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| WoS | SCOPUS | Document Type | Document Title | Abstract | Authors | Affiliation | ResearcherID (WoS) | AuthorsID (SCOPUS) | Author Email(s) | Journal Name | JCR Abbreviation | ISSN | eISSN | Volume | Issue | WoS Edition | WoS Category | JCR Year | IF | JCR (%) | FWCI | FWCI Update Date | WoS Citation | SCOPUS Citation | Keywords (WoS) | KeywordsPlus (WoS) | Keywords (SCOPUS) | KeywordsPlus (SCOPUS) | Language | Publication Stage | Publication Year | Publication Date | DOI | JCR Link | DOI Link | WOS Link | SCOPUS Link |
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| ○ | ○ | Article | Search for Supersymmetry with a Compressed Mass Spectrum in Events with a Soft τ Lepton, a Highly Energetic Jet, and Large Missing Transverse Momentum in Proton-Proton Collisions at √s=13 TeV | The first search for supersymmetry in events with an experimental signature of one soft, hadronically decaying tau lepton, one energetic jet from initial-state radiation, and large transverse momentum imbalance is presented. These event signatures are consistent with direct or indirect production of scalar tau leptons ((tau) over tilde) in supersymmetric models that exhibit coannihilation between the (tau) over tilde and the lightest neutralino ((chi) over tilde (0)(1)), and that could generate the observed relic density of dark matter. The data correspond to an integrated luminosity of 77.2 fb(-1) of proton-proton collisions at root s = 13 TeV collected with the CMS detector at the LHC in 2016 and 2017. The results are interpreted in a supersymmetric scenario with a small mass difference (Delta m) between the chargino ((chi) over tilde (+/-)(1)) or next-to-lightest neutralino ((chi) over tilde (0)(2)), and the (chi) over tilde (0)(1). The mass of the (tau) over tilde is assumed to be the average of the (chi) over tilde (1)(+/-) and (chi) over tilde (0)(1) masses. The data are consistent with standard model background predictions. Upper limits at 95% confidence level are set on the sum of the (chi) over tilde (+/-)(1). (chi) over tilde (0)(2), and (tau) over tilde production cross sections for Delta m ((chi) over tilde (+/-)(1),(chi) over tilde (0)(1)) = 50 GeV, resulting in a lower limit of 290 GeVon the mass of the (chi) over tilde (+/-)(1), which is the most stringent to date and surpasses the bounds from the LEP experiments. | Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Ambrogi, F.; Bergauer, T.; Brandstetter, J.; Dragicevic, M.; Eroe, J.; Del Valle, A. Escalante; Flechl, M.; Fruehwirth, R.; Jeitler, M.; Krammer, N.; Kraetschmer, I; Liko, D.; Madlener, T.; Mikulec, I; Rad, N.; