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Sample records for modeling tev class

  1. Hadronic modeling of TeV AGN: Gammas and neutrinos

    NASA Astrophysics Data System (ADS)

    Cerruti, M.; Zech, A.; Emery, G.; Guarin, D.

    2017-01-01

    Blazar emission models are usually divided into two big families, leptonic and hadronic, according to the particles which are responsible for the gamma-ray component of their spectral energy distributions. Even though leptonic models have been successful in explaining the gamma-ray emission from most blazars, hadronic models still present an intriguing alternative. They have the unique advantage to link the emission of photons, neutrinos, and cosmic rays from the astrophysical source. We have developed a stationary one-zone lepto-hadronic code to model the emission from blazars in both leptonic and hadronic scenarios. In this contribution we focus on hadronic modeling of a few selected TeV blazars of the BL Lac type and the radio galaxy Centaurus A detected by Cherenkov telescopes. We study in particular their associated neutrino emission, for different hadronic scenarios, and how it compares to the sensitivity of current neutrino detectors.

  2. New classes of polytropic models

    NASA Astrophysics Data System (ADS)

    Ngubelanga, S. A.; Maharaj, S. D.

    2017-03-01

    The gravitational behaviour of a dense compact object is studied in isotropic coordinates. We obtain new classes of static, spherically symmetric classes of solutions to the Einstein-Maxwell field equations by assuming an anisotropic pressures and a barotropic equation of state which is polytropic. Four new classes of exact solutions are found with different polytropic indices. We can regain masses of several observed astronomical objects. A physical analysis indicates that the models are well behaved.

  3. A GENERAL RELATIVISTIC EXTERNAL COMPTON-SCATTERING MODEL FOR TeV EMISSION FROM M87

    SciTech Connect

    Cui Yudong; Yuan Yefei; Li Yanrong; Wang Jianmin

    2012-02-20

    M87 is the first detected non-blazar extragalactic tera-electron-volt (TeV) source with rapid variation and a very flat spectrum in the TeV band. To explain the two peaks in the spectral energy distribution of the nucleus of M87, which is similar to that of blazars, the most commonly adopted models are the synchrotron self-Compton-scattering models and the external inverse Compton (EIC) scattering models. Considering that there is no correlated variation in the soft band (from radio to X-ray) matching the TeV variation and that the TeV sources should not suffer from {gamma}{gamma} absorption due to the flat TeV spectrum, the EIC models are advantageous in modeling the TeV emission from M87. In this paper, we propose a self-consistent EIC model to explain the flat TeV spectrum of M87 within the framework of fully general relativity, where the background soft photons are from the advection-dominated accretion flow around the central black hole, and the high-energy electrons are from the mini-jets that are powered by the magnetic reconnection in the main jet. In our model, both the TeV flares observed in the years 2005 and 2008 could be well explained: the {gamma}{gamma} absorption for TeV photons is very low, even inside the region very close to the black hole 20R{sub g} {approx} 50R{sub g} ; at the same region, the average EIC cooling time ({approx}10{sup 2} {approx} 10{sup 3} s) is short, which is consistent with the observed timescale of the TeV variation. Furthermore, we also discuss the possibility that the accompanying X-ray flare in 2008 is due to the direct synchrotron radiation of the mini-jets.

  4. A Model Chemistry Class.

    ERIC Educational Resources Information Center

    Summerlin, Lee; Borgford, Christie

    1989-01-01

    Described is an activity which uses a 96-well reaction plate and soda straws to construct a model of the periodic table of the elements. The model illustrates the ionization energies of the various elements. Construction of the model and related concepts are discussed. (CW)

  5. A Model Chemistry Class.

    ERIC Educational Resources Information Center

    Summerlin, Lee; Borgford, Christie

    1989-01-01

    Described is an activity which uses a 96-well reaction plate and soda straws to construct a model of the periodic table of the elements. The model illustrates the ionization energies of the various elements. Construction of the model and related concepts are discussed. (CW)

  6. Modeling of 10 GeV-1 TeV laser-plasma accelerators using Lorentz boosted simulations

    SciTech Connect

    Vay, J. -L.; Geddes, C. G. R.; Esarey, E.; Schroeder, C. B.; Leemans, W. P.; Cormier-Michel, E.; Grote, D. P.

    2011-12-13

    We study modeling of laser-plasma wakefield accelerators in an optimal frame of reference [J.-L. Vay, Phys. Rev. Lett. 98, 130405 (2007)] that allows direct and efficient full-scale modeling of deeply depleted and beam loaded laser-plasma stages of 10 GeV-1 TeV (parameters not computationally accessible otherwise). This verifies the scaling of plasmaaccelerators to very high energies and accurately models the laser evolution and the accelerated electron beam transverse dynamics and energy spread. Over 4, 5, and 6 orders of magnitude speedup is achieved for the modeling of 10 GeV, 100 GeV, and 1 TeV class stages, respectively. Agreement at the percentage level is demonstrated between simulations using different frames of reference for a 0.1 GeV class stage. In addition, obtaining these speedups and levels of accuracy was permitted by solutions for handling data input (in particular, particle and laser beams injection) and output in a relativistically boosted frame of reference, as well as mitigation of a high-frequency instability that otherwise limits effectiveness.

  7. A Latent Class Model for Rating Data.

    ERIC Educational Resources Information Center

    Rost, Jurgen

    1985-01-01

    A latent class model for rating data is presented which provides an alternative to the latent trait approach of analyzing test data. It is the analog of Andrich's binomial Rasch model for Lazarsfeld's latent class analysis (LCA). Response probabilities for rating categories follow a binomial distribution and depend on class-specific item…

  8. Consequences of Fitting Nonidentified Latent Class Models

    ERIC Educational Resources Information Center

    Abar, Beau; Loken, Eric

    2012-01-01

    Latent class models are becoming more popular in behavioral research. When models with a large number of latent classes relative to the number of manifest indicators are estimated, researchers must consider the possibility that the model is not identified. It is not enough to determine that the model has positive degrees of freedom. A well-known…

  9. Bayesian variable selection for latent class models.

    PubMed

    Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria

    2011-09-01

    In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.

  10. Left-right model with TeV fermionic dark matter and unification

    NASA Astrophysics Data System (ADS)

    Bandyopadhyay, Triparno; Raychaudhuri, Amitava

    2017-08-01

    The ingredients for a model with a TeV right-handed scale, gauge coupling unification, and suitable dark matter candidates lie at the heart of left-right symmetry with broken D-parity. After detailing the contents of such a model, with SU(2)R self-conjugate fermions at the right-handed scale aiding in unification of couplings, we explore its dark matter implications and collider signatures.

  11. Resonant leptogenesis in the minimal B-L extended standard model at TeV

    SciTech Connect

    Iso, Satoshi; Orikasa, Yuta; Okada, Nobuchika

    2011-05-01

    We investigate the resonant leptogenesis scenario in the minimal B-L extended standard model with the B-L symmetry breaking at the TeV scale. Through detailed analysis of the Boltzmann equations, we show how much the resultant baryon asymmetry via leptogenesis is enhanced or suppressed, depending on the model parameters, in particular, the neutrino Dirac-Yukawa couplings and the TeV scale Majorana masses of heavy degenerate neutrinos. In order to consider a realistic case, we impose a simple ansatz for the model parameters and analyze the neutrino oscillation parameters and the baryon asymmetry via leptogenesis as a function of only a single CP phase. We find that for a fixed CP phase all neutrino oscillation data and the observed baryon asymmetry of the present Universe can be simultaneously reproduced.

  12. Search for dijet resonances in proton-proton collisions at √{ s} = 13TeV and constraints on dark matter and other models

    NASA Astrophysics Data System (ADS)

    Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; König, A.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rad, N.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Strauss, J.; Waltenberger, W.; Wulz, C.-E.; Dvornikov, O.; Makarenko, V.; Mossolov, V.; Suarez Gonzalez, J.; Zykunov, V.; Shumeiko, N.; Alderweireldt, S.; De Wolf, E. A.; Janssen, X.; Lauwers, J.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; Daci, N.; De Bruyn, I.; Deroover, K.; Lowette, S.; Moortgat, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Léonard, A.; Luetic, J.; Maerschalk, T.; Marinov, A.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Yonamine, R.; Zenoni, F.; Zhang, F.; Cimmino, A.; Cornelis, T.; Dobur, D.; Fagot, A.; Gul, M.; Khvastunov, I.; Poyraz, D.; Salva, S.; Schöfbeck, R.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Bakhshiansohi, H.; Beluffi, C.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; De Visscher, S.; Delaere, C.; Delcourt, M.; Francois, B.; Giammanco, A.; Jafari, A.; Komm, M.; Krintiras, G.; Lemaitre, V.; Magitteri, A.; Mertens, A.; Musich, M.; Piotrzkowski, K.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Wertz, S.; Beliy, N.; Aldá Júnior, W. L.; Alves, F. L.; Alves, G. A.; Brito, L.; 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.; Da Silveira, G. G.; 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.; 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.; Fang, W.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Cheng, T.; Jiang, C. H.; Leggat, D.; Liu, Z.; Romeo, F.; Ruan, M.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Zhang, H.; Zhao, J.; Ban, Y.; Chen, G.; 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.; González Hernández, C. F.; Ruiz Alvarez, J. D.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Puljak, I.; Ribeiro Cipriano, P. M.; Sculac, T.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Ferencek, D.; Kadija, K.; Mesic, B.; Susa, T.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Tsiakkouri, D.; Finger, M.; Finger, M.; Carrera Jarrin, E.; Ellithi Kamel, A.; Mahmoud, M. A.; Radi, A.; Kadastik, M.; Perrini, L.; Raidal, M.; Tiko, A.; Veelken, C.; Eerola, P.; Pekkanen, J.; Voutilainen, M.; Härkönen, J.; Järvinen, T.; Karimäki, V.; Kinnunen, R.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; 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.; Ghosh, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Kucher, I.; Locci, E.; Machet, M.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Abdulsalam, A.; Antropov, I.; Baffioni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Chapon, E.; Charlot, C.; Davignon, O.; Granier de Cassagnac, R.; Jo, M.; Lisniak, S.; Miné, P.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Regnard, S.; Salerno, R.; Sirois, Y.; Strebler, T.; Yilmaz, Y.; Zabi, A.; Zghiche, 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.; Le Bihan, A.-C.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Carrillo Montoya, C. A.; Chierici, R.; Contardo, D.; Courbon, B.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Grenier, G.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Popov, A.; Sabes, D.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Toriashvili, T.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Feld, L.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Preuten, M.; Schomakers, C.; Schulz, J.; Verlage, T.; Albert, A.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Endres, M.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Güth, A.; Hamer, M.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Knutzen, S.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Olschewski, M.; Padeken, K.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Cherepanov, V.; Flügge, G.; Kargoll, B.; Kress, T.; Künsken, A.; Lingemann, J.; Müller, T.; Nehrkorn, A.; Nowack, A.; Pistone, C.; Pooth, O.; Stahl, A.; Aldaya Martin, M.; Arndt, T.; Asawatangtrakuldee, C.; Beernaert, K.; Behnke, O.; Behrens, U.; Bin Anuar, A. A.; Borras, K.; Campbell, A.; Connor, P.; Contreras-Campana, C.; Costanza, F.; Diez Pardos, C.; Dolinska, G.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Eren, E.; Gallo, E.; Garay Garcia, J.; Geiser, A.; Gizhko, A.; Grados Luyando, J. M.; Grohsjean, A.; Gunnellini, P.; Harb, A.; Hauk, J.; Hempel, M.; Jung, H.; Kalogeropoulos, A.; Karacheban, O.; Kasemann, M.; Keaveney, J.; Kleinwort, C.; Korol, I.; Krücker, D.; Lange, W.; Lelek, A.; Lenz, T.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I.-A.; Meyer, A. B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Ntomari, E.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Roland, B.; Sahin, M. Ö.; Saxena, P.; Schoerner-Sadenius, T.; Seitz, C.; Spannagel, S.; Stefaniuk, N.; Van Onsem, G. P.; Walsh, R.; Wissing, C.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Dreyer, T.; Garutti, E.; Gonzalez, D.; Haller, J.; Hoffmann, M.; Junkes, A.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Lapsien, T.; Marchesini, I.; Marconi, D.; Meyer, M.; Niedziela, M.; Nowatschin, D.; Pantaleo, F.; Peiffer, T.; Perieanu, A.; Poehlsen, J.; Sander, C.; Scharf, C.; Schleper, P.; Schmidt, A.; Schumann, S.; Schwandt, J.; Stadie, H.; Steinbrück, G.; Stober, F. M.; Stöver, M.; Tholen, H.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Vormwald, B.; Akbiyik, M.; Barth, C.; Baur, S.; Baus, C.; Berger, J.; Butz, E.; Caspart, R.; Chwalek, T.; Colombo, F.; De Boer, W.; Dierlamm, A.; Fink, S.; Freund, B.; Friese, R.; Giffels, M.; Gilbert, A.; Goldenzweig, P.; Haitz, D.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Katkov, I.; Kudella, S.; Mildner, H.; Mozer, M. U.; Müller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Röcker, S.; Roscher, F.; Schröder, M.; Shvetsov, I.; Sieber, G.; Simonis, H. J.; Ulrich, R.; 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.; Topsis-Giotis, I.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Tziaferi, E.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Loukas, N.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Filipovic, N.; Pasztor, G.; Bencze, G.; Hajdu, C.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Makovec, A.; Molnar, J.; Szillasi, Z.; Bartók, M.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Bahinipati, S.; Bhowmik, S.; Choudhury, S.; Mal, P.; Mandal, K.; Nayak, A.; Sahoo, D. K.; Sahoo, N.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chawla, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, A.; Kaur, M.; Kumar, R.; Kumari, P.; Mehta, A.; Mittal, M.; Singh, J. B.; Walia, G.; Kumar, Ashok; Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Keshri, S.; Malhotra, S.; Naimuddin, M.; Nishu, N.; Ranjan, K.; Sharma, R.; Sharma, V.; Bhattacharya, R.; Bhattacharya, S.; Chatterjee, K.; Dey, S.; Dutt, S.; Dutta, S.; Ghosh, S.; Majumdar, N.; Modak, A.; Mondal, K.; Mukhopadhyay, S.; Nandan, S.; Purohit, A.; Roy, A.; Roy, D.; Roy Chowdhury, S.; Sarkar, S.; Sharan, M.; Thakur, S.; Behera, P. K.; Chudasama, R.; Dutta, D.; Jha, V.; Kumar, V.; Mohanty, A. K.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.; Topkar, A.; Aziz, T.; Dugad, S.; Kole, G.; Mahakud, B.; Mitra, S.; Mohanty, G. B.; Parida, B.; Sur, N.; Sutar, B.; Banerjee, S.; Dewanjee, R. K.; Ganguly, S.; Guchait, M.; Jain, Sa.; Kumar, S.; Maity, M.; Majumder, G.; Mazumdar, K.; Sarkar, T.; Wickramage, N.; Chauhan, S.; Dube, S.; Hegde, V.; Kapoor, A.; Kothekar, K.; Pandey, S.; Rane, A.; Sharma, S.; Chenarani, S.; Eskandari Tadavani, E.; Etesami, S. M.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Paktinat Mehdiabadi, S.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Calabria, C.; Caputo, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.; Albergo, S.; Costa, S.; Di Mattia, A.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Lenzi, P.; Meschini, M.; Paoletti, S.; Russo, L.; Sguazzoni, G.; Strom, D.; Viliani, L.; Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.; Primavera, F.; Calvelli, V.; Ferro, F.; Monge, M. R.; Robutti, E.; Tosi, S.; Brianza, L.; Brivio, F.; Ciriolo, V.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Ghezzi, A.; Govoni, P.; Malberti, M.; Malvezzi, S.; Manzoni, R. A.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Pigazzini, S.; Ragazzi, S.; Tabarelli de Fatis, T.; Buontempo, S.; Cavallo, N.; De Nardo, G.; Di Guida, S.; Esposito, M.; Fabozzi, F.; Fienga, F.; Iorio, A. O. M.; Lanza, G.; Lista, L.; Meola, S.; Paolucci, P.; Sciacca, C.; Thyssen, F.; Azzi, P.; Bacchetta, N.; Benato, L.; Bisello, D.; Boletti, A.; Dall'Osso, M.; De Castro Manzano, P.; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Passaseo, M.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Sgaravatto, M.; Simonetto, F.; Torassa, E.; Ventura, S.; Zanetti, M.; Zotto, P.; Zumerle, G.; Braghieri, A.; Fallavollita, F.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.; Alunni Solestizi, L.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Leonardi, R.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fedi, G.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Barone, L.; Cavallari, F.; Cipriani, M.; Del Re, D.; Diemoz, M.; D'Imperio, G.; Gelli, S.; Longo, E.; Margaroli, F.; Marzocchi, B.; Meridiani, P.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Biino, C.; Cartiglia, N.; Cenna, F.; Costa, M.; Covarelli, R.; Degano, A.; Demaria, N.; Finco, L.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Monteno, M.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Ravera, F.; Romero, A.; Ruspa, M.; Sacchi, R.; Shchelina, K.; Sola, V.; Solano, A.; Staiano, A.; Traczyk, P.; Belforte, S.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Zanetti, A.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Lee, S.; Lee, S. 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J.; Kellams, N.; Lannon, K.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Smith, G.; Taroni, S.; Wayne, M.; Wolf, M.; Woodard, A.; Alimena, J.; Antonelli, L.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Francis, B.; Hart, A.; Hill, C.; Hughes, R.; Ji, W.; Liu, B.; Luo, W.; Puigh, D.; Winer, B. L.; Wulsin, H. W.; Cooperstein, S.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Lange, D.; Luo, J.; Marlow, D.; Medvedeva, T.; Mei, K.; Ojalvo, I.; Olsen, J.; Palmer, C.; Piroué, P.; Stickland, D.; Svyatkovskiy, A.; Tully, C.; Malik, S.; Barker, A.; Barnes, V. E.; Folgueras, S.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, A. W.; Khatiwada, A.; Miller, D. H.; Neumeister, N.; Schulte, J. F.; Shi, X.; Sun, J.; Wang, F.; Xie, W.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Chen, Z.; Ecklund, K. M.; Geurts, F. J. M.; Guilbaud, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Duh, Y. t.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Agapitos, A.; Chou, J. P.; Gershtein, Y.; Gómez Espinosa, T. A.; Halkiadakis, E.; Heindl, M.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Kyriacou, S.; Lath, A.; Nash, K.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Delannoy, A. G.; Foerster, M.; Heideman, J.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Juska, E.; Kamon, T.; Mueller, R.; Pakhotin, Y.; Patel, R.; Perloff, A.; Perniè, L.; Rathjens, D.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; De Guio, F.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Gurpinar, E.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Peltola, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Sun, X.; Wang, Y.; Wolfe, E.; Xia, F.; Clarke, C.; Harr, R.; Karchin, P. E.; Sturdy, J.; Belknap, D. A.; Buchanan, J.; Caillol, C.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Perry, T.; Pierro, G. A.; Polese, G.; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Taylor, D.; Woods, N.

    2017-06-01

    A search is presented for narrow resonances decaying to dijet final states in proton-proton collisions at √{ s} = 13TeV using data corresponding to an integrated luminosity of 12.9 fb-1. The dijet mass spectrum is well described by a smooth parameterization and no significant evidence for the production of new particles is observed. Upper limits at 95% confidence level are reported on the production cross section for narrow resonances with masses above 0.6 TeV. In the context of specific models, the limits exclude string resonances with masses below 7.4 TeV, scalar diquarks below 6.9 TeV, axigluons and colorons below 5.5 TeV, excited quarks below 5.4 TeV, color-octet scalars below 3.0 TeV, W‧ bosons below 2.7 TeV, Z‧ bosons below 2.1 TeV and between 2.3 and 2.6 TeV, and RS gravitons below 1.9 TeV. These extend previous limits in the dijet channel. Vector and axial-vector mediators in a simplified model of interactions between quarks and dark matter are excluded below 2.0 TeV. The first limits in the dijet channel on dark matter mediators are presented as functions of dark matter mass and are compared to the exclusions of dark matter in direct detection experiments.

  13. Search for dijet resonances in proton-proton collisions at sqrt(s) = 13 TeV and constraints on dark matter and other models

    SciTech Connect

    Sirunyan, Albert M; et al.

    2016-11-10

    A search is presented for narrow resonances decaying to dijet final states in proton-proton collisions at sqrt(s)=13 TeV using data corresponding to an integrated luminosity of 12.9 inverse femtobarns. The dijet mass spectrum is well described by a smooth parameterization and no significant evidence for the production of new particles is observed. Upper limits at 95% confidence level are reported on the production cross section for narrow resonances with masses above 0.6 TeV. In the context of specific models, the limits exclude string resonances with masses below 7.4 TeV, scalar diquarks below 6.9 TeV, axigluons and colorons below 5.5 TeV, excited quarks below 5.4 TeV, color-octet ascalars below 3.0 TeV, W' bosons below 2.7 TeV, Z' bosons below 2.1 TeV and between 2.3 and 2.6 TeV, and RS gravitons below 1.9 TeV. These extend previous limits in the dijet channel. Vector and axial-vector mediators in a simplified model of interactions between quarks and dark matter are excluded below 2.0 TeV. The first limits in the dijet channel on dark matter mediators are presented as functions of dark matter mass and are compared to the exclusions of dark matter in direct detection experiments.

  14. Search for dijet resonances in proton-proton collisions at $\\sqrt{s}=$ 13 TeV and constraints on dark matter and other models

    SciTech Connect

    Sirunyan, Albert M; et al.

    2016-11-10

    A search is presented for narrow resonances decaying to dijet final states in proton-proton collisions at sqrt(s)=13 TeV using data corresponding to an integrated luminosity of 12.9 inverse femtobarns. The dijet mass spectrum is well described by a smooth parameterization and no significant evidence for the production of new particles is observed. Upper limits at 95% confidence level are reported on the production cross section for narrow resonances with masses above 0.6 TeV. In the context of specific models, the limits exclude string resonances with masses below 7.4 TeV, scalar diquarks below 6.9 TeV, axigluons and colorons below 5.5 TeV, excited quarks below 5.4 TeV, color-octet ascalars below 3.0 TeV, W' bosons below 2.7 TeV, Z' bosons below 2.1 TeV and between 2.3 and 2.6 TeV, and RS gravitons below 1.9 TeV. These extend previous limits in the dijet channel. Vector and axial-vector mediators in a simplified model of interactions between quarks and dark matter are excluded below 2.0 TeV. The first limits in the dijet channel on dark matter mediators are presented as functions of dark matter mass and are compared to the exclusions of dark matter in direct detection experiments.

  15. Java classes for nonprocedural variogram modeling

    NASA Astrophysics Data System (ADS)

    Faulkner, Barton R.

    2002-04-01

    A set of Java TM classes was written for variogram modeling to support research for US EPA's Regional Vulnerability Assessment Program (ReVA). The modeling objectives of this research program are to use conceptual programming tools for numerical analysis for regional risk assessment. The classes presented use of object-oriented design elements, and their use is described for the benefit of programmers. To help facilitate their use, class diagrams and standard JavaDoc commenting were employed. Java's support for polymorphism and inheritance is used and these are described as ways to promote extension of these classes for other geostatistical applications. Among the advantages is the ease of programming, code reuse, and conceptual, rather than procedural implementation. A graphical application for variogram modeling that uses the classes is also provided and described. It can also be used by non-programmers. This application uses a generalized least-squares fitting algorithm for robust parametric variogram model fitting through the variogram cloud. This feature makes this program unique from other freely available variogram modeling programs, though the classes are presented primarily so they may be extended for use in other Java programs. More traditional variogram plotting and fitting utilities are also provided. This application is graphical and platform-neutral. It uses classes of the recently proposed Java API for linear algebra, called the JAMA package.

  16. Variable Assessment in Latent Class Models

    PubMed Central

    Zhang, Q.; Ip, E. H.

    2014-01-01

    The latent class model provides an important platform for jointly modeling mixed-mode data — i.e., discrete and continuous data with various parametric distributions. Multiple mixed-mode variables are used to cluster subjects into latent classes. While the mixed-mode latent class analysis is a powerful tool for statisticians, few studies are focused on assessing the contribution of mixed-mode variables in discriminating latent classes. Novel measures are derived for assessing both absolute and relative impacts of mixed-mode variables in latent class analysis. Specifically, the expected posterior gradient and the Kolmogorov variation of the posterior distribution, as well as related properties are studied. Numerical results are presented to illustrate the measures. PMID:24910486

  17. modeling lunar seisms in class

    NASA Astrophysics Data System (ADS)

    Blancou, Emmanuelle

    2017-04-01

    Students are taught that the internal structure of the Earth has been described by analyzing seismometer data collected at the surface of the Earth. With this in mind, a group of 17-years old students asked whether lunar seisms could be used to explore the internal structure of the Moon. Seismometers placed during Apollo 12, 14, 15 and 16 missions recorded many seismic events. The signals obtained on the Moon are different form those recorded on Earth and are due to meteorite impact, lunar tides and thermal variations. Students tried to model meteorite impacts and thermal moonquakes to determine whether they can be distinguished based on their seismic signature. To this aim, the impact of meteorites were modeled by a metallic ball falling in sand and thermal moonquakes were modeled by storing hydrates rocks on a freezer during a week and then upon a bain marie. Signal were collected in both conditions with microphones. Data showed distinctive feature depending on vibration origin.

  18. Les Houches 2015: Physics at TeV Colliders Standard Model Working Group Report

    SciTech Connect

    Andersen, J.R.; et al.

    2016-05-16

    This Report summarizes the proceedings of the 2015 Les Houches workshop on Physics at TeV Colliders. Session 1 dealt with (I) new developments relevant for high precision Standard Model calculations, (II) the new PDF4LHC parton distributions, (III) issues in the theoretical description of the production of Standard Model Higgs bosons and how to relate experimental measurements, (IV) a host of phenomenological studies essential for comparing LHC data from Run I with theoretical predictions and projections for future measurements in Run II, and (V) new developments in Monte Carlo event generators.

  19. Nonparametric Bayes Stochastically Ordered Latent Class Models

    PubMed Central

    Yang, Hongxia; O’Brien, Sean; Dunson, David B.

    2012-01-01

    Latent class models (LCMs) are used increasingly for addressing a broad variety of problems, including sparse modeling of multivariate and longitudinal data, model-based clustering, and flexible inferences on predictor effects. Typical frequentist LCMs require estimation of a single finite number of classes, which does not increase with the sample size, and have a well-known sensitivity to parametric assumptions on the distributions within a class. Bayesian nonparametric methods have been developed to allow an infinite number of classes in the general population, with the number represented in a sample increasing with sample size. In this article, we propose a new nonparametric Bayes model that allows predictors to flexibly impact the allocation to latent classes, while limiting sensitivity to parametric assumptions by allowing class-specific distributions to be unknown subject to a stochastic ordering constraint. An efficient MCMC algorithm is developed for posterior computation. The methods are validated using simulation studies and applied to the problem of ranking medical procedures in terms of the distribution of patient morbidity. PMID:22505787

  20. Associated TeV Emission from the Double-Synchrotron Model for Large-Scale Quasar Jets

    NASA Astrophysics Data System (ADS)

    Whitley, Kevin Michael; Meyer, Eileen T.; Georganopoulos, Markos

    2017-01-01

    When the Chandra X-ray telescope launched, it discovered that the radio jets sometimes produced by active galactic nuclei (AGN) were anomalously bright in the X-ray band. The dominant model for explaining this X-ray emission is presently that the synchrotron-emitting electrons in the jet are Compton-scattering Cosmic Microwave Background photons (IC/CMB). This model requires highly relativistic bulk jet speeds far from the core of the AGN and a cutoff of the electron energy distribution in the jet below TeV energies. Based on recent results ruling out the IC/CMB model for anomalous X-ray jets, we consider instead that X-rays from a second electron energy distribution reaching up to TeV energies. As a mandatory process, IC/CMB emission at TeV energies will result from these jets, at a level dependent on the magnetic field and Doppler boosting factor. Using publicly available multi-wavelength observations of around three dozen anomalously X-ray bright jets, we predict the TeV gamma ray production of each jet subject to these unknowns. This will allow us to construct a catalog of potential TeV-producing active galaxies to observe with TeV telescopes currently being constructed. Such observations could be used to determine which, if either, of these two models is correct in explaining the X-ray emission of these jets.

  1. Search for dijet resonances in proton-proton collisions at $$\\sqrt(s) = $$ 13 TeV and constraints on dark matter and other models

    DOE PAGES

    Sirunyan, Albert M.

    2017-02-14

    A search is presented for narrow resonances decaying to dijet final states in proton-proton collisions atmore » $$\\sqrt(s) = $$ 13 TeV using data corresponding to an integrated luminosity of 12.9 inverse femtobarns. The dijet mass spectrum is well described by a smooth parameterization and no significant evidence for the production of new particles is observed. Upper limits at 95% confidence level are reported on the production cross section for narrow resonances with masses above 0.6 TeV. In the context of specific models, the limits exclude string resonances with masses below 7.4 TeV, scalar diquarks below 6.9 TeV, axigluons and colorons below 5.5 TeV, excited quarks below 5.4 TeV, color-octet ascalars below 3.0 TeV, W' bosons below 2.7 TeV, Z' bosons below 2.1 TeV and between 2.3 and 2.6 TeV, and RS gravitons below 1.9 TeV. These extend previous limits in the dijet channel. Vector and axial-vector mediators in a simplified model of interactions between quarks and dark matter are excluded below 2.0 TeV. The first limits in the dijet channel on dark matter mediators are presented as functions of dark matter mass and are compared to the exclusions of dark matter in direct detection experiments.« less

  2. Search for dijet resonances in proton–proton collisions at s=13TeV and constraints on dark matter and other models

    DOE PAGES

    Sirunyan, A. M.; Tumasyan, A.; Adam, W.; ...

    2017-06-01

    A search is presented for narrow resonances decaying to dijet final states in proton-proton collisions atmore » $$\\sqrt(s) = $$ 13 TeV using data corresponding to an integrated luminosity of 12.9 inverse femtobarns. The dijet mass spectrum is well described by a smooth parameterization and no significant evidence for the production of new particles is observed. Upper limits at 95% confidence level are reported on the production cross section for narrow resonances with masses above 0.6 TeV. In the context of specific models, the limits exclude string resonances with masses below 7.4 TeV, scalar diquarks below 6.9 TeV, axigluons and colorons below 5.5 TeV, excited quarks below 5.4 TeV, color-octet ascalars below 3.0 TeV, W' bosons below 2.7 TeV, Z' bosons below 2.1 TeV and between 2.3 and 2.6 TeV, and RS gravitons below 1.9 TeV. These extend previous limits in the dijet channel. Vector and axial-vector mediators in a simplified model of interactions between quarks and dark matter are excluded below 2.0 TeV. The first limits in the dijet channel on dark matter mediators are presented as functions of dark matter mass and are compared to the exclusions of dark matter in direct detection experiments.« less

  3. Model independent search for new phenomena in pp collisions at√s=1.96 TeV

    DOE PAGES

    Abazov, Victor Mukhamedovich; Abbott, Braden Keim; Acharya, Bannanje Sripath; ...

    2012-05-24

    We describe a model independent search for physics beyond the standard model in lepton final states. We examine 117 final states using 1.1 fb-1 of pp collisions data at √s = 1.96 TeV collected with the D0 detector. We conclude that all observed discrepancies between data and model can be attributed to uncertainties in the standard model background modeling, and hence we do not see any evidence for physics beyond the standard model.

  4. The latent class multitrait-multimethod model.

    PubMed

    Oberski, Daniel L; Hagenaars, Jacques A P; Saris, Willem E

    2015-12-01

    A latent class multitrait-multimethod (MTMM) model is proposed to estimate random and systematic measurement error in categorical survey questions while making fewer assumptions than have been made so far in such evaluations, allowing for possible extreme response behavior and other nonmonotone effects. The method is a combination of the MTMM research design of Campbell and Fiske (1959), the basic response model for survey questions of Saris and Andrews (1991), and the latent class factor model of Vermunt and Magidson (2004, pp. 227-230). The latent class MTMM model thus combines an existing design, model, and method to allow for the estimation of the degree to and manner in which survey questions are affected by systematic measurement error. Starting from a general form of the response function for a survey question, we present the MTMM experimental approach to identification of the response function's parameters. A "trait-method biplot" is introduced as a means of interpreting the estimates of systematic measurement error, whereas the quality of the questions can be evaluated by item information curves and the item information function. An experiment from the European Social Survey is analyzed and the results are discussed, yielding valuable insights into the functioning of a set of example questions on the role of women in society in 2 countries.

  5. Hybrid Model of IRT and Latent Class Models.

    ERIC Educational Resources Information Center

    Yamamoto, Kentaro

    This study developed a hybrid of item response theory (IRT) models and latent class models, which combined the strengths of each type of model. The primary motivation for developing the new model is to describe characteristics of examinees' knowledge at the time of the examination. Hence, the application of the model lies mainly in so-called…

  6. A Comprehensive Model of In-Class and Out-Of-Class Learning in Higher Education.

    ERIC Educational Resources Information Center

    Shaeffer, James M.; Menges, Robert J.

    A model of in-class and out-of-class learning in higher education is discussed, and the literature related to out-of-class factors and time factors in teaching-learning is reviewed. Three related models of school learning are briefly reviewed. John Carroll's model (1963) proposes that the degree of student learning is determined by the time needed…

  7. A new class of relativistic stellar models

    NASA Astrophysics Data System (ADS)

    Haggag, Salah

    1995-03-01

    Einstein field equations for a static and spherically symmetric perfect fluid are considered. A formulation given by Patino and Rago is used to obtain a class of nine solutions, two of them are Tolman solutions I, IV and the remaining seven are new. The solutions are the correct ones corresponding to expressions derived by Patino and Rago which have been shown by Knutsen to be incorrect. Similar to Tolan solution IV each of the new solutions satisfies energy conditions inside a sphere in some range of two independent parameters. Besides, each solution could be matched to the exterior Schwarzschild solution at a boundary where the pressure vanishes and thus the solutions constitute a class of new physically reasonable stellar models.

  8. Unitarity sum rules, three-site moose model, and the ATLAS 2 TeV diboson anomalies

    NASA Astrophysics Data System (ADS)

    Abe, Tomohiro; Nagai, Ryo; Okawa, Shohei; Tanabashi, Masaharu

    2015-09-01

    We investigate W' interpretations for the ATLAS 2 TeV diboson anomalies. The roles of the unitarity sum rules, which ensure the perturbativity of the longitudinal vector boson scattering amplitudes, are emphasized. We find the unitarity sum rules and the custodial symmetry are powerful enough to predict various nontrivial relations among W W Z', W Z W', W W h , W W'h and Z Z'h coupling strengths in a model independent manner. We also perform surveys in the general parameter space of W' models and find the ATLAS 2 TeV diboson anomalies may be interpreted as a W' particle of the three-site moose model, i.e., a Kaluza-Klein like particle in a deconstructed extra dimension model. It is also shown that the nonstandard-model-like Higgs boson is favored by the present data to interpret the ATLAS diboson anomalies as the consequences of the W' and Z' bosons.

  9. Unrestricted Mixture Models for Class Identification in Growth Mixture Modeling

    ERIC Educational Resources Information Center

    Liu, Min; Hancock, Gregory R.

    2014-01-01

    Growth mixture modeling has gained much attention in applied and methodological social science research recently, but the selection of the number of latent classes for such models remains a challenging issue, especially when the assumption of proper model specification is violated. The current simulation study compared the performance of a linear…

  10. Interaction of model class A1, class A2, and class Y amphipathic helical peptides with membranes.

    PubMed

    Mishra, V K; Palgunachari, M N

    1996-08-27

    To test the hypothesis that differences in the lipid affinity of exchangeable apolipoproteins are due to the presence of different classes of amphipathic alpha-helical motifs which differ primarily in the distribution of charged amino acid residues, we designed and synthesized model peptides mimicking class A1, class A2, and class Y amphipathic helices present in these apolipoproteins. Both class A1 and class A2 helices have positive residues at the polar-nonpolar interface and negative residues at the center of the polar face. However, clustering of positive and negative residues is less exact in class A1 compared to class A2 helices. The class Y helices have two negative residue clusters on the polar face separating the two arms and the base of the Y motif formed by three positive residue clusters. The lipid affinities of three 18 residue model peptides representing these classes, Ac-18A1-NH2 (Ac-ELLEKWAEKLAALKEALK-NH2), Ac-18A2-NH2 (Ac-ELLEKWKEALAALAEKLK-NH2), and Ac-18Y-NH2 (Ac-ELLKAWKEALEALKEKLA-NH2), were determined by right-angle light scattering, circular dichroism spectroscopy, differential scanning calorimetry, and fluorescence spectroscopy. The observed rank order of lipid affinity of these three peptides is: Ac-18A2-NH2 > Ac-18Y-NH2 > Ac-18A1-NH2. This order is consistent with the known lipid affinity of exchangeable apolipoproteins containing class A1, class A2, and class Y helices (class A2 > class Y > class A1). Results of this study illustrate the important role of interfacial lysine residues in modulating the lipid affinity of amphipathic helices and suggest that the effect of interfacial lysine residues in increasing lipid affinity is additive. We propose that interfacial lysine residues, in addition to widening the hydrophobic face because of snorkeling, also help anchor the amphipathic helix in the lipid bilayer.

  11. Les Houches Physics at TeV Colliders 2005 Beyond the Standard Model Working Group: Summary Report

    SciTech Connect

    Allanach, B.C.; Grojean, C.; Skands, P.; Accomando, E.; Azuelos, G.; Baer, H.; Balazs, C.; Belanger, G.; Benakli, K.; Boudjema, F.; Brelier, B.; Bunichev, V.; Cacciapaglia, G.; Carena, M.; Choudhury, D.; Delsart, P.-A.; De Sanctis, U.; Desch, K.; Dobrescu, B.A.; Dudko, L.; El Kacimi, M.; /Saclay, SPhT /CERN /Fermilab /INFN, Turin /Turin U. /Montreal U. /TRIUMF /Florida State U. /Argonne /Annecy, LAPTH /Paris, LPTHE /Moscow State U. /Cornell U., CIHEP /Delhi U. /Milan U. /INFN, Milan /Freiburg U. /Cadi Ayyad U., Marrakech /Orsay, LPT /Oslo U. /Lancaster U.

    2006-03-17

    The work contained herein constitutes a report of the ''Beyond the Standard Model'' working group for the Workshop ''Physics at TeV Colliders'', Les Houches, France, 2-20 May, 2005. We present reviews of current topics as well as original research carried out for the workshop. Supersymmetric and non-supersymmetric models are studied, as well as computational tools designed in order to facilitate their phenomenology.

  12. A class of well behaved charged superdense star models of embedding class one

    NASA Astrophysics Data System (ADS)

    Gupta, Y. K.; Kumar, Jitendra

    2011-12-01

    A class of well behaved charged superdense star models of embedding class one is obtained by taking perfect fluid to be interior matter. In the process we come across the models for white dwarf, quark and neutron stars. Maximum mass of the star of this class is found to be 6.716998 M Θ with its radius is 18.92112 Km. In the absence of charge the models reduce to Schwarzchild's interior model with constant density.

  13. Model independent search for new phenomena in pp collisions at√s=1.96 TeV

    SciTech Connect

    Abazov, Victor Mukhamedovich; Abbott, Braden Keim; Acharya, Bannanje Sripath; Adams, Mark Raymond; Adams, Todd; Alexeev, Guennadi D; Alkhazov, Georgiy D; Alton, Andrew K; Alverson, George O; Alves, Gilvan Augusto; Aoki, Masato

    2012-05-24

    We describe a model independent search for physics beyond the standard model in lepton final states. We examine 117 final states using 1.1 fb-1 of pp collisions data at √s = 1.96 TeV collected with the D0 detector. We conclude that all observed discrepancies between data and model can be attributed to uncertainties in the standard model background modeling, and hence we do not see any evidence for physics beyond the standard model.

  14. Search for physics beyond the standard model in dilepton mass spectra in proton-proton collisions at √s = 8 TeV

    SciTech Connect

    Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M.; Erö, J.; Fabjan, C.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Knünz, V.; Krammer, M.; Krätschmer, I.; Liko, D.; Mikulec, I.; Rabady, D.; Rahbaran, B.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Taurok, A.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C. -E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Bansal, M.; Bansal, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Knutsson, A.; Luyckx, S.; Ochesanu, S.; Rougny, R.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D’Hondt, J.; Daci, N.; Heracleous, N.; Keaveney, J.; Lowette, S.; Maes, M.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Villella, I.; Caillol, C.; Clerbaux, B.; De Lentdecker, G.; Dobur, D.; Favart, L.; Gay, A. P. R.; Grebenyuk, A.; Léonard, A.; Mohammadi, A.; Perniè, L.; Reis, T.; Seva, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.; Zenoni, F.; Adler, V.; Beernaert, K.; Benucci, L.; Cimmino, A.; Costantini, S.; Crucy, S.; Dildick, S.; Fagot, A.; Garcia, G.; Mccartin, J.; Ocampo Rios, A. A.; Ryckbosch, D.; Salva Diblen, S.; Sigamani, M.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bruno, G.; Castello, R.; Caudron, A.; Ceard, L.; Da Silveira, G. G.; Delaere, C.; du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Nuttens, C.; Pagano, D.; Perrini, L.; Pin, A.; Piotrzkowski, K.; Popov, A.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Vizan Garcia, J. M.; Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.; Aldá Júnior, W. L.; Alves, G. A.; Brito, L.; Correa Martins Junior, M.; Dos Reis Martins, T.; Mora Herrera, C.; Pol, M. E.; Carvalho, W.; Chinellato, J.; Custódio, A.; Da Costa, E. M.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Malbouisson, H.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santaolalla, J.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Bernardes, C. A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Aleksandrov, A.; Genchev, V.; Iaydjiev, P.; Marinov, A.; Piperov, S.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Hadjiiska, R.; Kozhuharov, V.; Litov, L.; Pavlov, B.; Petkov, P.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Du, R.; Jiang, C. H.; Plestina, R.; Romeo, F.; Tao, J.; Wang, Z.; Asawatangtrakuldee, C.; Ban, Y.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Zou, W.; Avila, C.; Chaparro Sierra, L. 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L.; Favaro, C.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Baffioni, S.; Beaudette, F.; Busson, P.; Charlot, C.; Dahms, T.; Dalchenko, M.; Dobrzynski, L.; Filipovic, N.; Florent, A.; Granier de Cassagnac, R.; Mastrolorenzo, L.; Miné, P.; Mironov, C.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Paganini, P.; Regnard, S.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Veelken, C.; Yilmaz, Y.; Zabi, A.; Agram, J. -L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J. -M.; Chabert, E. C.; Collard, C.; Conte, E.; Fontaine, J. -C.; Gelé, D.; Goerlach, U.; Goetzmann, C.; Le Bihan, A. -C.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Beaupere, N.; Boudoul, G.; Bouvier, E.; Brochet, S.; Carrillo Montoya, C. A.; Chasserat, J.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Kurca, T.; Lethuillier, M.; Mirabito, L.; Perries, S.; Ruiz Alvarez, J. D.; Sabes, D.; Sgandurra, L.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Xiao, H.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Bontenackels, M.; Edelhoff, M.; Feld, L.; Hindrichs, O.; Klein, K.; Ostapchuk, A.; Perieanu, A.; Raupach, F.; Sammet, J.; Schael, S.; Weber, H.; Wittmer, B.; Zhukov, V.; Ata, M.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Erdmann, M.; Fischer, R.; Güth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Klingebiel, D.; Knutzen, S.; Kreuzer, P.; Merschmeyer, M.; Meyer, A.; Millet, P.; Olschewski, M.; Padeken, K.; Papacz, P.; Pook, T.; Reithler, H.; Schmitz, S. A.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Weber, M.; Cherepanov, V.; Erdogan, Y.; Flügge, G.; Geenen, H.; Geisler, M.; Haj Ahmad, W.; Heister, A.; Hoehle, F.; Kargoll, B.; Kress, T.; Kuessel, Y.; Künsken, A.; Lingemann, J.; Nowack, A.; Nugent, I. M.; Perchalla, L.; Pooth, O.; Stahl, A.; Asin, I.; Bartosik, N.; Behr, J.; Behrenhoff, W.; Behrens, U.; Bell, A. J.; Bergholz, M.; Bethani, A.; Borras, K.; Burgmeier, A.; Cakir, A.; Calligaris, L.; Campbell, A.; Choudhury, S.; Costanza, F.; Diez Pardos, C.; Dooling, S.; Dorland, T.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Flucke, G.; Garay Garcia, J.; Geiser, A.; Gunnellini, P.; Hauk, J.; Hempel, M.; Horton, D.; Jung, H.; Kalogeropoulos, A.; Kasemann, M.; Katsas, P.; Kieseler, J.; Kleinwort, C.; Krücker, D.; Lange, W.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Lutz, B.; Mankel, R.; Marfin, I.; Melzer-Pellmann, I. -A.; Meyer, A. B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Naumann-Emme, S.; Nayak, A.; Novgorodova, O.; Ntomari, E.; Perrey, H.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Ribeiro Cipriano, P. M.; Roland, B.; Ron, E.; Sahin, M. Ö.; Salfeld-Nebgen, J.; Saxena, P.; Schmidt, R.; Schoerner-Sadenius, T.; Schröder, M.; Seitz, C.; Spannagel, S.; Vargas Trevino, A. D. R.; Walsh, R.; Wissing, C.; Aldaya Martin, M.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Erfle, J.; Garutti, E.; Goebel, K.; Görner, M.; Haller, J.; Hoffmann, M.; Höing, R. S.; Kirschenmann, H.; Klanner, R.; Kogler, R.; Lange, J.; Lapsien, T.; Lenz, T.; Marchesini, I.; Ott, J.; Peiffer, T.; Pietsch, N.; Poehlsen, J.; Poehlsen, T.; Rathjens, D.; Sander, C.; Schettler, H.; Schleper, P.; Schlieckau, E.; Schmidt, A.; Seidel, M.; Sola, V.; Stadie, H.; Steinbrück, G.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Barth, C.; Baus, C.; Berger, J.; Böser, C.; Butz, E.; Chwalek, T.; De Boer, W.; Descroix, A.; Dierlamm, A.; Feindt, M.; Frensch, F.; Giffels, M.; Hartmann, F.; Hauth, T.; Husemann, U.; Katkov, I.; Kornmayer, A.; Kuznetsova, E.; Lobelle Pardo, P.; Mozer, M. U.; Müller, Th.; Nürnberg, A.; Quast, G.; Rabbertz, K.; Ratnikov, F.; Röcker, S.; Simonis, H. J.; Stober, F. M.; Ulrich, R.; Wagner-Kuhr, J.; Wayand, S.; Weiler, T.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Giakoumopoulou, V. A.; Kyriakis, A.; Loukas, D.; Markou, A.; Markou, C.; Psallidas, A.; Topsis-Giotis, I.; Agapitos, A.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Stiliaris, E.; Aslanoglou, X.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Bencze, G.; Hajdu, C.; Hidas, P.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Molnar, J.; Palinkas, J.; Szillasi, Z.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Swain, S. K.; Beri, S. B.; Bhatnagar, V.; Gupta, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, M.; Kumar, R.; Mittal, M.; Nishu, N.; Singh, J. B.; Kumar, Ashok; Kumar, Arun; Ahuja, S.; Bhardwaj, A.; Choudhary, B. 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S.; Colaleo, A.; Creanza, D.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Zito, G.; Abbiendi, G.; Benvenuti, A. C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.; Travaglini, R.; Albergo, S.; Cappello, G.; Chiorboli, M.; Costa, S.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Ciulli, V.; Civinini, C.; D’Alessandro, R.; Focardi, E.; Gallo, E.; Gonzi, S.; Gori, V.; Lenzi, P.; Meschini, M.; Paoletti, S.; Sguazzoni, G.; Tropiano, A.; Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.; Ferretti, R.; Ferro, F.; Lo Vetere, M.; Robutti, E.; Tosi, S.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Gerosa, R.; Ghezzi, A.; Govoni, P.; Lucchini, M. T.; Malvezzi, S.; Manzoni, R. A.; Martelli, A.; Marzocchi, B.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Redaelli, N.; Tabarelli de Fatis, T.; Buontempo, S.; Cavallo, N.; Di Guida, S.; Fabozzi, F.; Iorio, A. O. M.; Lista, L.; Meola, S.; Merola, M.; Paolucci, P.; Azzi, P.; Bacchetta, N.; Bisello, D.; Branca, A.; Carlin, R.; Checchia, P.; Dall’Osso, M.; Dorigo, T.; Dosselli, U.; Galanti, M.; Gasparini, F.; Gasparini, U.; Giubilato, P.; Gozzelino, A.; Kanishchev, K.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Tosi, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.; Gabusi, M.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vitulo, P.; Biasini, M.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Spiezia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Broccolo, G.; Castaldi, R.; Ciocci, M. A.; Dell’Orso, R.; Donato, S.; Fiori, F.; Foà, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Moon, C. S.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Serban, A. T.; Spagnolo, P.; Squillacioti, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Vernieri, C.; Barone, L.; Cavallari, F.; D’imperio, G.; Del Re, D.; Diemoz, M.; Grassi, M.; Jorda, C.; Longo, E.; Margaroli, F.; Meridiani, P.; Micheli, F.; Nourbakhsh, S.; Organtini, G.; Paramatti, R.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Traczyk, P.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bellan, R.; Biino, C.; Cartiglia, N.; Casasso, S.; Costa, M.; Degano, A.; Demaria, N.; Finco, L.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Musich, M.; Obertino, M. M.; Ortona, G.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Potenza, A.; Romero, A.; Ruspa, M.; Sacchi, R.; Solano, A.; Staiano, A.; Tamponi, U.; Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Gobbo, B.; La Licata, C.; Marone, M.; Schizzi, A.; Umer, T.; Zanetti, A.; Chang, S.; Kropivnitskaya, A.; Nam, S. K.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Kong, D. J.; Lee, S.; Oh, Y. D.; Park, H.; Sakharov, A.; Son, D. C.; Kim, T. J.; Kim, J. Y.; Song, S.; Choi, S.; Gyun, D.; Hong, B.; Jo, M.; Kim, H.; Kim, Y.; Lee, B.; Lee, K. S.; Park, S. K.; Roh, Y.; Yoo, H. D.; Choi, M.; Kim, J. H.; Park, I. C.; Ryu, G.; Ryu, M. S.; Choi, Y.; Choi, Y. K.; Goh, J.; Kim, D.; Kwon, E.; Lee, J.; Seo, H.; Yu, I.; Juodagalvis, A.; Komaragiri, J. R.; Ali, M. A. B. Md; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-de La Cruz, I.; Hernandez-Almada, A.; Lopez-Fernandez, R.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Vazquez Valencia, F.; Pedraza, I.; Salazar Ibarguen, H. A.; Casimiro Linares, E.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Reucroft, S.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Khalid, S.; Khan, W. A.; Khurshid, T.; Shah, M. A.; Shoaib, M.; Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Górski, M.; Kazana, M.; Nawrocki, K.; Romanowska-Rybinska, K.; Szleper, M.; Zalewski, P.; Brona, G.; Bunkowski, K.; Cwiok, M.; Dominik, W.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Wolszczak, W.; Bargassa, P.; Beirão Da Cruz E Silva, C.; Faccioli, P.; Ferreira Parracho, P. G.; Gallinaro, M.; Lloret Iglesias, L.; Nguyen, F.; Rodrigues Antunes, J.; Seixas, J.; Varela, J.; Vischia, P.; Golutvin, I.; Gorbunov, I.; Kamenev, A.; Karjavin, V.; Konoplyanikov, V.; Kozlov, G.; Lanev, A.; Malakhov, A.; Matveev, V.; Moisenz, P.; Palichik, V.; Perelygin, V.; Savina, M.; Shmatov, S.; Shulha, S.; Skatchkov, N.; Smirnov, V.; Zarubin, A.; Golovtsov, V.; Ivanov, Y.; Kim, V.; Levchenko, P.; Murzin, V.; Oreshkin, V.; Smirnov, I.; Sulimov, V.; Uvarov, L.; Vavilov, S.; Vorobyev, A.; Vorobyev, An.; Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.; Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Safronov, G.; Semenov, S.; Spiridonov, A.; Stolin, V.; Vlasov, E.; Zhokin, A.; Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Leonidov, A.; Mesyats, G.; Rusakov, S. V.; Vinogradov, A.; Belyaev, A.; Boos, E.; Bunichev, V.; Dubinin, M.; Dudko, L.; Gribushin, A.; Klyukhin, V.; Kodolova, O.; Lokhtin, I.; Obraztsov, S.; Perfilov, M.; Savrin, V.; Snigirev, A.; Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Krychkine, V.; Petrov, V.; Ryutin, R.; Sobol, A.; Tourtchanovitch, L.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.; Adzic, P.; Ekmedzic, M.; Milosevic, J.; Rekovic, V.; Alcaraz Maestre, J.; Battilana, C.; Calvo, E.; Cerrada, M.; Chamizo Llatas, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Domínguez Vázquez, D.; Escalante Del Valle, A.; Fernandez Bedoya, C.; Fernández Ramos, J. P.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Navarro De Martino, E.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.; Albajar, C.; de Trocóniz, J. F.; Missiroli, M.; Moran, D.; Brun, H.; Cuevas, J.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Duarte Campderros, J.; Fernandez, M.; Gomez, G.; Graziano, A.; Lopez Virto, A.; Marco, J.; Marco, R.; Martinez Rivero, C.; Matorras, F.; Munoz Sanchez, F. J.; Piedra Gomez, J.; Rodrigo, T.; Rodríguez-Marrero, A. Y.; Ruiz-Jimeno, A.; Scodellaro, L.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Auffray, E.; Auzinger, G.; Bachtis, M.; Baillon, P.; Ball, A. H.; Barney, D.; Benaglia, A.; Bendavid, J.; Benhabib, L.; Benitez, J. F.; Bernet, C.; Bloch, P.; Bocci, A.; Bonato, A.; Bondu, O.; Botta, C.; Breuker, H.; Camporesi, T.; Cerminara, G.; Colafranceschi, S.; D’Alfonso, M.; d’Enterria, D.; Dabrowski, A.; David, A.; De Guio, F.; De Roeck, A.; De Visscher, S.; Di Marco, E.; Dobson, M.; Dordevic, M.; Dorney, B.; Dupont-Sagorin, N.; Elliott-Peisert, A.; Eugster, J.; Franzoni, G.; Funk, W.; Gigi, D.; Gill, K.; Giordano, D.; Girone, M.; Glege, F.; Guida, R.; Gundacker, S.; Guthoff, M.; Hammer, J.; Hansen, M.; Harris, P.; Hegeman, J.; Innocente, V.; Janot, P.; Kousouris, K.; Krajczar, K.; Lecoq, P.; Lourenço, C.; Magini, N.; Malgeri, L.; Mannelli, M.; Marrouche, J.; Masetti, L.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Morovic, S.; Mulders, M.; Musella, P.; Orsini, L.; Pape, L.; Perez, E.; Perrozzi, L.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Pierini, M.; Pimiä, M.; Piparo, D.; Plagge, M.; Racz, A.; Rolandi, G.; Rovere, M.; Sakulin, H.; Schäfer, C.; Schwick, C.; Sharma, A.; Siegrist, P.; Silva, P.; Simon, M.; Sphicas, P.; Spiga, D.; Steggemann, J.; Stieger, B.; Stoye, M.; Takahashi, Y.; Treille, D.; Tsirou, A.; Veres, G. I.; Wardle, N.; Wöhri, H. K.; Wollny, H.; Zeuner, W. D.; Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Renker, D.; Rohe, T.; Bachmair, F.; Bäni, L.; Bianchini, L.; Buchmann, M. A.; Casal, B.; Chanon, N.; Dissertori, G.; Dittmar, M.; Donegà, M.; Dünser, M.; Eller, P.; Grab, C.; Hits, D.; Hoss, J.; Lustermann, W.; Mangano, B.; Marini, A. C.; Martinez Ruiz del Arbol, P.; Masciovecchio, M.; Meister, D.; Mohr, N.; Nägeli, C.; Nessi-Tedaldi, F.; Pandolfi, F.; Pauss, F.; Peruzzi, M.; Quittnat, M.; Rebane, L.; Rossini, M.; Starodumov, A.; Takahashi, M.; Theofilatos, K.; Wallny, R.; Weber, H. A.; Amsler, C.; Canelli, M. F.; Chiochia, V.; De Cosa, A.; Hinzmann, A.; Hreus, T.; Kilminster, B.; Lange, C.; Millan Mejias, B.; Ngadiuba, J.; Robmann, P.; Ronga, F. J.; Taroni, S.; Verzetti, M.; Yang, Y.; Cardaci, M.; Chen, K. H.; Ferro, C.; Kuo, C. M.; Lin, W.; Lu, Y. J.; Volpe, R.; Yu, S. S.; Chang, P.; Chang, Y. H.; Chang, Y. W.; Chao, Y.; Chen, K. F.; Chen, P. H.; Dietz, C.; Grundler, U.; Hou, W. -S.; Kao, K. Y.; Lei, Y. J.; Liu, Y. F.; Lu, R. -S.; Majumder, D.; Petrakou, E.; Tzeng, Y. M.; Wilken, R.; Asavapibhop, B.; Singh, G.; Srimanobhas, N.; Suwonjandee, N.; Adiguzel, A.; Bakirci, M. N.; Cerci, S.; Dozen, C.; Dumanoglu, I.; Eskut, E.; Girgis, S.; Gokbulut, G.; Gurpinar, E.; Hos, I.; Kangal, E. E.; Kayis Topaksu, A.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Sunar Cerci, D.; Tali, B.; Topakli, H.; Vergili, M.; Akin, I. V.; Bilin, B.; Bilmis, S.; Gamsizkan, H.; Karapinar, G.; Ocalan, K.; Sekmen, S.; Surat, U. E.; Yalvac, M.; Zeyrek, M.; Gülmez, E.; Isildak, B.; Kaya, M.; Kaya, O.; Cankocak, K.; Vardarlı, F. I.; Levchuk, L.; Sorokin, P.; Brooke, J. J.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Meng, Z.; Newbold, D. M.; Paramesvaran, S.; Poll, A.; Senkin, S.; Smith, V. J.; Williams, T.; Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Womersley, W. J.; Worm, S. D.; Baber, M.; Bainbridge, R.; Buchmuller, O.; Burton, D.; Colling, D.; Cripps, N.; Cutajar, M.; Dauncey, P.; Davies, G.; Della Negra, M.; Dunne, P.; Ferguson, W.; Fulcher, J.; Futyan, D.; Gilbert, A.; Hall, G.; Iles, G.; Jarvis, M.; Karapostoli, G.; Kenzie, M.; Lane, R.; Lucas, R.; Lyons, L.; Magnan, A. -M.; Malik, S.; Mathias, B.; Nash, J.; Nikitenko, A.; Pela, J.; Pesaresi, M.; Petridis, K.; Raymond, D. M.; Rogerson, S.; Rose, A.; Seez, C.; Sharp, P.; Tapper, A.; Vazquez Acosta, M.; Virdee, T.; Zenz, S. C.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Leggat, D.; Leslie, D.; Martin, W.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.; Dittmann, J.; Hatakeyama, K.; Kasmi, A.; Liu, H.; Scarborough, T.; Charaf, O.; Cooper, S. I.; Henderson, C.; Rumerio, P.; Avetisyan, A.; Bose, T.; Fantasia, C.; Lawson, P.; Richardson, C.; Rohlf, J.; John, J. St.; Sulak, L.; Alimena, J.; Berry, E.; Bhattacharya, S.; Christopher, G.; Cutts, D.; Demiragli, Z.; Dhingra, N.; Ferapontov, A.; Garabedian, A.; Heintz, U.; Kukartsev, G.; Laird, E.; Landsberg, G.; Luk, M.; Narain, M.; Segala, M.; Sinthuprasith, T.; Speer, T.; Swanson, J.; Breedon, R.; Breto, G.; Calderon De La Barca Sanchez, M.; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Gardner, M.; Ko, W.; Lander, R.; Miceli, T.; Mulhearn, M.; Pellett, D.; Pilot, J.; Ricci-Tam, F.; Searle, M.; Shalhout, S.; Smith, J.; Squires, M.; Stolp, D.; Tripathi, M.; Wilbur, S.; Yohay, R.; Cousins, R.; Everaerts, P.; Farrell, C.; Hauser, J.; Ignatenko, M.; Rakness, G.; Takasugi, E.; Valuev, V.; Weber, M.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Hanson, G.; Heilman, J.; Ivova Rikova, M.; Jandir, P.; Kennedy, E.; Lacroix, F.; Long, O. R.; Luthra, A.; Malberti, M.; Nguyen, H.; Olmedo Negrete, M.; Shrinivas, A.; Sumowidagdo, S.; Wimpenny, S.; Andrews, W.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; D’Agnolo, R. T.; Evans, D.; Holzner, A.; Kelley, R.; Klein, D.; Lebourgeois, M.; Letts, J.; Macneill, I.; Olivito, D.; Padhi, S.; Palmer, C.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Sudano, E.; Tadel, M.; Tu, Y.; Vartak, A.; Welke, C.; Würthwein, F.; Yagil, A.; Barge, D.; Bradmiller-Feld, J.; Campagnari, C.; Danielson, T.; Dishaw, A.; Dutta, V.; Flowers, K.; Franco Sevilla, M.; Geffert, P.; George, C.; Golf, F.; Gouskos, L.; Incandela, J.; Justus, C.; Mccoll, N.; Richman, J.; Stuart, D.; To, W.; West, C.; Yoo, J.; Apresyan, A.; Bornheim, A.; Bunn, J.; Chen, Y.; Duarte, J.; Mott, A.; Newman, H. B.; Pena, C.; Rogan, C.; Spiropulu, M.; Timciuc, V.; Vlimant, J. R.; Wilkinson, R.; Xie, S.; Zhu, R. Y.; Azzolini, V.; Calamba, A.; Carlson, B.; Ferguson, T.; Iiyama, Y.; Paulini, M.; Russ, J.; Vogel, H.; Vorobiev, I.; Cumalat, J. P.; Ford, W. T.; Gaz, A.; Luiggi Lopez, E.; Nauenberg, U.; Smith, J. G.; Stenson, K.; Ulmer, K. A.; Wagner, S. R.; Alexander, J.; Chatterjee, A.; Chu, J.; Dittmer, S.; Eggert, N.; Mirman, N.; Nicolas Kaufman, G.; Patterson, J. R.; Ryd, A.; Salvati, E.; Skinnari, L.; Sun, W.; Teo, W. D.; Thom, J.; Thompson, J.; Tucker, J.; Weng, Y.; Winstrom, L.; Wittich, P.; Winn, D.; Abdullin, S.; Albrow, M.; Anderson, J.; Apollinari, G.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Cheung, H. W. K.; Chlebana, F.; Cihangir, S.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gao, Y.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hanlon, J.; Hare, D.; Harris, R. M.; Hirschauer, J.; Hooberman, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Kaadze, K.; Klima, B.; Kreis, B.; Kwan, S.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, T.; Lykken, J.; Maeshima, K.; Marraffino, J. M.; Martinez Outschoorn, V. I.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mishra, K.; Mrenna, S.; Musienko, Y.; Nahn, S.; Newman-Holmes, C.; O’Dell, V.; Prokofyev, O.; Sexton-Kennedy, E.; Sharma, S.; Soha, A.; Spalding, W. J.; Spiegel, L.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vidal, R.; Whitbeck, A.; Whitmore, J.; Yang, F.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Carver, M.; Cheng, T.; Curry, D.; Das, S.; De Gruttola, M.; Di Giovanni, G. P.; Field, R. D.; Fisher, M.; Furic, I. K.; Hugon, J.; Konigsberg, J.; Korytov, A.; Kypreos, T.; Low, J. F.; Matchev, K.; Milenovic, P.; Mitselmakher, G.; Muniz, L.; Rinkevicius, A.; Shchutska, L.; Snowball, M.; Sperka, D.; Yelton, J.; Zakaria, M.; Hewamanage, S.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Adams, T.; Askew, A.; Bochenek, J.; Diamond, B.; Haas, J.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Prosper, H.; Veeraraghavan, V.; Weinberg, M.; Baarmand, M. M.; Hohlmann, M.; Kalakhety, H.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Bazterra, V. E.; Berry, D.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Khalatyan, S.; Kurt, P.; Moon, D. H.; O’Brien, C.; Silkworth, C.; Turner, P.; Varelas, N.; Albayrak, E. A.; Bilki, B.; Clarida, W.; Dilsiz, K.; Duru, F.; Haytmyradov, M.; Merlo, J. -P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Rahmat, R.; Sen, S.; Tan, P.; Tiras, E.; Wetzel, J.; Yetkin, T.; Yi, K.; Barnett, B. A.; Blumenfeld, B.; Bolognesi, S.; Fehling, D.; Gritsan, A. V.; Maksimovic, P.; Martin, C.; Swartz, M.; Baringer, P.; Bean, A.; Benelli, G.; Bruner, C.; Kenny, R. P.; Malek, M.; Murray, M.; Noonan, D.; Sanders, S.; Sekaric, J.; Stringer, R.; Wang, Q.; Wood, J. S.; Chakaberia, I.; Ivanov, A.; Khalil, S.; Makouski, M.; Maravin, Y.; Saini, L. K.; Shrestha, S.; Skhirtladze, N.; Svintradze, I.; Gronberg, J.; Lange, D.; Rebassoo, F.; Wright, D.; Baden, A.; Belloni, A.; Calvert, B.; Eno, S. C.; Gomez, J. A.; Hadley, N. J.; Kellogg, R. G.; Kolberg, T.; Lu, Y.; Marionneau, M.; Mignerey, A. C.; Pedro, K.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Apyan, A.; Barbieri, R.; Bauer, G.; Busza, W.; Cali, I. A.; Chan, M.; Di Matteo, L.; Gomez Ceballos, G.; Goncharov, M.; Gulhan, D.; Klute, M.; Lai, Y. S.; Lee, Y. -J.; Levin, A.; Luckey, P. D.; Ma, T.; Paus, C.; Ralph, D.; Roland, C.; Roland, G.; Stephans, G. S. F.; Stöckli, F.; Sumorok, K.; Velicanu, D.; Veverka, J.; Wyslouch, B.; Yang, M.; Zanetti, M.; Zhukova, V.; Dahmes, B.; Gude, A.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Mans, J.; Pastika, N.; Rusack, R.; Singovsky, A.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Gonzalez Suarez, R.; Keller, J.; Knowlton, D.; Kravchenko, I.; Lazo-Flores, J.; Malik, S.; Meier, F.; Snow, G. R.; Zvada, M.; Dolen, J.; Godshalk, A.; Iashvili, I.; Kharchilava, A.; Kumar, A.; Rappoccio, S.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Haley, J.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Trocino, D.; Wang, R. -J.; Wood, D.; Zhang, J.; Hahn, K. A.; Kubik, A.; Mucia, N.; Odell, N.; Pollack, B.; Pozdnyakov, A.; Schmitt, M.; Stoynev, S.; Sung, K.; Velasco, M.; Won, S.; Brinkerhoff, A.; Chan, K. M.; Drozdetskiy, A.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Luo, W.; Lynch, S.; Marinelli, N.; Pearson, T.; Planer, M.; Ruchti, R.; Valls, N.; Wayne, M.; Wolf, M.; Woodard, A.; Antonelli, L.; Brinson, J.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Hart, A.; Hill, C.; Hughes, R.; Kotov, K.; Ling, T. Y.; Puigh, D.; Rodenburg, M.; Smith, G.; Winer, B. L.; Wolfe, H.; Wulsin, H. W.; Driga, O.; Elmer, P.; Hebda, P.; Hunt, A.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Piroué, P.; Quan, X.; Saka, H.; Stickland, D.; Tully, C.; Werner, J. S.; Zuranski, A.; Brownson, E.; Mendez, H.; Ramirez Vargas, J. E.; Barnes, V. E.; Benedetti, D.; Bortoletto, D.; De Mattia, M.; Gutay, L.; Hu, Z.; Jha, M. K.; Jones, M.; Jung, K.; Kress, M.; Leonardo, N.; Lopes Pegna, D.; Maroussov, V.; Miller, D. H.; Neumeister, N.; Radburn-Smith, B. C.; Shi, X.; Shipsey, I.; Silvers, D.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Zablocki, J.; Zheng, Y.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Ecklund, K. M.; Geurts, F. J. M.; Li, W.; Michlin, B.; Padley, B. P.; Redjimi, R.; Roberts, J.; Zabel, J.; Betchart, B.; Bodek, A.; Covarelli, R.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Garcia-Bellido, A.; Goldenzweig, P.; Han, J.; Harel, A.; Khukhunaishvili, A.; Petrillo, G.; Vishnevskiy, D.; Ciesielski, R.; Demortier, L.; Goulianos, K.; Lungu, G.; Mesropian, C.; Arora, S.; Barker, A.; Chou, J. P.; Contreras-Campana, C.; Contreras-Campana, E.; Duggan, D.; Ferencek, D.; Gershtein, Y.; Gray, R.; Halkiadakis, E.; Hidas, D.; Kaplan, S.; Lath, A.; Panwalkar, S.; Park, M.; Patel, R.; Salur, S.; Schnetzer, S.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Rose, K.; Spanier, S.; York, A.; Bouhali, O.; Castaneda Hernandez, A.; Eusebi, R.; Flanagan, W.; Gilmore, J.; Kamon, T.; Khotilovich, V.; Krutelyov, V.; Montalvo, R.; Osipenkov, I.; Pakhotin, Y.; Perloff, A.; Roe, J.; Rose, A.; Safonov, A.; Sakuma, T.; Suarez, I.; Tatarinov, A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kovitanggoon, K.; Kunori, S.; Lee, S. W.; Libeiro, T.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Sharma, M.; Sheldon, P.; Snook, B.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Boutle, S.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Lin, C.; Neu, C.; Wood, J.; Clarke, C.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Sturdy, J.; Belknap, D. A.; Carlsmith, D.; Cepeda, M.; Dasu, S.; Dodd, L.; Duric, S.; Friis, E.; Hall-Wilton, R.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Lazaridis, C.; Levine, A.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ross, I.; Sarangi, T.; Savin, A.; Smith, W. H.; Taylor, D.; Verwilligen, P.; Vuosalo, C.; Woods, N.

    2015-04-07

    Dimuon and dielectron mass spectra, obtained from data resulting from proton-proton collisions at 8 TeV and recorded by the CMS experiment, are used to search for both narrow resonances and broad deviations from standard model predictions. The data correspond to an integrated luminosity of 20.6 (19.7) fb–1 for the dimuon (dielectron) channel. No evidence for non-standard-model physics is observed and 95% confidence level limits are set on parameters from a number of new physics models. The narrow resonance analyses exclude a Sequential Standard Model Z SSM ' resonance lighter than 2.90 TeV, a superstring-inspired Z ψ ' lighter than 2.57 TeV, and Randall-Sundrum Kaluza-Klein gravitons with masses below 2.73, 2.35, and 1.27 TeV for couplings of 0.10, 0.05, and 0.01, respectively. A notable feature is that the limits have been calculated in a model-independent way to enable straightforward reinterpretation in any model predicting a resonance structure. The observed events are also interpreted within the framework of two non-resonant analyses: one based on a large extra dimensions model and one based on a quark and lepton compositeness model with a left-left isoscalar contact interaction. Lower limits are established on MS, the scale characterizing the onset of quantum gravity, which range from 4.9 to 3.3 TeV, where the number of additional spatial dimensions varies from 3 to 7. Thus lower limits on Λ, the energy scale parameter for the contact interaction, are found to be 12.0 (15.2) TeV for destructive (constructive) interference in the dimuon channel and 13.5 (18.3) TeV in the dielectron channel.

  15. Search for physics beyond the standard model in dilepton mass spectra in proton-proton collisions at √s = 8 TeV

    DOE PAGES

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

    2015-04-07

    Dimuon and dielectron mass spectra, obtained from data resulting from proton-proton collisions at 8 TeV and recorded by the CMS experiment, are used to search for both narrow resonances and broad deviations from standard model predictions. The data correspond to an integrated luminosity of 20.6 (19.7) fb–1 for the dimuon (dielectron) channel. No evidence for non-standard-model physics is observed and 95% confidence level limits are set on parameters from a number of new physics models. The narrow resonance analyses exclude a Sequential Standard Model Z SSM ' resonance lighter than 2.90 TeV, a superstring-inspired Z ψ ' lighter than 2.57more » TeV, and Randall-Sundrum Kaluza-Klein gravitons with masses below 2.73, 2.35, and 1.27 TeV for couplings of 0.10, 0.05, and 0.01, respectively. A notable feature is that the limits have been calculated in a model-independent way to enable straightforward reinterpretation in any model predicting a resonance structure. The observed events are also interpreted within the framework of two non-resonant analyses: one based on a large extra dimensions model and one based on a quark and lepton compositeness model with a left-left isoscalar contact interaction. Lower limits are established on MS, the scale characterizing the onset of quantum gravity, which range from 4.9 to 3.3 TeV, where the number of additional spatial dimensions varies from 3 to 7. Thus lower limits on Λ, the energy scale parameter for the contact interaction, are found to be 12.0 (15.2) TeV for destructive (constructive) interference in the dimuon channel and 13.5 (18.3) TeV in the dielectron channel.« less

  16. Decelerating Flows in the TeV Blazars

    NASA Technical Reports Server (NTRS)

    Kazanas, Demosthenes

    2004-01-01

    TeV emission from a class of BL Lacertae (BL) objects is commonly modeled as radiation from relativistically moving homogeneous plasma blobs. In the context of these models, the blob Lorentz factors needed to reproduce the (corrected for absorption by the IR background) TeV emission are large ($\\delta \\gtrsim 50$) are required to reproduce via Synchrotron-Self Compton (SSC) the observed TeV emission. The main reason for this is that stronger beaming eases the problem of the lack of $\\sim$ IR-UV synchrotron seed photons needed to produce the de-absorbed $\\sim$ few TeV peak of the spectral energy distribution (SED). However, such high Doppler factors are in strong disagreement with the unified scheme, according to which BLs are FR I radio galaxies with their jets closely aligned to the line of sight. Here, motivated by the detection of sub-luminal velocities in the sub-pc scale jets of the best studied TeV blazars, MKN 421 and MKN 501. we examine the possibility that the relativistic flow in the TeV BLs is longitudinally decelerating. In this case, the problem of the missing seed photons is solved due to Upstream Compton (UC) scattering, a process in which the upstream energetic electrons from the fast base of the flow 'see' the synchrotron seed photons produced in the slow part of the flow relativistically beamed. Modest Lorentz factors ($\\Gamma kim 15s). decelerating down to values compatible with the recent radio interferometric observations, reproduce the $\\sim$ few TeV peak energy of these sources. Furthermore, such decelerating flows are shown to be in agreement with the BL - FR I unification.

  17. Bianchi class A models in Sàez-Ballester's theory

    NASA Astrophysics Data System (ADS)

    Socorro, J.; Espinoza-García, Abraham

    2012-08-01

    We apply the Sàez-Ballester (SB) theory to Bianchi class A models, with a barotropic perfect fluid in a stiff matter epoch. We obtain exact classical solutions à la Hamilton for Bianchi type I, II and VIh=-1 models. We also find exact quantum solutions to all Bianchi Class A models employing a particular ansatz for the wave function of the universe.

  18. A Model for Teaching Large Classes: Facilitating a "Small Class Feel"

    ERIC Educational Resources Information Center

    Lynch, Rosealie P.; Pappas, Eric

    2017-01-01

    This paper presents a model for teaching large classes that facilitates a "small class feel" to counteract the distance, anonymity, and formality that often characterize large lecture-style courses in higher education. One author (E. P.) has been teaching a 300-student general education critical thinking course for ten years, and the…

  19. A dynamic trajectory class model for intensive longitudinal categorical outcome.

    PubMed

    Lin, Haiqun; Han, Ling; Peduzzi, Peter N; Murphy, Terrence E; Gill, Thomas M; Allore, Heather G

    2014-07-10

    This paper presents a novel dynamic latent class model for a longitudinal response that is frequently measured as in our prospective study of older adults with monthly data on activities of daily living for more than 10 years. The proposed method is especially useful when the longitudinal response is measured much more frequently than other relevant covariates. The trajectory classes are latent classes that represent distinct temporal patterns of the longitudinal response wherein an individual may remain in a trajectory class or switch to another as the class membership predictors are updated periodically over time. The identification of a common set of trajectory classes allows changes among the temporal patterns to be distinguished from local fluctuations in the response. Within a trajectory class, the longitudinal response is modeled by a class-specific generalized linear mixed model. An informative event such as death is jointly modeled by class-specific probability of the event through shared random effects with that for the longitudinal response. We do not impose the conditional independence assumption given the classes. We illustrate the method by analyzing the change over time in activities of daily living trajectory class among 754 older adults with 70,500 person-months of follow-up in the Precipitating Events Project. We also investigate the impact of jointly modeling the class-specific probability of the event on the parameter estimates in a simulation study. The primary contribution of our paper is the periodic updating of trajectory classes for a longitudinal categorical response without assuming conditional independence.

  20. A Repeated Trajectory Class Model for Intensive Longitudinal Categorical Outcome

    PubMed Central

    Lin, Haiqun; Han, Ling; Peduzzi, Peter N.; Murphy, Terrence E.; Gill, Thomas M.; Allore, Heather G.

    2014-01-01

    This paper presents a novel repeated latent class model for a longitudinal response that is frequently measured as in our prospective study of older adults with monthly data on activities of daily living (ADL) for more than ten years. The proposed method is especially useful when the longitudinal response is measured much more frequently than other relevant covariates. The repeated trajectory classes represent distinct temporal patterns of the longitudinal response wherein an individual’s membership in the trajectory classes may renew or change over time. Within a trajectory class, the longitudinal response is modeled by a class-specific generalized linear mixed model. Effectively, an individual may remain in a trajectory class or switch to another as the class membership predictors are updated periodically over time. The identification of a common set of trajectory classes allows changes among the temporal patterns to be distinguished from local fluctuations in the response. An informative event such as death is jointly modeled by class-specific probability of the event through shared random effects. We do not impose the conditional independence assumption given the classes. The method is illustrated by analyzing the change over time in ADL trajectory class among 754 older adults with 70500 person-months of follow-up in the Precipitating Events Project. We also investigate the impact of jointly modeling the class-specific probability of the event on the parameter estimates in a simulation study. The primary contribution of our paper is the periodic updating of trajectory classes for a longitudinal categorical response without assuming conditional independence. PMID:24519416

  1. Instructor Modeling in Teacher Education Classes.

    ERIC Educational Resources Information Center

    Wilson, Jill

    1987-01-01

    This article argues that direct modeling by teacher education instructors would be more beneficial than videotaped models in helping students acquire and use specific skills. Research on modeling by cooperating teachers and videotapes is reviewed. (Author/MT)

  2. Les Houches ''Physics at TeV Colliders 2003'' Beyond the Standard Model Working Group: Summary Report

    SciTech Connect

    Allanach, B

    2004-03-01

    The work contained herein constitutes a report of the ''Beyond the Standard Model'' working group for the Workshop ''Physics at TeV Colliders'', Les Houches, France, 26 May-6 June, 2003. The research presented is original, and was performed specifically for the workshop. Tools for calculations in the minimal supersymmetric standard model are presented, including a comparison of the dark matter relic density predicted by public codes. Reconstruction of supersymmetric particle masses at the LHC and a future linear collider facility is examined. Less orthodox supersymmetric signals such as non-pointing photons and R-parity violating signals are studied. Features of extra dimensional models are examined next, including measurement strategies for radions and Higgs', as well as the virtual effects of Kaluza Klein modes of gluons. Finally, there is an update on LHC Z' studies.

  3. A one parameter class of fractional Maxwell-like models

    NASA Astrophysics Data System (ADS)

    Colombaro, Ivano; Giusti, Andrea; Mainardi, Francesco

    2017-06-01

    In this paper we discuss a one parameter modification of the well known fractional Maxwell model of viscoelasticity. Such models appear to be particularly interesting because they describe the short time asymptotic limit of a more general class of viscoelastic models known in the literature as Bessel models.

  4. Some classes of renormalizable tensor models

    NASA Astrophysics Data System (ADS)

    Geloun, Joseph Ben; Livine, Etera R.

    2013-08-01

    We identify new families of renormalizable tensor models from anterior renormalizable tensor models via a mapping capable of reducing or increasing the rank of the theory without having an effect on the renormalizability property. Mainly, a version of the rank 3 tensor model as defined by Ben Geloun and Samary [Ann. Henri Poincare 14, 1599 (2013); e-print arXiv:1201.0176 [hep-th

  5. Simulation Modeling of a Facility Layout in Operations Management Classes

    ERIC Educational Resources Information Center

    Yazici, Hulya Julie

    2006-01-01

    Teaching quantitative courses can be challenging. Similarly, layout modeling and lean production concepts can be difficult to grasp in an introductory OM (operations management) class. This article describes a simulation model developed in PROMODEL to facilitate the learning of layout modeling and lean manufacturing. Simulation allows for the…

  6. Simulation Modeling of a Facility Layout in Operations Management Classes

    ERIC Educational Resources Information Center

    Yazici, Hulya Julie

    2006-01-01

    Teaching quantitative courses can be challenging. Similarly, layout modeling and lean production concepts can be difficult to grasp in an introductory OM (operations management) class. This article describes a simulation model developed in PROMODEL to facilitate the learning of layout modeling and lean manufacturing. Simulation allows for the…

  7. Class Ranking Models for Deans' Letters and Their Psychometric Evaluation.

    ERIC Educational Resources Information Center

    Blacklow, Robert S.; And Others

    1991-01-01

    A study developed and evaluated five class ranking models for graduating medical students (n=1,283) in which performance data from both basic and clinical sciences could be used to study the models' predictive validity. Two models yielded higher validity; one is recommended for balance of clinical and basic science measures. (MSE)

  8. Universality classes for models of inflation

    SciTech Connect

    Binétruy, P.; Kiritsis, E.; Mabillard, J.; Pieroni, M.; Rosset, C. E-mail: mpieroni@apc.univ-paris7.fr

    2015-04-01

    We show that the cosmological evolution of a scalar field in a potential can be obtained from a renormalisation group equation. The slow roll regime of inflation models is understood in this context as the slow evolution close to a fixed point, described by the methods of renormalisation group. This explains in part the universality observed in the predictions of a certain number of inflation models. We illustrate this behavior on a certain number of examples and discuss it in the context of the AdS/CFT correspondence.

  9. Credibility analysis of risk classes by generalized linear model

    NASA Astrophysics Data System (ADS)

    Erdemir, Ovgucan Karadag; Sucu, Meral

    2016-06-01

    In this paper generalized linear model (GLM) and credibility theory which are frequently used in nonlife insurance pricing are combined for reliability analysis. Using full credibility standard, GLM is associated with limited fluctuation credibility approach. Comparison criteria such as asymptotic variance and credibility probability are used to analyze the credibility of risk classes. An application is performed by using one-year claim frequency data of a Turkish insurance company and results of credible risk classes are interpreted.

  10. An approximation formula for a class of Markov reliability models

    NASA Technical Reports Server (NTRS)

    White, A. L.

    1984-01-01

    A way of considering a small but often used class of reliability model and approximating algebraically the systems reliability is shown. The models considered are appropriate for redundant reconfigurable digital control systems that operate for a short period of time without maintenance, and for such systems the method gives a formula in terms of component fault rates, system recovery rates, and system operating time.

  11. Teaching Service Modelling to a Mixed Class: An Integrated Approach

    ERIC Educational Resources Information Center

    Deng, Jeremiah D.; Purvis, Martin K.

    2015-01-01

    Service modelling has become an increasingly important area in today's telecommunications and information systems practice. We have adapted a Network Design course in order to teach service modelling to a mixed class of both the telecommunication engineering and information systems backgrounds. An integrated approach engaging mathematics teaching…

  12. Spurious Latent Classes in the Mixture Rasch Model

    ERIC Educational Resources Information Center

    Alexeev, Natalia; Templin, Jonathan; Cohen, Allan S.

    2011-01-01

    Mixture Rasch models have been used to study a number of psychometric issues such as goodness of fit, response strategy differences, strategy shifts, and multidimensionality. Although these models offer the potential for improving understanding of the latent variables being measured, under some conditions overextraction of latent classes may…

  13. Spurious Latent Classes in the Mixture Rasch Model

    ERIC Educational Resources Information Center

    Alexeev, Natalia; Templin, Jonathan; Cohen, Allan S.

    2011-01-01

    Mixture Rasch models have been used to study a number of psychometric issues such as goodness of fit, response strategy differences, strategy shifts, and multidimensionality. Although these models offer the potential for improving understanding of the latent variables being measured, under some conditions overextraction of latent classes may…

  14. Using Latent Class Analysis to Model Temperament Types

    ERIC Educational Resources Information Center

    Loken, Eric

    2004-01-01

    Mixture models are appropriate for data that arise from a set of qualitatively different subpopulations. In this study, latent class analysis was applied to observational data from a laboratory assessment of infant temperament at four months of age. The EM algorithm was used to fit the models, and the Bayesian method of posterior predictive checks…

  15. Muon capture in a general class of weak models

    NASA Astrophysics Data System (ADS)

    Botella, F. J.

    1985-10-01

    We study muon capture by 12C in a general class of weak models. There is always a parameter characteristic of the weak model that can be extracted in a nuclear-model-independent way from the average polarization Pav, the longitudinal polarization PNL and the asymmetry α in the angular distribution of recoils. For a less general class of models the asymmetry α is unnecessary. Using the experimental values of PNL and Pav we get a lower bound for the mass of the right-handed gauge boson of the left-right-symmetric model, MWR>=2.5MWL, in a nuclear-model-independent way. The dependence of this bound on the experimental values is also discussed.

  16. Search for Heavy Neutrinos and WR Bosons with Right-Handed Couplings in a Left-Right Symmetric Model in pp Collisions at s=7TeV

    NASA Astrophysics Data System (ADS)

    Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Aguilo, E.; Bergauer, T.; Dragicevic, M.; Erö, J.; Fabjan, C.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hammer, J.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Knünz, V.; Krammer, M.; Krätschmer, I.; Liko, D.; Mikulec, I.; Pernicka, M.; Rahbaran, B.; Rohringer, C.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Taurok, A.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Bansal, M.; Bansal, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Luyckx, S.; Mucibello, L.; Ochesanu, S.; Roland, B.; Rougny, R.; Selvaggi, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D'Hondt, J.; Gonzalez Suarez, R.; Kalogeropoulos, A.; Maes, M.; Olbrechts, A.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Villella, I.; Clerbaux, B.; De Lentdecker, G.; Dero, V.; Gay, A. P. R.; Hreus, T.; Léonard, A.; Marage, P. E.; Mohammadi, A.; Reis, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.; Adler, V.; Beernaert, K.; Cimmino, A.; Costantini, S.; Garcia, G.; Grunewald, M.; Klein, B.; Lellouch, J.; Marinov, A.; Mccartin, J.; Ocampo Rios, A. A.; Ryckbosch, D.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Walsh, S.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Bruno, G.; Castello, R.; Ceard, L.; Delaere, C.; du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Lemaitre, V.; Liao, J.; Militaru, O.; Nuttens, C.; Pagano, D.; Pin, A.; Piotrzkowski, K.; Vizan Garcia, J. M.; Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.; Alves, G. A.; Correa Martins Junior, M.; Martins, T.; Pol, M. E.; Souza, M. H. G.; Aldá Júnior, W. L.; Carvalho, W.; Custódio, A.; Da Costa, E. M.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Malbouisson, H.; Malek, M.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santoro, A.; Soares Jorge, L.; Sznajder, A.; Vilela Pereira, A.; Anjos, T. S.; Bernardes, C. A.; Dias, F. A.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Lagana, C.; Marinho, F.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Genchev, V.; Iaydjiev, P.; Piperov, S.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Tcholakov, V.; Trayanov, R.; Vutova, M.; Dimitrov, A.; Hadjiiska, R.; Kozhuharov, V.; Litov, L.; Pavlov, B.; Petkov, P.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Jiang, C. H.; Liang, D.; Liang, S.; Meng, X.; Tao, J.; Wang, J.; Wang, X.; Wang, Z.; Xiao, H.; Xu, M.; Zang, J.; Zhang, Z.; Asawatangtrakuldee, C.; Ban, Y.; Guo, Y.; Li, W.; Liu, S.; Mao, Y.; Qian, S. J.; Teng, H.; Wang, D.; Zhang, L.; Zou, W.; Avila, C.; Gomez, J. P.; Gomez Moreno, B.; Osorio Oliveros, A. F.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Plestina, R.; Polic, D.; Puljak, I.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Duric, S.; Kadija, K.; Luetic, J.; Mekterovic, D.; Morovic, S.; Attikis, A.; Galanti, M.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Finger, M.; Finger, M., Jr.; Assran, Y.; Elgammal, S.; Ellithi Kamel, A.; Khalil, S.; Mahmoud, M. A.; Radi, A.; Kadastik, M.; Müntel, M.; Raidal, M.; Rebane, L.; Tiko, A.; Eerola, P.; Fedi, G.; Voutilainen, M.; Härkönen, J.; Heikkinen, A.; Karimäki, V.; Kinnunen, R.; Kortelainen, M. J.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Peltola, T.; Tuominen, E.; Tuominiemi, J.; Tuovinen, E.; Ungaro, D.; Wendland, L.; Banzuzi, K.; Karjalainen, A.; Korpela, A.; Tuuva, T.; Besancon, M.; Choudhury, S.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Malcles, J.; Millischer, L.; Nayak, A.; Rander, J.; Rosowsky, A.; Titov, M.; Baffioni, S.; Beaudette, F.; Benhabib, L.; Bianchini, L.; Bluj, M.; Broutin, C.; Busson, P.; Charlot, C.; Daci, N.; Dahms, T.; Dalchenko, M.; Dobrzynski, L.; Florent, A.; Granier de Cassagnac, R.; Haguenauer, M.; Miné, P.; Mironov, C.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Paganini, P.; Sabes, D.; Salerno, R.; Sirois, Y.; Veelken, C.; Zabi, A.; Agram, J.-L.; Andrea, J.; Bloch, D.; Bodin, D.; Brom, J.-M.; Cardaci, M.; Chabert, E. C.; Collard, C.; Conte, E.; Drouhin, F.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Juillot, P.; Le Bihan, A.-C.; Van Hove, P.; Fassi, F.; Mercier, D.; Beauceron, S.; Beaupere, N.; Bondu, O.; Boudoul, G.; Chasserat, J.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Kurca, T.; Lethuillier, M.; Mirabito, L.; Perries, S.; Sgandurra, L.; Sordini, V.; Tschudi, Y.; Verdier, P.; Viret, S.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Calpas, B.; Edelhoff, M.; Feld, L.; Heracleous, N.; Hindrichs, O.; Jussen, R.; Klein, K.; Merz, J.

    2012-12-01

    Results are presented from a search for heavy, right-handed muon neutrinos, Nμ, and right-handed WR bosons, which arise in the left-right symmetric extensions of the standard model. The analysis is based on a 5.0fb-1 sample of proton-proton collisions at a center-of-mass energy of 7 TeV, collected by the CMS detector at the Large Hadron Collider. No evidence is observed for an excess of events over the standard model expectation. For models with exact left-right symmetry, heavy right-handed neutrinos are excluded at 95% confidence level for a range of neutrino masses below the WR mass, dependent on the value of MWR. The excluded region in the two-dimensional (MWR, MNμ) mass plane extends to MWR=2.5TeV.

  17. Clusterization in a class of models of classical field theory

    NASA Astrophysics Data System (ADS)

    Makhankov, V. G.

    Using a simple nonlinear field theory as an example, problems of soliton existence, stability, dynamics, and statistics for the U (p,q) internal symmetry group in the R sub D,1 space-time are discussed. A number of mathematical and physical models belonging to this class are considered: in particular, the sine-Gordon equation (SG), 'phi-four' theory, the nonlinear Schroedinger equation (NSE), and the Landau-Lifshitz (LL) model, gauge equivalent to the latter. The existence of small-amplitude expansions makes it possible to calculate the soliton formfactors not only for solvable models such as SG, NSE, and LL in R sub 1,1 space, but also for a broader class of models in R sub D,1. Soliton stability properties imply that clustering (soliton creation) occurs at finite temperature in the framework of the models of more than one spatial dimension considered and may be regarded as a phase transition.

  18. Supervised Bayesian latent class models for high-dimensional data

    PubMed Central

    Desantis, Stacia M.; Houseman, E. Andrés; Coull, Brent A.; Nutt, Catherine L.; Betensky, Rebecca A.

    2013-01-01

    High-grade gliomas are the most common primary brain tumors in adults and are typically diagnosed using histopathology. However, these diagnostic categories are highly heterogeneous and do not always correlate well with survival. In an attempt to refine these diagnoses, we make several immunohistochemical measurements of YKL-40, a gene previously shown to be differentially expressed between diagnostic groups. We propose two latent class models for classification and variable selection in the presence of high-dimensional binary data, fit by using Bayesian Markov chain Monte Carlo techniques. Penalization and model selection are incorporated in this setting via prior distributions on the unknown parameters. The methods provide valid parameter estimates under conditions in which standard supervised latent class models do not, and outperform two-stage approaches to variable selection and parameter estimation in a variety of settings. We study the properties of these methods in simulations, and apply these methodologies to the glioma study for which identifiable three-class parameter estimates cannot be obtained without penalization. With penalization, the resulting latent classes correlate well with clinical tumor grade and offer additional information on survival prognosis that is not captured by clinical diagnosis alone. The inclusion of YKL-40 features also increases the precision of survival estimates. Fitting models with and without YKL-40 highlights a subgroup of patients who have glioblastoma (GBM) diagnosis but appear to have better prognosis than the typical GBM patient. PMID:22495652

  19. Identifiability Results for Several Classes of Linear Compartment Models.

    PubMed

    Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa

    2015-08-01

    Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.

  20. Universality class of the conserved Manna model in one dimension

    NASA Astrophysics Data System (ADS)

    Lee, Sang Bub

    2014-06-01

    The nonequilibrium absorbing phase transition of the discrete conserved Manna model was studied via Monte Carlo simulations on a one-dimensional chain, using the natural initial states with a sequential update. The critical density of the particles was found to be smaller than the recently reported value, and the order-parameter exponent was considerably different from the directed percolation (DP) value. The influence of quenched disorder was also studied on a diluted strip of Lx×Ly lattice sites with Lx≫Ly, and the results were compared with those of the contact process (CP). It was found that the Manna model and the CP exhibited distinctly different behaviors; the CP exhibited nonuniversal power-law decreases of active-site densities in the Griffith phase, whereas the Manna model showed a standard critical behavior. These results consistently suggest that the Manna model belongs to a universality class that is different from the DP class.

  1. A model for a multi-class classification machine

    NASA Astrophysics Data System (ADS)

    Rau, Albrecht; Nadal, Jean-Pierre

    1992-06-01

    We consider the properties of multi-class neural networks, where each neuron can be in several different states. The motivations for considering such systems are manifold. In image processing for example, the different states correspond to the different grey tone levels. Another multi-class classification task implemented on a feed-forward network is the analysis of DNA sequences or the prediction of the secondary structure of proteins from the sequence of amino acids. To investigate the behaviour of such systems, one specific dynamical rule - the “winner-take-all” rule - is studied. Gauge invariances of the model are analysed. For a multi-class perceptron with N Q-state input neurons and Q‧-state output neuron, the maximal number of patterns that can be stored in the large N limit is found to be proportional to N(Q - 1) ƒ(Q‧), where ƒ( Q‧) is a slowly increasing and bounded function of order 1.

  2. Electroweak vacuum stability in presence of singlet scalar dark matter in TeV scale seesaw models

    NASA Astrophysics Data System (ADS)

    Garg, Ila; Goswami, Srubabati; Vishnudath, K. N.; Khan, Najimuddin

    2017-09-01

    We consider singlet extensions of the standard model, both in the fermion and in the scalar sector, to account for the generation of neutrino mass at the TeV scale and the existence of dark matter, respectively. For the neutrino sector we consider models with extra singlet fermions that can generate neutrino mass via the so-called inverse or linear seesaw mechanism, whereas a singlet scalar is introduced as the candidate for dark matter. We show that although these two sectors are disconnected at low energy, the coupling constants of both the sectors get correlated at the high-energy scale by the constraints coming from the perturbativity and stability/metastability of the electroweak vacuum. The singlet fermions try to destabilize the electroweak vacuum while the singlet scalar aids the stability. As an upshot, the electroweak vacuum may attain absolute stability even up to the Planck scale for suitable values of the parameters. We delineate the parameter space for the singlet fermion and the scalar couplings for which the electroweak vacuum remains stable/metastable and at the same time gives the correct relic density and neutrino masses and mixing angles as observed.

  3. Extending positive CLASS results across multiple instructors and multiple classes of Modeling Instruction

    NASA Astrophysics Data System (ADS)

    Brewe, Eric; Traxler, Adrienne; de la Garza, Jorge; Kramer, Laird H.

    2013-12-01

    We report on a multiyear study of student attitudes measured with the Colorado Learning Attitudes about Science Survey in calculus-based introductory physics taught with the Modeling Instruction curriculum. We find that five of six instructors and eight of nine sections using Modeling Instruction showed significantly improved attitudes from pre- to postcourse. Cohen’s d effect sizes range from 0.08 to 0.95 for individual instructors. The average effect was d=0.45, with a 95% confidence interval of (0.26-0.64). These results build on previously published results showing positive shifts in attitudes from Modeling Instruction classes. We interpret these data in light of other published positive attitudinal shifts and explore mechanistic explanations for similarities and differences with other published positive shifts.

  4. Analysis of SPI drought class transitions using loglinear models

    NASA Astrophysics Data System (ADS)

    Moreira, Elsa E.; Paulo, Ana A.; Pereira, Luís S.; Mexia, João T.

    2006-11-01

    SummaryA total period of 67 years of standardized precipitation index (SPI) data sets were divided into three periods of 22/23 years and a loglinear modeling approach has been used to investigate differences relative to drought class transitions among these three periods. The study was applied to several locations in Alentejo region, southern Portugal, and four drought severity classes were considered. The drought class transitions were computed for the three periods to form a 3-dimensional contingency table. The application of loglinear modeling to these data allowed the comparison of the three periods in terms of probabilities of transition between drought classes in order to detect a possible trend in time evolution of droughts which could be related to climate change. Results show that the drought behavior for the first and last periods is similar, both showing worse drought events than the second. If just the second and third periods were compared one could conclude that droughts were aggravating and easily this behavior could be attributed to climate change, supporting the common assumption that a trend for progressive aggravation of drought occurrence exists. Therefore, results are more consistent with the existence of a long-term natural periodicity; however, this hypothesis should be tested using longer time series.

  5. Two classes of ODE models with switch-like behavior

    PubMed Central

    Just, Winfried; Korb, Mason; Elbert, Ben; Young, Todd

    2013-01-01

    In cases where the same real-world system can be modeled both by an ODE system ⅅ and a Boolean system 𝔹, it is of interest to identify conditions under which the two systems will be consistent, that is, will make qualitatively equivalent predictions. In this note we introduce two broad classes of relatively simple models that provide a convenient framework for studying such questions. In contrast to the widely known class of Glass networks, the right-hand sides of our ODEs are Lipschitz-continuous. We prove that if 𝔹 has certain structures, consistency between ⅅ and 𝔹 is implied by sufficient separation of time scales in one class of our models. Namely, if the trajectories of 𝔹 are “one-stepping” then we prove a strong form of consistency and if 𝔹 has a certain monotonicity property then there is a weaker consistency between ⅅ and 𝔹. These results appear to point to more general structure properties that favor consistency between ODE and Boolean models. PMID:24244061

  6. Homology Modeling of Class A G Protein-Coupled Receptors

    PubMed Central

    Costanzi, Stefano

    2012-01-01

    G protein-coupled receptors (GPCRs) are a large superfamily of membrane bound signaling proteins that hold great pharmaceutical interest. Since experimentally elucidated structures are available only for a very limited number of receptors, homology modeling has become a widespread technique for the construction of GPCR models intended to study the structure-function relationships of the receptors and aid the discovery and development of ligands capable of modulating their activity. Through this chapter, various aspects involved in the constructions of homology models of the serpentine domain of the largest class of GPCRs, known as class A or rhodopsin family, are illustrated. In particular, the chapter provides suggestions, guidelines and critical thoughts on some of the most crucial aspect of GPCR modeling, including: collection of candidate templates and a structure-based alignment of their sequences; identification and alignment of the transmembrane helices of the query receptor to the corresponding domains of the candidate templates; selection of one or more templates receptor; election of homology or de novo modeling for the construction of specific extracellular and intracellular domains; construction of the three-dimensional models, with special consideration to extracellular regions, disulfide bridges, and interhelical cavity; validation of the models through controlled virtual screening experiments. PMID:22323225

  7. Analysis of a waterborne disease model with socioeconomic classes.

    PubMed

    Collins, O C; Robertson, Suzanne L; Govinder, K S

    2015-11-01

    Waterborne diseases such as cholera continue to pose serious public health problems in the world today. Transmission parameters can vary greatly with socioeconomic class (SEC) and the availability of clean water. We formulate a multi-patch waterborne disease model such that each patch represents a particular SEC with its own water source, allowing individuals to move between SECs. For a 2-SEC model, we investigate the conditions under which each SEC is responsible for driving a cholera outbreak. We determine the effect of SECs on disease transmission dynamics by comparing the basic reproduction number of the 2-SEC model to that of a homogeneous model that does not take SECs into account. We conclude by extending several results of the 2-SEC model to an n-SEC model. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Latent class model characterization of neighborhood socioeconomic status

    PubMed Central

    Michael, Yvonne; Hyslop, Terry

    2016-01-01

    Purpose Neighborhood-level socioeconomic status (NSES) can influence breast cancer mortality and poorer health outcomes are observed in deprived neighborhoods. Commonly used NSES indexes are difficult to interpret. Latent class models allow for alternative characterization of NSES for use in studies of cancer causes and control. Methods Breast cancer data was from a cohort of women diagnosed at an academic medical center in Philadelphia, PA. NSES variables were defined using Census data. Latent class modeling was used to characterize NSES. Results Complete data was available for 1,664 breast cancer patients diagnosed between 1994 and 2002. Two separate latent variables, each with 2-classes (LC2) best represented NSES. LC2 demonstrated strong associations with race and tumor stage and size. Conclusions Latent variable models identified specific characteristics associated with advantaged or disadvantaged neighborhoods, potentially improving our understanding of the impact of socioeconomic influence on breast cancer prognosis. Improved classification will enhance our ability to identify vulnerable populations and prioritize the targeting of cancer control efforts. PMID:26797452

  9. An Agent Based Model for Social Class Emergence

    NASA Astrophysics Data System (ADS)

    Yang, Xiaoxiang; Rodriguez Segura, Daniel; Lin, Fei; Mazilu, Irina

    We present an open system agent-based model to analyze the effects of education and the society-specific wealth transactions on the emergence of social classes. Building on previous studies, we use realistic functions to model how years of education affect the income level. Numerical simulations show that the fraction of an individual's total transactions that is invested rather than consumed can cause wealth gaps between different income brackets in the long run. In an attempt to incorporate the network effects, we also explore how the probability of interactions among agents depending on the spread of their income brackets affects wealth distribution.

  10. Sloppy-model universality class and the Vandermonde matrix.

    PubMed

    Waterfall, Joshua J; Casey, Fergal P; Gutenkunst, Ryan N; Brown, Kevin S; Myers, Christopher R; Brouwer, Piet W; Elser, Veit; Sethna, James P

    2006-10-13

    In a variety of contexts, physicists study complex, nonlinear models with many unknown or tunable parameters to explain experimental data. We explain why such systems so often are sloppy: the system behavior depends only on a few "stiff" combinations of the parameters and is unchanged as other "sloppy" parameter combinations vary by orders of magnitude. We observe that the eigenvalue spectra for the sensitivity of sloppy models have a striking, characteristic form with a density of logarithms of eigenvalues which is roughly constant over a large range. We suggest that the common features of sloppy models indicate that they may belong to a common universality class. In particular, we motivate focusing on a Vandermonde ensemble of multiparameter nonlinear models and show in one limit that they exhibit the universal features of sloppy models.

  11. X-ray variability and prediction of TeV emission in the HBL 1ES 1101-232

    NASA Astrophysics Data System (ADS)

    Wolter, A.; Tavecchio, F.; Caccianiga, A.; Ghisellini, G.; Tagliaferri, G.

    2000-05-01

    1ES 1101-232 is a bright BL Lac of the High frequency peak class. We present here the results of two BeppoSAX observations in which the source has shown a variation of about 30% in flux with a corresponding spectral variability. We interpret the overall spectral energy distribution in terms of an homogeneous SSC model and, by using also the TeV upper limit from a short Mark 6 pointing, derive constraints on the physical parameters of the source, in particular on the magnetic field strength. The overall Spectral Energy Distribution makes 1ES 1101-232 a very promising candidate for TeV detection.

  12. Classification of universality classes for quasideterministic sandpile models

    NASA Astrophysics Data System (ADS)

    Lee, Sang Bub

    2017-07-01

    The critical behavior of the two-state rotational sandpile model proposed by Santra et al. [Phys. Rev. E 75, 041122 (2007), 10.1103/PhysRevE.75.041122] and the locally deterministic and globally stochastic three-state sandpile model are investigated via Monte Carlo simulations. Through these simulations, we are able to estimate critical exponents that characterize the avalanche properties, i.e., the probability distributions of the avalanche size, area, lifetime, and gyration radius, and the expectation values of the avalanche size and area against time and of the size against area. The results are compared with those of the known universality classes. The two models are found to yield consistent results within the range of statistical error, and appear to be consistent with the stochastic two-state Manna sandpile model; therefore, both models appear to belong to the Manna universality class. Our results contradict the earlier conclusion of Santra et al., which we attribute to the slow convergence of the probability distribution to the asymptotic power-law behavior, particularly for the size and lifetime of avalanches.

  13. Poverty and Depression among Men: The Social Class Worldview Model and Counseling Implications.

    ERIC Educational Resources Information Center

    Liu, William M.

    This paper outlines a theory for understanding social class in men's lives, and argues that poverty and depression are a function of social class and internalized classism. It begins by defining poverty, then explains the Social Class Worldview Model, which is a subjective social class model, and the Modern Classism Theory, which allows clinicians…

  14. Search for a Standard Model Higgs Boson in WH→ℓvb bmacr in p pmacr Collisions at s=1.96TeV

    NASA Astrophysics Data System (ADS)

    Aaltonen, T.; Adelman, J.; Akimoto, T.; Álvarez González, B.; Amerio, S.; Amidei, D.; Anastassov, A.; Annovi, A.; Antos, J.; Apollinari, G.; Apresyan, A.; Arisawa, T.; Artikov, A.; Ashmanskas, W.; Attal, A.; Aurisano, A.; Azfar, F.; Azzurri, P.; Badgett, W.; Barbaro-Galtieri, A.; Barnes, V. E.; Barnett, B. A.; Bartsch, V.; Bauer, G.; Beauchemin, P.-H.; Bedeschi, F.; Beecher, D.; Behari, S.; Bellettini, G.; Bellinger, J.; Benjamin, D.; Beretvas, A.; Beringer, J.; Bhatti, A.; Binkley, M.; Bisello, D.; Bizjak, I.; Blair, R. E.; Blocker, C.; Blumenfeld, B.; Bocci, A.; Bodek, A.; Boisvert, V.; Bolla, G.; Bortoletto, D.; Boudreau, J.; Boveia, A.; Brau, B.; Bridgeman, A.; Brigliadori, L.; Bromberg, C.; Brubaker, E.; Budagov, J.; Budd, H. S.; Budd, S.; Burke, S.; Burkett, K.; Busetto, G.; Bussey, P.; Buzatu, A.; Byrum, K. L.; Cabrera, S.; Calancha, C.; Campanelli, M.; Campbell, M.; Canelli, F.; Canepa, A.; Carls, B.; Carlsmith, D.; Carosi, R.; Carrillo, S.; Carron, S.; Casal, B.; Casarsa, M.; Castro, A.; Catastini, P.; Cauz, D.; Cavaliere, V.; Cavalli-Sforza, M.; Cerri, A.; Cerrito, L.; Chang, S. H.; Chen, Y. C.; Chertok, M.; Chiarelli, G.; Chlachidze, G.; Chlebana, F.; Cho, K.; Chokheli, D.; Chou, J. P.; Choudalakis, G.; Chuang, S. H.; Chung, K.; Chung, W. H.; Chung, Y. S.; Chwalek, T.; Ciobanu, C. I.; Ciocci, M. A.; Clark, A.; Clark, D.; Compostella, G.; Convery, M. E.; Conway, J.; Cordelli, M.; Cortiana, G.; Cox, C. A.; Cox, D. J.; Crescioli, F.; Cuenca Almenar, C.; Cuevas, J.; Culbertson, R.; Cully, J. C.; Dagenhart, D.; Datta, M.; Davies, T.; de Barbaro, P.; de Cecco, S.; Deisher, A.; de Lorenzo, G.; Dell'Orso, M.; Deluca, C.; Demortier, L.; Deng, J.; Deninno, M.; Derwent, P. F.; di Giovanni, G. P.; Dionisi, C.; di Ruzza, B.; Dittmann, J. R.; D'Onofrio, M.; Donati, S.; Dong, P.; Donini, J.; Dorigo, T.; Dube, S.; Efron, J.; Elagin, A.; Erbacher, R.; Errede, D.; Errede, S.; Eusebi, R.; Fang, H. C.; Farrington, S.; Fedorko, W. T.; Feild, R. G.; Feindt, M.; Fernandez, J. P.; Ferrazza, C.; Field, R.; Flanagan, G.; Forrest, R.; Frank, M. J.; Franklin, M.; Freeman, J. C.; Furic, I.; Gallinaro, M.; Galyardt, J.; Garberson, F.; Garcia, J. E.; Garfinkel, A. F.; Genser, K.; Gerberich, H.; Gerdes, D.; Gessler, A.; Giagu, S.; Giakoumopoulou, V.; Giannetti, P.; Gibson, K.; Gimmell, J. L.; Ginsburg, C. M.; Giokaris, N.; Giordani, M.; Giromini, P.; Giunta, M.; Giurgiu, G.; Glagolev, V.; Glenzinski, D.; Gold, M.; Goldschmidt, N.; Golossanov, A.; Gomez, G.; Gomez-Ceballos, G.; Goncharov, M.; González, O.; Gorelov, I.; Goshaw, A. T.; Goulianos, K.; Gresele, A.; Grinstein, S.; Grosso-Pilcher, C.; Group, R. C.; Grundler, U.; Guimaraes da Costa, J.; Gunay-Unalan, Z.; Haber, C.; Hahn, K.; Hahn, S. R.; Halkiadakis, E.; Han, B.-Y.; Han, J. Y.; Happacher, F.; Hara, K.; Hare, D.; Hare, M.; Harper, S.; Harr, R. F.; Harris, R. M.; Hartz, M.; Hatakeyama, K.; Hays, C.; Heck, M.; Heijboer, A.; Heinrich, J.; Henderson, C.; Herndon, M.; Heuser, J.; Hewamanage, S.; Hidas, D.; Hill, C. S.; Hirschbuehl, D.; Hocker, A.; Hou, S.; Houlden, M.; Hsu, S.-C.; Huffman, B. T.; Hughes, R. E.; Husemann, U.; Hussein, M.; Husemann, U.; Huston, J.; Incandela, J.; Introzzi, G.; Iori, M.; Ivanov, A.; James, E.; Jayatilaka, B.; Jeon, E. J.; Jha, M. K.; Jindariani, S.; Johnson, W.; Jones, M.; Joo, K. K.; Jun, S. Y.; Jung, J. E.; Junk, T. R.; Kamon, T.; Kar, D.; Karchin, P. E.; Kato, Y.; Kephart, R.; Keung, J.; Khotilovich, V.; Kilminster, B.; Kim, D. H.; Kim, H. S.; Kim, H. W.; Kim, J. E.; Kim, M. J.; Kim, S. B.; Kim, S. H.; Kim, Y. K.; Kimura, N.; Kirsch, L.; Klimenko, S.; Knuteson, B.; Ko, B. R.; Kondo, K.; Kong, D. J.; Konigsberg, J.; Korytov, A.; Kotwal, A. V.; Kreps, M.; Kroll, J.; Krop, D.; Krumnack, N.; Kruse, M.; Krutelyov, V.; Kubo, T.; Kuhr, T.; Kulkarni, N. P.; Kurata, M.; Kwang, S.; Laasanen, A. T.; Lami, S.; Lammel, S.; Lancaster, M.; Lander, R. L.; Lannon, K.; Lath, A.; Latino, G.; Lazzizzera, I.; Lecompte, T.; Lee, E.; Lee, H. S.; Lee, S. W.; Leone, S.; Lewis, J. D.; Lin, C.-S.; Linacre, J.; Lindgren, M.; Lipeles, E.; Lister, A.; Litvintsev, D. O.; Liu, C.; Liu, T.; Lockyer, N. S.; Loginov, A.; Loreti, M.; Lovas, L.; Lucchesi, D.; Luci, C.; Lueck, J.; Lujan, P.; Lukens, P.; Lungu, G.; Lyons, L.; Lys, J.; Lysak, R.; MacQueen, D.; Madrak, R.; Maeshima, K.; Makhoul, K.; Maki, T.; Maksimovic, P.; Malde, S.; Malik, S.; Manca, G.; Manousakis-Katsikakis, A.; Margaroli, F.; Marino, C.; Marino, C. P.; Martin, A.; Martin, V.; Martínez, M.; Martínez-Ballarín, R.; Maruyama, T.; Mastrandrea, P.; Masubuchi, T.; Mathis, M.; Mattson, M. E.; Mazzanti, P.; McFarland, K. S.; McIntyre, P.; McNulty, R.; Mehta, A.; Mehtala, P.; Menzione, A.; Merkel, P.; Mesropian, C.; Miao, T.; Miladinovic, N.; Miller, R.; Mills, C.; Milnik, M.; Mitra, A.; Mitselmakher, G.; Miyake, H.; Moggi, N.; Moon, C. S.; Moore, R.; Morello, M. J.; Morlock, J.; Movilla Fernandez, P.; Mülmenstädt, J.; Mukherjee, A.; Muller, Th.; Mumford, R.; Murat, P.; Mussini, M.; Nachtman, J.; Nagai, Y.; Nagano, A.; Naganoma, J.; Nakamura, K.; Nakano, I.; Napier, A.; Necula, V.; Nett, J.; Neu, C.; Neubauer, M. S.; Neubauer, S.; Nielsen, J.; Nodulman, L.; Norman, M.; Norniella, O.; Nurse, E.; Oakes, L.; Oh, S. H.; Oh, Y. D.; Oksuzian, I.; Okusawa, T.; Orava, R.; Osterberg, K.; Pagan Griso, S.; Palencia, E.; Papadimitriou, V.; Papaikonomou, A.; Paramonov, A. A.; Parks, B.; Pashapour, S.; Patrick, J.; Pauletta, G.; Paulini, M.; Paus, C.; Peiffer, T.; Pellett, D. E.; Penzo, A.; Phillips, T. J.; Piacentino, G.; Pianori, E.; Pinera, L.; Pitts, K.; Plager, C.; Pondrom, L.; Poukhov, O.; Pounder, N.; Prakoshyn, F.; Pronko, A.; Proudfoot, J.; Ptohos, F.; Pueschel, E.; Punzi, G.; Pursley, J.; Rademacker, J.; Rahaman, A.; Ramakrishnan, V.; Ranjan, N.; Redondo, I.; Renton, P.; Renz, M.; Rescigno, M.; Richter, S.; Rimondi, F.; Ristori, L.; Robson, A.; Rodrigo, T.; Rodriguez, T.; Rogers, E.; Rolli, S.; Roser, R.; Rossi, M.; Rossin, R.; Roy, P.; Ruiz, A.; Russ, J.; Rusu, V.; Saarikko, H.; Safonov, A.; Sakumoto, W. K.; Saltó, O.; Santi, L.; Sarkar, S.; Sartori, L.; Sato, K.; Savoy-Navarro, A.; Schlabach, P.; Schmidt, A.; Schmidt, E. E.; Schmidt, M. A.; Schmidt, M. P.; Schmitt, M.; Schwarz, T.; Scodellaro, L.; Scribano, A.; Scuri, F.; Sedov, A.; Seidel, S.; Seiya, Y.; Semenov, A.; Sexton-Kennedy, L.; Sforza, F.; Sfyrla, A.; Shalhout, S. Z.; Shears, T.; Shepard, P. F.; Shimojima, M.; Shiraishi, S.; Shochet, M.; Shon, Y.; Shreyber, I.; Sidoti, A.; Sinervo, P.; Sisakyan, A.; Slaughter, A. J.; Slaunwhite, J.; Sliwa, K.; Smith, J. R.; Snider, F. D.; Snihur, R.; Soha, A.; Somalwar, S.; Sorin, V.; Spalding, J.; Spreitzer, T.; Squillacioti, P.; Stanitzki, M.; St. Denis, R.; Stelzer, B.; Stelzer-Chilton, O.; Stentz, D.; Strologas, J.; Strycker, G. L.; Stuart, D.; Suh, J. S.; Sukhanov, A.; Suslov, I.; Suzuki, T.; Taffard, A.; Takashima, R.; Takeuchi, Y.; Tanaka, R.; Tecchio, M.; Teng, P. K.; Terashi, K.; Thom, J.; Thompson, A. S.; Thompson, G. A.; Thomson, E.; Tipton, P.; Ttito-Guzmán, P.; Tkaczyk, S.; Toback, D.; Tokar, S.; Tollefson, K.; Tomura, T.; Tonelli, D.; Torre, S.; Torretta, D.; Totaro, P.; Tourneur, S.; Trovato, M.; Tsai, S.-Y.; Tu, Y.; Turini, N.; Ukegawa, F.; Vallecorsa, S.; van Remortel, N.; Varganov, A.; Vataga, E.; Vázquez, F.; Velev, G.; Vellidis, C.; Vidal, M.; Vidal, R.; Vila, I.; Vilar, R.; Vine, T.; Vogel, M.; Volobouev, I.; Volpi, G.; Wagner, P.; Wagner, R. G.; Wagner, R. L.; Wagner, W.; Wagner-Kuhr, J.; Wakisaka, T.; Wallny, R.; Wang, S. M.; Warburton, A.; Waters, D.; Weinberger, M.; Weinelt, J.; Wester, W. C., III; Whitehouse, B.; Whiteson, D.; Wicklund, A. B.; Wicklund, E.; Wilbur, S.; Williams, G.; Williams, H. H.; Wilson, P.; Winer, B. L.; Wittich, P.; Wolbers, S.; Wolfe, C.; Wright, T.; Wu, X.; Würthwein, F.; Xie, S.; Yagil, A.; Yamamoto, K.; Yamaoka, J.; Yang, U. K.; Yang, Y. C.; Yao, W. M.; Yeh, G. P.; Yoh, J.; Yorita, K.; Yoshida, T.; Yu, G. B.; Yu, I.; Yu, S. S.; Yun, J. C.; Zanello, L.; Zanetti, A.; Zhang, X.; Zheng, Y.; Zucchelli, S.

    2009-09-01

    We present a search for a standard model Higgs boson produced in association with a W boson using 2.7fb-1 of integrated luminosity of p pmacr collision data taken at s=1.96TeV. Limits on the Higgs boson production rate are obtained for masses between 100 and 150GeV/c2. Through the use of multivariate techniques, the analysis achieves an observed (expected) 95% confidence level upper limit of 5.6 (4.8) times the theoretically expected production cross section for a standard model Higgs boson with a mass of 115GeV/c2.

  15. Search for a standard model Higgs boson in WH --> lvbb in pp collisions at square root s = 1.96 TeV.

    PubMed

    Aaltonen, T; Adelman, J; Akimoto, T; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Apresyan, A; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzurri, P; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Beecher, D; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Beringer, J; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burke, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Calancha, C; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carls, B; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Chwalek, T; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Cordelli, M; Cortiana, G; Cox, C A; Cox, D J; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lorenzo, G; Dell'orso, M; Deluca, C; Demortier, L; Deng, J; Deninno, M; Derwent, P F; di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Elagin, A; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Frank, M J; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Genser, K; Gerberich, H; Gerdes, D; Gessler, A; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Han, B-Y; Han, J Y; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hays, C; Heck, M; Heijboer, A; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Hussein, M; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jayatilaka, B; Jeon, E J; Jha, M K; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, H W; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Knuteson, B; Ko, B R; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhr, T; Kulkarni, N P; Kurata, M; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; Lecompte, T; Lee, E; Lee, H S; Lee, S W; Leone, S; Lewis, J D; Lin, C-S; Linacre, J; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, C; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lovas, L; Lucchesi, D; Luci, C; Lueck, J; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Macqueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Mathis, M; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzione, A; Merkel, P; Mesropian, C; Miao, T; Miladinovic, N; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moggi, N; Moon, C S; Moore, R; Morello, M J; Morlock, J; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Nett, J; Neu, C; Neubauer, M S; Neubauer, S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Osterberg, K; Pagan Griso, S; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Peiffer, T; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Pueschel, E; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Renton, P; Renz, M; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Safonov, A; Sakumoto, W K; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savoy-Navarro, A; Schlabach, P; Schmidt, A; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sforza, F; Sfyrla, A; Shalhout, S Z; Shears, T; Shepard, P F; Shimojima, M; Shiraishi, S; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spreitzer, T; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Strycker, G L; Stuart, D; Suh, J S; Sukhanov, A; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thompson, G A; Thomson, E; Tipton, P; Ttito-Guzmán, P; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Tourneur, S; Trovato, M; Tsai, S-Y; Tu, Y; Turini, N; Ukegawa, F; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vogel, M; Volobouev, I; Volpi, G; Wagner, P; Wagner, R G; Wagner, R L; Wagner, W; Wagner-Kuhr, J; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Weinelt, J; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Wilbur, S; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Würthwein, F; Xie, S; Yagil, A; Yamamoto, K; Yamaoka, J; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zhang, X; Zheng, Y; Zucchelli, S

    2009-09-04

    We present a search for a standard model Higgs boson produced in association with a W boson using 2.7 fb(-1) of integrated luminosity of pp collision data taken at square root s = 1.96 TeV. Limits on the Higgs boson production rate are obtained for masses between 100 and 150 GeV/c(2). Through the use of multivariate techniques, the analysis achieves an observed (expected) 95% confidence level upper limit of 5.6 (4.8) times the theoretically expected production cross section for a standard model Higgs boson with a mass of 115 GeV/c(2).

  16. Investigations of Standard Model and Exotic Monophoton Signatures at Center of Mass Energy = 7 TeV

    NASA Astrophysics Data System (ADS)

    Miceli, Tia Marie

    This dissertation describes the search for large extra dimensions. Such extra dimensions could solve the hierarchy problem, one of the greatest puzzles in the field of particle physics. The hierarchy problem is the mystery of why gravity is significantly weaker than the electromagnetic, strong, and weak forces. A solution would unify the strength of gravity with the strength of the other forces. There are many possible tests for extra dimensions. Presented is the analysis of proton-proton collisions with final states in which a single photon recoils from an invisible particle. These data were collected with the Compact Muon Solenoid detector during the 2011 Large Hadron Collider run at CERN, in Geneva, Switzerland, and correspond to an integrated luminosity of 5 fb-1 of proton-proton collisions at a center of mass energy of 7 TeV. Backgrounds are estimated using data driven techniques for those due to W → nue, hadronic jets, and non-collision backgrounds such as beam remnants faking a photon, and anomalous energy deposits in the electromagnetic calorimeter. The less significant backgrounds are estimated using Monte Carlo simulation, Wgamma, dijet, and jet + gamma. The irreducible Z(nunu)+ gamma background was estimated in Monte Carlo for the extra dimension search, but in later studies this component should be estimated by using a ratio of the measured Zgamma → mumugamma cross section. The results are consistent with the predicted Standard Model cross section of Z bosons decaying to neutrino pairs. Subsequently, the worlds best limits are placed on the monophoton topology of large extra dimensions. The same analysis provides a measurement of the Z(nunu)+gamma cross section consistent with next to leading order predictions.

  17. Evaluation of Structural Equation Mixture Models: Parameter Estimates and Correct Class Assignment

    ERIC Educational Resources Information Center

    Tueller, Stephen; Lubke, Gitta

    2010-01-01

    Structural equation mixture models (SEMMs) are latent class models that permit the estimation of a structural equation model within each class. Fitting SEMMs is illustrated using data from 1 wave of the Notre Dame Longitudinal Study of Aging. Based on the model used in the illustration, SEMM parameter estimation and correct class assignment are…

  18. Modelling of anisotropic compact stars of embedding class one

    NASA Astrophysics Data System (ADS)

    Bhar, Piyali; Maurya, S. K.; Gupta, Y. K.; Manna, Tuhina

    2016-10-01

    In the present article, we have constructed static anisotropic compact star models of Einstein field equations for the spherical symmetric metric of embedding class one. By assuming the particular form of the metric function ν, we have solved the Einstein field equations for anisotropic matter distribution. The anisotropic models represent the realistic compact objects such as SAX J 1808.4-3658 (SS1), Her X-1, Vela X-12, PSR J1614-2230 and Cen X-3. We have reported our results in details for the compact star Her X-1 on the ground of physical properties such as pressure, density, velocity of sound, energy conditions, TOV equation and red-shift etc. Along with these, we have also discussed about the stability of the compact star models. Finally we made a comparison between our anisotropic stars with the realistic objects on the key aspects as central density, central pressure, compactness and surface red-shift.

  19. Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?

    PubMed

    Lamont, Andrea E; Vermunt, Jeroen K; Van Horn, M Lee

    2016-01-01

    Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we tested the effects of violating an implicit assumption often made in these models; that is, independent variables in the model are not directly related to latent classes. Results indicate that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. In addition, we tested whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a reanalysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted.

  20. Regression mixture models: Does modeling the covariance between independent variables and latent classes improve the results?

    PubMed Central

    Lamont, Andrea E.; Vermunt, Jeroen K.; Van Horn, M. Lee

    2016-01-01

    Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted. PMID:26881956

  1. Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

    ERIC Educational Resources Information Center

    Nylund, Karen L.; Asparouhov, Tihomir; Muthen, Bengt O.

    2007-01-01

    Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study…

  2. Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

    ERIC Educational Resources Information Center

    Nylund, Karen L.; Asparouhov, Tihomir; Muthen, Bengt O.

    2007-01-01

    Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study…

  3. Search for minimal supersymmetric standard model Higgs Bosons H / A and for a Z^' } boson in the τ τ final state produced in pp collisions at √{s}= 13 TeV with the ATLAS detector

    NASA Astrophysics Data System (ADS)

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J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Dehghanian, N.; Deigaard, I.; Del Gaudio, M.; Del Peso, J.; Del Prete, T.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; della Volpe, D.; Delmastro, M.; Delsart, P. A.; DeMarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Denysiuk, D.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; Di Ciaccio, A.; Di Ciaccio, L.; Di Clemente, W. K.; Di Donato, C.; Di Girolamo, A.; Di Girolamo, B.; Di Micco, B.; Di Nardo, R.; Di Simone, A.; Di Sipio, R.; Di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Cornell, S. Díez; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dolejsi, J.; Dolezal, Z.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Duarte-Campderros, J.; Duchovni, E.; Duckeck, G.; Ducu, O. A.; Duda, D.; Dudarev, A.; Dudder, A. Chr.; Duffield, E. M.; Duflot, L.; Dührssen, M.; Dumancic, M.; Dunford, M.; Duran Yildiz, H.; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Dyndal, M.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Edwards, N. C.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; Kacimi, M. El; Ellajosyula, V.; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Ennis, J. S.; Erdmann, J.; Ereditato, A.; Ernis, G.; Ernst, J.; Ernst, M.; Errede, S.; Ertel, E.; Escalier, M.; Esch, H.; Escobar, C.; Esposito, B.; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Ezzi, M.; Fabbri, F.; Fabbri, L.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farina, C.; Farina, E. M.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Faucci Giannelli, M.; Favareto, A.; Fawcett, W. J.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, H.; Fenyuk, A. B.; Feremenga, L.; Fernandez Martinez, P.; Fernandez Perez, S.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; de Lima, D. E. Ferreira; Ferrer, A.; Ferrere, D.; Ferretti, C.; Ferretto Parodi, A.; Fiedler, F.; Filipčič, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Firan, A.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, G. T.; Fletcher, R. R. M.; Flick, T.; Flores Castillo, L. R.; Flowerdew, M. J.; Forcolin, G. T.; Formica, A.; Forti, A.; Foster, A. 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H.; Hillier, S. J.; Hinchliffe, I.; Hines, E.; Hinman, R. R.; Hirose, M.; Hirschbuehl, D.; Hobbs, J.; Hod, N.; Hodgkinson, M. C.; Hodgson, P.; Hoecker, A.; Hoeferkamp, M. R.; Hoenig, F.; Hohn, D.; Holmes, T. R.; Homann, M.; Honda, T.; Hong, T. M.; Hooberman, B. H.; Hopkins, W. H.; Horii, Y.; Horton, A. J.; Hostachy, J.-Y.; Hou, S.; Hoummada, A.; Howarth, J.; Hoya, J.; Hrabovsky, M.; Hristova, I.; Hrivnac, J.; Hryn'ova, T.; Hrynevich, A.; Hsu, C.; Hsu, P. J.; Hsu, S.-C.; Hu, Q.; Hu, S.; Huang, Y.; Hubacek, Z.; Hubaut, F.; Huegging, F.; Huffman, T. B.; Hughes, E. 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Sales; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sanchez, A.; Sánchez, J.; Sanchez Martinez, V.; Sandaker, H.; Sandbach, R. L.; Sander, H. G.; Sandhoff, M.; Sandoval, C.; Sankey, D. P. C.; Sannino, M.; Sansoni, A.; Santoni, C.; Santonico, R.; Santos, H.; Santoyo Castillo, I.; Sapp, K.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sato, K.; Sauvan, E.; Savage, G.; Savard, P.; Savic, N.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Scarcella, M.; Scarfone, V.; Schaarschmidt, J.; Schacht, P.; Schachtner, B. M.; Schaefer, D.; Schaefer, L.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Scharf, V.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schier, S.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schneider, B.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schott, M.; Schouwenberg, J. F. P.; Schovancova, J.; Schramm, S.; Schreyer, M.; Schuh, N.; Schulte, A.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwartzman, A.; Schwarz, T. A.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Schwindt, T.; Sciolla, G.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seifert, F.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Seliverstov, D. M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shirabe, S.; Shiyakova, M.; Shmeleva, A.; Shoaleh Saadi, D.; Shochet, M. J.; Shojaii, S.; Shope, D. R.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sickles, A. M.; Sidebo, P. E.; Sidiropoulou, O.; Sidorov, D.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, D.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Sivoklokov, S. Yu.; Sjölin, J.; Skinner, M. B.; Skottowe, H. P.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smestad, L.; Smiesko, J.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snyder, I. M.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Soh, D. A.; Sokhrannyi, G.; Solans Sanchez, C. A.; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Song, H. Y.; Sood, A.; Sopczak, A.; Sopko, V.; Sorin, V.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; Denis, R. D. St.; Stabile, A.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanescu-Bellu, M.; Stanitzki, M. M.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stillings, J. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Susinno, G.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Sykora, I.; Sykora, T.; Ta, D.; Taccini, C.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tan, K. G.; Tanaka, J.; Tanaka, M.; Tanaka, R.; Tanaka, S.; Tanioka, R.; Tannenwald, B. B.; Tapia Araya, S.; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Tavares Delgado, A.; Tayalati, Y.; Taylor, A. C.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teischinger, F. A.; Teixeira-Dias, P.; Temming, K. K.; Temple, D.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, E. N.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Thomson, M.; Tibbetts, M. J.; Ticse Torres, R. E.; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorov, T.; Todorova-Nova, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Tornambe, P.; Torrence, E.; Torres, H.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Trefzger, T.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tu, Y.; Tudorache, A.; Tudorache, V.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turecek, D.; Turgeman, D.; Turra, R.; Tuts, P. M.; Tyndel, M.; Ucchielli, G.; Ueda, I.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Vacavant, L.; Vacek, V.; Vachon, B.; Valderanis, C.; Valdes Santurio, E.; Valencic, N.; Valentinetti, S.; Valero, A.; Valery, L.; Valkar, S.; Valls Ferrer, J. A.; Van Den Wollenberg, W.; Van Der Deijl, P. C.; van der Graaf, H.; van Eldik, N.; van Gemmeren, P.; Van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vanguri, R.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vasquez, G. A.; Vazeille, F.; Vazquez Schroeder, T.; Veatch, J.; Veeraraghavan, V.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vest, A.; Vetterli, M. C.; Viazlo, O.; Vichou, I.; Vickey, T.; Vickey Boeriu, O. E.; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vlasak, M.; Vogel, M.; Vokac, P.; Volpi, G.; Volpi, M.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Vykydal, Z.; Wagner, P.; Wagner, W.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, K.; Wang, R.; Wang, S. M.; Wang, T.; Wang, T.; Wang, W.; Wang, X.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, S.; Weber, M. S.; Weber, S. W.; Weber, S. A.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, M. D.; Werner, P.; Wessels, M.; Wetter, J.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A.; White, M. J.; White, R.; Whiteson, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wittkowski, J.; Wolf, T. M. H.; Wolter, M. W.; Wolters, H.; Worm, S. D.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wozniak, K. W.; Wu, M.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xu, D.; Xu, L.; Yabsley, B.; Yacoob, S.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yen, A. L.; Yildirim, E.; Yorita, K.; Yoshida, R.; Yoshihara, K.; Young, C.; Young, C. J. S.; Youssef, S.; Yu, D. R.; Yu, J.; Yu, J. M.; Yu, J.; Yuan, L.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanello, L.; Zanzi, D.; Zeitnitz, C.; Zeman, M.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zengel, K.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, L.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Nedden, M. zur; Zwalinski, L.

    2016-11-01

    A search for neutral Higgs bosons of the minimal supersymmetric standard model (MSSM) and for a heavneutral Z^' } boson is performed using a data sample corresponding to an integrated luminosity of 3.2 fb^{-1} from proton-proton collisions at √{s} = 13 {TeV} recorded by the ATLAS detector at the LHC. The heavy resonance is assumed to decay to a τ ^+ τ ^- pair with at least one τ lepton decaying to final states with hadrons and a neutrino. The search is performed in the mass range of 0.2-1.2 {TeV} for the MSSM neutral Higgs bosons and 0.5-2.5 {TeV} for the heavy neutral Z^' } boson. The data are in good agreement with the background predicted by the Standard Model. The results are interpreted in MSSM and Z^' } benchmark scenarios. The most stringent constraints on the MSSM m_A-tan β space exclude at 95 % confidence level (CL) tan β > 7.6 for m_A = 200 {GeV} in the mh^{ {mod+}} MSSM scenario. For the Sequential Standard Model, a Z^' }_SSM mass up to 1.90 {TeV} is excluded at 95 % CL and masses up to 1.82-2.17 {TeV} are excluded for a Z^' }_{SFM} of the strong flavour model.

  4. On the General Class of Models of Adiabatic Evolution

    NASA Astrophysics Data System (ADS)

    Sun, Jie; Lu, Songfeng; Liu, Fang

    2016-10-01

    The general class of models of adiabatic evolution was proposed to speed up the usual adiabatic computation in the case of quantum search problem. It was shown [8] that, by temporarily increasing the ground state energy of a time-dependent Hamiltonian to a suitable quantity, the quantum computation can perform the calculation in time complexity O(1). But it is also known that if the overlap between the initial and final states of the system is zero, then the computation based on the generalized models of adiabatic evolution can break down completely. In this paper, we find another severe limitation for this class of adiabatic evolution-based algorithms, which should be taken into account in applications. That is, it is still possible that this kind of evolution designed to deal with the quantum search problem fails completely if the interpolating paths in the system Hamiltonian are chosen inappropriately, while the usual adiabatic evolutions can do the same job relatively effectively. This implies that it is not always recommendable to use nonlinear paths in adiabatic computation. On the contrary, the usual simple adiabatic evolution may be sufficient for effective use.

  5. Search for a Low Mass Standard Model Higgs Boson at D0 in ppbar Collisions at $\\sqrt{s}=1.96 TeV$

    SciTech Connect

    Rangel, Murilo

    2010-02-01

    We present combined searches for the low mass Standard Model Higgs boson at a center-of-mass energy of {radical}s = 1.96 TeV, using up to 5 fb{sup -1} of data collected with the D0 detector at the Fermilab Tevatron collider. The major contributing processes are associated production (WH {yields} lvbb, ZH {yields} vvbb, ZH {yields} llbb). The significant improvements across the full mass range resulting from the larger data sets and improved analyses as well as future prospects are discussed.

  6. Physical models for class-room teaching in hydrology

    NASA Astrophysics Data System (ADS)

    Rodhe, A.

    2012-03-01

    Hydrology teaching benefits from the fact that many important processes can be illustrated and explained with simple physical models. A set of mobile physical models has been developed and used during many years of lecturing at basic university level teaching in hydrology. One model, with which many phenomena can be demonstrated, consists of a 1.0 m long plexiglass container containing an about 0.25 m deep open sand aquifer through which water is circulated. The model can be used for showing the groundwater table and its influence on the water content in the unsaturated zone and for quantitative determination of hydraulic properties such as the storage coefficient and the saturated hydraulic conductivity. It is also well suited for discussions on the runoff process and the significance of recharge and discharge areas for groundwater. The flowpaths of water and contaminant dispersion can be illustrated in tracer experiments using flourescent or colour dye. This and a few other physical models, with suggested demonstrations and experiments, are described in this article. The finding from using models in class-room teaching is that it creates curiosity among the students, promotes discussions and most likely deepens the understanding of the basic processes.

  7. Inflationary field excursion in broad classes of scalar field models

    NASA Astrophysics Data System (ADS)

    Banerjee, Argha; Koley, Ratna

    2016-12-01

    In single-field slow-roll inflation models, the height and slope of the potential are made to satisfy certain conditions to match with observations. This in turn translates into bounds on the number of e -foldings and the excursion of the scalar field during inflation. In this work we consider broad classes of inflationary models to study how much the field excursion starting from the horizon exit to the end of inflation, Δ ϕ , can vary for the set of inflationary parameters given by Planck. We also derive an upper bound on the number of e -foldings between the horizon exit of a cosmologically interesting mode and the end of inflation. We comment on the possibility of having super-Planckian and sub-Planckian field excursions within the framework of single-field slow-roll inflation.

  8. Latent Class Models in action: bridging social capital & Internet usage.

    PubMed

    Neves, Barbara Barbosa; Fonseca, Jaime R S

    2015-03-01

    This paper explores how Latent Class Models (LCM) can be applied in social research, when the basic assumptions of regression models cannot be validated. We examine the usefulness of this method with data collected from a study on the relationship between bridging social capital and the Internet. Social capital is defined here as the resources that are potentially available in one's social ties. Bridging is a dimension of social capital, usually related to weak ties (acquaintances), and a source of instrumental resources such as information. The study surveyed a stratified random sample of 417 inhabitants of Lisbon, Portugal. We used LCM to create the variable bridging social capital, but also to estimate the relationship between bridging social capital and Internet usage when we encountered convergence problems with the logistic regression analysis. We conclude by showing a positive relationship between bridging and Internet usage, and by discussing the potential of LCM for social science research. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Evaluation of consumer satisfaction using the tetra-class model.

    PubMed

    Clerfeuille, Fabrice; Poubanne, Yannick; Vakrilova, Milena; Petrova, Guenka

    2008-09-01

    A number of studies have shown the importance of consumers' satisfaction toward pharmacy services. The measurement of patient satisfaction through different elements of services provided is challenging within the context of a dynamic economic environment. Patient satisfaction is the result of long-term established habits and expectations to the pharmacy as an institution. Few studies to date have attempted to discern whether these changes have led to increased patient satisfaction and loyalty, particularly within developing nations. The objective of this study was to evaluate the elements of the services provided in Bulgarian pharmacies and their contribution to consumer satisfaction using a tetra-class model. Three main hypotheses were tested in pharmacies to validate the model in the case of complex services. Additionally, the contribution of the different service elements to the clients' satisfaction was studied. The analysis was based on a survey of customers in central and district pharmacies in Sofia, Bulgaria. The data were analyzed through a correspondence analysis which was applied to the results of the 752 distributed questionnaires. It was observed that different dimensions of the pharmacies contribute uniquely to customer satisfaction, with consumer gender contributing greatly toward satisfaction, with type/location of pharmacy, consumer age, and educational degree also playing a part. The duration of time over which the consumers have been clients at a given pharmacy influences the subsequent service categorization. This research demonstrated that the tetra-class model is suitable for application in the pharmaceutical sector. The model results could be beneficial for both researchers and pharmacy managers.

  10. Latent Class Analysis with Distal Outcomes: A Flexible Model-Based Approach

    ERIC Educational Resources Information Center

    Lanza, Stephanie T.; Tan, Xianming; Bray, Bethany C.

    2013-01-01

    Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with…

  11. Latent Class Analysis with Distal Outcomes: A Flexible Model-Based Approach

    ERIC Educational Resources Information Center

    Lanza, Stephanie T.; Tan, Xianming; Bray, Bethany C.

    2013-01-01

    Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with…

  12. Modeling and Optimization of Class-E Amplifier at Subnominal Condition in a Wireless Power Transfer System for Biomedical Implants.

    PubMed

    Liu, Hao; Shao, Qi; Fang, Xuelin

    2017-02-01

    For the class-E amplifier in a wireless power transfer (WPT) system, the design parameters are always determined by the nominal model. However, this model neglects the conduction loss and voltage stress of MOSFET and cannot guarantee the highest efficiency in the WPT system for biomedical implants. To solve this problem, this paper proposes a novel circuit model of the subnominal class-E amplifier. On a WPT platform for capsule endoscope, the proposed model was validated to be effective and the relationship between the amplifier's design parameters and its characteristics was analyzed. At a given duty ratio, the design parameters with the highest efficiency and safe voltage stress are derived and the condition is called 'optimal subnominal condition.' The amplifier's efficiency can reach the highest of 99.3% at the 0.097 duty ratio. Furthermore, at the 0.5 duty ratio, the measured efficiency of the optimal subnominal condition can reach 90.8%, which is 15.2% higher than that of the nominal condition. Then, a WPT experiment with a receiving unit was carried out to validate the feasibility of the optimized amplifier. In general, the design parameters of class-E amplifier in a WPT system for biomedical implants can be determined with the proposed optimization method in this paper.

  13. Search for physics beyond the standard model in events with a Z boson, jets, and missing transverse energy in pp collisions at √{ s} = 7 TeV

    NASA Astrophysics Data System (ADS)

    Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M.; Erö, J.; Fabjan, C.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hammer, J.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Krammer, M.; Liko, D.; Mikulec, I.; Pernicka, M.; Rahbaran, B.; Rohringer, C.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Taurok, A.; Teischinger, F.; Wagner, P.; Waltenberger, W.; Walzel, G.; Widl, E.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Bansal, S.; Cerny, K.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Luyckx, S.; Maes, T.; Mucibello, L.; Ochesanu, S.; Roland, B.; Rougny, R.; Selvaggi, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D'Hondt, J.; Gonzalez Suarez, R.; Kalogeropoulos, A.; Maes, M.; Olbrechts, A.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Villella, I.; Charaf, O.; Clerbaux, B.; De Lentdecker, G.; Dero, V.; Gay, A. P. R.; Hreus, T.; Léonard, A.; Marage, P. E.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Adler, V.; Beernaert, K.; Cimmino, A.; Costantini, S.; Garcia, G.; Grunewald, M.; Klein, B.; Lellouch, J.; Marinov, A.; Mccartin, J.; Ocampo Rios, A. A.; Ryckbosch, D.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Vanelderen, L.; Verwilligen, P.; Walsh, S.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Bruno, G.; Ceard, L.; Delaere, C.; du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Lemaitre, V.; Liao, J.; Militaru, O.; Nuttens, C.; Pagano, D.; Pin, A.; Piotrzkowski, K.; Schul, N.; Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.; Alves, G. A.; Correa Martins, M.; De Jesus Damiao, D.; Martins, T.; Pol, M. E.; Souza, M. H. G.; Aldá Júnior, W. L.; Carvalho, W.; Custódio, A.; Da Costa, E. M.; De Oliveira Martins, C.; Fonseca De Souza, S.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Oguri, V.; Prado Da Silva, W. L.; Santoro, A.; Silva Do Amaral, S. M.; Soares Jorge, L.; Sznajder, A.; Anjos, T. S.; Bernardes, C. A.; Dias, F. A.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Lagana, C.; Marinho, F.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Genchev, V.; Iaydjiev, P.; Piperov, S.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Tcholakov, V.; Trayanov, R.; Vutova, M.; Dimitrov, A.; Hadjiiska, R.; Karadzhinova, A.; Kozhuharov, V.; Litov, L.; Pavlov, B.; Petkov, P.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Jiang, C. H.; Liang, D.; Liang, S.; Meng, X.; Tao, J.; Wang, J.; Wang, J.; Wang, X.; Wang, Z.; Xiao, H.; Xu, M.; Zang, J.; Zhang, Z.; Asawatangtrakuldee, C.; Ban, Y.; Guo, S.; Guo, Y.; Li, W.; Liu, S.; Mao, Y.; Qian, S. J.; Teng, H.; Wang, S.; Zhu, B.; Zou, W.; Avila, C.; Gomez Moreno, B.; Osorio Oliveros, A. F.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Plestina, R.; Polic, D.; Puljak, I.; Antunovic, Z.; Dzelalija, M.; Kovac, M.; Brigljevic, V.; Duric, S.; Kadija, K.; Luetic, J.; Morovic, S.; Attikis, A.; Galanti, M.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Finger, M.; Finger, M.; Assran, Y.; Elgammal, S.; Ellithi Kamel, A.; Khalil, S.; Mahmoud, M. A.; Radi, A.; Kadastik, M.; Müntel, M.; Raidal, M.; Rebane, L.; Tiko, A.; Azzolini, V.; Eerola, P.; Fedi, G.; Voutilainen, M.; Czellar, S.; Härkönen, J.; Heikkinen, A.; Karimäki, V.; Kinnunen, R.; Kortelainen, M. J.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Peltola, T.; Tuominen, E.; Tuominiemi, J.; Tuovinen, E.; Ungaro, D.; Wendland, L.; Banzuzi, K.; Korpela, A.; Tuuva, T.; Sillou, D.; Besancon, M.; Choudhury, S.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Malcles, J.; Millischer, L.; Rander, J.; Rosowsky, A.; Shreyber, I.; Titov, M.; Baffioni, S.; Beaudette, F.; Benhabib, L.; Bianchini, L.; Bluj, M.; Broutin, C.; Busson, P.; Charlot, C.; Daci, N.; Dahms, T.; Dobrzynski, L.; Granier de Cassagnac, R.; Haguenauer, M.; Miné, P.; Mironov, C.; Ochando, C.; Paganini, P.; Sabes, D.; Salerno, R.; Sirois, Y.; Veelken, C.; Zabi, A.; Agram, J.-L.; Andrea, J.; Bloch, D.; Bodin, D.; Brom, J.-M.; Cardaci, M.; Chabert, E. C.; Collard, C.; Conte, E.; Drouhin, F.; Ferro, C.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Juillot, P.; Karim, M.; Le Bihan, A.-C.; Van Hove, P.; Fassi, F.; Mercier, D.; Baty, C.; Beauceron, S.; Beaupere, N.; Bedjidian, M.; Bondu, O.; Boudoul, G.; Boumediene, D.; Brun, H.; Chasserat, J.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Falkiewicz, A.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Kurca, T.; Le Grand, T.; Lethuillier, M.; Mirabito, L.; Perries, S.; Sordini, V.; Tosi, S.; Tschudi, Y.; Verdier, P.; Viret, S.; Rurua, L.; Anagnostou, G.; Beranek, S.; Edelhoff, M.; Feld, L.; Heracleous, N.

    2012-09-01

    A search is presented for physics beyond the standard model (BSM) in events with a Z boson, jets, and missing transverse energy (ETmiss). This signature is motivated by BSM physics scenarios, including supersymmetry. The study is performed using a sample of proton-proton collision data collected at √{ s} = 7 TeV with the CMS experiment at the LHC, corresponding to an integrated luminosity of 4.98 fb-1. The contributions from the dominant standard model backgrounds are estimated from data using two complementary strategies, the jet-Z balance technique and a method based on modeling ETmiss with data control samples. In the absence of evidence for BSM physics, we set limits on the non-standard-model contributions to event yields in the signal regions and interpret the results in the context of simplified model spectra. Additional information is provided to facilitate tests of other BSM physics models.

  14. BL LAC candidates for TeV observations

    SciTech Connect

    Massaro, F.; Paggi, A.; Errando, M.; D'Abrusco, R.; Masetti, N.; Tosti, G.; Funk, S.

    2013-07-01

    BL Lac objects are the most numerous class of extragalactic TeV-detected sources. One of the biggest difficulties in investigating their TeV emission is due to their limited number, since only 47 BL Lac objects are known to be TeV emitters. In this paper, we propose new criteria to select TeV BL Lac candidates based on infrared and X-ray observations. We apply our selection criteria to the BL Lac objects listed in the ROMA-BZCAT catalog, thereby identifying 41 potential TeV emitters. We then perform a search over a more extended sample combining the ROSAT bright source catalog and the WISE all-sky survey, revealing 54 additional candidates for TeV observations. Our investigation also led to a tentative classification of 16 unidentified X-ray sources as BL Lac candidates. This analysis provides new interesting BL Lac targets for future observations with ground-based Cherenkov telescopes.

  15. Model Selection Information Criteria for Non-Nested Latent Class Models.

    ERIC Educational Resources Information Center

    Lin, Ting Hsiang; Dayton, C. Mitchell

    1997-01-01

    The use of these three model selection information criteria for latent class models was studied for nonnested models: (1) Akaike's information criterion (H. Akaike, 1973) (AIC); (2) the Schwarz information (G. Schwarz, 1978) (SIC) criterion; and (3) the Bozdogan version of the AIC (CAIC) (H. Bozdogan, 1987). Situations in which each is preferable…

  16. Surprising phenomena in a rich new class of inflationary models

    SciTech Connect

    Vaudrevange, Pascal M.; Podolsky, Dmitry I.; Starkman, Glenn D. E-mail: podolsky@phys.cwru.edu

    2010-04-01

    We report on a new class of fast-roll inflationary models. In a huge part of its parameter space, inflationary perturbations exhibit quite unusual phenomena such as scalar and tensor modes freezing out at widely different times, as well as scalar modes reentering the horizon during inflation. One specific point in parameter space is characterized by extraordinary behavior of the scalar perturbations. Freeze-out of scalar perturbations as well as particle production at horizon crossing are absent. Also the behavior of the perturbations around this quasi-de Sitter background is dual to a quantum field theory in flat space-time. Finally, the form of the primordial power spectrum is determined by the interaction between different modes of scalar perturbations.

  17. Global attractor for a class of Kirchhoff models

    NASA Astrophysics Data System (ADS)

    Zhijian, Yang; Baoxia, Jin

    2009-03-01

    The paper studies the longtime behavior of solutions to the initial boundary value problem (IBVP) for a class of Kirchhoff models arising in elastoplastic flow utt-div{|∇u|m -1∇u}-Δut+Δ2u+h(ut)+g(u)=f(x). By combining the decomposition idea with the operate technique, it proves that under rather mild conditions, the dynamical system associated with above-mentioned IBVP possesses in different phase spaces a global attractor which is connected, respectively. For application, the fact shows that for the concerned elastoplastic flow the permanent regime (global attractor) can be observed when the excitation starts from any bounded set in phase space.

  18. Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues - A tutorial.

    PubMed

    Oliveri, Paolo

    2017-08-22

    Qualitative data modelling is a fundamental branch of pattern recognition, with many applications in analytical chemistry, and embraces two main families: discriminant and class-modelling methods. The first strategy is appropriate when at least two classes are meaningfully defined in the problem under study, while the second strategy is the right choice when the focus is on a single class. For this reason, class-modelling methods are also referred to as one-class classifiers. Although, in the food analytical field, most of the issues would be properly addressed by class-modelling strategies, the use of such techniques is rather limited and, in many cases, discriminant methods are forcedly used for one-class problems, introducing a bias in the outcomes. Key aspects related to the development, optimisation and validation of suitable class models for the characterisation of food products are critically analysed and discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Spatial Latent Class Analysis Model for Spatially Distributed Multivariate Binary Data

    PubMed Central

    Wall, Melanie M.; Liu, Xuan

    2009-01-01

    A spatial latent class analysis model that extends the classic latent class analysis model by adding spatial structure to the latent class distribution through the use of the multinomial probit model is introduced. Linear combinations of independent Gaussian spatial processes are used to develop multivariate spatial processes that are underlying the categorical latent classes. This allows the latent class membership to be correlated across spatially distributed sites and it allows correlation between the probabilities of particular types of classes at any one site. The number of latent classes is assumed fixed but is chosen by model comparison via cross-validation. An application of the spatial latent class analysis model is shown using soil pollution samples where 8 heavy metals were measured to be above or below government pollution limits across a 25 square kilometer region. Estimation is performed within a Bayesian framework using MCMC and is implemented using the OpenBUGS software. PMID:20161235

  20. Searches for Higgs bosons in pp collisions at √{ s} = 7 and 8 TeV in the context of four-generation and fermiophobic models

    NASA Astrophysics Data System (ADS)

    Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Aguilo, E.; Bergauer, T.; Dragicevic, M.; Erö, J.; Fabjan, C.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hammer, J.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Knünz, V.; Krammer, M.; Liko, D.; Mikulec, I.; Pernicka, M.; Rahbaran, B.; Rohringer, C.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Taurok, A.; Waltenberger, W.; Walzel, G.; Widl, E.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Bansal, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Luyckx, S.; Mucibello, L.; Ochesanu, S.; Roland, B.; Rougny, R.; Selvaggi, M.; Staykova, Z.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D'Hondt, J.; Gonzalez Suarez, R.; Kalogeropoulos, A.; Maes, M.; Olbrechts, A.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Villella, I.; Clerbaux, B.; De Lentdecker, G.; Dero, V.; Gay, A. P. R.; Hreus, T.; Léonard, A.; Marage, P. E.; Mohammadi, A.; Reis, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.; Adler, V.; Beernaert, K.; Cimmino, A.; Costantini, S.; Garcia, G.; Grunewald, M.; Klein, B.; Lellouch, J.; Marinov, A.; Mccartin, J.; Ocampo Rios, A. A.; Ryckbosch, D.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Verwilligen, P.; Walsh, S.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Bruno, G.; Castello, R.; Ceard, L.; Delaere, C.; du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Lemaitre, V.; Liao, J.; Militaru, O.; Nuttens, C.; Pagano, D.; Pin, A.; Piotrzkowski, K.; Schul, N.; Vizan Garcia, J. M.; Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.; Alves, G. A.; Correa Martins Junior, M.; De Jesus Damiao, D.; Martins, T.; Pol, M. E.; Souza, M. H. G.; Aldá Júnior, W. L.; Carvalho, W.; Custódio, A.; Da Costa, E. M.; De Oliveira Martins, C.; Fonseca De Souza, S.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Oguri, V.; Prado Da Silva, W. L.; Santoro, A.; Soares Jorge, L.; Sznajder, A.; Anjos, T. S.; Bernardes, C. A.; Dias, F. A.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Lagana, C.; Marinho, F.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Genchev, V.; Iaydjiev, P.; Piperov, S.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Tcholakov, V.; Trayanov, R.; Vutova, M.; Dimitrov, A.; Hadjiiska, R.; Kozhuharov, V.; Litov, L.; Pavlov, B.; Petkov, P.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Jiang, C. H.; Liang, D.; Liang, S.; Meng, X.; Tao, J.; Wang, J.; Wang, X.; Wang, Z.; Xiao, H.; Xu, M.; Zang, J.; Zhang, Z.; Asawatangtrakuldee, C.; Ban, Y.; Guo, S.; Guo, Y.; Li, Q.; Li, W.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Zhang, L.; Zhu, B.; Zou, W.; Avila, C.; Gomez, J. P.; Gomez Moreno, B.; Osorio Oliveros, A. F.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Plestina, R.; Polic, D.; Puljak, I.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Duric, S.; Kadija, K.; Luetic, J.; Morovic, S.; Attikis, A.; Galanti, M.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Finger, M.; Finger, M.; Assran, Y.; Elgammal, S.; Ellithi Kamel, A.; Mahmoud, M. A.; Radi, A.; Kadastik, M.; Müntel, M.; Raidal, M.; Rebane, L.; Tiko, A.; Eerola, P.; Fedi, G.; Voutilainen, M.; Härkönen, J.; Heikkinen, A.; Karimäki, V.; Kinnunen, R.; Kortelainen, M. J.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Peltola, T.; Tuominen, E.; Tuominiemi, J.; Tuovinen, E.; Ungaro, D.; Wendland, L.; Banzuzi, K.; Karjalainen, A.; Korpela, A.; Tuuva, T.; Besancon, M.; Choudhury, S.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Malcles, J.; Millischer, L.; Nayak, A.; Rander, J.; Rosowsky, A.; Shreyber, I.; Titov, M.; Baffioni, S.; Beaudette, F.; Benhabib, L.; Bianchini, L.; Bluj, M.; Broutin, C.; Busson, P.; Charlot, C.; Daci, N.; Dahms, T.; Dobrzynski, L.; Granier de Cassagnac, R.; Haguenauer, M.; Miné, P.; Mironov, C.; Nguyen, M.; Ochando, C.; Paganini, P.; Sabes, D.; Salerno, R.; Sirois, Y.; Veelken, C.; Zabi, A.; Agram, J.-L.; Andrea, J.; Bloch, D.; Bodin, D.; Brom, J.-M.; Cardaci, M.; Chabert, E. C.; Collard, C.; Conte, E.; Drouhin, F.; Ferro, C.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Juillot, P.; Le Bihan, A.-C.; Van Hove, P.; Fassi, F.; Mercier, D.; Beauceron, S.; Beaupere, N.; Bondu, O.; Boudoul, G.; Chasserat, J.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Kurca, T.; Lethuillier, M.; Mirabito, L.; Perries, S.; Sordini, V.; Tschudi, Y.; Verdier, P.; Viret, S.; Tsamalaidze, Z.; Anagnostou, G.; Beranek, S.; Edelhoff, M.; Feld, L.; Heracleous, N.; Hindrichs, O.; Jussen, R.; Klein, K.; Merz, J.; Ostapchuk, A.; Perieanu, A.; Raupach, F.; Sammet, J.; Schael, S.

    2013-08-01

    Searches are reported for Higgs bosons in the context of either the standard model extended to include a fourth generation of fermions (SM4) with masses of up to 600 GeV or fermiophobic models. For the former, results from three decay modes (ττ, WW, and ZZ) are combined, whilst for the latter the diphoton decay is exploited. The analysed proton-proton collision data correspond to integrated luminosities of up to 5.1 fb-1 at 7 TeV and up to 5.3 fb-1 at 8 TeV. The observed results exclude the SM4 Higgs boson in the mass range 110-600 GeV at 99% confidence level (CL), and in the mass range 110-560 GeV at 99.9% CL. A fermiophobic Higgs boson is excluded in the mass range 110-147 GeV at 95% CL, and in the range 110-133 GeV at 99% CL. The recently observed boson with a mass near 125 GeV is not consistent with either an SM4 or a fermiophobic Higgs boson.

  1. Measurement of the branching ratios for the standard model Higgs decays into muon pairs and into Z boson pairs at a 1.4 TeV CLIC

    SciTech Connect

    Milutinović-Dumbelović, Gordana Božović-Jelisavčić, Ivanka; Kačarević, Goran; Lukić, Strahinja; Pandurović, Mila; Smiljanić, Ivan; Grefe, Christian; Roloff, Philipp

    2016-03-25

    The measurement of the Higgs production cross-section times the branching ratios for its decays into μ{sup +}μ{sup −} and ZZ* pairs at a 1.4 TeV CLIC collider is investigated in this paper. The Standard Model Higgs boson with a mass of 126 GeV is dominantly produced via WW fusion in e{sup +}e{sup −} collisions at 1.4 TeV centre-of-mass energy. Analyses of both decay channels are based on a full simulation of the CLIC-ILD detector. All relevant physics and beam-induced background processes are taken into account. An integrated luminosity of 1.5 ab{sup −1} and unpolarised beams are assumed. For the H→ZZ* decay, the purely hadronic final state (ZZ* → qq̄qq̄) is considered as well as ZZ* decays into two jets and two leptons (ZZ* → qql{sup +}l{sup −}). It is shown that the branching ratio for the Higgs decay into a muon pair times the Higgs production cross-section can be measured with 38% statistical uncertainty. It is also shown that the statistical uncertainty of the Higgs branching ratio for decay into a Z boson pair times the Higgs production cross-section can be measured with a precision of 18.3% and 5.6% for the hadronic and semi-leptonic ZZ* decays, respectively.

  2. Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches

    ERIC Educational Resources Information Center

    Lubke, Gitta; Tueller, Stephen

    2010-01-01

    Taxometric procedures such as MAXEIG and factor mixture modeling (FMM) are used in latent class clustering, but they have very different sets of strengths and weaknesses. Taxometric procedures, popular in psychiatric and psychopathology applications, do not rely on distributional assumptions. Their sole purpose is to detect the presence of latent…

  3. Search for physics beyond the standard model in events with two leptons, jets, and missing transverse momentum in pp collisions at √s = 8 TeV

    SciTech Connect

    Khachatryan, Vardan

    2015-04-22

    Our search is presented for physics beyond the standard model in final states with two opposite-sign same-flavor leptons, jets, and missing transverse momentum. The data sample corresponds to an integrated luminosity of 19.4 fb-1 of proton-proton collisions at √s = 8 TeV collected with the CMS detector at the CERN LHC in 2012. The analysis focuses on searches for a kinematic edge in the invariant mass distribution of the oppositesign same-flavor lepton pair and for final states with an on-shell Z boson. Furthermore, the observations are consistent with expectations from standard model processes and are interpreted in terms of upper limits on the production of supersymmetric particles.

  4. Search for Physics Beyond the Standard Model Using Multilepton Signatures in pp Collisions at sqrt(s)=7 TeV

    SciTech Connect

    Chatrchyan, Serguei; et al.

    2011-10-01

    A search for physics beyond the standard model in events with at least three leptons and any number of jets is presented. The data sample corresponds to 35 inverse picobarns of integrated luminosity in pp collisions at sqrt(s) = 7 TeV collected by the CMS experiment at the LHC. A number of exclusive multileptonic channels are investigated and standard model backgrounds are suppressed by requiring sufficient missing transverse energy, invariant mass inconsistent with that of the Z boson, or high jet activity. Control samples in data are used to ascertain the robustness of background evaluation techniques and to minimise the reliance on simulation. The observations are consistent with background expectations. These results constrain previously unexplored regions of supersymmetric parameter space.

  5. Search for physics beyond the standard model in events with two leptons, jets, and missing transverse momentum in pp collisions at √s = 8 TeV

    DOE PAGES

    Khachatryan, Vardan

    2015-04-22

    Our search is presented for physics beyond the standard model in final states with two opposite-sign same-flavor leptons, jets, and missing transverse momentum. The data sample corresponds to an integrated luminosity of 19.4 fb-1 of proton-proton collisions at √s = 8 TeV collected with the CMS detector at the CERN LHC in 2012. The analysis focuses on searches for a kinematic edge in the invariant mass distribution of the oppositesign same-flavor lepton pair and for final states with an on-shell Z boson. Furthermore, the observations are consistent with expectations from standard model processes and are interpreted in terms of uppermore » limits on the production of supersymmetric particles.« less

  6. Thor: Modeling of a Megabar Class Pulsed Power Accelerator

    NASA Astrophysics Data System (ADS)

    Haill, T. A.; Reisman, D. B.; Stoltzfus, B. S.; Austin, K. N.; Stygar, W. A.; Brown, J. L.; Davis, J.-P.; Waisman, E. M.

    2015-06-01

    Thor is a compact, economical machine to drive megabar-class shockless compression material physics experiments and multi-mega-ampere HEDP experiments for the physics community. It is capable of driving peak currents up to 7 MA with rise times of 200-500 ns, resulting in material pressures between 1 to 5 Mbar depending upon the load design, and incorporates a pulse tailoring capability required to maintain shockless loading of many materials. Thor is modular in nature with 200 capacitive bricks triggered in groups by independent, de-coupled switches. The current pulse at the load is a simple linear combination of the 200 time-shifted basis pulses. This enables a variety of experiments including shockless compression experiments using smooth ramped pulses, shock-ramp compression experiments using tailored pulses, and strength measurement experiments using flat top pulses. This paper overviews the Thor design and describes an equivalent circuit model of the machine that drives MHD simulations of the load region. 3D ALEGRA MHD simulations explore topics such as the uniformity of the magnetic field along the stripline load and the design modifications to improve uniformity. Optimized current drives and simulations of the aforementioned applications are also presented. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Company, for the U.S. DOE's NNSA under Contract DE-AC04-94AL85000.

  7. MODELING PHOTODISINTEGRATION-INDUCED TeV PHOTON EMISSION FROM LOW-LUMINOSITY GAMMA-RAY BURSTS

    SciTech Connect

    Liu Xuewen; Wu Xuefeng; Lu Tan E-mail: xfwu@pmo.ac.cn

    2012-05-15

    Ultra-high-energy cosmic-ray heavy nuclei have recently been considered as originating from nearby low-luminosity gamma-ray bursts that are associated with Type Ibc supernovae. Unlike the power-law decay in long duration gamma-ray bursts, the light curve of these bursts exhibits complex UV/optical behavior: shock breakout dominated thermal radiation peaks at about 1 day, and, after that, nearly constant emission sustained by radioactive materials for tens of days. We show that the highly boosted heavy nuclei at PeV energy interacting with the UV/optical photon field will produce considerable TeV photons via the photodisintegration/photo-de-excitation process. It was later predicted that a thermal-like {gamma}-ray spectrum peaks at about a few TeV, which may serve as evidence of nucleus acceleration. The future observations by the space telescope Fermi and by the ground atmospheric Cherenkov telescopes such as H.E.S.S., VERITAS, and MAGIC will shed light on this prediction.

  8. Emanations of dark matter: Muon anomalous magnetic moment, radiative neutrino mass, and novel leptogenesis at the TeV scale

    SciTech Connect

    Hambye, Thomas; Kannike, Kristjan; Raidal, Martti; Ma, Ernest

    2007-05-01

    The evidence for dark matter signals a new class of particles at the TeV scale, which may manifest themselves indirectly through loop effects. In a simple model we show that these loop effects may be responsible for the enhanced muon anomalous magnetic moment, for the neutrino mass, as well as for leptogenesis in a novel way. This scenario can be verified at LHC and/or ILC experiments.

  9. Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes

    PubMed Central

    Kim, Eun Sook; Wang, Yan

    2017-01-01

    Population heterogeneity in growth trajectories can be detected with growth mixture modeling (GMM). It is common that researchers compute composite scores of repeated measures and use them as multiple indicators of growth factors (baseline performance and growth) assuming measurement invariance between latent classes. Considering that the assumption of measurement invariance does not always hold, we investigate the impact of measurement noninvariance on class enumeration and parameter recovery in GMM through a Monte Carlo simulation study (Study 1). In Study 2, we examine the class enumeration and parameter recovery of the second-order growth mixture modeling (SOGMM) that incorporates measurement models at the first order level. Thus, SOGMM estimates growth trajectory parameters with reliable sources of variance, that is, common factor variance of repeated measures and allows heterogeneity in measurement parameters between latent classes. The class enumeration rates are examined with information criteria such as AIC, BIC, sample-size adjusted BIC, and hierarchical BIC under various simulation conditions. The results of Study 1 showed that the parameter estimates of baseline performance and growth factor means were biased to the degree of measurement noninvariance even when the correct number of latent classes was extracted. In Study 2, the class enumeration accuracy of SOGMM depended on information criteria, class separation, and sample size. The estimates of baseline performance and growth factor mean differences between classes were generally unbiased but the size of measurement noninvariance was underestimated. Overall, SOGMM is advantageous in that it yields unbiased estimates of growth trajectory parameters and more accurate class enumeration compared to GMM by incorporating measurement models. PMID:28928691

  10. Addressing the Problem of Switched Class Labels in Latent Variable Mixture Model Simulation Studies

    ERIC Educational Resources Information Center

    Tueller, Stephen J.; Drotar, Scott; Lubke, Gitta H.

    2011-01-01

    The discrimination between alternative models and the detection of latent classes in the context of latent variable mixture modeling depends on sample size, class separation, and other aspects that are related to power. Prior to a mixture analysis it is useful to investigate model performance in a simulation study that reflects the research…

  11. Addressing the Problem of Switched Class Labels in Latent Variable Mixture Model Simulation Studies

    ERIC Educational Resources Information Center

    Tueller, Stephen J.; Drotar, Scott; Lubke, Gitta H.

    2011-01-01

    The discrimination between alternative models and the detection of latent classes in the context of latent variable mixture modeling depends on sample size, class separation, and other aspects that are related to power. Prior to a mixture analysis it is useful to investigate model performance in a simulation study that reflects the research…

  12. Search for the Standard Model Higgs boson in the H to tau+ tau- decay mode in sqrt(s) = 7 TeV pp collisions with ATLAS

    SciTech Connect

    Aad, Georges

    2014-11-12

    A search for the neutral Higgs bosons predicted by the Minimal Supersymmetric Standard Model (MSSM) is reported. The analysis is performed on data from proton-proton collisions at a centre-of-mass energy of 8TeV collected with the ATLAS detector at the Large Hadron Collider. The samples used for this search were collected in 2012 and correspond to integrated luminosities in the range 19.5-20.3 fb-1. The MSSM Higgs bosons are searched for in the τ τ final state. No significant excess over the expected background is observed, and exclusion limits are derived for the production cross section times branching fraction of a scalar particle as a function of its mass. The results are also interpreted in the MSSM parameter space for various benchmark scenarios.

  13. Search for the Standard Model Higgs boson in the H to tau+ tau- decay mode in sqrt(s) = 7 TeV pp collisions with ATLAS

    DOE PAGES

    Aad, Georges

    2014-11-12

    A search for the neutral Higgs bosons predicted by the Minimal Supersymmetric Standard Model (MSSM) is reported. The analysis is performed on data from proton-proton collisions at a centre-of-mass energy of 8TeV collected with the ATLAS detector at the Large Hadron Collider. The samples used for this search were collected in 2012 and correspond to integrated luminosities in the range 19.5-20.3 fb-1. The MSSM Higgs bosons are searched for in the τ τ final state. No significant excess over the expected background is observed, and exclusion limits are derived for the production cross section times branching fraction of a scalarmore » particle as a function of its mass. The results are also interpreted in the MSSM parameter space for various benchmark scenarios.« less

  14. Search for neutral minimal supersymmetric standard model Higgs bosons decaying to tau pairs in pp collisions at √s=7 TeV.

    PubMed

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Giordano, D; Glege, F; Garrido, R Gomez-Reino; Gouzevitch, M; Govoni, P; Gowdy, S; Guiducci, L; Hansen, M; Hartl, C; Harvey, J; Hegeman, J; Hegner, B; Hoffmann, H F; Honma, A; Innocente, V; Janot, P; Kaadze, K; Karavakis, E; Lecoq, P; Lourenço, C; Mäki, T; Malberti, M; Malgeri, L; Mannelli, M; Masetti, L; Maurisset, A; Meijers, F; Mersi, S; Meschi, E; Moser, R; Mozer, M U; Mulders, M; Nesvold, E; Nguyen, M; Orimoto, T; Orsini, L; Perez, E; Petrilli, A; Pfeiffer, A; Pierini, M; Pimiä, M; Polese, G; Racz, A; Antunes, J Rodrigues; Rolandi, G; Rommerskirchen, T; Rovelli, C; Rovere, M; Sakulin, H; Schäfer, C; Schwick, C; Segoni, I; Sharma, A; Siegrist, P; Simon, M; Sphicas, P; Spiropulu, M; Stoye, M; Tropea, P; Tsirou, A; Vichoudis, P; Voutilainen, M; Zeuner, W D; Bertl, W; Deiters, K; Erdmann, W; Gabathuler, K; Horisberger, R; Ingram, Q; Kaestli, H C; König, S; Kotlinski, D; Langenegger, U; Meier, F; Renker, D; Rohe, T; Sibille, J; Starodumov, A; Bortignon, P; Caminada, L; Chanon, N; Chen, Z; 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Ceballos, G Gomez; Goncharov, M; Hahn, K A; Harris, P; Kim, Y; Klute, M; Lee, Y-J; Li, W; Loizides, C; Luckey, P D; Ma, T; Nahn, S; Paus, C; Ralph, D; Roland, C; Roland, G; Rudolph, M; Stephans, G S F; Stöckli, F; Sumorok, K; Sung, K; Wenger, E A; Xie, S; Yang, M; Yilmaz, Y; Yoon, A S; Zanetti, M; Cooper, S I; Cushman, P; Dahmes, B; De Benedetti, A; Dudero, P R; Franzoni, G; Haupt, J; Klapoetke, K; Kubota, Y; Mans, J; Rekovic, V; Rusack, R; Sasseville, M; Singovsky, A; Cremaldi, L M; Godang, R; Kroeger, R; Perera, L; Rahmat, R; Sanders, D A; Summers, D; Bloom, K; Bose, S; Butt, J; Claes, D R; Dominguez, A; Eads, M; Keller, J; Kelly, T; Kravchenko, I; Lazo-Flores, J; Malbouisson, H; Malik, S; Snow, G R; Baur, U; Godshalk, A; Iashvili, I; Jain, S; Kharchilava, A; Kumar, A; Shipkowski, S P; Smith, K; Alverson, G; Barberis, E; Baumgartel, D; Boeriu, O; Chasco, M; Reucroft, S; Swain, J; Trocino, D; Wood, D; Zhang, J; Anastassov, A; Kubik, A; Odell, N; Ofierzynski, R A; Pollack, B; Pozdnyakov, A; 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Boulahouache, C; Cuplov, V; Ecklund, K M; Geurts, F J M; Padley, B P; Redjimi, R; Roberts, J; Zabel, J; Betchart, B; Bodek, A; Chung, Y S; Covarelli, R; de Barbaro, P; Demina, R; Eshaq, Y; Flacher, H; Garcia-Bellido, A; Goldenzweig, P; Gotra, Y; Han, J; Harel, A; Miner, D C; Orbaker, D; Petrillo, G; Vishnevskiy, D; Zielinski, M; Bhatti, A; Ciesielski, R; Demortier, L; Goulianos, K; Lungu, G; Malik, S; Mesropian, C; Yan, M; Atramentov, O; Barker, A; Duggan, D; Gershtein, Y; Gray, R; Halkiadakis, E; Hidas, D; Hits, D; Lath, A; Panwalkar, S; Patel, R; Richards, A; Rose, K; Schnetzer, S; Somalwar, S; Stone, R; Thomas, S; Cerizza, G; Hollingsworth, M; Spanier, S; Yang, Z C; York, A; Asaadi, J; Eusebi, R; Gilmore, J; Gurrola, A; Kamon, T; Khotilovich, V; Montalvo, R; Nguyen, C N; Osipenkov, I; Pakhotin, Y; Pivarski, J; Safonov, A; Sengupta, S; Tatarinov, A; Toback, D; Weinberger, M; Akchurin, N; Bardak, C; Damgov, J; Jeong, C; Kovitanggoon, K; Lee, S W; Roh, Y; Sill, A; Volobouev, I; Wigmans, R; Yazgan, E; Appelt, E; Brownson, E; Engh, D; Florez, C; Gabella, W; Issah, M; Johns, W; Kurt, P; Maguire, C; Melo, A; Sheldon, P; Snook, B; Tuo, S; Velkovska, J; Arenton, M W; Balazs, M; Boutle, S; Cox, B; Francis, B; Hirosky, R; Ledovskoy, A; Lin, C; Neu, C; Yohay, R; Gollapinni, S; Harr, R; Karchin, P E; Lamichhane, P; Mattson, M; Milstène, C; Sakharov, A; Anderson, M; Bachtis, M; Bellinger, J N; Carlsmith, D; Dasu, S; Efron, J; Flood, K; Gray, L; Grogg, K S; Grothe, M; Hall-Wilton, R; Herndon, M; Klabbers, P; Klukas, J; Lanaro, A; Lazaridis, C; Leonard, J; Loveless, R; Mohapatra, A; Palmonari, F; Reeder, D; Ross, I; Savin, A; Smith, W H; Swanson, J; Weinberg, M

    2011-06-10

    A search for neutral minimal supersymmetric standard model (MSSM) Higgs bosons in pp collisions at the LHC at a center-of-mass energy of 7 TeV is presented. The results are based on a data sample corresponding to an integrated luminosity of 36  pb(-1) recorded by the CMS experiment. The search uses decays of the Higgs bosons to tau pairs. No excess is observed in the tau-pair invariant-mass spectrum. The resulting upper limits on the Higgs boson production cross section times branching fraction to tau pairs, as a function of the pseudoscalar Higgs boson mass, yield stringent new bounds in the MSSM parameter space.

  15. Search for Standard Model Production of Four Top Quarks in the Lepton + Jets Channel in pp Collisions at $$\\sqrt{s}$$ = 8 TeV

    DOE PAGES

    Khachatryan, Vardan

    2014-11-27

    Our search is presented for standard model (SM) production of four top quarks (t¯tt¯t) in pp collisions in the lepton + jets channel. The data correspond to an integrated luminosity of 19.6 fb-1 recorded at a centre-of-mass energy of 8 TeV with the CMS detector at the CERN LHC. The expected cross section for SM t¯tt¯t production is σSMt¯tt¯t≈1fb. A combination of kinematic reconstruction and multivariate techniques is used to distinguish between the small signal and large background. We determined that the data are consistent with expectations of the SM, and an upper limit of 32 fb is set atmore » a 95% confidence level on the cross section for producing four top quarks in the SM, where a limit of 32 ± 17 fb is expected.« less

  16. Search for Standard Model Production of Four Top Quarks in the Lepton + Jets Channel in pp Collisions at $\\sqrt{s}$ = 8 TeV

    SciTech Connect

    Khachatryan, Vardan

    2014-11-27

    Our search is presented for standard model (SM) production of four top quarks (t¯tt¯t) in pp collisions in the lepton + jets channel. The data correspond to an integrated luminosity of 19.6 fb-1 recorded at a centre-of-mass energy of 8 TeV with the CMS detector at the CERN LHC. The expected cross section for SM t¯tt¯t production is σSMt¯tt¯t≈1fb. A combination of kinematic reconstruction and multivariate techniques is used to distinguish between the small signal and large background. We determined that the data are consistent with expectations of the SM, and an upper limit of 32 fb is set at a 95% confidence level on the cross section for producing four top quarks in the SM, where a limit of 32 ± 17 fb is expected.

  17. A latent class approach to treatment readiness corresponds to a transtheoretical ("Stages of Change") model.

    PubMed

    Harrell, Paul Truman; Trenz, R C; Scherer, M; Martins, S S; Latimer, W W

    2013-09-01

    Motivation for treatment among people with substance use problems is an important aspect of treatment success. Models for treatment motivation are widely debated. Latent Class Analysis can help to demonstrate the appropriateness of available models. The current study utilizes Latent Class Analysis to analyze treatment readiness statements as they relate to the reduction or cessation of marijuana, cocaine, and opioid use among 539 cocaine and opioid users recruited from the community of Baltimore, MD, USA. Participants completed an in-person structured interview including demographic questions, a treatment readiness questionnaire with items on Intention to Stop Use (ISU) and Problem Recognition (PR), current substance abuse treatment utilization, and urinalysis testing for marijuana, cocaine, and heroin. Latent class models were fit to the treatment readiness questionnaire. A four-class model provided the best fit with one class low on both ISU and PR ("Pre-contemplative"), a second class low on ISU but high on PR ("Contemplative"), a third class high on both ("Preparation/Action"), and a final class high on ISU but low on PR ("Post-Action"). Compared to the "Contemplative" class, the "Pre-contemplative" class was significantly more likely to be positive for marijuana, and the "Post-Action" class was significantly less likely to be positive for opioids. The "Preparation/Action" class was significantly more likely to be in treatment. With the exception of the "Post-Action" class, the analysis appears similar to the "Stages of Change" model and suggests that problem recognition and intention to stop use are important domains in the model. However, further longitudinal research is needed to assess predictive validity of model.

  18. On Inclusion of Covariates for Class Enumeration of Growth Mixture Models.

    PubMed

    Li, Libo; Hser, Yih-Ing

    2011-01-01

    In this article, we directly questioned the common practice in growth mixture model (GMM) applications that exclusively rely on the fitting model without covariates for GMM class enumeration. We provided theoretical and simulation evidence to demonstrate that exclusion of covariates from GMM class enumeration could be problematic in many cases. Based on our findings, we provided recommendations for examining the class enumeration by the fitting model without covariates and discussed the potential of covariate inclusion as a remedy for the weakness of GMM class enumeration without including covariates. A real example on the development of children's cumulative exposure to risk factors for adolescent substance use was provided to illustrate our methodological developments.

  19. Modeling Subsurface Reactive Flows Using Leadership-Class Computing

    SciTech Connect

    Mills, Richard T; Hammond, Glenn; Lichtner, Peter; Sripathi, Vamsi K; Mahinthakumar, Gnanamanika; Smith, Barry F

    2009-01-01

    We describe our experiences running PFLOTRAN - a code for simulation of coupled hydro-thermal-chemical processes in variably saturated, non-isothermal, porous media - on leadership-class supercomputers, including initial experiences running on the petaflop incarnation of Jaguar, the Cray XT5 at the National Center for Computational Sciences at Oak Ridge National Laboratory. PFLOTRAN utilizes fully implicit time-stepping and is built on top of the Portable, Extensible Toolkit for Scientific Computation (PETSc). We discuss some of the hurdles to 'at scale' performance with PFLOTRAN and the progress we have made in overcoming them on leadership-class computer architectures.

  20. Modeling subsurface reactive flows using leadership-class computing

    NASA Astrophysics Data System (ADS)

    Tran Mills, Richard; Hammond, Glenn E.; Lichtner, Peter C.; Sripathi, Vamsi; Kumar Mahinthakumar, G.; Smith, Barry F.

    2009-07-01

    We describe our experiences running PFLOTRAN-a code for simulation of coupled hydro-thermal-chemical processes in variably saturated, non-isothermal, porous media- on leadership-class supercomputers, including initial experiences running on the petaflop incarnation of Jaguar, the Cray XT5 at the National Center for Computational Sciences at Oak Ridge National Laboratory. PFLOTRAN utilizes fully implicit time-stepping and is built on top of the Portable, Extensible Toolkit for Scientific Computation (PETSc). We discuss some of the hurdles to "at scale" performance with PFLOTRAN and the progress we have made in overcoming them on leadership-class computer architectures.

  1. Extending Positive CLASS Results across Multiple Instructors and Multiple Classes of Modeling Instruction

    ERIC Educational Resources Information Center

    Brewe, Eric; Traxler, Adrienne; de la Garza, Jorge; Kramer, Laird H.

    2013-01-01

    We report on a multiyear study of student attitudes measured with the Colorado Learning Attitudes about Science Survey in calculus-based introductory physics taught with the Modeling Instruction curriculum. We find that five of six instructors and eight of nine sections using Modeling Instruction showed significantly improved attitudes from pre-…

  2. Extending Positive CLASS Results across Multiple Instructors and Multiple Classes of Modeling Instruction

    ERIC Educational Resources Information Center

    Brewe, Eric; Traxler, Adrienne; de la Garza, Jorge; Kramer, Laird H.

    2013-01-01

    We report on a multiyear study of student attitudes measured with the Colorado Learning Attitudes about Science Survey in calculus-based introductory physics taught with the Modeling Instruction curriculum. We find that five of six instructors and eight of nine sections using Modeling Instruction showed significantly improved attitudes from pre-…

  3. Using multi-class queuing network to solve performance models of e-business sites.

    PubMed

    Zheng, Xiao-ying; Chen, De-ren

    2004-01-01

    Due to e-business's variety of customers with different navigational patterns and demands, multi-class queuing network is a natural performance model for it. The open multi-class queuing network(QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

  4. Measurement and Structural Model Class Separation in Mixture CFA: ML/EM versus MCMC

    ERIC Educational Resources Information Center

    Depaoli, Sarah

    2012-01-01

    Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the…

  5. The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models

    ERIC Educational Resources Information Center

    Park, Jungkyu; Yu, Hsiu-Ting

    2016-01-01

    The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…

  6. The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models

    ERIC Educational Resources Information Center

    Park, Jungkyu; Yu, Hsiu-Ting

    2016-01-01

    The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…

  7. Modeling and Verbalizations of Lower-Class, Black, Preschool Children: Educational Implications.

    ERIC Educational Resources Information Center

    Gottfried, Adele E.

    Two purposes guided this study: (1) to investigate the effects of modeling on the verbalizations of lower-class, black, preschool children; and (2) to investigate the relationships between the dialect employed by the model and children's language production. As subjects, 72 black, preschool children in lower-class neighborhood day care centers of…

  8. On Inclusion of Covariates for Class Enumeration of Growth Mixture Models

    ERIC Educational Resources Information Center

    Li, Libo; Hser, Yih-Ing

    2011-01-01

    In this article, we directly question the common practice in growth mixture model (GMM) applications that exclusively rely on the fitting model without covariates for GMM class enumeration. We provide theoretical and simulation evidence to demonstrate that exclusion of covariates from GMM class enumeration could be problematic in many cases. Based…

  9. Measurement and Structural Model Class Separation in Mixture CFA: ML/EM versus MCMC

    ERIC Educational Resources Information Center

    Depaoli, Sarah

    2012-01-01

    Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the…

  10. Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

    ERIC Educational Resources Information Center

    Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu

    2011-01-01

    Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…

  11. Distance Learning Class Model for Teaching a Foreign Language in University-Level Education Context

    ERIC Educational Resources Information Center

    Lee, Sun-Min

    2012-01-01

    This study aims to introduce the distance learning class model for a foreign language in university-level education context, and to prove that this class model is effective in cultivating the motivation and interest of university students for learning a foreign language. This distance learning lesson consists of two parts: Online chatting session,…

  12. Structural Validity of CLASS K-3 in Primary Grades: Testing Alternative Models

    ERIC Educational Resources Information Center

    Sandilos, Lia E.; Shervey, Sarah Wollersheim; DiPerna, James C.; Lei, Puiwa; Cheng, Weiyi

    2017-01-01

    This study examined the internal structure of the Classroom Assessment Scoring System (CLASS; K-3 version). The original CLASS K-3 model (Pianta, La Paro, & Hamre, 2008) and 5 alternative models were tested using confirmatory factor analysis with a sample of first- and second-grade classrooms (N = 141). Findings indicated that a slightly…

  13. Distance Learning Class Model for Teaching a Foreign Language in University-Level Education Context

    ERIC Educational Resources Information Center

    Lee, Sun-Min

    2012-01-01

    This study aims to introduce the distance learning class model for a foreign language in university-level education context, and to prove that this class model is effective in cultivating the motivation and interest of university students for learning a foreign language. This distance learning lesson consists of two parts: Online chatting session,…

  14. Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

    ERIC Educational Resources Information Center

    Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu

    2011-01-01

    Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…

  15. Science/Technology/Society: Model Lessons for Secondary Science Classes.

    ERIC Educational Resources Information Center

    Pearson, Janice V., Ed.

    This volume contains 35 lessons designed to be used in secondary science classes to introduce the science/technology/society (STS) themes and issues. While the first 11 lessons focus on general STS themes, the other 24 lessons cover specific STS issues that fall under such categories as population growth, water resources, world hunger, food…

  16. Multilevel Latent Class Analysis: Parametric and Nonparametric Models

    ERIC Educational Resources Information Center

    Finch, W. Holmes; French, Brian F.

    2014-01-01

    Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture…

  17. Multilevel Latent Class Analysis: Parametric and Nonparametric Models

    ERIC Educational Resources Information Center

    Finch, W. Holmes; French, Brian F.

    2014-01-01

    Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture…

  18. Time-dependent modelling of the Markarian 501 X-ray and TeV gamma-ray data taken during 1997 March and April

    NASA Astrophysics Data System (ADS)

    Krawczynski, H.; Coppi, P. S.; Aharonian, F.

    2002-11-01

    If the high-energy emission from TeV blazars is produced by the Synchrotron Self-Compton (SSC) mechanism, then simultaneous X-ray and gamma-ray observations of these objects are a powerful probe of the electron (and positron) populations responsible for this emission. Understanding the emitting particle distributions and their temporal evolution in turn allows us to probe physical conditions in the inner blazar jet and test, for example, various acceleration scenarios. Furthermore, by constraining the SSC model parameters, such observations enable us to predict the intrinsic (unabsorbed) gamma-ray energy spectra of these sources, a major uncertainty in current attempts to use gamma-ray observations to constrain the intensity of the Diffuse Extragalactic Background Radiation (DEBRA) at optical/infrared wavelengths. As a next step in testing the SSC model and as a demonstration of the potential power of coordinated X-ray and gamma-ray observations, we model in detail the X-ray and gamma-ray light curves of the TeV blazar Mrk 501 during its 1997 April-May outburst with a time-dependent SSC model. Extensive, quasi-simultaneous X-ray and gamma-ray coverage exists for this period. We discuss and explore quantitatively several of the flare scenarios presented in the literature. We show that simple two-component models (with a soft, steady X-ray component plus a variable SSC component) involving substantial pre-acceleration of electrons to Lorentz factors on the order of γmin= 105 describe the data train surprisingly well. All considered models imply an emission region that is strongly out of equipartition and low radiative efficiencies (ratio between kinetic jet luminosity and comoving radiative luminosity) of 1 per-mill and less. Degeneracy in both, model variant and jet parameters, prevents us from using the time-resolved SSC calculations to tighten substantially the constraints on the amount of extragalactic gamma-ray extinction by the DEBRA in the relevant 0.5-50

  19. Analyzing Homogeneity and Heterogeneity of Change Using Rasch and Latent Class Models: A Comparative and Integrative Approach.

    ERIC Educational Resources Information Center

    Meiser, Thorsten; And Others

    1995-01-01

    The mixed Rasch model integrates Rasch and latent class approaches by dividing the population into classes that conform to Rasch models with class-specific parameters, enabling the modeling of qualitatively different patterns of change with the homogeneity assumption retained within, but not between, classes. An empirical example is given. (SLD)

  20. Bottomonium suppression at sNN=2.76 TeV using a model based on color screening and gluonic dissociation with collisional damping

    NASA Astrophysics Data System (ADS)

    Ganesh, S.; Mishra, M.

    2013-10-01

    We present a model to explain the bottomonium suppression in Pb+Pb collisions at midrapidity obtained from Large Hadron Collider (LHC) energy, sNN=2.76 TeV. The model consists of two decoupled mechanisms, namely, color screening during bottomonium production followed by gluon induced dissociation along with collisional damping. The quasiparticle model (QPM) is used as equation of state (EOS) for the quark-gluon plasma (QGP) medium. The feed-down from higher Υ states, such as Υ(1P), Υ(2S), and Υ(2P), dilated formation times for bottomonium states, and viscous effect of the QGP medium are other ingredients included in the current formulation. We further assume that the QGP is expanding according to (1+1)-dimensional Bjorken's boost invariant scaling law. The net suppression (in terms of pT integrated survival probability) for bottomonium states at midrapidity is obtained as a function of centrality, and the result is then compared both quantitatively and qualitatively with the recent LHC experimental data in the midrapidity region recently published by the CMS Collaboration. We find that the current model, based on Debye color screening plus gluonic dissociation along with collisional damping, better describes the centrality dependence of bottomonium suppression at LHC energy as compared to the color screening model alone.

  1. A class of cellular automata modeling winnerless competition

    NASA Astrophysics Data System (ADS)

    Afraimovich, V.; Ordaz, F. C.; Urías, J.

    2002-06-01

    Neural units introduced by Rabinovich et al. ("Sensory coding with dynamically competitive networks," UCSD and CIT, February 1999) motivate a class of cellular automata (CA) where spatio-temporal encoding is feasible. The spatio-temporal information capacity of a CA is estimated by the information capacity of the attractor set, which happens to be finitely specified. Two-dimensional CA are studied in detail. An example is given for which the attractor is not a subshift.

  2. Combined results of searches for the standard model Higgs boson in pp collisions at sqrt(s) = 7 TeV

    SciTech Connect

    Chatrchyan, Serguei; et al.

    2012-03-01

    Combined results are reported from searches for the standard model Higgs boson in proton-proton collisions at sqrt(s)=7 TeV in five Higgs boson decay modes: gamma pair, b-quark pair, tau lepton pair, W pair, and Z pair. The explored Higgs boson mass range is 110-600 GeV. The analysed data correspond to an integrated luminosity of 4.6-4.8 inverse femtobarns. The expected excluded mass range in the absence of the standard model Higgs boson is 118-543 GeV at 95% CL. The observed results exclude the standard model Higgs boson in the mass range 127-600 GeV at 95% CL, and in the mass range 129-525 GeV at 99% CL. An excess of events above the expected standard model background is observed at the low end of the explored mass range making the observed limits weaker than expected in the absence of a signal. The largest excess, with a local significance of 3.1 sigma, is observed for a Higgs boson mass hypothesis of 124 GeV. The global significance of observing an excess with a local significance greater than 3.1 sigma anywhere in the search range 110-600 (110-145) GeV is estimated to be 1.5 sigma (2.1 sigma). More data are required to ascertain the origin of this excess.

  3. Search for the standard model Higgs boson decaying into two photons in pp collisions at sqrt(s)=7 TeV

    SciTech Connect

    Chatrchyan, S.; et al.,

    2012-04-01

    A search for a Higgs boson decaying into two photons is described. The analysis is performed using a dataset recorded by the CMS experiment at the LHC from pp collisions at a centre-of-mass energy of 7 TeV, which corresponds to an integrated luminosity of 4.8 inverse femtobarns. Limits are set on the cross section of the standard model Higgs boson decaying to two photons. The expected exclusion limit at 95% confidence level is between 1.4 and 2.4 times the standard model cross section in the mass range between 110 and 150 GeV. The analysis of the data excludes, at 95% confidence level, the standard model Higgs boson decaying into two photons in the mass range 128 to 132 GeV. The largest excess of events above the expected standard model background is observed for a Higgs boson mass hypothesis of 124 GeV with a local significance of 3.1 sigma. The global significance of observing an excess with a local significance greater than 3.1 sigma anywhere in the search range 110-150 GeV is estimated to be 1.8 sigma. More data are required to ascertain the origin of this excess.

  4. ELASTIC SCATTERING OF PROTONS FROM $\\sqrt{s} = 23.5~{\\rm GeV}$ to 7 TeV FROM A GENERALIZED BIALAS-BZDAK MODEL

    NASA Astrophysics Data System (ADS)

    Csörgő, T.; Nemes, F.

    2014-01-01

    The Bialas-Bzdak model of elastic proton-proton scattering is generalized to the case when the real part of the parton-parton level forward scattering amplitude is nonvanishing. Such a generalization enables the model to describe well the dip region of the differential cross-section of elastic scattering at the intersecting storage rings (ISR) energies, and improves significantly the ability of the model to describe also the recent TOTEM data at √ {s} = 7 TeV LHC energy. Within this framework, both the increase of the total cross-section, as well as the decrease of the location of the dip with increasing colliding energies, is related to the increase of the quark-diquark distance and to the increase of the "fragility" of the protons with increasing energies. In addition, we present and test the validity of two new phenomenological relations: one of them relates the total p+p cross-section to an effective, model-independent proton radius, while the other relates the position of the dip in the differential elastic cross-section to the measured value of the total cross-section.

  5. Comparison of Criteria for Choosing the Number of Classes in Bayesian Finite Mixture Models

    PubMed Central

    Nasserinejad, Kazem; van Rosmalen, Joost; de Kort, Wim; Lesaffre, Emmanuel

    2017-01-01

    Identifying the number of classes in Bayesian finite mixture models is a challenging problem. Several criteria have been proposed, such as adaptations of the deviance information criterion, marginal likelihoods, Bayes factors, and reversible jump MCMC techniques. It was recently shown that in overfitted mixture models, the overfitted latent classes will asymptotically become empty under specific conditions for the prior of the class proportions. This result may be used to construct a criterion for finding the true number of latent classes, based on the removal of latent classes that have negligible proportions. Unlike some alternative criteria, this criterion can easily be implemented in complex statistical models such as latent class mixed-effects models and multivariate mixture models using standard Bayesian software. We performed an extensive simulation study to develop practical guidelines to determine the appropriate number of latent classes based on the posterior distribution of the class proportions, and to compare this criterion with alternative criteria. The performance of the proposed criterion is illustrated using a data set of repeatedly measured hemoglobin values of blood donors. PMID:28081166

  6. Comparison of Criteria for Choosing the Number of Classes in Bayesian Finite Mixture Models.

    PubMed

    Nasserinejad, Kazem; van Rosmalen, Joost; de Kort, Wim; Lesaffre, Emmanuel

    2017-01-01

    Identifying the number of classes in Bayesian finite mixture models is a challenging problem. Several criteria have been proposed, such as adaptations of the deviance information criterion, marginal likelihoods, Bayes factors, and reversible jump MCMC techniques. It was recently shown that in overfitted mixture models, the overfitted latent classes will asymptotically become empty under specific conditions for the prior of the class proportions. This result may be used to construct a criterion for finding the true number of latent classes, based on the removal of latent classes that have negligible proportions. Unlike some alternative criteria, this criterion can easily be implemented in complex statistical models such as latent class mixed-effects models and multivariate mixture models using standard Bayesian software. We performed an extensive simulation study to develop practical guidelines to determine the appropriate number of latent classes based on the posterior distribution of the class proportions, and to compare this criterion with alternative criteria. The performance of the proposed criterion is illustrated using a data set of repeatedly measured hemoglobin values of blood donors.

  7. Stochastic models of cover class dynamics. [remote sensing of vegetation

    NASA Technical Reports Server (NTRS)

    Barringer, T. H.; Robinson, V. B.

    1981-01-01

    Investigations related to satellite remote sensing of vegetation have been concerned with questions of signature identification and extension, cover inventory accuracy, and change detection and monitoring. Attention is given to models of ecological succession, present directions in successional modeling and analysis, nondynamic spatial models, issues in the analysis of spatial data, and aspects of spatial modeling. Issues in time-series analysis are considered along with dynamic spatial models, and problems of model specification and identification.

  8. Search for standard model Higgs bosons decaying to w-boson pairs in proton-anti-proton collisions at √s= 1.96 TeV

    SciTech Connect

    Hidas, Dean Andrew

    2008-01-01

    This thesis describes a search for standard model Higgs bosons decaying to W boson pairs in proton-anti-proton collisions at a center of mass energy of 1.96 TeV using the CDF II detector. The decay to W bosons is dominant for Higgs masses greater than about 135 GeV. The final state examined consists of two leptons and missing transverse energy from the leptonic decay of one or more W bosons. The signal production mechanisms included are gluon fusion, associated production with a W or Z boson, and vector boson fusion. Matrix element calculations and artificial neural networks are used to discriminate signal from background for Higgs masses in the range 110 ≤MH ≤ 200 GeV. No significant excess of events is observed at any of the Higgs masses investigated. Upper limits on the standard model Higgs cross section are set at 95% confidence for each Higgs mass investigated, the most stringent limit being 1.63 times the predicted standard model cross section for a Higgs mass of 160 GeV.

  9. Search for a two-Higgs-boson doublet using a simplified model in pp collisions at sqrt[s] = 1.96 TeV.

    PubMed

    Aaltonen, T; Adelman, J; Álvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Appel, J A; Arisawa, T; Artikov, A; Asaadi, J; Ashmanskas, W; Auerbach, B; Aurisano, A; Azfar, F; Badgett, W; Bae, T; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Barria, P; Bartos, P; Bauce, M; Bedeschi, F; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Bhatti, A; Bisello, D; Bizjak, I; Bland, K R; Blumenfeld, B; Bocci, A; Bodek, A; Bortoletto, D; Boudreau, J; Boveia, A; Brigliadori, L; Bromberg, C; Brucken, E; Budagov, J; Budd, H S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Calamba, A; Calancha, C; Camarda, S; Campanelli, M; Campbell, M; Canelli, F; Carls, B; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chung, W H; Chung, Y S; Ciocci, M A; Clark, A; Clarke, C; Compostella, G; Convery, M E; Conway, J; Corbo, M; Cordelli, M; Cox, C A; Cox, D J; Crescioli, F; Cuevas, J; Culbertson, R; Dagenhart, D; d'Ascenzo, N; Datta, M; de Barbaro, P; Dell'Orso, M; Demortier, L; Deninno, M; Devoto, F; d'Errico, M; Di Canto, A; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Dorigo, M; Dorigo, T; Ebina, K; Elagin, A; Eppig, A; Erbacher, R; Errede, S; Ershaidat, N; Eusebi, R; Farrington, S; Feindt, M; Fernandez, J P; Field, R; Flanagan, G; Forrest, R; Frank, M J; Franklin, M; Freeman, J C; Funakoshi, Y; Furic, I; Gallinaro, M; Garcia, J E; Garfinkel, A F; Garosi, P; Gerberich, H; Gerchtein, E; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Ginsburg, C M; Giokaris, N; Giromini, P; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldin, D; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Grinstein, S; Grosso-Pilcher, C; Group, R C; Guimaraes da Costa, J; Hahn, S R; Halkiadakis, E; Hamaguchi, A; Han, J Y; Happacher, F; Hara, K; Hare, D; Hare, M; Harr, R F; Hatakeyama, K; Hays, C; Heck, M; Heinrich, J; Herndon, M; Hewamanage, S; Hocker, A; Hopkins, W; Horn, D; Hou, S; Hughes, R E; Hurwitz, M; Husemann, U; Hussain, N; Hussein, M; Huston, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jang, D; Jayatilaka, B; Jeon, E J; Jindariani, S; Johnstone, A; Jones, M; Joo, K K; Jun, S Y; Junk, T R; Kamon, T; Karchin, P E; Kasmi, A; Kato, Y; Ketchum, W; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kim, Y J; Kimura, N; Kirby, M; Klimenko, S; Knoepfel, K; Kondo, K; Kong, D J; Konigsberg, J; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Kruse, M; Krutelyov, V; Kuhr, T; Kurata, M; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; LeCompte, T; Lee, E; Lee, H S; Lee, J S; Lee, S W; Leo, S; Leone, S; Lewis, J D; Limosani, A; Lin, C-J; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, C; Liu, H; Liu, Q; Liu, T; Lockwitz, S; Loginov, A; Lucchesi, D; Lueck, J; Lujan, P; Lukens, P; Lungu, G; Lys, J; Lysak, R; Madrak, R; Maeshima, K; Maestro, P; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Martínez, M; Mastrandrea, P; Matera, K; Mattson, M E; Mazzacane, A; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Mesropian, C; Miao, T; Mietlicki, D; Mitra, A; Miyake, H; Moed, S; Moggi, N; Mondragon, M N; Moon, C S; Moore, R; Morello, M J; Morlock, J; Movilla Fernandez, P; Mukherjee, A; Muller, Th; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Naganoma, J; Nakano, I; Napier, A; Nett, J; Neu, C; Neubauer, M S; Nielsen, J; Nodulman, L; Noh, S Y; Norniella, O; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Ortolan, L; Pagan Griso, S; Pagliarone, C; Palencia, E; Papadimitriou, V; Paramonov, A A; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pilot, J; Pitts, K; Plager, C; Pondrom, L; Poprocki, S; Potamianos, K; Prokoshin, F; Pranko, A; Ptohos, F; Punzi, G; Rahaman, A; Ramakrishnan, V; Ranjan, N; Rao, K; Redondo, I; Renton, P; Rescigno, M; Riddick, T; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Ruffini, F; Ruiz, A; Russ, J; Rusu, V; Safonov, A; Sakumoto, W K; Sakurai, Y; Santi, L; Sato, K; Saveliev, V; Savoy-Navarro, A; Schlabach, P; Schmidt, A; Schmidt, E E; Schwarz, T; Scodellaro, L; Scribano, A; Scuri, F; Seidel, S; Seiya, Y; Semenov, A; Sforza, F; Shalhout, S Z; Shears, T; Shepard, P F; Shimojima, M; Shochet, M; Shreyber-Tecker, I; Simonenko, A; Sinervo, P; Sliwa, K; Smith, J R; Snider, F D; Soha, A; Sorin, V; Song, H; Squillacioti, P; Stancari, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Strycker, G L; Sudo, Y; Sukhanov, A; Suslov, I; Takemasa, K; Takeuchi, Y; Tang, J; Tecchio, M; Teng, P K; Thom, J; Thome, J; Thompson, G A; Thomson, E; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Trovato, M; Truong, A; Ukegawa, F; Uozumi, S; Varganov, A; Vázquez, F; Velev, G; Vellidis, C; Vidal, M; Vila, I; Vilar, R; Vizán, J; Vogel, M; Volpi, G; Wagner, P; Wagner, R L; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Wester, W C; Whiteson, D; Wicklund, A B; Wicklund, E; Wilbur, S; Wick, F; Williams, H H; Wilson, J S; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, H; Wright, T; Wu, X; Wu, Z; Yamamoto, K; Yamato, D; Yang, T; Yang, U K; Yang, Y C; Yao, W-M; Yeh, G P; Yi, K; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanetti, A; Zeng, Y; Zhou, C; Zucchelli, S

    2013-03-22

    We present a search for new particles in an extension to the standard model that includes a heavy Higgs boson (H(0)), a lighter charged Higgs boson (H(±)), and an even lighter Higgs boson h(0), with decays leading to a W-boson pair and a bottom-antibottom quark pair in the final state. We use events with exactly one lepton, missing transverse momentum, and at least four jets in data corresponding to an integrated luminosity of 8.7 fb(-1) collected by the CDF II detector in proton-antiproton collisions at sqrt[s]= 1.96 TeV. We find the data to be consistent with standard model predictions and report the results in terms of a simplified Higgs-cascade-decay model, setting 95% confidence level upper limits on the product of cross section and branching fraction from 1.3 pb to 15 fb as a function of H(0) and H(±) masses for m(h)(0) = 126 GeV/c(2).

  10. Search for the Standard Model Higgs Boson Decaying to Bottom Quarks in Proton-Proton Collisions at 8 TeV

    NASA Astrophysics Data System (ADS)

    Silkworth, Inga

    A search for the standard model Higgs boson (H) decaying to bottom quarks and produced in association with a Z boson is presented. The search uses 8 TeV center-of-mass energy proton-proton collision data recorded by the Compact Muon Solenoid experiment at the Large Hadron Collider corresponding to integrated luminosity of 19.0 inverse femtobarns. The Z boson is reconstructed using two oppositely charged leptons -- either electrons or muons. Two techniques for reconstructing the Higgs candidate are discussed: the standard method using two jets reconstructed with the anti-kt algorithm and a second technique using jet substructure that was developed for highly boosted massive particles. Upper limits, at the 95% confidence level, on the production cross section times the branching ratio, with respect to the standard model expectations, are derived for a Higgs boson in a mass range 110-135 GeV. The results from the ZH channel are combined with five other channels, and an excess of events is observed consistent with the standard model Higgs boson with a local significance of 2.1 standard deviations at 125 GeV.

  11. Search for a Two-Higgs-Boson Doublet Using a Simplified Model in pp̄ Collisions at √s=1.96 TeV

    SciTech Connect

    Aaltonen, T.; Adelman, J.; Álvarez González, B.; Amerio, S.; Amidei, D.; Anastassov, A.; Annovi, A.; Antos, J.; Apollinari, G.; Appel, J. A.; Arisawa, T.; Artikov, A.; Asaadi, J.; Ashmanskas, W.; Auerbach, B.; Aurisano, A.; Azfar, F.; Badgett, W.; Bae, T.; Barbaro-Galtieri, A.; Barnes, V. E.; Barnett, B. A.; Barria, P.; Bartos, P.; Bauce, M.; Bedeschi, F.; Behari, S.; Bellettini, G.; Bellinger, J.; Benjamin, D.; Beretvas, A.; Bhatti, A.; Bisello, D.; Bizjak, I.; Bland, K. R.; Blumenfeld, B.; Bocci, A.; Bodek, A.; Bortoletto, D.; Boudreau, J.; Boveia, A.; Brigliadori, L.; Bromberg, C.; Brucken, E.; Budagov, J.; Budd, H. S.; Burkett, K.; Busetto, G.; Bussey, P.; Buzatu, A.; Calamba, A.; Calancha, C.; Camarda, S.; Campanelli, M.; Campbell, M.; Canelli, F.; Carls, B.; Carlsmith, D.; Carosi, R.; Carrillo, S.; Carron, S.; Casal, B.; Casarsa, M.; Castro, A.; Catastini, P.; Cauz, D.; Cavaliere, V.; Cavalli-Sforza, M.; Cerri, A.; Cerrito, L.; Chen, Y. C.; Chertok, M.; Chiarelli, G.; Chlachidze, G.; Chlebana, F.; Cho, K.; Chokheli, D.; Chung, W. H.; Chung, Y. S.; Ciocci, M. A.; Clark, A.; Clarke, C.; Compostella, G.; Convery, M. E.; Conway, J.; Corbo, M.; Cordelli, M.; Cox, C. A.; Cox, D. J.; Crescioli, F.; Cuevas, J.; Culbertson, R.; Dagenhart, D.; d’Ascenzo, N.; Datta, M.; de Barbaro, P.; Dell’Orso, M.; Demortier, L.; Deninno, M.; Devoto, F.; d’Errico, M.; Di Canto, A.; Di Ruzza, B.; Dittmann, J. R.; D’Onofrio, M.; Donati, S.; Dong, P.; Dorigo, M.; Dorigo, T.; Ebina, K.; Elagin, A.; Eppig, A.; Erbacher, R.; Errede, S.; Ershaidat, N.; Eusebi, R.; Farrington, S.; Feindt, M.; Fernandez, J. P.; Field, R.; Flanagan, G.; Forrest, R.; Frank, M. J.; Franklin, M.; Freeman, J. C.; Funakoshi, Y.; Furic, I.; Gallinaro, M.; Garcia, J. E.; Garfinkel, A. F.; Garosi, P.; Gerberich, H.; Gerchtein, E.; Giagu, S.; Giakoumopoulou, V.; Giannetti, P.; Gibson, K.; Ginsburg, C. M.; Giokaris, N.; Giromini, P.; Giurgiu, G.; Glagolev, V.; Glenzinski, D.; Gold, M.; Goldin, D.; Goldschmidt, N.; Golossanov, A.; Gomez, G.; Gomez-Ceballos, G.; Goncharov, M.; González, O.; Gorelov, I.; Goshaw, A. T.; Goulianos, K.; Grinstein, S.; Grosso-Pilcher, C.; Group, R. C.; Guimaraes da Costa, J.; Hahn, S. R.; Halkiadakis, E.; Hamaguchi, A.; Han, J. Y.; Happacher, F.; Hara, K.; Hare, D.; Hare, M.; Harr, R. F.; Hatakeyama, K.; Hays, C.; Heck, M.; Heinrich, J.; Herndon, M.; Hewamanage, S.; Hocker, A.; Hopkins, W.; Horn, D.; Hou, S.; Hughes, R. E.; Hurwitz, M.; Husemann, U.; Hussain, N.; Hussein, M.; Huston, J.; Introzzi, G.; Iori, M.; Ivanov, A.; James, E.; Jang, D.; Jayatilaka, B.; Jeon, E. J.; Jindariani, S.; Johnstone, A.; Jones, M.; Joo, K. K.; Jun, S. Y.; Junk, T. R.; Kamon, T.; Karchin, P. E.; Kasmi, A.; Kato, Y.; Ketchum, W.; Keung, J.; Khotilovich, V.; Kilminster, B.; Kim, D. H.; Kim, H. S.; Kim, J. E.; Kim, M. J.; Kim, S. B.; Kim, S. H.; Kim, Y. K.; Kim, Y. J.; Kimura, N.; Kirby, M.; Klimenko, S.; Knoepfel, K.; Kondo, K.; Kong, D. J.; Konigsberg, J.; Kotwal, A. V.; Kreps, M.; Kroll, J.; Krop, D.; Kruse, M.; Krutelyov, V.; Kuhr, T.; Kurata, M.; Kwang, S.; Laasanen, A. T.; Lami, S.; Lammel, S.; Lancaster, M.; Lander, R. L.; Lannon, K.; Lath, A.; Latino, G.; LeCompte, T.; Lee, E.; Lee, H. S.; Lee, J. S.; Lee, S. W.; Leo, S.; Leone, S.; Lewis, J. D.; Limosani, A.; Lin, C.-J.; Lindgren, M.; Lipeles, E.; Lister, A.; Litvintsev, D. O.; Liu, C.; Liu, H.; Liu, Q.; Liu, T.; Lockwitz, S.; Loginov, A.; Lucchesi, D.; Lueck, J.; Lujan, P.; Lukens, P.; Lungu, G.; Lys, J.; Lysak, R.; Madrak, R.; Maeshima, K.; Maestro, P.; Malik, S.; Manca, G.; Manousakis-Katsikakis, A.; Margaroli, F.; Marino, C.; Martínez, M.; Mastrandrea, P.; Matera, K.; Mattson, M. E.; Mazzacane, A.; Mazzanti, P.; McFarland, K. S.; McIntyre, P.; McNulty, R.; Mehta, A.; Mehtala, P.; Mesropian, C.; Miao, T.; Mietlicki, D.; Mitra, A.; Miyake, H.; Moed, S.; Moggi, N.; Mondragon, M. N.; Moon, C. S.; Moore, R.; Morello, M. J.; Morlock, J.; Movilla Fernandez, P.; Mukherjee, A.; Muller, Th.; Murat, P.; Mussini, M.; Nachtman, J.; Nagai, Y.; Naganoma, J.; Nakano, I.; Napier, A.; Nett, J.; Neu, C.; Neubauer, M. S.; Nielsen, J.; Nodulman, L.; Noh, S. Y.; Norniella, O.; Oakes, L.; Oh, S. H.; Oh, Y. D.; Oksuzian, I.; Okusawa, T.; Orava, R.; Ortolan, L.; Pagan Griso, S.; Pagliarone, C.; Palencia, E.; Papadimitriou, V.; Paramonov, A. A.; Patrick, J.; Pauletta, G.; Paulini, M.; Paus, C.; Pellett, D. E.; Penzo, A.; Phillips, T. J.; Piacentino, G.; Pianori, E.; Pilot, J.; Pitts, K.; Plager, C.; Pondrom, L.; Poprocki, S.; Potamianos, K.; Prokoshin, F.; Pranko, A.; Ptohos, F.; Punzi, G.; Rahaman, A.; Ramakrishnan, V.; Ranjan, N.; Rao, K.; Redondo, I.; Renton, P.; Rescigno, M.; Riddick, T.; Rimondi, F.; Ristori, L.; Robson, A.; Rodrigo, T.; Rodriguez, T.; Rogers, E.; Rolli, S.; Roser, R.; Ruffini, F.; Ruiz, A.; Russ, J.; Rusu, V.; Safonov, A.; Sakumoto, W. K.; Sakurai, Y.; Santi, L.; Sato, K.; Saveliev, V.; Savoy-Navarro, A.; Schlabach, P.; Schmidt, A.; Schmidt, E. E.; Schwarz, T.; Scodellaro, L.; Scribano, A.; Scuri, F.; Seidel, S.; Seiya, Y.; Semenov, A.; Sforza, F.; Shalhout, S. Z.; Shears, T.; Shepard, P. F.; Shimojima, M.; Shochet, M.; Shreyber-Tecker, I.; Simonenko, A.; Sinervo, P.; Sliwa, K.; Smith, J. R.; Snider, F. D.; Soha, A.; Sorin, V.; Song, H.; Squillacioti, P.; Stancari, M.; Denis, R. St.; Stelzer, B.; Stelzer-Chilton, O.; Stentz, D.; Strologas, J.; Strycker, G. L.; Sudo, Y.; Sukhanov, A.; Suslov, I.; Takemasa, K.; Takeuchi, Y.; Tang, J.; Tecchio, M.; Teng, P. K.; Thom, J.; Thome, J.; Thompson, G. A.; Thomson, E.; Toback, D.; Tokar, S.; Tollefson, K.; Tomura, T.; Tonelli, D.; Torre, S.; Torretta, D.; Totaro, P.; Trovato, M.; Truong, A.; Ukegawa, F.; Uozumi, S.; Varganov, A.; Vázquez, F.; Velev, G.; Vellidis, C.; Vidal, M.; Vila, I.; Vilar, R.; Vizán, J.; Vogel, M.; Volpi, G.; Wagner, P.; Wagner, R. L.; Wakisaka, T.; Wallny, R.; Wang, S. M.; Warburton, A.; Waters, D.; Wester, W. C.; Whiteson, D.; Wicklund, A. B.; Wicklund, E.; Wilbur, S.; Wick, F.; Williams, H. H.; Wilson, J. S.; Wilson, P.; Winer, B. L.; Wittich, P.; Wolbers, S.; Wolfe, H.; Wright, T.; Wu, X.; Wu, Z.; Yamamoto, K.; Yamato, D.; Yang, T.; Yang, U. K.; Yang, Y. C.; Yao, W.-M.; Yeh, G. P.; Yi, K.; Yoh, J.; Yorita, K.; Yoshida, T.; Yu, G. B.; Yu, I.; Yu, S. S.; Yun, J. C.; Zanetti, A.; Zeng, Y.; Zhou, C.; Zucchelli, S.

    2013-03-18

    We present a search for new particles in an extension to the standard model that includes a heavy Higgs boson (H⁰), a lighter charged Higgs boson (H±), and an even lighter Higgs boson h⁰, with decays leading to a W-boson pair and a bottom-antibottom quark pair in the final state. We use events with exactly one lepton, missing transverse momentum, and at least four jets in data corresponding to an integrated luminosity of 8.7 fb⁻¹ collected by the CDF II detector in proton-antiproton collisions at √s=1.96 TeV. We find the data to be consistent with standard model predictions and report the results in terms of a simplified Higgs-cascade-decay model, setting 95% confidence level upper limits on the product of cross section and branching fraction from 1.3 pb to 15 fb as a function of H⁰ and H± masses for m⁰h=126 GeV/c²

  12. Search for a Two-Higgs-Boson Doublet Using a Simplified Model in pp̄ Collisions at √s=1.96 TeV

    DOE PAGES

    Aaltonen, T.; Adelman, J.; Álvarez González, B.; ...

    2013-03-18

    We present a search for new particles in an extension to the standard model that includes a heavy Higgs boson (H⁰), a lighter charged Higgs boson (H±), and an even lighter Higgs boson h⁰, with decays leading to a W-boson pair and a bottom-antibottom quark pair in the final state. We use events with exactly one lepton, missing transverse momentum, and at least four jets in data corresponding to an integrated luminosity of 8.7 fb⁻¹ collected by the CDF II detector in proton-antiproton collisions at √s=1.96 TeV. We find the data to be consistent with standard model predictions and reportmore » the results in terms of a simplified Higgs-cascade-decay model, setting 95% confidence level upper limits on the product of cross section and branching fraction from 1.3 pb to 15 fb as a function of H⁰ and H± masses for m⁰h=126 GeV/c²« less

  13. The Biplot as a diagnostic tool of local dependence in latent class models. A medical application.

    PubMed

    Sepúlveda, R; Vicente-Villardón, J L; Galindo, M P

    2008-05-20

    Latent class models (LCMs) can be used to assess diagnostic test performance when no reference test (a gold standard) is available, considering two latent classes representing disease or non-disease status. One of the basic assumptions in such models is that of local or conditional independence: all indicator variables (tests) are statistically independent within each latent class. However, in practice this assumption is often violated; hence, the two-LCM fits the data poorly. In this paper, we propose the use of Biplot methods to identify the conditional dependence between pairs of manifest variables within each latent class. Additionally, we propose incorporating such dependence in the corresponding latent class using the log-linear formulation of the model.

  14. On the initial value problem for a class of discrete velocity models.

    PubMed

    Bellandi, Davide

    2017-02-01

    In this paper we investigate the initial value problem for a class of hyperbolic systems relating the mathematical modeling of a class of complex phenomena, with emphasis on vehicular traffic flow. Existence and uniqueness for large times of solutions, a basic requisite both for models building and for their numerical implementation, are obtained under weak hypotheses on the terms modeling the interaction among agents. The results are then compared with the existing literature on the subject.

  15. A Connectionist Model of Stimulus Class Formation with a Yes/No Procedure and Compound Stimuli

    ERIC Educational Resources Information Center

    Tovar, Angel E.; Chavez, Alvaro Torres

    2012-01-01

    We analyzed stimulus class formation in a human study and in a connectionist model (CM) with a yes/no procedure, using compound stimuli. In the human study, the participants were six female undergraduate students; the CM was a feed-forward back-propagation network. Two 3-member stimulus classes were trained with a similar procedure in both the…

  16. Who Plans to Go to University? Statistical Modelling of Potential Working-Class Participants

    ERIC Educational Resources Information Center

    Collier, Tim; Gilchrist, Robert; Phillips, David

    2003-01-01

    This article develops a model for potential working-class applicants to university, using multinomial logit regression to analyse a national UK sample, drawn from lower middle and working-class adults aged 16-30. Influential factors are belief in ability, preference for university rather than earning money, qualifications and family encouragement;…

  17. The Impact of Misspecifying Class-Specific Residual Variances in Growth Mixture Models

    ERIC Educational Resources Information Center

    Enders, Craig K.; Tofighi, Davood

    2008-01-01

    The purpose of this study was to examine the impact of misspecifying a growth mixture model (GMM) by assuming that Level-1 residual variances are constant across classes, when they do, in fact, vary in each subpopulation. Misspecification produced bias in the within-class growth trajectories and variance components, and estimates were…

  18. Evaluation of the Curriculum of English Preparatory Classes at Yildiz Technical University Using CIPP Model

    ERIC Educational Resources Information Center

    Akpur, Ugur; Alci, Bülent; Karatas, Hakan

    2016-01-01

    The purpose of this study is to evaluate the instruction program of preparatory classes at Yildiz Technical University using CIPP model. A total of 54 teachers and 753 university students attending preparatory classes in the Academic Year of 2014-2015 formed the study group. The research is based on a questionnaire applied to teachers and…

  19. Mentoring for Inclusion: A Model Class for Special and General Educators.

    ERIC Educational Resources Information Center

    Hobbs, Tim; Westling, David L.

    2002-01-01

    Describes a model class for instructing general and special educators in best practices in inclusive education. The class emphasized cooperative learning and decision making, highly structured problem solving activities, and use of in vivo case studies involving children with disabilities in local schools. These components were designed to model…

  20. Showcasing Modeling Strategies in the ESOL Writing Class: Blending Rhetorical Fluency with Grammatical Accuracy

    ERIC Educational Resources Information Center

    Pandey, Anjali

    2010-01-01

    This paper argues for an overt innovational shift in praxis, as well as classroom configuration in the ESOL writing class by calling for a move away from the current foci on process-based pedagogies for newcomer populations, to an explicit teaching of modeling strategies with concomitant practice opportunities provided in the ESOL writing class.…

  1. A Connectionist Model of Stimulus Class Formation with a Yes/No Procedure and Compound Stimuli

    ERIC Educational Resources Information Center

    Tovar, Angel E.; Chavez, Alvaro Torres

    2012-01-01

    We analyzed stimulus class formation in a human study and in a connectionist model (CM) with a yes/no procedure, using compound stimuli. In the human study, the participants were six female undergraduate students; the CM was a feed-forward back-propagation network. Two 3-member stimulus classes were trained with a similar procedure in both the…

  2. Multivariate regression model for partitioning tree volume of white oak into round-product classes

    Treesearch

    Daniel A. Yaussy; David L. Sonderman

    1984-01-01

    Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

  3. Interpretation of distributions on Kpi0 in pFe, nFe, and piFe interactions at 0.5-5.0 TeV in the framework of contemporary models

    NASA Astrophysics Data System (ADS)

    Avakyan, V. V.; Garyaka, A. P.; Zazyan, G. Z.

    Fluctuations of the observable parameters of nuclear electromagnetic cascades induced by 0.5-5.0 TeV cosmic ray protons, neutrons and pions are studied by the ionization calorimeter of the PION setup. The distributions of the inelasticity coefficient Kpi0 are measured for pFe, nFe and (pi)Fe interactions and compared with the predictions of various models. The probability of the inelastic charge exchange reaction in the (pi)Fe interactions is defined.

  4. Search for Standard Model Higgs Boson Produced in Association with a Top-Antitop Quark Pair in 1.96 TeV Proton-Antiproton Collisions

    SciTech Connect

    Lai, Stanley T.

    2007-01-01

    This thesis describes the first search for Standard Model Higgs boson production in association with a top-antitop quark pair in proton-antiproton collisions at a centre of mass energy of 1.96 TeV. The integrated luminosity for othis search corresponds to 319 pb-1 of data recorded by the Collider Detector at Fermilab. We outline the even selection criteria, evaluate the even acceptance and estimate backgrounds from Standard Model sources. These events are observed that satisfy our event selection, while 2.16 ± 0.66 events are expected from background processes. no significant excess of events above background is thus observed, and we set 95% confidence level upper limits on the production cross section for this process as a function of the Higgs mass. For a Higgs boson mass of 115 GeV/c2 we find that σ$t\\bar{t}H$ x BR (Hbb) < 690 fb at 95% C.L. These are the first limits set for $t\\bar{t}H$ production. This search also allows us to anticipate the challenges and necessary strategies needed for future searches of $t\\bar{t}H$ production.

  5. Search for standard model production of four top quarks in proton-proton collisions at √{ s} = 13 TeV

    NASA Astrophysics Data System (ADS)

    Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; König, A.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rad, N.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Strauss, J.; Waltenberger, W.; Wulz, C.-E.; Dvornikov, O.; Makarenko, V.; Mossolov, V.; Suarez Gonzalez, J.; Zykunov, V.; Shumeiko, N.; Alderweireldt, S.; De Wolf, E. A.; Janssen, X.; Lauwers, J.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; Daci, N.; De Bruyn, I.; Deroover, K.; Lontkovskyi, D.; Lowette, S.; Moortgat, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Léonard, A.; Luetic, J.; Maerschalk, T.; Marinov, A.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Yonamine, R.; Zenoni, F.; Zhang, F.; Cimmino, A.; Cornelis, T.; Dobur, D.; Fagot, A.; Gul, M.; Khvastunov, I.; Poyraz, D.; Salva, S.; Schöfbeck, R.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Bakhshiansohi, H.; Beluffi, C.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; De Visscher, S.; Delaere, C.; Delcourt, M.; Francois, B.; Giammanco, A.; Jafari, A.; Komm, M.; Krintiras, G.; Lemaitre, V.; Magitteri, A.; Mertens, A.; Musich, M.; Piotrzkowski, K.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Wertz, S.; Beliy, N.; Aldá Júnior, W. L.; Alves, F. L.; Alves, G. A.; Brito, L.; 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.; Da Silveira, G. G.; 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.; 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.; Fang, W.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Cheng, T.; Jiang, C. H.; Leggat, D.; Liu, Z.; Romeo, F.; Ruan, M.; Shaheen, S. 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B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Popov, A.; Sabes, D.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Toriashvili, T.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Feld, L.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Preuten, M.; Schomakers, C.; Schulz, J.; Verlage, T.; Albert, A.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Endres, M.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Güth, A.; Hamer, M.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Knutzen, S.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Olschewski, M.; Padeken, K.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Cherepanov, V.; Flügge, G.; Kargoll, B.; Kress, T.; Künsken, A.; Lingemann, J.; Müller, T.; Nehrkorn, A.; Nowack, A.; Pistone, C.; Pooth, O.; Stahl, A.; Aldaya Martin, M.; Arndt, T.; Asawatangtrakuldee, C.; Beernaert, K.; Behnke, O.; Behrens, U.; Bin Anuar, A. A.; Borras, K.; Campbell, A.; Connor, P.; Contreras-Campana, C.; Costanza, F.; Diez Pardos, C.; Dolinska, G.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Eren, E.; Gallo, E.; Garay Garcia, J.; Geiser, A.; Gizhko, A.; Grados Luyando, J. M.; Grohsjean, A.; Gunnellini, P.; Harb, A.; Hauk, J.; Hempel, M.; Jung, H.; Kalogeropoulos, A.; Karacheban, O.; Kasemann, M.; Keaveney, J.; Kleinwort, C.; Korol, I.; Krücker, D.; Lange, W.; Lelek, A.; Lenz, T.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I.-A.; Meyer, A. B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Roland, B.; Sahin, M. Ö.; Saxena, P.; Schoerner-Sadenius, T.; Spannagel, S.; Stefaniuk, N.; Van Onsem, G. P.; Walsh, R.; Wissing, C.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Dreyer, T.; Garutti, E.; Gonzalez, D.; Haller, J.; Hoffmann, M.; Junkes, A.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Lapsien, T.; Marchesini, I.; Marconi, D.; Meyer, M.; Niedziela, M.; Nowatschin, D.; Pantaleo, F.; Peiffer, T.; Perieanu, A.; Poehlsen, J.; Scharf, C.; Schleper, P.; Schmidt, A.; Schumann, S.; Schwandt, J.; Stadie, H.; Steinbrück, G.; Stober, F. M.; Stöver, M.; Tholen, H.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Vormwald, B.; Akbiyik, M.; Barth, C.; Baur, S.; Baus, C.; Berger, J.; Butz, E.; Caspart, R.; Chwalek, T.; Colombo, F.; De Boer, W.; Dierlamm, A.; Fink, S.; Freund, B.; Friese, R.; Giffels, M.; Gilbert, A.; Goldenzweig, P.; Haitz, D.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Katkov, I.; Kudella, S.; Mildner, H.; Mozer, M. U.; Müller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Röcker, S.; Roscher, F.; Schröder, M.; Shvetsov, I.; Sieber, G.; Simonis, H. J.; Ulrich, R.; 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.; Topsis-Giotis, I.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Tziaferi, E.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Loukas, N.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Filipovic, N.; Pasztor, G.; Bencze, G.; Hajdu, C.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Makovec, A.; Molnar, J.; Szillasi, Z.; Bartók, M.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Komaragiri, J. R.; Bahinipati, S.; Bhowmik, S.; Choudhury, S.; Mal, P.; Mandal, K.; Nayak, A.; Sahoo, D. K.; Sahoo, N.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chawla, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, A.; Kaur, M.; Kumar, R.; Kumari, P.; Mehta, A.; Mittal, M.; Singh, J. B.; Walia, G.; Kumar, Ashok; Bhardwaj, A.; Choudhary, B. C.; Garg, R. 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M.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Paktinat Mehdiabadi, S.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Calabria, C.; Caputo, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. 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M.; Lanza, G.; Lista, L.; Meola, S.; Paolucci, P.; Sciacca, C.; Thyssen, F.; Azzi, P.; Bacchetta, N.; Benato, L.; Bisello, D.; Boletti, A.; Carlin, R.; Carvalho Antunes De Oliveira, A.; Checchia, P.; Dall'Osso, M.; De Castro Manzano, P.; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Zanetti, M.; Zotto, P.; Zumerle, G.; Braghieri, A.; Fallavollita, F.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.; Alunni Solestizi, L.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Leonardi, R.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fedi, G.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Barone, L.; Cavallari, F.; Cipriani, M.; Del Re, D.; Diemoz, M.; Gelli, S.; Longo, E.; Margaroli, F.; Marzocchi, B.; Meridiani, P.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Biino, C.; Cartiglia, N.; Cenna, F.; Costa, M.; Covarelli, R.; Degano, A.; Demaria, N.; Finco, L.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Monteno, M.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Ravera, F.; Romero, A.; Ruspa, M.; Sacchi, R.; Shchelina, K.; Sola, V.; Solano, A.; Staiano, A.; Traczyk, P.; Belforte, S.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Zanetti, A.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Lee, S.; Lee, S. W.; Oh, Y. D.; Sekmen, S.; Son, D. C.; Yang, Y. C.; Lee, A.; Kim, H.; Brochero Cifuentes, J. A.; Kim, T. J.; Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Ha, S.; Hong, B.; Jo, Y.; Kim, Y.; Lee, K.; Lee, K. S.; Lee, S.; Lim, J.; Park, S. K.; Roh, Y.; Almond, J.; Kim, J.; Lee, H.; Oh, S. B.; Radburn-Smith, B. C.; Seo, S. h.; Yang, U. K.; Yoo, H. D.; Yu, G. B.; Choi, M.; Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.; Ryu, G.; Ryu, M. S.; Choi, Y.; Goh, J.; Hwang, C.; Lee, J.; Yu, I.; Dudenas, V.; Juodagalvis, A.; Vaitkus, J.; Ahmed, I.; Ibrahim, Z. A.; Md Ali, M. A. B.; Mohamad Idris, F.; Wan Abdullah, W. A. T.; Yusli, M. N.; Zolkapli, Z.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-De La Cruz, I.; Hernandez-Almada, A.; Lopez-Fernandez, R.; Magaña Villalba, R.; Mejia Guisao, J.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Oropeza Barrera, C.; Vazquez Valencia, F.; Carpinteyro, S.; Pedraza, I.; Salazar Ibarguen, H. A.; Uribe Estrada, C.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Khan, W. A.; Saddique, A.; Shah, M. A.; Shoaib, M.; Waqas, M.; Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Górski, M.; Kazana, M.; Nawrocki, K.; Romanowska-Rybinska, K.; Szleper, M.; Zalewski, P.; Bunkowski, K.; Byszuk, A.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Walczak, M.; Bargassa, P.; Beirão Da Cruz E Silva, C.; Calpas, B.; Di Francesco, A.; Faccioli, P.; Ferreira Parracho, P. G.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Lloret Iglesias, L.; Nemallapudi, M. V.; Rodrigues Antunes, J.; Seixas, J.; Toldaiev, O.; Vadruccio, D.; Varela, J.; Vischia, P.; Afanasiev, S.; Bunin, P.; Gavrilenko, M.; Golutvin, I.; Gorbunov, I.; Kamenev, A.; Karjavin, V.; Lanev, A.; Malakhov, A.; Matveev, V.; Palichik, V.; Perelygin, V.; Shmatov, S.; Shulha, S.; Skatchkov, N.; Smirnov, V.; Voytishin, N.; Zarubin, A.; Chtchipounov, L.; Golovtsov, V.; Ivanov, Y.; Kim, V.; Kuznetsova, E.; Murzin, V.; Oreshkin, V.; Sulimov, V.; Vorobyev, A.; Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.; Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Pozdnyakov, I.; Safronov, G.; Spiridonov, A.; Toms, M.; Vlasov, E.; Zhokin, A.; Bylinkin, A.; Chadeeva, M.; Rusinov, V.; Tarkovskii, E.; Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Leonidov, A.; Terkulov, A.; Baskakov, A.; Belyaev, A.; Boos, E.; Bunichev, V.; Dubinin, M.; Dudko, L.; Klyukhin, V.; Kodolova, O.; Korneeva, N.; Lokhtin, I.; Miagkov, I.; Obraztsov, S.; Perfilov, M.; Savrin, V.; Snigirev, A.; Blinov, V.; Skovpen, Y.; Shtol, D.; Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Elumakhov, D.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Krychkine, V.; Petrov, V.; Ryutin, R.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.; Adzic, P.; Cirkovic, P.; Devetak, D.; Dordevic, M.; Milosevic, J.; Rekovic, V.; Alcaraz Maestre, J.; Barrio Luna, M.; Calvo, E.; Cerrada, M.; Chamizo Llatas, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Escalante Del Valle, A.; Fernandez Bedoya, C.; Fernández Ramos, J. P.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Navarro De Martino, E.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.; de Trocóniz, J. F.; Missiroli, M.; Moran, D.; Cuevas, J.; Fernandez Menendez, J.; Gonzalez Caballero, I.; González Fernández, J. R.; Palencia Cortezon, E.; Sanchez Cruz, S.; Suárez Andrés, I.; Vizan Garcia, J. M.; Cabrillo, I. J.; Calderon, A.; Curras, E.; Fernandez, M.; Garcia-Ferrero, J.; Gomez, G.; Lopez Virto, A.; Marco, J.; Martinez Rivero, C.; Matorras, F.; Piedra Gomez, J.; Rodrigo, T.; Ruiz-Jimeno, A.; Scodellaro, L.; Trevisani, N.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Auffray, E.; Auzinger, G.; Baillon, P.; Ball, A. 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I.; Henderson, C.; Rumerio, P.; West, C.; Arcaro, D.; Avetisyan, A.; Bose, T.; Gastler, D.; Rankin, D.; Richardson, C.; Rohlf, J.; Sulak, L.; Zou, D.; Benelli, G.; Cutts, D.; Garabedian, A.; Hakala, J.; Heintz, U.; Hogan, J. M.; Jesus, O.; Kwok, K. H. M.; Laird, E.; Landsberg, G.; Mao, Z.; Narain, M.; Piperov, S.; Sagir, S.; Spencer, E.; Syarif, R.; Breedon, R.; Burns, D.; Calderon De La Barca Sanchez, M.; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Flores, C.; Funk, G.; Gardner, M.; Ko, W.; Lander, R.; Mclean, C.; Mulhearn, M.; Pellett, D.; Pilot, J.; Shalhout, S.; Shi, M.; Smith, J.; Squires, M.; Stolp, D.; Tos, K.; Tripathi, M.; Bachtis, M.; Bravo, C.; Cousins, R.; Dasgupta, A.; Florent, A.; Hauser, J.; Ignatenko, M.; Mccoll, N.; Saltzberg, D.; Schnaible, C.; Valuev, V.; Weber, M.; Bouvier, E.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Ghiasi Shirazi, S. M. A.; Hanson, G.; Heilman, J.; Jandir, P.; Kennedy, E.; Lacroix, F.; Long, O. R.; Olmedo Negrete, M.; Paneva, M. I.; Shrinivas, A.; Si, W.; Wang, L.; Wei, H.; Wimpenny, S.; Yates, B. R.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; Derdzinski, M.; Gerosa, R.; Holzner, A.; Klein, D.; Krutelyov, V.; Letts, J.; Macneill, I.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Tadel, M.; Vartak, A.; Wasserbaech, S.; Welke, C.; Wood, J.; Würthwein, F.; Yagil, A.; Zevi Della Porta, G.; Amin, N.; Bhandari, R.; Bradmiller-Feld, J.; Campagnari, C.; Dishaw, A.; Dutta, V.; Franco Sevilla, M.; George, C.; Golf, F.; Gouskos, L.; Gran, J.; Heller, R.; Incandela, J.; Mullin, S. D.; Ovcharova, A.; Qu, H.; Richman, J.; Stuart, D.; Suarez, I.; Yoo, J.; Anderson, D.; Bendavid, J.; Bornheim, A.; Bunn, J.; Duarte, J.; Lawhorn, J. M.; Mott, A.; Newman, H. B.; Pena, C.; Spiropulu, M.; Vlimant, J. R.; Xie, S.; Zhu, R. Y.; Andrews, M. B.; Ferguson, T.; Paulini, M.; Russ, J.; Sun, M.; Vogel, H.; Vorobiev, I.; Weinberg, M.; Cumalat, J. P.; Ford, W. T.; Jensen, F.; Johnson, A.; Krohn, M.; Mulholland, T.; Stenson, K.; Wagner, S. R.; Alexander, J.; Chaves, J.; Chu, J.; Dittmer, S.; Mcdermott, K.; Mirman, N.; Nicolas Kaufman, G.; Patterson, J. R.; Rinkevicius, A.; Ryd, A.; Skinnari, L.; Soffi, L.; Tan, S. M.; Tao, Z.; Thom, J.; Tucker, J.; Wittich, P.; Zientek, M.; Winn, D.; Abdullin, S.; Albrow, M.; Apollinari, G.; Apresyan, A.; Banerjee, S.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Cheung, H. W. K.; Chlebana, F.; Cihangir, S.; Cremonesi, M.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hare, D.; Harris, R. M.; Hasegawa, S.; Hirschauer, J.; Hu, Z.; Jayatilaka, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Klima, B.; Kreis, B.; Lammel, S.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, M.; Liu, T.; Lopes De Sá, R.; Lykken, J.; Maeshima, K.; Magini, N.; Marraffino, J. M.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mrenna, S.; Nahn, S.; O'Dell, V.; Pedro, K.; Prokofyev, O.; Rakness, G.; Ristori, L.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Stoynev, S.; Strait, J.; Strobbe, N.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vernieri, C.; Verzocchi, M.; Vidal, R.; Wang, M.; Weber, H. A.; Whitbeck, A.; Wu, Y.; 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.; Low, J. F.; Ma, P.; Matchev, K.; Mei, H.; Mitselmakher, G.; Rank, D.; Shchutska, L.; Sperka, D.; Thomas, L.; Wang, J.; Wang, S.; Yelton, J.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Ackert, A.; Adams, T.; Askew, A.; Bein, S.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Prosper, H.; Santra, A.; Yohay, R.; Baarmand, M. M.; Bhopatkar, V.; Colafranceschi, S.; Hohlmann, M.; Noonan, D.; Roy, T.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Jung, K.; Sandoval Gonzalez, I. D.; Varelas, N.; Wang, H.; 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.; Blumenfeld, B.; Cocoros, A.; Eminizer, N.; Fehling, D.; Feng, L.; Gritsan, A. V.; Maksimovic, P.; Roskes, J.; Sarica, U.; Swartz, M.; Xiao, M.; Xin, Y.; You, C.; Al-bataineh, A.; Baringer, P.; Bean, A.; Boren, S.; Bowen, J.; Castle, J.; Forthomme, L.; Kenny, R. P.; Khalil, S.; Kropivnitskaya, A.; Majumder, D.; Mcbrayer, W.; Murray, M.; Sanders, S.; Stringer, R.; Tapia Takaki, J. D.; Wang, Q.; Ivanov, A.; Kaadze, K.; Maravin, Y.; Mohammadi, A.; Saini, L. K.; Skhirtladze, N.; Toda, S.; Rebassoo, F.; Wright, D.; Anelli, C.; Baden, A.; Baron, O.; Belloni, A.; Calvert, B.; Eno, S. C.; Ferraioli, C.; Gomez, J. A.; Hadley, N. J.; Jabeen, S.; Jeng, G. Y.; Kellogg, R. G.; Kolberg, T.; Kunkle, J.; Mignerey, A. C.; Ricci-Tam, F.; Shin, Y. H.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Abercrombie, D.; Allen, B.; Apyan, A.; Azzolini, V.; Barbieri, R.; Baty, A.; Bi, R.; Bierwagen, K.; Brandt, S.; Busza, W.; Cali, I. A.; D'Alfonso, M.; Demiragli, Z.; Di Matteo, L.; Gomez Ceballos, G.; Goncharov, M.; Hsu, D.; Iiyama, Y.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Krajczar, K.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Maier, B.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Roland, C.; Roland, G.; Salfeld-Nebgen, J.; Stephans, G. S. F.; Tatar, K.; Varma, M.; Velicanu, D.; Veverka, J.; Wang, J.; Wang, T. W.; Wyslouch, B.; Yang, M.; Benvenuti, A. C.; Chatterjee, R. M.; Evans, A.; Hansen, P.; Kalafut, S.; Kao, S. C.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Claes, D. R.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Kravchenko, I.; Malta Rodrigues, A.; Meier, F.; Monroy, J.; Siado, J. E.; Snow, G. R.; Stieger, B.; Alyari, M.; Dolen, J.; Godshalk, A.; Harrington, C.; Iashvili, I.; Kaisen, J.; Nguyen, D.; Parker, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Teixeira De Lima, R.; Trocino, D.; Wang, R.-J.; Wood, D.; Bhattacharya, S.; Charaf, O.; Hahn, K. A.; Kumar, A.; Mucia, N.; Odell, N.; Pollack, B.; Schmitt, M. H.; Sung, K.; Trovato, M.; Velasco, M.; Dev, N.; Hildreth, M.; Hurtado Anampa, K.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Rupprecht, N.; Smith, G.; Taroni, S.; Wayne, M.; Wolf, M.; Woodard, A.; Alimena, J.; Antonelli, L.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Francis, B.; Hart, A.; Hill, C.; Hughes, R.; Ji, W.; Liu, B.; Luo, W.; Puigh, D.; Winer, B. L.; Wulsin, H. W.; Cooperstein, S.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Lange, D.; Luo, J.; Marlow, D.; Medvedeva, T.; Mei, K.; Ojalvo, I.; Olsen, J.; Palmer, C.; Piroué, P.; Stickland, D.; Svyatkovskiy, A.; Tully, C.; Malik, S.; Barker, A.; Barnes, V. E.; Folgueras, S.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, A. W.; Khatiwada, A.; Miller, D. H.; Neumeister, N.; Schulte, J. F.; Shi, X.; Sun, J.; Wang, F.; Xie, W.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Chen, Z.; Ecklund, K. M.; Geurts, F. J. M.; Guilbaud, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Duh, Y. t.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Lo, K. H.; Tan, P.; Verzetti, M.; Agapitos, A.; Chou, J. P.; Gershtein, Y.; Gómez Espinosa, T. A.; Halkiadakis, E.; Heindl, M.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Kyriacou, S.; Lath, A.; Nash, K.; Osherson, M.; Saka, H.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Delannoy, A. G.; Foerster, M.; Heideman, J.; Riley, G.; Rose, K.; Spanier, S.; Thapa, K.; Bouhali, O.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Juska, E.; Kamon, T.; Mueller, R.; Pakhotin, Y.; Patel, R.; Perloff, A.; Perniè, L.; Rathjens, D.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; De Guio, F.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Gurpinar, E.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Peltola, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Sheldon, P.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Sun, X.; Wang, Y.; Wolfe, E.; Xia, F.; Clarke, C.; Harr, R.; Karchin, P. E.; Sturdy, J.; Belknap, D. A.; Buchanan, J.; Caillol, C.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Perry, T.; Pierro, G. A.; Polese, G.; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Taylor, D.; Woods, N.; CMS Collaboration

    2017-09-01

    A search for events containing four top quarks (t t bar t t bar) is reported from proton-proton collisions recorded by the CMS experiment at √{ s} = 13 TeV and corresponding to an integrated luminosity of 2.6 fb-1. The analysis considers the single-lepton (e or μ)+jets and the opposite-sign dilepton (μ+μ-, μ±e∓, or e+e-)+jets channels. It uses boosted decision trees to combine information on the global event and jet properties to distinguish between t t bar t t bar and t t bar production. The number of events observed after all selection requirements is consistent with expectations from background and standard model signal predictions, and an upper limit is set on the cross section for t t bar t t bar production in the standard model of 94 fb at 95% confidence level (10.2 × the prediction), with an expected limit of 118 fb. This is combined with the results from the published CMS search in the same-sign dilepton channel, resulting in an improved limit of 69 fb at 95% confidence level (7.4 × the prediction), with an expected limit of 71 fb. These are the strongest constraints on the rate of t t bar t t bar production to date.

