Using MOST to reveal the secrets of the mischievous Wolf-Rayet binary CV Ser

NASA Astrophysics Data System (ADS)

David-Uraz, Alexandre; Moffat, Anthony F. J.; Chené, André-Nicolas; Rowe, Jason F.; Lange, Nicholas; Guenther, David B.; Kuschnig, Rainer; Matthews, Jaymie M.; Rucinski, Slavek M.; Sasselov, Dimitar; Weiss, Werner W.

2012-11-01

The Wolf-Rayet (WR) binary CV Serpentis (= WR113, WC8d + O8-9IV) has been a source of mystery since it was shown that its atmospheric eclipses change with time over decades, in addition to its sporadic dust production. The first high-precision time-dependent photometric observations obtained with the Microvariability and Oscillations of STars (MOST) space telescope in 2009 show two consecutive eclipses over the 29-d orbit, with varying depths. A subsequent MOST run in 2010 showed a seemingly asymmetric eclipse profile. In order to help make sense of these observations, parallel optical spectroscopy was obtained from the Mont Megantic Observatory (2009, 2010) and from the Dominion Astrophysical Observatory (2009). Assuming these depth variations are entirely due to electron scattering in a β-law wind, an unprecedented 62 per cent increase in M⊙ is observed over one orbital period. Alternatively, no change in mass-loss rate would be required if a relatively small fraction of the carbon ions in the wind globally recombined and coaggulated to form carbon dust grains. However, it remains a mystery as to how this could occur. There also seems to be evidence for the presence of corotating interaction regions (CIR) in the WR wind: a CIR-like signature is found in the light curves, implying a potential rotation period for the WR star of 1.6 d. Finally, a new circular orbit is derived, along with constraints for the wind collision.

Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound.

PubMed

Virmani, Jitendra; Kumar, Vinod; Kalra, Naveen; Khandelwal, Niranjan

2014-08-01

A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.

Nickel speciation in several serpentine (ultramafic) topsoils via bulk synchrotron-based techniques

USDA-ARS?s Scientific Manuscript database

Serpentine soils are extensively studied because of their unique soil chemical properties and flora. They commonly have high magnesium-to-calcium ratios and elevated concentrations of trace metals including nickel, cobalt, and chromium. Several nickel hyperaccumulator plants are native to serpenti...

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns.

PubMed

Haghnegahdar, A A; Kolahi, S; Khojastepour, L; Tajeripour, F

2018-03-01

Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages.

Centrality dependence of charged particle multiplicity at midrapidity in Au+Au collisions at (sNN)=130 GeV

NASA Astrophysics Data System (ADS)

Back, B. B.; Baker, M. D.; Barton, D. S.; Betts, R. R.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Decowski, M. P.; Garcia, E.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Heintzelman, G. A.; Henderson, C.; Hołyński, R.; Hofman, D. J.; Holzman, B.; Johnson, E.; Kane, J. L.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Lin, W. T.; Manly, S.; McLeod, D.; Michałowski, J.; Mignerey, A. C.; Mülmenstädt, J.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sarin, P.; Sawicki, P.; Skulski, W.; Steadman, S. G.; Stephans, G. S.; Steinberg, P.; Stodulski, M.; Sukhanov, A.; Tang, J.-L.; Teng, R.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wadsworth, B.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2002-03-01

We present a measurement of the pseudorapidity density of primary charged particles near midrapidity in Au+Au collisions at (sNN)=130 GeV as a function of the number of participating nucleons. The pseudorapidity density, dNch/dη\\|\\|η\\|<1/(1/2), rises from 2.87+/-0.21 in peripheral events (~83) to 3.45+/-0.18 in central events (~353), which is 53+/-8% higher than pp&; collisions at a similar center-of-mass energy. This is consistent with an additional contribution to charged particle production that scales with the number of binary nucleon-nucleon collisions (Ncoll).

The NN-explore Exoplanet Stellar Speckle Imager: Instrument Description and Preliminary Results

NASA Astrophysics Data System (ADS)

Scott, Nicholas J.; Howell, Steve B.; Horch, Elliott P.; Everett, Mark E.

2018-05-01

A new speckle and wide-field imaging instrument for the WIYN telescope called NN-EXPLORE Exoplanet Stellar Speckle Imager (NESSI) is described. NESSI offers simultaneous two-color diffraction-limited imaging and wide-field traditional imaging for validation and characterization of transit and precision RV exoplanet studies. Many exoplanet targets will come from the NASA K2 and Transiting Exoplanet Survey Satellite (TESS) missions. NESSI is capable of resolving close binaries at sub-arcsecond separations down to the diffraction limit and >6 mag contrast difference in the visible band on targets as faint as 14th mag. Preliminary results from the instrument commissioning at WIYN and demonstrations of the instrument’s capabilities are presented.

Centrality dependence of the charged particle multiplicity near midrapidity in Au+Au collisions at (sNN)=130 and 200 GeV

NASA Astrophysics Data System (ADS)

Back, B. B.; Ballintijn, M.; Baker, M. D.; Barton, D. S.; Betts, R. R.; Bickley, A.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Corbo, J.; Decowski, M. P.; Garcia, E.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Heintzelman, G.; Henderson, C.; Hicks, D.; Hofman, D.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; McLeod, D.; Michałowski, J.; Mignerey, A.; Mülmenstädt, J.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Rafelski, M.; Rbeiz, M.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sagerer, J.; Sarin, P.; Sawicki, P.; Skulski, W.; Steadman, S. G.; Steinberg, P.; Stephans, G. S.; Stodulski, M.; Sukhanov, A.; Tang, J.-L.; Teng, R.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wadsworth, B.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2002-06-01

The PHOBOS experiment has measured the charged particle multiplicity at midrapidity in Au+Au collisions at (sNN)=200 GeV as a function of the collision centrality. Results on dNch/dη\\|\\|η\\|<1 divided by the number of participating nucleon pairs /2 are presented as a function of . As was found from similar data at (sNN)=130 GeV, the data can be equally well described by parton saturation models and two-component fits, which include contributions that scale as Npart and the number of binary collisions Ncoll. We compare the data at the two energies by means of the ratio R200/130 of the charged particle multiplicity for the two different energies as a function of . For events with >100, we find that this ratio is consistent with a constant value of 1.14+/-0.01(stat)+/-0.05(syst).

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns

PubMed Central

Haghnegahdar, A.A.; Kolahi, S.; Khojastepour, L.; Tajeripour, F.

2018-01-01

Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. Results: K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. Conclusion: We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages. PMID:29732343

Centrality dependence of charged jet production in p-Pb collisions at √{s_NN} = 5.02 TeV

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, S.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Dénes, E.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, A. S.; Gonzalez, V.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Haake, R.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, H.; Kim, J. S.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kostarakis, P.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lapidus, K.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lehner, S.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; León Vargas, H.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martin Blanco, J.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Montes, E.; Moreira De Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paić, G.; Pal, S. K.; Pan, J.; Pandey, A. K.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Pereira Da Costa, H.; Peresunko, D.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ruzza, B. D.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schmidt, M.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shahzad, M. I.; Shangaraev, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Souza, R. D. de; Sozzi, F.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Szabo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Tikhonov, A.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Valencia Palomo, L.; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vázquez Doce, O.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; Haller, B. von; Vorobyev, I.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yang, P.; Yano, S.; Yasin, Z.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.

2016-05-01

Measurements of charged jet production as a function of centrality are presented for p-Pb collisions recorded at √{s_{NN}}= 5.02 TeV with the ALICE detector. Centrality classes are determined via the energy deposit in neutron calorimeters at zero degree, close to the beam direction, to minimise dynamical biases of the selection. The corresponding number of participants or binary nucleon-nucleon collisions is determined based on the particle production in the Pb-going rapidity region. Jets have been reconstructed in the central rapidity region from charged particles with the anti-k_{T} algorithm for resolution parameters R = 0.2 and R = 0.4 in the transverse momentum range 20 to 120 GeV/ c. The reconstructed jet momentum and yields have been corrected for detector effects and underlying-event background. In the five centrality bins considered, the charged jet production in p-Pb collisions is consistent with the production expected from binary scaling from pp collisions. The ratio of jet yields reconstructed with the two different resolution parameters is also independent of the centrality selection, demonstrating the absence of major modifications of the radial jet structure in the reported centrality classes.

Centrality and pseudorapidity dependence of charged hadron production at intermediate p{sub T} in Au+Au collisions at {radical}s{sub NN} = 130 GeV

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

Adams, J.; Aggarwal, M.M.; Ahammed, Z.

2004-04-15

We present STAR measurements of charged hadron production as a function of centrality in Au + Au collisions at {radical}s{sub NN} = 130 GeV. The measurements cover a phase space region of 0.2 < p{sub T} < 6.0 GeV/c in transverse momentum and -1 < {eta} < 1 in pseudorapidity. Inclusive transverse momentum distributions of charged hadrons in the pseudorapidity region 0.5 < |{eta}| < 1 are reported and compared to our previously published results for |{eta}| < 0.5. No significant difference is seen for inclusive p{sub T} distributions of charged hadrons in these two pseudorapidity bins. We measured dN/d{eta}more » distributions and truncated mean p{sub T} in a region of p{sub T} > p{sub T}{sup cut}, and studied the results in the framework of participant and binary scaling. No clear evidence is observed for participant scaling of charged hadron yield in the measured p{sub T} region. The relative importance of hard scattering process is investigated through binary scaling fraction of particle production.« less

Centrality and pseudorapidity dependence of charged hadron production at intermediate p{sub t} in Au+Au collisions at {radical}s{sub NN} = 130 GeV

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

Adams, J.; Aggarwal, M.M.; Ahammed, Z.

2004-04-15

We present STAR measurements of charged hadron production as a function of centrality in Au + Au collisions at {radical}s{sub NN} = 130 GeV. The measurements cover a phase space region of 0.2 < p{sub T} < 6.0 GeV/c in transverse momentum and 11 < {eta} < 1 in pseudorapidity. Inclusive transverse momentum distributions of charged hadrons in the pseudorapidity region 0.5 < |{eta}| < 1 are reported and compared to our previously published results for |{eta}| < 0.5. No significant difference is seen for inclusive p{sub T} distributions of charged hadrons in these two pseudorapidity bins. We measured dN/d{eta}more » distributions and truncated mean p{sub T} in a region of p{sub T} > P{sub T}{sup cut}, and studied the results in the framework of participant and binary scaling. No clear evidence is observed for participant scaling of charged hadron yield in the measured pT region. The relative importance of hard scattering process is investigated through binary scaling fraction of particle production.« less

Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory.

PubMed

Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R; Malley, James D; Ziegler, Andreas

2014-07-01

Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Heavy-quark production and elliptic flow in Au+Au collisions at √s NN=62.4 GeV

DOE PAGES

Adare, A.

2015-04-28

In this study, we present measurements of electrons and positrons from the semileptonic decays of heavy-flavor hadrons at midrapidity (|y|< 0.35) in Au+Au collisions at √s NN = 62.4 GeV. The data were collected in 2010 by the PHENIX experiment that included the new hadron-blind detector. The invariant yield of electrons from heavy-flavor decays is measured as a function of transverse momentum in the range 1 < p e T < 5 GeV/c. The invariant yield per binary collision is slightly enhanced above the p+p reference in Au+Au 0%–20%, 20%–40%, and 40%–60% centralities at a comparable level. At this lowmore » beam energy this may be a result of the interplay between initial-state Cronin effects, final-state flow, and energy loss in medium. The v₂ of electrons from heavy-flavor decays is nonzero when averaged between 1.3 < p e T < 2.5 GeV/c for 0%–40% centrality collisions at √s NN = 62.4 GeV. For 20%–40% centrality collisions, the v₂ at √s NN = 62.4 GeV is smaller than that for heavy-flavor decays at √s NN = 200 GeV. The v₂ of the electrons from heavy-flavor decay at the lower beam energy is also smaller than v₂ for pions. Both results indicate that the heavy-quarks interact with the medium formed in these collisions, but they may not be at the same level of thermalization with the medium as observed at √s NN = 200 GeV.« less

Improved Monte Carlo Glauber predictions at present and future nuclear colliders

NASA Astrophysics Data System (ADS)

Loizides, Constantin; Kamin, Jason; d'Enterria, David

2018-05-01

We present the results of an improved Monte Carlo Glauber (MCG) model of relevance for collisions involving nuclei at center-of-mass energies of the BNL Relativistic Heavy Ion Collider (√{sNN}=0.2 TeV), CERN Large Hadron Collider (LHC) (√{sNN}=2.76 -8.8 TeV ), and proposed future hadron colliders (√{sNN}≈10 -63 TeV). The inelastic p p cross sections as a function of √{sNN} are obtained from a precise data-driven parametrization that exploits the many available measurements at LHC collision energies. We describe the nuclear density of a lead nucleus with two separated two-parameter Fermi distributions for protons and neutrons to account for their different densities close to the nuclear periphery. Furthermore, we model the nucleon degrees of freedom inside the nucleus through a lattice with a minimum nodal separation, combined with a "recentering and reweighting" procedure, that overcomes some limitations of previous MCG approaches. The nuclear overlap function, number of participant nucleons and binary nucleon-nucleon collisions, participant eccentricity and triangularity, overlap area, and average path length are presented in intervals of percentile centrality for lead-lead (PbPb) and proton-lead (p Pb ) collisions at all collision energies. We demonstrate for collisions at √{sNN}=5.02 TeV that the central values of the Glauber quantities change by up to 7% in a few bins of reaction centrality, due to the improvements implemented, though typically they remain within the previously assigned systematic uncertainties, while their new associated uncertainties are generally smaller (mostly below 5%) at all centralities than for earlier calculations. Tables for all quantities versus centrality at present and foreseen collision energies involving Pb nuclei, as well as for collisions of XeXe at √{sNN}=5.44 TeV , and AuAu and CuCu at √{sNN}=0.2 TeV , are provided. The source code for the improved Monte Carlo Glauber model is made publicly available.

PG 1316+678: A young pre-cataclysmic binary with weak reflection effects

NASA Astrophysics Data System (ADS)

Shimansky, V. V.; Borisov, N. V.; Bikmaev, I. F.; Sakhibullin, N. A.; Shimanskaya, N. N.; Spiridonova, O. I.; Irtuganov, E. N.

2013-03-01

The PG 1316+678 star is classified as a pre-cataclysmic binary, as is evidenced by its photometric and spectroscopic observations. Its orbital period is determined to be P orb = 3.3803d, which coincides with the photometric period. The intensities of the emission HI and HeI lines are shown to vary synchronously with the brightness of the object (Δ m V = 0.065 m , Δ m R = 0.08 m ). These variations arise as the UV radiation from the DAO white dwarf is reflected from the surface of the cold companion. The parameters of the binary are estimated and the time of its evolution after the common-envelope phase is determined to be t ≈ 240 000 years. Thus, PG 1316+678 is a young pre-cataclysmic NN Ser variable with the smallest known photometric reflection effect.

Measurement of Z boson production in Pb-Pb collisions at sqrt[s(NN)]=2.76  TeV with the ATLAS detector.

PubMed

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2013-01-11

The ATLAS experiment has observed 1995 Z boson candidates in data corresponding to 0.15  nb(-1) of integrated luminosity obtained in the 2011 LHC Pb+Pb run at sqrt[s(NN)]=2.76  TeV. The Z bosons are reconstructed via dielectron and dimuon decay channels, with a background contamination of less than 3%. Results from the two channels are consistent and are combined. Within the statistical and systematic uncertainties, the per-event Z boson yield is proportional to the number of binary collisions estimated by the Glauber model. The elliptic anisotropy of the azimuthal distribution of the Z boson with respect to the event plane is found to be consistent with zero.

Forward neutral pion production in p + p and d + Au collisions at square root sNN=200 GeV.

PubMed

Adams, J; Aggarwal, M M; Ahammed, Z; Amonett, J; Anderson, B D; Arkhipkin, D; Averichev, G S; Badyal, S K; Bai, Y; Balewski, J; Barannikova, O; Barnby, L S; Baudot, J; Bekele, S; Belaga, V V; Bellingeri-Laurikainen, A; Bellwied, R; Berger, J; Bezverkhny, B I; Bharadwaj, S; Bhasin, A; Bhati, A K; Bhatia, V S; Bichsel, H; Bielcik, J; Bielcikova, J; Billmeier, A; Bland, L C; Blyth, C O; Blyth, S-L; Bonner, B E; Botje, M; Boucham, A; Bouchet, J; Brandin, A V; Bravar, A; Bystersky, M; Cadman, R V; Cai, X Z; Caines, H; Sánchez, M Calderón de la Barca; Catu, O; Cebra, D; Chajecki, Z; Chaloupka, P; Chattopadhyay, S; Chen, H F; Chen, J H; Chen, Y; Cheng, J; Cherney, M; Chikanian, A; Choi, H A; Christie, W; Coffin, J P; Cormier, T M; Cosentino, M R; Cramer, J G; Crawford, H J; Das, D; Das, S; Daugherity, M; de Moura, M M; Dedovich, T G; Dephillips, M; Derevschikov, A A; Didenko, L; Dietel, T; Dogra, S M; Dong, W J; Dong, X; Draper, J E; Du, F; Dubey, A K; Dunin, V B; Dunlop, J C; Mazumdar, M R Dutta; Eckardt, V; Edwards, W R; Efimov, L G; Emelianov, V; Engelage, J; Eppley, G; Erazmus, B; Estienne, M; Fachini, P; Faivre, J; Fatemi, R; Fedorisin, J; Filimonov, K; Filip, P; Finch, E; Fine, V; Fisyak, Y; Fornazier, K S F; Fox, B D; Fu, J; Gagliardi, C A; Gaillard, L; Gans, J; Ganti, M S; Geurts, F; Ghazikhanian, V; Ghosh, P; Gonzalez, J E; Gorbunov, Y G; Gos, H; Grachov, O; Grebenyuk, O; Grosnick, D; Guertin, S M; Guo, Y; Gupta, A; Gupta, N; Gutierrez, T D; Hallman, T J; Hamed, A; Harris, J W; Heinz, M; Henry, T W; Hepplemann, S; Hippolyte, B; Hirsch, A; Hjort, E; Hoffmann, G W; Horner, M J; Huang, H Z; Huang, S L; Hughes, E W; Humanic, T J; Igo, G; Ishihara, A; Jacobs, P; Jacobs, W W; Jiang, H; Jones, P G; Judd, E G; Kabana, S; Kang, K; Kaplan, M; Keane, D; Kechechyan, A; Khodyrev, V Yu; Kim, B C; Kiryluk, J; Kisiel, A; Kislov, E M; Klein, S R; Koetke, D D; Kollegger, T; Kopytine, M; Kotchenda, L; Kowalik, K L; Kramer, M; Kravtsov, P; Kravtsov, V I; Krueger, K; Kuhn, C; Kulikov, A I; Kumar, A; Kutuev, R Kh; Kuznetsov, A A; Lamb, R; Lamont, M A C; Landgraf, J M; Lange, S; Laue, F; Lauret, J; Lebedev, A; Lednicky, R; Lee, C-H; Lehocka, S; Levine, M J; Li, C; Li, Q; Li, Y; Lin, G; Lindenbaum, S J; Lisa, M A; Liu, F; Liu, H; Liu, J; Liu, L; Liu, Q J; Liu, Z; Ljubicic, T; Llope, W J; Long, H; Longacre, R S; Lopez-Noriega, M; Love, W A; Lu, Y; Ludlam, T; Lynn, D; Ma, G L; Ma, J G; Ma, Y G; Magestro, D; Mahajan, S; Mahapatra, D P; Majka, R; Mangotra, L K; Manweiler, R; Margetis, S; Markert, C; Martin, L; Marx, J N; Matis, H S; Matulenko, Yu A; McClain, C J; McShane, T S; Melnick, Yu; Meschanin, A; Miller, M L; Minaev, N G; Mironov, C; Mischke, A; Mishra, D K; Mitchell, J; Mioduszewski, S; Mohanty, B; Molnar, L; Moore, C F; Morozov, D A; Munhoz, M G; Nandi, B K; Nayak, S K; Nayak, T K; Nelson, J M; Netrakanti, P K; Nikitin, V A; Nogach, L V; Nurushev, S B; Odyniec, G; Ogawa, A; Okorokov, V; Oldenburg, M; Olson, D; Pal, S K; Panebratsev, Y; Panitkin, S Y; Pavlinov, A I; Pawlak, T; Peitzmann, T; Perevoztchikov, V; Perkins, C; Peryt, W; Petrov, V A; Phatak, S C; Picha, R; Planinic, M; Pluta, J; Porile, N; Porter, J; Poskanzer, A M; Potekhin, M; Potrebenikova, E; Potukuchi, B V K S; Prindle, D; Pruneau, C; Putschke, J; Rakness, G; Raniwala, R; Raniwala, S; Ravel, O; Ray, R L; Razin, S V; Reichhold, D; Reid, J G; Reinnarth, J; Renault, G; Retiere, F; Ridiger, A; Ritter, H G; Roberts, J B; Rogachevskiy, O V; Romero, J L; Rose, A; Roy, C; Ruan, L; Russcher, M J; Sahoo, R; Sakrejda, I; Salur, S; Sandweiss, J; Sarsour, M; Savin, I; Sazhin, P S; Schambach, J; Scharenberg, R P; Schmitz, N; Schweda, K; Seger, J; Selyuzhenkov, I; Seyboth, P; Shabetai, A; Shahaliev, E; Shao, M; Shao, W; Sharma, M; Shen, W Q; Shestermanov, K E; Shimanskiy, S S; Sichtermann, E; Simon, F; Singaraju, R N; Smirnov, N; Snellings, R; Sood, G; Sorensen, P; Sowinski, J; Speltz, J; Spinka, H M; Srivastava, B; Stadnik, A; Stanislaus, T D S; Stock, R; Stolpovsky, A; Strikhanov, M; Stringfellow, B; Suaide, A A P; Sugarbaker, E; Sumbera, M; Surrow, B; Swanger, M; Symons, T J M; de Toledo, A Szanto; Tai, A; Takahashi, J; Tang, A H; Tarnowsky, T; Thein, D; Thomas, J H; Timmins, A R; Timoshenko, S; Tokarev, M; Trainor, T A; Trentalange, S; Tribble, R E; Tsai, O D; Ulery, J; Ullrich, T; Underwood, D G; Buren, G Van; van der Kolk, N; van Leeuwen, M; Molen, A M Vander; Varma, R; Vasilevski, I M; Vasiliev, A N; Vernet, R; Vigdor, S E; Viyogi, Y P; Vokal, S; Voloshin, S A; Waggoner, W T; Wang, F; Wang, G; Wang, G; Wang, X L; Wang, Y; Wang, Y; Wang, Z M; Ward, H; Watson, J W; Webb, J C; Westfall, G D; Wetzler, A; Whitten, C; Wieman, H; Wissink, S W; Witt, R; Wood, J; Wu, J; Xu, N; Xu, Q H; Xu, Z; Xu, Z Z; Yepes, P; Yoo, I-K; Yurevich, V I; Zborovsky, I; Zhang, H; Zhang, W M; Zhang, Y; Zhang, Z P; Zhong, C; Zoulkarneev, R; Zoulkarneeva, Y; Zubarev, A N; Zuo, J X

2006-10-13

Measurements of the production of forward pi0 mesons from p + p and d + Au collisions at square root sNN=200 GeV are reported. The p + p yield generally agrees with next-to-leading order perturbative QCD calculations. The d + Au yield per binary collision is suppressed as eta increases, decreasing to approximately 30% of the p + p yield at eta =4.00, well below shadowing expectations. Exploratory measurements of azimuthal correlations of the forward pi0 with charged hadrons at eta approximately 0 show a recoil peak in p + p that is suppressed in d + Au at low pion energy. These observations are qualitatively consistent with a saturation picture of the low-x gluon structure of heavy nuclei.

Particle-type dependence of azimuthal anisotropy and nuclear modification of particle production in Au+Au collisions at square root of sNN=200 GeV.

PubMed

Adams, J; Adler, C; Aggarwal, M M; Ahammed, Z; Amonett, J; Anderson, B D; Anderson, M; Arkhipkin, D; Averichev, G S; Badyal, S K; Balewski, J; Barannikova, O; Barnby, L S; Baudot, J; Bekele, S; Belaga, V V; Bellwied, R; Berger, J; Bezverkhny, B I; Bhardwaj, S; Bhaskar, P; Bhati, A K; Bichsel, H; Billmeier, A; Bland, L C; Blyth, C O; Bonner, B E; Botje, M; Boucham, A; Brandin, A; Bravar, A; Cadman, R V; Cai, X Z; Caines, H; Calderón de la Barca Sánchez, M; Carroll, J; Castillo, J; Castro, M; Cebra, D; Chaloupka, P; Chattopadhyay, S; Chen, H F; Chen, Y; Chernenko, S P; Cherney, M; Chikanian, A; Choi, B; Christie, W; Coffin, J P; Cormier, T M; Cramer, J G; Crawford, H J; Das, D; Das, S; Derevschikov, A A; Didenko, L; Dietel, T; Dong, W J; Dong, X; Draper, J E; Du, F; Dubey, A K; Dunin, V B; Dunlop, J C; Dutta Majumdar, M R; Eckardt, V; Efimov, L G; Emelianov, V; Engelage, J; Eppley, G; Erazmus, B; Estienne, M; Fachini, P; Faine, V; Faivre, J; Fatemi, R; Filimonov, K; Filip, P; Finch, E; Fisyak, Y; Flierl, D; Foley, K J; Fu, J; Gagliardi, C A; Gagunashvili, N; Gans, J; Ganti, M S; Gaudichet, L; Germain, M; Geurts, F; Ghazikhanian, V; Ghosh, P; Gonzalez, J E; Grachov, O; Grigoriev, V; Gronstal, S; Grosnick, D; Guedon, M; Guertin, S M; Gupta, A; Gushin, E; Gutierrez, T D; Hallman, T J; Hardtke, D; Harris, J W; Heinz, M; Henry, T W; Heppelmann, S; Herston, T; Hippolyte, B; Hirsch, A; Hjort, E; Hoffmann, G W; Horsley, M; Huang, H Z; Huang, S L; Humanic, T J; Igo, G; Ishihara, A; Jacobs, P; Jacobs, W W; Janik, M; Jiang, H; Johnson, I; Jones, P G; Judd, E G; Kabana, S; Kaneta, M; Kaplan, M; Keane, D; Khodyrev, V Yu; Kiryluk, J; Kisiel, A; Klay, J; Klein, S R; Klyachko, A; Koetke, D D; Kollegger, T; Kopytine, M; Kotchenda, L; Kovalenko, A D; Kramer, M; Kravtsov, P; Kravtsov, V I; Krueger, K; Kuhn, C; Kulikov, A I; Kumar, A; Kunde, G J; Kunz, C L; Kutuev, R Kh; Kuznetsov, A A; Lamont, M A C; Landgraf, J M; Lange, S; Lansdell, C P; Lasiuk, B; Laue, F; Lauret, J; Lebedev, A; Lednický, R; LeVine, M J; Li, C; Li, Q; Lindenbaum, S J; Lisa, M A; Liu, F; Liu, L; Liu, Z; Liu, Q J; Ljubicic, T; Llope, W J; Long, H; Longacre, R S; Lopez-Noriega, M; Love, W A; Ludlam, T; Lynn, D; Ma, J; Ma, Y G; Magestro, D; Mahajan, S; Mangotra, L K; Mahapatra, D P; Majka, R; Manweiler, R; Margetis, S; Markert, C; Martin, L; Marx, J; Matis, H S; Matulenko, Yu A; McShane, T S; Meissner, F; Melnick, Yu; Meschanin, A; Messer, M; Miller, M L; Milosevich, Z; Minaev, N G; Mironov, C; Mishra, D; Mitchell, J; Mohanty, B; Molnar, L; Moore, C F; Mora-Corral, M J; Morozov, D A; Morozov, V; de Moura, M M; Munhoz, M G; Nandi, B K; Nayak, S K; Nayak, T K; Nelson, J M; Nevski, P; Nikitin, V A; Nogach, L V; Norman, B; Nurushev, S B; Odyniec, G; Ogawa, A; Okorokov, V; Oldenburg, M; Olson, D; Paic, G; Pandey, S U; Pal, S K; Panebratsev, Y; Panitkin, S Y; Pavlinov, A I; Pawlak, T; Perevoztchikov, V; Perkins, C; Peryt, W; Petrov, V A; Phatak, S C; Picha, R; Planinic, M; Pluta, J; Porile, N; Porter, J; Poskanzer, A M; Potekhin, M; Potrebenikova, E; Potukuchi, B V K S; Prindle, D; Pruneau, C; Putschke, J; Rai, G; Rakness, G; Raniwala, R; Raniwala, S; Ravel, O; Ray, R L; Razin, S V; Reichhold, D; Reid, J G; Renault, G; Retiere, F; Ridiger, A; Ritter, H G; Roberts, J B; Rogachevski, O V; Romero, J L; Rose, A; Roy, C; Ruan, L J; Sahoo, R; Sakrejda, I; Salur, S; Sandweiss, J; Savin, I; Schambach, J; Scharenberg, R P; Schmitz, N; Schroeder, L S; Schweda, K; Seger, J; Seliverstov, D; Seyboth, P; Shahaliev, E; Shao, M; Sharma, M; Shestermanov, K E; Shimanskii, S S; Singaraju, R N; Simon, F; Skoro, G; Smirnov, N; Snellings, R; Sood, G; Sorensen, P; Sowinski, J; Spinka, H M; Srivastava, B; Stanislaus, S; Stock, R; Stolpovsky, A; Strikhanov, M; Stringfellow, B; Struck, C; Suaide, A A P; Sugarbaker, E; Suire, C; Sumbera, M; Surrow, B; Symons, T J M; de Toledo, A Szanto; Szarwas, P; Tai, A; Takahashi, J; Tang, A H; Thein, D; Thomas, J H; Tikhomirov, V; Tokarev, M; Tonjes, M B; Trainor, T A; Trentalange, S; Tribble, R E; Trivedi, M D; Trofimov, V; Tsai, O; Ullrich, T; Underwood, D G; Van Buren, G; VanderMolen, A M; Vasiliev, A N; Vasiliev, M; Vigdor, S E; Viyogi, Y P; Voloshin, S A; Waggoner, W; Wang, F; Wang, G; Wang, X L; Wang, Z M; Ward, H; Watson, J W; Wells, R; Westfall, G D; Whitten, C; Wieman, H; Willson, R; Wissink, S W; Witt, R; Wood, J; Wu, J; Xu, N; Xu, Z; Xu, Z Z; Yamamoto, E; Yepes, P; Yurevich, V I; Zanevski, Y V; Zborovský, I; Zhang, H; Zhang, W M; Zhang, Z P; Zołnierczuk, P A; Zoulkarneev, R; Zoulkarneeva, J; Zubarev, A N

2004-02-06

We present STAR measurements of the azimuthal anisotropy parameter v(2) and the binary-collision scaled centrality ratio R(CP) for kaons and lambdas (Lambda+Lambda) at midrapidity in Au+Au collisions at square root of s(NN)=200 GeV. In combination, the v(2) and R(CP) particle-type dependencies contradict expectations from partonic energy loss followed by standard fragmentation in vacuum. We establish p(T) approximately 5 GeV/c as the value where the centrality dependent baryon enhancement ends. The K(0)(S) and Lambda+Lambda v(2) values are consistent with expectations of constituent-quark-number scaling from models of hadron formation by parton coalescence or recombination.

Centrality dependence of charged jet production in p–Pb collisions at $$\\sqrt{s_\\mathrm{NN}}$$ = 5.02 TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2016-05-17

Measurements of charged jet production as a function of centrality are presented for p–Pb collisions recorded atmore » $$\\sqrt{s_\\mathrm{NN}}$$= 5.02 TeV with the ALICE detector. Centrality classes are determined via the energy deposit in neutron calorimeters at zero degree, close to the beam direction, to minimise dynamical biases of the selection. The corresponding number of participants or binary nucleon–nucleon collisions is determined based on the particle production in the Pb-going rapidity region. Jets have been reconstructed in the central rapidity region from charged particles with the anti-k T algorithm for resolution parameters R = 0.2 and R = 0.4 in the transverse momentum range 20 to 120 GeV/c. The reconstructed jet momentum and yields have been corrected for detector effects and underlying-event background. In the five centrality bins considered, the charged jet production in p–Pb collisions is consistent with the production expected from binary scaling from pp collisions. The ratio of jet yields reconstructed with the two different resolution parameters is also independent of the centrality selection, demonstrating the absence of major modifications of the radial jet structure in the reported centrality classes.« less

Non-conservative evolution in Algols: where is the matter?

NASA Astrophysics Data System (ADS)

Deschamps, R.; Braun, K.; Jorissen, A.; Siess, L.; Baes, M.; Camps, P.

2015-05-01

Context. There is indirect evidence of non-conservative evolutions in Algols. However, the systemic mass-loss rate is poorly constrained by observations and generally set as a free parameter in binary-star evolution simulations. Moreover, systemic mass loss may lead to observational signatures that still need to be found. Aims: Within the "hotspot" ejection mechanism, some of the material that is initially transferred from the companion star via an accretion stream is expelled from the system due to the radiative energy released on the gainer's surface by the impacting material. The objective of this paper is to retrieve observable quantities from this process and to compare them with observations. Methods: We investigate the impact of the outflowing gas and the possible presence of dust grains on the spectral energy distribution (SED). We used the 1D plasma code Cloudy and compared the results with the 3D Monte-Carlo radiative transfer code Skirt for dusty simulations. The circumbinary mass-distribution and binary parameters were computed with state-of-the-art binary calculations done with the Binstar evolution code. Results: The outflowing material reduces the continuum flux level of the stellar SED in the optical and UV. Because of the time-dependence of this effect, it may help to distinguish between different ejection mechanisms. If present, dust leads to observable infrared excesses, even with low dust-to-gas ratios, and traces the cold material at large distances from the star. By searching for this dust emission in the WISE catalogue, we found a small number of Algols showing infrared excesses, among which the two rather surprising objects SX Aur and CZ Vel. We find that some binary B[e] stars show the same strong Balmer continuum as we predict with our models. However, direct evidence of systemic mass loss is probably not observable in genuine Algols, since these systems no longer eject mass through the hotspot mechanism. Furthermore, owing to its high velocity, the outflowing material dissipates in a few hundred years. If hot enough, the hotspot may produce highly ionised species, such as Si iv, and observable characteristics that are typical of W Ser systems. Conclusions: If present, systemic mass loss leads to clear observational imprints. These signatures are not to be found in genuine Algols but in the closely related β Lyraes, W Serpentis stars, double periodic variables, symbiotic Algols, and binary B[e] stars. We emphasise the need for further observations of such objects where systemic mass loss is most likely to occur. Appendices are available in electronic form at http://www.aanda.org

Energy dependence of J/ψ production in Au + Au collisions at √{sNN} = 39 , 62.4 and 200GeV

NASA Astrophysics Data System (ADS)

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; Aggarwal, M. M.; Ahammed, Z.; Ajitanand, N. N.; Alekseev, I.; Anderson, D. M.; Aoyama, R.; Aparin, A.; Arkhipkin, D.; Aschenauer, E. C.; Ashraf, M. U.; Attri, A.; Averichev, G. S.; Bai, X.; Bairathi, V.; Behera, A.; Bellwied, R.; Bhasin, A.; Bhati, A. K.; Bhattarai, P.; Bielcik, J.; Bielcikova, J.; Bland, L. C.; Bordyuzhin, I. G.; Bouchet, J.; Brandenburg, J. D.; Brandin, A. V.; Brown, D.; Bunzarov, I.; Butterworth, J.; Caines, H.; Calderón de la Barca Sánchez, M.; Campbell, J. M.; Cebra, D.; Chakaberia, I.; Chaloupka, P.; Chang, Z.; Chankova-Bunzarova, N.; Chatterjee, A.; Chattopadhyay, S.; Chen, X.; Chen, J. H.; Chen, X.; Cheng, J.; Cherney, M.; Christie, W.; Contin, G.; Crawford, H. J.; Das, S.; De Silva, L. C.; Debbe, R. R.; Dedovich, T. G.; Deng, J.; Derevschikov, A. A.; Didenko, L.; Dilks, C.; Dong, X.; Drachenberg, J. L.; Draper, J. E.; Dunkelberger, L. E.; Dunlop, J. C.; Efimov, L. G.; Elsey, N.; Engelage, J.; Eppley, G.; Esha, R.; Esumi, S.; Evdokimov, O.; Ewigleben, J.; Eyser, O.; Fatemi, R.; Fazio, S.; Federic, P.; Federicova, P.; Fedorisin, J.; Feng, Z.; Filip, P.; Finch, E.; Fisyak, Y.; Flores, C. E.; Fujita, J.; Fulek, L.; Gagliardi, C. A.; Garand, D.; Geurts, F.; Gibson, A.; Girard, M.; Grosnick, D.; Gunarathne, D. S.; Guo, Y.; Gupta, A.; Gupta, S.; Guryn, W.; Hamad, A. I.; Hamed, A.; Harlenderova, A.; Harris, J. W.; He, L.; Heppelmann, S.; Heppelmann, S.; Hirsch, A.; Hoffmann, G. W.; Horvat, S.; Huang, B.; Huang, H. Z.; Huang, T.; Huang, X.; Humanic, T. J.; Huo, P.; Igo, G.; Jacobs, W. W.; Jentsch, A.; Jia, J.; Jiang, K.; Jowzaee, S.; Judd, E. G.; Kabana, S.; Kalinkin, D.; Kang, K.; Kauder, K.; Ke, H. W.; Keane, D.; Kechechyan, A.; Khan, Z.; Kikoła, D. P.; Kisel, I.; Kisiel, A.; Kochenda, L.; Kocmanek, M.; Kollegger, T.; Kosarzewski, L. K.; Kraishan, A. F.; Kravtsov, P.; Krueger, K.; Kulathunga, N.; Kumar, L.; Kvapil, J.; Kwasizur, J. H.; Lacey, R.; Landgraf, J. M.; Landry, K. D.; Lauret, J.; Lebedev, A.; Lednicky, R.; Lee, J. H.; Li, Y.; Li, X.; Li, W.; Li, C.; Lidrych, J.; Lin, T.; Lisa, M. A.; Liu, Y.; Liu, H.; Liu, F.; Liu, P.; Ljubicic, T.; Llope, W. J.; Lomnitz, M.; Longacre, R. S.; Luo, S.; Luo, X.; Ma, G. L.; Ma, L.; Ma, Y. G.; Ma, R.; Magdy, N.; Majka, R.; Mallick, D.; Margetis, S.; Markert, C.; Matis, H. S.; Meehan, K.; Mei, J. C.; Miller, Z. W.; Minaev, N. G.; Mioduszewski, S.; Mishra, D.; Mizuno, S.; Mohanty, B.; Mondal, M. M.; Morozov, D. A.; Mustafa, M. K.; Nasim, Md.; Nayak, T. K.; Nelson, J. M.; Nie, M.; Nigmatkulov, G.; Niida, T.; Nogach, L. V.; Nonaka, T.; Nurushev, S. B.; Odyniec, G.; Ogawa, A.; Oh, K.; Okorokov, V. A.; Olvitt, D.; Page, B. S.; Pak, R.; Pandit, Y.; Panebratsev, Y.; Pawlik, B.; Pei, H.; Perkins, C.; Pile, P.; Pluta, J.; Poniatowska, K.; Porter, J.; Posik, M.; Pruthi, N. K.; Przybycien, M.; Putschke, J.; Qiu, H.; Quintero, A.; Ramachandran, S.; Ray, R. L.; Reed, R.; Rehbein, M. J.; Ritter, H. G.; Roberts, J. B.; Rogachevskiy, O. V.; Romero, J. L.; Roth, J. D.; Ruan, L.; Rusnak, J.; Rusnakova, O.; Sahoo, N. R.; Sahu, P. K.; Salur, S.; Sandweiss, J.; Saur, M.; Schambach, J.; Schmah, A. M.; Schmidke, W. B.; Schmitz, N.; Schweid, B. R.; Seger, J.; Sergeeva, M.; Seyboth, P.; Shah, N.; Shahaliev, E.; Shanmuganathan, P. V.; Shao, M.; Sharma, A.; Sharma, M. K.; Shen, W. Q.; Shi, S. S.; Shi, Z.; Shou, Q. Y.; Sichtermann, E. P.; Sikora, R.; Simko, M.; Singha, S.; Skoby, M. J.; Smirnov, N.; Smirnov, D.; Solyst, W.; Song, L.; Sorensen, P.; Spinka, H. M.; Srivastava, B.; Stanislaus, T. D. S.; Strikhanov, M.; Stringfellow, B.; Sugiura, T.; Sumbera, M.; Summa, B.; Sun, X.; Sun, Y.; Sun, X. M.; Surrow, B.; Svirida, D. N.; Tang, A. H.; Tang, Z.; Taranenko, A.; Tarnowsky, T.; Tawfik, A.; Thäder, J.; Thomas, J. H.; Timmins, A. R.; Tlusty, D.; Todoroki, T.; Tokarev, M.; Trentalange, S.; Tribble, R. E.; Tribedy, P.; Tripathy, S. K.; Trzeciak, B. A.; Tsai, O. D.; Ullrich, T.; Underwood, D. G.; Upsal, I.; Van Buren, G.; van Nieuwenhuizen, G.; Vasiliev, A. N.; Videbæk, F.; Vokal, S.; Voloshin, S. A.; Vossen, A.; Wang, G.; Wang, Y.; Wang, F.; Wang, Y.; Webb, J. C.; Webb, G.; Wen, L.; Westfall, G. D.; Wieman, H.; Wissink, S. W.; Witt, R.; Wu, Y.; Xiao, Z. G.; Xie, W.; Xie, G.; Xu, J.; Xu, N.; Xu, Q. H.; Xu, Y. F.; Xu, Z.; Yang, Y.; Yang, Q.; Yang, C.; Yang, S.; Ye, Z.; Ye, Z.; Yi, L.; Yip, K.; Yoo, I.-K.; Yu, N.; Zbroszczyk, H.; Zha, W.; Zhang, Z.; Zhang, X. P.; Zhang, J. B.; Zhang, S.; Zhang, J.; Zhang, Y.; Zhang, J.; Zhang, S.; Zhao, J.; Zhong, C.; Zhou, L.; Zhou, C.; Zhu, X.; Zhu, Z.; Zyzak, M.; STAR Collaboration

2017-08-01

The inclusive J / ψ transverse momentum spectra and nuclear modification factors are reported at mid-rapidity (| y | < 1.0) in Au + Au collisions at √{sNN} = 39, 62.4 and 200 GeV taken by the STAR experiment. A suppression of J / ψ production, with respect to the production in p + p scaled by the number of binary nucleon-nucleon collisions, is observed in central Au + Au collisions at these three energies. No significant energy dependence of nuclear modification factors is found within uncertainties. The measured nuclear modification factors can be described by model calculations that take into account both suppression of direct J / ψ production due to the color screening effect and J / ψ regeneration from recombination of uncorrelated charm-anticharm quark pairs.

Collision geometry scaling of Au+Au pseudorapidity density from √(sNN )=19.6 to 200 GeV

NASA Astrophysics Data System (ADS)

Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Decowski, M. P.; García, E.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Heintzelman, G. A.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; McLeod, D.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sagerer, J.; Sarin, P.; Sawicki, P.; Skulski, W.; Steinberg, P.; Stephans, G. S.; Sukhanov, A.; Tonjes, M. B.; Tang, J.-L.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2004-08-01

The centrality dependence of the midrapidity charged particle multiplicity in Au+Au heavy-ion collisions at √(sNN )=19.6 and 200 GeV is presented. Within a simple model, the fraction of hard (scaling with number of binary collisions) to soft (scaling with number of participant pairs) interactions is consistent with a value of x=0.13±0.01 (stat) ±0.05 (syst) at both energies. The experimental results at both energies, scaled by inelastic p ( p¯ ) +p collision data, agree within systematic errors. The ratio of the data was found not to depend on centrality over the studied range and yields a simple linear scale factor of R200/19.6 =2.03±0.02 (stat) ±0.05 (syst) .

Azimuthal Anisotropy in U +U and Au +Au Collisions at RHIC

NASA Astrophysics Data System (ADS)

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; Aggarwal, M. M.; Ahammed, Z.; Alekseev, I.; Alford, J.; Aparin, A.; Arkhipkin, D.; Aschenauer, E. C.; Averichev, G. S.; Banerjee, A.; Bellwied, R.; Bhasin, A.; Bhati, A. K.; Bhattarai, P.; Bielcik, J.; Bielcikova, J.; Bland, L. C.; Bordyuzhin, I. G.; Bouchet, J.; Brandin, A. V.; Bunzarov, I.; Burton, T. P.; Butterworth, J.; Caines, H.; Calderón de la Barca Sánchez, M.; Campbell, J. M.; Cebra, D.; Cervantes, M. C.; Chakaberia, I.; Chaloupka, P.; Chang, Z.; Chattopadhyay, S.; Chen, J. H.; Chen, X.; Cheng, J.; Cherney, M.; Christie, W.; Contin, G.; Crawford, H. J.; Das, S.; De Silva, L. C.; Debbe, R. R.; Dedovich, T. G.; Deng, J.; Derevschikov, A. A.; di Ruzza, B.; Didenko, L.; Dilks, C.; Dong, X.; Drachenberg, J. L.; Draper, J. E.; Du, C. M.; Dunkelberger, L. E.; Dunlop, J. C.; Efimov, L. G.; Engelage, J.; Eppley, G.; Esha, R.; Evdokimov, O.; Eyser, O.; Fatemi, R.; Fazio, S.; Federic, P.; Fedorisin, J.; Feng, Z.; Filip, P.; Fisyak, Y.; Flores, C. E.; Fulek, L.; Gagliardi, C. A.; Garand, D.; Geurts, F.; Gibson, A.; Girard, M.; Greiner, L.; Grosnick, D.; Gunarathne, D. S.; Guo, Y.; Gupta, S.; Gupta, A.; Guryn, W.; Hamad, A.; Hamed, A.; Haque, R.; Harris, J. W.; He, L.; Heppelmann, S.; Heppelmann, S.; Hirsch, A.; Hoffmann, G. W.; Hofman, D. J.; Horvat, S.; Huang, H. Z.; Huang, B.; Huang, X.; Huck, P.; Humanic, T. J.; Igo, G.; Jacobs, W. W.; Jang, H.; Jiang, K.; Judd, E. G.; Kabana, S.; Kalinkin, D.; Kang, K.; Kauder, K.; Ke, H. W.; Keane, D.; Kechechyan, A.; Khan, Z. H.; Kikola, D. P.; Kisel, I.; Kisiel, A.; Koetke, D. D.; Kollegger, T.; Kosarzewski, L. K.; Kotchenda, L.; Kraishan, A. F.; Kravtsov, P.; Krueger, K.; Kulakov, I.; Kumar, L.; Kycia, R. A.; Lamont, M. A. C.; Landgraf, J. M.; Landry, K. D.; Lauret, J.; Lebedev, A.; Lednicky, R.; Lee, J. H.; Li, W.; Li, Y.; Li, C.; Li, Z. M.; Li, X.; Li, X.; Lisa, M. A.; Liu, F.; Ljubicic, T.; Llope, W. J.; Lomnitz, M.; Longacre, R. S.; Luo, X.; Ma, L.; Ma, R.; Ma, Y. G.; Ma, G. L.; Magdy, N.; Majka, R.; Manion, A.; Margetis, S.; Markert, C.; Masui, H.; Matis, H. S.; McDonald, D.; Meehan, K.; Minaev, N. G.; Mioduszewski, S.; Mohanty, B.; Mondal, M. M.; Morozov, D. A.; Mustafa, M. K.; Nandi, B. K.; Nasim, Md.; Nayak, T. K.; Nigmatkulov, G.; Nogach, L. V.; Noh, S. Y.; Novak, J.; Nurushev, S. B.; Odyniec, G.; Ogawa, A.; Oh, K.; Okorokov, V.; Olvitt, D. L.; Page, B. S.; Pak, R.; Pan, Y. X.; Pandit, Y.; Panebratsev, Y.; Pawlik, B.; Pei, H.; Perkins, C.; Peterson, A.; Pile, P.; Planinic, M.; Pluta, J.; Poljak, N.; Poniatowska, K.; Porter, J.; Posik, M.; Poskanzer, A. M.; Pruthi, N. K.; Putschke, J.; Qiu, H.; Quintero, A.; Ramachandran, S.; Raniwala, S.; Raniwala, R.; Ray, R. L.; Ritter, H. G.; Roberts, J. B.; Rogachevskiy, O. V.; Romero, J. L.; Roy, A.; Ruan, L.; Rusnak, J.; Rusnakova, O.; Sahoo, N. R.; Sahu, P. K.; Sakrejda, I.; Salur, S.; Sandweiss, J.; Sarkar, A.; Schambach, J.; Scharenberg, R. P.; Schmah, A. M.; Schmidke, W. B.; Schmitz, N.; Seger, J.; Seyboth, P.; Shah, N.; Shahaliev, E.; Shanmuganathan, P. V.; Shao, M.; Sharma, B.; Sharma, M. K.; Shen, W. Q.; Shi, S. S.; Shou, Q. Y.; Sichtermann, E. P.; Sikora, R.; Simko, M.; Skoby, M. J.; Smirnov, D.; Smirnov, N.; Song, L.; Sorensen, P.; Spinka, H. M.; Srivastava, B.; Stanislaus, T. D. S.; Stepanov, M.; Stock, R.; Strikhanov, M.; Stringfellow, B.; Sumbera, M.; Summa, B. J.; Sun, X.; Sun, X. M.; Sun, Z.; Sun, Y.; Surrow, B.; Svirida, D. N.; Szelezniak, M. A.; Tang, Z.; Tang, A. H.; Tarnowsky, T.; Tawfik, A. N.; Thomas, J. H.; Timmins, A. R.; Tlusty, D.; Tokarev, M.; Trentalange, S.; Tribble, R. E.; Tribedy, P.; Tripathy, S. K.; Trzeciak, B. A.; Tsai, O. D.; Ullrich, T.; Underwood, D. G.; Upsal, I.; Van Buren, G.; van Nieuwenhuizen, G.; Vandenbroucke, M.; Varma, R.; Vasiliev, A. N.; Vertesi, R.; Videbaek, F.; Viyogi, Y. P.; Vokal, S.; Voloshin, S. A.; Vossen, A.; Wang, F.; Wang, Y.; Wang, H.; Wang, J. S.; Wang, Y.; Wang, G.; Webb, G.; Webb, J. C.; Wen, L.; Westfall, G. D.; Wieman, H.; Wissink, S. W.; Witt, R.; Wu, Y. F.; Xiao, Z.; Xie, W.; Xin, K.; Xu, Y. F.; Xu, N.; Xu, Z.; Xu, Q. H.; Xu, H.; Yang, Y.; Yang, Y.; Yang, C.; Yang, S.; Yang, Q.; Ye, Z.; Yepes, P.; Yi, L.; Yip, K.; Yoo, I.-K.; Yu, N.; Zbroszczyk, H.; Zha, W.; Zhang, X. P.; Zhang, J. B.; Zhang, J.; Zhang, Z.; Zhang, S.; Zhang, Y.; Zhang, J. L.; Zhao, F.; Zhao, J.; Zhong, C.; Zhou, L.; Zhu, X.; Zoulkarneeva, Y.; Zyzak, M.; STAR Collaboration

2015-11-01

Collisions between prolate uranium nuclei are used to study how particle production and azimuthal anisotropies depend on initial geometry in heavy-ion collisions. We report the two- and four-particle cumulants, v2{2 } and v2{4 }, for charged hadrons from U +U collisions at √{sNN }=193 GeV and Au +Au collisions at √{sNN}=200 GeV . Nearly fully overlapping collisions are selected based on the energy deposited by spectators in zero degree calorimeters (ZDCs). Within this sample, the observed dependence of v2{2 } on multiplicity demonstrates that ZDC information combined with multiplicity can preferentially select different overlap configurations in U +U collisions. We also show that v2 vs multiplicity can be better described by models, such as gluon saturation or quark participant models, that eliminate the dependence of the multiplicity on the number of binary nucleon-nucleon collisions.

Charged hadron transverse momentum distributions in Au+Au collisions at √sNN=200 GeV

NASA Astrophysics Data System (ADS)

Back, B. B.; Baker, M. D.; Barton, D. S.; Betts, R. R.; Ballintijn, M.; Bickley, A. A.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Decowski, M. P.; García, E.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Heintzelman, G. A.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Lee, J. W.; Manly, S.; McLeod, D.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sagerer, J.; Sarin, P.; Sawicki, P.; Skulski, W.; Steadman, S. G.; Steinberg, P.; Stephans, G. S. F.; Sukhanov, A.; Tang, J.-L.; Teng, R.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Veres, G. I.; Wadsworth, B.; Wolfs, F. L. H.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2004-01-01

We present transverse momentum distributions of charged hadrons produced in Au+Au collisions at sNN=200 GeV. The spectra were measured for transverse momenta pT from 0.25 to 4.5 GeV/c in a pseudorapidity range of 0.2<η<1.4. The evolution of the spectra is studied as a function of collision centrality, from 65 to 344 participating nucleons. The results are compared to data from proton-antiproton collisions and Au+Au collisions at lower RHIC energies. We find a significant change of the spectral shape between proton-antiproton and semi-peripheral Au+Au collisions. Comparing semi-peripheral to central Au+Au collisions, we find that the yields at high pT exhibit approximate scaling with the number of participating nucleons, rather than scaling with the number of binary collisions.

Observation of D 0 meson nuclear modifications in Au + Au collisions at s NN = 200 GeV

DOE PAGES

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; ...

2014-09-30

We report the first measurement of charmed-hadron (D 0) production via the hadronic decay channel (D 0→K -+π +) in Au+Au collisions at √ sNN=200 GeV with the STAR experiment. The charm production cross section per nucleon-nucleon collision at midrapidity scales with the number of binary collisions, N bin, from p+p to central Au+Au collisions. The D 0 meson yields in central Au+Aucollisions are strongly suppressed compared to those in p+p scaled by N bin, for transverse momenta p T>3 GeV/c, demonstrating significant energy loss of charm quarks in the hot and dense medium. An enhancement at intermediate p Tmore » is also observed. Model calculations including strong charm-medium interactions and coalescence hadronization describe our measurements.« less

Physical Parameters of Components in Close Binary Systems: IV

NASA Astrophysics Data System (ADS)

Gazeas, K. D.; Baran, A.; Niarchos, P.; Zola, S.; Kreiner, J. M.; Ogloza, W.; Rucinski, S. M.; Zakrzewski, B.; Siwak, M.; Pigulski, A.; Drozdz, M.

2005-03-01

The paper presents new geometric, photometric and absolute parameters, derived from combined spectroscopic and photometric solutions, for ten contact binary systems. The analysis shows that three systems (EF Boo, GM Dra and SW Lac) are of W-type with shallow to moderate contact. Seven systems (V417 Aql, AH Aur, YY CrB, UX Eri, DZ Psc, GR Vir and NN Vir) are of A-type in a deep contact configuration. For six systems (V417 Aql, YY CrB, GM Dra, UX Eri, SW Lac and GR Vir) a spot model is introduced to explain the O'Connell effect in their light curves. The photometric and geometric elements of the systems are combined with the spectroscopic data taken at David Dunlap Observatory to yield the absolute parameters of the components.

Prospects of second generation artificial intelligence tools in calibration of chemical sensors.

PubMed

Braibanti, Antonio; Rao, Rupenaguntla Sambasiva; Ramam, Veluri Anantha; Rao, Gollapalli Nageswara; Rao, Vaddadi Venkata Panakala

2005-05-01

Multivariate data driven calibration models with neural networks (NNs) are developed for binary (Cu++ and Ca++) and quaternary (K+, Ca++, NO3- and Cl-) ion-selective electrode (ISE) data. The response profiles of ISEs with concentrations are non-linear and sub-Nernstian. This task represents function approximation of multi-variate, multi-response, correlated, non-linear data with unknown noise structure i.e. multi-component calibration/prediction in chemometric parlance. Radial distribution function (RBF) and Fuzzy-ARTMAP-NN models implemented in the software packages, TRAJAN and Professional II, are employed for the calibration. The optimum NN models reported are based on residuals in concentration space. Being a data driven information technology, NN does not require a model, prior- or posterior- distribution of data or noise structure. Missing information, spikes or newer trends in different concentration ranges can be modeled through novelty detection. Two simulated data sets generated from mathematical functions are modeled as a function of number of data points and network parameters like number of neurons and nearest neighbors. The success of RBF and Fuzzy-ARTMAP-NNs to develop adequate calibration models for experimental data and function approximation models for more complex simulated data sets ensures AI2 (artificial intelligence, 2nd generation) as a promising technology in quantitation.

Long-term eclipse timing of white dwarf binaries: an observational hint of a magnetic mechanism at work

NASA Astrophysics Data System (ADS)

Bours, M. C. P.; Marsh, T. R.; Parsons, S. G.; Dhillon, V. S.; Ashley, R. P.; Bento, J. P.; Breedt, E.; Butterley, T.; Caceres, C.; Chote, P.; Copperwheat, C. M.; Hardy, L. K.; Hermes, J. J.; Irawati, P.; Kerry, P.; Kilkenny, D.; Littlefair, S. P.; McAllister, M. J.; Rattanasoon, S.; Sahman, D. I.; Vučković, M.; Wilson, R. W.

2016-08-01

We present a long-term programme for timing the eclipses of white dwarfs in close binaries to measure apparent and/or real variations in their orbital periods. Our programme includes 67 close binaries, both detached and semi-detached and with M-dwarfs, K-dwarfs, brown dwarfs or white dwarfs secondaries. In total, we have observed more than 650 white dwarf eclipses. We use this sample to search for orbital period variations and aim to identify the underlying cause of these variations. We find that the probability of observing orbital period variations increases significantly with the observational baseline. In particular, all binaries with baselines exceeding 10 yr, with secondaries of spectral type K2 - M5.5, show variations in the eclipse arrival times that in most cases amount to several minutes. In addition, among those with baselines shorter than 10 yr, binaries with late spectral type (>M6), brown dwarf or white dwarf secondaries appear to show no orbital period variations. This is in agreement with the so-called Applegate mechanism, which proposes that magnetic cycles in the secondary stars can drive variability in the binary orbits. We also present new eclipse times of NN Ser, which are still compatible with the previously published circumbinary planetary system model, although only with the addition of a quadratic term to the ephemeris. Finally, we conclude that we are limited by the relatively short observational baseline for many of the binaries in the eclipse timing programme, and therefore cannot yet draw robust conclusions about the cause of orbital period variations in evolved, white dwarf binaries.

Tracking spin-axis orbital alignment in selected binary systems: the Torun Rossiter-McLaughlin effect survey

NASA Astrophysics Data System (ADS)

Sybilski, P.; Pawłaszek, R. K.; Sybilska, A.; Konacki, M.; Hełminiak, K. G.; Kozłowski, S. K.; Ratajczak, M.

2018-07-01

We have obtained high-resolution spectra of four eclipsing binary systems (FM Leo, NN Del, V963 Cen and AI Phe) with the view to gaining an insight into the relative orientations of their stellar spin axes and orbital axes. The so-called Rossiter-McLaughlin (RM) effect, i.e. the fact that the broadening and the amount of blue or redshift in the spectra during an eclipse depends on the tilt of the spin axis of the background star, has the potential of reconciling observations and theoretical models if such a tilt is found. We analyse the RM effect by disentangling the spectra, removing the front component and measuring the remaining, distorted lines with a broadening function (BF) obtained from single-value decomposition (SVD), weighting by the intensity centre of the BF in the eclipse. All but one of our objects show no significant misalignment, suggesting that aligned systems are dominant. We provide stellar as well as orbital parameters for our systems. With five measured spin-orbit angles, we increase significantly (from 9 to 14) the number of stars for which it has been measured. The spin-orbit angle β calculated for AI Phe's secondary component shows a misalignment of 87±17°. NN Del, with a large separation of components and a long dynamical time-scale for circularization and synchronization, is an example of a close to primordial spin-orbit angle measurement.

Measurement of the production of high-pT electrons from heavy-flavour hadron decays in Pb-Pb collisions at √{sNN} = 2.76 TeV

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, S.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; An, M.; Andrei, C.; Andrews, H. A.; Andronic, A.; Anguelov, V.; Anson, C.; Antičić, T.; Antinori, F.; Antonioli, P.; Anwar, R.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Beltran, L. G. E.; Belyaev, V.; Bencedi, G.; Beole, S.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Boldizsár, L.; Bombara, M.; Bonora, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buhler, P.; Buitron, S. A. I.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Caines, H.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa Del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crkovská, J.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; de, S.; de Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; de Falco, A.; de Gruttola, D.; De Marco, N.; de Pasquale, S.; de Souza, R. D.; Deisting, A.; Deloff, A.; Deplano, C.; Dhankher, P.; di Bari, D.; di Mauro, A.; di Nezza, P.; di Ruzza, B.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Duggal, A. K.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erhardt, F.; Espagnon, B.; Esumi, S.; Eulisse, G.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Francisco, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gajdosova, K.; Gallio, M.; Galvan, C. D.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Garg, K.; Garg, P.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Gay Ducati, M. B.; Germain, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, A. S.; Gonzalez, V.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Graczykowski, L. K.; Graham, K. L.; Greiner, L.; Grelli, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Gruber, L.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Guzman, I. B.; Haake, R.; Hadjidakis, C.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Herrmann, F.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Hladky, J.; Horak, D.; Hosokawa, R.; Hristov, P.; Hughes, C.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Ippolitov, M.; Irfan, M.; Isakov, V.; Islam, M. S.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacak, B.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Khuntia, A.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, H.; Kim, J. S.; Kim, J.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kundu, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lapidus, K.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lazaridis, L.; Lea, R.; Leardini, L.; Lee, S.; Lehas, F.; Lehner, S.; Lehrbach, J.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Llope, W.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lupi, M.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Mao, Y.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzilli, M.; Mazzoni, M. A.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Mhlanga, S.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Mishra, T.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montes, E.; Moreira de Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Münning, K.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Myers, C. J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Negrao de Oliveira, R. A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Ohlson, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pacik, V.; Pagano, D.; Pagano, P.; Paić, G.; Pal, S. K.; Palni, P.; Pan, J.; Pandey, A. K.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, J.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Peng, X.; Pereira da Costa, H.; Peresunko, D.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Poppenborg, H.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Pozdniakov, V.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Rana, D. B.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Ratza, V.; Ravasenga, I.; Read, K. F.; Redlich, K.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rodríguez Cahuantzi, M.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Sas, M. H. P.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schmidt, M.; Schukraft, J.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sett, P.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singhal, V.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Sozzi, F.; Spiriti, E.; Sputowska, I.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Suzuki, K.; Swain, S.; Szabo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thakur, D.; Thomas, D.; Tieulent, R.; Tikhonov, A.; Timmins, A. R.; Toia, A.; Tripathy, S.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Umaka, E. N.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; van der Maarel, J.; van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vázquez Doce, O.; Vechernin, V.; Veen, A. M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Vértesi, R.; Vickovic, L.; Vigolo, S.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Virgili, T.; Vislavicius, V.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Voscek, D.; Vranic, D.; Vrláková, J.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Willems, G. A.; Williams, M. C. S.; Windelband, B.; Winn, M.; Witt, W. E.; Yalcin, S.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zmeskal, J.; Alice Collaboration

2017-08-01

Electrons from heavy-flavour hadron decays (charm and beauty) were measured with the ALICE detector in Pb-Pb collisions at a centre-of-mass of energy √{sNN} = 2.76 TeV. The transverse momentum (pT) differential production yields at mid-rapidity were used to calculate the nuclear modification factor RAA in the interval 3

Υ production in U + U collisions at s N N = 193 GeV measured with the STAR experiment

DOE PAGES

Adamczyk, L.

2016-12-15

We present a measurement of the inclusive production of ¡ mesons in U+U collisions at √sNN = 193 GeV at mid-rapidity (|y| < 1). Previous studies in central Au+Au collisions at √sNN = 200 GeV show a suppression of ¡(1S+2S+3S) production relative to expectations from the ¡ yield in p+p collisions scaled by the number of binary nucleon-nucleon collisions (N coll), with an indication that the ¡(1S) state is also suppressed. The present measurement extends the number of participant nucleons in the collision (N part) by 20% compared to Au+Au collisions, and allows us to study a system with highermore » energy density. We observe a suppression in both the ¡(1S+2S+3S) and ¡(1S) yields in central U+U data, which consolidates and extends the previously observed suppression trend in Au+Au collisions.« less

Identified baryon and meson distributions at large transverse momenta from Au + Au collisions at square root sNN=200 GeV.

PubMed

Abelev, B I; Aggarwal, M M; Ahammed, Z; Anderson, B D; Anderson, M; Arkhipkin, D; Averichev, G S; Bai, Y; Balewski, J; Barannikova, O; Barnby, L S; Baudot, J; Bekele, S; Belaga, V V; Bellingeri-Laurikainen, A; Bellwied, R; Benedosso, F; Bhardwaj, S; Bhasin, A; Bhati, A K; Bichsel, H; Bielcik, J; Bielcikova, J; Bland, L C; Blyth, S-L; Bonner, B E; Botje, M; Bouchet, J; Brandin, A V; Bravar, A; Burton, T P; Bystersky, M; Cadman, R V; Cai, X Z; Caines, H; Calderón de la Barca Sánchez, M; Castillo, J; Catu, O; Cebra, D; Chajecki, Z; Chaloupka, P; Chattopadhyay, S; Chen, H F; Chen, J H; Cheng, J; Cherney, M; Chikanian, A; Christie, W; Coffin, J P; Cormier, T M; Cosentino, M R; Cramer, J G; Crawford, H J; Das, D; Das, S; Dash, S; Daugherity, M; de Moura, M M; Dedovich, T G; Dephillips, M; Derevschikov, A A; Didenko, L; Dietel, T; Djawotho, P; Dogra, S M; Dong, W J; Dong, X; Draper, J E; Du, F; Dunin, V B; Dunlop, J C; Dutta Mazumdar, M R; Eckardt, V; Edwards, W R; Efimov, L G; Emelianov, V; Engelage, J; Eppley, G; Erazmus, B; Estienne, M; Fachini, P; Fatemi, R; Fedorisin, J; Filip, P; Finch, E; Fine, V; Fisyak, Y; Fu, J; Gagliardi, C A; Gaillard, L; Ganti, M S; Ghazikhanian, V; Ghosh, P; Gonzalez, J E; Gorbunov, Y G; Gos, H; Grebenyuk, O; Grosnick, D; Guertin, S M; Guimaraes, K S F F; Gupta, N; Gutierrez, T D; Haag, B; Hallman, T J; Hamed, A; Harris, J W; He, W; Heinz, M; Henry, T W; Hepplemann, S; Hippolyte, B; Hirsch, A; Hjort, E; Hoffman, A M; Hoffmann, G W; Horner, M J; Huang, H Z; Huang, S L; Hughes, E W; Humanic, T J; Igo, G; Jacobs, P; Jacobs, W W; Jakl, P; Jia, F; Jiang, H; Jones, P G; Judd, E G; Kabana, S; Kang, K; Kapitan, J; Kaplan, M; Keane, D; Kechechyan, A; Khodyrev, V Yu; Kim, B C; Kiryluk, J; Kisiel, A; Kislov, E M; Klein, S R; Kocoloski, A; Koetke, D D; Kollegger, T; Kopytine, M; Kotchenda, L; Kouchpil, V; Kowalik, K L; Kramer, M; Kravtsov, P; Kravtsov, V I; Krueger, K; Kuhn, C; Kulikov, A I; Kumar, A; Kuznetsov, A A; Lamont, M A C; Landgraf, J M; Lange, S; Lapointe, S; Laue, F; Lauret, J; Lebedev, A; Lednicky, R; Lee, C-H; Lehocka, S; Levine, M J; Li, C; Li, Q; Li, Y; Lin, G; Lin, X; Lindenbaum, S J; Lisa, M A; Liu, F; Liu, H; Liu, J; Liu, L; Liu, Z; Ljubicic, T; Llope, W J; Long, H; Longacre, R S; Love, W A; Lu, Y; Ludlam, T; Lynn, D; Ma, G L; Ma, J G; Ma, Y G; Magestro, D; Mahapatra, D P; Majka, R; Mangotra, L K; Manweiler, R; Margetis, S; Markert, C; Martin, L; Matis, H S; Matulenko, Yu A; McClain, C J; McShane, T S; Melnick, Yu; Meschanin, A; Millane, J; Miller, M L; Minaev, N G; Mioduszewski, S; Mironov, C; Mischke, A; Mishra, D K; Mitchell, J; Mohanty, B; Molnar, L; Moore, C F; Morozov, D A; Munhoz, M G; Nandi, B K; Nattrass, C; Nayak, T K; Nelson, J M; Nepali, N S; Netrakanti, P K; Nogach, L V; Nurushev, S B; Odyniec, G; Ogawa, A; Okorokov, V; Oldenburg, M; Olson, D; Pachr, M; Pal, S K; Panebratsev, Y; Panitkin, S Y; Pavlinov, A I; Pawlak, T; Peitzmann, T; Perevoztchikov, V; Perkins, C; Peryt, W; Phatak, S C; Picha, R; Planinic, M; Pluta, J; Poljak, N; Porile, N; Porter, J; Poskanzer, A M; Potekhin, M; Potrebenikova, E; Potukuchi, B V K S; Prindle, D; Pruneau, C; Putschke, J; Rakness, G; Raniwala, R; Raniwala, S; Ray, R L; Razin, S V; Reinnarth, J; Relyea, D; Ridiger, A; Ritter, H G; Roberts, J B; Rogachevskiy, O V; Romero, J L; Rose, A; Roy, C; Ruan, L; Russcher, M J; Sahoo, R; Sakuma, T; Salur, S; Sandweiss, J; Sarsour, M; Sazhin, P S; Schambach, J; Scharenberg, R P; Schmitz, N; Seger, J; Selyuzhenkov, I; Seyboth, P; Shabetai, A; Shahaliev, E; Shao, M; Sharma, M; Shen, W Q; Shimanskiy, S S; Sichtermann, E P; Simon, F; Singaraju, R N; Smirnov, N; Snellings, R; Sood, G; Sorensen, P; Sowinski, J; Speltz, J; Spinka, H M; Srivastava, B; Stadnik, A; Stanislaus, T D S; Stock, R; Stolpovsky, A; Strikhanov, M; Stringfellow, B; Suaide, A A P; Subba, N L; Sugarbaker, E; Sumbera, M; Sun, Z; Surrow, B; Swanger, M; Symons, T J M; Szanto de Toledo, A; Tai, A; Takahashi, J; Tang, A H; Tarnowsky, T; Thein, D; Thomas, J H; Timmins, A R; Timoshenko, S; Tokarev, M; Trainor, T A; Trentalange, S; Tribble, R E; Tsai, O D; Ulery, J; Ullrich, T; Underwood, D G; Van Buren, G; van der Kolk, N; van Leeuwen, M; Vander Molen, A M; Varma, R; Vasilevski, I M; Vasiliev, A N; Vernet, R; Vigdor, S E; Viyogi, Y P; Vokal, S; Voloshin, S A; Waggoner, W T; Wang, F; Wang, G; Wang, J S; Wang, X L; Wang, Y; Watson, J W; Webb, J C; Westfall, G D; Wetzler, A; Whitten, C; Wieman, H; Wissink, S W; Witt, R; Wood, J; Wu, J; Xu, N; Xu, Q H; Xu, Z; Yepes, P; Yoo, I-K; Yurevich, V I; Zhan, W; Zhang, H; Zhang, W M; Zhang, Y; Zhang, Z P; Zhao, Y; Zhong, C; Zoulkarneev, R; Zoulkarneeva, Y; Zubarev, A N; Zuo, J X

2006-10-13

Transverse momentum spectra of pi+/-, p, and p up to 12 GeV/c at midrapidity in centrality selected Au + Au collisions at square root sNN=200 GeV are presented. In central Au + Au collisions, both pi +/- and p(p) show significant suppression with respect to binary scaling at pT approximately >4 GeV/c. Protons and antiprotons are less suppressed than pi+/-, in the range 1.5 approximately < pT approximately < 6 GeV/c. The pi-/pi+ and p/p ratios show at most a weak pT dependence and no significant centrality dependence. The p/pi ratios in central Au + Au collisions approach the values in p + p and d + Au collisions at pT approximately >5 GeV/c. The results at high pT indicate that the partonic sources of pi+/-, p, and p have similar energy loss when traversing the nuclear medium.

{phi} meson production in Au + Au and p + p collisions at {radical}s{sub NN}=200 GeV

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

Adams, J.; Adler, C.; Aggarwal, M.M.

2004-06-01

We report the STAR measurement of {psi} meson production in Au + Au and p + p collisions at {radical}s{sub NN} = 200 GeV. Using the event mixing technique, the {psi} spectra and yields are obtained at midrapidity for five centrality bins in Au+Au collisions and for non-singly-diffractive p+p collisions. It is found that the {psi} transverse momentum distributions from Au+Au collisions are better fitted with a single-exponential while the p+p spectrum is better described by a double-exponential distribution. The measured nuclear modification factors indicate that {psi} production in central Au+Au collisions is suppressed relative to peripheral collisions when scaledmore » by the number of binary collisions (). The systematics of versus centrality and the constant {psi}/K{sup -} ratio versus beam species, centrality, and collision energy rule out kaon coalescence as the dominant mechanism for {psi} production.« less

System-size dependence of open-heavy-flavor production in nucleus-nucleus collisions at √sNN =200 GeV

NASA Astrophysics Data System (ADS)

Adare, A.; Afanasiev, S.; Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Al-Bataineh, H.; Alexander, J.; Aoki, K.; Apadula, N.; Aphecetche, L.; Armendariz, R.; Aronson, S. H.; Asai, J.; Atomssa, E. T.; Averbeck, R.; Awes, T. C.; Azmoun, B.; Babintsev, V.; Baksay, G.; Baksay, L.; Baldisseri, A.; Barish, K. N.; Barnes, P. D.; Bassalleck, B.; Bathe, S.; Batsouli, S.; Baublis, V.; Baumgart, S.; Bazilevsky, A.; Belikov, S.; Bennett, R.; Berdnikov, Y.; Bickley, A. A.; Boissevain, J. G.; Borel, H.; Boyle, K.; Brooks, M. L.; Buesching, H.; Bumazhnov, V.; Bunce, G.; Butsyk, S.; Campbell, S.; Chang, B. S.; Charvet, J.-L.; Chernichenko, S.; Chi, C. Y.; Chiba, J.; Chiu, M.; Choi, I. J.; Chujo, T.; Chung, P.; Churyn, A.; Cianciolo, V.; Cleven, C. R.; Cole, B. A.; Comets, M. P.; Constantin, P.; Csanád, M.; Csörgő, T.; Dahms, T.; Das, K.; David, G.; Deaton, M. B.; Dehmelt, K.; Delagrange, H.; Denisov, A.; D'Enterria, D.; Deshpande, A.; Desmond, E. J.; Dietzsch, O.; Dion, A.; Donadelli, M.; Drapier, O.; Drees, A.; Dubey, A. K.; Durham, J. M.; Durum, A.; Dzhordzhadze, V.; Efremenko, Y. V.; Egdemir, J.; Ellinghaus, F.; Emam, W. S.; Enokizono, A.; En'yo, H.; Esumi, S.; Eyser, K. O.; Fields, D. E.; Finger, M.; Finger, M.; Fleuret, F.; Fokin, S. L.; Fraenkel, Z.; Frantz, J. E.; Franz, A.; Frawley, A. D.; Fujiwara, K.; Fukao, Y.; Fusayasu, T.; Gadrat, S.; Garishvili, I.; Glenn, A.; Gong, H.; Gonin, M.; Gosset, J.; Goto, Y.; Granier de Cassagnac, R.; Grau, N.; Greene, S. V.; Grosse Perdekamp, M.; Gunji, T.; Gustafsson, H.-Å.; Hachiya, T.; Hadj Henni, A.; Haegemann, C.; Haggerty, J. S.; Hamagaki, H.; Han, R.; Harada, H.; Hartouni, E. P.; Haruna, K.; Haslum, E.; Hayano, R.; He, X.; Heffner, M.; Hemmick, T. K.; Hester, T.; Hiejima, H.; Hill, J. C.; Hobbs, R.; Hohlmann, M.; Holzmann, W.; Homma, K.; Hong, B.; Horaguchi, T.; Hornback, D.; Ichihara, T.; Iinuma, H.; Imai, K.; Inaba, M.; Inoue, Y.; Isenhower, D.; Isenhower, L.; Ishihara, M.; Isobe, T.; Issah, M.; Isupov, A.; Jacak, B. V.; Jia, J.; Jin, J.; Jinnouchi, O.; Johnson, B. M.; Joo, K. S.; Jouan, D.; Kajihara, F.; Kametani, S.; Kamihara, N.; Kamin, J.; Kaneta, M.; Kang, J. H.; Kanou, H.; Kawall, D.; Kazantsev, A. V.; Khanzadeev, A.; Kikuchi, J.; Kim, D. H.; Kim, D. J.; Kim, E.; Kinney, E.; Kiss, Á.; Kistenev, E.; Kiyomichi, A.; Klay, J.; Klein-Boesing, C.; Kochenda, L.; Kochetkov, V.; Komkov, B.; Konno, M.; Kotchetkov, D.; Kozlov, A.; Král, A.; Kravitz, A.; Kubart, J.; Kunde, G. J.; Kurihara, N.; Kurita, K.; Kweon, M. J.; Kwon, Y.; Kyle, G. S.; Lacey, R.; Lai, Y. S.; Lajoie, J. G.; Lebedev, A.; Lee, D. M.; Lee, M. K.; Lee, T.; Leitch, M. J.; Leite, M. A. L.; Lenzi, B.; Li, X.; Liška, T.; Litvinenko, A.; Liu, M. X.; Love, B.; Lynch, D.; Maguire, C. F.; Makdisi, Y. I.; Malakhov, A.; Malik, M. D.; Manko, V. I.; Mao, Y.; Mašek, L.; Masui, H.; Matathias, F.; McCumber, M.; McGaughey, P. L.; McGlinchey, D.; Miake, Y.; Mikeš, P.; Miki, K.; Miller, T. E.; Milov, A.; Mioduszewski, S.; Mishra, M.; Mitchell, J. T.; Mitrovski, M.; Morreale, A.; Morrison, D. P.; Moukhanova, T. V.; Mukhopadhyay, D.; Murata, J.; Nagamiya, S.; Nagata, Y.; Nagle, J. L.; Naglis, M.; Nakagawa, I.; Nakamiya, Y.; Nakamura, T.; Nakano, K.; Newby, J.; Nguyen, M.; Norman, B. E.; Nouicer, R.; Nyanin, A. S.; O'Brien, E.; Oda, S. X.; Ogilvie, C. A.; Ohnishi, H.; Oka, M.; Okada, K.; Omiwade, O. O.; Oskarsson, A.; Ouchida, M.; Ozawa, K.; Pak, R.; Pal, D.; Palounek, A. P. T.; Pantuev, V.; Papavassiliou, V.; Park, J.; Park, W. J.; Pate, S. F.; Pei, H.; Peng, J.-C.; Pereira, H.; Peresedov, V.; Peressounko, D. Yu.; Pinkenburg, C.; Purschke, M. L.; Purwar, A. K.; Qu, H.; Rak, J.; Rakotozafindrabe, A.; Ravinovich, I.; Read, K. F.; Rembeczki, S.; Reuter, M.; Reygers, K.; Riabov, V.; Riabov, Y.; Roche, G.; Romana, A.; Rosati, M.; Rosendahl, S. S. E.; Rosnet, P.; Rukoyatkin, P.; Rykov, V. L.; Sahlmueller, B.; Saito, N.; Sakaguchi, T.; Sakai, S.; Sakata, H.; Samsonov, V.; Sato, S.; Sawada, S.; Seele, J.; Seidl, R.; Semenov, V.; Seto, R.; Sharma, D.; Shein, I.; Shevel, A.; Shibata, T.-A.; Shigaki, K.; Shimomura, M.; Shoji, K.; Sickles, A.; Silva, C. L.; Silvermyr, D.; Silvestre, C.; Sim, K. S.; Singh, C. P.; Singh, V.; Skutnik, S.; Slunečka, M.; Soldatov, A.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; Sourikova, I. V.; Staley, F.; Stankus, P. W.; Stenlund, E.; Stepanov, M.; Ster, A.; Stoll, S. P.; Sugitate, T.; Suire, C.; Sziklai, J.; Tabaru, T.; Takagi, S.; Takagui, E. M.; Taketani, A.; Tanaka, Y.; Tanida, K.; Tannenbaum, M. J.; Taranenko, A.; Tarján, P.; Thomas, T. L.; Togawa, M.; Toia, A.; Tojo, J.; Tomášek, L.; Torii, H.; Towell, R. S.; Tram, V.-N.; Tserruya, I.; Tsuchimoto, Y.; Vale, C.; Valle, H.; van Hecke, H. W.; Velkovska, J.; Vértesi, R.; Vinogradov, A. A.; Virius, M.; Vrba, V.; Vznuzdaev, E.; Wagner, M.; Walker, D.; Wang, X. R.; Watanabe, Y.; Wessels, J.; White, S. N.; Winter, D.; Woody, C. L.; Wysocki, M.; Xie, W.; Yamaguchi, Y. L.; Yanovich, A.; Yasin, Z.; Ying, J.; Yokkaichi, S.; Young, G. R.; Younus, I.; Yushmanov, I. E.; Zajc, W. A.; Zaudtke, O.; Zhang, C.; Zhou, S.; Zimányi, J.; Zolin, L.; Phenix Collaboration

2014-09-01

The PHENIX Collaboration at the Relativistic Heavy Ion Collider has measured open-heavy-flavor production in Cu +Cu collisions at √sNN =200 GeV through the measurement of electrons at midrapidity that originate from semileptonic decays of charm and bottom hadrons. In peripheral Cu +Cu collisions an enhanced production of electrons is observed relative to p +p collisions scaled by the number of binary collisions. In the transverse momentum range from 1 to 5 GeV/c the nuclear modification factor is RAA˜1.4. As the system size increases to more central Cu +Cu collisions, the enhancement gradually disappears and turns into a suppression. For pT>3 GeV/c, the suppression reaches RAA˜0.8 in the most central collisions. The pT and centrality dependence of RAA in Cu +Cu collisions agree quantitatively with RAA in d +Au and Au +Au collisions, if compared at a similar number of participating nucleons .

