2,244 results on '"Tuscaloosa, Alabama"'
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2. Strategies and performance of the CMS silicon tracker alignment during LHC Run 2
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Yerevan Physics Institute, Yerevan, Tumasyan Institut für Hochenergiephysik, Armenia A., Vienna, Adam, Austria W., Andrejkovic, J. W., Bergauer, T., Blöch, D., Chatterjee, S., Dragicevic, M., Escalante Del Valle, A., Frühwirth1, R., Hinger, V., Jeitler1, M., Krammer, N., Lechner, L., Liko, D., Mikulec, I., Paulitsch, P., Pitters, F. M., Schieck1, J., Schöfbeck, R., Schwarz, D., Steininger, H., Templ, S., Waltenberger, W., Wulz1 Institute for Nuclear Problems, C. -E., Minsk, Chekhovsky, Belarus V., Litomin, A., Makarenko Universiteit Antwerpen, V., Antwerpen, Beaumont, Belgium W., Darwish2, M. R., De Wolf, E. A., Janssen, T., Kello3, T., Lelek, A., Rejeb Sfar, H., Van Mechelen, P., Van Putte, S., Van Remortel Vrije Universiteit Brussel, N., Brussel, Blekman, Belgium F., Bols, E. S., D’Hondt, J., Delcourt, M., El Faham, H., Lowette, S., Moortgat, S., Morton, A., Muller, D., Sahasransu, A. R., Tavernier, S., Van Doninck, W., Van Mulders Université Libre de Bruxelles, P., Bruxelles, Allard, Belgium Y., Beghin, D., Bilin, B., Clerbaux, B., De Lentdecker, G., Deng, W., Favart, L., Grebenyuk, A., Hohov, D., Kalsi, A. K., Khalilzadeh, A., Lee, K., Mahdavikhorrami, M., Makarenko, I., Moureaux, L., Pétré, L., Popov, A., Postiau, N., Robert, F., Song, Z., Starling, E., Thomas, L., Vanden Bemden, M., Vander Velde, C., Vanlaer, P., Vannerom, D., Wezenbeek, L., Yang Ghent University, Y., Ghent, Cornelis, Belgium T., Dobur, D., Knolle, J., Lambrecht, L., Mestdach, G., Niedziela, M., Roskas, C., Samalan, A., Skovpen, K., Tytgat, M., Ver- massen, B., Vit Université Catholique de Louvain, M., Louvain-la-Neuve, Benecke, Belgium A., Bethani, A., Bruno, G., Bury, F., Caputo, C., David, P., Deblaere, A., Delaere, C., Donertas, I. S., Giammanco, A., Jaffel, K., Jain, Sa., Lemaitre, V., Mondal, K., Prisciandaro, J., Szilasi, N., Taliercio, A., Teklishyn, M., Tran, T. T., Vischia, P., Wertz Centro Brasileiro de Pesquisas Fisicas, S., Rio de Janeiro, Alves, Brazil G. A., Hensel, C., Moraes Universidade do Estado do Rio de Janeiro, A., Aldá Júnior, Brazil W. L., Alves Gallo Pereira, M., Barroso Ferreira Filho, M., Brandao Malbouisson, H., Carvalho, W., Chinellato4, J., Da Costa, E. M., Da Silveira5, G. G., De Jesus Damiao, D., Fonseca De Souza, S., Matos Figueiredo, D., Mora Herrera, C., Mota Amarilo, K., Mundim, L., Nogima, H., Rebello Teles, P., Santoro, A., Silva Do Amaral, S. M., Sznajder, A., Thiel, M., Torres Da Silva De Araujo6, F., Vilela Pereira Universidade Estadual Paulista (a), A., Universidade Federal do ABC (b), São, Paulo, Bernardes5, Brazil C. A., Calligaris, L., Fernandez Perez Tomei, T. R., Gre- gores, E. M., Lemos, D. S., Mercadante, P. G., Novaes, S. F., and Methods in Physics Research, Sandra S. Padula 36 The CMS Collaboration Nuclear Inst., A 1037 (2022) 166795 Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia, Aleksandrov, Bulgaria A., Antchev, G., Hadjiiska, R., Iaydjiev, P., Misheva, M., Rodozov, M., Shopova, M., Sultanov University of Sofia, G., Dimitrov, Bulgaria A., Ivanov, T., Litov, L., Pavlov, B., Petkov, P., Petrov Beihang University, A., Beijing, Cheng, China T., Javaid7, T., Mittal, M., Wang3, H., Yuan Department of Physics, L., Tsinghua, University, Ahmad, China M., Bauer, G., Dozen8, C., Hu, Z., Martins9, J., Wang, Y., Yi10, K., 11 Institute of High Energy Physics, Chapon, China E., Chen7, G. M., Chen7, H. S., Chen, M., Iemmi, F., Kapoor, A., Leggat, D., Liao, H., Liu7, Z. -A., Milosevic, V., Monti, F., Sharma, R., Tao, J., Thomas-Wilsker, J., Wang, J., Zhang, H., Zhao State Key Laboratory of Nuclear Physics and Technology, J., Peking Uni- versity, Agapitos, China A., An, Y., Ban, Y., Chen, C., Levin, A., Li, Q., Lyu, X., Mao, Y., Qian, S. J., Wang, D., Xiao Sun Yat-Sen University, Q. Wang12 J., Guangzhou, China M., Lu, You Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University, Z., Shanghai, Gao3, China X., Okawa Zhejiang University, H., Hangzhou, China, Zhejiang, Lin, China Z., Xiao Universidad de Los Andes, M., Bogota, Avila, Colombia C., Cabrera, A., Florez, C., Fraga Universidad de Antioquia, J., Medellin, Mejia Guisao, Colombia J., Ramirez, F., Ruiz Alvarez, J. D., Salazar González University of Split, C. A., Faculty of Electrical Engineering, Mechanical Engi- neering and Naval Architecture, Split, Giljanovic, Croatia D., Godinovic, N., Lelas, D., Puljak University of Split, I., Faculty of Science, Antunovic, Croatia Z., Kovac, M., Sculac Institute Rudjer Boskovic, T., Zagreb, Brigljevic, Croatia V., Ferencek, D., Majumder, D., Mishra, S., Roguljic, M., Starodumov13, A., Susa University of Cyprus, T., Nicosia, Attikis, Cyprus A., Christoforou, K., Erodotou, E., Ioannou, A., Kole, G., Kolosova, M., Konstantinou, S., Mousa, J., Nicolaou, C., Ptochos, F., Razis, P. A., Rykaczewski, H., Saka Charles University, H., Prague, Finger14, Czech Republic M., M. Finger Jr., 14, Kveton Escuela Politecnica Nacional, A., Quito, Ayala Universidad San Francisco de Quito, Ecuador E., Carrera Jarrin Academy of Scientific Research and Technology of the Arab Republic of Egypt, Ecuador E., Egyptian Network of High Energy Physics, Cairo, Abdalla15, Egypt H., Assran16, Y., 17 Center for High Energy Physics (CHEP-FU), Fayoum, University, El-, Fayoum, Mahmoud, Egypt M. A., Mohammed National Institute of Chemical Physics and Biophysics, Y., Tallinn, Ahmed18, Estonia I., Bhowmik, S., Dewanjee, R. K., Ehataht, K., Kadastik, M., Nandan, S., Nielsen, C., Pata, J., Raidal, M., Tani, L., Veelken Department of Physics, C., University of Helsinki, Helsinki, Eerola, Finland P., Forthomme, L., Kirschenmann, H., Osterberg, K., Vouti- lainen Helsinki Institute of Physics, M., Bharthuar, Finland S., Brücken, E., Garcia, F., Havukainen, J., Kim, M. S., Kinnunen, R., Lampén, T., Lassila-Perini, K., Lehti, S., Lindén, T., Lotti, M., Martikainen, L., Myllymäki, M., Ott, J., Siikonen, H., Tuominen, E., Tuominiemi Lappeenranta University of Technology, J., Lappeenranta, Luukka, Finland P., Petrow, H., Tuuva IRFU, T., Cea, Université, Paris-Saclay, Gif-sur-Yvette, Amendola, France C., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Faure, J. L., Ferri, F., Ganjour, S., Givernaud, A., Gras, P., Hamel de Monchenault, G., Jarry, P., Lenzi, B., Locci, E., Malcles, J., Rander, J., Rosowsky, A., Sahin, M. Ö., Savoy-Navarro19, A., Titov, M., Yu Laboratoire Leprince-Ringuet, G. B., Cnrs/in2p3, Ecole, Polytechnique, Insti- tut Polytechnique de Paris, Palaiseau, Ahuja, France S., Beaudette, F., Bonanomi, M., Buchot Perraguin, A., Busson, P., Cappati, A., Charlot, C., Davignon, O., Diab, B., Falmagne, G., Ghosh, S., Granier de Cassagnac, R., Hakimi, A., Kucher, I., Motta, J., Nguyen, M., Ochando, C., Paganini, P., Rembser, J., Salerno, R., Sarkar, U., Sauvan, J. B., Sirois, Y., Tarabini, A., Zabi, A., Zghiche Université de Strasbourg, A., Cnrs, IPHC UMR 7178, Strasbourg, Agram20, France J. -L., Andrea, J., Apparu, D., Bloch, D., Bonnin, C., Bourgatte, G., Brom, J. -M., Chabert, E. C., Charles, L., Collard, C., Dangelser, E., Darej, D., Fontaine20, J. -C., Goerlach, U., Grimault, C., Gross, L., Haas, C., Krauth, M., Le Bihan, A. -C., Nibigira, E., Ollivier-henry, N., Silva Jiménez, E., Van Hove Institut de Physique des 2 Infinis de Lyon (IP2I ), P., Villeurbanne, Asilar, France E., Baulieu, G., Beauceron, S., Bernet, C., Boudoul, G., Camen, C., Caponetto, L., Carle, A., Chanon, N., Contardo, D., Dené, P., Depasse, P., Dupasquier, T., El Mamouni, H., Fay, J., Galbit, G., Gascon, S., Gouze- vitch, M., Ille, B., Laktineh, I. B., Lattaud, H., Lesauvage, A., Lethuillier, M., Lumb, N., Mirabito, L., Nodari, B., Perries, S., Shchablo, K., Sordini, V., Torterotot, L., Touquet, G., Vander Donckt, M., Viret Georgian Technical University, S., Tbilisi, Lomidze, Georgia I., Toriashvili21, T., Tsamalaidze14 RWTH Aachen University, Z., Physikalisches Institut, I., Aachen, Autermann, Germany C., Botta, V., Feld, L., Karpinski, W., Kiesel, M. K., Klein, K., Lipinski, M., Louis, D., Meuser, D., Pauls, A., Pierschel, G., Rauch, M. P., Röwert, N., Schomakers, C., Schulz, J., Teroerde, M., Wlochal RWTH Aachen University, M., Physikalisches Institut A, Iii., Dodonova, Ger- many A., Eliseev, D., Erdmann, M., Fackeldey, P., Fischer, B., Hebbeker, T., Hoepfner, K., Ivone, F., Mastrolorenzo, L., and Methods in Physics Research, M. 37 The CMS Collaboration Nuclear Inst., A 1037 (2022) 166795 Merschmeyer, Meyer, A., Mocellin, G., Mondal, S., Mukherjee, S., Noll, D., Novak, A., Pook, T., Pozdnyakov, A., Rath, Y., Reithler, H., Roemer, J., Schmidt, A., Schuler, S. C., Sharma, A., Vigilante, L., Wiedenbeck, S., Zaleski RWTH Aachen University, S., Physikalisches Institut B, Iii., Dziwok, Ger- many C., Flügge, G., Haj Ahmad22, W., Hlushchenko, O., Kress, T., Nowack, A., Pistone, C., Pooth, O., Roy, D., Sert, H., Stahl18, A., Ziemons, T., Zotz Deutsches Elektronen-Synchrotron, A., Hamburg, Aarup Petersen, Germany H., Aldaya Martin, M., Asmuss, P., Baxter, S., Bayat- makou, M., Behnke, O., Bermúdez Martínez, A., Bertsche, D., Bhattacharya, S., Bin Anuar, A. A., Borras23, K., Brunner, D., Campbell, A., Cardini, A., Cheng, C., Colombina, F., Consuegra Rodríguez, S., Correia Silva, G., Danilov, V., De Silva, M., Didukh, L., Domínguez Damiani, D., Eckerlin, G., Eckstein, D., Estevez Banos, L. I., Filatov, O., Gallo24, E., Geiser, A., Giraldi, A., Grados Luyando, J. M., Grohsjean, A., Guthoff, M., Jafari25, A., Jomhari, N. Z., Jung, H., Kasem23, A., Kasemann, M., Kaveh, H., Kleinwort, C., Krücker, D., Lange, W., Lidrych, J., Lipka, K., Lohmann26, W., Mankel, R., Maser, H., Melzer-Pellmann, I. -A., Mendizabal Morentin, M., Metwally, J., Meyer, A. B., Meyer, M., Mittag, G., Mnich, J., Muhl, C., Mussgiller, A., Otarid, Y., Pérez Adán, D., Pitzl, D., Raspereza, A., Reichelt, O., Ribeiro Lopes, B., Rübenach, J., Saggio, A., Saibel, A., Savitskyi, M., Scham27, M., Scheurer, V., Schütze, P., Schwanenberger24, C., Shchedrolosiev, M., Shevchenko, R., Sosa Ricardo, R. E., Stafford, D., Stever, R., Tonon, N., Van De Klundert, M., Velyka, A., Walsh, R., Walter, D., Wen, Y., Wichmann, K., Wiens, L., Wissing, C., Wuchterl, S., Zuber University of Hamburg, A., Aggleton, Germany R., Albrecht, S., Bein, S., Benato, L., Biskop, H., Buhmann, P., Connor, P., De Leo, K., Eich, M., Feindt, F., Fröhlich, A., Garbers, C., Garutti, E., Gunnellini, P., Hajheidari, M., Haller, J., Hinzmann, A., Jabusch, H. R., Kasieczka, G., Klanner, R., Kogler, R., Kramer, T., Kutzner, V., Lange, J., Lange, T., Lobanov, A., Malara, A., Martens, S., Mrowietz, M., Niemeyer, C. E. N., Nigamova, A., Nissan, Y., Pena Rodriguez, K. J., Rieger, O., Schleper, P., Schröder, M., Schwandt, J., Sonneveld, J., Stadie, H., Steinbrück, G., Tews, A., Vormwald, B., Wellhausen, J., Zoi Karlsruher Institut fuer Technologie, I., Karlsruhe, Ardila-Perez, Germany L. E., Balzer, M., Barvich, T., Bechtel, J., Blank, T., Brom- mer, S., Burkart, M., Butz, E., Caselle, M., Caspart, R., Chwalek, T., De Boer†, W., Dierlamm, A., Droll, A., El Morabit, K., Faltermann, N., Giffels, M., Gosewisch, J. o., Gottmann, A., Hartmann18, F., Heidecker, C., Husemann, U., Keicher, P., Koppenhofer, R., Maier, S., Metzler, M., Mitra, S., Müller, Th., Neufeld, M., Neukum, M., Nürnberg, A., Quast, G., Rabbertz, K., Rauser, J., Sander, O., Savoiu, D., Schell, D., Schnepf, M., Seith, D., Shvetsov, I., Simonis, H. J., Stanulla, J., Steck, P., Ulrich, R., Van Der Linden, J., Von Cube, R. F., Wassmer, M., Weber, M., Weddigen, A., Wieland, S., Wittig, F., Wolf, R., Wozniewski, S., Wunsch Institute of Nuclear and Particle Physics (INPP), S., Ncsr, Demokritos, Aghia, Paraskevi, Anagnostou, Greece G., Assiouras, P., Daskalakis, G., Geralis, T., Kazas, I., Kyriakis, A., Loukas, D., Papadopoulos, A., Stakia National and Kapodistrian University of Athens, A., Athens, Diamantopoulou, Greece M., Karasavvas, D., Karathanasis, G., Kontaxakis, P., Koraka, C. K., Manousakis-Katsikakis, A., Panagiotou, A., Papavergou, I., Saoulidou, N., Theofilatos, K., Tziaferi, E., Vellidis, K., Vourliotis National Technical University of Athens, E., Bakas, Greece G., Kousouris, K., Papakrivopoulos, I., Tsipolitis, G., Zacharopoulou, A., Zografos University of Ioánnina, A., Ioánnina, Adamidis, Greece K., Bestintzanos, I., Evangelou, I., Foudas, C., Gianneios, P., Katsoulis, P., Kokkas, P., Manthos, N., Papadopoulos, I., Strologas MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, J., Eötvös Loránd University, Budapest, Csanad, Hungary M., Farkas, K., Gadallah28, M. M. A., Lökös29, S., Major, P., Mandal, K., Mehta, A., Pasztor, G., Rd ́l, A. J., Surányi, O., Veres Wigner Research Centre for Physics, G. I., Balazs, Hungary T., Bartók30, M., Bencze, G., Hajdu, C., Horvath31, D., Márton, K., Sikler, F., Veszpremi Institute of Nuclear Research ATOMKI, V., Debrecen, Czellar, Hungary S., Karancsi30, J., Molnar, J., Szillasi, Z., Teyssier Institute of Physics, D., University of Debrecen, Raics, Hungary P., Trocsanyi32, Z. L., Ujvari Karoly Robert Campus, B., MATE Institute of Technology, Gyongyos, Csorgo33, Hun- gary