  6. Search for the standard model Higgs boson in the decay channel H→ZZ→4ℓ in pp collisions at √s=7 TeV.

    PubMed

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Vaughan, J; Weng, Y; Winstrom, L; Wittich, P; Biselli, A; Winn, D; Abdullin, S; Albrow, M; Anderson, J; Apollinari, G; Atac, M; Bakken, J A; Bauerdick, L A T; Beretvas, A; Berryhill, J; Bhat, P C; Bloch, I; Burkett, K; Butler, J N; Chetluru, V; Cheung, H W K; Chlebana, F; Cihangir, S; Cooper, W; Eartly, D P; Elvira, V D; Esen, S; Fisk, I; Freeman, J; Gao, Y; Gottschalk, E; Green, D; Gutsche, O; Hanlon, J; Harris, R M; Hirschauer, J; Hooberman, B; Jensen, H; Jindariani, S; Johnson, M; Joshi, U; Klima, B; Kunori, S; Kwan, S; Leonidopoulos, C; Lincoln, D; Lipton, R; Lykken, J; Maeshima, K; Marraffino, J M; Maruyama, S; Mason, D; McBride, P; Miao, T; Mishra, K; Mrenna, S; Musienko, Y; Newman-Holmes, C; O'Dell, V; Pivarski, J; Pordes, R; Prokofyev, O; Schwarz, T; Sexton-Kennedy, E; Sharma, S; Spalding, W J; Spiegel, L; Tan, P; Taylor, L; Tkaczyk, S; Uplegger, L; Vaandering, E W; Vidal, R; Whitmore, J; Wu, W; Yang, F; Yumiceva, F; Yun, J C; Acosta, D; Avery, P; Bourilkov, D; Chen, M; Das, S; De Gruttola, M; Di Giovanni, G P; Dobur, D; Drozdetskiy, A; Field, R D; Fisher, M; Fu, Y; Furic, I K; Gartner, J; Goldberg, S; Hugon, J; Kim, B; Konigsberg, J; Korytov, A; Kropivnitskaya, A; Kypreos, T; Low, J F; Matchev, K; Milenovic, P; Mitselmakher, G; Muniz, L; Remington, R; Rinkevicius, A; Schmitt, M; Scurlock, B; Sellers, P; Skhirtladze, N; Snowball, M; Wang, D; Yelton, J; Zakaria, M; Gaultney, V; Lebolo, L M; Linn, S; Markowitz, P; Martinez, G; Rodriguez, J L; Adams, T; Askew, A; Bochenek, J; Chen, J; Diamond, B; Gleyzer, S V; Haas, J; Hagopian, S; Hagopian, V; Jenkins, M; Johnson, K F; Prosper, H; Sekmen, S; Veeraraghavan, V; Weinberg, M; Baarmand, M M; Dorney, B; Hohlmann, M; Kalakhety, H; Vodopiyanov, I; Adams, M R; Anghel, I M; Apanasevich, L; Bai, Y; Bazterra, V E; Betts, R R; Callner, J; Cavanaugh, R; Dragoiu, C; Gauthier, L; Gerber, C E; Hofman, D J; Khalatyan, S; Kunde, G J; Lacroix, F; Malek, M; O'Brien, C; Silkworth, C; Silvestre, C; Strom, D; Varelas, N; Akgun, U; Albayrak, E A; 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Kubik, A; Mucia, N; Odell, N; Ofierzynski, R A; Pollack, B; Pozdnyakov, A; Schmitt, M; Stoynev, S; Velasco, M; Won, S; Antonelli, L; Berry, D; Brinkerhoff, A; Hildreth, M; Jessop, C; Karmgard, D J; Kolb, J; Lannon, K; Luo, W; Lynch, S; Marinelli, N; Morse, D M; Pearson, T; Ruchti, R; Slaunwhite, J; Valls, N; Wayne, M; Wolf, M; Ziegler, J; Bylsma, B; Durkin, L S; Hill, C; Killewald, P; Kotov, K; Ling, T Y; Puigh, D; Rodenburg, M; Vuosalo, C; Williams, G; Adam, N; Berry, E; Elmer, P; Gerbaudo, D; Halyo, V; Hebda, P; Hegeman, J; Hunt, A; Laird, E; Lopes Pegna, D; Lujan, P; Marlow, D; Medvedeva, T; Mooney, M; Olsen, J; Piroué, P; Quan, X; Raval, A; Saka, H; Stickland, D; Tully, C; Werner, J S; Zuranski, A; Acosta, J G; Huang, X T; Lopez, A; Mendez, H; Oliveros, S; Ramirez Vargas, J E; Zatserklyaniy, A; Alagoz, E; Barnes, V E; Benedetti, D; Bolla, G; Bortoletto, D; De Mattia, M; Everett, A; Gutay, L; Hu, Z; Jones, M; Koybasi, O; Kress, M; Laasanen, A T; Leonardo, N; Maroussov, V; Merkel, P; 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Osipenkov, I; Pakhotin, Y; Perloff, A; Roe, J; Safonov, A; Sakuma, T; Sengupta, S; Suarez, I; Tatarinov, A; Toback, D; Akchurin, N; Bardak, C; Damgov, J; Dudero, P R; Jeong, C; Kovitanggoon, K; Lee, S W; Libeiro, T; Mane, P; Roh, Y; Sill, A; Volobouev, I; Wigmans, R; Appelt, E; Brownson, E; Engh, D; Florez, C; Gabella, W; Gurrola, A; Issah, M; Johns, W; Kurt, P; Maguire, C; Melo, A; Sheldon, P; Snook, B; Tuo, S; Velkovska, J; Arenton, M W; Balazs, M; Boutle, S; Conetti, S; Cox, B; Francis, B; Goadhouse, S; Goodell, J; Hirosky, R; Ledovskoy, A; Lin, C; Neu, C; Wood, J; Yohay, R; Gollapinni, S; Harr, R; Karchin, P E; Kottachchi Kankanamge Don, C; Lamichhane, P; Mattson, M; Milstène, C; Sakharov, A; Anderson, M; Bachtis, M; Belknap, D; Bellinger, J N; Bernardini, J; Borrello, L; Carlsmith, D; Cepeda, M; Dasu, S; Efron, J; Friis, E; Gray, L; Grogg, K S; Grothe, M; Hall-Wilton, R; Herndon, M; Hervé, A; Klabbers, P; Klukas, J; Lanaro, A; Lazaridis, C; Leonard, J; Loveless, R; Mohapatra, A; Ojalvo, I; Pierro, G A; Ross, I; Savin, A; Smith, W H; Swanson, J

    2012-03-16

    A search for a Higgs boson in the four-lepton decay channel H→ZZ, with each Z boson decaying to an electron or muon pair, is reported. The search covers Higgs boson mass hypotheses in the range of 110TeV from the LHC. Seventy-two events are observed with four-lepton invariant mass m(4ℓ)>100 GeV (with 13 below 160 GeV), while 67.1±6.0 (9.5±1.3) events are expected from background. The four-lepton mass distribution is consistent with the expectation of standard model background production of ZZ pairs. Upper limits at 95% confidence level exclude the standard model Higgs boson in the ranges of 134-158 GeV, 180-305 GeV, and 340-465 GeV. Small excesses of events are observed around masses of 119, 126, and 320 GeV, making the observed limits weaker than expected in the absence of a signal.

  7. Inferring Markov chains: Bayesian estimation, model comparison, entropy rate, and out-of-class modeling.

    PubMed

    Strelioff, Christopher C; Crutchfield, James P; Hübler, Alfred W

    2007-07-01

    Markov chains are a natural and well understood tool for describing one-dimensional patterns in time or space. We show how to infer kth order Markov chains, for arbitrary k , from finite data by applying Bayesian methods to both parameter estimation and model-order selection. Extending existing results for multinomial models of discrete data, we connect inference to statistical mechanics through information-theoretic (type theory) techniques. We establish a direct relationship between Bayesian evidence and the partition function which allows for straightforward calculation of the expectation and variance of the conditional relative entropy and the source entropy rate. Finally, we introduce a method that uses finite data-size scaling with model-order comparison to infer the structure of out-of-class processes.

  8. Deriving the Dividend Discount Model in the Intermediate Microeconomics Class

    ERIC Educational Resources Information Center

    Norman, Stephen; Schlaudraff, Jonathan; White, Karianne; Wills, Douglas

    2013-01-01

    In this article, the authors show that the dividend discount model can be derived using the basic intertemporal consumption model that is introduced in a typical intermediate microeconomics course. This result will be of use to instructors who teach microeconomics to finance students in that it demonstrates the value of utility maximization in…

  9. Theory and Practice: An Integrative Model Linking Class and Field

    ERIC Educational Resources Information Center

    Lesser, Joan Granucci; Cooper, Marlene

    2006-01-01

    Social work has evolved over the years taking on the challenges of the times. The profession now espouses a breadth of theoretical approaches and treatment modalities. We have developed a model to help graduate social work students master the skill of integrating theory and social work practice. The Integrative Model has five components: (l) The…

  10. Deriving the Dividend Discount Model in the Intermediate Microeconomics Class

    ERIC Educational Resources Information Center

    Norman, Stephen; Schlaudraff, Jonathan; White, Karianne; Wills, Douglas

    2013-01-01

    In this article, the authors show that the dividend discount model can be derived using the basic intertemporal consumption model that is introduced in a typical intermediate microeconomics course. This result will be of use to instructors who teach microeconomics to finance students in that it demonstrates the value of utility maximization in…

  11. Constraints on the pMSSM, AMSB model and on other models from the search for long-lived charged particles in proton-proton collisions at √s = 8 TeV

    SciTech Connect

    Khachatryan, Vardan

    2015-07-17

    Stringent limits are set on the long-lived lepton-like sector of the phenomenological minimal supersymmetric standard model (pMSSM) and the anomaly-mediated supersymmetry breaking (AMSB) model. We derived the limits from the results presented in a recent search for long-lived charged particles in proton–proton collisions, based on data collected by the CMS detector at a centre-of-mass energy of 8 TeV at the Large Hadron Collider. In the pMSSM parameter sub-space considered, 95.9 % of the points predicting charginos with a lifetime of at least 10 ns are excluded. Furthermore, these constraints on the pMSSM are the first obtained at the LHC. Charginos with a lifetime greater than 100 ns and masses up to about 800 GeV in the AMSB model are also excluded. Furthermore, the method described can also be used to set constraints on other models.

  12. Modeling self-consistent multi-class dynamic traffic flow

    NASA Astrophysics Data System (ADS)

    Cho, Hsun-Jung; Lo, Shih-Ching

    2002-09-01

    In this study, we present a systematic self-consistent multiclass multilane traffic model derived from the vehicular Boltzmann equation and the traffic dispersion model. The multilane domain is considered as a two-dimensional space and the interaction among vehicles in the domain is described by a dispersion model. The reason we consider a multilane domain as a two-dimensional space is that the driving behavior of road users may not be restricted by lanes, especially motorcyclists. The dispersion model, which is a nonlinear Poisson equation, is derived from the car-following theory and the equilibrium assumption. Under the concept that all kinds of users share the finite section, the density is distributed on a road by the dispersion model. In addition, the dynamic evolution of the traffic flow is determined by the systematic gas-kinetic model derived from the Boltzmann equation. Multiplying Boltzmann equation by the zeroth, first- and second-order moment functions, integrating both side of the equation and using chain rules, we can derive continuity, motion and variance equation, respectively. However, the second-order moment function, which is the square of the individual velocity, is employed by previous researches does not have physical meaning in traffic flow. Although the second-order expansion results in the velocity variance equation, additional terms may be generated. The velocity variance equation we propose is derived from multiplying Boltzmann equation by the individual velocity variance. It modifies the previous model and presents a new gas-kinetic traffic flow model. By coupling the gas-kinetic model and the dispersion model, a self-consistent system is presented.

  13. Search for the neutral Higgs bosons of the minimal supersymmetric standard model in pp collisions at sqrt{s}=7 TeV with the ATLAS detector

    NASA Astrophysics Data System (ADS)

    Aad, G.; Abajyan, T.; Abbott, B.; Abdallah, J.; Khalek, S. Abdel; Abdelalim, A. A.; Abdinov, O.; Aben, R.; Abi, B.; Abolins, M.; AbouZeid, O. S.; Abramowicz, H.; Abreu, H.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Addy, T. N.; Adelman, J.; Adomeit, S.; Adragna, P.; Adye, T.; Aefsky, S.; Aguilar-Saavedra, J. A.; Agustoni, M.; Ahlen, S. P.; Ahles, F.; Ahmad, A.; Ahsan, M.; Aielli, G.; Åkesson, T. P. A.; Akimoto, G.; Akimov, A. V.; Alam, M. A.; Albert, J.; Albrand, S.; Aleksa, M.; Aleksandrov, I. N.; Alessandria, F.; Alexa, C.; Alexander, G.; Alexandre, G.; Alexopoulos, T.; Alhroob, M.; Aliev, M.; Alimonti, G.; Alison, J.; Allbrooke, B. M. M.; Allison, L. J.; Allport, P. P.; Allwood-Spiers, S. E.; Almond, J.; Aloisio, A.; Alon, R.; Alonso, A.; Alonso, F.; Altheimer, A.; Alvarez Gonzalez, B.; Alviggi, M. G.; Amako, K.; Amelung, C.; Ammosov, V. V.; Amor dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amram, N.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Andrieux, M.-L.; Anduaga, X. S.; Angelidakis, S.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A.; Anjos, N.; Annovi, A.; Antonaki, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Aoun, S.; Bella, L. Aperio; Apolle, R.; Arabidze, G.; Aracena, I.; Arai, Y.; Arce, A. T. H.; Arfaoui, S.; Arguin, J.-F.; Argyropoulos, S.; Arik, E.; Arik, M.; Armbruster, A. J.; Arnaez, O.; Arnal, V.; Artamonov, A.; Artoni, G.; Arutinov, D.; Asai, S.; Ask, S.; Åsman, B.; Asquith, L.; Assamagan, K.; Astbury, A.; Atkinson, M.; Aubert, B.; Auge, E.; Augsten, K.; Aurousseau, M.; Avolio, G.; Axen, D.; Azuelos, G.; Azuma, Y.; Baak, M. A.; Baccaglioni, G.; Bacci, C.; Bach, A. M.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Mayes, J. Backus; Badescu, E.; Bagnaia, P.; Bai, Y.; Bailey, D. C.; Bain, T.; Baines, J. T.; Baker, O. 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M.; Sellden, B.; Sellers, G.; Seman, M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Serkin, L.; Seuster, R.; Severini, H.; Sfyrla, A.; Shabalina, E.; Shamim, M.; Shan, L. Y.; Shank, J. T.; Shao, Q. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Sherman, D.; Sherwood, P.; Shimizu, S.; Shimojima, M.; Shin, T.; Shiyakova, M.; Shmeleva, A.; Shochet, M. J.; Short, D.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silbert, O.; Silva, J.; Silver, Y.; Silverstein, D.; Silverstein, S. B.; Simak, V.; Simard, O.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simoniello, R.; Simonyan, M.; Sinervo, P.; Sinev, N. B.; Sipica, V.; Siragusa, G.; Sircar, A.; Sisakyan, A. N.; Sivoklokov, S. Yu.; Sjölin, J.; Sjursen, T. B.; Skinnari, L. A.; Skottowe, H. P.; Skovpen, K.; Skubic, P.; Slater, M.; Slavicek, T.; Sliwa, K.; Smakhtin, V.; Smart, B. H.; Smestad, L.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, B. C.; Smith, K. M.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snow, S. W.; Snow, J.; Snyder, S.; Sobie, R.; Sodomka, J.; Soffer, A.; Solans, C. A.; Solar, M.; Solc, J.; Soldatov, E. Yu.; Soldevila, U.; Solfaroli Camillocci, E.; Solodkov, A. A.; Solovyanov, O. V.; Solovyev, V.; Soni, N.; Sood, A.; Sopko, V.; Sopko, B.; Sosebee, M.; Soualah, R.; Soueid, P.; Soukharev, A.; South, D.; Spagnolo, S.; Spanò, F.; Spighi, R.; Spigo, G.; Spiwoks, R.; Spousta, M.; Spreitzer, T.; Spurlock, B.; Denis, R. D. St.; Stahlman, J.; Stamen, R.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanescu-Bellu, M.; Stanitzki, M. M.; Stapnes, S.; Starchenko, E. A.; Stark, J.; Staroba, P.; Starovoitov, P.; Staszewski, R.; Staude, A.; Stavina, P.; Steele, G.; Steinbach, P.; Steinberg, P.; Stekl, I.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stern, S.; Stewart, G. A.; Stillings, J. A.; Stockton, M. C.; Stoebe, M.; Stoerig, K.; Stoicea, G.; Stonjek, S.; Strachota, P.; Stradling, A. R.; Straessner, A.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strang, M.; Strauss, E.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Strong, J. A.; Stroynowski, R.; Stugu, B.; Stumer, I.; Stupak, J.; Sturm, P.; Styles, N. A.; Soh, D. A.; Su, D.; Subramania, HS.; Subramaniam, R.; Succurro, A.; Sugaya, Y.; Suhr, C.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Susinno, G.; Sutton, M. R.; Suzuki, Y.; Suzuki, Y.; Svatos, M.; Swedish, S.; Sykora, I.; Sykora, T.; Sánchez, J.; Ta, D.; Tackmann, K.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takahashi, Y.; Takai, H.; Takashima, R.; Takeda, H.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A.; Tamsett, M. C.; Tan, K. G.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tanaka, S.; Tanasijczuk, A. J.; Tani, K.; Tannoury, N.; Tapprogge, S.; Tardif, D.; Tarem, S.; Tarrade, F.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tassi, E.; Tayalati, Y.; Taylor, C.; Taylor, F. E.; Taylor, G. N.; Taylor, W.; Teinturier, M.; Teischinger, F. A.; Teixeira Dias Castanheira, M.; Teixeira-Dias, P.; Temming, K. K.; Ten Kate, H.; Teng, P. K.; Terada, S.; Terashi, K.; Terron, J.; Testa, M.; Teuscher, R. J.; Therhaag, J.; Theveneaux-Pelzer, T.; Thoma, S.; Thomas, J. P.; Thompson, E. N.; Thompson, P. D.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Thomson, M.; Thong, W. M.; Thun, R. P.; Tian, F.; Tibbetts, M. J.; Tic, T.; Tikhomirov, V. O.; Tikhonov, Y. A.; Timoshenko, S.; Tiouchichine, E.; Tipton, P.; Tisserant, S.; Todorov, T.; Todorova-Nova, S.; Toggerson, B.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tollefson, K.; Tomoto, M.; Tompkins, L.; Toms, K.; Tonoyan, A.; Topfel, C.; Topilin, N. D.; Torrence, E.; Torres, H.; Pastor, E. Torró; Toth, J.; Touchard, F.; Tovey, D. R.; Trefzger, T.; Tremblet, L.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Triplett, N.; Trischuk, W.; Trocmé, B.; Troncon, C.; Trottier-McDonald, M.; True, P.; Trzebinski, M.; Trzupek, A.; Tsarouchas, C.; Tseng, J. C.-L.; Tsiakiris, M.; Tsiareshka, P. V.; Tsionou, D.; Tsipolitis, G.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsukerman, I. I.; Tsulaia, V.; Tsung, J.-W.; Tsuno, S.; Tsybychev, D.; Tua, A.; Tudorache, A.; Tudorache, V.; Tuggle, J. M.; Turala, M.; Turecek, D.; Cakir, I. Turk; Turra, R.; Tuts, P. M.; Tykhonov, A.; Tylmad, M.; Tyndel, M.; Tzanakos, G.; Uchida, K.; Ueda, I.; Ueno, R.; Ughetto, M.; Ugland, M.; Uhlenbrock, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Unno, Y.; Urbaniec, D.; Urquijo, P.; Usai, G.; Vacavant, L.; Vacek, V.; Vachon, B.; Vahsen, S.; Valentinetti, S.; Valero, A.; Valery, L.; Valkar, S.; Gallego, E. Valladolid; Vallecorsa, S.; Valls Ferrer, J. A.; Van Berg, R.; Van Der Deijl, P. C.; van der Geer, R.; van der Graaf, H.; Van Der Leeuw, R.; van der Poel, E.; van der Ster, D.; van Eldik, N.; van Gemmeren, P.; Van Nieuwkoop, J.; van Vulpen, I.; Vanadia, M.; Vandelli, W.; Vaniachine, A.; Vankov, P.; Vannucci, F.; Vari, R.; Varnes, E. W.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vassilakopoulos, V. I.; Vazeille, F.; Schroeder, T. Vazquez; Vegni, G.; Veillet, J. J.; Veloso, F.; Veness, R.; Veneziano, S.; Ventura, A.; Ventura, D.; Venturi, M.; Venturi, N.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vest, A.; Vetterli, M. C.; Vichou, I.; Vickey, T.; Vickey Boeriu, O. E.; Viehhauser, G. H. A.; Viel, S.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinek, E.; Vinogradov, V. B.; Virchaux, M.; Virzi, J.; Vitells, O.; Viti, M.; Vivarelli, I.; Vaque, F. Vives; Vlachos, S.; Vladoiu, D.; Vlasak, M.; Vogel, A.; Vokac, P.; Volpi, G.; Volpi, M.; Volpini, G.; von der Schmitt, H.; von Radziewski, H.; von Toerne, E.; Vorobel, V.; Vorwerk, V.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Milosavljevic, M. Vranjes; Vrba, V.; Vreeswijk, M.; Vu Anh, T.; Vuillermet, R.; Vukotic, I.; Wagner, W.; Wagner, P.; Wahlen, H.; Wahrmund, S.; Wakabayashi, J.; Walch, S.; Walder, J.; Walker, R.; Walkowiak, W.; Wall, R.; Waller, P.; Walsh, B.; Wang, C.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, R.; Wang, S. M.; Wang, T.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Warsinsky, M.; Washbrook, A.; Wasicki, C.; Watanabe, I.; Watkins, P. M.; Watson, A. T.; Watson, I. J.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, A. T.; Waugh, B. M.; Weber, M. S.; Webster, J. S.; Weidberg, A. R.; Weigell, P.; Weingarten, J.; Weiser, C.; Wells, P. S.; Wenaus, T.; Wendland, D.; Weng, Z.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Werth, M.; Wessels, M.; Wetter, J.; Weydert, C.; Whalen, K.; White, A.; White, M. J.; White, S.; Whitehead, S. R.; Whiteson, D.; Whittington, D.; Wicke, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wijeratne, P. A.; Wildauer, A.; Wildt, M. A.; Wilhelm, I.; Wilkens, H. G.; Will, J. Z.; Williams, E.; Williams, H. H.; Williams, S.; Willis, W.; Willocq, S.; Wilson, J. A.; Wilson, M. G.; Wilson, A.; Wingerter-Seez, I.; Winkelmann, S.; Winklmeier, F.; Wittgen, M.; Wollstadt, S. J.; Wolter, M. W.; Wolters, H.; Wong, W. C.; Wooden, G.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wozniak, K. W.; Wraight, K.; Wright, M.; Wrona, B.; Wu, S. L.; Wu, X.; Wu, Y.; Wulf, E.; Wynne, B. M.; Xella, S.; Xiao, M.; Xie, S.; Xu, C.; Xu, D.; Xu, L.; Yabsley, B.; Yacoob, S.; Yamada, M.; Yamaguchi, H.; Yamamoto, A.; Yamamoto, K.; Yamamoto, S.; Yamamura, T.; Yamanaka, T.; Yamauchi, K.; Yamazaki, T.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, U. K.; Yang, Y.; Yang, Z.; Yanush, S.; Yao, L.; Yasu, Y.; Yatsenko, E.; Ye, J.; Ye, S.; Yen, A. L.; Yilmaz, M.; Yoosoofmiya, R.; Yorita, K.; Yoshida, R.; Yoshihara, K.; Young, C.; Young, C. J.; Youssef, S.; Yu, D.; Yu, D. R.; Yu, J.; Yu, J.; Yuan, L.; Yurkewicz, A.; Zabinski, B.; Zaidan, R.; Zaitsev, A. M.; Zanello, L.; Zanzi, D.; Zaytsev, A.; Zeitnitz, C.; Zeman, M.; Zemla, A.; Zenin, O.; Ženiš, T.; Zinonos, Z.; Zerwas, D.; Zevi della Porta, G.; Zhang, D.; Zhang, H.; Zhang, J.; Zhang, X.; Zhang, Z.; Zhao, L.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, N.; Zhou, Y.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhuravlov, V.; Zibell, A.; Zieminska, D.; Zimin, N. I.; Zimmermann, R.; Zimmermann, S.; Zimmermann, S.; Ziolkowski, M.; Zitoun, R.; Živković, L.; Zmouchko, V. V.; Zobernig, G.; Zoccoli, A.; zur Nedden, M.; Zutshi, V.; Zwalinski, L.

    2013-02-01

    A search for neutral Higgs bosons of the Minimal Supersymmetric Standard Model (MSSM) is reported. The analysis is based on a sample of proton-proton collisions at a centre-of-mass energy of 7 TeV recorded with the ATLAS detector at the Large Hadron Collider. The data were recorded in 2011 and correspond to an integrated luminosity of 4.7 fb-1 to 4.8 fb-1. Higgs boson decays into oppositely-charged muon or τ lepton pairs are considered for final states requiring either the presence or absence of b-jets. No statistically significant excess over the expected background is observed and exclusion limits at the 95% confidence level are derived. The exclusion limits are for the production cross-section of a generic neutral Higgs boson, ϕ, as a function of the Higgs boson mass and for h/ A/ H production in the MSSM as a function of the parameters m A and tan β in the m_h^{max } scenario for m A in the range of 90 GeV to 500 GeV.[Figure not available: see fulltext.

  14. Search for the standard model Higgs boson produced in association with W and Z bosons in pp collisions at √{s}=7 TeV

    NASA Astrophysics Data System (ADS)

    Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Aguilo, E.; Bergauer, T.; Dragicevic, M.; Erö, J.; Fabjan, C.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hammer, J.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesen-hofer, W.; Knünz, V.; Krammer, M.; Liko, D.; Mikulec, I.; Pernicka, M.; Rahbaran, B.; Rohringer, C.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Taurok, A.; Waltenberger, W.; Walzel, G.; Widl, E.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Bansal, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Luyckx, S.; Mucibello, L.; Ochesanu, S.; Roland, B.; Rougny, R.; Selvaggi, M.; Staykova, Z.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D'Hondt, J.; Gonzalez Suarez, R.; Kalogeropoulos, A.; Maes, M.; Olbrechts, A.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Villella, I.; Clerbaux, B.; De Lentdecker, G.; Dero, V.; Gay, A. P. R.; Hreus, T.; Léonard, A.; Marage, P. E.; Mohammadi, A.; Reis, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.; Adler, V.; Beernaert, K.; Cimmino, A.; Costantini, S.; Garcia, G.; Grunewald, M.; Klein, B.; Lellouch, J.; Marinov, A.; Mccartin, J.; Ocampo Rios, A. A.; Ryckbosch, D.; Strobbe, N.; Thyssen, F.; Tytgat, M.; Verwilligen, P.; Walsh, S.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Bruno, G.; Castello, R.; Ceard, L.; Delaere, C.; du Pree, T.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Lemaitre, V.; Liao, J.; Militaru, O.; Nuttens, C.; Pagano, D.; Pin, A.; Piotrzkowski, K.; Schul, N.; Vizan Garcia, J. M.; Beliy, N.; Caebergs, T.; Daubie, E.; Hammad, G. H.; Alves, G. A.; Correa Martins, M.; De Jesus Damiao, D.; Martins, T.; Pol, M. E.; Souza, M. H. G.; Aldá, W. L.; Carvalho, W.; Custódio, A.; Da Costa, E. M.; De Oliveira Martins, C.; Fonseca De Souza, S.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Oguri, V.; Prado Da Silva, W. L.; Santoro, A.; Soares Jorge, L.; Sznajder, A.; Bernardes, C. A.; Dias, F. A.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Lagana, C.; Marinho, F.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Genchev, V.; Iaydjiev, P.; Piperov, S.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Tcholakov, V.; Trayanov, R.; Vutova, M.; Dimitrov, A.; Hadjiiska, R.; Kozhuharov, V.; Litov, L.; Pavlov, B.; Petkov, P.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Jiang, C. H.; Liang, D.; Liang, S.; Meng, X.; Tao, J.; Wang, J.; Wang, X.; Wang, Z.; Xiao, H.; Xu, M.; Zang, J.; Zhang, Z.; Asawatangtrakuldee, C.; Ban, Y.; Guo, S.; Guo, Y.; Li, W.; Liu, S.; Mao, Y.; Qian, S. J.; Teng, H.; Wang, D.; Zhang, L.; Zhu, B.; Zou, W.; Avila, C.; Gomez, J. P.; Gomez Moreno, B.; Osorio Oliveros, A. F.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Plestina, R.; Polic, D.; Puljak, I.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Duric, S.; Kadija, K.; Luetic, J.; Morovic, S.; Attikis, A.; Galanti, M.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Finger, M.; Finger, M.; Assran, Y.; Elgammal, S.; Ellithi Kamel, A.; Khalil, S.; Mahmoud, M. A.; Radi, A.; Kadastik, M.; Müntel, M.; Raidal, M.; Rebane, L.; Tiko, A.; Eerola, P.; Fedi, G.; Voutilainen, M.; Härkönen, J.; Heikkinen, A.; Karimäki, V.; Kinnunen, R.; Kortelainen, M. J.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Peltola, T.; Tuominen, E.; Tuominiemi, J.; Tuovinen, E.; Ungaro, D.; Wendland, L.; Banzuzi, K.; Karjalainen, A.; Korpela, A.; Tuuva, T.; Besancon, M.; Choudhury, S.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Malcles, J.; Millischer, L.; Nayak, A.; Rander, J.; Rosowsky, A.; Shreyber, I.; Titov, M.; Baffioni, S.; Beaudette, F.; Benhabib, L.; Bianchini, L.; Bluj, M.; Broutin, C.; Busson, P.; Charlot, C.; Daci, N.; Dahms, T.; Dobrzynski, L.; Granier de Cassagnac, R.; Haguenauer, M.; Miné, P.; Mironov, C.; Nguyen, M.; Ochando, C.; Paganini, P.; Sabes, D.; Salerno, R.; Sirois, Y.; Veelken, C.; Zabi, A.; Agram, J.-L.; Andrea, J.; Bloch, D.; Bodin, D.; Brom, J.-M.; Cardaci, M.; Chabert, E. C.; Collard, C.; Conte, E.; Drouhin, F.; Ferro, C.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Juillot, P.; Le Bihan, A.-C.; Van Hove, P.; Fassi, F.; Mercier, D.; Beauceron, S.; Beaupere, N.; Bondu, O.; Boudoul, G.; Chasserat, J.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Kurca, T.; Lethuillier, M.; Mirabito, L.; Perries, S.; Sordini, V.; Tschudi, Y.; Verdier, P.; Viret, S.; Tsamalaidze, Z.; Anagnostou, G.; Beranek, S.; Edelhoff, M.; Feld, L.; Heracleous, N.; Hindrichs, O.; Jussen, R.; Klein, K.; Merz, J.; Ostapchuk, A.; Perieanu, A.; Raupach, F.; Sammet, J.; Schael, S.; Sprenger, D.; Weber, H.

    2012-11-01

    A search for the Higgs boson produced in association with a W or Z boson in proton-proton collisions at a center-of-mass energy of 7 TeV is performed with the CMS detector at the LHC using the full 2011 data sample, from an integrated luminosity of 5 fb-1. Higgs boson decay modes to ττ and WW are explored by selecting events with three or four leptons in the final state. No excess above background expectations is observed, resulting in exclusion limits on the product of Higgs associated production cross section and decay branching fraction for Higgs boson masses between 110 and 200 GeV in these channels. Combining these results with other CMS associated production searches using the same dataset in the H→ γγ and H→ b overline{b} decay modes, the cross section for associated Higgs boson production 3.3 times the standard model expectation or larger is ruled out at the 95% confidence level for a Higgs boson mass of 125 GeV.[Figure not available: see fulltext.

  15. A Simple Class of Bayesian Nonparametric Autoregression Models.

    PubMed

    Di Lucca, Maria Anna; Guglielmi, Alessandra; Müller, Peter; Quintana, Fernando A

    2013-03-01

    We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. We discuss implementation and applications of the models to a sequence of waiting times between eruptions of the Old Faithful Geyser, and to a dataset consisting of sequences of recurrence indicators for tumors in the bladder of several patients.

  16. Mathematical modeling of a class of multibody flexible spacecraft structures

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul, G.

    1994-01-01

    A mathematical model for a general multibody flexible spacecraft is obtained. The generic spacecraft considered consists of a flexible central body to which a number of flexible multibody structures are attached. The coordinate systems used in the derivation allow effective decoupling of the translational motion of the entire spacecraft from its rotational motion about its center of mass. The derivation assumes that the deformations in the bodies are only due to elastic motions. The dynamic model derived is a closed-form vector-matrix differential equation. The model developed can be used for analysis and simulation of many realistic spacecraft configurations.

  17. The job content questionnaire in various occupational contexts: applying a latent class model

    PubMed Central

    Santos, Kionna Oliveira Bernardes; de Araújo, Tânia Maria; Karasek, Robert

    2017-01-01

    Objective To evaluate Job Content Questionnaire(JCQ) performance using the latent class model. Methods We analysed cross-sectional studies conducted in Brazil and examined three occupational categories: petroleum industry workers (n=489), teachers (n=4392) and primary healthcare workers (3078)and 1552 urban workers from a representative sample of the city of Feira de Santana in Bahia, Brazil. An appropriate number of latent classes was extracted and described each occupational category using latent class analysis, a multivariate method that evaluates constructs and takes into account the latent characteristics underlying the structure of measurement scales. The conditional probabilities of workers belonging to each class were then analysed graphically. Results Initially, the latent class analysis extracted four classes corresponding to the four job types (active, passive, low strain and high strain) proposed by the Job-Strain model (JSM) and operationalised by the JCQ. However, after taking into consideration the adequacy criteria to evaluate the number of extracted classes, three classes (active, low strain and high strain) were extracted from the studies of urban workers and teachers and four classes (active, passive, low strain and high strain) from the study of primary healthcare and petroleum industry workers. Conclusion The four job types proposed by the JSM were identified among primary healthcare and petroleum industry workers—groups with relatively high levels of skill discretion and decision authority. Three job types were identified for teachers and urban workers; however, passive job situations were not found within these groups. The latent class analysis enabled us to describe the conditional standard responses of the job types proposed by the model, particularly in relation to active jobs and high and low strain situations. PMID:28515185

  18. The job content questionnaire in various occupational contexts: applying a latent class model.

    PubMed

    Santos, Kionna Oliveira Bernardes; Araújo, Tânia Maria de; Carvalho, Fernando Martins; Karasek, Robert

    2017-05-17

    To evaluate Job Content Questionnaire(JCQ) performance using the latent class model. We analysed cross-sectional studies conducted in Brazil and examined three occupational categories: petroleum industry workers (n=489), teachers (n=4392) and primary healthcare workers (3078)and 1552 urban workers from a representative sample of the city of Feira de Santana in Bahia, Brazil. An appropriate number of latent classes was extracted and described each occupational category using latent class analysis, a multivariate method that evaluates constructs and takes into accountthe latent characteristics underlying the structure of measurement scales. The conditional probabilities of workers belonging to each class were then analysed graphically. Initially, the latent class analysis extracted four classes corresponding to the four job types (active, passive, low strain and high strain) proposed by the Job-Strain model (JSM) and operationalised by the JCQ. However, after taking into consideration the adequacy criteria to evaluate the number of extracted classes, three classes (active, low strain and high strain) were extracted from the studies of urban workers and teachers and four classes (active, passive, low strain and high strain) from the study of primary healthcare and petroleum industry workers. The four job types proposed by the JSM were identified among primary healthcare and petroleum industry workers-groups with relatively high levels of skill discretion and decision authority. Three job types were identified for teachers and urban workers; however, passive job situations were not found within these groups. The latent class analysis enabled us to describe the conditional standard responses of the job types proposed by the model, particularly in relation to active jobs and high and low strain situations. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless

  19. A Class of Markov Models for Longitudinal Ordinal Data

    PubMed Central

    Lee, Keunbaik; Daniels, Michael J.

    2009-01-01

    Summary Generalized linear models with serial dependence are often used for short longitudinal series. Heagerty (2002, Biometrics 58, 342–351) has proposed marginalized transition models for the analysis of longitudinal binary data. In this article, we extend this work to accommodate longitudinal ordinal data. Fisher-scoring algorithms are developed for estimation. Methods are illustrated on quality-of-life data from a recent colorectal cancer clinical trial. PMID:18078479

  20. The transfer of contextual control over equivalence classes through equivalence classes: a possible model of social stereotyping.

    PubMed Central

    Kohlenberg, B S; Hayes, S C; Hayes, L J

    1991-01-01

    In Experiment 1, subjects acquired conditional equivalence classes controlled by three male and three female names as contextual stimuli. When equivalence relations were tested using new names not used in training (three male and three female), contextual control remained intact. Thus, generalized control of the composition of conditional equivalence classes by characteristically gender-identified names was shown. A basic analysis of this finding was tested in Experiment 2. Contextual equivalence classes were established using as contextual stimuli nonrepresentational visual figures that were members of additional pretrained three-member equivalence classes. When other stimuli in the pretrained equivalence classes were used as contextual stimuli, the conditional equivalence classes remained intact. Control subjects showed that this effect depended on the equivalence relations established in pretraining. The results show that contextual control over equivalence classes can transfer through equivalence classes. The implications of this phenomenon for social stereotyping are discussed. PMID:1774542

  1. On the Use of Covariates in a Latent Class Signal Detection Model, with Applications to Constructed Response Scoring

    ERIC Educational Resources Information Center

    Wang, Zijian Gerald

    2012-01-01

    A latent class signal detection (SDT) model was recently introduced as an alternative to traditional item response theory (IRT) methods in the analysis of constructed response data. This class of models can be represented as restricted latent class models and differ from the IRT approach in the way the latent construct is conceptualized. One…

  2. On the Use of Covariates in a Latent Class Signal Detection Model, with Applications to Constructed Response Scoring

    ERIC Educational Resources Information Center

    Wang, Zijian Gerald

    2012-01-01

    A latent class signal detection (SDT) model was recently introduced as an alternative to traditional item response theory (IRT) methods in the analysis of constructed response data. This class of models can be represented as restricted latent class models and differ from the IRT approach in the way the latent construct is conceptualized. One…

  3. Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach

    PubMed Central

    Lanza, Stephanie T.; Tan, Xianming; Bray, Bethany C.

    2014-01-01

    Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with categorical, continuous, or count distributions. A Monte Carlo simulation study is conducted to compare the performance of the new technique to two commonly used classify-analyze techniques: maximum-probability assignment and multiple pseudo-class draws. Simulation results show that the model-based approach produces substantially less biased estimates of the effect compared to either classify-analyze technique, particularly when the association between the latent class variable and the distal outcome is strong. In addition, we show that only the model-based approach is consistent. The approach is demonstrated empirically: latent classes of adolescent depression are used to predict smoking, grades, and delinquency. SAS syntax for implementing this approach using PROC LCA and a corresponding macro are provided. PMID:25419096

  4. Likelihood analysis of spatial capture-recapture models for stratified or class structured populations

    USGS Publications Warehouse

    Royle, J. Andrew; Sutherland, Christopher S.; Fuller, Angela K.; Sun, Catherine C.

    2015-01-01

    We develop a likelihood analysis framework for fitting spatial capture-recapture (SCR) models to data collected on class structured or stratified populations. Our interest is motivated by the necessity of accommodating the problem of missing observations of individual class membership. This is particularly problematic in SCR data arising from DNA analysis of scat, hair or other material, which frequently yields individual identity but fails to identify the sex. Moreover, this can represent a large fraction of the data and, given the typically small sample sizes of many capture-recapture studies based on DNA information, utilization of the data with missing sex information is necessary. We develop the class structured likelihood for the case of missing covariate values, and then we address the scaling of the likelihood so that models with and without class structured parameters can be formally compared regardless of missing values. We apply our class structured model to black bear data collected in New York in which sex could be determined for only 62 of 169 uniquely identified individuals. The models containing sex-specificity of both the intercept of the SCR encounter probability model and the distance coefficient, and including a behavioral response are strongly favored by log-likelihood. Estimated population sex ratio is strongly influenced by sex structure in model parameters illustrating the importance of rigorous modeling of sex differences in capture-recapture models.

  5. Evolution of perturbations in distinct classes of canonical scalar field models of dark energy

    SciTech Connect

    Jassal, H. K.

    2010-04-15

    Dark energy must cluster in order to be consistent with the equivalence principle. The background evolution can be effectively modeled by either a scalar field or by a barotropic fluid. The fluid model can be used to emulate perturbations in a scalar field model of dark energy, though this model breaks down at large scales. In this paper we study evolution of dark energy perturbations in canonical scalar field models: the classes of thawing and freezing models. The dark energy equation of state evolves differently in these classes. In freezing models, the equation of state deviates from that of a cosmological constant at early times. For thawing models, the dark energy equation of state remains near that of the cosmological constant at early times and begins to deviate from it only at late times. Since the dark energy equation of state evolves differently in these classes, the dark energy perturbations too evolve differently. In freezing models, since the equation of state deviates from that of a cosmological constant at early times, there is a significant difference in evolution of matter perturbations from those in the cosmological constant model. In comparison, matter perturbations in thawing models differ from the cosmological constant only at late times. This difference provides an additional handle to distinguish between these classes of models and this difference should manifest itself in the integrated Sachs-Wolfe effect.

  6. Similarity between class A and class B G-protein-coupled receptors exemplified through calcitonin gene-related peptide receptor modelling and mutagenesis studies

    PubMed Central

    Vohra, Shabana; Taddese, Bruck; Conner, Alex C.; Poyner, David R.; Hay, Debbie L.; Barwell, James; Reeves, Philip J.; Upton, Graham J. G.; Reynolds, Christopher A.

    2013-01-01

    Modelling class B G-protein-coupled receptors (GPCRs) using class A GPCR structural templates is difficult due to lack of homology. The plant GPCR, GCR1, has homology to both class A and class B GPCRs. We have used this to generate a class A–class B alignment, and by incorporating maximum lagged correlation of entropy and hydrophobicity into a consensus score, we have been able to align receptor transmembrane regions. We have applied this analysis to generate active and inactive homology models of the class B calcitonin gene-related peptide (CGRP) receptor, and have supported it with site-directed mutagenesis data using 122 CGRP receptor residues and 144 published mutagenesis results on other class B GPCRs. The variation of sequence variability with structure, the analysis of polarity violations, the alignment of group-conserved residues and the mutagenesis results at 27 key positions were particularly informative in distinguishing between the proposed and plausible alternative alignments. Furthermore, we have been able to associate the key molecular features of the class B GPCR signalling machinery with their class A counterparts for the first time. These include the [K/R]KLH motif in intracellular loop 1, [I/L]xxxL and KxxK at the intracellular end of TM5 and TM6, the NPXXY/VAVLY motif on TM7 and small group-conserved residues in TM1, TM2, TM3 and TM7. The equivalent of the class A DRY motif is proposed to involve Arg2.39, His2.43 and Glu3.46, which makes a polar lock with T6.37. These alignments and models provide useful tools for understanding class B GPCR function. PMID:23235263

  7. Dynamic behaviors of a class of HIV compartmental models

    NASA Astrophysics Data System (ADS)

    Chen, Xiaoyan; Huang, Lihong; Yu, Pei

    2015-06-01

    Based on heterogeneities in drug efficacy (either spatial or phenotypic), two HIV compartmental models were proposed in Callaway and Perelson (2002) to study the HIV virus dynamics under drug treatment. In this paper, we provide a global analysis on the two models, including the positivity and boundedness of solutions and the global stability of equilibrium solutions. In particular, we show that when the basic reproduction number R0 ⩽ 1 (for which the infection equilibrium does not exist), the infection-free equilibrium is globally asymptotically stable; while when R0 > 1 (for which the infection equilibrium exists), the infection equilibrium is globally asymptotically stable.

  8. Stochastic Control for a Class of Random Evolution Models

    SciTech Connect

    Hongler, Max-Olivier Soner, Halil Mete Streit, Ludwig

    2004-03-15

    We construct the explicit connection existing between a solvable model of the discrete velocities non-linear Boltzmann equation and the Hamilton-Bellman-Jacobi equation associated with a simple optimal control of a piecewise deterministic process. This study extends the known relation that exists between the Burgers equation and a simple controlled diffusion problem. In both cases the resulting partial differential equations can be linearized via a logarithmic transformation and hence offer the possibility to solve physically relevant non-linear field models in full generality.

  9. Climate Ocean Modeling on a Beowulf Class System

    NASA Technical Reports Server (NTRS)

    Cheng, B. N.; Chao, Y.; Wang, P.; Bondarenko, M.

    2000-01-01

    With the growing power and shrinking cost of personal computers. the availability of fast ethernet interconnections, and public domain software packages, it is now possible to combine them to build desktop parallel computers (named Beowulf or PC clusters) at a fraction of what it would cost to buy systems of comparable power front supercomputer companies. This led as to build and assemble our own sys tem. specifically for climate ocean modeling. In this article, we present our experience with such a system, discuss its network performance, and provide some performance comparison data with both HP SPP2000 and Cray T3E for an ocean Model used in present-day oceanographic research.

  10. A class of three-loop models with neutrino mass and dark matter

    NASA Astrophysics Data System (ADS)

    Chen, Chian-Shu; McDonald, Kristian L.; Nasri, Salah

    2014-06-01

    We study a class of three-loop models for neutrino mass in which dark matter plays a key role in enabling the mass diagram. The simplest models in this class have Majorana dark matter and include the proposal of Krauss, Nasri and Trodden; we identify the remaining related models, including the viable colored variants. The next-to-simplest models use either more multiplets and/or a slight modification of the loop-diagram, and predict inert N-tuplet scalar dark matter.

  11. Estimation of Parameters in Latent Class Models with Constraints on the Parameters.

    DTIC Science & Technology

    1986-06-01

    parameter. This rules out models which characterize each Pkj in terms of conjoint effects of item and state parameters, as the Rasch model does, for... example . It also rules out models that impose ordering constraints on the Pkj’S. Thus, while many interesting models can be cast in terms of equality...ONR86-1 m 4 4. TITLE (mnd Subtitle) S. TYPE OF REPORT & PERIOD COVERED Estimation of Parameters in Latent Class Models with Constraints on the

  12. First steps toward a new class of statistical convection models

    NASA Astrophysics Data System (ADS)

    Bristow, W. A.

    2009-12-01

    The pioneering work of Gary Abel, Mervyn Freeman [2002, 2006], and Murray Parkinson [2006] have examined the complex nature of convection observed by SuperDARN. They have shown that the convection velocity is highly variable in both time and space, with spatial and temporal correlations without characteristic scales. Current statistical convection models do not account for any variability beyond that of the IMF time series with which they are driven. They do, however, produce a pattern of convection that seem to represent the average of the observations. This work presents the initial steps toward developing a new convection model that will account for the variable nature of velocity, while retaining the average behavior of current models. The presentation examines the statistics of the velocity fluctuations and random number generators that have the same distributions. The next steps are to to enforce some temporal and spatial correlations that will generate the convection patterns observed on average. Abel, G. A., M. P. Freeman, (2002) A statistical analysis of ionospheric velocity and magnetic field power spectra at the time of pulsed ionospheric flows. J. Geophys. Res. 107, 1470, doi:10/1029/2002JA009402 Abel, G. A., M. P. Freeman, and G. Chisham (2006) Spatial structure of ionospheric convection velocities in regions of open and closed magnetic field topology. Geophys. Res. Lett. 33, doi 10.1029/2006GL027919 Parkinson, M. L., (2006) Dynamical critical scaling of electric field fluctuations in the greater cusp and magnetotail implied by HF radar observations of F-region Doppler velocity. Ann. Geophys., 24, 689-705

  13. Nested partially latent class models for dependent binary data; estimating disease etiology.

    PubMed

    Wu, Zhenke; Deloria-Knoll, Maria; Zeger, Scott L

    2017-04-01

    The Pneumonia Etiology Research for Child Health (PERCH) study seeks to use modern measurement technology to infer the causes of pneumonia for which gold-standard evidence is unavailable. Based on case-control data, the article describes a latent variable model designed to infer the etiology distribution for the population of cases, and for an individual case given her measurements. We assume each observation is drawn from a mixture model for which each component represents one disease class. The model conisidered here addresses a major limitation of the traditional latent class approach by taking account of residual dependence among multivariate binary outcomes given disease class, hence reducing estimation bias, retaining efficiency and offering more valid inference. Such "local dependence" on each subject is induced in the model by nesting latent subclasses within each disease class. Measurement precision and covariation can be estimated using the control sample for whom the class is known. In a Bayesian framework, we use stick-breaking priors on the subclass indicators for model-averaged inference across different numbers of subclasses. Assessment of model fit and individual diagnosis are done using posterior samples drawn by Gibbs sampling. We demonstrate the utility of the method on simulated and on the motivating PERCH data. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  14. An introduction to latent variable mixture modeling (part 2): longitudinal latent class growth analysis and growth mixture models.

    PubMed

    Berlin, Kristoffer S; Parra, Gilbert R; Williams, Natalie A

    2014-03-01

    Pediatric psychologists are often interested in finding patterns in heterogeneous longitudinal data. Latent variable mixture modeling is an emerging statistical approach that models such heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns. The purpose of the second of a 2-article set is to offer a nontechnical introduction to longitudinal latent variable mixture modeling. 3 latent variable approaches to modeling longitudinal data are reviewed and distinguished. Step-by-step pediatric psychology examples of latent growth curve modeling, latent class growth analysis, and growth mixture modeling are provided using the Early Childhood Longitudinal Study-Kindergarten Class of 1998-1999 data file. Latent variable mixture modeling is a technique that is useful to pediatric psychologists who wish to find groupings of individuals who share similar longitudinal data patterns to determine the extent to which these patterns may relate to variables of interest.

  15. Two-particle correlations with identified triggers in p -Pb collisions at √{sNN}=5.02 TeV using a multiphase transport model

    NASA Astrophysics Data System (ADS)

    Sarkar, Debojit; Choudhury, Subikash; Chattopadhyay, Subhasis

    2016-10-01

    We report calculations regarding recent measurements of near-side jetlike yields obtained from the angular correlations with pions and protons as trigger particles and unindentified charged hadrons as associated at intermediate transverse momentum (pT). Events generated from the string melting version of a multiphase transport model (AMPT) and HIJING are analyzed to calculate the near-side jetlike yields associated with pion and proton triggers in p -Pb collisions at 5.02 TeV. Multiplicity dependence of the near-side jetlike yield is studied by subtracting the so-called long-range correlations. In A -A collisions, at intermediate pT, jetlike yield associated with baryon trigger shows a gradual suppression from peripheral to central events following an initial rise. This suppression was interpreted as a combined effect of parton energy loss due to jet-quenching and particle production via quark recombination generating anomalous baryon to meson enhancement at intermediate pT. Similar enhancement was also observed in small collision systems like p -Pb at CERN Large Hadron Collider (LHC) energy. In absence of any significant jet-medium interplay in small systems, trigger species dependence of the associated jetlike yield may be attributed dominantly to the dynamics of quark coalescence and/or radial flow. The multiplicity evolution of the trigger species dependence of jetlike yields, obtained from AMPT, found to be completely different from the same when extracted from a hydrodynamical model, EPOS. With the data available at LHC, these results may be tested to achieve further insight on the underlying dynamics in small collision systems, and the mechanism of hadronization at intermediate pT as well.

  16. Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons.

    PubMed

    Pospischil, Martin; Toledo-Rodriguez, Maria; Monier, Cyril; Piwkowska, Zuzanna; Bal, Thierry; Frégnac, Yves; Markram, Henry; Destexhe, Alain

    2008-11-01

    We review here the development of Hodgkin-Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are "fast spiking", "regular spiking", "intrinsically bursting" and "low-threshold spike" cells. For each class, we fit "minimal" HH type models to experimental data. The models contain the minimal set of voltage-dependent currents to account for the data. To obtain models as generic as possible, we used data from different preparations in vivo and in vitro, such as rat somatosensory cortex and thalamus, guinea-pig visual and frontal cortex, ferret visual cortex, cat visual cortex and cat association cortex. For two cell classes, we used automatic fitting procedures applied to several cells, which revealed substantial cell-to-cell variability within each class. The selection of such cellular models constitutes a necessary step towards building network simulations of the thalamocortical system with realistic cellular dynamical properties.

  17. Educational Criteria for Evaluating Simple Class Diagrams Made by Novices for Conceptual Modeling

    ERIC Educational Resources Information Center

    Kayama, Mizue; Ogata, Shinpei; Asano, David K.; Hashimoto, Masami

    2016-01-01

    Conceptual modeling is one of the most important learning topics for higher education and secondary education. The goal of conceptual modeling in this research is to draw a class diagram using given notation to satisfy the given requirements. In this case, the subjects are asked to choose concepts to satisfy the given requirements and to correctly…

  18. Academic Achievement Effects of an In-Class Service Model on Students with and without Disabilities.

    ERIC Educational Resources Information Center

    Saint-Laurent, Lise; Dionne, Jean; Giasson, Jocelyne; Royer, Egide; Simard, Claude; Pierard, Brigitte

    1998-01-01

    This study evaluated an in-class service model for third graders at risk of school failure in 13 schools. The model included collaborative consultation, cooperative teaching, parent involvement, and strategic and adapted instruction. Significant effects were found on writing scores for students at risk (but not students with learning disabilities)…

  19. Achieving World-Class Schools: Mastering School Improvement Using a Genetic Model.

    ERIC Educational Resources Information Center

    Kimmelman, Paul L.; Kroeze, David J.