Seasonal variation and potential sources of Cryptosporidium contamination in surface waters of Chao Phraya River and Bang Pu Nature Reserve pier, Thailand.

PubMed

Koompapong, Khuanchai; Sukthana, Yaowalark

2012-07-01

Using molecular techniques, a longitudinal study was conducted with the aims at identifying the seasonal difference of Cryptosporidium contamination in surface water as well as analyzing the potential sources based on species information. One hundred forty-four water samples were collected, 72 samples from the Chao Phraya River, Thailand, collected in the summer, rainy and cool seasons and 72 samples from sea water at Bang Pu Nature Reserve pier, collected before, during and after the presence of migratory seagulls. Total prevalence of Cryptosporidium contamination in river and sea water locations was 11% and 6%, respectively. The highest prevalence was observed at the end of rainy season continuing into the cool season in river water (29%) and in sea water (12%). During the rainy season, prevalence of Cryptosporidium was 4% in river and sea water samples, but none in summer season. All positive samples from the river was C. parvum, while C. meleagridis (1), and C. serpentis (1) were obtained from sea water. To the best of our knowledge, this is the first genetic study in Thailand of Cryptosporidium spp contamination in river and sea water locations and the first report of C. serpentis, suggesting that humans, household pets, farm animals, wildlife and migratory birds may be the potential sources of the parasites. The findings are of use for implementing preventive measures to reduce the transmission of cryptosporidiosis to both humans and animals.

W and Z boson production in p-Pb collisions at √{s_{NN}}=5.02 TeV

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, S.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; An, M.; Andrei, C.; Andrews, H. A.; Andronic, A.; Anguelov, V.; Anson, C.; Antičić, T.; Antinori, F.; Antonioli, P.; Anwar, R.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Beltran, L. G. E.; Belyaev, V.; Bencedi, G.; Beole, S.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Boldizsár, L.; Bombara, M.; Bonora, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossù, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buhler, P.; Buitron, S. A. I.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Caines, H.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crkovská, J.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; De Souza, R. D.; Deisting, A.; Deloff, A.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Di Ruzza, B.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Duggal, A. K.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erhardt, F.; Espagnon, B.; Esumi, S.; Eulisse, G.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Francisco, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gajdosova, K.; Gallio, M.; Galvan, C. D.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Garg, K.; Garg, P.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Gay Ducati, M. B.; Germain, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, A. S.; Gonzalez, V.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Graczykowski, L. K.; Graham, K. L.; Greiner, L.; Grelli, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Gruber, L.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Guzman, I. B.; Haake, R.; Hadjidakis, C.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Herrmann, F.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Hladky, J.; Horak, D.; Hosokawa, R.; Hristov, P.; Hughes, C.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Ippolitov, M.; Irfan, M.; Isakov, V.; Islam, M. S.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacak, B.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Khuntia, A.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, H.; Kim, J. S.; Kim, J.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kundu, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lapidus, K.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lazaridis, L.; Lea, R.; Leardini, L.; Lee, S.; Lehas, F.; Lehner, S.; Lehrbach, J.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Llope, W.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lupi, M.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Mao, Y.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Mathis, A. M.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzilli, M.; Mazzoni, M. A.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Mhlanga, S.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Mishra, T.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montes, E.; Moreira De Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Münning, K.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Myers, C. J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Negrao De Oliveira, R. A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Ohlson, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pacik, V.; Pagano, D.; Pagano, P.; Paić, G.; Pal, S. K.; Palni, P.; Pan, J.; Pandey, A. K.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, J.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Peng, X.; Pereira Da Costa, H.; Peresunko, D.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Poppenborg, H.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Pozdniakov, V.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Rana, D. B.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Ratza, V.; Ravasenga, I.; Read, K. F.; Redlich, K.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rodríguez Cahuantzi, M.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Sas, M. H. P.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schmidt, M.; Schukraft, J.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sett, P.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singhal, V.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Sozzi, F.; Spiriti, E.; Sputowska, I.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Suzuki, K.; Swain, S.; Szabo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thakur, D.; Thomas, D.; Tieulent, R.; Tikhonov, A.; Timmins, A. R.; Toia, A.; Tripathy, S.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Umaka, E. N.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vázquez Doce, O.; Vechernin, V.; Veen, A. M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Vértesi, R.; Vickovic, L.; Vigolo, S.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Virgili, T.; Vislavicius, V.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Voscek, D.; Vranic, D.; Vrláková, J.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Willems, G. A.; Williams, M. C. S.; Windelband, B.; Winn, M.; Witt, W. E.; Yalcin, S.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zmeskal, J.

2017-02-01

The W and Z boson production was measured via the muonic decay channel in proton-lead collisions at √{s_{NN}}=5.02 TeV at the Large Hadron Collider with the ALICE detector. The measurement covers backward (-4.46 < y cms < -2.96) and forward (2.03 < y cms < 3.53) rapidity regions, corresponding to Pb-going and p-going directions, respectively. The Z-boson production cross section, with dimuon invariant mass of 60 < m μμ < 120 GeV/ c 2 and muon transverse momentum ( p T μ ) larger than 20 GeV/ c, is measured. The production cross section and charge asymmetry of muons from W-boson decays with p T μ > 10 GeV/ c are determined. The results are compared to theoretical calculations both with and without including the nuclear modification of the parton distribution functions. The W-boson production is also studied as a function of the collision centrality: the cross section of muons from W-boson decays is found to scale with the average number of binary nucleon-nucleon collisions within uncertainties.

Φ meson production in d+Au collisions at √s NN = 200 GeV

DOE PAGES

Adare, A.

2015-10-19

The PHENIX Collaboration has measured Φ meson production in d+Au collisions at √s NN=200 GeV using the dimuon and dielectron decay channels. The Φ meson is measured in the forward (backward) d-going (Au-going) direction, 1.2 < y < 2.2 (–2.2 < y < –1.2) in the transverse-momentum (p T) range from 1–7 GeV/c and at midrapidity |y|<0.35 in the p T range below 7 GeV/c. The Φ meson invariant yields and nuclear-modification factors as a function of p T, rapidity, and centrality are reported. An enhancement of Φ meson production is observed in the Au-going direction, while suppression is seenmore » in the d-going direction, and no modification is observed at midrapidity relative to the yield in p+p collisions scaled by the number of binary collisions. As a result, similar behavior was previously observed for inclusive charged hadrons and open heavy flavor, indicating similar cold-nuclear-matter effects.« less

Measurement of the production of high- p T electrons from heavy-flavour hadron decays in Pb–Pb collisions at s NN = 2.76  TeV

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

Adam, J.; Adamová, D.; Aggarwal, M. M.

Electrons from heavy-flavour hadron decays (charm and beauty) were measured with the ALICE detector in Pb–Pb collisions at a centre-of-mass of energy √s NN =2.76 TeV. The transverse momentum (p T ) differential production yields at mid-rapidity were used to calculate the nuclear modification factor R AA in the interval 3 < p T <18 GeV/c. The R AA shows a strong suppression compared to binary scaling of pp collisions at the same energy (up to a factor of 4) in the 10% most central Pb–Pb collisions. There is a centrality trend of suppression, and a weaker suppression (down tomore » a factor of 2) in semi-peripheral (50–80%) collisions is observed. The suppression of electrons in this broad p T interval indicates that both charm and beauty quarks lose energy when they traverse the hot medium formed in Pb–Pb collisions at LHC.« less

J/ψ production and nuclear effects in p-Pb collisions at $$ \\sqrt{{{{\\mathrm{s}}_{\\mathrm{NN}}}}} $$ = 5.02 TeV

DOE PAGES

Abelev, B.; Adam, J.; Adamová, D.; ...

2014-02-18

We studied inclusive J/ψ production with the ALICE detector in p-Pb collisions at the nucleon-nucleon center of mass energy √s NN = 5.02 TeV at the CERN LHC. The measurement is performed in the center of mass rapidity domains 2.03 < y cms < 3.53 and -4.46 < y cms < -2.96, down to zero transverse momentum, studying the μ + μ - decay mode. In this paper, the J/ψ production cross section and the nuclear modification factor R pPb for the rapidities under study are presented. Moreover, while at forward rapidity, corresponding to the proton direction, amore » suppression of the J/ψ yield with respect to binary-scaled pp collisions is observed, in the backward region no suppression is present. Finally, the ratio of the forward and backward yields is also measured differentially in rapidity and transverse momentum. Theoretical predictions based on nuclear shadowing, as well as on models including, in addition, a contribution from partonic energy loss, are in fair agreement with the experimental results.« less

Measurement of the production of high- p T electrons from heavy-flavour hadron decays in Pb–Pb collisions at s NN = 2.76  TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2017-05-29

Electrons from heavy-flavour hadron decays (charm and beauty) were measured with the ALICE detector in Pb–Pb collisions at a centre-of-mass of energy √s NN =2.76 TeV. The transverse momentum (p T ) differential production yields at mid-rapidity were used to calculate the nuclear modification factor R AA in the interval 3 < p T <18 GeV/c. The R AA shows a strong suppression compared to binary scaling of pp collisions at the same energy (up to a factor of 4) in the 10% most central Pb–Pb collisions. There is a centrality trend of suppression, and a weaker suppression (down tomore » a factor of 2) in semi-peripheral (50–80%) collisions is observed. The suppression of electrons in this broad p T interval indicates that both charm and beauty quarks lose energy when they traverse the hot medium formed in Pb–Pb collisions at LHC.« less

Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints.

PubMed

van der Ploeg, Tjeerd; Austin, Peter C; Steyerberg, Ewout W

2014-12-22

Modern modelling techniques may potentially provide more accurate predictions of binary outcomes than classical techniques. We aimed to study the predictive performance of different modelling techniques in relation to the effective sample size ("data hungriness"). We performed simulation studies based on three clinical cohorts: 1282 patients with head and neck cancer (with 46.9% 5 year survival), 1731 patients with traumatic brain injury (22.3% 6 month mortality) and 3181 patients with minor head injury (7.6% with CT scan abnormalities). We compared three relatively modern modelling techniques: support vector machines (SVM), neural nets (NN), and random forests (RF) and two classical techniques: logistic regression (LR) and classification and regression trees (CART). We created three large artificial databases with 20 fold, 10 fold and 6 fold replication of subjects, where we generated dichotomous outcomes according to different underlying models. We applied each modelling technique to increasingly larger development parts (100 repetitions). The area under the ROC-curve (AUC) indicated the performance of each model in the development part and in an independent validation part. Data hungriness was defined by plateauing of AUC and small optimism (difference between the mean apparent AUC and the mean validated AUC <0.01). We found that a stable AUC was reached by LR at approximately 20 to 50 events per variable, followed by CART, SVM, NN and RF models. Optimism decreased with increasing sample sizes and the same ranking of techniques. The RF, SVM and NN models showed instability and a high optimism even with >200 events per variable. Modern modelling techniques such as SVM, NN and RF may need over 10 times as many events per variable to achieve a stable AUC and a small optimism than classical modelling techniques such as LR. This implies that such modern techniques should only be used in medical prediction problems if very large data sets are available.

Ionic fluids with r-6 pair interactions have power-law electrostatic screening

NASA Astrophysics Data System (ADS)

Kjellander, Roland; Forsberg, Björn

2005-06-01

The decay behaviour of radial distribution functions for large distances r is investigated for classical Coulomb fluids where the ions interact with an r-6 potential (e.g. a dispersion interaction) in addition to the Coulombic and the short-range repulsive potentials (e.g. a hard core). The pair distributions and the density-density (NN), charge-density (QN) and charge-charge (QQ) correlation functions are investigated analytically and by Monte Carlo simulations. It is found that the NN correlation function ultimately decays like r-6 for large r, just as it does for fluids of electroneutral particles interacting with an r-6 potential. The prefactor is proportional to the squared compressibility in both cases. The QN correlations decay in general like r-8 and the QQ correlations like r-10 in the ionic fluid. The average charge density around an ion decays generally like r-8 and the average electrostatic potential like r-6. This behaviour is in stark contrast to the decay behaviour for classical Coulomb fluids in the absence of the r-6 potential, where all these functions decay exponentially for large r. The power-law decays are, however, the same as for quantum Coulomb fluids. This indicates that the inclusion of the dispersion interaction as an effective r-6 interaction potential in classical systems yields the same decay behaviour for the pair correlations as in quantum ionic systems. An exceptional case is the completely symmetric binary electrolyte for which only the NN correlation has a power-law decay but not the QQ correlations. These features are shown by an analysis of the bridge function.

Cold-nuclear-matter effects on heavy-quark production at forward and backward rapidity in d + Au collisions at √sNN = 200  GeV.

PubMed

Adare, A; Aidala, C; Ajitanand, N N; Akiba, Y; Akimoto, R; Al-Bataineh, H; Al-Ta'ani, H; Alexander, J; Andrews, K R; Angerami, A; Aoki, K; Apadula, N; Appelt, E; Aramaki, Y; Armendariz, R; Aschenauer, E C; Atomssa, E T; Averbeck, R; Awes, T C; Azmoun, B; Babintsev, V; Bai, M; Baksay, G; Baksay, L; Bannier, B; Barish, K N; Bassalleck, B; Basye, A T; Bathe, S; Baublis, V; Baumann, C; Bazilevsky, A; Belikov, S; Belmont, R; Ben-Benjamin, J; Bennett, R; Bhom, J H; Blau, D S; Bok, J S; Boyle, K; Brooks, M L; Broxmeyer, D; Buesching, H; Bumazhnov, V; Bunce, G; Butsyk, S; Campbell, S; Caringi, A; Castera, P; Chen, C-H; Chi, C Y; Chiu, M; Choi, I J; Choi, J B; Choudhury, R K; Christiansen, P; Chujo, T; Chung, P; Chvala, O; Cianciolo, V; Citron, Z; Cole, B A; Conesa Del Valle, Z; Connors, M; Csanád, M; Csörgő, T; Dahms, T; Dairaku, S; Danchev, I; Das, K; Datta, A; David, G; Dayananda, M K; Denisov, A; Deshpande, A; Desmond, E J; Dharmawardane, K V; Dietzsch, O; Dion, A; Donadelli, M; Drapier, O; Drees, A; Drees, K A; Durham, J M; Durum, A; Dutta, D; D'Orazio, L; Edwards, S; Efremenko, Y V; Ellinghaus, F; Engelmore, T; Enokizono, A; En'yo, H; Esumi, S; Fadem, B; Fields, D E; Finger, M; Finger, M; Fleuret, F; Fokin, S L; Fraenkel, Z; Frantz, J E; Franz, A; Frawley, A D; Fujiwara, K; Fukao, Y; Fusayasu, T; Gal, C; Garishvili, I; Glenn, A; Gong, H; Gong, X; Gonin, M; Goto, Y; Granier de Cassagnac, R; Grau, N; Greene, S V; Grim, G; Grosse Perdekamp, M; Gunji, T; Guo, L; Gustafsson, H-Å; Haggerty, J S; Hahn, K I; Hamagaki, H; Hamblen, J; Han, R; Hanks, J; Harper, C; Hashimoto, K; Haslum, E; Hayano, R; He, X; Heffner, M; Hemmick, T K; Hester, T; Hill, J C; Hohlmann, M; Hollis, R S; Holzmann, W; Homma, K; Hong, B; Horaguchi, T; Hori, Y; Hornback, D; Huang, S; Ichihara, T; Ichimiya, R; Iinuma, H; Ikeda, Y; Imai, K; Inaba, M; Iordanova, A; Isenhower, D; Ishihara, M; Issah, M; Ivanischev, D; Iwanaga, Y; Jacak, B V; Jia, J; Jiang, X; Jin, J; John, D; Johnson, B M; Jones, T; Joo, K S; Jouan, D; Jumper, D S; Kajihara, F; Kamin, J; Kaneti, S; Kang, B H; Kang, J H; Kang, J S; Kapustinsky, J; Karatsu, K; Kasai, M; Kawall, D; Kawashima, M; Kazantsev, A V; Kempel, T; Khanzadeev, A; Kijima, K M; Kikuchi, J; Kim, A; Kim, B I; Kim, D J; Kim, E-J; Kim, Y-J; Kim, Y K; Kinney, E; Kiss, A; Kistenev, E; Kleinjan, D; Kline, P; Kochenda, L; Komkov, B; Konno, M; Koster, J; Kotov, D; Král, A; Kravitz, A; Kunde, G J; Kurita, K; Kurosawa, M; Kwon, Y; Kyle, G S; Lacey, R; Lai, Y S; Lajoie, J G; Lebedev, A; Lee, D M; Lee, J; Lee, K B; Lee, K S; Lee, S H; Lee, S R; Leitch, M J; Leite, M A L; Li, X; Lichtenwalner, P; Liebing, P; Lim, S H; Linden Levy, L A; Liška, T; Liu, H; Liu, M X; Love, B; Lynch, D; Maguire, C F; Makdisi, Y I; Malik, M D; Manion, A; Manko, V I; Mannel, E; Mao, Y; Masui, H; Matathias, F; McCumber, M; McGaughey, P L; McGlinchey, D; McKinney, C; Means, N; Mendoza, M; Meredith, B; Miake, Y; Mibe, T; Mignerey, A C; Miki, K; Milov, A; Mitchell, J T; Miyachi, Y; Mohanty, A K; Moon, H J; Morino, Y; Morreale, A; Morrison, D P; Motschwiller, S; Moukhanova, T V; Murakami, T; Murata, J; Nagamiya, S; Nagle, J L; Naglis, M; Nagy, M I; Nakagawa, I; Nakamiya, Y; Nakamura, K R; Nakamura, T; Nakano, K; Nam, S; Newby, J; Nguyen, M; Nihashi, M; Nouicer, R; Nyanin, A S; Oakley, C; O'Brien, E; Oda, S X; Ogilvie, C A; Oka, M; Okada, K; Onuki, Y; Oskarsson, A; Ouchida, M; Ozawa, K; Pak, R; Pantuev, V; Papavassiliou, V; Park, B H; Park, I H; Park, S K; Park, W J; Pate, S F; Patel, L; Pei, H; Peng, J-C; Pereira, H; Peressounko, D Yu; Petti, R; Pinkenburg, C; Pisani, R P; Proissl, M; Purschke, M L; Qu, H; Rak, J; Ravinovich, I; Read, K F; Rembeczki, S; Reygers, K; Riabov, V; Riabov, Y; Richardson, E; Roach, D; Roche, G; Rolnick, S D; Rosati, M; Rosen, C A; Rosendahl, S S E; Ružička, P; Sahlmueller, B; Saito, N; Sakaguchi, T; Sakashita, K; Samsonov, V; Sano, S; Sarsour, M; Sato, T; Savastio, M; Sawada, S; Sedgwick, K; Seele, J; Seidl, R; Seto, R; Sharma, D; Shein, I; Shibata, T-A; Shigaki, K; Shim, H H; Shimomura, M; Shoji, K; Shukla, P; Sickles, A; Silva, C L; Silvermyr, D; Silvestre, C; Sim, K S; Singh, B K; Singh, C P; Singh, V; Slunečka, M; Sodre, T; Soltz, R A; Sondheim, W E; Sorensen, S P; Sourikova, I V; Stankus, P W; Stenlund, E; Stoll, S P; Sugitate, T; Sukhanov, A; Sun, J; Sziklai, J; Takagui, E M; Takahara, A; Taketani, A; Tanabe, R; Tanaka, Y; Taneja, S; Tanida, K; Tannenbaum, M J; Tarafdar, S; Taranenko, A; Tennant, E; Themann, H; Thomas, D; Thomas, T L; Togawa, M; Toia, A; Tomášek, L; Tomášek, M; Torii, H; Towell, R S; Tserruya, I; Tsuchimoto, Y; Utsunomiya, K; Vale, C; Valle, H; van Hecke, H W; Vazquez-Zambrano, E; Veicht, A; Velkovska, J; Vértesi, R; Virius, M; Vossen, A; Vrba, V; Vznuzdaev, E; Wang, X R; Watanabe, D; Watanabe, K; Watanabe, Y; Watanabe, Y S; Wei, F; Wei, R; Wessels, J; White, S N; Winter, D; Woody, C L; Wright, R M; Wysocki, M; Yamaguchi, Y L; Yamaura, K; Yang, R; Yanovich, A; Ying, J; Yokkaichi, S; Yoo, J S; You, Z; Young, G R; Younus, I; Yushmanov, I E; Zajc, W A; Zelenski, A; Zhou, S

2014-06-27

The PHENIX experiment has measured open heavy-flavor production via semileptonic decay over the transverse momentum range 1 < p(T) < 6  GeV/c at forward and backward rapidity (1.4 < |y| < 2.0) in d+Au and p + p collisions at √sNN = 200  GeV. In central d+Au collisions, relative to the yield in p + p collisions scaled by the number of binary nucleon-nucleon collisions, a suppression is observed at forward rapidity (in the d-going direction) and an enhancement at backward rapidity (in the Au-going direction). Predictions using nuclear-modified-parton-distribution functions, even with additional nuclear-p(T) broadening, cannot simultaneously reproduce the data at both rapidity ranges, which implies that these models are incomplete and suggests the possible importance of final-state interactions in the asymmetric d + Au collision system. These results can be used to probe cold-nuclear-matter effects, which may significantly affect heavy-quark production, in addition to helping constrain the magnitude of charmonia-breakup effects in nuclear matter.

W and Z boson production in p-Pb collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=5.02 $$ TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2017-02-15

The W and Z boson production was measured via the muonic decay channel in proton-lead collisions at √ sNN = 5.02 TeV at the Large Hadron Collider with the ALICE detector. The measurement covers backward (–4.46 < y cms < –2.96) and forward (2.03 < y cms < 3.53) rapidity regions, corresponding to Pb-going and p-going directions, respectively. The Z-boson production cross section, with dimuon invariant mass of 60 < m μμ < 120 GeV/c 2 and muon transverse momentum (p T μ) larger than 20 GeV/c, is measured. The production cross section and charge asymmetry of muons from W-bosonmore » decays with p T μ > 10 GeV/c are determined. The results are compared to theoretical calculations both with and without including the nuclear modification of the parton distribution functions. The W-boson production is also studied as a function of the collision centrality: the cross section of muons from W-boson decays is found to scale with the average number of binary nucleon-nucleon collisions within uncertainties.« less

Measurement of prompt D-meson production in p-Pb collisions at √(s(NN))=5.02 TeV.

PubMed

Abelev, B; Adam, J; Adamová, D; Aggarwal, M M; Aglieri Rinella, G; Agnello, M; Agostinelli, A; Agrawal, N; Ahammed, Z; Ahmad, N; Ahmed, I; Ahn, S U; Ahn, S A; Aimo, I; Aiola, S; Ajaz, M; Akindinov, A; Alam, S N; Aleksandrov, D; Alessandro, B; Alexandre, D; Alici, A; Alkin, A; Alme, J; Alt, T; Altinpinar, S; Altsybeev, I; Alves Garcia Prado, C; Andrei, C; Andronic, A; Anguelov, V; Anielski, J; Antičić, T; Antinori, F; Antonioli, P; Aphecetche, L; Appelshäuser, H; Arcelli, S; Armesto, N; Arnaldi, R; Aronsson, T; Arsene, I C; Arslandok, M; Augustinus, A; Averbeck, R; Awes, T C; Azmi, M D; Bach, M; Badalà, A; Baek, Y W; Bagnasco, S; Bailhache, R; Bala, R; Baldisseri, A; Baltasar Dos Santos Pedrosa, F; Baral, R C; Barbera, R; Barile, F; Barnaföldi, G G; Barnby, L S; Barret, V; Bartke, J; Basile, M; Bastid, N; Basu, S; Bathen, B; Batigne, G; Batyunya, B; Batzing, P C; Baumann, C; Bearden, I G; Beck, H; Bedda, C; Behera, N K; Belikov, I; Bellini, F; Bellwied, R; Belmont-Moreno, E; Belmont, R; Belyaev, V; Bencedi, G; Beole, S; Berceanu, I; Bercuci, A; Berdnikov, Y; Berenyi, D; Berger, M E; Bertens, R A; Berzano, D; Betev, L; Bhasin, A; Bhat, I R; Bhati, A K; Bhattacharjee, B; Bhom, J; Bianchi, L; Bianchi, N; Bianchin, C; Bielčík, J; Bielčíková, J; Bilandzic, A; Bjelogrlic, S; Blanco, F; Blau, D; Blume, C; Bock, F; Bogdanov, A; Bøggild, H; Bogolyubsky, M; Böhmer, F V; Boldizsár, L; Bombara, M; Book, J; Borel, H; Borissov, A; Bossú, F; Botje, M; Botta, E; Böttger, S; Braun-Munzinger, P; Bregant, M; Breitner, T; Broker, T A; Browning, T A; Broz, M; Bruna, E; Bruno, G E; Budnikov, D; Buesching, H; Bufalino, S; Buncic, P; Busch, O; Buthelezi, Z; Caffarri, D; Cai, X; Caines, H; Calero Diaz, L; Caliva, A; Calvo Villar, E; Camerini, P; Carena, F; Carena, W; Castillo Castellanos, J; Casula, E A R; Catanescu, V; Cavicchioli, C; Ceballos Sanchez, C; Cepila, J; Cerello, P; Chang, B; Chapeland, S; Charvet, J L; Chattopadhyay, S; Chattopadhyay, S; Chelnokov, V; Cherney, M; Cheshkov, C; Cheynis, B; Chibante Barroso, V; Chinellato, D D; Chochula, P; Chojnacki, M; Choudhury, S; Christakoglou, P; Christensen, C H; Christiansen, P; Chujo, T; Chung, S U; Cicalo, C; Cifarelli, L; Cindolo, F; Cleymans, J; Colamaria, F; Colella, D; Collu, A; Colocci, M; Conesa Balbastre, G; Conesa Del Valle, Z; Connors, M E; Contreras, J G; Cormier, T M; Corrales Morales, Y; Cortese, P; Cortés Maldonado, I; Cosentino, M R; Costa, F; Crochet, P; Cruz Albino, R; Cuautle, E; Cunqueiro, L; Dainese, A; Dang, R; Danu, A; Das, D; Das, I; Das, K; Das, S; Dash, A; Dash, S; De, S; Delagrange, H; Deloff, A; Dénes, E; D'Erasmo, G; De Caro, A; de Cataldo, G; de Cuveland, J; De Falco, A; De Gruttola, D; De Marco, N; De Pasquale, S; de Rooij, R; Diaz Corchero, M A; Dietel, T; Dillenseger, P; Divià, R; Di Bari, D; Di Liberto, S; Di Mauro, A; Di Nezza, P; Djuvsland, Ø; Dobrin, A; Dobrowolski, T; Domenicis Gimenez, D; Dönigus, B; Dordic, O; Dørheim, S; Dubey, A K; Dubla, A; Ducroux, L; Dupieux, P; Dutta Majumdar, A K; Hilden, T E; Ehlers, R J; Elia, D; Engel, H; Erazmus, B; Erdal, H A; Eschweiler, D; Espagnon, B; Esposito, M; Estienne, M; Esumi, S; Evans, D; Evdokimov, S; Fabris, D; Faivre, J; Falchieri, D; Fantoni, A; Fasel, M; Fehlker, D; Feldkamp, L; Felea, D; Feliciello, A; Feofilov, G; Ferencei, J; Fernández Téllez, A; Ferreiro, E G; Ferretti, A; Festanti, A; Figiel, J; Figueredo, M A S; Filchagin, S; Finogeev, D; Fionda, F M; Fiore, E M; Floratos, E; Floris, M; Foertsch, S; Foka, P; Fokin, S; Fragiacomo, E; Francescon, A; Frankenfeld, U; Fuchs, U; Furget, C; Fusco Girard, M; Gaardhøje, J J; Gagliardi, M; Gago, A M; Gallio, M; Gangadharan, D R; Ganoti, P; Garabatos, C; Garcia-Solis, E; Gargiulo, C; Garishvili, I; Gerhard, J; Germain, M; Gheata, A; Gheata, M; Ghidini, B; Ghosh, P; Ghosh, S K; Gianotti, P; Giubellino, P; Gladysz-Dziadus, E; Glässel, P; Gomez Ramirez, A; González-Zamora, P; Gorbunov, S; Görlich, L; Gotovac, S; Graczykowski, L K; Grelli, A; Grigoras, A; Grigoras, C; Grigoriev, V; Grigoryan, A; Grigoryan, S; Grinyov, B; Grion, N; Grosse-Oetringhaus, J F; Grossiord, J-Y; Grosso, R; Guber, F; Guernane, R; Guerzoni, B; Guilbaud, M; Gulbrandsen, K; Gulkanyan, H; Gumbo, M; Gunji, T; Gupta, A; Gupta, R; Khan, K H; Haake, R; Haaland, Ø; Hadjidakis, C; Haiduc, M; Hamagaki, H; Hamar, G; Hanratty, L D; Hansen, A; Harris, J W; Hartmann, H; Harton, A; Hatzifotiadou, D; Hayashi, S; Heckel, S T; Heide, M; Helstrup, H; Herghelegiu, A; Herrera Corral, G; Hess, B A; Hetland, K F; Hippolyte, B; Hladky, J; Hristov, P; Huang, M; Humanic, T J; Hussain, N; Hutter, D; Hwang, D S; Ilkaev, R; Ilkiv, I; Inaba, M; Innocenti, G M; Ionita, C; Ippolitov, M; Irfan, M; Ivanov, M; Ivanov, V; Jachołkowski, A; Jacobs, P M; Jahnke, C; Jang, H J; Janik, M A; Jayarathna, P H S Y; Jena, C; Jena, S; Jimenez Bustamante, R T; Jones, P G; Jung, H; Jusko, A; Kadyshevskiy, V; Kalcher, S; Kalinak, P; Kalweit, A; Kamin, J; Kang, J H; Kaplin, V; Kar, S; Karasu Uysal, A; Karavichev, O; Karavicheva, T; Karpechev, E; Kebschull, U; Keidel, R; Keijdener, D L D; Khan, M M; Khan, P; Khan, S A; Khanzadeev, A; Kharlov, Y; Kileng, B; Kim, B; Kim, D W; Kim, D J; Kim, J S; Kim, M; Kim, M; Kim, S; Kim, T; Kirsch, S; Kisel, I; Kiselev, S; Kisiel, A; Kiss, G; Klay, J L; Klein, J; Klein-Bösing, C; Kluge, A; Knichel, M L; Knospe, A G; Kobdaj, C; Kofarago, M; Köhler, M K; Kollegger, T; Kolojvari, A; Kondratiev, V; Kondratyeva, N; Konevskikh, A; Kovalenko, V; Kowalski, M; Kox, S; Koyithatta Meethaleveedu, G; Kral, J; Králik, I; Kravčáková, A; Krelina, M; Kretz, M; Krivda, M; Krizek, F; Kryshen, E; Krzewicki, M; Kučera, V; Kucheriaev, Y; Kugathasan, T; Kuhn, C; Kuijer, P G; Kulakov, I; Kumar, J; Kurashvili, P; Kurepin, A; Kurepin, A B; Kuryakin, A; Kushpil, S; Kweon, M J; Kwon, Y; Ladron de Guevara, P; Lagana Fernandes, C; Lakomov, I; Langoy, R; Lara, C; Lardeux, A; Lattuca, A; La Pointe, S L; La Rocca, P; Lea, R; Leardini, L; Lee, G R; Legrand, I; Lehnert, J; Lemmon, R C; Lenti, V; Leogrande, E; Leoncino, M; León Monzón, I; Lévai, P; Li, S; Lien, J; Lietava, R; Lindal, S; Lindenstruth, V; Lippmann, C; Lisa, M A; Ljunggren, H M; Lodato, D F; Loenne, P I; Loggins, V R; Loginov, V; Lohner, D; Loizides, C; Lopez, X; López Torres, E; Lu, X-G; Luettig, P; Lunardon, M; Luparello, G; Luzzi, C; Ma, R; Maevskaya, A; Mager, M; Mahapatra, D P; Mahmood, S M; Maire, A; Majka, R D; Malaev, M; Maldonado Cervantes, I; Malinina, L; Mal'Kevich, D; Malzacher, P; Mamonov, A; Manceau, L; Manko, V; Manso, F; Manzari, V; Marchisone, M; Mareš, J; Margagliotti, G V; Margotti, A; Marín, A; Markert, C; Marquard, M; Martashvili, I; Martin, N A; Martinengo, P; Martínez, M I; Martínez García, G; Martin Blanco, J; Martynov, Y; Mas, A; Masciocchi, S; Masera, M; Masoni, A; Massacrier, L; Mastroserio, A; Matyja, A; Mayer, C; Mazer, J; Mazzoni, M A; Meddi, F; Menchaca-Rocha, A; Meninno, E; Mercado Pérez, J; Meres, M; Miake, Y; Mikhaylov, K; Milano, L; Milosevic, J; Mischke, A; Mishra, A N; Miśkowiec, D; Mitra, J; Mitu, C M; Mlynarz, J; Mohammadi, N; Mohanty, B; Molnar, L; Montaño Zetina, L; Montes, E; Morando, M; Moreira De Godoy, D A; Moretto, S; Morreale, A; Morsch, A; Muccifora, V; Mudnic, E; Mühlheim, D; Muhuri, S; Mukherjee, M; Müller, H; Munhoz, M G; Murray, S; Musa, L; Musinsky, J; Nandi, B K; Nania, R; Nappi, E; Nattrass, C; Nayak, K; Nayak, T K; Nazarenko, S; Nedosekin, A; Nicassio, M; Niculescu, M; Nielsen, B S; Nikolaev, S; Nikulin, S; Nikulin, V; Nilsen, B S; Noferini, F; Nomokonov, P; Nooren, G; Norman, J; Nyanin, A; Nystrand, J; Oeschler, H; Oh, S; Oh, S K; Okatan, A; Olah, L; Oleniacz, J; Oliveira Da Silva, A C; Onderwaater, J; Oppedisano, C; Ortiz Velasquez, A; Oskarsson, A; Otwinowski, J; Oyama, K; Ozdemir, M; Sahoo, P; Pachmayer, Y; Pachr, M; Pagano, P; Paić, G; Painke, F; Pajares, C; Pal, S K; Palmeri, A; Pant, D; Papikyan, V; Pappalardo, G S; Pareek, P; Park, W J; Parmar, S; Passfeld, A; Patalakha, D I; Paticchio, V; Paul, B; Pawlak, T; Peitzmann, T; Pereira Da Costa, H; Pereira De Oliveira Filho, E; Peresunko, D; Pérez Lara, C E; Pesci, A; Peskov, V; Pestov, Y; Petráček, V; Petran, M; Petris, M; Petrovici, M; Petta, C; Piano, S; Pikna, M; Pillot, P; Pinazza, O; Pinsky, L; Piyarathna, D B; Płoskoń, M; Planinic, M; Pluta, J; Pochybova, S; Podesta-Lerma, P L M; Poghosyan, M G; Pohjoisaho, E H O; Polichtchouk, B; Poljak, N; Pop, A; Porteboeuf-Houssais, S; Porter, J; Potukuchi, B; Prasad, S K; Preghenella, R; Prino, F; Pruneau, C A; Pshenichnov, I; Puddu, G; Pujahari, P; Punin, V; Putschke, J; Qvigstad, H; Rachevski, A; Raha, S; Rak, J; Rakotozafindrabe, A; Ramello, L; Raniwala, R; Raniwala, S; Räsänen, S S; Rascanu, B T; Rathee, D; Rauf, A W; Razazi, V; Read, K F; Real, J S; Redlich, K; Reed, R J; Rehman, A; Reichelt, P; Reicher, M; Reidt, F; Renfordt, R; Reolon, A R; Reshetin, A; Rettig, F; Revol, J-P; Reygers, K; Riabov, V; Ricci, R A; Richert, T; Richter, M; Riedler, P; Riegler, W; Riggi, F; Rivetti, A; Rocco, E; Rodríguez Cahuantzi, M; Rodriguez Manso, A; Røed, K; Rogochaya, E; Rohni, S; Rohr, D; Röhrich, D; Romita, R; Ronchetti, F; Ronflette, L; Rosnet, P; Rossi, A; Roukoutakis, F; Roy, A; Roy, C; Roy, P; Rubio Montero, A J; Rui, R; Russo, R; Ryabinkin, E; Ryabov, Y; Rybicki, A; Sadovsky, S; Šafařík, K; Sahlmuller, B; Sahoo, R; Sahu, P K; Saini, J; Sakai, S; Salgado, C A; Salzwedel, J; Sambyal, S; Samsonov, V; Sanchez Castro, X; Sánchez Rodríguez, F J; Šándor, L; Sandoval, A; Sano, M; Santagati, G; Sarkar, D; Scapparone, E; Scarlassara, F; Scharenberg, R P; Schiaua, C; Schicker, R; Schmidt, C; Schmidt, H R; Schuchmann, S; Schukraft, J; Schulc, M; Schuster, T; Schutz, Y; Schwarz, K; Schweda, K; Scioli, G; Scomparin, E; Scott, R; Segato, G; Seger, J E; Sekiguchi, Y; Selyuzhenkov, I; Seo, J; Serradilla, E; Sevcenco, A; Shabetai, A; Shabratova, G; Shahoyan, R; Shangaraev, A; Sharma, N; Sharma, S; Shigaki, K; Shtejer, K; Sibiriak, Y; Siddhanta, S; Siemiarczuk, T; Silvermyr, D; Silvestre, C; Simatovic, G; Singaraju, R; Singh, R; Singha, S; Singhal, V; Sinha, B C; Sinha, T; Sitar, B; Sitta, M; Skaali, T B; Skjerdal, K; Slupecki, M; Smirnov, N; Snellings, R J M; Søgaard, C; Soltz, R; Song, J; Song, M; Soramel, F; Sorensen, S; Spacek, M; Spiriti, E; Sputowska, I; Spyropoulou-Stassinaki, M; Srivastava, B K; Stachel, J; Stan, I; Stefanek, G; Steinpreis, M; Stenlund, E; Steyn, G; Stiller, J H; Stocco, D; Stolpovskiy, M; Strmen, P; Suaide, A A P; Sugitate, T; Suire, C; Suleymanov, M; Sultanov, R; Šumbera, M; Susa, T; Symons, T J M; Szabo, A; Szanto de Toledo, A; Szarka, I; Szczepankiewicz, A; Szymanski, M; Takahashi, J; Tangaro, M A; Tapia Takaki, J D; Tarantola Peloni, A; Tarazona Martinez, A; Tarzila, M G; Tauro, A; Tejeda Muñoz, G; Telesca, A; Terrevoli, C; Thäder, J; Thomas, D; Tieulent, R; Timmins, A R; Toia, A; Trubnikov, V; Trzaska, W H; Tsuji, T; Tumkin, A; Turrisi, R; Tveter, T S; Ullaland, K; Uras, A; Usai, G L; Vajzer, M; Vala, M; Valencia Palomo, L; Vallero, S; Vande Vyvre, P; Van Der Maarel, J; Van Hoorne, J W; van Leeuwen, M; Vargas, A; Vargyas, M; Varma, R; 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Zimmermann, A; Zimmermann, M B; Zinovjev, G; Zoccarato, Y; Zyzak, M

2014-12-05

The p_{T}-differential production cross sections of the prompt charmed mesons D^{0}, D^{+}, D^{*+}, and D_{s}^{+} and their charge conjugate in the rapidity interval -0.96

Cloaca prolapse and cystitis in green iguana (Iguana iguana) caused by a novel Cryptosporidium species.

PubMed

Kik, Marja J L; van Asten, Alphons J A M; Lenstra, Johannes A; Kirpensteijn, Jolle

2011-01-10

Cryptosporidium infection was associated with colitis and cystitis in 2 green iguanas (Iguana iguana). The disease was characterized by a chronic clinical course of cloacal prolapses and cystitis. Histological examination of the gut and urinary bladder showed numerous Cryptosporidium developmental stages on the surface of the epithelium with mixed inflammatory response in the lamina propria. Cryptosporidium oocysts were visualised in a cytological preparation of the faeces. Based on the small subunit ribosomal RNA gene the cryptosporidia were characterized as belonging to the intestinal cryptosporidial lineage, but not to Cryptosporidium saurophilum or Cryptosporidium serpentis species. Copyright © 2010 Elsevier B.V. All rights reserved.

The light curve of CV Serpentis, the sometimes-eclipsing Wolf-Rayet star

NASA Technical Reports Server (NTRS)

Schild, R.; Liller, W.

1975-01-01

New photoelectric observations of the B-magnitude of CV Ser made in 1973 and 1974 show no clear evidence of an eclipse, but they establish night-to-night variability of several percent, a systematic brightness change of 0.035 mag during a portion of the single orbit observed in 1973, and irregular flaring in 1974. We made iris photometer measurements of Harvard patrol plates taken between 1905 June and 1953 July, and find no evidence of a very deep eclipse such as observed by Hjellming and Hiltner. We present several new light curves and discuss then in the light of the recent results of Cowley et al.

Charged hadron transverse momentum distributions in Au+Au collisions at S=200 GeV

NASA Astrophysics Data System (ADS)

Roland, Christof; PHOBOS Collaboration; Back, B. B.; Baker, M. D.; Barton, D. S.; Betts, R. R.; Ballintijn, M.; Bickley, A. A.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Decowski, M. P.; García, E.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Heintzelman, G. A.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; McLeod, D.; Michałowski, J.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sagerer, J.; Sarin, P.; Sawicki, P.; Skulski, W.; Steadman, S. G.; Steinberg, P.; Stephans, G. S. F.; Stodulski, M.; Sukhanov, A.; Tang, J.-L.; Teng, R.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wadsworth, B.; Wolfs, F. L. H.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2003-03-01

We present transverse momentum distributions of charged hadrons produced in Au+Au collisions at sqrt(s_NN) = 200 GeV. The evolution of the spectra for transverse momenta p_T from 0.25 to 5GeV/c is studied as a function of collision centrality over a range from 65 to 344 participating nucleons. We find a significant change of the spectral shape between proton-antiproton and peripheral Au+Au collisions. Comparing peripheral to central Au+Au collisions, we find that the yields at the highest p_T exhibit approximate scaling with the number of participating nucleons, rather than scaling with the number of binary collisions.

A Human Platelet Calcium Calculator Trained by Pairwise Agonist Scanning

PubMed Central

Lee, Mei Yan; Diamond, Scott L.

2015-01-01

Since platelet intracellular calcium mobilization [Ca(t)]i controls granule release, cyclooxygenase-1 and integrin activation, and phosphatidylserine exposure, blood clotting simulations require prediction of platelet [Ca(t)]i in response to combinatorial agonists. Pairwise Agonist Scanning (PAS) deployed all single and pairwise combinations of six agonists (ADP, convulxin, thrombin, U46619, iloprost and GSNO used at 0.1, 1, and 10xEC50; 154 conditions including a null condition) to stimulate platelet P2Y1/P2Y12 GPVI, PAR1/PAR4, TP, IP receptors, and guanylate cyclase, respectively, in Factor Xa-inhibited (250 nM apixaban), diluted platelet rich plasma that had been loaded with the calcium dye Fluo-4 NW. PAS of 10 healthy donors provided [Ca(t)]i data for training 10 neural networks (NN, 2-layer/12-nodes) per donor. Trinary stimulations were then conducted at all 0.1x and 1xEC50 doses (160 conditions) as was a sampling of 45 higher ordered combinations (four to six agonists). The NN-ensemble average was a calcium calculator that accurately predicted [Ca (t)]i beyond the single and binary training set for trinary stimulations (R = 0.924). The 160 trinary synergy scores, a normalized metric of signaling crosstalk, were also well predicted (R = 0.850) as were the calcium dynamics (R = 0.871) and high-dimensional synergy scores (R = 0.695) for the 45 higher ordered conditions. The calculator even predicted sequential addition experiments (n = 54 conditions, R = 0.921). NN-ensemble is a fast calcium calculator, ideal for multiscale clotting simulations that include spatiotemporal concentrations of ADP, collagen, thrombin, thromboxane, prostacyclin, and nitric oxide. PMID:25723389

Study of Z production in PbPb and pp collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=2.76 $$ TeV in the dimuon and dielectron decay channels

DOE PAGES

Chatrchyan, Serguei

2015-03-04

We found that the production of Z bosons is studied in the dimuon and dielectron decay channels in PbPb and pp collisions at √s NN=2.76 TeV, using data collected by the CMS experiment at the LHC. The PbPb data sample corresponds to an integrated luminosity of about 166 μb -1, while the pp data sample collected in 2013 at the same nucleon-nucleon centre-of-mass energy has an integrated luminosity of 5.4 pb -1. The Z boson yield is measured as a function of rapidity, transverse momentum, and collision centrality. The ratio of PbPb to pp yields, scaled by the number ofmore » inelastic nucleon-nucleon collisions, is found to be 1.06 ± 0.05 (stat) ± 0.08 (syst) in the dimuon channel and 1.02 ± 0.08 (stat) ± 0.15 (syst) in the dielectron channel, for centrality-integrated Z boson production. Finally, this binary collision scaling is seen to hold in the entire kinematic region studied, as expected for a colourless probe that is unaffected by the hot and dense QCD medium produced in heavy ion collisions.« less

WNN 92; Proceedings of the 3rd Workshop on Neural Networks: Academic/Industrial/NASA/Defense, Auburn Univ., AL, Feb. 10-12, 1992 and South Shore Harbour, TX, Nov. 4-6, 1992

NASA Technical Reports Server (NTRS)

Padgett, Mary L. (Editor)

1993-01-01

The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.

Evidence from d+Au measurements for final-state suppression of high-p(T) hadrons in Au+Au collisions at RHIC.

PubMed

Adams, J; Adler, C; Aggarwal, M M; Ahammed, Z; Amonett, J; Anderson, B D; Anderson, M; Arkhipkin, D; Averichev, G S; Badyal, S K; Balewski, J; Barannikova, O; Barnby, L S; Baudot, J; Bekele, S; Belaga, V V; Bellwied, R; Berger, J; Bezverkhny, B I; Bhardwaj, S; Bhaskar, P; Bhati, A K; Bichsel, H; Billmeier, A; Bland, L C; Blyth, C O; Bonner, B E; Botje, M; Boucham, A; Brandin, A; Bravar, A; Cadman, R V; Cai, X Z; Caines, H; Calderón de la Barca Sánchez, M; Carroll, J; Castillo, J; Castro, M; Cebra, D; Chaloupka, P; Chattopadhyay, S; Chen, H F; Chen, Y; Chernenko, S P; Cherney, M; Chikanian, A; Choi, B; Christie, W; Coffin, J P; Cormier, T M; Cramer, J G; Crawford, H J; Das, D; Das, S; Derevschikov, A A; Didenko, L; Dietel, T; Dong, X; Draper, J E; Du, F; Dubey, A K; Dunin, V B; Dunlop, J C; Dutta Majumdar, M R; Eckardt, V; Efimov, L G; Emelianov, V; Engelage, J; Eppley, G; Erazmus, B; Fachini, P; Faine, V; Faivre, J; Fatemi, R; Filimonov, K; Filip, P; Finch, E; Fisyak, Y; Flierl, D; Foley, K J; Fu, J; Gagliardi, C A; Ganti, M S; Gagunashvili, N; Gans, J; Gaudichet, L; Germain, M; Geurts, F; Ghazikhanian, V; Ghosh, P; Gonzalez, J E; Grachov, O; Grigoriev, V; Gronstal, S; Grosnick, D; Guedon, M; Guertin, S M; Gupta, A; Gushin, E; Gutierrez, T D; Hallman, T J; Hardtke, D; Harris, J W; Heinz, M; Henry, T W; Heppelmann, S; Herston, T; Hippolyte, B; Hirsch, A; Hjort, E; Hoffmann, G W; Horsley, M; Huang, H Z; Huang, S L; Humanic, T J; Igo, G; Ishihara, A; Jacobs, P; Jacobs, W W; Janik, M; Johnson, I; Jones, P G; Judd, E G; Kabana, S; Kaneta, M; Kaplan, M; Keane, D; Kiryluk, J; Kisiel, A; Klay, J; Klein, S R; Klyachko, A; Koetke, D D; Kollegger, T; Konstantinov, A S; Kopytine, M; Kotchenda, L; Kovalenko, A D; Kramer, M; Kravtsov, P; Krueger, K; Kuhn, C; Kulikov, A I; Kumar, A; Kunde, G J; Kunz, C L; Kutuev, R Kh; Kuznetsov, A A; Lamont, M A C; Landgraf, J M; Lange, S; Lansdell, C P; Lasiuk, B; Laue, F; Lauret, J; Lebedev, A; Lednický, R; Leontiev, V M; LeVine, M J; Li, C; Li, Q; Lindenbaum, S J; Lisa, M A; Liu, F; Liu, L; Liu, Z; Liu, Q J; Ljubicic, T; Llope, W J; Long, H; Longacre, R S; Lopez-Noriega, M; Love, W A; Ludlam, T; Lynn, D; Ma, J; Ma, Y G; Magestro, D; Mahajan, S; Mangotra, L K; Mahapatra, D P; Majka, R; Manweiler, R; Margetis, S; Markert, C; Martin, L; Marx, J; Matis, H S; Matulenko, Yu A; McShane, T S; Meissner, F; Melnick, Yu; Meschanin, A; Messer, M; Miller, M L; Milosevich, Z; Minaev, N G; Mironov, C; Mishra, D; Mitchell, J; Mohanty, B; Molnar, L; Moore, C F; Mora-Corral, M J; Morozov, V; de Moura, M M; Munhoz, M G; Nandi, B K; Nayak, S K; Nayak, T K; Nelson, J M; Nevski, P; Nikitin, V A; Nogach, L V; Norman, B; Nurushev, S B; Odyniec, G; Ogawa, A; Okorokov, V; Oldenburg, M; Olson, D; Paic, G; Pandey, S U; Pal, S K; Panebratsev, Y; Panitkin, S Y; Pavlinov, A I; Pawlak, T; Perevoztchikov, V; Peryt, W; Petrov, V A; Phatak, S C; Picha, R; Planinic, M; Pluta, J; Porile, N; Porter, J; Poskanzer, A M; Potekhin, M; Potrebenikova, E; Potukuchi, B V K S; Prindle, D; Pruneau, C; Putschke, J; Rai, G; Rakness, G; Raniwala, R; Raniwala, S; Ravel, O; Ray, R L; Razin, S V; Reichhold, D; Reid, J G; Renault, G; Retiere, F; Ridiger, A; Ritter, H G; Roberts, J B; Rogachevski, O V; Romero, J L; Rose, A; Roy, C; Ruan, L J; Rykov, V; Sahoo, R; Sakrejda, I; Salur, S; Sandweiss, J; Savin, I; Schambach, J; Scharenberg, R P; Schmitz, N; Schroeder, L S; Schweda, K; Seger, J; Seliverstov, D; Seyboth, P; Shahaliev, E; Shao, M; Sharma, M; Shestermanov, K E; Shimanskii, S S; Singaraju, R N; Simon, F; Skoro, G; Smirnov, N; Snellings, R; Sood, G; Sorensen, P; Sowinski, J; Spinka, H M; Srivastava, B; Stanislaus, S; Stock, R; Stolpovsky, A; Strikhanov, M; Stringfellow, B; Struck, C; Suaide, A A P; Sugarbaker, E; Suire, C; Sumbera, M; Surrow, B; Symons, T J M; Szanto de Toledo, A; Szarwas, P; Tai, A; Takahashi, J; Tang, A H; Thein, D; Thomas, J H; Tikhomirov, V; Tokarev, M; Tonjes, M B; Trainor, T A; Trentalange, S; Tribble, R E; Trivedi, M D; Trofimov, V; Tsai, O; Ullrich, T; Underwood, D G; Van Buren, G; VanderMolen, A M; Vasiliev, A N; Vasiliev, M; Vigdor, S E; Viyogi, Y P; Voloshin, S A; Waggoner, W; Wang, F; Wang, G; Wang, X L; Wang, Z M; Ward, H; Watson, J W; Wells, R; Westfall, G D; Whitten, C; Wieman, H; Willson, R; Wissink, S W; Witt, R; Wood, J; Wu, J; Xu, N; Xu, Z; Xu, Z Z; Yakutin, A E; Yamamoto, E; Yang, J; Yepes, P; Yurevich, V I; Zanevski, Y V; Zborovský, I; Zhang, H; Zhang, H Y; Zhang, W M; Zhang, Z P; Zołnierczuk, P A; Zoulkarneev, R; Zoulkarneeva, J; Zubarev, A N

2003-08-15

We report measurements of single-particle inclusive spectra and two-particle azimuthal distributions of charged hadrons at high transverse momentum (high p(T)) in minimum bias and central d+Au collisions at sqrt[s(NN)]=200 GeV. The inclusive yield is enhanced in d+Au collisions relative to binary-scaled p+p collisions, while the two-particle azimuthal distributions are very similar to those observed in p+p collisions. These results demonstrate that the strong suppression of the inclusive yield and back-to-back correlations at high p(T) previously observed in central Au+Au collisions are due to final-state interactions with the dense medium generated in such collisions.

An efficient synthetic method for organometallic radicals: structures and properties of gold(i)-(nitronyl nitroxide)-2-ide complexes.

PubMed

Suzuki, Shuichi; Kira, Sayaka; Kozaki, Masatoshi; Yamamura, Masaki; Hasegawa, Toru; Nabeshima, Tatsuya; Okada, Keiji

2017-02-21

One-pot synthesis of (nitronyl nitroxide)-gold(i)-phosphine (NN-Au-P) complexes has been developed using chloro(tetrahydrothiophene)gold(i), phosphine ligands, nitronyl nitroxide radicals, and sodium hydroxide. The NN-Au-P complexes can be easily handled because they were quite stable under aerated conditions in both solution and crystalline states. They showed weak absorption bands with vibrational structures in the 450-650 nm region. The oxidation potentials assigned to the NN moieties of NN-Au-P complexes with aromatic phosphines were observed around -0.1 V vs. Fc/Fc + (-0.11 V for NN-Au-1, -0.08 V for NN-Au-2, -0.13 V for NN-Au-5, and -0.07 V for NN-Au-6), somewhat lower than that of NN-Au-P complexes with aliphatic phosphines (-0.25 V for NN-Au-3 and -0.17 V for NN-Au-4).

Molecular and characterization of NnPPO cDNA from lotus (Nelumbo nucifera) in rhizome browning.

PubMed

Dong, C; Yu, A Q; Yang, M G; Zhou, M Q; Hu, Z L

2016-04-30

The complete cDNA (NnPPO) of polyphenol oxidase in Nelumbo nucifera was successfully isolated, using Rapid amplification cDNA end (RACE) assays. The full-length cDNA of NnPPO was 2069 bp in size, containing a 1791 bp open reading frame coding 597 amino acids. The putative NnPPO possessed the conserved active sites and domains for PPO function. Phylogenetic analysis revealed that NnPPO shared high homology with PPO of high plants, and the homology modeling proved that NnPPO had the typical structure of PPO family. In order to characterize the role of NnPPO, Real-time PCR assay demonstrated that NnPPO mRNA was expressed in different tissues of N. nucifera including young leave, rhizome, flower, root and leafstalk, with the highest expression in rhizome. Patterns of NnPPO expression in rhizome illustrated its mRNA level was significantly elevated, which was consistent with the change of NnPPO activity during rhizome browning. Therefore, transcriptional activation of NnPPO was probably the main reason causing rhizome browning.

40 CFR Table Nn-2 to Subpart Nn of... - Default Values for Calculation Methodology 2 of This Subpart

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) MANDATORY GREENHOUSE GAS REPORTING Suppliers of Natural Gas and Natural Gas Liquids Pt. 98, Subpt. NN, Table NN-2 Table NN-2 to Subpart NN of Part 98.../Unit) 1 Natural Gas Mscf 0.0544 Propane Barrel 0.241 Normal butane Barrel 0.281 Ethane Barrel 0.170...

A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality.

PubMed

Wang, Xueyi

2012-02-08

The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.

40 CFR Table Nn-1 to Subpart Nn of... - Default Factors for Calculation Methodology 1 of This Subpart

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) MANDATORY GREENHOUSE GAS REPORTING Suppliers of Natural Gas and Natural Gas Liquids Pt. 98, Subpt. NN, Table NN-1 Table NN-1 to Subpart NN of Part 98... CO2emission factor (kg CO2/MMBtu) Natural Gas 1.026 MMBtu/Mscf 53.06 Propane 3.84 MMBtu/bbl 62.87 Normal...

Measurement of electrons from heavy-flavour hadron decays in p-Pb collisions at √{sNN} = 5.02TeV

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anielski, J.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. 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T.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. 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F.; Grossiord, J.-Y.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Haake, R.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Heide, M.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Hosokawa, R.; Hristov, P.; Huang, M.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jung, H.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, H.; Kim, J. S.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobayashi, T.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Kretz, M.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; León Vargas, H.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martin Blanco, J.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Massacrier, L.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Minervini, L. M.; Mischke, A.; Mishra, A. N.; Miśkowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Montes, E.; Moreira De Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Munzer, R. H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Nayak, K.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, P.; Paić, G.; Pal, S. K.; Pan, J.; Pandey, A. K.; Papcun, P.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Pereira Da Costa, H.; Pereira De Oliveira Filho, E.; Peresunko, D.; Pérez Lara, C. E.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Płoskoń, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Revol, J.-P.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Romita, R.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schuster, T.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Shigaki, K.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Søgaard, C.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Sozzi, F.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stefanek, G.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tangaro, M. A.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vajzer, M.; Vala, M.; Valencia Palomo, L.; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Venaruzzo, M.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Vyushin, A.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilde, M.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yaldo, C. G.; Yang, H.; Yang, P.; Yano, S.; Yasar, C.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.

2016-03-01

The production of electrons from heavy-flavour hadron decays was measured as a function of transverse momentum (pT) in minimum-bias p-Pb collisions at √{sNN} = 5.02 TeV using the ALICE detector at the LHC. The measurement covers the pT interval 0.5

Measurement of electrons from heavy-flavour hadron decays in p–Pb collisions at s NN = 5.02 TeV

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

Adam, J.; Adamová, D.; Aggarwal, M. M.

2015-12-31

We measured the production of electrons from heavy-flavour hadron decays as a function of transverse momentum (p T) in minimum-bias p-Pb collisions at √s NN = 5.02 TeV using the ALICE detector at the LHC. Our measurement covers the p T interval 0.5 < p T < 12 GeV/c and the rapidity range -1.065 < y cms < 0.135 in the centre-of-mass reference frame. The contribution of electrons from background sources was subtracted using an invariant mass approach. The nuclear modification factor R-pPb was calculated by comparing the p T-differential invariant cross section in p-Pb collisions to a pp referencemore » at the same centre-of-mass energy, which was obtained by interpolating measurements at √s = 2.76 TeV and √= 7 TeV. The R pPb is consistent with unity within uncertainties of about 25%, which become larger for p T below 1 GeV/c. Furthermore, these measurements show that heavy-flavour production is consistent with binary scaling, so that a suppression in the high-p T yield in Pb-Pb collisions has to be attributed to effects induced by the hot medium produced in the final state. The data in p-Pb collisions are described by recent model calculations that include cold nuclear matter effects.« less

Post-boosting of classification boundary for imbalanced data using geometric mean.

PubMed

Du, Jie; Vong, Chi-Man; Pun, Chi-Man; Wong, Pak-Kin; Ip, Weng-Fai

2017-12-01

In this paper, a novel imbalance learning method for binary classes is proposed, named as Post-Boosting of classification boundary for Imbalanced data (PBI), which can significantly improve the performance of any trained neural networks (NN) classification boundary. The procedure of PBI simply consists of two steps: an (imbalanced) NN learning method is first applied to produce a classification boundary, which is then adjusted by PBI under the geometric mean (G-mean). For data imbalance, the geometric mean of the accuracies of both minority and majority classes is considered, that is statistically more suitable than the common metric accuracy. PBI also has the following advantages over traditional imbalance methods: (i) PBI can significantly improve the classification accuracy on minority class while improving or keeping that on majority class as well; (ii) PBI is suitable for large data even with high imbalance ratio (up to 0.001). For evaluation of (i), a new metric called Majority loss/Minority advance ratio (MMR) is proposed that evaluates the loss ratio of majority class to minority class. Experiments have been conducted for PBI and several imbalance learning methods over benchmark datasets of different sizes, different imbalance ratios, and different dimensionalities. By analyzing the experimental results, PBI is shown to outperform other imbalance learning methods on almost all datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Differential processing of pro-neurotensin/neuromedin N and relationship to pro-hormone convertases.

PubMed

Kitabgi, Patrick

2006-10-01

Neurotensin (NT) is synthesized as part of a larger precursor that also contains neuromedin N (NN), a six amino acid neurotensin-like peptide. NT and NN are located in the C-terminal region of the precursor (pro-NT/NN) where they are flanked and separated by three Lys-Arg sequences. A fourth dibasic sequence is present in the middle of the precursor. Dibasics are the consensus sites recognized and cleaved by endoproteases that belong to the recently identified family of pro-protein convertases (PCs). In tissues that express pro-NT/NN, the three C-terminal Lys-Arg sites are differentially processed, whereas the middle dibasic is poorly cleaved. Pro-NT/NN processing gives rise mainly to NT and NN in the brain, to NT and a large peptide ending with the NN sequence at its C-terminus (large NN) in the gut and to NT, large NN and a large peptide ending with the NT sequence (large NT) in the adrenals. Recent evidence indicates that PC1, PC2 and PC5-A are the pro-hormone convertases responsible for the processing patterns observed in the gut, brain and adrenals, respectively. As NT, NN, large NT and large NN are all endowed with biological activity, the evidence reviewed here supports the idea that post-translational processing of pro-NT/NN in tissues may generate biological diversity.

Reformulated Neural Network (ReNN): a New Alternative for Data-driven Modelling in Hydrology and Water Resources Engineering

NASA Astrophysics Data System (ADS)

Razavi, S.; Tolson, B.; Burn, D.; Seglenieks, F.

2012-04-01

Reformulated Neural Network (ReNN) has been recently developed as an efficient and more effective alternative to feedforward multi-layer perceptron (MLP) neural networks [Razavi, S., and Tolson, B. A. (2011). "A new formulation for feedforward neural networks." IEEE Transactions on Neural Networks, 22(10), 1588-1598, DOI: 1510.1109/TNN.2011.2163169]. This presentation initially aims to introduce the ReNN to the water resources community and then demonstrates ReNN applications to water resources related problems. ReNN is essentially equivalent to a single-hidden-layer MLP neural network but defined on a new set of network variables which is more effective than the traditional set of network weights and biases. The main features of the new network variables are that they are geometrically interpretable and each variable has a distinct role in forming the network response. ReNN is more efficiently trained as it has a less complex error response surface. In addition to the ReNN training efficiency, the interpretability of the ReNN variables enables the users to monitor and understand the internal behaviour of the network while training. Regularization in the ReNN response can be also directly measured and controlled. This feature improves the generalization ability of the network. The appeal of the ReNN is demonstrated with two ReNN applications to water resources engineering problems. In the first application, the ReNN is used to model the rainfall-runoff relationships in multiple watersheds in the Great Lakes basin located in northeastern North America. Modelling inflows to the Great Lakes are of great importance to the management of the Great Lakes system. Due to the lack of some detailed physical data about existing control structures in many subwatersheds of this huge basin, the data-driven approach to modelling such as the ReNN are required to replace predictions from a physically-based rainfall runoff model. Unlike traditional MLPs, the ReNN does not necessarily require an independent set of data for validation as the ReNN has the capability to control and verify the network degree of regularization. As such, the ReNN can be very beneficial in this case study as the data available in this case study is limited. In the second application, ReNN is fitted on the response function of the SWAT hydrologic model to act as a fast-to-run response surface surrogate (i.e., metamodel) of the original computationally intensive SWAT model. Besides the training efficiency gains, the ReNN applications demonstrate how the ReNN interpretability could help users develop more reliable networks which perform predictably better in terms of generalization.

Prohormone convertases differentially process pro-neurotensin/neuromedin N in tissues and cell lines.

PubMed

Kitabgi, Patrick

2006-08-01

Neurotensin (NT) is synthesized as part of a larger precursor that also contains neuromedin N (NN), a six-amino acid neurotensin-like peptide. NT and NN are located in the C-terminal region of the precursor (pro-NT/NN) where they are flanked and separated by three Lys-Arg sequences. A fourth dibasic sequence is present in the middle of the precursor. Dibasics are the consensus sites recognized and cleaved by specialized endoproteases that belong to the family of proprotein convertases (PCs). In tissues that express pro-NT/NN, the three C-terminal Lys-Arg sites are differentially processed, whereas the middle dibasic is poorly cleaved. Processing gives rise mainly to NT and NN in the brain, to NT and a large peptide with a C-terminal NN moiety (large NN) in the gut, and to NT, large NN, and a large peptide with a C-terminal NT moiety (large NT) in the adrenals. Recent evidence indicates that PC1, PC2, and PC5-A are the prohormone convertases responsible for the processing patterns observed in the gut, brain, and adrenals, respectively. As NT, NN, large NT, and large NN are all endowed with biological activity, the evidence reviewed in this paper supports the idea that posttranslational processing of pro-NT/NN in tissues may generate biological diversity of pathophysiological relevance.

Neurotensin and neuromedin N are differentially processed from a common precursor by prohormone convertases in tissues and cell lines.

PubMed

Kitabgi, Patrick

2010-01-01

Neurotensin (NT) is synthesized as part of a larger precursor that also contains neuromedin N (NN), a six amino acid NT-like peptide. NT and NN are located in the C-terminal region of the precursor (pro-NT/NN) where they are flanked and separated by three Lys-Arg sequences. A fourth dibasic sequence is present in the middle of the precursor. Dibasics are the consensus sites recognized and cleaved by specialized endoproteases that belong to the family of proprotein convertases (PCs). In tissues that express pro-NT/NN, the three C-terminal Lys-Arg sites are differentially processed, whereas the middle dibasic is poorly cleaved. Processing gives rise mainly to NT and NN in the brain, NT and a large peptide with a C-terminal NN moiety (large NN) in the gut, and NT, large NN, and a large peptide with a C-terminal NT moiety (large NT) in the adrenals. Recent evidence indicates that PC1, PC2, and PC5-A are the prohormone convertases responsible for the processing patterns observed in the gut, brain, and adrenals, respectively. As NT, NN, large NT, and large NN are all endowed with biological activity, the evidence reviewed here supports the idea that posttranslational processing of pro-NT/NN in tissues may generate biological diversity of pathophysiological relevance.

Oxygen reduction reaction catalyzed by platinum nanonetwork prepared by template free one step synthesis for polymer electrolyte membrane fuel cells

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

Narayanamoorthy, B.; Kumar, B.V.V.S. Pavan; Eswaramoorthy, M.

2014-07-01

Highlights: • Supportless Pt nanonetwork (Pt NN) synthesized by novel template free one step method as per our earlier reported procedure. • Electrocatalytic activity of Pt NN studied taking oxygen reduction reaction in acid medium. • Kinetic and thermodynamic parameters were deduced under hydrodynamic conditions. • ORR mechanistic pathway was proposed based on kinetic rate constants. • ADT analysis found enhanced stability (5000 cycles) for Pt NN than Pt NN/VC and reported Pt/C. - Abstract: The reduction reaction of molecular oxygen (ORR) was investigated using supportless Pt nanonetwork (Pt NN) electrocatalyst in sulfuric acid medium. Pt NN was prepared bymore » template free borohydride reduction. The transmission electron microscope images revealed a network like nano-architecture having an average cluster size of 30 nm. The electrochemical characterization of supportless and Vulcan carbon supported Pt NN (Pt NN/VC) was carried out using rotating disc and ring disc electrodes at various temperatures. Kinetic and thermodynamic parameters were estimated under hydrodynamic conditions and compared with Pt NN/VC and reported Pt/C catalysts. The accelerated durability test revealed that supportless Pt NN is quite stable for 5000 potential cycles with 22% reduction in electrochemical surface area (ECSA). While the initial limiting current density has in fact increased by 11.6%, whereas Pt NN/VC suffered nearly 55% loss in ECSA and 13% loss in limiting current density confirming an enhanced stability of supportless Pt NN morphology for ORR compared to conventional Pt/C ORR catalysts in acid medium.« less

Application of the Wavy Mechanical Face Seal to Submarine Seal Design

DTIC Science & Technology

1984-07-01

i2), IIIMRT(±.2i2) 146 COMMON IVMATC12,. 1.),. BMAT (360). RHAi(12),RHB(i𔃼) 150 COMMON RCP, RCM, RF, RS 160 REAL*4 MTM. MTP, JXP, JYP, JX%’PJTP, JXM...NN-I. N 3326 NC-n(NN-l)*12+1 330 OMRTCNC)=BM’IT(NC)eCVXP(NN) 3349 BMAT (NC.I)- BMAT (NC..I)eCYPNN) 3359 IPIRT(NC+5)u=tIAT(NC.S)+CMTP(NN) 3360 OMAT(NC4𔄀... BMAT (NC+6)4CYfl(NN) 3378 SM’AT(NC.7)onBMRT(NC+7)+CYYfl(NN) 3380 700 BflRT(NC.i±)-BflAT(NCi±)CMT(NN) 3390 C 349 C ENFORCE ZERO DISPLACEMENT BOUNDARY

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

NASA Astrophysics Data System (ADS)

Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu

2016-01-01

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

Neuromedin N: high affinity interaction with brain neurotensin receptors and rapid inactivation by brain synaptic peptidases.

PubMed

Checler, F; Vincent, J P; Kitabgi, P

1986-07-31

Neuromedin N (NN) is a novel neurotensin (NT)-like hexapeptide recently isolated from porcine spinal cord. NN competitively inhibited the binding of monoiodinated [Trp11]NT to rat brain synaptic membranes with a 19-fold lower potency than NT. In the presence of 1 mM 1,10-phenanthroline or 10 microM bestatin, the potency of NN relative to NT was increased about 5-fold. NN was readily degraded by rat brain synaptic membranes, and NN-(2-6) was the major degradation product. NN-(2-6) did not bind to NT receptors at concentrations up to 1 microM whether or not peptidase inhibitors were present in the binding assay. The rate of degradation by synaptic membranes was nearly 2.5 times higher for NN than for NT. NN degradation by membranes was totally prevented by 1,10-phenanthroline and markedly inhibited by bestatin. The presence of NN in the central nervous system, its high potency to interact with brain NT receptors and its rapid inactivation by brain synaptic peptidases make it a potential neurotransmitter candidate acting at the NT receptor.

Influence of slaughter weight and stress gene genotype on the water-holding capacity and protein gel characteristics of three porcine muscles.

PubMed

Sutton, D S; Ellis, M; Lan, Y; McKeith, F K; Wilson, E R

1997-06-01

The longissimus lumborum, gluteus medius, and the triceps brachii muscles from 40 animals were used to evaluate the effect of stress gene genotype (non-mutant, NN and mono-mutant, Nn) and live weight at slaughter (110 kg and 140 kg) on the processing quality of fresh pork. The 45 minute and ultimate pH measurements did not differ between genotypes. Total percent protein was not different between samples taken from NN or Nn pigs, nor were there any differences in salt-soluble protein. The M. longissimus lumborum from Nn pigs possessed lower water-holding capacity values and lost greater amounts of water upon cooking. In addition, Nn pigs had lower subjective color and firmness scores which suggest a higher incidence of pale, soft and exudative pork. Slaughter weight did not affect total protein, salt-soluble protein, Minolta L(∗), a(∗) and b(∗) values or subjective color, firmness and marbling scores. Back fat thickness and loineye area increased as slaughter weight increased. Overall, this study suggested that Nn pigs have reduced water retention properties which may result in lower yields in processed meat items. Slaughter weight had limited effects on the processing quality of meat from NN or Nn pigs. There were no interactions of significance between stress gene genotype and slaughter weight, suggesting that the differences in muscle quality and functional properties between NN and Nn pigs are maintained over the slaughter weights used in this study.

40 CFR Table Nn-2 to Subpart Hh of... - Lookup Default Values for Calculation Methodology 2 of This Subpart

Code of Federal Regulations, 2011 CFR

2011-07-01

... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) MANDATORY GREENHOUSE GAS REPORTING Municipal Solid Waste Landfills Pt. 98, Subpt. NN, Table NN-2 Table NN-2 to Subpart HH of Part 98—Lookup Default Values...

Reinforcement learning neural-network-based controller for nonlinear discrete-time systems with input constraints.

PubMed

He, Pingan; Jagannathan, S

2007-04-01

A novel adaptive-critic-based neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of actuator constraints. The constraints of the actuator are treated in the controller design as the saturation nonlinearity. The adaptive critic NN controller architecture based on state feedback includes two NNs: the critic NN is used to approximate the "strategic" utility function, whereas the action NN is employed to minimize both the strategic utility function and the unknown nonlinear dynamic estimation errors. The critic and action NN weight updates are derived by minimizing certain quadratic performance indexes. Using the Lyapunov approach and with novel weight updates, the uniformly ultimate boundedness of the closed-loop tracking error and weight estimates is shown in the presence of NN approximation errors and bounded unknown disturbances. The proposed NN controller works in the presence of multiple nonlinearities, unlike other schemes that normally approximate one nonlinearity. Moreover, the adaptive critic NN controller does not require an explicit offline training phase, and the NN weights can be initialized at zero or random. Simulation results justify the theoretical analysis.

Measurement of D-meson production versus multiplicity in p-Pb collisions at √{{s}_{NN}}=5.02 TeV

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anielski, J.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Benacek, P.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Dénes, E.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, A.; Gheata, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, V.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grossiord, J.-Y.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Haake, R.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Huang, M.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jung, H.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, H.; Kim, J. S.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Kretz, M.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; León Vargas, H.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martin Blanco, J.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Massacrier, L.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Minervini, L. M.; Mischke, A.; Mishra, A. N.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Montes, E.; Moreira De Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, P.; Paić, G.; Pal, S. K.; Pan, J.; Pandey, A. K.; Papcun, P.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Pereira Da Costa, H.; Peresunko, D.; Pérez Lara, C. E.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Revol, J.-P.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Romita, R.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schuster, T.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Shigaki, K.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Søgaard, C.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; de Souza, R. D.; Sozzi, F.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stefanek, G.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tangaro, M. A.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vajzer, M.; Vala, M.; Valencia Palomo, L.; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Venaruzzo, M.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilde, M.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yaldo, C. G.; Yang, H.; Yang, P.; Yano, S.; Yasar, C.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.

2016-08-01

The measurement of prompt D-meson production as a function of multiplicity in p-Pb collisions at √{s_{NN}}=5.02 TeV with the ALICE detector at the LHC is reported. D0, D+ and D∗+ mesons are reconstructed via their hadronic decay channels in the centre-of-mass rapidity range -0 .96 < y cms < 0 .04 and transverse momentum interval 1

Prediction of the electron redundant SinNn fullerenes

NASA Astrophysics Data System (ADS)

Yang, Huihui; Song, Yan; Zhang, Yan; Chen, Hongshan

2018-05-01

The stabilities and electronic structures of SimAln-mNn and SinNn (n = 16, 20, m = 12 and n = 24, m = 16) fullerene-like cages have been investigated using density functional method B3LYP and the second-order perturbation theory MP2. The results show that the SimAln-mNn and SinNn fullerenes are more stable than the AlN counterparts. Comparing with the corresponding AlnNn cages, one silicon atom in each Si2N2 square protrudes and the excess electrons reside as lone pair electrons at the outside of the protrudent Si atoms. Analyses on the electronic structures suggest that the Sisbnd N bonds are covalent bonding with strong polarity. The ELF (electron localization function) shows large electron pair probability between Si and N atoms. The orbital interactions between Si and N are stronger than that between Al and N atoms; the overlap integral is 0.40 per Sisbnd N bond in SinNn and 0.34 per Alsbnd N bond in AlnNn. The AIM (atoms in molecule) charges on the Al atoms in AlnNn and SimAln-mNn are 2.37 and 2.40. The charges on the in-plane and protrudent Si atoms are about 2.88 and 1.50 respectively. Considering the large local dipole moments around the protrudent Si atoms, the electrostatic interactions are also favorable to the SiN cages.

Design of fuzzy system by NNs and realization of adaptability

NASA Technical Reports Server (NTRS)

Takagi, Hideyuki

1993-01-01

The issue of designing and tuning fuzzy membership functions by neural networks (NN's) was started by NN-driven Fuzzy Reasoning in 1988. NN-driven fuzzy reasoning involves a NN embedded in the fuzzy system which generates membership values. In conventional fuzzy system design, the membership functions are hand-crafted by trial and error for each input variable. In contrast, NN-driven fuzzy reasoning considers several variables simultaneously and can design a multidimensional, nonlinear membership function for the entire subspace.

Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

NASA Astrophysics Data System (ADS)

Manikumari, N.; Murugappan, A.; Vinodhini, G.

2017-07-01

Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

Evidence that PC2 is the endogenous pro-neurotensin convertase in rMTC 6-23 cells and that PC1- and PC2-transfected PC12 cells differentially process pro-neurotensin.

PubMed

Rovère, C; Barbero, P; Kitabgi, P

1996-05-10

The neuropeptide precursor proneurotensin/neuromedin N (pro-NT/NN) is mainly expressed and differentially processed in the brain and in the small intestine. We showed previously that rMTC 6-23 cells process pro-NT/NN with a pattern similar to brain tissue and increase pro-NT/NN expression in response to dexamethasone, and that PC12 cells also produce pro-NT/NN but are virtually unable to process it. In addition, PC12 cells were reported to be devoid of the prohormone convertases PC1 and PC2. The present study was designed to identify the proprotein convertase(s) (PC) involved in pro-NT/NN processing in rMTC 6-23 cells and to compare PC1- and PC2-transfected PC12 cells for their ability to process pro-NT/NN. rMTC 6-23 cells were devoid of PC1, PC4, and PC5 but expressed furin and PC2. Stable expression of antisense PC2 RNA in rMTC 6-23 cells led to a 90% decrease in PC2 protein levels that correlated with a > 80% reduction of pro-NT/NN processing. PC2 expression was stimulated by dexamethasone in a time- and concentration-dependent manner. Stable PC12/PC2 transfectants processed pro-NT/NN with a pattern similar to that observed in the brain and in rMTC 6-23 cells. In contrast, stable PC12/PC1 transfectants reproduced the pro-NT/NN processing pattern seen in the gut. We conclude that (i) PC2 is the major pro-NT/NN convertase in rMTC 6-23 cells; (ii) its expression is coregulated with that of pro-NT/NN in this cell line; and (iii) PC2 and PC1 differentially process pro-NT/NN with brain and intestinal phenotype, respectively.

Nearest neighbor-density-based clustering methods for large hyperspectral images

NASA Astrophysics Data System (ADS)

Cariou, Claude; Chehdi, Kacem

2017-10-01

We address the problem of hyperspectral image (HSI) pixel partitioning using nearest neighbor - density-based (NN-DB) clustering methods. NN-DB methods are able to cluster objects without specifying the number of clusters to be found. Within the NN-DB approach, we focus on deterministic methods, e.g. ModeSeek, knnClust, and GWENN (standing for Graph WatershEd using Nearest Neighbors). These methods only require the availability of a k-nearest neighbor (kNN) graph based on a given distance metric. Recently, a new DB clustering method, called Density Peak Clustering (DPC), has received much attention, and kNN versions of it have quickly followed and showed their efficiency. However, NN-DB methods still suffer from the difficulty of obtaining the kNN graph due to the quadratic complexity with respect to the number of pixels. This is why GWENN was embedded into a multiresolution (MR) scheme to bypass the computation of the full kNN graph over the image pixels. In this communication, we propose to extent the MR-GWENN scheme on three aspects. Firstly, similarly to knnClust, the original labeling rule of GWENN is modified to account for local density values, in addition to the labels of previously processed objects. Secondly, we set up a modified NN search procedure within the MR scheme, in order to stabilize of the number of clusters found from the coarsest to the finest spatial resolution. Finally, we show that these extensions can be easily adapted to the three other NN-DB methods (ModeSeek, knnClust, knnDPC) for pixel clustering in large HSIs. Experiments are conducted to compare the four NN-DB methods for pixel clustering in HSIs. We show that NN-DB methods can outperform a classical clustering method such as fuzzy c-means (FCM), in terms of classification accuracy, relevance of found clusters, and clustering speed. Finally, we demonstrate the feasibility and evaluate the performances of NN-DB methods on a very large image acquired by our AISA Eagle hyperspectral imaging sensor.

Immunological and biochemical characterization of processing products from the neurotensin/neuromedin N precursor in the rat medullary thyroid carcinoma 6-23 cell line.

PubMed Central

Bidard, J N; de Nadai, F; Rovere, C; Moinier, D; Laur, J; Martinez, J; Cuber, J C; Kitabgi, P

1993-01-01

Neurotensin (NT) and neuromedin N (NN) are two related biologically active peptides that are encoded in the same precursor molecule. In the rat, the precursor consists of a 169-residue polypeptide starting with an N-terminal signal peptide and containing in its C-terminal region one copy each of NT and NN. NN precedes NT and is separated from it by a Lys-Arg sequence. Two other Lys-Arg sequences flank the N-terminus of NN and the C-terminus of NT. A fourth Lys-Arg sequence occurs near the middle of the precursor and is followed by an NN-like sequence. Finally, an Arg-Arg pair is present within the NT moiety. The four Lys-Arg doublets represent putative processing sites in the precursor molecule. The present study was designed to investigate the post-translational processing of the NT/NN precursor in the rat medullary thyroid carcinoma (rMTC) 6-23 cell line, which synthesizes large amounts of NT upon dexamethasone treatment. Five region-specific antisera recognizing the free N- or C-termini of sequences adjacent to the basic doublets were produced, characterized and used for immunoblotting and radioimmunoassay studies in combination with gel filtration, reverse-phase h.p.l.c. and trypsin digestion of rMTC 6-23 cell extracts. Because two of the antigenic sequences, i.e. NN and the NN-like sequence, start with a lysine residue that is essential for recognition by their respective antisera, a micromethod by which trypsin specifically cleaves at arginine residues was developed. The results show that dexamethasone-treated rMTC 6-23 cells produced comparable amounts of NT, NN and a peptide corresponding to a large N-terminal precursor fragment lacking the NN and NT moieties. This large fragment was purified. N-Terminal sequencing revealed that it started at residue Ser23 of the prepro-NT/NN sequence, and thus established the Cys22-Ser23 bond as the cleavage site of the signal peptide. Two other large N-terminal fragments bearing respectively the NN and NT sequences at their C-termini were present in lower amounts. The NN-like sequence was internal to all the large fragments. There was no evidence for the presence of peptides with the NN-like sequence at their N-termini. This shows that, in rMTC 6-23 cells, the precursor is readily processed at the three Lys-Arg doublets that flank and separate the NT and NN sequences. In contrast, the Lys-Arg doublet that precedes the NN-like sequence is not processed in this system.(ABSTRACT TRUNCATED AT 400 WORDS) Images Figure 3 PMID:8471039

Nearest Neighbor Classification of Stationary Time Series: An Application to Anesthesia Level Classification by EEG Analysis.