T., Nemes33, F., Novak Indian Institute of Science (IISc), T., Bangalore, Choudhury, India S., Komaragiri, J. R., Kumar, D., Panwar, L., Tiwari National Institute of Science Education and Research, P. C., Hbni, Bhubaneswar, Bahinipati34, India S., Das, A. K., Kar, C., Mal, P., Mishra, T., Mohanty, R., Muraleedharan Nair Bindhu35, V. K., Nayak35, A., Saha, P., Sur, N., Swain, S. K., Vats35 Punjab University, D., Chandigarh, Bansal, India S., Beri, S. B., Bhatnagar, V., Chaudhary, G., Chauhan, S., Dhingra36, N., Gupta, R., Kaur, A., Kaur, M., Kaur, S., Kumari, P., Meena, M., Sandeep, K., Singh, J. B., Virdi University of Delhi, A. K., Delhi, Ahmed, India A., Bhardwaj, A., Choudhary, B. C., Gola, M., Jain, C., Jain, G., Keshri, S., Kumar, A., Naimuddin, M., Priyanka, P., Ranjan, K., Saumya, S., Shah Saha Institute of Nuclear Physics, A., Hbni, Kolkata, Bharti37, India M., Bhattacharya, R., Bhowmik, D., Dutta, S., Gomber38, B., Maity39, M., Palit, P., Rout, P. K., Saha, G., Sahu, B., Sarkar, S., Sharan, M., Singh37, B., Thakur37 Indian Institute of Technology Madras, S., Madras, Behera, India P. K., Behera, S. C., Kalbhor, P., Muhammad, A., Pradhan, R., Pujahari, P. R., Sikdar Bhabha Atomic Research Centre, A. K., Mumbai, Dutta, India D., Jha, V., Kumar, V., Mishra, D. K., Naskar40, K., Netrakanti, P. K., Pant, L. M., Shukla Tata Institute of Fundamental Research-A, P., Aziz, India T., Dugad, S., Kumar Tata Institute of Fundamental Research-B, M., Banerjee, India S., Chudasama, R., Guchait, M., Karmakar, S., Kumar, S., Majumder, G., Mazumdar, K., Mukherjee Indian Institute of Science Education and Research (IISER), S., Pune, Alpana, India K., Dube, S., Kansal, B., Laha, A., Pandey, S., Rane, A., Rastogi, A., and Methods in Physics Research, S. Sharma 38 The CMS Collaboration Nuclear Inst., A 1037 (2022) 166795 Isfahan University of Technology, Isfahan, Bakhshiansohi12, Iran H., Khazaie, E., Zeinali41 Institute for Research in Fundamental Sciences (IPM), M., Tehran, Abbas, Iran S. M., Chenarani42, S., Etesami, S. M., Khakzad, M., Mohammadi Najafabadi University College Dublin, M., Dublin, Bari, Italy, Bari, Ramos, D., Bologna, Italy, Bologna, Lo Meoa, S., Grosso, G., Lusiani, E., Pavia, Italy, Pavia, Perugia, Italy, Peru-, Gia, Baldinelli, G., Bianchi, F., Magherinib, M., Pisa, Italy, Italy, Albergo, S., Costa, S., Di Mattia, A., Saizu, M. A., Tricomi, A., Tuve, C., Potenza, R., Catania, Italy, Manca, Mandorli, G., Massa, M., Mazzoni, E., Messineo, A., Scrib- anoa, A., Firenze, Italy, Firenze, Frascati, Benussi, Italy L., Bianco, S., Genova, Italy, Gen-, Ova, Milano, Italy, Milano, Boldrini, G., Pinolini, B. S., Napoli, Italy, Potenza, Italy, Padova, Italy, Trento, Beccherle, R., Rome, Italy, Rome, Torino, Italy, Novara, Coli, S., Rivettia, A., Trieste, Italy, Trieste, Daegu, Dogra, Korea S., Huh, C., Kim, B., Kim, D. H., Kim, G. N., Kim, J., Lee, J., Lee, S. W., Moon, C. S., Y. D., Oh, Pak, S. I., Radburn-Smith, B. C., Sekmen, S., Yang Chonnam National University, Y. C., Institute for Universe and Elementary Particles, Kwangju, Kim, Korea H., D’Amantea, V., Di Domenicoa, M. R., D, A 1037 (2022) 166795 Hanyang University, Seoul, Francois, Korea B., Kim, T. J., Park Korea University, J., Cho, Korea S., Choi, S., Go, Y., Hong, B., Lee, K. S., Lim, J., Park, J., Park, S. K., Yoo Kyung Hee University, J., Department of Physics, Republic of Korea, Goh, Korea J., Gurtu Sejong University, A., Kim, Korea H. S., Kim Seoul National University, Y., Almond, Korea J., Bhyun, J. H., Choi, J., Jeon, S., Kim, J. S., Ko, S., Kwon, H., Lee, H., Lee, S., B. H., Oh, Oh, M., S. B., Oh, Seo, H., Yang, U. K., Yoon University of Seoul, I., Jang, Korea W., Kang, D. Y., Kang, Y., Kim, S., Ko, B., Lee, J. S. H., Lee, Y., Park, I. C., Roh, Y., Ryu, M. S., Song, D., Watson, I. J., Yang Yonsei University, S., Korea S., Ha, Yoo Sungkyunkwan University, H. D., Suwon, Choi, Korea M., Yu College of Engineering and Technology, I., American University of the Middle East (AUM), Egaila, Kuwait, Dasman, Beyrouthy, Kuwait T., Maghrbi Riga Technical University, Y., Riga, Veckalns49 Vilnius University, Latvia V., Vilnius, Ambrozas, Lithuania M., Carvalho Antunes De Oliveira, A., Juodagalvis, A., Rinkevicius, A., Tamulaitis National Centre for Particle Physics, G., Universiti, Malaya, Kuala, Lumpur, Bin Norjoharuddeen, Malaysia N., Wan Abdullah, W. A. T., Yusli, M. N., Zolkapli Universidad de Sonora (UNISON), Z., Hermosillo, Benitez, Mexico J. F., Castaneda Hernandez, A., Coello, M. León., Murillo Quijada, J. A., Sehrawat, A., Valencia Palomo Centro de Investigacion y de Estudios Avanzados del IPN, L., Mexico, City, Ayala, Mexico G., Castilla-Valdez, H., De La Cruz-Burelo, E., Heredia-De La Cruz50, I., Lopezfernandez, R., Mondragon Herrera, C. A., Perez Navarro, D. A., Sánchez Hernández Universidad Iberoamericana, A., Carrillo Moreno, Mexico S., Oropeza Barrera, C., Vazquez Valencia Benemerita Universidad Autonoma de Puebla, F., Puebla, Pedraza, Mexico I., Salazar Ibarguen, H. A., Uribe Estrada University of Montenegro, C., Podgorica, Mijuskovic51, Montenegro J., Raicevic University of Auckland, N., Auckland, Krofcheck University of Canterbury, New Zealand D., Christchurch, Butler National Centre for Physics, New Zealand P. H., Quaid-I-Azam, University, Islamabad, Ahmad, Pak- istan A., Asghar, M. I., Awais, A., Awan, M. I. M., Hoorani, H. R., Khan, W. A., Shah, M. A., Shoaib, M., Waqas AGH University of Science and Technology Faculty of Computer Sci- ence, M., Electronics and Telecommunications, Krakow, Avati, Poland V., Grzanka, L., Malawski National Centre for Nuclear Research, M., Swierk, Bialkowska, Poland H., Bluj, M., Boimska, B., Górski, M., Kazana, M., Szleper, M., Zalewski Institute of Experimental Physics, P., Faculty of Physics, University of Warsaw, Warsaw, Bunkowski, Poland K., Doroba, K., Kalinowski, A., Konecki, M., Krolikowski Laboratório de Instrumentção e Física Experimental de Partículas, J., Lisboa, Araujo, Portugal M., Bargassa, P., Bastos, D., Boletti, A., Faccioli, P., Gallinaro, M., Hollar, J., Leonardo, N., Niknejad, T., Pisano, M., Seixas, J., Toldaiev, O., Varela Joint Institute for Nuclear Research, J., Dubna, Afanasiev, Russia S., Budkouski, D., Golutvin, I., Gorbunov, I., Karjavine, V., Korenkov, V., Lanev, A., Malakhov, A., Matveev52, V., Palichik, V., Perelygin, V., Savina, M., Seitova, D., Shalaev, V., Shmatov, S., Shulha, S., Smirnov, V., Teryaev, O., Voytishin, N., Yuldashev54, B. S., Zarubin, A., Zhizhin Petersburg Nuclear Physics Institute, I., Petersburg), Gatchina (St., Gavrilov, Russia G., Golovtcov, V., Ivanov, Y., Kim55, V., Kuznetsova56, E., Murzin, V., Oreshkin, V., Smirnov, I., Sosnov, D., Sulimov, V., Uvarov, L., Volkov, S., Vorobyev Institute for Nuclear Research, A., Moscow, Andreev, Russia Yu., Dermenev, A., Gninenko, S., Golubev, N., Karneyeu, A., Kirpichnikov, D., Kirsanov, M., Krasnikov, N., Pashenkov, A., Pivovarov, G., Alikhanov of NRC ‘Kurchatov Institute’, A. Toropin Institute for Theoretical and Experimental Physics named by A. I., Epshteyn, Russia V., Gavrilov, V., Lychkovskaya, N., Nikitenko57, A., Popov, V., Stepennov, A., Toms, M., Vlasov, E., Zhokin Moscow Institute of Physics and Technology, A., Aushev National Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI), Russia T., Bychkova, Russia O., Chadeeva58, M., Parygin, P., Popova, E., Rusinov, V., Lebedev Physical Institute, D. Selivanova P. 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C., Nabili, S., Palmer, C., Seidel, M., Skuja, A., Wang, L., Wong Massachusetts Institute of Technology, K., Cambridge, Massachusetts, Abercrombie, USA D., Andreassi, G., Bi, R., Brandt, S., Busza, W., Cali, I. A., D’Alfonso, M., Eysermans, J., Freer, C., Gomez Ceballos, G., Goncharov, M., Harris, P., Hu, M., Klute, M., Kovalskyi, D., Krupa, J., Lee, Y. -J., Mironov, C., Paus, C., Rankin, D., Roland, C., Roland, G., Shi, Z., Stephans, G. S. F., Wang, Z., Wyslouch University of Minnesota, B., Minneapolis, Minnesota, Chatterjee, USA R. M., Evans, A., Hansen, P., Hiltbrand, J., Jain, Sh., Krohn, M., Kubota, Y., Mans, J., Revering, M., Rusack, R., Saradhy, R., Schroeder, N., Strobbe, N., Wadud University of Mississippi, M. A., Oxford, Mississippi, Acosta, USA J. G., Cremaldi, L. M., Oliveros, S., Perera, L., Summers† University of Nebraska-Lincoln, D., Lincoln, Nebraska, Avdeeva, USA E., Bloom, K., Bryson, M., Claes, D. R., Fangmeier, C., Finco, L., Golf, F., Joo, C., Kravchenko, I., Meier, F., Musich, M., Reed, I., Siado, J. E., Snow†, G. R., Tabb, W., Yan, F., Zecchinelli State University of New York at Buffalo, A. G., Buffalo, Agarwal, USA G., Bandyopadhyay, H., Hay, L., Iashvili, I., Kharchilava, A., Mclean, C., Nguyen, D., Pekkanen, J., Rappoccio, S., Williams Northeastern University, A., Alverson, USA G., Barberis, E., Haddad, Y., Hortiangtham, A., Li, J., Madi- gan, G., Marzocchi, B., Morse, D. M., Nguyen, V., Orimoto, T., Parker, A., Skinnari, L., Tishelman-Charny, A., Wamorkar, T., Wang, B., Wisecarver, A., Wood Northwestern University, D., Evanston, Illinois, Bhattacharya, USA S., Bueghly, J., Chen, Z., Gilbert, A., Gunter, T., Hahn, K. A., Liu, Y., Odell, N., Schmitt, M. H., Sung, K., Velasco University of Notre Dame, M., Notre, Dame, Indiana, Band, USA R., Bucci, R., Das, A., Dev, N., Goldouzian, R., Hildreth, M., Hurtado Anampa, K., Jessop, C., Lannon, K., Lawrence, J., Loukas, N., Lut- ton, D., Marinelli, N., Mcalister, I., Mccauley, T., Mcgrady, C., Mohrman, K., Musienko52, Y., Ruchti, R., Siddireddy, P., Townsend, A., Wayne, M., Wightman, A., Zarucki, M., Zygala The Ohio State University, L., Columbus, Ohio, Bylsma, USA B., Cardwell, B., Durkin, L. S., Francis, B., Hill, C., Nunez Ornelas, M., Wei, K., Winer, B. L., Yates Princeton University, B. R., Princeton, New, Jersey, Addesa, USA F. M., Bonham, B., Das, P., Dezoort, G., Elmer, P., Frankenthal, A., Greenberg, B., Haubrich, N., Higginbotham, S., Kalogeropoulos, A., Kopp, G., Kwan, S., Lange, D., Marlow, D., Mei, K., Ojalvo, I., Olsen, J., Stickland, D., Tully University of Puerto Rico, C., Mayaguez, Puerto, Rico, Malik, USA S., Norberg, S., Ramirez Vargas Purdue University, J. E., West, Lafayette, Bakshi, USA A. S., Barnes, V. E., Chawla, R., Das, S., Gutay, L., Jones, M., Jung, A. W., Karmarkar, S., Kondratyev, D., Koshy, A. M., Liu, M., Negro, G., Neumeister, N., Paspalaki, G., Piperov, S., Purohit, A., Schulte, J. F., Stojanovic19, M., Thieman, J., Wang, F., Xiao, R., Xie Purdue University Northwest, W., Hammond, Indiana, Dolen, USA J., Parashar Rice University, N., Houston, Texas, Baty, USA A., Decaro, M., Dildick, S., Ecklund, K. M., Freed, S., Gardner, P., Geurts, F. J. M., Li, W., Liu, H., Nussbaum, T., Padley, B. P., Redjimi, R., Shi, W., Stahl Leiton, A. G., Yang, S., Zhang, L., Zhang University of Rochester, Y., Rochester, Bodek, USA A., de Barbaro, P., Demina, R., Dulemba, J. L., Fallon, C., Ferbel, T., Galanti, M., Garcia-Bellido, A., Hindrichs, O., Khukhunaishvili, A., Ranken, E., Taus Rutgers, R., The State University of New Jersey, Piscataway, Bartz, USA E., Chiarito, B., Chou, J. P., Gandrakota, A., Gershtein, Y., Halki- adakis, E., Hart, A., Heindl, M., Karacheban26, O., Laflotte, I., Lath, A., Montalvo, R., Nash, K., Osherson, M., Salur, S., Schnetzer, S., Somalwar, S., Stone, R., Thayil, S. A., Thomas, S., Wang University of Tennessee, H., Knoxville, Tennessee, Acharya, USA H., Delannoy, A. G., Fiorendi, S., Spanier Texas A&, S., University, M, College, Station, Texas, Bouhali98, USA O., Dalchenko, M., Delgado, A., Eusebi, R., Gilmore, J., and Methods in Physics Research, T. 43 The CMS Collaboration Nuclear Inst., A 1037 (2022) 166795 Huang, Kamon99, T., Kim, H., Luo, S., Malhotra, S., Mueller, R., Overton, D., Rathjens, D., Safonov Texas Tech University, A., Lubbock, Texas, Akchurin, USA N., Damgov, J., Hegde, V., Kunori, S., Lamichhane, K., Mengke, T., Muthumuni, S., Peltola, T., Volobouev, I., Whitbeck Vanderbilt University, A., Nashville, Tennessee, Appelt, USA E., D’Angelo, P., Greene, S., Gurrola, A., Johns, W., Melo, A., Ni, H., Padeken, K., Romeo, F., Sheldon, P., Tuo, S., Velkovska University of Virginia, J., Charlottesville, Virginia, Arenton, USA M. W., Cox, B., Cummings, G., Hakala, J., Hirosky, R., Joyce, M., Ledovskoy, A., Li, A., Neu, C., Perez Lara, C. E., Tannenwald, B., White, S., Wolfe Wayne State University, E., Detroit, Michigan, Poudyal University of Wisconsin - Madison, USA N., Madison, Wi, Wisconsin, Black, USA K., Bose, T., Caillol, C., Dasu, S., De Bruyn, I., Everaerts, P., Fienga, F., Galloni, C., He, H., Herndon, M., Hervé, A., Hussain, U., Lanaro, A., Loeliger, A., Loveless, R., Madhusudanan Sreekala, J., Mallampalli, A., Mohammadi, A., Pinna, D., Savin, A., Shang, V., Smith, W. H., Teague, D., Trembath-Reichert, S., Vetens 30 Also at Institute of Physics, W., Hungary 31 Also at Institute of Nuclear Research ATOMKI, Hungary 32 Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Hungary 33 Also at Wigner Research Centre for Physics, Hungary 34 Also at IIT Bhubaneswar, Bhubaneswar, India 35 Also at Institute of Physics, Khalsa College, India 36 Also at G. H. G., Punjab, India 37 Also at Shoolini University, Solan, India 38 Also at University of Hyderabad, Hyderabad, India 39 Also at