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Prediction models for clustered data: comparison of a random intercept and standard regression model

PubMed Central

2013-01-01

Background When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Methods Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. Results The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. Conclusion The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters. PMID:23414436
Prediction models for clustered data: comparison of a random intercept and standard regression model.

PubMed

Bouwmeester, Walter; Twisk, Jos W R; Kappen, Teus H; van Klei, Wilton A; Moons, Karel G M; Vergouwe, Yvonne

2013-02-15

When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.
Validation of behave fire behavior predictions in oak savannas

USGS Publications Warehouse

Grabner, Keith W.; Dwyer, John; Cutter, Bruce E.

1997-01-01

Prescribed fire is a valuable tool in the restoration and management of oak savannas. BEHAVE, a fire behavior prediction system developed by the United States Forest Service, can be a useful tool when managing oak savannas with prescribed fire. BEHAVE predictions of fire rate-of-spread and flame length were validated using four standardized fuel models: Fuel Model 1 (short grass), Fuel Model 2 (timber and grass), Fuel Model 3 (tall grass), and Fuel Model 9 (hardwood litter). Also, a customized oak savanna fuel model (COSFM) was created and validated. Results indicate that standardized fuel model 2 and the COSFM reliably estimate mean rate-of-spread (MROS). The COSFM did not appreciably reduce MROS variation when compared to fuel model 2. Fuel models 1, 3, and 9 did not reliably predict MROS. Neither the standardized fuel models nor the COSFM adequately predicted flame lengths. We concluded that standardized fuel model 2 should be used with BEHAVE when predicting fire rates-of-spread in established oak savannas.
The Risk GP Model: the standard model of prediction in medicine.

PubMed

Fuller, Jonathan; Flores, Luis J

2015-12-01

With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to a target population. In the second step, the risk measure is particularized or transformed to yield probabilistic information relevant to a patient from the target population. Hence, we call the approach the Risk Generalization-Particularization (Risk GP) Model. There are serious problems at both stages, especially with the extent to which the required assumptions will hold and the extent to which we have evidence for the assumptions. Given that there are other models of prediction that use different assumptions, we should not inflexibly commit ourselves to one standard model. Instead, model pluralism should be standard in medical prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.
Cp Asymmetries in B0DECAYS Beyond the Standard Model

NASA Astrophysics Data System (ADS)

Dib, Claudio O.; London, David; Nir, Yosef

Of the many ingredients of the Standard Model that are relevant to the analysis of CP asymmetries in B0 decays, some are likely to hold even beyond the Standard Model while others are sensitive to new physics. Consequently, certain predictions are maintained while others may show dramatic deviations from the Standard Model. Many classes of models may show clear signatures when the asymmetries are measured: four quark generations, Z-mediated flavor-changing neutral currents, supersymmetry and “real superweak” models. On the other hand, models of left-right symmetry and multi-Higgs sectors with natural flavor conservation are unlikely to modify the Standard Model predictions.
Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator.

PubMed

Dubay, Rickey; Hassan, Marwan; Li, Chunying; Charest, Meaghan

2014-09-01

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck IMRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yuan, Lulin, E-mail: lulin.yuan@duke.edu; Wu, Q. Jackie; Yin, Fang-Fang

2014-02-15

Purpose: Sparing of single-side parotid gland is a common practice in head-and-neck (HN) intensity modulated radiation therapy (IMRT) planning. It is a special case of dose sparing tradeoff between different organs-at-risk. The authors describe an improved mathematical model for predicting achievable dose sparing in parotid glands in HN IMRT planning that incorporates single-side sparing considerations based on patient anatomy and learning from prior plan data. Methods: Among 68 HN cases analyzed retrospectively, 35 cases had physician prescribed single-side parotid sparing preferences. The single-side sparing model was trained with cases which had single-side sparing preferences, while the standard model was trainedmore » with the remainder of cases. A receiver operating characteristics (ROC) analysis was performed to determine the best criterion that separates the two case groups using the physician's single-side sparing prescription as ground truth. The final predictive model (combined model) takes into account the single-side sparing by switching between the standard and single-side sparing models according to the single-side sparing criterion. The models were tested with 20 additional cases. The significance of the improvement of prediction accuracy by the combined model over the standard model was evaluated using the Wilcoxon rank-sum test. Results: Using the ROC analysis, the best single-side sparing criterion is (1) the predicted median dose of one parotid is higher than 24 Gy; and (2) that of the other is higher than 7 Gy. This criterion gives a true positive rate of 0.82 and a false positive rate of 0.19, respectively. For the bilateral sparing cases, the combined and the standard models performed equally well, with the median of the prediction errors for parotid median dose being 0.34 Gy by both models (p = 0.81). For the single-side sparing cases, the standard model overestimates the median dose by 7.8 Gy on average, while the predictions by the combined model differ from actual values by only 2.2 Gy (p = 0.005). Similarly, the sum of residues between the modeled and the actual plan DVHs is the same for the bilateral sparing cases by both models (p = 0.67), while the standard model predicts significantly higher DVHs than the combined model for the single-side sparing cases (p = 0.01). Conclusions: The combined model for predicting parotid sparing that takes into account single-side sparing improves the prediction accuracy over the previous model.« less
Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck IMRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yuan, Lulin, E-mail: lulin.yuan@duke.edu; Wu, Q. Jackie; Yin, Fang-Fang

Purpose: Sparing of single-side parotid gland is a common practice in head-and-neck (HN) intensity modulated radiation therapy (IMRT) planning. It is a special case of dose sparing tradeoff between different organs-at-risk. The authors describe an improved mathematical model for predicting achievable dose sparing in parotid glands in HN IMRT planning that incorporates single-side sparing considerations based on patient anatomy and learning from prior plan data. Methods: Among 68 HN cases analyzed retrospectively, 35 cases had physician prescribed single-side parotid sparing preferences. The single-side sparing model was trained with cases which had single-side sparing preferences, while the standard model was trainedmore » with the remainder of cases. A receiver operating characteristics (ROC) analysis was performed to determine the best criterion that separates the two case groups using the physician's single-side sparing prescription as ground truth. The final predictive model (combined model) takes into account the single-side sparing by switching between the standard and single-side sparing models according to the single-side sparing criterion. The models were tested with 20 additional cases. The significance of the improvement of prediction accuracy by the combined model over the standard model was evaluated using the Wilcoxon rank-sum test. Results: Using the ROC analysis, the best single-side sparing criterion is (1) the predicted median dose of one parotid is higher than 24 Gy; and (2) that of the other is higher than 7 Gy. This criterion gives a true positive rate of 0.82 and a false positive rate of 0.19, respectively. For the bilateral sparing cases, the combined and the standard models performed equally well, with the median of the prediction errors for parotid median dose being 0.34 Gy by both models (p = 0.81). For the single-side sparing cases, the standard model overestimates the median dose by 7.8 Gy on average, while the predictions by the combined model differ from actual values by only 2.2 Gy (p = 0.005). Similarly, the sum of residues between the modeled and the actual plan DVHs is the same for the bilateral sparing cases by both models (p = 0.67), while the standard model predicts significantly higher DVHs than the combined model for the single-side sparing cases (p = 0.01). Conclusions: The combined model for predicting parotid sparing that takes into account single-side sparing improves the prediction accuracy over the previous model.« less
Observation of the rare Bs0 →µ+µ- decay from the combined analysis of CMS and LHCb data

NASA Astrophysics Data System (ADS)

Cms Collaboration; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M.; Erö, J.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Knünz, V.; Krammer, M.; Krätschmer, I.; Liko, D.; Mikulec, I.; Rabady, D.; Rahbaran, B.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Bansal, S.; Cornelis, T.; de Wolf, E. A.; Janssen, X.; Knutsson, A.; Lauwers, J.; Luyckx, S.; Ochesanu, S.; Rougny, R.; van de Klundert, M.; van Haevermaet, H.; van Mechelen, P.; van Remortel, N.; van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D'Hondt, J.; Daci, N.; Heracleous, N.; Keaveney, J.; Lowette, S.; Maes, M.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; van Doninck, W.; van Mulders, P.; van Onsem, G. P.; Villella, I.; Caillol, C.; Clerbaux, B.; de Lentdecker, G.; Dobur, D.; Favart, L.; Gay, A. P. R.; Grebenyuk, A.; Léonard, A.; Mohammadi, A.; Perniè, L.; Randle-Conde, A.; Reis, T.; Seva, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.; Zenoni, F.; Adler, V.; Beernaert, K.; Benucci, L.; Cimmino, A.; Costantini, S.; Crucy, S.; Dildick, S.; Fagot, A.; Garcia, G.; McCartin, J.; Ocampo Rios, A. A.; Ryckbosch, D.; Salva Diblen, S.; Sigamani, M.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bruno, G.; Castello, R.; Caudron, A.; Ceard, L.; da Silveira, G. G.; Delaere, C.; Du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Nuttens, C.; Pagano, D.; Perrini, L.; Pin, A.; Piotrzkowski, K.; Popov, A.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Vizan Garcia, J. M.; Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.; Aldá Júnior, W. L.; Alves, G. A.; Brito, L.; Correa Martins Junior, M.; Dos Reis Martins, T.; Mora Herrera, C.; Pol, M. E.; Rebello Teles, P.; Carvalho, W.; Chinellato, J.; Custódio, A.; da Costa, E. M.; de Jesus Damiao, D.; de Oliveira Martins, C.; Fonseca de Souza, S.; Malbouisson, H.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Prado da Silva, W. L.; Santaolalla, J.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Bernardes, C. A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Aleksandrov, A.; Genchev, V.; Hadjiiska, R.; Iaydjiev, P.; Marinov, A.; Piperov, S.; Rodozov, M.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Cheng, T.; Du, R.; Jiang, C. H.; Plestina, R.; Romeo, F.; Tao, J.; Wang, Z.; Asawatangtrakuldee, C.; Ban, Y.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Zou, W.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; Gomez Moreno, B.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Polic, D.; Puljak, I.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Kadija, K.; Luetic, J.; Mekterovic, D.; Sudic, L.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Bodlak, M.; Finger, M.; Finger, M., Jr.; Assran, Y.; Ellithi Kamel, A.; Mahmoud, M. A.; Radi, A.; Kadastik, M.; Murumaa, M.; Raidal, M.; Tiko, A.; Eerola, P.; Fedi, G.; Voutilainen, M.; Härkönen, J.; Karimäki, V.; Kinnunen, R.; Kortelainen, M. J.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Peltola, T.; Tuominen, E.; Tuominiemi, J.; Tuovinen, E.; Wendland, L.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Favaro, C.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Baffioni, S.; Beaudette, F.; Busson, P.; Charlot, C.; Dahms, T.; Dalchenko, M.; Dobrzynski, L.; Filipovic, N.; Florent, A.; Granier de Cassagnac, R.; Mastrolorenzo, L.; Miné, P.; Mironov, C.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Regnard, S.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Veelken, C.; Yilmaz, Y.; Zabi, A.; Agram, J.-L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J.-M.; Chabert, E. C.; Collard, C.; Conte, E.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Goetzmann, C.; Le Bihan, A.-C.; Skovpen, K.; van Hove, P.; Gadrat, S.; Beauceron, S.; Beaupere, N.; Boudoul, G.; Bouvier, E.; Brochet, S.; Carrillo Montoya, C. A.; Chasserat, J.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Kurca, T.; Lethuillier, M.; Mirabito, L.; Perries, S.; Ruiz Alvarez, J. D.; Sabes, D.; Sgandurra, L.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Xiao, H.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Bontenackels, M.; Edelhoff, M.; Feld, L.; Heister, A.; Hindrichs, O.; Klein, K.; Ostapchuk, A.; Raupach, F.; Sammet, J.; Schael, S.; Schulte, J. F.; Weber, H.; Wittmer, B.; Zhukov, V.; Ata, M.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Erdmann, M.; Fischer, R.; Güth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Klingebiel, D.; Knutzen, S.; Kreuzer, P.; Merschmeyer, M.; Meyer, A.; Millet, P.; Olschewski, M.; Padeken, K.; Papacz, P.; Reithler, H.; Schmitz, S. A.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Weber, M.; Cherepanov, V.; Erdogan, Y.; Flügge, G.; Geenen, H.; Geisler, M.; Haj Ahmad, W.; Hoehle, F.; Kargoll, B.; Kress, T.; Kuessel, Y.; Künsken, A.; Lingemann, J.; Nowack, A.; Nugent, I. M.; Pooth, O.; Stahl, A.; Aldaya Martin, M.; Asin, I.; Bartosik, N.; Behr, J.; Behrens, U.; Bell, A. J.; Bethani, A.; Borras, K.; Burgmeier, A.; Cakir, A.; Calligaris, L.; Campbell, A.; Choudhury, S.; Costanza, F.; Diez Pardos, C.; Dolinska, G.; Dooling, S.; Dorland, T.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Flucke, G.; Garay Garcia, J.; Geiser, A.; Gunnellini, P.; Hauk, J.; Hempel, M.; Jung, H.; Kalogeropoulos, A.; Kasemann, M.; Katsas, P.; Kieseler, J.; Kleinwort, C.; Korol, I.; Krücker, D.; Lange, W.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Lutz, B.; Mankel, R.; Marfin, I.; Melzer-Pellmann, I.-A.; Meyer, A. B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Naumann-Emme, S.; Nayak, A.; Ntomari, E.; Perrey, H.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Ribeiro Cipriano, P. M.; Roland, B.; Ron, E.; Sahin, M. Ö.; Salfeld-Nebgen, J.; Saxena, P.; Schoerner-Sadenius, T.; Schröder, M.; Seitz, C.; Spannagel, S.; Vargas Trevino, A. D. R.; Walsh, R.; Wissing, C.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Erfle, J.; Garutti, E.; Goebel, K.; Görner, M.; Haller, J.; Hoffmann, M.; Höing, R. S.; Junkes, A.; Kirschenmann, H.; Klanner, R.; Kogler, R.; Lange, J.; Lapsien, T.; Lenz, T.; Marchesini, I.; Ott, J.; Peiffer, T.; Perieanu, A.; Pietsch, N.; Poehlsen, J.; Poehlsen, T.; Rathjens, D.; Sander, C.; Schettler, H.; Schleper, P.; Schlieckau, E.; Schmidt, A.; Seidel, M.; Sola, V.; Stadie, H.; Steinbrück, G.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Barth, C.; Baus, C.; Berger, J.; Böser, C.; Butz, E.; Chwalek, T.; de Boer, W.; Descroix, A.; Dierlamm, A.; Feindt, M.; Frensch, F.; Giffels, M.; Gilbert, A.; Hartmann, F.; Hauth, T.; Husemann, U.; Katkov, I.; Kornmayer, A.; Kuznetsova, E.; Lobelle Pardo, P.; Mozer, M. U.; Müller, T.; Müller, Th.; Nürnberg, A.; Quast, G.; Rabbertz, K.; Röcker, S.; Simonis, H. J.; Stober, F. M.; Ulrich, R.; Wagner-Kuhr, J.; Wayand, S.; Weiler, T.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Giakoumopoulou, V. A.; Kyriakis, A.; Loukas, D.; Markou, A.; Markou, C.; Psallidas, A.; Topsis-Giotis, I.; Agapitos, A.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Stiliaris, E.; Aslanoglou, X.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Strologas, J.; Bencze, G.; Hajdu, C.; Hidas, P.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Molnar, J.; Palinkas, J.; Szillasi, Z.; Makovec, A.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Sahoo, N.; Swain, S. K.; Beri, S. B.; Bhatnagar, V.; Gupta, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, M.; Kumar, R.; Mittal, M.; Nishu, N.; Singh, J. B.; Ashok Kumar; Arun Kumar; Ahuja, S.; Bhardwaj, A.; Choudhary, B. C.; Kumar, A.; Malhotra, S.; Naimuddin, M.; Ranjan, K.; Sharma, V.; Banerjee, S.; Bhattacharya, S.; Chatterjee, K.; Dutta, S.; Gomber, B.; Jain, Sa.; Jain, Sh.; Khurana, R.; Modak, A.; Mukherjee, S.; Roy, D.; Sarkar, S.; Sharan, M.; Abdulsalam, A.; Dutta, D.; Kailas, S.; Kumar, V.; Mohanty, A. K.; Pant, L. M.; Shukla, P.; Topkar, A.; Aziz, T.; Banerjee, S.; Bhowmik, S.; Chatterjee, R. M.; Dewanjee, R. K.; Dugad, S.; Ganguly, S.; Ghosh, S.; Guchait, M.; Gurtu, A.; Kole, G.; Kumar, S.; Maity, M.; Majumder, G.; Mazumdar, K.; Mohanty, G. B.; Parida, B.; Sudhakar, K.; Wickramage, N.; Bakhshiansohi, H.; Behnamian, H.; Etesami, S. M.; Fahim, A.; Goldouzian, R.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Paktinat Mehdiabadi, S.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Calabria, C.; Chhibra, S. S.; Colaleo, A.; Creanza, D.; de Filippis, N.; de Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Abbiendi, G.; Benvenuti, A. C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.; Travaglini, R.; Albergo, S.; Cappello, G.; Chiorboli, M.; Costa, S.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Gallo, E.; Gonzi, S.; Gori, V.; Lenzi, P.; Meschini, M.; Paoletti, S.; Sguazzoni, G.; Tropiano, A.; Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.; Ferretti, R.; Ferro, F.; Lo Vetere, M.; Robutti, E.; Tosi, S.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Gerosa, R.; Ghezzi, A.; Govoni, P.; Lucchini, M. T.; Malvezzi, S.; Manzoni, R. A.; Martelli, A.; Marzocchi, B.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Redaelli, N.; Tabarelli de Fatis, T.; Buontempo, S.; Cavallo, N.; di Guida, S.; Fabozzi, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Merola, M.; Paolucci, P.; Azzi, P.; Bacchetta, N.; Bisello, D.; Branca, A.; Carlin, R.; Checchia, P.; Dall'Osso, M.; Dorigo, T.; Dosselli, U.; Galanti, M.; Gasparini, F.; Gasparini, U.; Giubilato, P.; Gozzelino, A.; Kanishchev, K.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Tosi, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.; Gabusi, M.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vitulo, P.; Biasini, M.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Spiezia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Broccolo, G.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fiori, F.; Foà, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Moon, C. S.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Serban, A. 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S.; Flowers, S.; Hart, A.; Hill, C.; Hughes, R.; Kotov, K.; Ling, T. Y.; Luo, W.; Puigh, D.; Rodenburg, M.; Winer, B. L.; Wolfe, H.; Wulsin, H. W.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Hunt, A.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Piroué, P.; Quan, X.; Saka, H.; Stickland, D.; Tully, C.; Werner, J. S.; Zuranski, A.; Brownson, E.; Malik, S.; Mendez, H.; Ramirez Vargas, J. E.; Barnes, V. E.; Benedetti, D.; Bortoletto, D.; de Mattia, M.; Gutay, L.; Hu, Z.; Jha, M. K.; Jones, M.; Jung, K.; Kress, M.; Leonardo, N.; Miller, D. H.; Neumeister, N.; Radburn-Smith, B. C.; Shi, X.; Shipsey, I.; Silvers, D.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Zablocki, J.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Ecklund, K. M.; Geurts, F. J. M.; Li, W.; Michlin, B.; Padley, B. P.; Redjimi, R.; Roberts, J.; Zabel, J.; Betchart, B.; Bodek, A.; Covarelli, R.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Garcia-Bellido, A.; Goldenzweig, P.; Han, J.; Harel, A.; Khukhunaishvili, A.; Korjenevski, S.; Petrillo, G.; Vishnevskiy, D.; Ciesielski, R.; Demortier, L.; Goulianos, K.; Mesropian, C.; Arora, S.; Barker, A.; Chou, J. P.; Contreras-Campana, C.; Contreras-Campana, E.; Duggan, D.; Ferencek, D.; Gershtein, Y.; Gray, R.; Halkiadakis, E.; Hidas, D.; Kaplan, S.; Lath, A.; Panwalkar, S.; Park, M.; Patel, R.; Salur, S.; Schnetzer, S.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Rose, K.; Spanier, S.; York, A.; Bouhali, O.; Castaneda Hernandez, A.; Eusebi, R.; Flanagan, W.; Gilmore, J.; Kamon, T.; Khotilovich, V.; Krutelyov, V.; Montalvo, R.; Osipenkov, I.; Pakhotin, Y.; Perloff, A.; Roe, J.; Rose, A.; Safonov, A.; Suarez, I.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kovitanggoon, K.; Kunori, S.; Lee, S. W.; Libeiro, T.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Sharma, M.; Sheldon, P.; Snook, B.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Boutle, S.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Lin, C.; Neu, C.; Wood, J.; Clarke, C.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Sturdy, J.; Belknap, D. A.; Carlsmith, D.; Cepeda, M.; Dasu, S.; Dodd, L.; Duric, S.; Friis, E.; Hall-Wilton, R.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Lazaridis, C.; Levine, A.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ross, I.; Sarangi, T.; Savin, A.; Smith, W. H.; Taylor, D.; Vuosalo, C.; Bediaga, I.; de Miranda, J. M.; Ferreira Rodrigues, F.; Gomes, A.; Massafferri, A.; Dos Reis, A. C.; Rodrigues, A. B.; Amato, S.; Carvalho Akiba, K.; de Paula, L.; Francisco, O.; Gandelman, M.; Hicheur, A.; Lopes, J. H.; Martins Tostes, D.; Nasteva, I.; Otalora Goicochea, J. M.; Polycarpo, E.; Potterat, C.; Rangel, M. S.; Salustino Guimaraes, V.; Souza de Paula, B.; Vieira, D.; An, L.; Gao, Y.; Jing, F.; Li, Y.; Yang, Z.; Yuan, X.; Zhang, Y.; Zhong, L.; Beaucourt, L.; Chefdeville, M.; Decamp, D.; Déléage, N.; Ghez, Ph.; Lees, J.-P.; Marchand, J. F.; Minard, M.-N.; Pietrzyk, B.; Qian, W.; T'jampens, S.; Tisserand, V.; Tournefier, E.; Ajaltouni, Z.; Baalouch, M.; Cogneras, E.; Deschamps, O.; El Rifai, I.; Grabalosa Gándara, M.; Henrard, P.; Hoballah, M.; Lefèvre, R.; Maratas, J.; Monteil, S.; Niess, V.; Perret, P.; Adrover, C.; Akar, S.; Aslanides, E.; Cogan, J.; Kanso, W.; Le Gac, R.; Leroy, O.; Mancinelli, G.; Mordà, A.; Perrin-Terrin, M.; Serrano, J.; Tsaregorodtsev, A.; Amhis, Y.; Barsuk, S.; Borsato, M.; Kochebina, O.; Lefrançois, J.; Machefert, F.; Martín Sánchez, A.; Nicol, M.; Robbe, P.; Schune, M.-H.; Teklishyn, M.; Vallier, A.; Viaud, B.; Wormser, G.; Ben-Haim, E.; Charles, M.; Coquereau, S.; David, P.; Del Buono, L.; Henry, L.; Polci, F.; Albrecht, J.; Brambach, T.; Cauet, Ch.; Deckenhoff, M.; Eitschberger, U.; Ekelhof, R.; Gavardi, L.; Kruse, F.; Meier, F.; Niet, R.; Parkinson, C. J.; Schlupp, M.; Shires, A.; Spaan, B.; Swientek, S.; Wishahi, J.; Aquines Gutierrez, O.; Blouw, J.; Britsch, M.; Fontana, M.; Popov, D.; Schmelling, M.; Volyanskyy, D.; Zavertyaev, M.; Bachmann, S.; Bien, A.; Comerma-Montells, A.; de Cian, M.; Dordei, F.; Esen, S.; Färber, C.; Gersabeck, E.; Grillo, L.; Han, X.; Hansmann-Menzemer, S.; Jaeger, A.; Kolpin, M.; Kreplin, K.; Krocker, G.; Leverington, B.; Marks, J.; Meissner, M.; Neuner, M.; Nikodem, T.; Seyfert, P.; Stahl, M.; Stahl, S.; Uwer, U.; Vesterinen, M.; Wandernoth, S.; Wiedner, D.; Zhelezov, A.; McNulty, R.; Wallace, R.; Zhang, W. C.; Palano, A.; Carbone, A.; Falabella, A.; Galli, D.; Marconi, U.; Moggi, N.; Mussini, M.; Perazzini, S.; Vagnoni, V.; Valenti, G.; Zangoli, M.; Bonivento, W.; Cadeddu, S.; Cardini, A.; Cogoni, V.; Contu, A.; Lai, A.; Liu, B.; Manca, G.; Oldeman, R.; Saitta, B.; Vacca, C.; Andreotti, M.; Baldini, W.; Bozzi, C.; Calabrese, R.; Corvo, M.; Fiore, M.; Fiorini, M.; Luppi, E.; Pappalardo, L. L.; Shapoval, I.; Tellarini, G.; Tomassetti, L.; Vecchi, S.; Anderlini, L.; Bizzeti, A.; Frosini, M.; Graziani, G.; Passaleva, G.; Veltri, M.; Bencivenni, G.; Campana, P.; de Simone, P.; Lanfranchi, G.; Palutan, M.; Rama, M.; Sarti, A.; Sciascia, B.; Vazquez Gomez, R.; Cardinale, R.; Fontanelli, F.; Gambetta, S.; Patrignani, C.; Petrolini, A.; Pistone, A.; Calvi, M.; Cassina, L.; Gotti, C.; Khanji, B.; Kucharczyk, M.; Matteuzzi, C.; Fu, J.; Geraci, A.; Neri, N.; Palombo, F.; Amerio, S.; Collazuol, G.; Gallorini, S.; Gianelle, A.; Lucchesi, D.; Lupato, A.; Morandin, M.; Rotondo, M.; Sestini, L.; Simi, G.; Stroili, R.; Bedeschi, F.; Cenci, R.; Leo, S.; Marino, P.; Morello, M. J.; Punzi, G.; Stracka, S.; Walsh, J.; Carboni, G.; Furfaro, E.; Santovetti, E.; Satta, A.; Alves, A. A., Jr.; Auriemma, G.; Bocci, V.; Martellotti, G.; Penso, G.; Pinci, D.; Santacesaria, R.; Satriano, C.; Sciubba, A.; Dziurda, A.; Kucewicz, W.; Lesiak, T.; Rachwal, B.; Witek, M.; Firlej, M.; Fiutowski, T.; Idzik, M.; Morawski, P.; Moron, J.; Oblakowska-Mucha, A.; Swientek, K.; Szumlak, T.; Batozskaya, V.; Klimaszewski, K.; Kurek, K.; Szczekowski, M.; Ukleja, A.; Wislicki, W.; Cojocariu, L.; Giubega, L.; Grecu, A.; Maciuc, F.; Orlandea, M.; Popovici, B.; Stoica, S.; Straticiuc, M.; Alkhazov, G.; Bondar, N.; Dzyuba, A.; Maev, O.; Sagidova, N.; Shcheglov, Y.; Vorobyev, A.; Belogurov, S.; Belyaev, I.; Egorychev, V.; Golubkov, D.; Kvaratskheliya, T.; Machikhiliyan, I. V.; Polyakov, I.; Savrina, D.; Semennikov, A.; Zhokhov, A.; Berezhnoy, A.; Korolev, M.; Leflat, A.; Nikitin, N.; Filippov, S.; Gushchin, E.; Kravchuk, L.; Bondar, A.; Eidelman, S.; Krokovny, P.; Kudryavtsev, V.; Shekhtman, L.; Vorobyev, V.; Artamonov, A.; Belous, K.; Dzhelyadin, R.; Guz, Yu.; Novoselov, A.; Obraztsov, V.; Popov, A.; Romanovsky, V.; Shapkin, M.; Stenyakin, O.; Yushchenko, O.; Badalov, A.; Calvo Gomez, M.; Garrido, L.; Gascon, D.; Graciani Diaz, R.; Graugés, E.; Marin Benito, C.; Picatoste Olloqui, E.; Rives Molina, V.; Ruiz, H.; Vilasis-Cardona, X.; Adeva, B.; Alvarez Cartelle, P.; Dosil Suárez, A.; Fernandez Albor, V.; Gallas Torreira, A.; García Pardiñas, J.; Hernando Morata, J. A.; Plo Casasus, M.; Romero Vidal, A.; Saborido Silva, J. J.; Sanmartin Sedes, B.; Santamarina Rios, C.; Vazquez Regueiro, P.; Vázquez Sierra, C.; Vieites Diaz, M.; Alessio, F.; Archilli, F.; Barschel, C.; Benson, S.; Buytaert, J.; Campora Perez, D.; Castillo Garcia, L.; Cattaneo, M.; Charpentier, Ph.; Cid Vidal, X.; Clemencic, M.; Closier, J.; Coco, V.; Collins, P.; Corti, G.; Couturier, B.; D'Ambrosio, C.; Dettori, F.; di Canto, A.; Dijkstra, H.; Durante, P.; Ferro-Luzzi, M.; Forty, R.; Frank, M.; Frei, C.; Gaspar, C.; Gligorov, V. V.; Granado Cardoso, L. A.; Gys, T.; Haen, C.; He, J.; Head, T.; van Herwijnen, E.; Jacobsson, R.; Johnson, D.; Joram, C.; Jost, B.; Karacson, M.; Karbach, T. M.; Lacarrere, D.; Langhans, B.; Lindner, R.; Linn, C.; Lohn, S.; Mapelli, A.; Matev, R.; Mathe, Z.; Neubert, S.; Neufeld, N.; Otto, A.; Panman, J.; Pepe Altarelli, M.; Rauschmayr, N.; Rihl, M.; Roiser, S.; Ruf, T.; Schindler, H.; Schmidt, B.; Schopper, A.; Schwemmer, R.; Sridharan, S.; Stagni, F.; Subbiah, V. K.; Teubert, F.; Thomas, E.; Tonelli, D.; Trisovic, A.; Ubeda Garcia, M.; Wicht, J.; Wyllie, K.; Battista, V.; Bay, A.; Blanc, F.; Dorigo, M.; Dupertuis, F.; Fitzpatrick, C.; Gianì, S.; Haefeli, G.; Jaton, P.; Khurewathanakul, C.; Komarov, I.; La Thi, V. N.; Lopez-March, N.; Märki, R.; Martinelli, M.; Muster, B.; Nakada, T.; Nguyen, A. D.; Nguyen, T. D.; Nguyen-Mau, C.; Prisciandaro, J.; Puig Navarro, A.; Rakotomiaramanana, B.; Rouvinet, J.; Schneider, O.; Soomro, F.; Szczypka, P.; Tobin, M.; Tourneur, S.; Tran, M. T.; Veneziano, G.; Xu, Z.; Anderson, J.; Bernet, R.; Bowen, E.; Bursche, A.; Chiapolini, N.; Chrzaszcz, M.; Elsasser, Ch.; Graverini, E.; Lionetto, F.; Lowdon, P.; Müller, K.; Serra, N.; Steinkamp, O.; Storaci, B.; Straumann, U.; Tresch, M.; Vollhardt, A.; Aaij, R.; Ali, S.; van Beuzekom, M.; David, P. N. Y.; de Bruyn, K.; Farinelli, C.; Heijne, V.; Hulsbergen, W.; Jans, E.; Koppenburg, P.; Kozlinskiy, A.; van Leerdam, J.; Merk, M.; Oggero, S.; Pellegrino, A.; Snoek, H.; van Tilburg, J.; Tsopelas, P.; Tuning, N.; de Vries, J. A.; Ketel, T.; Koopman, R. F.; Lambert, R. W.; Martinez Santos, D.; Raven, G.; Schiller, M.; Syropoulos, V.; Tolk, S.; Dovbnya, A.; Kandybei, S.; Raniuk, I.; Okhrimenko, O.; Pugatch, V.; Bifani, S.; Farley, N.; Griffith, P.; Kenyon, I. R.; Lazzeroni, C.; Mazurov, A.; McCarthy, J.; Pescatore, L.; Watson, N. K.; Williams, M. P.; Adinolfi, M.; Benton, J.; Brook, N. H.; Cook, A.; Coombes, M.; Dalseno, J.; Hampson, T.; Harnew, S. T.; Naik, P.; Price, E.; Prouve, C.; Rademacker, J. H.; Richards, S.; Saunders, D. M.; Skidmore, N.; Souza, D.; Velthuis, J. J.; Voong, D.; Barter, W.; Bettler, M.-O.; Cliff, H. V.; Evans, H.-M.; Garra Tico, J.; Gibson, V.; Gregson, S.; Haines, S. C.; Jones, C. R.; Sirendi, M.; Smith, J.; Ward, D. R.; Wotton, S. A.; Wright, S.; Back, J. J.; Blake, T.; Craik, D. C.; Crocombe, A. C.; Dossett, D.; Gershon, T.; Kreps, M.; Langenbruch, C.; Latham, T.; O'Hanlon, D. P.; Pilař, T.; Poluektov, A.; Reid, M. M.; Silva Coutinho, R.; Wallace, C.; Whitehead, M.; Easo, S.; Nandakumar, R.; Papanestis, A.; Ricciardi, S.; Wilson, F. F.; Carson, L.; Clarke, P. E. L.; Cowan, G. A.; Eisenhardt, S.; Ferguson, D.; Lambert, D.; Luo, H.; Morris, A.-B.; Muheim, F.; Needham, M.; Playfer, S.; Alexander, M.; Beddow, J.; Dean, C.-T.; Eklund, L.; Hynds, D.; Karodia, S.; Longstaff, I.; Ogilvy, S.; Pappagallo, M.; Sail, P.; Skillicorn, I.; Soler, F. J. P.; Spradlin, P.; Affolder, A.; Bowcock, T. J. V.; Brown, H.; Casse, G.; Donleavy, S.; Dreimanis, K.; Farry, S.; Fay, R.; Hennessy, K.; Hutchcroft, D.; Liles, M.; McSkelly, B.; Patel, G. D.; Price, J. D.; Pritchard, A.; Rinnert, K.; Shears, T.; Smith, N. A.; Ciezarek, G.; Cunliffe, S.; Currie, R.; Egede, U.; Fol, P.; Golutvin, A.; Hall, S.; McCann, M.; Owen, P.; Patel, M.; Petridis, K.; Redi, F.; Sepp, I.; Smith, E.; Sutcliffe, W.; Websdale, D.; Appleby, R. B.; Barlow, R. J.; Bird, T.; Bjørnstad, P. M.; Borghi, S.; Brett, D.; Brodzicka, J.; Capriotti, L.; Chen, S.; de Capua, S.; Dujany, G.; Gersabeck, M.; Harrison, J.; Hombach, C.; Klaver, S.; Lafferty, G.; McNab, A.; Parkes, C.; Pearce, A.; Reichert, S.; Rodrigues, E.; Rodriguez Perez, P.; Smith, M.; Cheung, S.-F.; Derkach, D.; Evans, T.; Gauld, R.; Greening, E.; Harnew, N.; Hill, D.; Hunt, P.; Hussain, N.; Jalocha, J.; John, M.; Lupton, O.; Malde, S.; Smith, E.; Stevenson, S.; Thomas, C.; Topp-Joergensen, S.; Torr, N.; Wilkinson, G.; Counts, I.; Ilten, P.; Williams, M.; Andreassen, R.; Davis, A.; de Silva, W.; Meadows, B.; Sokoloff, M. D.; Sun, L.; Todd, J.; Andrews, J. E.; Hamilton, B.; Jawahery, A.; Wimberley, J.; Artuso, M.; Blusk, S.; Borgia, A.; Britton, T.; Ely, S.; Gandini, P.; Garofoli, J.; Gui, B.; Hadjivasiliou, C.; Jurik, N.; Kelsey, M.; Mountain, R.; Pal, B. K.; Skwarnicki, T.; Stone, S.; Wang, J.; Xing, Z.; Zhang, L.; Baesso, C.; Cruz Torres, M.; Göbel, C.; Molina Rodriguez, J.; Xie, Y.; Milanes, D. A.; Grünberg, O.; Heß, M.; Voß, C.; Waldi, R.; Likhomanenko, T.; Malinin, A.; Shevchenko, V.; Ustyuzhanin, A.; Martinez Vidal, F.; Oyanguren, A.; Ruiz Valls, P.; Sanchez Mayordomo, C.; Onderwater, C. J. G.; Wilschut, H. W.; Pesen, E.

2015-06-01

The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. The probabilities, or branching fractions, of the strange B meson () and the B0 meson decaying into two oppositely charged muons (μ+ and μ-) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the and decays are very rare, with about four of the former occurring for every billion mesons produced, and one of the latter occurring for every ten billion B0 mesons. A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb (Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton-proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the µ+µ- decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. Furthermore, we obtained evidence for the µ+µ- decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton-proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of and B0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.
Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.
Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611
Automated next-to-leading order predictions for new physics at the LHC: The case of colored scalar pair production

DOE PAGES

Degrande, Céline; Fuks, Benjamin; Hirschi, Valentin; ...

2015-05-05

We present for the first time the full automation of collider predictions matched with parton showers at the next-to-leading accuracy in QCD within nontrivial extensions of the standard model. The sole inputs required from the user are the model Lagrangian and the process of interest. As an application of the above, we explore scenarios beyond the standard model where new colored scalar particles can be pair produced in hadron collisions. Using simplified models to describe the new field interactions with the standard model, we present precision predictions for the LHC within the MadGraph5_aMC@NLO framework.
Comparison of Predictive Modeling Methods of Aircraft Landing Speed

NASA Technical Reports Server (NTRS)

Diallo, Ousmane H.

2012-01-01

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.
Modeling to Predict Escherichia coli at Presque Isle Beach 2, City of Erie, Erie County, Pennsylvania

USGS Publications Warehouse

Zimmerman, Tammy M.

2008-01-01

The Lake Erie beaches in Pennsylvania are a valuable recreational resource for Erie County. Concentrations of Escherichia coli (E. coli) at monitored beaches in Presque Isle State Park in Erie, Pa., occasionally exceed the single-sample bathing-water standard of 235 colonies per 100 milliliters resulting in potentially unsafe swimming conditions and prompting beach managers to post public advisories or to close beaches to recreation. To supplement the current method for assessing recreational water quality (E. coli concentrations from the previous day), a predictive regression model for E. coli concentrations at Presque Isle Beach 2 was developed from data collected during the 2004 and 2005 recreational seasons. Model output included predicted E. coli concentrations and exceedance probabilities--the probability that E. coli concentrations would exceed the standard. For this study, E. coli concentrations and other water-quality and environmental data were collected during the 2006 recreational season at Presque Isle Beach 2. The data from 2006, an independent year, were used to test (validate) the 2004-2005 predictive regression model and compare the model performance to the current method. Using 2006 data, the 2004-2005 model yielded more correct responses and better predicted exceedances of the standard than the use of E. coli concentrations from the previous day. The differences were not pronounced, however, and more data are needed. For example, the model correctly predicted exceedances of the standard 11 percent of the time (1 out of 9 exceedances that occurred in 2006) whereas using the E. coli concentrations from the previous day did not result in any correctly predicted exceedances. After validation, new models were developed by adding the 2006 data to the 2004-2005 dataset and by analyzing the data in 2- and 3-year combinations. Results showed that excluding the 2004 data (using 2005 and 2006 data only) yielded the best model. Explanatory variables in the 2005-2006 model were log10 turbidity, bird count, and wave height. The 2005-2006 model correctly predicted when the standard would not be exceeded (specificity) with a response of 95.2 percent (178 out of 187 nonexceedances) and correctly predicted when the standard would be exceeded (sensitivity) with a response of 64.3 percent (9 out of 14 exceedances). In all cases, the results from predictive modeling produced higher percentages of correct predictions than using E. coli concentrations from the previous day. Additional data collected each year can be used to test and possibly improve the model. The results of this study will aid beach managers in more rapidly determining when waters are not safe for recreational use and, subsequently, when to close a beach or post an advisory.
Kindergarten Predictors of Math Learning Disability

PubMed Central

Mazzocco, Michèle M. M.; Thompson, Richard E.

2009-01-01

The aim of the present study was to address how to effectively predict mathematics learning disability (MLD). Specifically, we addressed whether cognitive data obtained during kindergarten can effectively predict which children will have MLD in third grade, whether an abbreviated test battery could be as effective as a standard psychoeducational assessment at predicting MLD, and whether the abbreviated battery corresponded to the literature on MLD characteristics. Participants were 226 children who enrolled in a 4-year prospective longitudinal study during kindergarten. We administered measures of mathematics achievement, formal and informal mathematics ability, visual-spatial reasoning, and rapid automatized naming and examined which test scores and test items from kindergarten best predicted MLD at grades 2 and 3. Statistical models using standardized scores from the entire test battery correctly classified ~80–83 percent of the participants as having, or not having, MLD. Regression models using scores from only individual test items were less predictive than models containing the standard scores, except for models using a specific subset of test items that dealt with reading numerals, number constancy, magnitude judgments of one-digit numbers, or mental addition of one-digit numbers. These models were as accurate in predicting MLD as was the model including the entire set of standard scores from the battery of tests examined. Our findings indicate that it is possible to effectively predict which kindergartners are at risk for MLD, and thus the findings have implications for early screening of MLD. PMID:20084182
Observation of the rare $$B^0_s\\to\\mu^+\\mu^-$$ decay from the combined analysis of CMS and LHCb data

DOE PAGES

Khachatryan, Vardan

2015-05-13

The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. We foudn that the probabilities, or branching fractions, of the strange B meson (B 0 2 ) and the B 0 meson decaying into two oppositely charged muons (μ + and μ -) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the B 0 2 → μ + and μ - and (B 0 → μ +more » and μ - decays are very rare, with about four of the former occurring for every billion mesons produced, and one of the latter occurring for every ten billion B 0 mesons1. A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN2 started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb (Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton–proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the μ + and μ -decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. We then obtained evidence for the B 0 → μ + and μ - decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton–proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of B 0 2 and B 0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.« less
Observation of the rare $$B^0_s\\to\\mu^+\\mu^-$$ decay from the combined analysis of CMS and LHCb data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khachatryan, Vardan

The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. We foudn that the probabilities, or branching fractions, of the strange B meson (B 0 2 ) and the B 0 meson decaying into two oppositely charged muons (μ + and μ -) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the B 0 2 → μ + and μ - and (B 0 → μ +more » and μ - decays are very rare, with about four of the former occurring for every billion mesons produced, and one of the latter occurring for every ten billion B 0 mesons1. A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN2 started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb (Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton–proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the μ + and μ -decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. We then obtained evidence for the B 0 → μ + and μ - decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton–proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of B 0 2 and B 0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.« less
Observation of the rare B(s)(0) →µ+µ− decay from the combined analysis of CMS and LHCb data.

PubMed

2015-06-04

The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. The probabilities, or branching fractions, of the strange B meson (B(s)(0)) and the B0 meson decaying into two oppositely charged muons (μ+ and μ−) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the B(s)(0) →µ+µ− and B(0) →µ+µ− decays are very rare, with about four of the former occurring for every billion mesons produced, and one of the latter occurring for every ten billion B0 mesons. A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb (Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton–proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the B(s)(0) → µ+µ− decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. Furthermore, we obtained evidence for the B(0) → µ+µ− decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton–proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of B(s)(0) and B0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.
Monte Carlo Simulation of Plumes Spectral Emission

DTIC Science & Technology

2005-06-07

ERIM experimental data for hot cell radiance has been performed. It has been shown that NASA standard infrared optical model [3] provides good...Influence of different optical models on predicted numerical data on hot cell radiance for ERIM experimental conditions has been studied. 7...prediction (solid line) of the Hot cell radiance. NASA Standard Infrared Radiation model ; averaged rotational line structure (JLBL=0); spectral
Search for Anomalous Production of Photon + Jets + Missing Transverse Energy in $$p\\bar{p}$$ Collisions at $$\\sqrt{s}=1.96$$~TeV Using the CDF II Detector

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hewamanage, Samantha Kaushalya

2011-01-01

A model-independent signature-based search for physics beyond the Standard Model is performed in the photon + jets + missing transverse energy channel in \\ppbar collisions at a center of mass energy of 1.96 TeV using the CDF II detector. Events with a photon + jets are predicted by the Standard Model and also by many theoretical models beyond the Standard Model. In the Standard Model, the main mechanisms for photon + jets production include quark-antiquark annihilation and quark-gluon scattering. No intrinsic missing transverse energy is present in any of these Standard Model processes. In this search, photon +more » $$\\geq$$1 jet and photon + $$\\geq$$2 jet events are analyzed with and without a minimum requirement on the missing transverse energy. Numerous mass distributions and kinematic distributions are studied and no significant excess over the background prediction is found. All results indicate good agreement with expectations of the Standard Model.« less

Tevatron constraints on models of the Higgs boson with exotic spin and parity using decays to bottom-antibottom quark pairs.

PubMed

Aaltonen, T; Abazov, V M; Abbott, B; Acharya, B S; Adams, M; Adams, T; Agnew, J P; Alexeev, G D; Alkhazov, G; Alton, A; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Appel, J A; Arisawa, T; Artikov, A; Asaadi, J; Ashmanskas, W; Askew, A; Atkins, S; Auerbach, B; Augsten, K; Aurisano, A; Avila, C; Azfar, F; Badaud, F; Badgett, W; Bae, T; Bagby, L; Baldin, B; Bandurin, D V; Banerjee, S; Barbaro-Galtieri, A; Barberis, E; Baringer, P; Barnes, V E; Barnett, B A; Barria, P; Bartlett, J F; Bartos, P; Bassler, U; Bauce, M; Bazterra, V; Bean, A; Bedeschi, F; Begalli, M; Behari, S; Bellantoni, L; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Beri, S B; Bernardi, G; Bernhard, R; Bertram, I; Besançon, M; Beuselinck, R; Bhat, P C; Bhatia, S; Bhatnagar, V; Bhatti, A; Bland, K R; Blazey, G; Blessing, S; Bloom, K; Blumenfeld, B; Bocci, A; Bodek, A; Boehnlein, A; Boline, D; Boos, E E; Borissov, G; Bortoletto, D; Borysova, M; Boudreau, J; Boveia, A; Brandt, A; Brandt, O; Brigliadori, L; Brock, R; Bromberg, C; Bross, A; Brown, D; Brucken, E; Bu, X B; Budagov, J; Budd, H S; Buehler, M; Buescher, V; Bunichev, V; Burdin, S; Burkett, K; Busetto, G; Bussey, P; Buszello, C P; Butti, P; Buzatu, A; Calamba, A; Camacho-Pérez, E; Camarda, S; Campanelli, M; Canelli, F; Carls, B; Carlsmith, D; Carosi, R; Carrillo, S; Casal, B; Casarsa, M; Casey, B C K; Castilla-Valdez, H; Castro, A; Catastini, P; Caughron, S; Cauz, D; Cavaliere, V; Cerri, A; Cerrito, L; Chakrabarti, S; Chan, K M; Chandra, A; Chapon, E; Chen, G; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Cho, K; Cho, S W; Choi, S; Chokheli, D; Choudhary, B; Cihangir, S; Claes, D; Clark, A; Clarke, C; Clutter, J; Convery, M E; Conway, J; Cooke, M; Cooper, W E; Corbo, M; Corcoran, M; Cordelli, M; Couderc, F; Cousinou, M-C; Cox, C A; Cox, D J; Cremonesi, M; Cruz, D; Cuevas, J; Culbertson, R; Cutts, D; Das, A; d'Ascenzo, N; Datta, M; Davies, G; de Barbaro, P; de Jong, S J; De La Cruz-Burelo, E; Déliot, F; Demina, R; Demortier, L; Deninno, M; Denisov, D; Denisov, S P; D'Errico, M; Desai, S; Deterre, C; DeVaughan, K; Devoto, F; Di Canto, A; Di Ruzza, B; Diehl, H T; Diesburg, M; Ding, P F; Dittmann, J R; Dominguez, A; Donati, S; D'Onofrio, M; Dorigo, M; Driutti, A; Dubey, A; Dudko, L V; Duperrin, A; Dutt, S; Eads, M; Ebina, K; Edgar, R; Edmunds, D; Elagin, A; Ellison, J; Elvira, V D; Enari, Y; Erbacher, R; Errede, S; Esham, B; Evans, H; Evdokimov, V N; Farrington, S; Fauré, A; Feng, L; Ferbel, T; Fernández Ramos, J P; Fiedler, F; Field, R; Filthaut, F; Fisher, W; Fisk, H E; Flanagan, G; Forrest, R; Fortner, M; Fox, H; Franklin, M; Freeman, J C; Frisch, H; Fuess, S; Funakoshi, Y; Galloni, C; Garbincius, P H; Garcia-Bellido, A; García-González, J A; Garfinkel, A F; Garosi, P; Gavrilov, V; Geng, W; Gerber, C E; Gerberich, H; Gerchtein, E; Gershtein, Y; Giagu, S; Giakoumopoulou, V; Gibson, K; Ginsburg, C M; Ginther, G; Giokaris, N; Giromini, P; Glagolev, V; Glenzinski, D; Gogota, O; Gold, M; Goldin, D; Golossanov, A; Golovanov, G; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González López, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gramellini, E; Grannis, P D; Greder, S; Greenlee, H; Grenier, G; Gris, Ph; Grivaz, J-F; Grohsjean, A; Grosso-Pilcher, C; Group, R C; Grünendahl, S; Grünewald, M W; Guillemin, T; Guimaraes da Costa, J; Gutierrez, G; Gutierrez, P; Hahn, S R; Haley, J; Han, J Y; Han, L; Happacher, F; Hara, K; Harder, K; Hare, M; Harel, A; Harr, R F; Harrington-Taber, T; Hatakeyama, K; Hauptman, J M; Hays, C; Hays, J; Head, T; Hebbeker, T; Hedin, D; Hegab, H; Heinrich, J; Heinson, A P; Heintz, U; Hensel, C; Heredia-De La Cruz, I; Herndon, M; Herner, K; Hesketh, G; Hildreth, M D; Hirosky, R; Hoang, T; Hobbs, J D; Hocker, A; Hoeneisen, B; Hogan, J; Hohlfeld, M; Holzbauer, J L; Hong, Z; Hopkins, W; Hou, S; Howley, I; Hubacek, Z; Hughes, R E; Husemann, U; Hussein, M; Huston, J; Hynek, V; Iashvili, I; Ilchenko, Y; Illingworth, R; Introzzi, G; Iori, M; Ito, A S; Ivanov, A; Jabeen, S; Jaffré, M; James, E; Jang, D; Jayasinghe, A; Jayatilaka, B; Jeon, E J; Jeong, M S; Jesik, R; Jiang, P; Jindariani, S; Johns, K; Johnson, E; Johnson, M; Jonckheere, A; Jones, M; Jonsson, P; Joo, K K; Joshi, J; Jun, S Y; Jung, A W; Junk, T R; Juste, A; Kajfasz, E; Kambeitz, M; Kamon, T; Karchin, P E; Karmanov, D; Kasmi, A; Kato, Y; Katsanos, I; Kaur, M; Kehoe, R; Kermiche, S; Ketchum, W; Keung, J; Khalatyan, N; Khanov, A; Kharchilava, A; Kharzheev, Y N; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S H; Kim, S B; Kim, Y J; Kim, Y K; Kimura, N; Kirby, M; Kiselevich, I; Knoepfel, K; Kohli, J M; Kondo, K; Kong, D J; Konigsberg, J; Kotwal, A V; Kozelov, A V; Kraus, J; Kreps, M; Kroll, J; Kruse, M; Kuhr, T; Kumar, A; Kupco, A; Kurata, M; Kurča, T; Kuzmin, V A; Laasanen, A T; Lammel, S; Lammers, S; Lancaster, M; Lannon, K; Latino, G; Lebrun, P; Lee, H S; Lee, H S; Lee, J S; Lee, S W; Lee, W M; Lei, X; Lellouch, J; Leo, S; Leone, S; Lewis, J D; Li, D; Li, H; Li, L; Li, Q Z; Lim, J K; Limosani, A; Lincoln, D; Linnemann, J; Lipaev, V V; Lipeles, E; Lipton, R; Lister, A; Liu, H; Liu, H; Liu, Q; Liu, T; Liu, Y; Lobodenko, A; Lockwitz, S; Loginov, A; Lokajicek, M; Lopes de Sa, R; Lucchesi, D; Lucà, A; Lueck, J; Lujan, P; Lukens, P; Luna-Garcia, R; Lungu, G; Lyon, A L; Lys, J; Lysak, R; Maciel, A K A; Madar, R; Madrak, R; Maestro, P; Magaña-Villalba, R; Malik, S; Malik, S; Malyshev, V L; Manca, G; Manousakis-Katsikakis, A; Mansour, J; Marchese, L; Margaroli, F; Marino, P; Martínez-Ortega, J; Matera, K; Mattson, M E; Mazzacane, A; Mazzanti, P; McCarthy, R; McGivern, C L; McNulty, R; Mehta, A; Mehtala, P; Meijer, M M; Melnitchouk, A; Menezes, D; Mercadante, P G; Merkin, M; Mesropian, C; Meyer, A; Meyer, J; Miao, T; Miconi, F; Mietlicki, D; Mitra, A; Miyake, H; Moed, S; Moggi, N; Mondal, N K; Moon, C S; Moore, R; Morello, M J; Mukherjee, A; Mulhearn, M; Muller, Th; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Naganoma, J; Nagy, E; Nakano, I; Napier, A; Narain, M; Nayyar, R; Neal, H A; Negret, J P; Nett, J; Neu, C; Neustroev, P; Nguyen, H T; Nigmanov, T; Nodulman, L; Noh, S Y; Norniella, O; Nunnemann, T; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Orduna, J; Ortolan, L; Osman, N; Osta, J; Pagliarone, C; Pal, A; Palencia, E; Palni, P; Papadimitriou, V; Parashar, N; Parihar, V; Park, S K; Parker, W; Partridge, R; Parua, N; Patwa, A; Pauletta, G; Paulini, M; Paus, C; Penning, B; Perfilov, M; Peters, Y; Petridis, K; Petrillo, G; Pétroff, P; Phillips, T J; Piacentino, G; Pianori, E; Pilot, J; Pitts, K; Plager, C; Pleier, M-A; Podstavkov, V M; Pondrom, L; Popov, A V; Poprocki, S; Potamianos, K; Pranko, A; Prewitt, M; Price, D; Prokopenko, N; Prokoshin, F; Ptohos, F; Punzi, G; Qian, J; Quadt, A; Quinn, B; Ratoff, P N; Razumov, I; Redondo Fernández, I; Renton, P; Rescigno, M; Rimondi, F; Ripp-Baudot, I; Ristori, L; Rizatdinova, F; Robson, A; Rodriguez, T; Rolli, S; Rominsky, M; Ronzani, M; Roser, R; Rosner, J L; Ross, A; Royon, C; Rubinov, P; Ruchti, R; Ruffini, F; Ruiz, A; Russ, J; Rusu, V; Sajot, G; Sakumoto, W K; Sakurai, Y; Sánchez-Hernández, A; Sanders, M P; Santi, L; Santos, A S; Sato, K; Savage, G; Saveliev, V; Savitskyi, M; Savoy-Navarro, A; Sawyer, L; Scanlon, T; Schamberger, R D; Scheglov, Y; Schellman, H; Schlabach, P; Schmidt, E E; Schwanenberger, C; Schwarz, T; Schwienhorst, R; Scodellaro, L; Scuri, F; Seidel, S; Seiya, Y; Sekaric, J; Semenov, A; Severini, H; Sforza, F; Shabalina, E; Shalhout, S Z; Shary, V; Shaw, S; Shchukin, A A; Shears, T; Shepard, P F; Shimojima, M; Shochet, M; Shreyber-Tecker, I; Simak, V; Simonenko, A; Skubic, P; Slattery, P; Sliwa, K; Smirnov, D; Smith, J R; Snider, F D; Snow, G R; Snow, J; Snyder, S; Söldner-Rembold, S; Song, H; Sonnenschein, L; Sorin, V; Soustruznik, K; St Denis, R; Stancari, M; Stark, J; Stentz, D; Stoyanova, D A; Strauss, M; Strologas, J; Sudo, Y; Sukhanov, A; Suslov, I; Suter, L; Svoisky, P; Takemasa, K; Takeuchi, Y; Tang, J; Tecchio, M; Teng, P K; Thom, J; Thomson, E; Thukral, V; Titov, M; Toback, D; Tokar, S; Tokmenin, V V; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Trovato, M; Tsai, Y-T; Tsybychev, D; Tuchming, B; Tully, C; Ukegawa, F; Uozumi, S; Uvarov, L; Uvarov, S; Uzunyan, S; Van Kooten, R; van Leeuwen, W M; Varelas, N; Varnes, E W; Vasilyev, I A; Vázquez, F; Velev, G; Vellidis, C; Verkheev, A Y; Vernieri, C; Vertogradov, L S; Verzocchi, M; Vesterinen, M; Vidal, M; Vilanova, D; Vilar, R; Vizán, J; Vogel, M; Vokac, P; Volpi, G; Wagner, P; Wahl, H D; Wallny, R; Wang, M H L S; Wang, S M; Warchol, J; Waters, D; Watts, G; Wayne, M; Weichert, J; Welty-Rieger, L; Wester, W C; Whiteson, D; Wicklund, A B; Wilbur, S; Williams, H H; Williams, M R J; Wilson, G W; Wilson, J S; Wilson, P; Winer, B L; Wittich, P; Wobisch, M; Wolbers, S; Wolfe, H; Wood, D R; Wright, T; Wu, X; Wu, Z; Wyatt, T R; Xie, Y; Yamada, R; Yamamoto, K; Yamato, D; Yang, S; Yang, T; Yang, U K; Yang, Y C; Yao, W-M; Yasuda, T; Yatsunenko, Y A; Ye, W; Ye, Z; Yeh, G P; Yi, K; Yin, H; Yip, K; Yoh, J; Yorita, K; Yoshida, T; Youn, S W; Yu, G B; Yu, I; Yu, J M; Zanetti, A M; Zeng, Y; Zennamo, J; Zhao, T G; Zhou, B; Zhou, C; Zhu, J; Zielinski, M; Zieminska, D; Zivkovic, L; Zucchelli, S

2015-04-17

Combined constraints from the CDF and D0 Collaborations on models of the Higgs boson with exotic spin J and parity P are presented and compared with results obtained assuming the standard model value JP=0+. Both collaborations analyzed approximately 10 fb(-) of proton-antiproton collisions with a center-of-mass energy of 1.96 TeV collected at the Fermilab Tevatron. Two models predicting exotic Higgs bosons with JP=0- and JP=2+ are tested. The kinematic properties of exotic Higgs boson production in association with a vector boson differ from those predicted for the standard model Higgs boson. Upper limits at the 95% credibility level on the production rates of the exotic Higgs bosons, expressed as fractions of the standard model Higgs boson production rate, are set at 0.36 for both the JP=0- hypothesis and the JP=2+ hypothesis. If the production rate times the branching ratio to a bottom-antibottom pair is the same as that predicted for the standard model Higgs boson, then the exotic bosons are excluded with significances of 5.0 standard deviations and 4.9 standard deviations for the JP=0- and JP=2+ hypotheses, respectively.
Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets.

PubMed

Sun, Jiangming; Carlsson, Lars; Ahlberg, Ernst; Norinder, Ulf; Engkvist, Ola; Chen, Hongming

2017-07-24

Conformal prediction has been proposed as a more rigorous way to define prediction confidence compared to other application domain concepts that have earlier been used for QSAR modeling. One main advantage of such a method is that it provides a prediction region potentially with multiple predicted labels, which contrasts to the single valued (regression) or single label (classification) output predictions by standard QSAR modeling algorithms. Standard conformal prediction might not be suitable for imbalanced data sets. Therefore, Mondrian cross-conformal prediction (MCCP) which combines the Mondrian inductive conformal prediction with cross-fold calibration sets has been introduced. In this study, the MCCP method was applied to 18 publicly available data sets that have various imbalance levels varying from 1:10 to 1:1000 (ratio of active/inactive compounds). Our results show that MCCP in general performed well on bioactivity data sets with various imbalance levels. More importantly, the method not only provides confidence of prediction and prediction regions compared to standard machine learning methods but also produces valid predictions for the minority class. In addition, a compound similarity based nonconformity measure was investigated. Our results demonstrate that although it gives valid predictions, its efficiency is much worse than that of model dependent metrics.
Cure modeling in real-time prediction: How much does it help?

PubMed

Ying, Gui-Shuang; Zhang, Qiang; Lan, Yu; Li, Yimei; Heitjan, Daniel F

2017-08-01

Various parametric and nonparametric modeling approaches exist for real-time prediction in time-to-event clinical trials. Recently, Chen (2016 BMC Biomedical Research Methodology 16) proposed a prediction method based on parametric cure-mixture modeling, intending to cover those situations where it appears that a non-negligible fraction of subjects is cured. In this article we apply a Weibull cure-mixture model to create predictions, demonstrating the approach in RTOG 0129, a randomized trial in head-and-neck cancer. We compare the ultimate realized data in RTOG 0129 to interim predictions from a Weibull cure-mixture model, a standard Weibull model without a cure component, and a nonparametric model based on the Bayesian bootstrap. The standard Weibull model predicted that events would occur earlier than the Weibull cure-mixture model, but the difference was unremarkable until late in the trial when evidence for a cure became clear. Nonparametric predictions often gave undefined predictions or infinite prediction intervals, particularly at early stages of the trial. Simulations suggest that cure modeling can yield better-calibrated prediction intervals when there is a cured component, or the appearance of a cured component, but at a substantial cost in the average width of the intervals. Copyright © 2017 Elsevier Inc. All rights reserved.
A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China

NASA Astrophysics Data System (ADS)

Liu, Zhenchen; Lu, Guihua; He, Hai; Wu, Zhiyong; He, Jian

2018-01-01

Reliable drought prediction is fundamental for water resource managers to develop and implement drought mitigation measures. Considering that drought development is closely related to the spatial-temporal evolution of large-scale circulation patterns, we developed a conceptual prediction model of seasonal drought processes based on atmospheric and oceanic standardized anomalies (SAs). Empirical orthogonal function (EOF) analysis is first applied to drought-related SAs at 200 and 500 hPa geopotential height (HGT) and sea surface temperature (SST). Subsequently, SA-based predictors are built based on the spatial pattern of the first EOF modes. This drought prediction model is essentially the synchronous statistical relationship between 90-day-accumulated atmospheric-oceanic SA-based predictors and SPI3 (3-month standardized precipitation index), calibrated using a simple stepwise regression method. Predictor computation is based on forecast atmospheric-oceanic products retrieved from the NCEP Climate Forecast System Version 2 (CFSv2), indicating the lead time of the model depends on that of CFSv2. The model can make seamless drought predictions for operational use after a year-to-year calibration. Model application to four recent severe regional drought processes in China indicates its good performance in predicting seasonal drought development, despite its weakness in predicting drought severity. Overall, the model can be a worthy reference for seasonal water resource management in China.
Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ?

PubMed

Kramer, Andrew A; Higgins, Thomas L; Zimmerman, Jack E

2015-02-01

To compare ICU performance using standardized mortality ratios generated by the Acute Physiology and Chronic Health Evaluation IVa and a National Quality Forum-endorsed methodology and examine potential reasons for model-based standardized mortality ratio differences. Retrospective analysis of day 1 hospital mortality predictions at the ICU level using Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models on the same patient cohort. Forty-seven ICUs at 36 U.S. hospitals from January 2008 to May 2013. Eighty-nine thousand three hundred fifty-three consecutive unselected ICU admissions. None. We assessed standardized mortality ratios for each ICU using data for patients eligible for Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum predictions in order to compare unit-level model performance, differences in ICU rankings, and how case-mix adjustment might explain standardized mortality ratio differences. Hospital mortality was 11.5%. Overall standardized mortality ratio was 0.89 using Acute Physiology and Chronic Health Evaluation IVa and 1.07 using National Quality Forum, the latter having a widely dispersed and multimodal standardized mortality ratio distribution. Model exclusion criteria eliminated mortality predictions for 10.6% of patients for Acute Physiology and Chronic Health Evaluation IVa and 27.9% for National Quality Forum. The two models agreed on the significance and direction of standardized mortality ratio only 45% of the time. Four ICUs had standardized mortality ratios significantly less than 1.0 using Acute Physiology and Chronic Health Evaluation IVa, but significantly greater than 1.0 using National Quality Forum. Two ICUs had standardized mortality ratios exceeding 1.75 using National Quality Forum, but nonsignificant performance using Acute Physiology and Chronic Health Evaluation IVa. Stratification by patient and institutional characteristics indicated that units caring for more severely ill patients and those with a higher percentage of patients on mechanical ventilation had the most discordant standardized mortality ratios between the two predictive models. Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models yield different ICU performance assessments due to differences in case-mix adjustment. Given the growing role of outcomes in driving prospective payment patient referral and public reporting, performance should be assessed by models with fewer exclusions, superior accuracy, and better case-mix adjustment.
Dark matter, constrained minimal supersymmetric standard model, and lattice QCD.

PubMed

Giedt, Joel; Thomas, Anthony W; Young, Ross D

2009-11-13

Recent lattice measurements have given accurate estimates of the quark condensates in the proton. We use these results to significantly improve the dark matter predictions in benchmark models within the constrained minimal supersymmetric standard model. The predicted spin-independent cross sections are at least an order of magnitude smaller than previously suggested and our results have significant consequences for dark matter searches.
Using a prescribed fire to test custom and standard fuel models for fire behaviour prediction in a non-native, grass-invaded tropical dry shrubland

Treesearch

Andrew D. Pierce; Sierra McDaniel; Mark Wasser; Alison Ainsworth; Creighton M. Litton; Christian P. Giardina; Susan Cordell; Ralf Ohlemuller

2014-01-01

Questions: Do fuel models developed for North American fuel types accurately represent fuel beds found in grass-invaded tropical shrublands? Do standard or custom fuel models for firebehavior models with in situ or RAWS measured fuel moistures affect the accuracy of predicted fire behavior in grass-invaded tropical shrublands? Location: Hawaiâi Volcanoes National...
Image Discrimination Predictions of a Single Channel Model with Contrast Gain Control

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Null, Cynthia H.

1995-01-01

Image discrimination models predict the number of just-noticeable-differences between two images. We report the predictions of a single channel model with contrast masking for a range of standard discrimination experiments. Despite its computational simplicity, this model has performed as well as a multiple channel model in an object detection task.
Tevatron constraints on models of the Higgs boson with exotic spin and parity using decays to bottom-antibottom quark pairs

DOE PAGES

Aaltonen, T.

2015-04-15

In this study, combined constraints from the CDF and D0 Collaborations on models of the Higgs boson with exotic spin J and parity P are presented and compared with results obtained assuming the standard model value J P = 0 +. Both collaborations analyzed approximately 10 fb –1 of proton-antiproton collisions with a center-of-mass energy of 1.96 TeV collected at the Fermilab Tevatron. Two models predicting exotic Higgs bosons with J P = 0 – and J P = 2 + are tested. The kinematic properties of exotic Higgs boson production in association with a vector boson differ from thosemore » predicted for the standard model Higgs boson. Upper limits at the 95% credibility level on the production rates of the exotic Higgs bosons, expressed as fractions of the standard model Higgs boson production rate, are set at 0.36 for both the J P = 0 – hypothesis and the J P = 2 + hypothesis. If the production rate times the branching ratio to a bottom-antibottom pair is the same as that predicted for the standard model Higgs boson, then the exotic bosons are excluded with significances of 5.0 standard deviations and 4.9 standard deviations for the J P = 0 – and J P = 2 + hypotheses, respectively.« less
Propeller aircraft interior noise model. II - Scale-model and flight-test comparisons

NASA Technical Reports Server (NTRS)

Willis, C. M.; Mayes, W. H.

1987-01-01

A program for predicting the sound levels inside propeller driven aircraft arising from sidewall transmission of airborne exterior noise is validated through comparisons of predictions with both scale-model test results and measurements obtained in flight tests on a turboprop aircraft. The program produced unbiased predictions for the case of the scale-model tests, with a standard deviation of errors of about 4 dB. For the case of the flight tests, the predictions revealed a bias of 2.62-4.28 dB (depending upon whether or not the data for the fourth harmonic were included) and the standard deviation of the errors ranged between 2.43 and 4.12 dB. The analytical model is shown to be capable of taking changes in the flight environment into account.
Predicting Rib Fracture Risk With Whole-Body Finite Element Models: Development and Preliminary Evaluation of a Probabilistic Analytical Framework

PubMed Central

Forman, Jason L.; Kent, Richard W.; Mroz, Krystoffer; Pipkorn, Bengt; Bostrom, Ola; Segui-Gomez, Maria

2012-01-01

This study sought to develop a strain-based probabilistic method to predict rib fracture risk with whole-body finite element (FE) models, and to describe a method to combine the results with collision exposure information to predict injury risk and potential intervention effectiveness in the field. An age-adjusted ultimate strain distribution was used to estimate local rib fracture probabilities within an FE model. These local probabilities were combined to predict injury risk and severity within the whole ribcage. The ultimate strain distribution was developed from a literature dataset of 133 tests. Frontal collision simulations were performed with the THUMS (Total HUman Model for Safety) model with four levels of delta-V and two restraints: a standard 3-point belt and a progressive 3.5–7 kN force-limited, pretensioned (FL+PT) belt. The results of three simulations (29 km/h standard, 48 km/h standard, and 48 km/h FL+PT) were compared to matched cadaver sled tests. The numbers of fractures predicted for the comparison cases were consistent with those observed experimentally. Combining these results with field exposure informantion (ΔV, NASS-CDS 1992–2002) suggests a 8.9% probability of incurring AIS3+ rib fractures for a 60 year-old restrained by a standard belt in a tow-away frontal collision with this restraint, vehicle, and occupant configuration, compared to 4.6% for the FL+PT belt. This is the first study to describe a probabilistic framework to predict rib fracture risk based on strains observed in human-body FE models. Using this analytical framework, future efforts may incorporate additional subject or collision factors for multi-variable probabilistic injury prediction. PMID:23169122
The Real World Significance of Performance Prediction

ERIC Educational Resources Information Center

Pardos, Zachary A.; Wang, Qing Yang; Trivedi, Shubhendu

2012-01-01

In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…
Standard Clock in primordial density perturbations and cosmic microwave background

NASA Astrophysics Data System (ADS)

Chen, Xingang; Namjoo, Mohammad Hossein

2014-12-01

Standard Clocks in the primordial epoch leave a special type of features in the primordial perturbations, which can be used to directly measure the scale factor of the primordial universe as a function of time a (t), thus discriminating between inflation and alternatives. We have started to search for such signals in the Planck 2013 data using the key predictions of the Standard Clock. In this Letter, we summarize the key predictions of the Standard Clock and present an interesting candidate example in Planck 2013 data. Motivated by this candidate, we construct and compute full Standard Clock models and use the more complete prediction to make more extensive comparison with data. Although this candidate is not yet statistically significant, we use it to illustrate how Standard Clocks appear in Cosmic Microwave Background (CMB) and how they can be further tested by future data. We also use it to motivate more detailed theoretical model building.
Predicting Middle Level State Standardized Test Results Using Family and Community Demographic Data

ERIC Educational Resources Information Center

Tienken, Christopher H.; Colella, Anthony; Angelillo, Christian; Fox, Meredith; McCahill, Kevin R.; Wolfe, Adam

2017-01-01

The use of standardized test results to drive school administrator evaluations pervades education policymaking in more than 40 states. However, the results of state standardized tests are strongly influenced by non-school factors. The models of best fit (n = 18) from this correlational, explanatory, longitudinal study predicted accurately the…
Maps of estimated nitrate and arsenic concentrations in basin-fill aquifers of the southwestern United States

USGS Publications Warehouse

Beisner, Kimberly R.; Anning, David W.; Paul, Angela P.; McKinney, Tim S.; Huntington, Jena M.; Bexfield, Laura M.; Thiros, Susan A.

2012-01-01

Human-health concerns and economic considerations associated with meeting drinking-water standards motivated a study of the vulnerability of basin-fill aquifers to nitrate contamination and arsenic enrichment in the southwestern United States. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid representing about 190,600 square miles of basin-fill aquifers in parts of Arizona, California, Colorado, Nevada, New Mexico, and Utah. The statistical models, referred to as classifiers, reflect natural and human-related factors that affect aquifer vulnerability to contamination and relate nitrate and arsenic concentrations to explanatory variables representing local- and basin-scale measures of source and aquifer susceptibility conditions. Geochemical variables were not used in concentration predictions because they were not available for the entire study area. The models were calibrated to assess model accuracy on the basis of measured values.Only 2 percent of the area underlain by basin-fill aquifers in the study area was predicted to equal or exceed the U.S. Environmental Protection Agency drinking-water standard for nitrate as N (10 milligrams per liter), whereas 43 percent of the area was predicted to equal or exceed the standard for arsenic (10 micrograms per liter). Areas predicted to equal or exceed the drinking-water standard for nitrate include basins in central Arizona near Phoenix; the San Joaquin Valley, the Santa Ana Inland, and San Jacinto Basins of California; and the San Luis Valley of Colorado. Much of the area predicted to equal or exceed the drinking-water standard for arsenic is within a belt of basins along the western portion of the Basin and Range Physiographic Province that includes almost all of Nevada and parts of California and Arizona. Predicted nitrate and arsenic concentrations are substantially lower than the drinking-water standards in much of the study area-about 93 percent of the area underlain by basin-fill aquifers was less than one-half the standard for nitrate as N (5.0 milligrams per liter), and 50 percent was less than one-half the standard for arsenic (5.0 micrograms per liter). The predicted concentrations and the improved understanding of the susceptibility and vulnerability of southwestern basin-fill aquifers to nitrate contamination and arsenic enrichment can be used by water managers as a qualitative tool to assess and protect the quality of groundwater resources in the Southwest.
The viscoelastic standard nonlinear solid model: predicting the response of the lumbar intervertebral disk to low-frequency vibrations.

PubMed

Groth, Kevin M; Granata, Kevin P

2008-06-01

Due to the mathematical complexity of current musculoskeletal spine models, there is a need for computationally efficient models of the intervertebral disk (IVD). The aim of this study is to develop a mathematical model that will adequately describe the motion of the IVD under axial cyclic loading as well as maintain computational efficiency for use in future musculoskeletal spine models. Several studies have successfully modeled the creep characteristics of the IVD using the three-parameter viscoelastic standard linear solid (SLS) model. However, when the SLS model is subjected to cyclic loading, it underestimates the load relaxation, the cyclic modulus, and the hysteresis of the human lumbar IVD. A viscoelastic standard nonlinear solid (SNS) model was used to predict the response of the human lumbar IVD subjected to low-frequency vibration. Nonlinear behavior of the SNS model was simulated by a strain-dependent elastic modulus on the SLS model. Parameters of the SNS model were estimated from experimental load deformation and stress-relaxation curves obtained from the literature. The SNS model was able to predict the cyclic modulus of the IVD at frequencies of 0.01 Hz, 0.1 Hz, and 1 Hz. Furthermore, the SNS model was able to quantitatively predict the load relaxation at a frequency of 0.01 Hz. However, model performance was unsatisfactory when predicting load relaxation and hysteresis at higher frequencies (0.1 Hz and 1 Hz). The SLS model of the lumbar IVD may require strain-dependent elastic and viscous behavior to represent the dynamic response to compressive strain.
Adjustment of regional regression models of urban-runoff quality using data for Chattanooga, Knoxville, and Nashville, Tennessee

USGS Publications Warehouse

Hoos, Anne B.; Patel, Anant R.

1996-01-01

Model-adjustment procedures were applied to the combined data bases of storm-runoff quality for Chattanooga, Knoxville, and Nashville, Tennessee, to improve predictive accuracy for storm-runoff quality for urban watersheds in these three cities and throughout Middle and East Tennessee. Data for 45 storms at 15 different sites (five sites in each city) constitute the data base. Comparison of observed values of storm-runoff load and event-mean concentration to the predicted values from the regional regression models for 10 constituents shows prediction errors, as large as 806,000 percent. Model-adjustment procedures, which combine the regional model predictions with local data, are applied to improve predictive accuracy. Standard error of estimate after model adjustment ranges from 67 to 322 percent. Calibration results may be biased due to sampling error in the Tennessee data base. The relatively large values of standard error of estimate for some of the constituent models, although representing significant reduction (at least 50 percent) in prediction error compared to estimation with unadjusted regional models, may be unacceptable for some applications. The user may wish to collect additional local data for these constituents and repeat the analysis, or calibrate an independent local regression model.
Standard Model and New physics for ɛ'k/ɛk

NASA Astrophysics Data System (ADS)

Kitahara, Teppei

2018-05-01

The first result of the lattice simulation and improved perturbative calculations have pointed to a discrepancy between data on ɛ'k/ɛk and the standard-model (SM) prediction. Several new physics (NP) models can explain this discrepancy, and such NP models are likely to predict deviations of ℬ(K → πvv) from the SM predictions, which can be probed precisely in the near future by NA62 and KOTO experiments. We present correlations between ɛ'k/ɛk and ℬ(K → πvv) in two types of NP scenarios: a box dominated scenario and a Z-penguin dominated one. It is shown that different correlations are predicted and the future precision measurements of K → πvv can distinguish both scenarios.
Automatically updating predictive modeling workflows support decision-making in drug design.

PubMed

Muegge, Ingo; Bentzien, Jörg; Mukherjee, Prasenjit; Hughes, Robert O

2016-09-01

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure-activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.
Modeling the Zeeman effect in high altitude SSMIS channels for numerical weather prediction profiles: comparing a fast model and a line-by-line model

NASA Astrophysics Data System (ADS)

Larsson, R.; Milz, M.; Rayer, P.; Saunders, R.; Bell, W.; Booton, A.; Buehler, S. A.; Eriksson, P.; John, V.

2015-10-01

We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high altitude Special Sensor Microwave Imager/Sounder channels 19-22. We find that the models agree well for channels 21 and 22 compared to the channels' system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Same channel, there is 1.2 K in average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Same channel, there is 1.3 K in average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies causing up to ± 7 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures.

Modeling the Zeeman effect in high-altitude SSMIS channels for numerical weather prediction profiles: comparing a fast model and a line-by-line model

NASA Astrophysics Data System (ADS)

Larsson, Richard; Milz, Mathias; Rayer, Peter; Saunders, Roger; Bell, William; Booton, Anna; Buehler, Stefan A.; Eriksson, Patrick; John, Viju O.

2016-03-01

We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high-altitude Special Sensor Microwave Imager/Sounder channels 19-22. We find that the models agree well for channels 21 and 22 compared to the channels' system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Concerning the same channel, there is 1.2 K on average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Regarding the same channel, there is 1.3 K on average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies, causing up to ±7 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures.
Combined search for the standard model Higgs boson decaying to bb using the D0 run II data set.

PubMed

Abazov, V M; Abbott, B; Acharya, B S; Adams, M; Adams, T; Alexeev, G D; Alkhazov, G; Alton, A; Alverson, G; Askew, A; Atkins, S; Augsten, K; Avila, C; Badaud, F; Bagby, L; Baldin, B; Bandurin, D V; Banerjee, S; Barberis, E; Baringer, P; Bartlett, J F; Bassler, U; Bazterra, V; Bean, A; Begalli, M; Bellantoni, L; Beri, S B; Bernardi, G; Bernhard, R; Bertram, I; Besançon, M; Beuselinck, R; Bhat, P C; Bhatia, S; Bhatnagar, V; Blazey, G; Blessing, S; Bloom, K; Boehnlein, A; Boline, D; Boos, E E; Borissov, G; Bose, T; Brandt, A; Brandt, O; Brock, R; Bross, A; Brown, D; Brown, J; Bu, X B; Buehler, M; Buescher, V; Bunichev, V; Burdin, S; Buszello, C P; Camacho-Pérez, E; Casey, B C K; Castilla-Valdez, H; Caughron, S; Chakrabarti, S; Chakraborty, D; Chan, K M; Chandra, A; Chapon, E; Chen, G; Chevalier-Théry, S; Cho, D K; Cho, S W; Choi, S; Choudhary, B; Cihangir, S; Claes, D; Clutter, J; Cooke, M; Cooper, W E; Corcoran, M; Couderc, F; Cousinou, M-C; Croc, A; Cutts, D; Das, A; Davies, G; de Jong, S J; De La Cruz-Burelo, E; Déliot, F; Demina, R; Denisov, D; Denisov, S P; Desai, S; Deterre, C; Devaughan, K; Diehl, H T; Diesburg, M; Ding, P F; Dominguez, A; Dubey, A; Dudko, L V; Duggan, D; Duperrin, A; Dutt, S; Dyshkant, A; Eads, M; Edmunds, D; Ellison, J; Elvira, V D; Enari, Y; Evans, H; Evdokimov, A; Evdokimov, V N; Facini, G; Feng, L; Ferbel, T; Fiedler, F; Filthaut, F; Fisher, W; Fisk, H E; Fortner, M; Fox, H; Fuess, S; Garcia-Bellido, A; García-González, J A; García-Guerra, G A; Gavrilov, V; Gay, P; Geng, W; Gerbaudo, D; Gerber, C E; Gershtein, Y; Ginther, G; Golovanov, G; Goussiou, A; Grannis, P D; Greder, S; Greenlee, H; Grenier, G; Gris, Ph; Grivaz, J-F; Grohsjean, A; Grünendahl, S; Grünewald, M W; Guillemin, T; Gutierrez, G; Gutierrez, P; Hagopian, S; Haley, J; Han, L; Harder, K; Harel, A; Hauptman, J M; Hays, J; Head, T; Hebbeker, T; Hedin, D; Hegab, H; Heinson, A P; Heintz, U; Hensel, C; Heredia De La Cruz, I; Herner, K; Hesketh, G; Hildreth, M D; Hirosky, R; Hoang, T; Hobbs, J D; Hoeneisen, B; Hogan, J; Hohlfeld, M; Howley, I; Hubacek, Z; Hynek, V; Iashvili, I; Ilchenko, Y; Illingworth, R; Ito, A S; Jabeen, S; Jaffré, M; Jayasinghe, A; Jeong, M S; Jesik, R; Jiang, P; Johns, K; Johnson, E; Johnson, M; Jonckheere, A; Jonsson, P; Joshi, J; Jung, A W; Juste, A; Kaadze, K; Kajfasz, E; Karmanov, D; Kasper, P A; Katsanos, I; Kehoe, R; Kermiche, S; Khalatyan, N; Khanov, A; Kharchilava, A; Kharzheev, Y N; Kiselevich, I; Kohli, J M; Kozelov, A V; Kraus, J; Kulikov, S; Kumar, A; Kupco, A; Kurča, T; Kuzmin, V A; Lammers, S; Landsberg, G; Lebrun, P; Lee, H S; Lee, S W; Lee, W M; Lei, X; Lellouch, J; Li, D; Li, H; Li, L; Li, Q Z; Lim, J K; Lincoln, D; Linnemann, J; Lipaev, V V; Lipton, R; Liu, H; Liu, Y; Lobodenko, A; Lokajicek, M; Lopes de Sa, R; Lubatti, H J; Luna-Garcia, R; Lyon, A L; Maciel, A K A; Madar, R; Magaña-Villalba, R; Malik, S; Malyshev, V L; Maravin, Y; Martínez-Ortega, J; McCarthy, R; McGivern, C L; Meijer, M M; Melnitchouk, A; Menezes, D; Mercadante, P G; Merkin, M; Meyer, A; Meyer, J; Miconi, F; Mondal, N K; Mulhearn, M; Nagy, E; Naimuddin, M; Narain, M; Nayyar, R; Neal, H A; Negret, J P; Neustroev, P; Nguyen, H T; Nunnemann, T; Orduna, J; Osman, N; Osta, J; Padilla, M; Pal, A; Parashar, N; Parihar, V; Park, S K; Partridge, R; Parua, N; Patwa, A; Penning, B; Perfilov, M; Peters, Y; Petridis, K; Petrillo, G; Pétroff, P; Pleier, M-A; Podesta-Lerma, P L M; Podstavkov, V M; Popov, A V; Prewitt, M; Price, D; Prokopenko, N; Qian, J; Quadt, A; Quinn, B; Rangel, M S; Ranjan, K; Ratoff, P N; Razumov, I; Renkel, P; Ripp-Baudot, I; Rizatdinova, F; Rominsky, M; Ross, A; Royon, C; Rubinov, P; Ruchti, R; Sajot, G; Salcido, P; Sánchez-Hernández, A; Sanders, M P; Santos, A S; Savage, G; Sawyer, L; Scanlon, T; Schamberger, R D; Scheglov, Y; Schellman, H; Schlobohm, S; Schwanenberger, C; Schwienhorst, R; Sekaric, J; Severini, H; Shabalina, E; Shary, V; Shaw, S; Shchukin, A A; Shivpuri, R K; Simak, V; Skubic, P; Slattery, P; Smirnov, D; Smith, K J; Snow, G R; Snow, J; Snyder, S; Söldner-Rembold, S; Sonnenschein, L; Soustruznik, K; Stark, J; Stoyanova, D A; Strauss, M; Suter, L; Svoisky, P; Takahashi, M; Titov, M; Tokmenin, V V; Tsai, Y-T; Tschann-Grimm, K; Tsybychev, D; Tuchming, B; Tully, C; Uvarov, L; Uvarov, S; Uzunyan, S; Van Kooten, R; van Leeuwen, W M; Varelas, N; Varnes, E W; Vasilyev, I A; Verdier, P; Verkheev, A Y; Vertogradov, L S; Verzocchi, M; Vesterinen, M; Vilanova, D; Vokac, P; Wahl, H D; Wang, M H L S; Wang, R-J; Warchol, J; Watts, G; Wayne, M; Weichert, J; Welty-Rieger, L; White, A; Wicke, D; Williams, M R J; Wilson, G W; Wobisch, M; Wood, D R; Wyatt, T R; Xie, Y; Yamada, R; Yang, S; Yang, W-C; Yasuda, T; Yatsunenko, Y A; Ye, W; Ye, Z; Yin, H; Yip, K; Youn, S W; Yu, J M; Zennamo, J; Zhao, T; Zhao, T G; Zhou, B; Zhu, J; Zielinski, M; Zieminska, D; Zivkovic, L

2012-09-21

We present the results of the combination of searches for the standard model Higgs boson produced in association with a W or Z boson and decaying into bb using the data sample collected with the D0 detector in pp collisions at √s = 1.96 TeV at the Fermilab Tevatron Collider. We derive 95% C.L. upper limits on the Higgs boson cross section relative to the standard model prediction in the mass range 100 GeV ≤ M(H) ≤ 150 GeV, and we exclude Higgs bosons with masses smaller than 102 GeV at the 95% C.L. In the mass range 120 GeV ≤ M(H) ≤145 GeV, the data exhibit an excess above the background prediction with a global significance of 1.5 standard deviations, consistent with the expectation in the presence of a standard model Higgs boson.
Comparing motor-vehicle crash risk of EU and US vehicles.

PubMed

Flannagan, Carol A C; Bálint, András; Klinich, Kathleen D; Sander, Ulrich; Manary, Miriam A; Cuny, Sophie; McCarthy, Michael; Phan, Vuthy; Wallbank, Caroline; Green, Paul E; Sui, Bo; Forsman, Åsa; Fagerlind, Helen

2018-08-01

This study examined the hypotheses that passenger vehicles meeting European Union (EU) safety standards have similar crashworthiness to United States (US) -regulated vehicles in the US driving environment, and vice versa. The first step involved identifying appropriate databases of US and EU crashes that include in-depth crash information, such as estimation of crash severity using Delta-V and injury outcome based on medical records. The next step was to harmonize variable definitions and sampling criteria so that the EU data could be combined and compared to the US data using the same or equivalent parameters. Logistic regression models of the risk of a Maximum injury according to the Abbreviated Injury Scale of 3 or greater, or fatality (MAIS3+F) in EU-regulated and US-regulated vehicles were constructed. The injury risk predictions of the EU model and the US model were each applied to both the US and EU standard crash populations. Frontal, near-side, and far-side crashes were analyzed together (termed "front/side crashes") and a separate model was developed for rollover crashes. For the front/side model applied to the US standard population, the mean estimated risk for the US-vehicle model is 0.035 (sd = 0.012), and the mean estimated risk for the EU-vehicle model is 0.023 (sd = 0.016). When applied to the EU front/side population, the US model predicted a 0.065 risk (sd = 0.027), and the EU model predicted a 0.052 risk (sd = 0.025). For the rollover model applied to the US standard population, the US model predicted a risk of 0.071 (sd = 0.024), and the EU model predicted 0.128 risk (sd = 0.057). When applied to the EU rollover standard population, the US model predicted a 0.067 risk (sd = 0.024), and the EU model predicted 0.103 risk (sd = 0.040). The results based on these methods indicate that EU vehicles most likely have a lower risk of MAIS3+F injury in front/side impacts, while US vehicles most likely have a lower risk of MAIS3+F injury in llroovers. These results should be interpreted with an understanding of the uncertainty of the estimates, the study limitations, and our recommendations for further study detailed in the report. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
Extracting falsifiable predictions from sloppy models.

PubMed

Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

2007-12-01

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.
Support Vector Machines for Differential Prediction

PubMed Central

Kuusisto, Finn; Santos Costa, Vitor; Nassif, Houssam; Burnside, Elizabeth; Page, David; Shavlik, Jude

2015-01-01

Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction. In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results. PMID:26158123
Support Vector Machines for Differential Prediction.

PubMed

Kuusisto, Finn; Santos Costa, Vitor; Nassif, Houssam; Burnside, Elizabeth; Page, David; Shavlik, Jude

Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction . In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results.
Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

PubMed Central

Lechevalier, D.; Ak, R.; Ferguson, M.; Law, K. H.; Lee, Y.-T. T.; Rachuri, S.

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain. PMID:29202125
Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML).

PubMed

Park, J; Lechevalier, D; Ak, R; Ferguson, M; Law, K H; Lee, Y-T T; Rachuri, S

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.
The prediction of nonlinear dynamic loads on helicopters from flight variables using artificial neural networks

NASA Technical Reports Server (NTRS)

Cook, A. B.; Fuller, C. R.; O'Brien, W. F.; Cabell, R. H.

1992-01-01

A method of indirectly monitoring component loads through common flight variables is proposed which requires an accurate model of the underlying nonlinear relationships. An artificial neural network (ANN) model learns relationships through exposure to a database of flight variable records and corresponding load histories from an instrumented military helicopter undergoing standard maneuvers. The ANN model, utilizing eight standard flight variables as inputs, is trained to predict normalized time-varying mean and oscillatory loads on two critical components over a range of seven maneuvers. Both interpolative and extrapolative capabilities are demonstrated with agreement between predicted and measured loads on the order of 90 percent to 95 percent. This work justifies pursuing the ANN method of predicting loads from flight variables.
Evaluating the Predictive Value of Growth Prediction Models

ERIC Educational Resources Information Center

Murphy, Daniel L.; Gaertner, Matthew N.

2014-01-01

This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…
Template CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated Affinities

PubMed Central

Cramer, Richard D.