DTIC Science & Technology

1980-12-05

classification procedures that are common in speech processing. The anesthesia level classification by EEG time series population screening problem example is in...formance. The use of the KL number type metric in NN rule classification, in a delete-one subj ect ’s EE-at-a-time KL-NN and KL- kNN classification of the...17 individual labeled EEG sample population using KL-NN and KL- kNN rules. The results obtained are shown in Table 1. The entries in the table indicate

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

Atamanik, E.; Thangadurai, V.

We report a comparative study of the dielectric properties of solid-state (ceramic method) synthesized NaNbO{sub 3} (NN), Na{sub 0.75}K{sub 0.25}NbO{sub 3} (K25NN), K{sub 0.5}Na{sub 0.5}NbO{sub 3} (KNN) and some composite materials containing In{sub 2}O{sub 3} and NN or KNN using an AC impedance method. Powder X-ray diffraction (PXRD) was employed to investigate the phase purity. No significant amount of impurity phase was observed for NN, K25NN, and KNN. Substitutions of 10, 15 and 25 mol% In{sup 3+} for Nb{sup 5+} in KNN and NN using solid-state reactions at 1150 deg. C resulted in composite materials. AC impedance studies of NN,more » KNN and K25NN in the temperature range of 500-800 deg. C showed a single semicircle (attributed to the bulk property) in the high-frequency range of 10{sup 3} to 10{sup 6} Hz. The individual contributions from the bulk and grain boundary on the dielectric properties were resolved and quantified from the impedance data. The calculated dielectric values for NN were consistent with previously reported in the literature. 10% Indium based KNN composite materials had the lowest dielectric loss 0.585 and the dielectric constant of 233 at 100 kHz at the temperature of 650 deg. C.« less

Influence of the halothane gene (HAL) on pork quality in two commercial crossbreeds.

PubMed

Silveira, A C P; Freitas, P F A; César, A S M; Cesar, A S M; Antunes, R C; Guimarães, E C; Batista, D F A; Torido, L C

2011-01-01

We evaluated the effect of the halothane (HAL) gene on the quality of pork in domestic pigs. Half-carcasses from two different commercial pig (Sus domestica) crossbreeds were analyzed, 46 of which were homozygous dominant (HAL(NN)) and 69 of which were heterozygous (HAL(Nn)) for the halothane gene. The measures included backfat thickness, lean meat percentage, carcass weight, pH 24 h after slaughtering, color, and drip loss; DNA was extracted from the haunch muscle. Swine with the HAL(Nn) genotype had less backfat thickness and higher lean meat percentages than swine with the HAL(NN) genotype. Yet, swine with the HAL(Nn) genotype had lower quality meat than those with the HAL(NN) swine. The pH at 24 h was lower in HAL(Nn) swine. The meat color was paler in HAL(Nn) animals, the drip loss was greater in those animals bearing the n allele, and the amount of intramuscular fat was not related to the halothane genotype. We conclude that bearers of the recessive allele of the halothane gene produce more meat, but with quality parameters that are inferior to those sought by consumers and industry.

Neural-network-based state feedback control of a nonlinear discrete-time system in nonstrict feedback form.

PubMed

Jagannathan, Sarangapani; He, Pingan

2008-12-01

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in discrete-time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.

A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms.

PubMed

Azami, Hamed; Escudero, Javier

2015-08-01

Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

A polyethylenimine-mimetic biodegradable polycation gene vector and the effect of amine composition in transfection efficiency.

PubMed

Shen, J; Zhao, D J; Li, W; Hu, Q L; Wang, Q W; Xu, F J; Tang, G P

2013-06-01

The low toxicity and efficient gene delivery of polymeric vectors remain the major barrier to the clinical application of non-viral gene therapy. Here, we present a poly-D, L-succinimide (PSI)-based biodegradable cationic polymer which mimicked the golden standard, branched polyethylenimine (PEI, ~25 kDa). To investigate the influence of 1°, 2°, 3° amine group ratio in the polymer, a series of PSI-based vectors (PSI-NN'x-NNy) grafted with different amine side chains of N,N-dimethyldipropylenetriamine (NN') and bis(3-aminopropyl)amine (NN) were first characterized and contrasted by biophysical measurements. The in vitro and in vivo biological assay demonstrated that PSI-NN'0.85-NN1 exhibited better transfection ability and biocompatibility than PEI. The present results suggest that such PEI-mimic biodegradable PSI-NN'0.85-NN1 possesses a good potential application for clinical gene delivery. Copyright © 2013 Elsevier Ltd. All rights reserved.

All Prime Contract Awards by State or Country, Place, and Contractor, FY 87. Part 4. Sage Hen, California-Yolo, California.

DTIC Science & Technology

1987-01-01

WWU LU @6In 9-a I zo 4 In Nc041l44 - an 0 .)-o4 0 a W: to:3 1~)It 0) U0 f6 -) D0 -’ Cat 1 = NN g -46 6 * 6ii O --.. -4 n O io o o L ~ nn i V)( n L nL...8217 80 I 3 NN N N NN N-CO Nj NN No NN0 N-I N4(o 0 00 0 8-I I6 86M0 t ( ACCA - 0 (f",J- 0 0C 00 ~ 0 Nr0. 004 w - C) V-000 jCl 0 0- 0 004n n-4 c 0Y w1 47

Energy dependence of J/ψ production in Au + Au collisions at s N N = 39 , 62.4  and  200 GeV

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

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.

The inclusive J/ψ transverse momentum spectra and nuclear modification factors are reported at mid-rapidity (|y|<1.0) in Au + Au collisions at √sNN = 39, 62.4 and 200 GeV taken by the STAR experiment. A suppression of J/ψ production, with respect to the production in p+p scaled by the number of binary nucleon–nucleon collisions, is observed in central Au + Au collisions at these three energies. No significant energy dependence of nuclear modification factors is found within uncertainties. The measured nuclear modification factors can be described by model calculations that take into account both suppression of direct J/ψ production due tomore » the color screening effect and J/ψ regeneration from recombination of uncorrelated charm–anticharm quark pairs.« less

Energy dependence of J/ψ production in Au + Au collisions at s N N = 39 , 62.4  and  200 GeV

DOE PAGES

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; ...

2017-05-10

The inclusive J/ψ transverse momentum spectra and nuclear modification factors are reported at mid-rapidity (|y|<1.0) in Au + Au collisions at √sNN = 39, 62.4 and 200 GeV taken by the STAR experiment. A suppression of J/ψ production, with respect to the production in p+p scaled by the number of binary nucleon–nucleon collisions, is observed in central Au + Au collisions at these three energies. No significant energy dependence of nuclear modification factors is found within uncertainties. The measured nuclear modification factors can be described by model calculations that take into account both suppression of direct J/ψ production due tomore » the color screening effect and J/ψ regeneration from recombination of uncorrelated charm–anticharm quark pairs.« less

Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening

NASA Astrophysics Data System (ADS)

Hsieh, Jui-Hua; Wang, Xiang S.; Teotico, Denise; Golbraikh, Alexander; Tropsha, Alexander

2008-09-01

The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed `binding decoys'. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor ( kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant Ki of 135 μM. Our studies suggest that validated QSAR models could complement structure based docking and scoring approaches in identifying promising hits by virtual screening of molecular libraries.

Isospin flip as a relativistic effect: NN interactions

NASA Technical Reports Server (NTRS)

Buck, W. W.

1993-01-01

Results are presented of an analytic relativistic calculation of a OBE nucleon-nucleon (NN) interaction employing the Gross equation. The calculation consists of a non-relativistic reduction that keeps the negative energy states. The result is compared to purely non-relativistic OBEP results and the relativistic effects are separated out. One finds that the resulting relativistic effects are expressable as a power series in (tau(sub 1))(tau(sub 2)) that agrees, qualitatively, with NN scattering. Upon G-parity transforming this NN potential, one obtains, qualitatively, a short range NN spectroscopy in which the S-states are the lowest states.

Effects of trailer design on animal welfare parameters and carcass and meat quality of three Pietrain crosses being transported over a long distance.

PubMed

Weschenfelder, A V; Torrey, S; Devillers, N; Crowe, T; Bassols, A; Saco, Y; Piñeiro, M; Saucier, L; Faucitano, L

2012-09-01

This study aimed at evaluating the effects of trailer design on stress responses and meat quality traits of 3 different pig crosses: 50% Pietrain breeding with halothane (HAL)(Nn) (50Nn); 50% Pietrain breeding with HAL(NN) (50NN); and 25% Pietrain breeding with HAL(NN) genotype (25NN). Over a 6-wk period, pigs (120 pigs/crossbreed) were transported for 7 h in either a pot-belly (PB) or flat-deck (FD) trailer (10 pigs/crossbreed(-1)·trailer(-1)·wk(-1)). Temperature (T) and relative humidity (RH) were monitored in each trailer. Behaviors during loading and unloading, time to load and unload, and latency to rest in lairage were recorded, whereas a sub-population of pigs (4 pigs/crossbreed(-1)·trailer(-1)·wk(-1)) was equipped with gastro-intestinal tract (GIT) temperature monitors. Blood samples were collected at exsanguination for measurement of cortisol, creatine kinase (CK), lactate, haptoglobin, and Pig-MAP concentrations. Meat quality data were collected at 24 h postmortem from the LM and semimembranosus (SM) and adductor (AD) muscles of all 360 pigs. Greater T were recorded in the PB trailer during transportation (P = 0.006) and unloading (P < 0.001). Delta GIT temperature was greater (P = 0.01) in pigs unloaded from the PB. At loading, pigs tended to move backwards more (P = 0.06) when loaded on the FD than the PB trailer. At unloading, an interaction was found between trailer type and crossbreed type, with a greater (P < 0.01) frequency of overlaps in 50NN and 25NN pigs and slips/falls in 50Nn and 50NN pigs from the FD than the PB trailer. Cortisol concentrations at slaughter were greater (P = 0.02) in pigs transported in the PB than FD trailer. Greater lactate concentrations were found in 50Nn and 50NN pigs (P = 0.003) and greater CK concentrations (P < 0.001) in 50Nn pigs. As expected, 50Nn pigs produced leaner (P < 0.001) carcasses, with greater (P = 0.01) dressing percentages, as well as lower (P < 0.001) ultimate pH values and greater (P < 0.001) drip loss percentages in the LM and greater (P = 0.002) drip losses and a paler color (greater L* values, P = 0.02) in the SM than 50NN pigs. When used for long distance transportation under controlled conditions, the PB trailer produced no detrimental effects on animal welfare or pork quality. Pigs with 50% Pietrain crossbreeding appear to be more responsive to transport stress, having the potential to produce acceptable carcass and pork quality, provided pigs are free of the HAL gene.

Molecular evolution and functional characterisation of an ancient phenylalanine ammonia-lyase gene (NnPAL1) from Nelumbo nucifera: novel insight into the evolution of the PAL family in angiosperms

PubMed Central

2014-01-01

Background Phenylalanine ammonia-lyase (PAL; E.C.4.3.1.5) is a key enzyme of the phenylpropanoid pathway in plant development, and it catalyses the deamination of phenylalanine to trans-cinnamic acid, leading to the production of secondary metabolites. This enzyme has been identified in many organisms, ranging from prokaryotes to higher plants. Because Nelumbo nucifera is a basal dicot rich in many secondary metabolites, it is a suitable candidate for research on the phenylpropanoid pathway. Results Three PAL members, NnPAL1, NnPAL2 and NnPAL3, have been identified in N. nucifera using genome-wide analysis. NnPAL1 contains two introns; however, both NnPAL2 and NnPAL3 have only one intron. Molecular and evolutionary analysis of NnPAL1 confirms that it is an ancient PAL member of the angiosperms and may have a different origin. However, PAL clusters, except NnPAL1, are monophyletic after the split between dicots and monocots. These observations suggest that duplication events remain an important occurrence in the evolution of the PAL gene family. Molecular assays demonstrate that the mRNA of the NnPAL1 gene is 2343 bp in size and encodes a 717 amino acid polypeptide. The optimal pH and temperature of the recombinant NnPAL1 protein are 9.0 and 55°C, respectively. The NnPAL1 protein retains both PAL and weak TAL catalytic activities with Km values of 1.07 mM for L-phenylalanine and 3.43 mM for L-tyrosine, respectively. Cis-elements response to environmental stress are identified and confirmed using real-time PCR for treatments with abscisic acid (ABA), indoleacetic acid (IAA), ultraviolet light, Neurospora crassa (fungi) and drought. Conclusions We conclude that the angiosperm PAL genes are not derived from a single gene in an ancestral angiosperm genome; therefore, there may be another ancestral duplication and vertical inheritance from the gymnosperms. The different evolutionary histories for PAL genes in angiosperms suggest different mechanisms of functional regulation. The expression patterns of NnPAL1 in response to stress may be necessary for the survival of N. nucifera since the Cretaceous Period. The discovery and characterisation of the ancient NnPAL1 help to elucidate PAL evolution in angiosperms. PMID:24884360

Integrated Sensing Processor, Phase 2

DTIC Science & Technology

2005-12-01

performance analysis for several baseline classifiers including neural nets, linear classifiers, and kNN classifiers. Use of CCDR as a preprocessing step...below the level of the benchmark non-linear classifier for this problem ( kNN ). Furthermore, the CCDR preconditioned kNN achieved a 10% improvement over...the benchmark kNN without CCDR. Finally, we found an important connection between intrinsic dimension estimation via entropic graphs and the optimal

A silk peptide fraction restores cognitive function in AF64A-induced Alzheimer disease model rats by increasing expression of choline acetyltransferase gene

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

Cha, Yeseul

This study investigated the effects of a silk peptide fraction obtained by incubating silk proteins with Protease N and Neutrase (SP-NN) on cognitive dysfunction of Alzheimer disease model rats. In order to elucidate underlying mechanisms, the effect of SP-NN on the expression of choline acetyltransferase (ChAT) mRNA was assessed in F3.ChAT neural stem cells and Neuro2a neuroblastoma cells; active amino acid sequence was identified using HPLC-MS. The expression of ChAT mRNA in F3.ChAT cells increased by 3.79-fold of the control level by treatment with SP-NN fraction. The active peptide in SP-NN was identified as tyrosine-glycine with 238.1 of molecular weight.more » Male rats were orally administered with SP-NN (50 or 300 mg/kg) and challenged with a cholinotoxin AF64A. As a result of brain injury and decreased brain acetylcholine level, AF64A induced astrocytic activation, resulting in impairment of learning and memory function. Treatment with SP-NN exerted recovering activities on acetylcholine depletion and brain injury, as well as cognitive deficit induced by AF64A. The results indicate that, in addition to a neuroprotective activity, the SP-NN preparation restores cognitive function of Alzheimer disease model rats by increasing the release of acetylcholine. - Highlights: • Cognition-enhancing effects of SP-NN, a silk peptide preparation, were investigated. • SP-NN enhanced ChAT mRNA expression in F3.ChAT neural stem cells and Neuro-2a neuroblastoma cells. • Active molecule was identified as a dipeptide composed of tyrosine-glycine. • SP-NN reversed cognitive dysfunction elicited by AF64A. • Neuroprotection followed by increased acetylcholine level was achieved with SP-NN.« less

Mean Conditions and Turbulence Statistics at Vandenberg AFB, California,

DTIC Science & Technology

1984-11-01

2NN M N NN ’CN N NN N4 I’~tJN NN NIJN I pIi JN’ W’N NCJ’ ViNNCJ m I’N’(I N’N CJ N N N C’ 0 ’o CD22 REPRODUCEDU AT CG0VFRr4?A.FJ1 uvrF 11.4 PiJ0t’ N

Anticancer Effect of Nemopilema nomurai Jellyfish Venom on HepG2 Cells and a Tumor Xenograft Animal Model

PubMed Central

Bae, Seong Kyeong; Kim, Munki; Pyo, Min Jung; Kim, Minkyung; Yang, Sujeoung; Yoon, Won Duk; Han, Chang Hoon

2017-01-01

Various kinds of animal venoms and their components have been widely studied for potential therapeutic applications. This study evaluated whether Nemopilema nomurai jellyfish venom (NnV) has anticancer activity. NnV strongly induced cytotoxicity of HepG2 cells through apoptotic cell death, as demonstrated by alterations of chromatic morphology, activation of procaspase-3, and an increase in the Bax/Bcl-2 ratio. Furthermore, NnV inhibited the phosphorylation of PI3K, PDK1, Akt, mTOR, p70S6K, and 4EBP1, whereas it enhanced the expression of p-PTEN. Interestingly, NnV also inactivated the negative feedback loops associated with Akt activation, as demonstrated by downregulation of Akt at Ser473 and mTOR at Ser2481. The anticancer effect of NnV was significant in a HepG2 xenograft mouse model, with no obvious toxicity. HepG2 cell death by NnV was inhibited by tetracycline, metalloprotease inhibitor, suggesting that metalloprotease component in NnV is closely related to the anticancer effects. This study demonstrates, for the first time, that NnV exerts highly selective cytotoxicity in HepG2 cells via dual inhibition of the Akt and mTOR signaling pathways, but not in normal cells. PMID:28785288

Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems.

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-01-01

This paper presents a novel adaptive neural network (NN) control of single-input and single-output uncertain nonlinear discrete-time systems under event sampled NN inputs. In this control scheme, the feedback signals are transmitted, and the NN weights are tuned in an aperiodic manner at the event sampled instants. After reviewing the NN approximation property with event sampled inputs, an adaptive state estimator (SE), consisting of linearly parameterized NNs, is utilized to approximate the unknown system dynamics in an event sampled context. The SE is viewed as a model and its approximated dynamics and the state vector, during any two events, are utilized for the event-triggered controller design. An adaptive event-trigger condition is derived by using both the estimated NN weights and a dead-zone operator to determine the event sampling instants. This condition both facilitates the NN approximation and reduces the transmission of feedback signals. The ultimate boundedness of both the NN weight estimation error and the system state vector is demonstrated through the Lyapunov approach. As expected, during an initial online learning phase, events are observed more frequently. Over time with the convergence of the NN weights, the inter-event times increase, thereby lowering the number of triggered events. These claims are illustrated through the simulation results.

Determination of the geophysical model function of the ERS-1 scatterometer by the use of neural networks

NASA Astrophysics Data System (ADS)

Mejia, Carlos; Thiria, Sylvie; Tran, Ngan; CréPon, Michel; Badran, Fouad

1998-06-01

We present a geophysical model function (GMF) for the ERS-1 scatterometer computed by the use of neural networks. The neural networks GMF (NN GMF) is calibrated with ERS-1 scatterometer sigma 0 collocated with European Center for Medium-Range Weather Forecasts (ECMWF) analyzed wind vectors. Four different NN GMFs have been computed: one for each antenna and an average NN GMF. These NN GMFs do not present any significant differences which means that the three antenna are quasi-identical. The NN GMFs exhibit a biharmonic dependence on the wind azimuth with a small upwind-downwind modulation as found on previous GMFs. In order to check the validity of the NN GMF systematic comparisons with the European Space Agency (ESA) C band model (CMOD4) GMF (version 2 of March 25, 1993) and the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) CMOD213 GMF are done. It is found that the NN GMFs are highly accurate and relevant functions to model the ERS-1 scatterometer sigma 0.

Transverse momentum dependence of D-meson production in Pb-Pb collisions at $$ \\sqrt{{\\mathrm{s}}_{\\mathrm{NN}}}=2.76 $$ TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2016-03-14

The production of prompt charmed mesons D 0, D + and D* +, and their antiparticles, was measured with the ALICE detector in Pb-Pb collisions at the centre-of-mass energy per nucleon pair, √ sNN, of 2.76 TeV. The production yields for rapidity |y| < 0.5 are presented as a function of transverse momentum, p T, in the interval 1–36 GeV/c for the centrality class 0–10% and in the interval 1–16 GeV/c for the centrality class 30–50%. The nuclear modification factor R AA was computed using a proton-proton reference at √s = 2.76 TeV, based on measurements at √s = 7more » TeV and on theoretical calculations. A maximum suppression by a factor of 5-6 with respect to binary-scaled pp yields is observed for the most central collisions at p T of about 10 GeV/c. A suppression by a factor of about 2-3 persists at the highest p T covered by the measurements. At low pT (1-3 GeV/c), the R AA has large uncertainties that span the range 0.35 (factor of about 3 suppression) to 1 (no suppression). In all p T intervals, the R AA is larger in the 30-50% centrality class compared to central collisions. Furthermore, the D-meson R AA is also compared with that of charged pions and, at large p T, charged hadrons, and with model calculations.« less

Transverse momentum dependence of D-meson production in Pb-Pb collisions at sqrt{{s}_{NN}}=2.76 TeV

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anielski, J.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botta, E.; Böttger, S.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. 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B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; León Vargas, H.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martin Blanco, J.; Martinengo, P.; Martínez, M. 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S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, P.; Paić, G.; Pal, S. K.; Pan, J.; Pandey, A. K.; Papcun, P.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Peitzmann, T.; Pereira Da Costa, H.; Pereira De Oliveira Filho, E.; Peresunko, D.; Pérez Lara, C. E.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Revol, J.-P.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Romita, R.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. 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S.; Windelband, B.; Winn, M.; Yaldo, C. G.; Yang, H.; Yang, P.; Yano, S.; Yasar, C.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.

2016-03-01

The production of prompt charmed mesons D0, D+ and D∗+, and their antiparticles, was measured with the ALICE detector in Pb-Pb collisions at the centre-of-mass energy per nucleon pair, sqrt{s_{NN}} , of 2 .76 TeV. The production yields for rapidity | y| < 0 .5 are presented as a function of transverse momentum, p T, in the interval 1-36 GeV /c for the centrality class 0-10% and in the interval 1-16 GeV /c for the centrality class 30-50%. The nuclear modification factor R AA was computed using a proton-proton reference at sqrt{s}=2.76 TeV, based on measurements at sqrt{s}=7 TeV and on theoretical calculations. A maximum suppression by a factor of 5-6 with respect to binary-scaled pp yields is observed for the most central collisions at p T of about 10 GeV /c. A suppression by a factor of about 2-3 persists at the highest p T covered by the measurements. At low p T (1-3 GeV /c), the R AA has large uncertainties that span the range 0.35 (factor of about 3 suppression) to 1 (no suppression). In all p T intervals, the R AA is larger in the 30-50% centrality class compared to central collisions. The D-meson R AA is also compared with that of charged pions and, at large p T, charged hadrons, and with model calculations. [Figure not available: see fulltext.

Centrality dependence of inclusive J/ ψ production in p-Pb collisions at √{s_{NN}}=5.02 TeV

NASA Astrophysics Data System (ADS)

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T.; Caffarri, D.; Cai, X.; Caines, H.; Calero Diaz, L.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Cavicchioli, C.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Chunhui, Z.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Dénes, E.; D'Erasmo, G.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Dobrowolski, T.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Eschweiler, D.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Felea, D.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Germain, M.; Gheata, A.; Gheata, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Grosse-Oetringhaus, J. F.; Grossiord, J.-Y.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gulkanyan, H.; Gunji, T.; Gupta, A.; Gupta, R.; Haake, R.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hansen, A.; Harris, J. W.; Hartmann, H.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Heide, M.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hilden, T. E.; Hillemanns, H.; Hippolyte, B.; Hosokawa, R.; Hristov, P.; Huang, M.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Ilkiv, I.; Inaba, M.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacobs, P. M.; Jadlovska, S.; Jahnke, C.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jung, H.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Khan, K. H.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, B.; Kim, D. W.; Kim, D. J.; Kim, H.; Kim, J. S.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobayashi, T.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Kral, J.; Králik, I.; Kravčáková, A.; Kretz, M.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kugathasan, T.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kushpil, S.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Legrand, I.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Luz, P. H. F. N. D.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martin Blanco, J.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Martynov, Y.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Massacrier, L.; Mastroserio, A.; Masui, H.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Minervini, L. M.; Mischke, A.; Mishra, A. N.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Montes, E.; Morando, M.; Moreira De Godoy, D. A.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Murray, S.; Musa, L.; Musinsky, J.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Nattrass, C.; Nayak, K.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, P.; Paić, G.; Pajares, C.; Pal, S. K.; Pan, J.; Pandey, A. K.; Pant, D.; Papcun, P.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Peitzmann, T.; Pereira Da Costa, H.; Pereira De Oliveira Filho, E.; Peresunko, D.; Pérez Lara, C. E.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Real, J. S.; Redlich, K.; Reed, R. J.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Rettig, F.; Revol, J.-P.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rivetti, A.; Rocco, E.; Rodríguez Cahuantzi, M.; Rodriguez Manso, A.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Romita, R.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salgado, C. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sanchez Castro, X.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schuster, T.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Seo, J.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Shigaki, K.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Søgaard, C.; Soltz, R.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stefanek, G.; Steinpreis, M.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Sultanov, R.; Šumbera, M.; Symons, T. J. M.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tangaro, M. A.; Tapia Takaki, J. D.; Tarantola Peloni, A.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vajzer, M.; Vala, M.; Valencia Palomo, L.; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Venaruzzo, M.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Vyushin, A.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Wang, Y.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Wessels, J. P.; Westerhoff, U.; Wiechula, J.; Wikne, J.; Wilde, M.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yaldo, C. G.; Yang, H.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zhu, X.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.

2015-11-01

We present a measurement of inclusive J/ ψ production in p-Pb collisions at √{s_{NN}}=5.02 TeV as a function of the centrality of the collision, as estimated from the energy deposited in the Zero Degree Calorimeters. The measurement is performed with the ALICE detector down to zero transverse momentum, p T, in the backward (-4 .46 < y cms < -2 .96) and forward (2 .03 < y cms < 3 .53) rapidity intervals in the dimuon decay channel and in the mid-rapidity region (-1 .37 < y cms < 0 .43) in the dielectron decay channel. The backward and forward rapidity intervals correspond to the Pb-going and p-going direction, respectively. The p T-differential J /ψ production cross section at backward and forward rapidity is measured for several centrality classes, together with the corresponding average p T and p T2 values. The nuclear modification factor is presented as a function of centrality for the three rapidity intervals, and as a function of p T for several centrality classes at backward and forward rapidity. At mid- and forward rapidity, the J /ψ yield is suppressed up to 40% compared to that in pp interactions scaled by the number of binary collisions. The degree of suppression increases towards central p-Pb collisions at forward rapidity, and with decreasing p T of the J /ψ. At backward rapidity, the nuclear modification factor is compatible with unity within the total uncertainties, with an increasing trend from peripheral to central p-Pb collisions. [Figure not available: see fulltext.

Prediction of Classroom Reverberation Time using Neural Network

NASA Astrophysics Data System (ADS)

Liyana Zainudin, Fathin; Kadir Mahamad, Abd; Saon, Sharifah; Nizam Yahya, Musli

2018-04-01

In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed from ODEON 12.10 software. The NN was conducted separately for 500Hz, 1000Hz, and 2000Hz as absorption coefficient that is one of the prominent input variable is frequency dependent. Mean squared error (MSE) and regression (R) values were obtained to examine the NN efficiency. Overall, the NN shows a good result with MSE < 0.005 and R > 0.9. The NN also managed to achieve a percentage of accuracy of 92.53% for 500Hz, 93.66% for 1000Hz, and 93.18% for 2000Hz and thus displays a good and efficient performance. Nevertheless, the optimum RT value is range between 0.75 – 0.9 seconds.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Hyperimmune bovine colostrum treatment of moribund Leopard geckos (Eublepharis macularius) infected with Cryptosporidium sp.

PubMed

Graczyk, T K; Cranfield, M R; Bostwick, E F

1999-01-01

Therapy based on the protective passive immunity of hyperimmune bovine colostrum (HBC) was applied to 12 moribund Leopard geckos (Eublepharis macularius) infected with Cryptosporidium sp. The geckos were lethargic and moderately to severely emaciated, weighing on average 36% of the baseline body weight value. Seven gastric HBC treatments at 1-week intervals each decreased the relative output of Cryptosporidium sp. oocysts and the prevalence of oocyst-positive fecal specimens. Histologically, after 8 weeks of therapy, seven out of 12 geckos had only single developmental stages of Cryptosporidium sp. in the intestinal epithelium, and three, one and one geckos had low, moderate and high numbers, respectively, of the pathogen developmental stages. The HBC therapy was efficacious in decreasing the parasite load in moribund geckos. Morphometric and immunologic analysis of Cryptosporidium sp. oocyst isolates originating from Leopard geckos (E. macularius) demonstrated differences between gecko-derived oocyst isolates and isolates of C. serpentis recovered from snakes.

Dilepton Production in p+p, Au+Au collisions at √{sNN} = 200GeV and U+U collisions at √{sNN} = 193GeV

NASA Astrophysics Data System (ADS)

Brandenburg, James D.; STAR Collaboration

2017-11-01

In this contribution we report e+e1 spectra with various invariant mass and pT differentials in Au+Au collisions at √{sNN} = 200GeV and U+U collisions at √{sNN} = 193GeV. The structure of the t (- t ≈ pT2) distributions of these mass regions will be shown and compared with the same distributions in ultra-peripheral collisions. Additionally, this contribution discusses first measurements of μ+μ- invariant mass spectra from STAR's recently installed Muon Telescope Detector (MTD) in p+p and Au+Au collisions at √{sNN} = 200GeV.

High-Accuracy Methods for Numerical Flow Analysis Using Adaptive Non-Linear Wavelets

DTIC Science & Technology

2012-08-01

n ji n ji  , )(~ ,, Opp n jin ji  , (2-8) )(~ 2/12/1  OEE nn  , )(~ 2/12/1 OFF nn  . Also, if the flux values are discretized with...jin ji  , (3-5) )(~ 2/12/1  OEE nn  , )(~ 2/12/1 OFF nn  . Also, if the flux values are discretized with the lth order of spatial

Transverse energy production and charged-particle multiplicity at midrapidity in various systems from √{sN N}=7.7 to 200 GeV

NASA Astrophysics Data System (ADS)

Adare, A.; Afanasiev, S.; Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Akimoto, R.; Al-Bataineh, H.; Alexander, J.; Alfred, M.; Al-Jamel, A.; Al-Ta'Ani, H.; Angerami, A.; Aoki, K.; Apadula, N.; Aphecetche, L.; Aramaki, Y.; Armendariz, R.; Aronson, S. H.; Asai, J.; Asano, H.; Aschenauer, E. C.; Atomssa, E. T.; Averbeck, R.; Awes, T. C.; Azmoun, B.; Babintsev, V.; Bai, M.; Bai, X.; Baksay, G.; Baksay, L.; Baldisseri, A.; Bandara, N. S.; Bannier, B.; Barish, K. N.; Barnes, P. D.; Bassalleck, B.; Basye, A. T.; Bathe, S.; Batsouli, S.; Baublis, V.; Bauer, F.; Baumann, C.; Baumgart, S.; Bazilevsky, A.; Beaumier, M.; Beckman, S.; Belikov, S.; Belmont, R.; Bennett, R.; Berdnikov, A.; Berdnikov, Y.; Bhom, J. H.; Bickley, A. A.; Bjorndal, M. T.; Black, D.; Blau, D. S.; Boissevain, J. G.; Bok, J. S.; Borel, H.; Boyle, K.; Brooks, M. L.; Brown, D. S.; Bryslawskyj, J.; Bucher, D.; Buesching, H.; Bumazhnov, V.; Bunce, G.; Burward-Hoy, J. M.; Butsyk, S.; Campbell, S.; Caringi, A.; Castera, P.; Chai, J.-S.; Chang, B. S.; Charvet, J.-L.; Chen, C.-H.; Chernichenko, S.; Chi, C. Y.; Chiba, J.; Chiu, M.; Choi, I. J.; Choi, J. B.; Choi, S.; Choudhury, R. K.; Christiansen, P.; Chujo, T.; Chung, P.; Churyn, A.; Chvala, O.; Cianciolo, V.; Citron, Z.; Cleven, C. R.; Cobigo, Y.; Cole, B. A.; Comets, M. P.; Conesa Del Valle, Z.; Connors, M.; Constantin, P.; Cronin, N.; Crossette, N.; Csanád, M.; Csörgő, T.; Dahms, T.; Dairaku, S.; Danchev, I.; Danley, T. W.; Das, K.; Datta, A.; Daugherity, M. S.; David, G.; Dayananda, M. K.; Deaton, M. B.; Deblasio, K.; Dehmelt, K.; Delagrange, H.; Denisov, A.; D'Enterria, D.; Deshpande, A.; Desmond, E. J.; Dharmawardane, K. V.; Dietzsch, O.; Ding, L.; Dion, A.; Diss, P. B.; Do, J. H.; Donadelli, M.; D'Orazio, L.; Drachenberg, J. L.; Drapier, O.; Drees, A.; Drees, K. A.; Dubey, A. K.; Durham, J. M.; Durum, A.; Dutta, D.; Dzhordzhadze, V.; Edwards, S.; Efremenko, Y. V.; Egdemir, J.; Ellinghaus, F.; Emam, W. S.; Engelmore, T.; Enokizono, A.; En'yo, H.; Espagnon, B.; Esumi, S.; Eyser, K. O.; Fadem, B.; Feege, N.; Fields, D. E.; Finger, M.; Finger, M.; Fleuret, F.; Fokin, S. L.; Forestier, B.; Fraenkel, Z.; Frantz, J. E.; Franz, A.; Frawley, A. D.; Fujiwara, K.; Fukao, Y.; Fung, S.-Y.; Fusayasu, T.; Gadrat, S.; Gainey, K.; Gal, C.; Gallus, P.; Garg, P.; Garishvili, A.; Garishvili, I.; Gastineau, F.; Ge, H.; Germain, M.; Giordano, F.; Glenn, A.; Gong, H.; Gong, X.; Gonin, M.; Gosset, J.; Goto, Y.; Granier de Cassagnac, R.; Grau, N.; Greene, S. V.; Grim, G.; Grosse Perdekamp, M.; Gu, Y.; Gunji, T.; Guo, L.; Guragain, H.; Gustafsson, H.-Å.; Hachiya, T.; Hadj Henni, A.; Haegemann, C.; Haggerty, J. S.; Hagiwara, M. N.; Hahn, K. I.; Hamagaki, H.; Hamblen, J.; Hamilton, H. F.; Han, R.; Han, S. Y.; Hanks, J.; Harada, H.; Hartouni, E. P.; Haruna, K.; Harvey, M.; Hasegawa, S.; Haseler, T. O. S.; Hashimoto, K.; Haslum, E.; Hasuko, K.; Hayano, R.; Hayashi, S.; He, X.; Heffner, M.; Hemmick, T. K.; Hester, T.; Heuser, J. M.; Hiejima, H.; Hill, J. C.; Hobbs, R.; Hohlmann, M.; Hollis, R. S.; Holmes, M.; Holzmann, W.; Homma, K.; Hong, B.; Horaguchi, T.; Hori, Y.; Hornback, D.; Hoshino, T.; Hotvedt, N.; Huang, J.; Huang, S.; Hur, M. G.; Ichihara, T.; Ichimiya, R.; Iinuma, H.; Ikeda, Y.; Imai, K.; Imazu, Y.; Imrek, J.; Inaba, M.; Inoue, Y.; Iordanova, A.; Isenhower, D.; Isenhower, L.; Ishihara, M.; Isinhue, A.; Isobe, T.; Issah, M.; Isupov, A.; Ivanishchev, D.; Iwanaga, Y.; Jacak, B. V.; Javani, M.; Jeon, S. J.; Jezghani, M.; Jia, J.; Jiang, X.; Jin, J.; Jinnouchi, O.; Johnson, B. M.; Jones, T.; Joo, K. S.; Jouan, D.; Jumper, D. S.; Kajihara, F.; Kametani, S.; Kamihara, N.; Kamin, J.; Kanda, S.; Kaneta, M.; Kaneti, S.; Kang, B. H.; Kang, J. H.; Kang, J. S.; Kanou, H.; Kapustinsky, J.; Karatsu, K.; Kasai, M.; Kawagishi, T.; Kawall, D.; Kawashima, M.; Kazantsev, A. V.; Kelly, S.; Kempel, T.; Key, J. A.; Khachatryan, V.; Khandai, P. K.; Khanzadeev, A.; Kijima, K. M.; Kikuchi, J.; Kim, A.; Kim, B. I.; Kim, C.; Kim, D. H.; Kim, D. J.; Kim, E.; Kim, E.-J.; Kim, G. W.; Kim, H. J.; Kim, K.-B.; Kim, M.; Kim, Y.-J.; Kim, Y. K.; Kim, Y.-S.; Kimelman, B.; Kinney, E.; Kiss, Á.; Kistenev, E.; Kitamura, R.; Kiyomichi, A.; Klatsky, J.; Klay, J.; Klein-Boesing, C.; Kleinjan, D.; Kline, P.; Koblesky, T.; Kochenda, L.; Kochetkov, V.; Kofarago, M.; Komatsu, Y.; Komkov, B.; Konno, M.; Koster, J.; Kotchetkov, D.; Kotov, D.; Kozlov, A.; Král, A.; Kravitz, A.; Krizek, F.; Kroon, P. J.; Kubart, J.; Kunde, G. J.; Kurihara, N.; Kurita, K.; Kurosawa, M.; Kweon, M. J.; Kwon, Y.; Kyle, G. S.; Lacey, R.; Lai, Y. S.; Lajoie, J. G.; Lebedev, A.; Le Bornec, Y.; Leckey, S.; Lee, B.; Lee, D. M.; Lee, G. H.; Lee, J.; Lee, K. B.; Lee, K. S.; Lee, M. K.; Lee, S.; Lee, S. H.; Lee, S. R.; Lee, T.; Leitch, M. J.; Leite, M. A. L.; Leitgab, M.; Lenzi, B.; Lewis, B.; Li, X.; Li, X. H.; Lichtenwalner, P.; Liebing, P.; Lim, H.; Lim, S. H.; Linden Levy, L. A.; Liška, T.; Litvinenko, A.; Liu, H.; Liu, M. X.; Love, B.; Lynch, D.; Maguire, C. F.; Makdisi, Y. I.; Makek, M.; Malakhov, A.; Malik, M. D.; Manion, A.; Manko, V. I.; Mannel, E.; Mao, Y.; Maruyama, T.; Mašek, L.; Masui, H.; Masumoto, S.; Matathias, F.; McCain, M. C.; McCumber, M.; McGaughey, P. L.; McGlinchey, D.; McKinney, C.; Means, N.; Meles, A.; Mendoza, M.; Meredith, B.; Miake, Y.; Mibe, T.; Midori, J.; Mignerey, A. C.; Mikeš, P.; Miki, K.; Miller, T. E.; Milov, A.; Mioduszewski, S.; Mishra, D. K.; Mishra, G. C.; Mishra, M.; Mitchell, J. T.; Mitrovski, M.; Miyachi, Y.; Miyasaka, S.; Mizuno, S.; Mohanty, A. K.; Mohapatra, S.; Montuenga, P.; Moon, H. J.; Moon, T.; Morino, Y.; Morreale, A.; Morrison, D. P.; Moskowitz, M.; Moss, J. M.; Motschwiller, S.; Moukhanova, T. V.; Mukhopadhyay, D.; Murakami, T.; Murata, J.; Mwai, A.; Nagae, T.; Nagamiya, S.; Nagashima, K.; Nagata, Y.; Nagle, J. L.; Naglis, M.; Nagy, M. I.; Nakagawa, I.; Nakagomi, H.; Nakamiya, Y.; Nakamura, K. R.; Nakamura, T.; Nakano, K.; Nam, S.; Nattrass, C.; Nederlof, A.; Netrakanti, P. K.; Newby, J.; Nguyen, M.; Nihashi, M.; Niida, T.; Nishimura, S.; Norman, B. E.; Nouicer, R.; Novák, T.; Novitzky, N.; Nukariya, A.; Nyanin, A. S.; Nystrand, J.; Oakley, C.; Obayashi, H.; O'Brien, E.; Oda, S. X.; Ogilvie, C. A.; Ohnishi, H.; Oide, H.; Ojha, I. D.; Oka, M.; Okada, K.; Omiwade, O. O.; Onuki, Y.; Orjuela Koop, J. D.; Osborn, J. D.; Oskarsson, A.; Otterlund, I.; Ouchida, M.; Ozawa, K.; Pak, R.; Pal, D.; Palounek, A. P. T.; Pantuev, V.; Papavassiliou, V.; Park, B. H.; Park, I. H.; Park, J.; Park, J. S.; Park, S.; Park, S. K.; Park, W. J.; Pate, S. F.; Patel, L.; Patel, M.; Pei, H.; Peng, J.-C.; Pereira, H.; Perepelitsa, D. V.; Perera, G. D. N.; Peresedov, V.; Peressounko, D. Yu.; Perry, J.; Petti, R.; Pinkenburg, C.; Pinson, R.; Pisani, R. P.; Proissl, M.; Purschke, M. L.; Purwar, A. K.; Qu, H.; Rak, J.; Rakotozafindrabe, A.; Ramson, B. J.; Ravinovich, I.; Read, K. F.; Rembeczki, S.; Reuter, M.; Reygers, K.; Reynolds, D.; Riabov, V.; Riabov, Y.; Richardson, E.; Rinn, T.; Riveli, N.; Roach, D.; Roche, G.; Rolnick, S. D.; Romana, A.; Rosati, M.; Rosen, C. A.; Rosendahl, S. S. E.; Rosnet, P.; Rowan, Z.; Rubin, J. G.; Rukoyatkin, P.; Ružička, P.; Rykov, V. L.; Ryu, M. S.; Ryu, S. S.; Sahlmueller, B.; Saito, N.; Sakaguchi, T.; Sakai, S.; Sakashita, K.; Sakata, H.; Sako, H.; Samsonov, V.; Sano, M.; Sano, S.; Sarsour, M.; Sato, H. D.; Sato, S.; Sato, T.; Sawada, S.; Schaefer, B.; Schmoll, B. K.; Sedgwick, K.; Seele, J.; Seidl, R.; Sekiguchi, Y.; Semenov, V.; Sen, A.; Seto, R.; Sett, P.; Sexton, A.; Sharma, D.; Shaver, A.; Shea, T. K.; Shein, I.; Shevel, A.; Shibata, T.-A.; Shigaki, K.; Shimomura, M.; Shohjoh, T.; Shoji, K.; Shukla, P.; Sickles, A.; Silva, C. L.; Silvermyr, D.; Silvestre, C.; Sim, K. S.; Singh, B. K.; Singh, C. P.; Singh, V.; Skolnik, M.; Skutnik, S.; Slunečka, M.; Smith, W. C.; Snowball, M.; Solano, S.; Soldatov, A.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; Sourikova, I. V.; Staley, F.; Stankus, P. W.; Steinberg, P.; Stenlund, E.; Stepanov, M.; Ster, A.; Stoll, S. P.; Stone, M. R.; Sugitate, T.; Suire, C.; Sukhanov, A.; Sullivan, J. P.; Sumita, T.; Sun, J.; Sziklai, J.; Tabaru, T.; Takagi, S.; Takagui, E. M.; Takahara, A.; Taketani, A.; Tanabe, R.; Tanaka, K. H.; Tanaka, Y.; Taneja, S.; Tanida, K.; Tannenbaum, M. J.; Tarafdar, S.; Taranenko, A.; Tarján, P.; Tennant, E.; Themann, H.; Thomas, D.; Thomas, T. L.; Tieulent, R.; Timilsina, A.; Todoroki, T.; Togawa, M.; Toia, A.; Tojo, J.; Tomášek, L.; Tomášek, M.; Torii, H.; Towell, C. L.; Towell, R.; Towell, R. S.; Tram, V.-N.; Tserruya, I.; Tsuchimoto, Y.; Tsuji, T.; Tuli, S. K.; Tydesjö, H.; Tyurin, N.; Vale, C.; Valle, H.; van Hecke, H. W.; Vargyas, M.; Vazquez-Zambrano, E.; Veicht, A.; Velkovska, J.; Vértesi, R.; Vinogradov, A. A.; Virius, M.; Voas, B.; Vossen, A.; Vrba, V.; Vznuzdaev, E.; Wagner, M.; Walker, D.; Wang, X. R.; Watanabe, D.; Watanabe, K.; Watanabe, Y.; Watanabe, Y. S.; Wei, F.; Wei, R.; Wessels, J.; Whitaker, S.; White, A. S.; White, S. N.; Willis, N.; Winter, D.; Wolin, S.; Woody, C. L.; Wright, R. M.; Wysocki, M.; Xia, B.; Xie, W.; Xue, L.; Yalcin, S.; Yamaguchi, Y. L.; Yamaura, K.; Yang, R.; Yanovich, A.; Yasin, Z.; Ying, J.; Yokkaichi, S.; Yoo, J. H.; Yoon, I.; You, Z.; Young, G. R.; Younus, I.; Yu, H.; Yushmanov, I. E.; Zajc, W. A.; Zaudtke, O.; Zelenski, A.; Zhang, C.; Zhou, S.; Zimamyi, J.; Zolin, L.; Zou, L.; Phenix Collaboration

2016-02-01

Measurements of midrapidity charged-particle multiplicity distributions, d Nch/d η , and midrapidity transverse-energy distributions, d ET/d η , are presented for a variety of collision systems and energies. Included are distributions for Au +Au collisions at √{sNN}=200 , 130, 62.4, 39, 27, 19.6, 14.5, and 7.7 GeV, Cu +Cu collisions at √{sNN}=200 and 62.4 GeV, Cu +Au collisions at √{sNN}=200 GeV, U +U collisions at √{sNN}=193 GeV, d +Au collisions at √{sNN}=200 GeV, 3He+Au collisions at √{sNN}=200 GeV, and p +p collisions at √{sNN}=200 GeV. Centrality-dependent distributions at midrapidity are presented in terms of the number of nucleon participants, Npart, and the number of constituent quark participants, Nqp. For all A +A collisions down to √{sNN}=7.7 GeV, it is observed that the midrapidity data are better described by scaling with Nqp than scaling with Npart. Also presented are estimates of the Bjorken energy density, ɛBJ, and the ratio of d ET/d η to d Nch/d η , the latter of which is seen to be constant as a function of centrality for all systems.

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

NASA Technical Reports Server (NTRS)

Aires, F.; Prigent, C.; Rossow, W. B.

2004-01-01

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

Alpha List of Prime Contract Awards. Oct 91 - Sep 92. FY 92. (Reichlmeter - Season All Industries Inc)

DTIC Science & Technology

1993-01-01

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Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations.

PubMed

Krasnopolsky, Vladimir; Nadiga, Sudhir; Mehra, Avichal; Bayler, Eric; Behringer, David

2016-01-01

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived "ocean color" (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA's operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed--signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN's generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.

Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

PubMed Central

Nadiga, Sudhir; Mehra, Avichal; Bayler, Eric; Behringer, David

2016-01-01

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived “ocean color” (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA's operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed—signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN's generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series. PMID:26819586

Advances in Telescope and Detector Technologies - Impacts on the Study and Understanding of Binary Star and Exoplanet Systems

NASA Astrophysics Data System (ADS)

Guinan, Edward F.; Engle, Scott; Devinney, Edward J.

2012-04-01

Current and planned telescope systems (both on the ground and in space) as well as new technologies will be discussed with emphasis on their impact on the studies of binary star and exoplanet systems. Although no telescopes or space missions are primarily designed to study binary stars (what a pity!), several are available (or will be shortly) to study exoplanet systems. Nonetheless those telescopes and instruments can also be powerful tools for studying binary and variable stars. For example, early microlensing missions (mid-1990s) such as EROS, MACHO and OGLE were initially designed for probing dark matter in the halos of galaxies but, serendipitously, these programs turned out to be a bonanza for the studies of eclipsing binaries and variable stars in the Magellanic Clouds and in the Galactic Bulge. A more recent example of this kind of serendipity is the Kepler Mission. Although Kepler was designed to discover exoplanet transits (and so far has been very successful, returning many planetary candidates), Kepler is turning out to be a ``stealth'' stellar astrophysics mission returning fundamentally important and new information on eclipsing binaries, variable stars and, in particular, providing a treasure trove of data of all types of pulsating stars suitable for detailed Asteroseismology studies. With this in mind, current and planned telescopes and networks, new instruments and techniques (including interferometers) are discussed that can play important roles in our understanding of both binary star and exoplanet systems. Recent advances in detectors (e.g. laser frequency comb spectrographs), telescope networks (both small and large - e.g. Super-WASP, HAT-net, RoboNet, Las Combres Observatory Global Telescope (LCOGT) Network), wide field (panoramic) telescope systems (e.g. Large Synoptic Survey Telescope (LSST) and Pan-Starrs), huge telescopes (e.g. the Thirty Meter Telescope (TMT), the Overwhelming Large Telescope (OWL) and the Extremely Large Telescope (ELT)), and space missions, such as the James Webb Space Telescope (JWST), the possible NASA Explorer Transiting Exoplanet Survey Satellite (TESS - recently approved for further study) and Gaia (due for launch during 2013) will all be discussed. Also highlighted are advances in interferometers (both on the ground and from space) and imaging now possible at sub-millimeter wavelengths from the Extremely Long Array (ELVA) and Atacama Large Millimeter Array (ALMA). High precision Doppler spectroscopy, for example with HARPS, HIRES and more recently the Carnegie Planet Finder Spectrograph, are currently returning RVs typically better than ~2-m/s for some brighter exoplanet systems. But soon it should be possible to measure Doppler shifts as small as ~10-cm/s - sufficiently sensitive for detecting Earth-size planets. Also briefly discussed is the impact these instruments will have on the study of eclipsing binaries, along with future possibilities of utilizing methods from the emerging field of Astroinformatics, including: the Virtual Observatory (VO) and the possibilities of analyzing these huge datasets using Neural Network (NN) and Artificial Intelligence (AI) technologies.

Reinforcement-learning-based dual-control methodology for complex nonlinear discrete-time systems with application to spark engine EGR operation.

PubMed

Shih, Peter; Kaul, Brian C; Jagannathan, S; Drallmeier, James A

2008-08-01

A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary one for the affine nonlinear discrete-time system but the controllers together offer the desired performance. The primary adaptive critic NN controller includes an NN observer for estimating the states and output, an NN critic, and two action NNs for generating virtual control and actual control inputs for the nonstrict feedback nonlinear discrete-time system, whereas an additional critic NN and an action NN are included for the affine nonlinear discrete-time system by assuming the state availability. All NN weights adapt online towards minimization of a certain performance index, utilizing gradient-descent-based rule. Using Lyapunov theory, the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates, and observer estimates are shown. The adaptive critic NN controller performance is evaluated on an SI engine operating with high EGR levels where the controller objective is to reduce cyclic dispersion in heat release while minimizing fuel intake. Simulation and experimental results indicate that engine out emissions drop significantly at 20% EGR due to reduction in dispersion in heat release thus verifying the dual-control approach.

Proteomic Investigation to Identify Anticancer Targets of Nemopilema nomurai Jellyfish Venom in Human Hepatocarcinoma HepG2 Cells

PubMed Central

Choudhary, Indu; Lee, Hyunkyoung; Pyo, Min Jung; Heo, Yunwi; Chae, Jinho; Yum, Seung Shic; Kang, Changkeun; Kim, Euikyung

2018-01-01

Nemopilema nomurai is a giant jellyfish that blooms in East Asian seas. Recently, N. nomurai venom (NnV) was characterized from a toxicological and pharmacological point of view. A mild dose of NnV inhibits the growth of various kinds of cancer cells, mainly hepatic cancer cells. The present study aims to identify the potential therapeutic targets and mechanism of NnV in the growth inhibition of cancer cells. Human hepatocellular carcinoma (HepG2) cells were treated with NnV, and its proteome was analyzed using two-dimensional gel electrophoresis, followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI/TOF/MS). The quantity of twenty four proteins in NnV-treated HepG2 cells varied compared to non-treated control cells. Among them, the amounts of fourteen proteins decreased and ten proteins showed elevated levels. We also found that the amounts of several cancer biomarkers and oncoproteins, which usually increase in various types of cancer cells, decreased after NnV treatment. The representative proteins included proliferating cell nuclear antigen (PCNA), glucose-regulated protein 78 (GRP78), glucose-6-phosphate dehydrogenase (G6PD), elongation factor 1γ (EF1γ), nucleolar and spindle-associated protein (NuSAP), and activator of 90 kDa heat shock protein ATPase homolog 1 (AHSA1). Western blotting also confirmed altered levels of PCNA, GRP78, and G6PD in NnV-treated HepG2 cells. In summary, the proteomic approach explains the mode of action of NnV as an anticancer agent. Further characterization of NnV may help to unveil novel therapeutic agents in cancer treatment. PMID:29748501

Pharmacological characterisation of a new oral GH secretagogue, NN703.

PubMed

Hansen, B S; Raun, K; Nielsen, K K; Johansen, P B; Hansen, T K; Peschke, B; Lau, J; Andersen, P H; Ankersen, M

1999-08-01

NN703 is a novel orally active GH secretagogue (GHS) derived from ipamorelin. NN703 stimulates GH release from rat pituitary cells in a dose-dependent manner with a potency and efficacy similar to that of GHRP-6. The effect is inhibited by known GHS antagonists, but not by a GH-releasing hormone antagonist. Binding of (35)S-MK677 to the human type 1A GHS receptor (GHS-R 1A) stably expressed on BHK cells was inhibited by GHRP-6 and MK677 as expected. NN703 was also able to inhibit the binding of (35)S-MK677. However, the observed K(i) value was lower than expected, as based on the observed potencies regarding GH release from rat pituitary cells. Similarly, the effect of NN703 on the GHS-R 1A-induced inositol phosphate turnover in these cells showed a lower potency, when compared with GHRP-6 and MK677, than that observed in rat pituitary cells. The effect of i.v. administration of NN703 on GH and cortisol release was studied in swine. The potency and efficacy of NN703 on GH release were determined to be 155+/-23 nmol/kg and 91+/-7 ng GH/ml plasma respectively. A 50% increase of cortisol, compared with basal levels, was observed for all the tested doses of NN703, but no dose-dependency was shown. The effect of NN703 on GH release after i. v. and oral dosing in beagle dogs was studied. NN703 dose-dependently increased the GH release after oral administration. At the highest dose (20 micromol/kg), a 35-fold increase in peak GH concentration was observed (49.5+/-17.8 ng/ml, mean+/-s.e.m.). After a single i.v. dose of 1 micromol/kg the peak GH plasma concentration was elevated to 38.5+/-19.6 ng/ml (mean+/-s.e.m.) approximately 30 min after dosing and returned to basal level after 360 min. The oral bioavailability was 30%. The plasma half-life of NN703 was 4.1+/-0.4 h. A long-term biological effect of NN703 was demonstrated in a rat study, where the body weight gain was measured during a 14-day once daily oral challenge with 100 micromol/kg. The body weight gain was significantly increased after 14 days as compared with a vehicle-treated group. In summary, we here describe an orally active and GH specific secretagogue, NN703. This compound acts through a similar mechanism as GHRP-6, but has a different receptor pharmacology. NN703 induced GH release in both swine and dogs after i.v. and/or p.o. administration, had a high degree of GH specificity in swine and significantly increased the body weight gain in rats.

Nuclear force from lattice QCD.

PubMed

Ishii, N; Aoki, S; Hatsuda, T

2007-07-13

The nucleon-nucleon (NN) potential is studied by lattice QCD simulations in the quenched approximation, using the plaquette gauge action and the Wilson quark action on a 32(4) [approximately (4.4 fm)(4)] lattice. A NN potential V(NN)(r) is defined from the equal-time Bethe-Salpeter amplitude with a local interpolating operator for the nucleon. By studying the NN interaction in the (1)S(0) and (3)S(1) channels, we show that the central part of V(NN)(r) has a strong repulsive core of a few hundred MeV at short distances (r approximately < 0.5 fm) surrounded by an attractive well at medium and long distances. These features are consistent with the known phenomenological features of the nuclear force.

Production of e+e- from U+U collisions at √{sNN } = 193 GeV and Au+Au collisions at √{sNN } = 19.6 , 27 , 39 , 62.4, and 200 GeV as measured by STAR

NASA Astrophysics Data System (ADS)

Butterworth, Joey; STAR Collaboration

2017-08-01

We present STAR's measurement of the e+e- continuum as a function of centrality, invariant mass, and transverse momentum for U+U collisions at √{sNN } = 193 GeV. Also reported are the acceptance-corrected e+e- invariant mass spectra for minimum-bias Au+Au collisions at √{sNN } = 27 , 39, and 62.4 GeV and U+U collisions at √{sNN } = 193 GeV. The connection between the integrated e+e- excess yields normalized by charge particle multiplicity (dNch / dy) at mid-rapidity and the lifetime of the fireball is discussed.

Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

NASA Astrophysics Data System (ADS)

Sanal Kumar, K. P.; Bhavani, R., Dr.

2017-08-01

Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

Transverse energy production and charged-particle multiplicity at midrapidity in various systems from s N N = 7.7 to 200 GeV

DOE PAGES

Adare, A.; Afanasiev, S.; Aidala, C.; ...

2016-02-03

Measurements of midrapidity charged-particle multiplicity distributions, dN ch/dη, and midrapidity transverse-energy distributions, dE T/dη, are presented for a variety of collision systems and energies. Included are distributions for Au+Au collisions at √s NN=200, 130, 62.4, 39, 27, 19.6, 14.5, and 7.7 GeV, Cu+Cu collisions at √s NN=200 and 62.4 GeV, Cu+Au collisions at √s NN=200 GeV, U+U collisions at√s NN=193 GeV, d+Au collisions at √s NN=200 GeV, He3+Au collisions at √s NN=200 GeV, and p+p collisions at √s NN=200 GeV. We present centrality-dependent distributions at midrapidity in terms of the number of nucleon participants, N part, and the number ofmore » constituent quark participants, N qp. For all A+A collisions down to √s NN=7.7 GeV, we observed that the midrapidity data are better described by scaling with N qp than scaling with N part. Finally, our estimates of the Bjorken energy density, ε BJ, and the ratio of dE T/dη to dN ch/dη are presented, the latter of which is seen to be constant as a function of centrality for all systems.« less

An Improvement To The k-Nearest Neighbor Classifier For ECG Database

NASA Astrophysics Data System (ADS)

Jaafar, Haryati; Hidayah Ramli, Nur; Nasir, Aimi Salihah Abdul

2018-03-01

The k nearest neighbor (kNN) is a non-parametric classifier and has been widely used for pattern classification. However, in practice, the performance of kNN often tends to fail due to the lack of information on how the samples are distributed among them. Moreover, kNN is no longer optimal when the training samples are limited. Another problem observed in kNN is regarding the weighting issues in assigning the class label before classification. Thus, to solve these limitations, a new classifier called Mahalanobis fuzzy k-nearest centroid neighbor (MFkNCN) is proposed in this study. Here, a Mahalanobis distance is applied to avoid the imbalance of samples distribition. Then, a surrounding rule is employed to obtain the nearest centroid neighbor based on the distributions of training samples and its distance to the query point. Consequently, the fuzzy membership function is employed to assign the query point to the class label which is frequently represented by the nearest centroid neighbor Experimental studies from electrocardiogram (ECG) signal is applied in this study. The classification performances are evaluated in two experimental steps i.e. different values of k and different sizes of feature dimensions. Subsequently, a comparative study of kNN, kNCN, FkNN and MFkCNN classifier is conducted to evaluate the performances of the proposed classifier. The results show that the performance of MFkNCN consistently exceeds the kNN, kNCN and FkNN with the best classification rates of 96.5%.

Collapse of the sea surface stability during the Miocene to Quartenary in the Western Pacific Ocean, indicated by Discoaster abundance and Coccolith size change

NASA Astrophysics Data System (ADS)

Sato, T.; Pratiwi, S. D.; Farida, M.

2013-12-01

We describe in detail the middle Miocene to Pleistocene paleoceanography of the Western Pacific Ocean based on calcareous nannofossils. Abundantly occurrence of discoasters, which indicates the stable sea surface stratification and the development of thermo- and nutri-cline, are found in the interval from NN2 to NN4 zones of the early Miocene. The relative abundance of discoaster is decreased in the NN4-5 zone and it changed to very rare above NN10 (B in Fig.1). These characteristics are found in both Sites 805 and 782. Focusing to the mean size of Reticulofenestra species, it decreased at NN4-5 zone (A in Fig 2), and lower part of NN11 (B in Fig. 2). The presence of larger size Reticulofenestra species also show the oligotrophic conditions of sea surface with thermocline. On the basis of these results, the collapse of the stability of the sea surface stratification in the Western Pacific Ocean progressed throughout the Miocene to Quaternary. As the results, nutrient conditions of sea surface in these area were changed in steps from oligotrophic to eutrophic conditions at NN4-5 and lower part of NN11 (A and B in Fig. 2). These datum related to collapse of sea surface conditions, is cleary correlated to the timing of the end of Mid-Miocene Climatic Optimum (A) and the intensify of the Asian Monsoon (B; Fig. 2).

Evaluation of Data Processing Techniques for Unobtrusive Gait Authentication

DTIC Science & Technology

2014-03-01

scatter plot depicting the performance of kNN , by TER, on all experimental mixtures...30  Table 9.  Mean TER of SVM and kNN performance with different voting parameters...performance on XYZ-axis data. ...........................................................51  Table 19.  kNN and SVM results in back pocket carrying

Energy dependence of acceptance-corrected dielectron excess mass spectrum at mid-rapidity in Au +Au collisions at √{sNN} = 19.6 and 200 GeV

NASA Astrophysics Data System (ADS)

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; Aggarwal, M. M.; Ahammed, Z.; Alekseev, I.; Alford, J.; Aparin, A.; Arkhipkin, D.; Aschenauer, E. C.; Averichev, G. S.; Banerjee, A.; Bellwied, R.; Bhasin, A.; Bhati, A. K.; Bhattarai, P.; Bielcik, J.; Bielcikova, J.; Bland, L. C.; Bordyuzhin, I. G.; Bouchet, J.; Brandin, A. V.; Bunzarov, I.; Burton, T. P.; Butterworth, J.; Caines, H.; Calder'on de la Barca S'anchez, M.; Campbell, J. M.; Cebra, D.; Cervantes, M. C.; Chakaberia, I.; Chaloupka, P.; Chang, Z.; Chattopadhyay, S.; Chen, J. H.; Chen, X.; Cheng, J.; Cherney, M.; Christie, W.; Codrington, M. J. M.; Contin, G.; Crawford, H. J.; Das, S.; De Silva, L. C.; Debbe, R. R.; Dedovich, T. G.; Deng, J.; Derevschikov, A. A.; di Ruzza, B.; Didenko, L.; Dilks, C.; Dong, X.; Drachenberg, J. L.; Draper, J. E.; Du, C. M.; Dunkelberger, L. E.; Dunlop, J. C.; Efimov, L. G.; Engelage, J.; Eppley, G.; Esha, R.; Evdokimov, O.; Eyser, O.; Fatemi, R.; Fazio, S.; Federic, P.; Fedorisin, J.; Feng; Filip, P.; Fisyak, Y.; Flores, C. E.; Fulek, L.; Gagliardi, C. A.; Garand, D.; Geurts, F.; Gibson, A.; Girard, M.; Greiner, L.; Grosnick, D.; Gunarathne, D. S.; Guo, Y.; Gupta, S.; Gupta, A.; Guryn, W.; Hamad, A.; Hamed, A.; Haque, R.; Harris, J. W.; He, L.; Heppelmann, S.; Hirsch, A.; Hoffmann, G. W.; Hofman, D. J.; Horvat, S.; Huang, H. Z.; Huang, X.; Huang, B.; Huck, P.; Humanic, T. J.; Igo, G.; Jacobs, W. W.; Jang, H.; Jiang, K.; Judd, E. G.; Kabana, S.; Kalinkin, D.; Kang, K.; Kauder, K.; Ke, H. W.; Keane, D.; Kechechyan, A.; Khan, Z. H.; Kikola, D. P.; Kisel, I.; Kisiel, A.; Klein, S. R.; Koetke, D. D.; Kollegger, T.; Kosarzewski, L. K.; Kotchenda, L.; Kraishan, A. F.; Kravtsov, P.; Krueger, K.; Kulakov, I.; Kumar, L.; Kycia, R. A.; Lamont, M. A. C.; Landgraf, J. M.; Landry, K. D.; Lauret, J.; Lebedev, A.; Lednicky, R.; Lee, J. H.; Li, X.; Li, X.; Li, W.; Li, Z. M.; Li, Y.; Li, C.; Lisa, M. A.; Liu, F.; Ljubicic, T.; Llope, W. J.; Lomnitz, M.; Longacre, R. S.; Luo, X.; Ma, L.; Ma, R.; Ma, G. L.; Ma, Y. G.; Magdy, N.; Majka, R.; Manion, A.; Margetis, S.; Markert, C.; Masui, H.; Matis, H. S.; McDonald, D.; Meehan, K.; Minaev, N. G.; Mioduszewski, S.; Mohanty, B.; Mondal, M. M.; Morozov, D. A.; Mustafa, M. K.; Nandi, B. K.; Nasim, Md.; Nayak, T. K.; Nigmatkulov, G.; Nogach, L. V.; Noh, S. Y.; Novak, J.; Nurushev, S. B.; Odyniec, G.; Ogawa, A.; Oh, K.; Okorokov, V.; Olvitt, D. L.; Page, B. S.; Pan, Y. X.; Pandit, Y.; Panebratsev, Y.; Pawlak, T.; Pawlik, B.; Pei, H.; Perkins, C.; Peterson, A.; Pile, P.; Planinic, M.; Pluta, J.; Poljak, N.; Poniatowska, K.; Porter, J.; Posik, M.; Poskanzer, A. M.; Pruthi, N. K.; Putschke, J.; Qiu, H.; Quintero, A.; Ramachandran, S.; Raniwala, R.; Raniwala, S.; Ray, R. L.; Ritter, H. G.; Roberts, J. B.; Rogachevskiy, O. V.; Romero, J. L.; Roy, A.; Ruan, L.; Rusnak, J.; Rusnakova, O.; Sahoo, N. R.; Sahu, P. K.; Sakrejda, I.; Salur, S.; Sandacz, A.; Sandweiss, J.; Sarkar, A.; Schambach, J.; Scharenberg, R. P.; Schmah, A. M.; Schmidke, W. B.; Schmitz, N.; Seger, J.; Seyboth, P.; Shah, N.; Shahaliev, E.; Shanmuganathan, P. V.; Shao, M.; Sharma, M. K.; Sharma, B.; Shen, W. Q.; Shi, S. S.; Shou, Q. Y.; Sichtermann, E. P.; Sikora, R.; Simko, M.; Skoby, M. J.; Smirnov, N.; Smirnov, D.; Solanki, D.; Song, L.; Sorensen, P.; Spinka, H. M.; Srivastava, B.; Stanislaus, T. D. S.; Stock, R.; Strikhanov, M.; Stringfellow, B.; Sumbera, M.; Summa, B. J.; Sun, Y.; Sun, Z.; Sun, X. M.; Sun, X.; Surrow, B.; Svirida, D. N.; Szelezniak, M. A.; Takahashi, J.; Tang, A. H.; Tang, Z.; Tarnowsky, T.; Tawfik, A. N.; Thomas, J. H.; Timmins, A. R.; Tlusty, D.; Tokarev, M.; Trentalange, S.; Tribble, R. E.; Tribedy, P.; Tripathy, S. K.; Trzeciak, B. A.; Tsai, O. D.; Ullrich, T.; Underwood, D. G.; Upsal, I.; Van Buren, G.; van Nieuwenhuizen, G.; Vandenbroucke, M.; Varma, R.; Vasiliev, A. N.; Vertesi, R.; Videbaek, F.; Viyogi, Y. P.; Vokal, S.; Voloshin, S. A.; Vossen, A.; Wang, Y.; Wang, F.; Wang, H.; Wang, J. S.; Wang, G.; Wang, Y.; Webb, J. C.; Webb, G.; Wen, L.; Westfall, G. D.; Wieman, H.; Wissink, S. W.; Witt, R.; Wu, Y. F.; Xiao, Z.; Xie, W.; Xin, K.; Xu, Z.; Xu, Q. H.; Xu, N.; Xu, H.; Xu, Y. F.; Yang, Y.; Yang, C.; Yang, S.; Yang, Q.; Yang, Y.; Ye, Z.; Yepes, P.; Yi, L.; Yip, K.; Yoo, I.-K.; Yu, N.; Zbroszczyk, H.; Zha, W.; Zhang, J. B.; Zhang, X. P.; Zhang, S.; Zhang, J.; Zhang, Z.; Zhang, Y.; Zhang, J. L.; Zhao, F.; Zhao, J.; Zhong, C.; Zhou, L.; Zhu, X.; Zoulkarneeva, Y.; Zyzak, M.

2015-11-01

The acceptance-corrected dielectron excess mass spectra, where the known hadronic sources have been subtracted from the inclusive dielectron mass spectra, are reported for the first time at mid-rapidity |yee | < 1 in minimum-bias Au +Au collisions at √{sNN} = 19.6 and 200 GeV. The excess mass spectra are consistently described by a model calculation with a broadened ρ spectral function for Mee < 1.1 GeV /c2. The integrated dielectron excess yield at √{sNN} = 19.6 GeV for 0.4

Identification of Disease Critical Genes Using Collective Meta-heuristic Approaches: An Application to Preeclampsia.

PubMed

Biswas, Surama; Dutta, Subarna; Acharyya, Sriyankar

2017-12-01

Identifying a small subset of disease critical genes out of a large size of microarray gene expression data is a challenge in computational life sciences. This paper has applied four meta-heuristic algorithms, namely, honey bee mating optimization (HBMO), harmony search (HS), differential evolution (DE) and genetic algorithm (basic version GA) to find disease critical genes of preeclampsia which affects women during gestation. Two hybrid algorithms, namely, HBMO-kNN and HS-kNN have been newly proposed here where kNN (k nearest neighbor classifier) is used for sample classification. Performances of these new approaches have been compared with other two hybrid algorithms, namely, DE-kNN and SGA-kNN. Three datasets of different sizes have been used. In a dataset, the set of genes found common in the output of each algorithm is considered here as disease critical genes. In different datasets, the percentage of classification or classification accuracy of meta-heuristic algorithms varied between 92.46 and 100%. HBMO-kNN has the best performance (99.64-100%) in almost all data sets. DE-kNN secures the second position (99.42-100%). Disease critical genes obtained here match with clinically revealed preeclampsia genes to a large extent.

Octahedral d[sup 6] Bis(maleimide) and Bis(maleic anhydride) complexes of molybdenum

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

Lai, Chen-Hsing; Cheng, Chien-Hong; Liao, Fen-Ling

1993-12-08

Mo(CO)[sub 3](CH[sub 3]CN)[sub 3] reacts with 2 equiv of alkene, where the alkene is maleimide (MI), N-phenylmaleimide (PhMI), or N-methylmaleimide, to give the corresponding Mo(CO)[sub 2](alkene)[sub 2](CH[sub 3]CN)[sub 2] complex (1a-c, respectively) in excellent yield. Dissolution of 1 in DMSO led to the substitution of acetonitrile ligands by DMSO to form the corresponding cis bis(DMSO) complexes 2a-c. Addition of 1 equiv of NN to 1 yields MO(CO)[sub 2](alkene)[sub 2](alkene)[sub 2](NN) (NN = en, alkene = PhMI (3b), MeMI (3c); NN = o-phenylenediamine, alkene = PhMI (4)). Treatment of Mo-(CO)[sub 4](NN) (NN = phen or bpy), with 2 equiv of alkenemore » in refluxed acetonitrile for 2 h gave Mo(CO)[sub 2]-(alkene)[sub 2](NN) (NN = phen, alkene = MI (5a), PhMI (5b); NN = bpy, alkene = MI (6a), PhMI (6b)). Treatment of Mo(CO)[sub 3](CH[sub 3]CN)[sub 3] with 2 equiv of maleic anhydride (MA) gave Mo(CO)[sub 2](MA)[sub 2](CH[sub 3]CN)[sub 2] (7). The acetonitrile ligands in 7 were replaced by DMSO molecules to give complex 8 as 7 was dissolved in DMSO. Similarly, the reaction of 7 with a bidentate ligand NN (phen or bpy) gave the substituted product Mo(CO)[sub 2](MA)[sub 2](NN) (9 or 10). The structures and conformations of 1b and 7 were determined by X-ray diffraction. Both molecules adopt an octahedral geometry with mutually perpendicular trans alkene ligands and each alkene ligand eclipses a N-Mo-CO vector. Each PhMI or MA is oriented so that the central nitrogen or oxygen atom points to a carbonyl group. 1b crystallizes in triclinic space group P1. There are three possible conformations for a trans bis(maleimide) or bis(maleic anhydride) complex (I-III). The results of X-ray and NMR studies indicated that the main conformation of complexes 1-10 is I both in the solid state and in solution.« less

Neural Network Technique for Global Ocean Color (Chl-a) Estimates Bridging Multiple Satellite Missions

NASA Astrophysics Data System (ADS)

Garraffo, Z. D.; Nadiga, S.; Krasnopolsky, V.; Mehra, A.; Bayler, E. J.; Kim, H. C.; Behringer, D.

2016-02-01

A Neural Network (NN) technique is used to produce consistent global ocean color estimates, bridging multiple satellite ocean color missions by linking ocean color variability - primarily driven by biological processes - with the physical processes of the upper ocean. Satellite-derived surface variables - sea-surface temperature (SST) and sea-surface height (SSH) fields - are used as signatures of upper-ocean dynamics. The NN technique employs adaptive weights that are tuned by applying statistical learning (training) algorithms to past data sets, providing robustness with respect to random noise, accuracy, fast emulations, and fault-tolerance. This study employs Sea-viewing Wide Field-of-View Sensor (SeaWiFS) chlorophyll-a data for 1998-2010 in conjunction with satellite SSH and SST fields. After interpolating all data sets to the same two-degree latitude-longitude grid, the annual mean was removed and monthly anomalies extracted . The NN technique wass trained for even years of that period and tested for errors and bias for the odd years. The NN output are assessed for: (i) bias, (ii) variability, (iii) root-mean-square error (RMSE), and (iv) cross-correlation. A Jacobian is evaluated to estimate the impact of each input (SSH, SST) on the NN chlorophyll-a estimates. The differences between an ensemble of NNs vs a single NN are examined. After the NN is trained for the SeaWiFS period, the NN is then applied and validated for 2005-2015, a period covered by other satellite missions — the Moderate Resolution Imaging Spectroradiometer (MODIS AQUA) and the Visible Imaging Infrared Radiometer Suite (VIIRS).

Altered processing of the neurotensin/neuromedin N precursor in PC2 knock down mice: a biochemical and immunohistochemical study.

PubMed

Villeneuve, Pierre; Feliciangeli, Sylvain; Croissandeau, Gilles; Seidah, Nabil G; Mbikay, Majambu; Kitabgi, Patrick; Beaudet, Alain

2002-08-01

Neurotensin (NT) and neuromedin N (NN) are generated by endoproteolytic cleavage of a common precursor molecule, pro-NT/NN. To gain insight into the role of prohormone convertases PC1, PC2, and PC7 in this process, we investigated the maturation of pro-NT/NN in the brain of PC7 (PC7-/-), PC2 (PC2-/-), and/or PC1 (PC1+/- and PC2-/-; PC1+/-) knock down mice. Inactivation of the PC7 gene was without effect, suggesting that this convertase is not involved in the processing of pro-NT/NN. By contrast, there was a 15% decrease in NT and a 50% decrease in NN levels, as measured by radioimmunoassay, in whole brain extracts from PC2 null as compared with wild type mice. Using immunohistochemistry, we found that this decrease in pro-NT/NN maturation products was uneven and that it was most pronounced in the medial preoptic area, lateral hypothalamus, and paraventricular hypothalamic nuclei. These results suggest that PC2 plays a critical role in the processing of pro-NT/NN in mouse brain and that its deficiency may be compensated to a regionally variable extent by other convertases. Previous data have suggested that PC1 might be subserving this role. However, there was no change in the maturation of pro-NT/NN in the brain of mice in which the PC1 gene had been partially inactivated, implying that complete PC1 knock down may be required for loss of function.

Implementation of an Adaptive Controller System from Concept to Flight Test

NASA Technical Reports Server (NTRS)

Larson, Richard R.; Burken, John J.; Butler, Bradley S.; Yokum, Steve

2009-01-01

The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a "floating limiter" (FL) concept that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly "floating" and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually "hit" the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The tunable metrics for the FL are (1) window size, (2) drift rate, and (3) persistence counter. Ultimate range limits are also included in case the NN command should drift slowly to a limit value that would cause the FL to be defeated. The FL has proven to work as intended. Both high-g transients and excessive structural loads are controlled with NN hard-over commands. This presentation discusses the FL design features and presents test cases. Simulation runs are included to illustrate the dramatic improvement made to the control of NN hard-over effects. A mission control room display from a flight playback is presented to illustrate the neural net fault display representation. The FL is very adaptable to various requirements and is independent of flight condition. It should be considered as a cost-effective safety monitor to control single-string inputs in general.

Neural Net Safety Monitor Design

NASA Technical Reports Server (NTRS)

Larson, Richard R.

2007-01-01

The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a floating limiter (FL) concept1 that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly floating and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually hit the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The tunable metrics for the FL are (1) window size, (2) drift rate, and (3) persistence counter. Ultimate range limits are also included in case the NN command should drift slowly to a limit value that would cause the FL to be defeated. The FL has proven to work as intended. Both high-g transients and excessive structural loads are controlled with NN hard-over commands. This presentation discusses the FL design features and presents test cases. Simulation runs are included to illustrate the dramatic improvement made to the control of NN hard-over effects. A mission control room display from a flight playback is presented to illustrate the neural net fault display representation. The FL is very adaptable to various requirements and is independent of flight condition. It should be considered as a cost-effective safety monitor to control single-string inputs in general.