University of Visva-Bharati, Santiniketan, India 40 Also at Indian Institute of Technology (IIT), India 41 Also at Sharif University of Technology, Iran 42 Also at Department of Physics, University of Science and Technology of Mazandaran, Behshahr, Iran 43 Now at INFN Sezione di Bari, Università di Bari, Politecnico di Bari, Italy 44 Also at Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Italy 45 Also at Centro Siciliano di Fisica Nucleare, e di Struttura Della Materia, Catania, Italy 46 Also at Horia Hulubei National Institute of Physics and Nuclear Engineering (IFIN-HH), Bucharest, Romania 47 Also at Universita di Napoli ‘Federico II’, Napoli, Italy 48 Also at Consiglio Nazionale delle Ricerche, - Istituto Officina dei Materiali, Perugia, Italy 49 Also at Riga Technical University, Latvia 50 Also at Consejo Nacional de Ciencia, y Tecnología, Mexico 51 Also at IRFU, France 52 Also at Institute for Nuclear Research, Russia 53 Now at National Research Nuclear University ‘Moscow Engineering Physics Institute’ (MEPhI), Russia 54 Also at Institute of Nuclear Physics of the Uzbekistan Academy of Sciences, Tashkent, Petersburg Polytechnic University, Uzbekistan 55 Also at St., Petersburg, St., Russia 56 Also at University of Florida, USA 57 Also at Imperial College, Lebedev Physical Institute, United Kingdom 58 Also at P. N., Russia 59 Also at California Institute of Technology, USA 60 Also at INFN Sezione di Padova, Università di Padova, Università di Trento, Trento, Italy, Padova, Italy 61 Also at Budker Institute of Nuclear Physics, Russia 62 Also at Faculty of Physics, University of Belgrade, Serbia 63 Also at Trincomalee Campus, Eastern, University, Sri, Lanka, Nilaveli, Sri Lanka 64 Also at INFN Sezione di Pavia, Università di Pavia, Italy 65 Also at National and Kapodistrian University of Athens, Greece 66 Also at Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzer- land 67 Also at Universität Zürich, Switzerland 68 Also at Stefan Meyer Institute for Subatomic Physics, Austria 69 Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules, IN2P3-CNRS, Annecyle-Vieux, France 70 Also at Şırnak University, Sirnak, Turkey 71 Also at Near East University, Research Center of Experimental Health Science, Turkey 72 Also at Konya Technical University, Konya, Turkey 73 Also at Piri Reis University, Turkey 74 Also at Adiyaman University, Adiyaman, Turkey 75 Also at Ozyegin University, Turkey 76 Also at Izmir Institute of Technology, Izmir, Turkey 77 Also at Necmettin Erbakan University, Turkey 78 Also at Bozok Universitetesi Rektörlügü, Yozgat, Turkey † Deceased, 1 Also at TU Wien, Wien, Austria, 2 Also at Institute of Basic and Applied Sciences, Faculty of Engineering, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt 3 Also 4 Also 5 Also 6 Also 7 Also 8 Also, 9 Also 10 Also at Nanjing Normal University Department of Physics, Nanjing, China 11 Now at The University of Iowa, USA 12 Also at Deutsches Elektronen-Synchrotron, Alikhanov of NRC ‘Kurchatov Institute’, Germany 13 Also at Institute for Theoretical and Experimental Physics named by A. I., Russia 14 Also at Joint Institute for Nuclear Research, Russia 15 Also at Cairo University, Egypt 16 Also at Suez University, Suez, Egypt 17 Now at British University in Egypt, Egypt 18 Also at CERN, Switzerland 19 Also at Purdue University, USA 20 Also at Université de Haute Alsace, Mulhouse, France 21 Also at Tbilisi State University, Georgia 22 Also at Erzincan Binali Yildirim University, Erzincan, Turkey 23 Also at RWTH Aachen University, Germany 24 Also at University of Hamburg, Germany 25 Also at Isfahan University of Technology, Iran 26 Also at Brandenburg University of Technology, Cottbus, Germany 27 Also at Forschungszentrum Julich, Juelich, Germany 28 Also at Physics Department, Assiut, University, Assiut, Egypt 29 Also at Karoly Robert Campus, Hungary at Université Libre de Bruxelles, Belgium at Universidade Estadual de Campinas, Campinas, Brazil at Federal University of Rio Grande do Sul, Porto, Alegre, Brazil at The University of the State of Amazonas, Manaus, Brazil at University of Chinese Academy of Sciences, China at Department of Physics, China at UFMS, Nova, Andradina, and Methods in Physics Research, Brazil 44 The CMS Collaboration Nuclear Inst., A 1037 (2022) 166795 79 Also at Marmara University, Turkey 80 Also at Milli Savunma University, Turkey 81 Also at Kafkas University, Kars, Turkey 82 Also at Istanbul Bilgi University, Turkey 83 Also at Hacettepe University, Turkey 84 Also at Istanbul University, - Cerrahpasa, Turkey 85 Also at Vrije Universiteit Brussel, Belgium 86 Also at School of Physics and Astronomy, University of Southampton, Southampton, United Kingdom 87 Also at Rutherford Appleton Laboratory, United Kingdom 88 Also at IPPP Durham University, Durham, United Kingdom 89 Also at Monash University, Clayton, Australia 90 Also at Universitá di Torino, Torino, Italy 91 Also at Bethel University, Paul, St., Minneapolis, USA 92 Also at Karamanoğlu Mehmetbey University, Karaman, Turkey 93 Also at Ain Shams University, Egypt 94 Also at Bingol University, Bingol, Turkey 95 Also at Georgian Technical University, Georgia 96 Also at Sinop University, Sinop, Turkey 97 Also at Erciyes University, Kayseri, Turkey 98 Also at Texas A, M University at Qatar, Doha, Qatar 99 Also at Kyungpook National University, Daegu, Korea, Universidad de Cantabria, CMS Collaboration, The CMS Collaboration, Department of Physics, Helsinki Institute of Physics, University of Zurich, Belforte, S., Candelise, V., Casarsa, M., Cossutti, F., DA ROLD, A., DELLA RICCA, G., Sorrentino, G., Vazzoler, F., ET AL (the CMS, Collaboration), Tumasyan, A, Adam, W, Andrejkovic, JW, Bergauer, T, Bloch, D, Chatterjee, S, Dragicevic, M, Del Valle, AE, Fruwirth, R, Hinger, V, Jeitler, M, Krammer, N, Lechner, L, Liko, D, Mikulec, I, Paulitsch, P, Pitters, FM, Schieck, J, Schofbeck, R, Schwarz, D, Steininger, H, Templ, S, Waltenberger, W, Wulz, E, Chekhovsky, V, Litomin, A, Makarenko, V, Beaumont, W, Darwish, MR, De Wolf, EA, Janssen, T, Kello, T, Lelek, A, Sfar, HR, Van Mechelen, P, Van Putte, S, Van Remortel, N, Blekman, F, Bols, ES, D'Hondt, J, Delcourt, M, El Faham, H, Lowette, S, Moortgat, S, Morton, A, Muller, D, Sahasransu, AR, Tavernier, S, Van Doninck, W, Van Mulders, P, Allard, Y, Beghin, D, Bilin, B, Clerbaux, B, De Lentdecker, G, Deng, W, Favart, L, Grebenyuk, A, Hohov, D, Kalsi, AK, Khalilzadeh, A, Lee, K, Mahdavikhorrami, M, Makarenko, I, Moureaux, L, Petre, L, Popov, A, Postiau, N, Robert, F, Song, Z, Starling, E, Thomas, L, Vanden Bemden, M, Vander Velde, C, Vanlaer, P, Vannerom, D, Wezenbeek, L, Yang, Y, Cornelis, T, Dobur, D, Knolle, J, Lambrecht, L, Mestdach, G, Niedziela, M, Roskas, C, Samalan, A, Skovpen, K, Tytgat, M, Vermassen, B, Vit, M, Benecke, A, Bethani, A, Bruno, G, Bury, F, Caputo, C, David, P, Deblaere, A, Delaere, C, Donertas, IS, Giammanco, A, Jaffel, K, Jain, S, Lemaitre, V, Mondal, K, Prisciandaro, J, Szilasi, N, Taliercio, A, Teklishyn, M, Tran, TT, Vischia, P, Wertz, S, Alves, GA, Hensel, C, Moraes, A, Alda, WL, Pereira, MAG, Ferreira, MB, Malbouisson, HB, Carvalho, W, Chinellato, J, Da Costa, EM, Da Silveira, GG, Damiao, DD, De Souza, SF, Figueiredo, DM, Herrera, CM, Amarilo, KM, Mundim, L, Nogima, H, Teles, PR, Santoro, A, Do Amaral, SMS, Sznajder, A, Thiel, M, De Araujo, FTD, Pereira, AV, Bernardes, CA, Calligaris, L, Tomei, TRFP, Gregores, EM, Lemos, DS, Mercadante, PG, Novaes, SF, Padula, SS, Aleksandrov, A, Antchev, G, Hadjiiska, R, Iaydjiev, P, Misheva, M, Rodozov, M, Shopova, M, Sultanov, G, Dimitrov, A, Ivanov, T, Litov, L, Pavlov, B, Petkov, P, Petrov, A, Cheng, T, Javaid, T, Mittal, M, Wang, H, Yuan, L, Ahmad, M, Bauer, G, Dozen, C, Hu, Z, Martins, J, Wang, Y, Yi, K, Chapon, E, Chen, GM, Chen, HS, Chen, M, Iemmi, F, Kapoor, A, Leggat, D, Liao, H, Liu, A, Milosevic, V, Monti, F, Sharma, R, Tao, J, Thomas-Wilsker, J, Wang, J, Zhang, H, Zhao, J, Agapitos, A, An, Y, Ban, Y, Chen, C, Levin, A, Li, Q, Lyu, X, Mao, Y, Qian, SJ, Wang, D, Wang, Q, Lu, M, You, Z, Gao, X, Okawa, H, Lin, Z, Xiao, M, Avila, C, Cabrera, A, Florez, C, Fraga, J, Guisao, JM, Ramirez, F, Alvarez, JDR, Gonzalez, CAS, Giljanovic, D, Godinovic, N, Lelas, D, Puljak, I, Antunovic, Z, Kovac, M, Sculac, T, Brigljevic, V, Ferencek, D, Majumder, D, Mishra, S, Roguljic, M, Starodumov, A, Susa, T, Attikis, A, Christoforou, K, Erodotou, E, Ioannou, A, Kole, G, Kolosova, M, Konstantinou, S, Mousa, J, Nicolaou, C, Ptochos, F, Razis, PA, Rykaczewski, H, Saka, H, Finger, M, Kveton, A, Ayala, E, Jarrin, EC, Abdalla, H, Assran, Y, Mahmoud, MA, Mohammed, Y, Ahmed, I, Bhowmik, S, Dewanjee, RK, Ehataht, K, Kadastik, M, Nandan, S, Nielsen, C, Pata, J, Raidal, M, Tani, L, Veelken, C, Eerola, P, Forthomme, L, Kirschenmann, H, Osterberg, K, Voutilainen, M, Bharthuar, S, Brucken, E, Garcia, F, Havukainen, J, Kim, MS, Kinnunen, R, Lampen, T, Lassila-Perini, K, Lehti, S, Linden, T, Lotti, M, Martikainen, L, Myllymaki, M, Ott, J, Siikonen, H, Tuominen, E, Tuominiemi, J, Luukka, P, Petrow, H, Tuuva, T, Amendola, C, Besancon, M, Couderc, F, Dejardin, M, Denegri, D, Faure, JL, Ferri, F, Ganjour, S, Givernaud, A, Gras, P, de Monchenault, GH, Jarry, P, Lenzi, B, Locci, E, Malcles, J, Rander, J, Rosowsky, A, Sahin, MO, Savoy-Navarro, A, Titov, M, Yu, GB, Ahuja, S, Beaudette, F, Bonanomi, M, Perraguin, AB, Busson, P, Cappati, A, Charlot, C, Davignon, O, Diab, B, Falmagne, G, Ghosh, S, de Cassagnac, RG, Hakimi, A, Kucher, I, Motta, J, Nguyen, M, Ochando, C, Paganini, P, Rembser, J, Salerno, R, Sarkar, U, Sauvan, JB, Sirois, Y, Tarabini, A, Zabi, A, Zghiche, A, Agram, L, Andrea, J, Apparu, D, Bonnin, C, Bourgatte, G, Brom, JM, Chabert, EC, Charles, L, Collard, C, Dangelser, E, Darej, D, Fontaine, C, Goerlach, U, Grimault, C, Gross, L, Haas, C, Krauth, M, Le Bihan, AC, Nibigira, E, Ollivier-henry, N, Jimenez, ES, Van Hove, P, Asilar, E, Baulieu, G, Beauceron, S, Bernet, C, Boudoul, G, Camen, C, Caponetto, L, Carle, A, Chanon, N, Contardo, D, Dene, P, Depasse, P, Dupasquier, T, El Mamouni, H, Fay, J, Galbit, G, Gascon, S, Gouzevitch, M, Ille, B, Laktineh, IB, Lattaud, H, Lesauvage, A, Lethuillier, M, Lumb, N, Mirabito, L, Nodari, B, Perries, S, Shchablo, K, Sordini, V, Torterotot, L, Touquet, G, Vander Donckt, M, Viret, S, Lomidze, I, Toriashvili, T, Tsamalaidze, Z, Autermann, C, Botta, V, Feld, L, Karpinski, W, Kiesel, MK, Klein, K, Lipinski, M, Louis, D, Meuser, D, Pauls, A, Pierschel, G, Rauch, MP, Rowert, N, Schomakers, C, Schulz, J, Teroerde, M, Wlochal, M, Dodonova, A, Eliseev, D, Erdmann, M, Fackeldey, P, Fischer, B, Hebbeker, T, Hoepfner, K, Ivone, F, Mastrolorenzo, L, Merschmeyer, M, Meyer, A, Mocellin, G, Mondal, S, Mukherjee, S, Noll, D, Novak, A, Pook, T, Pozdnyakov, A, Rath, Y, Reithler, H, Roemer, J, Schmidt, A, Schuler, SC, Sharma, A, Vigilante, L, Wiedenbeck, S, Zaleski, S, Dziwok, C, Flugge, G, Ahmad, WH, Hlushchenko, O, Kress, T, Nowack, A, Pistone, C, Pooth, O, Roy, D, Sert, H, Stahl, A, Ziemons, T, Zotz, A, Petersen, HA, Martin, MA, Asmuss, P, Baxter, S, Bayatmakou, M, Behnke, O, Martinez, AB, Bertsche, D, Bhattacharya, S, Bin Anuar, AA, Borras, K, Brunner, D, Campbell, A, Cardini, A, Cheng, C, Colombina, F, Rodriguez, SC, Silva, GC, Danilov, V, De Silva, M, Didukh, L, Damiani, DD, Eckerlin, G, Eckstein, D, Banos, LIE, Filatov, O, Gallo, E, Geiser, A, Giraldi, A, Luyando, JMG, Grohsjean, A, Guthoff, M, Jafari, A, Jomhari, NZ, Jung, H, Kasem, A, Kasemann, M, Kaveh, H, Kleinwort, C, Krucker, D, Lange, W, Lidrych, J, Lipka, K, Lohmann, W, Mankel, R, Maser, H, Melzer-Pellmann, IA, Morentin, MM, Metwally, J, Meyer, AB, Meyer, M, Mittag, G, Mnich, J, Muhl, C, Mussgiller, A, Otarid, Y, Adan, DP, Pitzl, D, Raspereza, A, Reichelt, O, Lopes, BR, Rubenach, J, Saggio, A, Saibel, A, Savitskyi, M, Scham, M, Scheurer, V, Schutze, P, Schwanenberger, C, Shchedrolosiev, M, Shevchenko, R, Ricardo, RES, Stafford, D, Stever, R, Tonon, N, Van De Klundert, M, Velyka, A, Walsh, R, Walter, D, Wen, Y, Wichmann, K, Wiens, L, Wissing, C, Wuchterl, S, Zuber, A, Aggleton, R, Albrecht, S, Bein, S, Benato, L, Biskop, H, Buhmann, P, Connor, P, De Leo, K, Eich, M, Feindt, F, Frohlich, A, Garbers, C, Garutti, E, Gunnellini, P, Hajheidari, M, Haller, J, Hinzmann, A, Jabusch, HR, Kasieczka, G, Klanner, R, Kogler, R, Kramer, T, Kutzner, V, Lange, J, Lange, T, Lobanov, A, Malara, A, Martens, S, Mrowietz, M, Niemeyer, CEN, Nigamova, A, Nissan, Y, Rodriguez, KJP, Rieger, O, Schleper, P, Schroder, M, Schwandt, J, Sonneveld, J, Stadie, H, Steinbruck, G, Tews, A, Vormwald, B, Wellhausen, J, Zoi, I, Ardila-Perez, LE, Balzer, M, Barvich, T, Bechtel, J, Blank, T, Brommer, S, Burkart, M, Butz, E, Caselle, M, Caspart, R, Chwalek, T, De Boer, W, Dierlamm, A, Droll, A, El Morabit, K, Faltermann, N, Giffels, M, Gosewisch, JO, Gottmann, A, Hartmann, F, Heidecker, C, Husemann, U, Keicher, P, Koppenhofer, R, Maier, S, Metzler, M, Mitra, S, Muller, T, Neufeld, M, Neukum, M, Nurnberg, A, Quast, G, Rabbertz, K, Rauser, J, Sander, O, Savoiu, D, Schell, D, Schnepf, M, 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Technology ,Physics - Instrumentation and Detectors ,Physics::Instrumentation and Detectors ,Tracker ,Legacy reprocessing ,measurement methods ,tracking detector, alignment ,Settore ING-INF/01 - Elettronica ,Physics, Particles & Fields ,PARTICLE PHYSICS ,LARGE HADRON COLLIDER ,CMS ,High Energy Physics - Experiment ,High Energy Physics - Experiment (hep-ex) ,tracking detector: alignment ,Detector ,Alignment ,MillePede-II ,HipPy ,VERTEX ,High energy physics ,Experimental particle physics ,LHC ,p p: scattering ,p p: colliding beams ,B: decay ,tau: hadronic decay ,interaction: gauge ,interaction: model ,transverse momentum: missing-energy ,new physics: search for ,mass spectrum: transverse ,black hole: quantum ,vector boson: mass ,W': leptonic decay ,sensitivity ,leptoquark: coupling ,CERN LHC Coll ,leptoquark: mass: lower limit ,anomaly ,channel cross section: upper limit ,effective field theory ,Higgs ,Detectors and Experimental Techniques ,Instruments & Instrumentation ,physics.ins-det ,Instrumentation ,detector, alignment ,Physics ,Instrumentation and Detectors (physics.ins-det) ,Nuclear & Particles Physics ,Physics, Nuclear ,Physical Sciences ,0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics ,Engineering sciences. Technology ,Particle Physics - Experiment ,performance ,Nuclear and High Energy Physics ,530 Physics ,0299 Other Physical Sciences ,FOS: Physical sciences ,10192 Physics Institute ,114 Physical sciences ,0201 Astronomical and Space Sciences ,ddc:530 ,3106 Nuclear and High Energy Physics ,CMS, performance ,Nuclear Science & Technology ,numerical calculations ,Science & Technology ,hep-ex ,3105 Instrumentation ,silicon ,Physics and Astronomy ,alignment [tracking detector] ,semiconductor detector - Abstract