    In providing its program for education reform, this book uses, as an analogy, the genetic model taken from the Human Genome project. In the first part, "Theoretical Underpinnings," the book explains why a genetic model can be used to improve school systems; describes the critical components of a world-class school system; and details the…

  20. Distinctive and Incompatible Properties of Two Common Classes of IRT Models for Graded Responses.

    ERIC Educational Resources Information Center

    Andrich, David

    1995-01-01

    Two classes of graded response models, one based on the work of L. L. Thurstone and the other on the work of G. Rasch, are juxtaposed and shown to satisfy important but mutually incompatible criteria and to reflect different response processes. Implications of the choice between these models are discussed. (Author/SLD)

  1. Achieving World-Class Schools: Mastering School Improvement Using a Genetic Model.

    ERIC Educational Resources Information Center

    Kimmelman, Paul L.; Kroeze, David J.

    In providing its program for education reform, this book uses, as an analogy, the genetic model taken from the Human Genome project. In the first part, "Theoretical Underpinnings," the book explains why a genetic model can be used to improve school systems; describes the critical components of a world-class school system; and details the…

  2. Search for the Standard Model Higgs boson in its associated production with a W vector boson in pp collisions at (s) = 1.96TeV

    NASA Astrophysics Data System (ADS)

    Hegab, Hatim H.

    In this dissertation, results from a search for the Standard Model (SM) Higgs boson, at the DZERO experiment is shown. The SM is the theoretical framework which describes particles of matter and force carrier gauge bosons. To solve the mass problem in the SM, the Higgs mechanism was introduced. The Higgs mechanism causes an electroweak symmetry breaking and a new massive scalar boson was postulated. This particle is the Higgs boson. A search for the Higgs boson has been ongoing at the Tevatron where protons and antiprotons were allowed to collide at a center-of-mass energy of 1.96 TeV. For a low mass Higgs that is lower than 135 GeV, the dominant decay mode is Higgs to a pair of b-quarks. Work in this dissertation concentrated on a Higgs in the mass range of 100 - 150 GeV, where a W vector boson is produced in association with the Higgs boson. The final state chosen is one which contains a lepton (electron or a muon) a neutrino and a pair of b-quarks. This study used data provided by the DZERO experiment and computing resources provided by Fermilab. Results presented here are the outcome of analyzing 5.3 inverse-fb of data from RunII period. The analysis used different techniques to increase the sensitivity of the study. Data were subdivided based on lepton flavor, number of jets in sample, jets identified as b -jets and dates of collected data. A multivariate analysis technique based on boosted decision trees were used to separate signal from background processes, physical and instrumental. A good agreement between data and simulated events was observed. An observed (expected) upper limit of 4.5 (4.8) for a Higgs of mass 115 GeV was set on the ratio of the Higgs production to its decay branching ratio at the 95% confidence level.

  3. A nonparametric vs. latent class model of general practitioner utilization: evidence from Canada.

    PubMed

    McLeod, Logan

    2011-12-01

    Predicting health care utilization is the foundation of many health economics analyses, such as calculating risk-adjustment capitation payments or measuring equity in health care utilization. The most common econometric models of physician utilization are parametric count data models, since the most common metric of physician utilization is the number of physician visits. This paper makes two distinct contributions to the literature analyzing GP utilization: (i) it is the first to use a nonparametric kernel conditional density estimator to model GP utilization and compare the predicted utilization with that from a latent class negative binomial model; and (ii) it uses panel data to control for the potential endogeneity between self-reported health status and the number of GP visits. The goodness-of-fit results show the kernel conditional density estimator provides a better fit to the observed distribution of GP visits than the latent class negative binomial model. There are some meaningful differences in how the predicted conditional mean number of GP visits changes with a change in an individual's characteristics, called the incremental effect (IE), between the kernel conditional density estimator and the latent class negative binomial model. The most notable differences are observed in the right tail of the distribution where the IEs from the latent class negative binomial model are up to 190 times the magnitude of the IEs from the kernel conditional density estimator.

  4. A general model of invariant chain association with class II major histocompatibility complex proteins.

    PubMed Central

    Lee, C; McConnell, H M

    1995-01-01

    The binding of invariant chain to major histocompatibility complex (MHC) proteins is an important step in processing of MHC class II proteins and in antigen presentation. The question of how invariant chain can bind to all MHC class II proteins is central to understanding these processes. We have employed molecular modeling to predict the structure of class II-associated invariant chain peptide (CLIP)-MHC protein complexes and to ask whether the predicted mode of association could be general across all MHC class II proteins. CLIP fits identically into the MHC class II alleles HLA-DR3, I-Ak, I-Au, and I-Ad, with a consistent pattern of hydrogen bonds, contacts, and hydrophobic burial and without bad contacts. Our model predicts the burial of CLIP residues Met-91 and Met-99 in the deep P1 and P9 anchor pockets and other detailed interactions, which we have compared with available data. The predicted pattern of I-A allele-specific effects on CLIP binding is very similar to that observed experimentally by alanine-scanning mutations of CLIP. Together, these results indicate that CLIP may bind in a single, general way across products of MHC class II alleles. Images Fig. 1 Fig. 2 Fig. 3 PMID:7667280

  5. Cultural models of self and social class disparities at organizational gateways and pathways.

    PubMed

    Townsend, Sarah Sm; Truong, Mindy

    2017-05-26

    Attaining a college degree has traditionally been assumed to be key to upward social and professional mobility. However, college graduates from working-class backgrounds achieve less career success in professional, white-collar workplaces compared to those from middle-class backgrounds. Using a cultural models approach, we examine how the independent cultural beliefs and practices promoted by professional organizations disadvantage people from working-class backgrounds, who espouse interdependent beliefs and practices. Our review illustrates how this disadvantage can manifest in two ways. First, despite relative equality in objective qualifications, it can occur at organizational gateways (e.g., interview and hiring decisions). Second, even after people from working-class backgrounds gain access to an organization, it can occur along organizational pathways (e.g., performance evaluations and assignment to high-profile tasks). Copyright © 2017. Published by Elsevier Ltd.

  6. Managerial performance and cost efficiency of Japanese local public hospitals: a latent class stochastic frontier model.

    PubMed

    Besstremyannaya, Galina

    2011-09-01

    The paper explores the link between managerial performance and cost efficiency of 617 Japanese general local public hospitals in 1999-2007. Treating managerial performance as unobservable heterogeneity, the paper employs a panel data stochastic cost frontier model with latent classes. Financial parameters associated with better managerial performance are found to be positively significant in explaining the probability of belonging to the more efficient latent class. The analysis of latent class membership was consistent with the conjecture that unobservable technological heterogeneity reflected in the existence of the latent classes is related to managerial performance. The findings may support the cause for raising efficiency of Japanese local public hospitals by enhancing the quality of management.

  7. Constraining minimal anomaly free U(1) extensions of the Standard Model

    NASA Astrophysics Data System (ADS)

    Ekstedt, Andreas; Enberg, Rikard; Ingelman, Gunnar; Löfgren, Johan; Mandal, Tanumoy

    2016-11-01

    We consider a class of minimal anomaly free U(1) extensions of the Standard Model with three generations of right-handed neutrinos and a complex scalar. Using electroweak precision constraints, new 13 TeV LHC data, and considering theoretical limitations such as perturbativity, we show that it is possible to constrain a wide class of models. By classifying these models with a single parameter, κ, we can put a model independent upper bound on the new U(1) gauge coupling g z . We find that the new dilepton data puts strong bounds on the parameters, especially in the mass region M Z ' ≲ 3 TeV.

  8. Inclusion: The Results of Attendance, Achievement, and Self-Concept in a Class-within-a-Class Model.

    ERIC Educational Resources Information Center

    Yeager, Noranne

    Class-Within-A-Class (CWC) is an inclusion program that has been in existence since 1984 at a midwestern suburban/rural school of approximately 1,200 K-12 students. Special and general educators collaborate in teaching both disabled and regular students in a regular classroom setting. Teachers participate in inservice programs covering effective…

  9. Search for the minimal universal extra dimension model at the LHC with {radical}(s)=7 TeV

    SciTech Connect

    Bhattacherjee, Biplob; Ghosh, Kirtiman

    2011-02-01

    The Universal Extra Dimension (UED) model is one of the popular extension of the standard model (SM) which offers interesting phenomenology. In the minimal UED (mUED) model, Kaluza-Klein (KK) parity conservation ensures that n=1 KK states can only be pair produced at colliders and the lightest KK particle is stable. In most of the parameter space, first KK excitation of SM hypercharge gauge boson is the lightest one and it can be a viable dark matter candidate. Thus, the decay of n=1 KK particles will always involve missing transverse energy.

  10. An evaluation of the use of covariates to assist in class enumeration in linear growth mixture modeling.

    PubMed

    Hu, Jinxiang; Leite, Walter L; Gao, Miao

    2017-03-08

    This study examined whether the inclusion of covariates that predict class membership improves class identification in a growth mixture modeling (GMM). We manipulated the degree of class separation, sample size, the magnitude of covariate effect on class membership, the covariance between the intercept and the slope, and fit two models with covariates and an unconditional model. We concluded that correct class identification in GMM requires large sample sizes and class separation, and that unconditional GMM performs better than GMM with covariates if the sample size and class separation are sufficiently large. With small sample sizes, GMM with covariates outperformed unconditional GMM, but the percentage of correct class enumeration was low across different fit criteria.

  11. State-space reduction and equivalence class sampling for a molecular self-assembly model.

    PubMed

    Packwood, Daniel M; Han, Patrick; Hitosugi, Taro

    2016-07-01

    Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.

  12. School climate and bullying victimization: a latent class growth model analysis.

    PubMed

    Gage, Nicholas A; Prykanowski, Debra A; Larson, Alvin

    2014-09-01

    Researchers investigating school-level approaches for bullying prevention are beginning to discuss and target school climate as a construct that (a) may predict prevalence and (b) be an avenue for school-wide intervention efforts (i.e., increasing positive school climate). Although promising, research has not fully examined and established the social-ecological link between school climate factors and bullying/peer aggression. To address this gap, we examined the association between school climate factors and bullying victimization for 4,742 students in Grades 3-12 across 3 school years in a large, very diverse urban school district using latent class growth modeling. Across 3 different models (elementary, secondary, and transition to middle school), a 3-class model was identified, which included students at high-risk for bullying victimization. Results indicated that, for all students, respect for diversity and student differences (e.g., racial diversity) predicted within-class decreases in reports of bullying. High-risk elementary students reported that adult support in school was a significant predictor of within-class reduction of bullying, and high-risk secondary students report peer support as a significant predictor of within-class reduction of bullying.

  13. Dynamics of a Class of HIV Infection Models with Cure of Infected Cells in Eclipse Stage.

    PubMed

    Maziane, Mehdi; Lotfi, El Mehdi; Hattaf, Khalid; Yousfi, Noura

    2015-12-01

    In this paper, we propose two HIV infection models with specific nonlinear incidence rate by including a class of infected cells in the eclipse phase. The first model is described by ordinary differential equations (ODEs) and generalizes a set of previously existing models and their results. The second model extends our ODE model by taking into account the diffusion of virus. Furthermore, the global stability of both models is investigated by constructing suitable Lyapunov functionals. Finally, we check our theoretical results with numerical simulations.

  14. Search for minimal supersymmetric standard model Higgs Bosons H / A and for a $$Z^{\\prime}$$ boson in the $$\\tau\\tau $$ final state produced in pp collisions at $$\\sqrt{s}$$= 13 TeV with the ATLAS detector

    DOE PAGES

    Aaboud, M.; Aad, G.; Abbott, B.; ...

    2016-10-27

    A search for neutral Higgs bosons of the minimal supersymmetric standard model (MSSM) and for a heavneutral Z ' boson is performed using a data sample corresponding to an integrated luminosity of 3.2 fb- 1 from proton–proton collisions at √s=13 TeV recorded by the ATLAS detector at the LHC. The heavy resonance is assumed to decay to a τ + τ - pair with at least one τ lepton decaying to final states with hadrons and a neutrino. The search is performed in the mass range of 0.2–1.2 TeV for the MSSM neutral Higgs bosons and 0.5–2.5 TeV for the heavy neutral Z ' boson. The data are in good agreement with the background predicted by the Standard Model. The results are interpreted in MSSM and Z ' benchmark scenarios. The most stringent constraints on the MSSM m A –tan β space exclude at 95 % confidence level (CL) tan β > 7.6 for m A = 200 GeV in the mmore » $$mod+\\atop{h}$$ MSSM scenario. For the Sequential Standard Model, a Z$$'\\atop{SSM}$$ mass up to 1.90 TeV is excluded at 95 % CL and masses up to 1.82–2.17 TeV are excluded for a Z'$$\\atop{SFM}$$ of the strong flavour model.« less

  15. Modelling a new class of anisotropic compact stars satisfying the Karmakar's condition

    NASA Astrophysics Data System (ADS)

    Bhar, Piyali

    2017-06-01

    The present paper provides a new class of interior solutions which satisfies the Karmakar condition (K.R. Karmarkar, Proc. Indian Acad. Sci. A 27, 56 (1948)). The model is generated by assuming a reasonable choice for the metric potential g_{rr} and the model parameters are obtained accordingly since, in the case of embedding class-I, the metric co-efficients are dependent on each other. We match our interior solution to the exterior Schwarzschild line element in the presence of thin shell. The proposed model does not suffer from central singularity. The model is discussed for a neutron star of mass 1.4 M_{⊙} and radius 10km. The potential stability condition of the model is also discussed.

  16. A latent class distance association model for cross-classified data with a categorical response variable.

    PubMed

    Vera, José Fernando; de Rooij, Mark; Heiser, Willem J

    2014-11-01

    In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low-dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented. © 2014 The British Psychological Society.

  17. Z boson pair production at the LHC to O(α) in TeV scale gravity models

    NASA Astrophysics Data System (ADS)

    Agarwal, Neelima; Ravindran, V.; Tiwari, Vivek Kumar; Tripathi, Anurag

    2010-05-01

    The first results on next-to-leading order QCD corrections to production of two Z bosons in hadronic collisions in the large extra dimension ADD model are presented. Various kinematical distributions are obtained to order α in QCD by taking into account all the parton level subprocesses. We estimate the impact of the QCD corrections on various observables and find that they are significant. We also show the reduction in factorization scale uncertainty when O(α) effects are included.

  18. Search for neutral Higgs bosons of the minimal supersymmetric standard model decaying to tau pairs in pp collisions at square root of s = 1.96 TeV.

    PubMed

    Abulencia, A; Acosta, D; Adelman, J; Affolder, T; Akimoto, T; Albrow, M G; Ambrose, D; Amerio, S; Amidei, D; Anastassov, A; Anikeev, K; Annovi, A; Antos, J; Aoki, M; Apollinari, G; Arguin, J-F; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Azfar, F; Azzi-Bacchetta, P; Azzurri, P; Bacchetta, N; Bachacou, H; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Baroiant, S; Bartsch, V; Bauer, G; Bedeschi, F; Behari, S; Belforte, S; Bellettini, G; Bellinger, J; Belloni, A; Ben-Haim, E; Benjamin, D; Beretvas, A; Beringer, J; Berry, T; Bhatti, A; Binkley, M; Bisello, D; Bishai, M; Blair, R E; Blocker, C; Bloom, K; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bolshov, A; Bortoletto, D; Boudreau, J; Bourov, S; Boveia, A; Brau, B; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burkett, K; Busetto, G; Bussey, P; Byrum, K L; Cabrera, S; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carlsmith, D; Carosi, R; Carron, S; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chapman, J; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, I; Cho, K; Chokheli, D; Chou, J P; Chu, P H; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Ciljak, M; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Coca, M; Connolly, A; Convery, M E; Conway, J; Cooper, B; Copic, K; Cordelli, M; Cortiana, G; Cruz, A; Cuevas, J; Culbertson, R; Cyr, D; Daronco, S; D'Auria, S; D'onofrio, M; Dagenhart, D; de Barbaro, P; De Cecco, S; Deisher, A; De Lentdecker, G; Dell'Orso, M; Demers, S; Demortier, L; Deng, J; Deninno, M; De Pedis, D; Derwent, P F; Dionisi, C; Dittmann, J; Dituro, P; Dörr, C; Dominguez, A; Donati, S; Donega, M; Dong, P; Donini, J; Dorigo, T; Dube, S; Ebina, K; Efron, J; Ehlers, J; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, I; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Field, R; Flanagan, G; Flores-Castillo, L R; Foland, A; Forrester, S; Foster, G W; Franklin, M; Freeman, J C; Fujii, Y; Furic, I; Gajjar, A; Gallinaro, M; Galyardt, J; Garcia, J E; Garcia Sciveres, M; Garfinkel, A F; Gay, C; Gerberich, H; Gerchtein, E; Gerdes, D; Giagu, S; Giannetti, P; Gibson, A; Gibson, K; Ginsburg, C; Giolo, K; Giordani, M; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Goldstein, J; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Gotra, Y; Goulianos, K; Gresele, A; Griffiths, M; Grinstein, S; Grosso-Pilcher, C; Grundler, U; Guimaraes da Costa, J; Haber, C; Hahn, S R; Hahn, K; Halkiadakis, E; Hamilton, A; Han, B-Y; Handler, R; Happacher, F; Hara, K; Hare, M; Harper, S; Harr, R F; Harris, R M; Hatakeyama, K; Hauser, J; Hays, C; Hayward, H; Heijboer, A; Heinemann, B; Heinrich, J; Hennecke, M; Herndon, M; Heuser, J; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Holloway, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Huston, J; Ikado, K; Incandela, J; Introzzi, G; Iori, M; Ishizawa, Y; Ivanov, A; Iyutin, B; James, E; Jang, D; Jayatilaka, B; Jeans, D; Jensen, H; Jeon, E J; Jones, M; Joo, K K; Jun, S Y; Junk, T R; Kamon, T; Kang, J; Karagoz-Unel, M; Karchin, P E; Kato, Y; Kemp, Y; Kephart, R; Kerzel, U; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, M S; Kim, S B; Kim, S H; Kim, Y K; Kirby, M; Kirsch, L; Klimenko, S; Klute, M; Knuteson, B; Ko, B R; Kobayashi, H; Kondo, K; Kong, D J; Konigsberg, J; Kordas, K; Korytov, A; Kotwal, A V; Kovalev, A; Kraus, J; Kravchenko, I; Kreps, M; Kreymer, A; Kroll, J; Krumnack, N; Kruse, M; Krutelyov, V; Kuhlmann, S E; Kusakabe, Y; Kwang, S; Laasanen, A T; Lai, S; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; Lecci, C; Lecompte, T; Lee, J; Lee, J; Lee, S W; Lefèvre, R; Leonardo, N; Leone, S; Levy, S; Lewis, J D; Li, K; Lin, C; Lin, C S; Lindgren, M; Lipeles, E; Liss, T M; Lister, A; Litvintsev, D O; Liu, T; Liu, Y; Lockyer, N S; Loginov, A; Loreti, M; Loverre, P; Lu, R-S; Lucchesi, D; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Lytken, E; Mack, P; Macqueen, D; Madrak, R; Maeshima, K; Maksimovic, P; Manca, G; Margaroli, F; Marginean, R; Marino, C; Martin, A; Martin, M; Martin, V; Martínez, M; Maruyama, T; Matsunaga, H; Mattson, M E; Mazini, R; Mazzanti, P; McFarland, K S; McGivern, D; McIntyre, P; McNamara, P; McNulty, R; Mehta, A; Menzemer, S; Menzione, A; Merkel, P; Mesropian, C; Messina, A; von der Mey, M; Miao, T; Miladinovic, N; Miles, J; Miller, R; Miller, J S; Mills, C; Milnik, M; Miquel, R; Miscetti, S; Mitselmakher, G; Miyamoto, A; Moggi, N; Mohr, B; Moore, R; Morello, M; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Mulhearn, M; Muller, Th; Mumford, R; Murat, P; Nachtman, J; Nahn, S; Nakano, I; Napier, A; Naumov, D; Necula, V; Neu, C; Neubauer, M S; Nielsen, J; Nigmanov, T; Nodulman, L; Norniella, O; Ogawa, T; Oh, S H; Oh, Y D; Okusawa, T; Oldeman, R; Orava, R; Osterberg, K; Pagliarone, C; Palencia, E; Paoletti, R; Papadimitriou, V; Papikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Piedra, J; Pitts, K; Plager, C; Pondrom, L; Pope, G; Portell, X; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Rakitin, A; Rappoccio, S; Ratnikov, F; Reisert, B; Rekovic, V; van Remortel, N; Renton, P; Rescigno, M; Richter, S; Rimondi, F; Rinnert, K; Ristori, L; Robertson, W J; Robson, A; Rodrigo, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Rott, C; Ruiz, A; Russ, J; Rusu, V; Ryan, D; Saarikko, H; Sabik, S; Safonov, A; Sakumoto, W K; Salamanna, G; Salto, O; Saltzberg, D; Sanchez, C; Santi, L; Sarkar, S; Sato, K; Savard, P; Savoy-Navarro, A; Scheidle, T; Schlabach, P; Schmidt, E E; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scott, A L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Semeria, F; Sexton-Kennedy, L; Sfiligoi, I; Shapiro, M D; Shears, T; Shepard, P F; Sherman, D; Shimojima, M; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sill, A; Sinervo, P; Sisakyan, A; Sjolin, J; Skiba, A; Slaughter, A J; Sliwa, K; Smirnov, D; Smith, J R; Snider, F D; Snihur, R; Soderberg, M; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spinella, F; Squillacioti, P; Stanitzki, M; Staveris-Polykalas, A; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Stuart, D; Suh, J S; Sukhanov, A; Sumorok, K; Sun, H; Suzuki, T; Taffard, A; Tafirout, R; Takashima, R; Takeuchi, Y; Takikawa, K; Tanaka, M; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Tether, S; Thom, J; Thompson, A S; Thomson, E; Tipton, P; Tiwari, V; Tkaczyk, S; Toback, D; Tollefson, K; Tomura, T; Tonelli, D; Tönnesmann, M; Torre, S; Torretta, D; Tourneur, S; Trischuk, W; Tsuchiya, R; Tsuno, S; Turini, N; Ukegawa, F; Unverhau, T; Uozumi, S; Usynin, D; Vacavant, L; Vaiciulis, A; Vallecorsa, S; Varganov, A; Vataga, E; Velev, G; Veramendi, G; Veszpremi, V; Vickey, T; Vidal, R; Vila, I; Vilar, R; Vollrath, I; Volobouev, I; Würthwein, F; Wagner, P; Wagner, R G; Wagner, R L; Wagner, W; Wallny, R; Walter, T; Wan, Z; Wang, M J; Wang, S M; Warburton, A; Ward, B; Waschke, S; Waters, D; Watts, T; Weber, M; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Worm, S; Wright, T; Wu, X; Wynne, S M; Yagil, A; Yamamoto, K; Yamaoka, J; Yamashita, Y; Yang, C; Yang, U K; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zaw, I; Zetti, F; Zhang, X; Zhou, J; Zucchelli, S

    2006-01-13

    We present a search for neutral supersymmetric Higgs bosons decaying to tau pairs produced in pp collisions at square root of s = 1.96 TeV. The data, corresponding to 310 pb(-1) integrated luminosity, were collected with the Collider Detector at Fermilab in run II of the Tevatron. No significant excess above the standard model backgrounds is observed. We set exclusion limits on the production cross section times branching fraction to tau pairs for Higgs boson masses in the range from 90 to 250 GeV/c2.

  19. Inferring the structure of latent class models using a genetic algorithm.

    PubMed

    van der Maas, Han L J; Raijmakers, Maartje E J; Visser, Ingmar

    2005-05-01

    Present optimization techniques in latent class analysis apply the expectation maximization algorithm or the Newton-Raphson algorithm for optimizing the parameter values of a prespecified model. These techniques can be used to find maximum likelihood estimates of the parameters, given the specified structure of the model, which is defined by the number of classes and, possibly, fixation and equality constraints. The model structure is usually chosen on theoretical grounds. A large variety of structurally different latent class models can be compared using goodness-of-fit indices of the chi-square family, Akaike's information criterion, the Bayesian information criterion, and various other statistics. However, finding the optimal structure for a given goodness-of-fit index often requires a lengthy search in which all kinds of model structures are tested. Moreover, solutions may depend on the choice of initial values for the parameters. This article presents a new method by which one can simultaneously infer the model structure from the data and optimize the parameter values. The method consists of a genetic algorithm in which any goodness-of-fit index can be used as a fitness criterion. In a number of test cases in which data sets from the literature were used, it is shown that this method provides models that fit equally well as or better than the models suggested in the original articles.

  20. Determining the Number of Latent Classes in Single- and Multi-Phase Growth Mixture Models

    PubMed Central

    Kim, Su-Young

    2014-01-01

    Stage-sequential (or multiphase) growth mixture models are useful for delineating potentially different growth processes across multiple phases over time and for determining whether latent subgroups exist within a population. These models are increasingly important as social behavioral scientists are interested in better understanding change processes across distinctively different phases, such as before and after an intervention. One of the less understood issues related to the use of growth mixture models is how to decide on the optimal number of latent classes. The performance of several traditionally used information criteria for determining the number of classes is examined through a Monte Carlo simulation study in single- and multi-phase growth mixture models. For thorough examination, the simulation was carried out in two perspectives: the models and the factors. The simulation in terms of the models was carried out to see the overall performance of the information criteria within and across the models, while the simulation in terms of the factors was carried out to see the effect of each simulation factor on the performance of the information criteria holding the other factors constant. The findings not only support that sample size adjusted BIC (ADBIC) would be a good choice under more realistic conditions, such as low class separation, smaller sample size, and/or missing data, but also increase understanding of the performance of information criteria in single- and multi-phase growth mixture models. PMID:24729675

  1. Search for the Standard Model Higgs boson decaying into $ b\\overline{b} $ produced in association with top quarks decaying hadronically in pp collisions at $ \\sqrt{s}=8 $ TeV with the ATLAS detector

    SciTech Connect

    Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Abeloos, B.; Aben, R.; Abolins, M.; AbouZeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Alconada Verzini, M. J.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alstaty, M.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Aracena, I.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barranco Navarro, L.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bedognetti, M.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, A. S.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Belyaev, N. L.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez, J.; Benitez Garcia, J. A.; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Beringer, J.; Berlendis, S.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertram, I. A.; Bertsche, C.; Bertsche, D.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Betancourt, C.; Bethke, S.; Bevan, A. J.; Bhimji, W.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Biedermann, D.; Bielski, R.; Biesuz, N. V.; Biglietti, M.; Bilbao De Mendizabal, J.; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biondi, S.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J. -B.; Blanco, J. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Boerner, D.; Bogaerts, J. A.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A. S.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Bortfeldt, J.; Bortoletto, D.; Bortolotto, V.; Bos, K.; Boscherini, D.; Bosman, M.; Bossio Sola, J. D.; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Breaden Madden, W. D.; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; Broughton, J. H.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruneliere, R.; Bruni, A.; Bruni, G.; Brunt, BH; Bruschi, M.; Bruscino, N.; Bryant, P.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burckhart, H.; Burdin, S.; Burgard, C. D.; Burghgrave, B.; Burka, K.; Burke, S.; Burmeister, I.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Cabrera Urbán, S.; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Caloba, L. P.; Calvet, D.; Calvet, S.; Calvet, T. P.; Camacho Toro, R.; Camarda, S.; Camarri, P.; Cameron, D.; Caminal Armadans, R.; Camincher, C.; Campana, S.; Campanelli, M.; Camplani, A.; Campoverde, A.; Canale, V.; Canepa, A.; Cano Bret, M.; Cantero, J.; Cantrill, R.; Cao, T.; Capeans Garrido, M. D. M.; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, I.; Carli, T.; Carlino, G.; Carminati, L.; Caron, S.; Carquin, E.; Carrillo-Montoya, G. D.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castaneda-Miranda, E.; Castelli, A.; Castillo Gimenez, V.; Castro, N. F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavallaro, E.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Ceradini, F.; Cerda Alberich, L.; Cerio, B. C.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cerv, M.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chan, S. K.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chatterjee, A.; Chau, C. C.; Chavez Barajas, C. A.; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, K.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, H. J.; Cheng, Y.; Cheplakov, A.; Cheremushkina, E.; Cherkaoui El Moursli, R.; Chernyatin, V.; Cheu, E.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chitan, A.; Chizhov, M. V.; Choi, K.; Chomont, A. R.; Chouridou, S.; Chow, B. K. 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A.; Viel, S.; Vigani, L.; Vigne, R.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vlasak, M.; Vogel, M.; Vokac, P.; Volpi, G.; Volpi, M.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Vykydal, Z.; Wagner, P.; Wagner, W.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, K.; Wang, R.; Wang, S. M.; Wang, T.; Wang, T.; Wang, X.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, S.; Weber, M. S.; Weber, S. W.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weiser, C.; Weits, H.; Wells, P. 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H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yen, A. L.; Yildirim, E.; Yorita, K.; Yoshida, R.; Yoshihara, K.; Young, C.; Young, C. J. S.; Youssef, S.; Yu, D. R.; Yu, J.; Yu, J. M.; Yu, J.; Yuan, L.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanello, L.; Zanzi, D.; Zeitnitz, C.; Zeman, M.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zengel, K.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, L.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; zur Nedden, M.; Zurzolo, G.; Zwalinski, L.

    2016-05-01

    In this paper, a search for Higgs boson production in association with a pair of top quarks ($ t\\overline{t} $H) is performed, where the Higgs boson decays to $ b\\overline{b} $ , and both top quarks decay hadronically. The data used correspond to an integrated luminosity of 20.3 fb –1 of pp collisions at √s = 8 TeV collected with the ATLAS detector at the Large Hadron Collider. The search selects events with at least six energetic jets and uses a boosted decision tree algorithm to discriminate between signal and Standard Model background. The dominant multijet background is estimated using a dedicated data-driven technique. For a Higgs boson mass of 125 GeV, an upper limit of 6.4 (5.4) times the Standard Model cross section is observed (expected) at 95% confidence level. The best-fit value for the signal strength is μ = 1.6 ± 2.6 times the Standard Model expectation for mH = 125 GeV. Combining all $ t\\overline{t}$H searches carried out by ATLAS at √s = 8 and 7 TeV, an observed (expected) upper limit of 3.1 (1.4) times the Standard Model expectation is obtained at 95% confidence level, with a signal strength μ = 1.7 ± 0.8.

  2. Search for the Standard Model Higgs boson decaying into $$ b\\overline{b} $$ produced in association with top quarks decaying hadronically in pp collisions at $$ \\sqrt{s}=8 $$ TeV with the ATLAS detector

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2016-05-01

    In this paper, a search for Higgs boson production in association with a pair of top quarks (more » $$ t\\overline{t} $$H) is performed, where the Higgs boson decays to $$ b\\overline{b} $$ , and both top quarks decay hadronically. The data used correspond to an integrated luminosity of 20.3 fb –1 of pp collisions at √s = 8 TeV collected with the ATLAS detector at the Large Hadron Collider. The search selects events with at least six energetic jets and uses a boosted decision tree algorithm to discriminate between signal and Standard Model background. The dominant multijet background is estimated using a dedicated data-driven technique. For a Higgs boson mass of 125 GeV, an upper limit of 6.4 (5.4) times the Standard Model cross section is observed (expected) at 95% confidence level. The best-fit value for the signal strength is μ = 1.6 ± 2.6 times the Standard Model expectation for mH = 125 GeV. Combining all $$ t\\overline{t}$$H searches carried out by ATLAS at √s = 8 and 7 TeV, an observed (expected) upper limit of 3.1 (1.4) times the Standard Model expectation is obtained at 95% confidence level, with a signal strength μ = 1.7 ± 0.8.« less

  3. Search for the Standard Model Higgs boson decaying into boverline{b} produced in association with top quarks decaying hadronically in pp collisions at √{s}=8 TeV with the ATLAS detector

    NASA Astrophysics Data System (ADS)

    Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Abeloos, B.; Aben, R.; Abolins, M.; AbouZeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Alconada Verzini, M. J.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alstaty, M.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Aracena, I.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. 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A.; Crispin Ortuzar, M.; Cristinziani, M.; Croft, V.; Crosetti, G.; Cuhadar Donszelmann, T.; Cummings, J.; Curatolo, M.; Cúth, J.; Cuthbert, C.; Czirr, H.; Czodrowski, P.; D'Auria, S.; D'Onofrio, M.; Da Cunha Sargedas De Sousa, M. J.; Da Via, C.; Dabrowski, W.; Dado, T.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Dang, N. P.; Daniells, A. C.; Dann, N. S.; Danninger, M.; Dano Hoffmann, M.; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Davey, W.; David, C.; Davidek, T.; Davies, M.; Davison, P.; Dawe, E.; Dawson, I.; Daya-Ishmukhametova, R. K.; De, K.; de Asmundis, R.; De Benedetti, A.; De Castro, S.; De Cecco, S.; De Groot, N.; de Jong, P.; De la Torre, H.; De Lorenzi, F.; De Pedis, D.; De Salvo, A.; De Sanctis, U.; De Santo, A.; De Vivie De Regie, J. B.; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Deigaard, I.; Del Peso, J.; Del Prete, T.; Delgove, D.; Deliot, F.; Delitzsch, C. 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M.; Friedrich, F.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Fullana Torregrosa, E.; Fusayasu, T.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gabrielli, A.; Gabrielli, A.; Gach, G. P.; Gadatsch, S.; Gadomski, S.; Gagliardi, G.; Gagnon, L. G.; Gagnon, P.; Galea, C.; Galhardo, B.; Gallas, E. J.; Gallop, B. J.; Gallus, P.; Galster, G.; Gan, K. K.; Gao, J.; Gao, Y.; Gao, Y. S.; Garay Walls, F. M.; García, C.; García Navarro, J. E.; Garcia-Sciveres, M.; Gardner, R. W.; Garelli, N.; Garonne, V.; Gascon Bravo, A.; Gatti, C.; Gaudiello, A.; Gaudio, G.; Gaur, B.; Gauthier, L.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gazis, E. N.; Gecse, Z.; Gee, C. N. P.; Geich-Gimbel, Ch.; Geisler, M. P.; Gemme, C.; Genest, M. H.; Geng, C.; Gentile, S.; George, S.; Gerbaudo, D.; Gershon, A.; Ghasemi, S.; Ghazlane, H.; Ghneimat, M.; Giacobbe, B.; Giagu, S.; Giannetti, P.; Gibbard, B.; Gibson, S. M.; Gignac, M.; Gilchriese, M.; Gillam, T. P. S.; Gillberg, D.; Gilles, G.; Gingrich, D. M.; Giokaris, N.; Giordani, M. P.; Giorgi, F. M.; Giorgi, F. M.; Giraud, P. F.; Giromini, P.; Giugni, D.; Giuli, F.; Giuliani, C.; Giulini, M.; Gjelsten, B. K.; Gkaitatzis, S.; Gkialas, I.; Gkougkousis, E. L.; Gladilin, L. K.; Glasman, C.; Glatzer, J.; Glaysher, P. C. F.; Glazov, A.; Goblirsch-Kolb, M.; Godlewski, J.; Goldfarb, S.; Golling, T.; Golubkov, D.; Gomes, A.; Gonçalo, R.; Goncalves Pinto Firmino Da Costa, J.; Gonella, L.; Gongadze, A.; González de la Hoz, S.; Gonzalez Parra, G.; Gonzalez-Sevilla, S.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorelov, I.; Gorini, B.; Gorini, E.; Gorišek, A.; Gornicki, E.; Goshaw, A. T.; Gössling, C.; Gostkin, M. I.; Goudet, C. R.; Goujdami, D.; Goussiou, A. G.; Govender, N.; Gozani, E.; Graber, L.; Grabowska-Bold, I.; Gradin, P. O. J.; Grafström, P.; Gramling, J.; Gramstad, E.; Grancagnolo, S.; Gratchev, V.; Gray, H. M.; Graziani, E.; Greenwood, Z. D.; Grefe, C.; Gregersen, K.; Gregor, I. 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L.; Pingel, A.; Pires, S.; Pirumov, H.; Pitt, M.; Plazak, L.; Pleier, M.-A.; Pleskot, V.; Plotnikova, E.; Plucinski, P.; Pluth, D.; Poettgen, R.; Poggioli, L.; Pohl, D.; Polesello, G.; Poley, A.; Policicchio, A.; Polifka, R.; Polini, A.; Pollard, C. S.; Polychronakos, V.; Pommès, K.; Pontecorvo, L.; Pope, B. G.; Popeneciu, G. A.; Popovic, D. S.; Poppleton, A.; Pospisil, S.; Potamianos, K.; Potrap, I. N.; Potter, C. J.; Potter, C. T.; Poulard, G.; Poveda, J.; Pozdnyakov, V.; Pozo Astigarraga, M. E.; Pralavorio, P.; Pranko, A.; Prell, S.; Price, D.; Price, L. E.; Primavera, M.; Prince, S.; Proissl, M.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Przybycien, M.; Puddu, D.; Puldon, D.; Purohit, M.; Puzo, P.; Qian, J.; Qin, G.; Qin, Y.; Quadt, A.; Quayle, W. B.; Queitsch-Maitland, M.; Quilty, D.; Raddum, S.; Radeka, V.; Radescu, V.; Radhakrishnan, S. K.; Radloff, P.; Rados, P.; Ragusa, F.; Rahal, G.; Raine, J. A.; Rajagopalan, S.; Rammensee, M.; Rangel-Smith, C.; Ratti, M. G.; Rauscher, F.; Rave, S.; Ravenscroft, T.; Raymond, M.; Read, A. L.; Readioff, N. P.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reeves, K.; Rehnisch, L.; Reichert, J.; Reisin, H.; Rembser, C.; Ren, H.; Rescigno, M.; Resconi, S.; Rezanova, O. L.; Reznicek, P.; Rezvani, R.; Richter, R.; Richter, S.; Richter-Was, E.; Ricken, O.; Ridel, M.; Rieck, P.; Riegel, C. J.; Rieger, J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rinaldi, L.; Ristić, B.; Ritsch, E.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Rizzi, C.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robson, A.; Roda, C.; Rodina, Y.; Rodriguez Perez, A.; Rodriguez Rodriguez, D.; Roe, S.; Rogan, C. S.; Røhne, O.; Romaniouk, A.; Romano, M.; Romano Saez, S. M.; Romero Adam, E.; Rompotis, N.; Ronzani, M.; Roos, L.; Ros, E.; Rosati, S.; Rosbach, K.; Rose, P.; Rosenthal, O.; Rossetti, V.; Rossi, E.; Rossi, L. P.; Rosten, J. H. N.; Rosten, R.; Rotaru, M.; Roth, I.; Rothberg, J.; Rousseau, D.; Royon, C. R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rud, V. I.; Rudolph, M. S.; Rühr, F.; Ruiz-Martinez, A.; Rurikova, Z.; Rusakovich, N. A.; Ruschke, A.; Russell, H. L.; Rutherfoord, J. P.; Ruthmann, N.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Ryu, S.; Ryzhov, A.; Rzehorz, G. F.; Saavedra, A. F.; Sabato, G.; Sacerdoti, S.; Sadrozinski, H. F.-W.; Sadykov, R.; Safai Tehrani, F.; Saha, P.; Sahinsoy, M.; Saimpert, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Salamon, A.; Salazar Loyola, J. E.; Salek, D.; Sales De Bruin, P. H.; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sanchez, A.; Sánchez, J.; Sanchez Martinez, V.; Sandaker, H.; Sandbach, R. L.; Sander, H. G.; Sandhoff, M.; Sandoval, C.; Sandstroem, R.; Sankey, D. P. C.; Sannino, M.; Sansoni, A.; Santoni, C.; Santonico, R.; Santos, H.; Santoyo Castillo, I.; Sapp, K.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sasaki, Y.; Sato, K.; Sauvage, G.; Sauvan, E.; Savage, G.; Savard, P.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Scarcella, M.; Scarfone, V.; Schaarschmidt, J.; Schacht, P.; Schaefer, D.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Scharf, V.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schneider, B.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schorlemmer, A. L. S.; Schott, M.; Schovancova, J.; Schramm, S.; Schreyer, M.; Schuh, N.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwanenberger, C.; Schwartzman, A.; Schwarz, T. A.; Schwegler, Ph.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Schwindt, T.; Sciolla, G.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seifert, F.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Seliverstov, D. M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shiyakova, M.; Shmeleva, A.; Shoaleh Saadi, D.; Shochet, M. J.; Shojaii, S.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sidebo, P. E.; Sidiropoulou, O.; Sidorov, D.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simard, O.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, D.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Sivoklokov, S. Yu.; Sjölin, J.; Sjursen, T. B.; Skinner, M. B.; Skottowe, H. P.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smestad, L.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Soh, D. A.; Sokhrannyi, G.; Solans Sanchez, C. A.; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Song, H. Y.; Sood, A.; Sopczak, A.; Sopko, V.; Sorin, V.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; Denis, R. D. St.; Stabile, A.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanescu-Bellu, M.; Stanitzki, M. M.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stillings, J. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Subramaniam, R.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Susinno, G.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Sykora, I.; Sykora, T.; Ta, D.; Taccini, C.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tam, J. Y. C.; Tan, K. G.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tannenwald, B. B.; Tannoury, N.; Tapia Araya, S.; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Tavares Delgado, A.; Tayalati, Y.; Taylor, A. C.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teischinger, F. A.; Teixeira-Dias, P.; Temming, K. K.; Temple, D.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, E. N.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Thomson, M.; Tibbetts, M. J.; Ticse Torres, R. E.; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorov, T.; Todorova-Nova, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Torrence, E.; Torres, H.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Trefzger, T.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tudorache, A.; Tudorache, V.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turecek, D.; Turgeman, D.; Turra, R.; Turvey, A. J.; Tuts, P. M.; Tyndel, M.; Ucchielli, G.; Ueda, I.; Ueno, R.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usanova, A.; Vacavant, L.; Vacek, V.; Vachon, B.; Valderanis, C.; Valdes Santurio, E.; Valencic, N.; Valentinetti, S.; Valero, A.; Valery, L.; Valkar, S.; Vallecorsa, S.; Valls Ferrer, J. A.; Van Den Wollenberg, W.; Van Der Deijl, P. C.; van der Geer, R.; van der Graaf, H.; van Eldik, N.; van Gemmeren, P.; Van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vanguri, R.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vazeille, F.; Vazquez Schroeder, T.; Veatch, J.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vest, A.; Vetterli, M. C.; Viazlo, O.; Vichou, I.; Vickey, T.; Vickey Boeriu, O. E.; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Vigne, R.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vlasak, M.; Vogel, M.; Vokac, P.; Volpi, G.; Volpi, M.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Vykydal, Z.; Wagner, P.; Wagner, W.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, K.; Wang, R.; Wang, S. M.; Wang, T.; Wang, T.; Wang, X.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, S.; Weber, M. S.; Weber, S. W.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Wessels, M.; Wetter, J.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A.; White, M. J.; White, R.; White, S.; Whiteson, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wittkowski, J.; Wollstadt, S. J.; Wolter, M. W.; Wolters, H.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wozniak, K. W.; Wu, M.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xu, D.; Xu, L.; Yabsley, B.; Yacoob, S.; Yakabe, R.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yen, A. L.; Yildirim, E.; Yorita, K.; Yoshida, R.; Yoshihara, K.; Young, C.; Young, C. J. S.; Youssef, S.; Yu, D. R.; Yu, J.; Yu, J. M.; Yu, J.; Yuan, L.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanello, L.; Zanzi, D.; Zeitnitz, C.; Zeman, M.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zengel, K.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, L.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; zur Nedden, M.; Zurzolo, G.; Zwalinski, L.

    2016-05-01

    A search for Higgs boson production in association with a pair of top quarks ( toverline{t}H ) is performed, where the Higgs boson decays to boverline{b} , and both top quarks decay hadronically. The data used correspond to an integrated luminosity of 20.3 fb-1 of pp collisions at √{s}=8 TeV collected with the ATLAS detector at the Large Hadron Collider. The search selects events with at least six energetic jets and uses a boosted decision tree algorithm to discriminate between signal and Standard Model background. The dominant multijet background is estimated using a dedicated data-driven technique. For a Higgs boson mass of 125 GeV, an upper limit of 6.4 (5.4) times the Standard Model cross section is observed (expected) at 95% confidence level. The best-fit value for the signal strength is μ = 1.6 ± 2.6 times the Standard Model expectation for m H = 125 GeV. Combining all toverline{t}H searches carried out by ATLAS at √{s}=8 and 7 TeV, an observed (expected) upper limit of 3.1 (1.4) times the Standard Model expectation is obtained at 95% confidence level, with a signal strength μ = 1.7 ± 0.8. [Figure not available: see fulltext.

  4. Direct photon pair production at the LHC to O(α) in TeV scale gravity models

    NASA Astrophysics Data System (ADS)

    Kumar, M. C.; Mathews, Prakash; Ravindran, V.; Tripathi, Anurag

    2009-09-01

    The first results on next-to-leading order QCD corrections to production of direct photon pairs in hadronic collisions in the extra dimension models — ADD and RS are presented. Various kinematical distributions are obtained to order α in QCD by taking into account all the parton level subprocesses. Our Monte Carlo based code incorporates all the experimental cuts suitable for physics studies at the LHC. We estimate the impact of the QCD corrections on various observables and find that they are significant. We also show the reduction in factorization scale uncertainty when O(α) effects are included.

  5. Time Dependent Multi Zone Modeling of X-ray and Gamma-ray Variability of the TeV Blazar Mrk 421.

    NASA Astrophysics Data System (ADS)

    Fossati, Giovanni; Chen, X.

    2010-03-01

    We present a new time-dependent multi-zone code and its first application to study the SSC emission of Blazar Mrk 421. The code couples Fokker-Planck and Monte Carlo methods. All the light travel time effects are fully considered, internal and external. It has long been realized that simple one-zone homogeneous models are not adequate to describe several aspects of the phenomenology, in particular those pertaining to the complex multiwavelength variability. Progress has been made by several groups but important trade-offs have always been necessary, such as neglecting internal light travel time or the inclusion of IC losses in the electron evolution. Our code fully accounts for all the relevant effects, and it also affords us significant freedom w.r.t. geometry and "variability". This latter is implemented as a shock traveling along the jet, with electrons being injected as it sweeps the blob. Results are compared with the 2001 observations of Mrk 421. We also analyzed cases including a pre-existing cospatial electron population contributing to the SED, and external radiation field. It seems to be possible to achieve adequate fits to the observations, but a there remain several open issues, such as a systematic soft X-ray intraband lag, and a delay of the gamma-ray flare with respect to the X-ray flare. The two principal challenges are: 1. The simulated VHE spectrum is always softer than the observed one. 2. The correlation between the TeV gamma-ray and X-ray does not reproduce the (super)quadratic relationship observed in multiple occasions. In fact we have not been able to reproduce anything close to it in this first suite of models. We will report also on the extension of the code, and case studies, to jet-in-jet and spine-layer configurations, as well as to the study of red blazars. We acknowledge support from Chandra Award AR9-0016X

  6. The Effect of the Green Class Model on Environmental Knowledge and Its Retention

    ERIC Educational Resources Information Center

    Uzun, Naim

    2009-01-01

    The purpose of the study is to investigate the effects of an applied environmental education project carried out using the green-class model on students' environmental knowledge and its retention. 101 7th grade students attending Nazim Akcan Primary School in the Altindag Province of Ankara participated in the study. The study was carried out in…

  7. How to Make a Math Modeling Class from Scratch in Six (Not-So) Easy Steps

    ERIC Educational Resources Information Center

    Gerhardt, Ira

    2017-01-01

    The recent introduction of a new course in mathematical modeling at Manhattan College has provided students with a valuable opportunity to gain practical experience utilizing tools in applying their mathematical abilities to a real-world problem. This paper describes the steps taken to create this class, from obtaining a real-world partner…

  8. Transfer beyond Workplace Literacy Classes: Twelve Case Studies and a Model.

    ERIC Educational Resources Information Center

    Mikulecky, Larry; And Others

    Twelve case studies of learners in workplace literacy programs at cosmetics and medical instruments firms and a municipal services department were conducted to identify key elements related to broad transfer of the skills learned in the classes to life outside the classroom. A general model of the factors (instructional elements, learner…

  9. Diagram, a Learning Environment for Initiation to Object-Oriented Modeling with UML Class Diagrams

    ERIC Educational Resources Information Center

    Py, Dominique; Auxepaules, Ludovic; Alonso, Mathilde

    2013-01-01

    This paper presents Diagram, a learning environment for object-oriented modelling (OOM) with UML class diagrams. Diagram an open environment, in which the teacher can add new exercises without constraints on the vocabulary or the size of the diagram. The interface includes methodological help, encourages self-correcting and self-monitoring, and…

  10. A Mixture Rasch Model-Based Computerized Adaptive Test for Latent Class Identification

    ERIC Educational Resources Information Center

    Jiao, Hong; Macready, George; Liu, Junhui; Cho, Youngmi

    2012-01-01

    This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback-Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions. When item separation was…

  11. The Effect of Direct Instruction Model on Intermediate Class Achievement and Attitudes toward English Grammar

    ERIC Educational Resources Information Center

    Kousar, Rubina

    2010-01-01

    This study was aimed at investigating the effect of the direct instruction model on intermediate class achievement and attitudes toward English grammar. It was an experimental study and the purpose was to explore the relative effectiveness of instructional methodology (independent variable) on students' achievement and attitude (dependent…

  12. A Mixture Rasch Model-Based Computerized Adaptive Test for Latent Class Identification

    ERIC Educational Resources Information Center

    Jiao, Hong; Macready, George; Liu, Junhui; Cho, Youngmi

    2012-01-01

    This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback-Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions. When item separation was…

  13. The Use of Cooperative Round Robin Discussion Model to Improve Students' Holistic Ability in TEFL Class

    ERIC Educational Resources Information Center

    Asari, Slamet; Ma'rifah, Ulfatul; Arifani, Yudhi

    2017-01-01

    This classroom action research is carried out within two cycles to breed a strategy on how a" Round Robin Discussion Learning Model" enhance students' critical thinking, presentation skills, confidence, and independent learning in Teaching English as a Foreign Language (TEFL) class. Pop-up quiz, teacher made-tests, classroom…

  14. It Takes One to Know One: A Class Exercise in Mental Models

    ERIC Educational Resources Information Center

    Wilson, Theresa

    2014-01-01

    An active learning class exercise is presented that gives students the personal experience of the decision-making limitations of mental models. This innovative exercise was shown to increase student learning through greater understanding of the concept and higher retention of knowledge. The results suggest that student critical thinking skills…

  15. A Class of Multidimensional IRT Models for Testing Unidimensionality and Clustering Items

    ERIC Educational Resources Information Center

    Bartolucci, Francesco

    2007-01-01

    We illustrate a class of multidimensional item response theory models in which the items are allowed to have different discriminating power and the latent traits are represented through a vector having a discrete distribution. We also show how the hypothesis of unidimensionality may be tested against a specific bidimensional alternative by using a…

  16. Using a Student-Manipulated Model to Enhance Student Learning in a Large Lecture Class

    ERIC Educational Resources Information Center

    Gray, Kyle; Steer, David; McConnell, David; Owens, Katharine

    2010-01-01

    Despite years of formal education, approximately one-third of all undergraduate students still cannot explain the causes of the seasons. Student manipulation of a handheld model is one approach to teaching this concept; however, the large number of students in many introductory classes can dissuade instructors from utilizing this teaching…

  17. Confirmatory Latent Class Analysis: Model Selection Using Bayes Factors and (Pseudo) Likelihood Ratio Statistics.

    ERIC Educational Resources Information Center

    Hoijtink, Herbert

    2001-01-01

    Discusses , in the context of confirmatory latent class analysis, model selection using Bayes factors and (pseudo) likelihood ratio statistics. Uses a small simulation study to show that in this context, Bayes factors and the pseudo likelihood ratio statistics have the best properties. (SLD)

  18. Diagram, a Learning Environment for Initiation to Object-Oriented Modeling with UML Class Diagrams

    ERIC Educational Resources Information Center

    Py, Dominique; Auxepaules, Ludovic; Alonso, Mathilde

    2013-01-01

    This paper presents Diagram, a learning environment for object-oriented modelling (OOM) with UML class diagrams. Diagram an open environment, in which the teacher can add new exercises without constraints on the vocabulary or the size of the diagram. The interface includes methodological help, encourages self-correcting and self-monitoring, and…

  19. The Structure of Student Satisfaction with College Services: A Latent Class Model

    ERIC Educational Resources Information Center

    Adwere-Boamah, Joseph

    2011-01-01

    Latent Class Analysis (LCA) was used to identify distinct groups of Community college students based on their self-ratings of satisfaction with student service programs. The programs were counseling, financial aid, health center, student programs and student government. The best fitting model to describe the data was a two Discrete-Factor model…

  20. Understanding Korean Transnational Girls in High School Science Classes: Beyond the Model Minority Stereotype

    ERIC Educational Resources Information Center

    Ryu, Minjung

    2015-01-01

    This study examines six Korean transnational girls enrolled in two advanced placement (AP) biology classes to understand their experiences in science classrooms at the intersection of race, language, and gender. Confronting the model minority stereotype for Asian students, which is particularly salient in science, technology, engineering, and…

  1. Design, Development and Validation of a Model of Problem Solving for Egyptian Science Classes

    ERIC Educational Resources Information Center

    Shahat, Mohamed A.; Ohle, Annika; Treagust, David F.; Fischer, Hans E.

    2013-01-01

    Educators and policymakers envision the future of education in Egypt as enabling learners to acquire scientific inquiry and problem-solving skills. In this article, we describe the validation of a model for problem solving and the design of instruments for evaluating new teaching methods in Egyptian science classes. The instruments were based on…

  2. World-Class Higher Education and the Emerging Chinese Model of the University

    ERIC Educational Resources Information Center

    Li, Jun

    2012-01-01

    China's recent quest to develop world-class universities is a significant phenomenon within the worldwide transformation of tertiary education. Taking a cultural approach and drawing on empirical findings, this article investigates the emerging Chinese model of the university, considering its key features and contributions to global communities.…

  3. Mathematical Modelling: A Path to Political Reflection in the Mathematics Class

    ERIC Educational Resources Information Center

    Jacobini, Otavio Roberto; Wodewotzki, Maria Lucia L.

    2006-01-01

    This paper describes the construction of pedagogical environments in mathematics classes, centred on mathematical modelling and denominated "investigative scenarios", which stimulate students to investigation, to formulation of problems and to political reflection, as well as the sharing of acquired knowledge with other persons in the community.…

  4. Design, Development and Validation of a Model of Problem Solving for Egyptian Science Classes

    ERIC Educational Resources Information Center

    Shahat, Mohamed A.; Ohle, Annika; Treagust, David F.; Fischer, Hans E.

    2013-01-01

    Educators and policymakers envision the future of education in Egypt as enabling learners to acquire scientific inquiry and problem-solving skills. In this article, we describe the validation of a model for problem solving and the design of instruments for evaluating new teaching methods in Egyptian science classes. The instruments were based on…

  5. Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data

    ERIC Educational Resources Information Center

    Lu, Zhenqiu Laura; Zhang, Zhiyong; Lubke, Gitta

    2011-01-01

    "Growth mixture models" (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class…

  6. The Effect of Direct Instruction Model on Intermediate Class Achievement and Attitudes toward English Grammar

    ERIC Educational Resources Information Center

    Kousar, Rubina

    2010-01-01

    This study was aimed at investigating the effect of the direct instruction model on intermediate class achievement and attitudes toward English grammar. It was an experimental study and the purpose was to explore the relative effectiveness of instructional methodology (independent variable) on students' achievement and attitude (dependent…

  7. Modeling Individual Differences in Unfolding Preference Data: A Restricted Latent Class Approach.

    ERIC Educational Resources Information Center

    Bockenholt, Ulf; Bockenholt, Ingo

    1990-01-01

    A latent-class scaling approach is presented for modeling paired comparison and "pick any/t" data obtained in preference studies. The utility of this approach is demonstrated through analysis of data from studies involving consumer preference and preference for political candidates. (SLD)

  8. Comparison of Maximum Likelihood and Pearson Chi-Square Statistics for Assessing Latent Class Models.

    ERIC Educational Resources Information Center

    Holt, Judith A.; Macready, George B.

    When latent class parameters are estimated, maximum likelihood and Pearson chi-square statistics can be derived for assessing the fit of the model to the data. This study used simulated data to compare these two statistics, and is based on mixtures of latent binomial distributions, using data generated from five dichotomous manifest variables.…

  9. Understanding Korean Transnational Girls in High School Science Classes: Beyond the Model Minority Stereotype

    ERIC Educational Resources Information Center

    Ryu, Minjung

    2015-01-01

    This study examines six Korean transnational girls enrolled in two advanced placement (AP) biology classes to understand their experiences in science classrooms at the intersection of race, language, and gender. Confronting the model minority stereotype for Asian students, which is particularly salient in science, technology, engineering, and…

  10. Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data

    ERIC Educational Resources Information Center

    Lu, Zhenqiu Laura; Zhang, Zhiyong; Lubke, Gitta

    2011-01-01

    "Growth mixture models" (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class…

  11. It Takes One to Know One: A Class Exercise in Mental Models

    ERIC Educational Resources Information Center

    Wilson, Theresa

    2014-01-01

    An active learning class exercise is presented that gives students the personal experience of the decision-making limitations of mental models. This innovative exercise was shown to increase student learning through greater understanding of the concept and higher retention of knowledge. The results suggest that student critical thinking skills…

  12. Social Class and Pedagogy: A Model for the Investigation of Pedagogic Variation

    ERIC Educational Resources Information Center

    Hoadley, Ursula

    2008-01-01

    This article addresses an enduring concern in the sociology of education: how social class differences are reproduced through schooling. In particular it focuses on the functioning of pedagogy in this regard. The article presents a model that elucidates the inner logic of pedagogy in order to reveal the structuring of inequality with respect to…

  13. Search for physics beyond the standard model in events with a Z boson, jets, and missing transverse energy in pp collisions at sqrt(s) = 7 TeV

    SciTech Connect

    Chatrchyan, S.; et al.,

    2012-09-01

    A search is presented for physics beyond the standard model (BSM) in events with a Z boson, jets, and missing transverse energy (MET). This signature is motivated by BSM physics scenarios, including supersymmetry. The study is performed using a sample of proton-proton collision data collected at sqrt(s) = 7 TeV with the CMS experiment at the LHC, corresponding to an integrated luminosity of 4.98 inverse femtobarns. The contributions from the dominant standard model backgrounds are estimated from data using two complementary strategies, the jet-Z balance technique and a method based on modeling MET with data control samples. In the absence of evidence for BSM physics, we set limits on the non-standard-model contributions to event yields in the signal regions and interpret the results in the context of simplified model spectra. Additional information is provided to facilitate tests of other BSM physics models.

  14. Prediction of hemoglobin in blood donors using a latent class mixed-effects transition model.

    PubMed

    Nasserinejad, Kazem; van Rosmalen, Joost; de Kort, Wim; Rizopoulos, Dimitris; Lesaffre, Emmanuel

    2016-02-20

    Blood donors experience a temporary reduction in their hemoglobin (Hb) value after donation. At each visit, the Hb value is measured, and a too low Hb value leads to a deferral for donation. Because of the recovery process after each donation as well as state dependence and unobserved heterogeneity, longitudinal data of Hb values of blood donors provide unique statistical challenges. To estimate the shape and duration of the recovery process and to predict future Hb values, we employed three models for the Hb value: (i) a mixed-effects models; (ii) a latent-class mixed-effects model; and (iii) a latent-class mixed-effects transition model. In each model, a flexible function was used to model the recovery process after donation. The latent classes identify groups of donors with fast or slow recovery times and donors whose recovery time increases with the number of donations. The transition effect accounts for possible state dependence in the observed data. All models were estimated in a Bayesian way, using data of new entrant donors from the Donor InSight study. Informative priors were used for parameters of the recovery process that were not identified using the observed data, based on results from the clinical literature. The results show that the latent-class mixed-effects transition model fits the data best, which illustrates the importance of modeling state dependence, unobserved heterogeneity, and the recovery process after donation. The estimated recovery time is much longer than the current minimum interval between donations, suggesting that an increase of this interval may be warranted. Copyright © 2015 John Wiley & Sons, Ltd.

  15. Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models.

    PubMed

    Yue, Chen; Chen, Shaojie; Sair, Haris I; Airan, Raag; Caffo, Brian S

    2015-09-01

    Data reproducibility is a critical issue in all scientific experiments. In this manuscript, the problem of quantifying the reproducibility of graphical measurements is considered. The image intra-class correlation coefficient (I2C2) is generalized and the graphical intra-class correlation coefficient (GICC) is proposed for such purpose. The concept for GICC is based on multivariate probit-linear mixed effect models. A Markov Chain Monte Carlo EM (mcm-cEM) algorithm is used for estimating the GICC. Simulation results with varied settings are demonstrated and our method is applied to the KIRBY21 test-retest dataset.