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

Zhang Yanxia; Ma He; Peng Nanbo

We apply one of the lazy learning methods, the k-nearest neighbor (kNN) algorithm, to estimate the photometric redshifts of quasars based on various data sets from the Sloan Digital Sky Survey (SDSS), the UKIRT Infrared Deep Sky Survey (UKIDSS), and the Wide-field Infrared Survey Explorer (WISE; the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample, and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN performs best when k is different with a special input pattern for a special data set. The best result belongs to the SDSS-UKIDSS-WISEmore » sample. The experimental results generally show that the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. Compared with the performance of various other methods of estimating the photometric redshifts of quasars, kNN based on KD-Tree shows superiority, exhibiting the best accuracy.« less

Measurement of KS0 and K*0 in p +p ,d +Au , and Cu + Cu collisions at √{sNN}=200 GeV

NASA Astrophysics Data System (ADS)

Adare, A.; Afanasiev, S.; Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Akimoto, R.; Al-Bataineh, H.; Alexander, J.; Alfred, M.; Angerami, A.; Aoki, K.; Apadula, N.; Aphecetche, L.; Aramaki, Y.; Armendariz, R.; Aronson, S. H.; Asai, J.; Asano, H.; Atomssa, E. T.; Averbeck, R.; Awes, T. C.; Azmoun, B.; Babintsev, V.; Bai, M.; Baksay, G.; Baksay, L.; Baldisseri, A.; Bandara, N. S.; Bannier, B.; Barish, K. N.; Barnes, P. D.; Bassalleck, B.; Basye, A. T.; Bathe, S.; Batsouli, S.; Baublis, V.; Baumann, C.; Bazilevsky, A.; Beaumier, M.; Beckman, S.; Belikov, S.; Belmont, R.; Bennett, R.; Berdnikov, A.; Berdnikov, Y.; Bhom, J. H.; Bickley, A. A.; Black, D.; Blau, D. S.; Boissevain, J. G.; Bok, J. S.; Borel, H.; Boyle, K.; Brooks, M. L.; Bryslawskyj, J.; Buesching, H.; Bumazhnov, V.; Bunce, G.; Butsyk, S.; Campbell, S.; Caringi, A.; Chang, B. S.; Charvet, J.-L.; Chen, C.-H.; Chernichenko, S.; Chi, C. Y.; Chiba, J.; Chiu, M.; Choi, I. J.; Choi, J. B.; Choudhury, R. K.; Christiansen, P.; Chujo, T.; Chung, P.; Churyn, A.; Chvala, O.; Cianciolo, V.; Citron, Z.; Cleven, C. R.; Cole, B. A.; Comets, M. P.; Conesa Del Valle, Z.; Connors, M.; Constantin, P.; Csanád, M.; Csörgő, T.; Dahms, T.; Dairaku, S.; Danchev, I.; Das, K.; Datta, A.; Daugherity, M. S.; David, G.; Dayananda, M. K.; Deaton, M. B.; Deblasio, K.; Dehmelt, K.; Delagrange, H.; Denisov, A.; D'Enterria, D.; Deshpande, A.; Desmond, E. J.; Dharmawardane, K. V.; Dietzsch, O.; Ding, L.; Dion, A.; Do, J. H.; Donadelli, M.; Drapier, O.; Drees, A.; Drees, K. A.; Dubey, A. K.; Durham, J. M.; Durum, A.; Dutta, D.; Dzhordzhadze, V.; D'Orazio, L.; Edwards, S.; Efremenko, Y. V.; Egdemir, J.; Ellinghaus, F.; Emam, W. S.; Engelmore, T.; Enokizono, A.; En'yo, H.; Esumi, S.; Eyser, K. O.; Fadem, B.; Feege, N.; Fields, D. E.; Finger, M.; Finger, M.; Fleuret, F.; Fokin, S. L.; Fraenkel, Z.; Frantz, J. E.; Franz, A.; Frawley, A. D.; Fujiwara, K.; Fukao, Y.; Fusayasu, T.; Gadrat, S.; Gal, C.; Gallus, P.; Garg, P.; Garishvili, I.; Ge, H.; Giordano, F.; Glenn, A.; Gong, H.; Gonin, M.; Gosset, J.; Goto, Y.; Granier de Cassagnac, R.; Grau, N.; Greene, S. V.; Grim, G.; Grosse Perdekamp, M.; Gu, Y.; Gunji, T.; Guragain, H.; Gustafsson, H.-Å.; Hachiya, T.; Hadj Henni, A.; Haegemann, C.; Haggerty, J. S.; Hahn, K. I.; Hamagaki, H.; Hamblen, J.; Han, R.; Han, S. Y.; Hanks, J.; Harada, H.; Hartouni, E. P.; Haruna, K.; Hasegawa, S.; Haslum, E.; Hayano, R.; He, X.; Heffner, M.; Hemmick, T. K.; Hester, T.; Hiejima, H.; Hill, J. C.; Hobbs, R.; Hohlmann, M.; Hollis, R. S.; Holzmann, W.; Homma, K.; Hong, B.; Horaguchi, T.; Hornback, D.; Hoshino, T.; Huang, S.; Ichihara, T.; Ichimiya, R.; Iinuma, H.; Ikeda, Y.; Imai, K.; Imazu, Y.; Inaba, M.; Inoue, Y.; Iordanova, A.; Isenhower, D.; Isenhower, L.; Ishihara, M.; Isobe, T.; Issah, M.; Isupov, A.; Ivanischev, D.; Ivanishchev, D.; Iwanaga, Y.; Jacak, B. V.; Jeon, S. J.; Jezghani, M.; Jia, J.; Jiang, X.; Jin, J.; Jinnouchi, O.; Johnson, B. M.; Jones, T.; Joo, E.; Joo, K. S.; Jouan, D.; Jumper, D. S.; Kajihara, F.; Kametani, S.; Kamihara, N.; Kamin, J.; Kaneta, M.; Kang, J. H.; Kang, J. S.; Kanou, H.; Kapustinsky, J.; Karatsu, K.; Kasai, M.; Kawall, D.; Kawashima, M.; Kazantsev, A. V.; Kempel, T.; Key, J. A.; Khachatryan, V.; Khanzadeev, A.; Kihara, K.; Kijima, K. M.; Kikuchi, J.; Kim, A.; Kim, B. I.; Kim, C.; Kim, D. H.; Kim, D. J.; Kim, E.; Kim, E.-J.; Kim, H.-J.; Kim, M.; Kim, Y.-J.; Kim, Y. K.; Kinney, E.; Kiss, Á.; Kistenev, E.; Kiyomichi, A.; Klatsky, J.; Klay, J.; Klein-Boesing, C.; Kleinjan, D.; Kline, P.; Koblesky, T.; Kochenda, L.; Kochetkov, V.; Kofarago, M.; Komkov, B.; Konno, M.; Koster, J.; Kotchetkov, D.; Kotov, D.; Kozlov, A.; Král, A.; Kravitz, A.; Kubart, J.; Kunde, G. J.; Kurihara, N.; Kurita, K.; Kurosawa, M.; Kweon, M. J.; Kwon, Y.; Kyle, G. S.; Lacey, R.; Lai, Y. S.; Lajoie, J. G.; Lebedev, A.; Lee, D. M.; Lee, J.; Lee, K. B.; Lee, K. S.; Lee, M. K.; Lee, S. H.; Lee, T.; Leitch, M. J.; Leite, M. A. L.; Leitgab, M.; Lenzi, B.; Li, X.; Lichtenwalner, P.; Liebing, P.; Lim, S. H.; Linden Levy, L. A.; Liška, T.; Litvinenko, A.; Liu, H.; Liu, M. X.; Love, B.; Lynch, D.; Maguire, C. F.; Makdisi, Y. I.; Makek, M.; Malakhov, A.; Malik, M. D.; Manion, A.; Manko, V. I.; Mannel, E.; Mao, Y.; Mašek, L.; Masui, H.; Matathias, F.; McCumber, M.; McGaughey, P. L.; McGlinchey, D.; McKinney, C.; Means, N.; Meles, A.; Mendoza, M.; Meredith, B.; Miake, Y.; Mibe, T.; Mignerey, A. C.; Mikeš, P.; Miki, K.; Miller, A. J.; Miller, T. E.; Milov, A.; Mioduszewski, S.; Mishra, D. K.; Mishra, M.; Mitchell, J. T.; Mitrovski, M.; Miyasaka, S.; Mizuno, S.; Mohanty, A. K.; Montuenga, P.; Moon, H. J.; Moon, T.; Morino, Y.; Morreale, A.; Morrison, D. P.; Moukhanova, T. V.; Mukhopadhyay, D.; Murakami, T.; Murata, J.; Mwai, A.; Nagamiya, S.; Nagata, Y.; Nagle, J. L.; Naglis, M.; Nagy, M. I.; Nakagawa, I.; Nakagomi, H.; Nakamiya, Y.; Nakamura, K. R.; Nakamura, T.; Nakano, K.; Nam, S.; Nattrass, C.; Netrakanti, P. K.; Newby, J.; Nguyen, M.; Nihashi, M.; Niida, T.; Norman, B. E.; Nouicer, R.; Novitzky, N.; Nyanin, A. S.; Oakley, C.; O'Brien, E.; Oda, S. X.; Ogilvie, C. A.; Ohnishi, H.; Oka, M.; Okada, K.; Omiwade, O. O.; Onuki, Y.; Orjuela Koop, J. D.; Oskarsson, A.; Ouchida, M.; Ozaki, H.; Ozawa, K.; Pak, R.; Pal, D.; Palounek, A. P. T.; Pantuev, V.; Papavassiliou, V.; Park, I. H.; Park, J.; Park, S.; Park, S. K.; Park, W. J.; Pate, S. F.; Patel, L.; Patel, M.; Pei, H.; Peng, J.-C.; Pereira, H.; Perepelitsa, D. V.; Perera, G. D. N.; Peresedov, V.; Peressounko, D. Yu.; Perry, J.; Petti, R.; Pinkenburg, C.; Pinson, R.; Pisani, R. P.; Proissl, M.; Purschke, M. L.; Purwar, A. K.; Qu, H.; Rak, J.; Rakotozafindrabe, A.; Ravinovich, I.; Read, K. F.; Rembeczki, S.; Reuter, M.; Reygers, K.; Reynolds, D.; Riabov, V.; Riabov, Y.; Richardson, E.; Riveli, N.; Roach, D.; Roche, G.; Rolnick, S. D.; Romana, A.; Rosati, M.; Rosen, C. A.; Rosendahl, S. S. E.; Rosnet, P.; Rowan, Z.; Rubin, J. G.; Rukoyatkin, P.; Ružička, P.; Rykov, V. L.; Sahlmueller, B.; Saito, N.; Sakaguchi, T.; Sakai, S.; Sakashita, K.; Sakata, H.; Sako, H.; Samsonov, V.; Sano, S.; Sarsour, M.; Sato, S.; Sato, T.; Sawada, S.; Schaefer, B.; Schmoll, B. K.; Sedgwick, K.; Seele, J.; Seidl, R.; Semenov, V.; Sen, A.; Seto, R.; Sett, P.; Sexton, A.; Sharma, D.; Shein, I.; Shevel, A.; Shibata, T.-A.; Shigaki, K.; Shimomura, M.; Shoji, K.; Shukla, P.; Sickles, A.; Silva, C. L.; Silvermyr, D.; Silvestre, C.; Sim, K. S.; Singh, B. K.; Singh, C. P.; Singh, V.; Skutnik, S.; Slunečka, M.; Soldatov, A.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; Sourikova, I. V.; Staley, F.; Stankus, P. W.; Stenlund, E.; Stepanov, M.; Ster, A.; Stoll, S. P.; Sugitate, T.; Suire, C.; Sukhanov, A.; Sumita, T.; Sun, J.; Sziklai, J.; Tabaru, T.; Takagi, S.; Takagui, E. M.; Takahara, A.; Taketani, A.; Tanabe, R.; Tanaka, Y.; Taneja, S.; Tanida, K.; Tannenbaum, M. J.; Tarafdar, S.; Taranenko, A.; Tarján, P.; Themann, H.; Thomas, D.; Thomas, T. L.; Timilsina, A.; Todoroki, T.; Togawa, M.; Toia, A.; Tojo, J.; Tomášek, L.; Tomášek, M.; Torii, H.; Towell, M.; Towell, R.; Towell, R. S.; Tram, V.-N.; Tserruya, I.; Tsuchimoto, Y.; Vale, C.; Valle, H.; van Hecke, H. W.; Vargyas, M.; Vazquez-Zambrano, E.; Veicht, A.; Velkovska, J.; Vértesi, R.; Vinogradov, A. A.; Virius, M.; Vrba, V.; Vznuzdaev, E.; Wagner, M.; Walker, D.; Wang, X. R.; Watanabe, D.; Watanabe, K.; Watanabe, Y.; Watanabe, Y. S.; Wei, F.; Wei, R.; Wessels, J.; Whitaker, S.; White, S. N.; Winter, D.; Wolin, S.; Woody, C. L.; Wright, R. M.; Wysocki, M.; Xia, B.; Xie, W.; Xue, L.; Yalcin, S.; Yamaguchi, Y. L.; Yamaura, K.; Yang, R.; Yanovich, A.; Yasin, Z.; Ying, J.; Yokkaichi, S.; Yoon, I.; You, Z.; Young, G. R.; Younus, I.; Yushmanov, I. E.; Zajc, W. A.; Zaudtke, O.; Zelenski, A.; Zhang, C.; Zhou, S.; Zimányi, J.; Zolin, L.; Phenix Collaboration

2014-11-01

The PHENIX experiment at the Relativistic Heavy Ion Collider has performed a systematic study of KS0 and K*0 meson production at midrapidity in p +p ,d +Au , and Cu +Cu collisions at √{s NN}=200 GeV. The KS0 and K*0 mesons are reconstructed via their KS0→π0(→γ γ ) π0(→γ γ ) and K*0→K±π∓ decay modes, respectively. The measured transverse-momentum spectra are used to determine the nuclear modification factor of KS0 and K*0 mesons in d +Au and Cu +Cu collisions at different centralities. In the d +Au collisions, the nuclear modification factor of KS0 and K*0 mesons is almost constant as a function of transverse momentum and is consistent with unity, showing that cold-nuclear-matter effects do not play a significant role in the measured kinematic range. In Cu +Cu collisions, within the uncertainties no nuclear modification is registered in peripheral collisions. In central collisions, both mesons show suppression relative to the expectations from the p +p yield scaled by the number of binary nucleon-nucleon collisions in the Cu +Cu system. In the pT range 2 - 5 GeV /c , the strange mesons (KS0,K*0) similarly to the ϕ meson with hidden strangeness, show an intermediate suppression between the more suppressed light quark mesons (π0) and the nonsuppressed baryons (p ,p ¯). At higher transverse momentum, pT>5 GeV /c , production of all particles is similarly suppressed by a factor of ≈2 .

Centrality dependence of particle production in p - Pb collisions at s NN = 5.02 TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2015-06-08

Here, we report measurements of the primary charged-particle pseudorapidity density and transverse momentum distributions in p–Pb collisions at √s NN = 5.02TeV and investigate their correlation with experimental observables sensitive to the centrality of the collision. Centrality classes are defined by using different event-activity estimators, i.e., charged-particle multiplicities measured in three different pseudorapidity regions as well as the energy measured at beam rapidity (zero degree). The procedures to determine the centrality, quantified by the number of participants (N part) or the number of nucleon-nucleon binary collisions (N coll) are described. We show that, in contrast to Pb-Pb collisions, in p–Pbmore » collisions large multiplicity fluctuations together with the small range of participants available generate a dynamical bias in centrality classes based on particle multiplicity. We propose to use the zero-degree energy, which we expect not to introduce a dynamical bias, as an alternative event-centrality estimator. Based on zero-degree energy-centrality classes, the N part dependence of particle production is studied. Under the assumption that the multiplicity measured in the Pb-going rapidity region scales with the number of Pb participants, an approximate independence of the multiplicity per participating nucleon measured at mid-rapidity of the number of participating nucleons is observed. Furthermore, at high-p T the p–Pb spectra are found to be consistent with the pp spectra scaled by N coll for all centrality classes. Our results represent valuable input for the study of the event-activity dependence of hard probes in p–Pb collisions and, hence, help to establish baselines for the interpretation of the Pb-Pb data.« less

Measurement of K 0 S and K *0 in p+p, d+Au, and Cu+Cu collisions at sqrt S NN = 200 GeV

DOE PAGES

Adare, A.; Aidala, C.

2014-11-01

The PHENIX experiment at the Relativistic Heavy Ion Collider has performed a systematic study of K 0 S and K *0 meson production at midrapidity in p+p, d+Au, and Cu+Cu collisions at sqrt S NN = 200 GeV. The K 0 S and K *0 mesons are reconstructed via their K 0 S and π 0(→γγ)π 0 (→γγ) and K *0 → K ± π ± decay modes, respectively. The measured transverse-momentum spectra are used to determine the nuclear modification factor of K 0 S and K *0 mesons in d+Au and Cu+Cu collisions at different centralities. In the d+Aumore » collisions, the nuclear modification factor of K 0 S and K *0 mesons is almost constant as a function of transverse momentum and is consistent with unity showing that cold-nuclear-matter effects do not play a significant role in the measured kinematic range. In Cu+Cu collisions, within the uncertainties no nuclear modification is registered in peripheral collisions. In central collisions, both mesons show suppression relative to the expectations from the p+p yield scaled by the number of binary nucleon-nucleon collisions in the Cu+Cu system. In the p T range 2–5 GeV/c, the strange mesons ( K 0 S, K *0) similarly to the Φ meson with hidden strangeness, show an intermediate suppression between the more suppressed light quark mesons (π 0) and the nonsuppressed baryons (p, p-bar). At higher transverse momentum, p T > 5 GeV/c, production of all particles is similarly suppressed by a factor of ≈2. (auth)« less

Centrality dependence of inclusive J/ψ production in p-Pb collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=5.02 $$ TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2015-11-19

Here, we present a measurement of inclusive J/Ψ production in p-Pb collisions at √S NN = 5.02 TeV as a function of the centrality of the collision, as estimated from the energy deposited in the Zero Degree Calorimeters. We also performed this measurement with the ALICE detector down to zero transverse momentum, p T, in the backward (-4.46 < y cms < -2.96) and forward (2.03 < y cms< 3.53) rapidity intervals in the dimuon decay channel and in the mid-rapidity region (-1.37 < y cms < 0.43) in the dielectron decay channel. The backward and forward rapidity intervals correspondmore » to the Pb-going and p-going direction, respectively. The p T-differential J/Ψ production cross section at backward and forward rapidity is measured for several centrality classes, together with the corresponding average p T and p T2 values. The nuclear modification factor is presented as a function of centrality for the three rapidity intervals, and as a function of p T for several centrality classes at backward and forward rapidity. At mid-and forward rapidity, the J/Ψ yield is suppressed up to 40% compared to that in pp interactions scaled by the number of binary collisions. Furthermore, the degree of suppression increases towards central p-Pb collisions at forward rapidity, and with decreasing p T of the J/Ψ. At backward rapidity, the nuclear modification factor is compatible with unity within the total uncertainties, with an increasing trend from peripheral to central p-Pb collisions.« less

An automated technique for potential differentiation of ovarian mature teratomas from other benign tumours using neural networks classification of 2D ultrasound static images: a pilot study

NASA Astrophysics Data System (ADS)

Al-karawi, Dhurgham; Sayasneh, A.; Al-Assam, Hisham; Jassim, Sabah; Page, N.; Timmerman, D.; Bourne, T.; Du, Hongbo

2017-05-01

Ovarian cysts are a common pathology in women of all age groups. It is estimated that 5-10% of women have a surgical intervention to remove an ovarian cyst in their lifetime. Given this frequency rate, characterization of ovarian masses is essential for optimal management of patients. Patients with benign ovarian masses can be managed conservatively if they are asymptomatic. Mature teratomas are common benign ovarian cysts that occur, in most cases, in premenopausal women. These ovarian cysts can contain different types of human tissue including bone, cartilage, fat, hair, or other tissue. If they are causing no symptoms, they can be harmless and may not require surgery. Subjective assessment by ultrasound examiners has a high diagnostic accuracy when characterising mature teratomas from other types of tumours. The aim of this study is to develop a computerised technique with the potential to characterise mature teratomas and distinguish them from other types of benign ovarian tumours. Local Binary Pattern (LBP) was applied to extract texture features that are specific in distinguishing teratomas. Neural Networks (NN) was then used as a classifier for recognising mature teratomas. A pilot sample set of 130 B-mode static ovarian ultrasound images (41 mature teratomas tumours and 89 other types of benign tumours) was used to test the effectiveness of the proposed technique. Test results show an average accuracy rate of 99.4% with a sensitivity of 100%, specificity of 98.8% and positive predictive value of 98.9%. This study demonstrates that the NN and LBP techniques can accurately classify static 2D B-mode ultrasound images of benign ovarian masses into mature teratomas and other types of benign tumours.

R{sub AA} of J/psi near midrapidity in heavy ion collisions at sq root(s{sub NN})=200 GeV

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

Song, Taesoo; Park, Woosung; Lee, Su Houng

2010-03-15

We build up a model to reproduce the experimentally measured R{sub AA} of J/psi near midrapidty in an Au+Au collision at sq root(s{sub NN})=200 GeV. The model takes into account the J/psi suppression from the quark-gluon plasma and hadron gas as well as the nuclear absorption of primordial charmonia and the regeneration effects at the hadronization stage and hence is a generalization of the two-component model introduced by Grandchamp and Rapp. The improvements in this work are twofold; the addition of the initial local temperature profile and a consistent use of QCD next-to-leading order (NLO) formula for both the dissociationmore » cross section in the hadron gas and the thermal decay widths in the quark-gluon plasma for the charmonium states. The initial local temperature profile is determined from the assumption that the local entropy density is proportional to a formula involving the number densities of the number of participants and of the binary collisions that reproduces the multiplicities of charged particles at chemical freeze-out. The initial local temperature profile brings about a kink in the R{sub AA} curve due to the initial melting of J/psi. The initially formed fireball, composed of weakly interacting quarks and gluons with thermal masses that are extracted from lattice QCD, follows an isentropic expansion with cylindrical symmetry. The fit reproduces well the Au+Au as well as the Cu+Cu data. The same method is applied to predict the R{sub AA} expected from the Pb+Pb collision at Large Hadron Collider (LHC) energy.« less

Estimation of Comfort/Disconfort Based on EEG in Massage by Use of Clustering according to Correration and Incremental Learning type NN

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Authors proposed the estimation method combining k-means algorithm and NN for evaluating massage. However, this estimation method has a problem that discrimination ratio is decreased to new user. There are two causes of this problem. One is that generalization of NN is bad. Another one is that clustering result by k-means algorithm has not high correlation coefficient in a class. Then, this research proposes k-means algorithm according to correlation coefficient and incremental learning for NN. The proposed k-means algorithm is method included evaluation function based on correlation coefficient. Incremental learning is method that NN is learned by new data and initialized weight based on the existing data. The effect of proposed methods are verified by estimation result using EEG data when testee is given massage.

Neural networks: further insights into error function, generalized weights and others

PubMed Central

2016-01-01

The article is a continuum of a previous one providing further insights into the structure of neural network (NN). Key concepts of NN including activation function, error function, learning rate and generalized weights are introduced. NN topology can be visualized with generic plot() function by passing a “nn” class object. Generalized weights assist interpretation of NN model with respect to the independent effect of individual input variables. A large variance of generalized weights for a covariate indicates non-linearity of its independent effect. If generalized weights of a covariate are approximately zero, the covariate is considered to have no effect on outcome. Finally, prediction of new observations can be performed using compute() function. Make sure that the feature variables passed to the compute() function are in the same order to that in the training NN. PMID:27668220

Communication: Fitting potential energy surfaces with fundamental invariant neural network

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

Shao, Kejie; Chen, Jun; Zhao, Zhiqiang

A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vector is proposed in the construction of potential energy surfaces for molecular systems involving identical atoms. Mathematically, FIs finitely generate the permutation invariant polynomial (PIP) ring. In combination with NN, fundamental invariant neural network (FI-NN) can approximate any function to arbitrary accuracy. Because FI-NN minimizes the size of input permutation invariant polynomials, it can efficiently reduce the evaluation time of potential energy, in particular for polyatomic systems. In this work, we provide the FIs for all possible molecular systems up to five atoms. Potential energymore » surfaces for OH{sub 3} and CH{sub 4} were constructed with FI-NN, with the accuracy confirmed by full-dimensional quantum dynamic scattering and bound state calculations.« less

Chronic exposure to KATP channel openers results in attenuated glucose sensing in hypothalamic GT1-7 neurons.

PubMed

Haythorne, Elizabeth; Hamilton, D Lee; Findlay, John A; Beall, Craig; McCrimmon, Rory J; Ashford, Michael L J

2016-12-01

Individuals with Type 1 diabetes (T1D) are often exposed to recurrent episodes of hypoglycaemia. This reduces hormonal and behavioural responses that normally counteract low glucose in order to maintain glucose homeostasis, with altered responsiveness of glucose sensing hypothalamic neurons implicated. Although the molecular mechanisms are unknown, pharmacological studies implicate hypothalamic ATP-sensitive potassium channel (K ATP ) activity, with K ATP openers (KCOs) amplifying, through cell hyperpolarization, the response to hypoglycaemia. Although initial findings, using acute hypothalamic KCO delivery, in rats were promising, chronic exposure to the KCO NN414 worsened the responses to subsequent hypoglycaemic challenge. To investigate this further we used GT1-7 cells to explore how NN414 affected glucose-sensing behaviour, the metabolic response of cells to hypoglycaemia and K ATP activity. GT1-7 cells exposed to 3 or 24 h NN414 exhibited an attenuated hyperpolarization to subsequent hypoglycaemic challenge or NN414, which correlated with diminished K ATP activity. The reduced sensitivity to hypoglycaemia was apparent 24 h after NN414 removal, even though intrinsic K ATP activity recovered. The NN414-modified glucose responsiveness was not associated with adaptations in glucose uptake, metabolism or oxidation. K ATP inactivation by NN414 was prevented by the concurrent presence of tolbutamide, which maintains K ATP closure. Single channel recordings indicate that NN414 alters K ATP intrinsic gating inducing a stable closed or inactivated state. These data indicate that exposure of hypothalamic glucose sensing cells to chronic NN414 drives a sustained conformational change to K ATP , probably by binding to SUR1, that results in loss of channel sensitivity to intrinsic metabolic factors such as MgADP and small molecule agonists. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

NpNn scheme and the saturation of collectivity in the A~=170 and 230 regions

NASA Astrophysics Data System (ADS)

Saha, M.; Sen, S.

1993-02-01

It is shown that the well known phenomenon of the saturation in the B(E20+1-->2+1), as well as the E+21 values near midshell in the even rare-earth and actinide nuclei, can be reproduced in the NpNn scheme through a very simple parametrization in terms of the maximum number of valence protons and neutrons available in the major shells under consideration. This parametrization leads to a product (NpNn)eff which is found to have a more universal character as a structure variable than the usual NpNn.

ESR study of molecular orientation and dynamics of nitronyl nitroxide radicals in CLPOT 1D nanochannels.

PubMed

Kobayashi, Hirokazu; Morinaga, Yuka; Fujimori, Etsuko; Asaji, Tetsuo

2014-07-10

New inclusion compounds (ICs) were prepared using the organic 1D nanochannels of 2,4,6-tris(4-chlorophenoxy)-1,3,5-triazine (CLPOT) as a nanosized template and nitronyl nitroxide (NN) radicals such as phenylnitronylnitroxide (PhNN) and p-nitrophenylnitronylnitroxide (p-NPNN). ESR measurements below 255 K for the CLPOT ICs diluted with spacer molecules gave rigid limit spectra similar to that for PhNN molecules in a glassy ethanol matrix at low temperature, which suggests isolation of the radical molecules. ESR measurements for them in the range of 290-400 K gave a modulated quintet ESR signal, which suggested uniaxial rotational diffusion of NN radicals in the nanochannels approximately around the principal y-axis of the g-tensors. In the ESR measurements to 430 K for the [(CLPOT)2-(p-NPNN)0.07] IC without spacers, the broader line width than the case in dilution was observed by inter-radical dipolar interaction. In every case, the rotational diffusion activation energies of NN radicals in the CLPOT nanochannels were several times larger than those of 2,2,6,6-tetramethyl-1-piperidinyloxyl (TEMPO) radical derivatives (4-X-TEMPO) in CLPOT nanochannels. This is expected due to the larger molecular size of NN radicals than 4-X-TEMPO or stronger interaction between NN radicals and the surrounding host or guest molecules.

The distance function effect on k-nearest neighbor classification for medical datasets.

PubMed

Hu, Li-Yu; Huang, Min-Wei; Ke, Shih-Wen; Tsai, Chih-Fong

2016-01-01

K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output. Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually. The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets. In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.

Ab Initio Calculations of the N-N Bond Dissociation for the Gas-phase RDX and HMX.

PubMed

Liu, Lin-Lin; Liu, Pei-Jin; Hu, Song-Qi; He, Guo-Qiang

2017-01-17

NO 2 fission is a vital factor for 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) decomposition. In this study, the geometry of the gas-phase RDX and HMX molecules was optimized, and the bond order and the bond dissociation energy of the N-N bonds were examined. Moreover, the rate constants of the gas-phase RDX and HMX conformers, concerning the N-N bond dissociation, were evaluated using the microcanonical variational transition state theory (μVT). The calculation results have shown that HMX is more stable than RDX in terms of the N-N bond dissociation, and the conformers stability parameters were as follows: RDXaaa < RDXaae < HMX I < HMX II. In addition, for the RDX conformers, the N-N bond of the pseudo-equatorial positioning of the nitro group was more stable than the N-N bond of the axial positioning of the nitro group, while the results were opposite in the case of the HMX conformers. Moreover, it has been shown that the dissociation rate constant of the N-N bond is influenced by the temperature significantly, thus the rate constants were much lower (<10 -10  s -1 ) when the temperature was less than 1000 K.

Ab Initio Calculations of the N-N Bond Dissociation for the Gas-phase RDX and HMX

PubMed Central

Liu, Lin-lin; Liu, Pei-jin; Hu, Song-qi; He, Guo-qiang

2017-01-01

NO2 fission is a vital factor for 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) decomposition. In this study, the geometry of the gas-phase RDX and HMX molecules was optimized, and the bond order and the bond dissociation energy of the N-N bonds were examined. Moreover, the rate constants of the gas-phase RDX and HMX conformers, concerning the N-N bond dissociation, were evaluated using the microcanonical variational transition state theory (μVT). The calculation results have shown that HMX is more stable than RDX in terms of the N-N bond dissociation, and the conformers stability parameters were as follows: RDXaaa < RDXaae < HMX I < HMX II. In addition, for the RDX conformers, the N-N bond of the pseudo-equatorial positioning of the nitro group was more stable than the N-N bond of the axial positioning of the nitro group, while the results were opposite in the case of the HMX conformers. Moreover, it has been shown that the dissociation rate constant of the N-N bond is influenced by the temperature significantly, thus the rate constants were much lower (<10−10 s−1) when the temperature was less than 1000 K. PMID:28094774

Ab Initio Calculations of the N-N Bond Dissociation for the Gas-phase RDX and HMX

NASA Astrophysics Data System (ADS)

Liu, Lin-Lin; Liu, Pei-Jin; Hu, Song-Qi; He, Guo-Qiang

2017-01-01

NO2 fission is a vital factor for 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) decomposition. In this study, the geometry of the gas-phase RDX and HMX molecules was optimized, and the bond order and the bond dissociation energy of the N-N bonds were examined. Moreover, the rate constants of the gas-phase RDX and HMX conformers, concerning the N-N bond dissociation, were evaluated using the microcanonical variational transition state theory (μVT). The calculation results have shown that HMX is more stable than RDX in terms of the N-N bond dissociation, and the conformers stability parameters were as follows: RDXaaa < RDXaae < HMX I < HMX II. In addition, for the RDX conformers, the N-N bond of the pseudo-equatorial positioning of the nitro group was more stable than the N-N bond of the axial positioning of the nitro group, while the results were opposite in the case of the HMX conformers. Moreover, it has been shown that the dissociation rate constant of the N-N bond is influenced by the temperature significantly, thus the rate constants were much lower (<10-10 s-1) when the temperature was less than 1000 K.

ϕ meson production in d +Au collisions at √{sN N}=200 GeV

NASA Astrophysics Data System (ADS)

Adare, A.; Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Al-Bataineh, H.; Alexander, J.; Alfred, M.; Angerami, A.; Aoki, K.; Apadula, N.; Aramaki, Y.; Asano, H.; Atomssa, E. T.; Averbeck, R.; Awes, T. C.; Azmoun, B.; Babintsev, V.; Bai, M.; Baksay, G.; Baksay, L.; Bandara, N. S.; Bannier, B.; Barish, K. N.; Bassalleck, B.; Basye, A. T.; Bathe, S.; Baublis, V.; Baumann, C.; Bazilevsky, A.; Beaumier, M.; Beckman, S.; Belikov, S.; Belmont, R.; Bennett, R.; Berdnikov, A.; Berdnikov, Y.; Bhom, J. H.; Blau, D. S.; Bok, J. S.; Boyle, K.; Brooks, M. L.; Bryslawskyj, J.; Buesching, H.; Bumazhnov, V.; Bunce, G.; Butsyk, S.; Campbell, S.; Caringi, A.; Chen, C.-H.; Chi, C. Y.; Chiu, M.; Choi, I. J.; Choi, J. B.; Choudhury, R. K.; Christiansen, P.; Chujo, T.; Chung, P.; Chvala, O.; Cianciolo, V.; Citron, Z.; Cole, B. A.; Conesa Del Valle, Z.; Connors, M.; Csanád, M.; Csörgő, T.; Dahms, T.; Dairaku, S.; Danchev, I.; Danley, D.; Das, K.; Datta, A.; Daugherity, M. S.; David, G.; Dayananda, M. K.; Deblasio, K.; Dehmelt, K.; Denisov, A.; Deshpande, A.; Desmond, E. J.; Dharmawardane, K. V.; Dietzsch, O.; Dion, A.; Diss, P. B.; Do, J. H.; Donadelli, M.; D'Orazio, L.; Drapier, O.; Drees, A.; Drees, K. A.; Durham, J. M.; Durum, A.; Dutta, D.; Edwards, S.; Efremenko, Y. V.; Ellinghaus, F.; Engelmore, T.; Enokizono, A.; En'yo, H.; Esumi, S.; Fadem, B.; Feege, N.; Fields, D. E.; Finger, M.; Finger, M.; Fleuret, F.; Fokin, S. L.; Fraenkel, Z.; Frantz, J. E.; Franz, A.; Frawley, A. D.; Fujiwara, K.; Fukao, Y.; Fusayasu, T.; Gal, C.; Gallus, P.; Garg, P.; Garishvili, I.; Ge, H.; Giordano, F.; Glenn, A.; Gong, H.; Gonin, M.; Goto, Y.; Granier de Cassagnac, R.; Grau, N.; Greene, S. V.; Grim, G.; Grosse Perdekamp, M.; Gunji, T.; Gustafsson, H.-Å.; Hachiya, T.; Haggerty, J. S.; Hahn, K. I.; Hamagaki, H.; Hamblen, J.; Hamilton, H. F.; Han, R.; Han, S. Y.; Hanks, J.; Hasegawa, S.; Haseler, T. O. S.; Hashimoto, K.; Haslum, E.; Hayano, R.; He, X.; Heffner, M.; Hemmick, T. K.; Hester, T.; Hill, J. C.; Hohlmann, M.; Hollis, R. S.; Holzmann, W.; Homma, K.; Hong, B.; Horaguchi, T.; Hornback, D.; Hoshino, T.; Hotvedt, N.; Huang, J.; Huang, S.; Ichihara, T.; Ichimiya, R.; Ikeda, Y.; Imai, K.; Inaba, M.; Iordanova, A.; Isenhower, D.; Ishihara, M.; Issah, M.; Ivanishchev, D.; Iwanaga, Y.; Jacak, B. V.; Jezghani, M.; Jia, J.; Jiang, X.; Jin, J.; Johnson, B. M.; Jones, T.; Joo, K. S.; Jouan, D.; Jumper, D. S.; Kajihara, F.; Kamin, J.; Kanda, S.; Kang, J. H.; Kapustinsky, J.; Karatsu, K.; Kasai, M.; Kawall, D.; Kawashima, M.; Kazantsev, A. V.; Kempel, T.; Key, J. A.; Khachatryan, V.; Khanzadeev, A.; Kijima, K. M.; Kikuchi, J.; Kim, A.; Kim, B. I.; Kim, C.; Kim, D. J.; Kim, E.-J.; Kim, G. W.; Kim, M.; Kim, Y.-J.; Kimelman, B.; Kinney, E.; Kiss, Á.; Kistenev, E.; Kitamura, R.; Klatsky, J.; Kleinjan, D.; Kline, P.; Koblesky, T.; Kochenda, L.; Komkov, B.; Konno, M.; Koster, J.; Kotov, D.; Král, A.; Kravitz, A.; Kunde, G. J.; Kurita, K.; Kurosawa, M.; Kwon, Y.; Kyle, G. S.; Lacey, R.; Lai, Y. S.; Lajoie, J. G.; Lebedev, A.; Lee, D. M.; Lee, J.; Lee, K. B.; Lee, K. S.; Lee, S.; Lee, S. H.; Leitch, M. J.; Leite, M. A. L.; Li, X.; Lichtenwalner, P.; Liebing, P.; Lim, S. H.; Linden Levy, L. A.; Liška, T.; Liu, H.; Liu, M. X.; Love, B.; Lynch, D.; Maguire, C. F.; Makdisi, Y. I.; Makek, M.; Malik, M. D.; Manion, A.; Manko, V. I.; Mannel, E.; Mao, Y.; Masui, H.; Matathias, F.; McCumber, M.; McGaughey, P. L.; McGlinchey, D.; McKinney, C.; Means, N.; Meles, A.; Mendoza, M.; Meredith, B.; Miake, Y.; Mibe, T.; Mignerey, A. C.; Miki, K.; Milov, A.; Mishra, D. K.; Mitchell, J. T.; Miyasaka, S.; Mizuno, S.; Mohanty, A. K.; Montuenga, P.; Moon, H. J.; Moon, T.; Morino, Y.; Morreale, A.; Morrison, D. P.; Moukhanova, T. V.; Murakami, T.; Murata, J.; Mwai, A.; Nagamiya, S.; Nagashima, K.; Nagle, J. L.; Naglis, M.; Nagy, M. I.; Nakagawa, I.; Nakagomi, H.; Nakamiya, Y.; Nakamura, K. R.; Nakamura, T.; Nakano, K.; Nam, S.; Nattrass, C.; Netrakanti, P. K.; Newby, J.; Nguyen, M.; Nihashi, M.; Niida, T.; Nishimura, S.; Nouicer, R.; Novak, T.; Novitzky, N.; Nyanin, A. S.; Oakley, C.; O'Brien, E.; Oda, S. X.; Ogilvie, C. A.; Oka, M.; Okada, K.; Onuki, Y.; Orjuela Koop, J. D.; Osborn, J. D.; Oskarsson, A.; Ouchida, M.; Ozawa, K.; Pak, R.; Pantuev, V.; Papavassiliou, V.; Park, I. H.; Park, J. S.; Park, S.; Park, S. K.; Park, W. J.; Pate, S. F.; Patel, M.; Pei, H.; Peng, J.-C.; Pereira, H.; Perepelitsa, D. V.; Perera, G. D. N.; Peressounko, D. Yu.; Perry, J.; Petti, R.; Pinkenburg, C.; Pinson, R.; Pisani, R. P.; Proissl, M.; Purschke, M. L.; Qu, H.; Rak, J.; Ramson, B. J.; Ravinovich, I.; Read, K. F.; Rembeczki, S.; Reygers, K.; Reynolds, D.; Riabov, V.; Riabov, Y.; Richardson, E.; Rinn, T.; Roach, D.; Roche, G.; Rolnick, S. D.; Rosati, M.; Rosen, C. A.; Rosendahl, S. S. E.; Rowan, Z.; Rubin, J. G.; Ružička, P.; Sahlmueller, B.; Saito, N.; Sakaguchi, T.; Sakashita, K.; Sako, H.; Samsonov, V.; Sano, S.; Sarsour, M.; Sato, S.; Sato, T.; Sawada, S.; Schaefer, B.; Schmoll, B. K.; Sedgwick, K.; Seele, J.; Seidl, R.; Sen, A.; Seto, R.; Sett, P.; Sexton, A.; Sharma, D.; Shein, I.; Shibata, T.-A.; Shigaki, K.; Shimomura, M.; Shoji, K.; Shukla, P.; Sickles, A.; Silva, C. L.; Silvermyr, D.; Silvestre, C.; Sim, K. S.; Singh, B. K.; Singh, C. P.; Singh, V.; Slunečka, M.; Snowball, M.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; Sourikova, I. V.; Stankus, P. W.; Stenlund, E.; Stepanov, M.; Stoll, S. P.; Sugitate, T.; Sukhanov, A.; Sumita, T.; Sun, J.; Sziklai, J.; Takagui, E. M.; Taketani, A.; Tanabe, R.; Tanaka, Y.; Taneja, S.; Tanida, K.; Tannenbaum, M. J.; Tarafdar, S.; Taranenko, A.; Themann, H.; Thomas, D.; Thomas, T. L.; Tieulent, R.; Timilsina, A.; Todoroki, T.; Togawa, M.; Toia, A.; Tomášek, L.; Tomášek, M.; Torii, H.; Towell, C. L.; Towell, R.; Towell, R. S.; Tserruya, I.; Tsuchimoto, Y.; Vale, C.; Valle, H.; van Hecke, H. W.; Vazquez-Zambrano, E.; Veicht, A.; Velkovska, J.; Vértesi, R.; Virius, M.; Vrba, V.; Vznuzdaev, E.; Wang, X. R.; Watanabe, D.; Watanabe, K.; Watanabe, Y.; Watanabe, Y. S.; Wei, F.; Wei, R.; Wessels, J.; White, A. S.; White, S. N.; Winter, D.; Woody, C. L.; Wright, R. M.; Wysocki, M.; Xia, B.; Xue, L.; Yalcin, S.; Yamaguchi, Y. L.; Yamaura, K.; Yang, R.; Yanovich, A.; Ying, J.; Yokkaichi, S.; Yoo, J. H.; Yoon, I.; You, Z.; Young, G. R.; Younus, I.; Yu, H.; Yushmanov, I. E.; Zajc, W. A.; Zelenski, A.; Zhou, S.; Zou, L.; Phenix Collaboration

2015-10-01

The PHENIX Collaboration has measured ϕ meson production in d +Au collisions at √{sNN}=200 GeV using the dimuon and dielectron decay channels. The ϕ meson is measured in the forward (backward) d -going (Au-going) direction, 1.2

Production of inclusive Υ(1S) and Υ(2S) in p–Pb collisions at s NN = 5.02 TeV

DOE PAGES

Abelev, B.

2014-11-22

We report on the production of inclusive Υ(1S) and Υ(2S) in p–Pb collisions at √ SNN = 5.02 TeV at the LHC. The measurement is performed with the ALICE detector at backward (-4.46 < y cms < -2.96) and forward (2.03 < y cms < 3.53) rapidity down to zero transverse momentum. The production cross sections of the Υ(1S) and Υ(2S) are presented, as well as the nuclear modification factor and the ratio of the forward to backward yields of Υ(1S). A suppression of the inclusive Υ(1S) yield in p–Pb collisions with respect to the yield from pp collisions scaledmore » by the number of binary nucleon–nucleon collisions is observed at forward rapidity but not at backward rapidity. Finally, the results are compared to theoretical model calculations including nuclear shadowing or partonic energy loss effects.« less

B -meson production at forward and backward rapidity in p +p and Cu + Au collisions at √{sN N}=200 GeV

NASA Astrophysics Data System (ADS)

Aidala, C.; Ajitanand, N. N.; Akiba, Y.; Akimoto, R.; Alexander, J.; Alfred, M.; Andrieux, V.; Aoki, K.; Apadula, N.; Asano, H.; Atomssa, E. T.; Awes, T. C.; Ayuso, C.; Azmoun, B.; Babintsev, V.; Bagoly, A.; Bai, M.; Bai, X.; Bandara, N. S.; Bannier, B.; Barish, K. N.; Bathe, S.; Baublis, V.; Baumann, C.; Baumgart, S.; Bazilevsky, A.; Beaumier, M.; Belmont, R.; Berdnikov, A.; Berdnikov, Y.; Black, D.; Blau, D. S.; Boer, M.; Bok, J. S.; Boyle, K.; Brooks, M. L.; Bryslawskyj, J.; Buesching, H.; Bumazhnov, V.; Butler, C.; Butsyk, S.; Campbell, S.; Canoa Roman, V.; Cervantes, R.; Chen, C.-H.; Chi, C. Y.; Chiu, M.; Choi, I. J.; Choi, J. B.; Choi, S.; Christiansen, P.; Chujo, T.; Cianciolo, V.; Citron, Z.; Cole, B. A.; Connors, M.; Cronin, N.; Crossette, N.; Csanád, M.; Csörgő, T.; Danley, T. W.; Datta, A.; Daugherity, M. S.; David, G.; Deblasio, K.; Dehmelt, K.; Denisov, A.; Deshpande, A.; Desmond, E. J.; Ding, L.; Dion, A.; Dixit, D.; Do, J. H.; D'Orazio, L.; Drapier, O.; Drees, A.; Drees, K. A.; Dumancic, M.; Durham, J. M.; Durum, A.; Elder, T.; Engelmore, T.; Enokizono, A.; En'yo, H.; Esumi, S.; Eyser, K. O.; Fadem, B.; Fan, W.; Feege, N.; Fields, D. E.; Finger, M.; Finger, M.; Fleuret, F.; Fokin, S. L.; Frantz, J. E.; Franz, A.; Frawley, A. D.; Fukao, Y.; Fukuda, Y.; Fusayasu, T.; Gainey, K.; Gal, C.; Gallus, P.; Garg, P.; Garishvili, A.; Garishvili, I.; Ge, H.; Giordano, F.; Glenn, A.; Gong, X.; Gonin, M.; Goto, Y.; Granier de Cassagnac, R.; Grau, N.; Greene, S. V.; Grosse Perdekamp, M.; Gu, Y.; Gunji, T.; Guragain, H.; Hachiya, T.; Haggerty, J. S.; Hahn, K. I.; Hamagaki, H.; Hamilton, H. F.; Han, S. Y.; Hanks, J.; Hasegawa, S.; Haseler, T. O. S.; Hashimoto, K.; Hayano, R.; He, X.; Hemmick, T. K.; Hester, T.; Hill, J. C.; Hill, K.; Hollis, R. S.; Homma, K.; Hong, B.; Hoshino, T.; Hotvedt, N.; Huang, J.; Huang, S.; Ichihara, T.; Ikeda, Y.; Imai, K.; Imazu, Y.; Imrek, J.; Inaba, M.; Iordanova, A.; Isenhower, D.; Isinhue, A.; Ito, Y.; Ivanishchev, D.; Jacak, B. V.; Jeon, S. J.; Jezghani, M.; Ji, Z.; Jia, J.; Jiang, X.; Johnson, B. M.; Joo, K. S.; Jorjadze, V.; Jouan, D.; Jumper, D. S.; Kamin, J.; Kanda, S.; Kang, B. H.; Kang, J. H.; Kang, J. S.; Kapukchyan, D.; Kapustinsky, J.; Karthas, S.; Kawall, D.; Kazantsev, A. V.; Key, J. A.; Khachatryan, V.; Khandai, P. K.; Khanzadeev, A.; Kijima, K. M.; Kim, C.; Kim, D. J.; Kim, E.-J.; Kim, M.; Kim, M. H.; Kim, Y.-J.; Kim, Y. K.; Kincses, D.; Kistenev, E.; Klatsky, J.; Kleinjan, D.; Kline, P.; Koblesky, T.; Kofarago, M.; Komkov, B.; Koster, J.; Kotchetkov, D.; Kotov, D.; Krizek, F.; Kudo, S.; Kurita, K.; Kurosawa, M.; Kwon, Y.; Lacey, R.; Lai, Y. S.; Lajoie, J. G.; Lallow, E. O.; Lebedev, A.; Lee, D. M.; Lee, G. H.; Lee, J.; Lee, K. B.; Lee, K. S.; Lee, S.; Lee, S. H.; Leitch, M. J.; Leitgab, M.; Leung, Y. H.; Lewis, B.; Lewis, N. A.; Li, X.; Li, X.; Lim, S. H.; Liu, L. D.; Liu, M. X.; Loggins, V.-R.; Loggins, V.-R.; Lökös, S.; Lovasz, K.; Lynch, D.; Maguire, C. F.; Majoros, T.; Makdisi, Y. I.; Makek, M.; Malaev, M.; Manion, A.; Manko, V. I.; Mannel, E.; Masuda, H.; McCumber, M.; McGaughey, P. L.; McGlinchey, D.; McKinney, C.; Meles, A.; Mendoza, M.; Meredith, B.; Metzger, W. J.; Miake, Y.; Mibe, T.; Mignerey, A. C.; Mihalik, D. E.; Milov, A.; Mishra, D. K.; Mitchell, J. T.; Mitsuka, G.; Miyasaka, S.; Mizuno, S.; Mohanty, A. K.; Mohapatra, S.; Montuenga, P.; Moon, T.; Morrison, D. P.; Morrow, S. I. M.; Moskowitz, M.; Moukhanova, T. V.; Murakami, T.; Murata, J.; Mwai, A.; Nagae, T.; Nagai, K.; Nagamiya, S.; Nagashima, K.; Nagashima, T.; Nagle, J. L.; Nagy, M. I.; Nakagawa, I.; Nakagomi, H.; Nakamiya, Y.; Nakamura, K. R.; Nakamura, T.; Nakano, K.; Nattrass, C.; Netrakanti, P. K.; Nihashi, M.; Niida, T.; Nouicer, R.; Novák, T.; Novitzky, N.; Novotny, R.; Nyanin, A. S.; O'Brien, E.; Ogilvie, C. A.; Oide, H.; Okada, K.; Orjuela Koop, J. D.; Osborn, J. D.; Oskarsson, A.; Ottino, G. J.; Ozawa, K.; Pak, R.; Pantuev, V.; Papavassiliou, V.; Park, I. H.; Park, J. S.; Park, S.; Park, S. K.; Pate, S. F.; Patel, L.; Patel, M.; Peng, J.-C.; Peng, W.; Perepelitsa, D. V.; Perera, G. D. N.; Peressounko, D. Yu.; Perezlara, C. E.; Perry, J.; Petti, R.; Phipps, M.; Pinkenburg, C.; Pisani, R. P.; Pun, A.; Purschke, M. L.; Qu, H.; Radzevich, P. V.; Rak, J.; Ravinovich, I.; Read, K. F.; Reynolds, D.; Riabov, V.; Riabov, Y.; Richardson, E.; Richford, D.; Rinn, T.; Riveli, N.; Roach, D.; Rolnick, S. D.; Rosati, M.; Rowan, Z.; Runchey, J.; Ryu, M. S.; Safonov, A. S.; Sahlmueller, B.; Saito, N.; Sakaguchi, T.; Sako, H.; Samsonov, V.; Sarsour, M.; Sato, K.; Sato, S.; Sawada, S.; Schaefer, B.; Schmoll, B. K.; Sedgwick, K.; Seele, J.; Seidl, R.; Sekiguchi, Y.; Sen, A.; Seto, R.; Sett, P.; Sexton, A.; Sharma, D.; Shaver, A.; Shein, I.; Shibata, T.-A.; Shigaki, K.; Shimomura, M.; Shioya, T.; Shoji, K.; Shukla, P.; Sickles, A.; Silva, C. L.; Silvermyr, D.; Singh, B. K.; Singh, C. P.; Singh, V.; Skoby, M. J.; Skolnik, M.; Slunečka, M.; Smith, K. L.; Snowball, M.; Solano, S.; Soltz, R. A.; Sondheim, W. E.; Sorensen, S. P.; Sourikova, I. V.; Stankus, P. W.; Steinberg, P.; Stenlund, E.; Stepanov, M.; Ster, A.; Stoll, S. P.; Stone, M. R.; Sugitate, T.; Sukhanov, A.; Sumita, T.; Sun, J.; Syed, S.; Sziklai, J.; Takahara, A.; Takeda, A.; Taketani, A.; Tanaka, Y.; Tanida, K.; Tannenbaum, M. J.; Tarafdar, S.; Taranenko, A.; Tarnai, G.; Tennant, E.; Tieulent, R.; Timilsina, A.; Todoroki, T.; Tomášek, M.; Torii, H.; Towell, C. L.; Towell, R. S.; Tserruya, I.; Ueda, Y.; Ujvari, B.; van Hecke, H. W.; Vargyas, M.; Vazquez-Carson, S.; Vazquez-Zambrano, E.; Veicht, A.; Velkovska, J.; Vértesi, R.; Virius, M.; Vrba, V.; Vukman, N.; Vznuzdaev, E.; Wang, X. R.; Wang, Z.; Watanabe, D.; Watanabe, K.; Watanabe, Y.; Watanabe, Y. S.; Wei, F.; Whitaker, S.; Wolin, S.; Wong, C. P.; Woody, C. L.; Wysocki, M.; Xia, B.; Xu, C.; Xu, Q.; Xue, L.; Yalcin, S.; Yamaguchi, Y. L.; Yamamoto, H.; Yanovich, A.; Yin, P.; Yokkaichi, S.; Yoo, J. H.; Yoon, I.; You, Z.; Younus, I.; Yu, H.; Yushmanov, I. E.; Zajc, W. A.; Zelenski, A.; Zharko, S.; Zhou, S.; Zou, L.; Phenix Collaboration

2017-12-01

The fraction of J /ψ mesons which come from B -meson decay, FB →J /ψ, is measured for J /ψ rapidity 1.2 <|y |<2.2 and pT>0 in p +p and Cu+Au collisions at √{sNN} = 200 GeV with the PHENIX detector. The extracted fraction is FB →J /ψ=0.025 ±0.006 (stat) ± 0.010(syst) for p +p collisions. For Cu+Au collisions, FB →J /ψ is 0.094 ± 0.028(stat) ± 0.037(syst) in the Au-going direction (-2.2

Factors Controlling Fluxes of Nitrous Oxide (N-N2O) in AN Upland Tropical Forest (atlantic Forest) - Brazil, Rio de Janeiro

NASA Astrophysics Data System (ADS)

Perry, I.; de Mello, W. Z.; McDowell, W. H.

2010-12-01

Atlantic Forest is located along the Brazilian coast and inland to Paraguay and Argentina. It has been largely devastated years ago by anthropogenic activities, such as agriculture and urbanization. Only ten percent of its original area remains (100.000 km2), which is concentrated on high lands. Atlantic Forest is a biodiversity hotspot that receives high nitrogen (N) input through atmospheric deposition in forests of Rio de Janeiro; however, not much is known about the consequences of this N addition. This study has been conducted in the Serra dos Orgaos National Park (SONP - 22.782 km2) located a few kilometers Northeast of Rio de Janeiro Metropolitan Region, Sea Mountain. The forest, characterized as Tropical Moist Forest, is rigorously protected. Vegetation varies along the altitudinal gradient, where the highest peak is at 2,200m asl. Previous studies reported that N atmospheric deposition in SONP varies from 14 to 24 kg ha-1 year-1. The high N deposition on tropical forests increases emission to the atmosphere of N-N2O, a greenhouse gas. There is a lack of N-N2O measurements in tropical forests, mainly in upland tropical forests. We present fluxes of N-N2O from a Brazilian upland tropical forest, and assess the factors controlling N-N2O fluxes. Samples were collected from eight grids (48m2), between 330-451m asl (Subtropical vegetation) and eight grids between 1137-1251m (Montane vegetation), during the dry (July 2008) and wet (Jan-Feb 2009) seasons. Daily, N-N2O (N=372) and soil (N=185) were collected. Nitrous oxide emission was 0,7 (lower altitude) and 0,3 kgN ha-1 year-1 (higher altitude), which is lower than in other upland tropical forests, such as Luquillo Experimental Forest, Puerto Rico, where atmospheric N input (4 kg ha-1 year-1) is not as high as in SONP. Water filled pore space, soil temperature, phosphorus and C:N are the main factors controlling N-N2O fluxes. Manganese was not a good indicator for presence or absence of N-N2O. Higher N-N2O fluxes occurred with a C:N ratio below 16, and also increased with WFPS, soil temperature and phosphorus. Nitrogen availability had little influence on N-N2O fluxes, while phosphorus might be a limiting nutrient for N-N2O production in SONP. *Presenting authot (E-mail: igperry@yahoo.com.br)

Predicting net joint moments during a weightlifting exercise with a neural network model.

PubMed

Kipp, Kristof; Giordanelli, Matthew; Geiser, Christopher

2018-06-06

The purpose of this study was to develop and train a Neural Network (NN) that uses barbell mass and motions to predict hip, knee, and ankle Net Joint Moments (NJM) during a weightlifting exercise. Seven weightlifters performed two cleans at 85% of their competition maximum while ground reaction forces and 3-D motion data were recorded. An inverse dynamics procedure was used to calculate hip, knee, and ankle NJM. Vertical and horizontal barbell motion data were extracted and, along with barbell mass, used as inputs to a NN. The NN was then trained to model the association between the mass and kinematics of the barbell and the calculated NJM for six weightlifters, the data from the remaining weightlifter was then used to test the performance of the NN - this was repeated 7 times with a k-fold cross-validation procedure to assess the NN accuracy. Joint-specific predictions of NJM produced coefficients of determination (r 2 ) that ranged from 0.79 to 0.95, and the percent difference between NN-predicted and inverse dynamics calculated peak NJM ranged between 5% and 16%. The NN was thus able to predict the spatiotemporal patterns and discrete peaks of the three NJM with reasonable accuracy, which suggests that it is feasible to predict lower extremity NJM from the mass and kinematics of the barbell. Future work is needed to determine whether combining a NN model with low cost technology (e.g., digital video and free digitising software) can also be used to predict NJM of weightlifters during field-testing situations, such as practice and competition, with comparable accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.

Effect of 0.25 and 2.0 MeV He-Ion Irradiation on Short-Range Ordering in Model (EFDA) Fe-Cr Alloys

NASA Astrophysics Data System (ADS)

Dubiel, Stanisław M.; Żukrowski, Jan; Serruys, Yves

2018-05-01

The effects of He+ irradiation on a distribution of Cr atoms in Fe100-x Cr x (x = 5.8, 10.75, 15.15) alloys were studied by 57Fe Conversion Electron Mössbauer Spectroscopy (CEMS). The alloys were irradiated with doses up to 12 × 1016 ions/cm2 with 0.25 and 2.0 MeV He+ ions. The distribution of Cr atoms within the first two coordination shells around Fe atoms was expressed with short-range order parameters α 1 (first-neighbor shell, 1NN), α 2 (second-neighbor shell, 2NN), and α 12 (1NN + 2NN). In non-irradiated alloys, α 1 >0 and α 2 <0 was revealed for all three samples. The value of α 12 ≈0, i.e., the distribution of Cr atoms averaged over 1NN and 2NN, was random. The effect of the irradiation of the Fe94.2Cr5.8 alloy was similar for the two energies of He+, viz., increase of number of Cr atoms in 1NN and decrease in 2NN. Consequently, the degree of ordering increased. For the other two samples, the effect of the irradiation depends on the composition, and is stronger for the less energetic ions where, for Fe89.25Cr10.75 alloy, the disordering disappeared and some traces of Cr clustering appeared. In Fe84.85Cr15.15 alloy, the clustering was clear. In the samples irradiated with 2. 0 MeV He+ ions, the ordering also survived in the samples with x = 10.75 and 15.15, yet its degree became smaller than in the Fe94.2Cr5.8 alloy.

Real-time estimation of lesion depth and control of radiofrequency ablation within ex vivo animal tissues using a neural network.

PubMed

Wang, Yearnchee Curtis; Chan, Terence Chee-Hung; Sahakian, Alan Varteres

2018-01-04

Radiofrequency ablation (RFA), a method of inducing thermal ablation (cell death), is often used to destroy tumours or potentially cancerous tissue. Current techniques for RFA estimation (electrical impedance tomography, Nakagami ultrasound, etc.) require long compute times (≥ 2 s) and measurement devices other than the RFA device. This study aims to determine if a neural network (NN) can estimate ablation lesion depth for control of bipolar RFA using complex electrical impedance - since tissue electrical conductivity varies as a function of tissue temperature - in real time using only the RFA therapy device's electrodes. Three-dimensional, cubic models comprised of beef liver, pork loin or pork belly represented target tissue. Temperature and complex electrical impedance from 72 data generation ablations in pork loin and belly were used for training the NN (403 s on Xeon processor). NN inputs were inquiry depth, starting complex impedance and current complex impedance. Training-validation-test splits were 70%-0%-30% and 80%-10%-10% (overfit test). Once the NN-estimated lesion depth for a margin reached the target lesion depth, RFA was stopped for that margin of tissue. The NN trained to 93% accuracy and an NN-integrated control ablated tissue to within 1.0 mm of the target lesion depth on average. Full 15-mm depth maps were calculated in 0.2 s on a single-core ARMv7 processor. The results show that a NN could make lesion depth estimations in real-time using less in situ devices than current techniques. With the NN-based technique, physicians could deliver quicker and more precise ablation therapy.

Estimating surface soil moisture from SMAP observations using a Neural Network technique.

PubMed

Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

2018-01-01

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

ϒ Production in Heavy-Ion Collisions from the STAR Experiment

NASA Astrophysics Data System (ADS)

Ye, Zaochen; STAR Collaboration

2017-08-01

In these proceedings, we present recent results of ϒ measurements in heavy-ion collisions from the STAR experiment at RHIC. Nuclear modification factors (RAA) for ϒ (1 S) and ϒ (1 S + 2 S + 3 S) in U+U collisions at √{sNN } = 193 GeV are measured through the di-electron channel and compared to those in Au+Au collisions at √{sNN } = 200 GeV and Pb+Pb collisions at √{sNN } = 2.76 TeV. The ratio between the ϒ (2 S + 3 S) and ϒ (1 S) yields in Au+Au collisions at √{sNN } = 200 GeV is measured in the di-muon channel and compared to those in p+p collisions and in Pb+Pb collisions at √{sNN } = 2.76 TeV. Prospects for future ϒ measurements with the STAR experiment are also discussed.

Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

NASA Astrophysics Data System (ADS)

Yang, Xiong; Liu, Derong; Wang, Ding

2014-03-01

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

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

Majidpour, Mostafa; Qiu, Charlie; Chu, Peter

Three algorithms for the forecasting of energy consumption at individual EV charging outlets have been applied to real world data from the UCLA campus. Out of these three algorithms, namely k-Nearest Neighbor (kNN), ARIMA, and Pattern Sequence Forecasting (PSF), kNN with k=1, was the best and PSF was the worst performing algorithm with respect to the SMAPE measure. The advantage of PSF is its increased robustness to noise by substituting the real valued time series with an integer valued one, and the advantage of NN is having the least SMAPE for our data. We propose a Modified PSF algorithm (MPSF)more » which is a combination of PSF and NN; it could be interpreted as NN on integer valued data or as PSF with considering only the most recent neighbor to produce the output. Some other shortcomings of PSF are also addressed in the MPSF. Results show that MPSF has improved the forecast performance.« less

Infrared spectroscopy and density functional calculations on titanium-dinitrogen complexes

NASA Astrophysics Data System (ADS)

Yoo, Hae-Wook; Choi, Changhyeok; Cho, Soo Gyeong; Jung, Yousung; Choi, Myong Yong

2018-04-01

Titanium-nitrogen complexes were generated by laser ablated titanium (Ti) atoms and N2 gas molecules in this study. These complexes were isolated on the pre-deposited solid Ar matrix on the pre-cooled KBr window (T ∼ 5.4 K), allowing infrared spectra to be measured. Laser ablation experiments with 15N2 isotope provided distinct isotopic shifts in the infrared spectra that strongly implicated the formation of titanium-nitrogen complexes, Ti(NN)x. Density functional theory (DFT) calculations were employed to investigate the molecular structures, electronic ground state, relative energies, and IR frequencies of the anticipated Ti(NN)x complexes. Based on laser ablation experiments and DFT calculations, we were able to assign multiple Ti(NN)x (x = 1-6) species. Particularly, Ti(NN)5 and Ti(NN)6, which have high nitrogen content, may serve as good precursors in preparing polynitrogens.

Air Quality Data for Metals 1970 Through 1974 from the ...

EPA Pesticide Factsheets

... Ln f i i Ln ' F P 1 .nin • • t LI ! p Ln p p i Ln ' Ln T Ln • Ln • Ln ! i i Ln • Ln ! Ln • Ln • Ln • • • t .nn7 > .ni^ • LD ' .nin i t t i .PZT ! .nn7 • ,nn= • ?ND. QTR. ...

Applying an efficient K-nearest neighbor search to forest attribute imputation

Treesearch

Andrew O. Finley; Ronald E. McRoberts; Alan R. Ek

2006-01-01

This paper explores the utility of an efficient nearest neighbor (NN) search algorithm for applications in multi-source kNN forest attribute imputation. The search algorithm reduces the number of distance calculations between a given target vector and each reference vector, thereby, decreasing the time needed to discover the NN subset. Results of five trials show gains...

Alternate Implosion Models for the Plane Parallel Diode.

DTIC Science & Technology

1982-04-02

higher density regions, perhaps altering the spectral mix of line and continuum radiation. 3) Examine the effect of the chemical potential (properly...inclusion of previous time levels (i-i, i-2...) in an estimate of AF would be a means of filtering the noise that might develop in this variable, and...NN’N NO G~l-0NNV . OO’)N aQ.’)4Ŕ N 4 . * .~ 0 I’) MMIs )NN4 P).N 44 NN 4 0. 00(0 0-0 NN 1 OWEI) ~4N Vi N 0 vm -N 0. 0N0 M a 40 0 I.) 0.0. N Nflmwo ONNOM

Efficient Analysis of Complex Structures

NASA Technical Reports Server (NTRS)

Kapania, Rakesh K.

2000-01-01

Last various accomplishments achieved during this project are : (1) A Survey of Neural Network (NN) applications using MATLAB NN Toolbox on structural engineering especially on equivalent continuum models (Appendix A). (2) Application of NN and GAs to simulate and synthesize substructures: 1-D and 2-D beam problems (Appendix B). (3) Development of an equivalent plate-model analysis method (EPA) for static and vibration analysis of general trapezoidal built-up wing structures composed of skins, spars and ribs. Calculation of all sorts of test cases and comparison with measurements or FEA results. (Appendix C). (4) Basic work on using second order sensitivities on simulating wing modal response, discussion of sensitivity evaluation approaches, and some results (Appendix D). (5) Establishing a general methodology of simulating the modal responses by direct application of NN and by sensitivity techniques, in a design space composed of a number of design points. Comparison is made through examples using these two methods (Appendix E). (6) Establishing a general methodology of efficient analysis of complex wing structures by indirect application of NN: the NN-aided Equivalent Plate Analysis. Training of the Neural Networks for this purpose in several cases of design spaces, which can be applicable for actual design of complex wings (Appendix F).

Temperature-Dependent Second Shell Interference in the First Shell Analysis of Crystalline InP X-ray Absorption Spectroscopy Data

NASA Astrophysics Data System (ADS)

Schnohr, Claudia S.; Araujo, Leandro L.; Ridgway, Mark C.

2014-09-01

Analysing only the first nearest neighbour (NN) scattering signal is a commonly used and often successful way to treat extended X-ray absorption fine structure data. However, using temperature-dependent measurements of InP as an example, we demonstrate how this approach can lead to erroneous first NN structural parameters in systems with a weak first but strong second NN scatterer. In such cases, particularly low temperature data may suffer from an overlap of first and second NN scattering signals caused by the Fourier transformation (FT) even if the dominant peaks appear to be well separated. The first NN structural parameters then vary as a function of the FT settings if only the first NN scattering contribution is considered in the analysis. Although this variation is small, it can also lead to significant differences in other calculated properties such as the Einstein temperature. We demonstrate that these variations can be avoided either by choosing an appropriate FT window or by including the scattering contributions of higher shells in the analysis. The latter is achieved by a path fitting approach and yields structural parameters independent of the FT settings used.

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

PubMed

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

Technical note: an R package for fitting sparse neural networks with application in animal breeding.

PubMed

Wang, Yangfan; Mi, Xue; Rosa, Guilherme J M; Chen, Zhihui; Lin, Ping; Wang, Shi; Bao, Zhenmin

2018-05-04

Neural networks (NNs) have emerged as a new tool for genomic selection (GS) in animal breeding. However, the properties of NN used in GS for the prediction of phenotypic outcomes are not well characterized due to the problem of over-parameterization of NN and difficulties in using whole-genome marker sets as high-dimensional NN input. In this note, we have developed an R package called snnR that finds an optimal sparse structure of a NN by minimizing the square error subject to a penalty on the L1-norm of the parameters (weights and biases), therefore solving the problem of over-parameterization in NN. We have also tested some models fitted in the snnR package to demonstrate their feasibility and effectiveness to be used in several cases as examples. In comparison of snnR to the R package brnn (the Bayesian regularized single layer NNs), with both using the entries of a genotype matrix or a genomic relationship matrix as inputs, snnR has greatly improved the computational efficiency and the prediction ability for the GS in animal breeding because snnR implements a sparse NN with many hidden layers.

A multiple-point spatially weighted k-NN method for object-based classification

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

2016-10-01

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

A silk peptide fraction restores cognitive function in AF64A-induced Alzheimer disease model rats by increasing expression of choline acetyltransferase gene.

PubMed

Cha, Yeseul; Lee, Sang Hoon; Jang, Su Kil; Guo, Haiyu; Ban, Young-Hwan; Park, Dongsun; Jang, Gwi Yeong; Yeon, Sungho; Lee, Jeong-Yong; Choi, Ehn-Kyoung; Joo, Seong Soo; Jeong, Heon-Sang; Kim, Yun-Bae

2017-01-01

This study investigated the effects of a silk peptide fraction obtained by incubating silk proteins with Protease N and Neutrase (SP-NN) on cognitive dysfunction of Alzheimer disease model rats. In order to elucidate underlying mechanisms, the effect of SP-NN on the expression of choline acetyltransferase (ChAT) mRNA was assessed in F3.ChAT neural stem cells and Neuro2a neuroblastoma cells; active amino acid sequence was identified using HPLC-MS. The expression of ChAT mRNA in F3.ChAT cells increased by 3.79-fold of the control level by treatment with SP-NN fraction. The active peptide in SP-NN was identified as tyrosine-glycine with 238.1 of molecular weight. Male rats were orally administered with SP-NN (50 or 300mg/kg) and challenged with a cholinotoxin AF64A. As a result of brain injury and decreased brain acetylcholine level, AF64A induced astrocytic activation, resulting in impairment of learning and memory function. Treatment with SP-NN exerted recovering activities on acetylcholine depletion and brain injury, as well as cognitive deficit induced by AF64A. The results indicate that, in addition to a neuroprotective activity, the SP-NN preparation restores cognitive function of Alzheimer disease model rats by increasing the release of acetylcholine. Copyright © 2016 Elsevier Inc. All rights reserved.

Central institutional review board review for an academic trial network.

PubMed

Kaufmann, Petra; O'Rourke, P Pearl

2015-03-01

Translating discoveries into therapeutics is often delayed by lengthy start-up periods for multicenter clinical trials. One cause of delay can be multiple institutional review board (IRB) reviews of the same protocol. When developing the Network for Excellence in Neuroscience Clinical Trials (NeuroNEXT; hereafter, NN), the National Institute of Neurological Disorders and Stroke (NINDS) established a central IRB (CIRB) based at Massachusetts General Hospital, the academic medical center that received the NN clinical coordinating center grant. The 25 NN sites, located at U.S. academic institutions, agreed to required CIRB use for NN trials. To delineate roles and establish legal relationships between the NN sites and the CIRB, the CIRB executed reliance agreements with the sites and their affiliates that hold federalwide assurance for the protection of human subjects (FWA); this took, on average, 84 days. The first NN protocol reviewed by the CIRB achieved full approval to allow participant enrollment within 56 days and went from grant award to the first patient visit in less than four months. The authors describe anticipated challenges related to institutional oversight responsibilities versus regulatory CIRB review as well as unanticipated challenges related to working with complex organizations that include multiple FWA-holding affiliates. The authors anticipate that CIRB use will decrease NN trial start-up time and thus promote efficient trial implementation. They plan to collect data on timelines and costs associated with CIRB use. The NINDS plans to promote CIRB use in future initiatives.

Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

NASA Astrophysics Data System (ADS)

Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

2018-04-01

Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

Pro-neurotensin/neuromedin N expression and processing in human colon cancer cell lines.

PubMed

Rovère, C; Barbero, P; Maoret, J J; Laburthe, M; Kitabgi, P

1998-05-08

The regulatory peptide neurotensin NT has been proposed to exert an autocrine trophic effect on human colon cancers. In the present study, pro-neurotensin/neuromedin N (proNT/NN) expression and processing were investigated in 13 human colon cancer cell lines using a combination of radioimmunoassay and HPLC techniques. All 13 cell lines displayed low to moderate levels of proNT/NN ranging from 10 to 250 fmol/mg protein. However, only 6 (HCT8, LoVo, HT29, C119A, LS174T, and coloDM320) processed the precursor. Three of the latter (HCT8, LS174T, and coloDM320) were analysed in detail with regard to proNT/NN processing pattern and were found to produce NT and large precursor fragments ending with the NT or NN sequence. They had no detectable level of NN. Such a processing pattern resembles that generated by the prohormone convertase PC5. Northern and Western blot analysis of prohormone convertase expression in the 3 cell lines revealed that they were devoid of PC1 and PC2, whereas they all expressed PC5. These data indicate that proNT/NN is a good marker of human colon cancer cell lines while NT is found in only about half of the cell lines. They also suggest that, in addition to NT, several proNT/NN-derived products, possibly generated by PC5, might exert an autocrine positive effect on human colon cancer growth.

Tri-Service Construction Guide Specifications

DTIC Science & Technology

1992-04-01

Equipment 11474 NN 9102 11757 Radiographic Darkroom Equipment 11476 VA 8202 11471 Revolving Darkroom Doors 11494 VA 8201 11491 Hydrotherapy Equipment...11494 NN 9102 11716 Hydrotherapy Equipment 11500 CE 9105 11500 Air Pollution Control 11500 NS 9103 13255 Cleaning for Process Piping Systems 11600 NN...Doors (11471) 4 11494 - Hydrotherapy Equipment (11491) 0 0 11500 - Air Pollution Control 0 11500 - Cleaning for Process Piping Systems (13255) 0 11600

Minimum Expected Risk Estimation for Near-neighbor Classification

DTIC Science & Technology

2006-04-01

We consider the problems of class probability estimation and classification when using near-neighbor classifiers, such as k-nearest neighbors ( kNN ...estimate for weighted kNN classifiers with different prior information, for a broad class of risk functions. Theory and simulations show how significant...the difference is compared to the standard maximum likelihood weighted kNN estimates. Comparisons are made with uniform weights, symmetric weights

Portable Language-Independent Adaptive Translation from OCR. Phase 1

DTIC Science & Technology

2009-04-01

including brute-force k-Nearest Neighbors ( kNN ), fast approximate kNN using hashed k-d trees, classification and regression trees, and locality...achieved by refinements in ground-truthing protocols. Recent algorithmic improvements to our approximate kNN classifier using hashed k-D trees allows...recent years discriminative training has been shown to outperform phonetic HMMs estimated using ML for speech recognition. Standard ML estimation

Translation of the assembling trajectory by preorganisation: a study of the magnetic properties of 1D polymeric unpaired electrons immobilised on a discrete nanoscopic scaffold.

PubMed

Praveen, Vakayil K; Yamamoto, Yohei; Fukushima, Takanori; Tsunobuchi, Yoshihide; Nakabayashi, Koji; Ohkoshi, Shin-ichi; Kato, Kenichi; Takata, Masaki; Aida, Takuzo

2015-01-25

A nitronyl nitroxide (NN)-appended hexabenzocoronene (HBC(NN)), when allowed to coassemble with bis(hexafluoroacetylacetonato)cobalt(II), forms a coaxial nanotubular architecture featuring NN-Co(II) coordinated copolymer chains immobilised on the outer and inner nanotube surfaces. Upon lowering the temperature, this nanotube has enhanced magnetic susceptibility below 10 K.

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks.

PubMed

Savran, Aydogan; Tasaltin, Ramazan; Becerikli, Yasar

2006-04-01

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.

Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks.

PubMed

Shen, Lin; Yang, Weitao

2018-03-13

Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of chemical reactions in a complex environment but also very time-consuming. The computational cost of QM/MM calculations during MD simulations can be reduced significantly using semiempirical QM/MM methods with lower accuracy. To achieve higher accuracy at the ab initio QM/MM level, a correction on the existing semiempirical QM/MM model is an attractive idea. Recently, we reported a neural network (NN) method as QM/MM-NN to predict the potential energy difference between semiempirical and ab initio QM/MM approaches. The high-level results can be obtained using neural network based on semiempirical QM/MM MD simulations, but the lack of direct MD samplings at the ab initio QM/MM level is still a deficiency that limits the applications of QM/MM-NN. In the present paper, we developed a dynamic scheme of QM/MM-NN for direct MD simulations on the NN-predicted potential energy surface to approximate ab initio QM/MM MD. Since some configurations excluded from the database for NN training were encountered during simulations, which may cause some difficulties on MD samplings, an adaptive procedure inspired by the selection scheme reported by Behler [ Behler Int. J. Quantum Chem. 2015 , 115 , 1032 ; Behler Angew. Chem., Int. Ed. 2017 , 56 , 12828 ] was employed with some adaptions to update NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN MD method to the free energy calculation and transition path optimization on chemical reactions in water. The results at the ab initio QM/MM level can be well reproduced using this method after 2-4 iteration cycles. The saving in computational cost is about 2 orders of magnitude. It demonstrates that the QM/MM-NN with direct MD simulations has great potentials not only for the calculation of thermodynamic properties but also for the characterization of reaction dynamics, which provides a useful tool to study chemical or biochemical systems in solution or enzymes.

Reconstruction of paleoceanographic significance in the Atlantic, Pacific, and the Indian Ocean during the Neogene based on calcareous nannofossil productivity and coccolith size distribution of Reticulofenestra - with special reference to formation of petroleum source rocks

NASA Astrophysics Data System (ADS)

Pratiwi, S. D.; Sato, T.; Ovinda, O.; Syavitri, D.

2017-12-01

We studied in detail the calcareous nannofossils assemblages from the ODP Sites of the western Pacific, Bahama Bank of Caribbean Sea, northwestern Pacific, Equatorial Pacific and the Indian Ocean to reconstruct the Cenozoic paleoceanographic evolution and correlate with the global events. The absolute abundant of coccolith (number/g) is gradually increased from NN6 throughout NN19 Zone, while the relative abundance of Discoaster is decreased in the Pacific Ocean. The size of Reticulofenestra increased five times throughout the section. However, it drastically decreased in NN8-10 (8.80 Ma), NN12-13 (5.40 Ma), NN14-NN15 (3.75 Ma), NN17/NN18 (2.52 Ma) and in NN19 Zone (0.80 Ma) in the western Pacific site. The characteristic of eutrophication condition determined by the high productivity of coccolith and the drastic decrease of the maximum size of Reticulofenestra are strongly related to the appearance of nutricline in the sea surface ocean. On the basis of the relationship between the changes of maximum sizes of Reticulofenestra and nutrient condition, these eutrophication events are clearly traceable in the western Pacific, Bahama Bank of Caribbean Sea, northwestern Pacific, Equatorial Pacific and the Indian Ocean. Two paleoceanographic events found in 8.80 Ma and 3.75 Ma are interpreted as a change to high nutrient condition resulted in the intensification of Asian Monsoon and closure of Panama Isthmus (Fig.). The upwelling of nutrient-rich oceanic waters may give rise to exceptionally high organic productivity. Organic carbon- rich facies accumulate preferentially during major intensification episodes. The timing of high productivity of coccolith during the middle to late Miocene is related and applicable to the formation of petroleum source rock and traceable to the Japan, marginal eastern North Pacific and California oil sites. This study suggests that the timing of the collapse of sea surface condition or eutrophication condition (8.00 Ma to 10.00 Ma) is correlated to the timing of formation petroleum source rocks in Circum Pacific based on calcareous nannofossils study.

Secure and Efficient k-NN Queries⋆

PubMed Central

Asif, Hafiz; Vaidya, Jaideep; Shafiq, Basit; Adam, Nabil

2017-01-01

Given the morass of available data, ranking and best match queries are often used to find records of interest. As such, k-NN queries, which give the k closest matches to a query point, are of particular interest, and have many applications. We study this problem in the context of the financial sector, wherein an investment portfolio database is queried for matching portfolios. Given the sensitivity of the information involved, our key contribution is to develop a secure k-NN computation protocol that can enable the computation k-NN queries in a distributed multi-party environment while taking domain semantics into account. The experimental results show that the proposed protocols are extremely efficient. PMID:29218333

Antiparallel spin does not always contain more information

NASA Astrophysics Data System (ADS)

Ghosh, Sibasish; Roy, Anirban; Sen, Ujjwal

2001-01-01

We show that the Bloch vectors lying on any great circle comprise the largest set SL for which the parallel states \\|n-->,n-->> can always be exactly transformed into the antiparallel states \\|n-->,-n-->>. Thus more information about n--> is not extractable from \\|n-->,-n-->> than from \\|n-->,n-->> by any measuring strategy, for n-->∈SL. Surprisingly this most general transformation reduces to just a flip operation on the second particle. We also show here that a probabilistic exact parallel to antiparallel transformation is not possible if the corresponding antiparallel states span the whole Hilbert space of the two qubits. These considerations allow us to generalize a conjecture of Gisin and Popescu [Phys. Rev. Lett. 83, 432 (1999)].

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2005-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Hybrid Neural Network and Support Vector Machine Method for Optimization

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2007-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

A comparison of neural network-based predictions of foF2 with the IRI-2012 model at conjugate points in Southeast Asia

NASA Astrophysics Data System (ADS)

Wichaipanich, Noraset; Hozumi, Kornyanat; Supnithi, Pornchai; Tsugawa, Takuya

2017-06-01

This paper presents the development of Neural Network (NN) model for the prediction of the F2 layer critical frequency (foF2) at three ionosonde stations near the magnetic equator of Southeast Asia. Two of these stations including Chiang Mai (18.76°N, 98.93°E, dip angle 12.7°N) and Kototabang (0.2°S, 100.3°E, dip angle 10.1°S) are at the conjugate points while Chumphon (10.72°N, 99.37°E, dip angle 3.0°N) station is near the equator. To produce the model, the feed forward network with backpropagation algorithm is applied. The NN is trained with the daily hourly values of foF2 during 2004-2012, except 2009, and the selected input parameters, which affect the foF2 variability, include day number (DN), hour number (HR), solar zenith angle (C), geographic latitude (θ), magnetic inclination (I), magnetic declination (D) and angle of meridian (M) relative to the sub-solar point, the 7-day mean of F10.7 (F10.7_7), the 81-day mean of SSN (SSN_81) and the 2-day mean of Ap (Ap_2). The foF2 data of 2009 and 2013 are then used for testing the NN model during the foF2 interpolation and extrapolation, respectively. To examine the performance of the proposed NN, the root mean square error (RMSE) of the observed foF2, the proposed NN model and the IRI-2012 (CCIR and URSI options) model are compared. In general, the results show the same trends in foF2 variation between the models (NN and IRI-2012) and the observations in that they are higher during the day and lower at night. Besides, the results demonstrate that the proposed NN model can predict the foF2 values more closely during daytime than during nighttime as supported by the lower RMSE values during daytime (0.5 ≤ RMSE ≤ 1.0 for Chumphon and Kototabang, 0.7 ≤ RMSE ≤ 1.2 at Chiang Mai) and with the highest levels during nighttime (0.8 ≤ RMSE ≤ 1.5 for Chumphon and Kototabang, 1.2 ≤ RMSE ≤ 2.0 at Chiang Mai). Furthermore, the NN model predicts the foF2 values more accurately than the IRI model at the three sites on average, as clearly seen on the yearly RMSE averages. The RMSE values of NN model are lower than those of both CCIR and URSI options, and in terms of the yearly percentage improvements, the NN model gives improvement of around 10-15% in 2009 and 10% in 2013 for Chiang Mai, 20-25% in 2009 and 5-10% in 2013 for Chumphon, and around 18-25% in 2009 and 20-30% in 2013 at Kototabang. Although the NN model predicts the foF2 values closely to the observed data and produces more accurate prediction than the IRI models, in some cases, the IRI model performs better than the NN model. Hence, there is still room for further improvement of the proposed NN model.

Delta-Isobar Production in the Hard Photodisintegration of a Deuteron

NASA Astrophysics Data System (ADS)

Granados, Carlos; Sargsian, Misak

2010-02-01

Hard photodisintegration of the deuteron in delta-isobar production channels is proposed as a useful process in identifying the quark structure of hadrons and of hadronic interactions at large momentum and energy transfer. The reactions are modeled using the hard re scattering model, HRM, following previous works on hard breakup of a nucleon nucleon (NN) system in light nuclei. Here,quantitative predictions through the HRM require the numerical input of fits of experimental NN hard elastic scattering cross sections. Because of the lack of data in hard NN scattering into δ-isobar channels, the cross section of the corresponding photodisintegration processes cannot be predicted in the same way. Instead, the corresponding NN scattering process is modeled through the quark interchange mechanism, QIM, leaving an unknown normalization parameter. The observables of interest are ratios of differential cross sections of δ-isobar production channels to NN breakup in deuteron photodisintegration. Both entries in these ratios are derived through the HRM and QIM so that normalization parameters cancel out and numerical predictions can be obtained. )

Prime Contract Awards Alphabetically by Contractor, by State or Country, and Place, FY 88. Part 19. (Synertech Associates, Inc.-Tucker Equipment Co., Inc.)