The strategies for and the performance of the CMS silicon tracking system alignment during the 2015-2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1037, ISSN:0168-9002, ISSN:1872-9576
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- 2022
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3. Measurement of jet fragmentation in PbPb and pp collisions at root s(NN)=2.76 TeV
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Chatrchyan1, S., Khachatryan1, V., Sirunyan1, A. M., Tumasyan1, A., Adam2, W., Bergauer2, T., Dragicevic2, M., Erö2, J., Fabjan2, C., A, Friedl2, M., Frühwirth2, R., Ghete2, V. M., Hartl2, C., Hörmann2, N., Hrubec2, J., Jeitler2, M., Kiesenhofer2, W., Knünz2, V., Krammer2, M., Krätschmer2, I., Liko2, D., Mikulec2, I., Rabady2, D., B, Rahbaran2, B., Rohringer2, H., Schöfbeck2, R., Strauss2, J., Taurok2, A., Treberer Treberspurg2, W., Waltenberger2, W., Wulz2, C. E., Mossolov3, V., Shumeiko3, N., Suarez Gonzalez3, J., Alderweireldt4, S., Bansal4, M., Bansal4, S., Cornelis4, T., De Wolf4, E. A., Janssen4, X., Knutsson4, A., Luyckx4, S., Mucibello4, L., Ochesanu4, S., Roland4, B., Rougny4, R., Van Haevermaet4, H., Van Mechelen4, P., Van Remortel4, N., Van Spilbeeck4, A., Blekman5, F., Blyweert5, S., D'Hondt5, J., Heracleous5, N., Kalogeropoulos5, A., Keaveney5, J., Kim5, T. 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H., Woods164 1Yerevan Physics Institute, N., Yerevan, Armenia 2Institut für Hochenergiephysik der OeAW, Wien, Austria 3National Centre for Particle, High Energy Physics, Minsk, Belarus 4Universiteit Antwerpen, Antwerpen, Belgium 5Vrije Universiteit Brussel, Brussel, Belgium 6Université Libre de Bruxelles, Bruxelles, Belgium 7Ghent University, Ghent, Belgium 8Université Catholique de Louvain, Louvain la Neuve, Belgium 9Université de Mons, Mons, Belgium 10Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil 11Universidade do Estado do Rio de Janeiro, Brazil 12aUniversidade Estadual Paulista, São, Paulo, Brazil 12bUniversidade Federal do ABC, Brazil 13Institute for Nuclear Research, Nuclear, Energy, Sofia, Bulgaria 14University of Sofia, Bulgaria 15Institute of High Energy Physics, Beijing, China 16State Key Laboratory of Nuclear Physics, Technology, Peking, University, China 17Universidad de Los Andes, Bogota, Colombia 18Technical University of Split, Split, Croatia 19University of Split, Croatia 20Institute Rudjer Boskovic, Zagreb, Croatia 21University of Cyprus, Nicosia, Cyprus 22Charles University, Prague, Czech Republic 23Academy of Scientific Research, Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt 24National Institute of Chemical Physics, Biophysics, Tallinn, Estonia 25Department of Physics, University of Helsinki, Helsinki, Finland 26Helsinki Institute of Physics, Finland 27Lappeenranta University of Technology, Lappeenranta, Finland, 28dsm/irfu, Cea/saclay, Gif sur Yvette, France 29Laboratoire Leprince Ringuet, Ecole, Polytechnique, In2p3, Cnrs, Palaiseau, France 30Institut Pluridisciplinaire Hubert Curien, Université de Strasbourg, Université de Haute Alsace Mulhouse, Cnrs/in2p3, Strasbourg, France 31Centre de Calcul de l'Institut National de Physique Nucleaire et de Physique des Particules, Cnrs/in2p3, Villeurbanne, France 32Université de Lyon, Université Claude Bernard Lyon, 1, Cnrs, In2p3, Institut de Physique Nucléaire de Lyon, Villeurbanne, France 33Institute of High Energy Physics, Informatization, Tbilisi State University, Tbilisi, Georgia 34RWTH Aachen University, Physikalisches Institut, I., Aachen, Germany 35RWTH Aachen University, Physikalisches Institut A, I. I. I., Germany 36RWTH Aachen University, Physikalisches Institut B, I. I. I., Germany 37Deutsches Elektronen Synchrotron, Hamburg, Germany 38University of Hamburg, Germany 39Institut für Experimentelle Kernphysik, Karlsruhe, Germany 40Institute of Nuclear, Particle, Physics, Ncsr, Demokritos, Aghia, Paraskevi, Greece 41University of Athens, Athens, Greece 42University of Ioánnina, Ioánnina, Greece 43Wigner Research Centre for Physics, Budapest, Hungary 44Institute of Nuclear Research ATOMKI, Debrecen, Hungary 45University of Debrecen, Hungary 46National Institute of Science Education, Research, Bhubaneswar, India 47Panjab University, Chandigarh, India 48University of Delhi, Delhi, India 49Saha Institute of Nuclear Physics, Kolkata, India 50Bhabha Atomic Research Centre, Mumbai, India 51Tata Institute of Fundamental Research, India 52Institute for Research in Fundamental Sciences, Tehran, Iran 53University College Dublin, Dublin, Ireland 54aINFN Sezione di Bari, Bari, Italy 54bUniversità di Bari, Italy 54cPolitecnico di Bari, Italy 55aINFN Sezione di Bologna, Bologna, Italy 55bUniversità di Bologna, Italy 56aINFN Sezione di Catania, Catania, Italy 56bUniversità di Catania, Italy, 56ccsfnsm, Italy 57aINFN Sezione di Firenze, Firenze, Italy 57bUniversità di Firenze, Italy 58INFN Laboratori Nazionali di Frascati, Frascati, Italy 59aINFN Sezione di Genova, Genova, Italy 59bUniversità di Genova, Italy 60aINFN Sezione di Milano Bicocca, Milano, Italy 60bUniversità di Milano Bicocca, Italy 61aINFN Sezione di Napoli, Napoli, Italy 61bUniversità di Napoli ‘Federico II', Italy 61cUniversità della Basilicata, Marconi, Italy 61dUniversità G., Italy 62aINFN Sezione di Padova, Padova, Italy 62bUniversità di Padova, Italy 62cUniversità di Trento, Italy 63aINFN Sezione di Pavia, Pavia, Italy 63bUniversità di Pavia, Italy 64aINFN Sezione di Perugia, Perugia, Italy 64bUniversità di Perugia, Italy 65aINFN Sezione di Pisa, Pisa, Italy 65bUniversità di Pisa, Italy 65cScuola Normale Superiore di Pisa, Italy 66aINFN Sezione di Roma, Roma, Italy 66bUniversità di Roma, Italy 67aINFN Sezione di Torino, Torino, Italy 67bUniversità di Torino, Italy 67cUniversità del Piemonte Orientale, Italy 68aINFN Sezione di Trieste, Trieste, Italy 68bUniversità di Trieste, Italy 69Kangwon National University, Chunchon, Korea 70Kyungpook National University, Daegu, Korea 71Chonnam National University, Institute for Universe, Elementary, Particles, Kwangju, Korea 72Korea University, Seoul, Korea 73University of Seoul, Korea 74Sungkyunkwan University, Suwon, Korea 75Vilnius University, Vilnius, Lithuania 76National Centre for Particle Physics, Universiti, Malaya, Kuala, Lumpur, de Estudios Avanzados del IPN, Malaysia 77Centro de Investigacion y., Mexico, City, Mexico 78Universidad Iberoamericana, Mexico 79Benemerita Universidad Autonoma de Puebla, Puebla, Mexico 80Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico 81University of Auckland, Auckland, New Zealand 82University of Canterbury, Christchurch, New Zealand 83National Centre for Physics, Azam University, Quaid I., Islamabad, Pakistan 84National Centre for Nuclear Research, Swierk, Poland 85Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Física Experimental de Partículas, Poland 86Laboratório de Instrumentação e., Lisboa, Portugal 87Joint Institute for Nuclear Research, Dubna, Russia 88Petersburg Nuclear Physics Institute, Gatchina, Russia 89Institute for Nuclear Research, Moscow, Russia 90Institute for Theoretical, Experimental, Physics, Lebedev Physical Institute, Russia 9. 1. P. N., Russia 92Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Russia 93State Research Center of Russian Federation, Institute for High Energy Physics, Protvino, Russia 94University of Belgrade, Vinca Institute of Nuclear Sciences, Belgrade, Tecnológicas, Serbia 95Centro de Investigaciones Energéticas Medioambientales y., Madrid, Spain 96Universidad Autónoma de Madrid, Spain 97Universidad de Oviedo, Oviedo, Spain 98Instituto de Física de Cantabria, CSIC–Universidad de Cantabria, Santander, Spain, 99cern, European Organization for Nuclear Research, Geneva, Switzerland 100Paul Scherrer Institut, Villigen, Switzerland 101Institute for Particle Physics, Eth, Zurich, Zurich, Switzerland 102Universität Zürich, Switzerland 103National Central University, Chung, Li, Taiwan 104National Taiwan University, Taipei, Taiwan 105Chulalongkorn University, Bangkok, Thailand 106Cukurova University, Adana, Turkey 107Middle East Technical University, Physics, Department, Ankara, Turkey 108Bogazici University, Istanbul, Turkey 109Istanbul Technical University, Turkey 110National Scientific Center, Kharkov Institute of Physics, Technology, Kharkov, Ukraine 111University of Bristol, Bristol, United Kingdom 112Rutherford Appleton Laboratory, Didcot, United Kingdom 113Imperial College, London, United Kingdom 114Brunel University, Uxbridge, United Kingdom 115Baylor University, Waco, Texas, USA 116The University of Alabama, Tuscaloosa, Alabama, USA 117Boston University, Boston, Massachusetts, USA 118Brown University, Providence, Rhode, Island, USA 119University of California, Davis, Davis, California, USA 120University of California, Los, Angeles, USA 121University of California, Riverside, Riverside, USA 122University of California, San, Diego, Jolla, La, USA 123University of California, Santa, Barbara, USA 124California Institute of Technology, Pasadena, California, USA 125Carnegie Mellon University, Pittsburgh, Pennsylvania, USA 126University of Colorado at Boulder, Boulder, Colorado, USA 127Cornell University, Ithaca, New, York, USA 128Fairfield University, Fairfield, Connecticut, USA 129Fermi National Accelerator Laboratory, Batavia, Illinois, USA 130University of Florida, Gainesville, Florida, USA 131Florida International University, Miami, Florida, USA 132Florida State University, Tallahassee, Florida, USA 133Florida Institute of Technology, Melbourne, Florida, USA 134University of Illinois at Chicago, Chicago, Illinois USA 135The University of Iowa, Iowa, City, Iowa, USA 136Johns Hopkins University, Baltimore, Maryland, USA 137The University of Kansas, Lawrence, Kansas, USA 138Kansas State University, Manhattan, Kansas, USA 139Lawrence Livermore National Laboratory, Livermore, California, USA 140University of Maryland, College, Park, Maryland, USA 141Massachusetts Institute of Technology, Cambridge, Massachusetts, USA 142University of Minnesota, Minneapolis, Minnesota, USA 143University of Mississippi, Oxford, Mississippi, USA 144University of Nebraska Lincoln, Lincoln, Nebraska, USA 145State University of New York at Buffalo, Buffalo, New York USA 146Northeastern University, USA 147Northwestern University, Evanston, Illinois, USA 148University of Notre Dame, Notre, Dame, Indiana, USA 149The Ohio State University, Columbus, Ohio, USA 150Princeton University, Princeton, New, Jersey, USA 151University of Puerto Rico, Mayaguez, Puerto, Rico, USA 152Purdue University, West, Lafayette, USA 153Purdue University Calumet, Hammond, Indiana, USA 154Rice University, Houston, Texas, USA 155University of Rochester, Rochester, USA 156The Rockefeller University, Usa, 157rutgers, The State University of New Jersey, Piscataway, USA 158University of Tennessee, Knoxville, Tennessee, USA 159Texas, A, University, M., College, Station, Texas, USA 160Texas Tech University, Lubbock, Texas, USA 161Vanderbilt University, Nashville, Tennessee, USA 162University of Virginia, Charlottesville, Virginia, USA 163Wayne State University, Detroit, Michigan, USA 164University of Wisconsin, Madison, Wisconsin, USA aVienna University of Technology, Vienna, Austria, Bcern, Switzerland cInstitut Pluridisciplinaire Hubert Curien, France dNational Institute of Chemical Physics, Estonia eSkobeltsyn Institute of Nuclear Physics, Russia fUniversidade Estadual de Campinas, Campinas, Brazil gCalifornia Institute of Technology, USA hLaboratoire Leprince Ringuet, France iZewail City of Science, Technology, Zewail, Egypt jSuez University, Suez, Egypt kBritish University in Egypt, Egypt lCairo University, Egypt mFayoum University, Fayoum, El, Egypt