  16. Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review.

    PubMed

    Zhou, Mo; Thayer, Winter Maxwell; Bridges, John F P

    2017-10-03

    Latent class analysis (LCA) has been increasingly used to explore preference heterogeneity, but the literature has not been systematically explored and hence best practices are not understood. We sought to document all applications of LCA in the stated-preference literature in health and to inform future studies by identifying current norms in published applications. We conducted a systematic review of the MEDLINE, EMBASE, EconLit, Web of Science, and PsycINFO databases. We included stated-preference studies that used LCA to explore preference heterogeneity in healthcare or public health. Two co-authors independently evaluated titles, abstracts, and full-text articles. Abstracted key outcomes included segmentation methods, preference elicitation methods, number of attributes and levels, sample size, model selection criteria, number of classes reported, and hypotheses tests. Study data quality and validity were assessed with the Purpose, Respondents, Explanation, Findings, and Significance (PREFS) quality checklist. We identified 2560 titles, 99 of which met the inclusion criteria for the review. Two-thirds of the studies focused on the preferences of patients and the general population. In total, 80% of the studies used discrete choice experiments. Studies used between three and 20 attributes, most commonly four to six. Sample size in LCAs ranged from 47 to 2068, with one-third between 100 and 300. Over 90% of the studies used latent class logit models for segmentation. Bayesian information criterion (BIC), Akaike information criterion (AIC), and log-likelihood (LL) were commonly used for model selection, and class size and interpretability were also considered in some studies. About 80% of studies reported two to three classes. The number of classes reported was not correlated with any study characteristics or study population characteristics (p > 0.05). Only 30% of the studies reported using statistical tests to detect significant variations in preferences

  17. Stochastic models and numerical algorithms for a class of regulatory gene networks.

    PubMed

    Fournier, Thomas; Gabriel, Jean-Pierre; Pasquier, Jerôme; Mazza, Christian; Galbete, José; Mermod, Nicolas

    2009-08-01

    Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

  18. A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data

    ERIC Educational Resources Information Center

    Vera, J. Fernando; Macias, Rodrigo; Heiser, Willem J.

    2009-01-01

    In this paper, we propose a cluster-MDS model for two-way one-mode continuous rating dissimilarity data. The model aims at partitioning the objects into classes and simultaneously representing the cluster centers in a low-dimensional space. Under the normal distribution assumption, a latent class model is developed in terms of the set of…

  19. New physics at the TeV scale

    NASA Astrophysics Data System (ADS)

    Chakdar, Shreyashi

    The Standard Model of particle physics is assumed to be a low-energy effective theory with new physics theoretically motivated to be around TeV scale. The thesis presents theories with new physics beyond the Standard Model in the TeV scale testable in the colliders. Work done in chapters 2, 3 and 5 in this thesis present some models incorporating different approaches of enlarging the Standard Model gauge group to a grand unified symmetry with each model presenting its unique signatures in the colliders. The study on leptoquarks gauge bosons in reference to TopSU(5) model in chapter 2 showed that their discovery mass range extends up to 1.5 TeV at 14 TeV LHC with luminosity of 100 fb--1. On the other hand, in chapter 3 we studied the collider phenomenology of TeV scale mirror fermions in Left-Right Mirror model finding that the reaches for the mirror quarks goes upto 750 GeV at the 14 TeV LHC with 300 fb--1 luminosity. In chapter 4 we have enlarged the bosonic symmetry to fermi-bose symmetry e.g. supersymmetry and have shown that SUSY with non-universalities in gaugino or scalar masses within high scale SUGRA set up can still be accessible at LHC with 14 TeV. In chapter 5, we performed a study in respect to the e+e-- collider and find that precise measurements of the higgs boson mass splittings up to ˜ 100 MeV may be possible with high luminosity in the International Linear Collider (ILC). In chapter 6 we have shown that the experimental data on neutrino masses and mixings are consistent with the proposed 4/5 parameter Dirac neutrino models yielding a solution for the neutrino masses with inverted mass hierarchy and large CP violating phase delta and thus can be tested experimentally. Chapter 7 of the thesis incorporates a warm dark matter candidate in context of two Higgs doublet model. The model has several testable consequences at colliders with the charged scalar and pseudoscalar being in few hundred GeV mass range. This thesis presents an endeavor to study

  20. Marginal models for correlated binary responses with multiple classes and multiple levels of nesting.

    PubMed

    Qaqish, B F; Liang, K Y

    1992-09-01

    A model for correlated binary data is presented. Marginal probabilities and odds ratios are allowed to have general regression structures that include multiple classes and multiple levels of nesting. Estimation is done through the generalized estimating equations approach of Liang and Zeger (1986, Biometrika 73, 13-22). They are contrasted with conditional models and recommendations for choosing between the two are given. Examples from genetic epidemiology are presented.

  1. Dynamics for a class of stochastic SIS epidemic models with nonlinear incidence and periodic coefficients

    NASA Astrophysics Data System (ADS)

    Rifhat, Ramziya; Wang, Lei; Teng, Zhidong

    2017-09-01

    In this paper, we investigate the dynamics of a class of periodic stochastic SIS epidemic models with general nonlinear incidence f(S , I) . Some sufficient conditions on the permanence in the mean and extinction of positive solutions with probability one are established. By using the Khasminskii's boundary periodic Markov processes, the existence of stochastic nontrivial periodic solution for the models is also obtained. The numerical simulations are given to illustrate the main theoretical results and some interesting conjectures are presented.

  2. Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8 $$\\,\\text {TeV}$$

    DOE PAGES

    Khachatryan, Vardan

    2015-05-14

    Properties of the Higgs boson with mass near 125GeV are measured in proton-proton collisions with the CMS experiment at the LHC. Comprehensive sets of production and decay measurements are combined. The decay channels include γγ, ZZ, WW, ττ, bb, and μμ pairs. The data samples were collected in 2011 and 2012 and correspond to integrated luminosities of up to 5.1fb-1 at 7TeV and up to 19.7fb-1 at 8TeV. From the high-resolution γγ and ZZ channels, the mass of the Higgs boson is determined to be 125.02+0.26–0.27 (stat) +0.14–0.15 (syst) GeV. For this mass value, the event yields obtained in themore » different analyses tagging specific decay channels and production mechanisms are consistent with those expected for the standard model Higgs boson. The combined best-fit signal relative to the standard model expectation is 1.00 ± 0.09(stat)+0.08–0.07 (theo) ± 0.07(syst) at the measured mass. The couplings of the Higgs boson are probed for deviations in magnitude from the standard model predictions in multiple ways, including searches for invisible and undetected decays. As a result, no significant deviations are found.« less

  3. Comparative modeling of marsupial MHC class I molecules identifies structural polymorphisms affecting functional motifs.

    PubMed

    Daly, Kerry; Church, W Bret; Nicholas, Kevin; Williamson, Peter

    2007-11-01

    Major histocompatibility complex (MHC) class I molecules are transmembrane glycoproteins that present antigenic peptides to CD8+ T cells and are subsequently important for the initiation of an immune response. In this study novel MHC class I sequences from the tammar wallaby (Macropus eugenii) have been characterized. Analysis and comparative modeling of these and existing marsupial molecules reveals potential functional polymorphisms within peptide-binding grooves, MHC assembly motifs and the T cell receptor recognition interface. In addition, we show that a previously identified marsupial-specific insertion is within a region, which is known as a putative NK cell receptor (Ly49A) binding site in the mouse, suggesting that this site may be functionally active in marsupials. Further, the analysis highlighted differences in structural and sequence based grouping of marsupial MHC class I molecules. (c) 2007 Wiley-Liss, Inc.

  4. Adulthood Social Class and Union Interest: A First Test of a Theoretical Model.

    PubMed

    Mellor, Steven

    2016-10-02

    A serial mediation model of union interest was tested. Based on theoretical notes provided by Mellor and Golay (in press), adulthood social class was positioned as a predictor of willingness to join a labor union, with success/failure attributions at work and willingness to share work goals positioned as intervening variables. Data from U.S. nonunion employees (N = 560) suggested full mediation after effects were adjusted for childhood social class. In sequence, adulthood social class predicted success/failure attributions at work, success/failure attributions at work predicted willingness to share work goals, and willingness to share work goals predicted willingness to join. Implications for socioeconomic status (SES) research and union expansion are discussed.

  5. Dynamic analysis of a hepatitis B model with three-age-classes

    NASA Astrophysics Data System (ADS)

    Zhang, Suxia; Zhou, Yicang

    2014-07-01

    Based on the fact that the likelihood of becoming chronically infected is dependent on age at primary infection Kane (1995) [2], Edmunds et al. (1993) [3], Medley et al. (2001) [4], and Ganem and Prince (2004) [6], we formulate a hepatitis B transmission model with three age classes. The reproduction number, R0 is defined and the dynamical behavior of the model is analyzed. It is proved that the disease-free equilibrium is globally stable if R0<1, and there exists at least one endemic equilibrium and that the disease is uniformly persistent if R0>1. The unique endemic equilibrium and its global stability is obtained in a special case. Simulations are also conducted to compare the dynamical behavior of the model with and without age classes.

  6. Constraints on the pMSSM, AMSB model and on other models from the search for long-lived charged particles in proton-proton collisions at √s = 8 TeV

    DOE PAGES

    Khachatryan, Vardan

    2015-07-17

    Stringent limits are set on the long-lived lepton-like sector of the phenomenological minimal supersymmetric standard model (pMSSM) and the anomaly-mediated supersymmetry breaking (AMSB) model. We derived the limits from the results presented in a recent search for long-lived charged particles in proton–proton collisions, based on data collected by the CMS detector at a centre-of-mass energy of 8 TeV at the Large Hadron Collider. In the pMSSM parameter sub-space considered, 95.9 % of the points predicting charginos with a lifetime of at least 10 ns are excluded. Furthermore, these constraints on the pMSSM are the first obtained at the LHC. Charginosmore » with a lifetime greater than 100 ns and masses up to about 800 GeV in the AMSB model are also excluded. Furthermore, the method described can also be used to set constraints on other models.« less

  7. Semiparametric Inference for a General Class of Models for Recurrent Events

    PubMed Central

    Peña, Edsel A.; Slate, Elizabeth H.; González, Juan R.

    2006-01-01

    Procedures for estimating the parameters of the general class of semiparametric models for recurrent events proposed by Peña and Hollander (2004) are developed. This class of models incorporates an effective age function encoding the effect of changes after each event occurrence such as the impact of an intervention, it models the impact of accumulating event occurrences on the unit, it admits a link function in which the effect of possibly time-dependent covariates are incorporated, and it allows the incorporation of unobservable frailty components which induce dependencies among the inter-event times for each unit. The estimation procedures are semiparametric in that a baseline hazard function is nonparametrically specified. The sampling distribution properties of the estimators are examined through a simulation study, and the consequences of mis-specifying the model are analyzed. The results indicate that the flexibility of this general class of models provides a safeguard for analyzing recurrent event data, even data possibly arising from a frailtyless mechanism. The estimation procedures are applied to real data sets arising in the biomedical and public health settings, as well as from reliability and engineering situations. In particular, the procedures are applied to a data set pertaining to times to recurrence of bladder cancer and the results of the analysis are compared to those obtained using three methods of analyzing recurrent event data. PMID:19823592

  8. Substructure and strong interactions at the TeV scale

    SciTech Connect

    Peskin, M.E.

    1985-12-01

    A review is given of the current status of the three main theoretical ideas relevant to strong-interaction 1 TeV physics. These are composite vector bosons, Higgs bosons (''Technicolor''), and matter fermions. All involve the assumption that some object which is assumed to be fundamental in the standard model actually has dynamical internal structure. Complex, mechanistic models of the new physics are discussed. A brief digression is then made on how the weak interaction allows probing for this new structure. Direct manifestations of new 1 TeV strong interactions are discussed. 125 refs., 18 figs. (LEW)

  9. Search for the standard model Higgs boson decaying to a $W$ pair in the fully leptonic final state in $pp$ collisions at $\\sqrt{s}=7$ TeV

    SciTech Connect

    Chatrchyan, Serguei; et al.

    2012-03-01

    A search for the standard model Higgs boson decaying to W+W- in pp collisions at sqrt(s) = 7 TeV is reported. The data are collected at the LHC with the CMS detector, and correspond to an integrated luminosity of 4.6 inverse femtobarns. The W+W- candidates are selected in events with two charged leptons and large missing transverse energy. No significant excess of events above the standard model background expectations is observed, and upper limits on the Higgs boson production relative to the standard model Higgs expectation are derived. The standard model Higgs boson is excluded in the mass range 129-270 GeV at 95% confidence level.

  10. Phenomenology of TeV little string theory from holography.

    PubMed

    Antoniadis, Ignatios; Arvanitaki, Asimina; Dimopoulos, Savas; Giveon, Amit

    2012-02-24

    We study the graviton phenomenology of TeV little string theory by exploiting its holographic gravity dual five-dimensional theory. This dual corresponds to a linear dilaton background with a large bulk that constrains the standard model fields on the boundary of space. The linear dilaton geometry produces a unique Kaluza-Klein graviton spectrum that exhibits a ~TeV mass gap followed by a near continuum of narrow resonances that are separated from each other by only ~30 GeV. Resonant production of these particles at the LHC is the signature of this framework that distinguishes it from large extra dimensions, where the Kaluza-Klein states are almost a continuum with no mass gap, and warped models, where the states are separated by a TeV.

  11. On the global well-posedness theory for a class of PDE models for criminal activity

    NASA Astrophysics Data System (ADS)

    Rodríguez, N.

    2013-10-01

    We study a class of ‘reaction-advection-diffusion’ system of partial differential equations, which can be taken as basic models for criminal activity. This class of models are based on routine activity theory and other theories, such as the ‘repeat and near-repeat victimization effect’ and were first introduced in Short et al. (2008) [11]. In these models the criminal density is advected by a velocity field that depends on a scalar field, which measures the appeal to commit a crime. We refer to this scalar field as the attractiveness field. We prove local well-posedness of solutions for the general class of models. Furthermore, we prove global well-posedness of solutions to a fully-parabolic system with a velocity field that depends logarithmically on the attractiveness field. Our final result is the global well-posedness of solutions the fully-parabolic system with velocity field that depends linearly on the attractiveness field for small initial mass.

  12. Longitudinal evaluation of dental arch asymmetry in Class II subdivision malocclusion with 3-dimensional digital models.

    PubMed

    Veli, Ilknur; Yuksel, Burcin; Uysal, Tancan

    2014-06-01

    Class II subdivision malocclusions with their asymmetric occlusal relationships often pose treatment difficulties. The aim of this study was to evaluate the longitudinal changes of dental arch asymmetry in untreated subjects with Class II subdivision malocclusion. From 706 files from the University of Michigan Growth Study, longitudinal records of 17 untreated subjects with Class II subdivision malocclusion were included this study. Dental arch changes at 3 consecutive longitudinal intervals, defined by the cervical vertebral maturation method, were analyzed on digital dental models. The average ages of the subjects were 12.4, 15.1, and 19.1 years at the 3 time periods, respectively. Maxillary and mandibular reference lines were constructed and used for the intra-arch asymmetry measurements. The Friedman test and analysis of variance with repeated measures were used to determine dental arch asymmetries at the P <0.05 level. All subjects were found to have a type 1 Class II subdivision malocclusion characterized by distal positioning of the mandibular first molar on the Class II side. No statistically significant intra-arch asymmetry changes were found for the maxillary and mandibular dental arches in any time period. Between the baseline and the final follow-up, the data indicated decreases in maxillary and mandibular intercanine arch widths and arch lengths symmetrically. The results of this study indicate that the dental arch asymmetry in patients with Class II subdivision malocclusions did not improve or worsen with growth. Copyright © 2014 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

  13. Exact and Approximate Solutions for a Class of Cooperative Stochastic Models

    NASA Astrophysics Data System (ADS)

    Melkerson, Rebecca; Beck, Gillenhaal; Hall-Mejia, Estevan; Nshimyumukiza, Sabin; da Fonseca, Carlos; Mazilu, Dan; Mazilu, Irina

    We present a class of cooperative sequential adsorption models with evaporation defined on general lattice structures. Using matrix algebra theory to solve the associated master equations, we find the time-dependent probability distributions. We discuss these models in the context of ionic self-assembly of silica nanoparticles in order to also find the time-dependent surface coverage. To test the limits of the matrix theory, we add the possibilities for evaporation either once the surface is fully covered or at intermediate steps. We justify our mathematical models by comparing the results to customized experiments and computer simulations. Washington and Lee University Summer Research Scholars.

  14. Damage modeling and statistical analysis of optics damage performance in MJ-class laser systems.

    PubMed

    Liao, Zhi M; Raymond, B; Gaylord, J; Fallejo, R; Bude, J; Wegner, P

    2014-11-17

    Modeling the lifetime of a fused silica optic is described for a multiple beam, MJ-class laser system. This entails combining optic processing data along with laser shot data to account for complete history of optic processing and shot exposure. Integrating with online inspection data allows for the construction of a performance metric to describe how an optic performs with respect to the model. This methodology helps to validate the damage model as well as allows strategic planning and identifying potential hidden parameters that are affecting the optic's performance.

  15. Universality class of the two-dimensional site-diluted Ising model.

    PubMed

    Martins, P H L; Plascak, J A

    2007-07-01

    In this work, we evaluate the probability distribution function of the order parameter for the two-dimensional site-diluted Ising model. Extensive Monte Carlo simulations have been performed for different spin concentrations p (0.70class of the diluted Ising model seems to be independent of the amount of dilution. Logarithmic corrections of the finite-size critical temperature behavior of the model can also be inferred even for such small lattices.

  16. Cosmological consequences of nearly conformal dynamics at the TeV scale

    SciTech Connect

    Konstandin, Thomas; Servant, Géraldine E-mail: geraldine.servant@cern.ch

    2011-12-01

    Nearly conformal dynamics at the TeV scale as motivated by the hierarchy problem can be characterized by a stage of significant supercooling at the electroweak epoch. This has important cosmological consequences. In particular, a common assumption about the history of the universe is that the reheating temperature is high, at least high enough to assume that TeV-mass particles were once in thermal equilibrium. However, as we discuss in this paper, this assumption is not well justified in some models of strong dynamics at the TeV scale. We then need to reexamine how to achieve baryogenesis in these theories as well as reconsider how the dark matter abundance is inherited. We argue that baryonic and dark matter abundances can be explained naturally in these setups where reheating takes place by bubble collisions at the end of the strongly first-order phase transition characterizing conformal symmetry breaking, even if the reheating temperature is below the electroweak scale ∼ 100 GeV. In particular, non-thermal production of heavy WIMPs during bubble collisions becomes a well-motivated possibility. We also discuss inflation as well as gravity wave smoking gun signatures of this class of models.

  17. Cosmological consequences of nearly conformal dynamics at the TeV scale

    NASA Astrophysics Data System (ADS)

    Konstandin, Thomas; Servant, Géraldine

    2011-12-01

    Nearly conformal dynamics at the TeV scale as motivated by the hierarchy problem can be characterized by a stage of significant supercooling at the electroweak epoch. This has important cosmological consequences. In particular, a common assumption about the history of the universe is that the reheating temperature is high, at least high enough to assume that TeV-mass particles were once in thermal equilibrium. However, as we discuss in this paper, this assumption is not well justified in some models of strong dynamics at the TeV scale. We then need to reexamine how to achieve baryogenesis in these theories as well as reconsider how the dark matter abundance is inherited. We argue that baryonic and dark matter abundances can be explained naturally in these setups where reheating takes place by bubble collisions at the end of the strongly first-order phase transition characterizing conformal symmetry breaking, even if the reheating temperature is below the electroweak scale ~ 100 GeV. In particular, non-thermal production of heavy WIMPs during bubble collisions becomes a well-motivated possibility. We also discuss inflation as well as gravity wave smoking gun signatures of this class of models.

  18. Class Evolution Tree: A Graphical Tool to Support Decisions on the Number of Classes in Exploratory Categorical Latent Variable Modeling for Rehabilitation Research

    ERIC Educational Resources Information Center

    Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa

    2011-01-01

    The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was…

  19. A Test of the Reciprocal-Effects Model of Academic Achievement and Academic Self-Concept in Regular Classes and Special Classes for the Gifted

    ERIC Educational Resources Information Center

    Preckel, Franzis; Schmidt, Isabelle; Stumpf, Eva; Motschenbacher, Monika; Vogl, Katharina; Schneider, Wolfgang

    2017-01-01

    According to the reciprocal-effects model (REM), prior academic self-concept (ASC) has a positive effect on subsequent achievement beyond what can be explained in terms of prior achievement and vice versa. The present study investigated the REM for students studying in special classes for the gifted compared to students studying in regular…

  20. Class Evolution Tree: A Graphical Tool to Support Decisions on the Number of Classes in Exploratory Categorical Latent Variable Modeling for Rehabilitation Research

    ERIC Educational Resources Information Center

    Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa

    2011-01-01

    The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was…

  1. Criterion for universality-class-independent critical fluctuations: example of the two-dimensional Ising model.

    PubMed

    Clusel, Maxime; Fortin, Jean-Yves; Holdsworth, Peter C W

    2004-10-01

    Order parameter fluctuations for the two-dimensional Ising model in the region of the critical temperature are presented. A locus of temperatures T(*) (L) and a locus of magnetic fields B(*) (L) are identified, for which the probability density function is similar to that for the two-dimensional XY model in the spin wave approximation. The characteristics of the fluctuations along these points are largely independent of universality class. We show that the largest range of fluctuations relative to the variance of the distribution occurs along these loci of points, rather than at the critical temperature itself and we discuss this observation in terms of intermittency. Our motivation is the identification of a generic form for fluctuations in correlated systems in accordance with recent experimental and numerical observations. We conclude that a universality-class-dependent form for the fluctuations is a particularity of critical phenomena related to the change in symmetry at a phase transition.

  2. Analysis of a Class of Low-Dimensional Models of Mutation and Predation

    NASA Astrophysics Data System (ADS)

    Abernethy, Gavin M.; McCartney, Mark

    We consider a class of simple two-dimensional discrete models representative of a system incorporating both mutation and predation. A selection of analytic and numerical results are presented, classifying the dynamic behavior of the system by means of Lyapunov exponents over a biologically-reasonable region of parameter space, and illustrating the occurrence of hyperchaos and a Neimark-Sacker bifurcation producing regions of quasiperiodicity.

  3. Search for Neutral Minimal Supersymmetric Standard Model Higgs Bosons Decaying to Tau Pairs in pp Collisions at sqrt[s]=7 TeV

    SciTech Connect

    Chatrchyan, Serguei; et al.

    2011-06-01

    A search for neutral MSSM Higgs bosons in pp collisions at the LHC at a center-of-mass energy of 7 TeV is presented. The results are based on a data sample corresponding to an integrated luminosity of 36 inverse picobarns recorded by the CMS experiment. The search uses decays of the Higgs bosons to tau pairs. No excess is observed in the tau-pair invariant-mass spectrum. The resulting upper limits on the Higgs boson production cross section times branching fraction to tau pairs, as a function of the pseudoscalar Higgs boson mass, yield stringent new bounds in the MSSM parameter space.

  4. GTC Optical Spectroscopy of TeV Blazars

    NASA Astrophysics Data System (ADS)

    Paiano, Simona; Falomo, R.; Treves, A.; Landoni, M.; Scarpa, R.; Righi, C.

    2016-10-01

    We carried out an extensive spectroscopic campaign of blazars of unknown or uncertain redshift which are either detected in the TeV band by Cherenkov telescopes or good candidate as TeV emitters. We used the OSIRIS spectrograph at the Gran Telescopio of Canarias (GTC) at La Palma to secure high signal-to-noise ratio spectra at intermediate resolution in the range 3800-10000 Ang. These observations allow us to confirm the blazar classification, to find new redshift and/or to set stringent lower limits. The results are discussed in the context of emission modeling of TeV sources and are of importance for the characterization of the Extragalactic Background Light.

  5. A Novel Multi-Class Ensemble Model for Classifying Imbalanced Biomedical Datasets

    NASA Astrophysics Data System (ADS)

    Bikku, Thulasi; Sambasiva Rao, N., Dr; Rao, Akepogu Ananda, Dr

    2017-08-01

    This paper mainly focuseson developing aHadoop based framework for feature selection and classification models to classify high dimensionality data in heterogeneous biomedical databases. Wide research has been performing in the fields of Machine learning, Big data and Data mining for identifying patterns. The main challenge is extracting useful features generated from diverse biological systems. The proposed model can be used for predicting diseases in various applications and identifying the features relevant to particular diseases. There is an exponential growth of biomedical repositories such as PubMed and Medline, an accurate predictive model is essential for knowledge discovery in Hadoop environment. Extracting key features from unstructured documents often lead to uncertain results due to outliers and missing values. In this paper, we proposed a two phase map-reduce framework with text preprocessor and classification model. In the first phase, mapper based preprocessing method was designed to eliminate irrelevant features, missing values and outliers from the biomedical data. In the second phase, a Map-Reduce based multi-class ensemble decision tree model was designed and implemented in the preprocessed mapper data to improve the true positive rate and computational time. The experimental results on the complex biomedical datasets show that the performance of our proposed Hadoop based multi-class ensemble model significantly outperforms state-of-the-art baselines.

  6. A Nonparametric Multidimensional Latent Class IRT Model in a Bayesian Framework.

    PubMed

    Bartolucci, Francesco; Farcomeni, Alessio; Scaccia, Luisa

    2017-09-12

    We propose a nonparametric item response theory model for dichotomously-scored items in a Bayesian framework. The model is based on a latent class (LC) formulation, and it is multidimensional, with dimensions corresponding to a partition of the items in homogenous groups that are specified on the basis of inequality constraints among the conditional success probabilities given the latent class. Moreover, an innovative system of prior distributions is proposed following the encompassing approach, in which the largest model is the unconstrained LC model. A reversible-jump type algorithm is described for sampling from the joint posterior distribution of the model parameters of the encompassing model. By suitably post-processing its output, we then make inference on the number of dimensions (i.e., number of groups of items measuring the same latent trait) and we cluster items according to the dimensions when unidimensionality is violated. The approach is illustrated by two examples on simulated data and two applications based on educational and quality-of-life data.

  7. SPI drought class prediction using log-linear models applied to wet and dry seasons

    NASA Astrophysics Data System (ADS)

    Moreira, Elsa E.

    2016-08-01

    A log-linear modelling for 3-dimensional contingency tables was used with categorical time series of SPI drought class transitions for prediction of monthly drought severity. Standardized Precipitation Index (SPI) time series in 12- and 6-month time scales were computed for 10 precipitation time series relative to GPCC datasets with 2.5° spatial resolution located over Portugal and with 112 years length (1902-2014). The aim was modelling two-month step class transitions for the wet and dry seasons of the year and then obtain probability ratios - Odds - as well as their respective confidence intervals to estimate how probable a transition is compared to another. The prediction results produced by the modelling applied to wet and dry season separately, for the 6- and the 12-month SPI time scale, were compared with the results produced by the same modelling without the split, using skill scores computed for the entire time series length. Results point to good prediction performances ranging from 70 to 80% in the percentage of corrects (PC) and 50-70% in the Heidke skill score (HSS), with the highest scores obtained when the modelling is applied to the SPI12. The adding up of the wet and dry seasons introduced in the modelling brought improvements in the predictions, of about 0.9-4% in the PC and 1.3-6.8% in the HSS, being the highest improvements obtained in the SPI6 application.

  8. The significance of the 1-TeV scale

    SciTech Connect

    Quigg, C.

    1988-01-01

    the Standard Model of elementary particle physics organizes our knowledge of the basic constituents and interactions, and achieves a wide-ranging synthesis of elementary phenomena, but leaves unanswered some fundamental questions. Important clues to a more complete understanding are to be found on the energy scale of 1 TeV, where the kinship between the weak and electromagnetic interactions will be illuminated. Exploration of the 1-TeV scale is an essential goal for the 1990s. 37 refs., 3 figs.

  9. Fixed-distance latent class models for the analysis of sets of two-way contingency tables.

    PubMed

    Formann, A K

    1993-06-01

    This paper develops and describes the application of modified latent class models for analyzing sets of two-way contingency tables. The proposed fixed-distance models differ from traditional latent class models in that the assumption of local stochastic independence is superseded by allowing interactions of the manifest variables within each class, which can be represented by a single association parameter. As an example, two data sets on eye color by hair color [collected in Caithness (N1 = 5,387) and Aberdeen (N2 = 22,361)] and fixed-distance models with up to six classes (three classes per data set) are considered, finally leading to satisfactory fit and rather simple interpretation.

  10. An investigation of difficulties experienced by students developing unified modelling language (UML) class and sequence diagrams

    NASA Astrophysics Data System (ADS)

    Sien, Ven Yu

    2011-12-01

    Object-oriented analysis and design (OOAD) is not an easy subject to learn. There are many challenges confronting students when studying OOAD. Students have particular difficulty abstracting real-world problems within the context of OOAD. They are unable to effectively build object-oriented (OO) models from the problem domain because they essentially do not know "what" to model. This article investigates the difficulties and misconceptions undergraduate students have with analysing systems using unified modelling language analysis class and sequence diagrams. These models were chosen because they represent important static and dynamic aspects of the software system under development. The results of this study will help students produce effective OO models, and facilitate software engineering lecturers design learning materials and approaches for introductory OOAD courses.

  11. Integrated ligand based pharmacophore model derived from diverse FAAH covalent ligand classes.

    PubMed

    Shen, Lingling; Huang, Hongwei; Makriyannis, Alexandros; Fisher, Luke S

    2012-12-01

    3D pharmacophore modeling is an important computational methodology for ligand-enzyme binding interactions in drug discovery. More specifically, a consensus pharmacophore model derived from diverse ligands is a key determinant upon which the prediction power of computational models is based for designing novel ligands. In this work, by merging the important pharmacophore features based on four classes of covalent FAAH ligands, and then integrating the exclusion volume spheres derived from the crystal structure, we created for the first time an integrated FAAH pharmacophore model to describe the ligand-enzyme binding interactions. This new integrated FAAH pharmacophore model can correctly predict the covalent ligand binding mode, which correlates with the SAR data. The study is expected to provide insights into novel covalent ligand-FAAH binding interactions, and facilitate the design of covalent ligands against FAAH.

  12. A Class of Semiparametric Transformation Models for Survival Data with a Cured Proportion

    PubMed Central

    Choi, Sangbum; Huang, Xuelin; Chen, Yi-Hau

    2013-01-01

    We propose a new class of semiparametric regression models based on a multiplicative frailty assumption with a discrete frailty, which may account for cured subgroup in population. The cure model framework is then recast as a problem with a transformation model. The proposed models can explain a broad range of nonproportional hazards structures along with a cured proportion. An efficient and simple algorithm based on the martingale process is developed to locate the nonparametric maximum likelihood estimator. Unlike existing expectation-maximization based methods, our approach directly maximizes a nonparametric likelihood function, and the calculation of consistent variance estimates is immediate. The proposed method is useful for resolving identifiability features embedded in semiparametric cure models. Simulation studies are presented to demonstrate the finite sample properties of the proposed method. A case study of stage III soft-tissue sarcoma is given as an illustration. PMID:23760878

  13. Verification of relationship model between Korean new elderly class's recovery resilience and productive aging.

    PubMed

    Cho, Gun-Sang; Kim, Dae-Sung; Yi, Eun-Surk

    2015-12-01

    The purpose of this study is to verification of relationship model between Korean new elderly class's recovery resilience and productive aging. As of 2013, this study sampled preliminary elderly people in Gyeonggi-do and other provinces nationwide. Data from a total of effective 484 subjects was analyzed. The collected data was processed using the IBM SPSS 20.0 and AMOS 20.0, and underwent descriptive statistical analysis, confirmatory factor analysis, and structure model verification. The path coefficient associated with model fitness was examined. The standardization path coefficient between recovery resilience and productive aging is β=0.975 (t=14.790), revealing a statistically significant positive effect. Thus, it was found that the proposed basic model on the direct path of recovery resilience and productive aging was fit for the model.

  14. Elliptic flow in s=200 GeV Au+Au collisions and s=2.76 TeV Pb+Pb collisions: Insights from viscous hydrodynamics+hadron cascade hybrid model

    NASA Astrophysics Data System (ADS)

    Song, Huichao; Bass, Steffen A.; Heinz, Ulrich

    2011-05-01

    Using the newly developed hybrid model VISHNU, which connects viscous hydrodynamics with a hadron cascade model, we study the differential and integrated elliptic flow v2 at different centrality bins for 200 A GeV Au+Au collisions and 2.76 A TeV Pb+Pb collisions. We find that the average quark-gluon plasma (QGP)-specific shear viscosity η/s slightly increases from Relativistic Heavy Ion Collider to Large Hadron Collider (LHC) energies. However, a further study assuming different temperature dependencies for (η/s)QGP shows that one cannot uniquely constrain the form of (η/s)QGP(T) by fitting the spectra and v2 alone. Based on our current understanding, the question whether the QGP fluid is more viscous or more perfect in the temperature regime reached by LHC energies is still open.

  15. Search for the standard model higgs Boson in the ZH-->nunubb channel in 5.2 fb{-1} of pp collisions at sqrt[s]=1.96 TeV.

    PubMed

    Abazov, V M; Abbott, B; Abolins, M; Acharya, B S; Adams, M; Adams, T; Aguilo, E; Alexeev, G D; Alkhazov, G; Alton, A; Alverson, G; Alves, G A; Ancu, L S; Aoki, M; Arnoud, Y; Arov, M; Askew, A; Asman, B; Atramentov, O; Avila, C; Backusmayes, J; Badaud, F; Bagby, L; Baldin, B; Bandurin, D V; Banerjee, S; Barberis, E; Barfuss, A-F; Baringer, P; Barreto, J; Bartlett, J F; Bassler, U; Bauer, D; Beale, S; Bean, A; Begalli, M; Begel, M; Belanger-Champagne, C; Bellantoni, L; Benitez, J A; Beri, S B; Bernardi, G; Bernhard, R; Bertram, I; Besançon, M; Beuselinck, R; Bezzubov, V A; Bhat, P C; Bhatnagar, V; Blazey, G; Blessing, S; Bloom, K; Boehnlein, A; Boline, D; Bolton, T A; Boos, E E; Borissov, G; Bose, T; Brandt, A; Brock, R; Brooijmans, G; Bross, A; Brown, D; Bu, X B; Buchholz, D; Buehler, M; Buescher, V; Bunichev, V; Burdin, S; Burnett, T H; Buszello, C P; Calfayan, P; Calpas, B; Calvet, S; Camacho-Pérez, E; Cammin, J; Carrasco-Lizarraga, M A; Carrera, E; Casey, B C K; Castilla-Valdez, H; Chakrabarti, S; Chakraborty, D; Chan, K M; Chandra, A; Cheu, E; Chevalier-Théry, S; Cho, D K; Cho, S W; Choi, S; Choudhary, B; Christoudias, T; Cihangir, S; Claes, D; Clutter, J; Cooke, M; Cooper, W E; Corcoran, M; Couderc, F; Cousinou, M-C; Cutts, D; Cwiok, M; Das, A; Davies, G; De, K; de Jong, S J; De La Cruz-Burelo, E; Devaughan, K; Déliot, F; Demarteau, M; Demina, R; Denisov, D; Denisov, S P; Desai, S; Diehl, H T; Diesburg, M; Dominguez, A; Dorland, T; Dubey, A; Dudko, L V; Duflot, L; Duggan, D; Duperrin, A; Dutt, S; Dyshkant, A; Eads, M; Edmunds, D; Ellison, J; Elvira, V D; Enari, Y; Eno, S; Evans, H; Evdokimov, A; Evdokimov, V N; Facini, G; Ferapontov, A V; Ferbel, T; Fiedler, F; Filthaut, F; Fisher, W; Fisk, H E; Fortner, M; Fox, H; Fuess, S; Gadfort, T; Galea, C F; Garcia-Bellido, A; Gavrilov, V; Gay, P; Geist, W; Geng, W; Gerbaudo, D; Gerber, C E; Gershtein, Y; Gillberg, D; Ginther, G; Golovanov, G; Gómez, B; Goussiou, A; Grannis, P D; Greder, S; Greenlee, H; Greenwood, Z D; Gregores, E M; Grenier, G; Gris, Ph; Grivaz, J-F; Grohsjean, A; Grünendahl, S; Grünewald, M W; Guo, F; Guo, J; Gutierrez, G; Gutierrez, P; Haas, A; Haefner, P; Hagopian, S; Haley, J; Hall, I; Han, L; Harder, K; Harel, A; Hauptman, J M; Hays, J; Hebbeker, T; Hedin, D; Hegeman, J G; Heinson, A P; Heintz, U; Hensel, C; Heredia-De La Cruz, I; Herner, K; Hesketh, G; Hildreth, M D; Hirosky, R; Hoang, T; Hobbs, J D; Hoeneisen, B; Hohlfeld, M; Hossain, S; Houben, P; Hu, Y; Hubacek, Z; Huske, N; Hynek, V; Iashvili, I; Illingworth, R; Ito, A S; Jabeen, S; Jaffré, M; Jain, S; Jamin, D; Jesik, R; Johns, K; Johnson, C; Johnson, M; Johnston, D; Jonckheere, A; Jonsson, P; Juste, A; Kajfasz, E; Karmanov, D; Kasper, P A; Katsanos, I; Kaushik, V; Kehoe, R; Kermiche, S; Khalatyan, N; Khanov, A; Kharchilava, A; Kharzheev, Y N; Khatidze, D; Kirby, M H; Kirsch, M; Kohli, J M; Kozelov, A V; Kraus, J; Kumar, A; Kupco, A; Kurca, T; Kuzmin, V A; Kvita, J; Lam, D; Lammers, S; Landsberg, G; Lebrun, P; Lee, H S; Lee, W M; Leflat, A; Lellouch, J; Li, L; Li, Q Z; Lietti, S M; Lim, J K; Lincoln, D; Linnemann, J; Lipaev, V V; Lipton, R; Liu, Y; Liu, Z; Lobodenko, A; Lokajicek, M; Love, P; Lubatti, H J; Luna-Garcia, R; Lyon, A L; Maciel, A K A; Mackin, D; Mättig, P; Magaña-Villalba, R; Mal, P K; Malik, S; Malyshev, V L; Maravin, Y; Martínez-Ortega, J; McCarthy, R; McGivern, C L; Meijer, M M; Melnitchouk, A; Mendoza, L; Menezes, D; Mercadante, P G; Merkin, M; Meyer, A; Meyer, J; Mommsen, R K; Mondal, N K; Moulik, T; Muanza, G S; Mulhearn, M; Mundal, O; Mundim, L; Nagy, E; Naimuddin, M; Narain, M; Nayyar, R; Neal, H A; Negret, J P; Neustroev, P; Nilsen, H; Nogima, H; Novaes, S F; Nunnemann, T; Obrant, G; Ochando, C; Onoprienko, D; Orduna, J; Osman, N; Osta, J; Otec, R; Otero Y Garzón, G J; Owen, M; Padilla, M; Padley, P; Pangilinan, M; Parashar, N; Parihar, V; Park, S-J; Park, S K; Parsons, J; Partridge, R; Parua, N; Patwa, A; Penning, B; Perfilov, M; Peters, K; Peters, Y; Pétroff, P; Piegaia, R; Piper, J; Pleier, M-A; Podesta-Lerma, P L M; Podstavkov, V M; Pol, M-E; Polozov, P; Popov, A V; Prewitt, M; Price, D; Protopopescu, S; Qian, J; Quadt, A; Quinn, B; Rangel, M S; Ranjan, K; Ratoff, P N; Razumov, I; Renkel, P; Rich, P; Rijssenbeek, M; Ripp-Baudot, I; Rizatdinova, F; Robinson, S; Rominsky, M; Royon, C; Rubinov, P; Ruchti, R; Safronov, G; Sajot, G; Sánchez-Hernández, A; Sanders, M P; Sanghi, B; Savage, G; Sawyer, L; Scanlon, T; Schaile, D; Schamberger, R D; Scheglov, Y; Schellman, H; Schliephake, T; Schlobohm, S; Schwanenberger, C; Schwienhorst, R; Sekaric, J; Severini, H; Shabalina, E; Shary, V; Shchukin, A A; Shivpuri, R K; Simak, V; Sirotenko, V; Skubic, P; Slattery, P; Smirnov, D; Snow, G R; Snow, J; Snyder, S; Söldner-Rembold, S; Sonnenschein, L; Sopczak, A; Sosebee, M; Soustruznik, K; Spurlock, B; Stark, J; Stolin, V; Stoyanova, D A; Strandberg, J; Strang, M A; Strauss, E; Strauss, M; Ströhmer, R; Strom, D; Stutte, L; Svoisky, P; Takahashi, M; Tanasijczuk, A; Taylor, W; Tiller, B; Titov, M; Tokmenin, V V; Tsybychev, D; Tuchming, B; Tully, C; Tuts, P M; Unalan, R; Uvarov, L; Uvarov, S; Uzunyan, S; van den Berg, P J; Van Kooten, R; van Leeuwen, W M; Varelas, N; Varnes, E W; Vasilyev, I A; Verdier, P; Vertogradov, L S; Verzocchi, M; Vesterinen, M; Vilanova, D; Vint, P; Vokac, P; Wahl, H D; Wang, M H L S; Warchol, J; Watts, G; Wayne, M; Weber, G; Weber, M; Wetstein, M; White, A; Wicke, D; Williams, M R J; Wilson, G W; Wimpenny, S J; Wobisch, M; Wood, D R; Wyatt, T R; Xie, Y; Xu, C; Yacoob, S; Yamada, R; Yang, W-C; Yasuda, T; Yatsunenko, Y A; Ye, Z; Yin, H; Yip, K; Yoo, H D; Youn, S W; Yu, J; Zeitnitz, C; Zelitch, S; Zhao, T; Zhou, B; Zhu, J; Zielinski, M; Zieminska, D; Zivkovic, L; Zutshi, V; Zverev, E G

    2010-02-19

    A search is performed for the standard model Higgs boson in 5.2 fb{-1} of pp collisions at sqrt[s]=1.96 TeV, collected with the D0 detector at the Fermilab Tevatron Collider. The final state considered is a pair of b jets and large missing transverse energy, as expected from pp-->ZH-->nunubb production. The search is also sensitive to the WH-->lnubb channel when the charged lepton is not identified. For a Higgs boson mass of 115 GeV, a limit is set at the 95% C.L. on the cross section multiplied by branching fraction for [pp-->(Z/W)H](H-->bb) that is a factor of 3.7 larger than the standard model value, consistent with the factor of 4.6 expected.

  16. Search for the standard model Higgs boson in the diphoton decay channel with 4.9 fb(-1) of pp collision data at √s=7 TeV with ATLAS.

    PubMed

    Aad, G; Abbott, B; Abdallah, J; Abdel Khalek, S; Abdelalim, A A; Abdesselam, A; Abdinov, O; Abi, B; Abolins, M; Abouzeid, O S; Abramowicz, H; Abreu, H; Acerbi, E; Acharya, B S; Adamczyk, L; Adams, D L; Addy, T N; Adelman, J; Aderholz, M; Adomeit, S; Adragna, P; Adye, T; Aefsky, S; Aguilar-Saavedra, J A; Aharrouche, M; Ahlen, S P; Ahles, F; Ahmad, A; Ahsan, M; Aielli, G; Akdogan, T; Akesson, T P A; Akimoto, G; Akimov, A V; Akiyama, A; Alam, M S; Alam, M A; Albert, J; Albrand, S; Aleksa, M; Aleksandrov, I N; Alessandria, F; Alexa, C; Alexander, G; Alexandre, G; Alexopoulos, T; Alhroob, M; Aliev, M; Alimonti, G; Alison, J; Aliyev, M; Allbrooke, B M M; Allport, P P; Allwood-Spiers, S E; Almond, J; Aloisio, A; Alon, R; Alonso, A; Alvarez Gonzalez, B; Alviggi, M G; Amako, K; Amaral, P; Amelung, C; Ammosov, V V; Amorim, A; Amorós, G; Amram, N; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, G; Anderson, K J; Andreazza, A; Andrei, V; Andrieux, M-L; Anduaga, X S; Angerami, A; Anghinolfi, F; Anisenkov, A; Anjos, N; Annovi, A; Antonaki, A; Antonelli, M; Antonov, A; Antos, J; Anulli, F; Aoun, S; Aperio Bella, L; Apolle, R; Arabidze, G; Aracena, I; Arai, Y; Arce, A T H; Arfaoui, S; Arguin, J-F; Arik, E; Arik, M; Armbruster, A J; Arnaez, O; Arnal, V; Arnault, C; Artamonov, A; Artoni, G; Arutinov, D; Asai, S; Asfandiyarov, R; Ask, S; Asman, B; Asquith, L; Assamagan, K; Astbury, A; Astvatsatourov, A; Aubert, B; Auge, E; Augsten, K; Aurousseau, M; Avolio, G; Avramidou, R; Axen, D; Ay, C; Azuelos, G; Azuma, Y; Baak, M A; Baccaglioni, G; Bacci, C; Bach, A M; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Badescu, E; Bagnaia, P; Bahinipati, S; Bai, Y; Bailey, D C; Bain, T; Baines, J T; Baker, O K; Baker, M D; Baker, S; Banas, E; Banerjee, P; Banerjee, Sw; Banfi, D; Bangert, A; Bansal, V; Bansil, H S; Barak, L; Baranov, S P; Barashkou, A; Barbaro Galtieri, A; Barber, T; Barberio, E L; Barberis, D; Barbero, M; Bardin, D Y; Barillari, T; Barisonzi, M; Barklow, T; Barlow, N; Barnett, B M; Barnett, R M; Baroncelli, A; Barone, G; Barr, A J; Barreiro, F; Barreiro Guimarães da Costa, J; Barrillon, P; Bartoldus, R; Barton, A E; Bartsch, V; Bates, R L; Batkova, L; Batley, J R; Battaglia, A; Battistin, M; Bauer, F; Bawa, H S; Beale, S; Beau, T; Beauchemin, P H; Beccherle, R; Bechtle, P; Beck, H P; Becker, S; Beckingham, M; Becks, K H; Beddall, A J; Beddall, A; Bedikian, S; Bednyakov, V A; Bee, C P; Begel, M; Behar Harpaz, S; Behera, P K; Beimforde, M; Belanger-Champagne, C; Bell, P J; Bell, W H; Bella, G; Bellagamba, L; Bellina, F; Bellomo, M; Belloni, A; Beloborodova, O; Belotskiy, K; Beltramello, O; Benary, O; Benchekroun, D; Benchouk, C; Bendel, M; Benekos, N; Benhammou, Y; Benhar Noccioli, E; Benitez Garcia, J A; Benjamin, D P; Benoit, M; Bensinger, J R; Benslama, K; Bentvelsen, S; Berge, D; Bergeaas Kuutmann, E; Berger, N; Berghaus, F; Berglund, E; Beringer, J; Bernat, P; Bernhard, R; Bernius, C; Berry, T; Bertella, C; Bertin, A; Bertinelli, F; Bertolucci, F; Besana, M I; Besson, N; Bethke, S; Bhimji, W; Bianchi, R M; Bianco, M; Biebel, O; Bieniek, S P; Bierwagen, K; Biesiada, J; Biglietti, M; Bilokon, H; Bindi, M; Binet, S; Bingul, A; Bini, C; Biscarat, C; Bitenc, U; Black, K M; Blair, R E; Blanchard, J-B; Blanchot, G; Blazek, T; Blocker, C; Blocki, J; Blondel, A; Blum, W; Blumenschein, U; Bobbink, G J; Bobrovnikov, V B; Bocchetta, S S; Bocci, A; Boddy, C R; Boehler, M; Boek, J; Boelaert, N; Bogaerts, J A; Bogdanchikov, A; Bogouch, A; Bohm, C; Bohm, J; Boisvert, V; Bold, T; Boldea, V; Bolnet, N M; Bomben, M; Bona, M; Bondarenko, V G; Bondioli, M; Boonekamp, M; Booth, C N; Bordoni, S; Borer, C; Borisov, A; Borissov, G; Borjanovic, I; Borri, M; Borroni, S; Bortolotto, V; Bos, K; Boscherini, D; Bosman, M; Boterenbrood, H; Botterill, D; Bouchami, J; Boudreau, J; Bouhova-Thacker, E V; Boumediene, D; Bourdarios, C; Bousson, N; Boveia, A; Boyd, J; Boyko, I R; Bozhko, N I; Bozovic-Jelisavcic, I; Bracinik, J; Braem, A; Branchini, P; Brandenburg, G W; 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Timoshenko, S; Tipton, P; Tique Aires Viegas, F J; Tisserant, S; Tobias, J; Toczek, B; Todorov, T; Todorova-Nova, S; Toggerson, B; Tojo, J; Tokár, S; Tokunaga, K; Tokushuku, K; Tollefson, K; Tomoto, M; Tompkins, L; Toms, K; Tong, G; Tonoyan, A; Topfel, C; Topilin, N D; Torchiani, I; Torrence, E; Torres, H; Torró Pastor, E; Toth, J; Touchard, F; Tovey, D R; Trefzger, T; Tremblet, L; Tricoli, A; Trigger, I M; Trincaz-Duvoid, S; Trinh, T N; Tripiana, M F; Trischuk, W; Trivedi, A; Trocmé, B; Troncon, C; Trottier-McDonald, M; Trzebinski, M; Trzupek, A; Tsarouchas, C; Tseng, J C-L; Tsiakiris, M; Tsiareshka, P V; Tsionou, D; Tsipolitis, G; Tsiskaridze, V; Tskhadadze, E G; Tsukerman, I I; Tsulaia, V; Tsung, J-W; Tsuno, S; Tsybychev, D; Tua, A; Tudorache, A; Tudorache, V; Tuggle, J M; Turala, M; Turecek, D; Turk Cakir, I; Turlay, E; Turra, R; Tuts, P M; Tykhonov, A; Tylmad, M; Tyndel, M; Tzanakos, G; Uchida, K; Ueda, I; Ueno, R; Ugland, M; Uhlenbrock, M; Uhrmacher, M; Ukegawa, F; Unal, G; Underwood, D G; Undrus, A; Unel, G; Unno, Y; Urbaniec, D; Usai, G; Uslenghi, M; Vacavant, L; Vacek, V; Vachon, B; Vahsen, S; Valenta, J; Valente, P; Valentinetti, S; Valkar, S; Valladolid Gallego, E; Vallecorsa, S; Valls Ferrer, J A; van der Graaf, H; van der Kraaij, E; Van Der Leeuw, R; van der Poel, E; van der Ster, D; van Eldik, N; van Gemmeren, P; van Kesteren, Z; van Vulpen, I; Vanadia, M; Vandelli, W; Vandoni, G; Vaniachine, A; Vankov, P; Vannucci, F; Varela Rodriguez, F; Vari, R; Varnes, E W; Varol, T; Varouchas, D; Vartapetian, A; Varvell, K E; Vassilakopoulos, V I; Vazeille, F; Vazquez Schroeder, T; Vegni, G; Veillet, J J; Vellidis, C; Veloso, F; Veness, R; Veneziano, S; Ventura, A; Ventura, D; Venturi, M; Venturi, N; Vercesi, V; Verducci, M; Verkerke, W; Vermeulen, J C; Vest, A; Vetterli, M C; Vichou, I; Vickey, T; Vickey Boeriu, O E; Viehhauser, G H A; Viel, S; Villa, M; Villaplana Perez, M; Vilucchi, E; Vincter, M G; Vinek, E; Vinogradov, V B; Virchaux, M; Virzi, J; Vitells, O; Viti, M; Vivarelli, I; Vives Vaque, F; Vlachos, S; Vladoiu, D; Vlasak, M; Vlasov, N; Vogel, A; Vokac, P; Volpi, G; Volpi, M; Volpini, G; von der Schmitt, H; von Loeben, J; von Radziewski, H; von Toerne, E; Vorobel, V; Vorobiev, A P; Vorwerk, V; Vos, M; Voss, R; Voss, T T; Vossebeld, J H; Vranjes, N; Vranjes Milosavljevic, M; Vrba, V; Vreeswijk, M; Vu Anh, T; Vuillermet, R; Vukotic, I; Wagner, W; Wagner, P; Wahlen, H; Wakabayashi, J; Walch, S; Walder, J; Walker, R; Walkowiak, W; Wall, R; Waller, P; Wang, C; Wang, H; Wang, H; Wang, J; Wang, J; Wang, J C; Wang, R; Wang, S M; Warburton, A; Ward, C P; Warsinsky, M; Wasicki, C; Watkins, P M; Watson, A T; Watson, I J; Watson, M F; Watts, G; Watts, S; Waugh, A T; Waugh, B M; Weber, M; Weber, M S; Weber, P; Weidberg, A R; Weigell, P; Weingarten, J; Weiser, C; Wellenstein, H; Wells, P S; Wenaus, T; Wendland, D; Wendler, S; Weng, Z; Wengler, T; Wenig, S; Wermes, N; Werner, M; Werner, P; Werth, M; Wessels, M; Wetter, J; Weydert, C; Whalen, K; Wheeler-Ellis, S J; Whitaker, S P; White, A; White, M J; Whitehead, S R; Whiteson, D; Whittington, D; Wicek, F; Wicke, D; Wickens, F J; Wiedenmann, W; Wielers, M; Wienemann, P; Wiglesworth, C; Wiik, L A M; Wijeratne, P A; Wildauer, A; Wildt, M A; Wilhelm, I; Wilkens, H G; Will, J Z; Williams, E; Williams, H H; Willis, W; Willocq, S; Wilson, J A; Wilson, M G; Wilson, A; Wingerter-Seez, I; Winkelmann, S; Winklmeier, F; Wittgen, M; Wolter, M W; Wolters, H; Wong, W C; Wooden, G; Wosiek, B K; Wotschack, J; Woudstra, M J; Wozniak, K W; Wraight, K; Wright, C; Wright, M; Wrona, B; Wu, S L; Wu, X; Wu, Y; Wulf, E; Wunstorf, R; Wynne, B M; Xella, S; Xiao, M; Xie, S; Xie, Y; Xu, C; Xu, D; Xu, G; Yabsley, B; Yacoob, S; Yamada, M; Yamaguchi, H; Yamamoto, A; Yamamoto, K; Yamamoto, S; Yamamura, T; Yamanaka, T; Yamaoka, J; Yamazaki, T; Yamazaki, Y; Yan, Z; Yang, H; Yang, U K; Yang, Y; Yang, Y; Yang, Z; Yanush, S; Yao, Y; Yasu, Y; Ybeles Smit, G V; Ye, J; Ye, S; Yilmaz, M; Yoosoofmiya, R; Yorita, K; Yoshida, R; Young, C; Youssef, S; Yu, D; Yu, J; Yu, J; Yuan, L; Yurkewicz, A; Zabinski, B; Zaets, V G; Zaidan, R; Zaitsev, A M; Zajacova, Z; Zanello, L; Zaytsev, A; Zeitnitz, C; Zeller, M; Zeman, M; Zemla, A; Zendler, C; Zenin, O; Zeniš, T; Zenonos, Z; Zenz, S; Zerwas, D; Zevi della Porta, G; Zhan, Z; Zhang, D; Zhang, H; Zhang, J; Zhang, X; Zhang, Z; Zhao, L; Zhao, T; Zhao, Z; Zhemchugov, A; Zheng, S; Zhong, J; Zhou, B; Zhou, N; Zhou, Y; Zhu, C G; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhuravlov, V; Zieminska, D; Zimmermann, R; Zimmermann, S; Zimmermann, S; Ziolkowski, M; Zitoun, R; Zivković, L; Zmouchko, V V; Zobernig, G; Zoccoli, A; Zolnierowski, Y; Zsenei, A; zur Nedden, M; Zutshi, V; Zwalinski, L

    2012-03-16

    A search for the standard model Higgs boson is performed in the diphoton decay channel. The data used correspond to an integrated luminosity of 4.9 fb(-1) collected with the ATLAS detector at the Large Hadron Collider in proton-proton collisions at a center-of-mass energy of √s=7 TeV. In the diphoton mass range 110-150 GeV, the largest excess with respect to the background-only hypothesis is observed at 126.5 GeV, with a local significance of 2.8 standard deviations. Taking the look-elsewhere effect into account in the range 110-150 GeV, this significance becomes 1.5 standard deviations. The standard model Higgs boson is excluded at 95% confidence level in the mass ranges of 113-115 GeV and 134.5-136 GeV.

  17. A class of spherical, truncated, anisotropic models for application to globular clusters

    NASA Astrophysics Data System (ADS)

    de Vita, Ruggero; Bertin, Giuseppe; Zocchi, Alice

    2016-05-01

    Recently, a class of non-truncated, radially anisotropic models (the so-called f(ν)-models), originally constructed in the context of violent relaxation and modelling of elliptical galaxies, has been found to possess interesting qualities in relation to observed and simulated globular clusters. In view of new applications to globular clusters, we improve this class of models along two directions. To make them more suitable for the description of small stellar systems hosted by galaxies, we introduce a "tidal" truncation by means of a procedure that guarantees full continuity of the distribution function. The new fT(ν)-models are shown to provide a better fit to the observed photometric and spectroscopic profiles for a sample of 13 globular clusters studied earlier by means of non-truncated models; interestingly, the best-fit models also perform better with respect to the radial-orbit instability. Then, we design a flexible but simple two-component family of truncated models to study the separate issues of mass segregation and multiple populations. We do not aim at a fully realistic description of globular clusters to compete with the description currently obtained by means of dedicated simulations. The goal here is to try to identify the simplest models, that is, those with the smallest number of free parameters, but still have the capacity to provide a reasonable description for clusters that are evidently beyond the reach of one-component models. With this tool, we aim at identifying the key factors that characterize mass segregation or the presence of multiple populations. To reduce the relevant parameter space, we formulate a few physical arguments based on recent observations and simulations. A first application to two well-studied globular clusters is briefly described and discussed.

  18. The political implications of psychosocial work redesign: a model of the psychosocial class structure.

    PubMed

    Karasek, R

    1989-01-01

    The goal of this article is to further illuminate the poorly understood distribution of psychosocial well-being at the national population level (the distribution of conventional economic well-being is, of course, well understood). After reviewing psychosocial phenomena that cannot be predicted by the "conventional" class structure, the author introduces a new definition of production output value (New Value), more closely related to psychosocial well-being. A psychosocial class structure model is presented based on nine occupational subgroups in four status levels, representing both conventional and New Value-based status differences. An empirical analysis of 38 occupations in the U.S. work force (U.S. Quality of Employment Surveys 1969, 1972, 1977) uses psychosocial job dimensions of decision latitude, psychological demands, physical exertion, and social support. The author reviews job redesign strategies for three New Value "impoverished" groups with routinized, bureaucratized, and commercialized jobs, and the conditions for economic stability and political participation related to the psychosocial class structure model. Conflicts arising between conventional and New Value-based policies are discussed.

  19. Forecasting oral absorption across biopharmaceutics classification system classes with physiologically based pharmacokinetic models.

    PubMed

    Hansmann, Simone; Darwich, Adam; Margolskee, Alison; Aarons, Leon; Dressman, Jennifer

    2016-12-01

    The aim of this study was (1) to determine how closely physiologically based pharmacokinetic (PBPK) models can predict oral bioavailability using a priori knowledge of drug-specific properties and (2) to examine the influence of the biopharmaceutics classification system class on the simulation success. Simcyp Simulator, GastroPlus(™) and GI-Sim were used. Compounds with published Biowaiver monographs (bisoprolol (BCS I), nifedipine (BCS II), cimetidine (BCS III), furosemide (BCS IV)) were selected to ensure availability of accurate and reproducible data for all required parameters. Simulation success was evaluated with the average fold error (AFE) and absolute average fold error (AAFE). Parameter sensitivity analysis (PSA) to selected parameters was performed. Plasma concentration-time profiles after intravenous administration were forecast within an AAFE < 3. The addition of absorption processes resulted in more variability in the prediction of the plasma profiles, irrespective of biopharmaceutics classification system (BCS) class. The reliability of literature permeability data was identified as a key issue in the accuracy of predicting oral drug absorption. For the four drugs studied, it appears that the forecasting accuracy of the PBPK models is related to the BCS class (BCS I > BCS II, BCS III > BCS IV). These results will need to be verified with additional drugs. © 2016 Royal Pharmaceutical Society.