DTIC Science & Technology

1988-01-01

1-r. -wOq* o 0 v-* vvqtq "V so 400960 I- WO La U," 5 U),U 1, 0 wwo Uft www owm flm w woqa : C0 NNO N "NN N - 0 000WIWo m’ V 0 U, -4-4- mmmmm0 -4 1-4...Om 0 000000000iini 50-4 I N N4 N -44-4 -4 -4-4 -4 N -4 -4 NN4 Nl NN N NN 50000 I -4 -4 -4 - -4 -4 -4 -4 -4 4-4 -4 4 -f -4 44444- 50000 5 -4 N 4 N -4 -4...PP=P 100.4 1 0000 00 ,00-0 1 0000 00 I00w I -4-4-4-4 .4- 500-t 1 444- .4.4 500(4 5 N N -4 N C4NN 4 N (4N N N NNNNC1 -4.4 N4 NINNNNNNNN SO)ON 5 CC4 NNN

Environmental Life Cycle Techniques for New Weapons Acquisition Systems

DTIC Science & Technology

2004-09-01

Amount) Total grenade War Caracterisation factors (MJ/nn) Unit (Impact indicator) Total grenade War Total of all compartments MJ 59200 82700... Caracterisation factors (Kg CFC11 eq/nn) Unit (Impact indicator) Total grenade War Total of all compartments kg CFC-11 0,000429 0,00046 Remaining...Substance Compartment Unit (Amount) Total grenade War Caracterisation factors (Kg C2H2/nn) Unit (Impact indicator) Total grenade War Total of all

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

Ramdasi, O. A.; Kolekar, Y. D.; Kambale, R. C., E-mail: rckambale@gmail.com

The plate-like NaNbO{sub 3} (NN) templates with (100) preferential orientation was synthesized from bismuth layer structured ferroelectric Bi{sub 2.5}Na{sub 3.5}Nb{sub 5}O{sub 18} (BNN) precursor by topochemical microcrystal conversion (TMC) method. The large platelets of BNN were first obtained by molten salt synthesis at the 1125 °C with a salt-to oxide weight ratio 1.5: 1. The anisotropic NN templates were derived from BNN at the 975 °C with BNN/ Na{sub 2}CO{sub 3} molar ratio of 1:1.5. The NaNbO{sub 3} templates have an average length of ~ 10-14 µm. The NN templates retains their elemental constitutes of Na, Nb and O inmore » stoichiometric proportion. The effect of ultrasonication on the orientation factor (F{sub h00}) of NN templates was understood by X-ray diffraction (XRD) and scanning electron microscopy (SEM) results. The degree of (100) orientation of as synthesized NN templates (~57%) was found to be increased (~89%) after ultrasonication. Moreover, the microstructure i.e. alignment / shape of as synthesized NN templates was changed from rectangular (110) orientation to square (100) orientation geometry after ultrasonication. Hence, ultrasonication is a cost effective approach to preparing the textured piezoelectric ceramics by the template grain growth technique using tape casting.« less

Synthesis mechanism and improved (100) oriented NaNbO3 templates by ultrasonication

NASA Astrophysics Data System (ADS)

Ramdasi, O. A.; Kolekar, Y. D.; Kim, D. J.; Song, T. K.; Kambale, R. C.

2016-05-01

The plate-like NaNbO3 (NN) templates with (100) preferential orientation was synthesized from bismuth layer structured ferroelectric Bi2.5Na3.5Nb5O18 (BNN) precursor by topochemical microcrystal conversion (TMC) method. The large platelets of BNN were first obtained by molten salt synthesis at the 1125 °C with a salt-to oxide weight ratio 1.5: 1. The anisotropic NN templates were derived from BNN at the 975 °C with BNN/ Na2CO3 molar ratio of 1:1.5. The NaNbO3 templates have an average length of ~ 10-14 µm. The NN templates retains their elemental constitutes of Na, Nb and O in stoichiometric proportion. The effect of ultrasonication on the orientation factor (Fh00) of NN templates was understood by X-ray diffraction (XRD) and scanning electron microscopy (SEM) results. The degree of (100) orientation of as synthesized NN templates (~57%) was found to be increased (~89%) after ultrasonication. Moreover, the microstructure i.e. alignment / shape of as synthesized NN templates was changed from rectangular (110) orientation to square (100) orientation geometry after ultrasonication. Hence, ultrasonication is a cost effective approach to preparing the textured piezoelectric ceramics by the template grain growth technique using tape casting.

Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM

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

Lin, Jian; Hamidouche, Khaled; Zheng, Jie

2015-08-05

Machine Learning algorithms are benefiting from the continuous improvement of programming models, including MPI, MapReduce and PGAS. k-Nearest Neighbors (k-NN) algorithm is a widely used machine learning algorithm, applied to supervised learning tasks such as classification. Several parallel implementations of k-NN have been proposed in the literature and practice. However, on high-performance computing systems with high-speed interconnects, it is important to further accelerate existing designs of the k-NN algorithm through taking advantage of scalable programming models. To improve the performance of k-NN on large-scale environment with InfiniBand network, this paper proposes several alternative hybrid MPI+OpenSHMEM designs and performs a systemicmore » evaluation and analysis on typical workloads. The hybrid designs leverage the one-sided memory access to better overlap communication with computation than the existing pure MPI design, and propose better schemes for efficient buffer management. The implementation based on k-NN program from MaTEx with MVAPICH2-X (Unified MPI+PGAS Communication Runtime over InfiniBand) shows up to 9.0% time reduction for training KDD Cup 2010 workload over 512 cores, and 27.6% time reduction for small workload with balanced communication and computation. Experiments of running with varied number of cores show that our design can maintain good scalability.« less

Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbedAllah, Loai

2016-12-22

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that ECkNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbdAllah, Loai

2016-12-01

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

FMRI 3D registration based on Fourier space subsets using neural networks.

PubMed

Freire, Luis C; Gouveia, Ana R; Godinho, Fernando M

2010-01-01

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy.

PubMed

Jia, Zi-Jun; Song, Yong-Duan

2017-06-01

This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

NASA Astrophysics Data System (ADS)

Tiwari, Shivendra N.; Padhi, Radhakant

2018-01-01

Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.

Centrality dependence of chemical freeze-out parameters from net-proton and net-charge fluctuations using a hadron resonance gas model

NASA Astrophysics Data System (ADS)

Adak, Rama Prasad; Das, Supriya; Ghosh, Sanjay K.; Ray, Rajarshi; Samanta, Subhasis

2017-07-01

We estimate chemical freeze-out parameters in Hadron Resonance Gas (HRG) and Excluded Volume HRG (EVHRG) models by fitting the experimental information of net-proton and net-charge fluctuations measured in Au + Au collisions by the STAR Collaboration at the BNL Relativistic Heavy Ion Collider (RHIC). We observe that chemical freeze-out parameters obtained from lower and higher order fluctuations are almost the same for √{sNN}>27 GeV, but tend to deviate from each other at lower √{sNN}. Moreover, these separations increase with decrease of √{sNN}, and for a fixed √{sNN} increase towards central collisions. Furthermore, we observe an approximate scaling behavior of (μB/T ) /(μB/T)central with (Npart) /(Npart)central for the parameters estimated from lower order fluctuations for 11.5 ≤√{sNN}≤200 GeV. Scaling is violated for the parameters estimated from higher order fluctuations for √{sNN}=11.5 and 19.6 GeV. It is observed that the chemical freeze-out parameter, which can describe σ2/M of net protons very well in all energies and centralities, cannot describe the s σ equally well, and vice versa.

The Miocene "Pteropod event" in the SW part of the Central Paratethys (Medvednica Mt., northern Croatia)

NASA Astrophysics Data System (ADS)

Bošnjak, Marija; Sremac, Jasenka; Vrsaljko, Davor; Aščić, Šimun; Bosak, Luka

2017-08-01

Deep marine Miocene deposits exposed sporadically in the Medvednica Mt. (northern Croatia) comprise pelagic organisms such as coccolithophores, planktic foraminifera and pteropods. The pteropod fauna from yellow marls at the Vejalnica locality (central part of Medvednica Mt.) encompasses abundant specimens of Vaginella austriaca Kittl, 1886, accompanied with scarce Clio fallauxi (Kittl, 1886). Calcareous nannoplankton points to the presence of NN5 nannozone at this locality. Highly fossiliferous grey marls at the Marija Bistrica locality (north-eastern area of Medvednica Mt.) comprise limacinid pteropods: Limacina valvatina (Reuss, 1867), L. gramensis (Rasmussen, 1968) and Limacina sp. Late Badenian (NN5 to NN6 nannozone) age of these marls is presumed on the basis of coccolithophores. Most of the determined pteropods on species level, except V. austriaca have been found and described from this region for the first time. New pteropod records from Croatia point to two pteropod horizons coinciding with the Badenian marine transgressions in Central Paratethys. These pteropod assemblages confirm the existence of W-E marine connection ("Transtethyan Trench Corridor") during the Badenian NN5 nannozone. Limacinids point to the possible immigration of the "North Sea fauna" through a northern European marine passage during the Late Badenian (end of NN5-beginning of NN6 zone), as previously presumed by some other authors.

Weighted Parzen Windows for Pattern Classification

DTIC Science & Technology

1994-05-01

Nearest-Neighbor Rule The k-Nearest-Neighbor ( kNN ) technique is nonparametric, assuming nothing about the distribution of the data. Stated succinctly...probabilities P(wj I x) from samples." Raudys and Jain [20:255] advance this interpretation by pointing out that the kNN technique can be viewed as the...34Parzen window classifier with a hyper- rectangular window function." As with the Parzen-window technique, the kNN classifier is more accurate as the

Hierarchical modeling of molecular energies using a deep neural network

NASA Astrophysics Data System (ADS)

Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

2018-06-01

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

Applications of neural networks to the studies of phase transitions of two-dimensional Potts models

NASA Astrophysics Data System (ADS)

Li, C.-D.; Tan, D.-R.; Jiang, F.-J.

2018-04-01

We study the phase transitions of two-dimensional (2D) Q-states Potts models on the square lattice, using the first principles Monte Carlo (MC) simulations as well as the techniques of neural networks (NN). We demonstrate that the ideas from NN can be adopted to study these considered phase transitions efficiently. In particular, even with a simple NN constructed in this investigation, we are able to obtain the relevant information of the nature of these phase transitions, namely whether they are first order or second order. Our results strengthen the potential applicability of machine learning in studying various states of matters. Subtlety of applying NN techniques to investigate many-body systems is briefly discussed as well.

Pseudo-scalar pi N coupling and relativistic proton-nucleus scattering

NASA Technical Reports Server (NTRS)

Gross, Franz; Maung, Khin Maung; Tjon, J. A.; Townsend, L. W.; Wallace, S. J.

1988-01-01

Relativistic p-Ca-40 elastic scattering observables are calculated using relativistic NN amplitudes obtained from the solution of a two-body relativistic equation in which one particle is kept on its mass-shell. Results at 200 MeV are presented for two sets of NN amplitudes, one with pure pseudo-vector coupling for the pion and another with a 25 percent admixture of pseudo-scaling coupling. Both give a very good fit to the positive energy on-shell NN data. Differences between the predictions of these two models (which are shown to be due only to the differences in their corresponding negative energy amplitudes) provide a measure of the uncertainty in contructing Dirac optical potentials from NN amplitudes.

[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

PubMed

Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui

2016-01-01

In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct recognition rate of the Adaboost-SRDA-NN model achieved 100% in the validation set. The overall results demonstrate that SRDA algorithm can effectively achieve the spectral feature information extraction to the spectral dimension reduction in model calibration process of qualitative analysis of NIR spectroscopy. In addition, the Adaboost lifting algorithm can improve the classification accuracy of the final model. The results obtained in this work can provide research foundation for developing online monitoring instruments for the monitoring of SSF process.

Enhancement technology improves palatability of normal and callipyge lambs.

PubMed

Everts, A K R; Wulf, D M; Wheeler, T L; Everts, A J; Weaver, A D; Daniel, J A

2010-12-01

The objective of this research was to determine if BPI Processing Technology (BPT) improved palatability of normal (NN) and callipyge (CN) lamb meat and to determine the mechanism by which palatability was improved. Ten ewe and 10 wether lambs of each phenotype were slaughtered, and carcass traits were assessed by a trained evaluator. The LM was removed at 2 d postmortem. Alternating sides served as controls (CON) or were treated with BPT. Muscles designated BPT were injected to a target 120% by weight with a patented solution containing water, ammonium hydroxide, carbon monoxide, and salt. Muscle pH, cooking loss, Warner-Bratzler shear force (WBS), sarcomere length, cooked moisture retention, and desmin degradation were measured. A trained sensory panel and a take-home consumer panel evaluated LM chops. Callipyge had a heavier BW and HCW, less adjusted fat thickness, reduced yield grades, and greater conformation scores than NN (P < 0.05). For LM, NN had shorter sarcomeres, smaller WBS values, greater juiciness ratings, more off-flavors, reduced consumer ratings for raw characteristics (like of portion size, like of color, like of leanness, overall like of appearance) and greater consumer ratings for eating characteristics (like of juiciness, like of flavor) than CN (P < 0.05). For LM, BPT had greater cooked moisture retention, smaller WBS values, greater juiciness ratings, less off-flavors, and greater consumer ratings for raw characteristics (like of portion size, like of color, overall like of appearance) and eating characteristics (like of juiciness, like of flavor) than CON (P < 0.05). Significant phenotype × treatment interactions occurred for LM muscle pH, desmin degradation, tenderness, consumer like of texture/tenderness, and consumer overall like of eating quality (P < 0.05). For LM, BPT increased muscle pH more for NN than CN (P < 0.01) and increased desmin degradation for NN but decreased desmin degradation for CN (P < 0.01). The BPT enhancement improved LM tenderness ratings for CN more than NN (P < 0.05). For consumer like of texture/tenderness, BPT improved ratings for CN more than NN (P < 0.01). For consumer overall like of eating quality, BPT improved ratings for CN more than NN (P < 0.05). In summary, BPT had little to no effect on sarcomere length and desmin degradation, but improved palatability of NN and CN lamb by increasing cooked moisture retention, improving consumer acceptability of CN to near-normal levels.

A 10 nN resolution thrust-stand for micro-propulsion devices

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

Chakraborty, Subha; Courtney, Daniel G.; Shea, Herbert, E-mail: herbert.shea@epfl.ch

We report on the development of a nano-Newton thrust-stand that can measure up to 100 μN thrust from different types of microthrusters with 10 nN resolution. The compact thrust-stand measures the impingement force of the particles emitted from a microthruster onto a suspended plate of size 45 mm × 45 mm and with a natural frequency over 50 Hz. Using a homodyne (lock-in) readout provides strong immunity to facility vibrations, which historically has been a major challenge for nano-Newton thrust-stands. A cold-gas thruster generating up to 50 μN thrust in air was first used to validate the thrust-stand. Better thanmore » 10 nN resolution and a minimum detectable thrust of 10 nN were achieved. Thrust from a miniature electrospray propulsion system generating up to 3 μN of thrust was measured with our thrust-stand in vacuum, and the thrust was compared with that computed from beam diagnostics, obtaining agreement within 50 nN to 150 nN. The 10 nN resolution obtained from this thrust-stand matches that from state-of-the-art nano-Newton thrust-stands, which measure thrust directly from the thruster by mounting it on a moving arm (but whose natural frequency is well below 1 Hz). The thrust-stand is the first of its kind to demonstrate less than 3 μN resolution by measuring the impingement force, making it capable of measuring thrust from different types of microthrusters, with the potential of easy upscaling for thrust measurement at much higher levels, simply by replacing the force sensor with other force sensors.« less

The effects of ryanodine receptor (RYR1) mutation on natural killer cell cytotoxicity, plasma cytokines and stress hormones during acute intermittent exercise in pigs.

PubMed

Ciepielewski, Z M; Stojek, W; Borman, A; Myślińska, D; Pałczyńska, P; Kamyczek, M

2016-04-01

Stress susceptibility has been mapped to a single recessive gene, the ryanodine receptor 1 (RYR1) gene or halothane (Hal) gene. Homozygous (Hal(nn)), mutated pigs are sensitive to halothane and susceptible to Porcine Stress Syndrome (PSS). Previous studies have shown that stress-susceptible RYR1 gene mutated homozygotes in response to restraint stress showed an increase in natural killer cell cytotoxicity (NKCC) accompanied by more pronounced stress-related hormone and anti-inflammatory cytokine changes. In order to determine the relationship of a RYR1 gene mutation with NKCC, plasma cytokines and stress-related hormones following a different stress model - exercise - 36 male pigs (representing different genotypes according to RYR1 gene mutation: NN, homozygous dominant; Nn, heterozygous; nn, homozygous recessive) were submitted to an intermittent treadmill walking. During the entire experiment the greatest level of NKCC and the greatest concentrations of interleukin (IL-) 6, IL-10, IL-12, interferon (IFN-)γ and tumor necrosis factor-α and stress-related hormones (adrenaline, prolactin, beta-endorphin) were observed in nn pigs, and the greatest concentration of IL-1 and growth hormone in NN pigs. Immunostimulatory effects of intermittent exercise on NKCC in nn pigs were concomitant with increases in IL-2, IL-12 and IFN-γ, the potent NKCC activators. Our findings suggest that stress-susceptible pigs RYR1 gene mutated pigs develop a greater level of NKCC and cytokine production in response to exercise stress. These results suggest that the heterogeneity of immunological and neuroendocrine response to exercise stress in pigs could be influenced by RYR1 gene mutation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Material discovery by combining stochastic surface walking global optimization with a neural network.

PubMed

Huang, Si-Da; Shang, Cheng; Zhang, Xiao-Jie; Liu, Zhi-Pan

2017-09-01

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a "Global-to-Global" approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO 2 , is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO 2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO 2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

A 10 nN resolution thrust-stand for micro-propulsion devices

NASA Astrophysics Data System (ADS)

Chakraborty, Subha; Courtney, Daniel G.; Shea, Herbert

2015-11-01

We report on the development of a nano-Newton thrust-stand that can measure up to 100 μN thrust from different types of microthrusters with 10 nN resolution. The compact thrust-stand measures the impingement force of the particles emitted from a microthruster onto a suspended plate of size 45 mm × 45 mm and with a natural frequency over 50 Hz. Using a homodyne (lock-in) readout provides strong immunity to facility vibrations, which historically has been a major challenge for nano-Newton thrust-stands. A cold-gas thruster generating up to 50 μN thrust in air was first used to validate the thrust-stand. Better than 10 nN resolution and a minimum detectable thrust of 10 nN were achieved. Thrust from a miniature electrospray propulsion system generating up to 3 μN of thrust was measured with our thrust-stand in vacuum, and the thrust was compared with that computed from beam diagnostics, obtaining agreement within 50 nN to 150 nN. The 10 nN resolution obtained from this thrust-stand matches that from state-of-the-art nano-Newton thrust-stands, which measure thrust directly from the thruster by mounting it on a moving arm (but whose natural frequency is well below 1 Hz). The thrust-stand is the first of its kind to demonstrate less than 3 μN resolution by measuring the impingement force, making it capable of measuring thrust from different types of microthrusters, with the potential of easy upscaling for thrust measurement at much higher levels, simply by replacing the force sensor with other force sensors.

Dredging Operations Technical Support Program. Mathematical Model of the Consolidation/Desiccation Processes in Dredged Material.

DTIC Science & Technology

1985-07-01

5695 BF(I) - BETA(LDF) * GOTO 7 5700 6 NN - N-i 5705 Ch - CI / (ES(N)-ES(NN)) 5710 AF(I) w ALPHA(N) + CM*(ALPHA(N)-ALPHA(NN)) 5715 DF(I) a BETA(N) 4...1OXP34t4SETTLEMENT DUE TO CONSOLIDATION - PF1O.4) 7845 113 FORMAT(/1OX932HSETTLEMENT DUE TO DESICCATION - PF1O.4) 7850 114 FORNAT(/1OX920HSURFACE

Electromagnetic Induction Spectroscopy for the Detection of Subsurface Targets

DTIC Science & Technology

2012-12-01

curves of the proposed method and that of Fails et al.. For the kNN ROC curve, k = 7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81...et al. [6] and Ramachandran et al. [7] both demonstrated success in detecting mines using the k-nearest-neighbor ( kNN ) algorithm based on the EMI...error is also included in the feature vector. The kNN labels an unknown target based on the closest targets in a training set. Collins et al. [2] and

PyNN: A Common Interface for Neuronal Network Simulators.

PubMed

Davison, Andrew P; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

2008-01-01

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.

The National Network of Libraries of Medicine's outreach to the public health workforce: 2001–2006

PubMed Central

Cogdill, Keith W.; Ruffin, Angela B.; Stavri, P. Zoë

2007-01-01

Objective: The paper provides an overview of the National Network of Libraries of Medicine's (NN/ LM's) outreach to the public health workforce from 2001 to 2006. Description: NN/LM conducts outreach through the activities of the Regional Medical Library (RML) staff and RML-sponsored projects led by NN/LM members. Between 2001 and 2006, RML staff provided training on information resources and information management for public health personnel at national, state, and local levels. The RMLs also contributed significantly to the Partners in Information Access for the Public Health Workforce collaboration. Methods: Data were extracted from telephone interviews with directors of thirty-seven NN/LM-sponsored outreach projects directed at the public health sector. A review of project reports informed the interviews, which were transcribed and subsequently coded for emergent themes using qualitative analysis software. Results: Analysis of interview data led to the identification of four major themes: training, collaboration, evaluation of outcomes, and challenges. Sixteen subthemes represented specific lessons learned from NN/LM members' outreach to the public health sector. Conclusions: NN/LM conducted extensive information-oriented outreach to the public health workforce during the 2001-to-2006 contract period. Lessons learned from this experience, most notably the value of collaboration and the need for flexibility, continue to influence outreach efforts in the current contract period. PMID:17641766

On prognostic models, artificial intelligence and censored observations.

PubMed

Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

2001-03-01

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation.

PubMed

Mall, Susmita; Chakraverty, S

2016-08-01

Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced by expansion block of input pattern using Hermite orthogonal polynomials. A feedforward neural network model with the unsupervised error backpropagation principle is used for modifying the network parameters and minimizing the computed error function. The Van der Pol-Duffing and Duffing oscillator equations may not be solved exactly. Here, approximate solutions of these types of equations have been obtained by applying the HeNN model for the first time. Three mathematical example problems and two real-life application problems of Van der Pol-Duffing oscillator equation, extracting the features of early mechanical failure signal and weak signal detection problems, are solved using the proposed HeNN method. HeNN approximate solutions have been compared with results obtained by the well known Runge-Kutta method. Computed results are depicted in term of graphs. After training the HeNN model, we may use it as a black box to get numerical results at any arbitrary point in the domain. Thus, the proposed HeNN method is efficient. The results reveal that this method is reliable and can be applied to other nonlinear problems too.

PyNN: A Common Interface for Neuronal Network Simulators

PubMed Central

Davison, Andrew P.; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

2008-01-01

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN. PMID:19194529

Output feedback control of a quadrotor UAV using neural networks.

PubMed

Dierks, Travis; Jagannathan, Sarangapani

2010-01-01

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Adaptive neural network decentralized backstepping output-feedback control for nonlinear large-scale systems with time delays.

PubMed

Tong, Shao Cheng; Li, Yong Ming; Zhang, Hua-Guang

2011-07-01

In this paper, two adaptive neural network (NN) decentralized output feedback control approaches are proposed for a class of uncertain nonlinear large-scale systems with immeasurable states and unknown time delays. Using NNs to approximate the unknown nonlinear functions, an NN state observer is designed to estimate the immeasurable states. By combining the adaptive backstepping technique with decentralized control design principle, an adaptive NN decentralized output feedback control approach is developed. In order to overcome the problem of "explosion of complexity" inherent in the proposed control approach, the dynamic surface control (DSC) technique is introduced into the first adaptive NN decentralized control scheme, and a simplified adaptive NN decentralized output feedback DSC approach is developed. It is proved that the two proposed control approaches can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the observer errors and the tracking errors converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approaches.

Improved biomass and lipid production in Synechocystis sp. NN using industrial wastes and nano-catalyst coupled transesterification for biodiesel production.

PubMed

Jawaharraj, Kalimuthu; Karpagam, Rathinasamy; Ashokkumar, Balasubramaniem; Kathiresan, Shanmugam; Moorthy, Innasi Muthu Ganesh; Arumugam, Muthu; Varalakshmi, Perumal

2017-10-01

In this study, the improved biomass (1.6 folds) and lipid (1.3 folds) productivities in Synechocystis sp. NN using agro-industrial wastes supplementation through hybrid response surface methodology-genetic algorithm (RSM-GA) for cost-effective methodologies for biodiesel production was achieved. Besides, efficient harvesting in Synechocystis sp. NN was achieved by electroflocculation (flocculation efficiency 97.8±1.2%) in 10min when compared to other methods. Furthermore, different pretreatment methods were employed for lipid extraction and maximum lipid content of 19.3±0.2% by Synechocystis sp. NN was attained by ultrasonication than microwave and liquid nitrogen assisted pretreatment methods. The highest FAME (fatty acid methyl ester) conversion of 36.5±8.3mg FAME/g biomass was obtained using titanium oxide as heterogeneous nano-catalyst coupled whole-cell transesterification based method. Conclusively, Synechocystis sp. NN may be used as a biodiesel feedstock and its fuel production can be enriched by hybrid RSM-GA and nano-catalyst technologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning.

PubMed

Yang, Xiong; Liu, Derong; Wang, Ding; Wei, Qinglai

2014-07-01

In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Differential regulation of gene expression of neurotensin and prohormone convertases PC1 and PC2 in the bovine ocular ciliary epithelium: possible implications on neurotensin processing.

PubMed

Ortego, Javier; Wollmann, Guido; Coca-Prados, Miguel

2002-11-15

Prohormone convertases PC1 and PC2 are enzymes involved in the intracellular processing of pro-neurotensin/neuromedin N (pro-NT/NN) through the regulated secretory pathway. In this study, we present evidence of the differential gene expression of pro-NT/NN, pro-PC1 and pro-PC2 in two cell lines established from the neuroendocrine ocular ciliary epithelium. Dexamethasone and forskolin were found to synergistically up-regulate NT/NN mRNA expression in both cell types. The pigmented cells released NT, and this release was enhanced by agents that induced its biosynthesis. In contrast, nonpigmented cells exhibited a significantly reduced neurotensin secretion in response to inducers, leading to an accumulation of the peptide. PC1 and PC2 mRNA expression was induced in a cell-specific manner by the same agents that enhanced pro-NT/NN biosynthesis. These results demonstrate cell-specific processing of pro-NT/NN by the ciliary epithelium. Copyright 2002 Elsevier Science Ireland Ltd.

Inactivation of neurotensin and neuromedin N by Zn metallopeptidases.

PubMed

Kitabgi, Patrick

2006-10-01

The two related peptides neurotensin (NT) and neuromedin N (NN) are efficiently inactivated by peptidases in vitro. Whereas NT is primarily degraded by a combination of three Zn metallo-endopeptidases, namely endopeptidases 24.11, 24.15 and 24.16, in all systems examined, NN is essentially inactivated by the Zn metallo-exopeptidase aminopeptidase M. In this paper we review the work that has led to the identification of the NT- and NN-degrading enzymes and to the purification and cloning of EP 24.16, a previously unidentified peptidase. We provide a brief description of the three NT-inactivating endopeptidases and of their specific and mixed inhibitors, some of them developed in the course of studying NT degradation. Finally, we review in vivo data obtained with these inhibitors that strongly support a physiological role for EP 24.11, 24.15 and 24.16 in the termination of NT-generated signals and for aminopeptidase in terminating NN action. Knowledge of the NT and NN inactivation mechanisms offers the perspective to develop metabolically stable analogs of these peptides with potential therapeutic value.

Determination of protein-dye association by near infrared fluorescence-detected circular dichroism.

PubMed

Meadows, F; Narayanan, N; Patonay, G

2000-01-10

Near-infrared (NIR) squarylium dye spectral properties were evaluated by absorption, fluorescence, circular dichroism (CD), and fluorescence-detected circular dichroism (FDCD). Substituents of the two NN dyes differed at R(1) and R(2), located symmetrically on the chromophore. The side chains of NN525 are R(1)=hexanoic acid, R(2)=butyl sulfonate and R(1)=R(2)=ethyl for NN127. FDCD signals were first confirmed by denaturing BSA with 2-8 M urea showing a diminution of dye FDCD peaks, but no change occurred in spectral properties of the dyes in urea. This indicated that the observed cotton effects occurred by noncovalent interactions with the secondary structure of the protein. The average BSA-dye association constants found by fluorescence, absorbance, and FDCD were 1.27 x 10(6) (n=1) and 3.3 x 10(6) M(-1) (n=1) for NN127 and NN525 respectively. These values were in good agreement when calculated by the three spectroscopic methods validating the use of NIRFDCD for optical parameter calculations. These results are useful to describe NIR squarylium dye labeling of BSA.

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

PubMed

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Control of Complex Dynamic Systems by Neural Networks

NASA Technical Reports Server (NTRS)

Spall, James C.; Cristion, John A.

1993-01-01

This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

{rho}-{omega} mixing and spin dependent charge-symmetry violating potential

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

Biswas, Subhrajyoti; Roy, Pradip; Dutt-Mazumder, Abhee K.

2008-10-15

We construct the charge symmetry violating (CSV) nucleon-nucleon potential induced by the {rho}{sup 0}-{omega} mixing due to the neutron-proton mass difference driven by the NN loop. Analytical expression for the two-body CSV potential is presented containing both the central and noncentral NN interaction. We show that the {rho}NN tensor interaction can significantly enhance the charge symmetry violating NN interaction even if the momentum dependent off-shell {rho}{sup 0}-{omega} mixing amplitude is considered. It is also shown that the inclusion of form factors removes the divergence arising out of the contact interaction. Consequently, we see that the precise size of the computedmore » scattering length difference depends on how the short-range aspects of the CSV potential are treated.« less

Nearest Neighbor Algorithms for Pattern Classification

NASA Technical Reports Server (NTRS)

Barrios, J. O.

1972-01-01

A solution of the discrimination problem is considered by means of the minimum distance classifier, commonly referred to as the nearest neighbor (NN) rule. The NN rule is nonparametric, or distribution free, in the sense that it does not depend on any assumptions about the underlying statistics for its application. The k-NN rule is a procedure that assigns an observation vector z to a category F if most of the k nearby observations x sub i are elements of F. The condensed nearest neighbor (CNN) rule may be used to reduce the size of the training set required categorize The Bayes risk serves merely as a reference-the limit of excellence beyond which it is not possible to go. The NN rule is bounded below by the Bayes risk and above by twice the Bayes risk.

Permutation entropy analysis of financial time series based on Hill's diversity number

NASA Astrophysics Data System (ADS)

Zhang, Yali; Shang, Pengjian

2017-12-01

In this paper the permutation entropy based on Hill's diversity number (Nn,r) is introduced as a new way to assess the complexity of a complex dynamical system such as stock market. We test the performance of this method with simulated data. Results show that Nn,r with appropriate parameters is more sensitive to the change of system and describes the trends of complex systems clearly. In addition, we research the stock closing price series from different data that consist of six indices: three US stock indices and three Chinese stock indices during different periods, Nn,r can quantify the changes of complexity for stock market data. Moreover, we get richer information from Nn,r, and obtain some properties about the differences between the US and Chinese stock indices.

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

NASA Astrophysics Data System (ADS)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Short-term estimation of GNSS TEC using a neural network model in Brazil

NASA Astrophysics Data System (ADS)

Ferreira, Arthur Amaral; Borges, Renato Alves; Paparini, Claudia; Ciraolo, Luigi; Radicella, Sandro M.

2017-10-01

This work presents a novel Neural Network (NN) model to estimate Total Electron Content (TEC) from Global Navigation Satellite Systems (GNSS) measurements in three distinct sectors in Brazil. The purpose of this work is to start the investigations on the development of a regional model that can be used to determine the vertical TEC over Brazil, aiming future applications on a near real-time frame estimations and short-term forecasting. The NN is used to estimate the GNSS TEC values at void locations, where no dual-frequency GNSS receiver that may be used as a source of data to GNSS TEC estimation is available. This approach is particularly useful for GNSS single-frequency users that rely on corrections of ionospheric range errors by TEC models. GNSS data from the first GLONASS network for research and development (GLONASS R&D network) installed in Latin America, and from the Brazilian Network for Continuous Monitoring of the GNSS (RMBC) were used on TEC calibration. The input parameters of the NN model are based on features known to influence TEC values, such as geographic location of the GNSS receiver, magnetic activity, seasonal and diurnal variations, and solar activity. Data from two ten-days periods (from DoY 154 to 163 and from 282 to 291) are used to train the network. Three distinct analyses have been carried out in order to assess time-varying and spatial performance of the model. At the spatial performance analysis, for each region, a set of stations is chosen to provide training data to the NN, and after the training procedure, the NN is used to estimate vTEC behavior for the test station which data were not presented to the NN in training process. An analysis is done by comparing, for each testing station, the estimated NN vTEC delivered by the NN and reference calibrated vTEC. Also, as a second analysis, the network ability to forecast one day after the time interval (DoY 292) based on information of the second period of investigation is also assessed in order to verify the feasibility on using low amount of data for short-term forecasting. In a third analysis, the spatial performance of the NN model is assessed and compared against CODE Global Ionospheric Maps during the geomagnetic storm registered on 13th and 14th October 2016. The results obtained from the three described analyses indicate that even using a ten-days period of data to train the network, the proposed NN model provides good spatial performance and presents to be a promising tool for short-term forecasting. The results obtained in the analysis presented a root mean squared error less than 7.9 TECU in all scenarios under investigation.

Highly potent growth hormone secretagogues: hybrids of NN703 and ipamorelin.

PubMed

Hansen, T K; Ankersen, M; Raun, K; Hansen, B S

2001-07-23

A series of NN703 analogues with lysine mimetics combined with naphthyl- or biphenylalanine in the core has been prepared and tested in vitro in a rat pituitary cell based assay and subsequently in vivo in pigs in a single dose at 50 nmol/kg. Re-introduction of certain pharmacophores in the C-terminal of NN703, which were originally removed during optimisation for oral bioavailability, led to unexpectedly potent compounds in vitro as well as in vivo.

Physical Oceanography Report. Helicopter-Based STD Data from MIZEX 83 (Marginal Ice Zone Experiment).

DTIC Science & Technology

1984-09-01

platform /sonde-cradle support system was developed at Lamont to specifically conform to the passenger compartment ofI Unfortunately, the 202 underwater...NN o Ito~,O rd-toc,-t CD 0 000-0 N-10 6, )-0ooUo06oOo owo V 000000 WIX ; OAO. N AIN to.6uIND- 6ro"~ooo’o NK 6-ooW m 0 0 Lai 0 .NN NN I 0 Z M’%W~0~N

Detecting the Difficulty Level of Foreign Language Texts

DTIC Science & Technology

2010-02-01

continuous tenses), as well as part- of- speech labels for words. The authors used a k-Nearest Neighbor ( kNN ) classifier (Cover and Hart, 1967; Mitchell, 1997...anticipate, and influence these situations and to operate in them is found in foreign language speech and text. For this reason, military linguists are...the language model system, LGR is the prediction of one of the grammar-based classifiers, and CkNN is a confidence value of the kNN prediction for the

Inverse analysis of turbidites by machine learning

NASA Astrophysics Data System (ADS)

Naruse, H.; Nakao, K.

2017-12-01

This study aims to propose a method to estimate paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine-learning technique. In this method, numerical simulation was repeated under various initial conditions, which produces a data set of characteristic features of turbidites. Then, this data set of turbidites is used for supervised training of a deep-learning neural network (NN). Quantities of characteristic features of turbidites in the training data set are given to input nodes of NN, and output nodes are expected to provide the estimates of initial condition of the turbidity current. The optimization of weight coefficients of NN is then conducted to reduce root-mean-square of the difference between the true conditions and the output values of NN. The empirical relationship with numerical results and the initial conditions is explored in this method, and the discovered relationship is used for inversion of turbidity currents. This machine learning can potentially produce NN that estimates paleo-hydraulic conditions from data of ancient turbidites. We produced a preliminary implementation of this methodology. A forward model based on 1D shallow-water equations with a correction of density-stratification effect was employed. This model calculates a behavior of a surge-like turbidity current transporting mixed-size sediment, and outputs spatial distribution of volume per unit area of each grain-size class on the uniform slope. Grain-size distribution was discretized 3 classes. Numerical simulation was repeated 1000 times, and thus 1000 beds of turbidites were used as the training data for NN that has 21000 input nodes and 5 output nodes with two hidden-layers. After the machine learning finished, independent simulations were conducted 200 times in order to evaluate the performance of NN. As a result of this test, the initial conditions of validation data were successfully reconstructed by NN. The estimated values show very small deviation from the true parameters. Comparing to previous inverse modeling of turbidity currents, our methodology is superior especially in the efficiency of computation. Also, our methodology has advantage in extensibility and applicability to various sediment transport processes such as pyroclastic flows or debris flows.

The South African English Smartphone Digits-in-Noise Hearing Test: Effect of Age, Hearing Loss, and Speaking Competence.

PubMed

Potgieter, Jenni-Marí; Swanepoel, De Wet; Myburgh, Hermanus Carel; Smits, Cas

2017-11-20

This study determined the effect of hearing loss and English-speaking competency on the South African English digits-in-noise hearing test to evaluate its suitability for use across native (N) and non-native (NN) speakers. A prospective cross-sectional cohort study of N and NN English adults with and without sensorineural hearing loss compared pure-tone air conduction thresholds to the speech reception threshold (SRT) recorded with the smartphone digits-in-noise hearing test. A rating scale was used for NN English listeners' self-reported competence in speaking English. This study consisted of 454 adult listeners (164 male, 290 female; range 16 to 90 years), of whom 337 listeners had a best ear four-frequency pure-tone average (4FPTA; 0.5, 1, 2, and 4 kHz) of ≤25 dB HL. A linear regression model identified three predictors of the digits-in-noise SRT, namely, 4FPTA, age, and self-reported English-speaking competence. The NN group with poor self-reported English-speaking competence (≤5/10) performed significantly (p < 0.01) poorer than the N and NN (≥6/10) groups on the digits-in-noise test. Screening characteristics of the test improved with separate cutoff values depending on English-speaking competence for the N and NN groups (≥6/10) and NN group alone (≤5/10). Logistic regression models, which include age in the analysis, showed a further improvement in sensitivity and specificity for both groups (area under the receiver operating characteristic curve, 0.962 and 0.903, respectively). Self-reported English-speaking competence had a significant influence on the SRT obtained with the smartphone digits-in-noise test. A logistic regression approach considering SRT, self-reported English-speaking competence, and age as predictors of best ear 4FPTA >25 dB HL showed that the test can be used as an accurate hearing screening tool for N and NN English speakers. The smartphone digits-in-noise test, therefore, allows testing in a multilingual population familiar with English digits using dynamic cutoff values that can be chosen according to self-reported English-speaking competence and age.

Immunohistochemical evidence for the involvement of protein convertases 5A and 2 in the processing of pro-neurotensin in rat brain.

PubMed

Villeneuve, P; Lafortune, L; Seidah, N G; Kitabgi, P; Beaudet, A

2000-08-28

The neuropeptides/neurotransmitters neurotensin (NT) and neuromedin (NN) are synthesized by endoproteolytic cleavage of a common inactive precursor, pro-NT/NN. In vitro studies have suggested that the prohormone convertases PC5A and PC2 might both be involved in this process. In the present study, we used dual immunohistochemical techniques to determine whether either one or both of these two convertases were co-localized with pro-NT/NN maturation products and could therefore be involved in the physiological processing of this propeptide in rat brain. PC2-immunoreactive neurons were present in all regions immunopositive for NT. All but three regions expressing NT were also immunopositive for PC5A. Dual localization of NT with either convertase revealed that NT was extensively co-localized with both PC5A and PC2, albeit with regional differences. These results strongly suggest that PC5A and PC2 may play a key role in the maturation of pro-NT/NN in mammalian brain. The regional variability in NT/PC co-localization patterns may account for the region-specific maturation profiles previously reported for pro-NT/NN. The high degree of overlap between PC5A and PC2 in most NT-rich areas further suggests that these two convertases may act jointly to process pro-NT/NN. At the subcellular level, PC5A was largely co-localized with the mid-cisternae Golgi marker MG-160. By contrast, PC2 was almost completely excluded from MG-160-immunoreactive compartments. These results suggest that PC5A, which is particularly efficient at cleaving the two C-terminal-most dibasics of pro-NT/NN, may be acting as early as in the Golgi apparatus to release NT, whereas PC2, which is considerably more active than PC5A in cleaving the third C-terminal doublet, may be predominantly involved further distally along the secretory pathway to release NN. Copyright 2000 Wiley-Liss, Inc.

B -meson production at forward and backward rapidity in p + p and Cu + Au collisions at s N N = 200 GeV

DOE PAGES

Aidala, C.; Ajitanand, N. N.; Akiba, Y.; ...

2017-12-04

The fraction of J/Ψ mesons which come from B-meson decay, F B→J/Ψ, is measured in this paper for J/Ψ rapidity 1.2 < |y| < 2.2 and p T > 0 in p + p and Cu+Au collisions at √ sNN = 200 GeV with the PHENIX detector. The extracted fraction is F B→J/Ψ = 0.025 ± 0.006 (stat) ± 0.010(syst) for p + p collisions. For Cu+Au collisions, F B→J/Ψ is 0.094 ± 0.028 (stat) ± 0.037(syst) in the Au-going direction (-2.2 < y < -1.2) and 0.089 ± 0.026(stat) ± 0.040(syst) in the Cu-going direction (1.2 < y

B -meson production at forward and backward rapidity in p + p and Cu + Au collisions at s N N = 200 GeV

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

Aidala, C.; Ajitanand, N. N.; Akiba, Y.

The fraction of J/Ψ mesons which come from B-meson decay, F B→J/Ψ, is measured in this paper for J/Ψ rapidity 1.2 < |y| < 2.2 and p T > 0 in p + p and Cu+Au collisions at √ sNN = 200 GeV with the PHENIX detector. The extracted fraction is F B→J/Ψ = 0.025 ± 0.006 (stat) ± 0.010(syst) for p + p collisions. For Cu+Au collisions, F B→J/Ψ is 0.094 ± 0.028 (stat) ± 0.037(syst) in the Au-going direction (-2.2 < y < -1.2) and 0.089 ± 0.026(stat) ± 0.040(syst) in the Cu-going direction (1.2 < y

Strange hadron production at low transverse momenta

NASA Astrophysics Data System (ADS)

Veres, Gábor I.; PHOBOS Collaboration; Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Becker, B.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Decowski, M. P.; García, E.; Gburek, T.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Harrington, A. S.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Holynski, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Khan, N.; Kulinich, P.; Kuo, C. M.; Lee, J. W.; Lin, W. T.; Manly, S.; Mignerey, A. C.; Noell, A.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Remsberg, L. P.; Roland, C.; Roland, G.; Sagerer, J.; Sarin, P.; Sawicki, P.; Sedykh, I.; Skulski, W.; Smith, C. E.; Steinberg, P.; Stephans, G. S. F.; Sukhanov, A.; Teng, R.; Tonjes, M. B.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wadsworth, B.; Wolfs, F. L. H.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wyslouch, B.; Zhang, J.

2004-01-01

Some of the latest results of the PHOBOS experiment from the \\sqrt{s_{NN}}= 200\\ GeV Au+Au data are discussed. Those relevant to strangeness production are emphasized. These observations relate to the nature of the matter created when heavy ions collide at the highest achieved energy. The invariant yields of strange and non-strange charged hadrons at very low transverse momentum have been measured, and used to differentiate between different dynamical scenarios. In the intermediate transverse momentum range, the measured ratios of strange and anti-strange kaons approach one, while the antibaryon to baryon ratio is still significantly less, independent of collision centrality and transverse momentum. At high transverse momenta, we find that central and peripheral Au+Au collisions produce similar numbers of charged hadrons per participant nucleon pair, rather than per binary nucleon-nucleon collision. Finally, we describe the upgrades of PHOBOS completed for the 2003 d+Au and p+p run, which extend the transverse momentum range over which particle identification is possible and, at the same time, implement a trigger system selective for high-pT particles.

A novel approach of an absolute coding pattern based on Hamiltonian graph

NASA Astrophysics Data System (ADS)

Wang, Ya'nan; Wang, Huawei; Hao, Fusheng; Liu, Liqiang

2017-02-01

In this paper, a novel approach of an optical type absolute rotary encoder coding pattern is presented. The concept is based on the principle of the absolute encoder to find out a unique sequence that ensures an unambiguous shaft position of any angular. We design a single-ring and a n-by-2 matrix absolute encoder coding pattern by using the variations of Hamiltonian graph principle. 12 encoding bits is used in the single-ring by a linear array CCD to achieve an 1080-position cycle encoding. Besides, a 2-by-2 matrix is used as an unit in the 2-track disk to achieve a 16-bits encoding pattern by using an area array CCD sensor (as a sample). Finally, a higher resolution can be gained by an electronic subdivision of the signals. Compared with the conventional gray or binary code pattern (for a 2n resolution), this new pattern has a higher resolution (2n*n) with less coding tracks, which means the new pattern can lead to a smaller encoder, which is essential in the industrial production.

2. Historic American Buildings Survey Frank O. Branzetti, Photographer Sept. ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

2. Historic American Buildings Survey Frank O. Branzetti, Photographer Sept. 4, 1940 (nn) 18- MILE STONE ROUTE 126, WAYLAND - Milestones MM, NN & OO, Various Wayland locations, Wayland, Middlesex County, MA

Effect of an InxGa1-xAs-GaAs blocking heterocathode metal contact on the GaAs TED operation

NASA Astrophysics Data System (ADS)

Arkusha, Yu. V.; Prokhorov, E. D.; Storozhenko, I. P.

2004-09-01

The frequency dependence of the generation efficiency of an mm- -nn:In:InxGaGa1-1-xAs- As-nn:GaAs-:GaAs-nn++:GaAs TED with the 2.5-mm long active region is calculated. The optimum values - which yield the diode maximum generation efficiency - for the :GaAs TED with the 2.5-mm long active region is calculated. The optimum values - which yield the diode maximum generation efficiency - for the nn:In:InxGaGa1-1-xAs cathode length, the cathode concentration of ionized impurities, and the height of the potential barrier on metal contact are determined.As cathode length, the cathode concentration of ionized impurities, and the height of the potential barrier on metal contact are determined.

Characterization and neutralization of Nemopilema nomurai (Scyphozoa: Rhizostomeae) jellyfish venom using polyclonal antibody.

PubMed

Kang, Changkeun; Han, Dae-Yong; Park, Kwang-Il; Pyo, Min-Jung; Heo, Yunwi; Lee, Hyunkyoung; Kim, Gon Sup; Kim, Euikyung

2014-08-01

Jellyfish stings have often caused serious health concerns for sea bathers especially in tropical waters. In the coastal areas of Korea, China and Japan, the blooming and stinging accidents of poisonous jellyfish species have recently increased, including Nemopilema nomurai. We have generated a polyclonal antibody against N. nomurai jellyfish venom (NnV) by the immunization of white rabbits with NnV antigen. In the present study, the antibody has been characterized for its neutralizing effect against NnV. At first, the presence of NnV polyclonal antibody has been confirmed from the immunized rabbit serum by Enzyme linked immunosorbent assay (ELISA). Then, the neutralizing activities of the polyclonal antibody have been investigated using cell-based toxicity test, hemolysis assay, and mice lethality test. When the polyclonal antibody was preincubated with NnV, it shows a high effectiveness in neutralizing the NnV toxicities in a concentration-dependent manner. Moreover, we explored proteomic analyses using 2-D SDS-PAGE and MALDI-TOF mass spectrometry to illustrate the molecular identities of the jellyfish venom. From this, 18 different protein families have been identified as jellyfish venom-derived proteins; the main findings of which are matrix metalloproteinase-14, astacin-like metalloprotease toxin 3 precursor. It is expected that the present results would have contributed to our understandings of the envenomation by N. nomurai, their treatment and some valuable knowledge on the pathological processes of the jellyfish stinging. Copyright © 2014 Elsevier Ltd. All rights reserved.

A modified artificial immune system based pattern recognition approach -- an application to clinic diagnostics

PubMed Central

Zhao, Weixiang; Davis, Cristina E.

2011-01-01

Objective This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. Methods and materials Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function – partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). Results The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of “replacement threshold = 0.3”, the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for study 2 are 0.49% (AIS-RBFPLS) and 6.61% (AIS-kNN); and (3) the proposed AIS-RBFPLS classification approaches also yielded better diagnosis results than two classical neural network approaches of BPNN and Ortho-RBF network. Conclusion In summary, this paper proposed a new machine learning method for complex systems by integrating the AIS system with RBFPLS. This new method demonstrates its satisfactory effect on classification accuracy for clinical diagnosis, and also indicates its wide potential applications to other diagnosis and detection problems. PMID:21515033

Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

PubMed

Mei, Gang; Xu, Nengxiong; Xu, Liangliang

2016-01-01

This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.

All Prime Contract Awards by State or Country, Place, and Contractor, FY 87. Part 21. Aberdeen, Washington-Classified Location, Domestic.

DTIC Science & Technology

1987-01-01

iNC*NNNN N I N IN (D 0~0 Nr I CJ " l l 1 1 N NN NN N * NNN NN NI NI Nl 001 I 0) 1 cl N N"N " C14 ciC4 0 1(4NIN 1 (NN NN N N IN COM 1 00 0 0 -1 000...000 I W IA I.- : COm -IN ML )0 4 n0- 1 - A10 ( N N M0 -0M n 00 44-4-4 M U) () 04 V(C)-CF JN00N)(’J4 4-4- - 4 Z M 0 0 0 0-4 0 -4 4 00 C 0 0 0-4 4 N0-4 0...0 V 0 - 004mI. I CoW00 < 0- I C O- COOOI - COLn 4 COA CO - CO C~O CO CO C)O4- COM - *I WOO< 40 -W 04 1W 4C 1W 1W -A -W 1W Z 04 MW 0 Q4 4 ., 4 0 44 1A

Resistance change effect in SrTiO3/Si (001) isotype heterojunction

NASA Astrophysics Data System (ADS)

Huang, Xiushi; Gao, Zhaomeng; Li, Pei; Wang, Longfei; Liu, Xiansheng; Zhang, Weifeng; Guo, Haizhong

2018-02-01

Resistance switching has been observed in double and multi-layer structures of ferroelectric films. The higher switching ratio opens up a vast path for emerging ferroelectric semiconductor devices. An n-n+ isotype heterojunction has been fabricated by depositing an oxide SrTiO3 layer on a conventional n-type Si (001) substrate (SrTiO3/Si) by pulsed laser disposition. Rectification and resistive switching behaviors in the n-n+ SrTiO3/Si heterojunction were observed by a conductive atomic force microscopy, and the n-n+ SrTiO3/Si heterojunction exhibits excellent endurance and retention characteristics. The possible mechanism was proposed based on the band structure of the n-n+ SrTiO3/Si heterojunction, and the observed electrical behaviors could be attributed to the modulation effect of the electric field reversal on the width of accumulation and the depletion region, as well as the height of potential of the n-n+ junction formed at the STO/Si interface. Moreover, oxygen vacancies are also indicated to play a crucial role in causing insulator to semiconductor transition. These results open the way to potential application in future microelectronic devices based on perovskite oxide layers on conventional semiconductors.

Nucleon shadowing effects in Cu + Cu and Au + Au collisions at RHIC within the HIJING code

NASA Astrophysics Data System (ADS)

Abdel-Waged, Khaled; Felemban, Nuha

2018-02-01

The centrality dependence of pseudorapidity density of charged particles ({{{d}}{N}}{{ch}}/{{d}}η ) in Cu + Cu (Au + Au) collisions at Relativistic Heavy Ion Collider energy of \\sqrt{{s}{{NN}}}=22.4, 62.4 and 200 (19.6, 62.4 and 200) GeV, is investigated within an improved HIJING code. The standard HIJING model is enhanced by a prescription for collective nucleon-nucleon (NN) interactions and more modern parton distribution functions. The collective NN-interactions are used to induce both cascade and nucleon shadowing effects. We find collective cascade broadens the pseudorapidity distributions in the tails (at | η | > {y}{beam}) above 25%-30% collision centrality to be consistent with the {{{d}}{N}}{{ch}}/{{d}}η data at \\sqrt{{s}{{NN}}} =19.6,22.4,62.4 {GeV}. The overall contribution of nucleon shadowing is shown to depress the whole shape of {{{d}}{N}}{{ch}}/{{d}}η in the primary interaction region (at | η | < {y}{beam}) for semiperipheral (20%-25%) and peripheral (≥slant 35 % {--}40 % ) Cu + Cu (Au + Au) interactions at \\sqrt{{s}{{NN}}}=200 {GeV}, in accordance with the PHOBOS data.

Event-Triggered Distributed Control of Nonlinear Interconnected Systems Using Online Reinforcement Learning With Exploration.

PubMed

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-09-07

In this paper, a distributed control scheme for an interconnected system composed of uncertain input affine nonlinear subsystems with event triggered state feedback is presented by using a novel hybrid learning scheme-based approximate dynamic programming with online exploration. First, an approximate solution to the Hamilton-Jacobi-Bellman equation is generated with event sampled neural network (NN) approximation and subsequently, a near optimal control policy for each subsystem is derived. Artificial NNs are utilized as function approximators to develop a suite of identifiers and learn the dynamics of each subsystem. The NN weight tuning rules for the identifier and event-triggering condition are derived using Lyapunov stability theory. Taking into account, the effects of NN approximation of system dynamics and boot-strapping, a novel NN weight update is presented to approximate the optimal value function. Finally, a novel strategy to incorporate exploration in online control framework, using identifiers, is introduced to reduce the overall cost at the expense of additional computations during the initial online learning phase. System states and the NN weight estimation errors are regulated and local uniformly ultimately bounded results are achieved. The analytical results are substantiated using simulation studies.

Centrality Dependence of the Charged-Particle Multiplicity Density at Midrapidity in Pb-Pb Collisions at sqrt[s_{NN}]=5.02  TeV.

PubMed

Adam, J; Adamová, D; Aggarwal, M M; Aglieri Rinella, G; Agnello, M; Agrawal, N; Ahammed, Z; Ahmad, S; Ahn, S U; Aiola, S; Akindinov, A; Alam, S N; Aleksandrov, D; Alessandro, B; Alexandre, D; Alfaro Molina, R; Alici, A; Alkin, A; Almaraz, J R M; Alme, J; Alt, T; Altinpinar, S; Altsybeev, I; Alves Garcia Prado, C; Andrei, C; Andronic, A; Anguelov, V; Antičić, T; Antinori, F; Antonioli, P; Aphecetche, L; Appelshäuser, H; Arcelli, S; Arnaldi, R; Arnold, O W; Arsene, I C; Arslandok, M; Audurier, B; Augustinus, A; Averbeck, R; Azmi, M D; Badalà, A; Baek, Y W; Bagnasco, S; Bailhache, R; Bala, R; Balasubramanian, S; Baldisseri, A; Baral, R C; Barbano, A M; Barbera, R; Barile, F; Barnaföldi, G G; Barnby, L S; Barret, V; Bartalini, P; Barth, K; Bartke, J; Bartsch, E; Basile, M; Bastid, N; Basu, S; Bathen, B; Batigne, G; Batista Camejo, A; Batyunya, B; Batzing, P C; Bearden, I G; Beck, H; Bedda, C; Behera, N K; Belikov, I; Bellini, F; Bello Martinez, H; Bellwied, R; Belmont, R; Belmont-Moreno, E; Belyaev, V; Benacek, P; Bencedi, G; Beole, S; Berceanu, I; Bercuci, A; Berdnikov, Y; Berenyi, D; Bertens, R A; Berzano, D; Betev, L; Bhasin, A; Bhat, I R; Bhati, A K; Bhattacharjee, B; Bhom, J; Bianchi, L; Bianchi, N; Bianchin, C; Bielčík, J; Bielčíková, J; Bilandzic, A; Biro, G; Biswas, R; Biswas, S; Bjelogrlic, S; Blair, J T; Blau, D; Blume, C; Bock, F; Bogdanov, A; Bøggild, H; Boldizsár, L; Bombara, M; Book, J; Borel, H; Borissov, A; Borri, M; Bossú, F; Botta, E; Bourjau, C; Braun-Munzinger, P; Bregant, M; Breitner, T; Broker, T A; Browning, T A; Broz, M; Brucken, E J; Bruna, E; Bruno, G E; Budnikov, D; Buesching, H; Bufalino, S; Buncic, P; Busch, O; Buthelezi, Z; Butt, J B; Buxton, J T; Caffarri, D; Cai, X; Caines, H; Calero Diaz, L; Caliva, A; Calvo Villar, E; Camerini, P; Carena, F; Carena, W; Carnesecchi, F; Castillo Castellanos, J; Castro, A J; Casula, E A R; Ceballos Sanchez, C; Cerello, P; Cerkala, J; Chang, B; Chapeland, S; Chartier, M; Charvet, J L; Chattopadhyay, S; Chattopadhyay, S; 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Kamin, J; Kang, J H; Kaplin, V; Kar, S; Karasu Uysal, A; Karavichev, O; Karavicheva, T; Karayan, L; Karpechev, E; Kebschull, U; Keidel, R; Keijdener, D L D; Keil, M; Mohisin Khan, M; Khan, P; Khan, S A; Khanzadeev, A; Kharlov, Y; Kileng, B; Kim, D W; Kim, D J; Kim, D; Kim, H; Kim, J S; Kim, M; Kim, S; Kim, T; Kirsch, S; Kisel, I; Kiselev, S; Kisiel, A; Kiss, G; Klay, J L; Klein, C; Klein, J; Klein-Bösing, C; Klewin, S; Kluge, A; Knichel, M L; Knospe, A G; Kobdaj, C; Kofarago, M; Kollegger, T; Kolojvari, A; Kondratiev, V; Kondratyeva, N; Kondratyuk, E; Konevskikh, A; Kopcik, M; Kostarakis, P; Kour, M; Kouzinopoulos, C; Kovalenko, O; Kovalenko, V; Kowalski, M; Koyithatta Meethaleveedu, G; Králik, I; Kravčáková, A; Kretz, M; Krivda, M; Krizek, F; Kryshen, E; Krzewicki, M; Kubera, A M; Kučera, V; Kuhn, C; Kuijer, P G; Kumar, A; Kumar, J; Kumar, L; Kumar, S; Kurashvili, P; Kurepin, A; Kurepin, A B; Kuryakin, A; Kweon, M J; Kwon, Y; La Pointe, S L; La Rocca, P; Ladron de Guevara, P; Lagana Fernandes, C; Lakomov, I; Langoy, R; Lara, C; Lardeux, A; Lattuca, A; Laudi, E; Lea, R; Leardini, L; Lee, G R; Lee, S; Lehas, F; Lemmon, R C; Lenti, V; Leogrande, E; León Monzón, I; León Vargas, H; Leoncino, M; Lévai, P; Li, S; Li, X; Lien, J; Lietava, R; Lindal, S; Lindenstruth, V; Lippmann, C; Lisa, M A; Ljunggren, H M; Lodato, D F; Loenne, P I; Loginov, V; Loizides, C; Lopez, X; López Torres, E; Lowe, A; Luettig, P; Lunardon, M; Luparello, G; Lutz, T H; Maevskaya, A; Mager, M; Mahajan, S; Mahmood, S M; Maire, A; Majka, R D; Malaev, M; Maldonado Cervantes, I; Malinina, L; Mal'Kevich, D; Malzacher, P; Mamonov, A; Manko, V; Manso, F; Manzari, V; Marchisone, M; Mareš, J; Margagliotti, G V; Margotti, A; Margutti, J; Marín, A; Markert, C; Marquard, M; Martin, N A; Martin Blanco, J; Martinengo, P; Martínez, M I; Martínez García, G; Martinez Pedreira, M; Mas, A; Masciocchi, S; Masera, M; Masoni, A; Massacrier, L; Mastroserio, A; Matyja, A; Mayer, C; Mazer, J; Mazzoni, M A; Mcdonald, D; Meddi, F; Melikyan, Y; Menchaca-Rocha, A; Meninno, E; Mercado Pérez, J; Meres, M; Miake, Y; Mieskolainen, M M; Mikhaylov, K; Milano, L; Milosevic, J; Minervini, L M; Mischke, A; Mishra, A N; Miśkowiec, D; Mitra, J; Mitu, C M; Mohammadi, N; Mohanty, B; Molnar, L; Montaño Zetina, L; Montes, E; Moreira De Godoy, D A; Moreno, L A P; Moretto, S; Morreale, A; Morsch, A; Muccifora, V; Mudnic, E; Mühlheim, D; Muhuri, S; Mukherjee, M; Mulligan, J D; Munhoz, M G; Munzer, R H; Murakami, H; Murray, S; Musa, L; Musinsky, J; Naik, B; Nair, R; Nandi, B K; Nania, R; Nappi, E; Naru, M U; Natal da Luz, H; Nattrass, C; Navarro, S R; Nayak, K; Nayak, R; Nayak, T K; Nazarenko, S; Nedosekin, A; Nellen, L; Ng, F; Nicassio, M; Niculescu, M; Niedziela, J; Nielsen, B S; Nikolaev, S; Nikulin, S; Nikulin, V; Noferini, F; Nomokonov, P; Nooren, G; Noris, J C C; Norman, J; Nyanin, A; Nystrand, J; Oeschler, H; Oh, S; Oh, S K; Ohlson, A; Okatan, A; Okubo, T; Olah, L; Oleniacz, J; Oliveira Da Silva, A C; Oliver, M H; Onderwaater, J; Oppedisano, C; 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Wiechula, J; Wikne, J; Wilk, G; Wilkinson, J; Williams, M C S; Windelband, B; Winn, M; Yang, H; Yang, P; Yano, S; Yasar, C; Yin, Z; Yokoyama, H; Yoo, I-K; Yoon, J H; Yurchenko, V; Yushmanov, I; Zaborowska, A; Zaccolo, V; Zaman, A; Zampolli, C; Zanoli, H J C; Zaporozhets, S; Zardoshti, N; Zarochentsev, A; Závada, P; Zaviyalov, N; Zbroszczyk, H; Zgura, I S; Zhalov, M; Zhang, H; Zhang, X; Zhang, Y; Zhang, C; Zhang, Z; Zhao, C; Zhigareva, N; Zhou, D; Zhou, Y; Zhou, Z; Zhu, H; Zhu, J; Zichichi, A; Zimmermann, A; Zimmermann, M B; Zinovjev, G; Zyzak, M

2016-06-03

The pseudorapidity density of charged particles, dN_{ch}/dη, at midrapidity in Pb-Pb collisions has been measured at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02  TeV. For the 5% most central collisions, we measure a value of 1943±54. The rise in dN_{ch}/dη as a function of sqrt[s_{NN}] is steeper than that observed in proton-proton collisions and follows the trend established by measurements at lower energy. The increase of dN_{ch}/dη as a function of the average number of participant nucleons, ⟨N_{part}⟩, calculated in a Glauber model, is compared with the previous measurement at sqrt[s_{NN}]=2.76  TeV. A constant factor of about 1.2 describes the increase in dN_{ch}/dη from sqrt[s_{NN}]=2.76 to 5.02 TeV for all centrality classes, within the measured range of 0%-80% centrality. The results are also compared to models based on different mechanisms for particle production in nuclear collisions.

Excitation functions of parameters extracted from three-source (net-)proton rapidity distributions in Au-Au and Pb-Pb collisions over an energy range from AGS to RHIC

NASA Astrophysics Data System (ADS)

Gao, Li-Na; Liu, Fu-Hu; Sun, Yan; Sun, Zhu; Lacey, Roy A.

2017-03-01

Experimental results of the rapidity spectra of protons and net-protons (protons minus antiprotons) emitted in gold-gold (Au-Au) and lead-lead (Pb-Pb) collisions, measured by a few collaborations at the alternating gradient synchrotron (AGS), super proton synchrotron (SPS), and relativistic heavy ion collider (RHIC), are described by a three-source distribution. The values of the distribution width σC and fraction kC of the central rapidity region, and the distribution width σF and rapidity shift Δ y of the forward/backward rapidity regions, are then obtained. The excitation function of σC increases generally with increase of the center-of-mass energy per nucleon pair √{s_{NN}}. The excitation function of σF shows a saturation at √{s_{NN}}=8.8 GeV. The excitation function of kC shows a minimum at √{s_{NN}}=8.8 GeV and a saturation at √{s_{NN}} ≈ 17 GeV. The excitation function of Δ y increases linearly with ln(√{s_{NN}}) in the considered energy range.

Old Torreon Navajo Day School, Cuba, NM: NN0030341

EPA Pesticide Factsheets

NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0030341 to Bureau of Indian Affairs Old Torreon Navajo Day School Wastewater Treatment Lagoon.

A Nonlinear Theory for Asymmetric Sandwich Plates with a First-Order Compressible Core Impacted by a Friedlander-Type Shock Loading (PREPRINT)

DTIC Science & Technology

2011-07-25

the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the...ntN (27b) 0dnu or 0 d nnN (27c) 0 dtu or 0 d ntN (27d) 03  au or  a tnt a nnn d nt d t d nn d n a nt a t a nn a n MMNuNuNuNu

Numerical Studies of the Radiation Condition at the Inlet of a Transonic Compressor Blade Row.

DTIC Science & Technology

1980-04-01

284-296 (1977). 2 aL.) ITH~rOU7 REFLECTOAJ &RWD b)WITH PJEFL.ECr-IOA, FIGLJ/AE I NV9 PATTERNhS EXPEC TED located a chord length or so upstream of the...C) W In -0N -W e --N 0 -00 e -’N -" M-M M V -NM ""NM N NM 0 - - - - - - - - - - - - --4 WUN NWCON NMWN NN VN N-CYN N N NOJcyN N-N N-N N O~j N 000 00

Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy.

PubMed

Akl, A I; Sobh, M A; Enab, Y M; Tattersall, J

2001-12-01

The effect of dialysis on patients is conventionally predicted using a formal mathematical model. This approach requires many assumptions of the processes involved, and validation of these may be difficult. The validity of dialysis urea modeling using a formal mathematical model has been challenged. Artificial intelligence using neural networks (NNs) has been used to solve complex problems without needing a mathematical model or an understanding of the mechanisms involved. In this study, we applied an NN model to study and predict concentrations of urea during a hemodialysis session. We measured blood concentrations of urea, patient weight, and total urea removal by direct dialysate quantification (DDQ) at 30-minute intervals during the session (in 15 chronic hemodialysis patients). The NN model was trained to recognize the evolution of measured urea concentrations and was subsequently able to predict hemodialysis session time needed to reach a target solute removal index (SRI) in patients not previously studied by the NN model (in another 15 chronic hemodialysis patients). Comparing results of the NN model with the DDQ model, the prediction error was 10.9%, with a not significant difference between predicted total urea nitrogen (UN) removal and measured UN removal by DDQ. NN model predictions of time showed a not significant difference with actual intervals needed to reach the same SRI level at the same patient conditions, except for the prediction of SRI at the first 30-minute interval, which showed a significant difference (P = 0.001). This indicates the sensitivity of the NN model to what is called patient clearance time; the prediction error was 8.3%. From our results, we conclude that artificial intelligence applications in urea kinetics can give an idea of intradialysis profiling according to individual clinical needs. In theory, this approach can be extended easily to other solutes, making the NN model a step forward to achieving artificial-intelligent dialysis control.

Neural networks for continuous online learning and control.

PubMed

Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

2006-11-01

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration

NASA Astrophysics Data System (ADS)

Xu, Zhenkai; Li, Yong; Rizos, Chris; Xu, Xiaosu

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field test data is processed to evaluate the performance of the proposed approach.

BIA Wingate High School WWTF, Fort Wingate, NM: NN0020958

EPA Pesticide Factsheets

NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0020958 to Bureau of Indian Affairs (BIA) Wingate High School Wastewater Treatment Lagoon, Fort Wingate, NM.

Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

PubMed

Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

2010-12-01

The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Λ N → NN EFT potentials and hypertriton non-mesonic weak decay

NASA Astrophysics Data System (ADS)

Pérez-Obiol, Axel; Entem, David R.; Nogga, Andreas

2018-05-01

The potential for the Λ N → NN weak transition, the main responsible for the non-mesonic weak decay of hypernuclei, has been developed within the framework of effective field theory (EFT) up to next-to-leading order (NLO). The leading order (LO) and NLO contributions have been calculated in both momentum and coordinate space, and have been organised into the different operators which mediate the N → NN transition. We compare the ranges of the one-meson and two-pion exchanges for each operator. The non-mesonic weak decay of the hypertriton has been computed within the plane-wave approximation using the LO weak potential and modern strong EFT NN potentials. Formally, two methods to calculate the final state interactions among the decay products are presented. We briefly comment on the calculation of the {}{{Λ }}{}3H{\\to }3 He+{π }- mesonic weak decay.

Relativistic Quark Model Based Description of Low Energy NN Scattering

NASA Astrophysics Data System (ADS)

Antalik, R.; Lyubovitskij, V. E.

A model describing the NN scattering phase shifts is developed. Two nucleon interactions induced by meson exchange forces are constructed starting from π, η, η‧ pseudoscalar-, the ρ, ϕ, ω vector-, and the ɛ(600), a0, f0(1400) scalar — meson-nucleon coupling constants, which we obtained within a relativistic quantum field theory based quark model. Working within the Blankenbecler-Sugar-Logunov-Tavkhelidze quasipotential dynamics, we describe the NN phase shifts in a relativistically invariant way. In this procedure we use phenomenological form factor cutoff masses and effective ɛ and ω meson-nucleon coupling constants, only. Resulting NN phase shifts are in a good agreement with both, the empirical data, and the entirely phenomenological Bonn OBEP model fit. While the quality of our description, evaluated as a ratio of our results to the Bonn OBEP model χ2 ones is about 1.2, other existing (semi)microscopic results gave qualitative results only.

e+e‑ Pair Production at Very Low Transverse Mometum in Au+Au Collisions at s NN = 200 GeV and U+U Collisions at sNN = 193 GeV at STAR

NASA Astrophysics Data System (ADS)

Yang, Shuai

We present the first measurements of e+e‑ pair production at very low transverse momentum (pT < 0.15 GeV/c) in Au + Au collisions at sNN = 200 GeV and U + U collisions at sNN = 193 GeV using the STAR detector at the Relativistic Heavy Ion Collider. A significant excess, with respect to known hadronic contributions, is observed in 60-80% central heavy-ion collisions over the whole Mee range. Remarkably, the excess almost entirely happens below pT ≈ 0.15 GeV/c, and can not be explained by a theoretical model calculation incorporating in-medium broadened ρ spectral function. Moreover, the observed excess yield has no significant centrality dependence. In addition, the steepness of pT2 distribution exhibits mild invariant mass and collision species dependence.

Peptidase inhibitors potentiate the effects of neurotensin and neuromedin N on self-stimulation of the medial prefrontal cortex.

PubMed

Fernández, R; Alba, F; Ferrer, J M

1996-02-29

The purpose of this study was to examine the possible role of endogenous peptidases in the inhibition of intracranial self-stimulation (ICSS) produced by injections of neurotensin (NT) and neuromedin N (NN) into the medial prefrontal cortex (MPC) of the rat. We studied the effects on ICSS of the MPC of the administration of thiorphan and bestatin, two specific inhibitors of the peptidases that inactivate NT and NN respectively. Microinjections into MPC of thiorphan (10 micrograms) and bestatin (25 micrograms) potentiated in inhibition of ICSS produced by the intracortical administration of NT (10 nmol) and NN (20 nmol) respectively. This potentiation affected both the amplitude and the duration of the inhibition of ICSS produced by the neuropeptides. Our data indicate that endogenous peptidases are involved in the inactivation of NT and NN in the prefrontal cortex.

Adaptive critic neural network-based object grasping control using a three-finger gripper.

PubMed

Jagannathan, S; Galan, Gustavo

2004-03-01

Grasping of objects has been a challenging task for robots. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. Moreover, the object has to be secured accurately and considerably fast without damaging it. Since the gripper, contact dynamics, and the object properties are not typically known beforehand, an adaptive critic neural network (NN)-based hybrid position/force control scheme is introduced. The feedforward action generating NN in the adaptive critic NN controller compensates the nonlinear gripper and contact dynamics. The learning of the action generating NN is performed on-line based on a critic NN output signal. The controller ensures that a three-finger gripper tracks a desired trajectory while applying desired forces on the object for manipulation. Novel NN weight tuning updates are derived for the action generating and critic NNs so that Lyapunov-based stability analysis can be shown. Simulation results demonstrate that the proposed scheme successfully allows fingers of a gripper to secure objects without the knowledge of the underlying gripper and contact dynamics of the object compared to conventional schemes.

Differential catabolic fate of neuromedin N and neurotensin in the canine intestinal mucosa.

PubMed

Barelli, H; Mao, Y K; Vincent, B; Daniel, E E; Vincent, J P; Checler, F

1993-01-01

We have established the peptidase content of a P2 fraction (enriched in synaptosomes) and plasma membranes prepared from canine intestinal mucosa. Fourteen exo- and endopeptidases were assayed with fluorimetric or chromogenic substrates and identified by means of specific peptidase inhibitors. Post-proline dipeptidyl aminopeptidase IV, aminopeptidase M, and carboxypeptidase A were the most abundant exopeptidases, while aminopeptidases A and B, dipeptidyl aminopeptidase, pyroglutamyl peptide hydrolase I, and carboxypeptidase B displayed little, if any, activity. Endopeptidase 24.11 was the only endopeptidase that was detected in high amount. By contrast, proline endopeptidase exhibited a low activity, while angiotensin-converting enzyme, endopeptidase 24.15, endopeptidase 24.16, and cathepsin B and D-like activities were not detected. The catabolic rates of the two related neuropeptides, neurotensin (NT) and neuromedin N (NN), established that NN was inactivated 16 to 24 times faster than NT by plasma membrane and P2 fractions, respectively. Furthermore, the two peptides underwent qualitatively distinct mechanisms of degradation. A phosphoramidon-sensitive formation of NT(1-10) was detected as the major NT catabolite, indicating that NT was susceptible to an endoproteolytic cleavage elicited by endopeptidase 24.11. By contrast, NN was inactivated by the action of an exopeptidase at its N-terminus, leading to the formation of [des-Lys1]NN. The occurrence of this NN metabolite was prevented by bestatin and actinonin, but not by the aminopeptidase B inhibitor, arphamenine B, indicating that the release of the N-terminal residue of NN was likely due to aminopeptidase M.(ABSTRACT TRUNCATED AT 250 WORDS)

Self-Organizing Map Neural Network-Based Nearest Neighbor Position Estimation Scheme for Continuous Crystal PET Detectors

NASA Astrophysics Data System (ADS)

Wang, Yonggang; Li, Deng; Lu, Xiaoming; Cheng, Xinyi; Wang, Liwei

2014-10-01

Continuous crystal-based positron emission tomography (PET) detectors could be an ideal alternative for current high-resolution pixelated PET detectors if the issues of high performance γ interaction position estimation and its real-time implementation are solved. Unfortunately, existing position estimators are not very feasible for implementation on field-programmable gate array (FPGA). In this paper, we propose a new self-organizing map neural network-based nearest neighbor (SOM-NN) positioning scheme aiming not only at providing high performance, but also at being realistic for FPGA implementation. Benefitting from the SOM feature mapping mechanism, the large set of input reference events at each calibration position is approximated by a small set of prototypes, and the computation of the nearest neighbor searching for unknown events is largely reduced. Using our experimental data, the scheme was evaluated, optimized and compared with the smoothed k-NN method. The spatial resolutions of full-width-at-half-maximum (FWHM) of both methods averaged over the center axis of the detector were obtained as 1.87 ±0.17 mm and 1.92 ±0.09 mm, respectively. The test results show that the SOM-NN scheme has an equivalent positioning performance with the smoothed k-NN method, but the amount of computation is only about one-tenth of the smoothed k-NN method. In addition, the algorithm structure of the SOM-NN scheme is more feasible for implementation on FPGA. It has the potential to realize real-time position estimation on an FPGA with a high-event processing throughput.

Chevron Mining, Inc. McKinley Mine, Gallup, NM: NN0029386

EPA Pesticide Factsheets

NPDES Permit #NN0029386 for the Chevron Mining, Inc. McKinley Mine to tributaries to the Puerco River located on Tribal, private, and public lands within the Navajo Nation and near the Arizona border in New Mexico.

Erosion and deposition mode in a developing foreland basin: Temporal and spatial distribution of provenance in southwestern Taiwan

NASA Astrophysics Data System (ADS)

Yang, K. M.; Kun-an, H.; Chien, C. W.; Leh-chyun, W.; Chi-Cheng, Y.

2017-12-01

The foreland basin in southwestern Taiwan offers an idealistic example for the study of tectonostratigraphy in basin development. The subsidence analysis indicates that the recent basin development went through at least two rapid subsidence events, along with back-and-forth migration of the forebulge. This study aims to explore the interaction between the uplifting forebulge and coevally subsiding foredeep primarily based on petrofacies analysis, the results of which were then interpreted with the well-established tectonostratigraphic and biostratigraphic frameworks to infer the erosion and deposition mode during the basin development. The craton had been the sediment source to the west of the study area in the pre-orogenic period. In the initial stage of foreland basin development, the forebulge slowly elevated and started to obstruct sediment supplies from the craton. Before the period of NN19, the forebulge not only became the barrier of the most cratonic sediment supplies but also shed a major amount of detritus into the adjacent area. In addition, regional topographic relief, which was formed by syn-orogenic normal faulting during the NN11-15, locally changed the composition and transportation modes of the sediments; the exposed basement of the footwall also became the source of the sediments shed into the adjacent depo-centers. After the NN19, whole area was influenced predominantly by the orogenic belt from the east. Large amounts of slate fragments began to appear in the middle NN19 and relative percentage of the metamorphic lithics was increased upward and northward. As the orogen moved westward along with the foreland basin development, the studied area changed from the distal to proximal parts of the foredeep and sediment sources were controlled mainly by river systems derived from the orogen. The metamorphic lithics decreased southward and concentrated in the central part of the study area, suggesting that the slate fragments which were transported parallel with the orientation of submarine canyons since NN13 to the south of the study area. We propose that 1) from NN13 to NN18, the episodic subsidence in the foreland basin implies episodic movement of the orogenic belt, and 2) since the period of NN19, the orogenic belt and foreland basin has been developing in a continuous and steady state.

Use of carbosilane dendrimer to switch macrophage polarization for the acquisition of antitumor functions

NASA Astrophysics Data System (ADS)

Perisé-Barrios, Ana J.; Gómez, Rafael; Corbí, Angel L.; de La Mata, Javier; Domínguez-Soto, Angeles; Muñoz-Fernandez, María A.

2015-02-01

Tumor microenvironment favors the escape from immunosurveillance by promoting immunosuppression and blunting pro-inflammatory responses. Since most tumor-associated macrophages (TAM) exhibit an M2-like tumor cell growth promoting polarization, we have studied the role of 2G-03NN24 carbosilane dendrimer in M2 macrophage polarization to evaluate the potential application of dendrimers in tumor immunotherapy. We found that the 2G-03NN24 dendrimer decreases LPS-induced IL-10 production from in vitro generated monocyte-derived M2 macrophages, and also switches their gene expression profile towards the acquisition of M1 polarization markers (INHBA, SERPINE1, FLT1, EGLN3 and ALDH1A2) and the loss of M2 polarization-associated markers (EMR1, IGF1, FOLR2 and SLC40A1). Furthermore, 2G-03NN24 dendrimer decreases STAT3 activation. Our results indicate that the 2G-03NN24 dendrimer can be a useful tool for antitumor therapy by virtue of its potential ability to limit the M2-like polarization of TAM.Tumor microenvironment favors the escape from immunosurveillance by promoting immunosuppression and blunting pro-inflammatory responses. Since most tumor-associated macrophages (TAM) exhibit an M2-like tumor cell growth promoting polarization, we have studied the role of 2G-03NN24 carbosilane dendrimer in M2 macrophage polarization to evaluate the potential application of dendrimers in tumor immunotherapy. We found that the 2G-03NN24 dendrimer decreases LPS-induced IL-10 production from in vitro generated monocyte-derived M2 macrophages, and also switches their gene expression profile towards the acquisition of M1 polarization markers (INHBA, SERPINE1, FLT1, EGLN3 and ALDH1A2) and the loss of M2 polarization-associated markers (EMR1, IGF1, FOLR2 and SLC40A1). Furthermore, 2G-03NN24 dendrimer decreases STAT3 activation. Our results indicate that the 2G-03NN24 dendrimer can be a useful tool for antitumor therapy by virtue of its potential ability to limit the M2-like polarization of TAM. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr04038d

Northern Edge Navajo Casino, Fruitland, NM: NN0030343

EPA Pesticide Factsheets

NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0030343) to the Navajo Tribal Utility Authority Northern Edge Navajo Casino Wastewater Treatment Facility, 2752 Indian Service Road 36, Fruitland, NM.

Elliptic flow of electrons from beauty-hadron decays extracted from Pb-Pb collision data at √{s_NN} = 2.76 TeV

NASA Astrophysics Data System (ADS)

Moreira de Godoy, D.; Herrmann, F.; Klasen, M.; Klein-Bösing, C.; Suaide, A. A. P.

2018-05-01

We present a calculation of the elliptic flow of electrons from beauty-hadron decays in semi-central Pb-Pb collisions at centre-of-mass energy per colliding nucleon pair, represented as √{s_NN}, of 2.76 TeV. The result is obtained by the subtraction of the charm-quark contribution in the elliptic flow of electrons from heavy-flavour hadron decays in semi-central Pb-Pb collisions at √{s_NN} = 2.76 TeV recently made publicly available by the ALICE collaboration.

Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

NASA Technical Reports Server (NTRS)

Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.