nBritish University in Egypt, Cairo, Egypt, Ain Shams University, Egypt oUniversité de Haute Alsace, Mulhouse, France pJoint Institute for Nuclear Research, Russia qBrandenburg University of Technology, Cottbus, Germany rThe University of Kansas, USA sInstitute of Nuclear Research ATOMKI, Hungary tEötvös Loránd University, Hungary uKing Abdulaziz University, Jeddah, Saudi Arabia vUniversity of Visva Bharati, Santiniketan, India wUniversity of Ruhuna, Matara, Sri Lanka xIsfahan University of Technology, Isfahan, Iran ySharif University of Technology, Iran zPlasma Physics Research Center, Science, Research, Branch, Islamic Azad University, Iran aaLaboratori Nazionali di Legnaro dell'INFN, Legnaro, Italy bbUniversità degli Studi di Siena, Siena, Italy ccCentre National de la Recherche Scientifique IN2P3, Paris, France ddPurdue University, USA eeUniversidad Michoacana de San Nicolas de Hidalgo, Morelia, Mexico ffInstitute for Nuclear Research, Petersburg State Polytechnical University, Russia g. g. S. t., Petersburg, S. t., Russia hhFaculty of Physics, University of Belgrade, Serbia iiFacoltà Ingegneria, Università di Roma, Sezione dell'INFN, Italy jjScuola Normale e., Italy kkUniversity of Athens, Greece llPaul Scherrer Institut, Switzerland mmInstitute for Theoretical, Russia nnDeceased ooAlbert Einstein Center for Fundamental Physics, Bern, Switzerland ppGaziosmanpasa University, Tokat, Turkey qqAdiyaman University, Adiyaman, Turkey rrCag University, Mersin, Turkey ssMersin University, Turkey ttIzmir Institute of Technology, Izmir, Turkey uuOzyegin University, Turkey vvKafkas University, Kars, Turkey wwIstanbul University, Faculty of Science, Turkey xxMimar Sinan University, Istanbul, Istanbul, Turkey yyKahramanmaras Sütcü Imam University, Kahramanmaras, Turkey zzRutherford Appleton Laboratory, United Kingdom aaaSchool of Physics, Astronomy, University of Southampton, Southampton, United Kingdom bbbINFN Sezione di Perugia, Università di Perugia, Italy cccUtah Valley University, Orem, Utah, USA dddUniversity of Belgrade, Serbia eeeArgonne National Laboratory, Lemont, Illinois, USA fffErzincan University, Erzincan, Turkey gggYildiz Technical University, Turkey hhhTexas, A., University at Qatar, M., Doha, Qatar iiiKyungpook National University, and Daegu, Korea
- Subjects
ENERGY ,Science & Technology ,Physics, Nuclear ,Energy ,Physics ,Astrophysics::High Energy Astrophysical Phenomena ,Physical Sciences ,High Energy Physics::Experiment ,Nuclear Experiment - Abstract
The jet fragmentation function of inclusive jets with transverse momentum pT above 100GeV/c in PbPb collisions has been measured using reconstructed charged particles with pT above 1GeV/c in a cone of radius 0.3 around the jet axis. A data sample of PbPb collisions collected in 2011 at a nucleon-nucleon center-of-mass energy of sNN=2.76TeV corresponding to an integrated luminosity of 150μb-1 is used. The results for PbPb collisions as a function of collision centrality and jet transverse momentum are compared to reference distributions based on pp data collected at the same center-of-mass energy in 2013, with an integrated luminosity of 5.3pb-1. A centrality-dependent modification of the fragmentation function is found. For the most central collisions, a significant enhancement is observed in the PbPb/pp fragmentation function ratio for charged particles with pT less than 3GeV/c. This enhancement is observed for all jet pT bins studied. © 2014 CERN, for the CMS Collaboration.
- Published
- 2014
4. Self-assembly of magnetic biofunctional nanoparticles
- Author
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Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, Michigan 48109, Chemistry Department, Penn State University, University Park, Pennsylvania 16802, Center for Materials for Information Technology, The University of Alabama, Tuscaloosa, Alabama 35487, Sun, Xiangcheng, Thode, C. J., Mabry, J. K., Harrell, J. W., Nikles, D. E., Sun, K., Wang, L. M., Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, Michigan 48109, Chemistry Department, Penn State University, University Park, Pennsylvania 16802, Center for Materials for Information Technology, The University of Alabama, Tuscaloosa, Alabama 35487, Sun, Xiangcheng, Thode, C. J., Mabry, J. K., Harrell, J. W., Nikles, D. E., Sun, K., and Wang, L. M.
- Abstract
Spherical, ferromagnetic FePt nanoparticles with a particle size of 3 nm were prepared by the simultaneous polyol reduction of Fe(acac)3Fe(acac)3 and Pt(acac)2Pt(acac)2 in phenyl ether in the presence of oleic acid and oleylamine. The oleic acid ligands can be replaced with 11-mercaptoundecanoic acid, giving particles that can be dispersed in water. Both x-ray diffraction and transmission electron microscopy indicated that FePt particles were not affected by ligands replacement. Dispersions of the FePt particles with 11-mercaptoundecanoic acid ligands and ammonium counter ions gave self-assembled films consisting of highly ordered hexagonal arrays of particles.
- Published
- 2011
5. Police stress and teacher stress at work and at home
- Author
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Center for Political Studies University of Michigan Ann Arbor, Michigan 48109, USA, Department of Criminal Justice University of Alabama Tuscaloosa, Alabama 35487-0320, USA, Sigler, Robert T., Wilson, Charles Norton, Allen, Zack, Center for Political Studies University of Michigan Ann Arbor, Michigan 48109, USA, Department of Criminal Justice University of Alabama Tuscaloosa, Alabama 35487-0320, USA, Sigler, Robert T., Wilson, Charles Norton, and Allen, Zack
- Abstract
This study compared police officers and teachers in three communities--which varied in size, geographical location, and economic base--for differences in perceived occupational stress and for differences in the patterns of perceived job stress, perceived nonjob stress, and both perceived job and life stressors. For police officers, higher levels of job stress were associated with higher levels on measures of perceived job stressors. This relationship varied from city to city, with the relationship holding for the city in which both police operations and school operations were relatively normal, with no difference between police and teachers in the city in which the school administration was in conflict with its employees, and with a reversal in the city in which the police administration was noted for its excellent management skills.
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- 2006
6. Object-oriented expert system control of flotation plants.
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Ynchausti R.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Hales L.B., Ynchausti R.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Hales L.B.
- Abstract
Higher levels of abstraction, generalisation and classification are benefits of an object-oriented approach. Object-oriented programming, design and expert systems are part of this technology which is shaping the future of real-time supervisory control. Goals of real-time supervisory control systems include: minimising variations in process operations, improving efficiency, maximising production and implementation of procedures for starting and stopping the process. Incorporating object-oriented technology in design, development and implementation of real-time supervisory control systems provides new opportunities for these tasks. Some of the opportunities and challenges are discussed in relation to the implementation of real-time supervisory control using an object-oriented expert system for a rougher flotation circuit., Higher levels of abstraction, generalisation and classification are benefits of an object-oriented approach. Object-oriented programming, design and expert systems are part of this technology which is shaping the future of real-time supervisory control. Goals of real-time supervisory control systems include: minimising variations in process operations, improving efficiency, maximising production and implementation of procedures for starting and stopping the process. Incorporating object-oriented technology in design, development and implementation of real-time supervisory control systems provides new opportunities for these tasks. Some of the opportunities and challenges are discussed in relation to the implementation of real-time supervisory control using an object-oriented expert system for a rougher flotation circuit.
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- 1993
7. Bridging the gap between plant management and process control.
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Bascur O.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Bascur O.A., and Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93
- Abstract
Emerging information technologies allow the gap between management business objectives and the process control level to be narrowed. Benefits come from enhanced energy management, operating costs reduction, enhanced scheduling of activities, reduced quality giveaway, enhanced market driven production and enhanced decision making. Plant management systems integrate performance monitoring by controlling the business rather than just throughput and product quality. Sound business based decisions about plant operations can be achieved by adequate dynamic performance monitoring at the plant level and enforcement of the desired targets at the process control level. Some of the key requirements to implement a process management system to meet stricter process performance standards are described. Extensions are made to facilitate conforming with regulations established by various governmental and industry monitoring agencies., Emerging information technologies allow the gap between management business objectives and the process control level to be narrowed. Benefits come from enhanced energy management, operating costs reduction, enhanced scheduling of activities, reduced quality giveaway, enhanced market driven production and enhanced decision making. Plant management systems integrate performance monitoring by controlling the business rather than just throughput and product quality. Sound business based decisions about plant operations can be achieved by adequate dynamic performance monitoring at the plant level and enforcement of the desired targets at the process control level. Some of the key requirements to implement a process management system to meet stricter process performance standards are described. Extensions are made to facilitate conforming with regulations established by various governmental and industry monitoring agencies.
- Published
- 1993
8. Real-time process control with fuzzy logic and genetic algorithms.
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Karr C.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Karr C.L., and Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93
- Abstract
Researchers at the US Bureau of Mines have developed a technique for producing adaptive control systems using fuzzy logic and genetic algorithms. These controllers can be used to efficiently manipulate complex systems in real-time. Furthermore, they can acheive an acceptable level of control despite limited feedback in the problem environment, evaluate the magnitude of these changes and alter the strategy being used to control the problem environment. Fuzzy logic is used to provide the controller with a rule-base that mimics the "rule-of-thumb" approach generally adopted by humans in the area of process control. Genetic algorithms are used to provide the controller with the adaptive capabilities necessary for the control of complex time-varying processes popular in the mineral processing industry. The effectiveness of this technique is demonstrated on a laboratory pH titration system. The pH system includes a changing setpoint, titrants with varing concentrations and the possible addition of a buffering solution., Researchers at the US Bureau of Mines have developed a technique for producing adaptive control systems using fuzzy logic and genetic algorithms. These controllers can be used to efficiently manipulate complex systems in real-time. Furthermore, they can acheive an acceptable level of control despite limited feedback in the problem environment, evaluate the magnitude of these changes and alter the strategy being used to control the problem environment. Fuzzy logic is used to provide the controller with a rule-base that mimics the "rule-of-thumb" approach generally adopted by humans in the area of process control. Genetic algorithms are used to provide the controller with the adaptive capabilities necessary for the control of complex time-varying processes popular in the mineral processing industry. The effectiveness of this technique is demonstrated on a laboratory pH titration system. The pH system includes a changing setpoint, titrants with varing concentrations and the possible addition of a buffering solution.
- Published
- 1993
9. Predictive control of flotation plants.
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Hodouin D., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Bazin C., Flament F., Hodouin D., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Bazin C., and Flament F.