  20. Statistical power of likelihood ratio and Wald tests in latent class models with covariates.

    PubMed

    Gudicha, Dereje W; Schmittmann, Verena D; Vermunt, Jeroen K

    2016-12-30

    This paper discusses power and sample-size computation for likelihood ratio and Wald testing of the significance of covariate effects in latent class models. For both tests, asymptotic distributions can be used; that is, the test statistic can be assumed to follow a central Chi-square under the null hypothesis and a non-central Chi-square under the alternative hypothesis. Power or sample-size computation using these asymptotic distributions requires specification of the non-centrality parameter, which in practice is rarely known. We show how to calculate this non-centrality parameter using a large simulated data set from the model under the alternative hypothesis. A simulation study is conducted evaluating the adequacy of the proposed power analysis methods, determining the key study design factor affecting the power level, and comparing the performance of the likelihood ratio and Wald test. The proposed power analysis methods turn out to perform very well for a broad range of conditions. Moreover, apart from effect size and sample size, an important factor affecting the power is the class separation, implying that when class separation is low, rather large sample sizes are needed to achieve a reasonable power level.

  1. Unsupervised segmentation of broad classes from waveforms: Towards a model of early phonological acquisition

    NASA Astrophysics Data System (ADS)

    Lin, Ying

    2005-09-01

    It is commonly held that an important aspect of early phonological acquisition is the ability to learn sound distributions, or statistical learning. Yet significant differences in lexical representations are often observed in studies of infant speech perception, suggesting a protracted process of phonological development. The goal of the current project is to develop a model that links holistic and segmental representation of spoken words, using tools from contemporary speech recognition. In the present stage, the model focuses on the pre-lexical level of phonological development, and tries to identify segmental representations from acoustic signals of isolated words. The segmental representations are based on units that correspond to acoustic phonetic classes, and learning involves updating the unit models in parallel with updating phonotactics. Starting from acoustic segmentations, the model iteratively updates knowledge of the units and phonotactics, and renews segmentation hypotheses regarding each word until convergence. The results of running this algorithm on TIMIT and infant-directed speech data suggest that the model approximately identifies segment-sized broad classes in an unsupervised manner. This statistical approach also provides a different perspective on the role of lexicon in phonological development.

  2. Examining Class Differences in Method Effects Related to Negative Wording: An Example using Rasch Mixture Modeling.

    PubMed

    Morgan, Grant B; DiStefano, Christine; Motl, Robert W

    This study presents a mixture Rasch-based approach to investigating method effects associated with negatively worded items to illustrate how responses to this method effect vary depending on group characteristics. Using college students' responses on the Rosenberg Self-Esteem scale (Rosenberg, M., 1989), four latent classes were identified using six personality measures associated with the presence of this method effect. In addition, Rasch-based parameter estimates suggested latent classes differed in their use of the scale, showing that the method effect associated with negatively worded items may be more prominent for subjects possessing selected personality traits than for others. The mixture model approach to investigating method effects provides a way to address systematic methodological variation that is left unaccounted when a heterogeneous population is analyzed as one group.

  3. Modelling the benefits of a new class of anthelmintic in combination.

    PubMed

    Leathwick, D M

    2012-05-04

    Since 2009 two new classes of anthelmintics have been registered for use in sheep in New Zealand. This raises challenging questions about how such new actives should be used, not only to minimise the development of resistance to them, thereby ensuring their availability as effective treatments for as long as possible, but also to minimise the further development of resistance to the other anthelmintic classes. One strategy which appears to offer considerable potential for slowing the development of resistance is the use of combinations of different anthelmintic classes, although this approach remains contentious in some countries. The potential benefit of using anthelmintics in combination is particularly relevant to two recently released anthelmintic compounds because one, monepantel, is presently only available as a single active product while the other, derquantel, is only available in combination with abamectin. A simulation modelling approach was used to investigate the potential benefits of using anthelmintics in combination. The rate at which resistance develops to a new 'active' when used alone was compared to an equivalent compound used in combination with a second compound from an alternative class (in this case, abamectin), when various levels of resistance occur to the second active. In addition, the potential of a new active to reduce further development of resistance to the second compound in the combination was evaluated. Finally, the use of combinations as compared to sequential or rotational use patterns, in the presence of side resistance between two actives was investigated. The modelling simulations suggest a significant advantage to both compounds when they are used in combination, especially if both initially have high efficacy. The development of resistance to the new active was delayed, although to a lesser extent, even when the efficacy of the second active in the combination was only 50%. Under a 'low-refugia' management environment

  4. Development of 72kV Class Environmentally-Benign CO2 Gas Circuit Breaker Model

    NASA Astrophysics Data System (ADS)

    Uchii, Toshiyuki; Hoshina, Yoshikazu; Miyazaki, Kensaku; Mori, Tadashi; Kawano, Hiromichi; Nakamoto, Tetsuya; Hirano, Yoshihiko

    Adopting CO2 as an alternative gas of SF6 in a gas circuit breaker from the environmental viewpoint, a 72kV-31.5kA class CO2 gas circuit breaker (CO2-GCB) model, which does not contain SF6 gas at all, was designed, and produced. In the CO2-GCB model, some effective technologies for current interruption by CO2 gas were adopted; namely, puffer pressure enhancing techniques utilizing arc energy during a current interruption and ablation phenomena of a polymer element located in the puffer cylinder. As a result of current interruption and electric insulation tests, the CO2-GCB model achieved practical levels of performance. Furthermore, it was found by carrying out a life cycle assessment (LCA) that the CO2-GCB model could reduce the global warming impact by about 40% compared to the latest SF6 gas circuit breaker in the same rating for 20 years operation including one maintenance opportunity.

  5. Class of gauge-invariant models of quantum electrodynamics with nonlocal interaction

    PubMed Central

    Mei, Tao

    2017-01-01

    We present a class of gauge-invariant models of quantum electrodynamics with nonlocal interaction. The models have translation, Lorentz and gauge invariance and reduce to the conventional local quantum electrodynamics under the appropriate limit conditions, both the equations of motion of the charged particle and electromagnetic field obtained by the action principle lead to the normal form of current conservation. Quantization of the models is realized by taking advantage of the formalism based on the Yang-Feldman equations and the Lehmann-Symanzik-Zimmermann reduction formulas. Finally, we employ a special choice of the models to calculate the vacuum polarization as an example to demonstrate the possibility of establishing a theory of quantum electrodynamics without divergence. PMID:28368044

  6. Class of gauge-invariant models of quantum electrodynamics with nonlocal interaction.

    PubMed

    Mei, Tao

    2017-04-03

    We present a class of gauge-invariant models of quantum electrodynamics with nonlocal interaction. The models have translation, Lorentz and gauge invariance and reduce to the conventional local quantum electrodynamics under the appropriate limit conditions, both the equations of motion of the charged particle and electromagnetic field obtained by the action principle lead to the normal form of current conservation. Quantization of the models is realized by taking advantage of the formalism based on the Yang-Feldman equations and the Lehmann-Symanzik-Zimmermann reduction formulas. Finally, we employ a special choice of the models to calculate the vacuum polarization as an example to demonstrate the possibility of establishing a theory of quantum electrodynamics without divergence.

  7. A Critical Protection Level Derived from Dengue Infection Mathematical Model Considering Asymptomatic and Symptomatic Classes

    NASA Astrophysics Data System (ADS)

    Anggriani, N.; Supriatna, A. K.; Soewono, E.

    2013-04-01

    In this paper we formulate a model of dengue fever transmission by considering the presence of asymptomatic and symptomatic compartments. The model takes the form as a system of differential equations representing a host-vector SIR (Susceptible - Infective -Recovered) disease transmission. It is assumed that both host and vector populations are constant. It is also assumed that reinfection of recovered hosts by the disease is possible due to a wanning immunity in human body. We analyze the model to determine the qualitative behavior of the model solution and use the concept of effective basic reproduction number (fraktur Rp) as a control criteria of the disease transmission. The effect of mosquito biting protection (e.g. by using insect repellent) is also considered. We compute the long-term ratio of the asymptomatic and symptomatic classes and show a condition for which the iceberg phenomenon could appear.

  8. Internal Model Control Based on LS-SVM for a Class of Nonlinear Process

    NASA Astrophysics Data System (ADS)

    Zhicheng, Zhao; Zhiyuan, Liu; Zhimin, Xia; Jinggang, Zhang

    Aiming at a class of nonlinear process, an internal model control (IMC) scheme based on least squares support vector machines (LS-SVM) is proposed in this paper. By using LS-SVM algorithm and selecting the Gaussian kernel function, the internal model and its inversion of the nonlinear process are constructed, and the shortcomings of process identification and modeling based on neural network could be overcome. Then, combing with LS-SVM, the structure of internal model control was designed. The simulation results show that the nonlinear process identification based on LS-SVM has higher precision and better generalization than RBF neural network (NN) method, and IMC based on LS-SVM could achieve a good dynamic performance and robustness.

  9. A new model for spherically symmetric charged compact stars of embedding class 1

    NASA Astrophysics Data System (ADS)

    Maurya, S. K.; Gupta, Y. K.; Ray, Saibal; Deb, Debabrata

    2017-01-01

    In the present study we search for a new stellar model with spherically symmetric matter and a charged distribution in a general relativistic framework. The model represents a compact star of embedding class 1. The solutions obtained here are general in nature, having the following two features: first of all, the metric becomes flat and also the expressions for the pressure, energy density, and electric charge become zero in all the cases if we consider the constant A=0, which shows that our solutions represent the so-called `electromagnetic mass model' [17], and, secondly, the metric function ν (r), for the limit n tending to infinity, converts to ν (r)=C{r}2+ ln B, which is the same as considered by Maurya et al. [11]. We have investigated several physical aspects of the model and find that all the features are acceptable within the requirements of contemporary theoretical studies and observational evidence.

  10. Performance modeling and analysis of consumer classes in large scale systems

    NASA Astrophysics Data System (ADS)

    Al-Shukri, Sh.; Lenin, R. B.; Ramaswamy, S.; Anand, A.; Narasimhan, V. L.; Abraham, J.; Varadan, Vijay

    2009-03-01

    Peer-to-Peer (P2P) networks have been used efficiently as building blocks as overlay networks for large-scale distributed network applications with Internet Protocol (IP) based bottom layer networks. With large scale Wireless Sensor Networks (WSNs) becoming increasingly realistic, it is important to overlay networks with WSNs in the bottom layer. The suitable mathematical (stochastic) model that can model the overlay network over WSNs is Queuing Networks with Multi-Class customers. In this paper, we discuss how these mathematical network models can be simulated using the object oriented simulation package OMNeT++. We discuss the Graphical User Interface (GUI) which is developed to accept the input parameter files and execute the simulation using this interface. We compare the simulation results with analytical formulas available in the literature for these mathematical models.

  11. A Hierarchical Multiobjective Routing Model for MPLS Networks with Two Service Classes

    NASA Astrophysics Data System (ADS)

    Craveirinha, José; Girão-Silva, Rita; Clímaco, João; Martins, Lúcia

    This work presents a model for multiobjective routing in MPLS networks formulated within a hierarchical network-wide optimization framework, with two classes of services, namely QoS and Best Effort (BE) services. The routing model uses alternative routing and hierarchical optimization with two optimization levels, including fairness objectives. Another feature of the model is the use of an approximate stochastic representation of the traffic flows in the network, based on the concept of effective bandwidth. The theoretical foundations of a heuristic strategy for finding “good” compromise solutions to the very complex bi-level routing optimization problem, based on a conjecture concerning the definition of marginal implied costs for QoS flows and BE flows, will be described. The main features of a first version of this heuristic based on a bi-objective shortest path model and some preliminary results for a benchmark network will also be revealed.

  12. Evidence for an unconventional universality class from a two-dimensional dimerized quantum heisenberg model.

    PubMed

    Wenzel, Sandro; Bogacz, Leszek; Janke, Wolfhard

    2008-09-19

    The two-dimensional J-J' dimerized quantum Heisenberg model is studied on the square lattice by means of (stochastic series expansion) quantum Monte Carlo simulations as a function of the coupling ratio alpha=J'/J. The critical point of the order-disorder quantum phase transition in the J-J' model is determined as alpha_c=2.5196(2) by finite-size scaling for up to approximately 10 000 quantum spins. By comparing six dimerized models we show, contrary to the current belief, that the critical exponents of the J-J' model are not in agreement with the three-dimensional classical Heisenberg universality class. This lends support to the notion of nontrivial critical excitations at the quantum critical point.

  13. Class of gauge-invariant models of quantum electrodynamics with nonlocal interaction

    NASA Astrophysics Data System (ADS)

    Mei, Tao

    2017-04-01

    We present a class of gauge-invariant models of quantum electrodynamics with nonlocal interaction. The models have translation, Lorentz and gauge invariance and reduce to the conventional local quantum electrodynamics under the appropriate limit conditions, both the equations of motion of the charged particle and electromagnetic field obtained by the action principle lead to the normal form of current conservation. Quantization of the models is realized by taking advantage of the formalism based on the Yang-Feldman equations and the Lehmann-Symanzik-Zimmermann reduction formulas. Finally, we employ a special choice of the models to calculate the vacuum polarization as an example to demonstrate the possibility of establishing a theory of quantum electrodynamics without divergence.

  14. Active-absorbing-state phase transition beyond directed percolation: a class of exactly solvable models.

    PubMed

    Basu, Urna; Mohanty, P K

    2009-04-01

    We introduce and solve a model of hardcore particles on a one-dimensional periodic lattice which undergoes an active-absorbing-state phase transition at finite density. In this model, an occupied site is defined to be active if its left neighbor is occupied and the right neighbor is vacant. Particles from such active sites hop stochastically to their right. We show that both the density of active sites and the survival probability vanish as the particle density is decreased below half. The critical exponents and spatial correlations of the model are calculated exactly using the matrix product ansatz. Exact analytical study of several variations of the model reveals that these nonequilibrium phase transitions belong to a new universality class different from the generic active-absorbing-state phase transition, namely, directed percolation.

  15. Bayesian inference in camera trapping studies for a class of spatial capture-recapture models

    USGS Publications Warehouse

    Royle, J. Andrew; Karanth, K. Ullas; Gopalaswamy, Arjun M.; Kumar, N. Samba

    2009-01-01

    We develop a class of models for inference about abundance or density using spatial capture-recapture data from studies based on camera trapping and related methods. The model is a hierarchical model composed of two components: a point process model describing the distribution of individuals in space (or their home range centers) and a model describing the observation of individuals in traps. We suppose that trap- and individual-specific capture probabilities are a function of distance between individual home range centers and trap locations. We show that the models can be regarded as generalized linear mixed models, where the individual home range centers are random effects. We adopt a Bayesian framework for inference under these models using a formulation based on data augmentation. We apply the models to camera trapping data on tigers from the Nagarahole Reserve, India, collected over 48 nights in 2006. For this study, 120 camera locations were used, but cameras were only operational at 30 locations during any given sample occasion. Movement of traps is common in many camera-trapping studies and represents an important feature of the observation model that we address explicitly in our application.

  16. TESTING MAGNETIC FIELD MODELS FOR THE CLASS 0 PROTOSTAR L1527

    SciTech Connect

    Davidson, J. A.; Li, Z.-Y.; Hull, C. L. H.; Plambeck, R. L.; Kwon, W.; Crutcher, R. M.; Looney, L. W.; Novak, G.; Chapman, N. L.; Matthews, B. C.; Stephens, I. W.; Tobin, J. J.; Jones, T. J.

    2014-12-20

    For the Class 0 protostar L1527 we compare 131 polarization vectors from SCUPOL/JCMT, SHARP/CSO, and TADPOL/CARMA observations with the corresponding model polarization vectors of four ideal-MHD, nonturbulent, cloud core collapse models. These four models differ by their initial magnetic fields before collapse; two initially have aligned fields (strong and weak) and two initially have orthogonal fields (strong and weak) with respect to the rotation axis of the L1527 core. Only the initial weak orthogonal field model produces the observed circumstellar disk within L1527. This is a characteristic of nearly all ideal-MHD, nonturbulent, core collapse models. In this paper we test whether this weak orthogonal model also has the best agreement between its magnetic field structure and that inferred from the polarimetry observations of L1527. We found that this is not the case; based on the polarimetry observations, the most favored model of the four is the weak aligned model. However, this model does not produce a circumstellar disk, so our result implies that a nonturbulent, ideal-MHD global collapse model probably does not represent the core collapse that has occurred in L1527. Our study also illustrates the importance of using polarization vectors covering a large area of a cloud core to determine the initial magnetic field orientation before collapse; the inner core magnetic field structure can be highly altered by a collapse, and so measurements from this region alone can give unreliable estimates of the initial field configuration before collapse.

  17. A Hybrid Latent Class Analysis Modeling Approach to Analyze Urban Expressway Crash Risk.

    PubMed

    Yu, Rongjie; Wang, Xuesong; Abdel-Aty, Mohamed

    2017-04-01

    Crash risk analysis is rising as a hot research topic as it could reveal the relationships between traffic flow characteristics and crash occurrence risk, which is beneficial to understand crash mechanisms which would further refine the design of Active Traffic Management System (ATMS). However, the majority of the current crash risk analysis studies have ignored the impact of geometric characteristics on crash risk estimation while recent studies proved that crash occurrence risk was affected by the various alignment features. In this study, a hybrid Latent Class Analysis (LCA) modeling approach was proposed to account for the heterogeneous effects of geometric characteristics. Crashes were first segmented into homogenous subgroups, where the optimal number of latent classes was identified based on bootstrap likelihood ratio tests. Then, separate crash risk analysis models were developed using Bayesian random parameter logistic regression technique; data from Shanghai urban expressway system were employed to conduct the empirical study. Different crash risk contributing factors were unveiled by the hybrid LCA approach and better model goodness-of-fit was obtained while comparing to an overall total crash model. Finally, benefits of the proposed hybrid LCA approach were discussed.

  18. Identification of a Syndrome Class of Neuropsychiatric Adverse Reactions to Mefloquine from Latent Class Modeling of FDA Adverse Event Reporting System Data.

    PubMed

    Nevin, Remington L; Leoutsakos, Jeannie-Marie

    2017-03-01

    Although mefloquine use is known to be associated with a risk of severe neuropsychiatric adverse reactions that are often preceded by prodromal symptoms, specific combinations of neurologic or psychiatric reactions associated with mefloquine use are not well described in the literature. This study sought to identify a distinct neuropsychiatric syndrome class associated with mefloquine use in reports of adverse events. Latent class modeling of US Food and Drug Administration Adverse Event Reporting System (FAERS) data was performed using indicators defined by the Medical Dictionary for Regulatory Activities neurologic and psychiatric high-level group terms, in a study dataset of FAERS reports (n = 5332) of reactions to common antimalarial drugs. A distinct neuropsychiatric syndrome class was identified that was strongly and significantly associated with reports of mefloquine use (odds ratio = 3.92, 95% confidence interval 2.91-5.28), defined by a very high probability of symptoms of deliria (82.7%) including confusion and disorientation, and a moderate probability of other severe psychiatric and neurologic symptoms including dementia and amnesia (18.6%) and seizures (18.1%). The syndrome class was also associated with symptoms that are considered prodromal including anxiety, depression, sleep disturbance, and abnormal dreams, and neurological symptoms such as dizziness, vertigo, and paresthesias. This study confirms in FAERS reports the existence of a severe mefloquine neuropsychiatric syndrome class associated with common symptoms that may be considered prodromal. Clinical identification of the characteristic symptoms of this syndrome class may aid in improving case finding in pharmacovigilance studies of more serious adverse reactions to the drug.

  19. Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8 $\\,\\text {TeV}$

    SciTech Connect

    Khachatryan, Vardan

    2015-05-14

    Properties of the Higgs boson with mass near 125GeV are measured in proton-proton collisions with the CMS experiment at the LHC. Comprehensive sets of production and decay measurements are combined. The decay channels include γγ, ZZ, WW, ττ, bb, and μμ pairs. The data samples were collected in 2011 and 2012 and correspond to integrated luminosities of up to 5.1fb-1 at 7TeV and up to 19.7fb-1 at 8TeV. From the high-resolution γγ and ZZ channels, the mass of the Higgs boson is determined to be 125.02+0.26–0.27 (stat) +0.14–0.15 (syst) GeV. For this mass value, the event yields obtained in the different analyses tagging specific decay channels and production mechanisms are consistent with those expected for the standard model Higgs boson. The combined best-fit signal relative to the standard model expectation is 1.00 ± 0.09(stat)+0.08–0.07 (theo) ± 0.07(syst) at the measured mass. The couplings of the Higgs boson are probed for deviations in magnitude from the standard model predictions in multiple ways, including searches for invisible and undetected decays. As a result, no significant deviations are found.

  20. Modelling, Simulation, Animation, and Real-Time Control (Mosart) for a Class of Electromechanical Systems: A System-Theoretic Approach

    ERIC Educational Resources Information Center

    Rodriguez, Armando A.; Metzger, Richard P.; Cifdaloz, Oguzhan; Dhirasakdanon, Thanate; Welfert, Bruno

    2004-01-01

    This paper describes an interactive modelling, simulation, animation, and real-time control (MoSART) environment for a class of 'cart-pendulum' electromechanical systems that may be used to enhance learning within differential equations and linear algebra classes. The environment is useful for conveying fundamental mathematical/systems concepts…

  1. Modelling, Simulation, Animation, and Real-Time Control (Mosart) for a Class of Electromechanical Systems: A System-Theoretic Approach

    ERIC Educational Resources Information Center

    Rodriguez, Armando A.; Metzger, Richard P.; Cifdaloz, Oguzhan; Dhirasakdanon, Thanate; Welfert, Bruno

    2004-01-01

    This paper describes an interactive modelling, simulation, animation, and real-time control (MoSART) environment for a class of 'cart-pendulum' electromechanical systems that may be used to enhance learning within differential equations and linear algebra classes. The environment is useful for conveying fundamental mathematical/systems concepts…

  2. Gauge coupling unification in a classically scale invariant model

    NASA Astrophysics Data System (ADS)

    Haba, Naoyuki; Ishida, Hiroyuki; Takahashi, Ryo; Yamaguchi, Yuya

    2016-02-01

    There are a lot of works within a class of classically scale invariant model, which is motivated by solving the gauge hierarchy problem. In this context, the Higgs mass vanishes at the UV scale due to the classically scale invariance, and is generated via the Coleman-Weinberg mechanism. Since the mass generation should occur not so far from the electroweak scale, we extend the standard model only around the TeV scale. We construct a model which can achieve the gauge coupling unification at the UV scale. In the same way, the model can realize the vacuum stability, smallness of active neutrino masses, baryon asymmetry of the universe, and dark matter relic abundance. The model predicts the existence vector-like fermions charged under SU(3) C with masses lower than 1 TeV, and the SM singlet Majorana dark matter with mass lower than 2.6 TeV.

  3. Probing radiative neutrino mass models using trilepton channel at the LHC

    NASA Astrophysics Data System (ADS)

    Cherigui, Dounia; Guella, Chahrazed; Ahriche, Amine; Nasri, Salah

    2016-11-01

    In this work, we probe a class of neutrino mass models through the lepton flavor violating interactions of a singlet charged scalar, S± at the LHC proton-proton collisions with 8 TeV and 14 TeV energies. This scalar couples to the leptons and induces many processes such as pp →ℓ±ℓ±ℓ∓ +E̸T. In our analysis we discuss the opposite sign same flavor leptons signal, as well as the background free channel with the tau contribution which can enhance the signal/background ratio for center of mass energies √{ s} = 8 TeV and √{ s} = 14 TeV.

  4. A new class of ensemble conserving algorithms for approximate quantum dynamics: Theoretical formulation and model problems

    SciTech Connect

    Smith, Kyle K. G.; Poulsen, Jens Aage Nyman, Gunnar; Rossky, Peter J.

    2015-06-28

    We develop two classes of quasi-classical dynamics that are shown to conserve the initial quantum ensemble when used in combination with the Feynman-Kleinert approximation of the density operator. These dynamics are used to improve the Feynman-Kleinert implementation of the classical Wigner approximation for the evaluation of quantum time correlation functions known as Feynman-Kleinert linearized path-integral. As shown, both classes of dynamics are able to recover the exact classical and high temperature limits of the quantum time correlation function, while a subset is able to recover the exact harmonic limit. A comparison of the approximate quantum time correlation functions obtained from both classes of dynamics is made with the exact results for the challenging model problems of the quartic and double-well potentials. It is found that these dynamics provide a great improvement over the classical Wigner approximation, in which purely classical dynamics are used. In a special case, our first method becomes identical to centroid molecular dynamics.

  5. A new class of ensemble conserving algorithms for approximate quantum dynamics: Theoretical formulation and model problems.

    PubMed

    Smith, Kyle K G; Poulsen, Jens Aage; Nyman, Gunnar; Rossky, Peter J

    2015-06-28

    We develop two classes of quasi-classical dynamics that are shown to conserve the initial quantum ensemble when used in combination with the Feynman-Kleinert approximation of the density operator. These dynamics are used to improve the Feynman-Kleinert implementation of the classical Wigner approximation for the evaluation of quantum time correlation functions known as Feynman-Kleinert linearized path-integral. As shown, both classes of dynamics are able to recover the exact classical and high temperature limits of the quantum time correlation function, while a subset is able to recover the exact harmonic limit. A comparison of the approximate quantum time correlation functions obtained from both classes of dynamics is made with the exact results for the challenging model problems of the quartic and double-well potentials. It is found that these dynamics provide a great improvement over the classical Wigner approximation, in which purely classical dynamics are used. In a special case, our first method becomes identical to centroid molecular dynamics.

  6. Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image.

    PubMed

    Kim, Tae-Kyun; Kittler, Josef

    2005-03-01

    We present a novel method of nonlinear discriminant analysis involving a set of locally linear transformations called "Locally Linear Discriminant Analysis (LLDA)." The underlying idea is that global nonlinear data structures are locally linear and local structures can be linearly aligned. Input vectors are projected into each local feature space by linear transformations found to yield locally linearly transformed classes that maximize the between-class covariance while minimizing the within-class covariance. In face recognition, linear discriminant analysis (LDA) has been widely adopted owing to its efficiency, but it does not capture nonlinear manifolds of faces which exhibit pose variations. Conventional nonlinear classification methods based on kernels such as generalized discriminant analysis (GDA) and support vector machine (SVM) have been developed to overcome the shortcomings of the linear method, but they have the drawback of high computational cost of classification and overfitting. Our method is for multiclass nonlinear discrimination and it is computationally highly efficient as compared to GDA. The method does not suffer from overfitting by virtue of the linear base structure of the solution. A novel gradient-based learning algorithm is proposed for finding the optimal set of local linear bases. The optimization does not exhibit a local-maxima problem. The transformation functions facilitate robust face recognition in a low-dimensional subspace, under pose variations, using a single model image. The classification results are given for both synthetic and real face data.

  7. The distribution of Fst and other genetic statistics for a class of population structure models.

    PubMed

    Leviyang, Sivan

    2011-02-01

    We examine genetic statistics used in the study of structured populations. In a 1999 paper, Wakeley observed that the coalescent process associated with the finite island model can be decomposed into a scattering phase and a collecting phase. This decomposition becomes exact in the large population limit with the coalescent at the end of the scattering phase converging to the Ewens sampling formula and the coalescent during the collecting phase converging to the Kingman coalescent. In this paper we introduce a class of limiting models, which we refer to as G/KC models, that generalize Wakeley's decomposition. G in G/KC represents a completely general limit for the scattering phase, while KC represents a Kingman coalescent limit for the collecting phase. We show that both the island and two-dimensional stepping stone models converge to G/KC models in the large population limit. We then derive the distribution of the statistic F(st) for all G/KC models under a large sample limit for the cases of strong or weak mutation, thereby deriving the large population, large sample limiting distribution of F(st) for the island and two-dimensional stepping stone models as a special case of a general formula. Our methods allow us to take the large population and large sample limits simultaneously. In the context of large population, large sample limits, we show that the variance of F(st) in the presence of weak mutation collapses as O(1/log d) where d is the number of demes sampled. Further, we show that this O(1/log d) is caused by a heavy tail in the distribution of F(st). Our analysis of F(st) can be extended to an entire class of genetic statistics, and we use our approach to examine homozygosity measures. Our analysis uses coalescent based methods.

  8. Molecular modeling indicates distinct classes of missense variants with mild and severe XLRS phenotypes.

    PubMed

    Sergeev, Yuri V; Vitale, Susan; Sieving, Paul A; Vincent, Ajoy; Robson, Anthony G; Moore, Anthony T; Webster, Andrew R; Holder, Graham E

    2013-12-01

    X-linked retinoschisis (XLRS) is a vitreo-retinal degeneration caused by mutations in the RS1 gene which encodes the protein retinoschisin (RS1), required for the structural and functional integrity of the retina. Data are presented from a group of 38 XLRS patients from Moorfields Eye Hospital (London, UK) who had one of 18 missense mutations in RS1. Patients were grouped based on mutation severity predicted by molecular modeling: mild (class I), moderate (intermediate) and severe (class II). Most patients had an electronegative scotopic bright flash electroretinogram (ERG) (reduced b/a-wave ratio) in keeping with predominant inner retinal dysfunction. An association between the type of structural RS1 alterations and the severity of b/a-wave reduction was found in all but the oldest group of patients, significant in patients aged 15-30 years. Severe RS1 missense changes were associated with a lower ERG b/a ratio than were mild changes, suggesting that the extent of inner retinal dysfunction is influenced by the effect of the mutations on protein structure. The majority of class I mutations showed no changes involving cysteine residues. Class II mutations caused severe perturbations due to the removal or insertion of cysteine residues or due to changes in the hydrophobic core. The ERG b/a ratio in intermediate cases was abnormal but showed significant variability, possibly related to the role of proline or arginine residues. We also conducted a second study, using a completely independent cohort, to indicate a genotype-ERG phenotype correlation.

  9. Search for a Low-Mass Standard Model Higgs Boson in the ττ Decay Channel in pp̄ Collisions at √s=1.96 TeV

    SciTech Connect

    Aaltonen, T.; Álvarez González, B.; Amerio, S.; Amidei, D.; Anastassov, A.; Annovi, A.; Antos, J.; Apollinari, G.; Appel, J. A.; Apresyan, A.; Arisawa, T.; Artikov, A.; Asaadi, J.; Ashmanskas, W.; Auerbach, B.; Aurisano, A.; Azfar, F.; Badgett, W.; Barbaro-Galtieri, A.; Barnes, V. E.; Barnett, B. A.; Barria, P.; Bartos, P.; Bauce, M.; Bauer, G.; Bedeschi, F.; Beecher, D.; Behari, S.; Bellettini, G.; Bellinger, J.; Benjamin, D.; Beretvas, A.; Bhatti, A.; Binkley, M.; Bisello, D.; Bizjak, I.; Bland, K. R.; Blumenfeld, B.; Bocci, A.; Bodek, A.; Bortoletto, D.; Boudreau, J.; Boveia, A.; Brau, B.; Brigliadori, L.; Brisuda, A.; Bromberg, C.; Brucken, E.; Bucciantonio, M.; Budagov, J.; Budd, H. S.; Budd, S.; Burkett, K.; Busetto, G.; Bussey, P.; Buzatu, A.; Calancha, C.; Camarda, S.; Campanelli, M.; Campbell, M.; Canelli, F.; Canepa, A.; Carls, B.; Carlsmith, D.; Carosi, R.; Carrillo, S.; Carron, S.; Casal, B.; Casarsa, M.; Castro, A.; Catastini, P.; Cauz, D.; Cavaliere, V.; Cavalli-Sforza, M.; Cerri, A.; Cerrito, L.; Chen, Y. C.; Chertok, M.; Chiarelli, G.; Chlachidze, G.; Chlebana, F.; Cho, K.; Chokheli, D.; Chou, J. P.; Chung, W. H.; Chung, Y. S.; Ciobanu, C. I.; Ciocci, M. A.; Clark, A.; Clarke, C.; Compostella, G.; Convery, M. E.; Conway, J.; Corbo, M.; Cordelli, M.; Cox, C. A.; Cox, D. J.; Crescioli, F.; Cuenca Almenar, C.; Cuevas, J.; Culbertson, R.; Dagenhart, D.; d’Ascenzo, N.; Datta, M.; de Barbaro, P.; De Cecco, S.; De Lorenzo, G.; Dell’Orso, M.; Deluca, C.; Demortier, L.; Deng, J.; Deninno, M.; Devoto, F.; d’Errico, M.; Di Canto, A.; Di Ruzza, B.; Dittmann, J. R.; D’Onofrio, M.; Donati, S.; Dong, P.; Dorigo, M.; Dorigo, T.; Ebina, K.; Elagin, A.; Eppig, A.; Erbacher, R.; Errede, D.; Errede, S.; Ershaidat, N.; Eusebi, R.; Fang, H. C.; Farrington, S.; Feindt, M.; Fernandez, J. P.; Ferrazza, C.; Field, R.; Flanagan, G.; Forrest, R.; Frank, M. J.; Franklin, M.; Freeman, J. C.; Funakoshi, Y.; Furic, I.; Gallinaro, M.; Galyardt, J.; Garcia, J. E.; Garfinkel, A. F.; Garosi, P.; Gerberich, H.; Gerchtein, E.; Giagu, S.; Giakoumopoulou, V.; Giannetti, P.; Gibson, K.; Ginsburg, C. M.; Giokaris, N.; Giromini, P.; Giunta, M.; Giurgiu, G.; Glagolev, V.; Glenzinski, D.; Gold, M.; Goldin, D.; Goldschmidt, N.; Golossanov, A.; Gomez, G.; Gomez-Ceballos, G.; Goncharov, M.; González, O.; Gorelov, I.; Goshaw, A. T.; Goulianos, K.; Grinstein, S.; Grosso-Pilcher, C.; Group, R. C.; Guimaraes da Costa, J.; Gunay-Unalan, Z.; Haber, C.; Hahn, S. R.; Halkiadakis, E.; Hamaguchi, A.; Han, J. Y.; Happacher, F.; Hara, K.; Hare, D.; Hare, M.; Harr, R. F.; Hatakeyama, K.; Hays, C.; Heck, M.; Heinrich, J.; Herndon, M.; Hewamanage, S.; Hidas, D.; Hocker, A.; Hopkins, W.; Horn, D.; Hou, S.; Hughes, R. E.; Hurwitz, M.; Husemann, U.; Hussain, N.; Hussein, M.; Huston, J.; Introzzi, G.; Iori, M.; Ivanov, A.; James, E.; Jang, D.; Jayatilaka, B.; Jeon, E. J.; Jha, M. K.; Jindariani, S.; Johnson, W.; Jones, M.; Joo, K. K.; Jun, S. Y.; Junk, T. R.; Kamon, T.; Karchin, P. E.; Kasmi, A.; Kato, Y.; Ketchum, W.; Keung, J.; Khotilovich, V.; Kilminster, B.; Kim, D. H.; Kim, H. S.; Kim, H. W.; Kim, J. E.; Kim, M. J.; Kim, S. B.; Kim, S. H.; Kim, Y. K.; Kimura, N.; Kirby, M.; Klimenko, S.; Kondo, K.; Kong, D. J.; Konigsberg, J.; Kotwal, A. V.; Kreps, M.; Kroll, J.; Krop, D.; Krumnack, N.; Kruse, M.; Krutelyov, V.; Kuhr, T.; Kurata, M.; Kwang, S.; Laasanen, A. T.; Lami, S.; Lammel, S.; Lancaster, M.; Lander, R. L.; Lannon, K.; Lath, A.; Latino, G.; LeCompte, T.; Lee, E.; Lee, H. S.; Lee, J. S.; Lee, S. W.; Leo, S.; Leone, S.; Lewis, J. D.; Limosani, A.; Lin, C.-J.; Linacre, J.; Lindgren, M.; Lipeles, E.; Lister, A.; Litvintsev, D. O.; Liu, C.; Liu, Q.; Liu, T.; Lockwitz, S.; Loginov, A.; Lucchesi, D.; Lueck, J.; Lujan, P.; Lukens, P.; Lungu, G.; Lys, J.; Lysak, R.; Madrak, R.; Maeshima, K.; Maestro, P.; Makhoul, K.; Malik, S.; Manca, G.; Manousakis-Katsikakis, A.; Margaroli, F.; Marino, C.; Martínez, M.; Martínez-Ballarín, R.; Mastrandrea, P.; Mattson, M. E.; Mazzanti, P.; McFarland, K. S.; McIntyre, P.; McNulty, R.; Mehta, A.; Mehtala, P.; Menzione, A.; Mesropian, C.; Miao, T.; Mietlicki, D.; Mitra, A.; Miyake, H.; Moed, S.; Moggi, N.; Mondragon, M. N.; Moon, C. S.; Moore, R.; Morello, M. J.; Morlock, J.; Movilla Fernandez, P.; Mukherjee, A.; Muller, Th.; Murat, P.; Mussini, M.; Nachtman, J.; Nagai, Y.; Naganoma, J.; Nakano, I.; Napier, A.; Nett, J.; Neu, C.; Neubauer, M. S.; Nielsen, J.; Nodulman, L.; Norniella, O.; Nurse, E.; Oakes, L.; Oh, S. H.; Oh, Y. D.; Oksuzian, I.; Okusawa, T.; Orava, R.; Ortolan, L.; Pagan Griso, S.; Pagliarone, C.; Palencia, E.; Papadimitriou, V.; Paramonov, A. A.; Patrick, J.; Pauletta, G.; Paulini, M.; Paus, C.; Pellett, D. E.; Penzo, A.; Phillips, T. J.; Piacentino, G.; Pianori, E.; Pilot, J.; Pitts, K.; Plager, C.; Pondrom, L.; Potamianos, K.; Poukhov, O.; Prokoshin, F.; Pronko, A.; Ptohos, F.; Pueschel, E.; Punzi, G.; Pursley, J.; Rahaman, A.; Ramakrishnan, V.; Ranjan, N.; Redondo, I.; Renton, P.; Rescigno, M.; Riddick, T.; Rimondi, F.; Ristori, L.; Robson, A.; Rodrigo, T.; Rodriguez, T.; Rogers, E.; Rolli, S.; Roser, R.; Rossi, M.; Rubbo, F.; Ruffini, F.; Ruiz, A.; Russ, J.; Rusu, V.; Safonov, A.; Sakumoto, W. K.; Sakurai, Y.; Santi, L.; Sartori, L.; Sato, K.; Saveliev, V.; Savoy-Navarro, A.; Schlabach, P.; Schmidt, A.; Schmidt, E. E.; Schmidt, M. P.; Schmitt, M.; Schwarz, T.; Scodellaro, L.; Scribano, A.; Scuri, F.; Sedov, A.; Seidel, S.; Seiya, Y.; Semenov, A.; Sforza, F.; Sfyrla, A.; Shalhout, S. Z.; Shears, T.; Shepard, P. F.; Shimojima, M.; Shiraishi, S.; Shochet, M.; Shreyber, I.; Simonenko, A.; Sinervo, P.; Sissakian, A.; Sliwa, K.; Smith, J. R.; Snider, F. D.; Soha, A.; Somalwar, S.; Sorin, V.; Squillacioti, P.; Stancari, M.; Stanitzki, M.; St. Denis, R.; Stelzer, B.; Stelzer-Chilton, O.; Stentz, D.; Strologas, J.; Strycker, G. L.; Sudo, Y.; Sukhanov, A.; Suslov, I.; Takemasa, K.; Takeuchi, Y.; Tang, J.; Tecchio, M.; Teng, P. K.; Thom, J.; Thome, J.; Thompson, G. A.; Thomson, E.; Ttito-Guzmán, P.; Tkaczyk, S.; Toback, D.; Tokar, S.; Tollefson, K.; Tomura, T.; Tonelli, D.; Torre, S.; Torretta, D.; Totaro, P.; Trovato, M.; Tu, Y.; Ukegawa, F.; Uozumi, S.; Varganov, A.; Vázquez, F.; Velev, G.; Vellidis, C.; Vidal, M.; Vila, I.; Vilar, R.; Vizán, J.; Vogel, M.; Volpi, G.; Wagner, P.; Wagner, R. L.; Wakisaka, T.; Wallny, R.; Wang, S. M.; Warburton, A.; Waters, D.; Weinberger, M.; Wester, W. C.; Whitehouse, B.; Whiteson, D.; Wicklund, A. B.; Wicklund, E.; Wilbur, S.; Wick, F.; Williams, H. H.; Wilson, J. S.; Wilson, P.; Winer, B. L.; Wittich, P.; Wolbers, S.; Wolfe, H.; Wright, T.; Wu, X.; Wu, Z.; Yamamoto, K.; Yamaoka, J.; Yang, T.; Yang, U. K.; Yang, Y. C.; Yao, W.-M.; Yeh, G. P.; Yi, K.; Yoh, J.; Yorita, K.; Yoshida, T.; Yu, G. B.; Yu, I.; Yu, S. S.; Yun, J. C.; Zanetti, A.; Zeng, Y.; Zucchelli, S.

    2012-05-02

    We report on a search for the standard model Higgs boson decaying into pairs of τ leptons in pp̄ collisions produced by the Tevatron at √s=1.96 TeV. The analyzed data sample was recorded by the CDFII detector and corresponds to an integrated luminosity of 6.0 fb⁻¹. The search is performed in the final state with one τ decaying leptonically and the second one identified through its semihadronic decay. Since no significant excess is observed, a 95% credibility level upper limit on the production cross section times branching ratio to the ττ final state is set for hypothetical Higgs boson masses between 100 and 150 GeV/c². For a Higgs boson of 120 GeV/c² the observed (expected) limit is 14.6 (15.3) the predicted value.

  10. Search for a Low-Mass Standard Model Higgs Boson in the ττ Decay Channel in pp̄ Collisions at √s=1.96 TeV

    DOE PAGES

    Aaltonen, T.; Álvarez González, B.; Amerio, S.; ...

    2012-05-02

    We report on a search for the standard model Higgs boson decaying into pairs of τ leptons in pp̄ collisions produced by the Tevatron at √s=1.96 TeV. The analyzed data sample was recorded by the CDFII detector and corresponds to an integrated luminosity of 6.0 fb⁻¹. The search is performed in the final state with one τ decaying leptonically and the second one identified through its semihadronic decay. Since no significant excess is observed, a 95% credibility level upper limit on the production cross section times branching ratio to the ττ final state is set for hypothetical Higgs boson massesmore » between 100 and 150 GeV/c². For a Higgs boson of 120 GeV/c² the observed (expected) limit is 14.6 (15.3) the predicted value.« less

  11. Model-independent measurement of $\\boldsymbol{t}$-channel single top quark production in $\\boldsymbol{p\\bar{p}}$ collisions at $\\boldsymbol{\\sqrt{s}=1.96}$ TeV

    SciTech Connect

    Abazov, Victor Mukhamedovich; Abbott, Braden Keim; Acharya, Bannanje Sripath; Adams, Mark Raymond; Adams, Todd; Alexeev, Guennadi D.; Alkhazov, Georgiy D.; Alton, Andrew K.; Alverson, George O.; Alves, Gilvan Augusto; Ancu, Lucian Stefan; /Nijmegen U. /Fermilab

    2011-05-01

    We present a model-independent measurement of t-channel electroweak production of single top quarks in p{bar p} collisions at {radical}s = 1.96 TeV. Using 5.4 fb{sup -1} of integrated luminosity collected by the D0 detector at the Fermilab Tevatron Collider, and selecting events containing an isolated electron or muon, missing transverse energy and one or two jets originating from the fragmentation of b quarks, we measure a cross section {sigma}(p{bar p} {yields} tqb + X) = 2.90 {+-} 0.59 (stat + syst) pb for a top quark mass of 172.5 GeV. The probability of the background to fluctuate and produce a signal as large as the one observed is 1.6 x 10{sup -8}, corresponding to a significance of 5.5 standard deviations.

  12. Search for the Standard Model Higgs produced in association with a vector boson and decaying to a b b pair in pp collisions at √{ s} = 13 TeV with the ATLAS detector

    NASA Astrophysics Data System (ADS)

    Montalbano, Alyssa; Atlas Collaboration

    2017-01-01

    The Standard Model (SM) Higgs boson has yet to be measured in its predicted dominant decay mode, b b , owing to the high quantum chromodynamics (QCD) background at the Large Hadron Collider. This decay mode provides the largest avenue for new physics if the H -> b b measurement differs from the SM predictions. We significantly reduce the large QCD background by requiring the Higgs boson to be produced with an associated vector boson (Z,W) which then decays in a leptonic mode. I will discuss recent results from this search in pp collisions at √{ s} = 13 TeV with the ATLAS detector, a data-driven estimate of the QCD background, and optimizations for the measurement of VH production.

  13. Search for the standard model Higgs boson produced in association with a vector boson and decaying into a tau pair in p p collisions at √{s }=8 TeV with the ATLAS detector

    NASA Astrophysics Data System (ADS)

    Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Aben, R.; Abolins, M.; Abouzeid, O. S.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Alconada Verzini, M. J.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Alimonti, G.; Alio, L.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Altheimer, A.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amram, N.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Arnaez, O.; Arnal, V.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Aurousseau, M.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bacci, C.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Bain, T.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Bansil, H. S.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisonzi, M.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Basye, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Beccherle, R.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, W. H.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez Garcia, J. A.; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Berghaus, F.; Beringer, J.; Bernard, C.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertsche, C.; Bertsche, D.; Besana, M. I.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Betancourt, C.; Bethke, S.; Bevan, A. J.; Bhimji, W.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Biedermann, D.; Bieniek, S. P.; Biglietti, M.; Bilbao de Mendizabal, J.; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biondi, S.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J.-B.; Blanco, J. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Bogaerts, J. A.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A. S.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Borroni, S.; Bortfeldt, J.; Bortolotto, V.; Bos, K.; Boscherini, D.; Bosman, M.; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Bousson, N.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bozic, I.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Brazzale, S. F.; Breaden Madden, W. D.; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Bristow, K.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Bronner, J.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; Brown, J.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruneliere, R.; Bruni, A.; Bruni, G.; Bruschi, M.; Bruscino, N.

    2016-05-01

    A search for the standard model Higgs boson produced in association with a vector boson with the decay H →τ τ is presented. The data correspond to 20.3 fb-1 of integrated luminosity from proton-proton collisions at √{s }=8 TeV recorded by the ATLAS experiment at the LHC during 2012. The data agree with the background expectation, and 95% confidence-level upper limits are placed on the cross section of this process. The observed (expected) limit, expressed in terms of the signal strength μ =σ /σSM for mH=125 GeV , is μ <5.6 (3.7). The measured value of the signal strength is μ =2.3 ±1.6 .

  14. Search for the standard model Higgs boson produced in association with a vector boson and decaying into a tau pair in pp collisions at s=8 TeV with the ATLAS detector

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2016-05-17

    A search for the standard model Higgs boson produced in association with a vector boson with the decay H → ττ is presented. The data correspond to 20.3 fb-1 of integrated luminosity from proton-proton collisions at √s=8 TeV recorded by the ATLAS experiment at the LHC during 2012. The data agree with the background expectation, and 95% confidence-level upper limits are placed on the cross section of this process. The observed (expected) limit, expressed in terms of the signal strength μ = σ/σSM for mH = 125 GeV, is μ<5.6 (3.7). Furthermore, the measured value of the signal strength ismore » μ = 2.3 ± 1.6.« less

  15. Model-independent measurement of t-channel single top quark production in p(p)over-bar collisions at,root s=1.96 TeV

    SciTech Connect

    Abazov, V.M.; Abbott, B.; Acharya, B. S.; Adams, M.; Adams, T.; Alexeev, G. D.; Alkhazov, G.; Alton, A.; Alverson, G.; Alves, G. A.; Ancu, L. S.; Aoki, M.; Arov, M.; Askew, A.; Asman, B.; Atramentov, O.; Avila, C.; BackusMayes, J.; Badaud, F.; Bagby, L.; Baldin, B.; Bandurin, D. V.; Banerjee, S.; Barberis, E.; Baringer, R.; Barreto, J.; Bartlett, J. F.; Bassler, U.; Bazterra, V.; Beale, S.; Bean, A.; Begalli, M.; Begel, M.; Belanger-Champagne, C.; Bellantoni, L.; Beri, S. B.; Bernardi, G.; Bernhard, R.; Bertram, I.; Besancon, M.; Beuselinck, R.; Bezzubov, V. A.; Bhat, P. C.; Bhatnagar, V.; Blazey, G.; Blessing, S.; Bloom, K.; Boehnlein, A.; Boline, D.; Boos, E. E.; Borissov, G.; Bose, T.; Brandt, A.; Brandt, O.; Brock, R.; Brooijmans, G.; Bross, A.; Brown, D.; Brown, J.; Bu, X. B.; Buehler, M.; Buescher, V.; Bunichev, V.; Burdin, S.; Burnett, T. H.; Buszello, C. P.; Calpas, B.; Camacho-Perez, E.; Carrasco-Lizarraga, M. A.; Casey, B. C. K.; Castilla-Valdez, H.; Chakrabarti, S.; Chakraborty, D.; Chan, K. M.; Chandra, A.; Chen, G.; Chevalier-Thery, S.; Cho, D. K.; Cho, S. W.; Choi, S.; Choudhary, B.; Cihangir, S.; Claes, D.; Clutter, J.; Cochran, J.; Cooke, M.; Cooper, W. E.; Corcoran, M.; Couderc, F.; Cousinou, M. -C.; Croc, A.; Cutts, D.; Das, A.; Davies, G.; De, K.; de Jong, S. J.; De La Cruz-Burelo, E.; Deliot, F.; Demarteau, M.; Demina, R.; Denisov, D.; Denisov, S. P.; Desai, S.; Deterre, C.; DeVaughan, K.; Diehl, H. T.; Diesburg, M.; Dominguez, A.; Dorland, T.; Dubey, A.; Dudko, L. V.; Duggan, D.; Duperrin, A.; Dutt, S.; Dyshkant, A.; Eads, M.; Edmunds, D.; Eller, P.; Ellison, J.; Elvira, V. D.; Enari, Y.; Evans, H.; Evdokimov, A.; Evdokimov, V. N.; Facini, G.; Ferbel, T.; Fiedler, F.; Filthaut, F.; Fisher, W.; Fisk, H. E.; Focke, C.; Fortner, M.; Fox, H.; Fuess, S.; Garcia-Bellido, A.; Gavrilov, V.; Gay, P.; Geng, W.; Gerbaudo, D.; Gerber, C. E.; Gershtein, Y.; Ginther, G.; Golovanov, G.; Goussiou, A.; Grannis, P. D.; Greder, S.; Greenlee, H.; Greenwood, Z. D.; Gregores, E. M.; Grenier, G.; Gris, Ph.; Grivaz, J. -F.; Grohsjean, A.; Gruenendahl, S.; Gruenewald, M. W.; Guillemin, T.; Guo, F.; Gutierrez, G.; Gutierrez, P.; Haas, A.; Hagopian, S.; Haley, J.; Han, L.; Harder, K.; Harel, A.; Hauptman, J. M.; Hays, J.; Head, T.; Hebbeker, T.; Hedin, D.; Hegab, H.; Heinson, A. P.; Heintz, U.; Hensel, C.; Heredia-De La Cruz, I.; Herner, K.; Hesketh, G.; Hildreth, M. D.; Hirosky, R.; Hoang, T.; Hobbs, J. D.; Hoeneisen, B.; Hohlfeld, M.; Hubacek, Z.; Huske, N.; Hynek, V.; Iashvili, I.; Illingworth, R.; Ito, A. S.; Jabeen, S.; Jaffre, M.; Jamin, D.; Jayasinghe, A.; Jesik, R.; Johns, K.; Johnson, M.; Johnston, D.; Jonckheere, A.; Jonsson, P.; Joshi, J.; Jung, A. W.; Juste, A.; Kaadze, K.; Kajfasz, E.; Karmanov, D.; Kasper, P. A.; Katsanos, I.; Kehoe, R.; Kermiche, S.; Khalatyan, N.; Khanov, A.; Kharchilava, A.; Kharzheev, Y. N.; Khatidze, D.; Kirby, M. H.; Kohli, J. M.; Kozelov, A. V.; Kraus, J.; Kulikov, S.; Kumar, A.; Kupco, A.; Kurca, T.; Kuzmin, V. A.; Kvita, J.; Lammers, S.; Landsberg, G.; Lebrun, P.; Lee, H. S.; Lee, S. W.; Lee, W. M.; Lellouch, J.; Li, L.; Li, Q. Z.; Lietti, S. M.; Lim, J. K.; Lincoln, D.; Linnemann, J.; Lipaev, V. V.; Lipton, R.; Liu, Y.; Liu, Z.; Lobodenko, A.; Lokajicek, M.; de Sa, R. Lopes; Lubatti, H. J.; Luna-Garcia, R.; Lyon, A. L.; Maciel, A. K. A.; Mackin, D.; Madar, R.; Magana-Villalba, R.; Malik, S.; Malyshev, V. L.; Maravin, Y.; Martinez-Ortega, J.; McCarthy, R.; McGivern, C. L.; Meijer, M. M.; et al.

    2011-11-17

    We present a model-independent measurement of t-channel electroweak production of single top quarks in p{bar p} collisions at {radical}s = 1.96 TeV. Using 5.4 fb{sup -1} of integrated luminosity collected by the D0 detector at the Fermilab Tevatron Collider, and selecting events containing an isolated electron or muon, missing transverse energy and one or two jets originating from the fragmentation of b quarks, we measure a cross section {sigma}(p{bar p} {yields} tqb + X) = 2.90 {+-} 0.59 (stat + syst) pb for a top quark mass of 172.5 GeV. The probability of the background to fluctuate and produce a signal as large as the one observed is 1.6 x 10{sup -8}, corresponding to a significance of 5.5 standard deviations.

  16. Search for a low mass Standard Model Higgs boson in the $\\tau-\\tau$ decay channel in $p\\bar{p}$ collisions at $\\sqrt{s}$ = 1.96 TeV

    SciTech Connect

    Aaltonen, T.; Alvarez Gonzalez, B.; Amerio, S.; Amidei, D.; Anastassov, A.; Annovi, A.; Antos, J.; Apollinari, G.; Appel, J.A.; Apresyan, A.; Arisawa, T.; /Waseda U. /Dubna, JINR

    2012-01-01

    We report on a search for the standard model Higgs boson decaying into pairs of {tau} leptons in p{bar p} collisions produced by the Tevatron at {radical}s = 1.96 TeV. The analyzed data sample was recorded by the CDFII detector and corresponds to an integrated luminosity of 6.0 fb{sup -1}. The search is performed in the final state with one {tau} decaying leptonically and the second one identified through its semi-hadronic decay. Since no significant excess is observed, a 95% credibility level upper limit on the production cross section times branching ratio to the {tau}{tau} final state is set for hypothetical Higgs boson masses between 100 and 150 GeV/c{sup 2}. For a Higgs boson of 120 GeV/c{sup 2} the observed (expected) limit is 14.6 (15.3) the predicted value.

  17. Centrality Dependence of the Charged-Particle Multiplicity Density at Midrapidity in Pb-Pb Collisions at √{sN N }=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.; 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.; 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, 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.; 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.; 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.; 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.; 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.; 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.; 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.; 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.; Alice Collaboration

    2016-06-01

    The pseudorapidity density of charged particles, d Nch/d η , at midrapidity in Pb-Pb collisions has been measured at a center-of-mass energy per nucleon pair of √{sN N}=5.02 TeV . For the 5% most central collisions, we measure a value of 1943 ±54 . The rise in d Nch/d η as a function of √{sN N} is steeper than that observed in proton-proton collisions and follows the trend established by measurements at lower energy. The increase of d Nch/d η as a function of the average number of participant nucleons, ⟨Npart⟩ , calculated in a Glauber model, is compared with the previous measurement at √{sN N}=2.76 TeV . A constant factor of about 1.2 describes the increase in d Nch/d η from √{sN N}=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.

  18. A class of stochastic delayed SIR epidemic models with generalized nonlinear incidence rate and temporary immunity

    NASA Astrophysics Data System (ADS)

    Fan, Kuangang; Zhang, Yan; Gao, Shujing; Wei, Xiang

    2017-09-01

    A class of SIR epidemic model with generalized nonlinear incidence rate is presented in this paper. Temporary immunity and stochastic perturbation are also considered. The existence and uniqueness of the global positive solution is achieved. Sufficient conditions guaranteeing the extinction and persistence of the epidemic disease are established. Moreover, the threshold behavior is discussed, and the threshold value R0 is obtained. We show that if R0 < 1, the disease eventually becomes extinct with probability one, whereas if R0 > 1, then the system remains permanent in the mean.

  19. Dynamic universality class of Model C from the functional renormalization group

    NASA Astrophysics Data System (ADS)

    Mesterházy, D.; Stockemer, J. H.; Palhares, L. F.; Berges, J.

    2013-11-01

    We establish new scaling properties for the universality class of Model C, which describes relaxational critical dynamics of a nonconserved order parameter coupled to a conserved scalar density. We find an anomalous diffusion phase, which satisfies weak dynamic scaling while the conserved density diffuses only asymptotically. The properties of the phase diagram for the dynamic critical behavior include a significantly extended weak scaling region, together with a strong and a decoupled scaling regime. These calculations are done directly in 2≤d≤4 space dimensions within the framework of the nonperturbative functional renormalization group. The scaling exponents characterizing the different phases are determined along with subleading indices featuring the stability properties.

  20. Measurements and Modeling of Total Solar Irradiance in X-class Solar Flares

    NASA Technical Reports Server (NTRS)

    Moore, Christopher S.; Chamberlin, Phillip Clyde; Hock, Rachel

    2014-01-01

    The Total Irradiance Monitor (TIM) from NASA's SOlar Radiation and Climate Experiment can detect changes in the total solar irradiance (TSI) to a precision of 2 ppm, allowing observations of variations due to the largest X-class solar flares for the first time. Presented here is a robust algorithm for determining the radiative output in the TIM TSI measurements, in both the impulsive and gradual phases, for the four solar flares presented in Woods et al., as well as an additional flare measured on 2006 December 6. The radiative outputs for both phases of these five flares are then compared to the vacuum ultraviolet (VUV) irradiance output from the Flare Irradiance Spectral Model (FISM) in order to derive an empirical relationship between the FISM VUV model and the TIM TSI data output to estimate the TSI radiative output for eight other X-class flares. This model provides the basis for the bolometric energy estimates for the solar flares analyzed in the Emslie et al. study.

  1. Symmetry classes of alternating sign matrices in a nineteen-vertex model

    NASA Astrophysics Data System (ADS)

    Hagendorf, Christian; Morin-Duchesne, Alexi

    2016-05-01

    The nineteen-vertex model of Fateev and Zamolodchikov on a periodic lattice with an anti-diagonal twist is investigated. Its inhomogeneous transfer matrix is shown to have a simple eigenvalue, with the corresponding eigenstate displaying intriguing combinatorial features. Similar results were previously found for the same model with a diagonal twist. The eigenstate for the anti-diagonal twist is explicitly constructed using the quantum separation of variables technique. A number of sum rules and special components are computed and expressed in terms of Kuperberg’s determinants for partition functions of the inhomogeneous six-vertex model. The computations of some components of the special eigenstate for the diagonal twist are also presented. In the homogeneous limit, the special eigenstates become eigenvectors of the Hamiltonians of the integrable spin-one XXZ chain with twisted boundary conditions. Their sum rules and special components for both twists are expressed in terms of generating functions arising in the weighted enumeration of various symmetry classes of alternating sign matrices (ASMs). These include half-turn symmetric ASMs, quarter-turn symmetric ASMs, vertically symmetric ASMs, vertically and horizontally perverse ASMs and double U-turn ASMs. As side results, new determinant and pfaffian formulas for the weighted enumeration of various symmetry classes of alternating sign matrices are obtained.

  2. Measurements and modeling of total solar irradiance in X-class solar flares

    SciTech Connect

    Moore, Christopher Samuel; Chamberlin, Phillip Clyde; Hock, Rachel

    2014-05-20

    The Total Irradiance Monitor (TIM) from NASA's SOlar Radiation and Climate Experiment can detect changes in the total solar irradiance (TSI) to a precision of 2 ppm, allowing observations of variations due to the largest X-class solar flares for the first time. Presented here is a robust algorithm for determining the radiative output in the TIM TSI measurements, in both the impulsive and gradual phases, for the four solar flares presented in Woods et al., as well as an additional flare measured on 2006 December 6. The radi