2015-01-01

The possible applicability of the new template CoMFA methodology to the prediction of unknown biological affinities was explored. For twelve selected targets, all ChEMBL binding affinities were used as training and/or prediction sets, making these 3D-QSAR models the most structurally diverse and among the largest ever. For six of the targets, X-ray crystallographic structures provided the aligned templates required as input (BACE, cdk1, chk2, carbonic anhydrase-II, factor Xa, PTP1B). For all targets including the other six (hERG, cyp3A4 binding, endocrine receptor, COX2, D2, and GABAa), six modeling protocols applied to only three familiar ligands provided six alternate sets of aligned templates. The statistical qualities of the six or seven models thus resulting for each individual target were remarkably similar. Also, perhaps unexpectedly, the standard deviations of the errors of cross-validation predictions accompanying model derivations were indistinguishable from the standard deviations of the errors of truly prospective predictions. These standard deviations of prediction ranged from 0.70 to 1.14 log units and averaged 0.89 (8x in concentration units) over the twelve targets, representing an average reduction of almost 50% in uncertainty, compared to the null hypothesis of “predicting” an unknown affinity to be the average of known affinities. These errors of prediction are similar to those from Tanimoto coefficients of fragment occurrence frequencies, the predominant approach to side effect prediction, which template CoMFA can augment by identifying additional active structural classes, by improving Tanimoto-only predictions, by yielding quantitative predictions of potency, and by providing interpretable guidance for avoiding or enhancing any specific target response. PMID:26065424
Development of an integrated model for heat transfer and dynamic growth of Clostridium perfringens during the cooling of cooked boneless ham.

PubMed

Amézquita, A; Weller, C L; Wang, L; Thippareddi, H; Burson, D E

2005-05-25

Numerous small meat processors in the United States have difficulties complying with the stabilization performance standards for preventing growth of Clostridium perfringens by 1 log10 cycle during cooling of ready-to-eat (RTE) products. These standards were established by the Food Safety and Inspection Service (FSIS) of the US Department of Agriculture in 1999. In recent years, several attempts have been made to develop predictive models for growth of C. perfringens within the range of cooling temperatures included in the FSIS standards. Those studies mainly focused on microbiological aspects, using hypothesized cooling rates. Conversely, studies dealing with heat transfer models to predict cooling rates in meat products do not address microbial growth. Integration of heat transfer relationships with C. perfringens growth relationships during cooling of meat products has been very limited. Therefore, a computer simulation scheme was developed to analyze heat transfer phenomena and temperature-dependent C. perfringens growth during cooling of cooked boneless cured ham. The temperature history of ham was predicted using a finite element heat diffusion model. Validation of heat transfer predictions used experimental data collected in commercial meat-processing facilities. For C. perfringens growth, a dynamic model was developed using Baranyi's nonautonomous differential equation. The bacterium's growth model was integrated into the computer program using predicted temperature histories as input values. For cooling cooked hams from 66.6 degrees C to 4.4 degrees C using forced air, the maximum deviation between predicted and experimental core temperature data was 2.54 degrees C. Predicted C. perfringens growth curves obtained from dynamic modeling showed good agreement with validated results for three different cooling scenarios. Mean absolute values of relative errors were below 6%, and deviations between predicted and experimental cell counts were within 0.37 log10 CFU/g. For a cooling process which was in exact compliance with the FSIS stabilization performance standards, a mean net growth of 1.37 log10 CFU/g was predicted. This study introduced the combination of engineering modeling and microbiological modeling as a useful quantitative tool for general food safety applications, such as risk assessment and hazard analysis and critical control points (HACCP) plans.
Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

PubMed

Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

2015-01-01

Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.
Clinical Predictive Modeling Development and Deployment through FHIR Web Services

PubMed Central

Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

2015-01-01

Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction. PMID:26958207
Practical approach to subject-specific estimation of knee joint contact force.

PubMed

Knarr, Brian A; Higginson, Jill S

2015-08-20

Compressive forces experienced at the knee can significantly contribute to cartilage degeneration. Musculoskeletal models enable predictions of the internal forces experienced at the knee, but validation is often not possible, as experimental data detailing loading at the knee joint is limited. Recently available data reporting compressive knee force through direct measurement using instrumented total knee replacements offer a unique opportunity to evaluate the accuracy of models. Previous studies have highlighted the importance of subject-specificity in increasing the accuracy of model predictions; however, these techniques may be unrealistic outside of a research setting. Therefore, the goal of our work was to identify a practical approach for accurate prediction of tibiofemoral knee contact force (KCF). Four methods for prediction of knee contact force were compared: (1) standard static optimization, (2) uniform muscle coordination weighting, (3) subject-specific muscle coordination weighting and (4) subject-specific strength adjustments. Walking trials for three subjects with instrumented knee replacements were used to evaluate the accuracy of model predictions. Predictions utilizing subject-specific muscle coordination weighting yielded the best agreement with experimental data; however this method required in vivo data for weighting factor calibration. Including subject-specific strength adjustments improved models' predictions compared to standard static optimization, with errors in peak KCF less than 0.5 body weight for all subjects. Overall, combining clinical assessments of muscle strength with standard tools available in the OpenSim software package, such as inverse kinematics and static optimization, appears to be a practical method for predicting joint contact force that can be implemented for many applications. Copyright © 2015 Elsevier Ltd. All rights reserved.
A New Approach of Juvenile Age Estimation using Measurements of the Ilium and Multivariate Adaptive Regression Splines (MARS) Models for Better Age Prediction.

PubMed

Corron, Louise; Marchal, François; Condemi, Silvana; Chaumoître, Kathia; Adalian, Pascal

2017-01-01

Juvenile age estimation methods used in forensic anthropology generally lack methodological consistency and/or statistical validity. Considering this, a standard approach using nonparametric Multivariate Adaptive Regression Splines (MARS) models were tested to predict age from iliac biometric variables of male and female juveniles from Marseilles, France, aged 0-12 years. Models using unidimensional (length and width) and bidimensional iliac data (module and surface) were constructed on a training sample of 176 individuals and validated on an independent test sample of 68 individuals. Results show that MARS prediction models using iliac width, module and area give overall better and statistically valid age estimates. These models integrate punctual nonlinearities of the relationship between age and osteometric variables. By constructing valid prediction intervals whose size increases with age, MARS models take into account the normal increase of individual variability. MARS models can qualify as a practical and standardized approach for juvenile age estimation. © 2016 American Academy of Forensic Sciences.
Bayesian averaging over Decision Tree models for trauma severity scoring.

PubMed

Schetinin, V; Jakaite, L; Krzanowski, W

2018-01-01

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. Copyright © 2017 Elsevier B.V. All rights reserved.
Asymmetries of the B →K*μ+μ- decay and the search of new physics beyond the standard model

NASA Astrophysics Data System (ADS)

Fu, Hai-Bing; Wu, Xing-Gang; Cheng, Wei; Zhong, Tao; Sun, Zhan

2018-03-01

In this paper, we compute the forward-backward asymmetry and the isospin asymmetry of the B →K*μ+μ- decay. The B →K* transition form factors (TFFs) are key components of the decay. To achieve a more accurate QCD prediction, we adopt a chiral correlator for calculating the QCD light cone sum rules for those TFFs with the purpose of suppressing the uncertain high-twist distribution amplitudes. Our predictions show that the asymmetries under the standard model and the minimal supersymmetric standard model with minimal flavor violation are close in shape for q2≥6 GeV2 and are consistent with the Belle, LHCb, and CDF data within errors. When q2<2 GeV2, their predictions behave quite differently. Thus, a careful study on the B →K*μ+μ- decay within the small q2 region could be helpful for searching new physics beyond the standard model. As a further application, we also apply the B →K* TFFs to the branching ratio and longitudinal polarization fraction of the B →K*ν ν ¯ decay within different models.
Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation.

PubMed

Cook, Sarah F; Roberts, Jessica K; Samiee-Zafarghandy, Samira; Stockmann, Chris; King, Amber D; Deutsch, Nina; Williams, Elaine F; Allegaert, Karel; Wilkins, Diana G; Sherwin, Catherine M T; van den Anker, John N

2016-01-01

The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Nonlinear mixed-effects models were constructed from paracetamol concentration-time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1-14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1-28.1). Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations.
Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation

PubMed Central

Cook, Sarah F.; Roberts, Jessica K.; Samiee-Zafarghandy, Samira; Stockmann, Chris; King, Amber D.; Deutsch, Nina; Williams, Elaine F.; Allegaert, Karel; Sherwin, Catherine M. T.; van den Anker, John N.

2017-01-01

Objectives The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Methods Nonlinear mixed-effects models were constructed from paracetamol concentration–time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. Results The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1–14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1–28.1). Conclusions Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations. PMID:26201306

Predictive Models and Computational Embryology

EPA Science Inventory

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...
Predicting Student Performance in a Collaborative Learning Environment

ERIC Educational Resources Information Center

Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol

2015-01-01

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Lei; Yang Jinmin

Little Higgs theory naturally predicts a light Higgs boson whose most important discovery channel at the LHC is the diphoton signal pp{yields}h{yields}{gamma}{gamma}. In this work, we perform a comparative study for this signal in some typical little Higgs models, namely, the littlest Higgs model, two littlest Higgs models with T-parity (named LHT-I and LHT-II), and the simplest little Higgs models. We find that compared with the standard model prediction, the diphoton signal rate is always suppressed and the suppression extent can be quite different for different models. The suppression is mild (< or approx. 10%) in the littlest Higgs modelmore » but can be quite severe ({approx_equal}90%) in other three models. This means that discovering the light Higgs boson predicted by the little Higgs theory through the diphoton channel at the LHC will be more difficult than discovering the standard model Higgs boson.« less
Measurement of inclusive radiative B-meson decay B decaying to X(S) meson-gamma

NASA Astrophysics Data System (ADS)

Ozcan, Veysi Erkcan

Radiative decays of the B meson, B→ Xsgamma, proceed via virtual flavor changing neutral current processes that are sensitive to contributions from high mass scales, either within the Standard Model of electroweak interactions or beyond. In the Standard Model, these transitions are sensitive to the weak interactions of the top quark, and relatively robust predictions of the inclusive decay rate exist. Significant deviation from these predictions could be interpreted as indications for processes not included in the minimal Standard Model, like interactions of charged Higgs or SUSY particles. The analysis of the inclusive photon spectrum from B→ Xsgamma decays is rather challenging due to high backgrounds from photons emitted in the decay of mesons in B decays as well as e+e- annihilation to low mass quark and lepton pairs. Based on 88.5 million BB events collected by the BABAR detector, the photon spectrum above 1.9 GeV is presented. By comparison of the first and second moments of the photon spectrum with QCD predictions (calculated in the kinetic scheme), QCD parameters describing the bound state of the b quark in the B meson are extracted: mb=4.45+/-0.16 GeV/c2m2 p=0.65+/-0.29 GeV2 These parameters are useful input to non-perturbative QCD corrections to the semileptonic B decay rate and the determination of the CKM parameter Vub. Based on these parameters and heavy quark expansion, the full branching fraction is obtained as: BRB→X sgEg >1.6GeV=4.050.32 stat+/-0.38syst +/-0.29model x10-4. This result is in good agreement with previous measurements, the statistical and systematic errors are comparable. It is also in good agreement with the theoretical Standard Model predictions, and thus within the present errors there is no indication of any interactions not accounted for in the Standard Model. This finding implies strong constraints on physics beyond the Standard Model.
The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model-Based Standardized Infection Ratio.

PubMed

Fukuda, Haruhisa; Kuroki, Manabu

2016-03-01

To develop and internally validate a surgical site infection (SSI) prediction model for Japan. Retrospective observational cohort study. We analyzed surveillance data submitted to the Japan Nosocomial Infections Surveillance system for patients who had undergone target surgical procedures from January 1, 2010, through December 31, 2012. Logistic regression analyses were used to develop statistical models for predicting SSIs. An SSI prediction model was constructed for each of the procedure categories by statistically selecting the appropriate risk factors from among the collected surveillance data and determining their optimal categorization. Standard bootstrapping techniques were applied to assess potential overfitting. The C-index was used to compare the predictive performances of the new statistical models with those of models based on conventional risk index variables. The study sample comprised 349,987 cases from 428 participant hospitals throughout Japan, and the overall SSI incidence was 7.0%. The C-indices of the new statistical models were significantly higher than those of the conventional risk index models in 21 (67.7%) of the 31 procedure categories (P<.05). No significant overfitting was detected. Japan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.
The Modeling of the Effects of Soiling, Its Mechanisms, and the Corresponding Abrasion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, Lin; Muller, Matthew; Deceglie, Michael

2016-02-24

Decreasing LCOE with predictive soiling loss models (using site data to predict annualized energy loss), quantification of different soiling mechanisms (using AFM-based characterization), and developing standards for PV module coatings.
Predictive Models and Computational Toxicology (II IBAMTOX)

EPA Science Inventory

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...
Towards automating measurements and predictions of Escherichia coli concentrations in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2012–14

USGS Publications Warehouse

Brady, Amie M. G.; Meg B. Plona,

2015-07-30

A computer program was developed to manage the nowcasts by running the predictive models and posting the results to a publicly accessible Web site daily by 9 a.m. The nowcasts were able to correctly predict E. coli concentrations above or below the water-quality standard at Jaite for 79 percent of the samples compared with the measured concentrations. In comparison, the persistence model (using the previous day’s sample concentration) correctly predicted concentrations above or below the water-quality standard in only 68 percent of the samples. To determine if the Jaite nowcast could be used for the stretch of the river between Lock 29 and Jaite, the model predictions for Jaite were compared with the measured concentrations at Lock 29. The Jaite nowcast provided correct responses for 77 percent of the Lock 29 samples, which was a greater percentage than the percentage of correct responses (58 percent) from the persistence model at Lock 29.
Incorporating Midbrain Adaptation to Mean Sound Level Improves Models of Auditory Cortical Processing

PubMed Central

Schoppe, Oliver; King, Andrew J.; Schnupp, Jan W.H.; Harper, Nicol S.

2016-01-01

Adaptation to stimulus statistics, such as the mean level and contrast of recently heard sounds, has been demonstrated at various levels of the auditory pathway. It allows the nervous system to operate over the wide range of intensities and contrasts found in the natural world. Yet current standard models of the response properties of auditory neurons do not incorporate such adaptation. Here we present a model of neural responses in the ferret auditory cortex (the IC Adaptation model), which takes into account adaptation to mean sound level at a lower level of processing: the inferior colliculus (IC). The model performs high-pass filtering with frequency-dependent time constants on the sound spectrogram, followed by half-wave rectification, and passes the output to a standard linear–nonlinear (LN) model. We find that the IC Adaptation model consistently predicts cortical responses better than the standard LN model for a range of synthetic and natural stimuli. The IC Adaptation model introduces no extra free parameters, so it improves predictions without sacrificing parsimony. Furthermore, the time constants of adaptation in the IC appear to be matched to the statistics of natural sounds, suggesting that neurons in the auditory midbrain predict the mean level of future sounds and adapt their responses appropriately. SIGNIFICANCE STATEMENT An ability to accurately predict how sensory neurons respond to novel stimuli is critical if we are to fully characterize their response properties. Attempts to model these responses have had a distinguished history, but it has proven difficult to improve their predictive power significantly beyond that of simple, mostly linear receptive field models. Here we show that auditory cortex receptive field models benefit from a nonlinear preprocessing stage that replicates known adaptation properties of the auditory midbrain. This improves their predictive power across a wide range of stimuli but keeps model complexity low as it introduces no new free parameters. Incorporating the adaptive coding properties of neurons will likely improve receptive field models in other sensory modalities too. PMID:26758822
Orbital Debris Modeling

NASA Technical Reports Server (NTRS)

Liou, J. C.

2012-01-01

Presentation outlne: (1) The NASA Orbital Debris (OD) Engineering Model -- A mathematical model capable of predicting OD impact risks for the ISS and other critical space assets (2) The NASA OD Evolutionary Model -- A physical model capable of predicting future debris environment based on user-specified scenarios (3) The NASA Standard Satellite Breakup Model -- A model describing the outcome of a satellite breakup (explosion or collision)
Determinants of Antibiotic Consumption - Development of a Model using Partial Least Squares Regression based on Data from India.

PubMed

Tamhankar, Ashok J; Karnik, Shreyasee S; Stålsby Lundborg, Cecilia

2018-04-23

Antibiotic resistance, a consequence of antibiotic use, is a threat to health, with severe consequences for resource constrained settings. If determinants for human antibiotic use in India, a lower middle income country, with one of the highest antibiotic consumption in the world could be understood, interventions could be developed, having implications for similar settings. Year wise data for India, for potential determinants and antibiotic consumption, was sourced from publicly available databases for the years 2000-2010. Data was analyzed using Partial Least Squares regression and correlation between determinants and antibiotic consumption was evaluated, formulating 'Predictors' and 'Prediction models'. The 'prediction model' with the statistically most significant predictors (root mean square errors of prediction for train set-377.0 and test set-297.0) formulated from a combination of Health infrastructure + Surface transport infrastructure (HISTI), predicted antibiotic consumption within 95% confidence interval and estimated an antibiotic consumption of 11.6 standard units/person (14.37 billion standard units totally; standard units = number of doses sold in the country; a dose being a pill, capsule, or ampoule) for India for 2014. The HISTI model may become useful in predicting antibiotic consumption for countries/regions having circumstances and data similar to India, but without resources to measure actual data of antibiotic consumption.
Updating the Standard Spatial Observer for Contrast Detection

NASA Technical Reports Server (NTRS)

Ahumada, Albert J.; Watson, Andrew B.

2011-01-01

Watson and Ahmuada (2005) constructed a Standard Spatial Observer (SSO) model for foveal luminance contrast signal detection based on the Medelfest data (Watson, 1999). Here we propose two changes to the model, dropping the oblique effect from the CSF and using the cone density data of Curcio et al. (1990) to estimate the variation of sensitivity with eccentricity. Dropping the complex images, and using medians to exclude outlier data points, the SSO model now accounts for essentially all the predictable variance in the data, with an RMS prediction error of only 0.67 dB.
Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate

PubMed Central

2013-01-01

Background The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Methods and findings Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China. The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Conclusions Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships. PMID:23497145
Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate.

PubMed

Yang, Qingsheng; Mwenda, Kevin M; Ge, Miao

2013-03-12

The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China.The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships.
Approaches to setting organism-based ballast water discharge standards

USGS Publications Warehouse

Lee, Henry; Reusser, Deborah A.; Frazier, Melanie

2013-01-01

As a vector by which foreign species invade coastal and freshwater waterbodies, ballast water discharge from ships is recognized as a major environmental threat. The International Maritime Organization (IMO) drafted an international treaty establishing ballast water discharge standards based on the number of viable organisms per volume of ballast discharge for different organism size classes. Concerns that the IMO standards are not sufficiently protective have initiated several state and national efforts in the United States to develop more stringent standards. We evaluated seven approaches to establishing discharge standards for the >50-μm size class: (1) expert opinion/management consensus, (2) zero detectable living organisms, (3) natural invasion rates, (4) reaction–diffusion models, (5) population viability analysis (PVA) models, (6) per capita invasion probabilities (PCIP), and (7) experimental studies. Because of the difficulty in synthesizing scientific knowledge in an unbiased and transparent fashion, we recommend the use of quantitative models instead of expert opinion. The actual organism concentration associated with a “zero detectable organisms” standard is defined by the statistical rigor of its monitoring program; thus it is not clear whether such a standard is as stringent as other standards. For several reasons, the natural invasion rate, reaction–diffusion, and experimental approaches are not considered suitable for generating discharge standards. PVA models can be used to predict the likelihood of establishment of introduced species but are limited by a lack of population vital rates for species characteristic of ballast water discharges. Until such rates become available, PVA models are better suited to evaluate relative efficiency of proposed standards rather than predicting probabilities of invasion. The PCIP approach, which is based on historical invasion rates at a regional scale, appears to circumvent many of the indicated problems, although it may underestimate invasions by asexual and parthenogenic species. Further research is needed to better define propagule dose–responses, densities at which Allee effects occur, approaches to predicting the likelihood of invasion from multi-species introductions, and generation of formal comparisons of approaches using standardized scenarios.
Dark matter and MOND dynamical models of the massive spiral galaxy NGC 2841

NASA Astrophysics Data System (ADS)

Samurović, S.; Vudragović, A.; Jovanović, M.

2015-08-01

We study dynamical models of the massive spiral galaxy NGC 2841 using both the Newtonian models with Navarro-Frenk-White (NFW) and isothermal dark haloes, as well as various MOND (MOdified Newtonian Dynamics) models. We use the observations coming from several publicly available data bases: we use radio data, near-infrared photometry as well as spectroscopic observations. In our models, we find that both tested Newtonian dark matter approaches can successfully fit the observed rotational curve of NGC 2841. The three tested MOND models (standard, simple and, for the first time applied to another spiral galaxy than the Milky Way, Bekenstein's toy model) provide fits of the observed rotational curve with various degrees of success: the best result was obtained with the standard MOND model. For both approaches, Newtonian and MOND, the values of the mass-to-light ratios of the bulge are consistent with the predictions from the stellar population synthesis (SPS) based on the Salpeter initial mass function (IMF). Also, for Newtonian and simple and standard MOND models, the estimated stellar mass-to-light ratios of the disc agree with the predictions from the SPS models based on the Kroupa IMF, whereas the toy MOND model provides too low a value of the stellar mass-to-light ratio, incompatible with the predictions of the tested SPS models. In all our MOND models, we vary the distance to NGC 2841, and our best-fitting standard and toy models use the values higher than the Cepheid-based distance to the galaxy NGC 2841, and the best-fitting simple MOND model is based on the lower value of the distance. The best-fitting NFW model is inconsistent with the predictions of the Λ cold dark matter cosmology, because the inferred concentration index is too high for the established virial mass.
A new seasonal-deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios.

PubMed

Chen, Min; Melaas, Eli K; Gray, Josh M; Friedl, Mark A; Richardson, Andrew D

2016-11-01

A spring phenology model that combines photoperiod with accumulated heating and chilling to predict spring leaf-out dates is optimized using PhenoCam observations and coupled into the Community Land Model (CLM) 4.5. In head-to-head comparison (using satellite data from 2003 to 2013 for validation) for model grid cells over the Northern Hemisphere deciduous broadleaf forests (5.5 million km 2 ), we found that the revised model substantially outperformed the standard CLM seasonal-deciduous spring phenology submodel at both coarse (0.9 × 1.25°) and fine (1 km) scales. The revised model also does a better job of representing recent (decadal) phenological trends observed globally by MODIS, as well as long-term trends (1950-2014) in the PEP725 European phenology dataset. Moreover, forward model runs suggested a stronger advancement (up to 11 days) of spring leaf-out by the end of the 21st century for the revised model. Trends toward earlier advancement are predicted for deciduous forests across the whole Northern Hemisphere boreal and temperate deciduous forest region for the revised model, whereas the standard model predicts earlier leaf-out in colder regions, but later leaf-out in warmer regions, and no trend globally. The earlier spring leaf-out predicted by the revised model resulted in enhanced gross primary production (up to 0.6 Pg C yr -1 ) and evapotranspiration (up to 24 mm yr -1 ) when results were integrated across the study region. These results suggest that the standard seasonal-deciduous submodel in CLM should be reconsidered, otherwise substantial errors in predictions of key land-atmosphere interactions and feedbacks may result. © 2016 John Wiley & Sons Ltd.
Validation of BEHAVE fire behavior predictions in oak savannas using five fuel models

Treesearch

Keith Grabner; John Dwyer; Bruce Cutter

1997-01-01

Prescribed fire is a valuable tool in the restoration and management of oak savannas. BEHAVE, a fire behavior prediction system developed by the United States Forest Service, can be a useful tool when managing oak savannas with prescribed fire. BEHAVE predictions of fire rate-of-spread and flame length were validated using four standardized fuel models: Fuel Model 1 (...
Water quality management using statistical analysis and time-series prediction model

NASA Astrophysics Data System (ADS)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.
Practical approach to subject-specific estimation of knee joint contact force

PubMed Central

Knarr, Brian A.; Higginson, Jill S.

2015-01-01

Compressive forces experienced at the knee can significantly contribute to cartilage degeneration. Musculoskeletal models enable predictions of the internal forces experienced at the knee, but validation is often not possible, as experimental data detailing loading at the knee joint is limited. Recently available data reporting compressive knee force through direct measurement using instrumented total knee replacements offer a unique opportunity to evaluate the accuracy of models. Previous studies have highlighted the importance of subject-specificity in increasing the accuracy of model predictions; however, these techniques may be unrealistic outside of a research setting. Therefore, the goal of our work was to identify a practical approach for accurate prediction of tibiofemoral knee contact force (KCF). Four methods for prediction of knee contact force were compared: (1) standard static optimization, (2) uniform muscle coordination weighting, (3) subject-specific muscle coordination weighting and (4) subject-specific strength adjustments. Walking trials for three subjects with instrumented knee replacements were used to evaluate the accuracy of model predictions. Predictions utilizing subject-specific muscle coordination weighting yielded the best agreement with experimental data, however this method required in vivo data for weighting factor calibration. Including subject-specific strength adjustments improved models’ predictions compared to standard static optimization, with errors in peak KCF less than 0.5 body weight for all subjects. Overall, combining clinical assessments of muscle strength with standard tools available in the OpenSim software package, such as inverse kinematics and static optimization, appears to be a practical method for predicting joint contact force that can be implemented for many applications. PMID:25952546

Numerical models of laser fusion of intestinal tissues.

PubMed

Pearce, John A

2009-01-01

Numerical models of continuous wave Tm:YAG thermal fusion in rat intestinal tissues were compared to experiment. Optical and thermal FDM models that included tissue damage based on Arrhenius kinetics were used to predict birefringence loss in collagen as the standard of comparison. The models also predicted collagen shrinkage, jellification and water loss. The inclusion of variable optical and thermal properties is essential to achieve favorable agreement between predicted and measured damage boundaries.
Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

PubMed Central

Parker, Joel S.; Mullins, Michael; Cheang, Maggie C.U.; Leung, Samuel; Voduc, David; Vickery, Tammi; Davies, Sherri; Fauron, Christiane; He, Xiaping; Hu, Zhiyuan; Quackenbush, John F.; Stijleman, Inge J.; Palazzo, Juan; Marron, J.S.; Nobel, Andrew B.; Mardis, Elaine; Nielsen, Torsten O.; Ellis, Matthew J.; Perou, Charles M.; Bernard, Philip S.

2009-01-01

Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. Conclusion Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy. PMID:19204204
What No Child Left Behind Leaves Behind: The Roles of IQ and Self-Control in Predicting Standardized Achievement Test Scores and Report Card Grades

PubMed Central

Duckworth, Angela L.; Quinn, Patrick D.; Tsukayama, Eli

2013-01-01

The increasing prominence of standardized testing to assess student learning motivated the current investigation. We propose that standardized achievement test scores assess competencies determined more by intelligence than by self-control, whereas report card grades assess competencies determined more by self-control than by intelligence. In particular, we suggest that intelligence helps students learn and solve problems independent of formal instruction, whereas self-control helps students study, complete homework, and behave positively in the classroom. Two longitudinal, prospective studies of middle school students support predictions from this model. In both samples, IQ predicted changes in standardized achievement test scores over time better than did self-control, whereas self-control predicted changes in report card grades over time better than did IQ. As expected, the effect of self-control on changes in report card grades was mediated in Study 2 by teacher ratings of homework completion and classroom conduct. In a third study, ratings of middle school teachers about the content and purpose of standardized achievement tests and report card grades were consistent with the proposed model. Implications for pedagogy and public policy are discussed. PMID:24072936
Whole-Motion Model of Perception during Forward- and Backward-Facing Centrifuge Runs

PubMed Central

Holly, Jan E.; Vrublevskis, Arturs; Carlson, Lindsay E.

2009-01-01

Illusory perceptions of motion and orientation arise during human centrifuge runs without vision. Asymmetries have been found between acceleration and deceleration, and between forward-facing and backward-facing runs. Perceived roll tilt has been studied extensively during upright fixed-carriage centrifuge runs, and other components have been studied to a lesser extent. Certain, but not all, perceptual asymmetries in acceleration-vs-deceleration and forward-vs-backward motion can be explained by existing analyses. The immediate acceleration-deceleration roll-tilt asymmetry can be explained by the three-dimensional physics of the external stimulus; in addition, longer-term data has been modeled in a standard way using physiological time constants. However, the standard modeling approach is shown in the present research to predict forward-vs-backward-facing symmetry in perceived roll tilt, contradicting experimental data, and to predict perceived sideways motion, rather than forward or backward motion, around a curve. The present work develops a different whole-motion-based model taking into account the three-dimensional form of perceived motion and orientation. This model predicts perceived forward or backward motion around a curve, and predicts additional asymmetries such as the forward-backward difference in roll tilt. This model is based upon many of the same principles as the standard model, but includes an additional concept of familiarity of motions as a whole. PMID:19208962
Inflation in the standard cosmological model

NASA Astrophysics Data System (ADS)

Uzan, Jean-Philippe

2015-12-01

The inflationary paradigm is now part of the standard cosmological model as a description of its primordial phase. While its original motivation was to solve the standard problems of the hot big bang model, it was soon understood that it offers a natural theory for the origin of the large-scale structure of the universe. Most models rely on a slow-rolling scalar field and enjoy very generic predictions. Besides, all the matter of the universe is produced by the decay of the inflaton field at the end of inflation during a phase of reheating. These predictions can be (and are) tested from their imprint of the large-scale structure and in particular the cosmic microwave background. Inflation stands as a window in physics where both general relativity and quantum field theory are at work and which can be observationally studied. It connects cosmology with high-energy physics. Today most models are constructed within extensions of the standard model, such as supersymmetry or string theory. Inflation also disrupts our vision of the universe, in particular with the ideas of chaotic inflation and eternal inflation that tend to promote the image of a very inhomogeneous universe with fractal structure on a large scale. This idea is also at the heart of further speculations, such as the multiverse. This introduction summarizes the connections between inflation and the hot big bang model and details the basics of its dynamics and predictions. xml:lang="fr"
Solar g-modes? Comparison of detected asymptotic g-mode frequencies with solar model predictions

NASA Astrophysics Data System (ADS)

Wood, Suzannah Rebecca; Guzik, Joyce Ann; Mussack, Katie; Bradley, Paul A.

2018-06-01

After many years of searching for solar gravity modes, Fossat et al. (2017) reported detection of the nearly equally spaced high-order g-modes periods using a 15-year time series of GOLF data from the SOHO spacecraft. Here we report progress towards and challenges associated with calculating and comparing g-mode period predictions for several previously published standard solar models using various abundance mixtures and opacities, as well as the predictions for some non-standard models incorporating early mass loss, and compare with the periods reported by Fossat et al (2017). Additionally, we have a side-by-side comparison of results of different stellar pulsation codes for calculating g-mode predictions. These comparisons will allow for testing of nonstandard physics input that affect the core, including an early more massive Sun and dynamic electron screening.
Derivation and Validation of a Risk Standardization Model for Benchmarking Hospital Performance for Health-Related Quality of Life Outcomes after Acute Myocardial Infarction

PubMed Central

Arnold, Suzanne V.; Masoudi, Frederick A.; Rumsfeld, John S.; Li, Yan; Jones, Philip G.; Spertus, John A.

2014-01-01

Background Before outcomes-based measures of quality can be used to compare and improve care, they must be risk-standardized to account for variations in patient characteristics. Despite the importance of health-related quality of life (HRQL) outcomes among patients with acute myocardial infarction (AMI), no risk-standardized models have been developed. Methods and Results We assessed disease-specific HRQL using the Seattle Angina Questionnaire at baseline and 1 year later in 2693 unselected AMI patients from 24 hospitals enrolled in the TRIUMPH registry. Using 57 candidate sociodemographic, economic, and clinical variables present on admission, we developed a parsimonious, hierarchical linear regression model to predict HRQL. Eleven variables were independently associated with poor HRQL after AMI, including younger age, prior CABG, depressive symptoms, and financial difficulties (R2=20%). The model demonstrated excellent internal calibration and reasonable calibration in an independent sample of 1890 AMI patients in a separate registry, although the model slightly over-predicted HRQL scores in the higher deciles. Among the 24 TRIUMPH hospitals, 1-year unadjusted HRQL scores ranged from 67–89. After risk-standardization, HRQL scores variability narrowed substantially (range=79–83), and the group of hospital performance (bottom 20%/middle 60%/top 20%) changed in 14 of the 24 hospitals (58% reclassification with risk-standardization). Conclusions In this predictive model for HRQL after AMI, we identified risk factors, including economic and psychological characteristics, associated with HRQL outcomes. Adjusting for these factors substantially altered the rankings of hospitals as compared with unadjusted comparisons. Using this model to compare risk-standardized HRQL outcomes across hospitals may identify processes of care that maximize this important patient-centered outcome. PMID:24163068
OPR-PPR, a Computer Program for Assessing Data Importance to Model Predictions Using Linear Statistics

USGS Publications Warehouse

Tonkin, Matthew J.; Tiedeman, Claire; Ely, D. Matthew; Hill, Mary C.

2007-01-01

The OPR-PPR program calculates the Observation-Prediction (OPR) and Parameter-Prediction (PPR) statistics that can be used to evaluate the relative importance of various kinds of data to simulated predictions. The data considered fall into three categories: (1) existing observations, (2) potential observations, and (3) potential information about parameters. The first two are addressed by the OPR statistic; the third is addressed by the PPR statistic. The statistics are based on linear theory and measure the leverage of the data, which depends on the location, the type, and possibly the time of the data being considered. For example, in a ground-water system the type of data might be a head measurement at a particular location and time. As a measure of leverage, the statistics do not take into account the value of the measurement. As linear measures, the OPR and PPR statistics require minimal computational effort once sensitivities have been calculated. Sensitivities need to be calculated for only one set of parameter values; commonly these are the values estimated through model calibration. OPR-PPR can calculate the OPR and PPR statistics for any mathematical model that produces the necessary OPR-PPR input files. In this report, OPR-PPR capabilities are presented in the context of using the ground-water model MODFLOW-2000 and the universal inverse program UCODE_2005. The method used to calculate the OPR and PPR statistics is based on the linear equation for prediction standard deviation. Using sensitivities and other information, OPR-PPR calculates (a) the percent increase in the prediction standard deviation that results when one or more existing observations are omitted from the calibration data set; (b) the percent decrease in the prediction standard deviation that results when one or more potential observations are added to the calibration data set; or (c) the percent decrease in the prediction standard deviation that results when potential information on one or more parameters is added.
Testing In College Admissions: An Alternative to the Traditional Predictive Model.

ERIC Educational Resources Information Center

Lunneborg, Clifford E.

1982-01-01

A decision-making or utility theory model (which deals effectively with affirmative action goals and allows standardized tests to be placed in the service of those goals) is discussed as an alternative to traditional predictive admissions. (Author/PN)
Ensemble Learning of QTL Models Improves Prediction of Complex Traits

PubMed Central

Bian, Yang; Holland, James B.

2015-01-01

Quantitative trait locus (QTL) models can provide useful insights into trait genetic architecture because of their straightforward interpretability but are less useful for genetic prediction because of the difficulty in including the effects of numerous small effect loci without overfitting. Tight linkage between markers introduces near collinearity among marker genotypes, complicating the detection of QTL and estimation of QTL effects in linkage mapping, and this problem is exacerbated by very high density linkage maps. Here we developed a thinning and aggregating (TAGGING) method as a new ensemble learning approach to QTL mapping. TAGGING reduces collinearity problems by thinning dense linkage maps, maintains aspects of marker selection that characterize standard QTL mapping, and by ensembling, incorporates information from many more markers-trait associations than traditional QTL mapping. The objective of TAGGING was to improve prediction power compared with QTL mapping while also providing more specific insights into genetic architecture than genome-wide prediction models. TAGGING was compared with standard QTL mapping using cross validation of empirical data from the maize (Zea mays L.) nested association mapping population. TAGGING-assisted QTL mapping substantially improved prediction ability for both biparental and multifamily populations by reducing both the variance and bias in prediction. Furthermore, an ensemble model combining predictions from TAGGING-assisted QTL and infinitesimal models improved prediction abilities over the component models, indicating some complementarity between model assumptions and suggesting that some trait genetic architectures involve a mixture of a few major QTL and polygenic effects. PMID:26276383
The Standard Model.

PubMed

Shears, Tara

2012-02-28

The Standard Model is the theory used to describe the interactions between fundamental particles and fundamental forces. It is remarkably successful at predicting the outcome of particle physics experiments. However, the theory has not yet been completely verified. In particular, one of the most vital constituents, the Higgs boson, has not yet been observed. This paper describes the Standard Model, the experimental tests of the theory that have led to its acceptance and its shortcomings.
Translational Modeling to Guide Study Design and Dose Choice in Obesity Exemplified by AZD1979, a Melanin‐concentrating Hormone Receptor 1 Antagonist

PubMed Central

Trägårdh, M; Lindén, D; Ploj, K; Johansson, A; Turnbull, A; Carlsson, B; Antonsson, M

2017-01-01

In this study, we present the translational modeling used in the discovery of AZD1979, a melanin‐concentrating hormone receptor 1 (MCHr1) antagonist aimed for treatment of obesity. The model quantitatively connects the relevant biomarkers and thereby closes the scaling path from rodent to man, as well as from dose to effect level. The complexity of individual modeling steps depends on the quality and quantity of data as well as the prior information; from semimechanistic body‐composition models to standard linear regression. Key predictions are obtained by standard forward simulation (e.g., predicting effect from exposure), as well as non‐parametric input estimation (e.g., predicting energy intake from longitudinal body‐weight data), across species. The work illustrates how modeling integrates data from several species, fills critical gaps between biomarkers, and supports experimental design and human dose‐prediction. We believe this approach can be of general interest for translation in the obesity field, and might inspire translational reasoning more broadly. PMID:28556607
Origins of the high flux hohlraum model

NASA Astrophysics Data System (ADS)

Rosen, M. D.; Hinkel, D. E.; Williams, E. A.; Callahan, D. A.; Town, R. P. J.; Scott, H. A.; Kruer, W. L.; Suter, L. J.

2010-11-01

We review how the ``high flux model'' (HFM) helped clarify the performance of the Autumn 09 National Ignition Campaign (NIC) gas filled/capsule imploding hohlraum energetics campaign. This campaign showed good laser-hohlraum coupling, reasonably high drive, and implosion symmetry control via cross beam transfer. Mysteries that remained included the level and spectrum of the Stimulated Raman light, the tendency towards pancaked implosions, and drive that exceeded (standard model) predictions early in the campaign, and lagged those predictions late in the campaign. The HFM uses a detailed configuration accounting (DCA) atomic physics and a generous flux limiter (f=0.2) both of which contribute to predicting a hohlraum plasma that is cooler than the standard, XSN average atom, f=0.05 model. This cooler plasma proved to be key in solving all of those mysteries. Despite past successes of the HFM in correctly modeling Omega Laser Au sphere data and NIC empty hohlraum drive, the model lacked some credibility for this energetics campaign, because it predicted too much hohlraum drive. Its credibility was then boosted by a re-evaluation of the initially reported SRS levels.
Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

PubMed

Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of more than 10% over the standard classification models, which can be translated to correct labeling of additional 400 - 500 readmissions for heart failure patients in the state of California over a year. Lastly, several key predictor identified from the HCUP data include the disposition location from discharge, the number of chronic conditions, and the number of acute procedures. It would be beneficial to apply simple decision rules obtained from the decision tree in an ad-hoc manner to guide the cohort stratification. It could be potentially beneficial to explore the effect of pairwise interactions between influential predictors when building the logistic regression models for different data strata. Judicious use of the ad-hoc CLR models developed offers insights into future development of prediction models for hospital readmissions, which can lead to better intuition in identifying high-risk patients and developing effective post-discharge care strategies. Lastly, this paper is expected to raise the awareness of collecting data on additional markers and developing necessary database infrastructure for larger-scale exploratory studies on readmission risk prediction.
Effects of stimulus order on discrimination processes in comparative and equality judgements: data and models.

PubMed

Dyjas, Oliver; Ulrich, Rolf

2014-01-01

In typical discrimination experiments, participants are presented with a constant standard and a variable comparison stimulus and their task is to judge which of these two stimuli is larger (comparative judgement). In these experiments, discrimination sensitivity depends on the temporal order of these stimuli (Type B effect) and is usually higher when the standard precedes rather than follows the comparison. Here, we outline how two models of stimulus discrimination can account for the Type B effect, namely the weighted difference model (or basic Sensation Weighting model) and the Internal Reference Model. For both models, the predicted psychometric functions for comparative judgements as well as for equality judgements, in which participants indicate whether they perceived the two stimuli to be equal or not equal, are derived and it is shown that the models also predict a Type B effect for equality judgements. In the empirical part, the models' predictions are evaluated. To this end, participants performed a duration discrimination task with comparative judgements and with equality judgements. In line with the models' predictions, a Type B effect was observed for both judgement types. In addition, a time-order error, as indicated by shifts of the psychometric functions, and differences in response times were observed only for the equality judgement. Since both models entail distinct additional predictions, it seems worthwhile for future research to unite the two models into one conceptual framework.
Fuel model selection for BEHAVE in midwestern oak savannas

USGS Publications Warehouse

Grabner, K.W.; Dwyer, J.P.; Cutter, B.E.

2001-01-01

BEHAVE, a fire behavior prediction system, can be a useful tool for managing areas with prescribed fire. However, the proper choice of fuel models can be critical in developing management scenarios. BEHAVE predictions were evaluated using four standardized fuel models that partially described oak savanna fuel conditions: Fuel Model 1 (Short Grass), 2 (Timber and Grass), 3 (Tall Grass), and 9 (Hardwood Litter). Although all four models yielded regressions with R2 in excess of 0.8, Fuel Model 2 produced the most reliable fire behavior predictions.
Trapped Radiation Model Uncertainties: Model-Data and Model-Model Comparisons

NASA Technical Reports Server (NTRS)

Armstrong, T. W.; Colborn, B. L.

2000-01-01

The standard AP8 and AE8 models for predicting trapped proton and electron environments have been compared with several sets of flight data to evaluate model uncertainties. Model comparisons are made with flux and dose measurements made on various U.S. low-Earth orbit satellites (APEX, CRRES, DMSP, LDEF, NOAA) and Space Shuttle flights, on Russian satellites (Photon-8, Cosmos-1887, Cosmos-2044), and on the Russian Mir Space Station. This report gives the details of the model-data comparisons-summary results in terms of empirical model uncertainty factors that can be applied for spacecraft design applications are given in a combination report. The results of model-model comparisons are also presented from standard AP8 and AE8 model predictions compared with the European Space Agency versions of AP8 and AE8 and with Russian-trapped radiation models.
Trapped Radiation Model Uncertainties: Model-Data and Model-Model Comparisons

NASA Technical Reports Server (NTRS)

Armstrong, T. W.; Colborn, B. L.

2000-01-01

The standard AP8 and AE8 models for predicting trapped proton and electron environments have been compared with several sets of flight data to evaluate model uncertainties. Model comparisons are made with flux and dose measurements made on various U.S. low-Earth orbit satellites (APEX, CRRES, DMSP. LDEF, NOAA) and Space Shuttle flights, on Russian satellites (Photon-8, Cosmos-1887, Cosmos-2044), and on the Russian Mir space station. This report gives the details of the model-data comparisons -- summary results in terms of empirical model uncertainty factors that can be applied for spacecraft design applications are given in a companion report. The results of model-model comparisons are also presented from standard AP8 and AE8 model predictions compared with the European Space Agency versions of AP8 and AE8 and with Russian trapped radiation models.
Investigation of Homogeneous Relaxation Model Parameters and their Implications for Gasoline Injectors

DOE PAGES

Saha, Kaushik; Som, Sibendu; Battistoni, Michele

2017-01-01

Flash boiling is known to be a common phenomenon for gasoline direct injection (GDI) engine sprays. The Homogeneous Relaxation Model has been adopted in many recent numerical studies for predicting cavitation and flash boiling. The Homogeneous Relaxation Model is assessed in this study. Sensitivity analysis of the model parameters has been documented to infer the driving factors for the flash-boiling predictions. The model parameters have been varied over a range and the differences in predictions of the extent of flashing have been studied. Apart from flashing in the near nozzle regions, mild cavitation is also predicted inside the gasoline injectors.more » The variation in the predicted time scales through the model parameters for predicting these two different thermodynamic phenomena (cavitation, flash) have been elaborated in this study. Turbulence model effects have also been investigated by comparing predictions from the standard and Re-Normalization Group (RNG) k-ε turbulence models.« less
A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction

PubMed Central

Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José Cricelio; Luna-Vázquez, Francisco Javier; Salinas-Ruiz, Josafhat; Herrera-Morales, José R.; Buenrostro-Mariscal, Raymundo

2017-01-01

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments. PMID:28391241

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José Cricelio; Luna-Vázquez, Francisco Javier; Salinas-Ruiz, Josafhat; Herrera-Morales, José R; Buenrostro-Mariscal, Raymundo

2017-06-07

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments. Copyright © 2017 Montesinos-López et al.
DSSTOX WEBSITE LAUNCH: IMPROVING PUBLIC ACCESS TO DATABASES FOR BUILDING STRUCTURE-TOXICITY PREDICTION MODELS

EPA Science Inventory

DSSTox Website Launch: Improving Public Access to Databases for Building Structure-Toxicity Prediction Models
Ann M. Richard
US Environmental Protection Agency, Research Triangle Park, NC, USA

Distributed: Decentralized set of standardized, field-delimited databases,...
Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...
The Mt. Hood challenge: cross-testing two diabetes simulation models.

PubMed

Brown, J B; Palmer, A J; Bisgaard, P; Chan, W; Pedula, K; Russell, A

2000-11-01

Starting from identical patients with type 2 diabetes, we compared the 20-year predictions of two computer simulation models, a 1998 version of the IMIB model and version 2.17 of the Global Diabetes Model (GDM). Primary measures of outcome were 20-year cumulative rates of: survival, first (incident) acute myocardial infarction (AMI), first stroke, proliferative diabetic retinopathy (PDR), macro-albuminuria (gross proteinuria, or GPR), and amputation. Standardized test patients were newly diagnosed males aged 45 or 75, with high and low levels of glycated hemoglobin (HbA(1c)), systolic blood pressure (SBP), and serum lipids. Both models generated realistic results and appropriate responses to changes in risk factors. Compared with the GDM, the IMIB model predicted much higher rates of mortality and AMI, and fewer strokes. These differences can be explained by differences in model architecture (Markov vs. microsimulation), different evidence bases for cardiovascular prediction (Framingham Heart Study cohort vs. Kaiser Permanente patients), and isolated versus interdependent prediction of cardiovascular events. Compared with IMIB, GDM predicted much higher lifetime costs, because of lower mortality and the use of a different costing method. It is feasible to cross-validate and explicate dissimilar diabetes simulation models using standardized patients. The wide differences in the model results that we observed demonstrate the need for cross-validation. We propose to hold a second 'Mt Hood Challenge' in 2001 and invite all diabetes modelers to attend.
Development of Predictive Models for the Growth Kinetics of Listeria monocytogenes on Fresh Pork under Different Storage Temperatures.

PubMed

Luo, Ke; Hong, Sung-Sam; Wang, Jun; Chung, Mi-Ja; Deog-Hwan, Oh

2015-05-01

This study was conducted to develop a predictive model to estimate the growth of Listeria monocytogenes on fresh pork during storage at constant temperatures (5, 10, 15, 20, 25, 30, and 35°C). The Baranyi model was fitted to growth data (log CFU per gram) to calculate the specific growth rate (SGR) and lag time (LT) with a high coefficient of determination (R(2) > 0.98). As expected, SGR increased with a decline in LT with rising temperatures in all samples. Secondary models were then developed to describe the variation of SGR and LT as a function of temperature. Subsequently, the developed models were validated with additional independent growth data collected at 7, 17, 27, and 37°C and from published reports using proportion of relative errors and proportion of standard error of prediction. The proportion of relative errors of the SGR and LT models developed herein were 0.79 and 0.18, respectively. In addition, the standard error of prediction values of the SGR and LT of L. monocytogenes ranged from 25.7 to 33.1% and from 44.92 to 58.44%, respectively. These results suggest that the model developed in this study was capable of predicting the growth of L. monocytogenes under various isothermal conditions.
Benchmarking test of empirical root water uptake models

NASA Astrophysics Data System (ADS)

dos Santos, Marcos Alex; de Jong van Lier, Quirijn; van Dam, Jos C.; Freire Bezerra, Andre Herman

2017-01-01

Detailed physical models describing root water uptake (RWU) are an important tool for the prediction of RWU and crop transpiration, but the hydraulic parameters involved are hardly ever available, making them less attractive for many studies. Empirical models are more readily used because of their simplicity and the associated lower data requirements. The purpose of this study is to evaluate the capability of some empirical models to mimic the RWU distribution under varying environmental conditions predicted from numerical simulations with a detailed physical model. A review of some empirical models used as sub-models in ecohydrological models is presented, and alternative empirical RWU models are proposed. All these empirical models are analogous to the standard Feddes model, but differ in how RWU is partitioned over depth or how the transpiration reduction function is defined. The parameters of the empirical models are determined by inverse modelling of simulated depth-dependent RWU. The performance of the empirical models and their optimized empirical parameters depends on the scenario. The standard empirical Feddes model only performs well in scenarios with low root length density R, i.e. for scenarios with low RWU compensation. For medium and high R, the Feddes RWU model cannot mimic properly the root uptake dynamics as predicted by the physical model. The Jarvis RWU model in combination with the Feddes reduction function (JMf) only provides good predictions for low and medium R scenarios. For high R, it cannot mimic the uptake patterns predicted by the physical model. Incorporating a newly proposed reduction function into the Jarvis model improved RWU predictions. Regarding the ability of the models to predict plant transpiration, all models accounting for compensation show good performance. The Akaike information criterion (AIC) indicates that the Jarvis (2010) model (JMII), with no empirical parameters to be estimated, is the best model. The proposed models are better in predicting RWU patterns similar to the physical model. The statistical indices point to them as the best alternatives for mimicking RWU predictions of the physical model.
The prediction of speech intelligibility in classrooms using computer models

NASA Astrophysics Data System (ADS)

Dance, Stephen; Dentoni, Roger

2005-04-01

Two classrooms were measured and modeled using the industry standard CATT model and the Web model CISM. Sound levels, reverberation times and speech intelligibility were predicted in these rooms using data for 7 octave bands. It was found that overall sound levels could be predicted to within 2 dB by both models. However, overall reverberation time was found to be accurately predicted by CATT 14% prediction error, but not by CISM, 41% prediction error. This compared to a 30% prediction error using classical theory. As for STI: CATT predicted within 11%, CISM to within 3% and Sabine to within 28% of the measured value. It should be noted that CISM took approximately 15 seconds to calculate, while CATT took 15 minutes. CISM is freely available on-line at www.whyverne.co.uk/acoustics/Pages/cism/cism.html
Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

NASA Astrophysics Data System (ADS)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.
Quantifying Uncertainty in Model Predictions for the Pliocene (Plio-QUMP): Initial results

USGS Publications Warehouse

Pope, J.O.; Collins, M.; Haywood, A.M.; Dowsett, H.J.; Hunter, S.J.; Lunt, D.J.; Pickering, S.J.; Pound, M.J.

2011-01-01

Examination of the mid-Pliocene Warm Period (mPWP; ~. 3.3 to 3.0. Ma BP) provides an excellent opportunity to test the ability of climate models to reproduce warm climate states, thereby assessing our confidence in model predictions. To do this it is necessary to relate the uncertainty in model simulations of mPWP climate to uncertainties in projections of future climate change. The uncertainties introduced by the model can be estimated through the use of a Perturbed Physics Ensemble (PPE). Developing on the UK Met Office Quantifying Uncertainty in Model Predictions (QUMP) Project, this paper presents the results from an initial investigation using the end members of a PPE in a fully coupled atmosphere-ocean model (HadCM3) running with appropriate mPWP boundary conditions. Prior work has shown that the unperturbed version of HadCM3 may underestimate mPWP sea surface temperatures at higher latitudes. Initial results indicate that neither the low sensitivity nor the high sensitivity simulations produce unequivocally improved mPWP climatology relative to the standard. Whilst the high sensitivity simulation was able to reconcile up to 6 ??C of the data/model mismatch in sea surface temperatures in the high latitudes of the Northern Hemisphere (relative to the standard simulation), it did not produce a better prediction of global vegetation than the standard simulation. Overall the low sensitivity simulation was degraded compared to the standard and high sensitivity simulations in all aspects of the data/model comparison. The results have shown that a PPE has the potential to explore weaknesses in mPWP modelling simulations which have been identified by geological proxies, but that a 'best fit' simulation will more likely come from a full ensemble in which simulations that contain the strengths of the two end member simulations shown here are combined. ?? 2011 Elsevier B.V.
Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease.

PubMed

Søreide, K; Thorsen, K; Søreide, J A

2015-02-01

Mortality prediction models for patients with perforated peptic ulcer (PPU) have not yielded consistent or highly accurate results. Given the complex nature of this disease, which has many non-linear associations with outcomes, we explored artificial neural networks (ANNs) to predict the complex interactions between the risk factors of PPU and death among patients with this condition. ANN modelling using a standard feed-forward, back-propagation neural network with three layers (i.e., an input layer, a hidden layer and an output layer) was used to predict the 30-day mortality of consecutive patients from a population-based cohort undergoing surgery for PPU. A receiver-operating characteristic (ROC) analysis was used to assess model accuracy. Of the 172 patients, 168 had their data included in the model; the data of 117 (70%) were used for the training set, and the data of 51 (39%) were used for the test set. The accuracy, as evaluated by area under the ROC curve (AUC), was best for an inclusive, multifactorial ANN model (AUC 0.90, 95% CIs 0.85-0.95; p < 0.001). This model outperformed standard predictive scores, including Boey and PULP. The importance of each variable decreased as the number of factors included in the ANN model increased. The prediction of death was most accurate when using an ANN model with several univariate influences on the outcome. This finding demonstrates that PPU is a highly complex disease for which clinical prognoses are likely difficult. The incorporation of computerised learning systems might enhance clinical judgments to improve decision making and outcome prediction.
Higher order QCD predictions for associated Higgs production with anomalous couplings to gauge bosons

NASA Astrophysics Data System (ADS)

Mimasu, Ken; Sanz, Verónica; Williams, Ciaran

2016-08-01

We present predictions for the associated production of a Higgs boson at NLO+PS accuracy, including the effect of anomalous interactions between the Higgs and gauge bosons. We present our results in different frameworks, one in which the interaction vertex between the Higgs boson and Standard Model W and Z bosons is parameterized in terms of general Lorentz structures, and one in which Electroweak symmetry breaking is manifestly linear and the resulting operators arise through a six-dimensional effective field theory framework. We present analytic calculations of the Standard Model and Beyond the Standard Model contributions, and discuss the phenomenological impact of the higher order pieces. Our results are implemented in the NLO Monte Carlo program MCFM, and interfaced to shower Monte Carlos through the Powheg box framework.
Embedding Quantum Mechanics Into a Broader Noncontextual Theory: A Conciliatory Result

NASA Astrophysics Data System (ADS)

Garola, Claudio; Sozzo, Sandro

2010-12-01

The extended semantic realism ( ESR) model embodies the mathematical formalism of standard (Hilbert space) quantum mechanics in a noncontextual framework, reinterpreting quantum probabilities as conditional instead of absolute. We provide here an improved version of this model and show that it predicts that, whenever idealized measurements are performed, a modified Bell-Clauser-Horne-Shimony-Holt ( BCHSH) inequality holds if one takes into account all individual systems that are prepared, standard quantum predictions hold if one considers only the individual systems that are detected, and a standard BCHSH inequality holds at a microscopic (purely theoretical) level. These results admit an intuitive explanation in terms of an unconventional kind of unfair sampling and constitute a first example of the unified perspective that can be attained by adopting the ESR model.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Saha, Kaushik; Som, Sibendu; Battistoni, Michele

Flash boiling is known to be a common phenomenon for gasoline direct injection (GDI) engine sprays. The Homogeneous Relaxation Model has been adopted in many recent numerical studies for predicting cavitation and flash boiling. The Homogeneous Relaxation Model is assessed in this study. Sensitivity analysis of the model parameters has been documented to infer the driving factors for the flash-boiling predictions. The model parameters have been varied over a range and the differences in predictions of the extent of flashing have been studied. Apart from flashing in the near nozzle regions, mild cavitation is also predicted inside the gasoline injectors.more » The variation in the predicted time scales through the model parameters for predicting these two different thermodynamic phenomena (cavitation, flash) have been elaborated in this study. Turbulence model effects have also been investigated by comparing predictions from the standard and Re-Normalization Group (RNG) k-ε turbulence models.« less
Model predictive control of P-time event graphs

NASA Astrophysics Data System (ADS)

Hamri, H.; Kara, R.; Amari, S.

2016-12-01

This paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.
A Simple Model Predicting Individual Weight Change in Humans

PubMed Central

Thomas, Diana M.; Martin, Corby K.; Heymsfield, Steven; Redman, Leanne M.; Schoeller, Dale A.; Levine, James A.

2010-01-01

Excessive weight in adults is a national concern with over 2/3 of the US population deemed overweight. Because being overweight has been correlated to numerous diseases such as heart disease and type 2 diabetes, there is a need to understand mechanisms and predict outcomes of weight change and weight maintenance. A simple mathematical model that accurately predicts individual weight change offers opportunities to understand how individuals lose and gain weight and can be used to foster patient adherence to diets in clinical settings. For this purpose, we developed a one dimensional differential equation model of weight change based on the energy balance equation is paired to an algebraic relationship between fat free mass and fat mass derived from a large nationally representative sample of recently released data collected by the Centers for Disease Control. We validate the model's ability to predict individual participants’ weight change by comparing model estimates of final weight data from two recent underfeeding studies and one overfeeding study. Mean absolute error and standard deviation between model predictions and observed measurements of final weights are less than 1.8 ± 1.3 kg for the underfeeding studies and 2.5 ± 1.6 kg for the overfeeding study. Comparison of the model predictions to other one dimensional models of weight change shows improvement in mean absolute error, standard deviation of mean absolute error, and group mean predictions. The maximum absolute individual error decreased by approximately 60% substantiating reliability in individual weight change predictions. The model provides a viable method for estimating individual weight change as a result of changes in intake and determining individual dietary adherence during weight change studies. PMID:24707319
Exploring the Standard Model of Particles

ERIC Educational Resources Information Center

Johansson, K. E.; Watkins, P. M.

2013-01-01

With the recent discovery of a new particle at the CERN Large Hadron Collider (LHC) the Higgs boson could be about to be discovered. This paper provides a brief summary of the standard model of particle physics and the importance of the Higgs boson and field in that model for non-specialists. The role of Feynman diagrams in making predictions for…
Hybrid BEM/empirical approach for scattering of correlated sources in rocket noise prediction

NASA Astrophysics Data System (ADS)

Barbarino, Mattia; Adamo, Francesco P.; Bianco, Davide; Bartoccini, Daniele

2017-09-01

Empirical models such as the Eldred standard model are commonly used for rocket noise prediction. Such models directly provide a definition of the Sound Pressure Level through the quadratic pressure term by uncorrelated sources. In this paper, an improvement of the Eldred Standard model has been formulated. This new formulation contains an explicit expression for the acoustic pressure of each noise source, in terms of amplitude and phase, in order to investigate the sources correlation effects and to propagate them through a wave equation. In particular, the correlation effects between adjacent and not-adjacent sources have been modeled and analyzed. The noise prediction obtained with the revised Eldred-based model has then been used for formulating an empirical/BEM (Boundary Element Method) hybrid approach that allows an evaluation of the scattering effects. In the framework of the European Space Agency funded program VECEP (VEga Consolidation and Evolution Programme), these models have been applied for the prediction of the aeroacoustics loads of the VEGA (Vettore Europeo di Generazione Avanzata - Advanced Generation European Carrier Rocket) launch vehicle at lift-off and the results have been compared with experimental data.
Taming Many-Parameter BSM Models with Bayesian Neural Networks

NASA Astrophysics Data System (ADS)

Kuchera, M. P.; Karbo, A.; Prosper, H. B.; Sanchez, A.; Taylor, J. Z.

2017-09-01

The search for physics Beyond the Standard Model (BSM) is a major focus of large-scale high energy physics experiments. One method is to look for specific deviations from the Standard Model that are predicted by BSM models. In cases where the model has a large number of free parameters, standard search methods become intractable due to computation time. This talk presents results using Bayesian Neural Networks, a supervised machine learning method, to enable the study of higher-dimensional models. The popular phenomenological Minimal Supersymmetric Standard Model was studied as an example of the feasibility and usefulness of this method. Graphics Processing Units (GPUs) are used to expedite the calculations. Cross-section predictions for 13 TeV proton collisions will be presented. My participation in the Conference Experience for Undergraduates (CEU) in 2004-2006 exposed me to the national and global significance of cutting-edge research. At the 2005 CEU, I presented work from the previous summer's SULI internship at Lawrence Berkeley Laboratory, where I learned to program while working on the Majorana Project. That work inspired me to follow a similar research path, which led me to my current work on computational methods applied to BSM physics.
Quantitative social dialectology: explaining linguistic variation geographically and socially.

PubMed

Wieling, Martijn; Nerbonne, John; Baayen, R Harald

2011-01-01

In this study we examine linguistic variation and its dependence on both social and geographic factors. We follow dialectometry in applying a quantitative methodology and focusing on dialect distances, and social dialectology in the choice of factors we examine in building a model to predict word pronunciation distances from the standard Dutch language to 424 Dutch dialects. We combine linear mixed-effects regression modeling with generalized additive modeling to predict the pronunciation distance of 559 words. Although geographical position is the dominant predictor, several other factors emerged as significant. The model predicts a greater distance from the standard for smaller communities, for communities with a higher average age, for nouns (as contrasted with verbs and adjectives), for more frequent words, and for words with relatively many vowels. The impact of the demographic variables, however, varied from word to word. For a majority of words, larger, richer and younger communities are moving towards the standard. For a smaller minority of words, larger, richer and younger communities emerge as driving a change away from the standard. Similarly, the strength of the effects of word frequency and word category varied geographically. The peripheral areas of the Netherlands showed a greater distance from the standard for nouns (as opposed to verbs and adjectives) as well as for high-frequency words, compared to the more central areas. Our findings indicate that changes in pronunciation have been spreading (in particular for low-frequency words) from the Hollandic center of economic power to the peripheral areas of the country, meeting resistance that is stronger wherever, for well-documented historical reasons, the political influence of Holland was reduced. Our results are also consistent with the theory of lexical diffusion, in that distances from the Hollandic norm vary systematically and predictably on a word by word basis.
Quantitative Social Dialectology: Explaining Linguistic Variation Geographically and Socially

PubMed Central

Wieling, Martijn; Nerbonne, John; Baayen, R. Harald

2011-01-01

In this study we examine linguistic variation and its dependence on both social and geographic factors. We follow dialectometry in applying a quantitative methodology and focusing on dialect distances, and social dialectology in the choice of factors we examine in building a model to predict word pronunciation distances from the standard Dutch language to 424 Dutch dialects. We combine linear mixed-effects regression modeling with generalized additive modeling to predict the pronunciation distance of 559 words. Although geographical position is the dominant predictor, several other factors emerged as significant. The model predicts a greater distance from the standard for smaller communities, for communities with a higher average age, for nouns (as contrasted with verbs and adjectives), for more frequent words, and for words with relatively many vowels. The impact of the demographic variables, however, varied from word to word. For a majority of words, larger, richer and younger communities are moving towards the standard. For a smaller minority of words, larger, richer and younger communities emerge as driving a change away from the standard. Similarly, the strength of the effects of word frequency and word category varied geographically. The peripheral areas of the Netherlands showed a greater distance from the standard for nouns (as opposed to verbs and adjectives) as well as for high-frequency words, compared to the more central areas. Our findings indicate that changes in pronunciation have been spreading (in particular for low-frequency words) from the Hollandic center of economic power to the peripheral areas of the country, meeting resistance that is stronger wherever, for well-documented historical reasons, the political influence of Holland was reduced. Our results are also consistent with the theory of lexical diffusion, in that distances from the Hollandic norm vary systematically and predictably on a word by word basis. PMID:21912639

Numerical prediction of pollutant dispersion and transport in an atmospheric boundary layer

NASA Astrophysics Data System (ADS)

Zeoli, Stéphanie; Bricteux, Laurent; Mech. Eng. Dpt. Team

2014-11-01

The ability to accurately predict concentration levels of air pollutant released from point sources is required in order to determine their environmental impact. A wall modeled large-eddy simulation (WMLES) of the ABL is performed using the OpenFoam based solver SOWFA (Churchfield and Lee, NREL). It uses Boussinesq approximation for buoyancy effects and takes into account Coriolis forces. A synthetic eddy method is proposed to properly model turbulence inlet velocity boundary conditions. This method will be compared with the standard pressure gradient forcing. WMLES are usually performed using a standard Smagorinsky model or its dynamic version. It is proposed here to investigate a subgrid scale (SGS) model with a better spectral behavior. To this end, a regularized variational multiscale (RVMs) model (Jeanmart and Winckelmans, 2007) is implemented together with standard wall function in order to preserve the dynamics of the large scales within the Ekman layer. The influence of the improved SGS model on the wind simulation and scalar transport will be discussed based on turbulence diagnostics.
An improved null model for assessing the net effects of multiple stressors on communities.

PubMed

Thompson, Patrick L; MacLennan, Megan M; Vinebrooke, Rolf D

2018-01-01

Ecological stressors (i.e., environmental factors outside their normal range of variation) can mediate each other through their interactions, leading to unexpected combined effects on communities. Determining whether the net effect of stressors is ecologically surprising requires comparing their cumulative impact to a null model that represents the linear combination of their individual effects (i.e., an additive expectation). However, we show that standard additive and multiplicative null models that base their predictions on the effects of single stressors on community properties (e.g., species richness or biomass) do not provide this linear expectation, leading to incorrect interpretations of antagonistic and synergistic responses by communities. We present an alternative, the compositional null model, which instead bases its predictions on the effects of stressors on individual species, and then aggregates them to the community level. Simulations demonstrate the improved ability of the compositional null model to accurately provide a linear expectation of the net effect of stressors. We simulate the response of communities to paired stressors that affect species in a purely additive fashion and compare the relative abilities of the compositional null model and two standard community property null models (additive and multiplicative) to predict these linear changes in species richness and community biomass across different combinations (both positive, negative, or opposite) and intensities of stressors. The compositional model predicts the linear effects of multiple stressors under almost all scenarios, allowing for proper classification of net effects, whereas the standard null models do not. Our findings suggest that current estimates of the prevalence of ecological surprises on communities based on community property null models are unreliable, and should be improved by integrating the responses of individual species to the community level as does our compositional null model. © 2017 John Wiley & Sons Ltd.
Evaluation of internal noise methods for Hotelling observers

NASA Astrophysics Data System (ADS)

Zhang, Yani; Pham, Binh T.; Eckstein, Miguel P.

2005-04-01

Including internal noise in computer model observers to degrade model observer performance to human levels is a common method to allow for quantitatively comparisons of human and model performance. In this paper, we studied two different types of methods for injecting internal noise to Hotelling model observers. The first method adds internal noise to the output of the individual channels: a) Independent non-uniform channel noise, b) Independent uniform channel noise. The second method adds internal noise to the decision variable arising from the combination of channel responses: a) internal noise standard deviation proportional to decision variable's standard deviation due to the external noise, b) internal noise standard deviation proportional to decision variable's variance caused by the external noise. We tested the square window Hotelling observer (HO), channelized Hotelling observer (CHO), and Laguerre-Gauss Hotelling observer (LGHO). The studied task was detection of a filling defect of varying size/shape in one of four simulated arterial segment locations with real x-ray angiography backgrounds. Results show that the internal noise method that leads to the best prediction of human performance differs across the studied models observers. The CHO model best predicts human observer performance with the channel internal noise. The HO and LGHO best predict human observer performance with the decision variable internal noise. These results might help explain why previous studies have found different results on the ability of each Hotelling model to predict human performance. Finally, the present results might guide researchers with the choice of method to include internal noise into their Hotelling models.
Comparison of Damage Path Predictions for Composite Laminates by Explicit and Standard Finite Element Analysis Tools

NASA Technical Reports Server (NTRS)

Bogert, Philip B.; Satyanarayana, Arunkumar; Chunchu, Prasad B.

2006-01-01

Splitting, ultimate failure load and the damage path in center notched composite specimens subjected to in-plane tension loading are predicted using progressive failure analysis methodology. A 2-D Hashin-Rotem failure criterion is used in determining intra-laminar fiber and matrix failures. This progressive failure methodology has been implemented in the Abaqus/Explicit and Abaqus/Standard finite element codes through user written subroutines "VUMAT" and "USDFLD" respectively. A 2-D finite element model is used for predicting the intra-laminar damages. Analysis results obtained from the Abaqus/Explicit and Abaqus/Standard code show good agreement with experimental results. The importance of modeling delamination in progressive failure analysis methodology is recognized for future studies. The use of an explicit integration dynamics code for simple specimen geometry and static loading establishes a foundation for future analyses where complex loading and nonlinear dynamic interactions of damage and structure will necessitate it.
Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

PubMed

Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

2018-05-01

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.
High-precision predictions for the light CP-even Higgs boson mass of the minimal supersymmetric standard model.

PubMed

Hahn, T; Heinemeyer, S; Hollik, W; Rzehak, H; Weiglein, G

2014-04-11

For the interpretation of the signal discovered in the Higgs searches at the LHC it will be crucial in particular to discriminate between the minimal Higgs sector realized in the standard model (SM) and its most commonly studied extension, the minimal supersymmetric standard model (MSSM). The measured mass value, having already reached the level of a precision observable with an experimental accuracy of about 500 MeV, plays an important role in this context. In the MSSM the mass of the light CP-even Higgs boson, Mh, can directly be predicted from the other parameters of the model. The accuracy of this prediction should at least match the one of the experimental result. The relatively high mass value of about 126 GeV has led to many investigations where the scalar top quarks are in the multi-TeV range. We improve the prediction for Mh in the MSSM by combining the existing fixed-order result, comprising the full one-loop and leading and subleading two-loop corrections, with a resummation of the leading and subleading logarithmic contributions from the scalar top sector to all orders. In this way for the first time a high-precision prediction for the mass of the light CP-even Higgs boson in the MSSM is possible all the way up to the multi-TeV region of the relevant supersymmetric particles. The results are included in the code FEYNHIGGS.
Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...
Translational Modeling to Guide Study Design and Dose Choice in Obesity Exemplified by AZD1979, a Melanin-concentrating Hormone Receptor 1 Antagonist.

PubMed

Gennemark, P; Trägårdh, M; Lindén, D; Ploj, K; Johansson, A; Turnbull, A; Carlsson, B; Antonsson, M

2017-07-01

In this study, we present the translational modeling used in the discovery of AZD1979, a melanin-concentrating hormone receptor 1 (MCHr1) antagonist aimed for treatment of obesity. The model quantitatively connects the relevant biomarkers and thereby closes the scaling path from rodent to man, as well as from dose to effect level. The complexity of individual modeling steps depends on the quality and quantity of data as well as the prior information; from semimechanistic body-composition models to standard linear regression. Key predictions are obtained by standard forward simulation (e.g., predicting effect from exposure), as well as non-parametric input estimation (e.g., predicting energy intake from longitudinal body-weight data), across species. The work illustrates how modeling integrates data from several species, fills critical gaps between biomarkers, and supports experimental design and human dose-prediction. We believe this approach can be of general interest for translation in the obesity field, and might inspire translational reasoning more broadly. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.
Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

PubMed Central

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450
Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

PubMed

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

NASA Astrophysics Data System (ADS)

Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

2008-11-01

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.
Underestimation of Variance of Predicted Health Utilities Derived from Multiattribute Utility Instruments.

PubMed

Chan, Kelvin K W; Xie, Feng; Willan, Andrew R; Pullenayegum, Eleanor M

2017-04-01

Parameter uncertainty in value sets of multiattribute utility-based instruments (MAUIs) has received little attention previously. This false precision leads to underestimation of the uncertainty of the results of cost-effectiveness analyses. The aim of this study is to examine the use of multiple imputation as a method to account for this uncertainty of MAUI scoring algorithms. We fitted a Bayesian model with random effects for respondents and health states to the data from the original US EQ-5D-3L valuation study, thereby estimating the uncertainty in the EQ-5D-3L scoring algorithm. We applied these results to EQ-5D-3L data from the Commonwealth Fund (CWF) Survey for Sick Adults ( n = 3958), comparing the standard error of the estimated mean utility in the CWF population using the predictive distribution from the Bayesian mixed-effect model (i.e., incorporating parameter uncertainty in the value set) with the standard error of the estimated mean utilities based on multiple imputation and the standard error using the conventional approach of using MAUI (i.e., ignoring uncertainty in the value set). The mean utility in the CWF population based on the predictive distribution of the Bayesian model was 0.827 with a standard error (SE) of 0.011. When utilities were derived using the conventional approach, the estimated mean utility was 0.827 with an SE of 0.003, which is only 25% of the SE based on the full predictive distribution of the mixed-effect model. Using multiple imputation with 20 imputed sets, the mean utility was 0.828 with an SE of 0.011, which is similar to the SE based on the full predictive distribution. Ignoring uncertainty of the predicted health utilities derived from MAUIs could lead to substantial underestimation of the variance of mean utilities. Multiple imputation corrects for this underestimation so that the results of cost-effectiveness analyses using MAUIs can report the correct degree of uncertainty.
Superheavy magnetic monopoles and the standard cosmology

NASA Astrophysics Data System (ADS)

Turner, M. S.

1984-10-01

The superheavy magnetic monopoles predicted to exist in grand unified theories (GUTs) are for particle physics, astrophysics and cosmology. Astrophysical and cosmological considerations are invaluable in the study of the properties of GUT monopoles. Because of the glut of monopoles predicted in the standard cosmology for the simplest GUTs. The simplest GUTs and the standard cosmology are not compatible. This is a very important piece of information about physics at unification energies and about the earliest movements of the Universe. The cosmological consequences of GUT monopoles within the context of the standard hot big bang model are reviewed.
Application of linear regression analysis in accuracy assessment of rolling force calculations

NASA Astrophysics Data System (ADS)

Poliak, E. I.; Shim, M. K.; Kim, G. S.; Choo, W. Y.

1998-10-01

Efficient operation of the computational models employed in process control systems require periodical assessment of the accuracy of their predictions. Linear regression is proposed as a tool which allows separate systematic and random prediction errors from those related to measurements. A quantitative characteristic of the model predictive ability is introduced in addition to standard statistical tests for model adequacy. Rolling force calculations are considered as an example for the application. However, the outlined approach can be used to assess the performance of any computational model.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Cornejo, Juan Carlos

The Standard Model has been a theory with the greatest success in describing the fundamental interactions of particles. As of the writing of this dissertation, the Standard Model has not been shown to make a false prediction. However, the limitations of the Standard Model have long been suspected by its lack of a description of gravity, nor dark matter. Its largest challenge to date, has been the observation of neutrino oscillations, and the implication that they may not be massless, as required by the Standard Model. The growing consensus is that the Standard Model is simply a lower energy effectivemore » field theory, and that new physics lies at much higher energies. The Q weak Experiment is testing the Electroweak theory of the Standard Model by making a precise determination of the weak charge of the proton (Q p w). Any signs of "new physics" will appear as a deviation to the Standard Model prediction. The weak charge is determined via a precise measurement of the parity-violating asymmetry of the electron-proton interaction via elastic scattering of a longitudinally polarized electron beam of an un-polarized proton target. The experiment required that the electron beam polarization be measured to an absolute uncertainty of 1%. At this level the electron beam polarization was projected to contribute the single largest experimental uncertainty to the parity-violating asymmetry measurement. This dissertation will detail the use of Compton scattering to determine the electron beam polarization via the detection of the scattered photon. I will conclude the remainder of the dissertation with an independent analysis of the blinded Q weak.« less
Three-dimensional quantitative structure-activity relationship studies on novel series of benzotriazine based compounds acting as Src inhibitors using CoMFA and CoMSIA.

PubMed

Gueto, Carlos; Ruiz, José L; Torres, Juan E; Méndez, Jefferson; Vivas-Reyes, Ricardo

2008-03-01

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of benzotriazine derivatives, as Src inhibitors. Ligand molecular superimposition on the template structure was performed by database alignment method. The statistically significant model was established of 72 molecules, which were validated by a test set of six compounds. The CoMFA model yielded a q(2)=0.526, non cross-validated R(2) of 0.781, F value of 88.132, bootstrapped R(2) of 0.831, standard error of prediction=0.587, and standard error of estimate=0.351 while the CoMSIA model yielded the best predictive model with a q(2)=0.647, non cross-validated R(2) of 0.895, F value of 115.906, bootstrapped R(2) of 0.953, standard error of prediction=0.519, and standard error of estimate=0.178. The contour maps obtained from 3D-QSAR studies were appraised for activity trends for the molecules analyzed. Results indicate that small steric volumes in the hydrophobic region, electron-withdrawing groups next to the aryl linker region, and atoms close to the solvent accessible region increase the Src inhibitory activity of the compounds. In fact, adding substituents at positions 5, 6, and 8 of the benzotriazine nucleus were generated new compounds having a higher predicted activity. The data generated from the present study will further help to design novel, potent, and selective Src inhibitors as anticancer therapeutic agents.
A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies and its application to four recent severe regional drought events in China

NASA Astrophysics Data System (ADS)

Liu, Z.; LU, G.; He, H.; Wu, Z.; He, J.

2017-12-01

Reliable drought prediction is fundamental for seasonal water management. Considering that drought development is closely related to the spatio-temporal evolution of large-scale circulation patterns, we develop a conceptual prediction model of seasonal drought processes based on atmospheric/oceanic Standardized Anomalies (SA). It is essentially the synchronous stepwise regression relationship between 90-day-accumulated atmospheric/oceanic SA-based predictors and 3-month SPI updated daily (SPI3). It is forced with forecasted atmospheric and oceanic variables retrieved from seasonal climate forecast systems, and it can make seamless drought prediction for operational use after a year-to-year calibration. Simulation and prediction of four severe seasonal regional drought processes in China were forced with the NCEP/NCAR reanalysis datasets and the NCEP Climate Forecast System Version 2 (CFSv2) operationally forecasted datasets, respectively. With the help of real-time correction for operational application, model application during four recent severe regional drought events in China revealed that the model is good at development prediction but weak in severity prediction. In addition to weakness in prediction of drought peak, the prediction of drought relief is possible to be predicted as drought recession. This weak performance may be associated with precipitation-causing weather patterns during drought relief. Based on initial virtual analysis on predicted 90-day prospective SPI3 curves, it shows that the 2009/2010 drought in Southwest China and 2014 drought in North China can be predicted and simulated well even for the prospective 1-75 day. In comparison, the prospective 1-45 day may be a feasible and acceptable lead time for simulation and prediction of the 2011 droughts in Southwest China and East China, after which the simulated and predicted developments clearly change.
Regression Simulation Model. Appendix X. Users Manual,

DTIC Science & Technology

1981-03-01

change as the prediction equations become refined. Whereas no notice will be provided when the changes are made, the programs will be modified such that...NATIONAL BUREAU Of STANDARDS 1963 A ___,_ __ _ __ _ . APPENDIX X ( R4/ EGRESSION IMULATION ’jDEL. Ape’A ’) 7 USERS MANUA submitted to The Great River...regression analysis and to establish a prediction equation (model). The prediction equation contains the partial regression coefficients (B-weights) which
Fusion of multiscale wavelet-based fractal analysis on retina image for stroke prediction.

PubMed

Che Azemin, M Z; Kumar, Dinesh K; Wong, T Y; Wang, J J; Kawasaki, R; Mitchell, P; Arjunan, Sridhar P

2010-01-01

In this paper, we present a novel method of analyzing retinal vasculature using Fourier Fractal Dimension to extract the complexity of the retinal vasculature enhanced at different wavelet scales. Logistic regression was used as a fusion method to model the classifier for 5-year stroke prediction. The efficacy of this technique has been tested using standard pattern recognition performance evaluation, Receivers Operating Characteristics (ROC) analysis and medical prediction statistics, odds ratio. Stroke prediction model was developed using the proposed system.
Probing new physics through Bs*→μ+μ- decay

NASA Astrophysics Data System (ADS)

Kumar, Dinesh; Saini, Jyoti; Gangal, Shireen; Das, Sanjeeda Bharati

2018-02-01

We perform a model independent analysis of new physics in Bs*→μ+μ- decay. We intend to identify new physics operator(s) which can provide large enhancement in the branching ratio of Bs*→μ+μ- above its standard model prediction. For this, we consider new physics in the form of vector, axial-vector, scalar and pseudoscalar operators. We find that scalar and pseudoscalar operators do not contribute to the branching ratio of Bs*→μ+μ- . We perform a global fit to all relevant b →s μ+μ- data for different new physics scenarios. For each of these scenarios, we predict Br (Bs*→μ+μ-) . We find that a significant enhancement in Br (Bs*→μ+μ-) is not allowed by any of these new physics operators. In fact, for all new physics scenarios providing a good fit to the data, the branching ratio of Bs*→μ+μ- is suppressed as compared to the standard model (SM) value. Hence the present b →s μ+μ- data indicates that the future measurement of Br (Bs*→μ+μ-) is expected to be suppressed in comparison to the standard model prediction.

Integrating Map Algebra and Statistical Modeling for Spatio- Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain.

PubMed

Evrendilek, Fatih

2007-12-12

This study aims at quantifying spatio-temporal dynamics of monthly mean dailyincident photosynthetically active radiation (PAR) over a vast and complex terrain such asTurkey. The spatial interpolation method of universal kriging, and the combination ofmultiple linear regression (MLR) models and map algebra techniques were implemented togenerate surface maps of PAR with a grid resolution of 500 x 500 m as a function of fivegeographical and 14 climatic variables. Performance of the geostatistical and MLR modelswas compared using mean prediction error (MPE), root-mean-square prediction error(RMSPE), average standard prediction error (ASE), mean standardized prediction error(MSPE), root-mean-square standardized prediction error (RMSSPE), and adjustedcoefficient of determination (R² adj. ). The best-fit MLR- and universal kriging-generatedmodels of monthly mean daily PAR were validated against an independent 37-year observeddataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifingmethod. The spatial variability patterns of monthly mean daily incident PAR were moreaccurately reflected in the surface maps created by the MLR-based models than in thosecreated by the universal kriging method, in particular, for spring (May) and autumn(November). The MLR-based spatial interpolation algorithms of PAR described in thisstudy indicated the significance of the multifactor approach to understanding and mappingspatio-temporal dynamics of PAR for a complex terrain over meso-scales.
Application of the predicted heat strain model in development of localized, threshold-based heat stress management guidelines for the construction industry.

PubMed

Rowlinson, Steve; Jia, Yunyan Andrea

2014-04-01

Existing heat stress risk management guidelines recommended by international standards are not practical for the construction industry which needs site supervision staff to make instant managerial decisions to mitigate heat risks. The ability of the predicted heat strain (PHS) model [ISO 7933 (2004). Ergonomics of the thermal environment analytical determination and interpretation of heat stress using calculation of the predicted heat strain. Geneva: International Standard Organisation] to predict maximum allowable exposure time (D lim) has now enabled development of localized, action-triggering and threshold-based guidelines for implementation by lay frontline staff on construction sites. This article presents a protocol for development of two heat stress management tools by applying the PHS model to its full potential. One of the tools is developed to facilitate managerial decisions on an optimized work-rest regimen for paced work. The other tool is developed to enable workers' self-regulation during self-paced work.
Making Predictions in a Changing World: The Benefits of Individual-Based Ecology

PubMed Central

Stillman, Richard A.; Railsback, Steven F.; Giske, Jarl; Berger, Uta; Grimm, Volker

2014-01-01

Ecologists urgently need a better ability to predict how environmental change affects biodiversity. We examine individual-based ecology (IBE), a research paradigm that promises better a predictive ability by using individual-based models (IBMs) to represent ecological dynamics as arising from how individuals interact with their environment and with each other. A key advantage of IBMs is that the basis for predictions—fitness maximization by individual organisms—is more general and reliable than the empirical relationships that other models depend on. Case studies illustrate the usefulness and predictive success of long-term IBE programs. The pioneering programs had three phases: conceptualization, implementation, and diversification. Continued validation of models runs throughout these phases. The breakthroughs that make IBE more productive include standards for describing and validating IBMs, improved and standardized theory for individual traits and behavior, software tools, and generalized instead of system-specific IBMs. We provide guidelines for pursuing IBE and a vision for future IBE research. PMID:26955076
Cognitive Control and Individual Differences in Economic Ultimatum Decision-Making

PubMed Central

De Neys, Wim; Novitskiy, Nikolay; Geeraerts, Leen; Ramautar, Jennifer; Wagemans, Johan

2011-01-01

Much publicity has been given to the fact that people's economic decisions often deviate from the rational predictions of standard economic models. In the classic ultimatum game, for example, most people turn down financial gains by rejecting unequal monetary splits. The present study points to neglected individual differences in this debate. After participants played the ultimatum game we tested for individual differences in cognitive control capacity of the most and least economic responders. The key finding was that people who were higher in cognitive control, as measured by behavioral (Go/No-Go performance) and neural (No-Go N2 amplitude) markers, did tend to behave more in line with the standard models and showed increased acceptance of unequal splits. Hence, the cognitively highest scoring decision-makers were more likely to maximize their monetary payoffs and adhere to the standard economic predictions. Findings question popular claims with respect to the rejection of standard economic models and the irrationality of human economic decision-making. PMID:22096522
A Model for Investigating Predictive Validity at Highly Selective Institutions.

ERIC Educational Resources Information Center

Gross, Alan L.; And Others

A statistical model for investigating predictive validity at highly selective institutions is described. When the selection ratio is small, one must typically deal with a data set containing relatively large amounts of missing data on both criterion and predictor variables. Standard statistical approaches are based on the strong assumption that…
The organization of irrational beliefs in posttraumatic stress symptomology: testing the predictions of REBT theory using structural equation modelling.

PubMed

Hyland, Philip; Shevlin, Mark; Adamson, Gary; Boduszek, Daniel

2014-01-01

This study directly tests a central prediction of rational emotive behaviour therapy (REBT) that has received little empirical attention regarding the core and intermediate beliefs in the development of posttraumatic stress symptoms. A theoretically consistent REBT model of posttraumatic stress disorder (PTSD) was examined using structural equation modelling techniques among a sample of 313 trauma-exposed military and law enforcement personnel. The REBT model of PTSD provided a good fit of the data, χ(2) = 599.173, df = 356, p < .001; standardized root mean square residual = .05 (confidence interval = .04-.05); standardized root mean square residual = .04; comparative fit index = .95; Tucker Lewis index = .95. Results demonstrated that demandingness beliefs indirectly affected the various symptom groups of PTSD through a set of secondary irrational beliefs that include catastrophizing, low frustration tolerance, and depreciation beliefs. Results were consistent with the predictions of REBT theory and provides strong empirical support that the cognitive variables described by REBT theory are critical cognitive constructs in the prediction of PTSD symptomology. © 2013 Wiley Periodicals, Inc.
Portable visible and near-infrared spectrophotometer for triglyceride measurements.