2007-01-01

In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

DTIC Science & Technology

1999-05-17

Experimental Results In this section, we compare kNN -mut which uses the weight vector obtained using mutual information as the fi- nal weight vector and...WAKNN against kNN , C4.5 [Qui93], RIPPER [Coh95], PEBLS [CS93], Rainbow [McC96], VSM [Low95] on several synthetic and real data sets. VSM is another k...obtained without this option. 3 C4.5 RIPPER PEBLS Rainbow kNN WAKNN Syn-1 100.0 100.0 100.0 100.0 77.3 100.0 Syn-2 67.5 69.5 62.0 50.0 66.0 68.8 Syn

Auto-Detection of Partial Discharges in Power Cables by Descrete Wavelet Transform

NASA Astrophysics Data System (ADS)

Yasuda, Yoh; Hara, Takehisa; Urano, Koji; Chen, Min

One of the serious problems that may happen in power XLPE cables is destruction of insulator. The best and conventional way to prevent such a crucial accident is generally supposed to ascertain partial corona discharges occurring at small void in organic insulator. However, there are some difficulties to detect those partial discharges because of existence of external noises in detected data, whose patterns are hardly identified at a glance. By the reason of the problem, there have been a number of researches on the way of development to accomplish detecting partial discharges by employing neural network (NN) system, which is widely known as the system for pattern recognition. We have been developing the NN system of the auto-detection for partial discharges, which we actually input numerical data of waveform itself into and obtained appropriate performance from. In this paper, we employed Descrete Wavelet Transform (DWT) to acquire more detailed transformed data in order to put them into the NN system. Employing DWT, we became able to express the waveform data in time-frequency space, and achieved effective detectiton of partial discharges by NN system. We present here the results using DWT analysis for partial discharges and noise signals which we obtained actually. Moreover, we present results out of the NN system which were dealt with those transformed data.

An Examination of Diameter Density Prediction with k-NN and Airborne Lidar

DOE PAGES

Strunk, Jacob L.; Gould, Peter J.; Packalen, Petteri; ...

2017-11-16

While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination ( R 2),more » and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km 2 Savannah River Site in South Carolina, USA. In conclusion, we evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.« less

Systemic neonatal candidosis: the karyotyping of Candida albicans strains isolated from neonates and health-workers.

PubMed

Ben Abdeljelil, J; Ben Saida, N; Saghrouni, F; Fathallah, A; Boukadida, J; Sboui, H; Ben Said, M

2010-01-01

Candida albicans has become an important cause of nosocomial infections in neonatal intensive care units (NICUs). The aim of the present study was to compare C. albicans strains isolated from neonates (NN) suffering from systemic candidosis and from nurses in order to determine the relatedness between NN and health workers' strains. Thirty-one C. albicans strains were isolated from 18 NN admitted to the NICU of the neonatology service of Farhat Hached Hospital of Sousse, Tunisia and suffering from systemic candidosis, together with five strains recovered from nurses suffering from C. albicans onychomycosis. Two additional strains were tested, one from an adult patient who developed a systemic candidosis and the second from an adult with inguinal intertrigo. All strains were karyotyped by pulsed-field gel electrophoresis (PFGE) with a CHEF-DR II system. Analysis of PFGE patterns yielded by the 38 strains tested led to the identification of three pulsotypes that were designated I, II and III, and consisted of six chromosomal bands with a size ranging from 700 to >2500 kbp. The most widespread was the pulsotype I, which was shared by 17 NN and the five nurses' strains. The identity between NN and nurses' strains is very suggestive of a nosocomial acquisition from health-workers.

Applications of Artificial Neural Networks in Structural Engineering with Emphasis on Continuum Models

NASA Technical Reports Server (NTRS)

Kapania, Rakesh K.; Liu, Youhua

1998-01-01

The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

An Examination of Diameter Density Prediction with k-NN and Airborne Lidar

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

Strunk, Jacob L.; Gould, Peter J.; Packalen, Petteri

While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination ( R 2),more » and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km 2 Savannah River Site in South Carolina, USA. In conclusion, we evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.« less

Attenuation of neurobehavioral and neurochemical abnormalities in animal model of cognitive deficits of Alzheimer's disease by fermented soybean nanonutraceutical.

PubMed

Bhatt, Prakash Chandra; Pathak, Shruti; Kumar, Vikas; Panda, Bibhu Prasad

2018-02-01

The present study was performed to evaluate the efficacy of nanonutraceuticals (NN) for attenuation of neurobehavioral and neurochemical abnormalities in Alzheimer's disease. Solid-state fermentation of soybean with Bacillus subtilis was performed to produce different metabolites (nattokinase, daidzin, genistin and glycitin and menaquinone-7). Intoxication of rats with colchicine caused impairment in learning and memory which was demonstrated in neurobehavioral paradigms (Morris water maze and passive avoidance) linked with decreased activity of acetylcholinesterase (AChE). NN treatment led to a significant increase in TLT in the retention trials as compared to acquisition trial TLT suggesting an improved learning and memory in rats. Further, treatment of NN caused an increase in the activity of AChE (42%), accompanied with a reduced activity of glutathione (42%), superoxide dismutase (43%) and catalase (41%). It also decreased the level of lipid peroxidation (28%) and protein carbonyl contents (30%) in hippocampus as compared to those treated with colchicine alone, suggesting a possible neuroprotective efficacy of NN. Interestingly, in silico studies also demonstrated an effective amyloid-β and BACE-1 inhibition activity. These findings clearly indicated that NN reversed colchicine-induced behavioral and neurochemical alterations through potent antioxidant activity and could possibly impart beneficial effects in cognitive defects associated with Alzheimer's disease.

Genome-Wide Identification and Evolution Analysis of Trehalose-6-Phosphate Synthase Gene Family in Nelumbo nucifera

PubMed Central

Jin, Qijiang; Hu, Xin; Li, Xin; Wang, Bei; Wang, Yanjie; Jiang, Hongwei; Mattson, Neil; Xu, Yingchun

2016-01-01

Trehalose-6-phosphate synthase (TPS) plays a key role in plant carbohydrate metabolism and the perception of carbohydrate availability. In the present work, the publicly available Nelumbo nucifera (lotus) genome sequence database was analyzed which led to identification of nine lotus TPS genes (NnTPS). It was found that at least two introns are included in the coding sequences of NnTPS genes. When the motif compositions were analyzed we found that NnTPS generally shared the similar motifs, implying that they have similar functions. The dN/dS ratios were always less than 1 for different domains and regions outside domains, suggesting purifying selection on the lotus TPS gene family. The regions outside TPS domain evolved relatively faster than NnTPS domains. A phylogenetic tree was constructed using all predicted coding sequences of lotus TPS genes, together with those from Arabidopsis, poplar, soybean, and rice. The result indicated that those TPS genes could be clearly divided into two main subfamilies (I-II), where each subfamily could be further divided into 2 (I) and 5 (II) subgroups. Analyses of divergence and adaptive evolution show that purifying selection may have been the main force driving evolution of plant TPS genes. Some of the critical sites that contributed to divergence may have been under positive selection. Transcriptome data analysis revealed that most NnTPS genes were predominantly expressed in sink tissues. Expression pattern of NnTPS genes under copper and submergence stress indicated that NNU_014679 and NNU_022788 might play important roles in lotus energy metabolism and participate in stress response. Our results can facilitate further functional studies of TPS genes in lotus. PMID:27746792

Investigation on the Bay of Bengal branch of summer monsoon during normal and delayed onset over Gangetic West Bengal

NASA Astrophysics Data System (ADS)

Das, Debanjana; Mondal, Paramita; Saha, Poulomi; Chaudhuri, Sutapa

2018-06-01

The regional features of Bay of Bengal (BOB) branch of summer monsoon (SM) are examined to identify the causes of delayed onset over Gangetic West Bengal (GWB) in the years having normal onset over Kerala coast. The normal onset over both GWB and Kerala is designated as Normal-Normal (NN) years, while delayed onset over GWB and normal onset over Kerala is termed Normal-Delayed (ND) years. The temperature gradient (TTg), winds at 850 and 150 hPa pressure levels, sea-surface temperature (SST), outgoing long wave radiation (OLR), low-level moisture convergence, instability, and rainfall rate (RR) are analyzed in this study using National Centers for Environmental Prediction and National Oceanic and Atmospheric Administration reanalysis dataset during the period from 1981 to 2015. The result shows that TTg over BOB plays a significant role in controlling the movement of BOB branch of SM. Warm SST is observed to prevail over north BOB during NN years. The divergence at 150 hPa and convergence at 850 hPa pressure levels are found to influence the propagation of BOB branch of SM during both NN and ND years. The winds at 850 hPa level converge over BOB and GWB during NN years, whereas winds converge more over eastern BOB and Indo-Chinese peninsula during ND years. Result depicts abundance of low-level (850-1000 hPa) moisture over eastern BOB and Indo-Chinese peninsula during ND years, whereas moisture is observed to converge over north and north-eastern BOB during NN years. The RR is observed to be slightly higher during NN than ND years. However, it may not be concluded from the analysis that delayed onset over GWB will be responsible for less RR over the study region.

Cardiac autonomic regulation is disturbed in children with euthyroid Hashimoto thyroiditis.

PubMed

Kilic, Ayhan; Gulgun, Mustafa; Tascilar, Mehmet Emre; Sari, Erkan; Yokusoglu, Mehmet

2012-03-01

Hashimoto thyroiditis (chronic autoimmune thyroiditis) is the most common form of thyroiditis in childhood. Previous studies have found autonomic dysfunction of varying magnitude in patients with autoimmune diseases, which is considered a cardiovascular risk factor. We aimed to evaluate the heart rate variability (HRV), a measure of cardiac autonomic modulation, in children with euthyroid Hashimoto thyroiditis (eHT). The study included 32 patients with eHT (27 girls and 5 boys; mean age 11 ± 4.1 years, range 8-16; body mass index 0.47 ± 0.69 kg/m(2)), as judged by normal or minimally elevated serum TSH levels (normal range: 0.34-5.6 mIU/l) and normal levels of free thyroid hormones (FT4 and FT3) and 38 euthyroid age-matched controls. Patients with eHT and control subjects underwent physical examination and 24-hour ambulatory ECG monitoring. Time-domain parameters of HRV were evaluated for cardiac autonomic functions. Children with eHT displayed significantly lower values of time-domain parameters of SDANN (standard deviation of the averages of NN intervals), RMSSD (square root of the mean of the sum of the squares of differences between adjacent NN intervals), NN50 counts (number of pairs of adjacent NN intervals differing by more than 50 ms) and PNN50 (NN50 count divided by the total number of all NN intervals) for each 5-min interval, compared to healthy controls (p < 0.05 for each), indicating the decreased beat-to-beat variation of heart rate. In conclusion, eHT is associated with disturbed autonomic regulation of heart rate. Hence, the children with eHT are at higher risk for developing cardiovascular diseases.

High-quality two-nucleon potentials up to fifth order of the chiral expansion

NASA Astrophysics Data System (ADS)

Entem, D. R.; Machleidt, R.; Nosyk, Y.

2017-08-01

We present NN potentials through five orders of chiral effective field theory ranging from leading order (LO) to next-to-next-to-next-to-next-to-leading order (N4LO ). The construction may be perceived as consistent in the sense that the same power counting scheme as well as the same cutoff procedures are applied in all orders. Moreover, the long-range parts of these potentials are fixed by the very accurate π N low-energy constants (LECs) as determined in the Roy-Steiner equations analysis by Hoferichter, Ruiz de Elvira, and coworkers. In fact, the uncertainties of these LECs are so small that a variation within the errors leads to effects that are essentially negligible, reducing the error budget of predictions considerably. The NN potentials are fit to the world NN data below the pion-production threshold of the year 2016. The potential of the highest order (N4LO ) reproduces the world NN data with the outstanding χ2/datum of 1.15, which is the highest precision ever accomplished for any chiral NN potential to date. The NN potentials presented may serve as a solid basis for systematic ab initio calculations of nuclear structure and reactions that allow for a comprehensive error analysis. In particular, the consistent order by order development of the potentials will make possible a reliable determination of the truncation error at each order. Our family of potentials is nonlocal and, generally, of soft character. This feature is reflected in the fact that the predictions for the triton binding energy (from two-body forces only) converges to about 8.1 MeV at the highest orders. This leaves room for three-nucleon-force contributions of moderate size.

Maternal Perinatal Diet Induces Developmental Programming of Bone Architecture

PubMed Central

Devlin, MJ; Grasemann, C; Cloutier, AM; Louis, L; Alm, C; Palmert, MR; Bouxsein, ML

2013-01-01

Maternal high fat diet can alter offspring metabolism via perinatal developmental programming. This study tests the hypothesis that maternal high fat diet also induces perinatal programming of offspring bone mass and strength. We compared skeletal acquisition in pups from C57Bl/6J mice fed high fat or normal diet from preconception through lactation. Three-week-old male and female pups from high fat (HF-N) and normal mothers (N-N) were weaned onto normal diet. Outcomes at 14 and 26 wks of age included body mass, body composition, whole body bone mineral content via pDXA, femoral cortical and trabecular architecture via μCT, and glucose tolerance. Female HF-N had normal body mass and glucose tolerance, with lower %body fat but higher serum leptin at 14 wks vs. N-N (p<0.05 for both). Whole body bone mineral content was 12% lower at 14 wks and 5% lower at 26 wks, but trabecular bone volume fraction was 20% higher at 14 wks in female HF-N vs. N-N (p<0.05 for all). Male HF-N had normal body mass and mildly impaired glucose tolerance, with lower %body fat at 14 wks and lower serum leptin at 26 wks vs. N-N (p<0.05 for both). Serum insulin was higher at 14 wks and lower at 26 wks in HF-N vs. N-N (p<0.05). Trabecular BV/TV was 34% higher and cortical bone area was 6% higher at 14 wks vs. N-N (p<0.05 for both). These data suggest maternal high fat diet has complex effects on offspring bone, supporting the hypothesis that maternal diet alters postnatal skeletal homeostasis. PMID:23503967

Maternal perinatal diet induces developmental programming of bone architecture.

PubMed

Devlin, M J; Grasemann, C; Cloutier, A M; Louis, L; Alm, C; Palmert, M R; Bouxsein, M L

2013-04-01

Maternal high-fat (HF) diet can alter offspring metabolism via perinatal developmental programming. This study tests the hypothesis that maternal HF diet also induces perinatal programming of offspring bone mass and strength. We compared skeletal acquisition in pups from C57Bl/6J mice fed HF or normal diet from preconception through lactation. Three-week-old male and female pups from HF (HF-N) and normal mothers (N-N) were weaned onto normal diet. Outcomes at 14 and 26 weeks of age included body mass, body composition, whole-body bone mineral content (WBBMC) via peripheral dual-energy X-ray absorptiometry, femoral cortical and trabecular architecture via microcomputed tomography, and glucose tolerance. Female HF-N had normal body mass and glucose tolerance, with lower body fat (%) but higher serum leptin at 14 weeks vs. N-N (P<0.05 for both). WBBMC was 12% lower at 14 weeks and 5% lower at 26 weeks, but trabecular bone volume fraction was 20% higher at 14 weeks in female HF-N vs. N-N (P<0.05 for all). Male HF-N had normal body mass and mildly impaired glucose tolerance, with lower body fat (%) at 14 weeks and lower serum leptin at 26 weeks vs. N-N (P<0.05 for both). Serum insulin was higher at 14 weeks and lower at 26 weeks in HF-N vs. N-N (P<0.05). Trabecular BV/TV was 34% higher and cortical bone area was 6% higher at 14 weeks vs. N-N (P<0.05 for both). These data suggest that maternal HF diet has complex effects on offspring bone, supporting the hypothesis that maternal diet alters postnatal skeletal homeostasis.

Measuring nanotechnology development through the study of the dividing pattern between developed and developing countries during 2000-2014

NASA Astrophysics Data System (ADS)

Jafari, Mostafa; Zarghami, Hamid Reza

2016-07-01

This paper investigates the global nanotechnology and nanoscience (NN) indicators in a developmental context, during three 5-year periods from 2000 to 2014. Through bibliometric analyses of the longitudinal data from well-known databases, the growth patterns of NN articles and patents were investigated. Furthermore, the causal relationships among these indicators and some characteristics of the 105 countries studied were examined using regression and correlation analyses leading to the identification of the top 20 "science and innovation giants," in terms of all indicators, as well as the existence of significant, yet different, correlations among the indicators in developing and developed countries. In general, China's growth rate (GR) in NN publications was found to surpass USA, from 2010 to 2014, leading to a change in the ranking of the top countries and moving China, with about 25 % of world's NN articles, to top. A different trend was distinguished for patents in the area of nanotechnology, where USA, as the origin of over half of the world's granted patents, has been the undisputed leader. The shares of developing countries (i.e., the percent ratios of the number of nanotech patents granted to the citizens of developing countries over the total number of nanotech patents granted worldwide) was found to be incompatible with the countries' shares in the total NN articles, indicating a poor correlation between the two factors. However, developing countries were found to be superior in the GR of both NN articles and patents. Finally, the top countries identified can be regarded as suitable for comparative studies, and benchmarking by researchers and policy makers.

Heteroleptic copper(I) complexes prepared from phenanthroline and bis-phosphine ligands.

PubMed

Kaeser, Adrien; Mohankumar, Meera; Mohanraj, John; Monti, Filippo; Holler, Michel; Cid, Juan-José; Moudam, Omar; Nierengarten, Iwona; Karmazin-Brelot, Lydia; Duhayon, Carine; Delavaux-Nicot, Béatrice; Armaroli, Nicola; Nierengarten, Jean-François

2013-10-21

Preparation of [Cu(NN)(PP)](+) derivatives has been systematically investigated starting from two libraries of phenanthroline (NN) derivatives and bis-phosphine (PP) ligands, namely, (A) 1,10-phenanthroline (phen), neocuproine (2,9-dimethyl-1,10-phenanthroline, dmp), bathophenanthroline (4,7-diphenyl-1,10-phenanthroline, Bphen), 2,9-diphenethyl-1,10-phenanthroline (dpep), and 2,9-diphenyl-1,10-phenanthroline (dpp); (B) bis(diphenylphosphino)methane (dppm), 1,2-bis(diphenylphosphino)ethane (dppe), 1,3-bis(diphenylphosphino)propane (dppp), 1,2-bis(diphenylphosphino)benzene (dppb), 1,1'-bis(diphenylphosphino)ferrocene (dppFc), and bis[(2-diphenylphosphino)phenyl] ether (POP). Whatever the bis-phosphine ligand, stable heteroleptic [Cu(NN)(PP)](+) complexes are obtained from the 2,9-unsubstituted-1,10-phenanthroline ligands (phen and Bphen). By contrast, heteroleptic complexes obtained from dmp and dpep are stable in the solid state, but a dynamic ligand exchange reaction is systematically observed in solution, and the homoleptic/heteroleptic ratio is highly dependent on the bis-phosphine ligand. Detailed analysis revealed that the dynamic equilibrium resulting from ligand exchange reactions is mainly influenced by the relative thermodynamic stability of the different possible complexes. Finally, in the case of dpp, only homoleptic complexes were obtained whatever the bis-phosphine ligand. Obviously, steric effects resulting from the presence of the bulky phenyl rings on the dpp ligand destabilize the heteroleptic [Cu(NN)(PP)](+) complexes. In addition to the remarkable thermodynamic stability of [Cu(dpp)2]BF4, this negative steric effect drives the dynamic complexation scenario toward almost exclusive formation of homoleptic [Cu(NN)2](+) and [Cu(PP)2](+) complexes. This work provides the definitive rationalization of the stability of [Cu(NN)(PP)](+) complexes, marking the way for future developments in this field.

Triggering Mechanism for Neutron Induced Single-Event Burnout in Power Devices

NASA Astrophysics Data System (ADS)

Shoji, Tomoyuki; Nishida, Shuichi; Hamada, Kimimori

2013-04-01

Cosmic ray neutrons can trigger catastrophic failures in power devices. It has been reported that parasitic transistor action causes single-event burnout (SEB) in power metal-oxide-semiconductor field-effect transistors (MOSFETs) and insulated gate bipolar transistors (IGBTs). However, power diodes do not have an inherent parasitic transistor. In this paper, we describe the mechanism triggering SEB in power diodes for the first time using transient device simulation. Initially, generated electron-hole pairs created by incident recoil ions generate transient current, which increases the electron density in the vicinity of the n-/n+ boundary. The space charge effect of the carriers leads to an increase in the strength of the electric field at the n-/n+ boundary. Finally, the onset of impact ionization at the n-/n+ boundary can trigger SEB. Furthermore, this failure is closely related to diode secondary breakdown. It was clarified that the impact ionization at the n-/n+ boundary is a key point of the mechanism triggering SEB in power devices.

All Prime Contract Awards by State or Country, Place, and Contractor. Part 21 (Stafford Virginia-Yellowstone Park)

DTIC Science & Technology

1990-01-01

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A Technical Analysis Information Fusion Approach for Stock Price Analysis and Modeling

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

In this paper, we address the problem of technical analysis information fusion in improving stock market index-level prediction. We present an approach for analyzing stock market price behavior based on different categories of technical analysis metrics and a multiple predictive system. Each category of technical analysis measures is used to characterize stock market price movements. The presented predictive system is based on an ensemble of neural networks (NN) coupled with particle swarm intelligence for parameter optimization where each single neural network is trained with a specific category of technical analysis measures. The experimental evaluation on three international stock market indices and three individual stocks show that the presented ensemble-based technical indicators fusion system significantly improves forecasting accuracy in comparison with single NN. Also, it outperforms the classical neural network trained with index-level lagged values and NN trained with stationary wavelet transform details and approximation coefficients. As a result, technical information fusion in NN ensemble architecture helps improving prediction accuracy.

S-wave Approach for \\varvec{nnp} and \\varvec{ppn} Systems with Phenomenological Correction for Singlet \\varvec{NN} Potentials

NASA Astrophysics Data System (ADS)

Vlahovic, B.; Suslov, V. M.; Filikhin, I.

2017-03-01

Three-nucleon systems are considered assuming the neutrons and protons to be distinguishable particles. The configuration space Faddeev equations within the s-wave approach are applied for studying bound state and scattering problems. The phenomenological Malfliet-Tjon MT I-III and Afnan-Tang ATS3 NN potentials are used with scaling factors chosen to reproduce the singlet nn, pp and np experimental scattering lengths. Numerical evaluation for the charge symmetry breaking energy is found to be about 50 keV for ^3H and ^3He nuclei. To determine any effects related to the nn ( pp) and np potential differences, the nd and pd breakup scattering calculations were performed at E_{lab}=4.0 and 14.1 MeV. We found the effects due to potential differences are small but noticeable. We discuss the dependence of calculated inelasticities and phase-shifts with respect to the chosen value for cutoff radius.

Measurement of inclusive jet production and nuclear modifications in pPb collisions at √{s_{_NN}} =5.02 TeV

NASA Astrophysics Data System (ADS)

Khachatryan, V.; 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.; Knünz, V.; König, A.; Krammer, M.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rad, N.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Schöfbeck, R.; Strauss, J.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Knutsson, A.; Lauwers, J.; Luyckx, S.; 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.; Heracleous, N.; Keaveney, J.; Lowette, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Van Parijs, I.; Barria, P.; Brun, H.; Caillol, C.; Clerbaux, B.; De Lentdecker, G.; Fang, W.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Léonard, A.; Maerschalk, T.; Marinov, A.; Perniè, L.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Yonamine, R.; Zenoni, F.; Zhang, F.; Beernaert, K.; Benucci, L.; Cimmino, A.; Crucy, S.; Dobur, D.; Fagot, A.; Garcia, G.; Gul, M.; Mccartin, J.; Ocampo Rios, A. A.; Poyraz, D.; Ryckbosch, D.; Salva, S.; Sigamani, M.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; Ceard, L.; Delaere, C.; Delcourt, M.; Favart, D.; Forthomme, L.; Giammanco, A.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Mertens, A.; Musich, M.; Nuttens, C.; Perrini, L.; Piotrzkowski, K.; Popov, A.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Beliy, N.; Hammad, G. H.; Aldá Júnior, W. L.; Alves, F. L.; Alves, G. A.; Brito, L.; Correa Martins Junior, M.; Hamer, M.; Hensel, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.; Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Custódio, A.; Da Costa, E. M.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Huertas Guativa, L. M.; Malbouisson, H.; Matos Figueiredo, D.; Mora Herrera, C.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Ahuja, S.; Bernardes, C. A.; De Souza Santos, A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Moon, C. S.; Novaes, S. F.; Padula, Sandra S.; Romero Abad, D.; Ruiz Vargas, J. C.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Cheng, T.; Du, R.; Jiang, C. H.; Leggat, D.; Plestina, R.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Zhang, H.; Asawatangtrakuldee, C.; Ban, Y.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; Gomez Moreno, B.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Puljak, I.; Ribeiro Cipriano, P. M.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Kadija, K.; Luetic, J.; Micanovic, S.; Sudic, L.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Bodlak, M.; Finger, M.; Finger, M.; Abdelalim, A. A.; Awad, A.; Mahrous, A.; Radi, A.; Calpas, B.; Kadastik, M.; Murumaa, M.; Raidal, M.; Tiko, A.; Veelken, C.; Eerola, P.; Pekkanen, J.; Voutilainen, M.; Härkönen, J.; Karimäki, V.; Kinnunen, R.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Peltola, T.; 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.; Machet, M.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Zghiche, A.; Abdulsalam, A.; Antropov, I.; Baffioni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Chapon, E.; Charlot, C.; Davignon, O.; Filipovic, N.; Granier de Cassagnac, R.; Jo, M.; Lisniak, S.; Mastrolorenzo, L.; Miné, P.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Regnard, S.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Strebler, T.; Yilmaz, Y.; Zabi, A.; Agram, J.-L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J.-M.; Buttignol, M.; Chabert, E. C.; Chanon, N.; Collard, C.; Conte, E.; Coubez, X.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Goetzmann, C.; Le Bihan, A.-C.; Merlin, J. A.; Skovpen, K.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Bouvier, E.; Carrillo Montoya, C. A.; Chierici, R.; Contardo, D.; Courbon, B.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Ruiz Alvarez, J. D.; Sabes, D.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Toriashvili, T.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Feld, L.; Heister, A.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Ostapchuk, A.; Preuten, M.; Raupach, F.; Schael, S.; Schulte, J. F.; Verlage, T.; Weber, H.; Zhukov, V.; Ata, M.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Endres, M.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Güth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Knutzen, S.; Kreuzer, P.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Olschewski, M.; Padeken, K.; Papacz, P.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Cherepanov, V.; Erdogan, Y.; Flügge, G.; Geenen, H.; Geisler, M.; Hoehle, F.; Kargoll, B.; Kress, T.; Künsken, A.; Lingemann, J.; Nehrkorn, A.; Nowack, A.; Nugent, I. M.; Pistone, C.; Pooth, O.; Stahl, A.; Aldaya Martin, M.; Asin, I.; Bartosik, N.; Behnke, O.; Behrens, U.; Borras, K.; Burgmeier, A.; Campbell, A.; Contreras-Campana, C.; Costanza, F.; Diez Pardos, C.; Dolinska, G.; Dooling, S.; Dorland, T.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Flucke, G.; Gallo, E.; Garcia, J. Garay; Geiser, A.; Gizhko, A.; Gunnellini, P.; Hauk, J.; Hempel, M.; Jung, H.; Kalogeropoulos, A.; Karacheban, O.; Kasemann, M.; Katsas, P.; Kieseler, J.; Kleinwort, C.; Korol, I.; Lange, W.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Mankel, R.; 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.; Roland, B.; Sahin, M. Ö.; Saxena, P.; Schoerner-Sadenius, T.; Seitz, C.; Spannagel, S.; Stefaniuk, N.; Trippkewitz, K. D.; Walsh, R.; Wissing, C.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Erfle, J.; Garutti, E.; Goebel, K.; Gonzalez, D.; Görner, M.; Haller, J.; Hoffmann, M.; Höing, R. S.; Junkes, A.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Lapsien, T.; Lenz, T.; Marchesini, I.; Marconi, D.; Meyer, M.; Nowatschin, D.; Ott, J.; Pantaleo, F.; Peiffer, T.; Perieanu, A.; Pietsch, N.; Poehlsen, J.; Rathjens, D.; Sander, C.; Scharf, C.; Schleper, P.; Schlieckau, E.; Schmidt, A.; Schumann, S.; Schwandt, J.; Sola, V.; Stadie, H.; Steinbrück, G.; Stober, F. M.; Tholen, H.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Vormwald, B.; Barth, C.; Baus, C.; Berger, J.; Böser, C.; Butz, E.; Chwalek, T.; Colombo, F.; De Boer, W.; Descroix, A.; Dierlamm, A.; Fink, S.; Frensch, F.; Friese, R.; Giffels, M.; Gilbert, A.; Haitz, D.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Katkov, I.; Kornmayer, A.; Lobelle Pardo, P.; Maier, B.; Mildner, H.; Mozer, M. U.; Müller, T.; Müller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Röcker, S.; Roscher, F.; Schröder, M.; Sieber, G.; Simonis, H. J.; Ulrich, R.; Wagner-Kuhr, J.; Wayand, S.; Weber, M.; Weiler, T.; Williamson, S.; Wöhrmann, C.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Giakoumopoulou, V. A.; Kyriakis, A.; Loukas, D.; Psallidas, A.; Topsis-Giotis, I.; Agapitos, A.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Tziaferi, E.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Loukas, N.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Strologas, J.; Bencze, G.; Hajdu, C.; Hazi, A.; Hidas, P.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Molnar, J.; Szillasi, Z.; Bartók, M.; Makovec, A.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Choudhury, S.; Mal, P.; Mandal, K.; Sahoo, D. K.; Sahoo, N.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chawla, R.; Gupta, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, A.; Kaur, M.; Kumar, R.; Mehta, A.; Mittal, M.; Singh, J. B.; Walia, G.; Kumar, Ashok; Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Malhotra, S.; Naimuddin, M.; Nishu, N.; Ranjan, K.; Sharma, R.; Sharma, V.; Bhattacharya, S.; Chatterjee, K.; Dey, S.; Dutta, S.; Majumdar, N.; Modak, A.; Mondal, K.; Mukhopadhyay, S.; Roy, A.; Roy, D.; Roy Chowdhury, S.; Sarkar, S.; Sharan, M.; Chudasama, R.; Dutta, D.; Jha, V.; 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.; Jain, Sa.; Kole, G.; Kumar, S.; Mahakud, B.; Maity, M.; Majumder, G.; Mazumdar, K.; Mitra, S.; Mohanty, G. B.; Parida, B.; Sarkar, T.; Sur, N.; Sutar, B.; Wickramage, N.; Chauhan, S.; Dube, S.; Kapoor, A.; Kothekar, K.; Sharma, S.; Bakhshiansohi, H.; Behnamian, H.; Etesami, S. 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M.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mrenna, S.; Nahn, S.; Newman-Holmes, C.; O'Dell, V.; Pedro, K.; Prokofyev, O.; Rakness, G.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Stoynev, S.; 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.; Milenovic, P.; Mitselmakher, G.; Rank, D.; Rossin, R.; Shchutska, L.; Snowball, M.; Sperka, D.; Terentyev, N.; Thomas, L.; Wang, J.; Wang, S.; Yelton, J.; Hewamanage, S.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Ackert, A.; Adams, J. R.; Adams, T.; Askew, A.; Bein, S.; Bochenek, J.; Diamond, B.; Haas, J.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Khatiwada, A.; Prosper, H.; Weinberg, M.; Baarmand, M. M.; Bhopatkar, V.; Colafranceschi, S.; Hohlmann, M.; Kalakhety, H.; Noonan, D.; Roy, T.; 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, l. D.; Turner, P.; Varelas, N.; Wu, Z.; Zakaria, M.; 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.; Anderson, I.; Barnett, B. A.; Blumenfeld, B.; Cocoros, A.; Eminizer, N.; Fehling, D.; Feng, L.; Gritsan, A. V.; Maksimovic, P.; Osherson, M.; Roskes, J.; Sarica, U.; Swartz, M.; Xiao, M.; Xin, Y.; You, C.; Baringer, P.; Bean, A.; Bruner, C.; Kenny, R. P.; Majumder, D.; Malek, M.; Mcbrayer, W.; Murray, M.; Sanders, S.; Stringer, R.; Wang, Q.; Ivanov, A.; Kaadze, K.; Khalil, S.; Makouski, M.; Maravin, Y.; Mohammadi, A.; Saini, L. K.; Skhirtladze, N.; Toda, S.; Lange, D.; Rebassoo, F.; Wright, D.; Anelli, C.; Baden, A.; Baron, O.; Belloni, A.; Calvert, B.; Eno, S. C.; Ferraioli, C.; Gomez, J. A.; Hadley, N. J.; Jabeen, S.; Kellogg, R. G.; Kolberg, T.; Kunkle, J.; Lu, Y.; Mignerey, A. C.; Shin, Y. H.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Apyan, A.; Barbieri, R.; Baty, A.; Bi, R.; Bierwagen, K.; Brandt, S.; Busza, W.; Cali, I. A.; Demiragli, Z.; Di Matteo, L.; Gomez Ceballos, G.; Goncharov, M.; Gulhan, D.; Iiyama, Y.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Roland, C.; Roland, G.; Salfeld-Nebgen, J.; Stephans, G. S. F.; Sumorok, K.; Tatar, K.; Varma, M.; Velicanu, D.; Veverka, J.; Wang, J.; Wang, T. W.; Wyslouch, B.; Yang, M.; Zhukova, V.; Benvenuti, A. C.; Dahmes, B.; Evans, A.; Finkel, A.; Gude, A.; Hansen, P.; Kalafut, S.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bartek, R.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Knowlton, D.; Kravchenko, I.; Meier, F.; Monroy, J.; Ratnikov, F.; Siado, J. E.; Snow, G. R.; Alyari, M.; Dolen, J.; George, J.; Godshalk, A.; Harrington, C.; Iashvili, I.; Kaisen, J.; Kharchilava, A.; Kumar, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Teixeira De Lima, R.; Trocino, D.; Wang, R.-J.; Wood, D.; Zhang, J.; Bhattacharya, S.; Hahn, K. A.; Kubik, A.; Low, J. F.; Mucia, N.; Odell, N.; Pollack, B.; Schmitt, M.; Sung, K.; Trovato, M.; Velasco, M.; Dev, N.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Smith, G.; Taroni, S.; 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.; Ji, W.; Ling, T. Y.; Liu, B.; Luo, W.; Puigh, D.; Rodenburg, M.; Winer, B. L.; Wulsin, H. W.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Palmer, C.; Piroué, P.; Stickland, D.; Tully, C.; Zuranski, A.; Malik, S.; Barker, A.; Barnes, V. E.; Benedetti, D.; Bortoletto, D.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, A. W.; Jung, K.; Kumar, A.; Miller, D. H.; Neumeister, N.; Radburn-Smith, B. C.; Shi, X.; Shipsey, I.; Silvers, D.; Sun, J.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; 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.; Redjimi, R.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Chou, J. P.; Contreras-Campana, E.; Ferencek, D.; Gershtein, Y.; Halkiadakis, E.; Heindl, M.; Hidas, D.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Lath, A.; Nash, K.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Foerster, M.; 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.; Krutelyov, V.; Mueller, R.; Osipenkov, I.; Pakhotin, Y.; Patel, R.; Perloff, A.; Rose, A.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Undleeb, S.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Ni, H.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Lin, C.; Neu, C.; Sinthuprasith, T.; Sun, X.; Wang, Y.; Wolfe, E.; Wood, J.; Xia, F.; Clarke, C.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Sturdy, J.; Belknap, D. A.; Carlsmith, D.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ruggles, T.; Sarangi, T.; Savin, A.; Sharma, A.; Smith, N.; Smith, W. H.; Taylor, D.; Verwilligen, P.; Woods, N.; CMS Collaboration

2016-07-01

Inclusive jet production in pPb collisions at a nucleon-nucleon (NN) center-of-mass energy of √{s_{_NN}} =5.02 TeV is studied with the CMS detector at the LHC. A data sample corresponding to an integrated luminosity of 30.1 nb^{-1} is analyzed. The jet transverse momentum spectra are studied in seven pseudorapidity intervals covering the range -2.0<η _{CM}< 1.5 in the NN center-of-mass frame. The jet production yields at forward and backward pseudorapidity are compared and no significant asymmetry about η _{CM} = 0 is observed in the measured kinematic range. The measurements in the pPb system are compared to reference jet spectra obtained by extrapolation from previous measurements in pp collisions at √{s}=7 TeV . In all pseudorapidity ranges, nuclear modifications in inclusive jet production are found to be small, as predicted by next-to-leading order perturbative QCD calculations that incorporate nuclear effects in the parton distribution functions.

Centrality Dependence of Charged Hadron Transverse Momentum Spectra in Au+Au Collisions from √(sNN)=62.4 to 200 GeV

NASA Astrophysics Data System (ADS)

Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Busza, W.; Carroll, A.; Chai, Z.; Decowski, M. P.; García, E.; Gburek, T.; George, N.; Gulbrandsen, K.; Halliwell, C.; Hamblen, J.; Hauer, M.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Khan, N.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Reed, C.; Roland, C.; Roland, G.; Sagerer, J.; Seals, H.; Sedykh, I.; Smith, C. E.; Stankiewicz, M. A.; Steinberg, P.; Stephans, G. S.; Sukhanov, A.; Tonjes, M. B.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Vaurynovich, S. S.; Verdier, R.; Veres, G. I.; Wenger, E.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wysłouch, B.

2005-03-01

We have measured transverse momentum distributions of charged hadrons produced in Au+Au collisions at √(sNN)=62.4 GeV. The spectra are presented for transverse momenta 0.25

Dynamical freeze-out criterion in a hydrodynamical description of Au + Au collisions at √{sNN}=200 GeV and Pb + Pb collisions at √{sNN}=2760 GeV

NASA Astrophysics Data System (ADS)

Ahmad, Saeed; Holopainen, Hannu; Huovinen, Pasi

2017-05-01

In hydrodynamical modeling of ultrarelativistic heavy-ion collisions, the freeze-out is typically assumed to take place at a surface of constant temperature or energy density. A more physical approach is to assume that freeze-out takes place at a surface of constant Knudsen number. We evaluate the Knudsen number as a ratio of the expansion rate of the system to the pion-scattering rate and apply the constant Knudsen number freeze-out criterion to the ideal hydrodynamical description of heavy-ion collisions at the Relativistic Heavy Ion Collider at BNL (√{sNN}=200 GeV) and the Large Hadron Collider (√{sNN}=2760 GeV) energies. We see that once the numerical values of freeze-out temperature and freeze-out Knudsen number are chosen to produce similar pT distributions, the elliptic and triangular anisotropies are similar too, in both event-by-event and averaged initial state calculations.

QUANTUM CALCULATIONS OF ENERGETICS OF RHENIUM CLUSTERS IN TUNGSTEN

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

Setyawan, Wahyu; Nandipati, Giridhar; Roche, Kenneth J.

2015-09-22

Density functional theory was employed to explore the energetic properties of clusters up to size 2 of Re in W. While WW<111> is the most stable intrinsic dumbbell, ReW<110> is more stable than ReW<111>. However, when they are trapped by a substitutional Re (Re_s), ReW<111> becomes more stable than ReW<110>. In this case, the most stable configuration forms a ReWRe crowdion with the W atom in between the Re atoms. Simulations of a ReW[111] (dumbbell’s vector is from Re to W) approaching a Re_s along [111] indicate that the binding energy decreases from 0.83 eV at the first nearest neighbormore » (NN1) to 0.10 eV at NN3 and ~0 at NN4. In addition, while ReW<111> and ReW<110> are stable near a Re_s at NN1, the ReW<100> instantaneously rotates toward ReW<111>.« less

Neural network potential for Al-Mg-Si alloys

NASA Astrophysics Data System (ADS)

Kobayashi, Ryo; Giofré, Daniele; Junge, Till; Ceriotti, Michele; Curtin, William A.

2017-10-01

The 6000 series Al alloys, which include a few percent of Mg and Si, are important in automotive and aviation industries because of their low weight, as compared to steels, and the fact their strength can be greatly improved through engineered precipitation. To enable atomistic-level simulations of both the processing and performance of this important alloy system, a neural network (NN) potential for the ternary Al-Mg-Si has been created. Training of the NN uses an extensive database of properties computed using first-principles density functional theory, including complex precipitate phases in this alloy. The NN potential accurately reproduces most of the pure Al properties relevant to the mechanical behavior as well as heat of solution, solute-solute, and solute-vacancy interaction energies, and formation energies of small solute clusters and precipitates that are required for modeling the early stage of precipitation and mechanical strengthening. This success not only enables future detailed studies of Al-Mg-Si but also highlights the ability of NN methods to generate useful potentials in complex alloy systems.

Study of J/ψ→pp̄ and J/ψ→nn̄

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

Ablikim, M.; Achasov, M. N.; Ambrose, D. J.

2012-08-31

The decays J/ψ→pp̄ and J/ψ→nn̄ have been investigated with a sample of 225.2×10⁶ J/ψ events collected with the BESIII detector at the BEPCII e⁺e⁻ collider. The branching fractions are determined to be B(J/ψ→pp̄)=(2.112±0.004±0.031)×10⁻³ and B(J/ψ→nn̄)=(2.07±0.01±0.17)×10⁻³. Distributions of the angle θ between the proton or antineutron and the beam direction are well described by the form 1+αcos²θ, and we find α=0.595±0.012±0.015 for J/ψ→pp̄ and α=0.50±0.04±0.21 for J/ψ→nn̄. Our branching-fraction results suggest a large phase angle between the strong and electromagnetic amplitudes describing the J/ψ→NN¯¯¯ decay.

Nucleon-nucleon interactions from dispersion relations: Elastic partial waves

NASA Astrophysics Data System (ADS)

Albaladejo, M.; Oller, J. A.

2011-11-01

We consider nucleon-nucleon (NN) interactions from chiral effective field theory. In this work we restrict ourselves to the elastic NN scattering. We apply the N/D method to calculate the NN partial waves taking as input the one-pion exchange discontinuity along the left-hand cut. This discontinuity is amenable to a chiral power counting as discussed by Lacour, Oller, and Meißner [Ann. Phys. (NY)APNYA60003-491610.1016/j.aop.2010.06.012 326, 241 (2011)], with one-pion exchange as its leading order contribution. The resulting linear integral equation for a partial wave with orbital angular momentum ℓ≥2 is solved in the presence of ℓ-1 constraints, so as to guarantee the right behavior of the D- and higher partial waves near threshold. The calculated NN partial waves are based on dispersion relations and are independent of regulator. This method can also be applied to higher orders in the calculation of the discontinuity along the left-hand cut and extended to triplet coupled partial waves.

Azolla-Anabaena Relationship 1

PubMed Central

Meeks, John C.; Steinberg, Nisan A.; Enderlin, Carol S.; Joseph, Cecillia M.; Peters, Gerald A.

1987-01-01

The major radioactive products of the fixation of [13N]N2 by Azolla caroliniana Willd.-Anabaena azollae Stras. were ammonium, glutamine, and glutamate, plus a small amount of alanine. Ammonium accounted for 70 and 32% of the total radioactivity recovered after fixation for 1 and 10 minutes, respectively. The presence of a substantial pool of [13N]N2-derived 13NH4+ after longer incubation periods was attributed to the spatial separation between the site of N2-fixation (Anabaena) and a second, major site of assimilation (Azolla). Initially, glutamine was the most highly radioactive organic product formed from [13N]N2, but after 10 minutes of fixation glutamate had 1.5 times more radiolabel than glutamine. These kinetics of radiolabeling, along with the effects of inhibitors of glutamine synthetase and glutamate synthase on assimilation of exogenous and [13N]N2-derived 13NH4+, indicate that ammonium assimilation occurred by the glutamate synthase cycle and that glutamate dehydrogenase played little or no role in the synthesis of glutamate by Azolla-Anabaena. PMID:16665538

Anion induced conformational preference of Cα NN motif residues in functional proteins.

PubMed

Patra, Piya; Ghosh, Mahua; Banerjee, Raja; Chakrabarti, Jaydeb

2017-12-01

Among different ligand binding motifs, anion binding C α NN motif consisting of peptide backbone atoms of three consecutive residues are observed to be important for recognition of free anions, like sulphate or biphosphate and participate in different key functions. Here we study the interaction of sulphate and biphosphate with C α NN motif present in different proteins. Instead of total protein, a peptide fragment has been studied keeping C α NN motif flanked in between other residues. We use classical force field based molecular dynamics simulations to understand the stability of this motif. Our data indicate fluctuations in conformational preferences of the motif residues in absence of the anion. The anion gives stability to one of these conformations. However, the anion induced conformational preferences are highly sequence dependent and specific to the type of anion. In particular, the polar residues are more favourable compared to the other residues for recognising the anion. © 2017 Wiley Periodicals, Inc.

Atomic force microscopy-guided fractionation reveals the influence of cranberry phytochemicals on adhesion of Escherichia coli.

PubMed

Gupta, Prachi; Song, Biqin; Neto, Catherine; Camesano, Terri A

2016-06-15

Cranberry juice has been long used to prevent infections because of its effect on the adhesion of the bacteria to the host surface. Proanthocyanidins (PACs) comprise of one of the major classes of phytochemicals found in cranberry, which have been extensively studied and found effective in combating adhesion of pathogenic bacteria. The role of other cranberry constituents in impacting bacterial adhesion haven't been studied very well. In this study, cranberry juice fractions were prepared, characterized and tested for their effect on the surface adhesion of the pathogenic clinical bacterial strain E. coli B78 and non-pathogenic control E. coli HB101. The preparations tested included crude cranberry juice extract (CCE); three fractions containing flavonoid classes including proanthocyanidins, anthocyanins and flavonols; selected sub-fractions, and commercially available flavonol glycoside, quercetin-3-O-galactoside. Atomic force microscopy (AFM) was used to quantify the adhesion forces between the bacterial surface and the AFM probe after the treatment with the cranberry fractions. Adhesion forces of the non-pathogenic, non fimbriated lab strain HB101 are small (average force 0.19 nN) and do not change with cranberry treatments, whereas the adhesion forces of the pathogenic, Dr adhesion E. coli strain B78 (average force of 0.42 nN) show a significant decrease when treated with cranberry juice extract or fractions (average force of 0.31 nN, 0.37 nN and 0.39 nN with CCE, Fraction 7 and Fraction 4 respectively). In particular, the fractions that contained flavonols in addition to PACs were more efficient at lowering the force of adhesion (average force of 0.31 nN-0.18 nN between different sub-fractions containing flavonols and PACs). The sub-fractions containing flavonol glycosides (from juice, fruit and commercial quercetin) all resulted in reduced adhesion of the pathogenic bacteria to the model probe. This strongly suggests the anti adhesive role of other classes of cranberry compounds in conjunction with already known PACs and may have implications for development of alternative anti bacterial treatments.

Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study.

PubMed

Briceño, Javier; Cruz-Ramírez, Manuel; Prieto, Martín; Navasa, Miguel; Ortiz de Urbina, Jorge; Orti, Rafael; Gómez-Bravo, Miguel-Ángel; Otero, Alejandra; Varo, Evaristo; Tomé, Santiago; Clemente, Gerardo; Bañares, Rafael; Bárcena, Rafael; Cuervas-Mons, Valentín; Solórzano, Guillermo; Vinaixa, Carmen; Rubín, Angel; Colmenero, Jordi; Valdivieso, Andrés; Ciria, Rubén; Hervás-Martínez, César; de la Mata, Manuel

2014-11-01

There is an increasing discrepancy between the number of potential liver graft recipients and the number of organs available. Organ allocation should follow the concept of benefit of survival, avoiding human-innate subjectivity. The aim of this study is to use artificial-neural-networks (ANNs) for donor-recipient (D-R) matching in liver transplantation (LT) and to compare its accuracy with validated scores (MELD, D-MELD, DRI, P-SOFT, SOFT, and BAR) of graft survival. 64 donor and recipient variables from a set of 1003 LTs from a multicenter study including 11 Spanish centres were included. For each D-R pair, common statistics (simple and multiple regression models) and ANN formulae for two non-complementary probability-models of 3-month graft-survival and -loss were calculated: a positive-survival (NN-CCR) and a negative-loss (NN-MS) model. The NN models were obtained by using the Neural Net Evolutionary Programming (NNEP) algorithm. Additionally, receiver-operating-curves (ROC) were performed to validate ANNs against other scores. Optimal results for NN-CCR and NN-MS models were obtained, with the best performance in predicting the probability of graft-survival (90.79%) and -loss (71.42%) for each D-R pair, significantly improving results from multiple regressions. ROC curves for 3-months graft-survival and -loss predictions were significantly more accurate for ANN than for other scores in both NN-CCR (AUROC-ANN=0.80 vs. -MELD=0.50; -D-MELD=0.54; -P-SOFT=0.54; -SOFT=0.55; -BAR=0.67 and -DRI=0.42) and NN-MS (AUROC-ANN=0.82 vs. -MELD=0.41; -D-MELD=0.47; -P-SOFT=0.43; -SOFT=0.57, -BAR=0.61 and -DRI=0.48). ANNs may be considered a powerful decision-making technology for this dataset, optimizing the principles of justice, efficiency and equity. This may be a useful tool for predicting the 3-month outcome and a potential research area for future D-R matching models. Copyright © 2014 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Proton-Nucleus Elastic Cross Sections Using Two-Body In-Medium Scattering Amplitudes

NASA Technical Reports Server (NTRS)

Tripathi, R. K.; Wilson, John W.; Cucinotta, Francis A.

2001-01-01

Recently, a method was developed of extracting nucleon-nucleon (NN) cross sections in the medium directly from experiment. The in-medium NN cross sections form the basic ingredients of several heavy-ion scattering approaches including the coupled-channel approach developed at the Langley Research Center. The ratio of the real to the imaginary part of the two-body scattering amplitude in the medium was investigated. These ratios are used in combination with the in-medium NN cross sections to calculate elastic proton-nucleus cross sections. The agreement is excellent with the available experimental data. These cross sections are needed for the radiation risk assessment of space missions.

Using neural networks to represent potential surfaces as sums of products.

PubMed

Manzhos, Sergei; Carrington, Tucker

2006-11-21

By using exponential activation functions with a neural network (NN) method we show that it is possible to fit potentials to a sum-of-products form. The sum-of-products form is desirable because it reduces the cost of doing the quadratures required for quantum dynamics calculations. It also greatly facilitates the use of the multiconfiguration time dependent Hartree method. Unlike potfit product representation algorithm, the new NN approach does not require using a grid of points. It also produces sum-of-products potentials with fewer terms. As the number of dimensions is increased, we expect the advantages of the exponential NN idea to become more significant.

Resonant Versus Anti-Resonant Tunneling at Carbon Nanotube A-B-A Heterostructures

NASA Technical Reports Server (NTRS)

Mingo, N.; Yang, Liu; Han, Jie; Anantram, M. P.

2001-01-01

Narrow antiresonances going to zero transmission are found to occur for general (2n,0)(n,n)(2n,0) carbon nanotube heterostructures, whereas the complementary configuration, (n,n)(2n,0)(n,n), displays simple resonant tunneling behaviour. We compute examples for different cases, and give a simple explanation for the appearance of antiresonances in one case but not in the other. Conditions and ranges for the occurrence of these different behaviors are stated. The phenomenon of anti-resonant tunneling, which has passed unnoticed in previous studies of nanotube heterostructures, adds up to the rich set of behaviors available to nanotube based quantum effect devices.

Genetic algorithms applied to the scheduling of the Hubble Space Telescope

NASA Technical Reports Server (NTRS)

Sponsler, Jeffrey L.

1989-01-01

A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better.

Nearest unlike neighbor (NUN): an aid to decision confidence estimation

NASA Astrophysics Data System (ADS)

Dasarathy, Belur V.

1995-09-01

The concept of nearest unlike neighbor (NUN), proposed and explored previously in the design of nearest neighbor (NN) based decision systems, is further exploited in this study to develop a measure of confidence in the decisions made by NN-based decision systems. This measure of confidence, on the basis of comparison with a user-defined threshold, may be used to determine the acceptability of the decision provided by the NN-based decision system. The concepts, associated methodology, and some illustrative numerical examples using the now classical Iris data to bring out the ease of implementation and effectiveness of the proposed innovations are presented.

Effects of smoking cessation on heart rate variability among long-term male smokers.

PubMed

Harte, Christopher B; Meston, Cindy M

2014-04-01

Cigarette smoking has been shown to adversely affect heart rate variability (HRV), suggesting dysregulation of cardiac autonomic function. Conversely, smoking cessation is posited to improve cardiac regulation. The aim of the present study was to examine the effects of smoking cessation on HRV among a community sample of chronic smokers. Sixty-two healthy male smokers enrolled in an 8-week smoking cessation program involving a nicotine transdermal patch treatment. Participants were assessed at baseline (while smoking regularly), at mid-treatment (while using a high-dose patch), and at follow-up, 4 weeks after patch discontinuation. Both time-domain (standard deviation of normal-to-normal (NN) intervals (SDNN), square root of the mean squared difference of successive NN intervals (RMSSD), and percent of NN intervals for which successive heartbeat intervals differed by at least 50 ms (pNN50)) and frequency-domain (low frequency (LF), high frequency (HF), LF/HF ratio) parameters of HRV were assessed at each visit. Successful quitters (n = 20), compared to those who relapsed (n = 42), displayed significantly higher SDNN, RMSSD, pNN50, LF, and HF at follow-up, when both nicotine and smoke free. Smoking cessation significantly enhances HRV in chronic male smokers, indicating improved autonomic modulation of the heart. Results suggest that these findings may be primarily attributable to nicotine discontinuation rather than tobacco smoke discontinuation alone.

Exploring the source of the mid-level hump for intensity discrimination in quiet and the effects of noisea)

PubMed Central

Roverud, Elin; Strickland, Elizabeth A.

2015-01-01

Intensity discrimination Weber fractions (WFs) measured for short, high-frequency tones in quiet are larger at mid levels than at lower or higher levels. The source of this “mid-level hump” is a matter of debate. One theory is that the mid-level hump reflects basilar-membrane compression, and that WFs decrease at higher levels due to spread-of-excitation cues. To test this theory, Experiment 1 measured the mid-level hump and growth-of-masking functions to estimate the basilar membrane input/output (I/O) function in the same listeners. Results showed the initial rise in WFs could be accounted for by the change in I/O function slope, but there was additional unexplained variability in WFs. Previously, Plack [(1998). J. Acoust. Soc. Am. 103(5), 2530–2538] showed that long-duration notched noise (NN) presented with the tone reduced the mid-level hump even with a temporal gap in the NN. Plack concluded the results were consistent with central profile analysis. However, simultaneous, forward, and backward NN were not examined separately, which may independently test peripheral and central mechanisms of the NN. Experiment 2 measured WFs at the mid-level hump in the presence of NN and narrowband noise of different durations and temporal positions relative to the tone. Results varied across subjects, but were consistent with more peripheral mechanisms. PMID:25786945

Computerized lateral endoscopic approach to invertebral bodies

NASA Astrophysics Data System (ADS)

Abbasi, Hamid R.; Hariri, Sanaz; Kim, Daniel; Shahidi, Ramin; Steinberg, Gary

2001-05-01

Spinal surgery is often necessary to ease back pain symptoms. Neuronavigation (NN) allows the surgeon to localize the position of his instruments in 3D using pre- operative CT scans registered to intra-operative marker positions in cranial surgeries. However, this tool is unavailable in spinal surgeries for a variety of reasons. For example, because of the spine's many degrees of freedom and flexibility, the geometric relationship of the skin to the internal spinal anatomy is not fixed. Guided by the currently available imperfect 2D images, it is difficult for the surgeon to correct a patient's spinal anomaly; thus surgical relief of back pain is often only temporary. The Image Guidance Laborator's (IGL) goal is to combine the direct optical control of traditional endoscopy with the 3D orientation of NN. This powerful tool requires registration of the patient's anatomy to the surgical navigation system using internal landmarks rather than skin markers. Pre- operative CT scans matched with intraoperative fluoroscopic images can overcome the problem of spinal movement in NN registration. The combination of endoscopy with fluoroscopic registration of vertebral bodies in a NN system provides a 3D intra-operative navigational system for spinal neurosurgery to visualize the internal surgical environment from any orientation in real time. The accuracy of this system integration is being evaluated by assessing the success of nucleotomies and marker implantations guided by NN-registered endoscopy.

Effects of ice massage of the head and spine on heart rate variability in healthy volunteers.

PubMed

Mooventhan, A; Nivethitha, L

2016-07-01

Ice massage (IM) is one of the treatment procedures used in hydrotherapy. Though its various physiological/therapeutic effects have been reported, effects of IM of the head and spine on heart rate variability (HRV) have not been studied. Thus, this study evaluated the effects of IM of the head and spine on HRV in healthy volunteers. Thirty subjects were randomly divided into 3 sessions: (1) IM, (2) tap water massage (TWM) and (3) prone rest (PR). Heart rate (HR) and HRV were assessed before and after each intervention session. A significant increase in the mean of the intervals between adjacent QRS complexes or the instantaneous HR (RRI), square root of mean of sum of squares of differences between adjacent normal to normal (NN) intervals (RMSSD), number of interval differences of successive NN intervals greater than 50 milliseconds (NN50), proportion derived by dividing NN50 by total number of NN intervals along with significant reduction in HR after IM session; significant increase in RRI along with significant reduction in HR after TWM, and a significant increase only in RMSSD after PR were observed. However, there was no significant difference between the sessions. Results of this study suggest that 20 min of IM of the head and spine is effective in reducing HR and improving HRV through vagal dominance in healthy volunteers.

Measurement of prompt D -meson production in p – Pb collisions at s N N = 5.02 TeV

DOE PAGES

Abelev, B.; Adam, J.; Adamová, D.; ...

2014-12-04

The p T-differential production cross sections of the prompt charmed mesons D 0, D +, D *+, and D + s and their charge conjugate in the rapidity interval –0.96 < y cms < 0.04 were measured in p–Pb collisions at a center-of-mass energy √s NN = 5.02 TeV with the ALICE detector at the LHC. The nuclear modification factor R pPb, quantifying the D-meson yield in p–Pb collisions relative to the yield in pp collisions scaled by the number of binary nucleon-nucleon collisions, is compatible within the 15%–20% uncertainties with unity in the transverse momentum interval 1 < pmore » T < 24 GeV/c. No significant difference among the R pPb of the four D-meson species is observed. The results are described within uncertainties by theoretical calculations that include initial-state effects. In conclusion, the measurement adds experimental evidence that the modification of the momentum spectrum of D mesons observed in Pb-Pb collisions with respect to pp collisions is due to strong final-state effects induced by hot partonic matter.« less

Proton-Nucleus Total Cross Sections in Coupled-Channel Approach

NASA Technical Reports Server (NTRS)

Tripathi, R. K.; Wilson, John W.; Cucinotta, Francis A.

2000-01-01

Recently, nucleon-nucleon (N-N) cross sections in the medium have been extracted directly from experiment. The in-medium N-N cross sections form the basic ingredients of several heavy-ion scattering approaches including the coupled-channel approach developed at the Langley Research Center. In the present study the ratio of the real to the imaginary part of the two-body scattering amplitude in the medium was investigated. These ratios are used in combination with the in-medium N-N cross sections to calculate total proton-nucleus cross sections. The agreement is excellent with the available experimental data. These cross sections are needed for the radiation risk assessment of space missions.

Bankruptcy prediction for credit risk using neural networks: a survey and new results.

PubMed

Atiya, A F

2001-01-01

The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast).

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

Bayegan, S.; Shalchi, M. A.; Hadizadeh, M. R.

The recently developed chiral nucleon-nucleon (NN) potential by E. Epelbaum, W. Gloeckle, and Ulf-G. Meissner, Nucl. Phys. A747, 362 (2005) has been employed to study the two-nucleon bound and scattering states. Chiral NN potential up to next-to-next-to-next-to leading order (N{sup 3}LO) is used to calculate the np differential cross section and deuteron binding energy in a realistic three dimensional approach. The obtained results based on this helicity representation are compared to the standard partial wave (PW) results. This comparison shows that the 3D approach provides the same accuracy in the description of NN observables and the results are in closemore » agreement with available experimental data.« less

Centrality and pseudorapidity dependence of elliptic flow for charged hadrons in Au+Au collisions at √(sNN)=200 GeV

NASA Astrophysics Data System (ADS)

Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Decowski, M. P.; García, E.; George, N. K.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Heintzelman, G. A.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; McLeod, D.; Mignerey, A. C.; Nguyen, M.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sagerer, J.; Sarin, P.; Sawicki, P.; Skulski, W.; Steinberg, P.; Stephans, G. S.; Sukhanov, A.; Tang, J.-L.; Tonjes, M. B.; Trzupek, A.; Vale, C. M.; Nieuwenhuizen, G. J.; Verdier, R.; Veres, G. I.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2005-11-01

This Rapid Communication describes the measurement of elliptic flow for charged particles in Au+Au collisions at √(sNN)=200 GeV using the PHOBOS detector at the Relativistic Heavy Ion Collider. The measured azimuthal anisotropy is presented over a wide range of pseudorapidity for three broad collision centrality classes for the first time at this energy. Two distinct methods of extracting the flow signal were used to reduce systematic uncertainties. The elliptic flow falls sharply with increasing |η| at 200 GeV for all the centralities studied, as observed for minimum-bias collisions at √(sNN)=130 GeV.

Chiral symmetry and the nucleon-nucleon interaction

DOE PAGES

Machleidt, Ruprecht

2016-04-20

We review how nuclear forces emerge from low-energy quantum chromodynamics (QCD) via chiral effective field theory (EFT). During the past two decades, this approach has evolved into a powerful tool to derive nuclear two- and many-body forces in a systematic and model-independent way. We then focus on the nucleon-nucleon (NN) interaction and show in detail how, governed by chiral symmetry, the long- and intermediate-range of the NN potential builds up order by order. We proceed up to sixth order in small momenta, where convergence is achieved. Lastly, the final result allows for a full assessment of the validity of themore » chiral EFT approach to the NN interaction.« less

21 CFR 172.829 - Neotame.

Code of Federal Regulations, 2011 CFR

2011-04-01

... percent on a dry basis. (2) Free dipeptide acid (N-[N-(3,3-dimethylbutyl)-L-α-aspartyl]-L-phenylalanine... Multipurpose Additives § 172.829 Neotame. (a) Neotame is the chemical N-[N-(3,3-dimethylbutyl)-L-α-aspartyl]-L-phenylalanine-1-methyl ester (CAS Reg. No. 165450-17-9). (b) Neotame meets the following specifications when it...

21 CFR 172.829 - Neotame.

Code of Federal Regulations, 2013 CFR

2013-04-01

... percent on a dry basis. (2) Free dipeptide acid (N-[N-(3,3-dimethylbutyl)-L-α-aspartyl]-L-phenylalanine... Multipurpose Additives § 172.829 Neotame. (a) Neotame is the chemical N-[N-(3,3-dimethylbutyl)-L-α-aspartyl]-L-phenylalanine-1-methyl ester (CAS Reg. No. 165450-17-9). (b) Neotame meets the following specifications when it...

21 CFR 172.829 - Neotame.

Code of Federal Regulations, 2010 CFR

2010-04-01

... percent on a dry basis. (2) Free dipeptide acid (N-[N-(3,3-dimethylbutyl)-L-α-aspartyl]-L-phenylalanine... Multipurpose Additives § 172.829 Neotame. (a) Neotame is the chemical N-[N-(3,3-dimethylbutyl)-L-α-aspartyl]-L-phenylalanine-1-methyl ester (CAS Reg. No. 165450-17-9). (b) Neotame meets the following specifications when it...

21 CFR 172.829 - Neotame.

Code of Federal Regulations, 2012 CFR

2012-04-01

... percent on a dry basis. (2) Free dipeptide acid (N-[N-(3,3-dimethylbutyl)-L-α-aspartyl]-L-phenylalanine... Multipurpose Additives § 172.829 Neotame. (a) Neotame is the chemical N-[N-(3,3-dimethylbutyl)-L-α-aspartyl]-L-phenylalanine-1-methyl ester (CAS Reg. No. 165450-17-9). (b) Neotame meets the following specifications when it...

40 CFR Table 2 to Subpart F of... - Organic Hazardous Air Pollutants

Code of Federal Regulations, 2010 CFR

2010-07-01

... (N,N-) 121697 Diethyl sulfate 64675 Dimethylbenzidine (3,3″-) 119937 Dimethylformamide (N,N-) 68122... Hexachlorobenzene 118741 Hexachlorobutadiene 87683 Hexachloroethane 67721 Hexane 110543 Hydroquinone 123319... ethylene glycol, diethylene glycol, and triethylene glycol R-(OCH2 CH2n-OR where: n=1, 2, or 3; R=alkyl or...

40 CFR Table 2 to Subpart F of... - Organic Hazardous Air Pollutants

Code of Federal Regulations, 2012 CFR

2012-07-01

... (N,N-) 121697 Diethyl sulfate 64675 Dimethylbenzidine (3,3′-) 119937 Dimethylformamide (N,N-) 68122... Hexachlorobenzene 118741 Hexachlorobutadiene 87683 Hexachloroethane 67721 Hexane 110543 Hydroquinone 123319... ethylene glycol, diethylene glycol, and triethylene glycol R-(OCH2 CH2n-OR where: n=1, 2, or 3; R=alkyl or...

Updated analysis of NN elastic scattering to 3 GeV

NASA Astrophysics Data System (ADS)

Arndt, R. A.; Briscoe, W. J.; Strakovsky, I. I.; Workman, R. L.

2007-08-01

A partial-wave analysis of NN elastic scattering data has been updated to include a number of recent measurements. Experiments carried out at the Cooler Synchrotron (COSY) by the EDDA Collaboration have had a significant impact above 1 GeV. Results are discussed in terms of the partial-wave and direct-reconstruction amplitudes.

Predicting lettuce canopy photosynthesis with statistical and neural network models

NASA Technical Reports Server (NTRS)

Frick, J.; Precetti, C.; Mitchell, C. A.

1998-01-01

An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future).

Development of a quartz tuning-fork-based force sensor for measurements in the tens of nanoNewton force range during nanomanipulation experiments

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

Oiko, V. T. A., E-mail: oiko@ifi.unicamp.br; Rodrigues, V.; Ugarte, D.

2014-03-15

Understanding the mechanical properties of nanoscale systems requires new experimental and theoretical tools. In particular, force sensors compatible with nanomechanical testing experiments and with sensitivity in the nN range are required. Here, we report the development and testing of a tuning-fork-based force sensor for in situ nanomanipulation experiments inside a scanning electron microscope. The sensor uses a very simple design for the electronics and it allows the direct and quantitative force measurement in the 1–100 nN force range. The sensor response is initially calibrated against a nN range force standard, as, for example, a calibrated Atomic Force Microscopy cantilever; subsequently,more » applied force values can be directly derived using only the electric signals generated by the tuning fork. Using a homemade nanomanipulator, the quantitative force sensor has been used to analyze the mechanical deformation of multi-walled carbon nanotube bundles, where we analyzed forces in the 5–40 nN range, measured with an error bar of a few nN.« less

Measurements of directed, elliptic, and triangular flow in Cu + Au collisions at s NN = 200 GeV

DOE PAGES

Adare, A.; Aidala, C.; Ajitanand, N. N.; ...

2016-11-28

In this paper, measurements of anisotropic flow Fourier coefficients (v n) for inclusive charged particles and identified hadrons π ± ,K ±, p, andmore » $$\\overline{p}$$ produced at midrapidity in Cu + Au collisions at √sNN = 200 GeV are presented. The data were collected in 2012 by the PHENIX experiment at the Relativistic Heavy-Ion Collider (RHIC). The particle azimuthal distributions with respect to different-order symmetry planes Ψ n ,for n = 1, 2, and 3 are studied as a function of transverse momentum p T over a broad range of collision centralities. Mass ordering, as expected from hydrodynamic flow, is observed for all three harmonics. The charged-particle results are compared with hydrodynamical and transport model calculations. In addition, we also compare these Cu + Au results with those in Cu + Cu and Au + Au collisions at the same √sNN and find that the v 2 and v 3, as a function of transverse momentum, follow a common scaling with 1/(ε nN 1/3 part).« less

All Prime Contract Awards by State or Country, Place, and Contractor, FY 88. Part 9. (Athens, Maine-Woodbine, Maryland).

DTIC Science & Technology

1988-01-01

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GeNN: a code generation framework for accelerated brain simulations

NASA Astrophysics Data System (ADS)

Yavuz, Esin; Turner, James; Nowotny, Thomas

2016-01-01

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

GeNN: a code generation framework for accelerated brain simulations.

PubMed

Yavuz, Esin; Turner, James; Nowotny, Thomas

2016-01-07

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

GeNN: a code generation framework for accelerated brain simulations

PubMed Central

Yavuz, Esin; Turner, James; Nowotny, Thomas

2016-01-01

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/. PMID:26740369

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation.

PubMed

Vargas-Meléndez, Leandro; Boada, Beatriz L; Boada, María Jesús L; Gauchía, Antonio; Díaz, Vicente

2016-08-31

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

Rationally designed n-n heterojunction with highly efficient solar hydrogen evolution.

PubMed

Xu, Miao; Ye, Tiannan; Dai, Fang; Yang, Jindi; Shen, Jingmei; He, Qingquan; Chen, Wenlong; Liang, Na; Zai, Jiantao; Qian, Xuefeng

2015-04-13

In most of the reported n-n heterojunction photocatalysts, both the conduction and valence bands of one semiconductor are more negative than those of the other semiconductor. In this work, we designed and synthesized a novel n-n heterojunction photocatalyst, namely CdS-ZnWO4 heterojunctions, in which ZnWO4 has more negative conduction band and more positive valence band than those of CdS. The hydrogen evolution rate of CdS-30 mol %-ZnWO4 reaches 31.46 mmol h(-1)  g(-1) under visible light, which is approximately 8 and 755 times higher than that of pure CdS and ZnWO4 under similar conditions, respectively. The location of the surface active sites is researched and a plausible mechanism of performance enhancement by the tuning of the structure is proposed based on the photoelectrochemical characterization. The results illustrate that this kind of nonconventional n-n heterojunctions is also suitable and highly efficient for solar hydrogen evolution. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Accelerator diagnosis and control by Neural Nets

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

Spencer, J.E.

1989-01-01

Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach tomore » 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.« less

Measurement of inclusive jet production and nuclear modifications in pPb collisions at $$\\sqrt{s_{_\\mathrm {NN}}} =5.02\\,\\mathrm{TeV} $$

DOE PAGES

Khachatryan, Vardan

2016-07-04

In this study, inclusive jet production in pPb collisions at a nucleon–nucleon (NN) center-of-mass energy of √ sNN = 5.02 TeV is studied with the CMS detector at the LHC. A data sample corresponding to an integrated luminosity of 30.1 nb -1 is analyzed. The jet transverse momentum spectra are studied in seven pseudorapidity intervals covering the range -2.0 < η CM < 1.5 in the NN center-of-mass frame. The jet production yields at forward and backward pseudorapidity are compared and no significant asymmetry about ηCM=0 is observed in the measured kinematic range. The measurements in the pPb system aremore » compared to reference jet spectra obtained by extrapolation from previous measurements in pp collisions at √s = 7 TeV. In all pseudorapidity ranges, nuclear modifications in inclusive jet production are found to be small, as predicted by next-to-leading order perturbative QCD calculations that incorporate nuclear effects in the parton distribution functions.« less

A neural network model for predicting weighted mean temperature

NASA Astrophysics Data System (ADS)

Ding, Maohua

2018-02-01

Water vapor is an important element of the Earth's atmosphere, and most of it concentrates at the bottom of the troposphere. Knowledge of the water vapor measured by Global Navigation Satellite Systems (GNSS) is an important direction of GNSS research. In particular, when the zenith wet delay is converted to precipitable water vapor, the weighted mean temperature T_m is a variable parameter to be determined in this conversion. The purpose of the study is getting a more accurate T_m model for global users by a combination of two different characteristics of T_m (i.e., the T_m seasonal variations and the relationships between T_m and surface meteorological elements). The modeling process was carried out by using the neural network technology. A multilayer feedforward neural network model (the NN) was established. The NN model is used with measurements of only surface temperature T_S . The NN was validated and compared with four other published global T_m models. The results show that the NN performed better than any of the four compared models on the global scale.

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

PubMed Central

Vargas-Meléndez, Leandro; Boada, Beatriz L.; Boada, María Jesús L.; Gauchía, Antonio; Díaz, Vicente

2016-01-01

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors’ estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator. PMID:27589763

Characterization and Selection of 3-(1-Naphthoyl)-Indole Derivative-Specific Alpaca VHH Antibodies Using a Phage Display Library.

PubMed

Nakayama, Hiroshi; Murakami, Akikazu; Yoshida, Maiko; Muraoka, Jin; Wakai, Junko; Kenjyou, Noriko; Ito, Yuji

2016-08-01

A new alpaca VHH antibody library against 3-(1-naphthoyl)-indole derivatives was developed from alpaca immunized with 7-(3-(1-naphthoyl)-1H-indol-1-yl)-heptanoic acid-keyhole limpet hemocyanin (Hep-KLH) protein conjugates as the immunogen. From this library, two 3-(1-naphthoyl)-indole derivative-specific clones, named NN01 and NN02, were isolated using biopanning technology. The binding specificity of these clones was confirmed using a competitive enzyme-linked immunosorbent assay (c-ELISA). Based on the results of c-ELISA, a median inhibitory concentration (IC50) of these two VHH antibodies, NN01 and NN02, in the case of 7-(3-(1-naphthoyl)-1H-indol-1-yl)-heptanoic acid (Hep; one of 3-(1-naphthoyl)-indole derivatives) as an inhibitor exhibited an approximate 3 × 10(-7) M and 6 × 10(-7) M, respectively. Thus, VHH antibodies produced in this study could be considered a useful tool for the detection of 3-(1-naphthoyl)-indole derivatives.

Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

NASA Astrophysics Data System (ADS)

Li, Wenwen; Ando, Yasunobu; Minamitani, Emi; Watanabe, Satoshi

2017-12-01

To clarify atomic diffusion in amorphous materials, which is important in novel information and energy devices, theoretical methods having both reliability and computational speed are eagerly anticipated. In the present study, we applied neural network (NN) potentials, a recently developed machine learning technique, to the study of atom diffusion in amorphous materials, using Li3PO4 as a benchmark material. The NN potential was used together with the nudged elastic band, kinetic Monte Carlo, and molecular dynamics methods to characterize Li vacancy diffusion behavior in the amorphous Li3PO4 model. By comparing these results with corresponding DFT calculations, we found that the average error of the NN potential is 0.048 eV in calculating energy barriers of diffusion paths, and 0.041 eV in diffusion activation energy. Moreover, the diffusion coefficients obtained from molecular dynamics are always consistent with those from ab initio molecular dynamics simulation, while the computation speed of the NN potential is 3-4 orders of magnitude faster than DFT. Lastly, the structure of amorphous Li3PO4 and the ion transport properties in it were studied with the NN potential using a large supercell model containing more than 1000 atoms. The formation of P2O7 units was observed, which is consistent with the experimental characterization. The Li diffusion activation energy was estimated to be 0.55 eV, which agrees well with the experimental measurements.

Machine learning search for variable stars

NASA Astrophysics Data System (ADS)

Pashchenko, Ilya N.; Sokolovsky, Kirill V.; Gavras, Panagiotis

2018-04-01

Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SVM), k Nearest Neighbours (kNN), Neural Nets (NN), Random Forests (RF), and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of Optical Gravitational Lensing Experiment phase two (OGLE-II) Large Magellanic Cloud (LMC) photometry (30 265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31 798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SVM, and RF show a higher efficiency compared to LR and kNN.

Effects of analogues of ethanolamine and choline on phospholipid metabolism in rat hepatocytes

PubMed Central

Åkesson, Björn

1977-01-01

1. Analogues of ethanolamine and choline were incubated with different labelled precursors of phospholipids and isolated hepatocytes and the effects on phospholipid synthesis were studied. 2. 2-Aminopropan-1-ol and 2-aminobutan-1-ol were the most efficient inhibitors of [14C]ethanolamine incorporation into phospholipids, whereas the incorporation of [3H]choline was inhibited most extensively by NN-diethylethanolamine and NN-dimethylethanolamine. 3. When the analogues were incubated with [3H]glycerol and hepatocytes, the appearance of 3H in unnatural phospholipids indicated that they were incorporated, at least in part, via CDP-derivatives. The distribution of [3H]glycerol among molecular species of phospholipids containing 2-aminopropan-1-ol and 1-aminopropan-2-ol was the same as in phosphatidylethanolamine. In other phospholipid analogues the distribution of 3H was more similar to that in phosphatidylcholine. 4. NN-Diethylethanolamine stimulated both the conversion of phosphatidylethanolamine into phosphatidylcholine and the incorporation of [Me-14C]methionine into phospholipids. Other N-alkyl- or NN-dialkyl-ethanolamines also stimulated [14C]methionine incorporation, but inhibited the conversion of phosphatidylethanolamine into phosphatidylcholine. This indicates that phosphatidyl-NN-diethylethanolamine is a poor methyl acceptor, in contrast with other N-alkylated phosphatidylethanolamines. 5. These results on the regulation of phospholipid metabolism in intact cells are discussed with respect to the possible control points. They also provide guidelines for future experiments on the manipulation of phospholipid polar-headgroup composition in primary cultures of hepatocytes. PMID:606244

Effects of analogles of ethanolamine and choline on phospholipid metabolism in rat hepatocytes.

PubMed

Akesson, B

1977-12-15

1. Analogues of ethanolamine and choline were incubated with different labelled precursors of phospholipids and isolated hepatocytes and the effects on phospholipid synthesis were studied. 2. 2-Aminopropan-1-ol and 2-aminobutan-1-ol were the most efficient inhibitors of [(14)C]ethanolamine incorporation into phospholipids, whereas the incorporation of [(3)H]choline was inhibited most extensively by NN-diethylethanolamine and NN-dimethylethanolamine. 3. When the analogues were incubated with [(3)H]glycerol and hepatocytes, the appearance of (3)H in unnatural phospholipids indicated that they were incorporated, at least in part, via CDP-derivatives. The distribution of [(3)H]glycerol among molecular species of phospholipids containing 2-aminopropan-1-ol and 1-aminopropan-2-ol was the same as in phosphatidylethanolamine. In other phospholipid analogues the distribution of (3)H was more similar to that in phosphatidylcholine. 4. NN-Diethylethanolamine stimulated both the conversion of phosphatidylethanolamine into phosphatidylcholine and the incorporation of [Me-(14)C]methionine into phospholipids. Other N-alkyl- or NN-dialkyl-ethanolamines also stimulated [(14)C]methionine incorporation, but inhibited the conversion of phosphatidylethanolamine into phosphatidylcholine. This indicates that phosphatidyl-NN-diethylethanolamine is a poor methyl acceptor, in contrast with other N-alkylated phosphatidylethanolamines. 5. These results on the regulation of phospholipid metabolism in intact cells are discussed with respect to the possible control points. They also provide guidelines for future experiments on the manipulation of phospholipid polar-headgroup composition in primary cultures of hepatocytes.

Dietary sodium manipulation during critical periods in development sensitize adult offspring to amphetamines

PubMed Central

McBride, Shawna M.; Culver, Bruce; Flynn, Francis W.

2008-01-01

This study examined critical periods in development to determine when offspring were most susceptible to dietary sodium manipulation leading to amphetamine sensitization. Wistar dams (n = 6–8/group) were fed chow containing low (0.12% NaCl; LN), normal (1% NaCl; NN), or high sodium (4% NaCl; HN) during the prenatal or early postnatal period (birth to 5 wk). Offspring were fed normal chow thereafter until testing at 6 mo. Body weight (BW), blood pressure (BP), fluid intake, salt preference, response to amphetamine, open field behavior, plasma adrenocorticotropin hormone (ACTH), plasma corticosterone (Cort), and adrenal gland weight were measured. BW was similar for all offspring. Offspring from the prenatal and postnatal HN group had increased BP, NaCl intake, and salt preference and decreased water intake relative to NN offspring. Prenatal HN offspring had greater BP than postnatal HN offspring. In response to amphetamine, both prenatal and postnatal LN and HN offspring had increased locomotor behavior compared with NN offspring. In a novel open field environment, locomotion was also increased in prenatal and postnatal LN and HN offspring compared with NN offspring. ACTH and Cort levels 30 min after restraint stress and adrenal gland weight measurement were greater in LN and HN offspring compared with NN offspring. These results indicate that early life experience with low- and high-sodium diets, during the prenatal or early postnatal period, is a stress that produces long-term changes in responsiveness to amphetamines and to subsequent stressors. PMID:18614766

A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging

PubMed Central

Kwon, Oh-Hun; Park, Hyunjin; Seo, Sang-Won; Na, Duk L.; Lee, Jong-Min

2015-01-01

The mean diffusivity (MD) value has been used to describe microstructural properties in Diffusion Tensor Imaging (DTI) in cortical gray matter (GM). Recently, researchers have applied a cortical surface generated from the T1-weighted volume. When the DTI data are analyzed using the cortical surface, it is important to assign an accurate MD value from the volume space to the vertex of the cortical surface, considering the anatomical correspondence between the DTI and the T1-weighted image. Previous studies usually sampled the MD value using the nearest-neighbor (NN) method or Linear method, even though there are geometric distortions in diffusion-weighted volumes. Here we introduce a Surface Guided Diffusion Mapping (SGDM) method to compensate for such geometric distortions. We compared our SGDM method with results using NN and Linear methods by investigating differences in the sampled MD value. We also projected the tissue classification results of non-diffusion-weighted volumes to the cortical midsurface. The CSF probability values provided by the SGDM method were lower than those produced by the NN and Linear methods. The MD values provided by the NN and Linear methods were significantly greater than those of the SGDM method in regions suffering from geometric distortion. These results indicate that the NN and Linear methods assigned the MD value in the CSF region to the cortical midsurface (GM region). Our results suggest that the SGDM method is an effective way to correct such mapping errors. PMID:26236180

Symmetric and asymmetric squarylium dyes as noncovalent protein labels: a study by fluorimetry and capillary electrophoresis.

PubMed

Welder, Frank; Paul, Beverly; Nakazumi, Hiroyuki; Yagi, Shigeyuki; Colyer, Christa L

2003-08-05

Noncovalent interactions between two squarylium dyes and various model proteins have been explored. NN127 and SQ-3 are symmetric and asymmetric squarylium dyes, respectively, the fluorescence emissions of which have been shown to be enhanced upon complexation with proteins such as bovine serum albumin (BSA), human serum albumin (HSA), beta-lactoglobulin A, and trypsinogen. Although these dyes are poorly soluble in aqueous solution, they can be dissolved first in methanol followed by dilution with aqueous buffer without precipitation, and are then suitable for use as fluorescent labels in protein determination studies. The nature of interactions between these dyes and proteins was studied using a variety of buffer systems, and it was found that electrostatic interactions are involved but not dominant. Dye/protein stoichiometries in the noncovalent complexes were found to be 1:1 for SQ-3, although various possible stoichiometries were found for NN127 depending upon pH and protein. Association constants on the order of 10(5) and 10(7) were found for noncovalent complexes of SQ-3 and NN127, respectively, with HSA, indicating stronger interactions of the symmetric dye with proteins. Finally, HSA complexes with NN127 were determined by capillary electrophoresis with laser-induced fluorescence detection (CE-LIF). In particular, NN127 shows promise as a reagent capable of fluorescently labeling analyte proteins for analysis by CE-LIF without itself being significantly fluorescent under the aqueous solution conditions studied herein.

Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework.

PubMed

Ning, Kaida; Chen, Bo; Sun, Fengzhu; Hobel, Zachary; Zhao, Lu; Matloff, Will; Toga, Arthur W

2018-08-01

A long-standing question is how to best use brain morphometric and genetic data to distinguish Alzheimer's disease (AD) patients from cognitively normal (CN) subjects and to predict those who will progress from mild cognitive impairment (MCI) to AD. Here, we use a neural network (NN) framework on both magnetic resonance imaging-derived quantitative structural brain measures and genetic data to address this question. We tested the effectiveness of NN models in classifying and predicting AD. We further performed a novel analysis of the NN model to gain insight into the most predictive imaging and genetics features and to identify possible interactions between features that affect AD risk. Data were obtained from the AD Neuroimaging Initiative cohort and included baseline structural MRI data and single nucleotide polymorphism (SNP) data for 138 AD patients, 225 CN subjects, and 358 MCI patients. We found that NN models with both brain and SNP features as predictors perform significantly better than models with either alone in classifying AD and CN subjects, with an area under the receiver operating characteristic curve (AUC) of 0.992, and in predicting the progression from MCI to AD (AUC=0.835). The most important predictors in the NN model were the left middle temporal gyrus volume, the left hippocampus volume, the right entorhinal cortex volume, and the APOE (a gene that encodes apolipoprotein E) ɛ4 risk allele. Furthermore, we identified interactions between the right parahippocampal gyrus and the right lateral occipital gyrus, the right banks of the superior temporal sulcus and the left posterior cingulate, and SNP rs10838725 and the left lateral occipital gyrus. Our work shows the ability of NN models to not only classify and predict AD occurrence but also to identify important AD risk factors and interactions among them. Copyright © 2018 Elsevier Inc. All rights reserved.

Handling limited datasets with neural networks in medical applications: A small-data approach.

PubMed

Shaikhina, Torgyn; Khovanova, Natalia A

2017-01-01

Single-centre studies in medical domain are often characterised by limited samples due to the complexity and high costs of patient data collection. Machine learning methods for regression modelling of small datasets (less than 10 observations per predictor variable) remain scarce. Our work bridges this gap by developing a novel framework for application of artificial neural networks (NNs) for regression tasks involving small medical datasets. In order to address the sporadic fluctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated. The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3%, outperforming an ensemble NN model by 11%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples). The significance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Comparison of Sleep Disorders between Real and Simulated 3,450-m Altitude.

PubMed

Heinzer, Raphaël; Saugy, Jonas J; Rupp, Thomas; Tobback, Nadia; Faiss, Raphael; Bourdillon, Nicolas; Rubio, José Haba; Millet, Grégoire P

2016-08-01

Hypoxia is known to generate sleep-disordered breathing but there is a debate about the pathophysiological responses to two different types of hypoxic exposure: normobaric hypoxia (NH) and hypobaric hypoxia (HH), which have never been directly compared. Our aim was to compare sleep disorders induced by these two types of altitude. Subjects were exposed to 26 h of simulated (NH) or real altitude (HH) corresponding to 3,450 m and a control condition (NN) in a randomized order. The sleep assessments were performed with nocturnal polysomnography (PSG) and questionnaires. Thirteen healthy trained males subjects volunteered for this study (mean ± SD; age 34 ± 9 y, body weight 76.2 ± 6.8 kg, height 179.7 ± 4.2 cm). Mean nocturnal oxygen saturation was further decreased during HH than in NH (81.2 ± 3.1 versus 83.6 ± 1.9%; P < 0.01) when compared to NN (95.5 ± 0.9%; P < 0.001). Heart rate was higher in HH than in NH (61 ± 10 versus 55 ± 6 bpm; P < 0.05) and NN (48 ± 5 bpm; P < 0.001). Total sleep time was longer in HH than in NH (351 ± 63 versus 317 ± 65 min, P < 0.05), and both were shorter compared to NN (388 ± 50 min, P < 0.05). Breathing frequency did not differ between conditions. Apnea-hypopnea index was higher in HH than in NH (20.5 [15.8-57.4] versus 11.4 [5.0-65.4]; P < 0.01) and NN (8.2 [3.9-8.8]; P < 0.001). Subjective sleep quality was similar between hypoxic conditions but lower than in NN. Our results suggest that HH has a greater effect on nocturnal breathing and sleep structure than NH. In HH, we observed more periodic breathing, which might arise from the lower saturation due to hypobaria, but needs to be confirmed. © 2016 Associated Professional Sleep Societies, LLC.

Accumulation of lead (Pb) in brown trout (Salmo trutta) from a lake downstream a former shooting range.

PubMed

Mariussen, Espen; Heier, Lene Sørlie; Teien, Hans Christian; Pettersen, Marit Nandrup; Holth, Tor Fredrik; Salbu, Brit; Rosseland, Bjørn Olav

2017-01-01

An environmental survey was performed in Lake Kyrtjønn, a small lake within an abandoned shooting range in the south of Norway. In Lake Kyrtjønn the total water concentrations of Pb (14µg/L), Cu (6.1µg/L) and Sb (1.3µg/L) were elevated compared to the nearby reference Lake Stitjønn, where the total concentrations of Pb, Cu and Sb were 0.76, 1.8 and 0.12µg/L, respectively. Brown trout (Salmo trutta) from Lake Kyrtjønn had very high levels of Pb in bone (104mg/kg w.w.), kidney (161mg/kg w.w.) and the gills (137mg/kg d.w), and a strong inhibition of the ALA-D enzyme activity were observed in the blood (24% of control). Dry fertilized brown trout eggs were placed in the small outlet streams from Lake Kyrtjønn and the reference lake for 6 months, and the concentrations of Pb and Cu in eggs from the Lake Kyrtjønn stream were significantly higher than in eggs from the reference. More than 90% of Pb accumulated in the egg shell, whereas more than 80% of the Cu and Zn accumulated in the egg interior. Pb in the lake sediments was elevated in the upper 2-5cm layer (410-2700mg/kg d.w), and was predominantly associated with redox sensitive fractions (e.g., organic materials, hydroxides) indicating low potential mobility and bioavailability of the deposited Pb. Only minor amounts of Cu and Sb were deposited in the sediments. The present work showed that the adult brown trout, as well as fertilized eggs and alevins, may be subjected to increased stress due to chronic exposure to Pb, whereas exposure to Cu, Zn and Sb were of less importance. Copyright © 2016 Elsevier Inc. All rights reserved.

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

White, Travis A.; Witt, Suzanne E.; Li, Zhanyong

Formamidinate-bridged Rh 2 II,II complexes containing diimine ligands of the formula cis-[Rh 2 II,II(μ-DTolF) 2(NN) 2] 2+ (Rh 2-NN 2), where DTolF = p-ditolylformamidinate and NN = dppn (benzo[i]dipyrido[3,2-a:2',3'-h]quinoxaline), dppz (dipyrido[3,2-a:2',3'-c]phenazine), and phen (1,10-phenanthroline), electrocatalytically reduce H + to H 2 in DMF solutions containing CH 3COOH at a glassy carbon electrode. Cathodic scans in the absence of acid display a Rh III,II/II,II reduction at -0.90 V vs Fc +/Fc followed by NN 0/– reduction at -1.13, -1.36, and -1.65 V for Rh 2-dppn 2, Rh 2-dppz 2, and Rh 2-phen 2, respectively. Upon the addition of acid, Rh 2-dppnmore » 2 and Rh 2-dppz 2 undergo reduction–protonation–reduction at each pyrazine-containing NN ligand prior to the Rh 2 II,II/II,I reduction. The Rh 2 II,I species is thus protonated at one of the metal centers, resulting in the formation of the corresponding Rh 2 II,III-hydride. In the case of Rh 2-phen 2, the reduction of the phen ligand is followed by intramolecular electron transfer to the Rh 2 II,II core in the presence of protons to form a Rh 2 II,III-hydride species. Further reduction and protonation at the Rh 2 core for all three complexes rapidly catalyzes H 2 formation with varied calculated turnover frequencies (TOF) and overpotential values (η): 2.6 × 10 4 s –1 and 0.56 V for Rh 2-dppn, 2.8 × 10 4 s –1 and 0.50 V for Rh 2-dppz 2, and 5.9 × 10 4 s –1 and 0.64 V for Rh 2-phen 2. Bulk electrolysis confirmed H 2 formation, and further CH 3COOH addition regenerates H 2 production, attesting to the robust nature of the architecture. The cis-[Rh 2 II,II(μ-DTolF) 2(NN) 2] 2+ architecture benefits by combining electron-rich formamidinate bridges, a redox-active Rh 2 II,II core, and electron-accepting NN diimine ligands to allow for the electrocatalysis of H + substrate to H 2 fuel.« less

New Rh 2 (II,II) Architecture for the Catalytic Reduction of H +

DOE PAGES

White, Travis A.; Witt, Suzanne E.; Li, Zhanyong; ...

2015-09-25

Formamidinate-bridged Rh 2 II,II complexes containing diimine ligands of the formula cis-[Rh 2 II,II(μ-DTolF) 2(NN) 2] 2+ (Rh 2-NN 2), where DTolF = p-ditolylformamidinate and NN = dppn (benzo[i]dipyrido[3,2-a:2',3'-h]quinoxaline), dppz (dipyrido[3,2-a:2',3'-c]phenazine), and phen (1,10-phenanthroline), electrocatalytically reduce H + to H 2 in DMF solutions containing CH 3COOH at a glassy carbon electrode. Cathodic scans in the absence of acid display a Rh III,II/II,II reduction at -0.90 V vs Fc +/Fc followed by NN 0/– reduction at -1.13, -1.36, and -1.65 V for Rh 2-dppn 2, Rh 2-dppz 2, and Rh 2-phen 2, respectively. Upon the addition of acid, Rh 2-dppnmore » 2 and Rh 2-dppz 2 undergo reduction–protonation–reduction at each pyrazine-containing NN ligand prior to the Rh 2 II,II/II,I reduction. The Rh 2 II,I species is thus protonated at one of the metal centers, resulting in the formation of the corresponding Rh 2 II,III-hydride. In the case of Rh 2-phen 2, the reduction of the phen ligand is followed by intramolecular electron transfer to the Rh 2 II,II core in the presence of protons to form a Rh 2 II,III-hydride species. Further reduction and protonation at the Rh 2 core for all three complexes rapidly catalyzes H 2 formation with varied calculated turnover frequencies (TOF) and overpotential values (η): 2.6 × 10 4 s –1 and 0.56 V for Rh 2-dppn, 2.8 × 10 4 s –1 and 0.50 V for Rh 2-dppz 2, and 5.9 × 10 4 s –1 and 0.64 V for Rh 2-phen 2. Bulk electrolysis confirmed H 2 formation, and further CH 3COOH addition regenerates H 2 production, attesting to the robust nature of the architecture. The cis-[Rh 2 II,II(μ-DTolF) 2(NN) 2] 2+ architecture benefits by combining electron-rich formamidinate bridges, a redox-active Rh 2 II,II core, and electron-accepting NN diimine ligands to allow for the electrocatalysis of H + substrate to H 2 fuel.« less

Neural network retrieval of soil moisture: application to SMOS

NASA Astrophysics Data System (ADS)

Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Kolasssa, Jana; Jimenez, Carlos; Cabot, Francois; Mahmoodi, Ali

2014-05-01

We present an efficient statistical soil moisture (SM) retrieval method using SMOS brightness temperatures (BTs) complemented with MODIS NDVI and ASCAT backscattering data. The method is based on a feed-forward neural network (hereafter NN) trained with SM from ECMWF model predictions or from the SMOS operational algorithm. The best compromise to retrieve SM with NNs from SMOS brightness temperatures in a large fraction of the swath (~ 670 km) is to use incidence angles from 25 to 60 degrees (in 7 bins of 5 deg width) for both H and V polarizations. The correlation coefficient (R) of the SM retrieved by the NN and the reference SM dataset (ECMWF or SMOS L3) is 0.8. The correlation coefficient increases to 0.91 when adding as input MODIS NDVI, ECOCLIMAP sand and clay fractions and one of the following data: (i) active microwaves observations (ASCAT backscattering coefficient at 40 deg incidence angle), (ii) ECMWF soil temperature. Finally, the correlation coefficient increases to R=0.94 when using a normalization index computed locally for each latitude-longitude point with the maximum and minimum BTs and the associated SM values from the local time series. Global maps of SM obtained with NNs reproduce well the spatial structures present in the reference SM datasets, implying that the NN works well for a wide range of ecosystems and physical conditions. In addition, the results of the NNs have been evaluated at selected locations for which in situ measurements are available such as the USDA-ARS watersheds (USA), the OzNet network (AUS) and USDA-NRCS SCAN network (USA). The time series of SM obtained with NNs reproduce the temporal behavior measured with in situ sensors. For well known sites where the in situ measurement is representative of a 40 km scale like the Little Washita watershed, the NN models show a very high correlation of (R = 0.8-0.9) and a low standard deviation of 0.02-0.04 m3/m3 with respect to the in situ measurements. When comparing with all the in situ stations, the average correlation coefficients and bias of NN SM with respect to in situ measurements are comparable to those of ECMWF and SMOS L3 SM (R = 0.6). The standard deviation of the difference (STTD) of those products with respect to in situ measurements is lower for NN SM, in particular for the NN models that use local information on the extreme BTs and associated SM values, for which average STDD is 0.03 m3/m3, twice as low as the average STDD values obtained with ECMWF and L3 SM (0.05-0.07 m3/m3). In conclusion, SM obtained using NN give results of comparable or better quality to other SM products. In addition, NNs are an efficient method to obtain SM from SMOS data (one year of SMOS observations can be inverted in less than 60 seconds). These results have been obtained in the framework of the SMOS+NN project funded by ESA and they open interesting perspectives such as a near real time processor and data assimilation in weather prediction models.

Mapping gradients of community composition with nearest-neighbour imputation: extending plot data for landscape analysis

Treesearch

Janet L. Ohmann; Matthew J. Gregory; Emilie B. Henderson; Heather M. Roberts

2011-01-01

Question: How can nearest-neighbour (NN) imputation be used to develop maps of multiple species and plant communities? Location: Western and central Oregon, USA, but methods are applicable anywhere. Methods: We demonstrate NN imputation by mapping woody plant communities for >100 000 km2 of diverse forests and woodlands. Species abundances on...

21 CFR 172.829 - Neotame.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) Neotame is the chemical N-[N-(3,3-dimethylbutyl)-L-α-aspartyl]-L-phenylalanine-1-methyl ester (CAS Reg. No... acid (N-[N-(3,3-dimethylbutyl)-L-α-aspartyl]-L-phenylalanine), not more than 1.5 percent. (3) Other... neotame is used as a sugar substitute tablet, L-leucine may be used as a lubricant in the manufacture of...

Using Squares to Sum Squares

ERIC Educational Resources Information Center

DeTemple, Duane

2010-01-01

Purely combinatorial proofs are given for the sum of squares formula, 1[superscript 2] + 2[superscript 2] + ... + n[superscript 2] = n(n + 1) (2n + 1) / 6, and the sum of sums of squares formula, 1[superscript 2] + (1[superscript 2] + 2[superscript 2]) + ... + (1[superscript 2] + 2[superscript 2] + ... + n[superscript 2]) = n(n + 1)[superscript 2]…

Correcting Erroneous N+N Structures in the Productions of French Users of English

ERIC Educational Resources Information Center

Garnier, Marie

2012-01-01

This article presents the preliminary steps to the implementation of detection and correction strategies for the erroneous use of N+N structures in the written productions of French-speaking advanced users of English. This research is carried out as part of the grammar checking project "CorrecTools", in which errors are detected and corrected…

Global Ocean Tides. Part VIII. The Semidiurnal Luni-Solar Declination Tide (K2), Atlas of Tidal Charts and Maps.

DTIC Science & Technology

1981-06-01

cOOOaaaN.4aO.4a~J aaaa.4’asasSSSasasasasaS ~ 5555 SS 55 5 55*5 5 5555a40.44090.490Sa5555 ’tea aCca -Na SSSCCNNO06440 C.4(40(4’ aaa.tNCA .4.4aaaaOOeaO.4NaSNON...8217f SS .4N’S C CN NN N h . 4 N N N N NNM4 4 nnMf - f n ’ f3 ~~~~~~~~~ C C .40.SNC r foo.N CNC’C*.C N C.N oCoNC CN.𔃺 NSCNNNCCC000000toa, .. o.44.NN N

A Tool for Verification and Validation of Neural Network Based Adaptive Controllers for High Assurance Systems

NASA Technical Reports Server (NTRS)

Gupta, Pramod; Schumann, Johann

2004-01-01

High reliability of mission- and safety-critical software systems has been identified by NASA as a high-priority technology challenge. We present an approach for the performance analysis of a neural network (NN) in an advanced adaptive control system. This problem is important in the context of safety-critical applications that require certification, such as flight software in aircraft. We have developed a tool to measure the performance of the NN during operation by calculating a confidence interval (error bar) around the NN's output. Our tool can be used during pre-deployment verification as well as monitoring the network performance during operation. The tool has been implemented in Simulink and simulation results on a F-15 aircraft are presented.

Vertices cannot be hidden from quantum spatial search for almost all random graphs

NASA Astrophysics Data System (ADS)

Glos, Adam; Krawiec, Aleksandra; Kukulski, Ryszard; Puchała, Zbigniew

2018-04-01

In this paper, we show that all nodes can be found optimally for almost all random Erdős-Rényi G(n,p) graphs using continuous-time quantum spatial search procedure. This works for both adjacency and Laplacian matrices, though under different conditions. The first one requires p=ω (log ^8(n)/n), while the second requires p≥ (1+ɛ )log (n)/n, where ɛ >0. The proof was made by analyzing the convergence of eigenvectors corresponding to outlying eigenvalues in the \\Vert \\cdot \\Vert _∞ norm. At the same time for p<(1-ɛ )log (n)/n, the property does not hold for any matrix, due to the connectivity issues. Hence, our derivation concerning Laplacian matrix is tight.

Geographic List of Prime Contract Awards. Oct 1992-Sep 1993. FY 1993. (Aberdeen, Washington-Laramie, Wyoming). Part 13

DTIC Science & Technology

1994-03-01

If LiOm iI NNNY - -" N-I CV II Li000 II ==Z z m W LsJ COZ Z he co 2E Z NZ 11 -4 1LiON, 1 ~ u- -4 NN NN- YŘ Nm -4 NNi N- -4 N~ N 11 w I Ui00 to 1 NN 2c...cm cj W Of CY C-j m cj 2c w x CY ;c (m cj N 11 U01n It 13 13 13 -3 ŗ 13 If U01W 11 n m m z m z M it LIOM 11 < < < 1:10 if f (.)ON 110" 0 Q to Q ca 0

Scaling of charged particle production in d+Au collisions at √(sNN)=200GeV

NASA Astrophysics Data System (ADS)

Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Becker, B.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Busza, W.; Carroll, A.; Decowski, M. P.; García, E.; Gburek, T.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Harrington, A. S.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Khan, N.; Kulinich, P.; Kuo, C. M.; Lee, J. W.; Lin, W. T.; Manly, S.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Roland, C.; Roland, G.; Sagerer, J.; Sarin, P.; Sedykh, I.; Skulski, W.; Smith, C. E.; Steinberg, P.; Stephans, G. S.; Sukhanov, A.; Tonjes, M. B.; Trzupek, A.; Vale, C.; Nieuwenhuizen, G. J.; Verdier, R.; Veres, G. I.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wysłouch, B.; Zhang, J.

2005-09-01

The measured pseudorapidity distributions of primary charged particles over a wide pseudorapidity range of |η|≤5.4 and integrated charged particle multiplicities in d+Au collisions at √(sNN)=200GeV are presented as a function of collision centrality. The longitudinal features of d+Au collisions at √(sNN)=200GeV are found to be very similar to those seen in p+A collisions at lower energies. The total multiplicity of charged particles is found to scale with the total number of participants according to NdAuch=1/2Nppch, and the energy dependence of the density of charged particles produced in the fragmentation region exhibits extended longitudinal scaling.

Photon production in Pb + Pb collisions at √{sNN } = 2.76 TeV

NASA Astrophysics Data System (ADS)

Fu, Yong-Ping; Xi, Qin

2018-02-01

We calculate the high energy photon production from the Pb + Pb collisions for different centrality classes at √{sNN } = 2.76 TeV Large Hadron Collider (LHC) energy. The jet energy loss in the jet fragmentation, jet-photon conversion and jet bremsstrahlung is considered by using the Wang-Huang-Sarcevic (WHS) and Baier-Dokshitzer-Mueller-Peigne-Schiff (BDMPS) models. We use the (1 + 1)-dimensional ideal relativistic hydrodynamics to study the collective transverse flow and space-time evolution of the quark gluon plasma (QGP). The numerical results agree well with the ALICE data of the direct photons from the Pb + Pb collisions (√{sNN } = 2.76 TeV) for 0-20%, 20-40% and 40-80% centrality classes.

All Prime Contract Awards by State or Country, Place, and Contractor, FY 84. Part 10 (Alliance, North Carolina - Woodburn, Oregon).

DTIC Science & Technology

1984-01-01

Highway, Suite 120 4 Arlington, VA 22202-4302 8a. NAME OF FUNDING iSPONSORING TBb. OFFICE SYMBOL 9 . PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER...CODES 18. SUBJECT TERMS (Continue on if necessary and identity by biock number) FIELD GROUP SUB-GROUP - --- D T 1Q C% 9 ABSTRACT (Continue on reverse it...ZNNCYNNNNNNNNNcl C ci Y YNNNNNNNCY 9 ~- Z N N~ m zN C3 0). N (N NY N N NN N \\ NN N N NN N N N Nm C* . D 0 0 0 0 0 0) (D( ~0 0 CD0LL0 0 0 Q0 00 00 00 0 M W UOoO 0

Molecular Identification of Cryptosporidium Species from Pet Snakes in Thailand.

PubMed

Yimming, Benjarat; Pattanatanang, Khampee; Sanyathitiseree, Pornchai; Inpankaew, Tawin; Kamyingkird, Ketsarin; Pinyopanuwat, Nongnuch; Chimnoi, Wissanuwat; Phasuk, Jumnongjit

2016-08-01

Cryptosporidium is an important pathogen causing gastrointestinal disease in snakes and is distributed worldwide. The main objectives of this study were to detect and identify Cryptosporidium species in captive snakes from exotic pet shops and snake farms in Thailand. In total, 165 fecal samples were examined from 8 snake species, boa constrictor (Boa constrictor constrictor), corn snake (Elaphe guttata), ball python (Python regius), milk snake (Lampropeltis triangulum), king snake (Lampropeltis getula), rock python (Python sebae), rainbow boa (Epicrates cenchria), and carpet python (Morelia spilota). Cryptosporidium oocysts were examined using the dimethyl sulfoxide (DMSO)-modified acid-fast staining and a molecular method based on nested-PCR, PCR-RFLP analysis, and sequencing amplification of the SSU rRNA gene. DMSO-modified acid-fast staining revealed the presence of Cryptosporidium oocysts in 12 out of 165 (7.3%) samples, whereas PCR produced positive results in 40 (24.2%) samples. Molecular characterization indicated the presence of Cryptosporidium parvum (mouse genotype) as the most common species in 24 samples (60%) from 5 species of snake followed by Cryptosporidium serpentis in 9 samples (22.5%) from 2 species of snake and Cryptosporidium muris in 3 samples (7.5%) from P. regius.

Molecular Identification of Cryptosporidium Species from Pet Snakes in Thailand

PubMed Central

Yimming, Benjarat; Pattanatanang, Khampee; Sanyathitiseree, Pornchai; Inpankaew, Tawin; Kamyingkird, Ketsarin; Pinyopanuwat, Nongnuch; Chimnoi, Wissanuwat; Phasuk, Jumnongjit

2016-01-01

Cryptosporidium is an important pathogen causing gastrointestinal disease in snakes and is distributed worldwide. The main objectives of this study were to detect and identify Cryptosporidium species in captive snakes from exotic pet shops and snake farms in Thailand. In total, 165 fecal samples were examined from 8 snake species, boa constrictor (Boa constrictor constrictor), corn snake (Elaphe guttata), ball python (Python regius), milk snake (Lampropeltis triangulum), king snake (Lampropeltis getula), rock python (Python sebae), rainbow boa (Epicrates cenchria), and carpet python (Morelia spilota). Cryptosporidium oocysts were examined using the dimethyl sulfoxide (DMSO)-modified acid-fast staining and a molecular method based on nested-PCR, PCR-RFLP analysis, and sequencing amplification of the SSU rRNA gene. DMSO-modified acid-fast staining revealed the presence of Cryptosporidium oocysts in 12 out of 165 (7.3%) samples, whereas PCR produced positive results in 40 (24.2%) samples. Molecular characterization indicated the presence of Cryptosporidium parvum (mouse genotype) as the most common species in 24 samples (60%) from 5 species of snake followed by Cryptosporidium serpentis in 9 samples (22.5%) from 2 species of snake and Cryptosporidium muris in 3 samples (7.5%) from P. regius. PMID:27658593

Nearest neighbor imputation using spatial–temporal correlations in wireless sensor networks

PubMed Central

Li, YuanYuan; Parker, Lynne E.

2016-01-01

Missing data is common in Wireless Sensor Networks (WSNs), especially with multi-hop communications. There are many reasons for this phenomenon, such as unstable wireless communications, synchronization issues, and unreliable sensors. Unfortunately, missing data creates a number of problems for WSNs. First, since most sensor nodes in the network are battery-powered, it is too expensive to have the nodes retransmit missing data across the network. Data re-transmission may also cause time delays when detecting abnormal changes in an environment. Furthermore, localized reasoning techniques on sensor nodes (such as machine learning algorithms to classify states of the environment) are generally not robust enough to handle missing data. Since sensor data collected by a WSN is generally correlated in time and space, we illustrate how replacing missing sensor values with spatially and temporally correlated sensor values can significantly improve the network’s performance. However, our studies show that it is important to determine which nodes are spatially and temporally correlated with each other. Simple techniques based on Euclidean distance are not sufficient for complex environmental deployments. Thus, we have developed a novel Nearest Neighbor (NN) imputation method that estimates missing data in WSNs by learning spatial and temporal correlations between sensor nodes. To improve the search time, we utilize a kd-tree data structure, which is a non-parametric, data-driven binary search tree. Instead of using traditional mean and variance of each dimension for kd-tree construction, and Euclidean distance for kd-tree search, we use weighted variances and weighted Euclidean distances based on measured percentages of missing data. We have evaluated this approach through experiments on sensor data from a volcano dataset collected by a network of Crossbow motes, as well as experiments using sensor data from a highway traffic monitoring application. Our experimental results show that our proposed 𝒦-NN imputation method has a competitive accuracy with state-of-the-art Expectation–Maximization (EM) techniques, while using much simpler computational techniques, thus making it suitable for use in resource-constrained WSNs. PMID:28435414

Neutron-neutron quasifree scattering in nd breakup at 10 MeV

NASA Astrophysics Data System (ADS)

Malone, R. C.; Crowe, B.; Crowell, A. S.; Cumberbatch, L. C.; Esterline, J. H.; Fallin, B. A.; Friesen, F. Q. L.; Han, Z.; Howell, C. R.; Markoff, D.; Ticehurst, D.; Tornow, W.; Witała, H.

2016-03-01

The neutron-deuteron (nd) breakup reaction provides a rich environment for testing theoretical models of the neutron-neutron (nn) interaction. Current theoretical predictions based on rigorous ab-initio calculations agree well with most experimental data for this system, but there remain a few notable discrepancies. The cross section for nn quasifree (QFS) scattering is one such anomaly. Two recent experiments reported cross sections for this particular nd breakup configuration that exceed theoretical calculations by almost 20% at incident neutron energies of 26 and 25 MeV [1, 2]. The theoretical values can be brought into agreement with these results by increasing the strength of the 1S0 nn potential matrix element by roughly 10%. However, this modification of the nn effective range parameter and/or the 1S0 scattering length causes substantial charge-symmetry breaking in the nucleon-nucleon force and suggests the possibility of a weakly bound di-neutron state [3]. We are conducting new measurements of the cross section for nn QFS in nd breakup. The measurements are performed at incident neutron beam energies below 20 MeV. The neutron beam is produced via the 2H(d, n)3He reaction. The target is a deuterated plastic cylinder. Our measurements utilize time-of-flight techniques with a pulsed neutron beam and detection of the two emitted neutrons in coincidence. A description of our initial measurements at 10 MeV for a single scattering angle will be presented along with preliminary results. Also, plans for measurements at other energies with broad angular coverage will be discussed.

Structural vascular disease in Africans: Performance of ethnic-specific waist circumference cut points using logistic regression and neural network analyses: The SABPA study.

PubMed

Botha, J; de Ridder, J H; Potgieter, J C; Steyn, H S; Malan, L

2013-10-01

A recently proposed model for waist circumference cut points (RPWC), driven by increased blood pressure, was demonstrated in an African population. We therefore aimed to validate the RPWC by comparing the RPWC and the Joint Statement Consensus (JSC) models via Logistic Regression (LR) and Neural Networks (NN) analyses. Urban African gender groups (N=171) were stratified according to the JSC and RPWC cut point models. Ultrasound carotid intima media thickness (CIMT), blood pressure (BP) and fasting bloods (glucose, high density lipoprotein (HDL) and triglycerides) were obtained in a well-controlled setting. The RPWC male model (LR ROC AUC: 0.71, NN ROC AUC: 0.71) was practically equal to the JSC model (LR ROC AUC: 0.71, NN ROC AUC: 0.69) to predict structural vascular -disease. Similarly, the female RPWC model (LR ROC AUC: 0.84, NN ROC AUC: 0.82) and JSC model (LR ROC AUC: 0.82, NN ROC AUC: 0.81) equally predicted CIMT as surrogate marker for structural vascular disease. Odds ratios supported validity where prediction of CIMT revealed -clinical -significance, well over 1, for both the JSC and RPWC models in African males and females (OR 3.75-13.98). In conclusion, the proposed RPWC model was substantially validated utilizing linear and non-linear analyses. We therefore propose ethnic-specific WC cut points (African males, ≥90 cm; -females, ≥98 cm) to predict a surrogate marker for structural vascular disease. © J. A. Barth Verlag in Georg Thieme Verlag KG Stuttgart · New York.

Signal peptide discrimination and cleavage site identification using SVM and NN.

PubMed

Kazemian, H B; Yusuf, S A; White, K

2014-02-01

About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd.

Nonperturbative NN scattering in {sup 3}S{sub 1}–{sup 3}D{sub 1} channels of EFT(⁄π)

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

Yang, Ji-Feng, E-mail: jfyang@phy.ecnu.edu.cn

2013-12-15

The closed-form T matrices in the {sup 3}S{sub 1}–{sup 3}D{sub 1} channels of EFT(⁄π) for NN scattering with the potentials truncated at order O(Q{sup 4}) are presented with the nonperturbative divergences parametrized in a general manner. The stringent constraints imposed by the closed form of the T matrices are exploited in the underlying theory perspective and turned into virtues in the implementation of subtractions and the manifestation of power counting rules in nonperturbative regimes, leading us to the concept of EFT scenario. A number of scenarios of the EFT description of NN scattering are compared with PSA data in termsmore » of effective range expansion and {sup 3}S{sub 1} phase shifts, showing that it is favorable to proceed in a scenario with conventional EFT couplings and sophisticated renormalization in order to have large NN scattering lengths. The informative utilities of fine tuning are demonstrated in several examples and naturally interpreted in the underlying theory perspective. In addition, some of the approaches adopted in the recent literature are also addressed in the light of EFT scenario. -- Highlights: •Closed-form unitary T matrices for NN scattering are obtained in EFT(⁄π). •Nonperturbative properties inherent in such closed-form T matrices are explored. •Nonperturbative renormalization is implemented through exploiting these properties. •Unconventional power counting of couplings is shown to be less favored by PSA data. •The ideas about nonperturbative renormalization here might have wider applications.« less

Modelling thermal comfort of visitors at urban squares in hot and arid climate using NN-ARX soft computing method

NASA Astrophysics Data System (ADS)

Kariminia, Shahab; Motamedi, Shervin; Shamshirband, Shahaboddin; Piri, Jamshid; Mohammadi, Kasra; Hashim, Roslan; Roy, Chandrabhushan; Petković, Dalibor; Bonakdari, Hossein

2016-05-01

Visitors utilize the urban space based on their thermal perception and thermal environment. The thermal adaptation engages the user's behavioural, physiological and psychological aspects. These aspects play critical roles in user's ability to assess the thermal environments. Previous studies have rarely addressed the effects of identified factors such as gender, age and locality on outdoor thermal comfort, particularly in hot, dry climate. This study investigated the thermal comfort of visitors at two city squares in Iran based on their demographics as well as the role of thermal environment. Assessing the thermal comfort required taking physical measurement and questionnaire survey. In this study, a non-linear model known as the neural network autoregressive with exogenous input (NN-ARX) was employed. Five indices of physiological equivalent temperature (PET), predicted mean vote (PMV), standard effective temperature (SET), thermal sensation votes (TSVs) and mean radiant temperature ( T mrt) were trained and tested using the NN-ARX. Then, the results were compared to the artificial neural network (ANN) and the adaptive neuro-fuzzy inference system (ANFIS). The findings showed the superiority of the NN-ARX over the ANN and the ANFIS. For the NN-ARX model, the statistical indicators of the root mean square error (RMSE) and the mean absolute error (MAE) were 0.53 and 0.36 for the PET, 1.28 and 0.71 for the PMV, 2.59 and 1.99 for the SET, 0.29 and 0.08 for the TSV and finally 0.19 and 0.04 for the T mrt.

A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Treesearch

Jay M. Ver Hoef; Hailemariam Temesgen; Sergio Gómez

2013-01-01

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically,...

K-nearest neighbor imputation of forest inventory variables in New Hampshire

Treesearch

Andrew Lister; Michael Hoppus; Raymond L. Czaplewski

2005-01-01

The k-nearest neighbor (kNN) method was used to map stand volume for a mosaic of 4 Landsat scenes covering the state of New Hampshire. Data for gross cubic foot volume and trees per acre were summarized from USDA Forest Service Forest Inventory and Analysis (FIA) plots and used as training for kNN. Six bands of...

Corrigendum to ;Wounded quarks and diquarks in heavy ion collisions; [Phys. Lett. B 649 (2007) 263

NASA Astrophysics Data System (ADS)

Bialas, A.; Bzdak, A.

2017-10-01

Numerical result in Eq. (15), σqq /σNN = 1.147 - 1.148, as given in [Phys. Lett. B 649 (2007) 263] should be corrected to σqq /σNN = 0.147 - 0.148. The conclusions of the original paper remain unchanged. We thank Partha Pratim Bhaduri for pointing out this typo.

Investigation of the particle-core structure of odd-mass nuclei in the NpNn scheme

NASA Astrophysics Data System (ADS)

Bucurescu, D.; Cata, G.; Cutoiu, D.; Dragulescu, E.; Ivasu, M.; Zamfir, N. V.; Gizon, A.; Gizon, J.

1989-10-01

The NpNn scheme is applied to data related to collective band structures determined by the unique parity shell model orbitals in odd-A nuclei from the mass regions A≌80-100 and A≌130. Simple systematics are obtained which give a synthetic picture of the evolution of the particle-core coupling in these nuclear regions.

Future of the beam energy scan program at RHIC

NASA Astrophysics Data System (ADS)

Odyniec, Grazyna

2015-05-01

The first exploratory phase of a very successful Beam Energy Scan Program at RHIC was completed in 2014 with Au+Au collisions at energies ranging from 7 to 39 GeV. Data sets taken earlier extended the upper limit of energy range to the √sNN of 200 GeV. This provided an initial look into the uncharted territory of the QCD phase diagram, which is considered to be the single most important graph of our field. The main results from BES phase I, although effected by large statistical errors (steeply increasing with decreasing energy), suggest that the highest potential for discovery of the QCD Critical Point lies bellow √sNN 20 GeV. Here, we discuss the plans and the preparation for phase II of the BES program, with an order of magnitude larger statistics, which is planned for 2018-2019. The BES II will focus on Au+Au collisions at √sNN from 20 to 7 GeV in collider mode, and from √sNN 7 to 3.5 GeV in the fixed target mode, which will be run concurrently with the collider mode operation.

Higher Moments of Net-Particle Multiplicity Distributions

NASA Astrophysics Data System (ADS)

Thäder, Jochen

2016-12-01

Studying fluctuations of conserved quantities, such as baryon number, strangeness, and charge, provides insights into the properties of matter created in high-energy nuclear collisions. Lattice QCD calculations suggest that higher moments of these quantities are sensitive to the phase structure of the hot and dense nuclear matter created in such collisions. In this paper, we present first experimental results of volume and temperature independent cumulant ratios of net-charge and net-proton distributions in Au+Au collisions at √{sNN} = 14.5 GeV completing the first RHIC Beam Energy Scan (BES-I) program for √{sNN} = 7.7 to 200GeV, together with the first measurement of fully corrected net-kaon results, measured with the STAR detector at RHIC at mid-rapidity and a transverse momentum up to pT = 2GeV/c. The pseudorapidity dependence of the √{sNN} = 14.5 GeV net-charge cumulant ratios is discussed. The estimated uncertainties on the ratio c4 /c2, the most statistics-hungry of the present observables, at √{sNN} = 7.7 GeV in the upcoming RHIC BES-II program will also be presented.

Prime Contract Awards Alphabetically by Contractor, State or Country, and Place. Part 16 (South Dakota [State of], Brookings, South Dakota - Tekna, Mountain View, California), FY1991

DTIC Science & Technology

1991-01-01

4 - %-) N6- NN 4 .4 C.4 0 .4N Ho. up r-( r- N4 Nn NO. N N- NN M 040 H >.A WIx 0 -Cl 4c. q*j r- W W4 en H~C t-4OHI C00 C4 (D V 00 000~0 o CO a ccN H𔃺...I- I-- ə IW C-4 /) I-I. z I.- 2 z 2 Z z 3 IS I We-i if < 00 1-1 0 #-4 1-0 I- 1-4 0- < IS I Web Uo IP- I-- If M" ISO a a a 0 0 a 1-i ,0W UZ IS Z 2w 2...C4 C4 C4 Cj C-4 e4 C4 C4 C,4 cm ൌ (4 cli 04 " cli cm CS1 C4 e4 cm C14 c4cljcqclI"NNe4rqNN CIA CS1 C4 eq C14 C14 C4 C4 N C4 CSI eq C4 1 131 wix

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes.

PubMed

Mougiakakou, Stavroula G; Prountzou, Aikaterini; Iliopoulou, Dimitra; Nikita, Konstantina S; Vazeou, Andriani; Bartsocas, Christos S

2006-01-01

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

Performance Evaluation of 14 Neural Network Architectures Used for Predicting Heat Transfer Characteristics of Engine Oils

NASA Astrophysics Data System (ADS)

Al-Ajmi, R. M.; Abou-Ziyan, H. Z.; Mahmoud, M. A.

2012-01-01

This paper reports the results of a comprehensive study that aimed at identifying best neural network architecture and parameters to predict subcooled boiling characteristics of engine oils. A total of 57 different neural networks (NNs) that were derived from 14 different NN architectures were evaluated for four different prediction cases. The NNs were trained on experimental datasets performed on five engine oils of different chemical compositions. The performance of each NN was evaluated using a rigorous statistical analysis as well as careful examination of smoothness of predicted boiling curves. One NN, out of the 57 evaluated, correctly predicted the boiling curves for all cases considered either for individual oils or for all oils taken together. It was found that the pattern selection and weight update techniques strongly affect the performance of the NNs. It was also revealed that the use of descriptive statistical analysis such as R2, mean error, standard deviation, and T and slope tests, is a necessary but not sufficient condition for evaluating NN performance. The performance criteria should also include inspection of the smoothness of the predicted curves either visually or by plotting the slopes of these curves.

Photophysical dynamics of the efficient emission and photosensitization of [Ir(pqi)2(NN)]+ complexes.

PubMed

Zanoni, Kassio P S; Ito, Akitaka; Grüner, Malte; Murakami Iha, Neyde Y; de Camargo, Andrea S S

2018-01-23

The photophysical dynamics of three complexes in the highly-emissive [Ir(pqi) 2 (NN)] + series were investigated aiming at unique photophysical features and applications in light-emitting and singlet oxygen sensitizing research fields. Rational elucidation and Franck-Condon analyses of the observed emission spectra in nitrile solutions at 298 and 77 K reveal the true emissive nature of the lowest-lying triplet excited state (T 1 ), consisting of a hybrid 3 MLCT/LC Ir(pqi)→pqi state. Emissive deactivations from T 1 occur mainly by very intense, yellow-orange phosphorescence with high quantum yields and radiative rates. The emission nature experimentally verified is corroborated by theoretical calculations (TD-DFT), with T 1 arising from a mixing of several transitions induced by the spin-orbit coupling, majorly ascribed to 3 MLCT/LC Ir(pqi)→pqi and increasing contributions of 3 MLCT/LLCT Ir(pqi)→NN . The microsecond-lived emission of T 1 is rapidly quenched by molecular oxygen, with an efficient generation of singlet oxygen. Our findings show that the photophysics of [Ir(pqi) 2 (NN)][PF 6 ] complexes is suitable for many applications, from the active layer of electroluminescent devices to photosensitizers for photodynamic therapy and theranostics.

Motion Control of Drives for Prosthetic Hand Using Continuous Myoelectric Signals

NASA Astrophysics Data System (ADS)

Purushothaman, Geethanjali; Ray, Kalyan Kumar

2016-03-01

In this paper the authors present motion control of a prosthetic hand, through continuous myoelectric signal acquisition, classification and actuation of the prosthetic drive. A four channel continuous electromyogram (EMG) signal also known as myoelectric signals (MES) are acquired from the abled-body to classify the six unique movements of hand and wrist, viz, hand open (HO), hand close (HC), wrist flexion (WF), wrist extension (WE), ulnar deviation (UD) and radial deviation (RD). The classification technique involves in extracting the features/pattern through statistical time domain (TD) parameter/autoregressive coefficients (AR), which are reduced using principal component analysis (PCA). The reduced statistical TD features and or AR coefficients are used to classify the signal patterns through k nearest neighbour (kNN) as well as neural network (NN) classifier and the performance of the classifiers are compared. Performance comparison of the above two classifiers clearly shows that kNN classifier in identifying the hidden intended motion in the myoelectric signals is better than that of NN classifier. Once the classifier identifies the intended motion, the signal is amplified to actuate the three low power DC motor to perform the above mentioned movements.

Advanced image collection, information extraction, and change detection in support of NN-20 broad area search and analysis

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

Petrie, G.M.; Perry, E.M.; Kirkham, R.R.

1997-09-01

This report describes the work performed at the Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy`s Office of Nonproliferation and National Security, Office of Research and Development (NN-20). The work supports the NN-20 Broad Area Search and Analysis, a program initiated by NN-20 to improve the detection and classification of undeclared weapons facilities. Ongoing PNNL research activities are described in three main components: image collection, information processing, and change analysis. The Multispectral Airborne Imaging System, which was developed to collect georeferenced imagery in the visible through infrared regions of the spectrum, and flown on a light aircraftmore » platform, will supply current land use conditions. The image information extraction software (dynamic clustering and end-member extraction) uses imagery, like the multispectral data collected by the PNNL multispectral system, to efficiently generate landcover information. The advanced change detection uses a priori (benchmark) information, current landcover conditions, and user-supplied rules to rank suspect areas by probable risk of undeclared facilities or proliferation activities. These components, both separately and combined, provide important tools for improving the detection of undeclared facilities.« less

Future of the Beam Energy Scan program at RHIC

DOE PAGES

Odyniec, Grazyna; Bravina, L.; Foka, Y.; ...

2015-05-29

The first exploratory phase of a very successful Beam Energy Scan Program at RHIC was completed in 2014 with Au+Au collisions at energies ranging from 7 to 39 GeV. Data sets taken earlier extended the upper limit of energy range to the √sNN of 200 GeV. This provided an initial look into the uncharted territory of the QCD phase diagram, which is considered to be the single most important graph of our field. The main results from BES phase I, although effected by large statistical errors (steeply increasing with decreasing energy), suggest that the highest potential for discovery of themore » QCD Critical Point lies bellow √sNN 20 GeV. Here, we discuss the plans and the preparation for phase II of the BES program, with an order of magnitude larger statistics, which is planned for 2018-2019. The BES II will focus on Au+Au collisions at √sNN from 20 to 7 GeV in collider mode, and from √sNN 7 to 3.5 GeV in the fixed target mode, which will be run concurrently with the collider mode operation.« less

Modeling of transport phenomena in tokamak plasmas with neural networks

DOE PAGES

Meneghini, Orso; Luna, Christopher J.; Smith, Sterling P.; ...

2014-06-23

A new transport model that uses neural networks (NNs) to yield electron and ion heat ux pro les has been developed. Given a set of local dimensionless plasma parameters similar to the ones that the highest delity models use, the NN model is able to efficiently and accurately predict the ion and electron heat transport pro les. As a benchmark, a NN was built, trained, and tested on data from the 2012 and 2013 DIII-D experimental campaigns. It is found that NN can capture the experimental behavior over the majority of the plasma radius and across a broad range ofmore » plasma regimes. Although each radial location is calculated independently from the others, the heat ux pro les are smooth, suggesting that the solution found by the NN is a smooth function of the local input parameters. This result supports the evidence of a well-de ned, non-stochastic relationship between the input parameters and the experimentally measured transport uxes. Finally, the numerical efficiency of this method, requiring only a few CPU-μs per data point, makes it ideal for scenario development simulations and real-time plasma control.« less

PREFACE: 11th International Conference on Nucleus-Nucleus Collisions (NN2012)

NASA Astrophysics Data System (ADS)

Li, Bao-An; Natowitz, Joseph B.

2013-03-01

The 11th International Conference on Nucleus-Nucleus Collisions (NN2012) was held from 27 May to 1 June 2012, in San Antonio, Texas, USA. It was jointly organized and hosted by The Cyclotron Institute at Texas A&M University, College Station and The Department of Physics and Astronomy at Texas A&M University-Commerce. Among the approximately 300 participants were a large number of graduate students and post-doctoral fellows. The Keynote Talk of the conference, 'The State of Affairs of Present and Future Nucleus-Nucleus Collision Science', was given by Dr Robert Tribble, University Distinguished Professor and Director of the TAMU Cyclotron Institute. During the conference a very well-received public lecture on neutrino astronomy, 'The ICEcube project', was given by Dr Francis Halzen, Hilldale and Gregory Breit Distinguished Professor at the University of Wisconsin, Madison. The Scientific program continued in the general spirit and intention of this conference series. As is typical of this conference a broad range of topics including fundamental areas of nuclear dynamics, structure, and applications were addressed in 42 plenary session talks, 150 parallel session talks, and 21 posters. The high quality of the work presented emphasized the vitality and relevance of the subject matter of this conference. Following the tradition, the NN2012 International Advisory Committee selected the host and site of the next conference in this series. The 12th International Conference on Nucleus-Nucleus Collisions (NN2015) will be held 21-26 June 2015 in Catania, Italy. It will be hosted by The INFN, Laboratori Nazionali del Sud, INFN, Catania and the Dipartimento di Fisica e Astronomia of the University of Catania. The NN2012 Proceedings contains the conference program and 165 articles organized into the following 10 sections 1. Heavy and Superheavy Elements 2. QCD and Hadron Physics 3. Relativistic Heavy-Ion Collisions 4. Nuclear Structure 5. Nuclear Energy and Applications of Nuclear Science and Technologies 6. Nuclear Reactions and Structure of Unstable Nuclei 7. Equation of State of Neutron-Rich Nuclear Matter, Clusters in Nuclei and Nuclear Reactions 8. Fusion and Fission 9. Nuclear Astrophysics 10. New Facilities and Detectors We would like to thank Texas A&M University and Texas A&M University-Commerce for their organizational support and for providing financial support for many students and postdocs and those who had special need. This support helped assure the success of NN2012. Special thanks also go to all members of the International Advisory Committee and the Local Organizing Committee (listed below) for their great work in advising upon, preparing and executing the NN2012 scientific program as well as the social events that all together made the NN2012 an enjoyable experience for both the participants and their companions. NN2012 International Advisory Committee N Auerbach (Israel) J Aysto (Finland) C Beck (France) S Cherubini (Italy) L Ferreira (Portugal) C Gagliardi (USA) S Gales (France) C Gale (Canada) W Gelletly (Great Britain) Paulo R S Gomes (Brazil) W Greiner (Germany) W Henning (USA) D Hinde (Australia) S Hofmann (Germany) M Hussein (Brazil) B Jacak (USA) S Kailas (India) W G Lynch (USA) Z Majka (Poland) L McLerran (USA) V Metag (Germany) K Morita (Japan) B Mueller (USA) D G Mueller (France) T Motobayashi (Japan) W Nazarewicz (USA) Y Oganessian (Russia) J Nolen (USA) E K Rehm (USA) N Rowley (France) B Sherrill (USA) J Schukraft (Switzerland) W Q Shen (China) A Stefanini (Italy) H Stoecker (Germany) A Szanto de Toledo (Brazil) U van Kolck (USA) W von Oertzen (Germany) M Wiescher (USA) N Xu (USA) N V Zamfir (Romania) W L Zhan (China) H Q Zhang (China) NN2012 Local Organizing Committee Marina Barbui Carlos Bertulani Robert Burch Jr Cheri Davis Cody Folden Kris Hagel John Hardy Bao-An Li (Co-Chair and Scientific Secretary) Joseph Natowitz (Co-Chair) Ralf Rapp Livius Trache Sherry Yennello Editors of NN2012 Proceedings Bao-An Li (Texas A&M University-Commerce) and Joseph Natowitz (Texas A&M University) 7 January 2013, Texas, USA

Stability, elastic and electronic properties of a novel BN2 sheet with extended hexagons with N-N bonds

NASA Astrophysics Data System (ADS)

Waters, Kevin; Pandey, Ravindra

2018-04-01

A new B-N monolayer material (BN2) consisting of a network of extended hexagons is predicted using density functional theory. The distinguishable nature of this 2D material is found to be the presence of the bonded N atoms (N-N) in the lattice. Analysis of the phonon dispersion curves show this phase of BN2 to be stable. The calculated elastic properties exhibit anisotropic mechanical properties that surpass graphene in the armchair direction. The BN2 monolayer is metallic with in-plane p states dominating the Fermi level. Novel applications resulting from a strong anisotropic mechanical strength together with the metallic properties of the BN2 sheet with the extended hexagons with N-N bonds may enable future innovation at the nanoscale.

On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning.

PubMed

Mizutani, Eiji; Demmel, James W

2003-01-01

This paper briefly introduces our numerical linear algebra approaches for solving structured nonlinear least squares problems arising from 'multiple-output' neural-network (NN) models. Our algorithms feature trust-region regularization, and exploit sparsity of either the 'block-angular' residual Jacobian matrix or the 'block-arrow' Gauss-Newton Hessian (or Fisher information matrix in statistical sense) depending on problem scale so as to render a large class of NN-learning algorithms 'efficient' in both memory and operation costs. Using a relatively large real-world nonlinear regression application, we shall explain algorithmic strengths and weaknesses, analyzing simulation results obtained by both direct and iterative trust-region algorithms with two distinct NN models: 'multilayer perceptrons' (MLP) and 'complementary mixtures of MLP-experts' (or neuro-fuzzy modular networks).

Generalization of the NpNn scheme to nonyrast levels of even-even nuclei

NASA Astrophysics Data System (ADS)

Zhao, Y. M.; Arima, A.

2003-07-01

In this Brief Report we present the systematics of excitation energies for even-even nuclei in two regions: the 50

End-growth/evaporation living polymerization kinetics revisited

NASA Astrophysics Data System (ADS)

Semenov, A. N.; Nyrkova, I. A.

2011-03-01

End-growth/evaporation kinetics in living polymer systems with "association-ready" free unimers (no initiator) is considered theoretically. The study is focused on the systems with long chains (typical aggregation number N ≫ 1) at long times. A closed system of continuous equations is derived and is applied to study the kinetics of the chain length distribution (CLD) following a jump of a parameter (T-jump) inducing a change of the equilibrium mean chain length from N0 to N. The continuous approach is asymptotically exact for t ≫ t1, where t1 is the dimer dissociation time. It yields a number of essentially new analytical results concerning the CLD kinetics in some representative regimes. In particular, we obtained the asymptotically exact CLD response (for N ≫ 1) to a weak T-jump (ɛ = N0/N - 1 ≪ 1). For arbitrary T-jumps we found that the longest relaxation time tmax = 1/γ is always quadratic in N (γ is the relaxation rate of the slowest normal mode). More precisely tmax ∝4N2 for N0 < 2N and tmax ∝NN0/(1 - N/N0) for N0 > 2N. The mean chain length Nn is shown to change significantly during the intermediate slow relaxation stage t1 ≪ t ≪ tmax . We predict that N_n(t)-N_n(0)∝ √{t} in the intermediate regime for weak (or moderate) T-jumps. For a deep T-quench inducing strong increase of the equilibrium Nn (N ≫ N0 ≫ 1), the mean chain length follows a similar law, N_n(t)∝ √{t}, while an opposite T-jump (inducing chain shortening, N0 ≫ N ≫ 1) leads to a power-law decrease of Nn: Nn(t)∝t-1/3. It is also shown that a living polymer system gets strongly polydisperse in the latter regime, the maximum polydispersity index r = Nw/Nn being r* ≈ 0.77N0/N ≫ 1. The concentration of free unimers relaxes mainly during the fast process with the characteristic time tf ˜ t1N0/N2. A nonexponential CLD dominated by short chains develops as a result of the fast stage in the case of N0 = 1 and N ≫ 1. The obtained analytical results are supported, in part, by comparison with numerical results found both previously and in the present paper.

Peripheral halo-functionalization in [Cu(N^N)(P^P)]+ emitters: influence on the performances of light-emitting electrochemical cells.

PubMed

Brunner, Fabian; Martínez-Sarti, Laura; Keller, Sarah; Pertegás, Antonio; Prescimone, Alessandro; Constable, Edwin C; Bolink, Henk J; Housecroft, Catherine E

2016-09-27

A series of heteroleptic [Cu(N^N)(P^P)][PF 6 ] complexes is described in which P^P = bis(2-(diphenylphosphino)phenyl)ether (POP) or 4,5-bis(diphenylphosphino)-9,9-dimethylxanthene (xantphos) and N^N = 4,4'-diphenyl-6,6'-dimethyl-2,2'-bipyridine substituted in the 4-position of the phenyl groups with atom X (N^N = 1 has X = F, 2 has X = Cl, 3 has X = Br, 4 has X = I; the benchmark N^N ligand with X = H is 5). These complexes have been characterized by multinuclear NMR spectroscopy, mass spectrometry, elemental analyses and cyclic voltammetry; representative single crystal structures are also reported. The solution absorption spectra are characterized by high energy bands (arising from ligand-centred transitions) which are red-shifted on going from X = H to X = I, and a broad metal-to-ligand charge transfer band with λ max in the range 387-395 nm. The ten complexes are yellow emitters in solution and yellow or yellow-orange emitters in the solid-state. For a given N^N ligand, the solution photoluminescence (PL) spectra show no significant change on going from [Cu(N^N)(POP)] + to [Cu(N^N)(xantphos)] + ; introducing the iodo-functionality into the N^N domain leads to a red-shift in λ compared to the complexes with the benchmark N^N ligand 5. In the solid state, [Cu(1)(POP)][PF 6 ] and [Cu(1)(xantphos)][PF 6 ] (fluoro-substituent) exhibit the highest PL quantum yields (74 and 25%, respectively) with values of τ 1/2 = 11.1 and 5.8 μs, respectively. Light-emitting electrochemical cells (LECs) with [Cu(N^N)(P^P)][PF 6 ] complexes in the emissive layer have been tested. Using a block-wave pulsed current driving mode, the best performing device employed [Cu(1)(xantphos)] + and this showed a maximum luminance (Lum max ) of 129 cd m -2 and a device lifetime (t 1/2 ) of 54 h; however, the turn-on time (time to reach Lum max ) was 4.1 h. Trends in performance data reveal that the introduction of fluoro-groups is beneficial, but that the incorporation of heavier halo-substituents leads to poor devices, probably due to a detrimental effect on charge transport; LECs with the iodo-functionalized N^N ligand 4 failed to show any electroluminescence after 50 h.

Hybrid NN/SVM Computational System for Optimizing Designs

NASA Technical Reports Server (NTRS)

Rai, Man Mohan

2009-01-01

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily oversimplified to fit the scope of this article, an SVM can be characterized as an algorithm that (1) effects a nonlinear mapping of input vectors into a higher-dimensional feature space and (2) involves a dual formulation of governing equations and constraints. One advantageous feature of the SVM approach is that an objective function (which one seeks to minimize to obtain coefficients that define an SVM mathematical model) is convex, so that unlike in the cases of many NN models, any local minimum of an SVM model is also a global minimum.

Army Training Study: Training Effectiveness Analysis (TEA) Summary. Volume 1. Armor.

DTIC Science & Technology

1978-08-08

c. r tf(e rx =r) rfs it ,, 0’( r’( r , a S’ f "’ u nn Ir Irf I I. * 222 ARM’- NN :TuDY TWANINC EFFCIUENEZ-: ANALISIS TEA : MMARY ’JLUM~ I ARMOR U...marginally above 50%, however, probably is not. 22 TABLE 10 TANK CREW QUALIFICATION PERFORMANCE ON TASK STANDARDS S TANDARD SATI S FACTORY Day

Predicting Diameter Distributions of Longleaf Pine Plantations: A Comparison Between Artificial Neural Networks and Other Accepted Methodologies

Treesearch

Daniel J. Leduc; Thomas G. Matney; Keith L. Belli; V. Clark Baldwin

2001-01-01

Artificial neural networks (NN) are becoming a popular estimation tool. Because they require no assumptions about the form of a fitting function, they can free the modeler from reliance on parametric approximating functions that may or may not satisfactorily fit the observed data. To date there have been few applications in forestry science, but as better NN software...

Chemoselective room temperature E1cB N-N cleavage of oxazolidinone hydrazides from enantioselective aldehyde α-hydrazination: synthesis of (+)-1,4-dideoxyallonojirimycin.

PubMed

Ferreira, Jasmin; Rees-Jones, Sophie C M; Ramaotsoa, Valerie; Msutu, Ath'enkosi; Hunter, Roger

2016-02-07

Room temperature E1cB N-N cleavage of oxazolidinone hydrazides via N-alkylation with diethyl bromomalonate and potassium or caesium carbonate as base in acetonitrile is presented. The new method has a much improved chemoselectivity, which is illustrated by a concise total synthesis of the piperidine iminosugar (+)-1,4-dideoxyallonojirimycin.

A New NASA Data Product: Tropospheric and Stratospheric Column Ozone in the Tropics Derived from TOMS Measurements

NASA Technical Reports Server (NTRS)

Ziemke, J. R.; Chandra, S.; Bhartia, P. K.

1999-01-01

Tropospheric column ozone (TCO) and stratospheric column ozone (SCO) gridded data in the tropics for 1979-present are now available from NASA Goddard Space Flight Center via either direct ftp, world-NN,ide-NN,eb, or electronic mail. This note provides a brief overview of the method used to derive the data set including validation and adjustments.

Exploitation of RF-DNA for Device Classification and Verification Using GRLVQI Processing

DTIC Science & Technology

2012-12-01

5 FLD Fisher’s Linear Discriminant . . . . . . . . . . . . . . . . . . . 6 kNN K-Nearest Neighbor...Neighbor ( kNN ), Support Vector Machine (SVM), and simple cross-correlation techniques [40, 57, 82, 88, 94, 95]. The RF-DNA fingerprinting research in...Expansion and the Dis- crete Gabor Transform on a Non-Separable Lattice”. 2000 IEEE Int’l Conf on Acoustics, Speech , and Signal Processing (ICASSP00

A Mixed Approach to Similarity Metric Selection in Affinity Propagation-Based WiFi Fingerprinting Indoor Positioning.

PubMed

Caso, Giuseppe; de Nardis, Luca; di Benedetto, Maria-Gabriella

2015-10-30

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms.

A Mixed Approach to Similarity Metric Selection in Affinity Propagation-Based WiFi Fingerprinting Indoor Positioning

PubMed Central

Caso, Giuseppe; de Nardis, Luca; di Benedetto, Maria-Gabriella

2015-01-01

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms. PMID:26528984

A neural network for real-time retrievals of PWV and LWP from Arctic millimeter-wave ground-based observations.

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

Cadeddu, M. P.; Turner, D. D.; Liljegren, J. C.

2009-07-01

This paper presents a new neural network (NN) algorithm for real-time retrievals of low amounts of precipitable water vapor (PWV) and integrated liquid water from millimeter-wave ground-based observations. Measurements are collected by the 183.3-GHz G-band vapor radiometer (GVR) operating at the Atmospheric Radiation Measurement (ARM) Program Climate Research Facility, Barrow, AK. The NN provides the means to explore the nonlinear regime of the measurements and investigate the physical boundaries of the operability of the instrument. A methodology to compute individual error bars associated with the NN output is developed, and a detailed error analysis of the network output is provided.more » Through the error analysis, it is possible to isolate several components contributing to the overall retrieval errors and to analyze the dependence of the errors on the inputs. The network outputs and associated errors are then compared with results from a physical retrieval and with the ARM two-channel microwave radiometer (MWR) statistical retrieval. When the NN is trained with a seasonal training data set, the retrievals of water vapor yield results that are comparable to those obtained from a traditional physical retrieval, with a retrieval error percentage of {approx}5% when the PWV is between 2 and 10 mm, but with the advantages that the NN algorithm does not require vertical profiles of temperature and humidity as input and is significantly faster computationally. Liquid water path (LWP) retrievals from the NN have a significantly improved clear-sky bias (mean of {approx}2.4 g/m{sup 2}) and a retrieval error varying from 1 to about 10 g/m{sup 2} when the PWV amount is between 1 and 10 mm. As an independent validation of the LWP retrieval, the longwave downwelling surface flux was computed and compared with observations. The comparison shows a significant improvement with respect to the MWR statistical retrievals, particularly for LWP amounts of less than 60 g/m{sup 2}.« less

Remote sensing of nutrient deficiency in Lactuca sativa using neural networks for terrestrial and advanced life support applications

NASA Astrophysics Data System (ADS)

Sears, Edie Seldon

2000-12-01

A remote sensing study using reflectance and fluorescence spectra of hydroponically grown Lactuca sativa (lettuce) canopies was conducted. An optical receiver was designed and constructed to interface with a commercial fiber optic spectrometer for data acquisition. Optical parameters were varied to determine effects of field of view and distance to target on vegetation stress assessment over the test plant growth cycle. Feedforward backpropagation neural networks (NN) were implemented to predict the presence of canopy stress. Effects of spatial and spectral resolutions on stress predictions of the neural network were also examined. Visual inspection and fresh mass values failed to differentiate among controls, plants cultivated with 25% of the recommended concentration of phosphorous (P), and those cultivated with 25% nitrogen (N) based on fresh mass and visual inspection. The NN's were trained on input vectors created using reflectance and test day, fluorescence and test day, and reflectance, fluorescence, and test day. Four networks were created representing four levels of spectral resolution: 100-nm NN, 10-nm NN, 1-nm NN, and 0.1-nm NN. The 10-nm resolution was found to be sufficient for classifying extreme nitrogen deficiency in freestanding hydroponic lettuce. As a result of leaf angle and canopy structure broadband scattering intensity in the 700-nm to 1000-nm range was found to be the most useful portion of the spectrum in this study. More subtle effects of "greenness" and fluorescence emission were believed to be obscured by canopy structure and leaf orientation. As field of view was not as found to be as significant as originally believed, systems implementing higher repetitions over more uniformly oriented, i.e. smaller, flatter, target areas would provide for more discernible neural network input vectors. It is believed that this technique holds considerable promise for early detection of extreme nitrogen deficiency. Further research is recommended using stereoscopic digital cameras to quantify leaf area index, leaf shape, and leaf orientation as well as reflectance. Given this additional information fluorescence emission may also prove a more useful biological assay of freestanding vegetation.

Measurement of electrons from beauty-hadron decays in p-Pb collisions at √{s_{NN}}=5.02 TeV and Pb-Pb collisions at √{s_{NN}}=2.76 TeV

NASA Astrophysics Data System (ADS)

Adam, J.; Adamová, D.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, S.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; An, M.; Andrei, C.; Andrews, H. A.; Andronic, A.; Anguelov, V.; Anson, C.; Antičić, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshäuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badalà, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnaföldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Basu, S.; Bathen, B.; Batigne, G.; Batista Camejo, A.; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont-Moreno, E.; Beltran, L. G. E.; Belyaev, V.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielčík, J.; Bielčíková, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Bøggild, H.; Boldizsár, L.; Bombara, M.; Bonora, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossú, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buhler, P.; Buitron, S. A. I.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castillo Castellanos, J.; Castro, A. J.; Casula, E. A. R.; Ceballos Sanchez, C.; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Chibante Barroso, V.; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Chung, S. U.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Conesa Balbastre, G.; Conesa del Valle, Z.; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Corrales Morales, Y.; Cortés Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crkovská, J.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, D.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; De Souza, R. D.; Deisting, A.; Deloff, A.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Di Ruzza, B.; Diaz Corchero, M. A.; Dietel, T.; Dillenseger, P.; Divià, R.; Djuvsland, Ø.; Dobrin, A.; Domenicis Gimenez, D.; Dönigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Duggal, A. K.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eulisse, G.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernández Téllez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Francisco, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Fusco Girard, M.; Gaardhøje, J. J.; Gagliardi, M.; Gago, A. M.; Gajdosova, K.; Gallio, M.; Galvan, C. D.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Garg, K.; Garg, P.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, M.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glässel, P.; Goméz Coral, D. M.; Gomez Ramirez, A.; Gonzalez, A. S.; Gonzalez, V.; González-Zamora, P.; Gorbunov, S.; Görlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Gruber, L.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Guzman, I. B.; Haake, R.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbär, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Herrmann, F.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Hughes, C.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Isakov, V.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacak, B.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Jimenez Bustamante, R. T.; Jones, P. G.; Jung, H.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kang, J. H.; Kaplin, V.; Kar, S.; Karasu Uysal, A.; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Mohisin Khan, M.; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Khuntia, A.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, H.; Kim, J. S.; Kim, J.; Kim, M.; Kim, M.; Kim, S.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein, J.; Klein-Bösing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Koyithatta Meethaleveedu, G.; Králik, I.; Kravčáková, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kučera, V.; Kuhn, C.; Kuijer, P. G.; Kumar, A.; Kumar, J.; Kumar, L.; Kumar, S.; Kundu, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lapidus, K.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lazaridis, L.; Lea, R.; Leardini, L.; Lee, S.; Lehas, F.; Lehner, S.; Lehrbach, J.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; León Monzón, I.; León Vargas, H.; Leoncino, M.; Lévai, P.; Li, S.; Li, X.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; López Torres, E.; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lupi, M.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Mao, Y.; Marchisone, M.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marín, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martinengo, P.; Martínez, M. I.; Martínez García, G.; Martinez Pedreira, M.; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzilli, M.; Mazzoni, M. A.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Mercado Pérez, J.; Meres, M.; Mhlanga, S.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Mishra, T.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montes, E.; Moreira De Godoy, D. A.; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Mühlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Münning, K.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Negrao De Oliveira, R. A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paić, G.; Pal, S. K.; Palni, P.; Pan, J.; Pandey, A. K.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, J.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Peng, X.; Pereira Da Costa, H.; Peresunko, D.; Perez Lezama, E.; Peskov, V.; Pestov, Y.; Petráček, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Poppenborg, H.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Rascanu, B. T.; Rathee, D.; Ratza, V.; Ravasenga, I.; Read, K. F.; Redlich, K.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rodríguez Cahuantzi, M.; Røed, K.; Rogochaya, E.; Rohr, D.; Röhrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Šafařík, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Šándor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schmidt, M.; Schukraft, J.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Šefčík, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shangaraev, A.; Sharma, A.; Sharma, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singhal, V.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; Sozzi, F.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Šumbera, M.; Sumowidagdo, S.; Suzuki, K.; Swain, S.; Szabo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Muñoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thäder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Tikhonov, A.; Timmins, A. R.; Toia, A.; Tripathy, S.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vande Vyvre, P.; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vázquez Doce, O.; Vechernin, V.; Veen, A. M.; Velure, A.; Vercellin, E.; Vergara Limón, S.; Vernet, R.; Vértesi, R.; Vickovic, L.; Vigolo, S.; Viinikainen, J.; Vilakazi, Z.; Villalobos Baillie, O.; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Virgili, T.; Vislavicius, V.; Vodopyanov, A.; Völkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Voscek, D.; Vranic, D.; Vrláková, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Wang, M.; Watanabe, D.; Watanabe, Y.; Weber, M.; Weber, S. G.; Weiser, D. F.; Wessels, J. P.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Willems, G. A.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yalcin, S.; Yang, P.; Yano, S.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yoon, J. H.; Yurchenko, V.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, H.; Zhang, X.; Zhang, Y.; Zhang, C.; Zhang, Z.; Zhao, C.; Zhigareva, N.; Zhou, D.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zhu, J.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zmeskal, J.

2017-07-01

The production of beauty hadrons was measured via semi-leptonic decays at mid-rapidity with the ALICE detector at the LHC in the transverse momentum interval 1

Open charm and dileptons from relativistic heavy-ion collisions

NASA Astrophysics Data System (ADS)

Song, Taesoo; Cassing, Wolfgang; Moreau, Pierre; Bratkovskaya, Elena

2018-06-01

Dileptons are considered as one of the cleanest signals of the quark-gluon plasma (QGP); however, the QGP radiation is masked by many background sources from either hadronic decays or semileptonic decays from correlated charm pairs. In this study, we investigate the relative contribution of these channels in heavy-ion collisions from √{sNN}=8 GeV to 5 TeV with a focus on the competition between the thermal QGP radiation and the semileptonic decays from correlated D -meson pairs. As a tool, we employ the parton-hadron-string dynamics (PHSD) transport approach to study dilepton spectra in Pb + Pb (Au + Au) collisions in a wide energy range, incorporating for the first time a fully microscopic treatment of the charm dynamics and their semileptonic decays. We find that the dileptons from correlated D -meson decays dominate the thermal radiation from the QGP in central Pb + Pb collisions at the intermediate masses (1.2 GeV 40 GeV, while for √{sNN}=8 to 20 GeV the contribution from D ,D ¯ decays to the intermediate mass dilepton spectra is subleading such that one should observe a rather clear signal from the QGP radiation. We furthermore study the pT spectra and the RA A(pT) of single electrons at different energies as well as the excitation function of the inverse slope of the mT spectra for intermediate-mass dileptons from the QGP and from charm decays. We find moderate but characteristic changes in the inverse slope parameter for √{sNN}> 20 GeV which can be observed experimentally in high statistics data. Additionally, we provide detailed predictions for dilepton spectra from Pb + Pb collisions at √{sNN}= 5.02 TeV.

Measurement of electrons from beauty-hadron decays in p-Pb collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=5.02 $$ TeV and Pb-Pb collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=2.76 $$ TeV

DOE PAGES

Adam, J.; Adamová, D.; Aggarwal, M. M.; ...

2017-07-11

The production of beauty hadrons was measured via semi-leptonic decays at mid-rapidity with the ALICE detector at the LHC in the transverse momentum interval 1sNN = 5.02 TeV and in 1.3 < pT< 8 GeV/c in the 20% most central Pb-Pb collisions at √ sNN = 2.76 TeV. The pp reference spectra at √ sNN = 5.02 TeV and √s = 2.76 TeV, needed for the calculation of the nuclear modification factors R pPb and R PbPb, were obtained by a pQCD-driven scaling of the cross section of electrons from beauty-hadron decays measured at √s = 7 TeV. In themore » p T interval 3 < p T < 8 GeV/c, a suppression of the yield of electrons from beauty-hadron decays is observed in Pb-Pb compared to pp collisions. Towards lower p T, the R PbPb values increase with large systematic uncertainties. The R pPb is consistent with unity within systematic uncertainties and is well described by theoretical calculations that include cold nuclear matter effects in p-Pb collisions. The measured R pPb and these calculations indicate that cold nuclear matter effects are small at high transverse momentum also in Pb-Pb collisions. Therefore, the observed reduction of R PbPb below unity at high p T may be ascribed to an effect of the hot and dense medium formed in Pb-Pb collisions.« less

The NnCenH3 protein and centromeric DNA sequence profiles of Nelumbo nucifera Gaertn. (sacred lotus) reveal the DNA structures and dynamics of centromeres in basal eudicots.

PubMed

Zhu, Zhixuan; Gui, Songtao; Jin, Jing; Yi, Rong; Wu, Zhihua; Qian, Qian; Ding, Yi

2016-09-01

Centromeres on eukaryotic chromosomes consist of large arrays of DNA repeats that undergo very rapid evolution. Nelumbo nucifera Gaertn. (sacred lotus) is a phylogenetic relict and an aquatic perennial basal eudicot. Studies concerning the centromeres of this basal eudicot species could provide ancient evolutionary perspectives. In this study, we characterized the centromeric marker protein NnCenH3 (sacred lotus centromere-specific histone H3 variant), and used a chromatin immunoprecipitation (ChIP)-based technique to recover the NnCenH3 nucleosome-associated sequences of sacred lotus. The properties of the centromere-binding protein and DNA sequences revealed notable divergence between sacred lotus and other flowering plants, including the following factors: (i) an NnCenH3 alternative splicing variant comprising only a partial centromere-targeting domain, (ii) active genes with low transcription levels in the NnCenH3 nucleosomal regions, and (iii) the prevalence of the Ty1/copia class of long terminal repeat (LTR) retrotransposons in the centromeres of sacred lotus chromosomes. In addition, the dynamic natures of the centromeric region showed that some of the centromeric repeat DNA sequences originated from telomeric repeats, and a pair of centromeres on the dicentric chromosome 1 was inactive in the metaphase cells of sacred lotus. Our characterization of the properties of centromeric DNA structure within the sacred lotus genome describes a centromeric profile in ancient basal eudicots and might provide evidence of the origins and evolution of centromeres. Furthermore, the identification of centromeric DNA sequences is of great significance for the assembly of the sacred lotus genome. © 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.

The Effect of Sex on Heart Rate Variability at High Altitude.

PubMed

Boos, Christopher John; Vincent, Emma; Mellor, Adrian; O'Hara, John; Newman, Caroline; Cruttenden, Richard; Scott, Phylip; Cooke, Mark; Matu, Jamie; Woods, David Richard

2017-12-01

There is evidence suggesting that high altitude (HA) exposure leads to a fall in heart rate variability (HRV) that is linked to the development of acute mountain sickness (AMS). The effects of sex on changes in HRV at HA and its relationship to AMS are unknown. HRV (5-min single-lead ECG) was measured in 63 healthy adults (41 men and 22 women) 18-56 yr of age at sea level (SL) and during a HA trek at 3619, 4600, and 5140 m, respectively. The main effects of altitude (SL, 3619 m, 4600 m, and 5140 m) and sex (men vs women) and their potential interaction were assessed using a factorial repeated-measures ANOVA. Logistic regression analyses were performed to assess the ability of HRV to predict AMS. Men and women were of similar age (31.2 ± 9.3 vs 31.7 ± 7.5 yr), ethnicity, and body and mass index. There was main effect for altitude on heart rate, SD of normal-to-normal (NN) intervals (SDNN), root mean square of successive differences (RMSSD), number of pairs of successive NN differing by >50 ms (NN50), NN50/total number of NN, very low-frequency power, low-frequency (LF) power, high-frequency (HF) power, and total power (TP). The most consistent effect on post hoc analysis was reduction in these HRV measures between 3619 and 5140 m at HA. Heart rate was significantly lower and SDNN, RMSSD, LF power, HF power, and TP were higher in men compared with women at HA. There was no interaction between sex and altitude for any of the HRV indices measured. HRV was not predictive of AMS development. Increasing HA leads to a reduction in HRV. Significant differences between men and women emerge at HA. HRV was not predictive of AMS.

Polymers with nearest- and next nearest-neighbor interactions on the Husimi lattice

NASA Astrophysics Data System (ADS)

Oliveira, Tiago J.

2016-04-01

The exact grand-canonical solution of a generalized interacting self-avoid walk (ISAW) model, placed on a Husimi lattice built with squares, is presented. In this model, beyond the traditional interaction {ω }1={{{e}}}{ɛ 1/{k}BT} between (nonconsecutive) monomers on nearest-neighbor (NN) sites, an additional energy {ɛ }2 is associated to next-NN (NNN) monomers. Three definitions of NNN sites/interactions are considered, where each monomer can have, effectively, at most two, four, or six NNN monomers on the Husimi lattice. The phase diagrams found in all cases have (qualitatively) the same thermodynamic properties: a non-polymerized (NP) and a polymerized (P) phase separated by a critical and a coexistence surface that meet at a tricritical (θ-) line. This θ-line is found even when one of the interactions is repulsive, existing for {ω }1 in the range [0,∞ ), i.e., for {ɛ }1/{k}BT in the range [-∞ ,∞ ). Thus, counterintuitively, a θ-point exists even for an infinite repulsion between NN monomers ({ω }1=0), being associated to a coil-‘soft globule’ transition. In the limit of an infinite repulsive force between NNN monomers, however, the coil-globule transition disappears, and only NP-P continuous transition is observed. This particular case, with {ω }2=0, is also solved exactly on the square lattice, using a transfer matrix calculation where a discontinuous NP-P transition is found. For attractive and repulsive forces between NN and NNN monomers, respectively, the model becomes quite similar to the semiflexible-ISAW one, whose crystalline phase is not observed here, as a consequence of the frustration due to competing NN and NNN forces. The mapping of the phase diagrams in canonical ones is discussed and compared with recent results from Monte Carlo simulations on the square lattice.

Measurement of electrons from beauty-hadron decays in p-Pb collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=5.02 $$ TeV and Pb-Pb collisions at $$ \\sqrt{s_{\\mathrm{NN}}}=2.76 $$ TeV

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

Adam, J.; Adamová, D.; Aggarwal, M. M.

The production of beauty hadrons was measured via semi-leptonic decays at mid-rapidity with the ALICE detector at the LHC in the transverse momentum interval 1sNN = 5.02 TeV and in 1.3 < pT< 8 GeV/c in the 20% most central Pb-Pb collisions at √ sNN = 2.76 TeV. The pp reference spectra at √ sNN = 5.02 TeV and √s = 2.76 TeV, needed for the calculation of the nuclear modification factors R pPb and R PbPb, were obtained by a pQCD-driven scaling of the cross section of electrons from beauty-hadron decays measured at √s = 7 TeV. In themore » p T interval 3 < p T < 8 GeV/c, a suppression of the yield of electrons from beauty-hadron decays is observed in Pb-Pb compared to pp collisions. Towards lower p T, the R PbPb values increase with large systematic uncertainties. The R pPb is consistent with unity within systematic uncertainties and is well described by theoretical calculations that include cold nuclear matter effects in p-Pb collisions. The measured R pPb and these calculations indicate that cold nuclear matter effects are small at high transverse momentum also in Pb-Pb collisions. Therefore, the observed reduction of R PbPb below unity at high p T may be ascribed to an effect of the hot and dense medium formed in Pb-Pb collisions.« less

A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals

PubMed Central

2014-01-01

Background Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. Results The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p < 0.001). The classification accuracies of the SVM and K-nn classifiers were found to be 92.19% and 98.26%, respectively. Conclusion Although the data used to train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database. PMID:24970564

Comparison between analog and digital neural network implementations for range-finding applications.

PubMed

Gatet, Laurent; Tap-Béteille, Hélène; Bony, Francis

2009-03-01

A neural network (NN) was developed in order to increase the distance range of a phase-shift laser range finder and to achieve surface recognition, by using two photoelectrical signals issued from the measurement system. The NN architecture consists of a multilayer perceptron (MLP) with two inputs, three neurons in the hidden layer, and one output. Depending on the application, the NN output has to resolve the ambiguity due to phase-shift measurement by linearizing the inverse of the square law, or to indicate an output voltage corresponding to the tested surface. This embedded system dedicated to optoelectronic measurements was successfully tested with an analog NN, implemented in 0.35- microm complimentary metal-oxide-semiconductor (CMOS) technology, resulting in a threefold increase in the distance range with respect to the one limited by the phase-shift measurement, and by discriminating four types of surfaces (a plastic surface, glossy paper, a painted wall, and a porous surface), at a remote distance between the range finder and the target varying from 0.5 m up to 1.25 m and with a laser beam angle varying between -pi/6 and pi/6 with respect to the target. In this type of application, NN analog implementation provides many advantages, notably use of a small silicon area, low power consumption and no analog-to-digital conversions (ADCs). Nevertheless, digital implementation allows ease of conception and reconfigurability and an embedded weight and bias update. This paper presents the complete measurement system and a comparison between both types of implementation, by developing the advantages and drawbacks relative to each method. An optimized mixed architecture, using both techniques, is then proposed and discussed at the end of the paper.

A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals.

PubMed

Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

2014-06-27

Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p < 0.001). The classification accuracies of the SVM and K-nn classifiers were found to be 92.19% and 98.26%, respectively. Although the data used to train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database.

Learning from adaptive neural dynamic surface control of strict-feedback systems.