- Abstract
A control stategy is proposed to meet a number of specifications for flotation control: to minimise the variations of concentrate and tails grades and recovery while limiting the amplitude variations of the manipulated variables. The strategy consists of determining future control actions which optimise performance criterion over a limited time horizon. The desired dynamic behaviour of the controlled flotation plant is specified using reference models. The strategy was tested on a 2-computer plant simulator working in an environment which emulates the industrial conditions., A control stategy is proposed to meet a number of specifications for flotation control: to minimise the variations of concentrate and tails grades and recovery while limiting the amplitude variations of the manipulated variables. The strategy consists of determining future control actions which optimise performance criterion over a limited time horizon. The desired dynamic behaviour of the controlled flotation plant is specified using reference models. The strategy was tested on a 2-computer plant simulator working in an environment which emulates the industrial conditions.
- Published
- 1993
10. A vision for knowledge engineering of mineral process systems.
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Sastry K.V.S., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Dontula P., Sastry K.V.S., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Dontula P.
- Abstract
Advances are made on a continual basis in the field of mineral processing both in the industrial sector and academia. These advances are not always fully utilised because of the inherent difficulties in the transfer of knowledge and reduction to practice. In recent years, knowledge engineering is being increasingly employed in other fields to overcome some of these difficulties. A description is presented of how knowledge engineering can be applied to the benefit of the mineral processing industry. A discussion of associated principles, techniques and advantages is included., Advances are made on a continual basis in the field of mineral processing both in the industrial sector and academia. These advances are not always fully utilised because of the inherent difficulties in the transfer of knowledge and reduction to practice. In recent years, knowledge engineering is being increasingly employed in other fields to overcome some of these difficulties. A description is presented of how knowledge engineering can be applied to the benefit of the mineral processing industry. A discussion of associated principles, techniques and advantages is included.
- Published
- 1993
11. The development of optical sensors for coal processing applications.
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Meenan G.F., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Oblad H.B., Meenan G.F., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Oblad H.B.
- Abstract
Two optoelectronic detectors were developed by CONSOL R&D for fine coal processing applications. One sensing device is used to estimate the amount of fine coal in the solids fraction of the froth flotation tailings slurry. The other, based on similar technology, is capable of measuring solids concentrations as high as 10 wt% in fine coal slurry streams. These patented optoelectronic sensors are installed in several CONSOL coal preparation plants and are an integral part of the fine-coal control systems., Two optoelectronic detectors were developed by CONSOL R&D for fine coal processing applications. One sensing device is used to estimate the amount of fine coal in the solids fraction of the froth flotation tailings slurry. The other, based on similar technology, is capable of measuring solids concentrations as high as 10 wt% in fine coal slurry streams. These patented optoelectronic sensors are installed in several CONSOL coal preparation plants and are an integral part of the fine-coal control systems.
- Published
- 1993
12. PC usage in the metal processing industry.
- Author
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Whittle S.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Cook R.E., Whittle S.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Cook R.E.
- Abstract
Personal computers are now used extensively throughout the process industry. Their use runs from low end data acqusition systems to high performance windows into the process area. Their applications range from CAD, CAM, CIM and CAE to factory floor scheduling, process planning quality control, facility management, cell control and publishing. A discussion is presented which traces the evolution of a zinc plant's control automation program beginning with a PC as an operator interface and migrating to higher level work stations. Discussion of an Open Industrial System and the role PCs have played in this facility is also included., Personal computers are now used extensively throughout the process industry. Their use runs from low end data acqusition systems to high performance windows into the process area. Their applications range from CAD, CAM, CIM and CAE to factory floor scheduling, process planning quality control, facility management, cell control and publishing. A discussion is presented which traces the evolution of a zinc plant's control automation program beginning with a PC as an operator interface and migrating to higher level work stations. Discussion of an Open Industrial System and the role PCs have played in this facility is also included.
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- 1993
13. Potentiometric and voltammetric sensors for monitoring flotation pulps.
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Zhou R., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Chander S., Zhou R., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Chander S.
- Abstract
The advantages and disadvantages of various techniques available for controlling reagents in mineral flotation systems are discussed. Emphasis is placed on potentiometric and voltammetric sensors for monitoring flotation pulps. Different methods of increasing the sensitivity of detection and of facilitating on-stream monitoring of solution species are also discussed. The potentiometric sensors are simple to use, but are limited by the availability of an electrode reversible to the species to be monitored. In some case the response signal could be unreliable due to the presence of interfering species. In comparison, voltammetric sensors require a higher degree of instrumentation which provides an opportunity to decrease interferences and increase levels of detection., The advantages and disadvantages of various techniques available for controlling reagents in mineral flotation systems are discussed. Emphasis is placed on potentiometric and voltammetric sensors for monitoring flotation pulps. Different methods of increasing the sensitivity of detection and of facilitating on-stream monitoring of solution species are also discussed. The potentiometric sensors are simple to use, but are limited by the availability of an electrode reversible to the species to be monitored. In some case the response signal could be unreliable due to the presence of interfering species. In comparison, voltammetric sensors require a higher degree of instrumentation which provides an opportunity to decrease interferences and increase levels of detection.
- Published
- 1993
14. Neural network coupled acoustic emission sensors for rock grinding and drilling.
- Author
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Nichols T.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Jung S.J., Prisbrey K., Wu G.L., Nichols T.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Jung S.J., Prisbrey K., and Wu G.L.
- Abstract
A project to evaluate the on-line detection of rock properties through acoustic emission sensors is described. These sensors, when attached to crushers, grinders and roof bolters, are robust, economical and relatively maintenance free. The objective of the study was to relate acoustic emissions to rock fracture mechanisms, including microprobe closures, linear elastic deformations and crack propagation. Acoustic emissions were evaluated from steel jacketed rock cores during indentation tests. In addition sensors were attached to the rotating shaft of a rock bolter drill. A back propagation neural network "learned" rock fracture characteristics and rock types with 99% classification accuracy. A neural network's calibration and comutational abilities suggest better acoustic emission sensor capabilities., A project to evaluate the on-line detection of rock properties through acoustic emission sensors is described. These sensors, when attached to crushers, grinders and roof bolters, are robust, economical and relatively maintenance free. The objective of the study was to relate acoustic emissions to rock fracture mechanisms, including microprobe closures, linear elastic deformations and crack propagation. Acoustic emissions were evaluated from steel jacketed rock cores during indentation tests. In addition sensors were attached to the rotating shaft of a rock bolter drill. A back propagation neural network "learned" rock fracture characteristics and rock types with 99% classification accuracy. A neural network's calibration and comutational abilities suggest better acoustic emission sensor capabilities.
- Published
- 1993
15. Evaluation of a PC image-based on-line coarse particle size analyser.
- Author
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Lin C.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Miller J.D., Yen Y.K., Lin C.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Miller J.D., and Yen Y.K.
- Abstract
A PC image-based, on-line coarse particle size analysis system is being developed for the control of crushing and grinding circuits. Initial results indicated that the system is accurate when tested with irregularly shaped particles which are stationary and do not overlap with each other. The system has now been successfully tested in the dynamic mode with a moving belt conveyor. On-line size analysis for actual plant operations is discussed and includes: the video system with respect to lighting and data acquisition requirements for a rapidly moving belt; and image sampling, specifically the number of observations needed for accurate sampling and the total time required to perform the calculations for a representative data set., A PC image-based, on-line coarse particle size analysis system is being developed for the control of crushing and grinding circuits. Initial results indicated that the system is accurate when tested with irregularly shaped particles which are stationary and do not overlap with each other. The system has now been successfully tested in the dynamic mode with a moving belt conveyor. On-line size analysis for actual plant operations is discussed and includes: the video system with respect to lighting and data acquisition requirements for a rapidly moving belt; and image sampling, specifically the number of observations needed for accurate sampling and the total time required to perform the calculations for a representative data set.
- Published
- 1993
16. Autonomous excavation of fragmented rock using machine vision.
- Author
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Qiang Ji, Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Sanford R.L., Qiang Ji, Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Sanford R.L.
- Abstract
A laboratory scale microcomputer-based autonomous excavation system has been developed that moves a pile of fragmented rock to a designated location using a shovel attachment on a robot arm. Navigation and excavation subsystems interactively direct a robot excavator to move the shovel to the rock pile, intelligently load its bucket using a video camera and dump the rocks to a designated area. Although research was conducted on a laboratory scale model, the system can serve as the basis for further work on a full-scale, robust excavation system., A laboratory scale microcomputer-based autonomous excavation system has been developed that moves a pile of fragmented rock to a designated location using a shovel attachment on a robot arm. Navigation and excavation subsystems interactively direct a robot excavator to move the shovel to the rock pile, intelligently load its bucket using a video camera and dump the rocks to a designated area. Although research was conducted on a laboratory scale model, the system can serve as the basis for further work on a full-scale, robust excavation system.
- Published
- 1993
17. Automatically controlled continuous mining machines, computer simulation of control system performance.
- Author
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Nelson M.G., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Nelson M.G., and Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93
- Abstract
A computer simulation of the automatic control of continuous mining machines has been prepared, similar to previous simulations of longwall mining systems and coal thickness sensors. The simulation allows for programmed variation of the horizon detection method, the azimuthal guidance system, the type and configuration of the miner, varying hardness in the unmined face material (leading to varying error in the machine heading) and the miner's cutting cycle. It simulates control of the miner in three axes. Use of the simulator makes it possible to anticipate problems arising from the complex character of underground mining, which include unpredictable multi-pass coupling and unpredictable variation in geological conditions., A computer simulation of the automatic control of continuous mining machines has been prepared, similar to previous simulations of longwall mining systems and coal thickness sensors. The simulation allows for programmed variation of the horizon detection method, the azimuthal guidance system, the type and configuration of the miner, varying hardness in the unmined face material (leading to varying error in the machine heading) and the miner's cutting cycle. It simulates control of the miner in three axes. Use of the simulator makes it possible to anticipate problems arising from the complex character of underground mining, which include unpredictable multi-pass coupling and unpredictable variation in geological conditions.
- Published
- 1993
18. Graphical simulation of continuous miner production systems.
- Author
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Zhao R., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Suboleski S.C., Zhao R., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Suboleski S.C.
- Abstract
A simulation/animation model (SAM) with several unique features has been developed for continuous mining in room-and pillar production systems. Initial cut data is generated by the program based on a short list of general section parameters and is graphically displayed on the screen for checking and editing. This portion of the program includes several intelligent features. For example, a different cut sequence will be generated for uni-directional section ventilation than for bi-directional flow. Once the data are accepted, the entire simulation may be viewed in animation. Effectively, much of the data are checked visually by watching the actions of the mining equipment. Following the simulation, results are summarised as bar and pie charts, as well as printed output. In addition to the extensive use of graphics, the program contains several advances in simulation techniques., A simulation/animation model (SAM) with several unique features has been developed for continuous mining in room-and pillar production systems. Initial cut data is generated by the program based on a short list of general section parameters and is graphically displayed on the screen for checking and editing. This portion of the program includes several intelligent features. For example, a different cut sequence will be generated for uni-directional section ventilation than for bi-directional flow. Once the data are accepted, the entire simulation may be viewed in animation. Effectively, much of the data are checked visually by watching the actions of the mining equipment. Following the simulation, results are summarised as bar and pie charts, as well as printed output. In addition to the extensive use of graphics, the program contains several advances in simulation techniques.
- Published
- 1993
19. Applications of neural networks in the automotive industry.
- Author
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Feldkamp L.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Davis L.I., Puskorius G.V., Yuan F., Feldkamp L.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Davis L.I., Puskorius G.V., and Yuan F.
- Abstract
Emerging technologies such as neural networks and fuzzy logic have many potential industrial applications in diagnostics, modelling and control. In the automotive industry, applications can be divided into those that impact manufacturing processes and those that relate directly to the product. The use of neural networks for modelling and control is described using a simulation of vehicle active suspension as an example., Emerging technologies such as neural networks and fuzzy logic have many potential industrial applications in diagnostics, modelling and control. In the automotive industry, applications can be divided into those that impact manufacturing processes and those that relate directly to the product. The use of neural networks for modelling and control is described using a simulation of vehicle active suspension as an example.
- Published
- 1993
20. The future use of computers in the mineral industry.
- Author
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Meloy T.P., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Castelli E., Williams M.C., Meloy T.P., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Castelli E., and Williams M.C.
- Abstract
Predictions of possible effective and economic applications of computer technology in the minerals industry are presented. The problems of using the emerging computer technology in the minerals industry are explored. Suggestions are also made on how the minerals industry can prepare itself for the computer revolution., Predictions of possible effective and economic applications of computer technology in the minerals industry are presented. The problems of using the emerging computer technology in the minerals industry are explored. Suggestions are also made on how the minerals industry can prepare itself for the computer revolution.
- Published
- 1993
21. Phosphate analysis by optical image processing: sensor development and in-plant testing.
- Author
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Adel G.T., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Chandler M.A., Gutierrez E.C., Richardson J.N., Yoon R.H., Adel G.T., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Chandler M.A., Gutierrez E.C., Richardson J.N., and Yoon R.H.
- Abstract
A rapid bench-top analyser has been developed to determine the P2O5 content and the CaO/P2O5 ratio in phosphate ores. The analyser utilises a portable computer equipped with a frame grabber to measure the reflective intensity (grey level) of phosphate samples placed in front of a television camera. P2O5 content is related to the average grey level of the sample, while the CaO/P2O5 ratio is determined from the amount of bright material present. The sensor is currently being used to analyse flotation concentrate samples at Texasgulf's phosphate operation near Aurora, North Carolina. Sensor development along with results from laboratory and in-plant tasting are discussed., A rapid bench-top analyser has been developed to determine the P2O5 content and the CaO/P2O5 ratio in phosphate ores. The analyser utilises a portable computer equipped with a frame grabber to measure the reflective intensity (grey level) of phosphate samples placed in front of a television camera. P2O5 content is related to the average grey level of the sample, while the CaO/P2O5 ratio is determined from the amount of bright material present. The sensor is currently being used to analyse flotation concentrate samples at Texasgulf's phosphate operation near Aurora, North Carolina. Sensor development along with results from laboratory and in-plant tasting are discussed.
- Published
- 1993
22. Principal components factor analysis in mineral processing.
- Author
-
Stanley D.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Harris J., Meredith D.L., Stanley D.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Harris J., and Meredith D.L.
- Abstract
Principal components analysis (PCA) is a versatile, multivariate statistical method that the US Bureau of Mines is applying to mineral processing data to achieve data simplification and pattern recognition. The principal components are obtained from an eigenanalysis of the data matrix. To achieve data simplification PCA replaces a large number of measured variables with a smaller set of principal components and removes a significant amount of the experimental error. Data simplification and error reduction make PCA useful in process control. Plots of the individual principal components form the basis of a pattern recognition technique that allows detection of significant variations in the data structure. These applications of PCA are illustrated with appropriate mineral processing data., Principal components analysis (PCA) is a versatile, multivariate statistical method that the US Bureau of Mines is applying to mineral processing data to achieve data simplification and pattern recognition. The principal components are obtained from an eigenanalysis of the data matrix. To achieve data simplification PCA replaces a large number of measured variables with a smaller set of principal components and removes a significant amount of the experimental error. Data simplification and error reduction make PCA useful in process control. Plots of the individual principal components form the basis of a pattern recognition technique that allows detection of significant variations in the data structure. These applications of PCA are illustrated with appropriate mineral processing data.