PubMed

Kobayashi, Takanori; Kato, Yukiko Hakariya; Tsukamoto, Megumi; Ikuta, Kazuyoshi; Sakudo, Akikazu

2009-01-01

An affordable and portable machine is required for the practical use of visible and near-infrared (Vis-NIR) spectroscopy. A portable fruit tester comprising a Vis-NIR spectrophotometer was modified for use in the transmittance mode and employed to quantify triglyceride levels in serum in combination with a chemometric analysis. Transmittance spectra collected in the 600- to 1100-nm region were subjected to a partial least-squares regression analysis and leave-out cross-validation to develop a chemometrics model for predicting triglyceride concentrations in serum. The model yielded a coefficient of determination in cross-validation (R2VAL) of 0.7831 with a standard error of cross-validation (SECV) of 43.68 mg/dl. The detection limit of the model was 148.79 mg/dl. Furthermore, masked samples predicted by the model yielded a coefficient of determination in prediction (R2PRED) of 0.6856 with a standard error of prediction (SEP) and detection limit of 61.54 and 159.38 mg/dl, respectively. The portable Vis-NIR spectrophotometer may prove convenient for the measurement of triglyceride concentrations in serum, although before practical use there remain obstacles, which are discussed.
Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8

NASA Astrophysics Data System (ADS)

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M.; Erö, J.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Knünz, V.; Krammer, M.; Krätschmer, I.; Liko, D.; Mikulec, I.; Rabady, D.; Rahbaran, B.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Bansal, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Knutsson, A.; Lauwers, J.; Luyckx, S.; Ochesanu, S.; Rougny, R.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D'Hondt, J.; Daci, N.; Heracleous, N.; Keaveney, J.; Lowette, S.; Maes, M.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Villella, I.; Caillol, C.; Clerbaux, B.; De Lentdecker, G.; Dobur, D.; Favart, L.; Gay, A. P. R.; Grebenyuk, A.; Léonard, A.; Mohammadi, A.; Perniè, L.; Randle-conde, A.; Reis, T.; Seva, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.; Zenoni, F.; Adler, V.; Beernaert, K.; Benucci, L.; Cimmino, A.; Costantini, S.; Crucy, S.; Fagot, A.; Garcia, G.; Mccartin, J.; Ocampo Rios, A. A.; Poyraz, D.; Ryckbosch, D.; Salva Diblen, S.; Sigamani, M.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bruno, G.; Castello, R.; Caudron, A.; Ceard, L.; Da Silveira, G. G.; Delaere, C.; du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Nuttens, C.; Pagano, D.; Perrini, L.; Pin, A.; Piotrzkowski, K.; Popov, A.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Vizan Garcia, J. M.; Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.; Júnior, W. L. Aldá; Alves, G. A.; Brito, L.; Correa Martins Junior, M.; Martins, T. Dos Reis; Molina, J.; Mora Herrera, C.; Pol, M. E.; Teles, P. Rebello; Carvalho, W.; Chinellato, J.; Custódio, A.; Da Costa, E. M.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Malbouisson, H.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santaolalla, J.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Bernardes, C. A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Aleksandrov, A.; Genchev, V.; Hadjiiska, R.; Iaydjiev, P.; Marinov, A.; Piperov, S.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Cheng, T.; Du, R.; Jiang, C. H.; Plestina, R.; Romeo, F.; Tao, J.; Wang, Z.; Asawatangtrakuldee, C.; Ban, Y.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Zhang, F.; Zhang, L.; Zou, W.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; Gomez Moreno, B.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Polic, D.; Puljak, I.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Kadija, K.; Luetic, J.; Mekterovic, D.; Sudic, L.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Bodlak, M.; Finger, M.; Finger, M.; Assran, Y.; Ellithi Kame, A.; Mahmoud, M. A.; Radi, A.; Kadastik, M.; Murumaa, M.; Raidal, M.; Tiko, A.; Eerola, P.; Voutilainen, M.; Härkönen, J.; Heikkilä, J. K.; Karimäki, V.; Kinnunen, R.; Kortelainen, M. J.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Peltola, T.; Tuominen, E.; Tuominiemi, J.; Tuovinen, E.; Wendland, L.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. 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A.; Chasserat, J.; Chierici, R.; Contardo, D.; Courbon, B.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Kurca, T.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Ruiz Alvarez, J. D.; Sabes, D.; Sgandurra, L.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Xiao, H.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Bontenackels, M.; Edelhoff, M.; Feld, L.; Heister, A.; Klein, K.; Lipinski, M.; Ostapchuk, A.; Preuten, M.; Raupach, F.; Sammet, J.; Schael, S.; Schulte, J. F.; Weber, H.; Wittmer, B.; Zhukov, V.; Ata, M.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Erdmann, M.; Fischer, R.; Güth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Klingebiel, D.; Knutzen, S.; Kreuzer, P.; Merschmeyer, M.; Meyer, A.; Millet, P.; Olschewski, M.; Padeken, K.; Papacz, P.; Reithler, H.; Schmitz, S. 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B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Naumann-Emme, S.; Nayak, A.; Ntomari, E.; Perrey, H.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Ribeiro Cipriano, P. M.; Roland, B.; Ron, E.; Sahin, M. Ö.; Salfeld-Nebgen, J.; Saxena, P.; Schoerner-Sadenius, T.; Schröder, M.; Seitz, C.; Spannagel, S.; Vargas Trevino, A. D. R.; Walsh, R.; Wissing, C.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Erfle, J.; Garutti, E.; Goebel, K.; Görner, M.; Haller, J.; Hoffmann, M.; Höing, R. S.; Junkes, A.; Kirschenmann, H.; Klanner, R.; Kogler, R.; Lapsien, T.; Lenz, T.; Marchesini, I.; Marconi, D.; Ott, J.; Peiffer, T.; Perieanu, A.; Pietsch, N.; Poehlsen, J.; Poehlsen, T.; Rathjens, D.; Sander, C.; Schettler, H.; Schleper, P.; Schlieckau, E.; Schmidt, A.; Seidel, M.; Sola, V.; Stadie, H.; Steinbrück, G.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Barth, C.; Baus, C.; Berger, J.; Böser, C.; Butz, E.; Chwalek, T.; De Boer, W.; Descroix, A.; Dierlamm, A.; Feindt, M.; Frensch, F.; Giffels, M.; Gilbert, A.; Hartmann, F.; Hauth, T.; Husemann, U.; Katkov, I.; Kornmayer, A.; Lobelle Pardo, P.; Mozer, M. U.; Müller, T.; Müller, Th.; Nürnberg, A.; Quast, G.; Rabbertz, K.; Röcker, S.; Simonis, H. J.; Stober, F. M.; Ulrich, R.; Wagner-Kuhr, J.; Wayand, S.; Weiler, T.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Giakoumopoulou, V. A.; Kyriakis, A.; Loukas, D.; Markou, A.; Markou, C.; Psallidas, A.; Topsis-Giotis, I.; Agapitos, A.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Stiliaris, E.; Tziaferi, E.; Aslanoglou, X.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Strologas, J.; Bencze, G.; Hajdu, C.; Hidas, P.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Molnar, J.; Palinkas, J.; Szillasi, Z.; Makovec, A.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Swain, S. K.; Beri, S. B.; Bhatnagar, V.; Gupta, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, M.; Kumar, R.; Mittal, M.; Nishu, N.; Singh, J. B.; Kumar, Ashok; Kumar, Arun; Ahuja, S.; Bhardwaj, A.; Choudhary, B. 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S.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Abbiendi, G.; Benvenuti, A. C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. 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M.; Lista, L.; Meola, S.; Merola, M.; Paolucci, P.; Azzi, P.; Bacchetta, N.; Biselloa, D.; Branca, A.; Carlin, R.; Checchia, P.; Dall'Osso, M.; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Kanishchev, K.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Tosi, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.; Gabusi, M.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vitulo, P.; Biasini, M.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Spiezia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Broccolo, G.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fedi, G.; Fiori, F.; Foà, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Moon, C. S.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Serban, A. T.; Spagnolo, P.; Squillacioti, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Vernieri, C.; Barone, L.; Cavallari, F.; D'imperio, G.; Del Re, D.; Diemoz, M.; Jorda, C.; Longo, E.; Margaroli, F.; Meridiani, P.; Micheli, F.; Organtini, G.; Paramatti, R.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Traczyk, P.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bellan, R.; Biino, C.; Cartiglia, N.; Casasso, S.; Costa, M.; Covarelli, R.; Degano, A.; Demaria, N.; Finco, L.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Musich, M.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Potenza, A.; Romero, A.; Ruspa, M.; Sacchi, R.; Solano, A.; Staiano, A.; Tamponi, U.; Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Gobbo, B.; La Licata, C.; Marone, M.; Schizzi, A.; Umer, T.; Zanetti, A.; Chang, S.; Kropivnitskaya, T. A.; Nam, S. K.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Kim, M. S.; Kong, D. J.; Lee, S.; Oh, Y. D.; Park, H.; Sakharov, A.; Son, D. C.; Kim, T. J.; Ryu, M. S.; Kim, J. Y.; Moon, D. H.; Song, S.; Choi, S.; Gyun, D.; Hong, B.; Jo, M.; Kim, H.; Kim, Y.; Lee, B.; Lee, K. S.; Park, S. K.; Roh, Y.; Yoo, H. D.; Choi, M.; Kim, J. H.; Park, I. C.; Ryu, G.; Choi, Y.; Choi, Y. K.; Goh, J.; Kim, D.; Kwon, E.; Lee, J.; Yu, I.; Juodagalvis, A.; Komaragiri, J. R.; Md Ali, M. A. B.; Wan Abdullah, W. A. T.; Casimiro Linares, E.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-de La Cruz, I.; Hernandez-Almada, A.; Lopez-Fernandez, R.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Vazquez Valencia, F.; Pedraza, I.; Salazar Ibarguen, H. A.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Reucroft, S.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Khan, W. 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F.; Missiroli, M.; Moran, D.; Brun, H.; Cuevas, J.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Duarte Campderros, J.; Fernandez, M.; Gomez, G.; Graziano, A.; Lopez Virto, A.; Marco, J.; Marco, R.; Martinez Rivero, C.; Matorras, F.; Munoz Sanchez, F. J.; Piedra Gomez, J.; Rodrigo, T.; Rodríguez-Marrero, A. Y.; Ruiz-Jimeno, A.; Scodellaro, L.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Auffray, E.; Auzinger, G.; Bachtis, M.; Baillon, P.; Ball, A. H.; Barney, D.; Benaglia, A.; Bendavid, J.; Benhabib, L.; Benitez, J. F.; Bloch, P.; Bocci, A.; Bonato, A.; Bondu, O.; Botta, C.; Breuker, H.; Camporesi, T.; Cerminara, G.; Colafranceschi, S.; D'Alfonso, M.; d'Enterria, D.; Dabrowski, A.; David, A.; De Guio, F.; De Roeck, A.; De Visscher, S.; Di Marco, E.; Dobson, M.; Dordevic, M.; Dorney, B.; Dupont-Sagorin, N.; Elliott-Peisert, A.; Franzoni, G.; Funk, W.; Gigi, D.; Gill, K.; Giordano, D.; Girone, M.; Glege, F.; Guida, R.; Gundacker, S.; Guthoff, M.; Hammer, J.; Hansen, M.; Harris, P.; Hegeman, J.; Innocente, V.; Janot, P.; Kousouris, K.; Krajczar, K.; Lecoq, P.; Lourenço, C.; Magini, N.; Malgeri, L.; Mannelli, M.; Marrouche, J.; Masetti, L.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Morovic, S.; Mulders, M.; Orfanelli, S.; Orsini, L.; Pape, L.; Perez, E.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pimiä, M.; Piparo, D.; Plagge, M.; Racz, A.; Rolandi, G.; Rovere, M.; Sakulin, H.; Schäfer, C.; Schwick, C.; Sharma, A.; Siegrist, P.; Silva, P.; Simon, M.; Sphicas, P.; Spiga, D.; Steggemann, J.; Stieger, B.; Stoye, M.; Takahashi, Y.; Treille, D.; Tsirou, A.; Veres, G. I.; Wardle, N.; Wöhri, H. K.; Wollny, H.; Zeuner, W. D.; Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Renker, D.; Rohe, T.; Bachmair, F.; Bäni, L.; Bianchini, L.; Buchmann, M. A.; Casal, B.; Chanon, N.; Dissertori, G.; Dittmar, M.; Donegà, M.; Dünser, M.; Eller, P.; Grab, C.; Hits, D.; Hoss, J.; Kasieczka, G.; Lustermann, W.; Mangano, B.; Marini, A. C.; Marionneau, M.; Martinez Ruiz del Arbol, P.; Masciovecchio, M.; Meister, D.; Mohr, N.; Musella, P.; Nägeli, C.; Nessi-Tedaldi, F.; Pandolfi, F.; Pauss, F.; Perrozzi, L.; Peruzzi, M.; Quittnat, M.; Rebane, L.; Rossini, M.; Starodumov, A.; Takahashi, M.; Theofilatos, K.; Wallny, R.; Weber, H. A.; Amsler, C.; Canelli, M. F.; Chiochia, V.; De Cosa, A.; Hinzmann, A.; Hreus, T.; Kilminster, B.; Lange, C.; Ngadiuba, J.; Pinna, D.; Robmann, P.; Ronga, F. J.; Taroni, S.; Yang, Y.; Cardaci, M.; Chen, K. H.; Ferro, C.; Kuo, C. M.; Lin, W.; Lu, Y. J.; Volpe, R.; Yu, S. S.; Chang, P.; Chang, Y. H.; Chao, Y.; Chen, K. F.; Chen, P. H.; Dietz, C.; Grundler, U.; Hou, W.-S.; Liu, Y. F.; Lu, R.-S.; Mi nano Moya, M.; Petrakou, E.; Tsai, J. F.; Tzeng, Y. M.; Wilken, R.; Asavapibhop, B.; Singh, G.; Srimanobhas, N.; Suwonjandee, N.; Adiguzel, A.; Bakirci, M. N.; Cerci, S.; Dozen, C.; Dumanoglu, I.; Eskut, E.; Girgis, S.; Gokbulut, G.; Guler, Y.; Gurpinar, E.; Hos, I.; Kangal, E. E.; Kayis Topaksu, A.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Sunar Cerci, D.; Tali, B.; Topakli, H.; Vergili, M.; Zorbilmez, C.; Akin, I. V.; Bilin, B.; Bilmis, S.; Gamsizkan, H.; Isildak, B.; Karapinar, G.; Ocalan, K.; Sekmen, S.; Surat, U. E.; Yalvac, M.; Zeyrek, M.; Albayrak, E. A.; Gülmez, E.; Kaya, M.; Kaya, O.; Yetkin, T.; Cankocak, K.; Vardarlı, F. I.; Levchuk, L.; Sorokin, P.; Brooke, J. J.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Meng, Z.; Newbold, D. M.; Paramesvaran, S.; Poll, A.; Sakuma, T.; Seif El Nasr-storey, S.; Senkin, S.; Smith, V. J.; Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.; Womersley, W. J.; Worm, S. D.; Baber, M.; Bainbridge, R.; Buchmuller, O.; Burton, D.; Colling, D.; Cripps, N.; Dauncey, P.; Davies, G.; Della Negra, M.; Dunne, P.; Elwood, A.; Ferguson, W.; Fulcher, J.; Futyan, D.; Hall, G.; Iles, G.; Jarvis, M.; Karapostoli, G.; Kenzie, M.; Lane, R.; Lucas, R.; Lyons, L.; Magnan, A.-M.; Malik, S.; Mathias, B.; Nash, J.; Nikitenko, A.; Pela, J.; Pesaresi, M.; Petridis, K.; Raymond, D. M.; Rogerson, S.; Rose, A.; Seez, C.; Sharp, P.; Tapper, A.; Vazquez Acosta, M.; Virdee, T.; Zenz, S. C.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Leggat, D.; Leslie, D.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.; Dittmann, J.; Hatakeyama, K.; Kasmi, A.; Liu, H.; Pastika, N.; Scarborough, T.; Wu, Z.; Charaf, O.; Cooper, S. I.; Henderson, C.; Rumerio, P.; Avetisyan, A.; Bose, T.; Fantasia, C.; Lawson, P.; Richardson, C.; Rohlf, J.; St. John, J.; Sulak, L.; Alimena, J.; Berry, E.; Bhattacharya, S.; Christopher, G.; Cutts, D.; Demiragli, Z.; Dhingra, N.; Ferapontov, A.; Garabedian, A.; Heintz, U.; Laird, E.; Landsberg, G.; Mao, Z.; Narain, M.; Sagir, S.; Sinthuprasith, T.; Speer, T.; Swanson, J.; Breedon, R.; Breto, G.; De La Barca Sanchez, M. Calderon; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Gardner, M.; Ko, W.; Lander, R.; Mulhearn, M.; Pellett, D.; Pilot, J.; Ricci-Tam, F.; Shalhout, S.; Smith, J.; Squires, M.; Stolp, D.; Tripathi, M.; Wilbur, S.; Yohay, R.; Cousins, R.; Everaerts, P.; Farrell, C.; Hauser, J.; Ignatenko, M.; Rakness, G.; Takasugi, E.; Valuev, V.; Weber, M.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Hanson, G.; Heilman, J.; Ivova Rikova, M.; Jandir, P.; Kennedy, E.; Lacroix, F.; Long, O. R.; Luthra, A.; Malberti, M.; Negrete, M. Olmedo; Shrinivas, A.; Sumowidagdo, S.; Wimpenny, S.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; D'Agnolo, R. T.; Holzner, A.; Kelley, R.; Klein, D.; Letts, J.; Macneill, I.; Olivito, D.; Padhi, S.; Palmer, C.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Tadel, M.; Tu, Y.; Vartak, A.; Welke, C.; Würthwein, F.; Yagil, A.; Zevi Della Porta, G.; Barge, D.; Bradmiller-Feld, J.; Campagnari, C.; Danielson, T.; Dishaw, A.; Dutta, V.; Flowers, K.; Franco Sevilla, M.; Geffert, P.; George, C.; Golf, F.; Gouskos, L.; Incandela, J.; Justus, C.; Mccoll, N.; Mullin, S. D.; Richman, J.; Stuart, D.; To, W.; West, C.; Yoo, J.; Apresyan, A.; Bornheim, A.; Bunn, J.; Chen, Y.; Duarte, J.; Mott, A.; Newman, H. B.; Pena, C.; Pierini, M.; Spiropulu, M.; Vlimant, R.; Wilkinson, R.; Xie, S.; Zhu, R. Y.; Azzolini, V.; Calamba, A.; Carlson, B.; Ferguson, T.; Iiyama, Y.; Paulini, M.; Russ, J.; Vogel, H.; Vorobiev, I.; Cumalat, J. P.; Ford, W. T.; Gaz, A.; Krohn, M.; Luiggi Lopez, E.; Nauenberg, U.; Smith, J. G.; Stenson, K.; Wagner, S. R.; Alexander, J.; Chatterjee, A.; Chaves, J.; Chu, J.; Dittmer, S.; Eggert, N.; Mirman, N.; Nicolas Kaufman, G.; Patterson, J. R.; Ryd, A.; Salvati, E.; Skinnari, L.; Sun, W.; Teo, W. D.; Thom, J.; Thompson, J.; Tucker, J.; Weng, Y.; Winstrom, L.; Wittich, P.; Winn, D.; Abdullin, S.; Albrow, M.; Anderson, J.; Apollinari, G.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Cheung, H. W. K.; Chlebana, F.; Cihangir, S.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hanlon, J.; Hare, D.; Harris, R. M.; Hirschauer, J.; Hooberman, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Klima, B.; Kreis, B.; Kwan, S.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, T.; Lopes De Sá, R.; Lykken, J.; Maeshima, K.; Marraffino, J. M.; Martinez Outschoorn, V. I.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mishra, K.; Mrenna, S.; Nahn, S.; Newman-Holmes, C.; O'Dell, V.; Prokofyev, O.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vidal, R.; Whitbeck, A.; Whitmore, J.; Yang, F.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Carver, M.; Curry, D.; Das, S.; De Gruttola, M.; Di Giovanni, G. P.; Field, R. D.; Fisher, M.; Furic, I. K.; Hugon, J.; Konigsberg, J.; Korytov, A.; Kypreos, T.; Low, J. F.; Matchev, K.; Mei, H.; Milenovic, P.; Mitselmakher, G.; Muniz, L.; Rinkevicius, A.; Shchutska, L.; Snowball, M.; Sperka, D.; Yelton, J.; Zakaria, M.; Hewamanage, S.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Adams, J. R.; Adams, T.; Askew, A.; Bochenek, J.; Diamond, B.; Haas, J.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Prosper, H.; Veeraraghavan, V.; Weinberg, M.; Baarmand, M. M.; Hohlmann, M.; Kalakhety, H.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Kurt, P.; O'Brien, C.; Sandoval Gonzalez, I. D.; Silkworth, C.; Turner, P.; Varelas, N.; Bilki, B.; Clarida, W.; Dilsiz, K.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Rahmat, R.; Sen, S.; Tan, P.; Tiras, E.; Wetzel, J.; Yi, K.; Anderson, I.; Barnett, B. A.; Blumenfeld, B.; Bolognesi, S.; Fehling, D.; Gritsan, A. V.; Maksimovic, P.; Martin, C.; Swartz, M.; Xiao, M.; Baringer, P.; Bean, A.; Benelli, G.; Bruner, C.; Gray, J.; Kenny, R. P.; Majumder, D.; Malek, M.; Murray, M.; Noonan, D.; Sanders, S.; Sekaric, J.; Stringer, R.; Wang, Q.; Wood, J. S.; Chakaberia, I.; Ivanov, A.; Kaadze, K.; Khalil, S.; Makouski, M.; Maravin, Y.; Saini, L. K.; Skhirtladze, N.; Svintradze, I.; Gronberg, J.; Lange, D.; Rebassoo, F.; Wright, D.; Baden, A.; Belloni, A.; Calvert, B.; Eno, S. C.; Gomez, J. A.; Hadley, N. J.; Jabeen, S.; Kellogg, R. G.; Kolberg, T.; Lu, Y.; Mignerey, A. C.; Pedro, K.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Apyan, A.; Barbieri, R.; Bierwagen, K.; Busza, W.; Cali, I. A.; Di Matteo, L.; Gomez Ceballos, G.; Goncharov, M.; Gulhan, D.; Klute, M.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Paus, C.; Ralph, D.; Roland, C.; Roland, G.; Stephans, G. S. F.; Sumorok, K.; Velicanu, D.; Veverka, J.; Wyslouch, B.; Yang, M.; Zanetti, M.; Zhukova, V.; Dahmes, B.; Gude, A.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Mans, J.; Nourbakhsh, S.; Rusack, R.; Singovsky, A.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Gonzalez Suarez, R.; Keller, J.; Knowlton, D.; Kravchenko, I.; Lazo-Flores, J.; Meier, F.; Ratnikov, F.; Snow, G. R.; Zvada, M.; Dolen, J.; Godshalk, A.; Iashvili, I.; Kharchilava, A.; Kumar, A.; Rappoccio, S.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Trocino, D.; Wang, R. J.; Wood, D.; Zhang, J.; Hahn, K. A.; Kubik, A.; Mucia, N.; Odell, N.; Pollack, B.; Pozdnyakov, A.; Schmitt, M.; Stoynev, S.; Sung, K.; Velasco, M.; Won, S.; Brinkerhoff, A.; Chan, K. M.; Drozdetskiy, A.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Lynch, S.; Marinelli, N.; Musienko, Y.; Pearson, T.; Planer, M.; Ruchti, R.; Smith, G.; Valls, N.; Wayne, M.; Wolf, M.; Woodard, A.; Antonelli, L.; Brinson, J.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Hart, A.; Hill, C.; Hughes, R.; Kotov, K.; Ling, T. Y.; Luo, W.; Puigh, D.; Rodenburg, M.; Winer, B. L.; Wolfe, H.; Wulsin, H. W.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Piroué, P.; Quan, X.; Saka, H.; Stickland, D.; Tully, C.; Werner, J. S.; Zuranski, A.; Brownson, E.; Malik, S.; Mendez, H.; Ramirez Vargas, J. E.; Barnes, V. E.; Benedetti, D.; Bortoletto, D.; Gutay, L.; Hu, Z.; Jha, M. K.; Jones, M.; Jung, K.; Kress, M.; Leonardo, N.; Miller, D. H.; Neumeister, N.; Primavera, F.; Radburn-Smith, B. C.; Shi, X.; Shipsey, I.; Silvers, D.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Zablocki, J.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Ecklund, K. M.; Geurts, F. J. M.; Li, W.; Michlin, B.; Padley, B. P.; Redjimi, R.; Roberts, J.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Goldenzweig, P.; Han, J.; Harel, A.; Hindrichs, O.; Khukhunaishvili, A.; Korjenevski, S.; Petrillo, G.; Verzetti, M.; Vishnevskiy, D.; Ciesielski, R.; Demortier, L.; Goulianos, K.; Mesropian, C.; Arora, S.; Barker, A.; Chou, J. P.; Contreras-Campana, C.; Contreras-Campana, E.; Duggan, D.; Ferencek, D.; Gershtein, Y.; Gray, R.; Halkiadakis, E.; Hidas, D.; Kaplan, S.; Lath, A.; Panwalkar, S.; Park, M.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Rose, K.; Spanier, S.; York, A.; Bouhali, O.; Castaneda Hernandez, A.; Dalchenko, M.; De Mattia, M.; Dildick, S.; Eusebi, R.; Flanagan, W.; Gilmore, J.; Kamon, T.; Khotilovich, V.; Krutelyov, V.; Montalvo, R.; Osipenkov, I.; Pakhotin, Y.; Patel, R.; Perloff, A.; Roe, J.; Rose, A.; Safonov, A.; Suarez, I.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kovitanggoon, K.; Kunori, S.; Lee, S. W.; Libeiro, T.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Sharma, M.; Sheldon, P.; Snook, B.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Boutle, S.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Lin, C.; Neu, C.; Wolfe, E.; Wood, J.; Clarke, C.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Sturdy, J.; Belknap, D. A.; Carlsmith, D.; Cepeda, M.; Dasu, S.; Dodd, L.; Duric, S.; Friis, E.; Hall-Wilton, R.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Lazaridis, C.; Levine, A.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ross, I.; Sarangi, T.; Savin, A.; Smith, W. H.; Taylor, D.; Vuosalo, C.; Woods, N.; Roinishvili, V.

2015-05-01

Properties of the Higgs boson with mass near 125 are measured in proton-proton collisions with the CMS experiment at the LHC. Comprehensive sets of production and decay measurements are combined. The decay channels include , , , , , and pairs. The data samples were collected in 2011 and 2012 and correspond to integrated luminosities of up to 5.1 at 7 and up to 19.7 at 8. From the high-resolution and channels, the mass of the Higgs boson is determined to be . For this mass value, the event yields obtained in the different analyses tagging specific decay channels and production mechanisms are consistent with those expected for the standard model Higgs boson. The combined best-fit signal relative to the standard model expectation is at the measured mass. The couplings of the Higgs boson are probed for deviations in magnitude from the standard model predictions in multiple ways, including searches for invisible and undetected decays. No significant deviations are found.
Particle-laden weakly swirling free jets: Measurements and predictions. Ph.D. Thesis - Pennsylvania State Univ.

NASA Technical Reports Server (NTRS)

Bulzan, Daniel L.

1988-01-01

A theoretical and experimental investigation of particle-laden, weakly swirling, turbulent free jets was conducted. Glass particles, having a Sauter mean diameter of 39 microns, with a standard deviation of 15 microns, were used. A single loading ratio (the mass flow rate of particles per unit mass flow rate of air) of 0.2 was used in the experiments. Measurements are reported for three swirl numbers, ranging from 0 to 0.33. The measurements included mean and fluctuating velocities of both phases, and particle mass flux distributions. Measurements were also completed for single-phase non-swirling and swirling jets, as baselines. Measurements were compared with predictions from three types of multiphase flow analysis, as follows: (1) locally homogeneous flow (LHF) where slip between the phases was neglected; (2) deterministic separated flow (DSF), where slip was considered but effects of turbulence/particle interactions were neglected; and (3) stochastic separated flow (SSF), where effects of both interphase slip and turbulence/particle interactions were considered using random sampling for turbulence properties in conjunction with random-walk computations for particle motion. Single-phase weakly swirling jets were considered first. Predictions using a standard k-epsilon turbulence model, as well as two versions modified to account for effects of streamline curvature, were compared with measurements. Predictions using a streamline curvature modification based on the flux Richardson number gave better agreement with measurements for the single-phase swirling jets than the standard k-epsilon model. For the particle-laden jets, the LHF and DSF models did not provide very satisfactory predictions. The LHF model generally overestimated the rate of decay of particle mean axial and angular velocities with streamwise distance, and predicted particle mass fluxes also showed poor agreement with measurements, due to the assumption of no-slip between phases. The DSF model also performed quite poorly for predictions of particle mass flux because turbulent dispersion of the particles was neglected. The SSF model, which accounts for both particle inertia and turbulent dispersion of the particles, yielded reasonably good predictions throughout the flow field for the particle-laden jets.
Cross-validation of Peak Oxygen Consumption Prediction Models From OMNI Perceived Exertion.

PubMed

Mays, R J; Goss, F L; Nagle, E F; Gallagher, M; Haile, L; Schafer, M A; Kim, K H; Robertson, R J

2016-09-01

This study cross-validated statistical models for prediction of peak oxygen consumption using ratings of perceived exertion from the Adult OMNI Cycle Scale of Perceived Exertion. 74 participants (men: n=36; women: n=38) completed a graded cycle exercise test. Ratings of perceived exertion for the overall body, legs, and chest/breathing were recorded each test stage and entered into previously developed 3-stage peak oxygen consumption prediction models. There were no significant differences (p>0.05) between measured and predicted peak oxygen consumption from ratings of perceived exertion for the overall body, legs, and chest/breathing within men (mean±standard deviation: 3.16±0.52 vs. 2.92±0.33 vs. 2.90±0.29 vs. 2.90±0.26 L·min(-1)) and women (2.17±0.29 vs. 2.02±0.22 vs. 2.03±0.19 vs. 2.01±0.19 L·min(-1)) participants. Previously developed statistical models for prediction of peak oxygen consumption based on subpeak OMNI ratings of perceived exertion responses were similar to measured peak oxygen consumption in a separate group of participants. These findings provide practical implications for the use of the original statistical models in standard health-fitness settings. © Georg Thieme Verlag KG Stuttgart · New York.
The modelling of heat, mass and solute transport in solidification systems

NASA Technical Reports Server (NTRS)

Voller, V. R.; Brent, A. D.; Prakash, C.

1989-01-01

The aim of this paper is to explore the range of possible one-phase models of binary alloy solidification. Starting from a general two-phase description, based on the two-fluid model, three limiting cases are identified which result in one-phase models of binary systems. Each of these models can be readily implemented in standard single phase flow numerical codes. Differences between predictions from these models are examined. In particular, the effects of the models on the predicted macro-segregation patterns are evaluated.
Multivariate calibration standardization across instruments for the determination of glucose by Fourier transform near-infrared spectrometry.

PubMed

Zhang, Lin; Small, Gary W; Arnold, Mark A

2003-11-01

The transfer of multivariate calibration models is investigated between a primary (A) and two secondary Fourier transform near-infrared (near-IR) spectrometers (B, C). The application studied in this work is the use of bands in the near-IR combination region of 5000-4000 cm(-)(1) to determine physiological levels of glucose in a buffered aqueous matrix containing varying levels of alanine, ascorbate, lactate, triacetin, and urea. The three spectrometers are used to measure 80 samples produced through a randomized experimental design that minimizes correlations between the component concentrations and between the concentrations of glucose and water. Direct standardization (DS), piecewise direct standardization (PDS), and guided model reoptimization (GMR) are evaluated for use in transferring partial least-squares calibration models developed with the spectra of 64 samples from the primary instrument to the prediction of glucose concentrations in 16 prediction samples measured with each secondary spectrometer. The three algorithms are evaluated as a function of the number of standardization samples used in transferring the calibration models. Performance criteria for judging the success of the calibration transfer are established as the standard error of prediction (SEP) for internal calibration models built with the spectra of the 64 calibration samples collected with each secondary spectrometer. These SEP values are 1.51 and 1.14 mM for spectrometers B and C, respectively. When calibration standardization is applied, the GMR algorithm is observed to outperform DS and PDS. With spectrometer C, the calibration transfer is highly successful, producing an SEP value of 1.07 mM. However, an SEP of 2.96 mM indicates unsuccessful calibration standardization with spectrometer B. This failure is attributed to differences in the variance structure of the spectra collected with spectrometers A and B. Diagnostic procedures are presented for use with the GMR algorithm that forecasts the successful calibration transfer with spectrometer C and the unsatisfactory results with spectrometer B.
[Study of building quantitative analysis model for chlorophyll in winter wheat with reflective spectrum using MSC-ANN algorithm].

PubMed

Liang, Xue; Ji, Hai-yan; Wang, Peng-xin; Rao, Zhen-hong; Shen, Bing-hui

2010-01-01

Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method (BP-ANN), and the numbers of principal components were calculated by the method of cross validation. The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat. The correlation coefficient (r) of calibration set was 0.9604, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively. The correlation coefficient (r) of predicted set was 0.9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4.21% respectively. It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.
Precision measurement of the weak charge of the proton.

PubMed

2018-05-01

Large experimental programmes in the fields of nuclear and particle physics search for evidence of physics beyond that explained by current theories. The observation of the Higgs boson completed the set of particles predicted by the standard model, which currently provides the best description of fundamental particles and forces. However, this theory's limitations include a failure to predict fundamental parameters, such as the mass of the Higgs boson, and the inability to account for dark matter and energy, gravity, and the matter-antimatter asymmetry in the Universe, among other phenomena. These limitations have inspired searches for physics beyond the standard model in the post-Higgs era through the direct production of additional particles at high-energy accelerators, which have so far been unsuccessful. Examples include searches for supersymmetric particles, which connect bosons (integer-spin particles) with fermions (half-integer-spin particles), and for leptoquarks, which mix the fundamental quarks with leptons. Alternatively, indirect searches using precise measurements of well predicted standard-model observables allow highly targeted alternative tests for physics beyond the standard model because they can reach mass and energy scales beyond those directly accessible by today's high-energy accelerators. Such an indirect search aims to determine the weak charge of the proton, which defines the strength of the proton's interaction with other particles via the well known neutral electroweak force. Because parity symmetry (invariance under the spatial inversion (x, y, z) → (-x, -y, -z)) is violated only in the weak interaction, it provides a tool with which to isolate the weak interaction and thus to measure the proton's weak charge 1 . Here we report the value 0.0719 ± 0.0045, where the uncertainty is one standard deviation, derived from our measured parity-violating asymmetry in the scattering of polarized electrons on protons, which is -226.5 ± 9.3 parts per billion (the uncertainty is one standard deviation). Our value for the proton's weak charge is in excellent agreement with the standard model 2 and sets multi-teraelectronvolt-scale constraints on any semi-leptonic parity-violating physics not described within the standard model. Our results show that precision parity-violating measurements enable searches for physics beyond the standard model that can compete with direct searches at high-energy accelerators and, together with astronomical observations, can provide fertile approaches to probing higher mass scales.
Standard model predictions for B→Kℓ(+)ℓ- with form factors from lattice QCD.

PubMed

Bouchard, Chris; Lepage, G Peter; Monahan, Christopher; Na, Heechang; Shigemitsu, Junko

2013-10-18

We calculate, for the first time using unquenched lattice QCD form factors, the standard model differential branching fractions dB/dq2(B→Kℓ(+)ℓ(-)) for ℓ=e, μ, τ and compare with experimental measurements by Belle, BABAR, CDF, and LHCb. We report on B(B→Kℓ(+)ℓ(-)) in q2 bins used by experiment and predict B(B→Kτ(+)τ(-))=(1.41±0.15)×10(-7). We also calculate the ratio of branching fractions R(e)(μ)=1.00029(69) and predict R(ℓ)(τ)=1.176(40), for ℓ=e, μ. Finally, we calculate the "flat term" in the angular distribution of the differential decay rate F(H)(e,μ,τ) in experimentally motivated q2 bins.
Some Unsolved Problems, Questions, and Applications of the Brightsen Nucleon Cluster Model

NASA Astrophysics Data System (ADS)

Smarandache, Florentin

2010-10-01

Brightsen Model is opposite to the Standard Model, and it was build on John Weeler's Resonating Group Structure Model and on Linus Pauling's Close-Packed Spheron Model. Among Brightsen Model's predictions and applications we cite the fact that it derives the average number of prompt neutrons per fission event, it provides a theoretical way for understanding the low temperature / low energy reactions and for approaching the artificially induced fission, it predicts that forces within nucleon clusters are stronger than forces between such clusters within isotopes; it predicts the unmatter entities inside nuclei that result from stable and neutral union of matter and antimatter, and so on. But these predictions have to be tested in the future at the new CERN laboratory.
3D-quantitative structure-activity relationship studies on benzothiadiazepine hydroxamates as inhibitors of tumor necrosis factor-alpha converting enzyme.

PubMed

Murumkar, Prashant R; Giridhar, Rajani; Yadav, Mange Ram

2008-04-01

A set of 29 benzothiadiazepine hydroxamates having selective tumor necrosis factor-alpha converting enzyme inhibitory activity were used to compare the quality and predictive power of 3D-quantitative structure-activity relationship, comparative molecular field analysis, and comparative molecular similarity indices models for the atom-based, centroid/atom-based, data-based, and docked conformer-based alignment. Removal of two outliers from the initial training set of molecules improved the predictivity of models. Among the 3D-quantitative structure-activity relationship models developed using the above four alignments, the database alignment provided the optimal predictive comparative molecular field analysis model for the training set with cross-validated r(2) (q(2)) = 0.510, non-cross-validated r(2) = 0.972, standard error of estimates (s) = 0.098, and F = 215.44 and the optimal comparative molecular similarity indices model with cross-validated r(2) (q(2)) = 0.556, non-cross-validated r(2) = 0.946, standard error of estimates (s) = 0.163, and F = 99.785. These models also showed the best test set prediction for six compounds with predictive r(2) values of 0.460 and 0.535, respectively. The contour maps obtained from 3D-quantitative structure-activity relationship studies were appraised for activity trends for the molecules analyzed. The comparative molecular similarity indices models exhibited good external predictivity as compared with that of comparative molecular field analysis models. The data generated from the present study helped us to further design and report some novel and potent tumor necrosis factor-alpha converting enzyme inhibitors.
Efficacy of monitoring and empirical predictive modeling at improving public health protection at Chicago beaches

USGS Publications Warehouse

Nevers, Meredith B.; Whitman, Richard L.

2011-01-01

Efforts to improve public health protection in recreational swimming waters have focused on obtaining real-time estimates of water quality. Current monitoring techniques rely on the time-intensive culturing of fecal indicator bacteria (FIB) from water samples, but rapidly changing FIB concentrations result in management errors that lead to the public being exposed to high FIB concentrations (type II error) or beaches being closed despite acceptable water quality (type I error). Empirical predictive models may provide a rapid solution, but their effectiveness at improving health protection has not been adequately assessed. We sought to determine if emerging monitoring approaches could effectively reduce risk of illness exposure by minimizing management errors. We examined four monitoring approaches (inactive, current protocol, a single predictive model for all beaches, and individual models for each beach) with increasing refinement at 14 Chicago beaches using historical monitoring and hydrometeorological data and compared management outcomes using different standards for decision-making. Predictability (R2) of FIB concentration improved with model refinement at all beaches but one. Predictive models did not always reduce the number of management errors and therefore the overall illness burden. Use of a Chicago-specific single-sample standard-rather than the default 235 E. coli CFU/100 ml widely used-together with predictive modeling resulted in the greatest number of open beach days without any increase in public health risk. These results emphasize that emerging monitoring approaches such as empirical models are not equally applicable at all beaches, and combining monitoring approaches may expand beach access.
A perturbative approach to the redshift space correlation function: beyond the Standard Model

NASA Astrophysics Data System (ADS)

Bose, Benjamin; Koyama, Kazuya

2017-08-01

We extend our previous redshift space power spectrum code to the redshift space correlation function. Here we focus on the Gaussian Streaming Model (GSM). Again, the code accommodates a wide range of modified gravity and dark energy models. For the non-linear real space correlation function used in the GSM we use the Fourier transform of the RegPT 1-loop matter power spectrum. We compare predictions of the GSM for a Vainshtein screened and Chameleon screened model as well as GR. These predictions are compared to the Fourier transform of the Taruya, Nishimichi and Saito (TNS) redshift space power spectrum model which is fit to N-body data. We find very good agreement between the Fourier transform of the TNS model and the GSM predictions, with <= 6% deviations in the first two correlation function multipoles for all models for redshift space separations in 50Mpch <= s <= 180Mpc/h. Excellent agreement is found in the differences between the modified gravity and GR multipole predictions for both approaches to the redshift space correlation function, highlighting their matched ability in picking up deviations from GR. We elucidate the timeliness of such non-standard templates at the dawn of stage-IV surveys and discuss necessary preparations and extensions needed for upcoming high quality data.
Estimation of Handling Qualities Parameters of the Tu-144 Supersonic Transport Aircraft from Flight Test Data

NASA Technical Reports Server (NTRS)

Curry, Timothy J.; Batterson, James G. (Technical Monitor)

2000-01-01

Low order equivalent system (LOES) models for the Tu-144 supersonic transport aircraft were identified from flight test data. The mathematical models were given in terms of transfer functions with a time delay by the military standard MIL-STD-1797A, "Flying Qualities of Piloted Aircraft," and the handling qualities were predicted from the estimated transfer function coefficients. The coefficients and the time delay in the transfer functions were estimated using a nonlinear equation error formulation in the frequency domain. Flight test data from pitch, roll, and yaw frequency sweeps at various flight conditions were used for parameter estimation. Flight test results are presented in terms of the estimated parameter values, their standard errors, and output fits in the time domain. Data from doublet maneuvers at the same flight conditions were used to assess the predictive capabilities of the identified models. The identified transfer function models fit the measured data well and demonstrated good prediction capabilities. The Tu-144 was predicted to be between level 2 and 3 for all longitudinal maneuvers and level I for all lateral maneuvers. High estimates of the equivalent time delay in the transfer function model caused the poor longitudinal rating.

The regionalization of national-scale SPARROW models for stream nutrients

USGS Publications Warehouse

Schwarz, Gregory E.; Alexander, Richard B.; Smith, Richard A.; Preston, Stephen D.

2011-01-01

This analysis modifies the parsimonious specification of recently published total nitrogen (TN) and total phosphorus (TP) national-scale SPAtially Referenced Regressions On Watershed attributes models to allow each model coefficient to vary geographically among three major river basins of the conterminous United States. Regionalization of the national models reduces the standard errors in the prediction of TN and TP loads, expressed as a percentage of the predicted load, by about 6 and 7%. We develop and apply a method for combining national-scale and regional-scale information to estimate a hybrid model that imposes cross-region constraints that limit regional variation in model coefficients, effectively reducing the number of free model parameters as compared to a collection of independent regional models. The hybrid TN and TP regional models have improved model fit relative to the respective national models, reducing the standard error in the prediction of loads, expressed as a percentage of load, by about 5 and 4%. Only 19% of the TN hybrid model coefficients and just 2% of the TP hybrid model coefficients show evidence of substantial regional specificity (more than ±100% deviation from the national model estimate). The hybrid models have much greater precision in the estimated coefficients than do the unconstrained regional models, demonstrating the efficacy of pooling information across regions to improve regional models.
Direct CP violation in K⁰→ππ: Standard Model Status.

PubMed

Gisbert, Hector; Pich, Antonio

2018-05-01

In 1988 the NA31 experiment presented the first evidence of direct CP violation in the K0→ππ decay amplitudes. A clear signal with a 7.2σ statistical significance was later established with the full data samples from the NA31, E731, NA48 and KTeV experiments, confirming that CP violation is associated with a ΔS=1 quark transition, as predicted by the Standard Model. However, the theoretical prediction for the measured ratio ε'/ε has been a subject of strong controversy along the years. Although the underlying physics was already clarified in 2001, the recent release of improved lattice data has revived again the theoretical debate. We review the current status, discussing in detail the different ingredients that enter into the calculation of this observable and the reasons why seemingly contradictory predictions were obtained in the past by several groups. An update of the Standard Model prediction is presented and the prospects for future improvements are analysed. Taking into account all known short-distance and long-distance contributions, one obtains Re(ε'/ε) = (15 ± 7) ·10-4, in good agreement with the experimental measurement. . © 2018 IOP Publishing Ltd.
Predicting mortality rates: Comparison of an administrative predictive model (hospital standardized mortality ratio) with a physiological predictive model (Acute Physiology and Chronic Health Evaluation IV)--A cross-sectional study.

PubMed

Toua, Rene Elaine; de Kock, Jacques Erasmus; Welzel, Tyson

2016-02-01

Direct comparison of mortality rates has limited value because most deaths are due to the disease process. Predicting the risk of death accurately remains a challenge. A cross-sectional study compared the expected mortality rate as calculated with an administrative model to a physiological model, Acute Physiology and Chronic Health Evaluation IV. The combined cohort and stratified samples (<0.1, 0.1-0.5, or >0.5 predicted mortality) were considered. A total of 47,982 patients were scored from 1 July 2013 to 30 June 2014, and 46,061 records were included in the analysis. A moderate correlation was shown for the combined cohort (Pearson correlation index, 0.618; 95% confidence interval [CI], 0.380-0.779; R(2) = 0.38). A very good correlation for the less than 10% stratum (Pearson correlation index, 0.884; R(2) = 0.78; 95% CI, 0.79-0.937) and a moderate correlation for 0.1 to 0.5 predicted mortality rates (Pearson correlation index, 0.782; R(2) = 0.61; 95% CI, 0.623-0.879). There was no significant positive correlation for the greater than 50% predicted mortality stratum (Pearson correlation index, 0.087; R(2) = 0.007; 95% CI, -0.23 to 0.387). At less than 0.1, the models are interchangeable, but in spite of a moderate correlation, greater than 0.1 hospital standardized mortality ratio cannot be used to predict mortality. Copyright © 2015 Elsevier Inc. All rights reserved.
Predicting county-level cancer incidence rates and counts in the United States

PubMed Central

Yu, Binbing

2018-01-01

Many countries, including the United States, publish predicted numbers of cancer incidence and death in current and future years for the whole country. These predictions provide important information on the cancer burden for cancer control planners, policymakers and the general public. Based on evidence from several empirical studies, the joinpoint (segmented-line linear regression) model has been adopted by the American Cancer Society to estimate the number of new cancer cases in the United States and in individual states since 2007. Recently, cancer incidence in smaller geographic regions such as counties and FIPS code regions is of increasing interest by local policymakers. The natural extension is to directly apply the joinpoint model to county-level cancer incidence data. The direct application has several drawbacks and its performance has not been evaluated. To address the concerns, we developed a spatial random-effects joinpoint model for county-level cancer incidence data. The proposed model was used to predict both cancer incidence rates and counts at the county level. The standard joinpoint model and the proposed method were compared through a validation study. The proposed method out-performed the standard joinpoint model for almost all cancer sites, especially for moderate or rare cancer sites and for counties with small population sizes. As an application, we predicted county-level prostate cancer incidence rates and counts for the year 2011 in Connecticut. PMID:23670947
On the internal target model in a tracking task

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Baron, S.

1981-01-01

An optimal control model for predicting operator's dynamic responses and errors in target tracking ability is summarized. The model, which predicts asymmetry in the tracking data, is dependent on target maneuvers and trajectories. Gunners perception, decision making, control, and estimate of target positions and velocity related to crossover intervals are discussed. The model provides estimates for means, standard deviations, and variances for variables investigated and for operator estimates of future target positions and velocities.
Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Manh, Cuong Do

2015-01-01

The Mekong Delta is highly vulnerable to climate change and a dengue endemic area in Vietnam. This study aims to examine the association between climate factors and dengue incidence and to identify the best climate prediction model for dengue incidence in Can Tho city, the Mekong Delta area in Vietnam. We used three different regression models comprising: standard multiple regression model (SMR), seasonal autoregressive integrated moving average model (SARIMA), and Poisson distributed lag model (PDLM) to examine the association between climate factors and dengue incidence over the period 2003-2010. We validated the models by forecasting dengue cases for the period of January-December, 2011 using the mean absolute percentage error (MAPE). Receiver operating characteristics curves were used to analyze the sensitivity of the forecast of a dengue outbreak. The results indicate that temperature and relative humidity are significantly associated with changes in dengue incidence consistently across the model methods used, but not cumulative rainfall. The Poisson distributed lag model (PDLM) performs the best prediction of dengue incidence for a 6, 9, and 12-month period and diagnosis of an outbreak however the SARIMA model performs a better prediction of dengue incidence for a 3-month period. The simple or standard multiple regression performed highly imprecise prediction of dengue incidence. We recommend a follow-up study to validate the model on a larger scale in the Mekong Delta region and to analyze the possibility of incorporating a climate-based dengue early warning method into the national dengue surveillance system. Copyright © 2014 Elsevier B.V. All rights reserved.
Satisfying Friendship Maintenance Expectations: The Role of Friendship Standards and Biological Sex

ERIC Educational Resources Information Center

Hall, Jeffrey A.; Larson, Kiley A.; Watts, Amber

2011-01-01

The ideal standards model predicts linear relationship among friendship standards, expectation fulfillment, and relationship satisfaction. Using a diary method, 197 participants reported on expectation fulfillment in interactions with one best, one close, and one casual friend (N = 591) over five days (2,388 interactions). Using multilevel…
Development of estrogen receptor beta binding prediction model using large sets of chemicals.

PubMed

Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

2017-11-03

We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .
Predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwestern United States

USGS Publications Warehouse

Anning, David W.; Paul, Angela P.; McKinney, Tim S.; Huntington, Jena M.; Bexfield, Laura M.; Thiros, Susan A.

2012-01-01

The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) is conducting a regional analysis of water quality in the principal aquifer systems across the United States. The Southwest Principal Aquifers (SWPA) study is building a better understanding of the susceptibility and vulnerability of basin-fill aquifers in the region to groundwater contamination by synthesizing baseline knowledge of groundwater-quality conditions in 16 basins previously studied by the NAWQA Program. The improved understanding of aquifer susceptibility and vulnerability to contamination is assisting in the development of tools that water managers can use to assess and protect the quality of groundwater resources.Human-health concerns and economic considerations associated with meeting drinking-water standards motivated a study of the vulnerability of basin-fill aquifers to nitrate contamination and arsenic enrichment in the southwestern United States. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents about 190,600 square miles of basin-fill aquifers in parts of Arizona, California, Colorado, Nevada, New Mexico, and Utah. The statistical models, referred to as classifiers, reflect natural and human-related factors that affect aquifer vulnerability to contamination and relate nitrate and arsenic concentrations to explanatory variables representing local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions. The classifiers were unbiased and fit the observed data well, and misclassifications were primarily due to statistical sampling error in the training datasets.The classifiers were designed to predict concentrations to be in one of six classes for nitrate, and one of seven classes for arsenic. Each classification scheme allowed for identification of areas with concentrations that were equal to or exceeding the U.S. Environmental Protection Agency drinking-water standard. Whereas 2.4 percent of the area underlain by basin-fill aquifers in the study area was predicted to equal or exceed this standard for nitrate (10 milligrams per liter as N; mg/L), 42.7 percent was predicted to equal or exceed the standard for arsenic (10 micrograms per liter; μg/L). Areas predicted to equal or exceed the drinking-water standard for nitrate include basins in central Arizona near Phoenix; the San Joaquin, Inland, and San Jacinto basins of California; and the San Luis Valley of Colorado. Much of the area predicted to equal or exceed the drinking-water standard for arsenic is within a belt of basins along the western portion of the Basin and Range Physiographic Province in Nevada, California, and Arizona. Predicted nitrate and arsenic concentrations are substantially lower than the drinking-water standards in much of the study area—about 93.0 percent of the area underlain by basin-fill aquifers was less than one-half the standard for nitrate (5.0 mg/L), and 50.2 percent was less than one-half the standard for arsenic (5.0 μg/L).
Form Factor Measurements at BESIII for an Improved Standard Model Prediction of the Muon g-2

NASA Astrophysics Data System (ADS)

Destefanis, Marco

The anomalous part of the magnetic moment of the muon, (g-2)μ, allows for one of the most precise tests of the Standard Model of particle physics. We report on recent results by the BESIII Collaboration of exclusive hadronic cross section channels, such as the 2π, 3π, and 4π final states. These measurements are of utmost importance for an improved calculation of the hadronic vacuum polarization contribution of (g-2)μ, which currenty is limiting the overall Standard Model prediction of this quantity. BESIII has furthermore also intiatated a programme of spacelike transition form factor measurements, which can be used for a determination of the hadronic light-by-light contribution of (g-2)μ in a data-driven approach. These results are of relevance in view of the new and direct measurements of (g-2)μ as foreseen at Fermilab/USA and J-PARC/Japan.
Search for Standard Model Production of Four Top Quarks at CMS in the Dilepton Channel at a Center of Mass Energy of 13 TeV

NASA Astrophysics Data System (ADS)

Heilman, Jesse Alan

The search for the production of four top quarks decaying in the dileptonic channel in proton-proton collisions at the LHC is presented. The analysis utilises the data recorded by the CMS experiment at sqrt{s} = 13 TeV in 2015, which corresponds to an integrated luminosity of 2.6 inverse femtobarns. A boosted decision tree algorithm is used to select signal and suppress background events. Upper limits on dileptonic four top quark production of 14.9 times the predicted standard model cross section observed and 22.3 +16.2-8.4 times the predicted standard model cross section expected are calculated at the 95% confidence level. A combination is then performed with a parallel analysis of the single lepton channel to extend the reach of the search.
A TCP model for external beam treatment of intermediate-risk prostate cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walsh, Sean; Putten, Wil van der

2013-03-15

Purpose: Biological models offer the ability to predict clinical outcomes. The authors describe a model to predict the clinical response of intermediate-risk prostate cancer to external beam radiotherapy for a variety of fractionation regimes. Methods: A fully heterogeneous population averaged tumor control probability model was fit to clinical outcome data for hyper, standard, and hypofractionated treatments. The tumor control probability model was then employed to predict the clinical outcome of extreme hypofractionation regimes, as utilized in stereotactic body radiotherapy. Results: The tumor control probability model achieves an excellent level of fit, R{sup 2} value of 0.93 and a root meanmore » squared error of 1.31%, to the clinical outcome data for hyper, standard, and hypofractionated treatments using realistic values for biological input parameters. Residuals Less-Than-Or-Slanted-Equal-To 1.0% are produced by the tumor control probability model when compared to clinical outcome data for stereotactic body radiotherapy. Conclusions: The authors conclude that this tumor control probability model, used with the optimized radiosensitivity values obtained from the fit, is an appropriate mechanistic model for the analysis and evaluation of external beam RT plans with regard to tumor control for these clinical conditions.« less
Measurement of electroweak-induced production of Wγ with two jets in pp collisions at $$ \\sqrt{s}=8 $$ TeV and constraints on anomalous quartic gauge couplings

DOE PAGES

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; ...

2017-06-20

A measurement of electroweak-induced production of Wγ and two jets is performed, where the W boson decays leptonically. The data used in the analysis correspond to an integrated luminosity of 19.7 fb –1 collected by the CMS experiment in √s = 8 TeV proton-proton collisions produced at the LHC. Candidate events are selected with exactly one muon or electron, missing transverse momentum, one photon, and two jets with large rapidity separation. An excess over the hypothesis of the standard model without electroweak production of Wγ with two jets is observed with a significance of 2.7 standard deviations. The crossmore » section measured in the fiducial region is 10.8 ± 4.1(stat) ± 3.4(syst) ± 0.3(lumi) fb, which is consistent with the standard model electroweak prediction. The total cross section for Wγ in association with two jets in the same fiducial region is measured to be 23.2 ± 4.3(stat) ± 1.7(syst) ± 0.6(lumi) fb, which is consistent with the standard model prediction from the combination of electroweak and quantum chromodynamics-induced processes. As a result, no deviations are observed from the standard model predictions and experimental limits on anomalous quartic gauge couplings f M,0–7/Λ 4, f T,0–2/Λ 4, and f T,5–7/Λ 4 are set at 95% confidence level.« less
Measurement of electroweak-induced production of Wγ with two jets in pp collisions at $$ \\sqrt{s}=8 $$ TeV and constraints on anomalous quartic gauge couplings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.

A measurement of electroweak-induced production of Wγ and two jets is performed, where the W boson decays leptonically. The data used in the analysis correspond to an integrated luminosity of 19.7 fb –1 collected by the CMS experiment in √s = 8 TeV proton-proton collisions produced at the LHC. Candidate events are selected with exactly one muon or electron, missing transverse momentum, one photon, and two jets with large rapidity separation. An excess over the hypothesis of the standard model without electroweak production of Wγ with two jets is observed with a significance of 2.7 standard deviations. The crossmore » section measured in the fiducial region is 10.8 ± 4.1(stat) ± 3.4(syst) ± 0.3(lumi) fb, which is consistent with the standard model electroweak prediction. The total cross section for Wγ in association with two jets in the same fiducial region is measured to be 23.2 ± 4.3(stat) ± 1.7(syst) ± 0.6(lumi) fb, which is consistent with the standard model prediction from the combination of electroweak and quantum chromodynamics-induced processes. As a result, no deviations are observed from the standard model predictions and experimental limits on anomalous quartic gauge couplings f M,0–7/Λ 4, f T,0–2/Λ 4, and f T,5–7/Λ 4 are set at 95% confidence level.« less
Constraint on Absolute Accuracy of Metacomprehension Assessments: The Anchoring and Adjustment Model vs. the Standards Model

ERIC Educational Resources Information Center

Kwon, Heekyung

2011-01-01

The objective of this study is to provide a systematic account of three typical phenomena surrounding absolute accuracy of metacomprehension assessments: (1) the absolute accuracy of predictions is typically quite low; (2) there exist individual differences in absolute accuracy of predictions as a function of reading skill; and (3) postdictions…
Experimental studies of electroweak physics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etzion, E.

1997-09-01

Some experimental new Electroweak physics results measured at the LEP/SLD and the TEVATRON are discussed. The excellent accuracy achieved by the experiments still yield no significant evidence for deviation from the Standard Model predictions, or signal to physics beyond the Standard Model. The Higgs particle still has not been discovered and a low bound is given to its mass.
Use of Standard Deviations as Predictors in Models Using Large-Scale International Data Sets

ERIC Educational Resources Information Center

Austin, Bruce; French, Brian; Adesope, Olusola; Gotch, Chad

2017-01-01

Measures of variability are successfully used in predictive modeling in research areas outside of education. This study examined how standard deviations can be used to address research questions not easily addressed using traditional measures such as group means based on index variables. Student survey data were obtained from the Organisation for…
Compton Scattering Polarimetry for the Determination of the Proton's Weak Charge Through Measurements of the Parity-Violating Asymmetry of 1H(e,e')p

NASA Astrophysics Data System (ADS)

Cornejo, Juan Carlos

The Standard Model has been a theory with the greatest success in describing the fundamental interactions of particles. As of the writing of this dissertation, the Standard Model has not been shown to make a false prediction. However, the limitations of the Standard Model have long been suspected by its lack of a description of gravity, nor dark matter. Its largest challenge to date, has been the observation of neutrino oscillations, and the implication that they may not be massless, as required by the Standard Model. The growing consensus is that the Standard Model is simply a lower energy effective field theory, and that new physics lies at much higher energies. The Qweak Experiment is testing the Electroweak theory of the Standard Model by making a precise determination of the weak charge of the proton (Qpw). Any signs of "new physics" will appear as a deviation to the Standard Model prediction. The weak charge is determined via a precise measurement of the parity-violating asymmetry of the electron-proton interaction via elastic scattering of a longitudinally polarized electron beam of an un-polarized proton target. The experiment required that the electron beam polarization be measured to an absolute uncertainty of 1 %. At this level the electron beam polarization was projected to contribute the single largest experimental uncertainty to the parity-violating asymmetry measurement. This dissertation will detail the use of Compton scattering to determine the electron beam polarization via the detection of the scattered photon. I will conclude the remainder of the dissertation with an independent analysis of the blinded Qweak.
Search for Muonic Dark Forces at BABAR

NASA Astrophysics Data System (ADS)

Godang, Romulus

2017-04-01

Many models of physics beyond Standard Model predict the existence of light Higgs states, dark photons, and new gauge bosons mediating interactions between dark sectors and the Standard Model. Using a full data sample collected with the BABAR detector at the PEP-II e+e- collider, we report searches for a light non-Standard Model Higgs boson, dark photon, and a new muonic dark force mediated by a gauge boson (Z') coupling only to the second and third lepton families. Our results significantly improve upon the current bounds and further constrain the remaining region of the allowed parameter space.
Search for physics beyond the standard model in events with τ leptons, jets, and large transverse momentum imbalance in pp collisions at [Formula: see text].

PubMed

Chatrchyan, S; Khachatryan, V; Sirunyan, A M; Tumasyan, A; Adam, W; Aguilo, E; Bergauer, T; Dragicevic, M; Erö, J; Fabjan, C; Friedl, M; Frühwirth, R; Ghete, V M; Hammer, J; Hörmann, N; Hrubec, J; Jeitler, M; Kiesenhofer, W; Knünz, V; Krammer, M; Krätschmer, I; Liko, D; Mikulec, I; Pernicka, M; Rahbaran, B; Rohringer, C; Rohringer, H; Schöfbeck, R; Strauss, J; Taurok, A; Waltenberger, W; Walzel, G; Widl, E; Wulz, C-E; Mossolov, V; Shumeiko, N; Suarez Gonzalez, J; Bansal, M; Bansal, S; Cornelis, T; De Wolf, E A; Janssen, X; Luyckx, S; Mucibello, L; Ochesanu, S; Roland, B; Rougny, R; Selvaggi, M; Staykova, Z; Van Haevermaet, H; Van Mechelen, P; Van Remortel, N; Van Spilbeeck, A; Blekman, F; Blyweert, S; D'Hondt, J; Gonzalez Suarez, R; Kalogeropoulos, A; Maes, M; Olbrechts, A; Van Doninck, W; Van Mulders, P; Van Onsem, G P; Villella, I; Clerbaux, B; De Lentdecker, G; Dero, V; Gay, A P R; Hreus, T; Léonard, A; Marage, P E; Mohammadi, A; Reis, T; Thomas, L; Vander Marcken, G; Vander Velde, C; 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Rose, A; Ryan, M J; Seez, C; Sharp, P; Sparrow, A; Stoye, M; Tapper, A; Vazquez Acosta, M; Virdee, T; Wakefield, S; Wardle, N; Whyntie, T; Chadwick, M; Cole, J E; Hobson, P R; Khan, A; Kyberd, P; Leggat, D; Leslie, D; Martin, W; Reid, I D; Symonds, P; Teodorescu, L; Turner, M; Hatakeyama, K; Liu, H; Scarborough, T; Charaf, O; Henderson, C; Rumerio, P; Avetisyan, A; Bose, T; Fantasia, C; Heister, A; Lawson, P; Lazic, D; Rohlf, J; Sperka, D; St John, J; Sulak, L; Alimena, J; Bhattacharya, S; Cutts, D; Ferapontov, A; Heintz, U; Jabeen, S; Kukartsev, G; Laird, E; Landsberg, G; Luk, M; Narain, M; Nguyen, D; Segala, M; Sinthuprasith, T; Speer, T; Tsang, K V; Breedon, R; Breto, G; Calderon De La Barca Sanchez, M; Chauhan, S; Chertok, M; Conway, J; Conway, R; Cox, P T; Dolen, J; Erbacher, R; Gardner, M; Houtz, R; Ko, W; Kopecky, A; Lander, R; Miceli, T; Pellett, D; Ricci-Tam, F; Rutherford, B; Searle, M; Smith, J; Squires, M; Tripathi, M; Vasquez Sierra, R; Andreev, V; Cline, D; Cousins, R; Duris, J; Erhan, S; Everaerts, P; Farrell, C; Hauser, J; Ignatenko, M; Jarvis, C; Plager, C; Rakness, G; Schlein, P; Traczyk, P; Valuev, V; Weber, M; Babb, J; Clare, R; Dinardo, M E; Ellison, J; Gary, J W; Giordano, F; Hanson, G; Jeng, G Y; Liu, H; Long, O R; Luthra, A; Nguyen, H; Paramesvaran, S; Sturdy, J; Sumowidagdo, S; Wilken, R; Wimpenny, S; Andrews, W; Branson, J G; Cerati, G B; Cittolin, S; Evans, D; Golf, F; Holzner, A; Kelley, R; Lebourgeois, M; Letts, J; Macneill, I; Mangano, B; Padhi, S; Palmer, C; Petrucciani, G; Pieri, M; Sani, M; Sharma, V; Simon, S; Sudano, E; Tadel, M; Tu, Y; Vartak, A; Wasserbaech, S; Würthwein, F; Yagil, A; Yoo, J; Barge, D; Bellan, R; Campagnari, C; D'Alfonso, M; Danielson, T; Flowers, K; Geffert, P; Incandela, J; Justus, C; Kalavase, P; Koay, S A; Kovalskyi, D; Krutelyov, V; Lowette, S; Mccoll, N; Pavlunin, V; Rebassoo, F; Ribnik, J; Richman, J; Rossin, R; Stuart, D; To, W; West, C; Apresyan, A; Bornheim, A; Chen, Y; Di Marco, E; Duarte, J; Gataullin, M; Ma, Y; Mott, A; Newman, H B; Rogan, C; Spiropulu, M; Timciuc, V; Veverka, J; Wilkinson, R; Xie, S; Yang, Y; Zhu, R Y; Akgun, B; Azzolini, V; Calamba, A; Carroll, R; Ferguson, T; Iiyama, Y; Jang, D W; Liu, Y F; Paulini, M; Vogel, H; Vorobiev, I; Cumalat, J P; Drell, B R; Edelmaier, C J; Ford, W T; Gaz, A; Heyburn, B; Luiggi Lopez, E; Smith, J G; Stenson, K; Ulmer, K A; Wagner, S R; Alexander, J; Chatterjee, A; Eggert, N; Gibbons, L K; Heltsley, B; Khukhunaishvili, A; Kreis, B; Mirman, N; Nicolas Kaufman, G; Patterson, J R; Ryd, A; Salvati, E; Sun, W; Teo, W D; Thom, J; Thompson, J; Tucker, J; Vaughan, J; Weng, Y; Winstrom, L; Wittich, P; Winn, D; Abdullin, S; Albrow, M; Anderson, J; Bauerdick, L A T; Beretvas, A; Berryhill, J; Bhat, P C; Bloch, I; Burkett, K; Butler, J N; Chetluru, V; Cheung, H W K; Chlebana, F; Elvira, V D; Fisk, I; Freeman, J; Gao, Y; Green, D; Gutsche, O; Hanlon, J; Harris, R M; Hirschauer, J; Hooberman, B; Jindariani, S; Johnson, M; Joshi, U; Kilminster, B; Klima, B; Kunori, S; Kwan, S; Leonidopoulos, C; Linacre, J; Lincoln, D; Lipton, R; Lykken, J; Maeshima, K; Marraffino, J M; Maruyama, S; Mason, D; McBride, P; Mishra, K; Mrenna, S; Musienko, Y; Newman-Holmes, C; O'Dell, V; Prokofyev, O; Sexton-Kennedy, E; Sharma, S; Spalding, W J; Spiegel, L; Tan, P; Taylor, L; Tkaczyk, S; Tran, N V; Uplegger, L; Vaandering, E W; Vidal, R; Whitmore, J; Wu, W; Yang, F; Yumiceva, F; Yun, J C; Acosta, D; Avery, P; Bourilkov, D; Chen, M; Cheng, T; Das, S; De Gruttola, M; Di Giovanni, G P; Dobur, D; Drozdetskiy, A; Field, R D; Fisher, M; Fu, Y; Furic, I K; Gartner, J; Hugon, J; Kim, B; Konigsberg, J; Korytov, A; Kropivnitskaya, A; Kypreos, T; Low, J F; Matchev, K; Milenovic, P; Mitselmakher, G; Muniz, L; Park, M; Remington, R; Rinkevicius, A; Sellers, P; Skhirtladze, N; Snowball, M; Yelton, J; Zakaria, M; Gaultney, V; Hewamanage, S; Lebolo, L M; Linn, S; Markowitz, P; Martinez, G; Rodriguez, J L; Adams, T; Askew, A; Bochenek, J; Chen, J; Diamond, B; Gleyzer, S V; Haas, J; Hagopian, S; Hagopian, V; Jenkins, M; Johnson, K F; Prosper, H; Veeraraghavan, V; Weinberg, M; Baarmand, M M; Dorney, B; Hohlmann, M; Kalakhety, H; Vodopiyanov, I; Adams, M R; Anghel, I M; Apanasevich, L; Bai, Y; Bazterra, V E; Betts, R R; Bucinskaite, I; Callner, J; Cavanaugh, R; Evdokimov, O; Gauthier, L; Gerber, C E; Hofman, D J; Khalatyan, S; Lacroix, F; Malek, M; O'Brien, C; Silkworth, C; Strom, D; Turner, P; Varelas, N; Akgun, U; Albayrak, E A; Bilki, B; Clarida, W; Duru, F; Griffiths, S; Merlo, J-P; Mermerkaya, H; Mestvirishvili, A; Moeller, A; Nachtman, J; Newsom, C R; Norbeck, E; Onel, Y; Ozok, F; Sen, S; Tiras, E; Wetzel, J; Yetkin, T; Yi, K; Barnett, B A; Blumenfeld, B; Bolognesi, S; Fehling, D; Giurgiu, G; Gritsan, A V; Guo, Z J; Hu, G; Maksimovic, P; Rappoccio, S; Swartz, M; Whitbeck, A; Baringer, P; Bean, A; Benelli, G; Kenny Iii, R P; Murray, M; Noonan, D; Sanders, S; Stringer, R; Tinti, G; Wood, J S; Zhukova, V; Barfuss, A F; Bolton, T; Chakaberia, I; Ivanov, A; Khalil, S; Makouski, M; 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Keller, J; Kravchenko, I; Lazo-Flores, J; Malbouisson, H; Malik, S; Snow, G R; Baur, U; Godshalk, A; Iashvili, I; Jain, S; Kharchilava, A; Kumar, A; Shipkowski, S P; Smith, K; Alverson, G; Barberis, E; Baumgartel, D; Chasco, M; Haley, J; Nash, D; Trocino, D; Wood, D; Zhang, J; Anastassov, A; Kubik, A; Mucia, N; Odell, N; Ofierzynski, R A; Pollack, B; Pozdnyakov, A; Schmitt, M; Stoynev, S; Velasco, M; Won, S; Antonelli, L; Berry, D; Brinkerhoff, A; Hildreth, M; Jessop, C; Karmgard, D J; Kolb, J; Lannon, K; Luo, W; Lynch, S; Marinelli, N; Morse, D M; Pearson, T; Planer, M; Ruchti, R; Slaunwhite, J; Valls, N; Wayne, M; Wolf, M; Bylsma, B; Durkin, L S; Hill, C; Hughes, R; Kotov, K; Ling, T Y; Puigh, D; Rodenburg, M; Vuosalo, C; Williams, G; Winer, B L; Adam, N; Berry, E; Elmer, P; Gerbaudo, D; Halyo, V; Hebda, P; Hegeman, J; Hunt, A; Jindal, P; Lopes Pegna, D; Lujan, P; Marlow, D; Medvedeva, T; Mooney, M; Olsen, J; Piroué, P; Quan, X; Raval, A; Safdi, B; Saka, H; Stickland, D; Tully, C; Werner, J S; Zuranski, A; Acosta, J G; Brownson, E; Huang, X T; Lopez, A; Mendez, H; Oliveros, S; Ramirez Vargas, J E; Zatserklyaniy, A; Alagoz, E; Barnes, V E; Benedetti, D; Bolla, G; Bortoletto, D; De Mattia, M; Everett, A; Hu, Z; Jones, M; Koybasi, O; Kress, M; Laasanen, A T; Leonardo, N; Maroussov, V; Merkel, P; Miller, D H; Neumeister, N; Shipsey, I; Silvers, D; Svyatkovskiy, A; Vidal Marono, M; Yoo, H D; Zablocki, J; Zheng, Y; Guragain, S; Parashar, N; Adair, A; Boulahouache, C; Ecklund, K M; Geurts, F J M; Padley, B P; Redjimi, R; Roberts, J; Zabel, J; Betchart, B; Bodek, A; Chung, Y S; Covarelli, R; de Barbaro, P; Demina, R; Eshaq, Y; Ferbel, T; Garcia-Bellido, A; Goldenzweig, P; Han, J; Harel, A; Miner, D C; Vishnevskiy, D; Zielinski, M; Bhatti, A; Ciesielski, R; Demortier, L; Goulianos, K; Lungu, G; Malik, S; Mesropian, C; Arora, S; Barker, A; Chou, J P; Contreras-Campana, C; Contreras-Campana, E; Duggan, D; Ferencek, D; Gershtein, Y; Gray, R; Halkiadakis, E; Hidas, D; Lath, A; Panwalkar, S; Park, M; Patel, R; Rekovic, V; Robles, J; Rose, K; Salur, S; Schnetzer, S; Seitz, C; Somalwar, S; Stone, R; Thomas, S; Cerizza, G; Hollingsworth, M; Spanier, S; Yang, Z C; York, A; Eusebi, R; Flanagan, W; Gilmore, J; Kamon, T; Khotilovich, V; Montalvo, R; Osipenkov, I; Pakhotin, Y; Perloff, A; Roe, J; Safonov, A; Sakuma, T; Sengupta, S; Suarez, I; Tatarinov, A; Toback, D; Akchurin, N; Damgov, J; Dragoiu, C; Dudero, P R; Jeong, C; Kovitanggoon, K; Lee, S W; Libeiro, T; Roh, Y; Volobouev, I; Appelt, E; Delannoy, A G; Florez, C; Greene, S; Gurrola, A; Johns, W; Johnston, C; Kurt, P; Maguire, C; Melo, A; Sharma, M; Sheldon, P; Snook, B; Tuo, S; Velkovska, J; Arenton, M W; Balazs, M; Boutle, S; Cox, B; Francis, B; Goodell, J; Hirosky, R; Ledovskoy, A; Lin, C; Neu, C; Wood, J; Yohay, R; Gollapinni, S; Harr, R; Karchin, P E; Kottachchi Kankanamge Don, C; Lamichhane, P; Sakharov, A; Anderson, M; Belknap, D A; Borrello, L; Carlsmith, D; Cepeda, M; Dasu, S; Friis, E; Gray, L; Grogg, K S; Grothe, M; Hall-Wilton, R; Herndon, M; Hervé, A; Klabbers, P; Klukas, J; Lanaro, A; Lazaridis, C; Leonard, J; Loveless, R; Mohapatra, A; Ojalvo, I; Palmonari, F; Pierro, G A; Ross, I; Savin, A; Smith, W H; Swanson, J

A search for physics beyond the standard model is performed with events having one or more hadronically decaying τ leptons, highly energetic jets, and large transverse momentum imbalance. The data sample corresponds to an integrated luminosity of 4.98 fb -1 of proton-proton collisions at [Formula: see text] collected with the CMS detector at the LHC in 2011. The number of observed events is consistent with predictions for standard model processes. Lower limits on the mass of the gluino in supersymmetric models are determined.

Urban Climate Resilience - Connecting climate models with decision support cyberinfrastructure using open standards

NASA Astrophysics Data System (ADS)

Bermudez, L. E.; Percivall, G.; Idol, T. A.

2015-12-01

Experts in climate modeling, remote sensing of the Earth, and cyber infrastructure must work together in order to make climate predictions available to decision makers. Such experts and decision makers worked together in the Open Geospatial Consortium's (OGC) Testbed 11 to address a scenario of population displacement by coastal inundation due to the predicted sea level rise. In a Policy Fact Sheet "Harnessing Climate Data to Boost Ecosystem & Water Resilience", issued by White House Office of Science and Technology (OSTP) in December 2014, OGC committed to increase access to climate change information using open standards. In July 2015, the OGC Testbed 11 Urban Climate Resilience activity delivered on that commitment with open standards based support for climate-change preparedness. Using open standards such as the OGC Web Coverage Service and Web Processing Service and the NetCDF and GMLJP2 encoding standards, Testbed 11 deployed an interoperable high-resolution flood model to bring climate model outputs together with global change assessment models and other remote sensing data for decision support. Methods to confirm model predictions and to allow "what-if-scenarios" included in-situ sensor webs and crowdsourcing. A scenario was in two locations: San Francisco Bay Area and Mozambique. The scenarios demonstrated interoperation and capabilities of open geospatial specifications in supporting data services and processing services. The resultant High Resolution Flood Information System addressed access and control of simulation models and high-resolution data in an open, worldwide, collaborative Web environment. The scenarios examined the feasibility and capability of existing OGC geospatial Web service specifications in supporting the on-demand, dynamic serving of flood information from models with forecasting capacity. Results of this testbed included identification of standards and best practices that help researchers and cities deal with climate-related issues. Results of the testbeds will now be deployed in pilot applications. The testbed also identified areas of additional development needed to help identify scientific investments and cyberinfrastructure approaches needed to improve the application of climate science research results to urban climate resilence.
Implications of Higgs searches on the four-generation standard model.

PubMed

Kuflik, Eric; Nir, Yosef; Volansky, Tomer

2013-03-01

Within the four-generation standard model, the Higgs couplings to gluons and to photons deviate in a significant way from the predictions of the three-generation standard model. As a consequence, large departures in several Higgs production and decay channels are expected. Recent Higgs search results, presented by ATLAS, CMS, and CDF, hint on the existence of a Higgs boson with a mass around 125 GeV. Using these results and assuming such a Higgs boson, we derive exclusion limits on the four-generation standard model. For m(H)=125 GeV, the model is excluded above 99.95% confidence level. For 124.5 GeV≤m(H)≤127.5 GeV, an exclusion limit above 99% confidence level is found.
Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

PubMed

Sila, Andrew M; Shepherd, Keith D; Pokhariyal, Ganesh P

2016-04-15

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.
Diesel engine emissions and combustion predictions using advanced mixing models applicable to fuel sprays

NASA Astrophysics Data System (ADS)

Abani, Neerav; Reitz, Rolf D.

2010-09-01

An advanced mixing model was applied to study engine emissions and combustion with different injection strategies ranging from multiple injections, early injection and grouped-hole nozzle injection in light and heavy duty diesel engines. The model was implemented in the KIVA-CHEMKIN engine combustion code and simulations were conducted at different mesh resolutions. The model was compared with the standard KIVA spray model that uses the Lagrangian-Drop and Eulerian-Fluid (LDEF) approach, and a Gas Jet spray model that improves predictions of liquid sprays. A Vapor Particle Method (VPM) is introduced that accounts for sub-grid scale mixing of fuel vapor and more accurately and predicts the mixing of fuel-vapor over a range of mesh resolutions. The fuel vapor is transported as particles until a certain distance from nozzle is reached where the local jet half-width is adequately resolved by the local mesh scale. Within this distance the vapor particle is transported while releasing fuel vapor locally, as determined by a weighting factor. The VPM model more accurately predicts fuel-vapor penetrations for early cycle injections and flame lift-off lengths for late cycle injections. Engine combustion computations show that as compared to the standard KIVA and Gas Jet spray models, the VPM spray model improves predictions of in-cylinder pressure, heat released rate and engine emissions of NOx, CO and soot with coarse mesh resolutions. The VPM spray model is thus a good tool for efficiently investigating diesel engine combustion with practical mesh resolutions, thereby saving computer time.
Search for jet extinction in the inclusive jet-pt spectrum from proton-proton collisions at sqrt(s) = 8 TeV

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khachatryan, Vardan; et al.,

The first search at the LHC for the extinction of QCD jet production is presented, using data collected with the CMS detector corresponding to an integrated luminosity of 10.7 inverse femtobarns of proton-proton collisions at a center-of-mass energy of 8 TeV. The extinction model studied in this analysis is motivated by the search for signatures of strong gravity at the TeV scale (terascale gravity) and assumes the existence of string couplings in the strong-coupling limit. In this limit, the string model predicts the suppression of all high-transverse-momentum standard model processes, including jet production, beyond a certain energy scale. To testmore » this prediction, the measured transverse-momentum spectrum is compared to the theoretical prediction of the standard model. No significant deficit of events is found at high transverse momentum. A 95% confidence level lower limit of 3.3 TeV is set on the extinction mass scale.« less
Search for jet extinction in the inclusive jet-pT spectrum from proton-proton collisions at √s =8 TeV

NASA Astrophysics Data System (ADS)

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M.; Erö, J.; Fabjan, C.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Knünz, V.; Krammer, M.; Krätschmer, I.; Liko, D.; Mikulec, I.; Rabady, D.; Rahbaran, B.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Taurok, A.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Bansal, M.; Bansal, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Knutsson, A.; Luyckx, S.; Ochesanu, S.; Roland, B.; Rougny, R.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D'Hondt, J.; Daci, N.; Heracleous, N.; Kalogeropoulos, A.; Keaveney, J.; Kim, T. J.; Lowette, S.; Maes, M.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Villella, I.; Caillol, C.; Clerbaux, B.; De Lentdecker, G.; Dobur, D.; Favart, L.; Gay, A. P. R.; Grebenyuk, A.; Léonard, A.; Mohammadi, A.; Perniè, L.; Reis, T.; Seva, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.; Adler, V.; Beernaert, K.; Benucci, L.; Cimmino, A.; Costantini, S.; Crucy, S.; Dildick, S.; Fagot, A.; Garcia, G.; Klein, B.; Mccartin, J.; Ocampo Rios, A. A.; Ryckbosch, D.; Salva Diblen, S.; Sigamani, M.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bruno, G.; Castello, R.; Caudron, A.; Ceard, L.; Da Silveira, G. G.; Delaere, C.; du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Jez, P.; Komm, M.; Lemaitre, V.; Liao, J.; Nuttens, C.; Pagano, D.; Pin, A.; Piotrzkowski, K.; Popov, A.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Vizan Garcia, J. M.; Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.; Alves, G. A.; Correa Martins Junior, M.; Dos Reis Martins, T.; Pol, M. E.; Aldá Júnior, W. 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M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.; Travaglini, R.; Albergo, S.; Cappello, G.; Chiorboli, M.; Costa, S.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Gallo, E.; Gonzi, S.; Gori, V.; Lenzi, P.; Meschini, M.; Paoletti, S.; Sguazzoni, G.; Tropiano, A.; Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.; Ferro, F.; Lo Vetere, M.; Robutti, E.; Tosi, S.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Gerosa, R.; Ghezzi, A.; Govoni, P.; Lucchini, M. T.; Malvezzi, S.; Manzoni, R. A.; Martelli, A.; Marzocchi, B.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Redaelli, N.; Tabarelli de Fatis, T.; Buontempo, S.; Cavallo, N.; Di Guida, S.; Fabozzi, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Merola, M.; Paolucci, P.; Azzi, P.; Bacchetta, N.; Bisello, D.; Branca, A.; Carlin, R.; Dall'Osso, M.; Dorigo, T.; Galanti, M.; Gasparini, F.; Giubilato, P.; Gozzelino, A.; Kanishchev, K.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Montecassiano, F.; Passaseo, M.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Tosi, M.; Vanini, S.; Zotto, P.; Zucchetta, A.; Zumerle, G.; Gabusi, M.; Ratti, S. P.; Riccardi, C.; Salvini, P.; Vitulo, P.; Biasini, M.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Mantovani, G.; Menichelli, M.; Romeo, F.; Saha, A.; Santocchia, A.; Spiezia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Broccolo, G.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fiori, F.; Foà, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Moon, C. S.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Serban, A. T.; Spagnolo, P.; Squillacioti, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Vernieri, C.; Barone, L.; Cavallari, F.; Del Re, D.; Diemoz, M.; Grassi, M.; Jorda, C.; Longo, E.; Margaroli, F.; Meridiani, P.; Micheli, F.; Nourbakhsh, S.; Organtini, G.; Paramatti, R.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Traczyk, P.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bellan, R.; Biino, C.; Cartiglia, N.; Casasso, S.; Costa, M.; Degano, A.; Demaria, N.; Finco, L.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Musich, M.; Obertino, M. M.; Ortona, G.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Potenza, A.; Romero, A.; Ruspa, M.; Sacchi, R.; Solano, A.; Staiano, A.; Tamponi, U.; Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Gobbo, B.; La Licata, C.; Marone, M.; Montanino, D.; Schizzi, A.; Umer, T.; Zanetti, A.; Chang, S.; Kropivnitskaya, A.; Nam, S. K.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Kong, D. J.; Lee, S.; Oh, Y. D.; Park, H.; Sakharov, A.; Son, D. C.; Kim, J. Y.; Song, S.; Choi, S.; Gyun, D.; Hong, B.; Jo, M.; Kim, H.; Kim, Y.; Lee, B.; Lee, K. S.; Park, S. K.; Roh, Y.; Choi, M.; Kim, J. H.; Park, I. C.; Park, S.; Ryu, G.; Ryu, M. S.; Choi, Y.; Choi, Y. K.; Goh, J.; Kwon, E.; Lee, J.; Seo, H.; Yu, I.; Juodagalvis, A.; Komaragiri, J. R.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-de La Cruz, I.; Lopez-Fernandez, R.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Vazquez Valencia, F.; Pedraza, I.; Salazar Ibarguen, H. A.; Casimiro Linares, E.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Reucroft, S.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Khalid, S.; Khan, W. A.; Khurshid, T.; Shah, M. A.; Shoaib, M.; Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Górski, M.; Kazana, M.; Nawrocki, K.; Romanowska-Rybinska, K.; Szleper, M.; Zalewski, P.; Brona, G.; Bunkowski, K.; Cwiok, M.; Dominik, W.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Wolszczak, W.; Bargassa, P.; Beirão Da Cruz E Silva, C.; Faccioli, P.; Ferreira Parracho, P. G.; Gallinaro, M.; Nguyen, F.; Rodrigues Antunes, J.; Seixas, J.; Varela, J.; Vischia, P.; Afanasiev, S.; Bunin, P.; Gavrilenko, M.; Golutvin, I.; Karjavin, V.; Konoplyanikov, V.; Lanev, A.; Malakhov, A.; Matveev, V.; Moisenz, P.; Palichik, V.; Perelygin, V.; Savina, M.; Shmatov, S.; Shulha, S.; Skatchkov, N.; Smirnov, V.; Zarubin, A.; Golovtsov, V.; Ivanov, Y.; Kim, V.; Levchenko, P.; Murzin, V.; Oreshkin, V.; Smirnov, I.; Sulimov, V.; Uvarov, L.; Vavilov, S.; Vorobyev, A.; Vorobyev, An.; Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.; Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Safronov, G.; Semenov, S.; Spiridonov, A.; Stolin, V.; Vlasov, E.; Zhokin, A.; Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Leonidov, A.; Mesyats, G.; Rusakov, S. V.; Vinogradov, A.; Belyaev, A.; Boos, E.; Dubinin, M.; Dudko, L.; Ershov, A.; Gribushin, A.; Klyukhin, V.; Kodolova, O.; Lokhtin, I.; Obraztsov, S.; Petrushanko, S.; Savrin, V.; Snigirev, A.; Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Krychkine, V.; Petrov, V.; Ryutin, R.; Sobol, A.; Tourtchanovitch, L.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.; Adzic, P.; Dordevic, M.; Ekmedzic, M.; Milosevic, J.; Alcaraz Maestre, J.; Battilana, C.; Calvo, E.; Cerrada, M.; Chamizo Llatas, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Domínguez Vázquez, D.; Escalante Del Valle, A.; Fernandez Bedoya, C.; Fernández Ramos, J. P.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Merino, G.; Navarro De Martino, E.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.; Albajar, C.; de Trocóniz, J. F.; Missiroli, M.; Brun, H.; Cuevas, J.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Lloret Iglesias, L.; Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Duarte Campderros, J.; Fernandez, M.; Gomez, G.; Graziano, A.; Lopez Virto, A.; Marco, J.; Marco, R.; Martinez Rivero, C.; Matorras, F.; Munoz Sanchez, F. J.; Piedra Gomez, J.; Rodrigo, T.; Rodríguez-Marrero, A. Y.; Ruiz-Jimeno, A.; Scodellaro, L.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Auffray, E.; Auzinger, G.; Bachtis, M.; Baillon, P.; Ball, A. H.; Barney, D.; Benaglia, A.; Bendavid, J.; Benhabib, L.; Benitez, J. F.; Bernet, C.; Bianchi, G.; Bloch, P.; Bocci, A.; Bonato, A.; Bondu, O.; Botta, C.; Breuker, H.; Camporesi, T.; Cerminara, G.; Christiansen, T.; Colafranceschi, S.; D'Alfonso, M.; d'Enterria, D.; Dabrowski, A.; David, A.; De Guio, F.; De Roeck, A.; De Visscher, S.; Dobson, M.; Dupont-Sagorin, N.; Elliott-Peisert, A.; Eugster, J.; Franzoni, G.; Funk, W.; Giffels, M.; Gigi, D.; Gill, K.; Giordano, D.; Girone, M.; Glege, F.; Guida, R.; Gundacker, S.; Guthoff, M.; Hammer, J.; Hansen, M.; Harris, P.; Hegeman, J.; Innocente, V.; Janot, P.; Kousouris, K.; Krajczar, K.; Lecoq, P.; Lourenço, C.; Magini, N.; Malgeri, L.; Mannelli, M.; Masetti, L.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Morovic, S.; Mulders, M.; Musella, P.; Orsini, L.; Pape, L.; Perez, E.; Perrozzi, L.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Pimiä, M.; Piparo, D.; Plagge, M.; Racz, A.; Rolandi, G.; Rovere, M.; Sakulin, H.; Schäfer, C.; Schwick, C.; Sekmen, S.; Sharma, A.; Siegrist, P.; Silva, P.; Simon, M.; Sphicas, P.; Spiga, D.; Steggemann, J.; Stieger, B.; Stoye, M.; Treille, D.; Tsirou, A.; Veres, G. I.; Vlimant, J. R.; Wardle, N.; Wöhri, H. K.; Zeuner, W. D.; Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; König, S.; Kotlinski, D.; Langenegger, U.; Renker, D.; Rohe, T.; Bachmair, F.; Bäni, L.; Bianchini, L.; Bortignon, P.; Buchmann, M. A.; Casal, B.; Chanon, N.; Deisher, A.; Dissertori, G.; Dittmar, M.; Donegà, M.; Dünser, M.; Eller, P.; Grab, C.; Hits, D.; Lustermann, W.; Mangano, B.; Marini, A. C.; Martinez Ruiz del Arbol, P.; Meister, D.; Mohr, N.; Nägeli, C.; Nef, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pauss, F.; Peruzzi, M.; Quittnat, M.; Rebane, L.; Ronga, F. J.; Rossini, M.; Starodumov, A.; Takahashi, M.; Theofilatos, K.; Wallny, R.; Weber, H. A.; Amsler, C.; Canelli, M. F.; Chiochia, V.; De Cosa, A.; Hinzmann, A.; Hreus, T.; Ivova Rikova, M.; Kilminster, B.; Millan Mejias, B.; Ngadiuba, J.; Robmann, P.; Snoek, H.; Taroni, S.; Verzetti, M.; Yang, Y.; Cardaci, M.; Chen, K. H.; Ferro, C.; Kuo, C. M.; Lin, W.; Lu, Y. J.; Volpe, R.; Yu, S. S.; Chang, P.; Chang, Y. H.; Chang, Y. W.; Chao, Y.; Chen, K. F.; Chen, P. H.; Dietz, C.; Grundler, U.; Hou, W.-S.; Kao, K. Y.; Lei, Y. J.; Liu, Y. F.; Lu, R.-S.; Majumder, D.; Petrakou, E.; Shi, X.; Tzeng, Y. M.; Wilken, R.; Asavapibhop, B.; Srimanobhas, N.; Suwonjandee, N.; Adiguzel, A.; Bakirci, M. N.; Cerci, S.; Dozen, C.; Dumanoglu, I.; Eskut, E.; Girgis, S.; Gokbulut, G.; Gurpinar, E.; Hos, I.; Kangal, E. E.; Kayis Topaksu, A.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Sogut, K.; Sunar Cerci, D.; Tali, B.; Topakli, H.; Vergili, M.; Akin, I. V.; Bilin, B.; Bilmis, S.; Gamsizkan, H.; Karapinar, G.; Ocalan, K.; Surat, U. E.; Yalvac, M.; Zeyrek, M.; Gülmez, E.; Isildak, B.; Kaya, M.; Kaya, O.; Bahtiyar, H.; Barlas, E.; Cankocak, K.; Vardarlı, F. I.; Yücel, M.; Levchuk, L.; Sorokin, P.; Brooke, J. J.; Clement, E.; Cussans, D.; Flacher, H.; Frazier, R.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Meng, Z.; Newbold, D. M.; Paramesvaran, S.; Poll, A.; Senkin, S.; Smith, V. J.; Williams, T.; Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Womersley, W. J.; Worm, S. D.; Baber, M.; Bainbridge, R.; Buchmuller, O.; Burton, D.; Colling, D.; Cripps, N.; Cutajar, M.; Dauncey, P.; Davies, G.; Della Negra, M.; Dunne, P.; Ferguson, W.; Fulcher, J.; Futyan, D.; Gilbert, A.; Hall, G.; Iles, G.; Jarvis, M.; Karapostoli, G.; Kenzie, M.; Lane, R.; Lucas, R.; Lyons, L.; Magnan, A.-M.; Malik, S.; Marrouche, J.; Mathias, B.; Nash, J.; Nikitenko, A.; Pela, J.; Pesaresi, M.; Petridis, K.; Raymond, D. M.; Rogerson, S.; Rose, A.; Seez, C.; Sharp, P.; Tapper, A.; Vazquez Acosta, M.; Virdee, T.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Leggat, D.; Leslie, D.; Martin, W.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.; Dittmann, J.; Hatakeyama, K.; Kasmi, A.; Liu, H.; Scarborough, T.; Charaf, O.; Cooper, S. I.; Henderson, C.; Rumerio, P.; Avetisyan, A.; Bose, T.; Fantasia, C.; Heister, A.; Lawson, P.; Richardson, C.; Rohlf, J.; Sperka, D.; St. John, J.; Sulak, L.; Alimena, J.; Bhattacharya, S.; Christopher, G.; Cutts, D.; Demiragli, Z.; Ferapontov, A.; Garabedian, A.; Heintz, U.; Jabeen, S.; Kukartsev, G.; Laird, E.; Landsberg, G.; Luk, M.; Narain, M.; Segala, M.; Sinthuprasith, T.; Speer, T.; Swanson, J.; Breedon, R.; Breto, G.; Calderon De La Barca Sanchez, M.; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Gardner, M.; Ko, W.; Lander, R.; Miceli, T.; Mulhearn, M.; Pellett, D.; Pilot, J.; Ricci-Tam, F.; Searle, M.; Shalhout, S.; Smith, J.; Squires, M.; Stolp, D.; Tripathi, M.; Wilbur, S.; Yohay, R.; Cousins, R.; Everaerts, P.; Farrell, C.; Hauser, J.; Ignatenko, M.; Rakness, G.; Takasugi, E.; Valuev, V.; Weber, M.; Babb, J.; Clare, R.; Ellison, J.; Gary, J. W.; Hanson, G.; Heilman, J.; Jandir, P.; Kennedy, E.; Lacroix, F.; Liu, H.; Long, O. R.; Luthra, A.; Malberti, M.; Nguyen, H.; Shrinivas, A.; Sturdy, J.; Sumowidagdo, S.; Wimpenny, S.; Andrews, W.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; D'Agnolo, R. T.; Evans, D.; Holzner, A.; Kelley, R.; Lebourgeois, M.; Letts, J.; Macneill, I.; Olivito, D.; Padhi, S.; Palmer, C.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Sudano, E.; Tadel, M.; Tu, Y.; Vartak, A.; Würthwein, F.; Yagil, A.; Yoo, J.; Barge, D.; Bradmiller-Feld, J.; Campagnari, C.; Danielson, T.; Dishaw, A.; Flowers, K.; Franco Sevilla, M.; Geffert, P.; George, C.; Golf, F.; Incandela, J.; Justus, C.; Mccoll, N.; Richman, J.; Stuart, D.; To, W.; West, C.; Apresyan, A.; Bornheim, A.; Bunn, J.; Chen, Y.; Di Marco, E.; Duarte, J.; Mott, A.; Newman, H. B.; Pena, C.; Rogan, C.; Spiropulu, M.; Timciuc, V.; Wilkinson, R.; Xie, S.; Zhu, R. Y.; Azzolini, V.; Calamba, A.; Carroll, R.; Ferguson, T.; Iiyama, Y.; Paulini, M.; Russ, J.; Vogel, H.; Vorobiev, I.; Cumalat, J. P.; Drell, B. R.; Ford, W. T.; Gaz, A.; Luiggi Lopez, E.; Nauenberg, U.; Smith, J. G.; Stenson, K.; Ulmer, K. A.; Wagner, S. R.; Alexander, J.; Chatterjee, A.; Chu, J.; Dittmer, S.; Eggert, N.; Hopkins, W.; Kreis, B.; Mirman, N.; Nicolas Kaufman, G.; Patterson, J. R.; Ryd, A.; Salvati, E.; Skinnari, L.; Sun, W.; Teo, W. D.; Thom, J.; Thompson, J.; Tucker, J.; Weng, Y.; Winstrom, L.; Wittich, P.; Winn, D.; Abdullin, S.; Albrow, M.; Anderson, J.; Apollinari, G.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Burkett, K.; Butler, J. N.; Cheung, H. W. K.; Chlebana, F.; Cihangir, S.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hanlon, J.; Hare, D.; Harris, R. M.; Hirschauer, J.; Hooberman, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Kaadze, K.; Klima, B.; Kwan, S.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, T.; Lykken, J.; Maeshima, K.; Marraffino, J. M.; Martinez Outschoorn, V. I.; Maruyama, S.; Mason, D.; McBride, P.; Mishra, K.; Mrenna, S.; Musienko, Y.; Nahn, S.; Newman-Holmes, C.; O'Dell, V.; Prokofyev, O.; Sexton-Kennedy, E.; Sharma, S.; Soha, A.; Spalding, W. J.; Spiegel, L.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vidal, R.; Whitbeck, A.; Whitmore, J.; Yang, F.; Acosta, D.; Avery, P.; Bourilkov, D.; Carver, M.; Cheng, T.; Curry, D.; Das, S.; De Gruttola, M.; Di Giovanni, G. P.; Field, R. D.; Fisher, M.; Furic, I. K.; Hugon, J.; Konigsberg, J.; Korytov, A.; Kypreos, T.; Low, J. F.; Matchev, K.; Milenovic, P.; Mitselmakher, G.; Muniz, L.; Rinkevicius, A.; Shchutska, L.; Skhirtladze, N.; Snowball, M.; Yelton, J.; Zakaria, M.; Gaultney, V.; Hewamanage, S.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Adams, T.; Askew, A.; Bochenek, J.; Diamond, B.; Haas, J.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Prosper, H.; Veeraraghavan, V.; Weinberg, M.; Baarmand, M. M.; Hohlmann, M.; Kalakhety, H.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Bazterra, V. E.; Berry, D.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Khalatyan, S.; Kurt, P.; Moon, D. H.; O'Brien, C.; Silkworth, C.; Turner, P.; Varelas, N.; Albayrak, E. A.; Bilki, B.; Clarida, W.; Dilsiz, K.; Duru, F.; Haytmyradov, M.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Rahmat, R.; Sen, S.; Tan, P.; Tiras, E.; Wetzel, J.; Yetkin, T.; Yi, K.; Barnett, B. A.; Blumenfeld, B.; Bolognesi, S.; Fehling, D.; Gritsan, A. V.; Maksimovic, P.; Martin, C.; Swartz, M.; Baringer, P.; Bean, A.; Benelli, G.; Bruner, C.; Gray, J.; Kenny, R. P.; Murray, M.; Noonan, D.; Sanders, S.; Sekaric, J.; Stringer, R.; Wang, Q.; Wood, J. S.; Barfuss, A. F.; Chakaberia, I.; Ivanov, A.; Khalil, S.; Makouski, M.; Maravin, Y.; Saini, L. K.; Shrestha, S.; Svintradze, I.; Gronberg, J.; Lange, D.; Rebassoo, F.; Wright, D.; Baden, A.; Calvert, B.; Eno, S. C.; Gomez, J. A.; Hadley, N. J.; Kellogg, R. G.; Kolberg, T.; Lu, Y.; Marionneau, M.; Mignerey, A. C.; Pedro, K.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Apyan, A.; Barbieri, R.; Bauer, G.; Busza, W.; Cali, I. A.; Chan, M.; Di Matteo, L.; Dutta, V.; Gomez Ceballos, G.; Goncharov, M.; Gulhan, D.; Klute, M.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Ma, T.; Paus, C.; Ralph, D.; Roland, C.; Roland, G.; Stephans, G. S. F.; Stöckli, F.; Sumorok, K.; Velicanu, D.; Veverka, J.; Wyslouch, B.; Yang, M.; Zanetti, M.; Zhukova, V.; Dahmes, B.; De Benedetti, A.; Gude, A.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Mans, J.; Pastika, N.; Rusack, R.; Singovsky, A.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Gonzalez Suarez, R.; Keller, J.; Knowlton, D.; Kravchenko, I.; Lazo-Flores, J.; Malik, S.; Meier, F.; Snow, G. R.; Dolen, J.; Godshalk, A.; Iashvili, I.; Kharchilava, A.; Kumar, A.; Rappoccio, S.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Haley, J.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Trocino, D.; Wood, D.; Zhang, J.; Hahn, K. A.; Kubik, A.; Mucia, N.; Odell, N.; Pollack, B.; Pozdnyakov, A.; Schmitt, M.; Stoynev, S.; Sung, K.; Velasco, M.; Won, S.; Brinkerhoff, A.; Chan, K. M.; Drozdetskiy, A.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Luo, W.; Lynch, S.; Marinelli, N.; Pearson, T.; Planer, M.; Ruchti, R.; Valls, N.; Wayne, M.; Wolf, M.; Woodard, A.; Antonelli, L.; Brinson, J.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Hill, C.; Hughes, R.; Kotov, K.; Ling, T. Y.; Puigh, D.; Rodenburg, M.; Smith, G.; Vuosalo, C.; Winer, B. L.; Wolfe, H.; Wulsin, H. W.; Berry, E.; Driga, O.; Elmer, P.; Hebda, P.; Hunt, A.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Piroué, P.; Quan, X.; Saka, H.; Stickland, D.; Tully, C.; Werner, J. S.; Zenz, S. C.; Zuranski, A.; Brownson, E.; Mendez, H.; Ramirez Vargas, J. E.; Alagoz, E.; Barnes, V. E.; Benedetti, D.; Bolla, G.; Bortoletto, D.; De Mattia, M.; Everett, A.; Hu, Z.; Jha, M. K.; Jones, M.; Jung, K.; Kress, M.; Leonardo, N.; Lopes Pegna, D.; Maroussov, V.; Merkel, P.; Miller, D. H.; Neumeister, N.; Radburn-Smith, B. C.; Shipsey, I.; Silvers, D.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Yoo, H. D.; Zablocki, J.; Zheng, Y.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Ecklund, K. M.; Geurts, F. J. M.; Li, W.; Michlin, B.; Padley, B. P.; Redjimi, R.; Roberts, J.; Zabel, J.; Betchart, B.; Bodek, A.; Covarelli, R.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Garcia-Bellido, A.; Goldenzweig, P.; Han, J.; Harel, A.; Khukhunaishvili, A.; Miner, D. C.; Petrillo, G.; Vishnevskiy, D.; Ciesielski, R.; Demortier, L.; Goulianos, K.; Lungu, G.; Mesropian, C.; Arora, S.; Barker, A.; Chou, J. P.; Contreras-Campana, C.; Contreras-Campana, E.; Duggan, D.; Ferencek, D.; Gershtein, Y.; Gray, R.; Halkiadakis, E.; Hidas, D.; Lath, A.; Panwalkar, S.; Park, M.; Patel, R.; Rekovic, V.; Salur, S.; Schnetzer, S.; Seitz, C.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Rose, K.; Spanier, S.; York, A.; Bouhali, O.; Eusebi, R.; Flanagan, W.; Gilmore, J.; Kamon, T.; Khotilovich, V.; Krutelyov, V.; Montalvo, R.; Osipenkov, I.; Pakhotin, Y.; Perloff, A.; Roe, J.; Rose, A.; Safonov, A.; Sakuma, T.; Suarez, I.; Tatarinov, A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kovitanggoon, K.; Kunori, S.; Lee, S. W.; Libeiro, T.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Sharma, M.; Sheldon, P.; Snook, B.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Boutle, S.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Lin, C.; Neu, C.; Wood, J.; Gollapinni, S.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Belknap, D. A.; Carlsmith, D.; Cepeda, M.; Dasu, S.; Duric, S.; Friis, E.; Hall-Wilton, R.; Herndon, M.; Hervé, A.; Klabbers, P.; Klukas, J.; Lanaro, A.; Lazaridis, C.; Levine, A.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ross, I.; Sarangi, T.; Savin, A.; Smith, W. H.; Woods, N.; CMS Collaboration

2014-08-01

The first search at the LHC for the extinction of QCD jet production is presented, using data collected with the CMS detector corresponding to an integrated luminosity of 10.7 fb-1 of proton-proton collisions at a center-of-mass energy of 8 TeV. The extinction model studied in this analysis is motivated by the search for signatures of strong gravity at the TeV scale (terascale gravity) and assumes the existence of string couplings in the strong-coupling limit. In this limit, the string model predicts the suppression of all high-transverse-momentum standard model processes, including jet production, beyond a certain energy scale. To test this prediction, the measured transverse-momentum spectrum is compared to the theoretical prediction of the standard model. No significant deficit of events is found at high transverse momentum. A 95% confidence level lower limit of 3.3 TeV is set on the extinction mass scale.
A standardized method for the calibration of thermodynamic data for the prediction of gas chromatographic retention times.