PubMed

Wang, Min; Wang, Cong

2015-06-01

Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics

PubMed Central

Stikic, Maja; Berka, Chris; Levendowski, Daniel J.; Rubio, Roberto F.; Tan, Veasna; Korszen, Stephanie; Barba, Douglas; Wurzer, David

2014-01-01

The objective of this study was to investigate the feasibility of physiological metrics such as ECG-derived heart rate and EEG-derived cognitive workload and engagement as potential predictors of performance on different training tasks. An unsupervised approach based on self-organizing neural network (NN) was utilized to model cognitive state changes over time. The feature vector comprised EEG-engagement, EEG-workload, and heart rate metrics, all self-normalized to account for individual differences. During the competitive training process, a linear topology was developed where the feature vectors similar to each other activated the same NN nodes. The NN model was trained and auto-validated on combat marksmanship training data from 51 participants that were required to make “deadly force decisions” in challenging combat scenarios. The trained NN model was cross validated using 10-fold cross-validation. It was also validated on a golf study in which additional 22 participants were asked to complete 10 sessions of 10 putts each. Temporal sequences of the activated nodes for both studies followed the same pattern of changes, demonstrating the generalization capabilities of the approach. Most node transition changes were local, but important events typically caused significant changes in the physiological metrics, as evidenced by larger state changes. This was investigated by calculating a transition score as the sum of subsequent state transitions between the activated NN nodes. Correlation analysis demonstrated statistically significant correlations between the transition scores and subjects' performances in both studies. This paper explored the hypothesis that temporal sequences of physiological changes comprise the discriminative patterns for performance prediction. These physiological markers could be utilized in future training improvement systems (e.g., through neurofeedback), and applied across a variety of training environments. PMID:25414629

How do quarks and gluons lose energy in the QGP?

DOE PAGES

Tannenbaum, M. J.

2015-03-10

RHIC introduced the method of hard scattering of partons as an in-situ probe of the the medium produced in A+A collisions. A suppression, R AA ≈ 0.2 relative to binary-scaling, was discovered for π⁰ production in the range 5 < ρ T < 20 GeV/c in central Au+Au collisions at √s NN = 200 GeV, and surprisingly also for single-electrons from the decay of heavy quarks. Both these results have been confirmed in Pb+Pb collisions at the LHC at √s NN = 2.76 TeV. Interestingly, in this ρ T range the LHC results for pions nearly overlap the RHIC results.more » Thus, due to the flatter spectrum, the energy loss in the medium at LHC in this ρ T range must be ~ 40% larger than at RHIC. Unique at the LHC are the beautiful measurements of the fractional transverse momentum imbalance 1 – (ρ-carot T2/ρ-carot T1) of di-jets in Pb+Pb collisions. At the Utrecht meeting in 2011, I corrected for the fractional imbalance of di-jets with the same cuts in p-p collisions and showed that the relative fractional jet imbalance in Pb+Pb/p-p is ≈ 15% for jets with 120 < ρ-carot T1 < 360 GeV/c. CMS later confirmed this much smaller imbalance compared to the same quantity derived from two-particle correlations of di-jet fragments at RHIC corresponding to ρ-carot T jet ≈ 10 – 20 GeV/c, which appear to show a much larger fractional jet imbalance ≈ 45% in this lower ρ-carot T range. The variation of apparent energy loss in the medium as a function of both ρ T and √s NN is striking and presents a challenge to both theory and experiment for improved understanding. There are many other such unresolved issues, for instance, the absence of evidence for a q-carot effect, due to momentum transferred to the medium by outgoing partons, which would widen the away-side di-jet and di-hadron correlations in a similar fashion as the k T-effect. Another issue well known from experiments at the CERN ISR, SpS and SpS collider is that parton-parton hard-collisions make negligible contribution to multiplicity or transverse energy production in p-p collisions–soft particles, with ρ T < 2 GeV/c, predominate. Thus an apparent hard scattering component for A+A multiplicity distributions based on a popular formula, dN AA ch/dη = [(1 - x) (N part)dN pp ch/dη2 + x (N colldN pp ch/dη], seems to be an unphysical way to understand the deviation from N part scaling. Based on recent p-p and d+A measurements, a more physical way is presented along with several other stimulating results and ideas from recent d+Au (p+Pb) measurements.« less

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

Bernstein, A. M., E-mail: bernstein@mit.edu

Small angle electron scattering with intense electron beams opens up the possibility of performing almost real photon induced reactions with thin, polarized hydrogen and few body targets, allowing for the detection of low energy charged particles. This promises to be much more effective than conventional photon tagging techniques. For photo-pion reactions some fundamental new possibilities include: tests of charge symmetry in the N-N system by measurement of the neutron-neutron scattering length a{sub nn} in the and ggrD → π{sup +}nn reaction; tests of isospin breaking due to the mass difference of the up and down quarks; measurements with polarized targetsmore » are sensitive to πN phase shifts and will test the validity of the Fermi-Watson (final state interaction) theorem. All of these experiments will test the accuracy and energy region of validity of chiral effective theories.« less

Practical aspects of using a neural network to solve inverse geophysical problems

NASA Astrophysics Data System (ADS)

Yakimenko, A. A.; Morozov, A. E.; Karavaev, D. A.

2018-05-01

In this paper, an approach to solve an inverse problem of geophysics, such as determining the position of an object (cavity or cavern) and its geometrical parameters according to the propagation picture of a wave field, is proposed. At present there are no fast and accurate methods for determining such parameters. In this paper, a method based on neural networks (NNs) is proposed and a possible architecture of the NN is presented. The results of experiments on implementing and training the NN are also presented. The model obtained shows the presence of an "understanding" of the input data, demonstrating answers that are similar to the original data. In the NN answers, one can identify a relationship between the quality of the network response and the number of waves that have passed through the medium’s object being investigated.

Neural network-based systems for handprint OCR applications.

PubMed

Ganis, M D; Wilson, C L; Blue, J L

1998-01-01

Over the last five years or so, neural network (NN)-based approaches have been steadily gaining performance and popularity for a wide range of optical character recognition (OCR) problems, from isolated digit recognition to handprint recognition. We present an NN classification scheme based on an enhanced multilayer perceptron (MLP) and describe an end-to-end system for form-based handprint OCR applications designed by the National Institute of Standards and Technology (NIST) Visual Image Processing Group. The enhancements to the MLP are based on (i) neuron activations functions that reduce the occurrences of singular Jacobians; (ii) successive regularization to constrain the volume of the weight space; and (iii) Boltzmann pruning to constrain the dimension of the weight space. Performance characterization studies of NN systems evaluated at the first OCR systems conference and the NIST form-based handprint recognition system are also summarized.

Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form.

PubMed

Chen, Bing; Zhang, Huaguang; Lin, Chong

2016-01-01

This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear nonstrict-feedback systems via output feedback. A novel adaptive NN backstepping output-feedback control approach is first proposed for nonlinear nonstrict-feedback systems. The monotonicity of system bounding functions and the structure character of radial basis function (RBF) NNs are used to overcome the difficulties that arise from nonstrict-feedback structure. A state observer is constructed to estimate the immeasurable state variables. By combining adaptive backstepping technique with approximation capability of radial basis function NNs, an output-feedback adaptive NN controller is designed through backstepping approach. It is shown that the proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. Two examples are used to illustrate the effectiveness of the proposed approach.

Observer-Based Adaptive NN Control for a Class of Uncertain Nonlinear Systems With Nonsymmetric Input Saturation.

PubMed

Yong-Feng Gao; Xi-Ming Sun; Changyun Wen; Wei Wang

2017-07-01

This paper is concerned with the problem of adaptive tracking control for a class of uncertain nonlinear systems with nonsymmetric input saturation and immeasurable states. The radial basis function of neural network (NN) is employed to approximate unknown functions, and an NN state observer is designed to estimate the immeasurable states. To analyze the effect of input saturation, an auxiliary system is employed. By the aid of adaptive backstepping technique, an adaptive tracking control approach is developed. Under the proposed adaptive tracking controller, the boundedness of all the signals in the closed-loop system is achieved. Moreover, distinct from most of the existing references, the tracking error can be bounded by an explicit function of design parameters and saturation input error. Finally, an example is given to show the effectiveness of the proposed method.

Charged-Particle Multiplicity Density at Midrapidity in Central Pb-Pb Collisions at sNN=2.76TeV

NASA Astrophysics Data System (ADS)

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W.; Fabris, D.; Faivre, J.; Falchieri, D.; Fantoni, A.; Fasel, M.; Fearick, R.; Fedunov, A.; Fehlker, D.; Fekete, V.; Felea, D.; Feofilov, G.; Fernández Téllez, A.; Ferretti, A.; Ferretti, R.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Fini, R.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Fragkiadakis, M.; Frankenfeld, U.; Fuchs, U.; Furano, F.; Furget, C.; Fusco Girard, M.; Gaardhøje, J. J.; Gadrat, S.; Gagliardi, M.; Gago, A.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Ganti, M. S.; Garabatos, C.; Garcia-Solis, E.; Garishvili, I.; Gemme, R.; Gerhard, J.; Germain, M.; Geuna, C.; Gheata, A.; Gheata, M.; Ghidini, B.; Ghosh, P.; Gianotti, P.; Girard, M. R.; Giraudo, G.; Giubellino, P.; Gladysz-Dziadus, E.; Glässel, P.; Gomez, R.; Ferreiro, E. G.; González Santos, H.; González-Trueba, L. H.; González-Zamora, P.; Gorbunov, S.; Gotovac, S.; Grabski, V.; Grajcarek, R.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gros, P.; Grosse-Oetringhaus, J. F.; Grossiord, J.-Y.; Grosso, R.; Guber, F.; Guernane, R.; Guerra Gutierrez, C.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Gutbrod, H.; Haaland, Ø.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Harris, J. W.; Hartig, M.; Hasch, D.; Hasegan, D.; Hatzifotiadou, D.; Hayrapetyan, A.; Heide, M.; Heinz, M.; Helstrup, H.; Herghelegiu, A.; Hernández, C.; Herrera Corral, G.; Herrmann, N.; Hetland, K. F.; Hicks, B.; Hille, P. T.; Hippolyte, B.; Horaguchi, T.; Hori, Y.; Hristov, P.; Hřivnáčová, I.; Huang, M.; Huber, S.; Humanic, T. J.; Hwang, D. S.; Ichou, R.; Ilkaev, R.; Ilkiv, I.; Inaba, M.; Incani, E.; Innocenti, G. M.; Innocenti, P. G.; Ippolitov, M.; Irfan, M.; Ivan, C.; Ivanov, A.; Ivanov, M.; Ivanov, V.; Jachołkowski, A.; Jacobs, P. M.; Jancurová, L.; Jangal, S.; Janik, R.; Jena, S.; Jirden, L.; Jones, G. T.; Jones, P. G.; Jovanović, P.; Jung, H.; Jung, W.; Jusko, A.; Kalcher, S.; Kaliňák, P.; Kalisky, M.; Kalliokoski, T.; Kalweit, A.; Kamermans, R.; Kanaki, K.; Kang, E.; Kang, J. H.; Kaplin, V.; Karavichev, O.; Karavicheva, T.; Karpechev, E.; Kazantsev, A.; Kebschull, U.; Keidel, R.; Khan, M. M.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. J.; Kim, D. S.; Kim, D. W.; Kim, H. N.; Kim, J. H.; Kim, J. S.; Kim, M.; Kim, M.; Kim, S.; Kim, S. H.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Klay, J. L.; Klein, J.; Klein-Bösing, C.; Kliemant, M.; Klovning, A.; Kluge, A.; Knichel, M. L.; Koch, K.; Köhler, M. 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K.; Lopez, X.; López Noriega, M.; López Torres, E.; Løvhøiden, G.; Lu, X.-G.; Luettig, P.; Lunardon, M.; Luparello, G.; Luquin, L.; Luzzi, C.; Ma, K.; Ma, R.; Madagodahettige-Don, D. M.; Maevskaya, A.; Mager, M.; Mahapatra, D. P.; Maire, A.; Mal'Kevich, D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Malzacher, P.; Mamonov, A.; Manceau, L.; Mangotra, L.; Manko, V.; Manso, F.; Manzari, V.; Mao, Y.; Mareš, J.; Margagliotti, G. V.; Margotti, A.; Marín, A.; Markert, C.; Martashvili, I.; Martinengo, P.; Martínez, M. I.; Martínez Davalos, A.; Martínez García, G.; Martynov, Y.; Masciocchi, S.; Masera, M.; Masoni, A.; Massacrier, L.; Mastromarco, M.; Mastroserio, A.; Matthews, Z. L.; Matyja, A.; Mayani, D.; Mayer, C.; Mazza, G.; Mazzoni, M. A.; Meddi, F.; Menchaca-Rocha, A.; Mendez Lorenzo, P.; Menis, I.; Mercado Pérez, J.; Meres, M.; Mereu, P.; Miake, Y.; Midori, J.; Milano, L.; Milosevic, J.; Mischke, A.; Miśkowiec, D.; Mitu, C.; Mlynarz, J.; Mohanty, A. K.; Mohanty, B.; Molnar, L.; Montaño Zetina, L.; Monteno, M.; Montes, E.; Morando, M.; Moreira de Godoy, D. A.; Moretto, S.; Morsch, A.; Muccifora, V.; Mudnic, E.; Muhuri, S.; Müller, H.; Munhoz, M. G.; Munoz, J.; Musa, L.; Musso, A.; Nandi, B. K.; Nania, R.; Nappi, E.; Nattrass, C.; Navach, F.; Navin, S.; Nayak, T. K.; Nazarenko, S.; Nazarov, G.; Nedosekin, A.; Nendaz, F.; Newby, J.; Nicassio, M.; Nielsen, B. S.; Niida, T.; Nikolaev, S.; Nikolic, V.; Nikulin, S.; Nikulin, V.; Nilsen, B. S.; Nilsson, M. S.; Noferini, F.; Nooren, G.; Novitzky, N.; Nyanin, A.; Nyatha, A.; Nygaard, C.; Nystrand, J.; Obayashi, H.; Ochirov, A.; Oeschler, H.; Oh, S. K.; Oleniacz, J.; Oppedisano, C.; Ortiz Velasquez, A.; Ortona, G.; Oskarsson, A.; Ostrowski, P.; Otterlund, I.; Otwinowski, J.; Oyama, K.; Ozawa, K.; Pachmayer, Y.; Pachr, M.; Padilla, F.; Pagano, P.; Jayarathna, S. P.; Paić, G.; Painke, F.; Pajares, C.; Pal, S.; Pal, S. K.; Palaha, A.; Palmeri, A.; Pappalardo, G. S.; Park, W. J.; Patalakha, D. I.; Paticchio, V.; Pavlinov, A.; Pawlak, T.; Peitzmann, T.; Peresunko, D.; Pérez Lara, C. E.; Perini, D.; Perrino, D.; Peryt, W.; Pesci, A.; Peskov, V.; Pestov, Y.; Peters, A. J.; Petráček, V.; Petran, M.; Petris, M.; Petrov, P.; Petrovici, M.; Petta, C.; Piano, S.; Piccotti, A.; Pikna, M.; Pillot, P.; Pinazza, O.; Pinsky, L.; Pitz, N.; Piuz, F.; Piyarathna, D. B.; Platt, R.; Płoskoń, M.; Pluta, J.; Pocheptsov, T.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polák, K.; Polichtchouk, B.; Pop, A.; Porteboeuf, S.; Pospíšil, V.; Potukuchi, B.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puddu, G.; Pulvirenti, A.; Punin, V.; Putiš, M.; Putschke, J.; Quercigh, E.; Qvigstad, H.; Rachevski, A.; Rademakers, A.; Rademakers, O.; Radomski, S.; Räihä, T. S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Ramírez Reyes, A.; Rammler, M.; Raniwala, R.; Raniwala, S.; Räsänen, S. S.; Read, K. F.; Real, J.; Redlich, K.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Rettig, F.; Revol, J.-P.; Reygers, K.; Ricaud, H.; Riccati, L.; Ricci, R. A.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Rodríguez Cahuantzi, M.; Rohr, D.; Röhrich, D.; Romita, R.; Ronchetti, F.; Rosinský, P.; Rosnet, P.; Rossegger, S.; Rossi, A.; Roukoutakis, F.; Rousseau, S.; Roy, C.; Roy, P.; Rubio Montero, A. J.; Rui, R.; Rivetti, A.; Rusanov, I.; Ryabinkin, E.; Rybicki, A.; Sadovsky, S.; Šafařík, K.; Sahoo, R.; Sahu, P. K.; Saini, J.; Saiz, P.; Sakai, S.; Sakata, D.; Salgado, C. A.; Samanta, T.; Sambyal, S.; Samsonov, V.; Sanchez Castro, X.; Šándor, L.; Sandoval, A.; Sano, M.; Sano, S.; Santo, R.; Santoro, R.; Sarkamo, J.; Saturnini, P.; Scapparone, E.; Scarlassara, F.; Scharenberg, R. P.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schreiner, S.; Schuchmann, S.; Schukraft, J.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, P. A.; Scott, R.; Segato, G.; Selyuzhenkov, I.; Senyukov, S.; Seo, J.; Serci, S.; Serradilla, E.; Sevcenco, A.; Sgura, I.; Shabratova, G.; Shahoyan, R.; Sharma, N.; Sharma, S.; Shigaki, K.; Shimomura, M.; Shtejer, K.; Sibiriak, Y.; Siciliano, M.; Sicking, E.; Siemiarczuk, T.; Silenzi, A.; Silvermyr, D.; Simonetti, G.; Singaraju, R.; Singh, R.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Skjerdal, K.; Smakal, R.; Smirnov, N.; Snellings, R.; Søgaard, C.; Soloviev, A.; Soltz, R.; Son, H.; Song, J.; Song, M.; Soos, C.; Soramel, F.; Spyropoulou-Stassinaki, M.; Srivastava, B. K.; Stachel, J.; Stan, I.; Stefanek, G.; Stefanini, G.; Steinbeck, T.; Steinpreis, M.; Stenlund, E.; Steyn, G.; Stocco, D.; Stock, R.; Stokkevag, C. H.; Stolpovskiy, M.; Strmen, P.; Suaide, A. A. P.; Subieta Vásquez, M. A.; Sugitate, T.; Suire, C.; Sukhorukov, M.; Šumbera, M.; Susa, T.; Swoboda, D.; Symons, T. J. M.; Szanto de Toledo, A.; Szarka, I.; Szostak, A.; Tagridis, C.; Takahashi, J.; Tapia Takaki, J. D.; Tauro, A.; Tavlet, M.; Tejeda Muñoz, G.; Telesca, A.; Terrevoli, C.; Thäder, J.; Thomas, D.; Thomas, J. H.; Tieulent, R.; Timmins, A. R.; Tlusty, D.; Toia, A.; Torii, H.; Toscano, L.; Tosello, F.; Traczyk, T.; Truesdale, D.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Turvey, A. J.; Tveter, T. S.; Ulery, J.; Ullaland, K.; Uras, A.; Urbán, J.; Urciuoli, G. M.; Usai, G. L.; Vacchi, A.; Vajzer, M.; Vala, M.; Valencia Palomo, L.; Vallero, S.; van der Kolk, N.; van Leeuwen, M.; Vande Vyvre, P.; Vannucci, L.; Vargas, A.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vechernin, V.; Veldhoen, M.; Venaruzzo, M.; Vercellin, E.; Vergara, S.; Vernekohl, D. C.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Vikhlyantsev, O.; Vilakazi, Z.; Villalobos Baillie, O.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Viyogi, Y. P.; Vodopyanov, A.; Voloshin, K.; Voloshin, S.; Volpe, G.; von Haller, B.; Vranic, D.; Øvrebekk, G.; Vrláková, J.; Vulpescu, B.; Vyushin, A.; Wagner, B.; Wagner, V.; Wan, R.; Wang, D.; Wang, Y.; Wang, Y.; Watanabe, K.; Wessels, J. P.; Westerhoff, U.; Wiechula, J.; Wikne, J.; Wilde, M.; Wilk, A.; Wilk, G.; Williams, M. C. S.; Windelband, B.; Xaplanteris Karampatsos, L.; Yang, H.; Yang, S.; Yasnopolskiy, S.; Yi, J.; Yin, Z.; Yokoyama, H.; Yoo, I.-K.; Yu, W.; Yuan, X.; Yushmanov, I.; Zabrodin, E.; Zach, C.; Zampolli, C.; Zaporozhets, S.; Zarochentsev, A.; Závada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zelnicek, P.; Zenin, A.; Zgura, I.; Zhalov, M.; Zhang, X.; Zhou, D.; Zichichi, A.; Zinovjev, G.; Zoccarato, Y.; Zynovyev, M.

2010-12-01

The first measurement of the charged-particle multiplicity density at midrapidity in Pb-Pb collisions at a center-of-mass energy per nucleon pair sNN=2.76TeV is presented. For an event sample corresponding to the most central 5% of the hadronic cross section, the pseudorapidity density of primary charged particles at midrapidity is 1584±4(stat)±76(syst), which corresponds to 8.3±0.4(syst) per participating nucleon pair. This represents an increase of about a factor 1.9 relative to pp collisions at similar collision energies, and about a factor 2.2 to central Au-Au collisions at sNN=0.2TeV. This measurement provides the first experimental constraint for models of nucleus-nucleus collisions at LHC energies.

Uncertainty quantification of effective nuclear interactions

DOE PAGES

Pérez, R. Navarro; Amaro, J. E.; Arriola, E. Ruiz

2016-03-02

We give a brief review on the development of phenomenological NN interactions and the corresponding quanti cation of statistical uncertainties. We look into the uncertainty of effective interactions broadly used in mean eld calculations through the Skyrme parameters and effective eld theory counter-terms by estimating both statistical and systematic uncertainties stemming from the NN interaction. We also comment on the role played by different tting strategies on the light of recent developments.

MEASUREMENT OF THE CORRELATION COEFFICIENT Cnn AT 90 AND AN ENERGY OF 315 MEV FOR THE ELASTIC PP-SCATTERING

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

Vasilevskii, I.M.; Vishnyakov, V.V.; Iliescu, E.

1960-01-01

The spin correlation coefficient C/sub nn/ was measured for p-p scattering at 90 deg (c.m.s.) and 315 Mev and found to be 0.52 plus or minus 0.20. Calibration experiments on the polarization of protons at 640 Mev gave C/ sub nn/ = 0.7 plus or minus 0.3. (D.L.C.)

Uncertainty quantification of effective nuclear interactions

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

Pérez, R. Navarro; Amaro, J. E.; Arriola, E. Ruiz

We give a brief review on the development of phenomenological NN interactions and the corresponding quanti cation of statistical uncertainties. We look into the uncertainty of effective interactions broadly used in mean eld calculations through the Skyrme parameters and effective eld theory counter-terms by estimating both statistical and systematic uncertainties stemming from the NN interaction. We also comment on the role played by different tting strategies on the light of recent developments.

Three Dimensional Object Recognition Using a Complex Autoregressive Model

DTIC Science & Technology

1993-12-01

3.4.2 Template Matching Algorithm ...................... 3-16 3.4.3 K-Nearest-Neighbor ( KNN ) Techniques ................. 3-25 3.4.4 Hidden Markov Model...Neighbor ( KNN ) Test Results ...................... 4-13 4.2.1 Single-Look 1-NN Testing .......................... 4-14 4.2.2 Multiple-Look 1-NN Testing...4-15 4.2.3 Discussion of KNN Test Results ...................... 4-15 4.3 Hidden Markov Model (HMM) Test Results

On Algorithms for Generating Computationally Simple Piecewise Linear Classifiers

DTIC Science & Technology

1989-05-01

suffers. - Waveform classification, e.g. speech recognition, seismic analysis (i.e. discrimination between earthquakes and nuclear explosions), target...assuming Gaussian distributions (B-G) d) Bayes classifier with probability densities estimated with the k-N-N method (B- kNN ) e) The -arest neighbour...range of classifiers are chosen including a fast, easy computable and often used classifier (B-G), reliable and complex classifiers (B- kNN and NNR

Reversible insertion of carbon dioxide into Pt(II)-hydroxo bonds.

PubMed

Lohr, Tracy L; Piers, Warren E; Parvez, Masood

2013-10-01

The reactivity of three monomeric diimine Pt(II) hydroxo complexes, (NN)Pt(OH)R (NN = bulky diimine ligand; R = OH, ; R = C6H5, ; R = CH3, ) towards carbon dioxide has been investigated. Insertion into the Pt-OH bonds was found to be facile and reversible at low temperature for all compounds; the reaction with bis-hydroxide gives an isolable κ(2)-carbonato compound , with elimination of water.

Lack of maintenance of gait pattern as measured by instrumental methods suggests psychogenic gait.

PubMed

Merello, Marcelo; Ballesteros, Diego; Rossi, Malco; Arena, Julieta; Crespo, Marcos; Cervio, Andres; Cuello Oderiz, Carolina; Rivero, Alberto; Cerquetti, Daniel; Risk, Marcelo; Balej, Jorge

2012-01-01

Fluctuation is a common feature of all psychogenic gait disorder (PGD) patterns. Whether this fluctuation involves only the degree of impairment or whether it affects the gait pattern itself remains an interesting question. We hypothesize that, on repeated measurements, both normal and abnormal gait may present quantitative differences while maintaining their basic underlying pattern; conversely, in psychogenic gait, the basic pattern appears not to be preserved. Using an optoelectronic system, data acquired from 19 normal subjects and 66 patients were applied to train a neural network (NN) and subsequently classify gait patterns into four different groups (normal, ataxic, spastic-paraparetic and parkinsonian). Five patients who fulfilled clinical criteria for psychogenic gait and six controls were then prospectively evaluated on two separate occasions, three months apart. Normal controls and ataxic, parkinsonian or spastic patients were correctly identified by the NN, and categorized within the corresponding groups at baseline as well as at a three-month follow-up evaluation. NN analysis showed that after three months, no PGD patient preserved the gait pattern detected at baseline, even though this finding was not clinically apparent. Modification of gait pattern detected by repeated kinematic measurement and NN analysis could suggest the presence of PGD, particularly in difficult-to-diagnose cases.

Conserved charge fluctuations using the D measure in heavy-ion collisions

NASA Astrophysics Data System (ADS)

Mishra, D. K.; Netrakanti, P. K.; Garg, P.

2017-05-01

We study the net-charge fluctuation D -measure variable, in high-energy heavy-ion collisions in heavy-ion jet interaction generator (HIJING), ultrarelativistic quantum molecular dynamics (UrQMD), and hadron resonance gas (HRG) models for various center-of-mass energies (√{sNN}). The effects of kinematic acceptance and resonance decay, in the pseudorapidity acceptance interval (Δ η ) and lower transverse momentum (pTmin) threshold, on fluctuation measures are discussed. A strong dependence of D with the Δ η in HIJING and UrQMD models is observed as opposed to results obtained from the HRG model. The dissipation of fluctuation signal is estimated by fitting the D measure as a function of the Δ η . An extrapolated function for higher Δ η values at lower √{sNN} is different from the results obtained from models. Particle species dependence of D and the effect of the pTmin selection threshold are discussed in HIJING and HRG models. The comparison of D , at midrapidity, of net-charge fluctuations at various √{sNN} obtained from the models with the data from the A Large Ion Collider Experiment (ALICE) experiment is discussed. The results from the present paper as a function of Δ η and √{sNN} will provide a baseline for comparison to experimental measurements.

Ab initio description of continuum effects in A=11 exotic systems with chiral NN+3N forces

NASA Astrophysics Data System (ADS)

Calci, Angelo; Navratil, Petr; Roth, Robert; Dohet-Eraly, Jeremy; Quaglioni, Sofia; Hupin, Guillaume

2016-09-01

Based on the fundamental symmetries of QCD, chiral effective field theory (EFT) provides two- (NN), three- (3N) and many-nucleon interactions in a consistent and systematically improvable scheme. The rapid developments to construct divers families of chiral NN+3N interactions and the conceptual and technical improvements of ab initio many-body approaches pose a great opportunity for nuclear physics. By studying particular interesting phenomena in nuclear structure and reaction observables one can discriminate between different forces and study the predictive power of chiral EFT. The accurate description of the 11Be nucleus, in particular, the ground-state parity inversion and exceptionally strong E1 transition between its two bound states constitute an enormous challenge for the developments of nuclear forces and many-body approaches. We present a sensitivity analysis of structure and reaction observables to different NN+3N interactions in 11Be and n+10Be as well as the mirror p+10C scattering using the ab initio NCSM with continuum (NCSMC). Supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under Work Proposal No. SCW1158. TRIUMF receives federal funding via a contribution agreement with the National Research Council of Canada.

Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

NASA Astrophysics Data System (ADS)

Guo, Qianjian; Fan, Shuo; Xu, Rufeng; Cheng, Xiang; Zhao, Guoyong; Yang, Jianguo

2017-05-01

Aiming at the problem of low machining accuracy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA(grey relational analysis) method is introduced into the selection of temperature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN (artificial neural network) model is presented, and ABC(artificial bee colony) algorithm is introduced to train the link weights of ANN, a new ABC-NN(Artificial bee colony-based neural network) modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR (least squares regression), ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 μm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

A case of persistent retrograde amnesia following a dissociative fugue: neuropsychological and neurofunctional underpinnings of loss of autobiographical memory and self-awareness.

PubMed

Hennig-Fast, Kristina; Meister, Franziska; Frodl, Thomas; Beraldi, Anna; Padberg, Frank; Engel, Rolf R; Reiser, Maximilian; Möller, Hans-Jürgen; Meindl, Thomas

2008-10-01

Autobiographical memory relies on complex interactions between episodic memory contents, associated emotions and a sense of self-continuity over the course of one's life. This paper reports a study based upon the case of the patient NN who suffered from a complete loss of autobiographical memory and awareness of identity subsequent to a dissociative fugue. Neuropsychological, behavioral, and functional neuroimaging tests converged on the conclusion that NN suffered from a selective retrograde amnesia following an episode of dissociative fugue, during which he had lost explicit knowledge and vivid memory of his personal past. NN's loss of self-related memories was mirrored in neurobiological changes after the fugue whereas his semantic memory remained intact. Although NN still claimed to suffer from a stable loss of autobiographical, self-relevant memories 1 year after the fugue state, a proportionate improvement in underlying fronto-temporal neuronal networks was evident at this point in time. In spite of this improvement in neuronal activation, his anterograde visual memory had been decreased. It is posited that our data provide evidence for the important role of visual processing in autobiographical memory as well as for the efficiency of protective control mechanisms that constitute functional retrograde amnesia.

Comparison of Vertical Drifts of ISR and Magnetometer Data Measurements at the Magnetic Equator

NASA Astrophysics Data System (ADS)

Condor P, P. J.

2014-12-01

We compare vertical drifts measured with the Jicamarca incoherent scatter radar (ISR) and drifts estimated from magnetometer data applying a Neural Network data processing technique. For the application of the Neural Network (NN) method, we use the magnitude of the horizontal (H) component of the magnetic field measured with magnetometers at Jicamarca and Piura (Peru). The data was collected between the years 2002 and 2013. In training the NN we use the difference between the magnitudes of the horizontal components (dH) measured at JRO (placed at the magnetic equator) and Piura (displaced 5° away). Additional parameters used are F10.7 and Ap indexes. The estimates obtained with the NN procedure are very good. We have an RMS error of 3.7 m/s using dH as an input of the NN while the error is 3.9 m/s when we use the component H of JRO as an input. The results are validated using the set of vertical drifts observations collected with the Jicamarca incoherent scatter radar. The estimated drifts can be accessed using the following website: http://jro.igp.gob.pe/driftnn. In the poster, we show the comparison of vertical drifts from 2002 to 2013 where we discuss the agreement between magnetometer and ISR data.

Green synthesis and characterization of ANbO3 (A = Na, K) nanopowders fabricated using a biopolymer

NASA Astrophysics Data System (ADS)

Khorrami, Gh. H.; Mousavi, M.; Khayatian, S. A.; Kompany, A.; Khorsand Zak, A.

2017-10-01

Lead-free sodium niobate (NaNbO3, NN) and potassium niobate (KNbO3, KN) nanopowders were successfully synthesized by a simple and green synthesis process in gelatin media. Gelatin, which is a biopolymer, was used as stabilizer. In order to determine the lowest calcination temperature needed to obtain pure NN and KN nanopowders, the produced gels were analyzed by thermogravometric analyzer (TGA). The produced gels were calcined at 500∘C and 600∘C. The structural and optical properties of the prepared powders were examined using X-ray diffraction (XRD) technique, transmission electron microscopy (TEM), and UV-Vis spectroscopy. The XRD results revealed that pure phase NN and KN nanopowders were formed at low temperature calcination of 500∘C and 600∘C, respectively. The Scherrer formula and size-strain plot (SSP) method were employed to estimate crystallite size and lattice strain of the samples. The TEM images show that the NN and KN samples calcined at 600∘C have cubic shape with an average particle size of 60.95 and 39.29 nm, respectively. The optical bandgap energy of the samples was calculated using UV-Vis diffused reflectance spectra of the samples and Kubelka-Munck relation.

Elliptic flow of electrons from heavy-flavor hadron decays in Au + Au collisions at s N N = 200 , 62.4, and 39 GeV

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

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.

Here, we present measurements of elliptic flow (v 2) of electrons from the decays of heavy-flavor hadrons (e HF) by the STAR experiment. For Au+Au collisions atmore » $$\\sqrt{s}$$$_ {NN}$$=200 GeV we report v 2, for transverse momentum (p T) between 0.2 and 7 GeV/c, using three methods: the event plane method (v 2{EP}), two-particle correlations (v 2{2}), and four-particle correlations (v 2{4}). For Au+Au collisions at $$\\sqrt{s}$$$_ {NN}$$=62.4 and 39 GeV we report v 2{2} for p T <2GeV/c. v 2{2} and v 2{4} are nonzero at low and intermediate p T at 200 GeV, and v 2{2} is consistent with zero at low p T at other energies. The v 2{2} at the two lower beam energies is systematically lower than at $$\\sqrt{s}$$$_ {NN}$$=200 GeV for p T <1GeV/c. This difference may suggest that charm quarks interact less strongly with the surrounding nuclear matter at those two lower energies compared to $$\\sqrt{s}$$$_ {NN}$$=200 GeV.« less

Dynamic learning from adaptive neural network control of a class of nonaffine nonlinear systems.

PubMed

Dai, Shi-Lu; Wang, Cong; Wang, Min

2014-01-01

This paper studies the problem of learning from adaptive neural network (NN) control of a class of nonaffine nonlinear systems in uncertain dynamic environments. In the control design process, a stable adaptive NN tracking control design technique is proposed for the nonaffine nonlinear systems with a mild assumption by combining a filtered tracking error with the implicit function theorem, input-to-state stability, and the small-gain theorem. The proposed stable control design technique not only overcomes the difficulty in controlling nonaffine nonlinear systems but also relaxes constraint conditions of the considered systems. In the learning process, the partial persistent excitation (PE) condition of radial basis function NNs is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition and an appropriate state transformation, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the implicit desired control input dynamics in the stable control process and of storing the learned knowledge in memory. Subsequently, an NN learning control design technique that effectively exploits the learned knowledge without re-adapting to the controller parameters is proposed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed design techniques.

Multiwavelet grading of prostate pathological images

NASA Astrophysics Data System (ADS)

Soltanian-Zadeh, Hamid; Jafari-Khouzani, Kourosh

2002-05-01

We have developed image analysis methods to automatically grade pathological images of prostate. The proposed method generates Gleason grades to images, where each image is assigned a grade between 1 and 5. This is done using features extracted from multiwavelet transformations. We extract energy and entropy features from submatrices obtained in the decomposition. Next, we apply a k-NN classifier to grade the image. To find optimal multiwavelet basis, preprocessing, and classifier, we use features extracted by different multiwavelets with either critically sampled preprocessing or repeated row preprocessing and different k-NN classifiers and compare their performances, evaluated by total misclassification rate (TMR). To evaluate sensitivity to noise, we add white Gaussian noise to images and compare the results (TMR's). We applied proposed methods to 100 images. We evaluated the first and second levels of decomposition using Geronimo, Hardin, and Massopust (GHM), Chui and Lian (CL), and Shen (SA4) multiwavelets. We also evaluated k-NN classifier for k=1,2,3,4,5. Experimental results illustrate that first level of decomposition is quite noisy. They also show that critically sampled preprocessing outperforms repeated row preprocessing and has less sensitivity to noise. Finally, comparison studies indicate that SA4 multiwavelet and k-NN classifier (k=1) generates optimal results (with smallest TMR of 3%).

Elliptic flow of electrons from heavy-flavor hadron decays in Au + Au collisions at s N N = 200 , 62.4, and 39 GeV

DOE PAGES

Adamczyk, L.; Adkins, J. K.; Agakishiev, G.; ...

2017-03-13

Here, we present measurements of elliptic flow (v 2) of electrons from the decays of heavy-flavor hadrons (e HF) by the STAR experiment. For Au+Au collisions atmore » $$\\sqrt{s}$$$_ {NN}$$=200 GeV we report v 2, for transverse momentum (p T) between 0.2 and 7 GeV/c, using three methods: the event plane method (v 2{EP}), two-particle correlations (v 2{2}), and four-particle correlations (v 2{4}). For Au+Au collisions at $$\\sqrt{s}$$$_ {NN}$$=62.4 and 39 GeV we report v 2{2} for p T <2GeV/c. v 2{2} and v 2{4} are nonzero at low and intermediate p T at 200 GeV, and v 2{2} is consistent with zero at low p T at other energies. The v 2{2} at the two lower beam energies is systematically lower than at $$\\sqrt{s}$$$_ {NN}$$=200 GeV for p T <1GeV/c. This difference may suggest that charm quarks interact less strongly with the surrounding nuclear matter at those two lower energies compared to $$\\sqrt{s}$$$_ {NN}$$=200 GeV.« less

Testing Spatial Symmetry Using Contingency Tables Based on Nearest Neighbor Relations

PubMed Central

Ceyhan, Elvan

2014-01-01

We consider two types of spatial symmetry, namely, symmetry in the mixed or shared nearest neighbor (NN) structures. We use Pielou's and Dixon's symmetry tests which are defined using contingency tables based on the NN relationships between the data points. We generalize these tests to multiple classes and demonstrate that both the asymptotic and exact versions of Pielou's first type of symmetry test are extremely conservative in rejecting symmetry in the mixed NN structure and hence should be avoided or only the Monte Carlo randomized version should be used. Under RL, we derive the asymptotic distribution for Dixon's symmetry test and also observe that the usual independence test seems to be appropriate for Pielou's second type of test. Moreover, we apply variants of Fisher's exact test on the shared NN contingency table for Pielou's second test and determine the most appropriate version for our setting. We also consider pairwise and one-versus-rest type tests in post hoc analysis after a significant overall symmetry test. We investigate the asymptotic properties of the tests, prove their consistency under appropriate null hypotheses, and investigate finite sample performance of them by extensive Monte Carlo simulations. The methods are illustrated on a real-life ecological data set. PMID:24605061

A synthetic NO reduction cycle on a bis(pyrazolato)-bridged dinuclear ruthenium complex including photo-induced transformation.

PubMed

Arikawa, Yasuhiro; Hiura, Junko; Tsuchii, Chika; Kodama, Mika; Matsumoto, Naoki; Umakoshi, Keisuke

2018-05-17

A synthetic NO reduction cycle (2NO + 2H+ + 2e- → N2O + H2O) on a dinuclear platform {(TpRu)2(μ-pz)2} (Tp = HB(pyrazol-1-yl)3) was achieved, where an unusual N-N coupling complex was included. Moreover, an interesting photo-induced conversion of the N-N coupling complex to an oxido-bridged complex was revealed.

Predictions for p+Pb collisions at √s NN = 5TeV: Comparison with data

DOE PAGES

None, None

2016-09-01

Predictions made prior to the LHC p+Pb run at √s NN = 5TeV are compared to currently available data. Some predictions shown here have been updated by including the same experimental cuts as the data. Here, some additional predictions are also presented, especially for quarkonia, that were provided to the experiments before the data were made public but were too late for the original publication.

Excess of J/ψ yield at very low transverse momenta in A+A collisions measured by the STAR experiment

NASA Astrophysics Data System (ADS)

Zha, Wangmei; STAR Collaboration

2017-08-01

In this article, we report the STAR measurements of J / ψ production at very low transverse momenta (pT) in hadronic Au+Au collisions at √{sNN } = 200 GeV and U+U collisions at √{sNN } = 193 GeV at mid-rapidity. Centrality dependence of J / ψ yields and nuclear modification factors at very low pT are presented.

Progress in adapting k-NN methods for forest mapping and estimation using the new annual Forest Inventory and Analysis data

Treesearch

Reija Haapanen; Kimmo Lehtinen; Jukka Miettinen; Marvin E. Bauer; Alan R. Ek

2002-01-01

The k-nearest neighbor (k-NN) method has been undergoing development and testing for applications with USDA Forest Service Forest Inventory and Analysis (FIA) data in Minnesota since 1997. Research began using the 1987-1990 FIA inventory of the state, the then standard 10-point cluster plots, and Landsat TM imagery. In the past year, research has moved to examine...

Observation of charge asymmetry dependence of pion elliptic flow and the possible chiral magnetic wave in heavy-ion collisions

DOE PAGES

Adamczyk, L.

2015-06-26

We present measurements of π⁻ and π⁺ elliptic flow, v₂, at midrapidity in Au+Au collisions at √s NN = 200, 62.4, 39, 27, 19.6, 11.5, and 7.7 GeV, as a function of event-by-event charge asymmetry, A ch, based on data from the STAR experiment at RHIC. We find that π⁻ (π⁺) elliptic flow linearly increases (decreases) with charge asymmetry for most centrality bins at √s NN = 27 GeV and higher. At √s NN = 200 GeV, the slope of the difference of v₂ between π⁻ and π⁺ as a function of A ch exhibits a centrality dependence, which ismore » qualitatively similar to calculations that incorporate a chiral magnetic wave effect. In addition, similar centrality dependence is also observed at lower energies.« less

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

Konobeevski, E. S., E-mail: konobeev@inr.ru; Burmistrov, Yu. M.; Zuyev, S. V.

The first results are obtained in a kinematically complete experiment devoted to measuring the n + d {sup {yields}}p + n + n reaction yield at energies in the range E{sub n} = 40-60 MeV and various angles of divergence of two neutrons ({Delta}{theta} = 4{sup o}, 6{sup o}, and 8{sup o}) in the geometry of neutron-neutron final-state interaction. The {sup 1}S{sub 0} neutron-neutron scattering length a{sub nn} is determined by comparing the experimental energy dependence of the reaction yield with the results of a simulation in the Watson-Migdal approximation, which depend on a{sub nn}. For E{sub n} = 40more » MeV and {Delta}{theta} = 6{sup o} (the best statistics in the experiment), the value a{sub nn} = -17.9 {+-} 1.0 fm was obtained. A further improvement of the experimental accuracy will make it possible to remove the existing disagreement of the results from different experiments.« less

Quantitative workflow based on NN for weighting criteria in landfill suitability mapping

NASA Astrophysics Data System (ADS)

Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Alkhasawneh, Mutasem Sh.; Aziz, Hamidi Abdul

2017-10-01

Our study aims to introduce a new quantitative workflow that integrates neural networks (NNs) and multi criteria decision analysis (MCDA). Existing MCDA workflows reveal a number of drawbacks, because of the reliance on human knowledge in the weighting stage. Thus, new workflow presented to form suitability maps at the regional scale for solid waste planning based on NNs. A feed-forward neural network employed in the workflow. A total of 34 criteria were pre-processed to establish the input dataset for NN modelling. The final learned network used to acquire the weights of the criteria. Accuracies of 95.2% and 93.2% achieved for the training dataset and testing dataset, respectively. The workflow was found to be capable of reducing human interference to generate highly reliable maps. The proposed workflow reveals the applicability of NN in generating landfill suitability maps and the feasibility of integrating them with existing MCDA workflows.

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

PubMed

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

Zero-sum two-player game theoretic formulation of affine nonlinear discrete-time systems using neural networks.

PubMed

Mehraeen, Shahab; Dierks, Travis; Jagannathan, S; Crow, Mariesa L

2013-12-01

In this paper, the nearly optimal solution for discrete-time (DT) affine nonlinear control systems in the presence of partially unknown internal system dynamics and disturbances is considered. The approach is based on successive approximate solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in optimal control. Successive approximation approach for updating control and disturbance inputs for DT nonlinear affine systems are proposed. Moreover, sufficient conditions for the convergence of the approximate HJI solution to the saddle point are derived, and an iterative approach to approximate the HJI equation using a neural network (NN) is presented. Then, the requirement of full knowledge of the internal dynamics of the nonlinear DT system is relaxed by using a second NN online approximator. The result is a closed-loop optimal NN controller via offline learning. A numerical example is provided illustrating the effectiveness of the approach.

Medium modified two-body scattering amplitude from proton-nucleus total cross-sections

NASA Technical Reports Server (NTRS)

Tripathi, R. K.; Wilson, J. W.; Cucinotta, F. A.

2001-01-01

Recently (R.K. Tripathi, J.W. Wilson, F.A. Cucinotta, Nucl. Instr. and Meth. B 145 (1998) 277; R.K. Tripathi, F.A. Cucinotta, J.W. Wilson, NASA-TP-1998-208438), we have extracted nucleon-nucleon (N-N) cross-sections in the medium directly from experiment. The in-medium N-N cross-sections form the basic ingredients of several heavy-ion scattering approaches including the coupled-channel approach developed at the NASA Langley Research Center. Here, we investigate the ratio of real to imaginary part of the two-body scattering amplitude in the medium. These ratios are used in combination with the in-medium N-N cross-sections to calculate total proton-nucleus cross-sections. The agreement is excellent with the available experimental data. These cross-sections are needed for the radiation risk assessment of space missions. c2001 Elsevier Science B.V. All rights reserved.

Ratios of charged antiparticles to particles near midrapidity in Au+Au collisions at (sNN)=200 GeV

NASA Astrophysics Data System (ADS)

Back, B. B.; Baker, M. D.; Barton, D. S.; Betts, R. R.; Ballintijn, M.; Bickley, A. A.; Bindel, R.; Budzanowski, A.; Busza, W.; Carroll, A.; Decowski, M. P.; Garcia, E.; George, N.; Gulbrandsen, K.; Gushue, S.; Halliwell, C.; Hamblen, J.; Heintzelman, G. A.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Katzy, J.; Khan, N.; Kucewicz, W.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; McLeod, D.; Michałowski, J.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Park, I. C.; Pernegger, H.; Reed, C.; Remsberg, L. P.; Reuter, M.; Roland, C.; Roland, G.; Rosenberg, L.; Sagerer, J.; Sarin, P.; Sawicki, P.; Skulski, W.; Steadman, S. G.; Steinberg, P.; Stephans, G. S.; Stodulski, M.; Sukhanov, A.; Tang, J.-L.; Teng, R.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Verdier, R.; Wadsworth, B.; Wolfs, F. L.; Wosiek, B.; Woźniak, K.; Wuosmaa, A. H.; Wysłouch, B.

2003-02-01

The ratios of charged antiparticles to particles have been obtained for pions, kaons, and protons near midrapidity in central Au+Au collisions at (sNN)=200 GeV. Ratios of <π->/<π+>=1.025±0.006(stat.)±0.018(syst.), /=0.95±0.03(stat.)±0.03(syst.), and / =0.73±0.02(stat.)±0.03(syst.) have been observed. The / and / ratios are consistent with a baryochemical potential μB of 27 MeV, roughly a factor of 2 smaller than in (sNN)=130 GeV collisions. The data are compared to results from lower energies and model calculations. Our accurate measurements of the particle ratios impose stringent constraints on current and future models dealing with baryon production and transport.

Elliptic flow of charm and strange hadrons in high-multiplicity pPb collisions at $$\\sqrt{s_{_\\mathrm{NN}}} =$$ 8.16 TeV

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

Sirunyan, Albert M; et al.

The elliptic azimuthal anisotropy coefficient (more » $$v_2$$) is measured for charm (D$^0$) and strange (K$$_\\mathrm{S}^0$$, $$\\Lambda$$, $$\\Xi^-$$, and $$\\Omega^-$$) hadrons, using a data sample of pPb collisions collected by the CMS experiment, at a nucleon-nucleon center-of-mass energy $$\\sqrt{s_{_\\mathrm{NN}}} =$$ 8.16 TeV. A significant positive $$v_2$$ signal from long-range azimuthal correlations is observed for all particle species in high-multiplicity pPb collisions. The measurement represents the first observation of possible long-range collectivity for open heavy flavor hadrons in small systems. The results suggest that charm quarks have a smaller $$v_2$$ than the lighter quarks, probably reflecting a weaker collective behavior. This effect is not seen in the larger PbPb collision system at $$\\sqrt{s_{_\\mathrm{NN}}} =$$ 5.02 TeV, also presented.« less

{ITALIC AB INITIO} Large-Basis no-Core Shell Model and its Application to Light Nuclei

NASA Astrophysics Data System (ADS)

Barrett, Bruce R.; Navratil, Petr; Ormand, W. E.; Vary, James P.

2002-01-01

We discuss the {ITALIC ab initio} No-Core Shell Model (NCSM). In this method the effective Hamiltonians are derived microscopically from realistic nucleon-nucleon (NN) potentials, such as the CD-Bonn and the Argonne AV18 NN potentials, as a function of the finite Harmonic Oscillator (HO) basis space. We present converged results, i.e. , up to 50 Ω and 18 Ω HO excitations, respectively, for the A=3 and 4 nucleon systems. Our results for these light systems are in agreement with results obtained by other exact methods. We also calculate properties of 6Li and 6He in model spaces up to 10 Ω and of 12C up to 6 Ω. Binding energies, rms radii, excitation spectra and electromagnetic properties are discussed. The favorable comparison with available data is a consequence of the underlying NN interaction rather than a phenomenological fit.

Adaptive output feedback NN control of a class of discrete-time MIMO nonlinear systems with unknown control directions.

PubMed

Li, Yanan; Yang, Chenguang; Ge, Shuzhi Sam; Lee, Tong Heng

2011-04-01

In this paper, adaptive neural network (NN) control is investigated for a class of block triangular multiinput-multioutput nonlinear discrete-time systems with each subsystem in pure-feedback form with unknown control directions. These systems are of couplings in every equation of each subsystem, and different subsystems may have different orders. To avoid the noncausal problem in the control design, the system is transformed into a predictor form by rigorous derivation. By exploring the properties of the block triangular form, implicit controls are developed for each subsystem such that the couplings of inputs and states among subsystems have been completely decoupled. The radial basis function NN is employed to approximate the unknown control. Each subsystem achieves a semiglobal uniformly ultimately bounded stability with the proposed control, and simulation results are presented to demonstrate its efficiency.

Nucleon-nucleon scattering in a strong external magnetic field and the neutrino emissivity

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

Bavarsad, E.; Mohammadi, R.; Haghighat, M.

The nucleon-nucleon scattering in a large magnetic background is considered to find its potential to change the neutrino emissivity of the neutron stars. For this purpose, we consider the one-pion-exchange approximation to find the nucleon-nucleon (NN) cross section in a background field as large as 10{sup 15}-10{sup 18} G. We show that the NN cross section in neutron stars with temperatures in the range 0.1-5 MeV can be changed up to the 1 order of magnitude with respect to the one in the absence of the magnetic field. In the limit of the soft neutrino emission, the neutrino emissivity canmore » be written in terms of the NN-scattering amplitude; therefore, the large magnetic fields can dramatically change the neutrino emissivity of the neutron stars as well.« less

A comparison of human performance in figural and navigational versions of the traveling salesman problem.

PubMed

Blaser, R E; Wilber, Julie

2013-11-01

Performance on a typical pen-and-paper (figural) version of the Traveling Salesman Problem was compared to performance on a room-sized navigational version of the same task. Nine configurations were designed to examine the use of the nearest-neighbor (NN), cluster approach, and convex-hull strategies. Performance decreased with an increasing number of nodes internal to the hull, and improved when the NN strategy produced the optimal path. There was no overall difference in performance between figural and navigational task modalities. However, there was an interaction between modality and configuration, with evidence that participants relied more heavily on the NN strategy in the figural condition. Our results suggest that participants employed similar, but not identical, strategies when solving figural and navigational versions of the problem. Surprisingly, there was no evidence that participants favored global strategies in the figural version and local strategies in the navigational version.

Neural networks for Higgs physics

NASA Astrophysics Data System (ADS)

Tentindo-Repond, Silvia; Bhat, Pushpalatha C.; Prosper, Harrison B.

2001-08-01

The main application of neural networks (NN) in Higgs physics so far has been to optimize the signal over background ratio. The positive result obtained imply that the use of NN will lead to a big reduction in the integrated luminosity required for the discovery of the Higgs in RunII. Neural Networks have also been recently used in Higgs physics to set up tagging algorithms to identify the heavy flavor content of jets. Whereas in the previous studies the NN b-tagging methods used are channel-independent, a channel-dependent method has been used in the present work. The signal pp¯→WH→lνbb¯ has been studied against the dominant background pp¯→Wbb¯, in an attempt to improve the signal over background ratio by trying to push the invariant mass of the background events further away from the signal. This result would get the equivalent effect of an improved mass resolution.

Riverine N2O concentrations, exports to estuary and emissions to atmosphere from the Changjiang River in response to increasing nitrogen loads

NASA Astrophysics Data System (ADS)

Yan, Weijin; Yang, Libiao; Wang, Fang; Wang, Jianing; Ma, Pei

2012-12-01

This study investigated the variations of dissolved N2O and emissions over diurnal and seasonal temporal scales in 2009, as well as the time series of riverine N2O export to estuary and emission to atmosphere in response to increasing anthropogenic nitrogen loads in the Changjiang River. For the diurnal study, N2O concentrations ranged from 0.26 to 0.34 and from 0.44 to 0.52 μg N-N2O L-1 in August and October 2009, respectively. The dissolved N2O was supersaturated with a mean value of 197%. Studies on N2O emissions, also taken in August and October, ranged from 2.67 to 11.6 and from 6.72 to 15.2 μg N-N2O m-2 h-1, respectively. For the seasonal study (June through December 2009), N2O concentrations ranged from 0.34 to 0.72 μg N-N2O L-1 and were supersaturated in all the samples (average 212%). N2O emissions ranged from 1.87 to 40.8 μg N-N2O m-2 h-1. Our study found no significant differences in diurnal patterns of N2O saturation but detected significant difference in seasonal patterns of N2O saturation: higher during summer while lower during autumn and winter. We found a significant relationship between dissolved N2O and river nitrate, which can predict the variation of N2O concentrations in the River. The net production of N2 ranged from 0.01 to 0.47 mg N-N2 L-1. These excess N2 values were significantly correlated to the N2O production and are suggestive of denitrification in the river. Applying the Global News model to the river system using measures taken during the 1970 to 2002 period, we estimated N2O emissions to atmosphere increased from 330 to 3650 ton N-N2O yr-1. During that same 1970-2002 period, N2O exports to estuary increased from 91 to 470 ton N-N2O yr-1. Taken together, the findings reported here suggest that both the river N2O concentrations and emissions would increase in response to rising anthropogenic nitrogen loads. Our study showed that the mean emission factor based on the ratio of the total N2O flux to NO3- flux is four times greater than the value of 0.0025 obtained with the methodology recommended by the Intergovernmental Panel on Climate Change. Thus, our findings reflect the open river channel rapid exchange of gases with the atmosphere.

Cryptosporidium Taxonomy: Recent Advances and Implications for Public Health

PubMed Central

Xiao, Lihua; Fayer, Ronald; Ryan, Una; Upton, Steve J.

2004-01-01

There has been an explosion of descriptions of new species of Cryptosporidium during the last two decades. This has been accompanied by confusion regarding the criteria for species designation, largely because of the lack of distinct morphologic differences and strict host specificity among Cryptosporidium spp. A review of the biologic species concept, the International Code of Zoological Nomenclature (ICZN), and current practices for Cryptosporidium species designation calls for the establishment of guidelines for naming Cryptosporidium species. All reports of new Cryptosporidium species should include at least four basic components: oocyst morphology, natural host specificity, genetic characterizations, and compliance with the ICZN. Altogether, 13 Cryptosporidium spp. are currently recognized: C. muris, C. andersoni, C. parvum, C. hominis, C. wrairi, C. felis, and C. cannis in mammals; C. baïleyi, C. meleagridis, and C. galli in birds; C. serpentis and C. saurophilum in reptiles; and C. molnari in fish. With the establishment of a framework for naming Cryptosporidium species and the availability of new taxonomic tools, there should be less confusion associated with the taxonomy of the genus Cryptosporidium. The clarification of Cryptosporidium taxonomy is also useful for understanding the biology of Cryptosporidium spp., assessing the public health significance of Cryptosporidium spp. in animals and the environment, characterizing transmission dynamics, and tracking infection and contamination sources. PMID:14726456

Resistant mechanism against nelfinavir of subtype C human immunodeficiency virus type 1 proteases

NASA Astrophysics Data System (ADS)

Liu, Xiaoqing; Dai, Qi; Li, Lihua; Xiu, Zhilong

2011-02-01

Human immunodeficiency virus type 1 protease (HIV-1 PR) is one of the major targets of anti-AIDS drug discovery, and the subtype C HIV-1 is infecting more and more humans. In this work, we executed computational simulations of subtype B (labeled B(WT)) HIV-1 PR, D30N mutant B (labeled B(D30N)) HIV-1 PR, subtype C (labeled C(Ref)) HIV-1 PR, D30N mutant C (labeled C(D30N)) HIV-1 PR, and D30N/N83T mutant C (labeled C(D30N/N83T)) HIV-1 PR with drug nelfinavir (NFV), aiming at clarifying (1) the resistant mechanism against NFV due to D30N mutation in subtype C HIV-1 PR; (2) the reason that the emergence rate of N83T mutation in C(D30N) HIV-1 PR is higher than that in B(D30N) HIV-1 PR; (3) the affinity of NFV with C(D30N/N83T) HIV-1 PR is higher than B(D30N) and C(D30N) HIV-1 PRs. The results indicate: (1) D30N mutation in subtype C HIV-1 PR reduces the hydrogen bond between the 30th residue and NFV, and the binding free energy contributions of some residues decrease; (2) the hydrogen bonds between the 83th/83'th residue and the 34th/34'th residue exist in both B(D30N) and C(D30N/N83T) HIV-1 PRs, while they disappear in C(D30N) HIV-1 PR. Meanwhile, the binding free energy contribution of N30 in C(D30N) is lower than that in B(D30N) and C(D30N/N83T); (3) N83T mutation makes some residues dislocate, and the contributions of these residues to binding free energy in C(D30N/N83T) increase comparing to those in B(D30N) and C(D30N). Our findings suggest that despite the nonactive site mutations, the polymorphisms regulate the emergence rates of these drug-resistant mutants.

Comparison of Sleep Disorders between Real and Simulated 3,450-m Altitude

PubMed Central

Heinzer, Raphaël; Saugy, Jonas J.; Rupp, Thomas; Tobback, Nadia; Faiss, Raphael; Bourdillon, Nicolas; Rubio, José Haba; Millet, Grégoire P.

2016-01-01

Study Objectives: Hypoxia is known to generate sleep-disordered breathing but there is a debate about the pathophysiological responses to two different types of hypoxic exposure: normobaric hypoxia (NH) and hypobaric hypoxia (HH), which have never been directly compared. Our aim was to compare sleep disorders induced by these two types of altitude. Methods: Subjects were exposed to 26 h of simulated (NH) or real altitude (HH) corresponding to 3,450 m and a control condition (NN) in a randomized order. The sleep assessments were performed with nocturnal polysomnography (PSG) and questionnaires. Thirteen healthy trained males subjects volunteered for this study (mean ± SD; age 34 ± 9 y, body weight 76.2 ± 6.8 kg, height 179.7 ± 4.2 cm). Results: Mean nocturnal oxygen saturation was further decreased during HH than in NH (81.2 ± 3.1 versus 83.6 ± 1.9%; P < 0.01) when compared to NN (95.5 ± 0.9%; P < 0.001). Heart rate was higher in HH than in NH (61 ± 10 versus 55 ± 6 bpm; P < 0.05) and NN (48 ± 5 bpm; P < 0.001). Total sleep time was longer in HH than in NH (351 ± 63 versus 317 ± 65 min, P < 0.05), and both were shorter compared to NN (388 ± 50 min, P < 0.05). Breathing frequency did not differ between conditions. Apnea-hypopnea index was higher in HH than in NH (20.5 [15.8–57.4] versus 11.4 [5.0–65.4]; P < 0.01) and NN (8.2 [3.9–8.8]; P < 0.001). Subjective sleep quality was similar between hypoxic conditions but lower than in NN. Conclusions: Our results suggest that HH has a greater effect on nocturnal breathing and sleep structure than NH. In HH, we observed more periodic breathing, which might arise from the lower saturation due to hypobaria, but needs to be confirmed. Citation: Heinzer R, Saugy JJ, Rupp T, Tobback N, Faiss R, Bourdillon N, Rubio JH, Millet GP. Comparison of sleep disorders between real and simulated 3,450-m altitude. SLEEP 2016;39(8):1517–1523. PMID:27166242

Wide field imaging - I. Applications of neural networks to object detection and star/galaxy classification

NASA Astrophysics Data System (ADS)

Andreon, S.; Gargiulo, G.; Longo, G.; Tagliaferri, R.; Capuano, N.

2000-12-01

Astronomical wide-field imaging performed with new large-format CCD detectors poses data reduction problems of unprecedented scale, which are difficult to deal with using traditional interactive tools. We present here NExt (Neural Extractor), a new neural network (NN) based package capable of detecting objects and performing both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first distinguished from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold; they are then classified as stars or as galaxies through diagnostic diagrams having variables chosen according to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of `what an object is' (i.e. it keeps all structures composed of more than one pixel) and performs the detection via an unsupervised NN, approaching detection as a clustering problem that has been thoroughly studied in the artificial intelligence literature. The first part of the NExt procedure consists of an optimal compression of the redundant information contained in the pixels via a mapping from pixel intensities to a subspace individualized through principal component analysis. At magnitudes fainter than the completeness limit, stars are usually almost indistinguishable from galaxies, and therefore the parameters characterizing the two classes do not lie in disconnected subspaces, thus preventing the use of unsupervised methods. We therefore adopted a supervised NN (i.e. a NN that first finds the rules to classify objects from examples and then applies them to the whole data set). In practice, each object is classified depending on its membership of the regions mapping the input feature space in the training set. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features we use a NN to select the most significant features among the large number of measured ones, and then we use these selected features to perform the classification task. In order to optimize the performance of the system, we implemented and tested several different models of NN. The comparison of the NExt performance with that of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.

Superexchange interaction in AIIBVI-based semimagnetic semiconductors. (in English)

NASA Astrophysics Data System (ADS)

Melnychuk, S. V.; Mykhaylevsky, Y. M.; Savchhuk, A. I.; Tryfonenko, D. M.

In this report Mn^{2+}, Fe^{2+} and Co^{2+} ions in the ground state in the presence of crystalline field T_d symmetry are considered. Superexchange interaction is performed via S, Se, Te ions. Theoretical calculations of the superexchange interaction integral J_{NN} have been carried out within the framework of Racah technique. Experimental values of J_{NN} for Cd_{1-x}Fe_{x}Te were obtained from the measurement of Faraday rotation temperature dependence.

Enhanced photoelectrochemical activity in all-oxide heterojunction devices based on correlated "metallic" oxides.

PubMed

Apgar, Brent A; Lee, Sungki; Schroeder, Lauren E; Martin, Lane W

2013-11-20

n-n Schottky, n-n ohmic, and p-n Schottky heterojunctions based on TiO2 /correlated "metallic" oxide couples exhibit strong solar-light absorption driven by the unique electronic structure of the "metallic" oxides. Photovoltaic and photocatalytic responses are driven by hot electron injection from the "metallic" oxide into the TiO2 , enabling new modalities of operation for energy systems. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Near-Neighbor Algorithms for Processing Bearing Data

DTIC Science & Technology

1989-05-10

neighbor algorithms need not be universally more cost -effective than brute force methods. While the data access time of near-neighbor techniques scales with...the number of objects N better than brute force, the cost of setting up the data structure could scale worse than (Continues) 20...for the near neighbors NN2 1 (i). Depending on the particular NN algorithm, the cost of accessing near neighbors for each ai E S1 scales as either N

DoD Fuel Facilities Criteria

DTIC Science & Technology

2015-04-27

Pantograph Feb-2010 UFGS 33 58 00 Leak Detection for Fueling Systems Apr-2008 UFGS 33 52 43.13 Aviation Fuel Piping Feb-2010 UFGS 33 59 00 Tightness of... Pipeline Pressure Testing Guidelines  Specifications  Questions 2 7/12/2017 3 7/12/2017 DoD Fuels Facilities Documents  Unified...UFGS)  Most in the 33 nn nn series  Associated with Standard Designs  Available on WBDG site  Coating Systems 4 7/12/2017 Pipeline

76 FR 61054 - Approval and Promulgation of State Implementation Plans; State of Colorado Regulation Number 3...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-03

....aa; II.D.1.bb; II.D.1.kk; II.D.1.nn; II.D.1.oo; II.D.1.aaa; II.D.1.bbb; II.D.1.ccc; II.D.1.fff; II.D...; II.D.1.y; II.D.1.aa; II.D.1.bb; II.D.1.kk; II.D.1.nn; II.D.1.oo; II.D.1.aaa; II.D.1.bbb; II.D.1.ccc...

A Comparative Evaluation of Anomaly Detection Algorithms for Maritime Video Surveillance

DTIC Science & Technology

2011-01-01

of k-means clustering and the k- NN Localized p-value Estimator ( KNN -LPE). K-means is a popular distance-based clustering algorithm while KNN -LPE...implemented the sparse cluster identification rule we described in Section 3.1. 2. k-NN Localized p-value Estimator ( KNN -LPE): We implemented this using...Average Density ( KNN -NAD): This was implemented as described in Section 3.4. Algorithm Parameter Settings The global and local density-based anomaly

Artificial astrocytes improve neural network performance.

PubMed

Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-04-19

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

Artificial Astrocytes Improve Neural Network Performance

PubMed Central

Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-01-01

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

System identification using Nuclear Norm & Tabu Search optimization

NASA Astrophysics Data System (ADS)

Ahmed, Asif A.; Schoen, Marco P.; Bosworth, Ken W.

2018-01-01

In recent years, subspace System Identification (SI) algorithms have seen increased research, stemming from advanced minimization methods being applied to the Nuclear Norm (NN) approach in system identification. These minimization algorithms are based on hard computing methodologies. To the authors’ knowledge, as of now, there has been no work reported that utilizes soft computing algorithms to address the minimization problem within the nuclear norm SI framework. A linear, time-invariant, discrete time system is used in this work as the basic model for characterizing a dynamical system to be identified. The main objective is to extract a mathematical model from collected experimental input-output data. Hankel matrices are constructed from experimental data, and the extended observability matrix is employed to define an estimated output of the system. This estimated output and the actual - measured - output are utilized to construct a minimization problem. An embedded rank measure assures minimum state realization outcomes. Current NN-SI algorithms employ hard computing algorithms for minimization. In this work, we propose a simple Tabu Search (TS) algorithm for minimization. TS algorithm based SI is compared with the iterative Alternating Direction Method of Multipliers (ADMM) line search optimization based NN-SI. For comparison, several different benchmark system identification problems are solved by both approaches. Results show improved performance of the proposed SI-TS algorithm compared to the NN-SI ADMM algorithm.

Modeling ionospheric foF 2 response during geomagnetic storms using neural network and linear regression techniques

NASA Astrophysics Data System (ADS)

Tshisaphungo, Mpho; Habarulema, John Bosco; McKinnell, Lee-Anne

2018-06-01

In this paper, the modeling of the ionospheric foF 2 changes during geomagnetic storms by means of neural network (NN) and linear regression (LR) techniques is presented. The results will lead to a valuable tool to model the complex ionospheric changes during disturbed days in an operational space weather monitoring and forecasting environment. The storm-time foF 2 data during 1996-2014 from Grahamstown (33.3°S, 26.5°E), South Africa ionosonde station was used in modeling. In this paper, six storms were reserved to validate the models and hence not used in the modeling process. We found that the performance of both NN and LR models is comparable during selected storms which fell within the data period (1996-2014) used in modeling. However, when validated on storm periods beyond 1996-2014, the NN model gives a better performance (R = 0.62) compared to LR model (R = 0.56) for a storm that reached a minimum Dst index of -155 nT during 19-23 December 2015. We also found that both NN and LR models are capable of capturing the ionospheric foF 2 responses during two great geomagnetic storms (28 October-1 November 2003 and 6-12 November 2004) which have been demonstrated to be difficult storms to model in previous studies.

Low Nourishment of Vitamin C Induces Glutathione Depletion and Oxidative Stress in Healthy Young Adults.

PubMed

Waly, Mostafa I; Al-Attabi, Zahir; Guizani, Nejib

2015-09-01

The present study was conducted to assess the status of vitamin C among healthy young adults in relation to serum antioxidant parameters [glutathione (GSH), thiols, and total antioxidant capacity, (TAC)], and oxidative stress markers [malondialdehyde (MDA), and nitrites plus nitrates (NN)]. A prospective study included 200 young adults, and their dietary intake was assessed by using food diaries. Fasting plasma vitamin C, serum levels of GSH, thiols, TAC, MDA, and NN were measured using biochemical assays. It was observed that 38% of the enrolled subjects, n=76, had an adequate dietary intake of vitamin C (ADI group). Meanwhile, 62%, n=124, had a low dietary intake of vitamin C (LDI group) as compared to the recommended dietary allowances. The fasting plasma level of vitamin C was significantly higher in the ADI group as compared to the LDI group. Oxidative stress in the sera of the LDI group was evidenced by depletion of GSH, low thiols levels, impairment of TAC, an elevation of MDA, and increased NN. In the ADI group, positive correlations were found between plasma vitamin C and serum antioxidant parameters (GSH, thiols, and TAC). Meanwhile, the plasma vitamin C was negatively correlated with serum MDA and NN levels. This study reveals a significant increase of oxidative stress status and reduced antioxidant capacity in sera from healthy young adults with low intake of the dietary antioxidant, vitamin C.

Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns.

PubMed

Bildirici, Melike; Ersin, Özgür

2014-01-01

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications.

Identified hadron transverse momentum spectra in Au+Au collisions at sNN=62.4 GeV

NASA Astrophysics Data System (ADS)

Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Busza, W.; Carroll, A.; Chai, Z.; Decowski, M. P.; García, E.; Gburek, T.; George, N.; Gulbrandsen, K.; Halliwell, C.; Hamblen, J.; Hauer, M.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Khan, N.; Kulinich, P.; Kuo, C. M.; Lin, W. T.; Manly, S.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Reed, C.; Roland, C.; Roland, G.; Sagerer, J.; Seals, H.; Sedykh, I.; Smith, C. E.; Stankiewicz, M. A.; Steinberg, P.; Stephans, G. S. F.; Sukhanov, A.; Tonjes, M. B.; Trzupek, A.; Vale, C.; Nieuwenhuizen, G. J. Van; Vaurynovich, S. S.; Verdier, R.; Veres, G. I.; Wenger, E.; Wolfs, F. L. H.; Wosiek, B.; Woźniak, K.; Wysłouch, B.

2007-02-01

Transverse momentum spectra of pions, kaons, protons, and antiprotons from Au+Au collisions at sNN = 62.4 GeV have been measured by the PHOBOS experiment at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. The identification of particles relies on three different methods: low momentum particles stopping in the first detector layers; the specific energy loss (dE/dx) in the silicon spectrometer, and time-of-flight measurement. These methods cover the transverse momentum ranges 0.03 0.2, 0.2 1.0, and 0.5 3.0 GeV/c, respectively. Baryons are found to have substantially harder transverse momentum spectra than mesons. The pT region in which the proton to pion ratio reaches unity in central Au+Au collisions at sNN = 62.4 GeV fits into a smooth trend as a function of collision energy. At low transverse mass, the spectra of various species exhibit a significant deviation from transverse mass scaling. The observed particle yields at very low pT are comparable to extrapolations from higher pT for kaons, protons and antiprotons. By comparing our results to Au+Au collisions at sNN = 200 GeV, we conclude that the net proton yield at midrapidity is proportional to the number of participant nucleons in the collision.

Finite-Time Attitude Tracking Control for Spacecraft Using Terminal Sliding Mode and Chebyshev Neural Network.

PubMed

An-Min Zou; Kumar, K D; Zeng-Guang Hou; Xi Liu

2011-08-01

A finite-time attitude tracking control scheme is proposed for spacecraft using terminal sliding mode and Chebyshev neural network (NN) (CNN). The four-parameter representations (quaternion) are used to describe the spacecraft attitude for global representation without singularities. The attitude state (i.e., attitude and velocity) error dynamics is transformed to a double integrator dynamics with a constraint on the spacecraft attitude. With consideration of this constraint, a novel terminal sliding manifold is proposed for the spacecraft. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated unknown function, a switch function is applied to generate a switching between the adaptive NN control and the robust controller. Meanwhile, a CNN, whose basis functions are implemented using only desired signals, is introduced to approximate the desired nonlinear function and bounded external disturbances online, and the robust term based on the hyperbolic tangent function is applied to counteract NN approximation errors in the adaptive neural control scheme. Most importantly, the finite-time stability in both the reaching phase and the sliding phase can be guaranteed by a Lyapunov-based approach. Finally, numerical simulations on the attitude tracking control of spacecraft in the presence of an unknown mass moment of inertia matrix, bounded external disturbances, and control input constraints are presented to demonstrate the performance of the proposed controller.

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

DOE PAGES

Jin, Xiaoli; Shi, Chunhai; Yu, Chang Yeon; ...

2017-05-19

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than themore » PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.« less

Effect of ploidy on scale-cover pattern in linear ornamental (koi) common carp Cyprinus carpio.

PubMed

Gomelsky, B; Schneider, K J; Glennon, R P; Plouffe, D A

2012-09-01

The effect of ploidy on scale-cover pattern in linear ornamental (koi) common carp Cyprinus carpio was investigated. To obtain diploid and triploid linear fish, eggs taken from a leather C. carpio female (genotype ssNn) and sperm taken from a scaled C. carpio male (genotype SSnn) were used for the production of control (no shock) and heat-shocked progeny. In heat-shocked progeny, the 2 min heat shock (40° C) was applied 6 min after insemination. Diploid linear fish (genotype SsNn) demonstrated a scale-cover pattern typical for this category with one even row of scales along lateral line and few scales located near operculum and at bases of fins. The majority (97%) of triploid linear fish (genotype SssNnn) exhibited non-typical scale patterns which were characterized by the appearance of additional scales on the body. The extent of additional scales in triploid linear fish was variable; some fish had large scales, which covered almost the entire body. Apparently, the observed difference in scale-cover pattern between triploid and diploid linear fish was caused by different phenotypic expression of gene N/n. Due to incomplete dominance of allele N, triploids Nnn demonstrate less profound reduction of scale cover compared with diploids Nn. © 2012 The Authors. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.

Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods.

PubMed

Balouchestani, Mohammadreza; Krishnan, Sridhar

2014-01-01

Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.

Unveiling magnetic interactions of ruthenium trichloride via constraining direction of orbital moments: Potential routes to realize a quantum spin liquid

NASA Astrophysics Data System (ADS)

Hou, Y. S.; Xiang, H. J.; Gong, X. G.

2017-08-01

Recent experiments reveal that the honeycomb ruthenium trichloride α -RuC l3 is a prime candidate of the Kitaev quantum spin liquid (QSL). However, there is no theoretical model which can properly describe its experimental dynamical response due to the lack of a full understanding of its magnetic interactions. Here, we propose a general scheme to calculate the magnetic interactions in systems (e.g., α -RuC l3 ) with nonnegligible orbital moments by constraining the directions of orbital moments. With this scheme, we put forward a minimal J1-K1-Γ1-J3-K3 model for α -RuC l3 and find that: (I) The third nearest neighbor (NN) antiferromagnetic Heisenberg interaction J3 stabilizes the zigzag antiferromagnetic order; (II) The NN symmetric off-diagonal exchange Γ1 plays a pivotal role in determining the preferred direction of magnetic moments and generating the spin wave gap. An exact diagonalization study on this model shows that the Kitaev QSL can be realized by suppressing the NN symmetric off-diagonal exchange Γ1 and the third NN Heisenberg interaction J3. Thus, we not only propose a powerful general scheme for investigating the intriguing magnetism of Jeff=1 /2 magnets, but also point out future directions for realizing the Kitaev QSL in the honeycomb ruthenium trichloride α -RuC l3 .

Effects of dietary aluminum, calcium, and phosphorus on egg and bone of European starlings

USGS Publications Warehouse

Miles, A.K.; Grue, C.E.; Pendleton, G.W.; Soares, J.H.

1993-01-01

Egg and bone of passerine birds nesting in acidified habitats may be affected by high levels of Al or P, or low levels of Ca. Nine treatments of three levels of dietary Al (target levels of 200, 1,000, and 5,000 ?g/g) and three levels of Ca:P (target levels of NN = 1.3% Ca: 0.9% P; LL = 0.19 Ca:0.45 P; LH = 0.19 Ca:1.65 P) were fed to 16-17 starling pairs during two breeding seasons. Eggs of starlings fed the LH diet were smaller and weighed less than eggs from the NN and LL treatments. Treatment effects on thickness, strength, and weight of eggshells were not consistent between seasons, probably because of differences in actual dietary levels of AI, Ca, and P or in incubation intervals. In one season, birds fed the highest Al diet had thicker eggshells than those from the other Al treatments (no effect from Ca:P); the following season, eggshells from the NN and LH treatments were thicker and stronger than those from the LL treatment. Eggshells from the NN treatment weighed more than those from the other Ca:P treatments. Starlings on the LH diet had the strongest femurs, but the effect was interactive with different levels of dietary AI. Effects of Ca:P on egg and bone were more evident than Al effects.

Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

PubMed Central

Bildirici, Melike; Ersin, Özgür

2014-01-01

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications. PMID:24977200

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

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

Jin, Xiaoli; Shi, Chunhai; Yu, Chang Yeon

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than themore » PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.« less

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

NASA Astrophysics Data System (ADS)

Wani, Omar; Beckers, Joost V. L.; Weerts, Albrecht H.; Solomatine, Dimitri P.

2017-08-01

A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on instance-based learning, it uses a k nearest-neighbour search for similar historical hydrometeorological conditions to determine uncertainty intervals from a set of historical errors, i.e. discrepancies between past forecast and observation. The performance of this method is assessed using test cases of hydrologic forecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect were made and their uncertainties were estimated using kNN resampling and two alternative uncertainty estimators: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Results show that kNN uncertainty estimation produces accurate and narrow uncertainty intervals with good probability coverage. Analysis also shows that the performance of this technique depends on the choice of search space. Nevertheless, the accuracy and reliability of uncertainty intervals generated using kNN resampling are at least comparable to those produced by QR and UNEEC. It is concluded that kNN uncertainty estimation is an interesting alternative to other post-processors, like QR and UNEEC, for estimating forecast uncertainty. Apart from its concept being simple and well understood, an advantage of this method is that it is relatively easy to implement.

Shape-selective recognition of DNA abasic sites by metallohelices: inhibition of human AP endonuclease 1.

PubMed

Malina, Jaroslav; Scott, Peter; Brabec, Viktor

2015-06-23

Loss of a base in DNA leading to creation of an abasic (AP) site leaving a deoxyribose residue in the strand, is a frequent lesion that may occur spontaneously or under the action of various physical and chemical agents. Progress in the understanding of the chemistry and enzymology of abasic DNA largely relies upon the study of AP sites in synthetic duplexes. We report here on interactions of diastereomerically pure metallo-helical 'flexicate' complexes, bimetallic triple-stranded ferro-helicates [Fe2(NN-NN)3](4+) incorporating the common NN-NN bis(bidentate) helicand, with short DNA duplexes containing AP sites in different sequence contexts. The results show that the flexicates bind to AP sites in DNA duplexes in a shape-selective manner. They preferentially bind to AP sites flanked by purines on both sides and their binding is enhanced when a pyrimidine is placed in opposite orientation to the lesion. Notably, the Λ-enantiomer binds to all tested AP sites with higher affinity than the Δ-enantiomer. In addition, the binding of the flexicates to AP sites inhibits the activity of human AP endonuclease 1, which is as a valid anticancer drug target. Hence, this finding indicates the potential of utilizing well-defined metallo-helical complexes for cancer chemotherapy. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Measurement of undisturbed di-nitrogen emissions from aquatic ecosystems

NASA Astrophysics Data System (ADS)

Qin, Shuping, Clough, Timothy, Lou, Jiafa; Hu, Chunsheng; Oenema, Oene; Wrage-Mönnig, Nicole; Zhang, Yuming

2016-04-01

Increased production of reactive nitrogen (Nr) from atmospheric di-nitrogen (N2) during the last century has greatly contributed to increased food production1-4. However, enriching the biosphere with Nr through N fertilizer production, combustion, and biological N2 fixation has also caused a series of negative effects on global ecosystems 5,6, especially aquatic ecosystems7. The main pathway converting Nr back into the atmospheric N2 pool is the last step of the denitrification process, i.e., the reduction of nitrous oxide (N2O) into N2 by micro-organisms7,8. Despite several attempts9,10, there is not yet an accurate, fast and direct method for measuring undisturbed N2 fluxes from denitrification in aquatic sediments at the field scale11-14. Such a method is essential to study the feedback of aquatic ecosystems to Nr inputs1,2,7. Here we show that the measurement of both N2O emission and its isotope signature can be used to infer the undisturbed N2 fluxes from aquatic ecosystems. The microbial reduction of N2O increases the natural abundance of 15N-N2O relative to 14N-N2O (δ15N-N2O). We observed linear relationships between δ15N-N2O and the logarithmic transformed N2O/(N2+N2O) emission ratios. Through independent measurements, we verified that the undisturbed N2 flux from aquatic ecosystems can be inferred from measurements of N2O emissions and the δ15N-N2O signature. Our method allows the determination of field-scale N2 fluxes from undisturbed aquatic ecosystems, and thereby allows model predictions of denitrification rates to be tested. The undisturbed N2 fluxes observed are almost one order of magnitude higher than those estimated by the traditional method, where perturbation of the system occurs, indicating that the ability of aquatic ecosystems to remove Nr may have been severely underestimated.

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification.

PubMed

Vrooman, Henri A; Cocosco, Chris A; van der Lijn, Fedde; Stokking, Rik; Ikram, M Arfan; Vernooij, Meike W; Breteler, Monique M B; Niessen, Wiro J

2007-08-01

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.