- Published
- 1993
23. Fuzzy logic and modern control: an analysis of controller performance under feedback errors.
- Author
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Ralston P.A.S., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Hahnert W.F., Stoll K.E., Ward T.L., Ralston P.A.S., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Hahnert W.F., Stoll K.E., and Ward T.L.
- Abstract
Fuzzy logic is well suited to the control of non-linear, time-varying systems and any system for which it is difficult to obtain an exact mathematical model. To design a good fuzzy controller, a thorough understanding of the desired process behaviour is required. It has been suggested that fuzzy controllers are robust. If they are, it is reasonable to explore whether or not they respond better than conventional controllers to errors in the feedback signal due to imperfect measurement or sensor degradation. It may be the case that simpler or less expensive sensors could be used in some processes. Research is presented which relies on an integrated array of computer programs that model and simulate: the process to be controlled; common feedback instrumentation; and both fuzzy logic controllers and conventional controllers. Various types of feedback sensor imperfections are imposed upon the system and the performance of the two controllers is compared. Each controller is tuned to its optimal performance in response to a setpoint change using perfect feedback measurement., Fuzzy logic is well suited to the control of non-linear, time-varying systems and any system for which it is difficult to obtain an exact mathematical model. To design a good fuzzy controller, a thorough understanding of the desired process behaviour is required. It has been suggested that fuzzy controllers are robust. If they are, it is reasonable to explore whether or not they respond better than conventional controllers to errors in the feedback signal due to imperfect measurement or sensor degradation. It may be the case that simpler or less expensive sensors could be used in some processes. Research is presented which relies on an integrated array of computer programs that model and simulate: the process to be controlled; common feedback instrumentation; and both fuzzy logic controllers and conventional controllers. Various types of feedback sensor imperfections are imposed upon the system and the performance of the two controllers is compared. Each controller is tuned to its optimal performance in response to a setpoint change using perfect feedback measurement.
- Published
- 1993
24. On-line reconciliation of mineral processing data.
- Author
-
Hodouin D., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Bazin C., Makni S., Hodouin D., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Bazin C., and Makni S.
- Abstract
Computer systems, installed in mineral processing plants, are designed to accumulate massive amounts of real-time data. Frequently these data are only partially processed because of the lack of adequate methods and the limited accuracy of mineral processing sensors. Methods are proposed to estimate missing data and reconcile sensor data on a real-time basis. They are based on dynamic extension of steady state techniques such as the node imbalance minimisation or the least squares mass balance adjustment techniques. The methods are illustrated for simulated and actual flotation data. Their upgrading ability could be used to improve monitoring, control and optimisation., Computer systems, installed in mineral processing plants, are designed to accumulate massive amounts of real-time data. Frequently these data are only partially processed because of the lack of adequate methods and the limited accuracy of mineral processing sensors. Methods are proposed to estimate missing data and reconcile sensor data on a real-time basis. They are based on dynamic extension of steady state techniques such as the node imbalance minimisation or the least squares mass balance adjustment techniques. The methods are illustrated for simulated and actual flotation data. Their upgrading ability could be used to improve monitoring, control and optimisation.
- Published
- 1993
25. Discete element modelling in mineral processing.
- Author
-
Mishra B.K., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Mishra B.K., and Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93
- Abstract
The potential merit of the discrete element method (DEM) as a numerical tool for analysing process engineering problems involving particulate material is discussed. In this method, the dynamic equilibrium equations for each distinct and deformable body are solved. Therefore, it readily lends itself to modelling large numbers of discrete, interacting bodies as in mineral processing operations where a charge of particulate material is processed. Here some applications of DEM are reported: motion of particles in the ball mill, mixer and jig. Although DEM has made very little headway in mineral processing research, it is shown that many other areas of industrial interest in the context of flow of particulate materials could be analysed by this method., The potential merit of the discrete element method (DEM) as a numerical tool for analysing process engineering problems involving particulate material is discussed. In this method, the dynamic equilibrium equations for each distinct and deformable body are solved. Therefore, it readily lends itself to modelling large numbers of discrete, interacting bodies as in mineral processing operations where a charge of particulate material is processed. Here some applications of DEM are reported: motion of particles in the ball mill, mixer and jig. Although DEM has made very little headway in mineral processing research, it is shown that many other areas of industrial interest in the context of flow of particulate materials could be analysed by this method.
- Published
- 1993
26. Application of fuzzy control techniques to a chaotic system.
- Author
-
Karr C.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Gentry E.J., Karr C.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Gentry E.J.
- Abstract
Researchers at the US Bureau of Mines have developed a control algorithm to efficiently manipulate chaotic systems. The control, based on both fuzzy logic and genetic algorithms, prescribes actions that drive the chaotic system to acceptable regions of state space. The use of fuzzy logic enables a person who has limited experience with a chaotic system to establish a set of rules for manipulating the problem environment and a set of membership functions that define terms used in the rule-set. Genetic algorithms are search algorithms based on natural genetics and are used in the control algorithm to select membership functions that provide for a suitable level of control. The Bureau-developed algorithm controls the computer simulation of a ball bouncing on an oscillating table, a system that exhibits chaotic behaviour., Researchers at the US Bureau of Mines have developed a control algorithm to efficiently manipulate chaotic systems. The control, based on both fuzzy logic and genetic algorithms, prescribes actions that drive the chaotic system to acceptable regions of state space. The use of fuzzy logic enables a person who has limited experience with a chaotic system to establish a set of rules for manipulating the problem environment and a set of membership functions that define terms used in the rule-set. Genetic algorithms are search algorithms based on natural genetics and are used in the control algorithm to select membership functions that provide for a suitable level of control. The Bureau-developed algorithm controls the computer simulation of a ball bouncing on an oscillating table, a system that exhibits chaotic behaviour.
- Published
- 1993
27. Automated surveying of mines using a laser rangefinder.
- Author
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Shaffer G., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Stentz A., Shaffer G., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Stentz A.
- Published
- 1993
28. A computer model simulating noise behaviour in the mining environment.
- Author
-
Rider J.P., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Burks J.A., Rider J.P., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Burks J.A.
- Abstract
The ability to accurately predict the sound field is essential for the estimation of probable worker noise exposure. The US Bureau of Mines is carrying out research into computer-based analytical techniques specifically geared to calculate the spatial variation of sound pressure levels within common mining environments. Initial modelling efforts describing the user interface, the simulation of energy acoustics and the graphical representation of sound pressure levels are presented. Computer software incorporating or adapting existing acoustical theory into a simulation model is being developed. Modelling efforts are emphasising portability and ease of use, with the user interface employing window technology. Input and output displays are graphically represented, providing a user with ease of use, simplified data entry and a better level of understanding and comprehension., The ability to accurately predict the sound field is essential for the estimation of probable worker noise exposure. The US Bureau of Mines is carrying out research into computer-based analytical techniques specifically geared to calculate the spatial variation of sound pressure levels within common mining environments. Initial modelling efforts describing the user interface, the simulation of energy acoustics and the graphical representation of sound pressure levels are presented. Computer software incorporating or adapting existing acoustical theory into a simulation model is being developed. Modelling efforts are emphasising portability and ease of use, with the user interface employing window technology. Input and output displays are graphically represented, providing a user with ease of use, simplified data entry and a better level of understanding and comprehension.
- Published
- 1993
29. Emerging computer techniques for the minerals industry and Noranda's commitment to excellence.
- Author
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Connell R.E., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Connell R.E., and Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93
- Abstract
A description is presented of the approaches that have been employed within the Noranda Group of Companies to ensure that they are utilising information technology effectively. The ways that information technology has been integrated or tied with other areas in the organisation, such as process control, and the kinds of people and skill sets required to support the area of information technology are discussed. The effect that information technology and automation have on streamlining and rethinking the way that business is carried out is also considered. The evolution of information technology is reviewed. The approach and the effect it has had on a diversified natural resources company in the mining and metals, forest products and oil and gas industries is discussed., A description is presented of the approaches that have been employed within the Noranda Group of Companies to ensure that they are utilising information technology effectively. The ways that information technology has been integrated or tied with other areas in the organisation, such as process control, and the kinds of people and skill sets required to support the area of information technology are discussed. The effect that information technology and automation have on streamlining and rethinking the way that business is carried out is also considered. The evolution of information technology is reviewed. The approach and the effect it has had on a diversified natural resources company in the mining and metals, forest products and oil and gas industries is discussed.
- Published
- 1993
30. Mine planning using Apple's Macintosh - overview of applications software and planning techniques.
- Author
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Sanford R.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Johnson P., Morley L.A., Novak T., Sanford R.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Johnson P., Morley L.A., and Novak T.
- Abstract
Apple's Macintosh computer had much of its significant development following the introduction of the the DOS-based PC. Current Macintosh use in mine-planning applications lags DOS machines since many users have made significant investments in hardware and training in other environments. Macintosh use is becoming more pronounced in many other industries and commercial software is of at least the quality of that available for DOS machines and many workstations. Attributes of the Macintosh computer family are described as related to many mine planning problems and solutions including software, memory considerations, workstations, DOS compatability and networking., Apple's Macintosh computer had much of its significant development following the introduction of the the DOS-based PC. Current Macintosh use in mine-planning applications lags DOS machines since many users have made significant investments in hardware and training in other environments. Macintosh use is becoming more pronounced in many other industries and commercial software is of at least the quality of that available for DOS machines and many workstations. Attributes of the Macintosh computer family are described as related to many mine planning problems and solutions including software, memory considerations, workstations, DOS compatability and networking.
- Published
- 1993
31. A futuristic look at process control in the minerals industry.
- Author
-
Hales L.B., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Ynchausti R.A., Hales L.B., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Ynchausti R.A.
- Abstract
Control systems of the future will probably develop towards more sophisticated user friendly computer based systems that use powerful software analysis and control techniques. Auto-tuning, adaptive control and process models will probably gain expanded use because of their transparent availability to the system designer. Control of individual unit operations will be optimised and coordinated through expert systems that monitor economic conditions and management directives and direct operations accordingly. Plant areas, considered cost centres for years, will probably be integrated into plant-wide control systems for economic control and optimisation. New control hardware, process sensors, control strategies and software development tools are presented, as well as an overview of the economics driving the continued development of advanced process control systems., Control systems of the future will probably develop towards more sophisticated user friendly computer based systems that use powerful software analysis and control techniques. Auto-tuning, adaptive control and process models will probably gain expanded use because of their transparent availability to the system designer. Control of individual unit operations will be optimised and coordinated through expert systems that monitor economic conditions and management directives and direct operations accordingly. Plant areas, considered cost centres for years, will probably be integrated into plant-wide control systems for economic control and optimisation. New control hardware, process sensors, control strategies and software development tools are presented, as well as an overview of the economics driving the continued development of advanced process control systems.
- Published
- 1993
32. Historical perspective on the personal computer - rampant growth! But where's it going and who's leading?
- Author
-
Gibbs R.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Gibbs R.L., and Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93
- Abstract
The history of the development of the personal computer is described. The future prospects for their development and possible standards which could be incorporated are also discussed., The history of the development of the personal computer is described. The future prospects for their development and possible standards which could be incorporated are also discussed.
- Published
- 1993
33. On-line analysis of phosphate rock slurry by prompt neutron activation technique.
- Author
-
Moudgil B.M., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Pinault J.L., Zhu S.L., Moudgil B.M., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Pinault J.L., and Zhu S.L.
- Abstract
With depletion of high grade phosphate deposits in Florida, it is necessary to develop new processes or modify the current circuits for phosphate flotation. Greater control of various streams is necessary to improve processing efficiency. This would require on-line analysis of phosphate rock slurry to provide basic information for the purposes of process control. The prompt neutron activation technique has an advantage over x-ray techniques as it can be used for the analysis of low atomic number elements. A prompt neutron activation probe was evaluated to determine the chemical composition of phosphate rock slurry in-situ. The technique allowed the analysis of Ca, Si and H using a 252Cf source. Direct measurement of P and Mg was not accurate due to interference from the rare earth elements associated with the Florida phosphate rock. The solids loading, particle size distribution and flotation reagents present in the slurry had no significant effect on the accuracy of the measurements. The technique was also found to be reliable under industrial conditions., With depletion of high grade phosphate deposits in Florida, it is necessary to develop new processes or modify the current circuits for phosphate flotation. Greater control of various streams is necessary to improve processing efficiency. This would require on-line analysis of phosphate rock slurry to provide basic information for the purposes of process control. The prompt neutron activation technique has an advantage over x-ray techniques as it can be used for the analysis of low atomic number elements. A prompt neutron activation probe was evaluated to determine the chemical composition of phosphate rock slurry in-situ. The technique allowed the analysis of Ca, Si and H using a 252Cf source. Direct measurement of P and Mg was not accurate due to interference from the rare earth elements associated with the Florida phosphate rock. The solids loading, particle size distribution and flotation reagents present in the slurry had no significant effect on the accuracy of the measurements. The technique was also found to be reliable under industrial conditions.
- Published
- 1993
34. Electrochemical control of sulphide flotation circuits.
- Author
-
Richardson P.E., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Gebhardt J.E., Rice D.A., Yoon R.H., Richardson P.E., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Gebhardt J.E., Rice D.A., and Yoon R.H.
- Abstract
An improved understanding of the significant role of electrochemical reactions in sulphide flotation has evolved in the past twenty years. This understanding has been accompanied by a corresponding increase in attempts to control the electrochemical potential of flotation pulps for monitoring and controlling sulphide flotation circuits to improve metallurgical performance. The basic electrochemistry of sulphide flotation and the use of electrochemical potential as a control parameter is discussed. Many of the problems that occur when applying electrochemical concepts to real flotation cells arise from uncontrolled oxidation during grinding and flotation, from process waters, from oxidised ores and from galvanic contacts. Better control of process waters and the electrochemical potentials of minerals during grinding and conditioning is very likely to minimise many of the reactions that are now uncontrolled and often adversely affect flotation., An improved understanding of the significant role of electrochemical reactions in sulphide flotation has evolved in the past twenty years. This understanding has been accompanied by a corresponding increase in attempts to control the electrochemical potential of flotation pulps for monitoring and controlling sulphide flotation circuits to improve metallurgical performance. The basic electrochemistry of sulphide flotation and the use of electrochemical potential as a control parameter is discussed. Many of the problems that occur when applying electrochemical concepts to real flotation cells arise from uncontrolled oxidation during grinding and flotation, from process waters, from oxidised ores and from galvanic contacts. Better control of process waters and the electrochemical potentials of minerals during grinding and conditioning is very likely to minimise many of the reactions that are now uncontrolled and often adversely affect flotation.
- Published
- 1993
35. Blast fragmentation model evaluation via image analysis.
- Author
-
Smith M.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Kemeny J.M., Smith M.L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Kemeny J.M.
- Abstract
In blasting, an optimal muckpile size distribution can be found in terms of mining and milling productivity and cost. Fragmentation control requires a means of modelling and evaluating blast results. A principal problem in the practical application of empirical blast fragmentation modelling is the evaluation of the muckpile's size distribution. Image analysis of video and photographic records of production size blasts at Cyprus's Miami and Thompson Creek mines is being used to provide data for estimating parameters relating to blast design, energy factor and rock quality. The application of image analysis to the estimation of particle size distribution is reviewed with regard to the problem of fragmentation modelling., In blasting, an optimal muckpile size distribution can be found in terms of mining and milling productivity and cost. Fragmentation control requires a means of modelling and evaluating blast results. A principal problem in the practical application of empirical blast fragmentation modelling is the evaluation of the muckpile's size distribution. Image analysis of video and photographic records of production size blasts at Cyprus's Miami and Thompson Creek mines is being used to provide data for estimating parameters relating to blast design, energy factor and rock quality. The application of image analysis to the estimation of particle size distribution is reviewed with regard to the problem of fragmentation modelling.