PubMed

McGinitie, Teague M; Ebrahimi-Najafabadi, Heshmatollah; Harynuk, James J

2014-02-21

A new method for calibrating thermodynamic data to be used in the prediction of analyte retention times is presented. The method allows thermodynamic data collected on one column to be used in making predictions across columns of the same stationary phase but with varying geometries. This calibration is essential as slight variances in the column inner diameter and stationary phase film thickness between columns or as a column ages will adversely affect the accuracy of predictions. The calibration technique uses a Grob standard mixture along with a Nelder-Mead simplex algorithm and a previously developed model of GC retention times based on a three-parameter thermodynamic model to estimate both inner diameter and stationary phase film thickness. The calibration method is highly successful with the predicted retention times for a set of alkanes, ketones and alcohols having an average error of 1.6s across three columns. Copyright © 2014 Elsevier B.V. All rights reserved.
Prediction of Airfoil Characteristics With Higher Order Turbulence Models

NASA Technical Reports Server (NTRS)

Gatski, Thomas B.

1996-01-01

This study focuses on the prediction of airfoil characteristics, including lift and drag over a range of Reynolds numbers. Two different turbulence models, which represent two different types of models, are tested. The first is a standard isotropic eddy-viscosity two-equation model, and the second is an explicit algebraic stress model (EASM). The turbulent flow field over a general-aviation airfoil (GA(W)-2) at three Reynolds numbers is studied. At each Reynolds number, predicted lift and drag values at different angles of attack are compared with experimental results, and predicted variations of stall locations with Reynolds number are compared with experimental data. Finally, the size of the separation zone predicted by each model is analyzed, and correlated with the behavior of the lift coefficient near stall. In summary, the EASM model is able to predict the lift and drag coefficients over a wider range of angles of attack than the two-equation model for the three Reynolds numbers studied. However, both models are unable to predict the correct lift and drag behavior near the stall angle, and for the lowest Reynolds number case, the two-equation model did not predict separation on the airfoil near stall.
Evaluation of an ARPS-based canopy flow modeling system for use in future operational smoke prediction efforts

Treesearch

M. T. Kiefer; S. Zhong; W. E. Heilman; J. J. Charney; X. Bian

2013-01-01

Efforts to develop a canopy flow modeling system based on the Advanced Regional Prediction System (ARPS) model are discussed. The standard version of ARPS is modified to account for the effect of drag forces on mean and turbulent flow through a vegetation canopy, via production and sink terms in the momentum and subgrid-scale turbulent kinetic energy (TKE) equations....
Does Attainment of Piaget's Formal Operational Level of Cognitive Development Predict Student Understanding of Scientific Models?

ERIC Educational Resources Information Center

Lahti, Richard Dennis, II.

2012-01-01

Knowledge of scientific models and their uses is a concept that has become a key benchmark in many of the science standards of the past 30 years, including the proposed Next Generation Science Standards. Knowledge of models is linked to other important nature of science concepts such as theory change which are also rising in prominence in newer…
Random Predictor Models for Rigorous Uncertainty Quantification: Part 1

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean and the variance of the model's parameters, thus of the predicted output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, can be bounded tightly and rigorously.
Curvaton scenario within the minimal supersymmetric standard model and predictions for non-Gaussianity.

PubMed

Mazumdar, Anupam; Nadathur, Seshadri

2012-03-16

We provide a model in which both the inflaton and the curvaton are obtained from within the minimal supersymmetric standard model, with known gauge and Yukawa interactions. Since now both the inflaton and curvaton fields are successfully embedded within the same sector, their decay products thermalize very quickly before the electroweak scale. This results in two important features of the model: first, there will be no residual isocurvature perturbations, and second, observable non-Gaussianities can be generated with the non-Gaussianity parameter f(NL)~O(5-1000) being determined solely by the combination of weak-scale physics and the standard model Yukawa interactions.
Decadal climate predictions improved by ocean ensemble dispersion filtering

NASA Astrophysics Data System (ADS)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its ensemble average, improves a prediction system. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Our study shows that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure applying the average during the model run, called ensemble dispersion filter, results in more accurate results than the standard prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhLB..762..421D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhLB..762..421D">Finding the strong CP problem at the LHC</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> D'Agnolo, Raffaele Tito; Hook, Anson 2016-11-01 We show that a class of parity based solutions to the strong CP problem predicts new colored particles with mass at the TeV scale, due to constraints from Planck suppressed operators. The new particles are copies of the Standard Model quarks and leptons. The new quarks can be produced at the LHC and are either collider stable or decay into Standard Model quarks through a Higgs, a W or a Z boson. We discuss some simple but generic predictions of the models for the LHC and find signatures not related to the traditional solutions of the hierarchy problem. We thus provide alternative motivation for new physics searches at the weak scale. We also briefly discuss the cosmological history of these models and how to obtain successful baryogenesis. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EPJWC.14201002A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EPJWC.14201002A">The new g-2 experiment at Fermilab</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Anastasi, A. 2017-04-01 There is a long standing discrepancy between the Standard Model prediction for the muon g-2 and the value measured by the Brookhaven E821 Experiment. At present the discrepancy stands at about three standard deviations, with an uncertainty dominated by the theoretical error. Two new proposals - at Fermilab and J-PARC - plan to improve the experimental uncertainty by a factor of 4, and it is expected that there will be a significant reduction in the uncertainty of the Standard Model prediction. I will review the status of the planned experiment at Fermilab, E989, which will analyse 21 times more muons than the BNL experiment and discuss how the systematic uncertainty will be reduced by a factor of 3 such that a precision of 0.14 ppm can be achieved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018PhRvD..97d3511A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018PhRvD..97d3511A">Gravitational leptogenesis, reheating, and models of neutrino mass</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Adshead, Peter; Long, Andrew J.; Sfakianakis, Evangelos I. 2018-02-01 Gravitational leptogenesis refers to a class of baryogenesis models in which the matter-antimatter asymmetry of the Universe arises through the standard model lepton-number gravitational anomaly. In these models chiral gravitational waves source a lepton asymmetry in standard model neutrinos during the inflationary epoch. We point out that gravitational leptogenesis can be successful in either the Dirac or Majorana neutrino mass scenario. In the Dirac mass scenario, gravitational leptogenesis predicts a relic abundance of sterile neutrinos that remain out of equilibrium, and the lepton asymmetry carried by the standard model sector is unchanged. In the Majorana mass scenario, the neutrinos participate in lepton-number-violating interactions that threaten to wash out the lepton asymmetry during postinflationary reheating. However, we show that a complete (exponential) washout of the lepton asymmetry is prevented if the lepton-number-violating interactions go out of equilibrium before all of the standard model Yukawa interactions come into equilibrium. The baryon and lepton asymmetries carried by right-chiral quarks and leptons are sequestered from the lepton-number violation, and the washout processes only suppress the predicted baryon asymmetry by a factor of ɛw .o .=±O (0.1 ). The sign of ɛw .o . depends on the model parameters in such a way that a future measurement of the primordial gravitational wave chirality would constrain the scale of lepton-number violation (heavy Majorana neutrino mass). </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19626881','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19626881">[Proximate analysis of straw by near infrared spectroscopy (NIRS)].</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Huang, Cai-jin; Han, Lu-jia; Liu, Xian; Yang, Zeng-ling 2009-04-01 Proximate analysis is one of the routine analysis procedures in utilization of straw for biomass energy use. The present paper studied the applicability of rapid proximate analysis of straw by near infrared spectroscopy (NIRS) technology, in which the authors constructed the first NIRS models to predict volatile matter and fixed carbon contents of straw. NIRS models were developed using Foss 6500 spectrometer with spectra in the range of 1,108-2,492 nm to predict the contents of moisture, ash, volatile matter and fixed carbon in the directly cut straw samples; to predict ash, volatile matter and fixed carbon in the dried milled straw samples. For the models based on directly cut straw samples, the determination coefficient of independent validation (R2v) and standard error of prediction (SEP) were 0.92% and 0.76% for moisture, 0.94% and 0.84% for ash, 0.88% and 0.82% for volatile matter, and 0.75% and 0.65% for fixed carbon, respectively. For the models based on dried milled straw samples, the determination coefficient of independent validation (R2v) and standard error of prediction (SEP) were 0.98% and 0.54% for ash, 0.95% and 0.57% for volatile matter, and 0.78% and 0.61% for fixed carbon, respectively. It was concluded that NIRS models can predict accurately as an alternative analysis method, therefore rapid and simultaneous analysis of multicomponents can be achieved by NIRS technology, decreasing the cost of proximate analysis for straw. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1221229','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1221229"></a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Aaltonen, Timo Antero In this study, a search for particles with the same mass and couplings as those of the standard model Higgs boson but different spin and parity quantum numbers is presented. We test two specific alternative Higgs boson hypotheses: a pseudoscalar Higgs boson with spin-parity J P = 0 – and a gravitonlike Higgs boson with J P = 2 +, assuming for both a mass of 125 GeV/c 2. We search for these exotic states produced in association with a vector boson and decaying into a bottom-antibottom quark pair. The vector boson is reconstructed through its decay into an electronmore » or muon pair, or an electron or muon and a neutrino, or it is inferred from an imbalance in total transverse momentum. We use expected kinematic differences between events containing exotic Higgs bosons and those containing standard model Higgs bosons. The data were collected by the CDF experiment at the Tevatron proton-antiproton collider, operating at a center-of-mass energy of √s = 1.96 TeV, and correspond to an integrated luminosity of 9.45 fb –1. We exclude deviations from the predictions of the standard model with a Higgs boson of mass 125 GeV/c 2 at the level of 5 standard deviations, assuming signal strengths for exotic boson production equal to the prediction for the standard model Higgs boson, and set upper limits of approximately 30% relative to the standard model rate on the possible rate of production of each exotic state.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003PhDT.......245S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003PhDT.......245S">Experimental and mathematical modeling of flow in headboxes</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Shariati, Mohammad Reza The fluid flow patterns in a paper-machine headbox have a strong influence on the quality of the paper produced by the machine. Due to increasing demand for high quality paper there is a need to investigate the details of the fluid flow in the paper machine headbox. The objective of this thesis is to use experimental and computational methods of modeling the flow inside a typical headbox in order to evaluate and understand the mean flow patterns and turbulence created there. In particular, spatial variations of the mean flow and of the turbulence quantities and the turbulence generated secondary flows are studied. In addition to the flow inside the headbox, the flow leaving the slice is also modeled both experimentally and computationally. Comparison of the experimental and numerical results indicated that streamwise mean components of the velocities in the headbox are predicted well by all the turbulence models considered in this study. However, the standard k-epsilon model and the algebraic turbulence models fail to predict the turbulence quantities accurately. Standard k-epsilon-model also fails to predict the direction and magnitude of the secondary flows. Significant improvements in the k-epsilon model predictions were achieved when the turbulence production term was artificially set to zero. This is justified by observations of the turbulent velocities from the experiments and by a consideration of the form of the kinetic energy equation. A better estimation of the Reynolds normal stress distribution and the degree of anisotropy of turbulence was achieved using the Reynolds stress turbulence model. Careful examination of the measured turbulence velocity results shows that after the initial decay of the turbulence in the headbox, there is a short region close to the exit, but inside the headbox, where the turbulent kinetic energy actually increases as a result of the distortion imposed by the contraction. The turbulence energy quickly resumes its decay in the free jet after the headbox. The overall conclusion from this thesis, obtained by comparison of experimental and computational simulations of the flow in a headbox, is that numerical simulations show great promise for predictions of headbox flows. Mean velocities and turbulence characteristics can now be predicted with fair accuracy by careful use of specialized turbulence models. Standard engineering turbulence models, such as the k-epsilon model and its immediate relatives, should not be used to estimate the turbulence quantities essential for predicting pulp fiber dispersion within the contracting region and free jet of a headbox, particularly when the overall contraction ratio is greater than about five. (Abstract shortened by UMI.) </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22973220','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22973220">An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Hertäg, Loreen; Hass, Joachim; Golovko, Tatiana; Durstewitz, Daniel 2012-01-01 For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean-input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx) model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ("in vivo-like") input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a "high-throughput" model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active">10</li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active">11</li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3979723','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3979723">Development of Standard Fuel Models in Boreal Forests of Northeast China through Calibration and Validation</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Cai, Longyan; He, Hong S.; Wu, Zhiwei; Lewis, Benard L.; Liang, Yu 2014-01-01 Understanding the fire prediction capabilities of fuel models is vital to forest fire management. Various fuel models have been developed in the Great Xing'an Mountains in Northeast China. However, the performances of these fuel models have not been tested for historical occurrences of wildfires. Consequently, the applicability of these models requires further investigation. Thus, this paper aims to develop standard fuel models. Seven vegetation types were combined into three fuel models according to potential fire behaviors which were clustered using Euclidean distance algorithms. Fuel model parameter sensitivity was analyzed by the Morris screening method. Results showed that the fuel model parameters 1-hour time-lag loading, dead heat content, live heat content, 1-hour time-lag SAV(Surface Area-to-Volume), live shrub SAV, and fuel bed depth have high sensitivity. Two main sensitive fuel parameters: 1-hour time-lag loading and fuel bed depth, were determined as adjustment parameters because of their high spatio-temporal variability. The FARSITE model was then used to test the fire prediction capabilities of the combined fuel models (uncalibrated fuel models). FARSITE was shown to yield an unrealistic prediction of the historical fire. However, the calibrated fuel models significantly improved the capabilities of the fuel models to predict the actual fire with an accuracy of 89%. Validation results also showed that the model can estimate the actual fires with an accuracy exceeding 56% by using the calibrated fuel models. Therefore, these fuel models can be efficiently used to calculate fire behaviors, which can be helpful in forest fire management. PMID:24714164 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19326251','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19326251">In silico prediction of nematic transition temperature for liquid crystals using quantitative structure-property relationship approaches.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Fatemi, Mohammad Hossein; Ghorbanzad'e, Mehdi 2009-11-01 Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for training, and an internal and external test set. Training and internal test sets were used for ANN model development, and the external test set was used for evaluation of the predictive power of the model. In order to build the models, a set of six descriptors were selected by the best multilinear regression procedure of the CODESSA program. These descriptors were: atomic charge weighted partial negatively charged surface area, relative negative charged surface area, polarity parameter/square distance, minimum most negative atomic partial charge, molecular volume, and the A component of moment of inertia, which encode geometrical and electronic characteristics of molecules. These descriptors were used as inputs to ANN. The optimized ANN model had 6:6:1 topology. The standard errors in the calculation of T (N) for the training, internal, and external test sets using the ANN model were 1.012, 4.910, and 4.070, respectively. To further evaluate the ANN model, a crossvalidation test was performed, which produced the statistic Q (2) = 0.9796 and standard deviation of 2.67 based on predicted residual sum of square. Also, the diversity test was performed to ensure the model's stability and prove its predictive capability. The obtained results reveal the suitability of ANN for the prediction of T (N) for liquid crystals using molecular structural descriptors. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/90388','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/90388">An experimental and theoretical study to relate uncommon rock/fluid properties to oil recovery. Final report</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Watson, R. Waterflooding is the most commonly used secondary oil recovery technique. One of the requirements for understanding waterflood performance is a good knowledge of the basic properties of the reservoir rocks. This study is aimed at correlating rock-pore characteristics to oil recovery from various reservoir rock types and incorporating these properties into empirical models for Predicting oil recovery. For that reason, this report deals with the analyses and interpretation of experimental data collected from core floods and correlated against measurements of absolute permeability, porosity. wettability index, mercury porosimetry properties and irreducible water saturation. The results of the radial-core the radial-core andmore » linear-core flow investigations and the other associated experimental analyses are presented and incorporated into empirical models to improve the predictions of oil recovery resulting from waterflooding, for sandstone and limestone reservoirs. For the radial-core case, the standardized regression model selected, based on a subset of the variables, predicted oil recovery by waterflooding with a standard deviation of 7%. For the linear-core case, separate models are developed using common, uncommon and combination of both types of rock properties. It was observed that residual oil saturation and oil recovery are better predicted with the inclusion of both common and uncommon rock/fluid properties into the predictive models.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20482816','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20482816">Assessment of hospital performance with a case-mix standardized mortality model using an existing administrative database in Japan.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Miyata, Hiroaki; Hashimoto, Hideki; Horiguchi, Hiromasa; Fushimi, Kiyohide; Matsuda, Shinya 2010-05-19 Few studies have examined whether risk adjustment is evenly applicable to hospitals with various characteristics and case-mix. In this study, we applied a generic prediction model to nationwide discharge data from hospitals with various characteristics. We used standardized data of 1,878,767 discharged patients provided by 469 hospitals from July 1 to October 31, 2006. We generated and validated a case-mix in-hospital mortality prediction model using 50/50 split sample validation. We classified hospitals into two groups based on c-index value (hospitals with c-index > or = 0.8; hospitals with c-index < 0.8) and examined differences in their characteristics. The model demonstrated excellent discrimination as indicated by the high average c-index and small standard deviation (c-index = 0.88 +/- 0.04). Expected mortality rate of each hospital was highly correlated with observed mortality rate (r = 0.693, p < 0.001). Among the studied hospitals, 446 (95%) had a c-index of >/=0.8 and were classified as the higher c-index group. A significantly higher proportion of hospitals in the lower c-index group were specialized hospitals and hospitals with convalescent wards. The model fits well to a group of hospitals with a wide variety of acute care events, though model fit is less satisfactory for specialized hospitals and those with convalescent wards. Further sophistication of the generic prediction model would be recommended to obtain optimal indices to region specific conditions. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3213024','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3213024">Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> 2011-01-01 Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. Conclusions We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting. PMID:22023778 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22023778','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22023778">Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Kennedy, Curtis E; Turley, James P 2011-10-24 Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/5835196-muon-pair-production-gev-le-radical-le-gev-using-amy-detector-tristan','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/5835196-muon-pair-production-gev-le-radical-le-gev-using-amy-detector-tristan">Muon pair production at 52 GeV le radical s le 57 GeV using the AMY Detector at TRISTAN</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Bacala, A.M. 1989-01-01 The reaction e{sup +}e{sup {minus}} {yields} {mu}{sup +}{mu}{sup {minus}} has been observed by the AMY Detector at 52 GeV {le} {radical}s {le} 57 GeV at the TRISTAN storage ring in Tsukuba, Japan. With an integrated luminosity of 18.6 pb{sup {minus}1}, this presents a new test of the standard model of the electroweak interactions in this previously unexplored energy region. The forward-backward charge asymmetry and the cross-section calculated at the various energies show agreement with the standard model predictions. The products of the vector and the axial-vector coupling constants of the electron and muon extracted from these measurements are also consistentmore » with theory. The data were combined at the average energy of {radical}s = 55.2 GeV. The measured asymmetry and cross-section are -34.4 {+-} 7.7% and 29.7 {+-} 2.1 pb respectively. This is in agreement with the standard model prediction of -28.3% for the asymmetry and 29.5 pb for the cross-section. The product of the axial couplings, g{sup e}{sub A}g{sup {mu}}{sub A} = 0.29 {+-} 0.07, and the product of the vector couplings, g{sup e}{sub V}g{sup {mu}}{sub V} = 0.01 {+-} 0.06, agree with the standard model predictions of 0.25 and .002 for these respective constants.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018AdWR..113..223L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018AdWR..113..223L">An efficient Bayesian data-worth analysis using a multilevel Monte Carlo method</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Lu, Dan; Ricciuto, Daniel; Evans, Katherine 2018-03-01 Improving the understanding of subsurface systems and thus reducing prediction uncertainty requires collection of data. As the collection of subsurface data is costly, it is important that the data collection scheme is cost-effective. Design of a cost-effective data collection scheme, i.e., data-worth analysis, requires quantifying model parameter, prediction, and both current and potential data uncertainties. Assessment of these uncertainties in large-scale stochastic subsurface hydrological model simulations using standard Monte Carlo (MC) sampling or surrogate modeling is extremely computationally intensive, sometimes even infeasible. In this work, we propose an efficient Bayesian data-worth analysis using a multilevel Monte Carlo (MLMC) method. Compared to the standard MC that requires a significantly large number of high-fidelity model executions to achieve a prescribed accuracy in estimating expectations, the MLMC can substantially reduce computational costs using multifidelity approximations. Since the Bayesian data-worth analysis involves a great deal of expectation estimation, the cost saving of the MLMC in the assessment can be outstanding. While the proposed MLMC-based data-worth analysis is broadly applicable, we use it for a highly heterogeneous two-phase subsurface flow simulation to select an optimal candidate data set that gives the largest uncertainty reduction in predicting mass flow rates at four production wells. The choices made by the MLMC estimation are validated by the actual measurements of the potential data, and consistent with the standard MC estimation. But compared to the standard MC, the MLMC greatly reduces the computational costs. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25910110','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25910110">Constraints on models of the Higgs boson with exotic spin and parity using decays to bottom-antibottom quarks in the full CDF data set.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Aaltonen, T; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Appel, J A; Arisawa, T; Artikov, A; Asaadi, J; Ashmanskas, W; Auerbach, B; Aurisano, A; Azfar, F; Badgett, W; Bae, T; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Barria, P; Bartos, P; Bauce, M; Bedeschi, F; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Bhatti, A; Bland, K R; Blumenfeld, B; Bocci, A; Bodek, A; Bortoletto, D; Boudreau, J; Boveia, A; Brigliadori, L; Bromberg, C; Brucken, E; Budagov, J; Budd, H S; Burkett, K; Busetto, G; Bussey, P; Butti, P; Buzatu, A; Calamba, A; Camarda, S; Campanelli, M; Canelli, F; Carls, B; Carlsmith, D; Carosi, R; Carrillo, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cerri, A; Cerrito, L; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Cho, K; Chokheli, D; Clark, A; Clarke, C; Convery, M E; Conway, J; Corbo, M; Cordelli, M; Cox, C A; Cox, D J; Cremonesi, M; Cruz, D; Cuevas, J; Culbertson, R; d'Ascenzo, N; Datta, M; de Barbaro, P; Demortier, L; Deninno, M; D'Errico, M; Devoto, F; Di Canto, A; Di Ruzza, B; Dittmann, J R; Donati, S; D'Onofrio, M; Dorigo, M; Driutti, A; Ebina, K; Edgar, R; Elagin, A; Erbacher, R; Errede, S; Esham, B; Farrington, S; Fernández Ramos, J P; Field, R; Flanagan, G; Forrest, R; Franklin, M; Freeman, J C; Frisch, H; Funakoshi, Y; Galloni, C; Garfinkel, A F; Garosi, P; Gerberich, H; Gerchtein, E; Giagu, S; Giakoumopoulou, V; Gibson, K; Ginsburg, C M; Giokaris, N; Giromini, P; Glagolev, V; Glenzinski, D; Gold, M; Goldin, D; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González López, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gramellini, E; Grosso-Pilcher, C; Group, R C; Guimaraes da Costa, J; Hahn, S R; Han, J Y; Happacher, F; Hara, K; Hare, M; Harr, R F; Harrington-Taber, T; Hatakeyama, K; Hays, C; Heinrich, J; Herndon, M; Hocker, A; Hong, Z; Hopkins, W; Hou, S; Hughes, R E; Husemann, U; Hussein, M; Huston, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jang, D; Jayatilaka, B; Jeon, E J; Jindariani, S; Jones, M; Joo, K K; Jun, S Y; Junk, T R; Kambeitz, M; Kamon, T; Karchin, P E; Kasmi, A; Kato, Y; Ketchum, W; Keung, J; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S H; Kim, S B; Kim, Y J; Kim, Y K; Kimura, N; Kirby, M; Knoepfel, K; Kondo, K; Kong, D J; Konigsberg, J; Kotwal, A V; Kreps, M; Kroll, J; Kruse, M; Kuhr, T; Kurata, M; Laasanen, A T; Lammel, S; Lancaster, M; Lannon, K; Latino, G; Lee, H S; Lee, J S; Leo, S; Leone, S; Lewis, J D; Limosani, A; Lipeles, E; Lister, A; Liu, H; Liu, Q; Liu, T; Lockwitz, S; Loginov, A; Lucchesi, D; Lucà, A; Lueck, J; Lujan, P; Lukens, P; Lungu, G; Lys, J; Lysak, R; Madrak, R; Maestro, P; Malik, S; Manca, G; Manousakis-Katsikakis, A; Marchese, L; Margaroli, F; Marino, P; Matera, K; Mattson, M E; Mazzacane, A; Mazzanti, P; McNulty, R; Mehta, A; Mehtala, P; Mesropian, C; Miao, T; Mietlicki, D; Mitra, A; Miyake, H; Moed, S; Moggi, N; Moon, C S; Moore, R; Morello, M J; Mukherjee, A; Muller, Th; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Naganoma, J; Nakano, I; Napier, A; Nett, J; Neu, C; Nigmanov, T; Nodulman, L; Noh, S Y; Norniella, O; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Ortolan, L; Pagliarone, C; Palencia, E; Palni, P; Papadimitriou, V; Parker, W; Pauletta, G; Paulini, M; Paus, C; Phillips, T J; Piacentino, G; Pianori, E; Pilot, J; Pitts, K; Plager, C; Pondrom, L; Poprocki, S; Potamianos, K; Pranko, A; Prokoshin, F; Ptohos, F; Punzi, G; Redondo Fernández, I; Renton, P; Rescigno, M; Rimondi, F; Ristori, L; Robson, A; Rodriguez, T; Rolli, S; Ronzani, M; Roser, R; Rosner, J L; Ruffini, F; Ruiz, A; Russ, J; Rusu, V; Sakumoto, W K; Sakurai, Y; Santi, L; Sato, K; Saveliev, V; Savoy-Navarro, A; Schlabach, P; Schmidt, E E; Schwarz, T; Scodellaro, L; Scuri, F; Seidel, S; Seiya, Y; Semenov, A; Sforza, F; Shalhout, S Z; Shears, T; Shepard, P F; Shimojima, M; Shochet, M; Shreyber-Tecker, I; Simonenko, A; Sliwa, K; Smith, J R; Snider, F D; Song, H; Sorin, V; St Denis, R; Stancari, M; Stentz, D; Strologas, J; Sudo, Y; Sukhanov, A; Suslov, I; Takemasa, K; Takeuchi, Y; Tang, J; Tecchio, M; Teng, P K; Thom, J; Thomson, E; Thukral, V; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Trovato, M; Ukegawa, F; Uozumi, S; Vázquez, F; Velev, G; Vellidis, C; Vernieri, C; Vidal, M; Vilar, R; Vizán, J; Vogel, M; Volpi, G; Wagner, P; Wallny, R; Wang, S M; Waters, D; Wester, W C; Whiteson, D; Wicklund, A B; Wilbur, S; Williams, H H; Wilson, J S; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, H; Wright, T; Wu, X; Wu, Z; Yamamoto, K; Yamato, D; Yang, T; Yang, U K; Yang, Y C; Yao, W-M; Yeh, G P; Yi, K; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Zanetti, A M; Zeng, Y; Zhou, C; Zucchelli, S 2015-04-10 A search for particles with the same mass and couplings as those of the standard model Higgs boson but different spin and parity quantum numbers is presented. We test two specific alternative Higgs boson hypotheses: a pseudoscalar Higgs boson with spin-parity J^{P}=0^{-} and a gravitonlike Higgs boson with J^{P}=2^{+}, assuming for both a mass of 125 GeV/c^{2}. We search for these exotic states produced in association with a vector boson and decaying into a bottom-antibottom quark pair. The vector boson is reconstructed through its decay into an electron or muon pair, or an electron or muon and a neutrino, or it is inferred from an imbalance in total transverse momentum. We use expected kinematic differences between events containing exotic Higgs bosons and those containing standard model Higgs bosons. The data were collected by the CDF experiment at the Tevatron proton-antiproton collider, operating at a center-of-mass energy of sqrt[s]=1.96 TeV, and correspond to an integrated luminosity of 9.45 fb^{-1}. We exclude deviations from the predictions of the standard model with a Higgs boson of mass 125 GeV/c^{2} at the level of 5 standard deviations, assuming signal strengths for exotic boson production equal to the prediction for the standard model Higgs boson, and set upper limits of approximately 30% relative to the standard model rate on the possible rate of production of each exotic state. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/44829','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/44829">Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense mixed conifer forest</a> <a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a> Marek K. Jakubowksi; Qinghua Guo; Brandon Collins; Scott Stephens; Maggi Kelly 2013-01-01 We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/808745','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/808745">Nuclear power plant life extension using subsize surveillance specimens. Performance report (4/15/92 - 4/14/98)</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Kumar, Arvind S. 2001-03-05 A new methodology to predict the Upper Shelf Energy (USE) of standard Charpy specimens (Full size) based on subsize specimens has been developed. The prediction methodology uses Finite Element Modeling (FEM) to model the fracture behavior. The inputs to FEM are the tensile properties of material and subsize Charpy specimen test data. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26133478','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26133478">Prediction of moisture variation during composting process: A comparison of mathematical models.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Wang, Yongjiang; Ai, Ping; Cao, Hongliang; Liu, Zhigang 2015-10-01 This study was carried out to develop and compare three models for simulating the moisture content during composting. Model 1 described changes in water content using mass balance, while Model 2 introduced a liquid-gas transferred water term. Model 3 predicted changes in moisture content without complex degradation kinetics. Average deviations for Model 1-3 were 8.909, 7.422 and 5.374 kg m(-3) while standard deviations were 10.299, 8.374 and 6.095, respectively. The results showed that Model 1 is complex and involves more state variables, but can be used to reveal the effect of humidity on moisture content. Model 2 tested the hypothesis of liquid-gas transfer and was shown to be capable of predicting moisture content during composting. Model 3 could predict water content well without considering degradation kinetics. Copyright © 2015 Elsevier Ltd. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5203752','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5203752">Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Maghooli, Keivan; Langarizadeh, Mostafa; Shahmoradi, Leila; Habibi-koolaee, Mahdi; Jebraeily, Mohamad; Bouraghi, Hamid 2016-01-01 Introduction: Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. Objective: we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD. Methods: we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model. Results: The proposed model had an accuracy of 94.84% ( Standard Deviation: 24.42) in order to correct prediction of the ESD disease. Conclusions: Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD. PMID:28077889 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24231260','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24231260">Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C 2014-03-01 To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19053524','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19053524">Predicting the accuracy of ligand overlay methods with Random Forest models.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Nandigam, Ravi K; Evans, David A; Erickson, Jon A; Kim, Sangtae; Sutherland, Jeffrey J 2008-12-01 The accuracy of binding mode prediction using standard molecular overlay methods (ROCS, FlexS, Phase, and FieldCompare) is studied. Previous work has shown that simple decision tree modeling can be used to improve accuracy by selection of the best overlay template. This concept is extended to the use of Random Forest (RF) modeling for template and algorithm selection. An extensive data set of 815 ligand-bound X-ray structures representing 5 gene families was used for generating ca. 70,000 overlays using four programs. RF models, trained using standard measures of ligand and protein similarity and Lipinski-related descriptors, are used for automatically selecting the reference ligand and overlay method maximizing the probability of reproducing the overlay deduced from X-ray structures (i.e., using rmsd < or = 2 A as the criteria for success). RF model scores are highly predictive of overlay accuracy, and their use in template and method selection produces correct overlays in 57% of cases for 349 overlay ligands not used for training RF models. The inclusion in the models of protein sequence similarity enables the use of templates bound to related protein structures, yielding useful results even for proteins having no available X-ray structures. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhDT.........9J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhDT.........9J">Constraints on models for the Higgs boson with exotic spin and parity</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Johnson, Emily Hannah The production of a Higgs boson in association with a vector boson at the Tevatron offers a unique opportunity to study models for the Higgs boson with exotic spin J and parity P assignments. At the Tevatron the V H system is produced near threshold. Different JP assignments of the Higgs boson can be distinguished by examining the behavior of the cross section near threshold. The relatively low backgrounds at the Tevatron compared to the LHC put us in a unique position to study the direct decay of the Higgs boson to fermions. If the Higgs sector is more complex than predicted, studying the spin and parity of the Higgs boson in all decay modes is important. In this Thesis we will examine the WH → lnu bb¯ production and decay mode using 9.7 fb-1 of data collected by the D0 experiment in an attempt to derive constraints on models containing exotic values for the spin and parity of the Higgs boson. In particular, we will examine models for a Higgs boson with J P = 0- and JP = 2+. We use a likelihood ratio to quantify the degree to which our data are incompatible with exotic JP predictions for a range of possible production rates. Assuming the production cross section times branching ratio of the signals in the models considered is equal to the standard model prediction, the WH → lnu bb¯ mode alone is unable to reject either exotic model considered. We will also discuss the combination of the ZH → llbb¯, WH → lnubb¯, and V H → nunu bb¯ production modes at the D0 experiment and with the CDF experiment. When combining all three production modes at the D0 experiment we reject the JP = 0- and J P = 2+ hypotheses at the 97.6% CL and at the 99.0% CL, respectively, when assuming the signal production cross section times branching ratio is equal to the standard model predicted value. When combining with the CDF experiment we reject the JP = 0- and JP = 2 + hypotheses with significances of 5.0 standard deviations and 4.9 standard deviations, respectively. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/18942','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/18942">Computer models for predicting the probability of violating CO air quality standards : the model SIMCO.</a> <a target="_blank" rel="noopener noreferrer" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a> 1982-01-01 This report presents the user instructions and data requirements for SIMCO, a combined simulation and probability computer model developed to quantify and evaluate carbon monoxide in roadside environments. The model permits direct determinations of t... </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22746340','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22746340">Protein and oil composition predictions of single soybeans by transmission Raman spectroscopy.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Schulmerich, Matthew V; Walsh, Michael J; Gelber, Matthew K; Kong, Rong; Kole, Matthew R; Harrison, Sandra K; McKinney, John; Thompson, Dennis; Kull, Linda S; Bhargava, Rohit 2012-08-22 The soybean industry requires rapid, accurate, and precise technologies for the analyses of seed/grain constituents. While the current gold standard for nondestructive quantification of economically and nutritionally important soybean components is near-infrared spectroscopy (NIRS), emerging technology may provide viable alternatives and lead to next generation instrumentation for grain compositional analysis. In principle, Raman spectroscopy provides the necessary chemical information to generate models for predicting the concentration of soybean constituents. In this communication, we explore the use of transmission Raman spectroscopy (TRS) for nondestructive soybean measurements. We show that TRS uses the light scattering properties of soybeans to effectively homogenize the heterogeneous bulk of a soybean for representative sampling. Working with over 1000 individual intact soybean seeds, we developed a simple partial least-squares model for predicting oil and protein content nondestructively. We find TRS to have a root-mean-standard error of prediction (RMSEP) of 0.89% for oil measurements and 0.92% for protein measurements. In both calibration and validation sets, the predicative capabilities of the model were similar to the error in the reference methods. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MPLB...3240038T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MPLB...3240038T">A comparative study of various inflow boundary conditions and turbulence models for wind turbine wake predictions</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Tian, Lin-Lin; Zhao, Ning; Song, Yi-Lei; Zhu, Chun-Ling 2018-05-01 This work is devoted to perform systematic sensitivity analysis of different turbulence models and various inflow boundary conditions in predicting the wake flow behind a horizontal axis wind turbine represented by an actuator disc (AD). The tested turbulence models are the standard k-𝜀 model and the Reynolds Stress Model (RSM). A single wind turbine immersed in both uniform flows and in modeled atmospheric boundary layer (ABL) flows is studied. Simulation results are validated against the field experimental data in terms of wake velocity and turbulence intensity. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1212527-search-standard-model-higgs-boson-decay-channel-hzz-atlas-detector','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1212527-search-standard-model-higgs-boson-decay-channel-hzz-atlas-detector">Search for the Standard Model Higgs boson in the decay channel H → Z Z ( * ) → 4 ℓ with the ATLAS detector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Aad, G.; Abbott, B.; Abdallah, J.; ... 2011-10-17 A search for the Standard Model Higgs boson in the decay channel H→ZZ(*)→ℓ+ℓ-ℓ'+ℓ'-, where ℓ=e,μ, is presented. Proton–proton collision data at s=7TeV recorded with the ATLAS detector and corresponding to an average integrated luminosity of 2.1fb -1 are compared to the Standard Model expectations. Upper limits on the production cross section of a Standard Model Higgs boson with a mass between 110 and 600GeV are derived. The observed (expected) 95% confidence level upper limit on the production cross section for a Higgs boson with a mass of 194 GeV, the region with the best expected sensitivity for this search, ismore » 0.99 (1.01) times the Standard Model prediction. The Standard Model Higgs boson is excluded at 95% confidence level in the mass ranges 191–197, 199–200 and 214–224 GeV« less </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active">11</li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active">12</li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016SPIE.9696E..0XZ','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016SPIE.9696E..0XZ">A standardized model for predicting flap failure using indocyanine green dye</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Zimmermann, Terence M.; Moore, Lindsay S.; Warram, Jason M.; Greene, Benjamin J.; Nakhmani, Arie; Korb, Melissa L.; Rosenthal, Eben L. 2016-03-01 Techniques that provide a non-invasive method for evaluation of intraoperative skin flap perfusion are currently available but underutilized. We hypothesize that intraoperative vascular imaging can be used to reliably assess skin flap perfusion and elucidate areas of future necrosis by means of a standardized critical perfusion threshold. Five animal groups (negative controls, n=4; positive controls, n=5; chemotherapy group, n=5; radiation group, n=5; chemoradiation group, n=5) underwent pre-flap treatments two weeks prior to undergoing random pattern dorsal fasciocutaneous flaps with a length to width ratio of 2:1 (3 x 1.5 cm). Flap perfusion was assessed via laser-assisted indocyanine green dye angiography and compared to standard clinical assessment for predictive accuracy of flap necrosis. For estimating flap-failure, clinical prediction achieved a sensitivity of 79.3% and a specificity of 90.5%. When average flap perfusion was more than three standard deviations below the average flap perfusion for the negative control group at the time of the flap procedure (144.3+/-17.05 absolute perfusion units), laser-assisted indocyanine green dye angiography achieved a sensitivity of 81.1% and a specificity of 97.3%. When absolute perfusion units were seven standard deviations below the average flap perfusion for the negative control group, specificity of necrosis prediction was 100%. Quantitative absolute perfusion units can improve specificity for intraoperative prediction of viable tissue. Using this strategy, a positive predictive threshold of flap failure can be standardized for clinical use. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/22676052-perturbative-approach-redshift-space-correlation-function-beyond-standard-model','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/22676052-perturbative-approach-redshift-space-correlation-function-beyond-standard-model">A perturbative approach to the redshift space correlation function: beyond the Standard Model</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Bose, Benjamin; Koyama, Kazuya, E-mail: benjamin.bose@port.ac.uk, E-mail: kazuya.koyama@port.ac.uk We extend our previous redshift space power spectrum code to the redshift space correlation function. Here we focus on the Gaussian Streaming Model (GSM). Again, the code accommodates a wide range of modified gravity and dark energy models. For the non-linear real space correlation function used in the GSM we use the Fourier transform of the RegPT 1-loop matter power spectrum. We compare predictions of the GSM for a Vainshtein screened and Chameleon screened model as well as GR. These predictions are compared to the Fourier transform of the Taruya, Nishimichi and Saito (TNS) redshift space power spectrum model whichmore » is fit to N-body data. We find very good agreement between the Fourier transform of the TNS model and the GSM predictions, with ≤ 6% deviations in the first two correlation function multipoles for all models for redshift space separations in 50Mpc h ≤ s ≤ 180Mpc/ h . Excellent agreement is found in the differences between the modified gravity and GR multipole predictions for both approaches to the redshift space correlation function, highlighting their matched ability in picking up deviations from GR. We elucidate the timeliness of such non-standard templates at the dawn of stage-IV surveys and discuss necessary preparations and extensions needed for upcoming high quality data.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27131183','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27131183">Predicting ground contact events for a continuum of gait types: An application of targeted machine learning using principal component analysis.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Osis, Sean T; Hettinga, Blayne A; Ferber, Reed 2016-05-01 An ongoing challenge in the application of gait analysis to clinical settings is the standardized detection of temporal events, with unobtrusive and cost-effective equipment, for a wide range of gait types. The purpose of the current study was to investigate a targeted machine learning approach for the prediction of timing for foot strike (or initial contact) and toe-off, using only kinematics for walking, forefoot running, and heel-toe running. Data were categorized by gait type and split into a training set (∼30%) and a validation set (∼70%). A principal component analysis was performed, and separate linear models were trained and validated for foot strike and toe-off, using ground reaction force data as a gold-standard for event timing. Results indicate the model predicted both foot strike and toe-off timing to within 20ms of the gold-standard for more than 95% of cases in walking and running gaits. The machine learning approach continues to provide robust timing predictions for clinical use, and may offer a flexible methodology to handle new events and gait types. Copyright © 2016 Elsevier B.V. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900051029&hterms=Inverter&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DInverter','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900051029&hterms=Inverter&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DInverter">Frequency domain model for analysis of paralleled, series-output-connected Mapham inverters</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Brush, Andrew S.; Sundberg, Richard C.; Button, Robert M. 1989-01-01 The Mapham resonant inverter is characterized as a two-port network driven by a selected periodic voltage. The two-port model is then used to model a pair of Mapham inverters connected in series and employing phasor voltage regulation. It is shown that the model is useful for predicting power output in paralleled inverter units, and for predicting harmonic current output of inverter pairs, using standard power flow techniques. Some sample results are compared to data obtained from testing hardware inverters. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890016778','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890016778">Frequency domain model for analysis of paralleled, series-output-connected Mapham inverters</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Brush, Andrew S.; Sundberg, Richard C.; Button, Robert M. 1989-01-01 The Mapham resonant inverter is characterized as a two-port network driven by a selected periodic voltage. The two-port model is then used to model a pair of Mapham inverters connected in series and employing phasor voltage regulation. It is shown that the model is useful for predicting power output in paralleled inverter units, and for predicting harmonic current output of inverter pairs, using standard power flow techniques. Some examples are compared to data obtained from testing hardware inverters. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880005807','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880005807">Numerical solution of turbulent flow past a backward facing step using a nonlinear K-epsilon model</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Speziale, C. G.; Ngo, Tuan 1987-01-01 The problem of turbulent flow past a backward facing step is important in many technological applications and has been used as a standard test case to evaluate the performance of turbulence models in the prediction of separated flows. It is well known that the commonly used kappa-epsilon (and K-l) models of turbulence yield inaccurate predictions for the reattachment points in this problem. By an analysis of the mean vorticity transport equation, it will be argued that the intrinsically inaccurate prediction of normal Reynolds stress differences by the Kappa-epsilon and K-l models is a major contributor to this problem. Computations using a new nonlinear kappa-epsilon model (which alleviates this deficiency) are made with the TEACH program. Comparisons are made between the improved results predicted by this nonlinear kappa-epsilon model and those obtained from the linear kappa-epsilon model as well as from second-order closure models. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1405162','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1405162">Search for physics beyond the standard model in events with τ leptons, jets, and large transverse momentum imbalance in pp collisions at $$\\sqrt{s}=7\\ \\mathrm{TeV}$$</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M. A search for physics beyond the standard model is performed with events having one or more hadronically decaying tau leptons, highly energetic jets, and large transverse momentum imbalance. The data sample corresponds to an integrated luminosity of 4.98 inverse femtobarns of proton-proton collisions at sqrt(s) = 7 TeV collected with the CMS detector at the LHC in 2011. The number of observed events is consistent with predictions for standard model processes. Lower limits on the mass of the gluino in supersymmetric models are determined. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017APS..APRS10004G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017APS..APRS10004G">Sakurai Prize: Extended Higgs Sectors--phenomenology and future prospects</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Gunion, John 2017-01-01 The discovery of a spin-0 state at 125 GeV with properties close to those predicted for the single Higgs boson of the Standard Model does not preclude the existence of additional Higgs bosons. In this talk, models with extended Higgs sectors are reviewed, including two-Higgs-doublet models with and without an extra singlet Higgs field and supersymmetric models. Special emphasis is given to the limit in which the couplings and properties of one of the Higgs bosons of the extended Higgs sector are very close to those predicted for the single Standard Model Higgs boson while the other Higgs bosons are relatively light, perhaps even having masses close to or below the SM-like 125 GeV state. Constraints on this type of scenario given existing data are summarized and prospects for observing these non-SM-like Higgs bosons are discussed. Supported by the Department of Energy. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JPhCS.938a2059P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPhCS.938a2059P">Model identification of new heavy Z‧ bosons at ILC with polarized beams</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Pankov, A. A.; Tsytrinov, A. V. 2017-12-01 Extra neutral gauge bosons, Z‧s, are predicted by many theoretical scenarios of physics beyond the Standard Model, and intensive searches for their signatures will be performed at present and future high energy colliders. It is quite possible that Z‧s are heavy enough to lie beyond the discovery reach expected at the CERN Large Hadron Collider LHC, in which case only indirect signatures of Z‧ exchanges may occur at future colliders, through deviations of the measured cross sections from the Standard Model predictions. We here discuss in this context the expected sensitivity to Z‧ parameters of fermion-pair production cross sections at the planned International Linear Collider (ILC), especially as regards the potential of distinguishing different Z‧ models once such deviations are observed. Specifically, we evaluate the discovery and identification reaches on Z‧ gauge bosons pertinent to the E 6, LR, ALR, and SSM classes of models at the ILC. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28725976','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28725976">Target-mediated drug disposition model for drugs with two binding sites that bind to a target with one binding site.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Gibiansky, Leonid; Gibiansky, Ekaterina 2017-10-01 The paper extended the TMDD model to drugs with two identical binding sites (2-1 TMDD). The quasi-steady-state (2-1 QSS), quasi-equilibrium (2-1 QE), irreversible binding (2-1 IB), and Michaelis-Menten (2-1 MM) approximations of the model were derived. Using simulations, the 2-1 QSS approximation was compared with the full 2-1 TMDD model. As expected and similarly to the standard TMDD for monoclonal antibodies (mAb), 2-1 QSS predictions were nearly identical to 2-1 TMDD predictions, except for times of fast changes following initiation of dosing, when equilibrium has not yet been reached. To illustrate properties of new equations and approximations, several variations of population PK data for mAbs with soluble (slow elimination of the complex) or membrane-bound (fast elimination of the complex) targets were simulated from a full 2-1 TMDD model and fitted to 2-1 TMDD models, to its approximations, and to the standard (1-1) QSS model. For a mAb with a soluble target, it was demonstrated that the 2-1 QSS model provided nearly identical description of the observed (simulated) free drug and total target concentrations, although there was some minor bias in predictions of unobserved free target concentrations. The standard QSS approximation also provided a good description of the observed data, but was not able to distinguish between free drug concentrations (with no target attached and both binding site free) and partially bound drug concentrations (with one of the binding sites occupied by the target). For a mAb with a membrane-bound target, the 2-1 MM approximation adequately described the data. The 2-1 QSS approximation converged 10 times faster than the full 2-1 TMDD, and its run time was comparable with the standard QSS model. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/6035288-modelling-milk-production-from-feed-intake-dairy-cattle','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/6035288-modelling-milk-production-from-feed-intake-dairy-cattle">Modelling milk production from feed intake in dairy cattle</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Clarke, D.L. 1985-05-01 Predictive models were developed for both Holstein and Jersey cows. Since Holsteins comprised eighty-five percent of the data, the predictive models developed for Holsteins were used for the development of a user-friendly computer model. Predictive models included: milk production (squared multiple correlation .73), natural log (ln) of milk production (.73), four percent fat-corrected milk (.67), ln four percent fat-corrected milk (.68), fat-free milk (.73), ln fat-free milk (.73), dry matter intake (.61), ln dry matter intake (.60), milk fat (.52), and ln milk fat (.56). The predictive models for ln milk production, ln fat-free milk and ln dry matter intakemore » were incorporated into a computer model. The model was written in standard Fortran for use on mainframe or micro-computers. Daily milk production, fat-free milk production, and dry matter intake were predicted on a daily basis with the previous day's dry matter intake serving as an independent variable in the prediction of the daily milk and fat-free milk production. 21 refs.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820012224','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820012224">Astrophysical tests for radiative decay of neutrinos and fundamental physics implications</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Stecker, F. W.; Brown, R. W. 1981-01-01 The radiative lifetime tau for the decay of massious neutrinos was calculated using various physical models for neutrino decay. The results were then related to the astrophysical problem of the detectability of the decay photons from cosmic neutrinos. Conversely, the astrophysical data were used to place lower limits on tau. These limits are all well below predicted values. However, an observed feature at approximately 1700 A in the ultraviolet background radiation at high galactic latitudes may be from the decay of neutrinos with mass approximately 14 eV. This would require a decay rate much larger than the predictions of standard models but could be indicative of a decay rate possible in composite models or other new physics. Thus an important test for substructure in leptons and quarks or other physics beyond the standard electroweak model may have been found. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1256709','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1256709">Les Houches 2015: Physics at TeV Colliders Standard Model Working Group Report</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Andersen, J.R.; et al. This Report summarizes the proceedings of the 2015 Les Houches workshop on Physics at TeV Colliders. Session 1 dealt with (I) new developments relevant for high precision Standard Model calculations, (II) the new PDF4LHC parton distributions, (III) issues in the theoretical description of the production of Standard Model Higgs bosons and how to relate experimental measurements, (IV) a host of phenomenological studies essential for comparing LHC data from Run I with theoretical predictions and projections for future measurements in Run II, and (V) new developments in Monte Carlo event generators. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4364920','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4364920">Predictive Inference Using Latent Variables with Covariates*</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Schofield, Lynne Steuerle; Junker, Brian; Taylor, Lowell J.; Black, Dan A. 2014-01-01 Plausible Values (PVs) are a standard multiple imputation tool for analysis of large education survey data that measures latent proficiency variables. When latent proficiency is the dependent variable, we reconsider the standard institutionally-generated PV methodology and find it applies with greater generality than shown previously. When latent proficiency is an independent variable, we show that the standard institutional PV methodology produces biased inference because the institutional conditioning model places restrictions on the form of the secondary analysts’ model. We offer an alternative approach that avoids these biases based on the mixed effects structural equations (MESE) model of Schofield (2008). PMID:25231627 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1438540','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1438540">Les Houches 2017: Physics at TeV Colliders Standard Model Working Group Report</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Andersen, J.R.; et al. This Report summarizes the proceedings of the 2017 Les Houches workshop on Physics at TeV Colliders. Session 1 dealt with (I) new developments relevant for high precision Standard Model calculations, (II) theoretical uncertainties and dataset dependence of parton distribution functions, (III) new developments in jet substructure techniques, (IV) issues in the theoretical description of the production of Standard Model Higgs bosons and how to relate experimental measurements, (V) phenomenological studies essential for comparing LHC data from Run II with theoretical predictions and projections for future measurements, and (VI) new developments in Monte Carlo event generators. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED283074.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED283074.pdf">Qualitative Differences in Real-Time Solution of Standardized Figural Analogies.</a> <a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a> Schiano, Diane J.; And Others Performance on standardized figural analogy tests is considered highly predictive of academic success. While information-processing models of analogy solution attribute performance differences to quantitative differences in processing parameters, the problem-solving literature suggests that qualitative differences in problem representation and… </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22524966','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22524966">Evaluation of apparent viscosity of Para rubber latex by diffuse reflection near-infrared spectroscopy.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Sirisomboon, Panmanas; Chowbankrang, Rawiphan; Williams, Phil 2012-05-01 Near-infrared spectroscopy in diffuse reflection mode was used to evaluate the apparent viscosity of Para rubber field latex and concentrated latex over the wavelength range of 1100 to 2500 nm, using partial least square regression (PLSR). The model with ten principal components (PCs) developed using the raw spectra accurately predicted the apparent viscosity with correlation coefficient (r), standard error of prediction (SEP), and bias of 0.974, 8.6 cP, and -0.4 cP, respectively. The ratio of the SEP to the standard deviation (RPD) and the ratio of the SEP to the range (RER) for the prediction were 4.4 and 16.7, respectively. Therefore, the model can be used for measurement of the apparent viscosity of field latex and concentrated latex in quality assurance and process control in the factory. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1327281','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1327281">The New Muon g₋2 experiment at Fermilab</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Venanzoni, Graziano 2016-06-02 There is a long standing discrepancy between the Standard Model prediction for the muon g-2 and the value measured by the Brookhaven E821 Experiment. At present the discrepancy stands at about three standard deviations, with a comparable accuracy between experiment and theory. Two new proposals -- at Fermilab and J-PARC -- plan to improve the experimental uncertainty by a factor of 4, and it is expected that there will be a significant reduction in the uncertainty of the Standard Model prediction. I will review the status of the planned experiment at Fermilab, E989, which will analyse 21 times more muonsmore » than the BNL experiment and discuss how the systematic uncertainty will be reduced by a factor of 3 such that a precision of 0.14 ppm can be achieved.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110024207','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110024207">The lagRST Model: A Turbulence Model for Non-Equilibrium Flows</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Lillard, Randolph P.; Oliver, A. Brandon; Olsen, Michael E.; Blaisdell, Gregory A.; Lyrintzis, Anastasios S. 2011-01-01 This study presents a new class of turbulence model designed for wall bounded, high Reynolds number flows with separation. The model addresses deficiencies seen in the modeling of nonequilibrium turbulent flows. These flows generally have variable adverse pressure gradients which cause the turbulent quantities to react at a finite rate to changes in the mean flow quantities. This "lag" in the response of the turbulent quantities can t be modeled by most standard turbulence models, which are designed to model equilibrium turbulent boundary layers. The model presented uses a standard 2-equation model as the baseline for turbulent equilibrium calculations, but adds transport equations to account directly for non-equilibrium effects in the Reynolds Stress Tensor (RST) that are seen in large pressure gradients involving shock waves and separation. Comparisons are made to several standard turbulence modeling validation cases, including an incompressible boundary layer (both neutral and adverse pressure gradients), an incompressible mixing layer and a transonic bump flow. In addition, a hypersonic Shock Wave Turbulent Boundary Layer Interaction with separation is assessed along with a transonic capsule flow. Results show a substantial improvement over the baseline models for transonic separated flows. The results are mixed for the SWTBLI flows assessed. Separation predictions are not as good as the baseline models, but the over prediction of the peak heat flux downstream of the reattachment shock that plagues many models is reduced. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20040040119&hterms=gravity+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dgravity%2Bmodel','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20040040119&hterms=gravity+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dgravity%2Bmodel">An Evaluation of Recent Gravity Models wrt. Altimeter Satellite Missions</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Lemoine, Frank G.; Zelensky, N. P.; Luthcke, S. B.; Beckley, B. D.; Chinn, D. S.; Rowlands, D. D. 2003-01-01 With the launch of CHAMP and GRACE, we have entered a new phase in the history of satellite geodesy. For the first time, geopotential models are now available based almost exclusively on satellite-satellite tracking either with GPS in the case of the CHAMP-based geopotential models, or co-orbital intersatellite ultra-precise ranging in the case of GRACE. Different groups have analyzed these data, and produced a series of geopotential models (e.g., EIGENlS, EIGEN2, GGM0lS, GGMOlC) that incorporate the new data. We will compare the performance of these "newer" geopotential models with the standard models now used for computations, (e.g., JGM-3, BGM-96, PGS7727, and GRIMS-C1) for TOPEX, JASON, Geosat-Follow-On (GFO), and Envisat using standard metrics such as SLR RMS of fit, altimeter crossovers, and orbit overlaps. Where covariances are available we can evaluate the predicted geographically correlated orbit error. These predicted results can be compared with the Earth-fixed differences between dynamic and reduced-dynamic orbits to test the predictive accuracy of the covariances, as well as to calibrate the error of the solutions. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active">12</li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active">13</li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhDT........41O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhDT........41O">Understanding pyrotechnic shock dynamics and response attenuation over distance</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Ott, Richard J. Pyrotechnic shock events used during stage separation on rocket vehicles produce high amplitude short duration structural response that can lead to malfunction or degradation of electronic components, cracks and fractures in brittle materials, local plastic deformation, and can cause materials to experience accelerated fatigue life. These transient loads propagate as waves through the structural media losing energy as they travel outward from the source. This work assessed available test data in an effort to better understand attenuation characteristics associated with wave propagation and attempted to update a historical standard defined by the Martin Marietta Corporation in the late 1960's using out of date data acquisition systems. Two data sets were available for consideration. The first data set came from a test that used a flight like cylinder used in NASA's Ares I-X program, and the second from a test conducted with a flat plate. Both data sets suggested that the historical standard was not a conservative estimate of shock attenuation with distance, however, the variation in the test data did not lend to recommending an update to the standard. Beyond considering attenuation with distance an effort was made to model the flat plate configuration using finite element analysis. The available flat plate data consisted of three groups of tests, each with a unique charge density linear shape charge (LSC) used to cut an aluminum plate. The model was tuned to a representative test using the lowest charge density LSC as input. The correlated model was then used to predict the other two cases by linearly scaling the input load based on the relative difference in charge density. The resulting model predictions were then compared with available empirical data. Aside from differences in amplitude due to nonlinearities associated with scaling the charge density of the LSC, the model predictions matched the available test data reasonably well. Finally, modeling best practices were recommended when using industry standard software to predict shock response on structures. As part of the best practices documented, a frequency dependent damping schedule that can be used in model development when no data is available is provided. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27431531','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27431531">Using the weighted area under the net benefit curve for decision curve analysis.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Talluri, Rajesh; Shete, Sanjay 2016-07-18 Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients. We propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest. We compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method. The proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/16868014','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/16868014">Prediction of 222Rn in Danish dwellings using geology and house construction information from central databases.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Andersen, Claus E; Raaschou-Nielsen, Ole; Andersen, Helle Primdal; Lind, Morten; Gravesen, Peter; Thomsen, Birthe L; Ulbak, Kaare 2007-01-01 A linear regression model has been developed for the prediction of indoor (222)Rn in Danish houses. The model provides proxy radon concentrations for about 21,000 houses in a Danish case-control study on the possible association between residential radon and childhood cancer (primarily leukaemia). The model was calibrated against radon measurements in 3116 houses. An independent dataset with 788 house measurements was used for model performance assessment. The model includes nine explanatory variables, of which the most important ones are house type and geology. All explanatory variables are available from central databases. The model was fitted to log-transformed radon concentrations and it has an R(2) of 40%. The uncertainty associated with individual predictions of (untransformed) radon concentrations is about a factor of 2.0 (one standard deviation). The comparison with the independent test data shows that the model makes sound predictions and that errors of radon predictions are only weakly correlated with the estimates themselves (R(2) = 10%). </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/43664','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/43664">Influence of forest roads standards and networks on water yield as predicted by the distributed hydrology-soil-vegetation model</a> <a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a> Salli F. Dymond; W. Michael Aust; Steven P. Prisley; Mark H. Eisenbies; James M. Vose 2013-01-01 Throughout the country, foresters are continually looking at the effects of logging and forest roads on stream discharge and overall stream health. In the Pacific Northwest, a distributed hydrology-soil-vegetation model (DHSVM) has been used to predict the effects of logging on peak discharge in mountainous regions. DHSVM uses elevation, meteorological, vegetation, and... </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Vroom%27s+AND+Expectancy+AND+Models&id=EJ260775','ERIC'); return false;" href="https://eric.ed.gov/?q=Vroom%27s+AND+Expectancy+AND+Models&id=EJ260775">Predicting Student Grade Point Average at a Community College from Scholastic Aptitude Tests and from Measures Representing Three Constructs in Vroom's Expectancy Theory Model of Motivation.</a> <a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a> Malloch, Douglas C.; Michael, William B. 1981-01-01 This study was designed to determine whether an unweighted linear combination of community college students' scores on standardized achievement tests and a measure of motivational constructs derived from Vroom's expectance theory model of motivation was predictive of academic success (grade point average earned during one quarter of an academic… </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23043771','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23043771">A clinical investigation of motivation to change standards and cognitions about failure in perfectionism.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Egan, Sarah J; Piek, Jan P; Dyck, Murray J; Rees, Clare S; Hagger, Martin S 2013-10-01 Clinical perfectionism is a transdiagnostic process that has been found to maintain eating disorders, anxiety disorders and depression. Cognitive behavioural models explaining the maintenance of clinical perfectionism emphasize the contribution of dichotomous thinking and resetting standards higher following both success and failure in meeting their goals. There has been a paucity of research examining the predictions of the models and motivation to change perfectionism. Motivation to change is important as individuals with clinical perfectionism often report many perceived benefits of their perfectionism; they are, therefore, likely to be ambivalent regarding changing perfectionism. The aim was to compare qualitative responses regarding questions about motivation to change standards and cognitions regarding failure to meet a personal standard in two contrasting groups with high and low negative perfectionism. Negative perfectionism refers to concern over not meeting personal standards. A clinical group with a range of axis 1 diagnoses who were elevated on negative perfectionism were compared to a group of athletes who were low on negative perfectionism. Results indicated that the clinical group perceived many negative consequences of their perfectionism. They also, however, reported numerous benefits and the majority stated that they would prefer not to change their perfectionism. The clinical group also reported dichotomous thinking and preferring to either keep standards the same or reset standards higher following failure, whilst the athlete group reported they would keep standards the same or set them lower. The findings support predictions of the cognitive behavioural model of clinical perfectionism. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012WRR....48.5520R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012WRR....48.5520R">Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Rings, Joerg; Vrugt, Jasper A.; Schoups, Gerrit; Huisman, Johan A.; Vereecken, Harry 2012-05-01 Bayesian model averaging (BMA) is a standard method for combining predictive distributions from different models. In recent years, this method has enjoyed widespread application and use in many fields of study to improve the spread-skill relationship of forecast ensembles. The BMA predictive probability density function (pdf) of any quantity of interest is a weighted average of pdfs centered around the individual (possibly bias-corrected) forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts, and reflect the individual models skill over a training (calibration) period. The original BMA approach presented by Raftery et al. (2005) assumes that the conditional pdf of each individual model is adequately described with a rather standard Gaussian or Gamma statistical distribution, possibly with a heteroscedastic variance. Here we analyze the advantages of using BMA with a flexible representation of the conditional pdf. A joint particle filtering and Gaussian mixture modeling framework is presented to derive analytically, as closely and consistently as possible, the evolving forecast density (conditional pdf) of each constituent ensemble member. The median forecasts and evolving conditional pdfs of the constituent models are subsequently combined using BMA to derive one overall predictive distribution. This paper introduces the theory and concepts of this new ensemble postprocessing method, and demonstrates its usefulness and applicability by numerical simulation of the rainfall-runoff transformation using discharge data from three different catchments in the contiguous United States. The revised BMA method receives significantly lower-prediction errors than the original default BMA method (due to filtering) with predictive uncertainty intervals that are substantially smaller but still statistically coherent (due to the use of a time-variant conditional pdf). </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H13K..05A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H13K..05A">The Global Integrated Drought Monitoring and Prediction System (GIDMaPS): Overview and Capabilities</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> AghaKouchak, A.; Hao, Z.; Farahmand, A.; Nakhjiri, N. 2013-12-01 Development of reliable monitoring and prediction indices and tools are fundamental to drought preparedness and management. Motivated by the Global Drought Information Systems (GDIS) activities, this paper presents the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which provides near real-time drought information using both remote sensing observations and model simulations. The monthly data from the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-Land), North American Land Data Assimilation System (NLDAS), and remotely sensed precipitation data are used as input to GIDMaPS. Numerous indices have been developed for drought monitoring based on various indicator variables (e.g., precipitation, soil moisture, water storage). Defining droughts based on a single variable (e.g., precipitation, soil moisture or runoff) may not be sufficient for reliable risk assessment and decision making. GIDMaPS provides drought information based on multiple indices including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. In other words, MSDI incorporates the meteorological and agricultural drought conditions for overall characterization of droughts. The seasonal prediction component of GIDMaPS is based on a persistence model which requires historical data and near-past observations. The seasonal drought prediction component is based on two input data sets (MERRA and NLDAS) and three drought indicators (SPI, SSI and MSDI). The drought prediction model provides the empirical probability of drought for different severity levels. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from several major droughts including the 2013 Namibia, 2012-2013 United States, 2011-2012 Horn of Africa, and 2010 Amazon Droughts will be presented. The results indicate that GIDMaPS advances our drought monitoring and prediction capabilities through integration of multiple data and indicators. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/20762216-burnout-prediction-model-based-around-char-morphology','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/20762216-burnout-prediction-model-based-around-char-morphology">A burnout prediction model based around char morphology</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Tao Wu; Edward Lester; Michael Cloke Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coalmore » particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1569361','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1569361">Cancer mortality among coke oven workers.</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Redmond, C K 1983-01-01 The OSHA standard for coke oven emissions, which went into effect in January 1977, sets a permissible exposure limit to coke oven emissions of 150 micrograms/m3 benzene-soluble fraction of total particulate matter (BSFTPM). Review of the epidemiologic evidence for the standard indicates an excess relative risk for lung cancer as high as 16-fold in topside coke oven workers with 15 years of exposure or more. There is also evidence for a consistent dose-response relationship in lung cancer mortality when duration and location of employment at the coke ovens are considered. Dose-response models fitted to these same data indicate that, while excess risks may still occur under the OSHA standard, the predicted levels of increased relative risk would be about 30-50% if a linear dose-response model is assumed and 3-7% if a quadratic model is assumed. Lung cancer mortality data for other steelworkers suggest the predicted excess risk has probably been somewhat overestimated, but lack of information on important confounding factors limits further dose-response analysis. PMID:6653539 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2013-title50-vol12/pdf/CFR-2013-title50-vol12-sec600-315.pdf','CFR2013'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2013-title50-vol12/pdf/CFR-2013-title50-vol12-sec600-315.pdf">50 CFR 600.315 - National Standard 2-Scientific Information.</a> <a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2013&page.go=Go">Code of Federal Regulations, 2013 CFR</a> 2013-10-01 ... from surveys or sampling programs, and models that are mathematical representations of reality... static and ideally entails developing and following a research plan with the following elements: Clear... predictions, or testing hypotheses; study design with an explicit and standardized method of collecting data... </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17763757','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17763757">[Rapid prediction of annual ring density of Paulownia elongate standing tress using near infrared spectroscopy].</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Jiang, Ze-Hui; Wang, Yu-Rong; Fei, Ben-Hua; Fu, Feng; Hse, Chung-Yun 2007-06-01 Rapid prediction of annual ring density of Paulownia elongate standing trees using near infrared spectroscopy was studied. It was non-destructive to collect the samples for trees, that is, the wood cores 5 mm in diameter were unthreaded at the breast height of standing trees instead of fallen trees. Then the spectra data were collected by autoscan method of NIR. The annual ring density was determined by mercury immersion. And the models were made and analyzed by the partial least square (PLS) and full cross validation in the 350-2 500 nm wavelength range. The results showed that high coefficients were obtained between the annual ring and the NIR fitted data. The correlation coefficient of prediction model was 0.88 and 0.91 in the middle diameter and bigger diameter, respectively. Moreover, high coefficients of correlation were also obtained between annual ring density laboratory-determined and the NIR fitted data in the middle diameter of Paulownia elongate standing trees, the correlation coefficient of calibration model and prediction model were 0.90 and 0.83, and the standard errors of calibration (SEC) and standard errors of prediction(SEP) were 0.012 and 0.016, respectively. The method can simply, rapidly and non-destructively estimate the annual ring density of the Paulownia elongate standing trees close to the cutting age. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26379782','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26379782">RRegrs: an R package for computer-aided model selection with multiple regression models.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L 2015-01-01 Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1514098W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1514098W">Supermodeling With A Global Atmospheric Model</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Wiegerinck, Wim; Burgers, Willem; Selten, Frank 2013-04-01 In weather and climate prediction studies it often turns out to be the case that the multi-model ensemble mean prediction has the best prediction skill scores. One possible explanation is that the major part of the model error is random and is averaged out in the ensemble mean. In the standard multi-model ensemble approach, the models are integrated in time independently and the predicted states are combined a posteriori. Recently an alternative ensemble prediction approach has been proposed in which the models exchange information during the simulation and synchronize on a common solution that is closer to the truth than any of the individual model solutions in the standard multi-model ensemble approach or a weighted average of these. This approach is called the super modeling approach (SUMO). The potential of the SUMO approach has been demonstrated in the context of simple, low-order, chaotic dynamical systems. The information exchange takes the form of linear nudging terms in the dynamical equations that nudge the solution of each model to the solution of all other models in the ensemble. With a suitable choice of the connection strengths the models synchronize on a common solution that is indeed closer to the true system than any of the individual model solutions without nudging. This approach is called connected SUMO. An alternative approach is to integrate a weighted averaged model, weighted SUMO. At each time step all models in the ensemble calculate the tendency, these tendencies are weighted averaged and the state is integrated one time step into the future with this weighted averaged tendency. It was shown that in case the connected SUMO synchronizes perfectly, the connected SUMO follows the weighted averaged trajectory and both approaches yield the same solution. In this study we pioneer both approaches in the context of a global, quasi-geostrophic, three-level atmosphere model that is capable of simulating quite realistically the extra-tropical circulation in the Northern Hemisphere winter. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=312032&Lab=NERL&keyword=solution+AND+English&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=312032&Lab=NERL&keyword=solution+AND+English&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50">Updates to In-Line Calculation of Photolysis Rates</a> <a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a> How photolysis rates are calculated affects ozone and aerosol concentrations predicted by the CMAQ model and the model?s run-time. The standard configuration of CMAQ uses the inline option that calculates photolysis rates by solving the radiative transfer equation for the needed ... </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..547..348M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..547..348M">Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan 2017-04-01 Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1332705','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1332705">Predictive Scheduling for Electric Vehicles Considering Uncertainty of Load and User Behaviors</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Wang, Bin; Huang, Rui; Wang, Yubo 2016-05-02 Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimizationmore » module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4814350','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4814350">Automated Clinical Assessment from Smart home-based Behavior Data</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen 2016-01-01 Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behaviour in the home and predicting standard clinical assessment scores of the residents. To accomplish this goal, we propose a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident’s daily behavior and predict the corresponding standard clinical assessment scores. CAAB uses statistical features that describe characteristics of a resident’s daily activity performance to train machine learning algorithms that predict the clinical assessment scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years using prediction and classification-based experiments. In the prediction-based experiments, we obtain a statistically significant correlation (r = 0.72) between CAAB-predicted and clinician-provided cognitive assessment scores and a statistically significant correlation (r = 0.45) between CAAB-predicted and clinician-provided mobility scores. Similarly, for the classification-based experiments, we find CAAB has a classification accuracy of 72% while classifying cognitive assessment scores and 76% while classifying mobility scores. These prediction and classification results suggest that it is feasible to predict standard clinical scores using smart home sensor data and learning-based data analysis. PMID:26292348 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhRvL.119r1804A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhRvL.119r1804A">Search for Dark Matter Produced in Association with a Higgs Boson Decaying to b b ¯ Using 36 fb-1 of p p Collisions at √{s }=13 TeV with the ATLAS Detector</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Aaboud, M.; Aad, G.; Abbott, B.; Abdinov, O.; Abeloos, B.; Abidi, S. H.; Abouzeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adachi, S.; Adamczyk, L.; Adelman, J.; Adersberger, M.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agheorghiesei, C.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akatsuka, S.; Akerstedt, H.; Åkesson, T. P. A.; Akilli, E.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albicocco, P.; Alconada Verzini, M. J.; Alderweireldt, S. C.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Ali, B.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alshehri, A. A.; Alstaty, M. I.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Angerami, A.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antel, C.; Antonelli, M.; Antonov, A.; Antrim, D. J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Araujo Ferraz, V.; Arce, A. T. H.; Ardell, R. E.; Arduh, F. 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S.; Danninger, M.; Dano Hoffmann, M.; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Daubney, T.; Davey, W.; David, C.; Davidek, T.; Davis, D. R.; Davison, P.; Dawe, E.; Dawson, I.; de, K.; de Asmundis, R.; de Benedetti, A.; de Castro, S.; de Cecco, S.; de Groot, N.; de Jong, P.; de la Torre, H.; de Lorenzi, F.; de Maria, A.; de Pedis, D.; de Salvo, A.; de Sanctis, U.; de Santo, A.; de Vasconcelos Corga, K.; de Vivie de Regie, J. B.; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Dehghanian, N.; Deigaard, I.; Del Gaudio, M.; Del Peso, J.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; Della Volpe, D.; Delmastro, M.; Delporte, C.; Delsart, P. A.; Demarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Denysiuk, D.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Devesa, M. R.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; di Bello, F. A.; di Ciaccio, A.; di Ciaccio, L.; di Clemente, W. K.; di Donato, C.; di Girolamo, A.; di Girolamo, B.; di Micco, B.; di Nardo, R.; di Petrillo, K. F.; di Simone, A.; di Sipio, R.; di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Díez Cornell, S.; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; Do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dolejsi, J.; Dolezal, Z.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Duarte-Campderros, J.; Dubreuil, A.; Duchovni, E.; Duckeck, G.; Ducourthial, A.; Ducu, O. A.; Duda, D.; Dudarev, A.; Dudder, A. Chr.; Duffield, E. M.; Duflot, L.; Dührssen, M.; Dumancic, M.; Dumitriu, A. E.; Duncan, A. K.; Dunford, M.; Duran Yildiz, H.; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Duvnjak, D.; Dyndal, M.; Dziedzic, B. S.; Eckardt, C.; Ecker, K. 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B.; Feremenga, L.; Fernandez Martinez, P.; Fernandez Perez, S.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; Ferreira de Lima, D. E.; Ferrer, A.; Ferrere, D.; Ferretti, C.; Fiedler, F.; Filipčič, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, R. R. M.; Flick, T.; Flierl, B. M.; Flores Castillo, L. R.; Flowerdew, M. J.; Forcolin, G. T.; Formica, A.; Förster, F. A.; Forti, A.; Foster, A. G.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Franchino, S.; Francis, D.; Franconi, L.; Franklin, M.; Frate, M.; Fraternali, M.; Freeborn, D.; Fressard-Batraneanu, S. M.; Freund, B.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Fusayasu, T.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gabrielli, A.; Gabrielli, A.; Gach, G. P.; Gadatsch, S.; Gadomski, S.; Gagliardi, G.; Gagnon, L. 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M.; Schaefer, D.; Schaefer, L.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schier, S.; Schildgen, L. K.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schott, M.; Schouwenberg, J. F. P.; Schovancova, J.; Schramm, S.; Schuh, N.; Schulte, A.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwartzman, A.; Schwarz, T. A.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Sciandra, A.; Sciolla, G.; Scornajenghi, M.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Semprini-Cesari, N.; Senkin, S.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Shen, Y.; Sherafati, N.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shipsey, I. P. J.; Shirabe, S.; Shiyakova, M.; Shlomi, J.; Shmeleva, A.; Shoaleh Saadi, D.; Shochet, M. J.; Shojaii, S.; Shope, D. R.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sickles, A. M.; Sidebo, P. E.; Sideras Haddad, E.; Sidiropoulou, O.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Siral, I.; Sivoklokov, S. Yu.; Sjölin, J.; Skinner, M. B.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smiesko, J.; Smirnov, N.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, J. W.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snyder, I. M.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Søgaard, A.; Soh, D. A.; Sokhrannyi, G.; Solans Sanchez, C. A.; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Sopczak, A.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spieker, T. M.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; St. Denis, R. D.; Stabile, A.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanitzki, M. M.; Stapf, B. S.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Stark, S. H.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultan, Dms; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Suruliz, K.; Suster, C. J. E.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Swift, S. P.; Sykora, I.; Sykora, T.; Ta, D.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Tahirovic, E.; Taiblum, N.; Takai, H.; Takashima, R.; Takasugi, E. H.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tanaka, J.; Tanaka, M.; Tanaka, R.; Tanaka, S.; Tanioka, R.; Tannenwald, B. B.; Tapia Araya, S.; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Tavares Delgado, A.; Tayalati, Y.; Taylor, A. C.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teixeira-Dias, P.; Temple, D.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thiele, F.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Tibbetts, M. J.; Ticse Torres, R. E.; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorova-Nova, S.; Todt, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Tornambe, P.; Torrence, E.; Torres, H.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Treado, C. J.; Trefzger, T.; Tresoldi, F.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tsang, K. W.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tu, Y.; Tudorache, A.; Tudorache, V.; Tulbure, T. T.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turgeman, D.; Turk Cakir, I.; Turra, R.; Tuts, P. M.; Ucchielli, G.; Ueda, I.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usui, J.; Vacavant, L.; Vacek, V.; Vachon, B.; Vadla, K. O. H.; Vaidya, A.; Valderanis, C.; Valdes Santurio, E.; Valente, M.; Valentinetti, S.; Valero, A.; Valéry, L.; Valkar, S.; Vallier, A.; Valls Ferrer, J. A.; van den Wollenberg, W.; van der Graaf, H.; van Gemmeren, P.; van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varni, C.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vasquez, G. A.; Vazeille, F.; Vazquez Schroeder, T.; Veatch, J.; Veeraraghavan, V.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, A. T.; Vermeulen, J. C.; Vetterli, M. C.; Viaux Maira, N.; Viazlo, O.; Vichou, I.; Vickey, T.; Vickey Boeriu, O. E.; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vishwakarma, A.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vogel, M.; Vokac, P.; Volpi, G.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Wagner, P.; Wagner, W.; Wagner-Kuhr, J.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, Q.; Wang, R.; Wang, S. M.; Wang, T.; Wang, W.; Wang, W.; Wang, Z.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, A. F.; Webb, S.; Weber, M. S.; Weber, S. W.; Weber, S. A.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weirich, M.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M. D.; Werner, P.; Wessels, M.; Weston, T. D.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A. S.; White, A.; White, M. J.; White, R.; Whiteson, D.; Whitmore, B. W.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winkels, E.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wobisch, M.; Wolf, T. M. H.; Wolff, R.; Wolter, M. W.; Wolters, H.; Wong, V. W. S.; Worm, S. D.; Wosiek, B. K.; Wotschack, J.; Wozniak, K. W.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xi, Z.; Xia, L.; Xu, D.; Xu, L.; Xu, T.; Yabsley, B.; Yacoob, S.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamatani, M.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yigitbasi, E.; Yildirim, E.; Yorita, K.; Yoshihara, K.; Young, C.; Young, C. J. S.; Yu, J.; Yu, J.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zacharis, G.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanzi, D.; Zeitnitz, C.; Zemaityte, G.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, L.; Zhang, M.; Zhang, P.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Y.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, M.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Zou, R.; Zur Nedden, M.; Zwalinski, L.; Atlas Collaboration 2017-11-01 Several extensions of the standard model predict associated production of dark-matter particles with a Higgs boson. Such processes are searched for in final states with missing transverse momentum and a Higgs boson decaying to a b b ¯ pair with the ATLAS detector using 36.1 fb-1 of p p collisions at a center-of-mass energy of 13 TeV at the LHC. The observed data are in agreement with the standard model predictions and limits are placed on the associated production of dark-matter particles and a Higgs boson. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1406401-search-dark-matter-produced-association-higgs-boson-decaying-bb-using-fb1-pp-collisions-tev-atlas-detector','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1406401-search-dark-matter-produced-association-higgs-boson-decaying-bb-using-fb1-pp-collisions-tev-atlas-detector">Search for Dark Matter Produced in Association with a Higgs Boson Decaying to b b ¯ Using 36 fb – 1 of p p Collisions at s = 13 TeV with the ATLAS Detector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Aaboud, M.; Aad, G.; Abbott, B. Several extensions of the standard model predict associated production of dark-matter particles with a Higgs boson. Such processes are searched for in final states with missing transverse momentum and a Higgs boson decaying to a bb pair with the ATLAS detector using 36.1 fb -1 of pp collisions at a center-of-mass energy of 13 TeV at the LHC. The observed data are in agreement with the standard model predictions and limits are placed on the associated production of dark-matter particles and a Higgs boson. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active">13</li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active">14</li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017APS..APRS10002D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017APS..APRS10002D">Sakurai Prize: The Future of Higgs Physics</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Dawson, Sally 2017-01-01 The discovery of the Higgs boson relied critically on precision calculations. The quantum contributions from the Higgs boson to the W and top quark masses suggested long before the Higgs discovery that a Standard Model Higgs boson should have a mass in the 100-200 GeV range. The experimental extraction of Higgs properties requires normalization to the predicted Higgs production and decay rates, for which higher order corrections are also essential. As Higgs physics becomes a mature subject, more and more precise calculations will be required. If there is new physics at high scales, it will contribute to the predictions and precision Higgs physics will be a window to beyond the Standard Model physics. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1406401-search-dark-matter-produced-association-higgs-boson-decaying-bb-using-fb1-pp-collisions-tev-atlas-detector','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1406401-search-dark-matter-produced-association-higgs-boson-decaying-bb-using-fb1-pp-collisions-tev-atlas-detector">Search for Dark Matter Produced in Association with a Higgs Boson Decaying to b b ¯ Using 36 fb – 1 of p p Collisions at s = 13 TeV with the ATLAS Detector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Aaboud, M.; Aad, G.; Abbott, B.; ... 2017-11-01 Several extensions of the standard model predict associated production of dark-matter particles with a Higgs boson. Such processes are searched for in final states with missing transverse momentum and a Higgs boson decaying to a bb pair with the ATLAS detector using 36.1 fb -1 of pp collisions at a center-of-mass energy of 13 TeV at the LHC. The observed data are in agreement with the standard model predictions and limits are placed on the associated production of dark-matter particles and a Higgs boson. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26756274','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26756274">Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Wang, Ming; Long, Qi 2016-09-01 Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to high-dimensional settings. The predictive accuracy of prediction models for cancer recurrence after prostatectomy is assessed by applying the proposed approaches. We find that the estimated predictive accuracy for the models in consideration is sensitive to NCAR assumption, and thus identify the best predictive model. Finally, we further evaluate the performance of the proposed methods in both settings of low-dimensional and high-dimensional data under CAR and NCAR through simulations. © 2016, The International Biometric Society. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19750025061&hterms=econometrics&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Deconometrics','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19750025061&hterms=econometrics&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Deconometrics">Crystal study and econometric model</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> 1975-01-01 An econometric model was developed that can be used to predict demand and supply figures for crystals over a time horizon roughly concurrent with that of NASA's Space Shuttle Program - that is, 1975 through 1990. The model includes an equation to predict the impact on investment in the crystal-growing industry. Actually, two models are presented. The first is a theoretical model which follows rather strictly the standard theoretical economic concepts involved in supply and demand analysis, and a modified version of the model was developed which, though not quite as theoretically sound, was testable utilizing existing data sources. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19790007427','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19790007427">Closed loop models for analyzing the effects of simulator characteristics. [digital simulation of human operators</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Baron, S.; Muralidharan, R.; Kleinman, D. L. 1978-01-01 The optimal control model of the human operator is used to develop closed loop models for analyzing the effects of (digital) simulator characteristics on predicted performance and/or workload. Two approaches are considered: the first utilizes a continuous approximation to the discrete simulation in conjunction with the standard optimal control model; the second involves a more exact discrete description of the simulator in a closed loop multirate simulation in which the optimal control model simulates the pilot. Both models predict that simulator characteristics can have significant effects on performance and workload. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2246184','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2246184">Mammographic density, breast cancer risk and risk prediction</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Vachon, Celine M; van Gils, Carla H; Sellers, Thomas A; Ghosh, Karthik; Pruthi, Sandhya; Brandt, Kathleen R; Pankratz, V Shane 2007-01-01 In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models. PMID:18190724 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27756408','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27756408">Prediction of chemo-response in serous ovarian cancer.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Gonzalez Bosquet, Jesus; Newtson, Andreea M; Chung, Rebecca K; Thiel, Kristina W; Ginader, Timothy; Goodheart, Michael J; Leslie, Kimberly K; Smith, Brian J 2016-10-19 Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA. We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets. The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets. These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018BoLMe.tmp..180S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018BoLMe.tmp..180S">Comparison of Speed-Up Over Hills Derived from Wind-Tunnel Experiments, Wind-Loading Standards, and Numerical Modelling</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Safaei Pirooz, Amir A.; Flay, Richard G. J. 2018-03-01 We evaluate the accuracy of the speed-up provided in several wind-loading standards by comparison with wind-tunnel measurements and numerical predictions, which are carried out at a nominal scale of 1:500 and full-scale, respectively. Airflow over two- and three-dimensional bell-shaped hills is numerically modelled using the Reynolds-averaged Navier-Stokes method with a pressure-driven atmospheric boundary layer and three different turbulence models. Investigated in detail are the effects of grid size on the speed-up and flow separation, as well as the resulting uncertainties in the numerical simulations. Good agreement is obtained between the numerical prediction of speed-up, as well as the wake region size and location, with that according to large-eddy simulations and the wind-tunnel results. The numerical results demonstrate the ability to predict the airflow over a hill with good accuracy with considerably less computational time than for large-eddy simulation. Numerical simulations for a three-dimensional hill show that the speed-up and the wake region decrease significantly when compared with the flow over two-dimensional hills due to the secondary flow around three-dimensional hills. Different hill slopes and shapes are simulated numerically to investigate the effect of hill profile on the speed-up. In comparison with more peaked hill crests, flat-topped hills have a lower speed-up at the crest up to heights of about half the hill height, for which none of the standards gives entirely satisfactory values of speed-up. Overall, the latest versions of the National Building Code of Canada and the Australian and New Zealand Standard give the best predictions of wind speed over isolated hills. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8503W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8503W">Identifying influential data points in hydrological model calibration and their impact on streamflow predictions</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Wright, David; Thyer, Mark; Westra, Seth 2015-04-01 Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this study establish the feasibility and importance of including influential point detection diagnostics as a standard tool in hydrological model calibration. They provide the hydrologist with important information on whether model calibration is susceptible to a small number of highly influent data points. This enables the hydrologist to make a more informed decision of whether to (1) remove/retain the calibration data; (2) adjust the calibration strategy and/or hydrological model to reduce the susceptibility of model predictions to a small number of influential observations. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70034478','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70034478">Repeatable source, site, and path effects on the standard deviation for empirical ground-motion prediction models</a> <a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a> Lin, P.-S.; Chiou, B.; Abrahamson, N.; Walling, M.; Lee, C.-T.; Cheng, C.-T. 2011-01-01 In this study, we quantify the reduction in the standard deviation for empirical ground-motion prediction models by removing ergodic assumption.We partition the modeling error (residual) into five components, three of which represent the repeatable source-location-specific, site-specific, and path-specific deviations from the population mean. A variance estimation procedure of these error components is developed for use with a set of recordings from earthquakes not heavily clustered in space.With most source locations and propagation paths sampled only once, we opt to exploit the spatial correlation of residuals to estimate the variances associated with the path-specific and the source-location-specific deviations. The estimation procedure is applied to ground-motion amplitudes from 64 shallow earthquakes in Taiwan recorded at 285 sites with at least 10 recordings per site. The estimated variance components are used to quantify the reduction in aleatory variability that can be used in hazard analysis for a single site and for a single path. For peak ground acceleration and spectral accelerations at periods of 0.1, 0.3, 0.5, 1.0, and 3.0 s, we find that the singlesite standard deviations are 9%-14% smaller than the total standard deviation, whereas the single-path standard deviations are 39%-47% smaller. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28154864','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28154864">Development of polyparameter linear free energy relationship models for octanol-air partition coefficients of diverse chemicals.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Jin, Xiaochen; Fu, Zhiqiang; Li, Xuehua; Chen, Jingwen 2017-03-22 The octanol-air partition coefficient (K OA ) is a key parameter describing the partition behavior of organic chemicals between air and environmental organic phases. As the experimental determination of K OA is costly, time-consuming and sometimes limited by the availability of authentic chemical standards for the compounds to be determined, it becomes necessary to develop credible predictive models for K OA . In this study, a polyparameter linear free energy relationship (pp-LFER) model for predicting K OA at 298.15 K and a novel model incorporating pp-LFERs with temperature (pp-LFER-T model) were developed from 795 log K OA values for 367 chemicals at different temperatures (263.15-323.15 K), and were evaluated with the OECD guidelines on QSAR model validation and applicability domain description. Statistical results show that both models are well-fitted, robust and have good predictive capabilities. Particularly, the pp-LFER model shows a strong predictive ability for polyfluoroalkyl substances and organosilicon compounds, and the pp-LFER-T model maintains a high predictive accuracy within a wide temperature range (263.15-323.15 K). </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.7206D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.7206D">Topsoil organic carbon content of Europe, a new map based on a generalised additive model</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> de Brogniez, Delphine; Ballabio, Cristiano; Stevens, Antoine; Jones, Robert J. A.; Montanarella, Luca; van Wesemael, Bas 2014-05-01 There is an increasing demand for up-to-date spatially continuous organic carbon (OC) data for global environment and climatic modeling. Whilst the current map of topsoil organic carbon content for Europe (Jones et al., 2005) was produced by applying expert-knowledge based pedo-transfer rules on large soil mapping units, the aim of this study was to replace it by applying digital soil mapping techniques on the first European harmonised geo-referenced topsoil (0-20 cm) database, which arises from the LUCAS (land use/cover area frame statistical survey) survey. A generalized additive model (GAM) was calibrated on 85% of the dataset (ca. 17 000 soil samples) and a backward stepwise approach selected slope, land cover, temperature, net primary productivity, latitude and longitude as environmental covariates (500 m resolution). The validation of the model (applied on 15% of the dataset), gave an R2 of 0.27. We observed that most organic soils were under-predicted by the model and that soils of Scandinavia were also poorly predicted. The model showed an RMSE of 42 g kg-1 for mineral soils and of 287 g kg-1 for organic soils. The map of predicted OC content showed the lowest values in Mediterranean countries and in croplands across Europe, whereas highest OC content were predicted in wetlands, woodlands and in mountainous areas. The map of standard error of the OC model predictions showed high values in northern latitudes, wetlands, moors and heathlands, whereas low uncertainty was mostly found in croplands. A comparison of our results with the map of Jones et al. (2005) showed a general agreement on the prediction of mineral soils' OC content, most probably because the models use some common covariates, namely land cover and temperature. Our model however failed to predict values of OC content greater than 200 g kg-1, which we explain by the imposed unimodal distribution of our model, whose mean is tilted towards the majority of soils, which are mineral. Finally, average OC content predictions for each land cover class compared well between models, with our model always showing smaller standard deviations. We concluded that the chosen model and covariates are appropriate for the prediction of OC content in European mineral soils. We presented in this work the first map of topsoil OC content at European scale based on a harmonised soil dataset. The associated uncertainty map shall support the end-users in a careful use of the predictions. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.usgs.gov/sir/2006/5159/','USGSPUBS'); return false;" href="https://pubs.usgs.gov/sir/2006/5159/">Monitoring and modeling to predict Escherichia coli at Presque Isle Beach 2, City of Erie, Erie County, Pennsylvania</a> <a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a> Zimmerman, Tammy M. 2006-01-01 The Lake Erie shoreline in Pennsylvania spans nearly 40 miles and is a valuable recreational resource for Erie County. Nearly 7 miles of the Lake Erie shoreline lies within Presque Isle State Park in Erie, Pa. Concentrations of Escherichia coli (E. coli) bacteria at permitted Presque Isle beaches occasionally exceed the single-sample bathing-water standard, resulting in unsafe swimming conditions and closure of the beaches. E. coli concentrations and other water-quality and environmental data collected at Presque Isle Beach 2 during the 2004 and 2005 recreational seasons were used to develop models using tobit regression analyses to predict E. coli concentrations. All variables statistically related to E. coli concentrations were included in the initial regression analyses, and after several iterations, only those explanatory variables that made the models significantly better at predicting E. coli concentrations were included in the final models. Regression models were developed using data from 2004, 2005, and the combined 2-year dataset. Variables in the 2004 model and the combined 2004-2005 model were log10 turbidity, rain weight, wave height (calculated), and wind direction. Variables in the 2005 model were log10 turbidity and wind direction. Explanatory variables not included in the final models were water temperature, streamflow, wind speed, and current speed; model results indicated these variables did not meet significance criteria at the 95-percent confidence level (probabilities were greater than 0.05). The predicted E. coli concentrations produced by the models were used to develop probabilities that concentrations would exceed the single-sample bathing-water standard for E. coli of 235 colonies per 100 milliliters. Analysis of the exceedence probabilities helped determine a threshold probability for each model, chosen such that the correct number of exceedences and nonexceedences was maximized and the number of false positives and false negatives was minimized. Future samples with computed exceedence probabilities higher than the selected threshold probability, as determined by the model, will likely exceed the E. coli standard and a beach advisory or closing may need to be issued; computed exceedence probabilities lower than the threshold probability will likely indicate the standard will not be exceeded. Additional data collected each year can be used to test and possibly improve the model. This study will aid beach managers in more rapidly determining when waters are not safe for recreational use and, subsequently, when to issue beach advisories or closings. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3675001','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3675001">Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Bornschein, Jörg; Henniges, Marc; Lücke, Jörg 2013-01-01 Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex. PMID:23754938 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19800051791&hterms=interior+design&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dinterior%2Bdesign','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19800051791&hterms=interior+design&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dinterior%2Bdesign">Prediction of the interior noise levels of high-speed propeller-driven aircraft</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Rennison, D. C.; Wilby, J. F.; Wilby, E. G. 1980-01-01 The theoretical basis for an analytical model developed to predict the interior noise levels of high-speed propeller-driven airplanes is presented. Particular emphasis is given to modeling the transmission of discrete tones through a fuselage element into a cavity, estimates for the mean and standard deviation of the acoustic power flow, the coupling between a non-homogeneous excitation and the fuselage vibration response, and the prediction of maximum interior noise levels. The model allows for convenient examination of the various roles of the excitation and fuselage structural characteristics on the fuselage vibration response and the interior noise levels, as is required for the design of model or prototype noise control validation tests. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011PMB....56.2103B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011PMB....56.2103B">Modeling late rectal toxicities based on a parameterized representation of the 3D dose distribution</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Buettner, Florian; Gulliford, Sarah L.; Webb, Steve; Partridge, Mike 2011-04-01 Many models exist for predicting toxicities based on dose-volume histograms (DVHs) or dose-surface histograms (DSHs). This approach has several drawbacks as firstly the reduction of the dose distribution to a histogram results in the loss of spatial information and secondly the bins of the histograms are highly correlated with each other. Furthermore, some of the complex nonlinear models proposed in the past lack a direct physical interpretation and the ability to predict probabilities rather than binary outcomes. We propose a parameterized representation of the 3D distribution of the dose to the rectal wall which explicitly includes geometrical information in the form of the eccentricity of the dose distribution as well as its lateral and longitudinal extent. We use a nonlinear kernel-based probabilistic model to predict late rectal toxicity based on the parameterized dose distribution and assessed its predictive power using data from the MRC RT01 trial (ISCTRN 47772397). The endpoints under consideration were rectal bleeding, loose stools, and a global toxicity score. We extract simple rules identifying 3D dose patterns related to a specifically low risk of complication. Normal tissue complication probability (NTCP) models based on parameterized representations of geometrical and volumetric measures resulted in areas under the curve (AUCs) of 0.66, 0.63 and 0.67 for predicting rectal bleeding, loose stools and global toxicity, respectively. In comparison, NTCP models based on standard DVHs performed worse and resulted in AUCs of 0.59 for all three endpoints. In conclusion, we have presented low-dimensional, interpretable and nonlinear NTCP models based on the parameterized representation of the dose to the rectal wall. These models had a higher predictive power than models based on standard DVHs and their low dimensionality allowed for the identification of 3D dose patterns related to a low risk of complication. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28686921','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28686921">E-waste Management and Refurbishment Prediction (EMARP) Model for Refurbishment Industries.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Resmi, N G; Fasila, K A 2017-10-01 This paper proposes a novel algorithm for establishing a standard methodology to manage and refurbish e-waste called E-waste Management And Refurbishment Prediction (EMARP), which can be adapted by refurbishing industries in order to improve their performance. Waste management, particularly, e-waste management is a serious issue nowadays. Computerization has been into waste management in different ways. Much of the computerization has happened in planning the waste collection, recycling and disposal process and also managing documents and reports related to waste management. This paper proposes a computerized model to make predictions for e-waste refurbishment. All possibilities for reusing the common components among the collected e-waste samples are predicted, thus minimizing the wastage. Simulation of the model has been done to analyse the accuracy in the predictions made by the system. The model can be scaled to accommodate the real-world scenario. Copyright © 2017 Elsevier Ltd. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70034624','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70034624">The Regionalization of National-Scale SPARROW Models for Stream Nutrients</a> <a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a> Schwarz, G.E.; Alexander, R.B.; Smith, R.A.; Preston, S.D. 2011-01-01 This analysis modifies the parsimonious specification of recently published total nitrogen (TN) and total phosphorus (TP) national-scale SPAtially Referenced Regressions On Watershed attributes models to allow each model coefficient to vary geographically among three major river basins of the conterminous United States. Regionalization of the national models reduces the standard errors in the prediction of TN and TP loads, expressed as a percentage of the predicted load, by about 6 and 7%. We develop and apply a method for combining national-scale and regional-scale information to estimate a hybrid model that imposes cross-region constraints that limit regional variation in model coefficients, effectively reducing the number of free model parameters as compared to a collection of independent regional models. The hybrid TN and TP regional models have improved model fit relative to the respective national models, reducing the standard error in the prediction of loads, expressed as a percentage of load, by about 5 and 4%. Only 19% of the TN hybrid model coefficients and just 2% of the TP hybrid model coefficients show evidence of substantial regional specificity (more than ??100% deviation from the national model estimate). The hybrid models have much greater precision in the estimated coefficients than do the unconstrained regional models, demonstrating the efficacy of pooling information across regions to improve regional models. ?? 2011 American Water Resources Association. This article is a U.S. Government work and is in the public domain in the USA. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016APS..DFD.L5007R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016APS..DFD.L5007R">Regularized Moment Equations and Shock Waves for Rarefied Granular Gas</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Reddy, Lakshminarayana; Alam, Meheboob 2016-11-01 It is well-known that the shock structures predicted by extended hydrodynamic models are more accurate than the standard Navier-Stokes model in the rarefied regime, but they fail to predict continuous shock structures when the Mach number exceeds a critical value. Regularization or parabolization is one method to obtain smooth shock profiles at all Mach numbers. Following a Chapman-Enskog-like method, we have derived the "regularized" version 10-moment equations ("R10" moment equations) for inelastic hard-spheres. In order to show the advantage of R10 moment equations over standard 10-moment equations, the R10 moment equations have been employed to solve the Riemann problem of plane shock waves for both molecular and granular gases. The numerical results are compared between the 10-moment and R10-moment models and it is found that the 10-moment model fails to produce continuous shock structures beyond an upstream Mach number of 1 . 34 , while the R10-moment model predicts smooth shock profiles beyond the upstream Mach number of 1 . 34 . The density and granular temperature profiles are found to be asymmetric, with their maxima occurring within the shock-layer. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1430518-beyond-standard-two-film-theory-computational-fluid-dynamics-simulations-carbon-dioxide-capture-wetted-wall-column','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1430518-beyond-standard-two-film-theory-computational-fluid-dynamics-simulations-carbon-dioxide-capture-wetted-wall-column">Beyond the standard two-film theory: Computational fluid dynamics simulations for carbon dioxide capture in a wetted wall column</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Wang, Chao; Xu, Zhijie; Lai, Canhai The standard two-film theory (STFT) is a diffusion-based mechanism that can be used to describe gas mass transfer across liquid film. Fundamental assumptions of the STFT impose serious limitations on its ability to predict mass transfer coefficients. To better understand gas absorption across liquid film in practical situations, a multiphase computational fluid dynamics (CFD) model fully equipped with mass transport and chemistry capabilities has been developed for solvent-based carbon dioxide (CO 2) capture to predict the CO 2 mass transfer coefficient in a wetted wall column. The hydrodynamics is modeled using a volume of fluid method, and the diffusive andmore » reactive mass transfer between the two phases is modeled by adopting a one-fluid formulation. We demonstrate that the proposed CFD model can naturally account for the influence of many important factors on the overall mass transfer that cannot be quantitatively explained by the STFT, such as the local variation in fluid velocities and properties, flow instabilities, and complex geometries. The CFD model also can predict the local mass transfer coefficient variation along the column height, which the STFT typically does not consider.« less </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active">14</li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active">15</li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1434865-beyond-standard-two-film-theory-computational-fluid-dynamics-simulations-carbon-dioxide-capture-wetted-wall-column','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1434865-beyond-standard-two-film-theory-computational-fluid-dynamics-simulations-carbon-dioxide-capture-wetted-wall-column">Beyond the standard two-film theory: Computational fluid dynamics simulations for carbon dioxide capture in a wetted wall column</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Wang, Chao; Xu, Zhijie; Lai, Canhai The standard two-film theory (STFT) is a diffusion-based mechanism that can be used to describe gas mass transfer across liquid film. Fundamental assumptions of the STFT impose serious limitations on its ability to predict mass transfer coefficients. To better understand gas absorption across liquid film in practical situations, a multiphase computational fluid dynamics (CFD) model fully equipped with mass transport and chemistry capabilities has been developed for solvent-based carbon dioxide (CO2) capture to predict the CO2 mass transfer coefficient in a wetted wall column. The hydrodynamics is modeled using a volume of fluid method, and the diffusive and reactive massmore » transfer between the two phases is modeled by adopting a one-fluid formulation. We demonstrate that the proposed CFD model can naturally account for the influence of many important factors on the overall mass transfer that cannot be quantitatively explained by the STFT, such as the local variation in fluid velocities and properties, flow instabilities, and complex geometries. The CFD model also can predict the local mass transfer coefficient variation along the column height, which the STFT typically does not consider.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1430518-beyond-standard-two-film-theory-computational-fluid-dynamics-simulations-carbon-dioxide-capture-wetted-wall-column','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1430518-beyond-standard-two-film-theory-computational-fluid-dynamics-simulations-carbon-dioxide-capture-wetted-wall-column">Beyond the standard two-film theory: Computational fluid dynamics simulations for carbon dioxide capture in a wetted wall column</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Wang, Chao; Xu, Zhijie; Lai, Canhai; ... 2018-03-27 The standard two-film theory (STFT) is a diffusion-based mechanism that can be used to describe gas mass transfer across liquid film. Fundamental assumptions of the STFT impose serious limitations on its ability to predict mass transfer coefficients. To better understand gas absorption across liquid film in practical situations, a multiphase computational fluid dynamics (CFD) model fully equipped with mass transport and chemistry capabilities has been developed for solvent-based carbon dioxide (CO 2) capture to predict the CO 2 mass transfer coefficient in a wetted wall column. The hydrodynamics is modeled using a volume of fluid method, and the diffusive andmore » reactive mass transfer between the two phases is modeled by adopting a one-fluid formulation. We demonstrate that the proposed CFD model can naturally account for the influence of many important factors on the overall mass transfer that cannot be quantitatively explained by the STFT, such as the local variation in fluid velocities and properties, flow instabilities, and complex geometries. The CFD model also can predict the local mass transfer coefficient variation along the column height, which the STFT typically does not consider.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1234895-top-quark-years-after-discovery','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1234895-top-quark-years-after-discovery">The top quark (20 years after the discovery)</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Boos, Eduard; Brandt, Oleg; Denisov, Dmitri; ... 2015-09-10 On the twentieth anniversary of the observation of the top quark, we trace our understanding of this heaviest of all known particles from the prediction of its existence, through the searches and discovery, to the current knowledge of its production mechanisms and properties. We also discuss the central role of the top quark in the Standard Model and the windows that it opens for seeking new physics beyond the Standard Model. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26420266','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26420266">On the Effect of Thermophysical Properties of Clothing on the Heat Strain Predicted by PHS Model.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> d'Ambrosio Alfano, Francesca Romana; Palella, Boris Igor; Riccio, Giuseppe; Malchaire, Jacques 2016-03-01 Procedures and equations reported in ISO 9920 for the correction of basic thermophysical clothing properties taking into account pumping effect and air movement are very different from those used by the Predicted Heat Strain (PHS) model in ISO 7933. To study the effect of these differences on the assessment of hot environments using the PHS model, an analysis focusing on the modelling of the dynamic thermal insulation and the vapour resistance of the clothing reported in ISO 9920 and ISO 7933 standards will be discussed in this paper. The results are useful evidence to start a discussion on the best practice for dealing with clothing thermophysical properties and underline the need to harmonize the entire set of standards in the field of the Ergonomics of the Thermal Environment. ISO 7933 is presently under revision. © The Author 2015. Published by Oxford University Press on behalf of the British Occupational Hygiene Society. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004PhyA..344..162H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004PhyA..344..162H">Confidence limits for data mining models of options prices</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Healy, J. V.; Dixon, M.; Read, B. J.; Cai, F. F. 2004-12-01 Non-parametric methods such as artificial neural nets can successfully model prices of financial options, out-performing the Black-Scholes analytic model (Eur. Phys. J. B 27 (2002) 219). However, the accuracy of such approaches is usually expressed only by a global fitting/error measure. This paper describes a robust method for determining prediction intervals for models derived by non-linear regression. We have demonstrated it by application to a standard synthetic example (29th Annual Conference of the IEEE Industrial Electronics Society, Special Session on Intelligent Systems, pp. 1926-1931). The method is used here to obtain prediction intervals for option prices using market data for LIFFE “ESX” FTSE 100 index options ( http://www.liffe.com/liffedata/contracts/month_onmonth.xls). We avoid special neural net architectures and use standard regression procedures to determine local error bars. The method is appropriate for target data with non constant variance (or volatility). </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/21325505-hyperquarks-bosonic-preon-bound-states','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/21325505-hyperquarks-bosonic-preon-bound-states">Hyperquarks and bosonic preon bound states</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Schmid, Michael L.; Buchmann, Alfons J. 2009-11-01 In a model in which leptons, quarks, and the recently introduced hyperquarks are built up from two fundamental spin-(1/2) preons, the standard model weak gauge bosons emerge as preon bound states. In addition, the model predicts a host of new composite gauge bosons, in particular, those responsible for hyperquark and proton decay. Their presence entails a left-right symmetric extension of the standard model weak interactions and a scheme for a partial and grand unification of nongravitational interactions based on, respectively, the effective gauge groups SU(6){sub P} and SU(9){sub G}. This leads to a prediction of the Weinberg angle at lowmore » energies in good agreement with experiment. Furthermore, using evolution equations for the effective coupling strengths, we calculate the partial and grand unification scales, the hyperquark mass scale, as well as the mass and decay rate of the lightest hyperhadron.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5795887','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5795887">A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> González-Calabuig, Andreu; Cetó, Xavier 2018-01-01 This work reports the applicability of a voltammetric sensor array able to quantify the content of 2,4-dinitrophenol, 4-nitrophenol, and picric acid in artificial samples using the electronic tongue (ET) principles. The ET is based on cyclic voltammetry signals, obtained from an array of metal disk electrodes and a graphite epoxy composite electrode, compressed using discrete wavelet transform with chemometric tools such as artificial neural networks (ANNs). ANNs were employed to build the quantitative prediction model. In this manner, a set of standards based on a full factorial design, ranging from 0 to 300 mg·L−1, was prepared to build the model; afterward, the model was validated with a completely independent set of standards. The model successfully predicted the concentration of the three considered phenols with a normalized root mean square error of 0.030 and 0.076 for the training and test subsets, respectively, and r ≥ 0.948. PMID:29342848 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29608318','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29608318">Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Yadav, Jaydeep; Korzekwa, Ken; Nagar, Swati 2018-05-07 Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min -1 , the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min -1 . These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHEP...10..132A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHEP...10..132A">Measurement of inclusive and differential cross sections in the H → ZZ * → 4 ℓ decay channel in pp collisions at √{s}=13 TeV with the ATLAS detector</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Aaboud, M.; Aad, G.; Abbott, B.; Abdinov, O.; Abeloos, B.; Abidi, S. H.; AbouZeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adachi, S.; Adamczyk, L.; Adelman, J.; Adersberger, M.; Adye, T.; Affolder, A. A.; Afik, Y.; Agatonovic-Jovin, T.; Agheorghiesei, C.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akatsuka, S.; Akerstedt, H.; Åkesson, T. P. A.; Akilli, E.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albicocco, P.; Alconada Verzini, M. J.; Alderweireldt, S. C.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Ali, B.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alshehri, A. A.; Alstaty, M. I.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amoroso, S.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Angerami, A.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antel, C.; Antonelli, M.; Antonov, A.; Antrim, D. J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Araujo Ferraz, V.; Arce, A. T. H.; Ardell, R. E.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Bagnaia, P.; Bahmani, M.; Bahrasemani, H.; Baines, J. T.; Bajic, M.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balli, F.; Balunas, W. K.; Banas, E.; Bandyopadhyay, A.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisits, M.-S.; Barkeloo, J. T.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska-Blenessy, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barranco Navarro, L.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Bechtle, P.; Beck, H. P.; Beck, H. C.; Becker, K.; Becker, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bedognetti, M.; Bee, C. P.; Beermann, T. A.; Begalli, M.; Begel, M.; Behr, J. K.; Bell, A. S.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Belyaev, N. L.; Benary, O.; Benchekroun, D.; Bender, M.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez, J.; Benjamin, D. P.; Benoit, M.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Beringer, J.; Berlendis, S.; Bernard, N. R.; Bernardi, G.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertram, I. A.; Bertsche, C.; Bertsche, D.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Bethani, A.; Bethke, S.; Bevan, A. J.; Beyer, J.; Bianchi, R. M.; Biebel, O.; Biedermann, D.; Bielski, R.; Bierwagen, K.; Biesuz, N. V.; Biglietti, M.; Billoud, T. R. V.; Bilokon, H.; Bindi, M.; Bingul, A.; Bini, C.; Biondi, S.; Bisanz, T.; Bittrich, C.; Bjergaard, D. M.; Black, J. E.; Black, K. M.; Blair, R. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blue, A.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Boerner, D.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bokan, P.; Bold, T.; Boldyrev, A. S.; Bolz, A. E.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Bortfeldt, J.; Bortoletto, D.; Bortolotto, V.; Boscherini, D.; Bosman, M.; Sola, J. D. Bossio; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Braren, F.; Bratzler, U.; Brau, B.; Brau, J. E.; Breaden Madden, W. D.; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Briglin, D. L.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; Broughton, J. H.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruni, A.; Bruni, G.; Bruni, L. S.; Bruno, S.; Brunt, BH; Bruschi, M.; Bruscino, N.; Bryant, P.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burch, T. J.; Burdin, S.; Burgard, C. D.; Burger, A. M.; Burghgrave, B.; Burka, K.; Burke, S.; Burmeister, I.; Burr, J. T. P.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Cabrera Urbán, S.; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Callea, G.; Caloba, L. P.; Calvente Lopez, S.; Calvet, D.; Calvet, S.; Calvet, T. P.; Camacho Toro, R.; Camarda, S.; Camarri, P.; Cameron, D.; Caminal Armadans, R.; Camincher, C.; Campana, S.; Campanelli, M.; Camplani, A.; Campoverde, A.; Canale, V.; Cano Bret, M.; Cantero, J.; Cao, T.; Capeans Garrido, M. D. M.; Caprini, I.; Caprini, M.; Capua, M.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, I.; Carli, T.; Carlino, G.; Carlson, B. T.; Carminati, L.; Carney, R. M. D.; Caron, S.; Carquin, E.; Carrá, S.; Carrillo-Montoya, G. D.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castelijn, R.; Castillo Gimenez, V.; Castro, N. F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavallaro, E.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Celebi, E.; Ceradini, F.; Cerda Alberich, L.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chan, S. K.; Chan, W. S.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chau, C. C.; Chavez Barajas, C. A.; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, C.; Chen, H.; Chen, J.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, H. J.; Cheplakov, A.; Cheremushkina, E.; Cherkaoui El Moursli, R.; Cheu, E.; Cheung, K.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chitan, A.; Chiu, Y. H.; Chizhov, M. V.; Choi, K.; Chomont, A. R.; Chouridou, S.; Chow, Y. S.; Christodoulou, V.; Chu, M. C.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Ciftci, A. K.; Cinca, D.; Cindro, V.; Cioara, I. A.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Citterio, M.; Ciubancan, M.; Clark, A.; Clark, B. L.; Clark, M. R.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Colasurdo, L.; Cole, B.; Colijn, A. P.; Collot, J.; Colombo, T.; Conde Muiño, P.; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Constantinescu, S.; Conti, G.; Conventi, F.; Cooke, M.; Cooper-Sarkar, A. M.; Cormier, F.; Cormier, K. J. R.; Corradi, M.; Corriveau, F.; Cortes-Gonzalez, A.; Costa, G.; Costa, M. J.; Costanzo, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Cranmer, K.; Crawley, S. J.; Creager, R. A.; Cree, G.; Crépé-Renaudin, S.; Crescioli, F.; Cribbs, W. A.; Cristinziani, M.; Croft, V.; Crosetti, G.; Cueto, A.; Cuhadar Donszelmann, T.; Cukierman, A. R.; Cummings, J.; Curatolo, M.; Cúth, J.; Czekierda, S.; Czodrowski, P.; D'amen, G.; D'Auria, S.; D'eramo, L.; D'Onofrio, M.; Da Cunha Sargedas De Sousa, M. J.; Da Via, C.; Dabrowski, W.; Dado, T.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Daneri, M. F.; Dang, N. P.; Daniells, A. C.; Dann, N. S.; Danninger, M.; Dano Hoffmann, M.; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Daubney, T.; Davey, W.; David, C.; Davidek, T.; Davis, D. R.; Davison, P.; Dawe, E.; Dawson, I.; De, K.; de Asmundis, R.; De Benedetti, A.; De Castro, S.; De Cecco, S.; De Groot, N.; de Jong, P.; De la Torre, H.; De Lorenzi, F.; De Maria, A.; De Pedis, D.; De Salvo, A.; De Sanctis, U.; De Santo, A.; De Vasconcelos Corga, K.; De Vivie De Regie, J. B.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Dehghanian, N.; Deigaard, I.; Del Gaudio, M.; Del Peso, J.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; della Volpe, D.; Delmastro, M.; Delporte, C.; Delsart, P. A.; DeMarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Denysiuk, D.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Devesa, M. R.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; Di Bello, F. A.; Di Ciaccio, A.; Di Ciaccio, L.; Di Clemente, W. K.; Di Donato, C.; Di Girolamo, A.; Di Girolamo, B.; Di Micco, B.; Di Nardo, R.; Di Petrillo, K. F.; Di Simone, A.; Di Sipio, R.; Di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Díez Cornell, S.; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dolejsi, J.; Dolezal, Z.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Duarte-Campderros, J.; Dubreuil, A.; Duchovni, E.; Duckeck, G.; Ducourthial, A.; Ducu, O. A.; Duda, D.; Dudarev, A.; Dudder, A. Chr.; Duffield, E. M.; Duflot, L.; Dührssen, M.; Dumancic, M.; Dumitriu, A. E.; Duncan, A. K.; Dunford, M.; Duran Yildiz, H.; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Duvnjak, D.; Dyndal, M.; Dziedzic, B. S.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; El Kacimi, M.; El Kosseifi, R.; Ellajosyula, V.; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Ennis, J. S.; Erdmann, J.; Ereditato, A.; Ernst, M.; Errede, S.; Escalier, M.; Escobar, C.; Esposito, B.; Estrada Pastor, O.; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Ezzi, M.; Fabbri, F.; Fabbri, L.; Fabiani, V.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farina, C.; Farina, E. M.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Faucci Giannelli, M.; Favareto, A.; Fawcett, W. J.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Fenton, M. J.; Fenyuk, A. B.; Feremenga, L.; Fernandez Martinez, P.; Fernandez Perez, S.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; Ferreira de Lima, D. E.; Ferrer, A.; Ferrere, D.; Ferretti, C.; Fiedler, F.; Filipčič, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, R. R. M.; Flick, T.; Flierl, B. M.; Flores Castillo, L. R.; Flowerdew, M. J.; Forcolin, G. T.; Formica, A.; Förster, F. A.; Forti, A.; Foster, A. G.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Franchino, S.; Francis, D.; Franconi, L.; Franklin, M.; Frate, M.; Fraternali, M.; Freeborn, D.; Fressard-Batraneanu, S. 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M.; Nakahama, Y.; Nakamura, K.; Nakamura, T.; Nakano, I.; Naranjo Garcia, R. F.; Narayan, R.; Narrias Villar, D. I.; Naryshkin, I.; Naumann, T.; Navarro, G.; Nayyar, R.; Neal, H. A.; Nechaeva, P. Yu.; Neep, T. J.; Negri, A.; Negrini, M.; Nektarijevic, S.; Nellist, C.; Nelson, A.; Nelson, M. E.; Nemecek, S.; Nemethy, P.; Nessi, M.; Neubauer, M. S.; Neumann, M.; Newman, P. R.; Ng, T. Y.; Nguyen Manh, T.; Nickerson, R. B.; Nicolaidou, R.; Nielsen, J.; Nikiforou, N.; Nikolaenko, V.; Nikolic-Audit, I.; Nikolopoulos, K.; Nilsen, J. K.; Nilsson, P.; Ninomiya, Y.; Nisati, A.; Nishu, N.; Nisius, R.; Nitsche, I.; Nitta, T.; Nobe, T.; Noguchi, Y.; Nomachi, M.; Nomidis, I.; Nomura, M. A.; Nooney, T.; Nordberg, M.; Norjoharuddeen, N.; Novgorodova, O.; Nozaki, M.; Nozka, L.; Ntekas, K.; Nurse, E.; Nuti, F.; O'connor, K.; O'Neil, D. C.; O'Rourke, A. A.; O'Shea, V.; Oakham, F. G.; Oberlack, H.; Obermann, T.; Ocariz, J.; Ochi, A.; Ochoa, I.; Ochoa-Ricoux, J. P.; Oda, S.; Odaka, S.; Oh, A.; Oh, S. H.; Ohm, C. C.; Ohman, H.; Oide, H.; Okawa, H.; Okumura, Y.; Okuyama, T.; Olariu, A.; Oleiro Seabra, L. F.; Olivares Pino, S. A.; Oliveira Damazio, D.; Olszewski, A.; Olszowska, J.; Onofre, A.; Onogi, K.; Onyisi, P. U. E.; Oppen, H.; Oreglia, M. J.; Oren, Y.; Orestano, D.; Orlando, N.; Orr, R. S.; Osculati, B.; Ospanov, R.; Otero y Garzon, G.; Otono, H.; Ouchrif, M.; Ould-Saada, F.; Ouraou, A.; Oussoren, K. P.; Ouyang, Q.; Owen, M.; Owen, R. E.; Ozcan, V. E.; Ozturk, N.; Pachal, K.; Pacheco Pages, A.; Pacheco Rodriguez, L.; Padilla Aranda, C.; Pagan Griso, S.; Paganini, M.; Paige, F.; Palacino, G.; Palazzo, S.; Palestini, S.; Palka, M.; Pallin, D.; Panagiotopoulou, E. St.; Panagoulias, I.; Pandini, C. E.; Panduro Vazquez, J. G.; Pani, P.; Panitkin, S.; Pantea, D.; Paolozzi, L.; Papadopoulou, Th. D.; Papageorgiou, K.; Paramonov, A.; Paredes Hernandez, D.; Parker, A. J.; Parker, M. A.; Parker, K. A.; Parodi, F.; Parsons, J. A.; Parzefall, U.; Pascuzzi, V. R.; Pasner, J. M.; Pasqualucci, E.; Passaggio, S.; Pastore, Fr.; Pataraia, S.; Pater, J. R.; Pauly, T.; Pearson, B.; Pedraza Lopez, S.; Pedro, R.; Peleganchuk, S. V.; Penc, O.; Peng, C.; Peng, H.; Penwell, J.; Peralva, B. S.; Perego, M. M.; Perepelitsa, D. V.; Peri, F.; Perini, L.; Pernegger, H.; Perrella, S.; Peschke, R.; Peshekhonov, V. D.; Peters, K.; Peters, R. F. Y.; Petersen, B. A.; Petersen, T. C.; Petit, E.; Petridis, A.; Petridou, C.; Petroff, P.; Petrolo, E.; Petrov, M.; Petrucci, F.; Pettersson, N. E.; Peyaud, A.; Pezoa, R.; Phillips, F. H.; Phillips, P. W.; Piacquadio, G.; Pianori, E.; Picazio, A.; Piccaro, E.; Pickering, M. A.; Piegaia, R.; Pilcher, J. E.; Pilkington, A. D.; Pinamonti, M.; Pinfold, J. L.; Pirumov, H.; Pitt, M.; Plazak, L.; Pleier, M.-A.; Pleskot, V.; Plotnikova, E.; Pluth, D.; Podberezko, P.; Poettgen, R.; Poggi, R.; Poggioli, L.; Pogrebnyak, I.; Pohl, D.; Pokharel, I.; Polesello, G.; Poley, A.; Policicchio, A.; Polifka, R.; Polini, A.; Pollard, C. S.; Polychronakos, V.; Pommès, K.; Ponomarenko, D.; Pontecorvo, L.; Popeneciu, G. A.; Pospisil, S.; Potamianos, K.; Potrap, I. N.; Potter, C. J.; Potti, H.; Poulsen, T.; Poveda, J.; Pozo Astigarraga, M. E.; Pralavorio, P.; Pranko, A.; Prell, S.; Price, D.; Primavera, M.; Prince, S.; Proklova, N.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Przybycien, M.; Puri, A.; Puzo, P.; Qian, J.; Qin, G.; Qin, Y.; Quadt, A.; Queitsch-Maitland, M.; Quilty, D.; Raddum, S.; Radeka, V.; Radescu, V.; Radhakrishnan, S. K.; Radloff, P.; Rados, P.; Ragusa, F.; Rahal, G.; Raine, J. A.; Rajagopalan, S.; Rangel-Smith, C.; Rashid, T.; Raspopov, S.; Ratti, M. G.; Rauch, D. M.; Rauscher, F.; Rave, S.; Ravinovich, I.; Rawling, J. H.; Raymond, M.; Read, A. L.; Readioff, N. P.; Reale, M.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reed, R. G.; Reeves, K.; Rehnisch, L.; Reichert, J.; Reiss, A.; Rembser, C.; Ren, H.; Rescigno, M.; Resconi, S.; Resseguie, E. D.; Rettie, S.; Reynolds, E.; Rezanova, O. L.; Reznicek, P.; Rezvani, R.; Richter, R.; Richter, S.; Richter-Was, E.; Ricken, O.; Ridel, M.; Rieck, P.; Riegel, C. J.; Rieger, J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rimoldi, M.; Rinaldi, L.; Ripellino, G.; Ristić, B.; Ritsch, E.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Rizzi, C.; Roberts, R. T.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robson, A.; Rocco, E.; Roda, C.; Rodina, Y.; Rodriguez Bosca, S.; Rodriguez Perez, A.; Rodriguez Rodriguez, D.; Roe, S.; Rogan, C. S.; Røhne, O.; Roloff, J.; Romaniouk, A.; Romano, M.; Romano Saez, S. M.; Romero Adam, E.; Rompotis, N.; Ronzani, M.; Roos, L.; Rosati, S.; Rosbach, K.; Rose, P.; Rosien, N.-A.; Rossi, E.; Rossi, L. P.; Rosten, J. H. N.; Rosten, R.; Rotaru, M.; Rothberg, J.; Rousseau, D.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rühr, F.; Ruiz-Martinez, A.; Rurikova, Z.; Rusakovich, N. A.; Russell, H. L.; Rutherfoord, J. P.; Ruthmann, N.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Ryu, S.; Ryzhov, A.; Rzehorz, G. F.; Saavedra, A. F.; Sabato, G.; Sacerdoti, S.; Sadrozinski, H. F.-W.; Sadykov, R.; Safai Tehrani, F.; Saha, P.; Sahinsoy, M.; Saimpert, M.; Saito, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Salazar Loyola, J. E.; Salek, D.; Sales De Bruin, P. H.; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sampsonidou, D.; Sánchez, J.; Sanchez Martinez, V.; Sanchez Pineda, A.; Sandaker, H.; Sandbach, R. L.; Sander, C. O.; Sandhoff, M.; Sandoval, C.; Sankey, D. P. C.; Sannino, M.; Sano, Y.; Sansoni, A.; Santoni, C.; Santos, H.; Santoyo Castillo, I.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sato, K.; Sauvan, E.; Savage, G.; Savard, P.; Savic, N.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Schaarschmidt, J.; Schacht, P.; Schachtner, B. M.; Schaefer, D.; Schaefer, L.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schier, S.; Schildgen, L. K.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schott, M.; Schouwenberg, J. F. P.; Schovancova, J.; Schramm, S.; Schuh, N.; Schulte, A.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwartzman, A.; Schwarz, T. A.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Sciandra, A.; Sciolla, G.; Scornajenghi, M.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Semprini-Cesari, N.; Senkin, S.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Shen, Y.; Sherafati, N.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shipsey, I. P. J.; Shirabe, S.; Shiyakova, M.; Shlomi, J.; Shmeleva, A.; Shoaleh Saadi, D.; Shochet, M. J.; Shojaii, S.; Shope, D. R.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sickles, A. M.; Sidebo, P. E.; Sideras Haddad, E.; Sidiropoulou, O.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Siral, I.; Sivoklokov, S. Yu.; Sjölin, J.; Skinner, M. B.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smiesko, J.; Smirnov, N.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, J. W.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snyder, I. M.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Søgaard, A.; Soh, D. A.; Sokhrannyi, G.; Solans Sanchez, C. A.; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Sopczak, A.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spieker, T. M.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; St. Denis, R. D.; Stabile, A.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanitzki, M. M.; Stapf, B. S.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Stark, S. H.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Stegler, M.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultan, DMS; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Suruliz, K.; Suster, C. J. E.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Swift, S. P.; Sykora, I.; Sykora, T.; Ta, D.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Tahirovic, E.; Taiblum, N.; Takai, H.; Takashima, R.; Takasugi, E. H.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tanaka, J.; Tanaka, M.; Tanaka, R.; Tanaka, S.; Tanioka, R.; Tannenwald, B. B.; Tapia Araya, S.; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Tavares Delgado, A.; Tayalati, Y.; Taylor, A. C.; Taylor, A. J.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teixeira-Dias, P.; Temple, D.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thiele, F.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Tibbetts, M. J.; Ticse Torres, R. E.; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorova-Nova, S.; Todt, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Tornambe, P.; Torrence, E.; Torres, H.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Treado, C. J.; Trefzger, T.; Tresoldi, F.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tsang, K. W.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tu, Y.; Tudorache, A.; Tudorache, V.; Tulbure, T. T.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turgeman, D.; Turk Cakir, I.; Turra, R.; Tuts, P. M.; Ucchielli, G.; Ueda, I.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usui, J.; Vacavant, L.; Vacek, V.; Vachon, B.; Vadla, K. O. H.; Vaidya, A.; Valderanis, C.; Valdes Santurio, E.; Valente, M.; Valentinetti, S.; Valero, A.; Valéry, L.; Valkar, S.; Vallier, A.; Valls Ferrer, J. A.; Van Den Wollenberg, W.; van der Graaf, H.; van Gemmeren, P.; Van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varni, C.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vasquez, G. A.; Vazeille, F.; Vazquez Furelos, D.; Vazquez Schroeder, T.; Veatch, J.; Veeraraghavan, V.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, A. T.; Vermeulen, J. C.; Vetterli, M. C.; Viaux Maira, N.; Viazlo, O.; Vichou, I.; Vickey, T.; Vickey Boeriu, O. E.; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Villa, M.; Perez, M. Villaplana; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vishwakarma, A.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vogel, M.; Vokac, P.; Volpi, G.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Wagner, P.; Wagner, W.; Wagner-Kuhr, J.; Wahlberg, H.; Wahrmund, S.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, Q.; Wang, R.-J.; Wang, R.; Wang, S. M.; Wang, T.; Wang, W.; Wang, W.; Wang, Z.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, A. F.; Webb, S.; Weber, M. S.; Weber, S. W.; Weber, S. A.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weirich, M.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M. D.; Werner, P.; Wessels, M.; Weston, T. D.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A. S.; White, A.; White, M. J.; White, R.; Whiteson, D.; Whitmore, B. W.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winkels, E.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wobisch, M.; Wolf, T. M. H.; Wolff, R.; Wolter, M. W.; Wolters, H.; Wong, V. W. S.; Worm, S. D.; Wosiek, B. K.; Wotschack, J.; Wozniak, K. W.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xi, Z.; Xia, L.; Xu, D.; Xu, L.; Xu, T.; Yabsley, B.; Yacoob, S.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamane, F.; Yamatani, M.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yigitbasi, E.; Yildirim, E.; Yorita, K.; Yoshihara, K.; Young, C.; Young, C. J. S.; Yu, J.; Yu, J.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zacharis, G.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanzi, D.; Zeitnitz, C.; Zemaityte, G.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, L.; Zhang, M.; Zhang, P.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Y.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, M.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Zou, R.; zur Nedden, M.; Zwalinski, L. 2017-10-01 Inclusive and differential fiducial cross sections of Higgs boson production in proton-proton collisions are measured in the H → ZZ * → 4 ℓ decay channel. The proton-proton collision data were produced at the Large Hadron Collider at a centre-of-mass energy of 13 TeV and recorded by the ATLAS detector in 2015 and 2016, corresponding to an integrated luminosity of 36.1 fb-1. The inclusive fiducial cross section in the H → ZZ * → 4ℓ decay channel is measured to be 3.62 ± 0.50(stat) - 0.20 + 0.25 (sys) fb, in agreement with the Standard Model prediction of 2 .91 ± 0 .13 fb. The cross section is also extrapolated to the total phase space including all Standard Model Higgs boson decays. Several differential fiducial cross sections are measured for observables sensitive to the Higgs boson production and decay, including kinematic distributions of jets produced in association with the Higgs boson. Good agreement is found between data and Standard Model predictions. The results are used to put constraints on anomalous Higgs boson interactions with Standard Model particles, using the pseudo-observable extension to the kappa-framework. [Figure not available: see fulltext. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020076305','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020076305">Dynamical Evolution of Planetary Embryos</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Wetherill, George W. 2002-01-01 During the past decade, progress has been made by relating the 'standard model' for the formation of planetary systems to computational and observational advances. A significant contribution to this has been provided by this grant. The consequence of this is that the rigor of the physical modeling has improved considerably. This has identified discrepancies between the predictions of the standard model and recent observations of extrasolar planets. In some cases, the discrepancies can be resolved by recognition of the stochastic nature of the planetary formation process, leading to variations in the final state of a planetary system. In other cases, it seems more likely that there are major deficiencies in the standard model, requiring our identifying variations to the model that are not so strongly constrained to our Solar System. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013BoLMe.146..447M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013BoLMe.146..447M">Aerodynamic Parameters of a UK City Derived from Morphological Data</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Millward-Hopkins, J. T.; Tomlin, A. S.; Ma, L.; Ingham, D. B.; Pourkashanian, M. 2013-03-01 Detailed three-dimensional building data and a morphometric model are used to estimate the aerodynamic roughness length z 0 and displacement height d over a major UK city (Leeds). Firstly, using an adaptive grid, the city is divided into neighbourhood regions that are each of a relatively consistent geometry throughout. Secondly, for each neighbourhood, a number of geometric parameters are calculated. Finally, these are used as input into a morphometric model that considers the influence of height variability to predict aerodynamic roughness length and displacement height. Predictions are compared with estimations made using standard tables of aerodynamic parameters. The comparison suggests that the accuracy of plan-area-density based tables is likely to be limited, and that height-based tables of aerodynamic parameters may be more accurate for UK cities. The displacement heights in the standard tables are shown to be lower than the current predictions. The importance of geometric details in determining z 0 and d is then explored. Height variability is observed to greatly increase the predicted values. However, building footprint shape only has a significant influence upon the predictions when height variability is not considered. Finally, we develop simple relations to quantify the influence of height variation upon predicted z 0 and d via the standard deviation of building heights. The difference in these predictions compared to the more complex approach highlights the importance of considering the specific shape of the building-height distributions. Collectively, these results suggest that to accurately predict aerodynamic parameters of real urban areas, height variability must be considered in detail, but it may be acceptable to make simple assumptions about building layout and footprint shape. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28765864','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28765864">Comparison of EHR-based diagnosis documentation locations to a gold standard for risk stratification in patients with multiple chronic conditions.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Martin, Shelby; Wagner, Jesse; Lupulescu-Mann, Nicoleta; Ramsey, Katrina; Cohen, Aaron; Graven, Peter; Weiskopf, Nicole G; Dorr, David A 2017-08-02 To measure variation among four different Electronic Health Record (EHR) system documentation locations versus 'gold standard' manual chart review for risk stratification in patients with multiple chronic illnesses. Adults seen in primary care with EHR evidence of at least one of 13 conditions were included. EHRs were manually reviewed to determine presence of active diagnoses, and risk scores were calculated using three different methodologies and five EHR documentation locations. Claims data were used to assess cost and utilization for the following year. Descriptive and diagnostic statistics were calculated for each EHR location. Criterion validity testing compared the gold standard verified diagnoses versus other EHR locations and risk scores in predicting future cost and utilization. Nine hundred patients had 2,179 probable diagnoses. About 70% of the diagnoses from the EHR were verified by gold standard. For a subset of patients having baseline and prediction year data (n=750), modeling showed that the gold standard was the best predictor of outcomes on average for a subset of patients that had these data. However, combining all data sources together had nearly equivalent performance for prediction as the gold standard. EHR data locations were inaccurate 30% of the time, leading to improvement in overall modeling from a gold standard from chart review for individual diagnoses. However, the impact on identification of the highest risk patients was minor, and combining data from different EHR locations was equivalent to gold standard performance. The reviewer's ability to identify a diagnosis as correct was influenced by a variety of factors, including completeness, temporality, and perceived accuracy of chart data. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002SPIE.4927...64W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002SPIE.4927...64W">Artificial Neural Network and application in calibration transfer of AOTF-based NIR spectrometer</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Wang, Wenbo; Jiang, Chengzhi; Xu, Kexin; Wang, Bin 2002-09-01 Chemometrics is widely applied to develop models for quantitative prediction of unknown samples in Near-infrared (NIR) spectroscopy. However, calibrated models generally fail when new instruments are introduced or replacement of the instrument parts occurs. Therefore, calibration transfer becomes necessary to avoid the costly, time-consuming recalibration of models. Piecewise Direct Standardization (PDS) has been proven to be a reference method for standardization. In this paper, Artificial Neural Network (ANN) is employed as an alternative to transfer spectra between instruments. Two Acousto-optic Tunable Filter NIR spectrometers are employed in the experiment. Spectra of glucose solution are collected on the spectrometers through transflectance mode. A Back propagation Network with two layers is employed to simulate the function between instruments piecewisely. Standardization subset is selected by Kennard and Stone (K-S) algorithm in the first two score space of Principal Component Analysis (PCA) of spectra matrix. In current experiment, it is noted that obvious nonlinearity exists between instruments and attempts are made to correct such nonlinear effect. Prediction results before and after successful calibration transfer are compared. Successful transfer can be achieved by adapting window size and training parameters. Final results reveal that ANN is effective in correcting the nonlinear instrumental difference and a only 1.5~2 times larger prediction error is expected after successful transfer. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29316622','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29316622">Interaction Analysis of Longevity Interventions Using Survival Curves.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Nowak, Stefan; Neidhart, Johannes; Szendro, Ivan G; Rzezonka, Jonas; Marathe, Rahul; Krug, Joachim 2018-01-06 A long-standing problem in ageing research is to understand how different factors contributing to longevity should be expected to act in combination under the assumption that they are independent. Standard interaction analysis compares the extension of mean lifespan achieved by a combination of interventions to the prediction under an additive or multiplicative null model, but neither model is fundamentally justified. Moreover, the target of longevity interventions is not mean life span but the entire survival curve. Here we formulate a mathematical approach for predicting the survival curve resulting from a combination of two independent interventions based on the survival curves of the individual treatments, and quantify interaction between interventions as the deviation from this prediction. We test the method on a published data set comprising survival curves for all combinations of four different longevity interventions in Caenorhabditis elegans . We find that interactions are generally weak even when the standard analysis indicates otherwise. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5872032','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5872032">Interaction Analysis of Longevity Interventions Using Survival Curves</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Nowak, Stefan; Neidhart, Johannes; Szendro, Ivan G.; Rzezonka, Jonas; Marathe, Rahul; Krug, Joachim 2018-01-01 A long-standing problem in ageing research is to understand how different factors contributing to longevity should be expected to act in combination under the assumption that they are independent. Standard interaction analysis compares the extension of mean lifespan achieved by a combination of interventions to the prediction under an additive or multiplicative null model, but neither model is fundamentally justified. Moreover, the target of longevity interventions is not mean life span but the entire survival curve. Here we formulate a mathematical approach for predicting the survival curve resulting from a combination of two independent interventions based on the survival curves of the individual treatments, and quantify interaction between interventions as the deviation from this prediction. We test the method on a published data set comprising survival curves for all combinations of four different longevity interventions in Caenorhabditis elegans. We find that interactions are generally weak even when the standard analysis indicates otherwise. PMID:29316622 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1347939','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1347939">Study of C P -violating charge asymmetries of single muons and like-sign dimuons in p p ¯ collisions</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Abazov, V. M.; Abbott, B.; Acharya, B. S. 2014-01-01 We measure the inclusive single muon charge asymmetry and the like-sign dimuon charge asymmetry inmore » $$p \\bar{p}$$ collisions using the full data set of 10.4 fb$$^{-1}$$ collected with the D0 detector at the Fermilab Tevatron. The standard model predictions of the charge asymmetries induced by CP violation are small in magnitude compared to the current experimental precision, so non-zero measurements could indicate new sources of CP violation. The measurements differ from the standard model predictions of CP violation in these asymmetries with a significance of 3.6 standard deviations. These results are interpreted in a framework of $B$ meson mixing within the CKM formalism to measure the relative width difference $$\\dgg$$ between the mass eigenstates of the $$\\Bd$$ meson system, and the semileptonic charge asymmetries $$\\asld$$ and $$\\asls$$ of $$\\Bd$$ and $$\\Bs$$ mesons respectively.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESS...18.3301H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESS...18.3301H">The importance of hydrological uncertainty assessment methods in climate change impact studies</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Honti, M.; Scheidegger, A.; Stamm, C. 2014-08-01 Climate change impact assessments have become more and more popular in hydrology since the middle 1980s with a recent boost after the publication of the IPCC AR4 report. From hundreds of impact studies a quasi-standard methodology has emerged, to a large extent shaped by the growing public demand for predicting how water resources management or flood protection should change in the coming decades. The "standard" workflow relies on a model cascade from global circulation model (GCM) predictions for selected IPCC scenarios to future catchment hydrology. Uncertainty is present at each level and propagates through the model cascade. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. Our hypothesis was that the relative importance of climatic and hydrologic uncertainty is (among other factors) heavily influenced by the uncertainty assessment method. To test this we carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on two small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment with two different likelihood functions. One was a time series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was an approximate likelihood function for the flow quantiles. The results showed that the expected climatic impact on flow quantiles was small compared to prediction uncertainty. The choice of uncertainty assessment method actually determined what sources of uncertainty could be identified at all. This demonstrated that one could arrive at rather different conclusions about the causes behind predictive uncertainty for the same hydrological model and calibration data when considering different objective functions for calibration. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2265071','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2265071">Visual Field Defects and Retinal Ganglion Cell Losses in Human Glaucoma Patients</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Harwerth, Ronald S.; Quigley, Harry A. 2007-01-01 Objective The depth of visual field defects are correlated with retinal ganglion cell densities in experimental glaucoma. This study was to determine whether a similar structure-function relationship holds for human glaucoma. Methods The study was based on retinal ganglion cell densities and visual thresholds of patients with documented glaucoma (Kerrigan-Baumrind, et al.) The data were analyzed by a model that predicted ganglion cell densities from standard clinical perimetry, which were then compared to histologic cell counts. Results The model, without free parameters, produced accurate and relatively precise quantification of ganglion cell densities associated with visual field defects. For 437 sets of data, the unity correlation for predicted vs. measured cell densities had a coefficient of determination of 0.39. The mean absolute deviation of the predicted vs. measured values was 2.59 dB, the mean and SD of the distribution of residual errors of prediction was -0.26 ± 3.22 dB. Conclusions Visual field defects by standard clinical perimetry are proportional to neural losses caused by glaucoma. Clinical Relevance The evidence for quantitative structure-function relationships provides a scientific basis of interpreting glaucomatous neuropathy from visual thresholds and supports the application of standard perimetry to establish the stage of the disease. PMID:16769839 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26117284','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26117284">A novel auto-tuning PID control mechanism for nonlinear systems.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Cetin, Meric; Iplikci, Serdar 2015-09-01 In this paper, a novel Runge-Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70005928','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70005928">Evaluating remedial alternatives for an acid mine drainage stream: A model post audit</a> <a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a> Runkel, Robert L.; Kimball, Briant A.; Walton-Day, Katherine; Verplanck, Philip L.; Broshears, Robert E. 2012-01-01 A post audit for a reactive transport model used to evaluate acid mine drainage treatment systems is presented herein. The post audit is based on a paired synoptic approach in which hydrogeochemical data are collected at low (existing conditions) and elevated (following treatment) pH. Data obtained under existing, low-pH conditions are used for calibration, and the resultant model is used to predict metal concentrations observed following treatment. Predictions for Al, As, Fe, H+, and Pb accurately reproduce the observed reduction in dissolved concentrations afforded by the treatment system, and the information provided in regard to standard attainment is also accurate (predictions correctly indicate attainment or nonattainment of water quality standards for 19 of 25 cases). Errors associated with Cd, Cu, and Zn are attributed to misspecification of sorbent mass (precipitated Fe). In addition to these specific results, the post audit provides insight in regard to calibration and sensitivity analysis that is contrary to conventional wisdom. Steps taken during the calibration process to improve simulations of As sorption were ultimately detrimental to the predictive results, for example, and the sensitivity analysis failed to bracket observed metal concentrations. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active">15</li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active">16</li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22074087','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22074087">Evaluating remedial alternatives for an acid mine drainage stream: a model post audit.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Runkel, Robert L; Kimball, Briant A; Walton-Day, Katherine; Verplanck, Philip L; Broshears, Robert E 2012-01-03 A post audit for a reactive transport model used to evaluate acid mine drainage treatment systems is presented herein. The post audit is based on a paired synoptic approach in which hydrogeochemical data are collected at low (existing conditions) and elevated (following treatment) pH. Data obtained under existing, low-pH conditions are used for calibration, and the resultant model is used to predict metal concentrations observed following treatment. Predictions for Al, As, Fe, H(+), and Pb accurately reproduce the observed reduction in dissolved concentrations afforded by the treatment system, and the information provided in regard to standard attainment is also accurate (predictions correctly indicate attainment or nonattainment of water quality standards for 19 of 25 cases). Errors associated with Cd, Cu, and Zn are attributed to misspecification of sorbent mass (precipitated Fe). In addition to these specific results, the post audit provides insight in regard to calibration and sensitivity analysis that is contrary to conventional wisdom. Steps taken during the calibration process to improve simulations of As sorption were ultimately detrimental to the predictive results, for example, and the sensitivity analysis failed to bracket observed metal concentrations. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1998PhLB..419..199C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1998PhLB..419..199C">String unification scale and the hyper-charge Kac-Moody level in the non-supersymmetric standard model</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Cho, Gi-Chol; Hagiwara, Kaoru 1998-02-01 The string theory predicts the unification of the gauge couplings and gravity. The minimal supersymmetric Standard Model, however, gives the unification scale ~2x1016 GeV which is significantly smaller than the string scale ~5x1017 GeV of the weak coupling heterotic string theory. We study the unification scale of the non-supersymmetric minimal Standard Model quantitatively at the two-loop level. We find that the unification scale should be at most ~4x1016 GeV and the desired Kac-Moody level of the hyper-charge coupling should be 1.33<~kY<~1.35. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/18717776','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/18717776">Predicting intentions to donate blood among nondonors in Australia: an extended theory of planned behavior.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Robinson, Natalie G; Masser, Barbara M; White, Katherine M; Hyde, Melissa K; Terry, Deborah J 2008-12-01 With an increasing demand for blood and blood products in Australia, there is a continual need to recruit blood donors. As such, it is important to investigate the factors that impact on nondonors' decision-making processes with regard to donating blood for the first time. Previous research has established the efficacy of the theory of planned behavior (TPB) in predicting blood donor intentions. The current research aimed to test a TPB model augmented with constructs implicated in previous blood donor research; specifically descriptive norm, moral norm, anticipated regret, and donation anxiety. Participants completed measures assessing the standard TPB variables of attitude, subjective norm, and perceived behavioral control (PBC) as well as descriptive norm, moral norm, donation anxiety, and anticipated regret. Path analysis examined the utility of the augmented TPB model to predict 195 non-blood donors' intentions to donate blood. A final revised model provided a very good fit to the data and included attitude, PBC, moral norm, descriptive norm, anticipated regret, and donation anxiety as direct predictors of intention, with these factors accounting for 70 percent of the variance in intentions to donate blood. A revised TPB model provided a more efficacious predictor of nondonors' intentions to donate than the standard TPB model and highlights the role that norm-based factors and affective-laden constructs play in predicting non-blood donors' intentions to donate. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20924933','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20924933">A simplified building airflow model for agent concentration prediction.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Jacques, David R; Smith, David A 2010-11-01 A simplified building airflow model is presented that can be used to predict the spread of a contaminant agent from a chemical or biological attack. If the dominant means of agent transport throughout the building is an air-handling system operating at steady-state, a linear time-invariant (LTI) model can be constructed to predict the concentration in any room of the building as a result of either an internal or external release. While the model does not capture weather-driven and other temperature-driven effects, it is suitable for concentration predictions under average daily conditions. The model is easily constructed using information that should be accessible to a building manager, supplemented with assumptions based on building codes and standard air-handling system design practices. The results of the model are compared with a popular multi-zone model for a simple building and are demonstrated for building examples containing one or more air-handling systems. The model can be used for rapid concentration prediction to support low-cost placement strategies for chemical and biological detection sensors. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19700135','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19700135">Echocardiography and risk prediction in advanced heart failure: incremental value over clinical markers.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Agha, Syed A; Kalogeropoulos, Andreas P; Shih, Jeffrey; Georgiopoulou, Vasiliki V; Giamouzis, Grigorios; Anarado, Perry; Mangalat, Deepa; Hussain, Imad; Book, Wendy; Laskar, Sonjoy; Smith, Andrew L; Martin, Randolph; Butler, Javed 2009-09-01 Incremental value of echocardiography over clinical parameters for outcome prediction in advanced heart failure (HF) is not well established. We evaluated 223 patients with advanced HF receiving optimal therapy (91.9% angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 92.8% beta-blockers, 71.8% biventricular pacemaker, and/or defibrillator use). The Seattle Heart Failure Model (SHFM) was used as the reference clinical risk prediction scheme. The incremental value of echocardiographic parameters for event prediction (death or urgent heart transplantation) was measured by the improvement in fit and discrimination achieved by addition of standard echocardiographic parameters to the SHFM. After a median follow-up of 2.4 years, there were 38 (17.0%) events (35 deaths; 3 urgent transplants). The SHFM had likelihood ratio (LR) chi(2) 32.0 and C statistic 0.756 for event prediction. Left ventricular end-systolic volume, stroke volume, and severe tricuspid regurgitation were independent echocardiographic predictors of events. The addition of these parameters to SHFM improved LR chi(2) to 72.0 and C statistic to 0.866 (P < .001 and P=.019, respectively). Reclassifying the SHFM-predicted risk with use of the echocardiography-added model resulted in improved prognostic separation. Addition of standard echocardiographic variables to the SHFM results in significant improvement in risk prediction for patients with advanced HF. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1171529','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1171529">Appliance Efficiency Standards and Price Discrimination</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Spurlock, Cecily Anna 2013-05-08 I explore the effects of two simultaneous changes in minimum energy efficiency and ENERGY STAR standards for clothes washers. Adapting the Mussa and Rosen (1978) and Ronnen (1991) second-degree price discrimination model, I demonstrate that clothes washer prices and menus adjusted to the new standards in patterns consistent with a market in which firms had been price discriminating. In particular, I show evidence of discontinuous price drops at the time the standards were imposed, driven largely by mid-low efficiency segments of the market. The price discrimination model predicts this result. On the other hand, in a perfectly competition market, pricesmore » should increase for these market segments. Additionally, new models proliferated in the highest efficiency market segment following the standard changes. Finally, I show that firms appeared to use different adaptation strategies at the two instances of the standards changing.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26589828','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26589828">Optimal weighted combinatorial forecasting model of QT dispersion of ECGs in Chinese adults.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Wen, Zhang; Miao, Ge; Xinlei, Liu; Minyi, Cen 2016-07-01 This study aims to provide a scientific basis for unifying the reference value standard of QT dispersion of ECGs in Chinese adults. Three predictive models including regression model, principal component model, and artificial neural network model are combined to establish the optimal weighted combination model. The optimal weighted combination model and single model are verified and compared. Optimal weighted combinatorial model can reduce predicting risk of single model and improve the predicting precision. The reference value of geographical distribution of Chinese adults' QT dispersion was precisely made by using kriging methods. When geographical factors of a particular area are obtained, the reference value of QT dispersion of Chinese adults in this area can be estimated by using optimal weighted combinatorial model and reference value of the QT dispersion of Chinese adults anywhere in China can be obtained by using geographical distribution figure as well. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29886747','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29886747">Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Holcomb, David A; Messier, Kyle P; Serre, Marc L; Rowny, Jakob G; Stewart, Jill R 2018-06-25 Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.usgs.gov/fs/2013/3069/pdf/fs2013-3069.pdf','USGSPUBS'); return false;" href="https://pubs.usgs.gov/fs/2013/3069/pdf/fs2013-3069.pdf">Real-time assessments of water quality: expanding nowcasting throughout the Great Lakes</a> <a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a> , 2013-01-01 Nowcasts are systems that inform the public of current bacterial water-quality conditions at beaches on the basis of predictive models. During 2010–12, the U.S. Geological Survey (USGS) worked with 23 local and State agencies to improve existing operational beach nowcast systems at 4 beaches and expand the use of predictive models in nowcasts at an additional 45 beaches throughout the Great Lakes. The predictive models were specific to each beach, and the best model for each beach was based on a unique combination of environmental and water-quality explanatory variables. The variables used most often in models to predict Escherichia coli (E. coli) concentrations or the probability of exceeding a State recreational water-quality standard included turbidity, day of the year, wave height, wind direction and speed, antecedent rainfall for various time periods, and change in lake level over 24 hours. During validation of 42 beach models during 2012, the models performed better than the current method to assess recreational water quality (previous day's E. coli concentration). The USGS will continue to work with local agencies to improve nowcast predictions, enable technology transfer of predictive model development procedures, and implement more operational systems during 2013 and beyond. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/28281','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/28281">Predicting Debris-Slide Locations in Northwestern California</a> <a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a> Mark E. Reid; Stephen D. Ellen; Dianne L. Brien; Juan de la Fuente; James N. Falls; Billie G. Hicks; Eric C. Johnson 2007-01-01 We tested four topographic models for predicting locations of debris-slide sources: 1) slope; 2) proximity to stream; 3) SHALSTAB with "standard" parameters; and 4) debris-slide-prone landforms, which delineates areas similar to "inner gorge" and "headwall swale" using experience-based rules. These approaches were compared in three diverse... </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014APS..DFDD30008M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014APS..DFDD30008M">A study of two subgrid-scale models and their effects on wake breakdown behind a wind turbine in uniform inflow</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Martinez, Luis; Meneveau, Charles 2014-11-01 Large Eddy Simulations (LES) of the flow past a single wind turbine with uniform inflow have been performed. A goal of the simulations is to compare two turbulence subgrid-scale models and their effects in predicting the initial breakdown, transition and evolution of the wake behind the turbine. Prior works have often observed negligible sensitivities to subgrid-scale models. The flow is modeled using an in-house LES with pseudo-spectral discretization in horizontal planes and centered finite differencing in the vertical direction. Turbines are represented using the actuator line model. We compare the standard constant-coefficient Smagorinsky subgrid-scale model with the Lagrangian Scale Dependent Dynamic model (LSDM). The LSDM model predicts faster transition to turbulence in the wake, whereas the standard Smagorinsky model predicts significantly delayed transition. The specified Smagorinsky coefficient is larger than the dynamic one on average, increasing diffusion thus delaying transition. A second goal is to compare the resulting near-blade properties such as local aerodynamic forces from the LES with Blade Element Momentum Theory. Results will also be compared with those of the SOWFA package, the wind energy CFD framework from NREL. This work is supported by NSF (IGERT and IIA-1243482) and computations use XSEDE resources, and has benefitted from interactions with Dr. M. Churchfield of NREL. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://rosap.ntl.bts.gov/view/dot/10018','DOTNTL'); return false;" href="https://rosap.ntl.bts.gov/view/dot/10018">Heliport noise model : methodology - draft report</a> <a target="_blank" rel="noopener noreferrer" href="http://ntlsearch.bts.gov/tris/index.do">DOT National Transportation Integrated Search</a> 1988-04-30 The Heliport Noise Model (HNM) is the United States standard for predicting civil helicopter noise exposure in the vicinity of heliports and airports. HNM Version 1 is the culmination of several years of work in helicopter noise research, field measu... </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1253596','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1253596">Constraints on Models for the Higgs Boson with Exotic Spin and Parity</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Johnson, Emily Hannah The production of a Higgs boson in association with a vector boson at the Tevatron offers a unique opportunity to study models for the Higgs boson with exotic spin J and parity P assignments. At the Tevatron the V H system is produced near threshold. Different JP assignments of the Higgs boson can be distinguished by examining the behavior of the cross section near threshold. The relatively low backgrounds at the Tevatron compared to the LHC put us in a unique position to study the direct decay of the Higgs boson to fermions. If the Higgs sector is more complexmore » than predicted, studying the spin and parity of the Higgs boson in all decay modes is important. In this Thesis we will examine the WH → ℓνb¯b production and decay mode using 9.7 fb -1 of data collected by the D0 experiment in an attempt to derive constraints on models containing exotic values for the spin and parity of the Higgs boson. In particular, we will examine models for a Higgs boson with JP = 0- and JP = 2+. We use a likelihood ratio to quantify the degree to which our data are incompatible with exotic JP predictions for a range of possible production rates. Assuming the production cross section times branching ratio of the signals in the models considered is equal to the standard model prediction, the WH → ℓνb¯b mode alone is unable to reject either exotic model considered. We will also discuss the combination of the ZH → ℓℓb¯b, WH → ℓνb¯b, and V H → ννb¯b production modes at the D0 experiment and with the CDF experiment. When combining all three production modes at the D0 experiment we reject the JP = 0- and JP = 2+ hypotheses at the 97.6% CL and at the 99.0% CL, respectively, when assuming the signal production cross section times branching ratio is equal to the standard model predicted value. When combining with the CDF experiment we reject the JP = 0- and JP = 2+ hypotheses with significances of 5.0 standard deviations and 4.9 standard deviations, respectively.abstract« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013PhRvD..88c1103A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013PhRvD..88c1103A">Signature-based search for delayed photons in exclusive photon plus missing transverse energy events from pp¯ collisions with s=1.96TeV</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Aaltonen, T.; Amerio, S.; Amidei, D.; Anastassov, A.; Annovi, A.; Antos, J.; Apollinari, G.; Appel, J. A.; Arisawa, T.; Artikov, A.; Asaadi, J.; Ashmanskas, W.; Auerbach, B.; Aurisano, A.; Azfar, F.; Badgett, W.; Bae, T.; Barbaro-Galtieri, A.; Barnes, V. E.; Barnett, B. A.; Barria, P.; Bartos, P.; Bauce, M.; Bedeschi, F.; Behari, S.; Bellettini, G.; Bellinger, J.; Benjamin, D.; Beretvas, A.; Bhatti, A.; Bland, K. R.; Blumenfeld, B.; Bocci, A.; Bodek, A.; Bortoletto, D.; Boudreau, J.; Boveia, A.; Brigliadori, L.; Bromberg, C.; Brucken, E.; Budagov, J.; Budd, H. S.; Burkett, K.; Busetto, G.; Bussey, P.; Butti, P.; Buzatu, A.; Calamba, A.; Camarda, S.; Campanelli, M.; Canelli, F.; Carls, B.; Carlsmith, D.; Carosi, R.; Carrillo, S.; Casal, B.; Casarsa, M.; Castro, A.; Catastini, P.; Cauz, D.; Cavaliere, V.; Cavalli-Sforza, M.; Cerri, A.; Cerrito, L.; Chen, Y. C.; Chertok, M.; Chiarelli, G.; Chlachidze, G.; Cho, K.; Chokheli, D.; Ciocci, M. A.; Clark, A.; Clarke, C.; Convery, M. E.; Conway, J.; Corbo, M.; Cordelli, M.; Cox, C. A.; Cox, D. J.; Cremonesi, M.; Cruz, D.; Cuevas, J.; Culbertson, R.; d'Ascenzo, N.; Datta, M.; De Barbaro, P.; Demortier, L.; Deninno, M.; d'Errico, M.; Devoto, F.; Di Canto, A.; Di Ruzza, B.; Dittmann, J. R.; D'Onofrio, M.; Donati, S.; Dorigo, M.; Driutti, A.; Ebina, K.; Edgar, R.; Elagin, A.; Erbacher, R.; Errede, S.; Esham, B.; Eusebi, R.; Farrington, S.; Fernández Ramos, J. P.; Field, R.; Flanagan, G.; Forrest, R.; Franklin, M.; Freeman, J. C.; Frisch, H.; Funakoshi, Y.; Garfinkel, A. F.; Garosi, P.; Gerberich, H.; Gerchtein, E.; Giagu, S.; Giakoumopoulou, V.; Gibson, K.; Ginsburg, C. M.; Giokaris, N.; Giromini, P.; Giurgiu, G.; Glagolev, V.; Glenzinski, D.; Gold, M.; Goldin, D.; Golossanov, A.; Gomez, G.; Gomez-Ceballos, G.; Goncharov, M.; González López, O.; Gorelov, I.; Goshaw, A. T.; Goulianos, K.; Gramellini, E.; Grinstein, S.; Grosso-Pilcher, C.; Group, R. C.; Guimaraes da Costa, J.; Hahn, S. R.; Han, J. Y.; Happacher, F.; Hara, K.; Hare, M.; Harr, R. F.; Harrington-Taber, T.; Hatakeyama, K.; Hays, C.; Heinrich, J.; Herndon, M.; Hocker, A.; Hong, Z.; Hopkins, W.; Hou, S.; Hughes, R. E.; Husemann, U.; Hussein, M.; Huston, J.; Introzzi, G.; Iori, M.; Ivanov, A.; James, E.; Jang, D.; Jayatilaka, B.; Jeon, E. J.; Jindariani, S.; Jones, M.; Joo, K. K.; Jun, S. Y.; Junk, T. R.; Kambeitz, M.; Kamon, T.; Karchin, P. E.; Kasmi, A.; Kato, Y.; Ketchum, W.; Keung, J.; Kilminster, B.; Kim, D. H.; Kim, H. S.; Kim, J. E.; Kim, M. J.; Kim, S. B.; Kim, S. H.; Kim, Y. J.; Kim, Y. K.; Kimura, N.; Kirby, M.; Knoepfel, K.; Kondo, K.; Kong, D. J.; Konigsberg, J.; Kotwal, A. V.; Kreps, M.; Kroll, J.; Kruse, M.; Kuhr, T.; Kurata, M.; Laasanen, A. T.; Lammel, S.; Lancaster, M.; Lannon, K.; Latino, G.; Lee, H. S.; Lee, J. S.; Leo, S.; Leone, S.; Lewis, J. D.; Limosani, A.; Lipeles, E.; Lister, A.; Liu, H.; Liu, Q.; Liu, T.; Lockwitz, S.; Loginov, A.; Lucà, A.; Lucchesi, D.; Lueck, J.; Lujan, P.; Lukens, P.; Lungu, G.; Lys, J.; Lysak, R.; Madrak, R.; Maestro, P.; Malik, S.; Manca, G.; Manousakis-Katsikakis, A.; Margaroli, F.; Marino, P.; Martínez, M.; Matera, K.; Mattson, M. E.; Mazzacane, A.; Mazzanti, P.; McNulty, R.; Mehta, A.; Mehtala, P.; Mesropian, C.; Miao, T.; Mietlicki, D.; Mitra, A.; Miyake, H.; Moed, S.; Moggi, N.; Moon, C. S.; Moore, R.; Morello, M. J.; Mukherjee, A.; Muller, Th.; Murat, P.; Mussini, M.; Nachtman, J.; Nagai, Y.; Naganoma, J.; Nakano, I.; Napier, A.; Nett, J.; Neu, C.; Nigmanov, T.; Nodulman, L.; Noh, S. Y.; Norniella, O.; Oakes, L.; Oh, S. H.; Oh, Y. D.; Oksuzian, I.; Okusawa, T.; Orava, R.; Ortolan, L.; Pagliarone, C.; Palencia, E.; Palni, P.; Papadimitriou, V.; Parker, W.; Pauletta, G.; Paulini, M.; Paus, C.; Phillips, T. J.; Piacentino, G.; Pianori, E.; Pilot, J.; Pitts, K.; Plager, C.; Pondrom, L.; Poprocki, S.; Potamianos, K.; Pranko, A.; Prokoshin, F.; Ptohos, F.; Punzi, G.; Ranjan, N.; Redondo Fernández, I.; Renton, P.; Rescigno, M.; Rimondi, F.; Ristori, L.; Robson, A.; Rodriguez, T.; Rolli, S.; Ronzani, M.; Roser, R.; Rosner, J. L.; Ruffini, F.; Ruiz, A.; Russ, J.; Rusu, V.; Sakumoto, W. K.; Sakurai, Y.; Santi, L.; Sato, K.; Saveliev, V.; Savoy-Navarro, A.; Schlabach, P.; Schmidt, E. E.; Schwarz, T.; Scodellaro, L.; Scuri, F.; Seidel, S.; Seiya, Y.; Semenov, A.; Sforza, F.; Shalhout, S. Z.; Shears, T.; Shepard, P. F.; Shimojima, M.; Shochet, M.; Shreyber-Tecker, I.; Simonenko, A.; Sinervo, P.; Sliwa, K.; Smith, J. R.; Snider, F. D.; Song, H.; Sorin, V.; Stancari, M.; Denis, R. St.; Stelzer, B.; Stelzer-Chilton, O.; Stentz, D.; Strologas, J.; Sudo, Y.; Sukhanov, A.; Suslov, I.; Takemasa, K.; Takeuchi, Y.; Tang, J.; Tecchio, M.; Teng, P. K.; Thom, J.; Thomson, E.; Thukral, V.; Toback, D.; Tokar, S.; Tollefson, K.; Tomura, T.; Tonelli, D.; Torre, S.; Torretta, D.; Totaro, P.; Trovato, M.; Ukegawa, F.; Uozumi, S.; Vázquez, F.; Velev, G.; Vellidis, C.; Vernieri, C.; Vidal, M.; Vilar, R.; Vizán, J.; Vogel, M.; Volpi, G.; Wagner, P.; Wallny, R.; Wang, S. M.; Warburton, A.; Waters, D.; Wester, W. C., III; Whiteson, D.; Wicklund, A. B.; Wilbur, S.; Williams, H. H.; Wilson, J. S.; Wilson, P.; Winer, B. L.; Wittich, P.; Wolbers, S.; Wolfe, H.; Wright, T.; Wu, X.; Wu, Z.; Yamamoto, K.; Yamato, D.; Yang, T.; Yang, U. K.; Yang, Y. C.; Yao, W.-M.; Yeh, G. P.; Yi, K.; Yoh, J.; Yorita, K.; Yoshida, T.; Yu, G. B.; Yu, I.; Zanetti, A. M.; Zeng, Y.; Zhou, C.; Zucchelli, S. 2013-08-01 We present the first signature-based search for delayed photons using an exclusive photon plus missing transverse energy final state. Events are reconstructed in a data sample from the CDF II detector corresponding to 6.3fb-1 of integrated luminosity from s=1.96TeV proton-antiproton collisions. Candidate events are selected if they contain a photon with an arrival time in the detector larger than expected from a promptly produced photon. The mean number of events from standard model sources predicted by the data-driven background model based on the photon timing distribution is 286±24. A total of 322 events are observed. A p value of 12% is obtained, showing consistency of the data with standard model predictions. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29086047','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29086047">RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Kaspi, Omer; Yosipof, Abraham; Senderowitz, Hanoch 2017-06-06 An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a "one stop shop" algorithm for developing and validating QSAR models, performing outlier removal, descriptors selection, model development and predictions for test set samples using applicability domain. For "future" predictions (i.e., for samples not included in the original test set) RANSAC provides a statistical estimate for the probability of obtaining reliable predictions, i.e., predictions within a pre-defined number of standard deviations from the true values. In this work we describe the first application of RNASAC in material informatics, focusing on the analysis of solar cells. We demonstrate that for three datasets representing different metal oxide (MO) based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties. These models were subsequently used to predict the properties of virtual solar cells libraries highlighting interesting dependencies of PV properties on MO compositions. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5731680','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5731680">A backwards glance at words: Using reversed-interior masked primes to test models of visual word identification</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Lupker, Stephen J. 2017-01-01 The experiments reported here used “Reversed-Interior” (RI) primes (e.g., cetupmor-COMPUTER) in three different masked priming paradigms in order to test between different models of orthographic coding/visual word recognition. The results of Experiment 1, using a standard masked priming methodology, showed no evidence of priming from RI primes, in contrast to the predictions of the Bayesian Reader and LTRS models. By contrast, Experiment 2, using a sandwich priming methodology, showed significant priming from RI primes, in contrast to the predictions of open bigram models, which predict that there should be no orthographic similarity between these primes and their targets. Similar results were obtained in Experiment 3, using a masked prime same-different task. The results of all three experiments are most consistent with the predictions derived from simulations of the Spatial-coding model. PMID:29244824 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1253623-bigg-models-platform-integrating-standardizing-sharing-genome-scale-models','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1253623-bigg-models-platform-integrating-standardizing-sharing-genome-scale-models">BiGG Models: A platform for integrating, standardizing and sharing genome-scale models</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> King, Zachary A.; Lu, Justin; Drager, Andreas; ... 2015-10-17 In this study, genome-scale metabolic models are mathematically structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scalemore » metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702785','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702785">BiGG Models: A platform for integrating, standardizing and sharing genome-scale models</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> King, Zachary A.; Lu, Justin; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A.; Ebrahim, Ali; Palsson, Bernhard O.; Lewis, Nathan E. 2016-01-01 Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data. PMID:26476456 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/18023062','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/18023062">Multivariate models for prediction of rheological characteristics of filamentous fermentation broth from the size distribution.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Petersen, Nanna; Stocks, Stuart; Gernaey, Krist V 2008-05-01 The main purpose of this article is to demonstrate that principal component analysis (PCA) and partial least squares regression (PLSR) can be used to extract information from particle size distribution data and predict rheological properties. Samples from commercially relevant Aspergillus oryzae fermentations conducted in 550 L pilot scale tanks were characterized with respect to particle size distribution, biomass concentration, and rheological properties. The rheological properties were described using the Herschel-Bulkley model. Estimation of all three parameters in the Herschel-Bulkley model (yield stress (tau(y)), consistency index (K), and flow behavior index (n)) resulted in a large standard deviation of the parameter estimates. The flow behavior index was not found to be correlated with any of the other measured variables and previous studies have suggested a constant value of the flow behavior index in filamentous fermentations. It was therefore chosen to fix this parameter to the average value thereby decreasing the standard deviation of the estimates of the remaining rheological parameters significantly. Using a PLSR model, a reasonable prediction of apparent viscosity (micro(app)), yield stress (tau(y)), and consistency index (K), could be made from the size distributions, biomass concentration, and process information. This provides a predictive method with a high predictive power for the rheology of fermentation broth, and with the advantages over previous models that tau(y) and K can be predicted as well as micro(app). Validation on an independent test set yielded a root mean square error of 1.21 Pa for tau(y), 0.209 Pa s(n) for K, and 0.0288 Pa s for micro(app), corresponding to R(2) = 0.95, R(2) = 0.94, and R(2) = 0.95 respectively. Copyright 2007 Wiley Periodicals, Inc. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22189378','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22189378">A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Mirkhani, Seyyed Alireza; Gharagheizi, Farhad; Sattari, Mehdi 2012-03-01 Evaluation of diffusion coefficients of pure compounds in air is of great interest for many diverse industrial and air quality control applications. In this communication, a QSPR method is applied to predict the molecular diffusivity of chemical compounds in air at 298.15K and atmospheric pressure. Four thousand five hundred and seventy nine organic compounds from broad spectrum of chemical families have been investigated to propose a comprehensive and predictive model. The final model is derived by Genetic Function Approximation (GFA) and contains five descriptors. Using this dedicated model, we obtain satisfactory results quantified by the following statistical results: Squared Correlation Coefficient=0.9723, Standard Deviation Error=0.003 and Average Absolute Relative Deviation=0.3% for the predicted properties from existing experimental values. Copyright © 2011 Elsevier Ltd. All rights reserved. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active">16</li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active">17</li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007IJBm...51..169V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007IJBm...51..169V">Validation of an individualised model of human thermoregulation for predicting responses to cold air</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> van Marken Lichtenbelt, Wouter D.; Frijns, Arjan J. H.; van Ooijen, Marieke J.; Fiala, Dusan; Kester, Arnold M.; van Steenhoven, Anton A. 2007-01-01 Most computer models of human thermoregulation are population based. Here, we individualised the Fiala model [Fiala et al. (2001) Int J Biometeorol 45:143 159] with respect to anthropometrics, body fat, and metabolic rate. The predictions of the adapted multisegmental thermoregulatory model were compared with measured skin temperatures of individuals. Data from two experiments, in which reclining subjects were suddenly exposed to mild to moderate cold environmental conditions, were used to study the effect on dynamic skin temperature responses. Body fat was measured by the three-compartment method combining underwater weighing and deuterium dilution. Metabolic rate was determined by indirect calorimetry. In experiment 1, the bias (mean difference) between predicted and measured mean skin temperature decreased from 1.8°C to -0.15°C during cold exposure. The standard deviation of the mean difference remained of the same magnitude (from 0.7°C to 0.9°C). In experiment 2 the bias of the skin temperature changed from 2.0±1.09°C using the standard model to 1.3±0.93°C using individual characteristics in the model. The inclusion of individual characteristics thus improved the predictions for an individual and led to a significantly smaller systematic error. However, a large part of the discrepancies in individual response to cold remained unexplained. Possible further improvements to the model accomplished by inclusion of more subject characteristics (i.e. body fat distribution, body shape) and model refinements on the level of (skin) blood perfusion, and control functions, are discussed. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17106897','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17106897">Material model measurements and predictions for a random pore poly(epsilon-caprolactone) scaffold.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Quinn, T P; Oreskovic, T L; Landis, F A; Washburn, N R 2007-07-01 We investigated material models for a polymeric scaffold used for bone. The material was made by co-extruding poly(epsilon-caprolactone) (PCL), a biodegradable polyester, and poly(ethylene oxide) (PEO). The water soluble PEO was removed resulting in a porous scaffold. The stress-strain curve in compression was fit with a phenomenological model in hyperbolic form. This material model will be useful for designers for quasi-static analysis as it provides a simple form that can easily be used in finite element models. The ASTM D-1621 standard recommends using a secant modulus based on 10% strain. The resulting modulus has a smaller scatter in its value compared with the coefficients of the hyperbolic model, and it is therefore easier to compare differences in material processing and ensure quality of the scaffold. A prediction of the small-strain elastic modulus was constructed from images of the microstructure. Each pixel of the micrographs was represented with a brick finite element and assigned the Young's modulus of bulk PCL or a value of 0 for a pore. A compressive strain was imposed on the model and the resulting stresses were calculated. The elastic constants of the scaffold were then computed with Hooke's law for a linear-elastic isotropic material. The model was able to predict the small-strain elastic modulus measured in the experiments to within one standard deviation. Thus, by knowing the microstructure of the scaffold, its bulk properties can be predicted from the material properties of the constituents. Copyright 2006 Wiley Periodicals, Inc. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JPS...378..311S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JPS...378..311S">New battery model considering thermal transport and partial charge stationary effects in photovoltaic off-grid applications</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Sanz-Gorrachategui, Iván; Bernal, Carlos; Oyarbide, Estanis; Garayalde, Erik; Aizpuru, Iosu; Canales, Jose María; Bono-Nuez, Antonio 2018-02-01 The optimization of the battery pack in an off-grid Photovoltaic application must consider the minimum sizing that assures the availability of the system under the worst environmental conditions. Thus, it is necessary to predict the evolution of the state of charge of the battery under incomplete daily charging and discharging processes and fluctuating temperatures over day-night cycles. Much of previous development work has been carried out in order to model the short term evolution of battery variables. Many works focus on the on-line parameter estimation of available charge, using standard or advanced estimators, but they are not focused on the development of a model with predictive capabilities. Moreover, normally stable environmental conditions and standard charge-discharge patterns are considered. As the actual cycle-patterns differ from the manufacturer's tests, batteries fail to perform as expected. This paper proposes a novel methodology to model these issues, with predictive capabilities to estimate the remaining charge in a battery after several solar cycles. A new non-linear state space model is proposed as a basis, and the methodology to feed and train the model is introduced. The new methodology is validated using experimental data, providing only 5% of error at higher temperatures than the nominal one. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160006277','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160006277">Random Predictor Models for Rigorous Uncertainty Quantification: Part 2</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P. 2015-01-01 This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean, the variance, and the range of the model's parameter, thus of the output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, is bounded rigorously. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24946833','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24946833">Predicting use of ineffective vegetable parenting practices with the Model of Goal Directed Behavior.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Baranowski, Tom; Beltran, Alicia; Chen, Tzu-An; Thompson, Debbe; O'Connor, Teresia; Hughes, Sheryl; Diep, Cassandra; Baranowski, Janice C 2015-04-01 Increasing a parent's ability to influence a child's vegetable intake may require reducing the parent's use of ineffective vegetable parenting practices. The present study was designed to understand the psychosocial influences on ineffective vegetable parenting practices. A cross-sectional web-based survey was conducted to model use of ineffective vegetable parenting practices using validated scales from a Model of Goal Directed Vegetable Parenting Practices. The dependent variable was a composite ineffective vegetable parenting practices index. The independent variables included validated subscales of intention, habit, perceived barriers, desire, competence, autonomy, relatedness, attitudes, norms, perceived behavioural control and anticipated emotions. Models were analysed using block regression with backward deletion. Parents of 307 pre-school children (3-5 years old). Variables significantly positively related to ineffective vegetable parenting practices in order of relationship strength included habit of controlling vegetable practices (standardized β=0·349, P<0·0001) and desire (standardized β=0·117, P=0·025). Variables significantly negatively related to ineffective vegetable parenting practices in order of relationship strength included perceived behavioural control of negative parenting practices (standardized β=-0·215, P<0000), the habit of active child involvement in vegetable selection (standardized β=-0·142, P=0·008), anticipated negative parent emotional response to child vegetable refusal (standardized β=-0·133, P=0·009), autonomy (standardized β=-0·118, P=0.014), attitude about negative effects of vegetables (standardized β=-0·118, P=0·015) and descriptive norms (standardized β=-0·103, P=0·032). The model accounted for 40·5 % of the variance in use of ineffective vegetable parenting practices. The present study is the first report of psychometrically tested scales to predict use of ineffective vegetable parenting practices. Innovative intervention procedures will need to be designed and tested to reduce ineffective vegetable parenting practices. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA616401','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA616401">ESPC Common Model Architecture</a> <a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a> 2014-09-30 1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. ESPC Common Model Architecture Earth System Modeling...Operational Prediction Capability (NUOPC) was established between NOAA and Navy to develop common software architecture for easy and efficient...development under a common model architecture and other software-related standards in this project. OBJECTIVES NUOPC proposes to accelerate </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA552926','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA552926">Optimizing Hyperspectral Imagery Anomaly Detection through Robust Parameter Design</a> <a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a> 2011-10-01 72 3.2.1 Standard RSM Model ( y (1)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.2.2 RPD Model Including N ×N ( y (2...LT surface plot for y (1) model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 3.6. LT surface plot for y (2) model...88 3.12. AutoGAD y (1) residual versus predicted plot. . . . . . . . . . . . . . . . . . . . . . . . 96 3.13 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1248216','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1248216">Heavy flavor decay of Zγ at CDF</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Timothy M. Harrington-Taber 2013-01-01 Diboson production is an important and frequently measured parameter of the Standard Model. This analysis considers the previously neglected pmore » $$\\bar{p}$$ →Z γ→ b$$\\bar{b}$$ channel, as measured at the Collider Detector at Fermilab. Using the entire Tevatron Run II dataset, the measured result is consistent with Standard Model predictions, but the statistical error associated with this method of measurement limits the strength of this correlation.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/21432936-searching-fourth-family-quarks-through-anomalous-decays','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/21432936-searching-fourth-family-quarks-through-anomalous-decays">Searching for the fourth family quarks through anomalous decays</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Sahin, M.; Sultansoy, S.; Turkoz, S. 2010-09-01 The flavor democracy hypothesis predicts the existence of the fourth standard model family. Because of the high masses of the fourth family quarks, their anomalous decays could be dominant if certain criteria are met. This will drastically change the search strategy at hadron colliders. We show that the fourth standard model family down quarks with masses up to 400-450 GeV can be observed (or excluded) via anomalous decays by Tevatron. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28000608','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28000608">CP violation in the B system.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Gershon, T; Gligorov, V V 2017-04-01 The phenomenon of CP violation is crucial to understand the asymmetry between matter and antimatter that exists in the Universe. Dramatic experimental progress has been made, in particular in measurements of the behaviour of particles containing the b quark, where CP violation effects are predicted by the Kobayashi-Maskawa mechanism that is embedded in the standard model. The status of these measurements and future prospects for an understanding of CP violation beyond the standard model are reviewed. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AIPC..698..168K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AIPC..698..168K">Probing Supersymmetry with Neutral Current Scattering Experiments</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Kurylov, A.; Ramsey-Musolf, M. J.; Su, S. 2004-02-01 We compute the supersymmetric contributions to the weak charges of the electron (QWe) and proton (QWp) in the framework of Minimal Supersymmetric Standard Model. We also consider the ratio of neutral current to charged current cross sections, R v and Rv¯ at v (v¯)-nucleus deep inelastic scattering, and compare the supersymmetric corrections with the deviations of these quantities from the Standard Model predictions implied by the recent NuTeV measurement. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70111686','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70111686">Partial least squares for efficient models of fecal indicator bacteria on Great Lakes beaches</a> <a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a> Brooks, Wesley R.; Fienen, Michael N.; Corsi, Steven R. 2013-01-01 At public beaches, it is now common to mitigate the impact of water-borne pathogens by posting a swimmer's advisory when the concentration of fecal indicator bacteria (FIB) exceeds an action threshold. Since culturing the bacteria delays public notification when dangerous conditions exist, regression models are sometimes used to predict the FIB concentration based on readily-available environmental measurements. It is hard to know which environmental parameters are relevant to predicting FIB concentration, and the parameters are usually correlated, which can hurt the predictive power of a regression model. Here the method of partial least squares (PLS) is introduced to automate the regression modeling process. Model selection is reduced to the process of setting a tuning parameter to control the decision threshold that separates predicted exceedances of the standard from predicted non-exceedances. The method is validated by application to four Great Lakes beaches during the summer of 2010. Performance of the PLS models compares favorably to that of the existing state-of-the-art regression models at these four sites. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JPS...277..239P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JPS...277..239P">Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Pohjoranta, Antti; Halinen, Matias; Pennanen, Jari; Kiviaho, Jari 2015-03-01 Generalized predictive control (GPC) is applied to control the maximum temperature in a solid oxide fuel cell (SOFC) stack and the temperature difference over the stack. GPC is a model predictive control method and the models utilized in this work are ARX-type (autoregressive with extra input), multiple input-multiple output, polynomial models that were identified from experimental data obtained from experiments with a complete SOFC system. The proposed control is evaluated by simulation with various input-output combinations, with and without constraints. A comparison with conventional proportional-integral-derivative (PID) control is also made. It is shown that if only the stack maximum temperature is controlled, a standard PID controller can be used to obtain output performance comparable to that obtained with the significantly more complex model predictive controller. However, in order to control the temperature difference over the stack, both the stack minimum and the maximum temperature need to be controlled and this cannot be done with a single PID controller. In such a case the model predictive controller provides a feasible and effective solution. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22716998','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22716998">Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Austin, Peter C; Steyerberg, Ewout W 2012-06-20 When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26257587','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26257587">Regularization Paths for Conditional Logistic Regression: The clogitL1 Package.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Reid, Stephen; Tibshirani, Rob 2014-07-01 We apply the cyclic coordinate descent algorithm of Friedman, Hastie, and Tibshirani (2010) to the fitting of a conditional logistic regression model with lasso [Formula: see text] and elastic net penalties. The sequential strong rules of Tibshirani, Bien, Hastie, Friedman, Taylor, Simon, and Tibshirani (2012) are also used in the algorithm and it is shown that these offer a considerable speed up over the standard coordinate descent algorithm with warm starts. Once implemented, the algorithm is used in simulation studies to compare the variable selection and prediction performance of the conditional logistic regression model against that of its unconditional (standard) counterpart. We find that the conditional model performs admirably on datasets drawn from a suitable conditional distribution, outperforming its unconditional counterpart at variable selection. The conditional model is also fit to a small real world dataset, demonstrating how we obtain regularization paths for the parameters of the model and how we apply cross validation for this method where natural unconditional prediction rules are hard to come by. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4527184','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4527184">Regularization Paths for Conditional Logistic Regression: The clogitL1 Package</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Reid, Stephen; Tibshirani, Rob 2014-01-01 We apply the cyclic coordinate descent algorithm of Friedman, Hastie, and Tibshirani (2010) to the fitting of a conditional logistic regression model with lasso (ℓ1) and elastic net penalties. The sequential strong rules of Tibshirani, Bien, Hastie, Friedman, Taylor, Simon, and Tibshirani (2012) are also used in the algorithm and it is shown that these offer a considerable speed up over the standard coordinate descent algorithm with warm starts. Once implemented, the algorithm is used in simulation studies to compare the variable selection and prediction performance of the conditional logistic regression model against that of its unconditional (standard) counterpart. We find that the conditional model performs admirably on datasets drawn from a suitable conditional distribution, outperforming its unconditional counterpart at variable selection. The conditional model is also fit to a small real world dataset, demonstrating how we obtain regularization paths for the parameters of the model and how we apply cross validation for this method where natural unconditional prediction rules are hard to come by. PMID:26257587 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2045089','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2045089">A method for modelling GP practice level deprivation scores using GIS</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Strong, Mark; Maheswaran, Ravi; Pearson, Tim; Fryers, Paul 2007-01-01 Background A measure of general practice level socioeconomic deprivation can be used to explore the association between deprivation and other practice characteristics. An area-based categorisation is commonly chosen as the basis for such a deprivation measure. Ideally a practice population-weighted area-based deprivation score would be calculated using individual level spatially referenced data. However, these data are often unavailable. One approach is to link the practice postcode to an area-based deprivation score, but this method has limitations. This study aimed to develop a Geographical Information Systems (GIS) based model that could better predict a practice population-weighted deprivation score in the absence of patient level data than simple practice postcode linkage. Results We calculated predicted practice level Index of Multiple Deprivation (IMD) 2004 deprivation scores using two methods that did not require patient level data. Firstly we linked the practice postcode to an IMD 2004 score, and secondly we used a GIS model derived using data from Rotherham, UK. We compared our two sets of predicted scores to "gold standard" practice population-weighted scores for practices in Doncaster, Havering and Warrington. Overall, the practice postcode linkage method overestimated "gold standard" IMD scores by 2.54 points (95% CI 0.94, 4.14), whereas our modelling method showed no such bias (mean difference 0.36, 95% CI -0.30, 1.02). The postcode-linked method systematically underestimated the gold standard score in less deprived areas, and overestimated it in more deprived areas. Our modelling method showed a small underestimation in scores at higher levels of deprivation in Havering, but showed no bias in Doncaster or Warrington. The postcode-linked method showed more variability when predicting scores than did the GIS modelling method. Conclusion A GIS based model can be used to predict a practice population-weighted area-based deprivation measure in the absence of patient level data. Our modelled measure generally had better agreement with the population-weighted measure than did a postcode-linked measure. Our model may also avoid an underestimation of IMD scores in less deprived areas, and overestimation of scores in more deprived areas, seen when using postcode linked scores. The proposed method may be of use to researchers who do not have access to patient level spatially referenced data. PMID:17822545 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1253179-interferometric-tests-planckian-quantum-geometry-models','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1253179-interferometric-tests-planckian-quantum-geometry-models">Interferometric tests of Planckian quantum geometry models</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Kwon, Ohkyung; Hogan, Craig J. 2016-04-19 The effect of Planck scale quantum geometrical effects on measurements with interferometers is estimated with standard physics, and with a variety of proposed extensions. It is shown that effects are negligible in standard field theory with canonically quantized gravity. Statistical noise levels are estimated in a variety of proposals for nonstandard metric fluctuations, and these alternatives are constrained using upper bounds on stochastic metric fluctuations from LIGO. Idealized models of several interferometer system architectures are used to predict signal noise spectra in a quantum geometry that cannot be described by a fluctuating metric, in which position noise arises from holographicmore » bounds on directional information. Lastly, predictions in this case are shown to be close to current and projected experimental bounds.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20366814','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20366814">Search for Higgs boson production in dilepton and missing energy final states with 5.4 fb(-1) of pp collisions at square root(s) = 1.96 TeV.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Abazov, V M; Abbott, B; Abolins, M; Acharya, B S; Adams, M; Adams, T; Aguilo, E; Alexeev, G D; Alkhazov, G; Alton, A; Alverson, G; Alves, G A; Ancu, L S; Aoki, M; Arnoud, Y; Arov, M; Askew, A; Asman, B; Atramentov, O; Avila, C; BackusMayes, J; Badaud, F; Bagby, L; Baldin, B; Bandurin, D V; Banerjee, S; Barberis, E; Barfuss, A-F; Baringer, P; Barreto, J; Bartlett, J F; Bassler, U; Bauer, D; Beale, S; Bean, A; Begalli, M; Begel, M; Belanger-Champagne, C; Bellantoni, L; Benitez, J A; Beri, S B; Bernardi, G; Bernhard, R; Bertram, I; Besançon, M; Beuselinck, R; Bezzubov, V A; Bhat, P C; Bhatnagar, V; Blazey, G; Blessing, S; Bloom, K; Boehnlein, A; Boline, D; Bolton, T A; Boos, E E; Borissov, G; Bose, T; Brandt, A; Brock, R; Brooijmans, G; Bross, A; Brown, D; Bu, X B; Buchholz, D; Buehler, M; Buescher, V; Bunichev, V; Burdin, S; Burnett, T H; Buszello, C P; Calfayan, P; Calpas, B; Calvet, S; Camacho-Pérez, E; Cammin, J; Carrasco-Lizarraga, M A; Carrera, E; Casey, B C K; Castilla-Valdez, H; Chakrabarti, S; Chakraborty, D; Chan, K M; Chandra, A; Cheu, E; Chevalier-Théry, S; Cho, D K; Cho, S W; Choi, S; Choudhary, B; Christoudias, T; Cihangir, S; Claes, D; Clutter, J; Cooke, M; Cooper, W E; Corcoran, M; Couderc, F; Cousinou, M-C; Cutts, D; Cwiok, M; Das, A; Davies, G; De, K; de Jong, S J; De la Cruz-Burelo, E; DeVaughan, K; Déliot, F; Demarteau, M; Demina, R; Denisov, D; Denisov, S P; Desai, S; Diehl, H T; Diesburg, M; Dominguez, A; Dorland, T; Dubey, A; Dudko, L V; Duflot, L; Duggan, D; Duperrin, A; Dutt, S; Dyshkant, A; Eads, M; Edmunds, D; Ellison, J; Elvira, V D; Enari, Y; Eno, S; Evans, H; Evdokimov, A; Evdokimov, V N; Facini, G; Ferapontov, A V; Ferbel, T; Fiedler, F; Filthaut, F; Fisher, W; Fisk, H E; Fortner, M; Fox, H; Fuess, S; Gadfort, T; Galea, C F; Garcia-Bellido, A; Gavrilov, V; Gay, P; Geist, W; Geng, W; Gerbaudo, D; Gerber, C E; Gershtein, Y; Gillberg, D; Ginther, G; Golovanov, G; Gómez, B; Goussiou, A; Grannis, P D; Greder, S; Greenlee, H; Greenwood, Z D; Gregores, E M; Grenier, G; Gris, Ph; Grivaz, J-F; Grohsjean, A; Grünendahl, S; Grünewald, M W; Guo, F; Guo, J; Gutierrez, G; Gutierrez, P; Haas, A; Haefner, P; Hagopian, S; Haley, J; Hall, I; Han, L; Harder, K; Harel, A; Hauptman, J M; Hays, J; Hebbeker, T; Hedin, D; Hegeman, J G; Heinson, A P; Heintz, U; Hensel, C; Heredia-De la Cruz, I; Herner, K; Hesketh, G; Hildreth, M D; Hirosky, R; Hoang, T; Hobbs, J D; Hoeneisen, B; Hohlfeld, M; Hossain, S; Houben, P; Hu, Y; Hubacek, Z; Huske, N; Hynek, V; Iashvili, I; Illingworth, R; Ito, A S; Jabeen, S; Jaffré, M; Jain, S; Jamin, D; Jesik, R; Johns, K; Johnson, C; Johnson, M; Johnston, D; Jonckheere, A; Jonsson, P; Juste, A; Kajfasz, E; Karmanov, D; Kasper, P A; Katsanos, I; Kaushik, V; Kehoe, R; Kermiche, S; Khalatyan, N; Khanov, A; Kharchilava, A; Kharzheev, Y N; Khatidze, D; Kirby, M H; Kirsch, M; Kohli, J M; Kozelov, A V; Kraus, J; Kumar, A; Kupco, A; Kurca, T; Kuzmin, V A; Kvita, J; Lam, D; Lammers, S; Landsberg, G; Lebrun, P; Lee, H S; Lee, W M; Leflat, A; Lellouch, J; Li, L; Li, Q Z; Lietti, S M; Lim, J K; Lincoln, D; Linnemann, J; Lipaev, V V; Lipton, R; Liu, Y; Liu, Z; Lobodenko, A; Lokajicek, M; Love, P; Lubatti, H J; Luna-Garcia, R; Lyon, A L; Maciel, A K A; Mackin, D; Mättig, P; Magaña-Villalba, R; Mal, P K; Malik, S; Malyshev, V L; Maravin, Y; Martínez-Ortega, J; McCarthy, R; McGivern, C L; Meijer, M M; Melnitchouk, A; Mendoza, L; Menezes, D; Mercadante, P G; Merkin, M; Meyer, A; Meyer, J; Mondal, N K; Moulik, T; Muanza, G S; Mulhearn, M; Mundal, O; Mundim, L; Nagy, E; Naimuddin, M; Narain, M; Nayyar, R; Neal, H A; Negret, J P; Neustroev, P; Nilsen, H; Nogima, H; Novaes, S F; Nunnemann, T; Obrant, G; Onoprienko, D; Orduna, J; Osman, N; Osta, J; Otec, R; Otero y Garzón, G J; Owen, M; Padilla, M; Padley, P; Pangilinan, M; Parashar, N; Parihar, V; Park, S-J; Park, S K; Parsons, J; Partridge, R; Parua, N; Patwa, A; Penning, B; Perfilov, M; Peters, K; Peters, Y; Pétroff, P; Piegaia, R; Piper, J; Pleier, M-A; Podesta-Lerma, P L M; Podstavkov, V M; Pol, M-E; Polozov, P; Popov, A V; Prewitt, M; Price, D; Protopopescu, S; Qian, J; Quadt, A; Quinn, B; Rangel, M S; Ranjan, K; Ratoff, P N; Razumov, I; Renkel, P; Rich, P; Rijssenbeek, M; Ripp-Baudot, I; Rizatdinova, F; Robinson, S; Rominsky, M; Royon, C; Rubinov, P; Ruchti, R; Safronov, G; Sajot, G; Sánchez-Hernández, A; Sanders, M P; Sanghi, B; Savage, G; Sawyer, L; Scanlon, T; Schaile, D; Schamberger, R D; Scheglov, Y; Schellman, H; Schliephake, T; Schlobohm, S; Schwanenberger, C; Schwienhorst, R; Sekaric, J; Severini, H; Shabalina, E; Shary, V; Shchukin, A A; Shivpuri, R K; Simak, V; Sirotenko, V; Skubic, P; Slattery, P; Smirnov, D; Snow, G R; Snow, J; Snyder, S; Söldner-Rembold, S; Sonnenschein, L; Sopczak, A; Sosebee, M; Soustruznik, K; Spurlock, B; Stark, J; Stolin, V; Stoyanova, D A; Strandberg, J; Strang, M A; Strauss, E; Strauss, M; Ströhmer, R; Strom, D; Stutte, L; Svoisky, P; Takahashi, M; Tanasijczuk, A; Taylor, W; Tiller, B; Titov, M; Tokmenin, V V; Tsybychev, D; Tuchming, B; Tully, C; Tuts, P M; Unalan, R; Uvarov, L; Uvarov, S; Uzunyan, S; van den Berg, P J; Van Kooten, R; van Leeuwen, W M; Varelas, N; Varnes, E W; Vasilyev, I A; Verdier, P; Vertogradov, L S; Verzocchi, M; Vesterinen, M; Vilanova, D; Vint, P; Vokac, P; Wahl, H D; Wang, M H L S; Warchol, J; Watts, G; Wayne, M; Weber, G; Weber, M; Wetstein, M; White, A; Wicke, D; Williams, M R J; Wilson, G W; Wimpenny, S J; Wobisch, M; Wood, D R; Wyatt, T R; Xie, Y; Xu, C; Yacoob, S; Yamada, R; Yang, W-C; Yasuda, T; Yatsunenko, Y A; Ye, Z; Yin, H; Yip, K; Yoo, H D; Youn, S W; Yu, J; Zeitnitz, C; Zelitch, S; Zhao, T; Zhou, B; Zhu, J; Zielinski, M; Zieminska, D; Zivkovic, L; Zutshi, V; Zverev, E G 2010-02-12 A search for the standard model Higgs boson is presented using events with two charged leptons and large missing transverse energy selected from 5.4 fb(-1) of integrated luminosity in pp collisions at square root(s) = 1.96 TeV collected with the D0 detector at the Fermilab Tevatron collider. No significant excess of events above background predictions is found, and observed (expected) upper limits at 95% confidence level on the rate of Higgs boson production are derived that are a factor of 1.55 (1.36) above the predicted standard model cross section at m(H) = 165 GeV. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1419649','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1419649">Search for Dark Matter Produced in Association with a Higgs Boson Decaying to b b ¯ Using 36 fb - 1 of p p Collisions at s = 13 TeV with the ATLAS Detector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Aaboud, M.; Aad, G.; Abbott, B. Several extensions of the standard model predict associated production of dark-matter particles with a Higgs boson. Such processes are searched for in final states with missing transverse momentum and a Higgs boson decaying to a b¯b pair with the ATLAS detector using 36.1 fb –1 of pp collisions at a center-of-mass energy of 13 TeV at the LHC. As a result, the observed data are in agreement with the standard model predictions and limits are placed on the associated production of dark-matter particles and a Higgs boson. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active">17</li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active">18</li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29219535','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29219535">Search for Dark Matter Produced in Association with a Higgs Boson Decaying to bb[over ¯] Using 36 fb^{-1} of pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Aaboud, M; Aad, G; Abbott, B; Abdinov, O; Abeloos, B; Abidi, S H; AbouZeid, O S; Abraham, N L; Abramowicz, H; Abreu, H; Abreu, R; Abulaiti, Y; Acharya, B S; Adachi, S; Adamczyk, L; Adelman, J; Adersberger, M; Adye, T; Affolder, A A; Agatonovic-Jovin, T; Agheorghiesei, C; Aguilar-Saavedra, J A; Ahlen, S P; Ahmadov, F; Aielli, G; Akatsuka, S; Akerstedt, H; Åkesson, T P A; Akilli, E; Akimov, A V; Alberghi, G L; Albert, J; Albicocco, P; Alconada Verzini, M J; Alderweireldt, S C; Aleksa, M; Aleksandrov, I N; Alexa, C; Alexander, G; Alexopoulos, T; Alhroob, M; Ali, B; Aliev, M; Alimonti, G; Alison, J; Alkire, S P; Allbrooke, B M M; Allen, B W; Allport, P P; Aloisio, A; Alonso, A; Alonso, F; Alpigiani, C; Alshehri, A A; Alstaty, M I; Alvarez Gonzalez, B; Álvarez Piqueras, D; Alviggi, M G; Amadio, B T; Amaral Coutinho, Y; Amelung, C; Amidei, D; Amor Dos Santos, S P; Amorim, A; Amoroso, S; Amundsen, G; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, J K; Anderson, K J; Andreazza, A; Andrei, V; Angelidakis, S; Angelozzi, I; Angerami, A; Anisenkov, A V; Anjos, N; Annovi, A; Antel, C; Antonelli, M; Antonov, A; Antrim, D J; Anulli, F; Aoki, M; Aperio Bella, L; Arabidze, G; Arai, Y; Araque, J P; Araujo Ferraz, V; Arce, A T H; Ardell, R E; Arduh, F A; Arguin, J-F; Argyropoulos, S; Arik, M; Armbruster, A J; Armitage, L J; Arnaez, O; Arnold, H; Arratia, M; Arslan, O; Artamonov, A; Artoni, G; Artz, S; Asai, S; Asbah, N; Ashkenazi, A; Asquith, L; Assamagan, K; Astalos, R; Atkinson, M; Atlay, N B; Augsten, K; Avolio, G; Axen, B; Ayoub, M K; Azuelos, G; Baas, A E; Baca, M J; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Bagnaia, P; Bahmani, M; Bahrasemani, H; Baines, J T; Bajic, M; Baker, O K; Baldin, E M; Balek, P; Balli, F; Balunas, W K; Banas, E; Bandyopadhyay, A; Banerjee, Sw; Bannoura, A A E; Barak, L; Barberio, E L; Barberis, D; Barbero, M; Barillari, T; Barisits, M-S; Barkeloo, J T; Barklow, T; Barlow, N; Barnes, S L; Barnett, B M; Barnett, R M; Barnovska-Blenessy, Z; Baroncelli, A; Barone, G; Barr, A J; Barranco Navarro, L; Barreiro, F; Barreiro Guimarães da Costa, J; Bartoldus, R; Barton, A E; Bartos, P; Basalaev, A; Bassalat, A; Bates, R L; Batista, S J; Batley, J R; Battaglia, M; Bauce, M; Bauer, F; Bawa, H S; Beacham, J B; Beattie, M D; Beau, T; Beauchemin, P H; Bechtle, P; Beck, H P; Beck, H C; Becker, K; Becker, M; Beckingham, M; Becot, C; Beddall, A J; Beddall, A; Bednyakov, V A; Bedognetti, M; Bee, C P; Beermann, T A; Begalli, M; Begel, M; Behr, J K; Bell, A S; Bella, G; Bellagamba, L; Bellerive, A; Bellomo, M; Belotskiy, K; Beltramello, O; Belyaev, N L; Benary, O; Benchekroun, D; Bender, M; 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Such processes are searched for in final states with missing transverse momentum and a Higgs boson decaying to a bb[over ¯] pair with the ATLAS detector using 36.1 fb^{-1} of pp collisions at a center-of-mass energy of 13 TeV at the LHC. The observed data are in agreement with the standard model predictions and limits are placed on the associated production of dark-matter particles and a Higgs boson. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28368573','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28368573">Predicting ESI/MS Signal Change for Anions in Different Solvents.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Kruve, Anneli; Kaupmees, Karl 2017-05-02 LC/ESI/MS is a technique widely used for qualitative and quantitative analysis in various fields. However, quantification is currently possible only for compounds for which the standard substances are available, as the ionization efficiency of different compounds in ESI source differs by orders of magnitude. In this paper we present an approach for quantitative LC/ESI/MS analysis without standard substances. This approach relies on accurately predicting the ionization efficiencies in ESI source based on a model, which uses physicochemical parameters of analytes. Furthermore, the model has been made transferable between different mobile phases and instrument setups by using a suitable set of calibration compounds. This approach has been validated both in flow injection and chromatographic mode with gradient elution. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1419649-search-dark-matter-produced-association-higgs-boson-decaying-bb-using-pp-collisions-atlas-detector','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1419649-search-dark-matter-produced-association-higgs-boson-decaying-bb-using-pp-collisions-atlas-detector">Search for Dark Matter Produced in Association with a Higgs Boson Decaying to b b ¯ Using 36 fb - 1 of p p Collisions at s = 13 TeV with the ATLAS Detector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Aaboud, M.; Aad, G.; Abbott, B.; ... 2017-11-01 Several extensions of the standard model predict associated production of dark-matter particles with a Higgs boson. Such processes are searched for in final states with missing transverse momentum and a Higgs boson decaying to a b¯b pair with the ATLAS detector using 36.1 fb –1 of pp collisions at a center-of-mass energy of 13 TeV at the LHC. As a result, the observed data are in agreement with the standard model predictions and limits are placed on the associated production of dark-matter particles and a Higgs boson. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/22218288-big-bang-nucleosynthesis-update','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/22218288-big-bang-nucleosynthesis-update">Big bang nucleosynthesis: An update</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Olive, Keith A. An update on the standard model of big bang nucleosynthesis (BBN) is presented. With the value of the baryon-tophoton ratio determined to high precision by WMAP, standard BBN is a parameter-free theory. In this context, the theoretical prediction for the abundances of D, {sup 4}He, and {sup 7}Li is discussed and compared to their observational determination. While concordance for D and {sup 4}He is satisfactory, the prediction for {sup 7}Li exceeds the observational determination by a factor of about four. Possible solutions to this problem are discussed. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110007949','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110007949">A Final Approach Trajectory Model for Current Operations</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Gong, Chester; Sadovsky, Alexander 2010-01-01 Predicting accurate trajectories with limited intent information is a challenge faced by air traffic management decision support tools in operation today. One such tool is the FAA's Terminal Proximity Alert system which is intended to assist controllers in maintaining safe separation of arrival aircraft during final approach. In an effort to improve the performance of such tools, two final approach trajectory models are proposed; one based on polynomial interpolation, the other on the Fourier transform. These models were tested against actual traffic data and used to study effects of the key final approach trajectory modeling parameters of wind, aircraft type, and weight class, on trajectory prediction accuracy. Using only the limited intent data available to today's ATM system, both the polynomial interpolation and Fourier transform models showed improved trajectory prediction accuracy over a baseline dead reckoning model. Analysis of actual arrival traffic showed that this improved trajectory prediction accuracy leads to improved inter-arrival separation prediction accuracy for longer look ahead times. The difference in mean inter-arrival separation prediction error between the Fourier transform and dead reckoning models was 0.2 nmi for a look ahead time of 120 sec, a 33 percent improvement, with a corresponding 32 percent improvement in standard deviation. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22670592','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22670592">QSAR, docking and ADMET studies of artemisinin derivatives for antimalarial activity targeting plasmepsin II, a hemoglobin-degrading enzyme from P. falciparum.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Qidwai, Tabish; Yadav, Dharmendra K; Khan, Feroz; Dhawan, Sangeeta; Bhakuni, R S 2012-01-01 This work presents the development of quantitative structure activity relationship (QSAR) model to predict the antimalarial activity of artemisinin derivatives. The structures of the molecules are represented by chemical descriptors that encode topological, geometric, and electronic structure features. Screening through QSAR model suggested that compounds A24, A24a, A53, A54, A62 and A64 possess significant antimalarial activity. Linear model is developed by the multiple linear regression method to link structures to their reported antimalarial activity. The correlation in terms of regression coefficient (r(2)) was 0.90 and prediction accuracy of model in terms of cross validation regression coefficient (rCV(2)) was 0.82. This study indicates that chemical properties viz., atom count (all atoms), connectivity index (order 1, standard), ring count (all rings), shape index (basic kappa, order 2), and solvent accessibility surface area are well correlated with antimalarial activity. The docking study showed high binding affinity of predicted active compounds against antimalarial target Plasmepsins (Plm-II). Further studies for oral bioavailability, ADMET and toxicity risk assessment suggest that compound A24, A24a, A53, A54, A62 and A64 exhibits marked antimalarial activity comparable to standard antimalarial drugs. Later one of the predicted active compound A64 was chemically synthesized, structure elucidated by NMR and in vivo tested in multidrug resistant strain of Plasmodium yoelii nigeriensis infected mice. The experimental results obtained agreed well with the predicted values. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29563892','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29563892">Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Megjhani, Murad; Terilli, Kalijah; Frey, Hans-Peter; Velazquez, Angela G; Doyle, Kevin William; Connolly, Edward Sander; Roh, David Jinou; Agarwal, Sachin; Claassen, Jan; Elhadad, Noemie; Park, Soojin 2018-01-01 Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone. 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt-Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and linear and kernel support vector machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset. Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/15017216','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/15017216"></a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Tanimoto, Naho The production cross section and the kinematic properties of the decay products of Wγ in the W → μv decay channel from pmore » $$\\bar{p}$$ collisions at √s = 1.96 TeV are presented. The measurement use the high p T muon data from the upgraded Collider Detector at Fermilab (CDF). The data were collected between March 2002 and September 2003. The total integrated luminosities are 192 pb -1 with the muon detector which covers the pseudorapidity region of |η| ≤ 0.6 and 175 pb -1 with the muon detector covering the region 0.6 ≤ |η| ≤ 1.0. In the Standard Model the μvγ final states occur due to Wγ → μvγ production and via muon Bremsstrahlung, W → μv → μvγ. W bosons are selected in their muon decay mode. Additionally, photons with transverse energy above 7 GeV, pseudorapidity in the central region (|η| < 1.1) and muon-photon angular separation ΔR(μ,γ) > 0.7 are selected. The author observes a total of 128 Wγ candidates, whereas the Standard Model expectation is 142.4 ± 9.5 events. The Wγ production cross section is found to be σ(p$$\\bar{p}$$ → μvγ) = 16.3 ± 2.3(stat.) ± 1.8(syst.) ± 1.2(lum.) [pb]. The theoretical prediction for this cross section is σ(p$$\\bar{p}$$ → lvγ) = 19.3 ± 1.4(th.) [pb]. The Standard Model predictions for several kinematic + variables are compared with data for E T γ > 7 GeV and ΔR(μ,γ) > 0.7. The measured cross section and the photon and W boson production kinematics are found to agree with the Standard Model predictions.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/10699681','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/10699681">Application of neural networks and sensitivity analysis to improved prediction of trauma survival.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Hunter, A; Kennedy, L; Henry, J; Ferguson, I 2000-05-01 The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27703197','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27703197">COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Zhu, Fan; Panwar, Bharat; Dodge, Hiroko H; Li, Hongdong; Hampstead, Benjamin M; Albin, Roger L; Paulson, Henry L; Guan, Yuanfang 2016-10-05 We present COMPASS, a COmputational Model to Predict the development of Alzheimer's diSease Spectrum, to model Alzheimer's disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer's Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores, and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall, and 91% accuracy at 10% recall. For (3), "genetic only" model has Pearson's correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26594695','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26594695">Model averaging and muddled multimodel inferences.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Cade, Brian S 2015-09-01 Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70174875','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70174875">Model averaging and muddled multimodel inferences</a> <a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a> Cade, Brian S. 2015-01-01 Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the tstatistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhDT.........2B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhDT.........2B">The search for the pair production of second-generation scalar leptoquarks and measurements of the differential cross sections of the W boson produced in association with jets with the CMS detector at the LHC</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Baumgartel, Darin C. Since the formulation of the Standard Model of particle physics, numerous experiments have sought to observe the signatures of the subatomic particles by examining the outcomes of charged particle collisions. Over time, advances in detector technology and scientific computing have allowed for unprecedented precision measurements of Standard Model phenomena and particle properties. Although the Standard Model has displayed remarkable predictive power, extensions to the Standard Model have been formulated to account for unexplained phenomena, and these extensions often infer the existence of additional subatomic particles. Consequently, experiments at particle colliders often endeavor to search for signatures of physics beyond the Standard Model. These searches and measurements are often complementary pursuits, as searches are often limited by the precision of estimations of the Standard Model backgrounds. At the forefront of present-day collider experiments is the Large Hadron Collider at CERN, which delivers proton-proton collisions with unprecedented energy and luminosity. Collisions are recorded with detectors located at interaction points along the ring of the Large Hadron Collider. The CMS detector is one of two general-purpose detectors at the Large Hadron Collider, and the high-precision detection of particles from collision events in the CMS detector make the CMS detector a powerful tool for both Standard-Model measurements and searches for new physics. The Standard Model is characterized by three generation of quarks and leptons. This correspondence between the generations of quarks and leptons is necessary to allow for the renormalizability of the Standard Model, but it is not an inherent property of the Standard Model. Motivated by this compelling symmetry, many theories and models propose the existence of leptoquark bosons which mediate transitions between quarks and leptons. Experimental constraints indicate that leptoquarks would couple to a single generation, and this thesis describes searches for leptoquarks produced in pairs and decaying to final states containing either two muons and two jets, or one muon, one muon-neutrino, and two jets. Searches are conducted with collision data at center-of-mass energies of both 7 TeV and 8 TeV. No compelling evidence for the existence of leptoquarks is found, and upper limits on the leptoquark mass and cross section are placed at the 95% confidence level. These limits are the most stringent to date, and are several times larger than limits placed previously at hadron collider experiments. While the pair production of massive leptoquark bosons yields final states which have strong kinematic differences from the Standard Model processes, the ability to exploit these differences is limited by the ability to accurately model the backgrounds. The most notable of these backgrounds is the production of a W boson in association with one or more jets. Since the W+jets process has a very large cross section and a final state containing missing energy, its contribution to the total Standard Model background is both nominally large and more difficult to discriminate against than backgrounds with only visible final state objects. Furthermore, estimates of this background are not easily improved by comparisons with data in control regions, and simulations of the background are often limited to leading-order predictions. To improve the understanding and modeling of this background for future endeavors, this thesis also presents measurements of the W+jets process differentially as a function of several variables, including the jet multiplicity, the individual jet transverse momenta and pseudorapidities, the angular separation between the jets and the muon, and the scalar sum of the transverse momenta of all jets. The agreement of these measurements with respect to predictions from event leading-order generators and next-to-leading-order calculations is assessed. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.usgs.gov/sir/2013/5166/pdf/sir2013-5166.pdf','USGSPUBS'); return false;" href="https://pubs.usgs.gov/sir/2013/5166/pdf/sir2013-5166.pdf">Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches</a> <a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a> Francy, Donna S.; Brady, Amie M.G.; Carvin, Rebecca B.; Corsi, Steven R.; Fuller, Lori M.; Harrison, John H.; Hayhurst, Brett A.; Lant, Jeremiah; Nevers, Meredith B.; Terrio, Paul J.; Zimmerman, Tammy M. 2013-01-01 Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as Escherichia coli (E. coli.). Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010-12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report. Local agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development. Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches that had at least 2 years of data (2010-11 and sometimes earlier) and for 1 beach that had 1 year of data. For most models, software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 hours, wave height, wind direction and speed, and antecedent rainfall for various time periods. Forty-two predictive models were validated against data collected during an independent year (2012) and compared to the current method for assessing recreational water quality-using the previous day’s E. coli concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 percent greater than the persistence model and overall correct responses greater than or equal to 80 percent, sensitivities (percentage of exceedances of the bathing-water standard that were correctly predicted by the model) greater than or equal to 50 percent, and specificities (percentage of nonexceedances correctly predicted by the model) greater than or equal to 85 percent. Out of 42 predictive models, 24 models yielded over-all correct responses that were at least 5 percent greater than the use of the persistence model. Predictive-model responses met the performance goals more often than the persistence-model responses in terms of overall correctness (28 versus 17 models, respectively), sensitivity (17 versus 4 models), and specificity (34 versus 25 models). Gaining knowledge of each beach and the factors that affect E. coli concentrations is important for developing good predictive models. Collection of additional years of data with a wide range of environmental conditions may also help to improve future model performance. The USGS will continue to work with local agencies in 2013 and beyond to develop and validate predictive models at beaches and improve existing nowcasts, restructuring monitoring activities to accommodate future uncertainties in funding and resources. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JApSp..84..804M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JApSp..84..804M">Partial Least Squares Regression Calibration of an Ultraviolet-Visible Spectrophotometer for Measurements of Chemical Oxygen Demand in Dye Wastewater</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Mai, W.; Zhang, J.-F.; Zhao, X.-M.; Li, Z.; Xu, Z.-W. 2017-11-01 Wastewater from the dye industry is typically analyzed using a standard method for measurement of chemical oxygen demand (COD) or by a single-wavelength spectroscopic method. To overcome the disadvantages of these methods, ultraviolet-visible (UV-Vis) spectroscopy was combined with principal component regression (PCR) and partial least squares regression (PLSR) in this study. Unlike the standard method, this method does not require digestion of the samples for preparation. Experiments showed that the PLSR model offered high prediction performance for COD, with a mean relative error of about 5% for two dyes. This error is similar to that obtained with the standard method. In this study, the precision of the PLSR model decreased with the number of dye compounds present. It is likely that multiple models will be required in reality, and the complexity of a COD monitoring system would be greatly reduced if the PLSR model is used because it can include several dyes. UV-Vis spectroscopy with PLSR successfully enhanced the performance of COD prediction for dye wastewater and showed good potential for application in on-line water quality monitoring. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/21371527-phenomenological-consequences-constrained-exceptional-supersymmetric-standard-model','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/21371527-phenomenological-consequences-constrained-exceptional-supersymmetric-standard-model">Phenomenological Consequences of the Constrained Exceptional Supersymmetric Standard Model</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Athron, Peter; King, S. F.; Miller, D. J. 2010-02-10 The Exceptional Supersymmetric Standard Model (E{sub 6}SSM) provides a low energy alternative to the MSSM, with an extra gauged U(1){sub N} symmetry, solving the mu-problem of the MSSM. Inspired by the possible embedding into an E{sub 6} GUT, the matter content fills three generations of E{sub 6} multiplets, thus predicting exciting exotic matter such as diquarks or leptoquarks. We present predictions from a constrained version of the model (cE{sub 6}SSM), with a universal scalar mass m{sub 0}, trilinear mass A and gaugino mass M{sub 1/2}. We reveal a large volume of the cE{sub 6}SSM parameter space where the correct breakdownmore » of the gauge symmetry is achieved and all experimental constraints satisfied. We predict a hierarchical particle spectrum with heavy scalars and light gauginos, while the new exotic matter can be light or heavy depending on parameters. We present representative cE{sub 6}SSM scenarios, demonstrating that there could be light exotic particles, like leptoquarks and a U(1){sub N} Z' boson, with spectacular signals at the LHC.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018PhLB..778..101S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018PhLB..778..101S">Search for Higgs boson pair production in events with two bottom quarks and two tau leptons in proton-proton collisions at √{ s } = 13TeV</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Ambrogi, F.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Grossmann, J.; Hrubec, J.; Jeitler, M.; König, A.; Krammer, N.; Krätschmer, I.; Liko, D.; Madlener, T.; Mikulec, I.; Pree, E.; Rabady, D.; Rad, N.; Rohringer, H.; Schieck, J.; Schöfbeck, R.; Spanring, M.; Spitzbart, D.; Strauss, J.; Waltenberger, W.; Wittmann, J.; Wulz, C.-E.; Zarucki, M.; Chekhovsky, V.; Mossolov, V.; Suarez Gonzalez, J.; De Wolf, E. A.; Di Croce, D.; Janssen, X.; Lauwers, J.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; De Bruyn, I.; De Clercq, J.; Deroover, K.; Flouris, G.; Lontkovskyi, D.; Lowette, S.; Moortgat, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Luetic, J.; Maerschalk, T.; Marinov, A.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Yonamine, R.; Zenoni, F.; Zhang, F.; Cimmino, A.; Cornelis, T.; Dobur, D.; Fagot, A.; Gul, M.; Khvastunov, I.; Poyraz, D.; Roskas, C.; Salva, S.; Tytgat, M.; Verbeke, W.; Zaganidis, N.; Bakhshiansohi, H.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; De Visscher, S.; Delaere, C.; Delcourt, M.; Francois, B.; Giammanco, A.; Jafari, A.; Komm, M.; Krintiras, G.; Lemaitre, V.; Magitteri, A.; Mertens, A.; Musich, M.; Piotrzkowski, K.; Quertenmont, L.; Vidal Marono, M.; Wertz, S.; Beliy, N.; Aldá Júnior, W. 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H.; Barney, D.; Bianco, M.; Bloch, P.; Bocci, A.; Botta, C.; Camporesi, T.; Castello, R.; Cepeda, M.; Cerminara, G.; Chapon, E.; Chen, Y.; d'Enterria, D.; Dabrowski, A.; Daponte, V.; David, A.; De Gruttola, M.; De Roeck, A.; Di Marco, E.; Dobson, M.; Dorney, B.; du Pree, T.; Dünser, M.; Dupont, N.; Elliott-Peisert, A.; Everaerts, P.; Franzoni, G.; Fulcher, J.; Funk, W.; Gigi, D.; Gill, K.; Glege, F.; Gulhan, D.; Gundacker, S.; Guthoff, M.; Harris, P.; Hegeman, J.; Innocente, V.; Janot, P.; Karacheban, O.; Kieseler, J.; Kirschenmann, H.; Knünz, V.; Kornmayer, A.; Kortelainen, M. J.; Lange, C.; Lecoq, P.; Lourenço, C.; Lucchini, M. T.; Malgeri, L.; Mannelli, M.; Martelli, A.; Meijers, F.; Merlin, J. A.; Mersi, S.; Meschi, E.; Milenovic, P.; Moortgat, F.; Mulders, M.; Neugebauer, H.; Orfanelli, S.; Orsini, L.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Racz, A.; Reis, T.; Rolandi, G.; Rovere, M.; Sakulin, H.; Schäfer, C.; Schwick, C.; Seidel, M.; Selvaggi, M.; Sharma, A.; Silva, P.; Sphicas, P.; Steggemann, J.; Stoye, M.; Tosi, M.; Treille, D.; Triossi, A.; Tsirou, A.; Veckalns, V.; Veres, G. I.; Verweij, M.; Wardle, N.; Zeuner, W. D.; Bertl, W.; Caminada, L.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.; Wiederkehr, S. A.; Bachmair, F.; Bäni, L.; Berger, P.; Bianchini, L.; Casal, B.; Dissertori, G.; Dittmar, M.; Donegà, M.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Kasieczka, G.; Klijnsma, T.; Lustermann, W.; Mangano, B.; Marionneau, M.; Meinhard, M. T.; Meister, D.; Micheli, F.; Musella, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Quittnat, M.; Schönenberger, M.; Shchutska, L.; Tavolaro, V. R.; Theofilatos, K.; Vesterbacka Olsson, M. L.; Wallny, R.; Zagozdzinska, A.; Zhu, D. H.; Aarrestad, T. K.; Amsler, C.; Canelli, M. F.; De Cosa, A.; Del Burgo, R.; Donato, S.; Galloni, C.; Hreus, T.; Kilminster, B.; Ngadiuba, J.; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Seitz, C.; Takahashi, Y.; Zucchetta, A.; Candelise, V.; Doan, T. H.; Jain, Sh.; Khurana, R.; Kuo, C. M.; Lin, W.; Pozdnyakov, A.; Yu, S. S.; Kumar, Arun; Chang, P.; Chao, Y.; Chen, K. F.; Chen, P. H.; Fiori, F.; Hou, W.-S.; Hsiung, Y.; Liu, Y. F.; Lu, R.-S.; Miñano Moya, M.; Paganis, E.; Psallidas, A.; Tsai, J. f.; Asavapibhop, B.; Kovitanggoon, K.; Singh, G.; Srimanobhas, N.; Adiguzel, A.; Boran, F.; Cerci, S.; Damarseckin, S.; Demiroglu, Z. S.; Dozen, C.; Dumanoglu, I.; Girgis, S.; Gokbulut, G.; Guler, Y.; Hos, I.; Kangal, E. E.; Kara, O.; Kayis Topaksu, A.; Kiminsu, U.; Oglakci, M.; Onengut, G.; Ozdemir, K.; Sunar Cerci, D.; Tali, B.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.; Bilin, B.; Karapinar, G.; Ocalan, K.; Yalvac, M.; Zeyrek, M.; Gülmez, E.; Kaya, M.; Kaya, O.; Tekten, S.; Yetkin, E. A.; Agaras, M. N.; Atay, S.; Cakir, A.; Cankocak, K.; Grynyov, B.; Levchuk, L.; Sorokin, P.; Aggleton, R.; Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Davignon, O.; Flacher, H.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Newbold, D. M.; Paramesvaran, S.; Poll, A.; Sakuma, T.; Seif El Nasr-storey, S.; Smith, D.; Smith, V. J.; Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Calligaris, L.; Cieri, D.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.; Bainbridge, R.; Breeze, S.; Buchmuller, O.; Bundock, A.; Casasso, S.; Citron, M.; Colling, D.; Corpe, L.; Dauncey, P.; Davies, G.; De Wit, A.; Della Negra, M.; Di Maria, R.; Elwood, A.; Haddad, Y.; Hall, G.; Iles, G.; James, T.; Lane, R.; Laner, C.; Lyons, L.; Magnan, A.-M.; Malik, S.; Mastrolorenzo, L.; Matsushita, T.; Nash, J.; Nikitenko, A.; Palladino, V.; Pesaresi, M.; Raymond, D. M.; Richards, A.; Rose, A.; Scott, E.; Seez, C.; Shtipliyski, A.; Summers, S.; Tapper, A.; Uchida, K.; Vazquez Acosta, M.; Virdee, T.; Winterbottom, D.; Wright, J.; Zenz, S. C.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.; Borzou, A.; Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Pastika, N.; Smith, C.; Bartek, R.; Dominguez, A.; Buccilli, A.; Cooper, S. I.; Henderson, C.; Rumerio, P.; West, C.; Arcaro, D.; Avetisyan, A.; Bose, T.; Gastler, D.; Rankin, D.; Richardson, C.; Rohlf, J.; Sulak, L.; Zou, D.; Benelli, G.; Cutts, D.; Garabedian, A.; Hakala, J.; Heintz, U.; Hogan, J. M.; Kwok, K. H. M.; Laird, E.; Landsberg, G.; Mao, Z.; Narain, M.; Piperov, S.; Sagir, S.; Syarif, R.; Yu, D.; Band, R.; Brainerd, C.; Burns, D.; Calderon De La Barca Sanchez, M.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Flores, C.; Funk, G.; Gardner, M.; Ko, W.; Lander, R.; Mclean, C.; Mulhearn, M.; Pellett, D.; Pilot, J.; Shalhout, S.; Shi, M.; Smith, J.; Squires, M.; Stolp, D.; Tos, K.; Tripathi, M.; Wang, Z.; Bachtis, M.; Bravo, C.; Cousins, R.; Dasgupta, A.; Florent, A.; Hauser, J.; Ignatenko, M.; Mccoll, N.; Saltzberg, D.; Schnaible, C.; Valuev, V.; Bouvier, E.