- Published
- 1993
36. Intelligent sensor data analysis using AI based techniques.
- Author
-
Lever P.J.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, King R.H., Lever P.J.A., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and King R.H.
- Abstract
Human operators, geological conditions, equipment changes, mining sequences and roof conditions are some of the continually changing operating conditions that drive a dynamic mining environment. These dynamic characteristics make it difficult to automatically analyse mining data because attributes change as the mining environment changes. Mine monitoring systems could provide mine managers with much more useful information than they do at present, if automatic analysis were possible. Automatic analysis of sensor data is also a key to autonomous mining systems. A description is presented of artificial intelligence (AI) and sensor fusion based sensor data analysis techniques developed over the past four years. These techniques were used to classify machine operating events by continuously monitoring machine power usage in a continuous miner section at an underground coal mine. The result is a report similar to an industrial engineering time study. First, numerical pattern classification techniques broadly identify operating events. However, domain knowledge about inter-machine and intra-machine interactions is necessary to identify and refine dynamic event boundaries. By implementing AI and sensor fusion based techniques it is possible to classify machine events more accurately and recognise more specific events. These techniques could be applied to different mining sensor data applications., Human operators, geological conditions, equipment changes, mining sequences and roof conditions are some of the continually changing operating conditions that drive a dynamic mining environment. These dynamic characteristics make it difficult to automatically analyse mining data because attributes change as the mining environment changes. Mine monitoring systems could provide mine managers with much more useful information than they do at present, if automatic analysis were possible. Automatic analysis of sensor data is also a key to autonomous mining systems. A description is presented of artificial intelligence (AI) and sensor fusion based sensor data analysis techniques developed over the past four years. These techniques were used to classify machine operating events by continuously monitoring machine power usage in a continuous miner section at an underground coal mine. The result is a report similar to an industrial engineering time study. First, numerical pattern classification techniques broadly identify operating events. However, domain knowledge about inter-machine and intra-machine interactions is necessary to identify and refine dynamic event boundaries. By implementing AI and sensor fusion based techniques it is possible to classify machine events more accurately and recognise more specific events. These techniques could be applied to different mining sensor data applications.
- Published
- 1993
37. The theory and development of a Michigan Tech/Outokumpu slurry ash analyser.
- Author
-
Kawatra S.K., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Kawatra S.K., and Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93
- Abstract
Outokumpu Electronics has designed a new on-line coal slurry ash analyser, based on an instrument developed at Michigan Technological University. The instrument determines ash content from measurements of x-ray back-scatter, iron fluorescence and low energy gamma ray absorption., Outokumpu Electronics has designed a new on-line coal slurry ash analyser, based on an instrument developed at Michigan Technological University. The instrument determines ash content from measurements of x-ray back-scatter, iron fluorescence and low energy gamma ray absorption.
- Published
- 1993
38. Designing an information management system for mineral processing plants.
- Author
-
Kuehn L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Jones D., Kosick G.A., Kuehn L., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Jones D., and Kosick G.A.
- Abstract
The design of an information management system (IMS) for a mineral processing plant requires prior consideration of many factors, which can be broadly categorised into two domains, process plant considerations and IMS considerations. The process plant considerations include: plant characteristics, management and personnel issues, existing organisational standards and policies and the level of existing hardware and software systems. The IMS considerations include: system functionality, system sizing and desired performance, hardware components, software components and system support issues. All of these aspects must be addressed in order to achieve a successful and cost effective system. These design issues are discussed and in doing so a guideline is provided for process engineers to tailor a system that is best suited to their specific application. An approach that has been used in two Canadian concentrators is also presented. Both of these plants have incorporated MillEX, a software design system designed specifically as the central components of a mineral processing IMS., The design of an information management system (IMS) for a mineral processing plant requires prior consideration of many factors, which can be broadly categorised into two domains, process plant considerations and IMS considerations. The process plant considerations include: plant characteristics, management and personnel issues, existing organisational standards and policies and the level of existing hardware and software systems. The IMS considerations include: system functionality, system sizing and desired performance, hardware components, software components and system support issues. All of these aspects must be addressed in order to achieve a successful and cost effective system. These design issues are discussed and in doing so a guideline is provided for process engineers to tailor a system that is best suited to their specific application. An approach that has been used in two Canadian concentrators is also presented. Both of these plants have incorporated MillEX, a software design system designed specifically as the central components of a mineral processing IMS.
- Published
- 1993
39. Emerging computer technology for the minerals industries.
- Author
-
Fries E.F., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Murphy J.N., Fries E.F., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Murphy J.N.
- Abstract
The availability of fast, low-cost computer hardware and advanced software is opening new opportunities for minerals extraction and processing. Programmable Logic Controllers (PLCs) are currently used extensively in the minerals industries. Future prospects for computing in the mineral processing industry is discussed. Subjects covered include: expert systems, intelligent signal processing, neural networks and hierarchical controllers as applied to mining and mineral processing operations., The availability of fast, low-cost computer hardware and advanced software is opening new opportunities for minerals extraction and processing. Programmable Logic Controllers (PLCs) are currently used extensively in the minerals industries. Future prospects for computing in the mineral processing industry is discussed. Subjects covered include: expert systems, intelligent signal processing, neural networks and hierarchical controllers as applied to mining and mineral processing operations.
- Published
- 1993
40. The use of computer spreadsheets in the mineral industry.
- Author
-
Meloy T.P., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Bevilacqua P., Williams M.C., Meloy T.P., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Bevilacqua P., and Williams M.C.
- Abstract
Speadsheets are ideally suited for engineering problems in the mineral industry. Essentially, a spreadsheet is a finite element analysis form of computer software and many mineral industry problems are readily solved by finite element element analysis techniques. The modelling of a two dimensional sieve cascadograph is described. A two dimensional sheet flotation cell and a sluice way with riffle are also modelled using the spreadsheet software., Speadsheets are ideally suited for engineering problems in the mineral industry. Essentially, a spreadsheet is a finite element analysis form of computer software and many mineral industry problems are readily solved by finite element element analysis techniques. The modelling of a two dimensional sieve cascadograph is described. A two dimensional sheet flotation cell and a sluice way with riffle are also modelled using the spreadsheet software.
- Published
- 1993
41. Genetic algorithms for controller training: effects of population size on training accuracy and efficiency.
- Author
-
Hahnert W.F., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, Ralston P.A.S., Hahnert W.F., Emerging computer techniques for the minerals industry Tuscaloosa, Alabama Feb-93Feb-93, and Ralston P.A.S.
- Abstract
In off-line training of a rule-based controller, the significant measure of successful training is the quality of control provided by the general rule set. In an adaptive or on-line control environment, performence is also measured by the ability to accurately maintain a satisfactory rule set, but within constraints on speed and/or resource availability. Very small population genetic algorithms, or microGAs, have been proposed as a means of capitalising on the hill climbing characteristics of faster local optimisation techniques while requiring less memory and retaining much of the robustness of the traditional, larger population genetic search. A traditional genetic algorithm and a similar microGA are developed and applied to two control problems. The performance of these algorithms is analysed with respect to: the quality of the rules learned; the rate at which learning occurs; and the memory resources required during learning., In off-line training of a rule-based controller, the significant measure of successful training is the quality of control provided by the general rule set. In an adaptive or on-line control environment, performence is also measured by the ability to accurately maintain a satisfactory rule set, but within constraints on speed and/or resource availability. Very small population genetic algorithms, or microGAs, have been proposed as a means of capitalising on the hill climbing characteristics of faster local optimisation techniques while requiring less memory and retaining much of the robustness of the traditional, larger population genetic search. A traditional genetic algorithm and a similar microGA are developed and applied to two control problems. The performance of these algorithms is analysed with respect to: the quality of the rules learned; the rate at which learning occurs; and the memory resources required during learning.
- Published
- 1993
42. Three photographs of wounded veterans from Northington General Hospital in Tuscaloosa, Alabama.
- Author
-
Northington PRO, Tuscaloosa, Alabama. and Northington PRO, Tuscaloosa, Alabama.
- Abstract
Top left: Nurses pushing three wounded veterans in wheelchairs, on their way to the USO club in Tuscaloosa; one of the men is from Greensboro, Alabama. Top right: Young women and wounded veterans eating a meal at the USO club in Tuscaloosa. Bottom: Wounded veterans watching a performance of Kay Kyser and Company (sponsored by the USO) at the hospital in Tuscaloosa; Kyser is on stage.
- Published
- 1943
43. Congressional Medal of Honor winner Henry E. Erwin speaking into a microphone, probably during a radio broadcast from Northington Hospital in Tuscaloosa, Alabama.
- Author
-
Northington PRO, Tuscaloosa, Alabama. and Northington PRO, Tuscaloosa, Alabama.
- Abstract
Erwin, a staff sergeant in the U.S. Army Air Corps, saved the crew of the bomber, "The City of Los Angeles, " by throwing a burning phosphoresce smoke bomb out a window of the plane.
- Published
- 1945
44. Alabama serviceman Beasley, a patient at Northington General Hospital in Tuscaloosa, Alabama.
- Author
-
Northington PRO, Tuscaloosa, Alabama. and Northington PRO, Tuscaloosa, Alabama.
- Abstract
Beasley is holding a cane.
- Published
- 1945
45. Master Sergeant Samuel Whitehead of Rogersville, Alabama.
- Author
-
Northington PRO, Tuscaloosa, Alabama. and Northington PRO, Tuscaloosa, Alabama.
- Abstract
"'They were swell.' That's what a bronze-star wearer from Northington General Hospital, Tuscaloosa, Ala., said about a couple of USO shows he got to see at the front lines in Italy. With those three words he also praised the motion pictures, games and dances financed by the War Chest. M/Sgt. Samuel Whitehead, Rogersville, Ala., is a Pre-Pearl Harbor man who served with the 10th 'Mountain' Division seven months in Italy. In April he was wounded in the jaw by rock fragments when a blocked tunnel at Lake Garda, near the Brenner Pass, was blasted by the engineers. He arrived at NGH August 12. During offensive operations in extremely difficult and mountainous terrain of the Appenines, Whitehead worked tirelessly in laying wire from rear positions to the advance command posts of the most forward organizations. At times he continued his work for more than 24 hours, laboring to keep up with the rapidly advancing front. It was this work for which he, as Chief Lineman of a construction platoon, was awarded the Bronze Star. 'I guess I sorta lost my school-girl beauty overseas,' he said. 'In civilian life I hope to use this signal corps experience in getting a telephone linesman job.'"
- Published
- 1945
46. 'Lt. Homer B. Pou, Northington General Hospital patient from Tuscaloosa, Ala., is shown with his first pay check since February 1944 - a check for $6,459!'
- Author
-
Northington PRO, Tuscaloosa, Alabama. and Northington PRO, Tuscaloosa, Alabama.
- Abstract
"'Back at our air base in England those USO units were really entertaining,' said Pou. 'Despite air raids, the show always went on.' Pou, first pilot on a B-17, was shot down March 8, 1944, by German fighters on the second large Berlin raid. Two of his crew were killed, the rest bailed out and were captured by Germans. It was thirty hours after his capture that the lieutenant received treatment for his wounded arm, which had been broken by shell fire. 'Our medical treatment was very inadequate,' said the lieutenant. 'And the food given us by the Germans consisted mostly of potatoes and bread.' Around the first of this year the prisoners were forced to march 90 kilometers from their camp, Stalag Luft No. 3, to Bavaria, where they were liberated April 28 by U.S. troops. Pou, who was in the hospital with an infection of his arm bone, was sent to an American hospital in France before traveling to the States aboard a liberty ship. He arrived at Northington June 5, 1945. 'At our P.O.W. camp we had an Alabama Club, composed of about 25 men from this state. Talking to our fellow Alabamians kept us from going off our nut.'"
- Published
- 1945
47. Private George Glenn of Holly Pond, Alabama.
- Author
-
Northington PRO, Tuscaloosa, Alabama. and Northington PRO, Tuscaloosa, Alabama.
- Abstract
"There were two Southern girls in the USO musical Infantryman Pfc. George Glenn, Holly Pond, Ala., saw when he was a patient in the 25th General Hospital in the Pacific. 'They made New Guinea seem like home,' he said. Wounded in Luzon January 25, 1945, by fragments from an artillery shell, Glenn was evacuated to New Guinea, where he had seen action the preceding summer. The battle experience that is most prominent in his memory took place at Wake Island off the northern coast of New Guinea, during June and July of 1944. His outfit, the Sixth Infantry Division, was supposed to take a well-fortified, rocky point infested with Japs dug into caves and armed with mortars and machine guns. Of the 35 men who were sent on reconnaissance patrol to size up the installation, only three returned. Glenn, who manned a machine gun, stayed at the front 38 days, during which time the Americans pushed forward 100 yards. But the persistent Yanks finally took the point after two months of constant fighting. 'We could really appreciate a USO show, movie or dance after battles like that one,' Glenn reported."
- Published
- 1945
48. Captain Herbert M. Vines of Bessemer, Alabama.
- Author
-
Northington PRO, Tuscaloosa, Alabama. and Northington PRO, Tuscaloosa, Alabama.
- Abstract
"A battery commander with the 5th Army in Italy, Capt. Herbert M. Vines, of Bessemer, now at Northington General Hospital, lost his left leg in the battle of Cassino. 'I heard the shell coming and dove under a truck. And, of course, that's where it landed. Shot most of my leg off - nothing much left - so I borrowed a knife and cut the few nerves and tendons holding it.' Capt. Vines, who wears the Silver Star for gallantry in action in Magnano, Italy, was overseas 11 months. During that time he saw several USO shows. 'They were really good,' he said, 'and there was a noticeable change in the spirits of the men after they'd seen them.'"
- Published
- 1945
49. Staff Sergeant Leonard Ozley of Birmingham, Alabama.
- Author
-
Northington PRO, Tuscaloosa, Alabama. and Northington PRO, Tuscaloosa, Alabama.
- Abstract
"'We had been in combat two months in France when they let half of us go back for a rest and for the first entertainment we had had since before D-Day. It was a USO show with only four in the cast, but don't think it didn't mean everything to us. The only trouble was, the MC had to cut the show short and announce that we were to be sent back to the front lines immediately; but it was something to remember.' S/Sgt. Leonard Ozley, Northington General Hospital patient, a Birmingham boy, wears the Purple Heart with an Oak Leaf Cluster. He was first wounded August 7 when a German machine gun sprayed his right arm. Making a rapid recovery, he was sent back to the front lines and was hit by a mortar shell December 16. At this time his skull was fractured and his right eye was put out. He was flown from France to a hospital in England and later sailed home by hospital ship. After a few months stay at William Beaumont General Hospital in El Paso, Texas, he was sent to Northington in Tuscaloosa. Ozley served with the 90th Infantry Division 10 months in the ETO. While spearheading for Patton, his battalion was isolated behind the enemy lines for two days until reinforcements were sent up. Their action made possible the capture of thousands of Nazis, along with their equipment, at Falaise Gap. Ozley, who is having his head shaved tomorrow, will have a plate put in his head soon."
- Published
- 1945
50. Student soldiers marching on the quad in front of Gorgas Library at the University of Alabama.
- Author
-
King, Yeatman, Tuscaloosa, Alabama and King, Yeatman, Tuscaloosa, Alabama
- Published
- 1941
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