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Ghiasi Shirazi, S. M. A.; Hanson, G.; Heilman, J.; Jandir, P.; Kennedy, E.; Lacroix, F.; Long, O. R.; Olmedo Negrete, M.; Paneva, M. I.; Shrinivas, A.; Si, W.; Wang, L.; Wei, H.; Wimpenny, S.; Yates, B. R.; Branson, J. G.; Cittolin, S.; Derdzinski, M.; Hashemi, B.; Holzner, A.; Klein, D.; Kole, G.; Krutelyov, V.; Letts, J.; Macneill, I.; Masciovecchio, M.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Tadel, M.; Vartak, A.; Wasserbaech, S.; Wood, J.; Würthwein, F.; Yagil, A.; Zevi Della Porta, G.; Amin, N.; Bhandari, R.; Bradmiller-Feld, J.; Campagnari, C.; Dishaw, A.; Dutta, V.; Franco Sevilla, M.; George, C.; Golf, F.; Gouskos, L.; Gran, J.; Heller, R.; Incandela, J.; Mullin, S. D.; Ovcharova, A.; Qu, H.; Richman, J.; Stuart, D.; Suarez, I.; Yoo, J.; Anderson, D.; Bendavid, J.; Bornheim, A.; Lawhorn, J. M.; Newman, H. B.; Nguyen, T.; Pena, C.; Spiropulu, M.; Vlimant, J. R.; Xie, S.; Zhang, Z.; Zhu, R. Y.; Andrews, M. B.; Ferguson, T.; Mudholkar, T.; Paulini, M.; Russ, J.; Sun, M.; Vogel, H.; Vorobiev, I.; Weinberg, M.; Cumalat, J. P.; Ford, W. T.; Jensen, F.; Johnson, A.; Krohn, M.; Leontsinis, S.; Mulholland, T.; Stenson, K.; Wagner, S. R.; Alexander, J.; Chaves, J.; Chu, J.; Dittmer, S.; Mcdermott, K.; Mirman, N.; Patterson, J. R.; Rinkevicius, A.; Ryd, A.; Skinnari, L.; Soffi, L.; Tan, S. M.; Tao, Z.; Thom, J.; Tucker, J.; Wittich, P.; Zientek, M.; Abdullin, S.; Albrow, M.; Apollinari, G.; Apresyan, A.; Apyan, A.; Banerjee, S.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Canepa, A.; Cerati, G. B.; Cheung, H. W. K.; Chlebana, F.; Cremonesi, M.; Duarte, J.; Elvira, V. D.; Freeman, J.; Gecse, Z.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Harris, R. M.; Hasegawa, S.; Hirschauer, J.; Hu, Z.; Jayatilaka, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Klima, B.; Kreis, B.; Lammel, S.; Lincoln, D.; Lipton, R.; Liu, M.; Liu, T.; Lopes De Sá, R.; Lykken, J.; Maeshima, K.; Magini, N.; Marraffino, J. M.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mrenna, S.; Nahn, S.; O'Dell, V.; Pedro, K.; Prokofyev, O.; Rakness, G.; Ristori, L.; Schneider, B.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Stoynev, S.; Strait, J.; Strobbe, N.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vernieri, C.; Verzocchi, M.; Vidal, R.; Wang, M.; Weber, H. A.; Whitbeck, A.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Brinkerhoff, A.; Carnes, A.; Carver, M.; Curry, D.; Das, S.; Field, R. D.; Furic, I. K.; Konigsberg, J.; Korytov, A.; Kotov, K.; Ma, P.; Matchev, K.; Mei, H.; Mitselmakher, G.; Rank, D.; Sperka, D.; Terentyev, N.; Thomas, L.; Wang, J.; Wang, S.; Yelton, J.; Joshi, Y. R.; Linn, S.; Markowitz, P.; Rodriguez, J. L.; Ackert, A.; Adams, T.; Askew, A.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Kolberg, T.; Martinez, G.; Perry, T.; Prosper, H.; Saha, A.; Santra, A.; Yohay, R.; Baarmand, M. M.; Bhopatkar, V.; Colafranceschi, S.; Hohlmann, M.; Noonan, D.; Roy, T.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Cavanaugh, R.; Chen, X.; Evdokimov, O.; Gerber, C. E.; Hangal, D. A.; Hofman, D. J.; Jung, K.; Kamin, J.; Sandoval Gonzalez, I. D.; Tonjes, M. B.; Trauger, H.; Varelas, N.; Wang, H.; Wu, Z.; Zhang, J.; Bilki, B.; Clarida, W.; Dilsiz, K.; Durgut, S.; Gandrajula, R. P.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Snyder, C.; Tiras, E.; Wetzel, J.; Yi, K.; Blumenfeld, B.; Cocoros, A.; Eminizer, N.; Fehling, D.; Feng, L.; Gritsan, A. V.; Maksimovic, P.; Roskes, J.; Sarica, U.; Swartz, M.; Xiao, M.; You, C.; Al-bataineh, A.; Baringer, P.; Bean, A.; Boren, S.; Bowen, J.; Castle, J.; Khalil, S.; Kropivnitskaya, A.; Majumder, D.; Mcbrayer, W.; Murray, M.; Royon, C.; Sanders, S.; Schmitz, E.; Stringer, R.; Tapia Takaki, J. D.; Wang, Q.; Ivanov, A.; Kaadze, K.; Maravin, Y.; Mohammadi, A.; Saini, L. K.; Skhirtladze, N.; Toda, S.; Rebassoo, F.; Wright, D.; Anelli, C.; Baden, A.; Baron, O.; Belloni, A.; Calvert, B.; Eno, S. C.; Ferraioli, C.; Hadley, N. J.; Jabeen, S.; Jeng, G. Y.; Kellogg, R. G.; Kunkle, J.; Mignerey, A. C.; Ricci-Tam, F.; Shin, Y. H.; Skuja, A.; Tonwar, S. C.; Abercrombie, D.; Allen, B.; Azzolini, V.; Barbieri, R.; Baty, A.; Bi, R.; Brandt, S.; Busza, W.; Cali, I. A.; D'Alfonso, M.; Demiragli, Z.; Gomez Ceballos, G.; Goncharov, M.; Hsu, D.; Iiyama, Y.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Maier, B.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Roland, C.; Roland, G.; Salfeld-Nebgen, J.; Stephans, G. S. F.; Tatar, K.; Velicanu, D.; Wang, J.; Wang, T. W.; Wyslouch, B.; Benvenuti, A. C.; Chatterjee, R. M.; Evans, A.; Hansen, P.; Kalafut, S.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Claes, D. R.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Kravchenko, I.; Monroy, J.; Siado, J. E.; Snow, G. R.; Stieger, B.; Alyari, M.; Dolen, J.; Godshalk, A.; Harrington, C.; Iashvili, I.; Nguyen, D.; Parker, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Teixeira De Lima, R.; Trocino, D.; Wood, D.; Bhattacharya, S.; Charaf, O.; Hahn, K. A.; Mucia, N.; Odell, N.; Pollack, B.; Schmitt, M. H.; Sung, K.; Trovato, M.; Velasco, M.; Dev, N.; Hildreth, M.; Hurtado Anampa, K.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Loukas, N.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Smith, G.; Taroni, S.; Wayne, M.; Wolf, M.; Woodard, A.; Alimena, J.; Antonelli, L.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Francis, B.; Hart, A.; Hill, C.; Ji, W.; Liu, B.; Luo, W.; Puigh, D.; Winer, B. L.; Wulsin, H. W.; Benaglia, A.; Cooperstein, S.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Higginbotham, S.; Lange, D.; Luo, J.; Marlow, D.; Mei, K.; Ojalvo, I.; Olsen, J.; Palmer, C.; Piroué, P.; Stickland, D.; Tully, C.; Malik, S.; Norberg, S.; Barker, A.; Barnes, V. E.; Folgueras, S.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, A. W.; Khatiwada, A.; Miller, D. H.; Neumeister, N.; Peng, C. C.; Schulte, J. F.; Sun, J.; Wang, F.; Xie, W.; Cheng, T.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Chen, Z.; Ecklund, K. M.; Geurts, F. J. M.; Guilbaud, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Bodek, A.; de Barbaro, P.; Demina, R.; Duh, Y. t.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Ciesielski, R.; Goulianos, K.; Mesropian, C.; Agapitos, A.; Chou, J. P.; Gershtein, Y.; Gómez Espinosa, T. A.; Halkiadakis, E.; Heindl, M.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Kyriacou, S.; Lath, A.; Montalvo, R.; Nash, K.; Osherson, M.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Delannoy, A. G.; Foerster, M.; Heideman, J.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Castaneda Hernandez, A.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Mueller, R.; Pakhotin, Y.; Patel, R.; Perloff, A.; Perniè, L.; Rathjens, D.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Damgov, J.; De Guio, F.; Dudero, P. R.; Faulkner, J.; Gurpinar, E.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Peltola, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Hirosky, R.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Sun, X.; Wang, Y.; Wolfe, E.; Xia, F.; Harr, R.; Karchin, P. E.; Sturdy, J.; Zaleski, S.; Brodski, M.; Buchanan, J.; Caillol, C.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Hussain, U.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Pierro, G. A.; Polese, G.; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Taylor, D.; Woods, N.; CMS Collaboration 2018-03-01 A search for the production of Higgs boson pairs in proton-proton collisions at a centre-of-mass energy of 13 TeV is presented, using a data sample corresponding to an integrated luminosity of 35.9fb-1 collected with the CMS detector at the LHC. Events with one Higgs boson decaying into two bottom quarks and the other decaying into two τ leptons are explored to investigate both resonant and nonresonant production mechanisms. The data are found to be consistent, within uncertainties, with the standard model background predictions. For resonant production, upper limits at the 95% confidence level are set on the production cross section for Higgs boson pairs as a function of the hypothesized resonance mass and are interpreted in the context of the minimal supersymmetric standard model. For nonresonant production, upper limits on the production cross section constrain the parameter space for anomalous Higgs boson couplings. The observed (expected) upper limit at 95% confidence level corresponds to about 30 (25) times the prediction of the standard model. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFM.H34B..03W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFM.H34B..03W">Predicting Fecal Indicator Bacteria Fate and Removal in Urban Stormwater at the Watershed Scale</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Wolfand, J.; Hogue, T. S.; Luthy, R. G. 2016-12-01 Urban stormwater is a major cause of water quality impairment, resulting in surface waters that fail to meet water quality standards and support their designated uses. Of the many stormwater pollutants, fecal indicator bacteria are particularly important to track because they are directly linked to pathogens which jeopardize public health; yet, their fate and transport in urban stormwater is poorly understood. Monitoring fecal bacteria in stormwater is possible, but due to the high variability of fecal indicators both spatially and temporally, single grab or composite samples do not fully capture fecal indicator loading. Models have been developed to predict fecal indicator bacteria at the watershed scale, but they are often limited to agricultural areas, or areas that receive frequent rainfall. Further, it is unclear whether best management practices (BMPs), such as bioretention or engineered wetlands, are able to reduce bacteria to meet water quality standards at watershed outlets. This research seeks to develop a model to predict fecal indicator bacteria in urban stormwater in a semi-arid climate at the watershed scale. Using the highly developed Ballona Creek watershed (89 mi2) located in Los Angeles County as a case study, several existing mechanistic models are coupled with a hydrologic model to predict fecal indicator concentrations (E. coli, enterococci, fecal coliform, and total coliform) at the outfall of Ballona Creek watershed, Santa Monica Bay. The hydrologic model was developed using InfoSWMM Sustain, calibrated for flow from WY 1998-2006 (NSE = 0.94; R2 = 0.95), and validated from WY 2007-2015 (NSE = 0.93; R2 = 0.95). The developed coupled model is being used to predict fecal indicator fate and transport and evaluate how BMPs can be optimized to reduce fecal indicator loading to surface waters and recreational beaches. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28843552','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28843552">Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions between two at-line instruments installed at two liquid detergent production plants.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Brouckaert, D; Uyttersprot, J-S; Broeckx, W; De Beer, T 2017-09-01 Calibration transfer of partial least squares (PLS) quantification models is established between two Raman spectrometers located at two liquid detergent production plants. As full recalibration of existing calibration models is time-consuming, labour-intensive and costly, it is investigated whether the use of mathematical correction methods requiring only a handful of standardization samples can overcome the dissimilarities in spectral response observed between both measurement systems. Univariate and multivariate standardization approaches are investigated, ranging from simple slope/bias correction (SBC), local centring (LC) and single wavelength standardization (SWS) to more complex direct standardization (DS) and piecewise direct standardization (PDS). The results of these five calibration transfer methods are compared reciprocally, as well as with regard to a full recalibration. Four PLS quantification models, each predicting the concentration of one of the four main ingredients in the studied liquid detergent composition, are aimed at transferring. Accuracy profiles are established from the original and transferred quantification models for validation purposes. A reliable representation of the calibration models performance before and after transfer is thus established, based on β-expectation tolerance intervals. For each transferred model, it is investigated whether every future measurement that will be performed in routine will be close enough to the unknown true value of the sample. From this validation, it is concluded that instrument standardization is successful for three out of four investigated calibration models using multivariate (DS and PDS) transfer approaches. The fourth transferred PLS model could not be validated over the investigated concentration range, due to a lack of precision of the slave instrument. Comparing these transfer results to a full recalibration on the slave instrument allows comparison of the predictive power of both Raman systems and leads to the formulation of guidelines for further standardization projects. It is concluded that it is essential to evaluate the performance of the slave instrument prior to transfer, even when it is theoretically identical to the master apparatus. Copyright © 2017 Elsevier B.V. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/22521648-number-tidal-dwarf-satellite-galaxies-dependence-bulge-index','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/22521648-number-tidal-dwarf-satellite-galaxies-dependence-bulge-index">THE NUMBER OF TIDAL DWARF SATELLITE GALAXIES IN DEPENDENCE OF BULGE INDEX</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> López-Corredoira, Martín; Kroupa, Pavel, E-mail: martinlc@iac.es, E-mail: pavel@astro.uni-bonn.de We show that a significant correlation (up to 5σ) emerges between the bulge index, defined to be larger for a larger bulge/disk ratio, in spiral galaxies with similar luminosities in the Galaxy Zoo 2 of the Sloan Digital Sky Survey and the number of tidal-dwarf galaxies in the catalog by Kaviraj et al. In the standard cold or warm dark matter cosmological models, the number of satellite galaxies correlates with the circular velocity of the dark matter host halo. In generalized gravity models without cold or warm dark matter, such a correlation does not exist, because host galaxies cannot capture infalling dwarfmore » galaxies due to the absence of dark-matter-induced dynamical friction. However, in such models, a correlation is expected to exist between the bulge mass and the number of satellite galaxies because bulges and tidal-dwarf satellite galaxies form in encounters between host galaxies. This is not predicted by dark matter models in which bulge mass and the number of satellites are a priori uncorrelated because higher bulge/disk ratios do not imply higher dark/luminous ratios. Hence, our correlation reproduces the prediction of scenarios without dark matter, whereas an explanation is not found readily from the a priori predictions of the standard scenario with dark matter. Further research is needed to explore whether some application of the standard theory may explain this correlation.« less </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active">18</li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active">19</li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AtmEn..39.4025G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AtmEn..39.4025G">A hybrid model for predicting carbon monoxide from vehicular exhausts in urban environments</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Gokhale, Sharad; Khare, Mukesh Several deterministic-based air quality models evaluate and predict the frequently occurring pollutant concentration well but, in general, are incapable of predicting the 'extreme' concentrations. In contrast, the statistical distribution models overcome the above limitation of the deterministic models and predict the 'extreme' concentrations. However, the environmental damages are caused by both extremes as well as by the sustained average concentration of pollutants. Hence, the model should predict not only 'extreme' ranges but also the 'middle' ranges of pollutant concentrations, i.e. the entire range. Hybrid modelling is one of the techniques that estimates/predicts the 'entire range' of the distribution of pollutant concentrations by combining the deterministic based models with suitable statistical distribution models ( Jakeman, et al., 1988). In the present paper, a hybrid model has been developed to predict the carbon monoxide (CO) concentration distributions at one of the traffic intersections, Income Tax Office (ITO), in the Delhi city, where the traffic is heterogeneous in nature and meteorology is 'tropical'. The model combines the general finite line source model (GFLSM) as its deterministic, and log logistic distribution (LLD) model, as its statistical components. The hybrid (GFLSM-LLD) model is then applied at the ITO intersection. The results show that the hybrid model predictions match with that of the observed CO concentration data within the 5-99 percentiles range. The model is further validated at different street location, i.e. Sirifort roadway. The validation results show that the model predicts CO concentrations fairly well ( d=0.91) in 10-95 percentiles range. The regulatory compliance is also developed to estimate the probability of exceedance of hourly CO concentration beyond the National Ambient Air Quality Standards (NAAQS) of India. It consists of light vehicles, heavy vehicles, three- wheelers (auto rickshaws) and two-wheelers (scooters, motorcycles, etc). </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011PhDT........73R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011PhDT........73R">Enhanced propagation modeling of directional aviation noise: A hybrid parabolic equation-fast field program method</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Rosenbaum, Joyce E. 2011-12-01 Commercial air traffic is anticipated to increase rapidly in the coming years. The impact of aviation noise on communities surrounding airports is, therefore, a growing concern. Accurate prediction of noise can help to mitigate the impact on communities and foster smoother integration of aerospace engineering advances. The problem of accurate sound level prediction requires careful inclusion of all mechanisms that affect propagation, in addition to correct source characterization. Terrain, ground type, meteorological effects, and source directivity can have a substantial influence on the noise level. Because they are difficult to model, these effects are often included only by rough approximation. This dissertation presents a model designed for sound propagation over uneven terrain, with mixed ground type and realistic meteorological conditions. The model is a hybrid of two numerical techniques: the parabolic equation (PE) and fast field program (FFP) methods, which allow for physics-based inclusion of propagation effects and ensure the low frequency content, a factor in community impact, is predicted accurately. Extension of the hybrid model to a pseudo-three-dimensional representation allows it to produce aviation noise contour maps in the standard form. In order for the model to correctly characterize aviation noise sources, a method of representing arbitrary source directivity patterns was developed for the unique form of the parabolic equation starting field. With this advancement, the model can represent broadband, directional moving sound sources, traveling along user-specified paths. This work was prepared for possible use in the research version of the sound propagation module in the Federal Aviation Administration's new standard predictive tool. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21553177','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21553177">The prediction of intelligence in preschool children using alternative models to regression.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E 2011-12-01 Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=248811&keyword=time+AND+influence+AND+HA&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=248811&keyword=time+AND+influence+AND+HA&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50">“Transference Ratios” to Predict Total Oxidized Sulfur and Nitrogen Deposition – Part I, Monitoring Results</a> <a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a> Use of model-predicted “transference ratios” is currently under consideration by the US EPA in the formulation of a Secondary National Ambient Air Quality Standard for oxidized nitrogen and oxidized sulfur. This term is an empirical parameter defined for oxidized sulfur (TS)as th... </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=watson&pg=6&id=EJ994646','ERIC'); return false;" href="https://eric.ed.gov/?q=watson&pg=6&id=EJ994646">Determining a Model to Predict Hispanic Preservice Teachers' Success on the Texas Examination of Educator Standards</a> <a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a> Zhang, Zhidong; Telese, James 2012-01-01 In this article, we report the regression relations between preservice teachers' academic characteristics and their performance on the Texas Examination of Educator Standards. These academic characteristics include grade point average, reading ability, and critical thinking. The studies indicate that the critical thinking was the best predictor… </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25133703','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25133703">Nanoparticle surface characterization and clustering through concentration-dependent surface adsorption modeling.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Chen, Ran; Zhang, Yuntao; Sahneh, Faryad Darabi; Scoglio, Caterina M; Wohlleben, Wendel; Haase, Andrea; Monteiro-Riviere, Nancy A; Riviere, Jim E 2014-09-23 Quantitative characterization of nanoparticle interactions with their surrounding environment is vital for safe nanotechnological development and standardization. A recent quantitative measure, the biological surface adsorption index (BSAI), has demonstrated promising applications in nanomaterial surface characterization and biological/environmental prediction. This paper further advances the approach beyond the application of five descriptors in the original BSAI to address the concentration dependence of the descriptors, enabling better prediction of the adsorption profile and more accurate categorization of nanomaterials based on their surface properties. Statistical analysis on the obtained adsorption data was performed based on three different models: the original BSAI, a concentration-dependent polynomial model, and an infinite dilution model. These advancements in BSAI modeling showed a promising development in the application of quantitative predictive modeling in biological applications, nanomedicine, and environmental safety assessment of nanomaterials. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19970002973','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19970002973">An Occupational Performance Test Validation Program for Fire Fighters at the Kennedy Space Center</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Schonfeld, Brian R.; Doerr, Donald F.; Convertino, Victor A. 1990-01-01 We evaluated performance of a modified Combat Task Test (CTT) and of standard fitness tests in 20 male subjects to assess the prediction of occupational performance standards for Kennedy Space Center fire fighters. The CTT consisted of stair-climbing, a chopping simulation, and a victim rescue simulation. Average CTT performance time was 3.61 +/- 0.25 min (SEM) and all CTT tasks required 93% to 97% maximal heart rate. By using scores from the standard fitness tests, a multiple linear regression model was fitted to each parameter: the stairclimb (r(exp 2) = .905, P less than .05), the chopping performance time (r(exp 2) = .582, P less than .05), the victim rescue time (r(exp 2) = .218, P = not significant), and the total performance time (r(exp 2) = .769, P less than .05). Treadmill time was the predominant variable, being the major predictor in two of four models. These results indicated that standardized fitness tests can predict performance on some CTT tasks and that test predictors were amenable to exercise training. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29073279','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29073279">Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Huang, Cai; Mezencev, Roman; McDonald, John F; Vannberg, Fredrik 2017-01-01 Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24840455','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24840455">Multiplicative effects model with internal standard in mobile phase for quantitative liquid chromatography-mass spectrometry.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Song, Mi; Chen, Zeng-Ping; Chen, Yao; Jin, Jing-Wen 2014-07-01 Liquid chromatography-mass spectrometry assays suffer from signal instability caused by the gradual fouling of the ion source, vacuum instability, aging of the ion multiplier, etc. To address this issue, in this contribution, an internal standard was added into the mobile phase. The internal standard was therefore ionized and detected together with the analytes of interest by the mass spectrometer to ensure that variations in measurement conditions and/or instrument have similar effects on the signal contributions of both the analytes of interest and the internal standard. Subsequently, based on the unique strategy of adding internal standard in mobile phase, a multiplicative effects model was developed for quantitative LC-MS assays and tested on a proof of concept model system: the determination of amino acids in water by LC-MS. The experimental results demonstrated that the proposed method could efficiently mitigate the detrimental effects of continuous signal variation, and achieved quantitative results with average relative predictive error values in the range of 8.0-15.0%, which were much more accurate than the corresponding results of conventional internal standard method based on the peak height ratio and partial least squares method (their average relative predictive error values were as high as 66.3% and 64.8%, respectively). Therefore, it is expected that the proposed method can be developed and extended in quantitative LC-MS analysis of more complex systems. Copyright © 2014 Elsevier B.V. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H41G1325J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H41G1325J">A Study on Active Disaster Management System for Standardized Emergency Action Plan using BIM and Flood Damage Estimation Techniques</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Jeong, C.; Om, J.; Hwang, J.; Joo, K.; Heo, J. 2013-12-01 In recent, the frequency of extreme flood has been increasing due to climate change and global warming. Highly flood damages are mainly caused by the collapse of flood control structures such as dam and dike. In order to reduce these disasters, the disaster management system (DMS) through flood forecasting, inundation mapping, EAP (Emergency Action Plan) has been studied. The estimation of inundation damage and practical EAP are especially crucial to the DMS. However, it is difficult to predict inundation and take a proper action through DMS in real emergency situation because several techniques for inundation damage estimation are not integrated and EAP is supplied in the form of a document in Korea. In this study, the integrated simulation system including rainfall frequency analysis, rainfall-runoff modeling, inundation prediction, surface runoff analysis, and inland flood analysis was developed. Using this system coupled with standard GIS data, inundation damage can be estimated comprehensively and automatically. The standard EAP based on BIM (Building Information Modeling) was also established in this system. It is, therefore, expected that the inundation damages through this study over the entire area including buildings can be predicted and managed. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006SPIE.6381E..08Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006SPIE.6381E..08Y">Prediction of ethanol in bottled Chinese rice wine by NIR spectroscopy</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Ying, Yibin; Yu, Haiyan; Pan, Xingxiang; Lin, Tao 2006-10-01 To evaluate the applicability of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining ethanol concentration of Chinese rice wine in square brown glass bottle, transmission spectra of 100 bottled Chinese rice wine samples were collected in the spectral range of 350-1200 nm. Statistical equations were established between the reference data and VIS-NIR spectra by partial least squares (PLS) regression method. Performance of three kinds of mathematical treatment of spectra (original spectra, first derivative spectra and second derivative spectra) were also discussed. The PLS models of original spectra turned out better results, with higher correlation coefficient in calibration (R cal) of 0.89, lower root mean standard error of calibration (RMSEC) of 0.165, and lower root mean standard error of cross validation (RMSECV) of 0.179. Using original spectra, PLS models for ethanol concentration prediction were developed. The R cal and the correlation coefficient in validation (R val) were 0.928 and 0.875, respectively; and the RMSEC and the root mean standard error of validation (RMSEP) were 0.135 (%, v v -1) and 0.177 (%, v v -1), respectively. The results demonstrated that VIS-NIR spectroscopy could be used to predict ethanol concentration in bottled Chinese rice wine. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130008633','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130008633">Using Random Forest Models to Predict Organizational Violence</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Levine, Burton; Bobashev, Georgly 2012-01-01 We present a methodology to access the proclivity of an organization to commit violence against nongovernment personnel. We fitted a Random Forest model using the Minority at Risk Organizational Behavior (MAROS) dataset. The MAROS data is longitudinal; so, individual observations are not independent. We propose a modification to the standard Random Forest methodology to account for the violation of the independence assumption. We present the results of the model fit, an example of predicting violence for an organization; and finally, we present a summary of the forest in a "meta-tree," </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19890049261&hterms=standard+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dstandard%2Bmodel','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19890049261&hterms=standard+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dstandard%2Bmodel">Primordial lithium and the standard model(s)</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Deliyannis, Constantine P.; Demarque, Pierre; Kawaler, Steven D.; Romanelli, Paul; Krauss, Lawrence M. 1989-01-01 The results of new theoretical work on surface Li-7 and Li-6 evolution in the oldest halo stars are presented, along with a new and refined analysis of the predicted primordial Li abundance resulting from big-bang nucleosynthesis. This makes it possible to determine the constraints which can be imposed on cosmology using primordial Li and both standard big-bang and stellar-evolution models. This leads to limits on the baryon density today of 0.0044-0.025 (where the Hubble constant is 100h km/sec Mpc) and imposes limitations on alternative nucleosynthesis scenarios. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017SPIE10399E..0MV','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017SPIE10399E..0MV">Predicting silicon pore optics</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Vacanti, Giuseppe; Barriére, Nicolas; Bavdaz, Marcos; Chatbi, Abdelhakim; Collon, Maximilien; Dekker, Danielle; Girou, David; Günther, Ramses; van der Hoeven, Roy; Landgraf, Boris; Sforzini, Jessica; Vervest, Mark; Wille, Eric 2017-09-01 Continuing improvement of Silicon Pore Optics (SPO) calls for regular extension and validation of the tools used to model and predict their X-ray performance. In this paper we present an updated geometrical model for the SPO optics and describe how we make use of the surface metrology collected during each of the SPO manufacturing runs. The new geometrical model affords the user a finer degree of control on the mechanical details of the SPO stacks, while a standard interface has been developed to make use of any type of metrology that can return changes in the local surface normal of the reflecting surfaces. Comparisons between the predicted and actual performance of samples optics will be shown and discussed. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/15359679','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15359679">Foot shape modeling.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Luximon, Ameersing; Goonetilleke, Ravindra S 2004-01-01 This study is an attempt to show how a "standard" foot can be parameterized using foot length, foot width, foot height, and a measure of foot curvature so that foot shape can be predicted using these simple anthropometric measures. The prediction model was generated using 40 Hong Kong Chinese men, and the model was validated using a different group of 25 Hong Kong Chinese men. The results show that each individual foot shape may be predicted to a mean accuracy of 2.1 mm for the left foot and 2.4 mm for the right foot. Application of this research includes the potential design and development of custom footwear without the necessity of expensive 3-D scanning of feet. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5874842','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5874842">Prediction of Phase Behavior of Spray-Dried Amorphous Solid Dispersions: Assessment of Thermodynamic Models, Standard Screening Methods and a Novel Atomization Screening Device with Regard to Prediction Accuracy</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Chavez, Pierre-François; Meeus, Joke; Robin, Florent; Schubert, Martin Alexander; Somville, Pascal 2018-01-01 The evaluation of drug–polymer miscibility in the early phase of drug development is essential to ensure successful amorphous solid dispersion (ASD) manufacturing. This work investigates the comparison of thermodynamic models, conventional experimental screening methods (solvent casting, quench cooling), and a novel atomization screening device based on their ability to predict drug–polymer miscibility, solid state properties (Tg value and width), and adequate polymer selection during the development of spray-dried amorphous solid dispersions (SDASDs). Binary ASDs of four drugs and seven polymers were produced at 20:80, 40:60, 60:40, and 80:20 (w/w). Samples were systematically analyzed using modulated differential scanning calorimetry (mDSC) and X-ray powder diffraction (XRPD). Principal component analysis (PCA) was used to qualitatively assess the predictability of screening methods with regards to SDASD development. Poor correlation was found between theoretical models and experimentally-obtained results. Additionally, the limited ability of usual screening methods to predict the miscibility of SDASDs did not guarantee the appropriate selection of lead excipient for the manufacturing of robust SDASDs. Contrary to standard approaches, our novel screening device allowed the selection of optimal polymer and drug loading and established insight into the final properties and performance of SDASDs at an early stage, therefore enabling the optimization of the scaled-up late-stage development. PMID:29518936 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006SPIE.6047E..22H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006SPIE.6047E..22H">Study of Vis/NIR spectroscopy measurement on acidity of yogurt</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> He, Yong; Feng, Shuijuan; Wu, Di; Li, Xiaoli 2006-09-01 A fast measurement of pH of yogurt using Vis/NIR-spectroscopy techniques was established in order to measuring the acidity of yogurt rapidly. 27 samples selected separately from five different brands of yogurt were measured by Vis/NIR-spectroscopy. The pH of yogurt on positions scanned by spectrum was measured by a pH meter. The mathematical model between pH and Vis/NIR spectral measurements was established and developed based on partial least squares (PLS) by using Unscramble V9.2. Then 25 unknown samples from 5 different brands were predicted based on the mathematical model. The result shows that The correlation coefficient of pH based on PLS model is more than 0.890, and standard error of calibration (SEC) is 0.037, standard error of prediction (SEP) is 0.043. Through predicting the pH of 25 samples of yogurt from 5 different brands, the correlation coefficient between predictive value and measured value of those samples is more than 0918. The results show the good to excellent prediction performances. The Vis/NIR spectroscopy technique had a significant greater accuracy for determining the value of pH. It was concluded that the VisINIRS measurement technique can be used to measure pH of yogurt fast and accurately, and a new method for the measurement of pH of yogurt was established. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003WRR....39.1151M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003WRR....39.1151M">Stochastic capture zone analysis of an arsenic-contaminated well using the generalized likelihood uncertainty estimator (GLUE) methodology</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Morse, Brad S.; Pohll, Greg; Huntington, Justin; Rodriguez Castillo, Ramiro 2003-06-01 In 1992, Mexican researchers discovered concentrations of arsenic in excess of World Heath Organization (WHO) standards in several municipal wells in the Zimapan Valley of Mexico. This study describes a method to delineate a capture zone for one of the most highly contaminated wells to aid in future well siting. A stochastic approach was used to model the capture zone because of the high level of uncertainty in several input parameters. Two stochastic techniques were performed and compared: "standard" Monte Carlo analysis and the generalized likelihood uncertainty estimator (GLUE) methodology. The GLUE procedure differs from standard Monte Carlo analysis in that it incorporates a goodness of fit (termed a likelihood measure) in evaluating the model. This allows for more information (in this case, head data) to be used in the uncertainty analysis, resulting in smaller prediction uncertainty. Two likelihood measures are tested in this study to determine which are in better agreement with the observed heads. While the standard Monte Carlo approach does not aid in parameter estimation, the GLUE methodology indicates best fit models when hydraulic conductivity is approximately 10-6.5 m/s, with vertically isotropic conditions and large quantities of interbasin flow entering the basin. Probabilistic isochrones (capture zone boundaries) are then presented, and as predicted, the GLUE-derived capture zones are significantly smaller in area than those from the standard Monte Carlo approach. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5089532','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5089532">The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS)</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> 2016-01-01 Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d–1 (RTD-model) and 0.03 ± 0.03 L d–1 (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d–1 and 0.0437 ± 0.02 L d–1, respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations. PMID:27363449 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880004485','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880004485">File Usage Analysis and Resource Usage Prediction: a Measurement-Based Study. Ph.D. Thesis</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Devarakonda, Murthy V.-S. 1987-01-01 A probabilistic scheme was developed to predict process resource usage in UNIX. Given the identity of the program being run, the scheme predicts CPU time, file I/O, and memory requirements of a process at the beginning of its life. The scheme uses a state-transition model of the program's resource usage in its past executions for prediction. The states of the model are the resource regions obtained from an off-line cluster analysis of processes run on the system. The proposed method is shown to work on data collected from a VAX 11/780 running 4.3 BSD UNIX. The results show that the predicted values correlate well with the actual. The coefficient of correlation between the predicted and actual values of CPU time is 0.84. Errors in prediction are mostly small. Some 82% of errors in CPU time prediction are less than 0.5 standard deviations of process CPU time. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active">19</li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active">20</li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900036554&hterms=Unix&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DTitle%26N%3D0%26No%3D30%26Ntt%3DUnix','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900036554&hterms=Unix&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DTitle%26N%3D0%26No%3D30%26Ntt%3DUnix">Predictability of process resource usage - A measurement-based study on UNIX</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Devarakonda, Murthy V.; Iyer, Ravishankar K. 1989-01-01 A probabilistic scheme is developed to predict process resource usage in UNIX. Given the identity of the program being run, the scheme predicts CPU time, file I/O, and memory requirements of a process at the beginning of its life. The scheme uses a state-transition model of the program's resource usage in its past executions for prediction. The states of the model are the resource regions obtained from an off-line cluster analysis of processes run on the system. The proposed method is shown to work on data collected from a VAX 11/780 running 4.3 BSD UNIX. The results show that the predicted values correlate well with the actual. The correlation coefficient betweeen the predicted and actual values of CPU time is 0.84. Errors in prediction are mostly small. Some 82 percent of errors in CPU time prediction are less than 0.5 standard deviations of process CPU time. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880004487','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880004487">Predictability of process resource usage: A measurement-based study of UNIX</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Devarakonda, Murthy V.; Iyer, Ravishankar K. 1987-01-01 A probabilistic scheme is developed to predict process resource usage in UNIX. Given the identity of the program being run, the scheme predicts CPU time, file I/O, and memory requirements of a process at the beginning of its life. The scheme uses a state-transition model of the program's resource usage in its past executions for prediction. The states of the model are the resource regions obtained from an off-line cluster analysis of processes run on the system. The proposed method is shown to work on data collected from a VAX 11/780 running 4.3 BSD UNIX. The results show that the predicted values correlate well with the actual. The correlation coefficient between the predicted and actual values of CPU time is 0.84. Errors in prediction are mostly small. Some 82% of errors in CPU time prediction are less than 0.5 standard deviations of process CPU time. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHEP...04..025K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHEP...04..025K">Search for physics beyond the standard model in dilepton mass spectra in proton-proton collisions at TeV</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M.; Erö, J.; Fabjan, C.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Knünz, V.; Krammer, M.; Krätschmer, I.; Liko, D.; Mikulec, I.; Rabady, D.; Rahbaran, B.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Taurok, A.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Bansal, M.; Bansal, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Knutsson, A.; Luyckx, S.; Ochesanu, S.; Rougny, R.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D'Hondt, J.; Daci, N.; Heracleous, N.; Keaveney, J.; Lowette, S.; Maes, M.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Villella, I.; Caillol, C.; Clerbaux, B.; De Lentdecker, G.; Dobur, D.; Favart, L.; Gay, A. P. R.; Grebenyuk, A.; Léonard, A.; Mohammadi, A.; Perniè, L.; Reis, T.; Seva, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.; Zenoni, F.; Adler, V.; Beernaert, K.; Benucci, L.; Cimmino, A.; Costantini, S.; Crucy, S.; Dildick, S.; Fagot, A.; Garcia, G.; Mccartin, J.; Ocampo Rios, A. A.; Ryckbosch, D.; Salva Diblen, S.; Sigamani, M.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bruno, G.; Castello, R.; Caudron, A.; Ceard, L.; Da Silveira, G. G.; Delaere, C.; du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Nuttens, C.; Pagano, D.; Perrini, L.; Pin, A.; Piotrzkowski, K.; Popov, A.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Vizan Garcia, J. M.; Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.; Aldá Júnior, W. L.; Alves, G. A.; Brito, L.; Correa Martins Junior, M.; Dos Reis Martins, T.; Mora Herrera, C.; Pol, M. E.; Carvalho, W.; Chinellato, J.; Custódio, A.; Da Costa, E. M.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Malbouisson, H.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santaolalla, J.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Bernardes, C. A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Aleksandrov, A.; Genchev, V.; Iaydjiev, P.; Marinov, A.; Piperov, S.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Hadjiiska, R.; Kozhuharov, V.; Litov, L.; Pavlov, B.; Petkov, P.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Du, R.; Jiang, C. H.; Plestina, R.; Romeo, F.; Tao, J.; Wang, Z.; Asawatangtrakuldee, C.; Ban, Y.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Zou, W.; Avila, C.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; Gomez Moreno, B.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Polic, D.; Puljak, I.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Kadija, K.; Luetic, J.; Mekterovic, D.; Sudic, L.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Bodlak, M.; Finger, M.; Finger, M.; Assran, Y.; Elgammal, S.; Mahmoud, M. A.; Radi, A.; Kadastik, M.; Murumaa, M.; Raidal, M.; Tiko, A.; Eerola, P.; Fedi, G.; Voutilainen, M.; Härkönen, J.; Karimäki, V.; Kinnunen, R.; Kortelainen, M. J.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Peltola, T.; Tuominen, E.; Tuominiemi, J.; Tuovinen, E.; Wendland, L.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Favaro, C.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Baffioni, S.; Beaudette, F.; Busson, P.; Charlot, C.; Dahms, T.; Dalchenko, M.; Dobrzynski, L.; Filipovic, N.; Florent, A.; Granier de Cassagnac, R.; Mastrolorenzo, L.; Miné, P.; Mironov, C.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Paganini, P.; Regnard, S.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Veelken, C.; Yilmaz, Y.; Zabi, A.; Agram, J.-L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J.-M.; Chabert, E. C.; Collard, C.; Conte, E.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Goetzmann, C.; Le Bihan, A.-C.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Beaupere, N.; Boudoul, G.; Bouvier, E.; Brochet, S.; Carrillo Montoya, C. A.; Chasserat, J.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Kurca, T.; Lethuillier, M.; Mirabito, L.; Perries, S.; Ruiz Alvarez, J. D.; Sabes, D.; Sgandurra, L.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Xiao, H.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Bontenackels, M.; Edelhoff, M.; Feld, L.; Hindrichs, O.; Klein, K.; Ostapchuk, A.; Perieanu, A.; Raupach, F.; Sammet, J.; Schael, S.; Weber, H.; Wittmer, B.; Zhukov, V.; Ata, M.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Erdmann, M.; Fischer, R.; Güth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Klingebiel, D.; Knutzen, S.; Kreuzer, P.; Merschmeyer, M.; Meyer, A.; Millet, P.; Olschewski, M.; Padeken, K.; Papacz, P.; Pook, T.; Reithler, H.; Schmitz, S. A.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Weber, M.; Cherepanov, V.; Erdogan, Y.; Flügge, G.; Geenen, H.; Geisler, M.; Haj Ahmad, W.; Heister, A.; Hoehle, F.; Kargoll, B.; Kress, T.; Kuessel, Y.; Künsken, A.; Lingemann, J.; Nowack, A.; Nugent, I. M.; Perchalla, L.; Pooth, O.; Stahl, A.; Asin, I.; Bartosik, N.; Behr, J.; Behrenhoff, W.; Behrens, U.; Bell, A. J.; Bergholz, M.; Bethani, A.; Borras, K.; Burgmeier, A.; Cakir, A.; Calligaris, L.; Campbell, A.; Choudhury, S.; Costanza, F.; Diez Pardos, C.; Dooling, S.; Dorland, T.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Flucke, G.; Garay Garcia, J.; Geiser, A.; Gunnellini, P.; Hauk, J.; Hempel, M.; Horton, D.; Jung, H.; Kalogeropoulos, A.; Kasemann, M.; Katsas, P.; Kieseler, J.; Kleinwort, C.; Krücker, D.; Lange, W.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Lutz, B.; Mankel, R.; Marfin, I.; Melzer-Pellmann, I.-A.; Meyer, A. B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Naumann-Emme, S.; Nayak, A.; Novgorodova, O.; Ntomari, E.; Perrey, H.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Ribeiro Cipriano, P. M.; Roland, B.; Ron, E.; Sahin, M. Ö.; Salfeld-Nebgen, J.; Saxena, P.; Schmidt, R.; Schoerner-Sadenius, T.; Schröder, M.; Seitz, C.; Spannagel, S.; Vargas Trevino, A. D. R.; Walsh, R.; Wissing, C.; Aldaya Martin, M.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Erfle, J.; Garutti, E.; Goebel, K.; Görner, M.; Haller, J.; Hoffmann, M.; Höing, R. S.; Kirschenmann, H.; Klanner, R.; Kogler, R.; Lange, J.; Lapsien, T.; Lenz, T.; Marchesini, I.; Ott, J.; Peiffer, T.; Pietsch, N.; Poehlsen, J.; Poehlsen, T.; Rathjens, D.; Sander, C.; Schettler, H.; Schleper, P.; Schlieckau, E.; Schmidt, A.; Seidel, M.; Sola, V.; Stadie, H.; Steinbrück, G.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Barth, C.; Baus, C.; Berger, J.; Böser, C.; Butz, E.; Chwalek, T.; De Boer, W.; Descroix, A.; Dierlamm, A.; Feindt, M.; Frensch, F.; Giffels, M.; Hartmann, F.; Hauth, T.; Husemann, U.; Katkov, I.; Kornmayer, A.; Kuznetsova, E.; Lobelle Pardo, P.; Mozer, M. U.; Müller, Th.; Nürnberg, A.; Quast, G.; Rabbertz, K.; Ratnikov, F.; Röcker, S.; Simonis, H. J.; Stober, F. M.; Ulrich, R.; Wagner-Kuhr, J.; Wayand, S.; Weiler, T.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Giakoumopoulou, V. A.; Kyriakis, A.; Loukas, D.; Markou, A.; Markou, C.; Psallidas, A.; Topsis-Giotis, I.; Agapitos, A.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Stiliaris, E.; Aslanoglou, X.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Bencze, G.; Hajdu, C.; Hidas, P.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Molnar, J.; Palinkas, J.; Szillasi, Z.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Swain, S. K.; Beri, S. B.; Bhatnagar, V.; Gupta, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, M.; Kumar, R.; Mittal, M.; Nishu, N.; Singh, J. B.; Kumar, Ashok; Kumar, Arun; Ahuja, S.; Bhardwaj, A.; Choudhary, B. C.; Kumar, A.; Malhotra, S.; Naimuddin, M.; Ranjan, K.; Sharma, V.; Banerjee, S.; Bhattacharya, S.; Chatterjee, K.; Dutta, S.; Gomber, B.; Jain, Sa.; Jain, Sh.; Khurana, R.; Modak, A.; Mukherjee, S.; Roy, D.; Sarkar, S.; Sharan, M.; Abdulsalam, A.; Dutta, D.; Kailas, S.; Kumar, V.; Mohanty, A. K.; Pant, L. M.; Shukla, P.; Topkar, A.; Aziz, T.; Banerjee, S.; Bhowmik, S.; Chatterjee, R. M.; Dewanjee, R. K.; Dugad, S.; Ganguly, S.; Ghosh, S.; Guchait, M.; Gurtu, A.; Kole, G.; Kumar, S.; Maity, M.; Majumder, G.; Mazumdar, K.; Mohanty, G. B.; Parida, B.; Sudhakar, K.; Wickramage, N.; Bakhshiansohi, H.; Behnamian, H.; Etesami, S. M.; Fahim, A.; Goldouzian, R.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Paktinat Mehdiabadi, S.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Barbone, L.; Calabria, C.; Chhibra, S. S.; Colaleo, A.; Creanza, D.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Zito, G.; Abbiendi, G.; Benvenuti, A. C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.; Travaglini, R.; Albergo, S.; Cappello, G.; Chiorboli, M.; Costa, S.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Gallo, E.; Gonzi, S.; Gori, V.; Lenzi, P.; Meschini, M.; Paoletti, S.; Sguazzoni, G.; Tropiano, A.; Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.; Ferretti, R.; Ferro, F.; Lo Vetere, M.; Robutti, E.; Tosi, S.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Gerosa, R.; Ghezzi, A.; Govoni, P.; Lucchini, M. T.; Malvezzi, S.; Manzoni, R. A.; Martelli, A.; Marzocchi, B.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Redaelli, N.; Tabarelli de Fatis, T.; Buontempo, S.; Cavallo, N.; Di Guida, S.; Fabozzi, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Merola, M.; Paolucci, P.; Azzi, P.; Bacchetta, N.; Bisello, D.; Branca, A.; Carlin, R.; Checchia, P.; Dall'Osso, M.; Dorigo, T.; Dosselli, U.; Galanti, M.; Gasparini, F.; Gasparini, U.; Giubilato, P.; Gozzelino, A.; Kanishchev, K.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Tosi, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.; Gabusi, M.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vitulo, P.; Biasini, M.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Spiezia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Broccolo, G.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fiori, F.; Foà, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Moon, C. S.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Serban, A. T.; Spagnolo, P.; Squillacioti, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Vernieri, C.; Barone, L.; Cavallari, F.; D'imperio, G.; Del Re, D.; Diemoz, M.; Grassi, M.; Jorda, C.; Longo, E.; Margaroli, F.; Meridiani, P.; Micheli, F.; Nourbakhsh, S.; Organtini, G.; Paramatti, R.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Traczyk, P.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bellan, R.; Biino, C.; Cartiglia, N.; Casasso, S.; Costa, M.; Degano, A.; Demaria, N.; Finco, L.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Musich, M.; Obertino, M. M.; Ortona, G.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Potenza, A.; Romero, A.; Ruspa, M.; Sacchi, R.; Solano, A.; Staiano, A.; Tamponi, U.; Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Gobbo, B.; La Licata, C.; Marone, M.; Schizzi, A.; Umer, T.; Zanetti, A.; Chang, S.; Kropivnitskaya, A.; Nam, S. K.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Kong, D. J.; Lee, S.; Oh, Y. D.; Park, H.; Sakharov, A.; Son, D. C.; Kim, T. J.; Kim, J. Y.; Song, S.; Choi, S.; Gyun, D.; Hong, B.; Jo, M.; Kim, H.; Kim, Y.; Lee, B.; Lee, K. S.; Park, S. K.; Roh, Y.; Yoo, H. D.; Choi, M.; Kim, J. H.; Park, I. C.; Ryu, G.; Ryu, M. S.; Choi, Y.; Choi, Y. K.; Goh, J.; Kim, D.; Kwon, E.; Lee, J.; Seo, H.; Yu, I.; Juodagalvis, A.; Komaragiri, J. R.; Ali, M. A. B. Md; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-de La Cruz, I.; Hernandez-Almada, A.; Lopez-Fernandez, R.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Vazquez Valencia, F.; Pedraza, I.; Salazar Ibarguen, H. A.; Casimiro Linares, E.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Reucroft, S.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Khalid, S.; Khan, W. A.; Khurshid, T.; Shah, M. A.; Shoaib, M.; Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Górski, M.; Kazana, M.; Nawrocki, K.; Romanowska-Rybinska, K.; Szleper, M.; Zalewski, P.; Brona, G.; Bunkowski, K.; Cwiok, M.; Dominik, W.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Wolszczak, W.; Bargassa, P.; Beirão Da Cruz E Silva, C.; Faccioli, P.; Ferreira Parracho, P. G.; Gallinaro, M.; Lloret Iglesias, L.; Nguyen, F.; Rodrigues Antunes, J.; Seixas, J.; Varela, J.; Vischia, P.; Golutvin, I.; Gorbunov, I.; Kamenev, A.; Karjavin, V.; Konoplyanikov, V.; Kozlov, G.; Lanev, A.; Malakhov, A.; Matveev, V.; Moisenz, P.; Palichik, V.; Perelygin, V.; Savina, M.; Shmatov, S.; Shulha, S.; Skatchkov, N.; Smirnov, V.; Zarubin, A.; Golovtsov, V.; Ivanov, Y.; Kim, V.; Levchenko, P.; Murzin, V.; Oreshkin, V.; Smirnov, I.; Sulimov, V.; Uvarov, L.; Vavilov, S.; Vorobyev, A.; Vorobyev, An.; Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.; Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Safronov, G.; Semenov, S.; Spiridonov, A.; Stolin, V.; Vlasov, E.; Zhokin, A.; Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Leonidov, A.; Mesyats, G.; Rusakov, S. V.; Vinogradov, A.; Belyaev, A.; Boos, E.; Bunichev, V.; Dubinin, M.; Dudko, L.; Gribushin, A.; Klyukhin, V.; Kodolova, O.; Lokhtin, I.; Obraztsov, S.; Perfilov, M.; Savrin, V.; Snigirev, A.; Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Krychkine, V.; Petrov, V.; Ryutin, R.; Sobol, A.; Tourtchanovitch, L.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.; Adzic, P.; Ekmedzic, M.; Milosevic, J.; Rekovic, V.; Alcaraz Maestre, J.; Battilana, C.; Calvo, E.; Cerrada, M.; Chamizo Llatas, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Domínguez Vázquez, D.; Escalante Del Valle, A.; Fernandez Bedoya, C.; Fernández Ramos, J. P.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Navarro De Martino, E.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.; Albajar, C.; de Trocóniz, J. F.; Missiroli, M.; Moran, D.; Brun, H.; Cuevas, J.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Duarte Campderros, J.; Fernandez, M.; Gomez, G.; Graziano, A.; Lopez Virto, A.; Marco, J.; Marco, R.; Martinez Rivero, C.; Matorras, F.; Munoz Sanchez, F. J.; Piedra Gomez, J.; Rodrigo, T.; Rodríguez-Marrero, A. Y.; Ruiz-Jimeno, A.; Scodellaro, L.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Auffray, E.; Auzinger, G.; Bachtis, M.; Baillon, P.; Ball, A. H.; Barney, D.; Benaglia, A.; Bendavid, J.; Benhabib, L.; Benitez, J. 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I.; Wardle, N.; Wöhri, H. K.; Wollny, H.; Zeuner, W. D.; Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Renker, D.; Rohe, T.; Bachmair, F.; Bäni, L.; Bianchini, L.; Buchmann, M. A.; Casal, B.; Chanon, N.; Dissertori, G.; Dittmar, M.; Donegà, M.; Dünser, M.; Eller, P.; Grab, C.; Hits, D.; Hoss, J.; Lustermann, W.; Mangano, B.; Marini, A. C.; Martinez Ruiz del Arbol, P.; Masciovecchio, M.; Meister, D.; Mohr, N.; Nägeli, C.; Nessi-Tedaldi, F.; Pandolfi, F.; Pauss, F.; Peruzzi, M.; Quittnat, M.; Rebane, L.; Rossini, M.; Starodumov, A.; Takahashi, M.; Theofilatos, K.; Wallny, R.; Weber, H. A.; Amsler, C.; Canelli, M. F.; Chiochia, V.; De Cosa, A.; Hinzmann, A.; Hreus, T.; Kilminster, B.; Lange, C.; Millan Mejias, B.; Ngadiuba, J.; Robmann, P.; Ronga, F. J.; Taroni, S.; Verzetti, M.; Yang, Y.; Cardaci, M.; Chen, K. H.; Ferro, C.; Kuo, C. M.; Lin, W.; Lu, Y. J.; Volpe, R.; Yu, S. S.; Chang, P.; Chang, Y. H.; Chang, Y. 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I.; Henderson, C.; Rumerio, P.; Avetisyan, A.; Bose, T.; Fantasia, C.; Lawson, P.; Richardson, C.; Rohlf, J.; John, J. St.; Sulak, L.; Alimena, J.; Berry, E.; Bhattacharya, S.; Christopher, G.; Cutts, D.; Demiragli, Z.; Dhingra, N.; Ferapontov, A.; Garabedian, A.; Heintz, U.; Kukartsev, G.; Laird, E.; Landsberg, G.; Luk, M.; Narain, M.; Segala, M.; Sinthuprasith, T.; Speer, T.; Swanson, J.; Breedon, R.; Breto, G.; Calderon De La Barca Sanchez, M.; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Gardner, M.; Ko, W.; Lander, R.; Miceli, T.; Mulhearn, M.; Pellett, D.; Pilot, J.; Ricci-Tam, F.; Searle, M.; Shalhout, S.; Smith, J.; Squires, M.; Stolp, D.; Tripathi, M.; Wilbur, S.; Yohay, R.; Cousins, R.; Everaerts, P.; Farrell, C.; Hauser, J.; Ignatenko, M.; Rakness, G.; Takasugi, E.; Valuev, V.; Weber, M.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Hanson, G.; Heilman, J.; Ivova Rikova, M.; Jandir, P.; Kennedy, E.; Lacroix, F.; Long, O. 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M.; Hirschauer, J.; Hooberman, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Kaadze, K.; Klima, B.; Kreis, B.; Kwan, S.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, T.; Lykken, J.; Maeshima, K.; Marraffino, J. M.; Martinez Outschoorn, V. I.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mishra, K.; Mrenna, S.; Musienko, Y.; Nahn, S.; Newman-Holmes, C.; O'Dell, V.; Prokofyev, O.; Sexton-Kennedy, E.; Sharma, S.; Soha, A.; Spalding, W. J.; Spiegel, L.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vidal, R.; Whitbeck, A.; Whitmore, J.; Yang, F.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Carver, M.; Cheng, T.; Curry, D.; Das, S.; De Gruttola, M.; Di Giovanni, G. P.; Field, R. D.; Fisher, M.; Furic, I. K.; Hugon, J.; Konigsberg, J.; Korytov, A.; Kypreos, T.; Low, J. 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J.; Kellams, N.; Lannon, K.; Luo, W.; Lynch, S.; Marinelli, N.; Pearson, T.; Planer, M.; Ruchti, R.; Valls, N.; Wayne, M.; Wolf, M.; Woodard, A.; Antonelli, L.; Brinson, J.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Hart, A.; Hill, C.; Hughes, R.; Kotov, K.; Ling, T. Y.; Puigh, D.; Rodenburg, M.; Smith, G.; Winer, B. L.; Wolfe, H.; Wulsin, H. W.; Driga, O.; Elmer, P.; Hebda, P.; Hunt, A.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Piroué, P.; Quan, X.; Saka, H.; Stickland, D.; Tully, C.; Werner, J. S.; Zuranski, A.; Brownson, E.; Mendez, H.; Ramirez Vargas, J. E.; Barnes, V. E.; Benedetti, D.; Bortoletto, D.; De Mattia, M.; Gutay, L.; Hu, Z.; Jha, M. K.; Jones, M.; Jung, K.; Kress, M.; Leonardo, N.; Lopes Pegna, D.; Maroussov, V.; Miller, D. H.; Neumeister, N.; Radburn-Smith, B. C.; Shi, X.; Shipsey, I.; Silvers, D.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Zablocki, J.; Zheng, Y.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Ecklund, K. M.; Geurts, F. J. M.; Li, W.; Michlin, B.; Padley, B. P.; Redjimi, R.; Roberts, J.; Zabel, J.; Betchart, B.; Bodek, A.; Covarelli, R.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Garcia-Bellido, A.; Goldenzweig, P.; Han, J.; Harel, A.; Khukhunaishvili, A.; Petrillo, G.; Vishnevskiy, D.; Ciesielski, R.; Demortier, L.; Goulianos, K.; Lungu, G.; Mesropian, C.; Arora, S.; Barker, A.; Chou, J. P.; Contreras-Campana, C.; Contreras-Campana, E.; Duggan, D.; Ferencek, D.; Gershtein, Y.; Gray, R.; Halkiadakis, E.; Hidas, D.; Kaplan, S.; Lath, A.; Panwalkar, S.; Park, M.; Patel, R.; Salur, S.; Schnetzer, S.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Rose, K.; Spanier, S.; York, A.; Bouhali, O.; Castaneda Hernandez, A.; Eusebi, R.; Flanagan, W.; Gilmore, J.; Kamon, T.; Khotilovich, V.; Krutelyov, V.; Montalvo, R.; Osipenkov, I.; Pakhotin, Y.; Perloff, A.; Roe, J.; Rose, A.; Safonov, A.; Sakuma, T.; Suarez, I.; Tatarinov, A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kovitanggoon, K.; Kunori, S.; Lee, S. W.; Libeiro, T.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Sharma, M.; Sheldon, P.; Snook, B.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Boutle, S.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Lin, C.; Neu, C.; Wood, J.; Clarke, C.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Sturdy, J.; Belknap, D. A.; Carlsmith, D.; Cepeda, M.; Dasu, S.; Dodd, L.; Duric, S.; Friis, E.; Hall-Wilton, R.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Lazaridis, C.; Levine, A.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ross, I.; Sarangi, T.; Savin, A.; Smith, W. H.; Taylor, D.; Verwilligen, P.; Vuosalo, C.; Woods, N. 2015-04-01 Dimuon and dielectron mass spectra, obtained from data resulting from proton-proton collisions at 8 TeV and recorded by the CMS experiment, are used to search for both narrow resonances and broad deviations from standard model predictions. The data correspond to an integrated luminosity of 20.6 (19.7) fb-1 for the dimuon (dielectron) channel. No evidence for non-standard-model physics is observed and 95% confidence level limits are set on parameters from a number of new physics models. The narrow resonance analyses exclude a Sequential Standard Model Z{SSM/'} resonance lighter than 2.90 TeV, a superstring-inspired Z{/ψ '} lighter than 2.57 TeV, and Randall-Sundrum Kaluza-Klein gravitons with masses below 2.73, 2.35, and 1.27 TeV for couplings of 0.10, 0.05, and 0.01, respectively. A notable feature is that the limits have been calculated in a model-independent way to enable straightforward reinterpretation in any model predicting a resonance structure. The observed events are also interpreted within the framework of two non-resonant analyses: one based on a large extra dimensions model and one based on a quark and lepton compositeness model with a left-left isoscalar contact interaction. Lower limits are established on MS, the scale characterizing the onset of quantum gravity, which range from 4.9 to 3.3 TeV, where the number of additional spatial dimensions varies from 3 to 7. Similarly, lower limits on Λ, the energy scale parameter for the contact interaction, are found to be 12.0 (15.2) TeV for destructive (constructive) interference in the dimuon channel and 13.5 (18.3) TeV in the dielectron channel. [Figure not available: see fulltext. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1234878-search-physics-beyond-standard-model-dilepton-mass-spectra-proton-proton-collisions-tev','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1234878-search-physics-beyond-standard-model-dilepton-mass-spectra-proton-proton-collisions-tev">Search for physics beyond the standard model in dilepton mass spectra in proton-proton collisions at √s = 8 TeV</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; ... 2015-04-07 Dimuon and dielectron mass spectra, obtained from data resulting from proton-proton collisions at 8 TeV and recorded by the CMS experiment, are used to search for both narrow resonances and broad deviations from standard model predictions. The data correspond to an integrated luminosity of 20.6 (19.7) fb –1 for the dimuon (dielectron) channel. No evidence for non-standard-model physics is observed and 95% confidence level limits are set on parameters from a number of new physics models. The narrow resonance analyses exclude a Sequential Standard Model Z SSM ' resonance lighter than 2.90 TeV, a superstring-inspired Z ψ ' lighter thanmore » 2.57 TeV, and Randall-Sundrum Kaluza-Klein gravitons with masses below 2.73, 2.35, and 1.27 TeV for couplings of 0.10, 0.05, and 0.01, respectively. A notable feature is that the limits have been calculated in a model-independent way to enable straightforward reinterpretation in any model predicting a resonance structure. The observed events are also interpreted within the framework of two non-resonant analyses: one based on a large extra dimensions model and one based on a quark and lepton compositeness model with a left-left isoscalar contact interaction. Lower limits are established on MS, the scale characterizing the onset of quantum gravity, which range from 4.9 to 3.3 TeV, where the number of additional spatial dimensions varies from 3 to 7. Thus lower limits on Λ, the energy scale parameter for the contact interaction, are found to be 12.0 (15.2) TeV for destructive (constructive) interference in the dimuon channel and 13.5 (18.3) TeV in the dielectron channel.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26563743','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26563743">Prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy: a systematic review and external validation study.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Hilkens, N A; Algra, A; Greving, J P 2016-01-01 ESSENTIALS: Prediction models may help to identify patients at high risk of bleeding on antiplatelet therapy. We identified existing prediction models for bleeding and validated them in patients with cerebral ischemia. Five prediction models were identified, all of which had some methodological shortcomings. Performance in patients with cerebral ischemia was poor. Background Antiplatelet therapy is widely used in secondary prevention after a transient ischemic attack (TIA) or ischemic stroke. Bleeding is the main adverse effect of antiplatelet therapy and is potentially life threatening. Identification of patients at increased risk of bleeding may help target antiplatelet therapy. This study sought to identify existing prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy and evaluate their performance in patients with cerebral ischemia. We systematically searched PubMed and Embase for existing prediction models up to December 2014. The methodological quality of the included studies was assessed with the CHARMS checklist. Prediction models were externally validated in the European Stroke Prevention Study 2, comprising 6602 patients with a TIA or ischemic stroke. We assessed discrimination and calibration of included prediction models. Five prediction models were identified, of which two were developed in patients with previous cerebral ischemia. Three studies assessed major bleeding, one studied intracerebral hemorrhage and one gastrointestinal bleeding. None of the studies met all criteria of good quality. External validation showed poor discriminative performance, with c-statistics ranging from 0.53 to 0.64 and poor calibration. A limited number of prediction models is available that predict intracranial hemorrhage or major bleeding in patients on antiplatelet therapy. The methodological quality of the models varied, but was generally low. Predictive performance in patients with cerebral ischemia was poor. In order to reliably predict the risk of bleeding in patients with cerebral ischemia, development of a prediction model according to current methodological standards is needed. © 2015 International Society on Thrombosis and Haemostasis. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70003681','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70003681">Model and parametric uncertainty in source-based kinematic models of earthquake ground motion</a> <a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a> Hartzell, Stephen; Frankel, Arthur; Liu, Pengcheng; Zeng, Yuehua; Rahman, Shariftur 2011-01-01 Four independent ground-motion simulation codes are used to model the strong ground motion for three earthquakes: 1994 Mw 6.7 Northridge, 1989 Mw 6.9 Loma Prieta, and 1999 Mw 7.5 Izmit. These 12 sets of synthetics are used to make estimates of the variability in ground-motion predictions. In addition, ground-motion predictions over a grid of sites are used to estimate parametric uncertainty for changes in rupture velocity. We find that the combined model uncertainty and random variability of the simulations is in the same range as the variability of regional empirical ground-motion data sets. The majority of the standard deviations lie between 0.5 and 0.7 natural-log units for response spectra and 0.5 and 0.8 for Fourier spectra. The estimate of model epistemic uncertainty, based on the different model predictions, lies between 0.2 and 0.4, which is about one-half of the estimates for the standard deviation of the combined model uncertainty and random variability. Parametric uncertainty, based on variation of just the average rupture velocity, is shown to be consistent in amplitude with previous estimates, showing percentage changes in ground motion from 50% to 300% when rupture velocity changes from 2.5 to 2.9 km/s. In addition, there is some evidence that mean biases can be reduced by averaging ground-motion estimates from different methods. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22194308','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22194308">A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Rivas, Elena; Lang, Raymond; Eddy, Sean R 2012-02-01 The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3264907','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3264907">A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Rivas, Elena; Lang, Raymond; Eddy, Sean R. 2012-01-01 The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases. PMID:22194308 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26265094','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26265094">Visual Predictive Check in Models with Time-Varying Input Function.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Largajolli, Anna; Bertoldo, Alessandra; Campioni, Marco; Cobelli, Claudio 2015-11-01 The nonlinear mixed effects models are commonly used modeling techniques in the pharmaceutical research as they enable the characterization of the individual profiles together with the population to which the individuals belong. To ensure a correct use of them is fundamental to provide powerful diagnostic tools that are able to evaluate the predictive performance of the models. The visual predictive check (VPC) is a commonly used tool that helps the user to check by visual inspection if the model is able to reproduce the variability and the main trend of the observed data. However, the simulation from the model is not always trivial, for example, when using models with time-varying input function (IF). In this class of models, there is a potential mismatch between each set of simulated parameters and the associated individual IF which can cause an incorrect profile simulation. We introduce a refinement of the VPC by taking in consideration a correlation term (the Mahalanobis or normalized Euclidean distance) that helps the association of the correct IF with the individual set of simulated parameters. We investigate and compare its performance with the standard VPC in models of the glucose and insulin system applied on real and simulated data and in a simulated pharmacokinetic/pharmacodynamic (PK/PD) example. The newly proposed VPC performance appears to be better with respect to the standard VPC especially for the models with big variability in the IF where the probability of simulating incorrect profiles is higher. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013SPIE.8651E..0TM','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013SPIE.8651E..0TM">Blind image quality assessment without training on human opinion scores</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Mittal, Anish; Soundararajan, Rajiv; Muralidhar, Gautam S.; Bovik, Alan C.; Ghosh, Joydeep 2013-03-01 We propose a family of image quality assessment (IQA) models based on natural scene statistics (NSS), that can predict the subjective quality of a distorted image without reference to a corresponding distortionless image, and without any training results on human opinion scores of distorted images. These `completely blind' models compete well with standard non-blind image quality indices in terms of subjective predictive performance when tested on the large publicly available `LIVE' Image Quality database. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMGC41E..05T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMGC41E..05T">Predicting the Vegetation Condition of the 2015/16 Drought across the Great Horn of Africa (GHA): Model Evaluation and Preliminary Result</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Tadesse, T.; Bayissa, Y. A.; Demisse, G. B.; Wardlow, B. 2017-12-01 The National Drought Mitigation Center (NDMC) funded by NASA has developed a new tool for predicting the general vegetation condition called: the "Vegetation outlook for the Greater Africa (VegOut-GHA)." In this study, the 2015/16 drought across the GHA that has been considered one of the worst in decades across the region was assessed and evaluated using the VegOut-GHA models and products. The VegOut-GHA maps (hindsight prediction maps) for the growing season (June - September) were generated to predict a standardized seasonal greenness (SSG) that is based on seasonally integrated normalized difference vegetation index (a measure that represents a general indicator of relative vegetation health within a growing season). The vegetation condition outlooks were made for 10-day, 1-month, 2-month, and 3-month in hindsight and compared to the observed values of the SSG. The VegOut-GHA model was evaluated and compared to crop yield and other satellite-derived data (e.g., standardized seasonal precipitation based on "Enhancing National Climate Services (ENACTS)" datasets for GHA). Thus, the VegOut-GHA model and its evaluation results will be discussed based on the 2015/2016 drought season in the region. This preliminary results suggest an opportunity to improve management of drought risk in agriculture and food security. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5489009','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5489009">Improving rational thermal comfort prediction by using subpopulation characteristics: A case study at Hermitage Amsterdam</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Kramer, Rick; Schellen, Lisje; Schellen, Henk; Kingma, Boris 2017-01-01 ABSTRACT This study aims to improve the prediction accuracy of the rational standard thermal comfort model, known as the Predicted Mean Vote (PMV) model, by (1) calibrating one of its input variables “metabolic rate,” and (2) extending it by explicitly incorporating the variable running mean outdoor temperature (RMOT) that relates to adaptive thermal comfort. The analysis was performed with survey data (n = 1121) and climate measurements of the indoor and outdoor environment from a one year-long case study undertaken at Hermitage Amsterdam museum in the Netherlands. The PMVs were calculated for 35 survey days using (1) an a priori assumed metabolic rate, (2) a calibrated metabolic rate found by fitting the PMVs to the thermal sensation votes (TSVs) of each respondent using an optimization routine, and (3) extending the PMV model by including the RMOT. The results show that the calibrated metabolic rate is estimated to be 1.5 Met for this case study that was predominantly visited by elderly females. However, significant differences in metabolic rates have been revealed between adults and elderly showing the importance of differentiating between subpopulations. Hence, the standard tabular values, which only differentiate between various activities, may be oversimplified for many cases. Moreover, extending the PMV model with the RMOT substantially improves the thermal sensation prediction, but thermal sensation toward extreme cool and warm sensations remains partly underestimated. PMID:28680934 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28680934','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28680934">Improving rational thermal comfort prediction by using subpopulation characteristics: A case study at Hermitage Amsterdam.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Kramer, Rick; Schellen, Lisje; Schellen, Henk; Kingma, Boris 2017-01-01 This study aims to improve the prediction accuracy of the rational standard thermal comfort model, known as the Predicted Mean Vote (PMV) model, by (1) calibrating one of its input variables "metabolic rate," and (2) extending it by explicitly incorporating the variable running mean outdoor temperature (RMOT) that relates to adaptive thermal comfort. The analysis was performed with survey data ( n = 1121) and climate measurements of the indoor and outdoor environment from a one year-long case study undertaken at Hermitage Amsterdam museum in the Netherlands. The PMVs were calculated for 35 survey days using (1) an a priori assumed metabolic rate, (2) a calibrated metabolic rate found by fitting the PMVs to the thermal sensation votes (TSVs) of each respondent using an optimization routine, and (3) extending the PMV model by including the RMOT. The results show that the calibrated metabolic rate is estimated to be 1.5 Met for this case study that was predominantly visited by elderly females. However, significant differences in metabolic rates have been revealed between adults and elderly showing the importance of differentiating between subpopulations. Hence, the standard tabular values, which only differentiate between various activities, may be oversimplified for many cases. Moreover, extending the PMV model with the RMOT substantially improves the thermal sensation prediction, but thermal sensation toward extreme cool and warm sensations remains partly underestimated. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25999783','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25999783">Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8[Formula: see text].</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Khachatryan, V; Sirunyan, A M; Tumasyan, A; Adam, W; Bergauer, T; Dragicevic, M; Erö, J; Friedl, M; Frühwirth, R; Ghete, V M; Hartl, C; Hörmann, N; Hrubec, J; Jeitler, M; Kiesenhofer, W; Knünz, V; Krammer, M; Krätschmer, I; Liko, D; Mikulec, I; Rabady, D; Rahbaran, B; Rohringer, H; Schöfbeck, R; Strauss, J; Treberer-Treberspurg, W; Waltenberger, W; Wulz, C-E; Mossolov, V; Shumeiko, N; Suarez Gonzalez, J; Alderweireldt, S; Bansal, S; Cornelis, T; De Wolf, E A; Janssen, X; Knutsson, A; Lauwers, J; Luyckx, S; Ochesanu, S; Rougny, R; Van De Klundert, M; Van Haevermaet, H; Van Mechelen, P; Van Remortel, N; Van Spilbeeck, A; Blekman, F; Blyweert, S; D'Hondt, J; Daci, N; Heracleous, N; Keaveney, J; Lowette, S; Maes, M; Olbrechts, A; Python, Q; Strom, D; Tavernier, S; Van Doninck, W; Van Mulders, P; Van Onsem, G P; Villella, I; Caillol, C; Clerbaux, B; De Lentdecker, G; Dobur, D; Favart, L; Gay, A P R; Grebenyuk, A; Léonard, A; Mohammadi, A; Perniè, L; Randle-Conde, A; Reis, T; Seva, T; Thomas, L; Vander Velde, C; Vanlaer, P; Wang, J; Zenoni, F; Adler, V; Beernaert, K; Benucci, L; Cimmino, A; Costantini, S; Crucy, S; Fagot, A; Garcia, G; Mccartin, J; Ocampo Rios, A A; Poyraz, D; Ryckbosch, D; Salva Diblen, S; Sigamani, M; Strobbe, N; Thyssen, F; Tytgat, M; Yazgan, E; Zaganidis, N; Basegmez, S; Beluffi, C; Bruno, G; Castello, R; Caudron, A; Ceard, L; Da Silveira, G G; Delaere, C; du Pree, T; Favart, D; Forthomme, L; Giammanco, A; Hollar, J; Jafari, A; Jez, P; Komm, M; Lemaitre, V; Nuttens, C; Pagano, D; Perrini, L; Pin, A; Piotrzkowski, K; Popov, A; Quertenmont, L; Selvaggi, M; Vidal Marono, M; Vizan Garcia, J M; Beliy, N; Caebergs, T; Daubie, E; Hammad, G H; Júnior, W L Aldá; Alves, G A; Brito, L; Correa Martins Junior, M; Martins, T Dos Reis; Molina, J; Mora Herrera, C; Pol, M E; Teles, P Rebello; Carvalho, W; Chinellato, J; Custódio, A; Da Costa, E M; De Jesus Damiao, D; De Oliveira Martins, C; Fonseca De Souza, S; Malbouisson, H; Matos Figueiredo, D; Mundim, L; Nogima, H; Prado Da Silva, W L; Santaolalla, J; Santoro, A; Sznajder, A; Tonelli Manganote, E J; Vilela Pereira, A; Bernardes, C A; Dogra, S; Fernandez Perez Tomei, T R; Gregores, E M; Mercadante, P G; Novaes, S F; Padula, Sandra S; Aleksandrov, A; Genchev, V; Hadjiiska, R; Iaydjiev, P; Marinov, A; Piperov, S; Rodozov, M; Stoykova, S; Sultanov, G; Vutova, M; Dimitrov, A; Glushkov, I; Litov, L; Pavlov, B; Petkov, P; Bian, J G; Chen, G M; Chen, H S; Chen, M; Cheng, T; Du, R; Jiang, C H; Plestina, R; Romeo, F; Tao, J; Wang, Z; Asawatangtrakuldee, C; Ban, Y; Liu, S; Mao, Y; Qian, S J; Wang, D; Xu, Z; Zhang, F; Zhang, L; Zou, W; Avila, C; Cabrera, A; Chaparro Sierra, L F; Florez, C; Gomez, J P; Gomez Moreno, B; Sanabria, J C; Godinovic, N; Lelas, D; Polic, D; Puljak, I; Antunovic, Z; Kovac, M; Brigljevic, V; Kadija, K; Luetic, J; Mekterovic, D; Sudic, L; Attikis, A; Mavromanolakis, G; Mousa, J; Nicolaou, C; Ptochos, F; Razis, P A; Rykaczewski, H; Bodlak, M; Finger, M; Finger, M; Assran, Y; Ellithi Kame, A; Mahmoud, M A; Radi, A; Kadastik, M; Murumaa, M; Raidal, M; Tiko, A; Eerola, P; Voutilainen, M; Härkönen, J; Heikkilä, J K; Karimäki, V; Kinnunen, R; Kortelainen, M J; Lampén, T; Lassila-Perini, K; Lehti, S; Lindén, T; Luukka, P; Mäenpää, T; Peltola, T; Tuominen, E; Tuominiemi, J; Tuovinen, E; Wendland, L; 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Kyberd, P; Leggat, D; Leslie, D; Reid, I D; Symonds, P; Teodorescu, L; Turner, M; Dittmann, J; Hatakeyama, K; Kasmi, A; Liu, H; Pastika, N; Scarborough, T; Wu, Z; Charaf, O; Cooper, S I; Henderson, C; Rumerio, P; Avetisyan, A; Bose, T; Fantasia, C; Lawson, P; Richardson, C; Rohlf, J; St John, J; Sulak, L; Alimena, J; Berry, E; Bhattacharya, S; Christopher, G; Cutts, D; Demiragli, Z; Dhingra, N; Ferapontov, A; Garabedian, A; Heintz, U; Laird, E; Landsberg, G; Mao, Z; Narain, M; Sagir, S; Sinthuprasith, T; Speer, T; Swanson, J; Breedon, R; Breto, G; De La Barca Sanchez, M Calderon; Chauhan, S; Chertok, M; Conway, J; Conway, R; Cox, P T; Erbacher, R; Gardner, M; Ko, W; Lander, R; Mulhearn, M; Pellett, D; Pilot, J; Ricci-Tam, F; Shalhout, S; Smith, J; Squires, M; Stolp, D; Tripathi, M; Wilbur, S; Yohay, R; Cousins, R; Everaerts, P; Farrell, C; Hauser, J; Ignatenko, M; Rakness, G; Takasugi, E; Valuev, V; Weber, M; Burt, K; Clare, R; Ellison, J; Gary, J W; Hanson, G; Heilman, J; Ivova Rikova, M; Jandir, P; Kennedy, E; Lacroix, F; Long, O R; Luthra, A; Malberti, M; Negrete, M Olmedo; Shrinivas, A; Sumowidagdo, S; Wimpenny, S; Branson, J G; Cerati, G B; Cittolin, S; D'Agnolo, R T; Holzner, A; Kelley, R; Klein, D; Letts, J; Macneill, I; Olivito, D; Padhi, S; Palmer, C; Pieri, M; Sani, M; Sharma, V; Simon, S; Tadel, M; Tu, Y; Vartak, A; Welke, C; Würthwein, F; Yagil, A; Zevi Della Porta, G; Barge, D; Bradmiller-Feld, J; Campagnari, C; Danielson, T; Dishaw, A; Dutta, V; Flowers, K; Franco Sevilla, M; Geffert, P; George, C; Golf, F; Gouskos, L; Incandela, J; Justus, C; Mccoll, N; Mullin, S D; Richman, J; Stuart, D; To, W; West, C; Yoo, J; Apresyan, A; Bornheim, A; Bunn, J; Chen, Y; Duarte, J; Mott, A; Newman, H B; Pena, C; Pierini, M; Spiropulu, M; Vlimant, R; Wilkinson, R; Xie, S; Zhu, R Y; Azzolini, V; Calamba, A; Carlson, B; Ferguson, T; Iiyama, Y; Paulini, M; Russ, J; Vogel, H; Vorobiev, I; Cumalat, J P; Ford, W T; Gaz, A; Krohn, M; Luiggi Lopez, E; Nauenberg, U; Smith, J G; Stenson, K; Wagner, S R; Alexander, J; Chatterjee, A; Chaves, J; Chu, J; Dittmer, S; Eggert, N; Mirman, N; Nicolas Kaufman, G; Patterson, J R; Ryd, A; Salvati, E; Skinnari, L; Sun, W; Teo, W D; Thom, J; Thompson, J; Tucker, J; Weng, Y; Winstrom, L; Wittich, P; Winn, D; Abdullin, S; Albrow, M; Anderson, J; Apollinari, G; Bauerdick, L A T; Beretvas, A; Berryhill, J; Bhat, P C; Bolla, G; Burkett, K; Butler, J N; Cheung, H W K; Chlebana, F; Cihangir, S; Elvira, V D; Fisk, I; Freeman, J; Gottschalk, E; Gray, L; Green, D; Grünendahl, S; Gutsche, O; Hanlon, J; Hare, D; Harris, R M; Hirschauer, J; Hooberman, B; Jindariani, S; Johnson, M; Joshi, U; Klima, B; Kreis, B; Kwan, S; Linacre, J; Lincoln, D; Lipton, R; Liu, T; Lopes De Sá, R; Lykken, J; Maeshima, K; Marraffino, J M; Martinez Outschoorn, V I; Maruyama, S; Mason, D; McBride, P; Merkel, P; Mishra, K; Mrenna, S; Nahn, S; Newman-Holmes, C; O'Dell, V; Prokofyev, O; Sexton-Kennedy, E; Soha, A; Spalding, W J; Spiegel, L; Taylor, L; Tkaczyk, S; Tran, N V; Uplegger, L; Vaandering, E W; Vidal, R; Whitbeck, A; Whitmore, J; Yang, F; Acosta, D; Avery, P; Bortignon, P; Bourilkov, D; Carver, M; Curry, D; Das, S; De Gruttola, M; Di Giovanni, G P; Field, R D; Fisher, M; Furic, I K; Hugon, J; Konigsberg, J; Korytov, A; Kypreos, T; Low, J F; Matchev, K; Mei, H; Milenovic, P; Mitselmakher, G; Muniz, L; Rinkevicius, A; Shchutska, L; Snowball, M; Sperka, D; Yelton, J; Zakaria, M; Hewamanage, S; Linn, S; Markowitz, P; Martinez, G; Rodriguez, J L; Adams, J R; Adams, T; Askew, A; Bochenek, J; Diamond, B; Haas, J; Hagopian, S; Hagopian, V; Johnson, K F; Prosper, H; Veeraraghavan, V; Weinberg, M; Baarmand, M M; Hohlmann, M; Kalakhety, H; Yumiceva, F; Adams, M R; Apanasevich, L; Berry, D; Betts, R R; Bucinskaite, I; Cavanaugh, R; Evdokimov, O; Gauthier, L; Gerber, C E; Hofman, D J; Kurt, P; O'Brien, C; Sandoval Gonzalez, I D; Silkworth, C; Turner, P; Varelas, N; Bilki, B; Clarida, W; Dilsiz, K; Haytmyradov, M; Khristenko, V; Merlo, J-P; Mermerkaya, H; Mestvirishvili, A; Moeller, A; Nachtman, J; Ogul, H; Onel, Y; Ozok, F; Penzo, A; Rahmat, R; Sen, S; Tan, P; Tiras, E; Wetzel, J; Yi, K; Anderson, I; Barnett, B A; Blumenfeld, B; Bolognesi, S; Fehling, D; Gritsan, A V; Maksimovic, P; Martin, C; Swartz, M; Xiao, M; Baringer, P; Bean, A; Benelli, G; Bruner, C; Gray, J; Kenny, R P; Majumder, D; Malek, M; Murray, M; Noonan, D; Sanders, S; Sekaric, J; Stringer, R; Wang, Q; Wood, J S; Chakaberia, I; Ivanov, A; Kaadze, K; Khalil, S; Makouski, M; Maravin, Y; Saini, L K; Skhirtladze, N; Svintradze, I; Gronberg, J; Lange, D; Rebassoo, F; Wright, D; Baden, A; Belloni, A; Calvert, B; Eno, S C; Gomez, J A; Hadley, N J; Jabeen, S; Kellogg, R G; Kolberg, T; Lu, Y; Mignerey, A C; Pedro, K; Skuja, A; Tonjes, M B; Tonwar, S C; Apyan, A; Barbieri, R; Bierwagen, K; Busza, W; Cali, I A; Di Matteo, L; Gomez Ceballos, G; Goncharov, M; Gulhan, D; Klute, M; Lai, Y S; Lee, Y-J; Levin, A; Luckey, P D; Paus, C; Ralph, D; Roland, C; Roland, G; Stephans, G S F; Sumorok, K; Velicanu, D; Veverka, J; Wyslouch, B; Yang, M; Zanetti, M; Zhukova, V; Dahmes, B; Gude, A; Kao, S C; Klapoetke, K; Kubota, Y; Mans, J; Nourbakhsh, S; Rusack, R; Singovsky, A; Tambe, N; Turkewitz, J; Acosta, J G; Oliveros, S; Avdeeva, E; Bloom, K; Bose, S; Claes, D R; Dominguez, A; Gonzalez Suarez, R; Keller, J; Knowlton, D; Kravchenko, I; Lazo-Flores, J; Meier, F; Ratnikov, F; Snow, G R; Zvada, M; Dolen, J; Godshalk, A; Iashvili, I; Kharchilava, A; Kumar, A; Rappoccio, S; Alverson, G; Barberis, E; Baumgartel, D; Chasco, M; Massironi, A; Morse, D M; Nash, D; Orimoto, T; Trocino, D; Wang, R J; Wood, D; Zhang, J; Hahn, K A; Kubik, A; Mucia, N; Odell, N; Pollack, B; Pozdnyakov, A; Schmitt, M; Stoynev, S; Sung, K; Velasco, M; Won, S; Brinkerhoff, A; Chan, K M; Drozdetskiy, A; Hildreth, M; Jessop, C; Karmgard, D J; Kellams, N; Lannon, K; Lynch, S; Marinelli, N; Musienko, Y; Pearson, T; Planer, M; Ruchti, R; Smith, G; Valls, N; Wayne, M; Wolf, M; Woodard, A; Antonelli, L; Brinson, J; Bylsma, B; Durkin, L S; Flowers, S; Hart, A; Hill, C; Hughes, R; Kotov, K; Ling, T Y; Luo, W; Puigh, D; Rodenburg, M; Winer, B L; Wolfe, H; Wulsin, H W; Driga, O; Elmer, P; Hardenbrook, J; Hebda, P; Koay, S A; Lujan, P; Marlow, D; Medvedeva, T; Mooney, M; Olsen, J; Piroué, P; Quan, X; Saka, H; Stickland, D; Tully, C; Werner, J S; Zuranski, A; Brownson, E; Malik, S; Mendez, H; Ramirez Vargas, J E; Barnes, V E; Benedetti, D; Bortoletto, D; Gutay, L; Hu, Z; Jha, M K; Jones, M; Jung, K; Kress, M; Leonardo, N; Miller, D H; Neumeister, N; Primavera, F; Radburn-Smith, B C; Shi, X; Shipsey, I; Silvers, D; Svyatkovskiy, A; Wang, F; Xie, W; Xu, L; Zablocki, J; Parashar, N; Stupak, J; Adair, A; Akgun, B; Ecklund, K M; Geurts, F J M; Li, W; Michlin, B; Padley, B P; Redjimi, R; Roberts, J; Zabel, J; Betchart, B; Bodek, A; de Barbaro, P; Demina, R; Eshaq, Y; Ferbel, T; Galanti, M; Garcia-Bellido, A; Goldenzweig, P; Han, J; Harel, A; Hindrichs, O; Khukhunaishvili, A; Korjenevski, S; Petrillo, G; Verzetti, M; Vishnevskiy, D; Ciesielski, R; Demortier, L; Goulianos, K; Mesropian, C; Arora, S; Barker, A; Chou, J P; Contreras-Campana, C; Contreras-Campana, E; Duggan, D; Ferencek, D; Gershtein, Y; Gray, R; Halkiadakis, E; Hidas, D; Kaplan, S; Lath, A; Panwalkar, S; Park, M; Salur, S; Schnetzer, S; Sheffield, D; Somalwar, S; Stone, R; Thomas, S; Thomassen, P; Walker, M; Rose, K; Spanier, S; York, A; Bouhali, O; Castaneda Hernandez, A; Dalchenko, M; De Mattia, M; Dildick, S; Eusebi, R; Flanagan, W; Gilmore, J; Kamon, T; Khotilovich, V; Krutelyov, V; Montalvo, R; Osipenkov, I; Pakhotin, Y; Patel, R; Perloff, A; Roe, J; Rose, A; Safonov, A; Suarez, I; Tatarinov, A; Ulmer, K A; Akchurin, N; Cowden, C; Damgov, J; Dragoiu, C; Dudero, P R; Faulkner, J; Kovitanggoon, K; Kunori, S; Lee, S W; Libeiro, T; Volobouev, I; Appelt, E; Delannoy, A G; Greene, S; Gurrola, A; Johns, W; Maguire, C; Mao, Y; Melo, A; Sharma, M; Sheldon, P; Snook, B; Tuo, S; Velkovska, J; Arenton, M W; Boutle, S; Cox, B; Francis, B; Goodell, J; Hirosky, R; Ledovskoy, A; Li, H; Lin, C; Neu, C; Wolfe, E; Wood, J; Clarke, C; Harr, R; Karchin, P E; Kottachchi Kankanamge Don, C; Lamichhane, P; Sturdy, J; Belknap, D A; Carlsmith, D; Cepeda, M; Dasu, S; Dodd, L; Duric, S; Friis, E; Hall-Wilton, R; Herndon, M; Hervé, A; Klabbers, P; Lanaro, A; Lazaridis, C; Levine, A; Loveless, R; Mohapatra, A; Ojalvo, I; Perry, T; Pierro, G A; Polese, G; Ross, I; Sarangi, T; Savin, A; Smith, W H; Taylor, D; Vuosalo, C; Woods, N; Roinishvili, V Properties of the Higgs boson with mass near 125[Formula: see text] are measured in proton-proton collisions with the CMS experiment at the LHC. Comprehensive sets of production and decay measurements are combined. The decay channels include [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] pairs. The data samples were collected in 2011 and 2012 and correspond to integrated luminosities of up to 5.1[Formula: see text] at 7[Formula: see text] and up to 19.7[Formula: see text] at 8[Formula: see text]. From the high-resolution [Formula: see text] and [Formula: see text] channels, the mass of the Higgs boson is determined to be [Formula: see text]. For this mass value, the event yields obtained in the different analyses tagging specific decay channels and production mechanisms are consistent with those expected for the standard model Higgs boson. The combined best-fit signal relative to the standard model expectation is [Formula: see text] at the measured mass. The couplings of the Higgs boson are probed for deviations in magnitude from the standard model predictions in multiple ways, including searches for invisible and undetected decays. No significant deviations are found. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1421818','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1421818">Measurement of inclusive and differential cross sections in the H → ZZ * → 4ℓ decay channel in pp collisions at $$ \\sqrt{s}=13 $$ TeV with the ATLAS detector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Aaboud, M.; Aad, G.; Abbott, B. Inclusive and differential fiducial cross sections of Higgs boson production in proton-proton collisions are measured in the H → ZZ* → 4ℓ decay channel. The proton-proton collision data were produced at the Large Hadron Collider at a centre-of-mass energy of 13 TeV and recorded by the ATLAS detector in 2015 and 2016, corresponding to an integrated luminosity of 36.1 fb –1. The inclusive fiducial cross section in the H → ZZ* → 4ℓ decay channel is measured to be 3.62±0.50(stat) –0.20 +0.25 (sys) fb, in agreement with the Standard Model prediction of 2.91 ± 0.13 fb. The cross section ismore » also extrapolated to the total phase space including all Standard Model Higgs boson decays. Several differential fiducial cross sections are measured for observables sensitive to the Higgs boson production and decay, including kinematic distributions of jets produced in association with the Higgs boson. Good agreement is found between data and Standard Model predictions. The results are used to put constraints on anomalous Higgs boson interactions with Standard Model particles, using the pseudo-observable extension to the kappa-framework.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1418808-measurement-inclusive-differential-cross-sections-zz-decay-channel-pp-collisions-sqrt-tev-atlas-detector','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1418808-measurement-inclusive-differential-cross-sections-zz-decay-channel-pp-collisions-sqrt-tev-atlas-detector">Measurement of inclusive and differential cross sections in the H → ZZ * → 4ℓ decay channel in pp collisions at $$ \\sqrt{s}=13 $$ TeV with the ATLAS detector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Aaboud, M.; Aad, G.; Abbott, B.; ... 2017-10-19 Inclusive and differential fiducial cross sections of Higgs boson production in proton-proton collisions are measured in the H → ZZ * → 4ℓ decay channel. The proton-proton collision data were produced at the Large Hadron Collider at a centre-of-mass energy of 13 TeV and recorded by the ATLAS detector in 2015 and 2016, corresponding to an integrated luminosity of 36.1 fb –1. The inclusive fiducial cross section in the H → ZZ * → 4ℓ decay channel is measured to be 3.62±0.50(stat) –0.20 +0.25 (sys) fb, in agreement with the Standard Model prediction of 2.91 ± 0.13 fb. The crossmore » section is also extrapolated to the total phase space including all Standard Model Higgs boson decays. Several differential fiducial cross sections are measured for observables sensitive to the Higgs boson production and decay, including kinematic distributions of jets produced in association with the Higgs boson. Good agreement is found between data and Standard Model predictions. Here, the results are used to put constraints on anomalous Higgs boson interactions with Standard Model particles, using the pseudo-observable extension to the kappa-framework.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1421818-measurement-inclusive-differential-cross-sections-zz-decay-channel-pp-collisions-sqrt-tev-atlas-detector','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1421818-measurement-inclusive-differential-cross-sections-zz-decay-channel-pp-collisions-sqrt-tev-atlas-detector">Measurement of inclusive and differential cross sections in the H → ZZ * → 4ℓ decay channel in pp collisions at $$ \\sqrt{s}=13 $$ TeV with the ATLAS detector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Aaboud, M.; Aad, G.; Abbott, B.; ... 2017-10-19 Inclusive and differential fiducial cross sections of Higgs boson production in proton-proton collisions are measured in the H → ZZ* → 4ℓ decay channel. The proton-proton collision data were produced at the Large Hadron Collider at a centre-of-mass energy of 13 TeV and recorded by the ATLAS detector in 2015 and 2016, corresponding to an integrated luminosity of 36.1 fb –1. The inclusive fiducial cross section in the H → ZZ* → 4ℓ decay channel is measured to be 3.62±0.50(stat) –0.20 +0.25 (sys) fb, in agreement with the Standard Model prediction of 2.91 ± 0.13 fb. The cross section ismore » also extrapolated to the total phase space including all Standard Model Higgs boson decays. Several differential fiducial cross sections are measured for observables sensitive to the Higgs boson production and decay, including kinematic distributions of jets produced in association with the Higgs boson. Good agreement is found between data and Standard Model predictions. The results are used to put constraints on anomalous Higgs boson interactions with Standard Model particles, using the pseudo-observable extension to the kappa-framework.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1418808','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1418808">Measurement of inclusive and differential cross sections in the H → ZZ * → 4ℓ decay channel in pp collisions at $$ \\sqrt{s}=13 $$ TeV with the ATLAS detector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Aaboud, M.; Aad, G.; Abbott, B. Inclusive and differential fiducial cross sections of Higgs boson production in proton-proton collisions are measured in the H → ZZ * → 4ℓ decay channel. The proton-proton collision data were produced at the Large Hadron Collider at a centre-of-mass energy of 13 TeV and recorded by the ATLAS detector in 2015 and 2016, corresponding to an integrated luminosity of 36.1 fb –1. The inclusive fiducial cross section in the H → ZZ * → 4ℓ decay channel is measured to be 3.62±0.50(stat) –0.20 +0.25 (sys) fb, in agreement with the Standard Model prediction of 2.91 ± 0.13 fb. The crossmore » section is also extrapolated to the total phase space including all Standard Model Higgs boson decays. Several differential fiducial cross sections are measured for observables sensitive to the Higgs boson production and decay, including kinematic distributions of jets produced in association with the Higgs boson. Good agreement is found between data and Standard Model predictions. Here, the results are used to put constraints on anomalous Higgs boson interactions with Standard Model particles, using the pseudo-observable extension to the kappa-framework.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/11230910','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/11230910">Application of a predictive Bayesian model to environmental accounting.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Anex, R P; Englehardt, J D 2001-03-30 Environmental accounting techniques are intended to capture important environmental costs and benefits that are often overlooked in standard accounting practices. Environmental accounting methods themselves often ignore or inadequately represent large but highly uncertain environmental costs and costs conditioned by specific prior events. Use of a predictive Bayesian model is demonstrated for the assessment of such highly uncertain environmental and contingent costs. The predictive Bayesian approach presented generates probability distributions for the quantity of interest (rather than parameters thereof). A spreadsheet implementation of a previously proposed predictive Bayesian model, extended to represent contingent costs, is described and used to evaluate whether a firm should undertake an accelerated phase-out of its PCB containing transformers. Variability and uncertainty (due to lack of information) in transformer accident frequency and severity are assessed simultaneously using a combination of historical accident data, engineering model-based cost estimates, and subjective judgement. Model results are compared using several different risk measures. Use of the model for incorporation of environmental risk management into a company's overall risk management strategy is discussed. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28088411','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28088411">Standardization of milk infrared spectra for the retroactive application of calibration models.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Bonfatti, V; Fleming, A; Koeck, A; Miglior, F 2017-03-01 The objective of this study was to standardize the infrared spectra obtained over time and across 2 milk laboratories of Canada to create a uniform historical database and allow (1) the retroactive application of calibration models for prediction of fine milk composition; and (2) the direct use of spectral information for the development of indicators of animal health and efficiency. Spectral variation across laboratories and over time was inspected by principal components analysis (PCA). Shifts in the PCA scores were detected over time, leading to the definition of different subsets of spectra having homogeneous infrared signal. To evaluate the possibility of using common equations on spectra collected by the 2 instruments and over time, we developed a standardization (STD) method. For each subset of data having homogeneous infrared signal, a total of 99 spectra corresponding to the percentiles of the distribution of the absorbance at each wavenumber were created and used to build the STD matrices. Equations predicting contents of saturated fatty acids, short-chain fatty acids, and C18:0 were created and applied on different subsets of spectra, before and after STD. After STD, bias and root mean squared error of prediction decreased by 66% and 32%, respectively. When calibration equations were applied to the historical nonstandardized database of spectra, shifts in the predictions could be observed over time for all investigated traits. Shifts in the distribution of the predictions over time corresponded to the shifts identified by the inspection of the PCA scores. After STD, shifts in the predicted fatty acid contents were greatly reduced. Standardization reduced spectral variability between instruments and over time, allowing the merging of milk spectra data from different instruments into a common database, the retroactive use of calibrations equations, or the direct use of the spectral data without restrictions. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active">20</li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active">21</li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=risk+AND+assessment&pg=3&id=EJ1044295','ERIC'); return false;" href="https://eric.ed.gov/?q=risk+AND+assessment&pg=3&id=EJ1044295">Assessing Academic Risk of Student-Athletes: Applicability of the NCAA Graduation Risk Overview Model to GPA</a> <a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a> Johnson, James 2013-01-01 In an effort to standardize academic risk assessment, the NCAA developed the graduation risk overview (GRO) model. Although this model was designed to assess graduation risk, its ability to predict grade-point average (GPA) remained unknown. Therefore, 134 individual risk assessments were made to determine GRO model effectiveness in the… </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29484676','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29484676">Parametric models to compute tryptophan fluorescence wavelengths from classical protein simulations.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Lopez, Alvaro J; Martínez, Leandro 2018-02-26 Fluorescence spectroscopy is an important method to study protein conformational dynamics and solvation structures. Tryptophan (Trp) residues are the most important and practical intrinsic probes for protein fluorescence due to the variability of their fluorescence wavelengths: Trp residues emit in wavelengths ranging from 308 to 360 nm depending on the local molecular environment. Fluorescence involves electronic transitions, thus its computational modeling is a challenging task. We show that it is possible to predict the wavelength of emission of a Trp residue from classical molecular dynamics simulations by computing the solvent-accessible surface area or the electrostatic interaction between the indole group and the rest of the system. Linear parametric models are obtained to predict the maximum emission wavelengths with standard errors of the order 5 nm. In a set of 19 proteins with emission wavelengths ranging from 308 to 352 nm, the best model predicts the maximum wavelength of emission with a standard error of 4.89 nm and a quadratic Pearson correlation coefficient of 0.81. These models can be used for the interpretation of fluorescence spectra of proteins with multiple Trp residues, or for which local Trp environmental variability exists and can be probed by classical molecular dynamics simulations. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3719851','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3719851">Geometric Metamorphosis</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Niethammer, Marc; Hart, Gabriel L.; Pace, Danielle F.; Vespa, Paul M.; Irimia, Andrei; Van Horn, John D.; Aylward, Stephen R. 2013-01-01 Standard image registration methods do not account for changes in image appearance. Hence, metamorphosis approaches have been developed which jointly estimate a space deformation and a change in image appearance to construct a spatio-temporal trajectory smoothly transforming a source to a target image. For standard metamorphosis, geometric changes are not explicitly modeled. We propose a geometric metamorphosis formulation, which explains changes in image appearance by a global deformation, a deformation of a geometric model, and an image composition model. This work is motivated by the clinical challenge of predicting the long-term effects of traumatic brain injuries based on time-series images. This work is also applicable to the quantification of tumor progression (e.g., estimating its infiltrating and displacing components) and predicting chronic blood perfusion changes after stroke. We demonstrate the utility of the method using simulated data as well as scans from a clinical traumatic brain injury patient. PMID:21995083 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3130495','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3130495">Predicting Flavonoid UGT Regioselectivity</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Jackson, Rhydon; Knisley, Debra; McIntosh, Cecilia; Pfeiffer, Phillip 2011-01-01 Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scores. These techniques included an application of time series distance functions to protein classification. Time series distances defined on the index sequences were used in nearest neighbor and support vector machine classifiers. Additionally, Bayesian neural network classifiers were applied to the index sequences. The experiments identified improvements over the nearest neighbor and support vector machine classifications relying on standard alignment similarity scores, as well as strong correlations between specific subsequences and regioselectivities. PMID:21747849 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhDT.......150S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhDT.......150S">Agricultural Drought Transition Periods In the United States Corn Belt Region</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Schiraldi, Nicholas J. Agricultural drought in the U.S. Corn Belt region (CBR) has tremendous global socioeconomic implications. Unfortunately, the weather and climate factors that contribute to transition events toward or away from such droughts, and how well those factors are predicted, are poorly understood. This dissertation focuses on the atmospheric circulation signals associated with agricultural drought transitions periods in the CBR that evolve over 20 and 60 days, and how well those circulation signals are predicted on seasonal to sub-seasonal time scales. Results show that amplification of an intraseasonal Rossby wave train across the Pacific Ocean into North America, which occurs coincident with intraseasonal tropical convection on its equatorward side, triggers these transition events, not shifts in the low frequency base state. This result is confirmed through composite analysis, trajectory analysis and a vertically integrated moisture budget. Trajectory analysis reveals similar source regions for air parcels associated with drought development and breakdown, but with a shift toward more parcels originating over the Gulf of Mexico during transitions away from drought. The primary result from the vertically integrated moisture budget demonstrates that advection and convergence of moisture on intraseasonal time scales dominates during these transitions. The primary conclusion drawn is that weather events are the primary driver of agricultural drought transitions occurring over 20 and 60 days. The seasonal to sub-seasonal hindcast dataset is used to investigate the prediction of the low frequency, intraseasonal and synoptic circulation patterns associated with 20 and 60-day drought transition periods. The forecast models assessed are the European Centre for Medium Range Prediction (ECMWF), National Center for Environment Prediction Climate Forecast System (NCEP) and the Australian Bureau of Meteorology (BoM). Results demonstrate that ECMWF and NCEP are not skillful in predicting the patterns associated with 20-day agricultural drought onset and decay, but have some skill during 60-day agricultural drought onset and decay events at lead F360-F480. BoM was not skillful in predicting the circulation patterns associated with either type of drought transition. Finally, a regression model is used to predict 30-day forward looking standardized precipitation anomalies in the CBR, which leverages lowpass and intraseasonal filtered geopotential height anomalies at 200 hPa as predictors. The statistical model is more skillful than climatology in predicting 1 to 30, through 27 to 57 day standardized precipitation anomalies during July, as measured by root mean square error. The regression model also is skillful in predicting the directional skew (above or below normal) of the forward looking standardized precipitation anomalies. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1715378A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1715378A">Improving Multi-Sensor Drought Monitoring, Prediction and Recovery Assessment Using Gravimetry Information</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Aghakouchak, Amir; Tourian, Mohammad J. 2015-04-01 Development of reliable drought monitoring, prediction and recovery assessment tools are fundamental to water resources management. This presentation focuses on how gravimetry information can improve drought assessment. First, we provide an overview of the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which offers near real-time drought information using remote sensing observations and model simulations. Then, we present a framework for integration of satellite gravimetry information for improving drought prediction and recovery assessment. The input data include satellite-based and model-based precipitation, soil moisture estimates and equivalent water height. Previous studies show that drought assessment based on one single indicator may not be sufficient. For this reason, GIDMaPS provides drought information based on multiple drought indicators including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. MSDI incorporates the meteorological and agricultural drought conditions and provides composite multi-index drought information for overall characterization of droughts. GIDMaPS includes a seasonal prediction component based on a statistical persistence-based approach. The prediction component of GIDMaPS provides the empirical probability of drought for different severity levels. In this presentation we present a new component in which the drought prediction information based on SPI, SSI and MSDI are conditioned on equivalent water height obtained from the Gravity Recovery and Climate Experiment (GRACE). Using a Bayesian approach, GRACE information is used to evaluate persistence of drought. Finally, the deficit equivalent water height based on GRACE is used for assessing drought recovery. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from 2014 California Drought will be presented. Further Reading: Hao Z., AghaKouchak A., Nakhjiri N., Farahmand A., 2014, Global Integrated Drought Monitoring and Prediction System, Scientific Data, 1:140001, 1-10, doi: 10.1038/sdata.2014.1. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011NHESS..11....1S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011NHESS..11....1S">Machine learning modelling for predicting soil liquefaction susceptibility</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Samui, P.; Sitharam, T. G. 2011-01-01 This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013MS%26E...52d2011H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013MS%26E...52d2011H">An evaluation of 1D loss model collections for the off-design performance prediction of automotive turbocharger compressors</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Harley, P.; Spence, S.; Early, J.; Filsinger, D.; Dietrich, M. 2013-12-01 Single-zone modelling is used to assess different collections of impeller 1D loss models. Three collections of loss models have been identified in literature, and the background to each of these collections is discussed. Each collection is evaluated using three modern automotive turbocharger style centrifugal compressors; comparisons of performance for each of the collections are made. An empirical data set taken from standard hot gas stand tests for each turbocharger is used as a baseline for comparison. Compressor range is predicted in this study; impeller diffusion ratio is shown to be a useful method of predicting compressor surge in 1D, and choke is predicted using basic compressible flow theory. The compressor designer can use this as a guide to identify the most compatible collection of losses for turbocharger compressor design applications. The analysis indicates the most appropriate collection for the design of automotive turbocharger centrifugal compressors. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23001060','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23001060">The spatial structure of a nonlinear receptive field.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Schwartz, Gregory W; Okawa, Haruhisa; Dunn, Felice A; Morgan, Josh L; Kerschensteiner, Daniel; Wong, Rachel O; Rieke, Fred 2012-11-01 Understanding a sensory system implies the ability to predict responses to a variety of inputs from a common model. In the retina, this includes predicting how the integration of signals across visual space shapes the outputs of retinal ganglion cells. Existing models of this process generalize poorly to predict responses to new stimuli. This failure arises in part from properties of the ganglion cell response that are not well captured by standard receptive-field mapping techniques: nonlinear spatial integration and fine-scale heterogeneities in spatial sampling. Here we characterize a ganglion cell's spatial receptive field using a mechanistic model based on measurements of the physiological properties and connectivity of only the primary excitatory circuitry of the retina. The resulting simplified circuit model successfully predicts ganglion-cell responses to a variety of spatial patterns and thus provides a direct correspondence between circuit connectivity and retinal output. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Orthopedic&pg=3&id=EJ1095911','ERIC'); return false;" href="https://eric.ed.gov/?q=Orthopedic&pg=3&id=EJ1095911">Application of ''Earl's Assessment "as", Assessment "for", and Assessment "of" Learning Model'' with Orthopaedic Assessment Clinical Competence</a> <a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a> Lafave, Mark R.; Katz, Larry; Vaughn, Norman 2013-01-01 Context: In order to study the efficacy of assessment methods, a theoretical framework of Earl's model of assessment was introduced. Objective: (1) Introduce the predictive learning assessment model (PLAM) as an application of Earl's model of learning; (2) test Earl's model of learning through the use of the Standardized Orthopedic Assessment Tool… </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28405845','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28405845">Anxiety after completion of treatment for early-stage breast cancer: a systematic review to identify candidate predictors and evaluate multivariable model development.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Harris, Jenny; Cornelius, Victoria; Ream, Emma; Cheevers, Katy; Armes, Jo 2017-07-01 The purpose of this review was to identify potential candidate predictors of anxiety in women with early-stage breast cancer (BC) after adjuvant treatments and evaluate methodological development of existing multivariable models to inform the future development of a predictive risk stratification model (PRSM). Databases (MEDLINE, Web of Science, CINAHL, CENTRAL and PsycINFO) were searched from inception to November 2015. Eligible studies were prospective, recruited women with stage 0-3 BC, used a validated anxiety outcome ≥3 months post-treatment completion and used multivariable prediction models. Internationally accepted quality standards were used to assess predictive risk of bias and strength of evidence. Seven studies were identified: five were observational cohorts and two secondary analyses of RCTs. Variability of measurement and selective reporting precluded meta-analysis. Twenty-one candidate predictors were identified in total. Younger age and previous mental health problems were identified as risk factors in ≥3 studies. Clinical variables (e.g. treatment, tumour grade) were not identified as predictors in any studies. No studies adhered to all quality standards. Pre-existing vulnerability to mental health problems and younger age increased the risk of anxiety after completion of treatment for BC survivors, but there was no evidence that chemotherapy was a predictor. Multiple predictors were identified but many lacked reproducibility or were not measured across studies, and inadequate reporting did not allow full evaluation of the multivariable models. The use of quality standards in the development of PRSM within supportive cancer care would improve model quality and performance, thereby allowing professionals to better target support for patients. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22968935','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22968935">Prediction of physical-chemical properties of crude oils by 1H NMR analysis of neat samples and chemometrics.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Masili, Alice; Puligheddu, Sonia; Sassu, Lorenzo; Scano, Paola; Lai, Adolfo 2012-11-01 In this work, we report the feasibility study to predict the properties of neat crude oil samples from 300-MHz NMR spectral data and partial least squares (PLS) regression models. The study was carried out on 64 crude oil samples obtained from 28 different extraction fields and aims at developing a rapid and reliable method for characterizing the crude oil in a fast and cost-effective way. The main properties generally employed for evaluating crudes' quality and behavior during refining were measured and used for calibration and testing of the PLS models. Among these, the UOP characterization factor K (K(UOP)) used to classify crude oils in terms of composition, density (D), total acidity number (TAN), sulfur content (S), and true boiling point (TBP) distillation yields were investigated. Test set validation with an independent set of data was used to evaluate model performance on the basis of standard error of prediction (SEP) statistics. Model performances are particularly good for K(UOP) factor, TAN, and TPB distillation yields, whose standard error of calibration and SEP values match the analytical method precision, while the results obtained for D and S are less accurate but still useful for predictions. Furthermore, a strategy that reduces spectral data preprocessing and sample preparation procedures has been adopted. The models developed with such an ample crude oil set demonstrate that this methodology can be applied with success to modern refining process requirements. Copyright © 2012 John Wiley & Sons, Ltd. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1345634','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1345634">Handbook of LHC Higgs Cross Sections: 4. Deciphering the Nature of the Higgs Sector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> de Florian, D. This Report summarizes the results of the activities of the LHC Higgs Cross Section Working Group in the period 2014-2016. The main goal of the working group was to present the state-of-the-art of Higgs physics at the LHC, integrating all new results that have appeared in the last few years. The first part compiles the most up-to-date predictions of Higgs boson production cross sections and decay branching ratios, parton distribution functions, and off-shell Higgs boson production and interference effects. The second part discusses the recent progress in Higgs effective field theory predictions, followed by the third part on pseudo-observables, simplifiedmore » template cross section and fiducial cross section measurements, which give the baseline framework for Higgs boson property measurements. The fourth part deals with the beyond the Standard Model predictions of various benchmark scenarios of Minimal Supersymmetric Standard Model, extended scalar sector, Next-to-Minimal Supersymmetric Standard Model and exotic Higgs boson decays. This report follows three previous working-group reports: Handbook of LHC Higgs Cross Sections: 1. Inclusive Observables (CERN-2011-002), Handbook of LHC Higgs Cross Sections: 2. Differential Distributions (CERN-2012-002), and Handbook of LHC Higgs Cross Sections: 3. Higgs properties (CERN-2013-004). The current report serves as the baseline reference for Higgs physics in LHC Run 2 and beyond.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1367708-search-standard-model-production-four-top-quarks-protonproton-collisions','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1367708-search-standard-model-production-four-top-quarks-protonproton-collisions">Search for standard model production of four top quarks in proton–proton collisions at s = 13 TeV</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Sirunyan, A. M.; Tumasyan, A.; Adam, W. A search for events containing four top quarks (more » $$t\\bar{t}t\\bar{t}$$) is reported from proton-proton collisions recorded by the CMS experiment at $$\\sqrt{s}$$ = 13 TeV and corresponding to an integrated luminosity of 2.6 fb -1 inverse femtobarns. The analysis considers the single-lepton (e or $$\\mu$$)+jets and the opposite-sign dilepton (μ +μ -, μ ± e ±, or e +e -) + jets channels. It uses boosted decision trees to combine information on the global event and jet properties to distinguish between ttbar-ttbar and ttbar production. The number of events observed after all selection requirements is consistent with expectations from background and standard model signal predictions, and an upper limit is set on the cross section for ttbar-ttbar production in the standard model of 94 fb at 95% confidence level (10.2 times the prediction), with an expected limit of 118 fb. This is combined with the results from the published CMS search in the same-sign dilepton channel, resulting in an improved limit of 69 fb at 95% confidence level (7.4 times the prediction), with an expected limit of 71 fb. These are the strongest constraints on the rate of $$t\\bar{t}t\\bar{t}$$ production to date.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1367708-search-standard-model-production-four-top-quarks-protonproton-collisions-nbsp-tev','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1367708-search-standard-model-production-four-top-quarks-protonproton-collisions-nbsp-tev">Search for standard model production of four top quarks in proton–proton collisions at s = 13 TeV</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Sirunyan, A. M.; Tumasyan, A.; Adam, W.; ... 2017-06-28 A search for events containing four top quarks (more » $$t\\bar{t}t\\bar{t}$$) is reported from proton-proton collisions recorded by the CMS experiment at $$\\sqrt{s}$$ = 13 TeV and corresponding to an integrated luminosity of 2.6 fb -1 inverse femtobarns. The analysis considers the single-lepton (e or $$\\mu$$)+jets and the opposite-sign dilepton (μ +μ -, μ ± e ±, or e +e -) + jets channels. It uses boosted decision trees to combine information on the global event and jet properties to distinguish between ttbar-ttbar and ttbar production. The number of events observed after all selection requirements is consistent with expectations from background and standard model signal predictions, and an upper limit is set on the cross section for ttbar-ttbar production in the standard model of 94 fb at 95% confidence level (10.2 times the prediction), with an expected limit of 118 fb. This is combined with the results from the published CMS search in the same-sign dilepton channel, resulting in an improved limit of 69 fb at 95% confidence level (7.4 times the prediction), with an expected limit of 71 fb. These are the strongest constraints on the rate of $$t\\bar{t}t\\bar{t}$$ production to date.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhLB..772..336S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhLB..772..336S">Search for standard model production of four top quarks in proton-proton collisions at √{ s} = 13 TeV</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; König, A.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rad, N.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Strauss, J.; Waltenberger, W.; Wulz, C.-E.; Dvornikov, O.; Makarenko, V.; Mossolov, V.; Suarez Gonzalez, J.; Zykunov, V.; Shumeiko, N.; Alderweireldt, S.; De Wolf, E. A.; Janssen, X.; Lauwers, J.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; Daci, N.; De Bruyn, I.; Deroover, K.; Lontkovskyi, D.; Lowette, S.; Moortgat, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Léonard, A.; Luetic, J.; Maerschalk, T.; Marinov, A.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Yonamine, R.; Zenoni, F.; Zhang, F.; Cimmino, A.; Cornelis, T.; Dobur, D.; Fagot, A.; Gul, M.; Khvastunov, I.; Poyraz, D.; Salva, S.; Schöfbeck, R.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Bakhshiansohi, H.; Beluffi, C.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; De Visscher, S.; Delaere, C.; Delcourt, M.; Francois, B.; Giammanco, A.; Jafari, A.; Komm, M.; Krintiras, G.; Lemaitre, V.; Magitteri, A.; Mertens, A.; Musich, M.; Piotrzkowski, K.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Wertz, S.; Beliy, N.; Aldá Júnior, W. 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M.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Paktinat Mehdiabadi, S.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Calabria, C.; Caputo, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. 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D.; Sekmen, S.; Son, D. C.; Yang, Y. C.; Lee, A.; Kim, H.; Brochero Cifuentes, J. A.; Kim, T. J.; Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Ha, S.; Hong, B.; Jo, Y.; Kim, Y.; Lee, K.; Lee, K. S.; Lee, S.; Lim, J.; Park, S. K.; Roh, Y.; Almond, J.; Kim, J.; Lee, H.; Oh, S. B.; Radburn-Smith, B. C.; Seo, S. h.; Yang, U. K.; Yoo, H. D.; Yu, G. B.; Choi, M.; Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.; Ryu, G.; Ryu, M. S.; Choi, Y.; Goh, J.; Hwang, C.; Lee, J.; Yu, I.; Dudenas, V.; Juodagalvis, A.; Vaitkus, J.; Ahmed, I.; Ibrahim, Z. A.; Md Ali, M. A. B.; Mohamad Idris, F.; Wan Abdullah, W. A. T.; Yusli, M. N.; Zolkapli, Z.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-De La Cruz, I.; Hernandez-Almada, A.; Lopez-Fernandez, R.; Magaña Villalba, R.; Mejia Guisao, J.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Oropeza Barrera, C.; Vazquez Valencia, F.; Carpinteyro, S.; Pedraza, I.; Salazar Ibarguen, H. A.; Uribe Estrada, C.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. 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P.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Navarro De Martino, E.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.; de Trocóniz, J. F.; Missiroli, M.; Moran, D.; Cuevas, J.; Fernandez Menendez, J.; Gonzalez Caballero, I.; González Fernández, J. R.; Palencia Cortezon, E.; Sanchez Cruz, S.; Suárez Andrés, I.; Vizan Garcia, J. M.; Cabrillo, I. J.; Calderon, A.; Curras, E.; Fernandez, M.; Garcia-Ferrero, J.; Gomez, G.; Lopez Virto, A.; Marco, J.; Martinez Rivero, C.; Matorras, F.; Piedra Gomez, J.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Auffray, E.; Auzinger, G.; Baillon, P.; Ball, A. H.; Barney, D.; Bloch, P.; Bocci, A.; Botta, C.; Camporesi, T.; Castello, R.; Cepeda, M.; Cerminara, G.; Chen, Y.; d'Enterria, D.; Dabrowski, A.; Daponte, V.; David, A.; De Gruttola, M.; De Roeck, A.; Di Marco, E.; Dobson, M.; Dorney, B.; du Pree, T.; Duggan, D.; Dünser, M.; Dupont, N.; Elliott-Peisert, A.; Everaerts, P.; Fartoukh, S.; Franzoni, G.; Fulcher, J.; Funk, W.; Gigi, D.; Gill, K.; Girone, M.; Glege, F.; Gulhan, D.; Gundacker, S.; Guthoff, M.; Harris, P.; Hegeman, J.; Innocente, V.; Janot, P.; Kieseler, J.; Kirschenmann, H.; Knünz, V.; Kornmayer, A.; Kortelainen, M. J.; Kousouris, K.; Krammer, M.; Lange, C.; Lecoq, P.; Lourenço, C.; Lucchini, M. T.; Malgeri, L.; Mannelli, M.; Martelli, A.; Meijers, F.; Merlin, J. A.; Mersi, S.; Meschi, E.; Milenovic, P.; Moortgat, F.; Morovic, S.; Mulders, M.; Neugebauer, H.; Orfanelli, S.; Orsini, L.; Pape, L.; Perez, E.; Peruzzi, M.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Racz, A.; Reis, T.; Rolandi, G.; Rovere, M.; Sakulin, H.; Sauvan, J. B.; Schäfer, C.; Schwick, C.; Seidel, M.; Sharma, A.; Silva, P.; Sphicas, P.; Steggemann, J.; Stoye, M.; Takahashi, Y.; Tosi, M.; Treille, D.; Triossi, A.; Tsirou, A.; Veckalns, V.; Veres, G. I.; Verweij, M.; Wardle, N.; Wöhri, H. K.; Zagozdzinska, A.; Zeuner, W. D.; Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.; Bachmair, F.; Bäni, L.; Bianchini, L.; Casal, B.; Dissertori, G.; Dittmar, M.; Donegà, M.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Kasieczka, G.; Lustermann, W.; Mangano, B.; Marionneau, M.; Martinez Ruiz del Arbol, P.; Masciovecchio, M.; Meinhard, M. T.; Meister, D.; Micheli, F.; Musella, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pata, J.; Pauss, F.; Perrin, G.; Perrozzi, L.; Quittnat, M.; Rossini, M.; Schönenberger, M.; Starodumov, A.; Tavolaro, V. R.; Theofilatos, K.; Wallny, R.; Aarrestad, T. K.; Amsler, C.; Caminada, L.; Canelli, M. F.; De Cosa, A.; Galloni, C.; Hinzmann, A.; Hreus, T.; Kilminster, B.; Ngadiuba, J.; Pinna, D.; Rauco, G.; Robmann, P.; Salerno, D.; Seitz, C.; Yang, Y.; Zucchetta, A.; Candelise, V.; Doan, T. H.; Jain, Sh.; Khurana, R.; Konyushikhin, M.; Kuo, C. M.; Lin, W.; Pozdnyakov, A.; Yu, S. S.; Kumar, Arun; Chang, P.; Chang, Y. H.; Chao, Y.; Chen, K. F.; Chen, P. H.; Fiori, F.; Hou, W.-S.; Hsiung, Y.; Liu, Y. F.; Lu, R.-S.; Miñano Moya, M.; Paganis, E.; Psallidas, A.; Tsai, J. f.; Asavapibhop, B.; Singh, G.; Srimanobhas, N.; Suwonjandee, N.; Adiguzel, A.; Bakirci, M. N.; Damarseckin, S.; Demiroglu, Z. S.; Dozen, C.; Eskut, E.; Girgis, S.; Gokbulut, G.; Guler, Y.; Hos, I.; Kangal, E. E.; Kara, O.; Kiminsu, U.; Oglakci, M.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Sunar Cerci, D.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.; Bilin, B.; Bilmis, S.; Isildak, B.; Karapinar, G.; Yalvac, M.; Zeyrek, M.; Gülmez, E.; Kaya, M.; Kaya, O.; Yetkin, E. A.; Yetkin, T.; Cakir, A.; Cankocak, K.; Sen, S.; Grynyov, B.; Levchuk, L.; Sorokin, P.; Aggleton, R.; Ball, F.; Beck, L.; Brooke, J. J.; Burns, D.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Newbold, D. M.; Paramesvaran, S.; Poll, A.; Sakuma, T.; Seif El Nasr-storey, S.; Smith, D.; Smith, V. J.; Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Calligaris, L.; Cieri, D.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. 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H.; Taylor, D.; Woods, N.; CMS Collaboration 2017-09-01 A search for events containing four top quarks (t t bar t t bar) is reported from proton-proton collisions recorded by the CMS experiment at √{ s} = 13 TeV and corresponding to an integrated luminosity of 2.6 fb-1. The analysis considers the single-lepton (e or μ)+jets and the opposite-sign dilepton (μ+μ-, μ±e∓, or e+e-)+jets channels. It uses boosted decision trees to combine information on the global event and jet properties to distinguish between t t bar t t bar and t t bar production. The number of events observed after all selection requirements is consistent with expectations from background and standard model signal predictions, and an upper limit is set on the cross section for t t bar t t bar production in the standard model of 94 fb at 95% confidence level (10.2 × the prediction), with an expected limit of 118 fb. This is combined with the results from the published CMS search in the same-sign dilepton channel, resulting in an improved limit of 69 fb at 95% confidence level (7.4 × the prediction), with an expected limit of 71 fb. These are the strongest constraints on the rate of t t bar t t bar production to date. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17699540','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17699540">Does consideration of either psychological or material disadvantage improve coronary risk prediction? Prospective observational study of Scottish men.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Macleod, John; Metcalfe, Chris; Smith, George Davey; Hart, Carole 2007-09-01 To assess the value of psychosocial risk factors in discriminating between individuals at higher and lower risk of coronary heart disease, using risk prediction equations. Prospective observational study. Scotland. 5191 employed men aged 35 to 64 years and free of coronary heart disease at study enrollment Area under receiver operating characteristic (ROC) curves for risk prediction equations including different risk factors for coronary heart disease. During the first 10 years of follow up, 203 men died of coronary heart disease and a further 200 were admitted to hospital with this diagnosis. Area under the ROC curve for the standard Framingham coronary risk factors was 74.5%. Addition of "vital exhaustion" and psychological stress led to areas under the ROC curve of 74.5% and 74.6%, respectively. Addition of current social class and lifetime social class to the standard Framingham equation gave areas under the ROC curve of 74.6% and 74.9%, respectively. In no case was there strong evidence for improved discrimination of the model containing the novel risk factor over the standard model. Consideration of psychosocial risk factors, including those that are strong independent predictors of heart disease, does not substantially influence the ability of risk prediction tools to discriminate between individuals at higher and lower risk of coronary heart disease. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3398763','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3398763">Drug response in a genetically engineered mouse model of multiple myeloma is predictive of clinical efficacy</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Chesi, Marta; Matthews, Geoffrey M.; Garbitt, Victoria M.; Palmer, Stephen E.; Shortt, Jake; Lefebure, Marcus; Stewart, A. Keith; Johnstone, Ricky W. 2012-01-01 The attrition rate for anticancer drugs entering clinical trials is unacceptably high. For multiple myeloma (MM), we postulate that this is because of preclinical models that overemphasize the antiproliferative activity of drugs, and clinical trials performed in refractory end-stage patients. We validate the Vk*MYC transgenic mouse as a faithful model to predict single-agent drug activity in MM with a positive predictive value of 67% (4 of 6) for clinical activity, and a negative predictive value of 86% (6 of 7) for clinical inactivity. We identify 4 novel agents that should be prioritized for evaluation in clinical trials. Transplantation of Vk*MYC tumor cells into congenic mice selected for a more aggressive disease that models end-stage drug-resistant MM and responds only to combinations of drugs with single-agent activity in untreated Vk*MYC MM. We predict that combinations of standard agents, histone deacetylase inhibitors, bromodomain inhibitors, and hypoxia-activated prodrugs will demonstrate efficacy in the treatment of relapsed MM. PMID:22451422 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19990007765','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19990007765">Prediction Accuracy of Error Rates for MPTB Space Experiment</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Buchner, S. P.; Campbell, A. B.; Davis, D.; McMorrow, D.; Petersen, E. L.; Stassinopoulos, E. G.; Ritter, J. C. 1998-01-01 This paper addresses the accuracy of radiation-induced upset-rate predictions in space using the results of ground-based measurements together with standard environmental and device models. The study is focused on two part types - 16 Mb NEC DRAM's (UPD4216) and 1 Kb SRAM's (AMD93L422) - both of which are currently in space on board the Microelectronics and Photonics Test Bed (MPTB). To date, ground-based measurements of proton-induced single event upset (SEM cross sections as a function of energy have been obtained and combined with models of the proton environment to predict proton-induced error rates in space. The role played by uncertainties in the environmental models will be determined by comparing the modeled radiation environment with the actual environment measured aboard MPTB. Heavy-ion induced upsets have also been obtained from MPTB and will be compared with the "predicted" error rate following ground testing that will be done in the near future. These results should help identify sources of uncertainty in predictions of SEU rates in space. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29533814','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29533814">Prediction of pork loin quality using online computer vision system and artificial intelligence model.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David 2018-06-01 The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active">21</li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active">22</li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17238260','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17238260">Random forest models to predict aqueous solubility.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Palmer, David S; O'Boyle, Noel M; Glen, Robert C; Mitchell, John B O 2007-01-01 Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r2 = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ChA%26A..41..517S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ChA%26A..41..517S">Influence of Convective Effect of Solar Winds on the CME Transit Time</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Sun, Lu-yuan 2017-10-01 Based on an empirical model for predicting the transit time of coronal mass ejections (CMEs) proposed by Gopalswamy, 52 CME events which are related to the geomagnetic storms of Dst < -50 nT, and 10 CME events which caused extremely strong geomagnetic storms (Dst < -200 nT) in 1996- 2007 are selected, and combined with the observational data of the interplanetary solar winds that collected by the ACE satellite at 1AU, to analyze the influence of convective effect of ambient solar winds on the prediction of the CME transit time when it arrives at a place of 1 AU. After taking the convective effect of ambient solar winds into account, the standard deviation of predictions is reduced from 16.5 to 11.4 hours for the 52 CME events, and the prediction error is less than 15 hours for 68% of these events; while the standard deviation of predictions is reduced from 10.6 to 6.5 hours for the 10 CME events that caused extremely strong geomagnetic storms, and the prediction error is less than 5 hours for 6 of the 10 events. These results show that taking the convective effect of ambient solar winds into account can reduce the standard deviation of the predicted CME transit time, hence the convective effect of solar winds plays an important role for predicting the transit times of CME events. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5862475','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5862475">Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Hermes, Helen E.; Teutonico, Donato; Preuss, Thomas G.; Schneckener, Sebastian 2018-01-01 The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations. PMID:29561908 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25746643','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25746643">A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Chun, Ting Sie; Malek, M A; Ismail, Amelia Ritahani 2015-01-01 The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25863339','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25863339">A strategy to establish Food Safety Model Repositories.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Plaza-Rodríguez, C; Thoens, C; Falenski, A; Weiser, A A; Appel, B; Kaesbohrer, A; Filter, M 2015-07-02 Transferring the knowledge of predictive microbiology into real world food manufacturing applications is still a major challenge for the whole food safety modelling community. To facilitate this process, a strategy for creating open, community driven and web-based predictive microbial model repositories is proposed. These collaborative model resources could significantly improve the transfer of knowledge from research into commercial and governmental applications and also increase efficiency, transparency and usability of predictive models. To demonstrate the feasibility, predictive models of Salmonella in beef previously published in the scientific literature were re-implemented using an open source software tool called PMM-Lab. The models were made publicly available in a Food Safety Model Repository within the OpenML for Predictive Modelling in Food community project. Three different approaches were used to create new models in the model repositories: (1) all information relevant for model re-implementation is available in a scientific publication, (2) model parameters can be imported from tabular parameter collections and (3) models have to be generated from experimental data or primary model parameters. All three approaches were demonstrated in the paper. The sample Food Safety Model Repository is available via: http://sourceforge.net/projects/microbialmodelingexchange/files/models and the PMM-Lab software can be downloaded from http://sourceforge.net/projects/pmmlab/. This work also illustrates that a standardized information exchange format for predictive microbial models, as the key component of this strategy, could be established by adoption of resources from the Systems Biology domain. Copyright © 2015. Published by Elsevier B.V. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=338089&Lab=NERL&keyword=Time+AND+Series+AND+Design&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=338089&Lab=NERL&keyword=Time+AND+Series+AND+Design&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50">Predicting Future-Year Ozone Concentrations: Integrated Observational-Modeling Approach for Probabilistic Evaluation of the Efficacy of Emission Control strategies</a> <a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a> Regional-scale air quality models are being used to demonstrate attainment of the ozone air quality standard. In current regulatory applications, a regional-scale air quality model is applied for a base year and a future year with reduced emissions using the same meteorological ... </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26328979','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26328979">Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S; Lin, Weili; Shen, Dinggang 2015-09-01 Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [(18)F]FDG PET image by using a low-dose brain [(18)F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. The authors employ a regression forest for predicting the standard-dose brain [(18)F]FDG PET image by low-dose brain [(18)F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [(18)F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [(18)F]FDG PET image and substantially enhanced image quality of low-dose brain [(18)F]FDG PET image. In this paper, the authors propose a framework to generate standard-dose brain [(18)F]FDG PET image using low-dose brain [(18)F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [(18)F]FDG PET can be well-predicted using MRI and low-dose brain [(18)F]FDG PET. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4545052','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4545052">Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang 2015-01-01 Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [18F]FDG PET image by using a low-dose brain [18F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [18F]FDG PET image by low-dose brain [18F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [18F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [18F]FDG PET image and substantially enhanced image quality of low-dose brain [18F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [18F]FDG PET image using low-dose brain [18F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [18F]FDG PET can be well-predicted using MRI and low-dose brain [18F]FDG PET. PMID:26328979 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/22581360-prediction-standard-dose-brain-pet-image-using-mri-low-dose-brain-sup-fdg-pet-images','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/22581360-prediction-standard-dose-brain-pet-image-using-mri-low-dose-brain-sup-fdg-pet-images">Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Kang, Jiayin; Gao, Yaozong; Shi, Feng Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. Asmore » yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET image and substantially enhanced image quality of low-dose brain [{sup 18}F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [{sup 18}F]FDG PET image using low-dose brain [{sup 18}F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [{sup 18}F]FDG PET can be well-predicted using MRI and low-dose brain [{sup 18}F]FDG PET.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24448052','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24448052">Role-modeling and medical error disclosure: a national survey of trainees.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Martinez, William; Hickson, Gerald B; Miller, Bonnie M; Doukas, David J; Buckley, John D; Song, John; Sehgal, Niraj L; Deitz, Jennifer; Braddock, Clarence H; Lehmann, Lisa Soleymani 2014-03-01 To measure trainees' exposure to negative and positive role-modeling for responding to medical errors and to examine the association between that exposure and trainees' attitudes and behaviors regarding error disclosure. Between May 2011 and June 2012, 435 residents at two large academic medical centers and 1,187 medical students from seven U.S. medical schools received anonymous, electronic questionnaires. The questionnaire asked respondents about (1) experiences with errors, (2) training for responding to errors, (3) behaviors related to error disclosure, (4) exposure to role-modeling for responding to errors, and (5) attitudes regarding disclosure. Using multivariate regression, the authors analyzed whether frequency of exposure to negative and positive role-modeling independently predicted two primary outcomes: (1) attitudes regarding disclosure and (2) nontransparent behavior in response to a harmful error. The response rate was 55% (884/1,622). Training on how to respond to errors had the largest independent, positive effect on attitudes (standardized effect estimate, 0.32, P < .001); negative role-modeling had the largest independent, negative effect (standardized effect estimate, -0.26, P < .001). Positive role-modeling had a positive effect on attitudes (standardized effect estimate, 0.26, P < .001). Exposure to negative role-modeling was independently associated with an increased likelihood of trainees' nontransparent behavior in response to an error (OR 1.37, 95% CI 1.15-1.64; P < .001). Exposure to role-modeling predicts trainees' attitudes and behavior regarding the disclosure of harmful errors. Negative role models may be a significant impediment to disclosure among trainees. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1327908','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1327908">Extended Glauert tip correction to include vortex rollup effects</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Maniaci, David; Schmitz, Sven Wind turbine loads predictions by blade-element momentum theory using the standard tip-loss correction have been shown to over-predict loading near the blade tip in comparison to experimental data. This over-prediction is theorized to be due to the assumption of light rotor loading, inherent in the standard tip-loss correction model of Glauert. A higher- order free-wake method, WindDVE, is used to compute the rollup process of the trailing vortex sheets downstream of wind turbine blades. Results obtained serve an exact correction function to the Glauert tip correction used in blade-element momentum methods. Lastly, it is found that accounting for the effectsmore » of tip vortex rollup within the Glauert tip correction indeed results in improved prediction of blade tip loads computed by blade-element momentum methods.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1327908-extended-glauert-tip-correction-include-vortex-rollup-effects','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1327908-extended-glauert-tip-correction-include-vortex-rollup-effects">Extended Glauert tip correction to include vortex rollup effects</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Maniaci, David; Schmitz, Sven 2016-10-03 Wind turbine loads predictions by blade-element momentum theory using the standard tip-loss correction have been shown to over-predict loading near the blade tip in comparison to experimental data. This over-prediction is theorized to be due to the assumption of light rotor loading, inherent in the standard tip-loss correction model of Glauert. A higher- order free-wake method, WindDVE, is used to compute the rollup process of the trailing vortex sheets downstream of wind turbine blades. Results obtained serve an exact correction function to the Glauert tip correction used in blade-element momentum methods. Lastly, it is found that accounting for the effectsmore » of tip vortex rollup within the Glauert tip correction indeed results in improved prediction of blade tip loads computed by blade-element momentum methods.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150002853','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150002853">Evaluation of Industry Standard Turbulence Models on an Axisymmetric Supersonic Compression Corner</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> DeBonis, James R. 2015-01-01 Reynolds-averaged Navier-Stokes computations of a shock-wave/boundary-layer interaction (SWBLI) created by a Mach 2.85 flow over an axisymmetric 30-degree compression corner were carried out. The objectives were to evaluate four turbulence models commonly used in industry, for SWBLIs, and to evaluate the suitability of this test case for use in further turbulence model benchmarking. The Spalart-Allmaras model, Menter's Baseline and Shear Stress Transport models, and a low-Reynolds number k- model were evaluated. Results indicate that the models do not accurately predict the separation location; with the SST model predicting the separation onset too early and the other models predicting the onset too late. Overall the Spalart-Allmaras model did the best job in matching the experimental data. However there is significant room for improvement, most notably in the prediction of the turbulent shear stress. Density data showed that the simulations did not accurately predict the thermal boundary layer upstream of the SWBLI. The effect of turbulent Prandtl number and wall temperature were studied in an attempt to improve this prediction and understand their effects on the interaction. The data showed that both parameters can significantly affect the separation size and location, but did not improve the agreement with the experiment. This case proved challenging to compute and should provide a good test for future turbulence modeling work. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29533721','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29533721">Prediction of Response to Neoadjuvant Chemotherapy and Radiation Therapy with Baseline and Restaging 18F-FDG PET Imaging Biomarkers in Patients with Esophageal Cancer.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Beukinga, Roelof J; Hulshoff, Jan Binne; Mul, Véronique E M; Noordzij, Walter; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Plukker, John T M 2018-06-01 Purpose To assess the value of baseline and restaging fluorine 18 ( 18 F) fluorodeoxyglucose (FDG) positron emission tomography (PET) radiomics in predicting pathologic complete response to neoadjuvant chemotherapy and radiation therapy (NCRT) in patients with locally advanced esophageal cancer. Materials and Methods In this retrospective study, 73 patients with histologic analysis-confirmed T1/N1-3/M0 or T2-4a/N0-3/M0 esophageal cancer were treated with NCRT followed by surgery (Chemoradiotherapy for Esophageal Cancer followed by Surgery Study regimen) between October 2014 and August 2017. Clinical variables and radiomic features from baseline and restaging 18 F-FDG PET were selected by univariable logistic regression and least absolute shrinkage and selection operator. The selected variables were used to fit a multivariable logistic regression model, which was internally validated by using bootstrap resampling with 20 000 replicates. The performance of this model was compared with reference prediction models composed of maximum standardized uptake value metrics, clinical variables, and maximum standardized uptake value at baseline NCRT radiomic features. Outcome was defined as complete versus incomplete pathologic response (tumor regression grade 1 vs 2-5 according to the Mandard classification). Results Pathologic response was complete in 16 patients (21.9%) and incomplete in 57 patients (78.1%). A prediction model combining clinical T-stage and restaging NCRT (post-NCRT) joint maximum (quantifying image orderliness) yielded an optimism-corrected area under the receiver operating characteristics curve of 0.81. Post-NCRT joint maximum was replaceable with five other redundant post-NCRT radiomic features that provided equal model performance. All reference prediction models exhibited substantially lower discriminatory accuracy. Conclusion The combination of clinical T-staging and quantitative assessment of post-NCRT 18 F-FDG PET orderliness (joint maximum) provided high discriminatory accuracy in predicting pathologic complete response in patients with esophageal cancer. © RSNA, 2018 Online supplemental material is available for this article. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JIEIA..97..273T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JIEIA..97..273T">Parametric Study of Shear Strength of Concrete Beams Reinforced with FRP Bars</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Thomas, Job; Ramadass, S. 2016-09-01 Fibre Reinforced Polymer (FRP) bars are being widely used as internal reinforcement in structural elements in the last decade. The corrosion resistance of FRP bars qualifies its use in severe and marine exposure conditions in structures. A total of eight concrete beams longitudinally reinforced with FRP bars were cast and tested over shear span to depth ratio of 0.5 and 1.75. The shear strength test data of 188 beams published in various literatures were also used. The model originally proposed by Indian Standard Code of practice for the prediction of shear strength of concrete beams reinforced with steel bars IS:456 (Plain and reinforced concrete, code of practice, fourth revision. Bureau of Indian Standards, New Delhi, 2000) is considered and a modification to account for the influence of the FRP bars is proposed based on regression analysis. Out of the 196 test data, 110 test data is used for the regression analysis and 86 test data is used for the validation of the model. In addition, the shear strength of 86 test data accounted for the validation is assessed using eleven models proposed by various researchers. The proposed model accounts for compressive strength of concrete ( f ck ), modulus of elasticity of FRP rebar ( E f ), longitudinal reinforcement ratio ( ρ f ), shear span to depth ratio ( a/ d) and size effect of beams. The predicted shear strength of beams using the proposed model and 11 models proposed by other researchers is compared with the corresponding experimental results. The mean of predicted shear strength to the experimental shear strength for the 86 beams accounted for the validation of the proposed model is found to be 0.93. The result of the statistical analysis indicates that the prediction based on the proposed model corroborates with the corresponding experimental data. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013IJCMS...250004R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013IJCMS...250004R">Mathematical-Artificial Neural Network Hybrid Model to Predict Roll Force during Hot Rolling of Steel</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Rath, S.; Sengupta, P. P.; Singh, A. P.; Marik, A. K.; Talukdar, P. 2013-07-01 Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70017477','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70017477">Prediction by regression and intrarange data scatter in surface-process studies</a> <a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a> Toy, T.J.; Osterkamp, W.R.; Renard, K.G. 1993-01-01 Modeling is a major component of contemporary earth science, and regression analysis occupies a central position in the parameterization, calibration, and validation of geomorphic and hydrologic models. Although this methodology can be used in many ways, we are primarily concerned with the prediction of values for one variable from another variable. Examination of the literature reveals considerable inconsistency in the presentation of the results of regression analysis and the occurrence of patterns in the scatter of data points about the regression line. Both circumstances confound utilization and evaluation of the models. Statisticians are well aware of various problems associated with the use of regression analysis and offer improved practices; often, however, their guidelines are not followed. After a review of the aforementioned circumstances and until standard criteria for model evaluation become established, we recommend, as a minimum, inclusion of scatter diagrams, the standard error of the estimate, and sample size in reporting the results of regression analyses for most surface-process studies. ?? 1993 Springer-Verlag. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19750034265&hterms=Questionnaire+Design&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DQuestionnaire%2BDesign','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19750034265&hterms=Questionnaire+Design&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DQuestionnaire%2BDesign">The use of subjective expert opinions in cost optimum design of aerospace structures. [probabilistic failure models</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Thomas, J. M.; Hanagud, S. 1975-01-01 The results of two questionnaires sent to engineering experts are statistically analyzed and compared with objective data from Saturn V design and testing. Engineers were asked how likely it was for structural failure to occur at load increments above and below analysts' stress limit predictions. They were requested to estimate the relative probabilities of different failure causes, and of failure at each load increment given a specific cause. Three mathematical models are constructed based on the experts' assessment of causes. The experts' overall assessment of prediction strength fits the Saturn V data better than the models do, but a model test option (T-3) based on the overall assessment gives more design change likelihood to overstrength structures than does an older standard test option. T-3 compares unfavorably with the standard option in a cost optimum structural design problem. The report reflects a need for subjective data when objective data are unavailable. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25515107','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25515107">An Integrative Model of Physiological Traits Can be Used to Predict Obstructive Sleep Apnea and Response to Non Positive Airway Pressure Therapy.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Owens, Robert L; Edwards, Bradley A; Eckert, Danny J; Jordan, Amy S; Sands, Scott A; Malhotra, Atul; White, David P; Loring, Stephen H; Butler, James P; Wellman, Andrew 2015-06-01 Both anatomical and nonanatomical traits are important in obstructive sleep apnea (OSA) pathogenesis. We have previously described a model combining these traits, but have not determined its diagnostic accuracy to predict OSA. A valid model, and knowledge of the published effect sizes of trait manipulation, would also allow us to predict the number of patients with OSA who might be effectively treated without using positive airway pressure (PAP). Fifty-seven subjects with and without OSA underwent standard clinical and research sleep studies to measure OSA severity and the physiological traits important for OSA pathogenesis, respectively. The traits were incorporated into a physiological model to predict OSA. The model validity was determined by comparing the model prediction of OSA to the clinical diagnosis of OSA. The effect of various trait manipulations was then simulated to predict the proportion of patients treated by each intervention. The model had good sensitivity (80%) and specificity (100%) for predicting OSA. A single intervention on one trait would be predicted to treat OSA in approximately one quarter of all patients. Combination therapy with two interventions was predicted to treat OSA in ∼50% of patients. An integrative model of physiological traits can be used to predict population-wide and individual responses to non-PAP therapy. Many patients with OSA would be expected to be treated based on known trait manipulations, making a strong case for the importance of non-anatomical traits in OSA pathogenesis and the effectiveness of non-PAP therapies. © 2015 Associated Professional Sleep Societies, LLC. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018PApGe.175.1019A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018PApGe.175.1019A">Ground Motion Prediction Model Using Adaptive Neuro-Fuzzy Inference Systems: An Example Based on the NGA-West 2 Data</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Ameur, Mourad; Derras, Boumédiène; Zendagui, Djawed 2018-03-01 Adaptive neuro-fuzzy inference systems (ANFIS) are used here to obtain the robust ground motion prediction model (GMPM). Avoiding a priori functional form, ANFIS provides fully data-driven predictive models. A large subset of the NGA-West2 database is used, including 2335 records from 580 sites and 137 earthquakes. Only shallow earthquakes and recordings corresponding to stations with measured V s30 properties are selected. Three basics input parameters are chosen: the moment magnitude ( Mw), the Joyner-Boore distance ( R JB) and V s30. ANFIS model output is the peak ground acceleration (PGA), peak ground velocity (PGV) and 5% damped pseudo-spectral acceleration (PSA) at periods from 0.01 to 4 s. A procedure similar to the random-effects approach is developed to provide between- and within-event standard deviations. The total standard deviation (SD) varies between [0.303 and 0.360] (log10 units) depending on the period. The ground motion predictions resulting from such simple three explanatory variables ANFIS models are shown to be comparable to the most recent NGA results (e.g., Boore et al., in Earthquake Spectra 30:1057-1085, 2014; Derras et al., in Earthquake Spectra 32:2027-2056, 2016). The main advantage of ANFIS compared to artificial neuronal network (ANN) is its simple and one-off topology: five layers. Our results exhibit a number of physically sound features: magnitude scaling of the distance dependency, near-fault saturation distance increasing with magnitude and amplification on soft soils. The ability to implement ANFIS model using an analytic equation and Excel is demonstrated. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active">22</li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active">23</li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PApGe.tmp..485A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PApGe.tmp..485A">Ground Motion Prediction Model Using Adaptive Neuro-Fuzzy Inference Systems: An Example Based on the NGA-West 2 Data</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Ameur, Mourad; Derras, Boumédiène; Zendagui, Djawed 2017-12-01 Adaptive neuro-fuzzy inference systems (ANFIS) are used here to obtain the robust ground motion prediction model (GMPM). Avoiding a priori functional form, ANFIS provides fully data-driven predictive models. A large subset of the NGA-West2 database is used, including 2335 records from 580 sites and 137 earthquakes. Only shallow earthquakes and recordings corresponding to stations with measured V s30 properties are selected. Three basics input parameters are chosen: the moment magnitude (Mw), the Joyner-Boore distance (R JB) and V s30. ANFIS model output is the peak ground acceleration (PGA), peak ground velocity (PGV) and 5% damped pseudo-spectral acceleration (PSA) at periods from 0.01 to 4 s. A procedure similar to the random-effects approach is developed to provide between- and within-event standard deviations. The total standard deviation (SD) varies between [0.303 and 0.360] (log10 units) depending on the period. The ground motion predictions resulting from such simple three explanatory variables ANFIS models are shown to be comparable to the most recent NGA results (e.g., Boore et al., in Earthquake Spectra 30:1057-1085, 2014; Derras et al., in Earthquake Spectra 32:2027-2056, 2016). The main advantage of ANFIS compared to artificial neuronal network (ANN) is its simple and one-off topology: five layers. Our results exhibit a number of physically sound features: magnitude scaling of the distance dependency, near-fault saturation distance increasing with magnitude and amplification on soft soils. The ability to implement ANFIS model using an analytic equation and Excel is demonstrated. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20691511','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20691511">Three-dimensional quantitative structure-activity relationship CoMSIA/CoMFA and LeapFrog studies on novel series of bicyclo [4.1.0] heptanes derivatives as melanin-concentrating hormone receptor R1 antagonists.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Morales-Bayuelo, Alejandro; Ayazo, Hernan; Vivas-Reyes, Ricardo 2010-10-01 Comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were performed on a series of bicyclo [4.1.0] heptanes derivatives as melanin-concentrating hormone receptor R1 antagonists (MCHR1 antagonists). Molecular superimposition of antagonists on the template structure was performed by database alignment method. The statistically significant model was established on sixty five molecules, which were validated by a test set of ten molecules. The CoMSIA model yielded the best predictive model with a q(2) = 0.639, non cross-validated R(2) of 0.953, F value of 92.802, bootstrapped R(2) of 0.971, standard error of prediction = 0.402, and standard error of estimate = 0.146 while the CoMFA model yielded a q(2) = 0.680, non cross-validated R(2) of 0.922, F value of 114.351, bootstrapped R(2) of 0.925, standard error of prediction = 0.364, and standard error of estimate = 0.180. CoMFA analysis maps were employed for generating a pseudo cavity for LeapFrog calculation. The contour maps obtained from 3D-QSAR studies were appraised for activity trends for the molecules analyzed. The results show the variability of steric and electrostatic contributions that determine the activity of the MCHR1 antagonist, with these results we proposed new antagonists that may be more potent than previously reported, these novel antagonists were designed from the addition of highly electronegative groups in the substituent di(i-C(3)H(7))N- of the bicycle [4.1.0] heptanes, using the model CoMFA which also was used for the molecular design using the technique LeapFrog. The data generated from the present study will further help to design novel, potent, and selective MCHR1 antagonists. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22048485','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22048485">Reliability of a new biokinetic model of zirconium in internal dosimetry: part I, parameter uncertainty analysis.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Li, Wei Bo; Greiter, Matthias; Oeh, Uwe; Hoeschen, Christoph 2011-12-01 The reliability of biokinetic models is essential in internal dose assessments and radiation risk analysis for the public, occupational workers, and patients exposed to radionuclides. In this paper, a method for assessing the reliability of biokinetic models by means of uncertainty and sensitivity analysis was developed. The paper is divided into two parts. In the first part of the study published here, the uncertainty sources of the model parameters for zirconium (Zr), developed by the International Commission on Radiological Protection (ICRP), were identified and analyzed. Furthermore, the uncertainty of the biokinetic experimental measurement performed at the Helmholtz Zentrum München-German Research Center for Environmental Health (HMGU) for developing a new biokinetic model of Zr was analyzed according to the Guide to the Expression of Uncertainty in Measurement, published by the International Organization for Standardization. The confidence interval and distribution of model parameters of the ICRP and HMGU Zr biokinetic models were evaluated. As a result of computer biokinetic modelings, the mean, standard uncertainty, and confidence interval of model prediction calculated based on the model parameter uncertainty were presented and compared to the plasma clearance and urinary excretion measured after intravenous administration. It was shown that for the most important compartment, the plasma, the uncertainty evaluated for the HMGU model was much smaller than that for the ICRP model; that phenomenon was observed for other organs and tissues as well. The uncertainty of the integral of the radioactivity of Zr up to 50 y calculated by the HMGU model after ingestion by adult members of the public was shown to be smaller by a factor of two than that of the ICRP model. It was also shown that the distribution type of the model parameter strongly influences the model prediction, and the correlation of the model input parameters affects the model prediction to a certain extent depending on the strength of the correlation. In the case of model prediction, the qualitative comparison of the model predictions with the measured plasma and urinary data showed the HMGU model to be more reliable than the ICRP model; quantitatively, the uncertainty model prediction by the HMGU systemic biokinetic model is smaller than that of the ICRP model. The uncertainty information on the model parameters analyzed in this study was used in the second part of the paper regarding a sensitivity analysis of the Zr biokinetic models. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19770017245','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19770017245">Boundary layer integral matrix procedure: Verification of models</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Bonnett, W. S.; Evans, R. M. 1977-01-01 The three turbulent models currently available in the JANNAF version of the Aerotherm Boundary Layer Integral Matrix Procedure (BLIMP-J) code were studied. The BLIMP-J program is the standard prediction method for boundary layer effects in liquid rocket engine thrust chambers. Experimental data from flow fields with large edge-to-wall temperature ratios are compared to the predictions of the three turbulence models contained in BLIMP-J. In addition, test conditions necessary to generate additional data on a flat plate or in a nozzle are given. It is concluded that the Cebeci-Smith turbulence model be the recommended model for the prediction of boundary layer effects in liquid rocket engines. In addition, the effects of homogeneous chemical reaction kinetics were examined for a hydrogen/oxygen system. Results show that for most flows, kinetics are probably only significant for stoichiometric mixture ratios. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130012907','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130012907">Structural Dynamics Modeling of HIRENASD in Support of the Aeroelastic Prediction Workshop</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Wieseman, Carol; Chwalowski, Pawel; Heeg, Jennifer; Boucke, Alexander; Castro, Jack 2013-01-01 An Aeroelastic Prediction Workshop (AePW) was held in April 2012 using three aeroelasticity case study wind tunnel tests for assessing the capabilities of various codes in making aeroelasticity predictions. One of these case studies was known as the HIRENASD model that was tested in the European Transonic Wind Tunnel (ETW). This paper summarizes the development of a standardized enhanced analytical HIRENASD structural model for use in the AePW effort. The modifications to the HIRENASD finite element model were validated by comparing modal frequencies, evaluating modal assurance criteria, comparing leading edge, trailing edge and twist of the wing with experiment and by performing steady and unsteady CFD analyses for one of the test conditions on the same grid, and identical processing of results. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhRvL.116h2003C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhRvL.116h2003C">High-Precision Differential Predictions for Top-Quark Pairs at the LHC</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Czakon, Michal; Heymes, David; Mitov, Alexander 2016-02-01 We present the first complete next-to-next-to-leading order (NNLO) QCD predictions for differential distributions in the top-quark pair production process at the LHC. Our results are derived from a fully differential partonic Monte Carlo calculation with stable top quarks which involves no approximations beyond the fixed-order truncation of the perturbation series. The NNLO corrections improve the agreement between existing LHC measurements [V. Khachatryan et al. (CMS Collaboration), Eur. Phys. J. C 75, 542 (2015)] and standard model predictions for the top-quark transverse momentum distribution, thus helping alleviate one long-standing discrepancy. The shape of the top-quark pair invariant mass distribution turns out to be stable with respect to radiative corrections beyond NLO which increases the value of this observable as a place to search for physics beyond the standard model. The results presented here provide essential input for parton distribution function fits, implementation of higher-order effects in Monte Carlo generators, as well as top-quark mass and strong coupling determination. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26967413','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26967413">High-Precision Differential Predictions for Top-Quark Pairs at the LHC.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Czakon, Michal; Heymes, David; Mitov, Alexander 2016-02-26 We present the first complete next-to-next-to-leading order (NNLO) QCD predictions for differential distributions in the top-quark pair production process at the LHC. Our results are derived from a fully differential partonic Monte Carlo calculation with stable top quarks which involves no approximations beyond the fixed-order truncation of the perturbation series. The NNLO corrections improve the agreement between existing LHC measurements [V. Khachatryan et al. (CMS Collaboration), Eur. Phys. J. C 75, 542 (2015)] and standard model predictions for the top-quark transverse momentum distribution, thus helping alleviate one long-standing discrepancy. The shape of the top-quark pair invariant mass distribution turns out to be stable with respect to radiative corrections beyond NLO which increases the value of this observable as a place to search for physics beyond the standard model. The results presented here provide essential input for parton distribution function fits, implementation of higher-order effects in Monte Carlo generators, as well as top-quark mass and strong coupling determination. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/10175331','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/10175331">A radial transmission line material measurement apparatus</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Warne, L.K.; Moyer, R.D.; Koontz, T.E. 1993-05-01 A radial transmission line material measurement sample apparatus (sample holder, offset short standards, measurement software, and instrumentation) is described which has been proposed, analyzed, designed, constructed, and tested. The purpose of the apparatus is to obtain accurate surface impedance measurements of lossy, possibly anisotropic, samples at low and intermediate frequencies (vhf and low uhf). The samples typically take the form of sections of the material coatings on conducting objects. Such measurements thus provide the key input data for predictive numerical scattering codes. Prediction of the sample surface impedance from the coaxial input impedance measurement is carried out by two techniques.more » The first is an analytical model for the coaxial-to-radial transmission line junction. The second is an empirical determination of the bilinear transformation model of the junction by the measurement of three full standards. The standards take the form of three offset shorts (and an additional lossy Salisbury load), which have also been constructed. The accuracy achievable with the device appears to be near one percent.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1098851-measurements-ww-wz-production-w+jets-final-states-pp-collisions','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1098851-measurements-ww-wz-production-w+jets-final-states-pp-collisions">Measurements of WW and WZ Production in W+jets Final States in pp̄ Collisions</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Abazov, V. M.; Abbott, B.; Acharya, B. S.; ... 2012-05-02 We study WW and WZ production with lνqq (l=e,μ) final states using data collected by the D0 detector at the Fermilab Tevatron Collider corresponding to 4.3 fb⁻¹ of integrated luminosity from pp̄ collisions at √s=1.96 TeV. Assuming the ratio between the production cross sections σ(WW) and σ(WZ) as predicted by the standard model, we measure the total WV (V=W,Z) cross section to be σ(WV)=19.6 +3.2 -3.0 pb and reject the background-only hypothesis at a level of 7.9 standard deviations. We also use b-jet discrimination to separate the WZ component from the dominant WW component. Simultaneously fitting WW and WZ contributions,more » we measure σ(WW)=15.9 +3.7-3.2 pb and σ(WZ)=3.3 +4.1 -3.3 pb, which is consistent with the standard model predictions.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20070012405','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070012405">Progress Toward Improving Jet Noise Predictions in Hot Jets</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Khavaran, Abbas; Kenzakowski, Donald C. 2007-01-01 An acoustic analogy methodology for improving noise predictions in hot round jets is presented. Past approaches have often neglected the impact of temperature fluctuations on the predicted sound spectral density, which could be significant for heated jets, and this has yielded noticeable acoustic under-predictions in such cases. The governing acoustic equations adopted here are a set of linearized, inhomogeneous Euler equations. These equations are combined into a single third order linear wave operator when the base flow is considered as a locally parallel mean flow. The remaining second-order fluctuations are regarded as the equivalent sources of sound and are modeled. It is shown that the hot jet effect may be introduced primarily through a fluctuating velocity/enthalpy term. Modeling this additional source requires specialized inputs from a RANS-based flowfield simulation. The information is supplied using an extension to a baseline two equation turbulence model that predicts total enthalpy variance in addition to the standard parameters. Preliminary application of this model to a series of unheated and heated subsonic jets shows significant improvement in the acoustic predictions at the 90 degree observer angle. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3703326','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3703326">A Review of Surface Water Quality Models</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Li, Shibei; Jia, Peng; Qi, Changjun; Ding, Feng 2013-01-01 Surface water quality models can be useful tools to simulate and predict the levels, distributions, and risks of chemical pollutants in a given water body. The modeling results from these models under different pollution scenarios are very important components of environmental impact assessment and can provide a basis and technique support for environmental management agencies to make right decisions. Whether the model results are right or not can impact the reasonability and scientificity of the authorized construct projects and the availability of pollution control measures. We reviewed the development of surface water quality models at three stages and analyzed the suitability, precisions, and methods among different models. Standardization of water quality models can help environmental management agencies guarantee the consistency in application of water quality models for regulatory purposes. We concluded the status of standardization of these models in developed countries and put forward available measures for the standardization of these surface water quality models, especially in developing countries. PMID:23853533 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19980200846','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19980200846">Improvement and Application of Atmospheric Radiative Transfer Models for Prediction of the Climatic Effects of Aerosol</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Bergstrom, Robert W.; Mlawer, Eli J.; Sokolik, Irina N.; Clough, Shepard A.; Toon, Owen B. 1998-01-01 This paper presents a radiative transfer model that has been developed to accurately predict the atmospheric radiant flux in both the infrared and the solar spectrum with a minimum of computational effort. The model is designed to be included in numerical climate models. To assess the accuracy of the model, the results are compared to other more detailed models for several standard cases in the solar and thermal spectrum. As the thermal spectrum has been treated in other publications, we focus here on the solar part of the spectrum. We perform several example calculations focussing on the question of absorption of solar radiation by gases and aerosols. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19980201056','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19980201056">Improvement and Application of Atmospheric Radiative Transfer Models for Prediction of the Climatic Effects of Aerosol</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Bergstrom, Robert W. 1998-01-01 This paper presents a radiative transfer model that has been developed to accurately predict the atmospheric radiant flux in both the infrared and the solar spectrum with a minimum of computational effort. The model is designed to be included in numerical climate models. To assess the accuracy of the model, the results are compared to other more detailed models for several standard cases in the solar and thermal spectrum. As the thermal spectrum has been treated in other publications we focus here on the solar part of the spectrum. We perform several example calculations focussing on the question of absorption of solar radiation by gases and aerosols. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhRvD..92a5007C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvD..92a5007C">Family nonuniversal Z' models with protected flavor-changing interactions</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Celis, Alejandro; Fuentes-Martín, Javier; Jung, Martin; Serôdio, Hugo 2015-07-01 We define a new class of Z' models with neutral flavor-changing interactions at tree level in the down-quark sector. They are related in an exact way to elements of the quark mixing matrix due to an underlying flavored U(1)' gauge symmetry, rendering these models particularly predictive. The same symmetry implies lepton-flavor nonuniversal couplings, fully determined by the gauge structure of the model. Our models allow us to address presently observed deviations from the standard model and specific correlations among the new physics contributions to the Wilson coefficients C9,10' ℓ can be tested in b →s ℓ+ℓ- transitions. We furthermore predict lepton-universality violations in Z' decays, testable at the LHC. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19800017470','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19800017470">Initial geomagnetic field model from MAGSAT</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Langel, R. A.; Estes, R. H.; Mead, G. D.; Fabiano, E. B.; Lancaster, E. R. 1980-01-01 Magsat data from magnetically quiet days were used to derive a thirteenth degree and order spherical harmonic geomagnetic field model, MGST(3/80). The model utilized both scalar and vector data and fit that data with standard deviations of 8, 52, 55 and 97 nT for the scalar magnitude, B sub r, B sub theta and B sub phi respectively. When compared with earlier models, the Earth's dipole moment continues to decrease at a rate of about 26 nT/year. Evaluation of earlier models with Magsat data shows that the scalar field at the Magsat epoch is best predicted by the POGO(2/72) model but that the AWC/75 and IGS/75 are better for predicting vector fields. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5119819','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5119819">Auditory Time-Frequency Masking for Spectrally and Temporally Maximally-Compact Stimuli</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Laback, Bernhard; Savel, Sophie; Ystad, Sølvi; Balazs, Peter; Meunier, Sabine; Kronland-Martinet, Richard 2016-01-01 Many audio applications perform perception-based time-frequency (TF) analysis by decomposing sounds into a set of functions with good TF localization (i.e. with a small essential support in the TF domain) using TF transforms and applying psychoacoustic models of auditory masking to the transform coefficients. To accurately predict masking interactions between coefficients, the TF properties of the model should match those of the transform. This involves having masking data for stimuli with good TF localization. However, little is known about TF masking for mathematically well-localized signals. Most existing masking studies used stimuli that are broad in time and/or frequency and few studies involved TF conditions. Consequently, the present study had two goals. The first was to collect TF masking data for well-localized stimuli in humans. Masker and target were 10-ms Gaussian-shaped sinusoids with a bandwidth of approximately one critical band. The overall pattern of results is qualitatively similar to existing data for long maskers. To facilitate implementation in audio processing algorithms, a dataset provides the measured TF masking function. The second goal was to assess the potential effect of auditory efferents on TF masking using a modeling approach. The temporal window model of masking was used to predict present and existing data in two configurations: (1) with standard model parameters (i.e. without efferents), (2) with cochlear gain reduction to simulate the activation of efferents. The ability of the model to predict the present data was quite good with the standard configuration but highly degraded with gain reduction. Conversely, the ability of the model to predict existing data for long maskers was better with than without gain reduction. Overall, the model predictions suggest that TF masking can be affected by efferent (or other) effects that reduce cochlear gain. Such effects were avoided in the experiment of this study by using maximally-compact stimuli. PMID:27875575 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27875575','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27875575">Auditory Time-Frequency Masking for Spectrally and Temporally Maximally-Compact Stimuli.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Necciari, Thibaud; Laback, Bernhard; Savel, Sophie; Ystad, Sølvi; Balazs, Peter; Meunier, Sabine; Kronland-Martinet, Richard 2016-01-01 Many audio applications perform perception-based time-frequency (TF) analysis by decomposing sounds into a set of functions with good TF localization (i.e. with a small essential support in the TF domain) using TF transforms and applying psychoacoustic models of auditory masking to the transform coefficients. To accurately predict masking interactions between coefficients, the TF properties of the model should match those of the transform. This involves having masking data for stimuli with good TF localization. However, little is known about TF masking for mathematically well-localized signals. Most existing masking studies used stimuli that are broad in time and/or frequency and few studies involved TF conditions. Consequently, the present study had two goals. The first was to collect TF masking data for well-localized stimuli in humans. Masker and target were 10-ms Gaussian-shaped sinusoids with a bandwidth of approximately one critical band. The overall pattern of results is qualitatively similar to existing data for long maskers. To facilitate implementation in audio processing algorithms, a dataset provides the measured TF masking function. The second goal was to assess the potential effect of auditory efferents on TF masking using a modeling approach. The temporal window model of masking was used to predict present and existing data in two configurations: (1) with standard model parameters (i.e. without efferents), (2) with cochlear gain reduction to simulate the activation of efferents. The ability of the model to predict the present data was quite good with the standard configuration but highly degraded with gain reduction. Conversely, the ability of the model to predict existing data for long maskers was better with than without gain reduction. Overall, the model predictions suggest that TF masking can be affected by efferent (or other) effects that reduce cochlear gain. Such effects were avoided in the experiment of this study by using maximally-compact stimuli. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.U51A..03M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.U51A..03M">Designing Collaborative Developmental Standards by Refactoring of the Earth Science Models, Libraries, Workflows and Frameworks.</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Mirvis, E.; Iredell, M. 2015-12-01 The operational (OPS) NOAA National Centers for Environmental Prediction (NCEP) suite, traditionally, consist of a large set of multi- scale HPC models, workflows, scripts, tools and utilities, which are very much depending on the variety of the additional components. Namely, this suite utilizes a unique collection of the in-house developed 20+ shared libraries (NCEPLIBS), certain versions of the 3-rd party libraries (like netcdf, HDF, ESMF, jasper, xml etc.), HPC workflow tool within dedicated (sometimes even vendors' customized) HPC system homogeneous environment. This domain and site specific, accompanied with NCEP's product- driven large scale real-time data operations complicates NCEP collaborative development tremendously by reducing chances to replicate this OPS environment anywhere else. The NOAA/NCEP's Environmental Modeling Center (EMC) missions to develop and improve numerical weather, climate, hydrological and ocean prediction through the partnership with the research community. Realizing said difficulties, lately, EMC has been taken an innovative approach to improve flexibility of the HPC environment by building the elements and a foundation for NCEP OPS functionally equivalent environment (FEE), which can be used to ease the external interface constructs as well. Aiming to reduce turnaround time of the community code enhancements via Research-to-Operations (R2O) cycle, EMC developed and deployed several project sub-set standards that already paved the road to NCEP OPS implementation standards. In this topic we will discuss the EMC FEE for O2R requirements and approaches in collaborative standardization, including NCEPLIBS FEE and models code version control paired with the models' derived customized HPC modules and FEE footprints. We will share NCEP/EMC experience and potential in the refactoring of EMC development processes, legacy codes and in securing model source code quality standards by using combination of the Eclipse IDE, integrated with the reverse engineering tools/APIs. We will also inform on collaborative efforts in the restructuring of the NOAA Environmental Modeling System (NEMS) - the multi- model and coupling framework, and transitioning FEE verification methodology. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2769639','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2769639">Predictive Monitoring for Improved Management of Glucose Levels</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Reifman, Jaques; Rajaraman, Srinivasan; Gribok, Andrei; Ward, W. Kenneth 2007-01-01 Background Recent developments and expected near-future improvements in continuous glucose monitoring (CGM) devices provide opportunities to couple them with mathematical forecasting models to produce predictive monitoring systems for early, proactive glycemia management of diabetes mellitus patients before glucose levels drift to undesirable levels. This article assesses the feasibility of data-driven models to serve as the forecasting engine of predictive monitoring systems. Methods We investigated the capabilities of data-driven autoregressive (AR) models to (1) capture the correlations in glucose time-series data, (2) make accurate predictions as a function of prediction horizon, and (3) be made portable from individual to individual without any need for model tuning. The investigation is performed by employing CGM data from nine type 1 diabetic subjects collected over a continuous 5-day period. Results With CGM data serving as the gold standard, AR model-based predictions of glucose levels assessed over nine subjects with Clarke error grid analysis indicated that, for a 30-minute prediction horizon, individually tuned models yield 97.6 to 100.0% of data in the clinically acceptable zones A and B, whereas cross-subject, portable models yield 95.8 to 99.7% of data in zones A and B. Conclusions This study shows that, for a 30-minute prediction horizon, data-driven AR models provide sufficiently-accurate and clinically-acceptable estimates of glucose levels for timely, proactive therapy and should be considered as the modeling engine for predictive monitoring of patients with type 1 diabetes mellitus. It also suggests that AR models can be made portable from individual to individual with minor performance penalties, while greatly reducing the burden associated with model tuning and data collection for model development. PMID:19885110 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27414041','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27414041">Predicting tibiotalar and subtalar joint angles from skin-marker data with dual-fluoroscopy as a reference standard.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Nichols, Jennifer A; Roach, Koren E; Fiorentino, Niccolo M; Anderson, Andrew E 2016-09-01 Evidence suggests that the tibiotalar and subtalar joints provide near six degree-of-freedom (DOF) motion. Yet, kinematic models frequently assume one DOF at each of these joints. In this study, we quantified the accuracy of kinematic models to predict joint angles at the tibiotalar and subtalar joints from skin-marker data. Models included 1 or 3 DOF at each joint. Ten asymptomatic subjects, screened for deformities, performed 1.0m/s treadmill walking and a balanced, single-leg heel-rise. Tibiotalar and subtalar joint angles calculated by inverse kinematics for the 1 and 3 DOF models were compared to those measured directly in vivo using dual-fluoroscopy. Results demonstrated that, for each activity, the average error in tibiotalar joint angles predicted by the 1 DOF model were significantly smaller than those predicted by the 3 DOF model for inversion/eversion and internal/external rotation. In contrast, neither model consistently demonstrated smaller errors when predicting subtalar joint angles. Additionally, neither model could accurately predict discrete angles for the tibiotalar and subtalar joints on a per-subject basis. Differences between model predictions and dual-fluoroscopy measurements were highly variable across subjects, with joint angle errors in at least one rotation direction surpassing 10° for 9 out of 10 subjects. Our results suggest that both the 1 and 3 DOF models can predict trends in tibiotalar joint angles on a limited basis. However, as currently implemented, neither model can predict discrete tibiotalar or subtalar joint angles for individual subjects. Inclusion of subject-specific attributes may improve the accuracy of these models. Copyright © 2016 Elsevier B.V. All rights reserved. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active">23</li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_24" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active">24</li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="461"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940012406','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940012406">Computation of confined coflow jets with three turbulence models</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Zhu, J.; Shih, T. H. 1993-01-01 A numerical study of confined jets in a cylindrical duct is carried out to examine the performance of two recently proposed turbulence models: an RNG-based K-epsilon model and a realizable Reynolds stress algebraic equation model. The former is of the same form as the standard K-epsilon model but has different model coefficients. The latter uses an explicit quadratic stress-strain relationship to model the turbulent stresses and is capable of ensuring the positivity of each turbulent normal stress. The flow considered involves recirculation with unfixed separation and reattachment points and severe adverse pressure gradients, thereby providing a valuable test of the predictive capability of the models for complex flows. Calculations are performed with a finite-volume procedure. Numerical credibility of the solutions is ensured by using second-order accurate differencing schemes and sufficiently fine grids. Calculations with the standard K-epsilon model are also made for comparison. Detailed comparisons with experiments show that the realizable Reynolds stress algebraic equation model consistently works better than does the standard K-epsilon model in capturing the essential flow features, while the RNG-based K-epsilon model does not seem to give improvements over the standard K-epsilon model under the flow conditions considered. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25970759','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25970759">Expected outcomes from topical haemoglobin spray in non-healing and worsening venous leg ulcers.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Arenberger, P; Elg, F; Petyt, J; Cutting, K 2015-05-01 To evaluate the effect of topical haemoglobin spray on treatment response and wound-closure rates in patients with chronic venous leg ulcers. A linear regression model was used to forecast healing outcomes over a 12-month period. Simulated data were taken from normal distributions based on post-hoc analysis of a 72-patient study in non-healing and worsening wounds (36 patients receiving standard care and 36 receiving standard care plus topical haemoglobin spray). Using a simulated 25,000 'patients' from each group, the proportion of wound closure over time was projected. Simulation results predicted a 55% wound closure rate at six months in the haemoglobin group, compared with 4% in the standard care group. Over a 12-month simulation period, a 43% overall reduction in wound burden was predicted. With the haemoglobin spray, 85% of wounds were expected to heal in 12 months, compared with 13% in the standard care group. Topical haemoglobin spray promises a more effective treatment for chronic venous leg ulcers than standard care alone in wounds that are non-healing or worsening. Further research is required to validate these predictions and to identify achievable outcomes in other chronic wound types. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29650478','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29650478">A strategy to improve the identification reliability of the chemical constituents by high-resolution mass spectrometry-based isomer structure prediction combined with a quantitative structure retention relationship analysis: Phthalide compounds in Chuanxiong as a test case.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Zhang, Qingqing; Huo, Mengqi; Zhang, Yanling; Qiao, Yanjiang; Gao, Xiaoyan 2018-06-01 High-resolution mass spectrometry (HRMS) provides a powerful tool for the rapid analysis and identification of compounds in herbs. However, the diversity and large differences in the content of the chemical constituents in herbal medicines, especially isomerisms, are a great challenge for mass spectrometry-based structural identification. In the current study, a new strategy for the structural characterization of potential new phthalide compounds was proposed by isomer structure predictions combined with a quantitative structure-retention relationship (QSRR) analysis using phthalide compounds in Chuanxiong as an example. This strategy consists of three steps. First, the structures of phthalide compounds were reasonably predicted on the basis of the structure features and MS/MS fragmentation patterns: (1) the collected raw HRMS data were preliminarily screened by an in-house database; (2) the MS/MS fragmentation patterns of the analogous compounds were summarized; (3) the reported phthalide compounds were identified, and the structures of the isomers were reasonably predicted. Second, the QSRR model was established and verified using representative phthalide compound standards. Finally, the retention times of the predicted isomers were calculated by the QSRR model, and the structures of these peaks were rationally characterized by matching retention times of the detected chromatographic peaks and the predicted isomers. A multiple linear regression QSRR model in which 6 physicochemical variables were screened was built using 23 phthalide standards. The retention times of the phthalide isomers in Chuanxiong were well predicted by the QSRR model combined with reasonable structure predictions (R 2 =0.955). A total of 81 peaks were detected from Chuanxiong and assigned to reasonable structures, and 26 potential new phthalide compounds were structurally characterized. This strategy can improve the identification efficiency and reliability of homologues in complex materials. Copyright © 2018 Elsevier B.V. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Obedience+AND+Authority&pg=2&id=EJ756871','ERIC'); return false;" href="https://eric.ed.gov/?q=Obedience+AND+Authority&pg=2&id=EJ756871">Adolescents' as Active Agents in the Socialization Process: Legitimacy of Parental Authority and Obligation to Obey as Predictors of Obedience</a> <a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a> Darling, Nancy; Cumsille, Patricio; Loreto Martinez, M. 2007-01-01 Adolescents' agreement with parental standards and beliefs about the legitimacy of parental authority and their own obligation to obey were used to predict adolescents' obedience, controlling for parental monitoring, rules, and rule enforcement. Hierarchical linear models were used to predict both between-adolescent and within-adolescent,… </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=150966&keyword=sources+AND+funding&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50','EPA-EIMS'); return false;" href="https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=150966&keyword=sources+AND+funding&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50">CONTRIBUTION OF NUTRIENTS AND E. COLI TO SURFACE WATER CONDITION IN THE OZARKS I. USING PARTIAL LEAST SQUARES PREDICTIONS WHEN STANDARD REGRESSION ASSUMPTIONS ARE VIOLATED</a> <a target="_blank" rel="noopener noreferrer" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a> We present here the application of PLS regression to predicting surface water total phosphorous, total ammonia and Escherichia coli from landscape metrics. The amount of variability in surface water constituents explained by each model reflects the composition of the contributi... </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Poisson&pg=5&id=EJ973860','ERIC'); return false;" href="https://eric.ed.gov/?q=Poisson&pg=5&id=EJ973860">Development of Abbreviated Nine-Item Forms of the Raven's Standard Progressive Matrices Test</a> <a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a> Bilker, Warren B.; Hansen, John A.; Brensinger, Colleen M.; Richard, Jan; Gur, Raquel E.; Gur, Ruben C. 2012-01-01 The Raven's Standard Progressive Matrices (RSPM) is a 60-item test for measuring abstract reasoning, considered a nonverbal estimate of fluid intelligence, and often included in clinical assessment batteries and research on patients with cognitive deficits. The goal was to develop and apply a predictive model approach to reduce the number of items… </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28531380','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28531380">Are traditional body fat equations and anthropometry valid to estimate body fat in children and adolescents living with HIV?</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Lima, Luiz Rodrigo Augustemak de; Martins, Priscila Custódio; Junior, Carlos Alencar Souza Alves; Castro, João Antônio Chula de; Silva, Diego Augusto Santos; Petroski, Edio Luiz The aim of this study was to assess the validity of traditional anthropometric equations and to develop predictive equations of total body and trunk fat for children and adolescents living with HIV based on anthropometric measurements. Forty-eight children and adolescents of both sexes (24 boys) aged 7-17 years, living in Santa Catarina, Brazil, participated in the study. Dual-energy X-ray absorptiometry was used as the reference method to evaluate total body and trunk fat. Height, body weight, circumferences and triceps, subscapular, abdominal and calf skinfolds were measured. The traditional equations of Lohman and Slaughter were used to estimate body fat. Multiple regression models were fitted to predict total body fat (Model 1) and trunk fat (Model 2) using a backward selection procedure. Model 1 had an R 2 =0.85 and a standard error of the estimate of 1.43. Model 2 had an R 2 =0.80 and standard error of the estimate=0.49. The traditional equations of Lohman and Slaughter showed poor performance in estimating body fat in children and adolescents living with HIV. The prediction models using anthropometry provided reliable estimates and can be used by clinicians and healthcare professionals to monitor total body and trunk fat in children and adolescents living with HIV. Copyright © 2017 Sociedade Brasileira de Infectologia. Published by Elsevier Editora Ltda. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHEP...09..088A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHEP...09..088A">Search for new phenomena in a lepton plus high jet multiplicity final state with the ATLAS experiment using √{s}=13 TeV proton-proton collision data</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Aaboud, M.; Aad, G.; Abbott, B.; Abdinov, O.; Abeloos, B.; Abidi, S. H.; AbouZeid, O. S.; Abraham, N. 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P.; Denysiuk, D.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Devesa, M. R.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; Di Bello, F. A.; Di Ciaccio, A.; Di Ciaccio, L.; Di Clemente, W. K.; Di Donato, C.; Di Girolamo, A.; Di Girolamo, B.; Di Micco, B.; Di Nardo, R.; Di Petrillo, K. F.; Di Simone, A.; Di Sipio, R.; Di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Cornell, S. Díez; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dolejsi, J.; Dolezal, Z.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Duarte-Campderros, J.; Dubreuil, A.; Duchovni, E.; Duckeck, G.; Ducourthial, A.; Ducu, O. A.; Duda, D.; Dudarev, A.; Dudder, A. Chr.; Duffield, E. M.; Duflot, L.; Dührssen, M.; Dumancic, M.; Dumitriu, A. E.; Duncan, A. K.; Dunford, M.; Yildiz, H. Duran; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Dyndal, M.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; El Kacimi, M.; El Kosseifi, R.; Ellajosyula, V.; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Ennis, J. S.; Erdmann, J.; Ereditato, A.; Ernst, M.; Errede, S.; Escalier, M.; Escobar, C.; Esposito, B.; Pastor, O. Estrada; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Ezzi, M.; Fabbri, F.; Fabbri, L.; Fabiani, V.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farina, C.; Farina, E. M.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Giannelli, M. Faucci; Favareto, A.; Fawcett, W. 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J.; Rieger, J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rimoldi, M.; Rinaldi, L.; Ripellino, G.; Ristić, B.; Ritsch, E.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Rizzi, C.; Roberts, R. T.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robson, A.; Rocco, E.; Roda, C.; Rodina, Y.; Bosca, S. Rodriguez; Perez, A. Rodriguez; Rodriguez, D. Rodriguez; Roe, S.; Rogan, C. S.; Røhne, O.; Roloff, J.; Romaniouk, A.; Romano, M.; Saez, S. M. Romano; Adam, E. Romero; Rompotis, N.; Ronzani, M.; Roos, L.; Rosati, S.; Rosbach, K.; Rose, P.; Rosien, N.-A.; Rossi, E.; Rossi, L. P.; Rosten, J. H. N.; Rosten, R.; Rotaru, M.; Rothberg, J.; Rousseau, D.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rühr, F.; Ruiz-Martinez, A.; Rurikova, Z.; Rusakovich, N. A.; Russell, H. L.; Rutherfoord, J. P.; Ruthmann, N.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Ryu, S.; Ryzhov, A.; Rzehorz, G. F.; Saavedra, A. F.; Sabato, G.; Sacerdoti, S.; Sadrozinski, H. F.-W.; Sadykov, R.; Tehrani, F. Safai; Saha, P.; Sahinsoy, M.; Saimpert, M.; Saito, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Loyola, J. E. Salazar; Salek, D.; De Bruin, P. H. Sales; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sampsonidou, D.; Sánchez, J.; Martinez, V. Sanchez; Pineda, A. Sanchez; Sandaker, H.; Sandbach, R. L.; Sander, C. O.; Sandhoff, M.; Sandoval, C.; Sankey, D. P. C.; Sannino, M.; Sano, Y.; Sansoni, A.; Santoni, C.; Santos, H.; Castillo, I. Santoyo; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sato, K.; Sauvan, E.; Savage, G.; Savard, P.; Savic, N.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Scarcella, M.; Schaarschmidt, J.; Schacht, P.; Schachtner, B. M.; Schaefer, D.; Schaefer, L.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schier, S.; Schildgen, L. K.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schott, M.; Schouwenberg, J. F. P.; Schovancova, J.; Schramm, S.; Schuh, N.; Schulte, A.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwartzman, A.; Schwarz, T. A.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Sciandra, A.; Sciolla, G.; Scornajenghi, M.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Semprini-Cesari, N.; Senkin, S.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Shen, Y.; Sherafati, N.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shipsey, I. P. J.; Shirabe, S.; Shiyakova, M.; Shlomi, J.; Shmeleva, A.; Saadi, D. Shoaleh; Shochet, M. J.; Shojaii, S.; Shope, D. R.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sickles, A. M.; Sidebo, P. E.; Haddad, E. Sideras; Sidiropoulou, O.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Siral, I.; Sivoklokov, S. Yu.; Sjölin, J.; Skinner, M. B.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smiesko, J.; Smirnov, N.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, J. W.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snyder, I. M.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Søgaard, A.; Soh, D. A.; Sokhrannyi, G.; Sanchez, C. A. Solans; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Sopczak, A.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spieker, T. M.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; Denis, R. D. St.; Stabile, A.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanitzki, M. M.; Stapf, B. S.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Stark, S. H.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultan, DMS; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Suruliz, K.; Suster, C. J. E.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Swift, S. P.; Sykora, I.; Sykora, T.; Ta, D.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takasugi, E. H.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tanaka, J.; Tanaka, M.; Tanaka, R.; Tanaka, S.; Tanioka, R.; Tannenwald, B. B.; Araya, S. Tapia; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Delgado, A. Tavares; Tayalati, Y.; Taylor, A. C.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teixeira-Dias, P.; Temple, D.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Tibbetts, M. J.; Torres, R. E. Ticse; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorova-Nova, S.; Todt, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Tornambe, P.; Torrence, E.; Torres, H.; Pastor, E. Torró; Toth, J.; Touchard, F.; Tovey, D. R.; Treado, C. J.; Trefzger, T.; Tresoldi, F.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tsang, K. W.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tu, Y.; Tudorache, A.; Tudorache, V.; Tulbure, T. T.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turgeman, D.; Cakir, I. Turk; Turra, R.; Tuts, P. M.; Ucchielli, G.; Ueda, I.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usui, J.; Vacavant, L.; Vacek, V.; Vachon, B.; Vaidya, A.; Valderanis, C.; Santurio, E. Valdes; Valentinetti, S.; Valero, A.; Valéry, L.; Valkar, S.; Vallier, A.; Ferrer, J. A. Valls; Van Den Wollenberg, W.; van der Graaf, H.; van Gemmeren, P.; Van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varni, C.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vasquez, G. A.; Vazeille, F.; Schroeder, T. Vazquez; Veatch, J.; Veeraraghavan, V.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, A. T.; Vermeulen, J. C.; Vetterli, M. C.; Maira, N. Viaux; Viazlo, O.; Vichou, I.; Vickey, T.; Boeriu, O. E. Vickey; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Villa, M.; Perez, M. Villaplana; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vishwakarma, A.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vogel, M.; Vokac, P.; Volpi, G.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Milosavljevic, M. Vranjes; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Wagner, P.; Wagner, W.; Wagner-Kuhr, J.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, Q.; Wang, R.; Wang, S. M.; Wang, T.; Wang, W.; Wang, W.; Wang, Z.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, A. F.; Webb, S.; Weber, M. S.; Weber, S. W.; Weber, S. A.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weirich, M.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M. D.; Werner, P.; Wessels, M.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A. S.; White, A.; White, M. J.; White, R.; Whiteson, D.; Whitmore, B. W.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winkels, E.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wobisch, M.; Wolf, T. M. H.; Wolff, R.; Wolter, M. W.; Wolters, H.; Wong, V. W. S.; Worm, S. D.; Wosiek, B. K.; Wotschack, J.; Wozniak, K. W.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xi, Z.; Xia, L.; Xu, D.; Xu, L.; Xu, T.; Yabsley, B.; Yacoob, S.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamatani, M.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Wong, K. H. Yau; Ye, J.; Ye, S.; Yeletskikh, I.; Yigitbasi, E.; Yildirim, E.; Yorita, K.; Yoshihara, K.; Young, C.; Young, C. J. S.; Yu, J.; Yu, J.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zacharis, G.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanzi, D.; Zeitnitz, C.; Zemaityte, G.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, L.; Zhang, M.; Zhang, P.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Y.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, M.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Zou, R.; zur Nedden, M.; Zwalinski, L. 2017-09-01 A search for new phenomena in final states characterized by high jet multiplicity, an isolated lepton (electron or muon) and either zero or at least three b-tagged jets is presented. The search uses 36.1 fb-1 of √{s}=13 TeV proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider in 2015 and 2016. The dominant sources of background are estimated using parameterized extrapolations, based on observables at medium jet multiplicity, to predict the b-tagged jet multiplicity distribution at the higher jet multiplicities used in the search. No significant excess over the Standard Model expectation is observed and 95% confidence-level limits are extracted constraining four simplified models of R-parity-violating supersymmetry that feature either gluino or top-squark pair production. The exclusion limits reach as high as 2.1 TeV in gluino mass and 1.2 TeV in top-squark mass in the models considered. In addition, an upper limit is set on the cross-section for Standard Model t\\overline{t}t\\overline{t} production of 60 fb (6.5 × the Standard Model prediction) at 95% confidence level. Finally, model-independent limits are set on the contribution from new phenomena to the signal-region yields. [Figure not available: see fulltext. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20100024271','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20100024271">Optimization of Regression Models of Experimental Data Using Confirmation Points</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Ulbrich, N. 2010-01-01 A new search metric is discussed that may be used to better assess the predictive capability of different math term combinations during the optimization of a regression model of experimental data. The new search metric can be determined for each tested math term combination if the given experimental data set is split into two subsets. The first subset consists of data points that are only used to determine the coefficients of the regression model. The second subset consists of confirmation points that are exclusively used to test the regression model. The new search metric value is assigned after comparing two values that describe the quality of the fit of each subset. The first value is the standard deviation of the PRESS residuals of the data points. The second value is the standard deviation of the response residuals of the confirmation points. The greater of the two values is used as the new search metric value. This choice guarantees that both standard deviations are always less or equal to the value that is used during the optimization. Experimental data from the calibration of a wind tunnel strain-gage balance is used to illustrate the application of the new search metric. The new search metric ultimately generates an optimized regression model that was already tested at regression model independent confirmation points before it is ever used to predict an unknown response from a set of regressors. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1251172','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1251172">Muon (g-2) Technical Design Report</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Grange, J. The Muon (g-2) Experiment, E989 at Fermilab, will measure the muon anomalous magnetic moment a factor-of-four more precisely than was done in E821 at the Brookhaven National Laboratory AGS. The E821 result appears to be greater than the Standard-Model prediction by more than three standard deviations. When combined with expected improvement in the Standard-Model hadronic contributions, E989 should be able to determine definitively whether or not the E821 result is evidence for physics beyond the Standard Model. After a review of the physics motivation and the basic technique, which will use the muon storage ring built at BNL and nowmore » relocated to Fermilab, the design of the new experiment is presented. This document was created in partial fulfillment of the requirements necessary to obtain DOE CD-2/3 approval.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19850017881','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19850017881">Care 3 model overview and user's guide, first revision</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Bavuso, S. J.; Petersen, P. L. 1985-01-01 A manual was written to introduce the CARE III (Computer-Aided Reliability Estimation) capability to reliability and design engineers who are interested in predicting the reliability of highly reliable fault-tolerant systems. It was also structured to serve as a quick-look reference manual for more experienced users. The guide covers CARE III modeling and reliability predictions for execution in the CDC CYber 170 series computers, DEC VAX-11/700 series computer, and most machines that compile ANSI Standard FORTRAN 77. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25213962','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25213962">Predicting glycogen concentration in the foot muscle of abalone using near infrared reflectance spectroscopy (NIRS).</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Fluckiger, Miriam; Brown, Malcolm R; Ward, Louise R; Moltschaniwskyj, Natalie A 2011-06-15 Near infrared reflectance spectroscopy (NIRS) was used to predict glycogen concentrations in the foot muscle of cultured abalone. NIR spectra of live, shucked and freeze-dried abalones were modelled against chemically measured glycogen data (range: 0.77-40.9% of dry weight (DW)) using partial least squares (PLS) regression. The calibration models were then used to predict glycogen concentrations of test abalone samples and model robustness was assessed from coefficient of determination of the validation (R2(val)) and standard error of prediction (SEP) values. The model for freeze-dried abalone gave the best prediction (R2(val) 0.97, SEP=1.71), making it suitable for quantifying glycogen. Models for live and shucked abalones had R2(val) of 0.86 and 0.90, and SEP of 3.46 and 3.07 respectively, making them suitable for producing estimations of glycogen concentration. As glycogen is a taste-active component associated with palatability in abalone, this study demonstrated the potential of NIRS as a rapid method to monitor the factors associated with abalone quality. Copyright © 2011 Elsevier Ltd. All rights reserved. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1212532-search-heavy-standard-model-higgs-boson-channel-hzz+-qq-using-atlas-detector','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1212532-search-heavy-standard-model-higgs-boson-channel-hzz+-qq-using-atlas-detector">Search for a heavy Standard Model Higgs boson in the channel H → Z Z → ℓ + ℓ - q q ¯ using the ATLAS detector</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a> Aad, G.; Abbott, B.; Abdallah, J.; ... 2011-12-01 Here, a search for a heavy Standard Model Higgs boson decaying via H → ZZ → l +l –qq¯, where l = e,μ, is presented. The search is performed using a data set of pp collisions at √s = 7TeV, corresponding to an integrated luminosity of 1.04 fb –1 collected in 2011 by the ATLAS detector at the CERN LHC collider. No significant excess of events above the estimated background is found. Upper limits at 95% confidence level on the production cross section (relative to that expected from the Standard Model) of a Higgs boson with a mass in themore » range between 200 and 600GeV are derived. Within this mass range, there is at present insufficient sensitivity to exclude a Standard Model Higgs boson. For a Higgs boson with a mass of , where the sensitivity is maximal, the observed and expected cross section upper limits are factors of 1.7 and 2.7, respectively, larger than the Standard Model prediction.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1996NuPhB.472...79M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1996NuPhB.472...79M">Nucleon decay in non-minimal supersymmetric SO(10)</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Macpherson, Alick L. 1996-02-01 Evaluation of nucleon decay modes and branching ratios in a non-minimal supersymmetric SO(10) grand unified theory is presented. The non-minimal GUT considered is the supersymmetrised version of the 'realistic' SO(10) model originally proposed by Harvey, Reiss and Ramond, which is realistic in that it gives acceptable charged fermion and neutrino masses within the context of a phenomenological fit to the low-energy standard model inputs. Despite a complicated Higgs sector, the SO(10) 10 Higgs superfield mass insertion is found to be the sole contribution to the tree-level F-term governing nucleon decay. The resulting dimension-5 operators that mediate nucleon decay give branching ratio predictions parameterised by a single parameter, the ratio of the Yukawa couplings of the 10 to the fermion generations. For parameter values corresponding to a lack of dominance of the third family self-coupling, the dominant nucleon decay modes are p → K + + overlineνμand n → K 0 + overlineνμ as expected. Further, the charged muon decay modes are enhanced by two orders of magnitude over the standard minimal SUSY SU(5) predictions, thus predicting a distinct spectrum of 'visible' modes. These charged muon decay modes, along with p → π + + overlineνμand n → π 0 + overlineνμ, which are moderately enhanced over the SUSY SU(5) prediction, suggest a distinguishing fingerprint of this particular GUT model, and if nucleon decay is observed at Super-KAMIOKANDE the predicted branching ratio spectrum can be used to determine the validity of this 'realistic' SO(10) SUSY GUT model. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23276108','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23276108">Inequality across consonantal contrasts in speech perception: evidence from mismatch negativity.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Cornell, Sonia A; Lahiri, Aditi; Eulitz, Carsten 2013-06-01 The precise structure of speech sound representations is still a matter of debate. In the present neurobiological study, we compared predictions about differential sensitivity to speech contrasts between models that assume full specification of all phonological information in the mental lexicon with those assuming sparse representations (only contrastive or otherwise not predictable information is stored). In a passive oddball paradigm, we studied the contrast sensitivity as reflected in the mismatch negativity (MMN) response to changes in the manner of articulation, as well as place of articulation of consonants in intervocalic positions of nonwords (manner of articulation: [edi ~ eni], [ezi ~ eni]; place of articulation: [edi ~ egi]). Models that assume full specification of all phonological information in the mental lexicon posit equal MMNs within each contrast (symmetric MMNs), that is, changes from standard [edi] to deviant [eni] elicit a similar MMN response as changes from standard [eni] to deviant [edi]. In contrast, models that assume sparse representations predict that only the [ezi] ~ [eni] reversals will evoke symmetric MMNs because of their conflicting fully specified manner features. Asymmetric MMNs are predicted, however, for the reversals of [edi] ~ [eni] and [edi] ~ [egi] because either a manner or place property in each pair is not fully specified in the mental lexicon. Our results show a pattern of symmetric and asymmetric MMNs that is in line with predictions of the featurally underspecified lexicon model that assumes sparse phonological representations. We conclude that the brain refers to underspecified phonological representations during speech perception. (PsycINFO Database Record (c) 2013 APA, all rights reserved). </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17995724','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17995724">Prediction of frozen food properties during freezing using product composition.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Boonsupthip, W; Heldman, D R 2007-06-01 Frozen water fraction (FWF), as a function of temperature, is an important parameter for use in the design of food freezing processes. An FWF-prediction model, based on concentrations and molecular weights of specific product components, has been developed. Published food composition data were used to determine the identity and composition of key components. The model proposed in this investigation had been verified using published experimental FWF data and initial freezing temperature data, and by comparison to outputs from previously published models. It was found that specific food components with significant influence on freezing temperature depression of food products included low molecular weight water-soluble compounds with molality of 50 micromol per 100 g food or higher. Based on an analysis of 200 high-moisture food products, nearly 45% of the experimental initial freezing temperature data were within an absolute difference (AD) of +/- 0.15 degrees C and standard error (SE) of +/- 0.65 degrees C when compared to values predicted by the proposed model. The predicted relationship between temperature and FWF for all analyzed food products provided close agreements with experimental data (+/- 0.06 SE). The proposed model provided similar prediction capability for high- and intermediate-moisture food products. In addition, the proposed model provided statistically better prediction of initial freezing temperature and FWF than previous published models. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21459847','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21459847">The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Roche, Daniel B; Buenavista, Maria T; Tetchner, Stuart J; McGuffin, Liam J 2011-07-01 The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence. Our guiding principles behind the server development were as follows: (i) to provide a simple unified resource that makes our prediction software accessible to all and (ii) to produce integrated output for predictions that can be easily interpreted. The output for predictions is presented as a simple table that summarizes all results graphically via plots and annotated 3D models. The raw machine readable data files for each set of predictions are also provided for developers, which comply with the Critical Assessment of Methods for Protein Structure Prediction (CASP) data standards. The server comprises an integrated suite of five novel methods: nFOLD4, for tertiary structure prediction; ModFOLD 3.0, for model quality assessment; DISOclust 2.0, for disorder prediction; DomFOLD 2.0 for domain prediction; and FunFOLD 1.0, for ligand binding site prediction. Predictions from the IntFOLD server were found to be competitive in several categories in the recent CASP9 experiment. The IntFOLD server is available at the following web site: http://www.reading.ac.uk/bioinf/IntFOLD/. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19920059811&hterms=standard+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dstandard%2Bmodel','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19920059811&hterms=standard+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dstandard%2Bmodel">Standard solar model. II - g-modes</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Guenther, D. B.; Demarque, P.; Pinsonneault, M. H.; Kim, Y.-C. 1992-01-01 The paper presents the g-mode oscillation for a set of modern solar models. Each solar model is based on a single modification or improvement to the physics of a reference solar model. Improvements were made to the nuclear reaction rates, the equation of state, the opacities, and the treatment of the atmosphere. The error in the predicted g-mode periods associated with the uncertainties in the model physics is predicted and the specific sensitivities of the g-mode periods and their period spacings to the different model structures are described. In addition, these models are compared to a sample of published observations. A remarkably good agreement is found between the 'best' solar model and the observations of Hill and Gu (1990). </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160008687','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160008687">The EST Model for Predicting Progressive Damage and Failure of Open Hole Bending Specimens</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Joseph, Ashith P. K.; Waas, Anthony M.; Pineda, Evan J. 2016-01-01 Progressive damage and failure in open hole composite laminate coupons subjected to flexural loading is modeled using Enhanced Schapery Theory (EST). Previous studies have demonstrated that EST can accurately predict the strength of open hole coupons under remote tensile and compressive loading states. This homogenized modeling approach uses single composite shell elements to represent the entire laminate in the thickness direction and significantly reduces computational cost. Therefore, when delaminations are not of concern or are active in the post-peak regime, the version of EST presented here is a good engineering tool for predicting deformation response. Standard coupon level tests provides all the input data needed for the model and they are interpreted in conjunction with finite element (FE) based simulations. Open hole bending test results of three different IM7/8552 carbon fiber composite layups agree well with EST predictions. The model is able to accurately capture the curvature change and deformation localization in the specimen at and during the post catastrophic load drop event. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28441634','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28441634">Spatial enhancement of ECG using diagnostic similarity score based lead selective multi-scale linear model.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Nallikuzhy, Jiss J; Dandapat, S 2017-06-01 In this work, a new patient-specific approach to enhance the spatial resolution of ECG is proposed and evaluated. The proposed model transforms a three-lead ECG into a standard twelve-lead ECG thereby enhancing its spatial resolution. The three leads used for prediction are obtained from the standard twelve-lead ECG. The proposed model takes advantage of the improved inter-lead correlation in wavelet domain. Since the model is patient-specific, it also selects the optimal predictor leads for a given patient using a lead selection algorithm. The lead selection algorithm is based on a new diagnostic similarity score which computes the diagnostic closeness between the original and the spatially enhanced leads. Standard closeness measures are used to assess the performance of the model. The similarity in diagnostic information between the original and the spatially enhanced leads are evaluated using various diagnostic measures. Repeatability and diagnosability are performed to quantify the applicability of the model. A comparison of the proposed model is performed with existing models that transform a subset of standard twelve-lead ECG into the standard twelve-lead ECG. From the analysis of the results, it is evident that the proposed model preserves diagnostic information better compared to other models. Copyright © 2017 Elsevier Ltd. All rights reserved. </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active">24</li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> </div> <div id="page_25" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active">25</li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="481"> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5014078','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5014078">Comparing language outcomes in monolingual and bilingual stroke patients</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Parker Jones, ‘Ōiwi; Grogan, Alice; Crinion, Jenny; Rae, Johanna; Ruffle, Louise; Leff, Alex P.; Seghier, Mohamed L.; Price, Cathy J.; Green, David W. 2015-01-01 Post-stroke prognoses are usually inductive, generalizing trends learned from one group of patients, whose outcomes are known, to make predictions for new patients. Research into the recovery of language function is almost exclusively focused on monolingual stroke patients, but bilingualism is the norm in many parts of the world. If bilingual language recruits qualitatively different networks in the brain, prognostic models developed for monolinguals might not generalize well to bilingual stroke patients. Here, we sought to establish how applicable post-stroke prognostic models, trained with monolingual patient data, are to bilingual stroke patients who had been ordinarily resident in the UK for many years. We used an algorithm to extract binary lesion images for each stroke patient, and assessed their language with a standard tool. We used feature selection and cross-validation to find ‘good’ prognostic models for each of 22 different language skills, using monolingual data only (174 patients; 112 males and 62 females; age at stroke: mean = 53.0 years, standard deviation = 12.2 years, range = 17.2–80.1 years; time post-stroke: mean = 55.6 months, standard deviation = 62.6 months, range = 3.1–431.9 months), then made predictions for both monolinguals and bilinguals (33 patients; 18 males and 15 females; age at stroke: mean = 49.0 years, standard deviation = 13.2 years, range = 23.1–77.0 years; time post-stroke: mean = 49.2 months, standard deviation = 55.8 months, range = 3.9–219.9 months) separately, after training with monolingual data only. We measured group differences by comparing prediction error distributions, and used a Bayesian test to search for group differences in terms of lesion-deficit associations in the brain. Our models distinguish better outcomes from worse outcomes equally well within each group, but tended to be over-optimistic when predicting bilingual language outcomes: our bilingual patients tended to have poorer language skills than expected, based on trends learned from monolingual data alone, and this was significant (P < 0.05, corrected for multiple comparisons) in 13/22 language tasks. Both patient groups appeared to be sensitive to damage in the same sets of regions, though the bilinguals were more sensitive than the monolinguals. PMID:25688076 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4229232','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4229232">Are Subject-Specific Musculoskeletal Models Robust to the Uncertainties in Parameter Identification?</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Valente, Giordano; Pitto, Lorenzo; Testi, Debora; Seth, Ajay; Delp, Scott L.; Stagni, Rita; Viceconti, Marco; Taddei, Fulvia 2014-01-01 Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur. PMID:25390896 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20070022496','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20070022496">Twist Model Development and Results from the Active Aeroelastic Wing F/A-18 Aircraft</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Lizotte, Andrew M.; Allen, Michael J. 2007-01-01 Understanding the wing twist of the active aeroelastic wing (AAW) F/A-18 aircraft is a fundamental research objective for the program and offers numerous benefits. In order to clearly understand the wing flexibility characteristics, a model was created to predict real-time wing twist. A reliable twist model allows the prediction of twist for flight simulation, provides insight into aircraft performance uncertainties, and assists with computational fluid dynamic and aeroelastic issues. The left wing of the aircraft was heavily instrumented during the first phase of the active aeroelastic wing program allowing deflection data collection. Traditional data processing steps were taken to reduce flight data, and twist predictions were made using linear regression techniques. The model predictions determined a consistent linear relationship between the measured twist and aircraft parameters, such as surface positions and aircraft state variables. Error in the original model was reduced in some cases by using a dynamic pressure-based assumption. This technique produced excellent predictions for flight between the standard test points and accounted for nonlinearities in the data. This report discusses data processing techniques and twist prediction validation, and provides illustrative and quantitative results. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050111580','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050111580">Twist Model Development and Results From the Active Aeroelastic Wing F/A-18 Aircraft</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Lizotte, Andrew; Allen, Michael J. 2005-01-01 Understanding the wing twist of the active aeroelastic wing F/A-18 aircraft is a fundamental research objective for the program and offers numerous benefits. In order to clearly understand the wing flexibility characteristics, a model was created to predict real-time wing twist. A reliable twist model allows the prediction of twist for flight simulation, provides insight into aircraft performance uncertainties, and assists with computational fluid dynamic and aeroelastic issues. The left wing of the aircraft was heavily instrumented during the first phase of the active aeroelastic wing program allowing deflection data collection. Traditional data processing steps were taken to reduce flight data, and twist predictions were made using linear regression techniques. The model predictions determined a consistent linear relationship between the measured twist and aircraft parameters, such as surface positions and aircraft state variables. Error in the original model was reduced in some cases by using a dynamic pressure-based assumption and by using neural networks. These techniques produced excellent predictions for flight between the standard test points and accounted for nonlinearities in the data. This report discusses data processing techniques and twist prediction validation, and provides illustrative and quantitative results. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3735601','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3735601">Perceptual Distortions in Pitch and Time Reveal Active Prediction and Support for an Auditory Pitch-Motion Hypothesis</a> <a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a> Henry, Molly J.; McAuley, J. Devin 2013-01-01 A number of accounts of human auditory perception assume that listeners use prior stimulus context to generate predictions about future stimulation. Here, we tested an auditory pitch-motion hypothesis that was developed from this perspective. Listeners judged either the time change (i.e., duration) or pitch change of a comparison frequency glide relative to a standard (referent) glide. Under a constant-velocity assumption, listeners were hypothesized to use the pitch velocity (Δf/Δt) of the standard glide to generate predictions about the pitch velocity of the comparison glide, leading to perceptual distortions along the to-be-judged dimension when the velocities of the two glides differed. These predictions were borne out in the pattern of relative points of subjective equality by a significant three-way interaction between the velocities of the two glides and task. In general, listeners’ judgments along the task-relevant dimension (pitch or time) were affected by expectations generated by the constant-velocity standard, but in an opposite manner for the two stimulus dimensions. When the comparison glide velocity was faster than the standard, listeners overestimated time change, but underestimated pitch change, whereas when the comparison glide velocity was slower than the standard, listeners underestimated time change, but overestimated pitch change. Perceptual distortions were least evident when the velocities of the standard and comparison glides were matched. Fits of an imputed velocity model further revealed increasingly larger distortions at faster velocities. The present findings provide support for the auditory pitch-motion hypothesis and add to a larger body of work revealing a role for active prediction in human auditory perception. PMID:23936462 </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23936462','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23936462">Perceptual distortions in pitch and time reveal active prediction and support for an auditory pitch-motion hypothesis.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Henry, Molly J; McAuley, J Devin 2013-01-01 A number of accounts of human auditory perception assume that listeners use prior stimulus context to generate predictions about future stimulation. Here, we tested an auditory pitch-motion hypothesis that was developed from this perspective. Listeners judged either the time change (i.e., duration) or pitch change of a comparison frequency glide relative to a standard (referent) glide. Under a constant-velocity assumption, listeners were hypothesized to use the pitch velocity (Δf/Δt) of the standard glide to generate predictions about the pitch velocity of the comparison glide, leading to perceptual distortions along the to-be-judged dimension when the velocities of the two glides differed. These predictions were borne out in the pattern of relative points of subjective equality by a significant three-way interaction between the velocities of the two glides and task. In general, listeners' judgments along the task-relevant dimension (pitch or time) were affected by expectations generated by the constant-velocity standard, but in an opposite manner for the two stimulus dimensions. When the comparison glide velocity was faster than the standard, listeners overestimated time change, but underestimated pitch change, whereas when the comparison glide velocity was slower than the standard, listeners underestimated time change, but overestimated pitch change. Perceptual distortions were least evident when the velocities of the standard and comparison glides were matched. Fits of an imputed velocity model further revealed increasingly larger distortions at faster velocities. The present findings provide support for the auditory pitch-motion hypothesis and add to a larger body of work revealing a role for active prediction in human auditory perception. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/22351361-analytic-formula-supercluster-mass-function','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/22351361-analytic-formula-supercluster-mass-function">An analytic formula for the supercluster mass function</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Lim, Seunghwan; Lee, Jounghun, E-mail: slim@astro.umass.edu, E-mail: jounghun@astro.snu.ac.kr 2014-03-01 We present an analytic formula for the supercluster mass function, which is constructed by modifying the extended Zel'dovich model for the halo mass function. The formula has two characteristic parameters whose best-fit values are determined by fitting to the numerical results from N-body simulations for the standard ΛCDM cosmology. The parameters are found to be independent of redshifts and robust against variation of the key cosmological parameters. Under the assumption that the same formula for the supercluster mass function is valid for non-standard cosmological models, we show that the relative abundance of the rich superclusters should be a powerful indicatormore » of any deviation of the real universe from the prediction of the standard ΛCDM model.« less </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28903421','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28903421">Development and validation of a preoperative prediction model for colorectal cancer T-staging based on MDCT images and clinical information.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Sa, Sha; Li, Jing; Li, Xiaodong; Li, Yongrui; Liu, Xiaoming; Wang, Defeng; Zhang, Huimao; Fu, Yu 2017-08-15 This study aimed to establish and evaluate the efficacy of a prediction model for colorectal cancer T-staging. T-staging was positively correlated with the level of carcinoembryonic antigen (CEA), expression of carbohydrate antigen 19-9 (CA19-9), wall deformity, blurred outer edges, fat infiltration, infiltration into the surrounding tissue, tumor size and wall thickness. Age, location, enhancement rate and enhancement homogeneity were negatively correlated with T-staging. The predictive results of the model were consistent with the pathological gold standard, and the kappa value was 0.805. The total accuracy of staging improved from 51.04% to 86.98% with the proposed model. The clinical, imaging and pathological data of 611 patients with colorectal cancer (419 patients in the training group and 192 patients in the validation group) were collected. A spearman correlation analysis was used to validate the relationship among these factors and pathological T-staging. A prediction model was trained with the random forest algorithm. T staging of the patients in the validation group was predicted by both prediction model and traditional method. The consistency, accuracy, sensitivity, specificity and area under the curve (AUC) were used to compare the efficacy of the two methods. The newly established comprehensive model can improve the predictive efficiency of preoperative colorectal cancer T-staging. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1995AtmRe..36..207M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1995AtmRe..36..207M">A comparison of selected models for estimating cable icing</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> McComber, Pierre; Druez, Jacques; Laflamme, Jean In many cold climate countries, it is becoming increasingly important to monitor transmission line icing. Indeed, by knowing in advance of localized danger for icing overloads, electric utilities can take measures in time to prevent generalized failure of the power transmission network. Recently in Canada, a study was made to compare the estimation of a few icing models working from meteorological data in estimating ice loads for freezing rain events. The models tested were using only standard meteorological parameters, i.e. wind speed and direction, temperature and precipitation rate. This study has shown that standard meteorological parameters can only achieve very limited accuracy, especially for longer icing events. However, with the help of an additional instrument monitoring the icing rate intensity, a significant improvement in model prediction might be achieved. The icing rate meter (IRM) which counts icing and de-icing cycles per unit time on a standard probe can be used to estimate the icing intensity. A cable icing estimation is then made by taking into consideration the accretion size, temperature, wind speed and direction, and precipitation rate. In this paper, a comparison is made between the predictions of two previously tested models (one obtained and the other reconstructed from their description in the public literature) and of a model based on the icing rate meter readings. The models are tested against nineteen events recorded on an icing test line at Mt. Valin, Canada, during the winter season 1991-1992. These events are mostly rime resulting from in-cloud icing. However, freezing rain and wet snow events were also recorded. Results indicate that a significant improvement in the estimation is attained by using the icing rate meter data together with the other standard meteorological parameters. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25739207','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25739207">[On-site evaluation of raw milk qualities by portable Vis/NIR transmittance technique].</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Wang, Jia-Hua; Zhang, Xiao-Wei; Wang, Jun; Han, Dong-Hai 2014-10-01 To ensure the material safety of dairy products, visible (Vis)/near infrared (NIR) spectroscopy combined with che- mometrics methods was used to develop models for fat, protein, dry matter (DM) and lactose on-site evaluation. A total of 88 raw milk samples were collected from individual livestocks in different years. The spectral of raw milk were measured by a porta- ble Vis/NIR spectrometer with diffused transmittance accessory. To remove the scatter effect and baseline drift, the diffused transmittance spectra were preprocessed by 2nd order derivative with Savitsky-Golay (polynomial order 2, data point 25). Changeable size moving window partial least squares (CSMWPLS) and genetic algorithms partial least squares (GAPLS) meth- ods were suggested to select informative regions for PLS calibration. The PLS and multiple linear regression (MLR) methods were used to develop models for predicting quality index of raw milk. The prediction performance of CSMWPLS models were similar to GAPLS models for fat, protein, DM and lactose evaluation, the root mean standard errors of prediction (RMSEP) were 0.115 6/0.103 3, 0.096 2/0.113 7, 0.201 3/0.123 7 and 0.077 4/0.066 8, and the relative standard deviations of prediction (RPD) were 8.99/10.06, 3.53/2.99, 5.76/9.38 and 1.81/2.10, respectively. Meanwhile, the MLR models were also cal- ibrated with 8, 10, 9 and 7 variables for fat, protein, DM and lactose, respectively. The prediction performance of MLR models was better than or close to PLS models. The MLR models to predict fat, protein, DM and lactose yielded the RMSEP of 0.107 0, 0.093 0, 0.136 0 and 0.065 8, and the RPD of 9.72, 3.66, 8.53 and 2.13, respectively. The results demonstrated the usefulness of Vis/NIR spectra combined with multivariate calibration methods as an objective and rapid method for the quality evaluation of complicated raw milks. And the results obtained also highlight the potential of portable Vis/NIR instruments for on-site assessing quality indexes of raw milk. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26319721','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26319721">Modeling the Effect of Storage Temperatures on the Growth of Listeria monocytogenes on Ready-to-Eat Ham and Sausage.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Luo, Ke; Hong, Sung-Sam; Oh, Deog-Hwan 2015-09-01 The aim of this study was to model the growth kinetics of Listeria monocytogenes on ready-to-eat ham and sausage at different temperatures (4 to 35°C). The observed data fitted well with four primary models (Baranyi, modified Gompertz, logistic, and Huang) with high coefficients of determination (R(2) > 0.98) at all measured temperatures. After the mean square error (0.009 to 0.051), bias factors (0.99 to1.06), and accuracy factors (1.01 to 1.09) were obtained in all models, the square root and the natural logarithm model were employed to describe the relation between temperature and specific growth rate (SGR) and lag time (LT) derived from the primary models. These models were validated against the independent data observed from additional experiments using the acceptable prediction zone method and the proportion of the standard error of prediction. All secondary models based on each of the four primary models were acceptable to describe the growth of the pathogen in the two samples. The validation results indicate that the optimal primary model for estimating the SGR was the Baranyi model, and the optimal primary model for estimating LT was the logistic model in ready-to-eat (RTE) ham. The Baranyi model was also the optimal model to estimate the SGR and LT in RTE sausage. These results could be used to standardize predictive models, which are commonly used to identify critical control points in hazard analysis and critical control point systems or for the quantitative microbial risk assessment to improve the food safety of RTE meat products. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26751103','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26751103">Testing the Bivalent Fear of Evaluation Model of Social Anxiety: The Relationship between Fear of Positive Evaluation, Social Anxiety, and Perfectionism.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Yap, Keong; Gibbs, Amy L; Francis, Andrew J P; Schuster, Sharynn E 2016-01-01 The Bivalent Fear of Evaluation (BFOE) model of social anxiety proposes that fear of negative evaluation (FNE) and fear of positive evaluation (FPE) play distinct roles in social anxiety. Research is however lacking in terms of how FPE is related to perfectionism and how these constructs interact to predict social anxiety. Participants were 382 individuals from the general community and included an oversampling of individuals with social anxiety. Measures of FPE, FNE, perfectionism, and social anxiety were administered. Results were mostly consistent with the predictions made by the BFOE model and showed that accounting for confounding variables, FPE correlated negatively with high standards but positively with maladaptive perfectionism. FNE was also positively correlated with maladaptive perfectionism, but there was no significant relationship between FNE and high standards. Also consistent with BFOE model, both FNE and FPE significantly moderated the relationship between maladaptive perfectionism and social anxiety with the relationship strengthened at high levels of FPE and FNE. These findings provide additional support for the BFOE model and implications are discussed. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JPhCS.880a2053C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPhCS.880a2053C">The Standard Model Algebra - a summary</a> <a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a> Cristinel Stoica, Ovidiu 2017-08-01 A generation of leptons and quarks and the gauge symmetries of the Standard Model can be obtained from the Clifford algebra ℂℓ 6. An instance of ℂℓ 6 is implicitly generated by the Dirac algebra combined with the electroweak symmetry, while the color symmetry gives another instance of ℂℓ 6 with a Witt decomposition. The minimal mathematical model proposed here results by identifying the two instances of ℂℓ 6. The left ideal decomposition generated by the Witt decomposition represents the leptons and quarks, and their antiparticles. The SU(3)c and U(1)em symmetries of the SM are the symmetries of this ideal decomposition. The patterns of electric charges, colors, chirality, weak isospins, and hypercharges, follow from this, without predicting additional particles or forces, or proton decay. The electroweak symmetry is present in its broken form, due to the geometry. The predicted Weinberg angle is given by sin2 W = 0.25. The model shares common features with previously known models, particularly with Chisholm and Farwell, 1996, Trayling and Baylis, 2004, and Furey, 2016. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/16209196','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/16209196">[Prediction of 137Cs accumulation in animal products in the territory of Semipalatinsk test site].</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Spiridonov, S I; Gontarenko, I A; Mukusheva, M K; Fesenko, S V; Semioshkina, N A 2005-01-01 The paper describes mathematical models for 137Cs behavior in the organism of horses and sheep pasturing on the bording area to the testing area "Ground Zero" of the Semipalatinsk Test Site. The models are parameterized on the base of the data from an experiment with the breeds of animals now commonly encountered within the Semipalatinsk Test Site. The predictive calculations with the models devised have shown that 137Cs concentrations in milk of horses and sheep pasturingon the testing area to "Ground Zero" can exceed the adopted standards during a long period of time. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/21308262-tachyon-cosmology-supernovae-data-big-brake-singularity','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/21308262-tachyon-cosmology-supernovae-data-big-brake-singularity">Tachyon cosmology, supernovae data, and the big brake singularity</a> <a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a> Keresztes, Z.; Gergely, L. A.; Gorini, V. 2009-04-15 We compare the existing observational data on type Ia supernovae with the evolutions of the Universe predicted by a one-parameter family of tachyon models which we have introduced recently [Phys. Rev. D 69, 123512 (2004)]. Among the set of the trajectories of the model which are compatible with the data there is a consistent subset for which the Universe ends up in a new type of soft cosmological singularity dubbed big brake. This opens up yet another scenario for the future history of the Universe besides the one predicted by the standard {lambda}CDM model. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040182282','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040182282">A Hybrid Approach To Tandem Cylinder Noise</a> <a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a> Lockard, David P. 2004-01-01 Aeolian tone generation from tandem cylinders is predicted using a hybrid approach. A standard computational fluid dynamics (CFD) code is used to compute the unsteady flow around the cylinders, and the acoustics are calculated using the acoustic analogy. The CFD code is nominally second order in space and time and includes several turbulence models, but the SST k - omega model is used for most of the calculations. Significant variation is observed between laminar and turbulent cases, and with changes in the turbulence model. A two-dimensional implementation of the Ffowcs Williams-Hawkings (FW-H) equation is used to predict the far-field noise. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/9561437','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/9561437">Secular changes in standards of bodily attractiveness in women: tests of a reproductive model.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Barber, N 1998-05-01 Since success at work is favored by a more slender body build while reproduction is favored by curvaceousness, standards of women's bodily attractiveness should be predictable from economic and reproductive variables. This hypothesis was tested in a replication and extension of a study by Silverstein, Perdue, Peterson, Vogel, and Fantini (1986) which looked at correlates of curvaceousness of Vogue models over time. As economic prosperity increased, and as women's participation in the economy, and higher education, increased, curvaceousness of the standards declined. As the proportion of single women to men, both aged 20-24 years, increased, and as the birth rate declined, curvaceousness was reduced. Results suggest that cultural standards of attractiveness are influenced by an evolved psychology of mate selection. </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=DURAND&id=EJ770879','ERIC'); return false;" href="https://eric.ed.gov/?q=DURAND&id=EJ770879">Resources and Commitment as Critical Factors in the Development of "Gifted" Athletes</a> <a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a> Baker, Joseph; Cote, Jean 2003-01-01 Several sport-specific talent detection models have been developed over the last 30 years (Durand-Bush & Salmela, 2001). However, these models have failed in at least one important standard of judgment--accurately predicting who will develop into an elite level athlete. The authors believe that the WICS model presented by Robert Sternberg also… </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.fs.usda.gov/treesearch/pubs/31914','TREESEARCH'); return false;" href="https://www.fs.usda.gov/treesearch/pubs/31914">Using HFire for spatial modeling of fire in shrublands</a> <a target="_blank" rel="noopener noreferrer" href="http://www.fs.usda.gov/treesearch/">Treesearch</a> Seth H. Peterson; Marco E. Morais; Jean M. Carlson; Philip E. Dennison; Dar A. Roberts; Max A. Moritz; David R. Weise 2009-01-01 An efficient raster fire-spread model named HFire is introduced. HFire can simulate single-fire events or long-term fire regimes, using the same fire-spread algorithm. This paper describes the HFire algorithm, benchmarks the model using a standard set of tests developed for FARSITE, and compares historical and predicted fire spread perimeters for three southern... </li> <li> <a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28506672','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28506672">Comparison of the Nosocomial Pneumonia Mortality Prediction (NPMP) model with standard mortality prediction tools.</a> <a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a> Srinivasan, M; Shetty, N; Gadekari, S; Thunga, G; Rao, K; Kunhikatta, V 2017-07-01 Severity or mortality prediction of nosocomial pneumonia could aid in the effective triage of patients and assisting physicians. To compare various severity assessment scoring systems for predicting intensive care unit (ICU) mortality in nosocomial pneumonia patients. A prospective cohort study was conducted in a tertiary care university-affiliated hospital in Manipal, India. One hundred patients with nosocomial pneumonia, admitted in the ICUs who developed pneumonia after >48h of admission, were included. The Nosocomial Pneumonia Mortality Prediction (NPMP) model, developed in our hospital, was compared with Acute Physiology and Chronic Health Evaluation II (APACHE II), Mortality Probability Model II (MPM 72 II), Simplified Acute Physiology Score II (SAPS II), Multiple Organ Dysfunction Score (MODS), Sequential Organ Failure Assessment (SOFA), Clinical Pulmonary Infection Score (CPIS), Ventilator-Associated Pneumonia Predisposition, Insult, Response, Organ dysfunction (VAP-PIRO). Data and clinical variables were collected on the day of pneumonia diagnosis. The outcome for the study was ICU mortality. The sensitivity and specificity of the various scoring systems was analysed by plotting receiver operating characteristic (ROC) curves and computing the area under the curve for each of the mortality predicting tools. NPMP, APACHE II, SAPS II, MPM 72 II, SOFA, and VAP-PIRO were found to have similar and acceptable discrimination power as assessed by the area under the ROC curve. The AUC values for the above scores ranged from 0.735 to 0.762. CPIS and MODS showed least discrimination. NPMP is a specific tool to predict mortality in nosocomial pneumonia and is comparable to other standard scores. Copyright © 2017 The Healthcare Infection Society. Published by Elsevier Ltd. 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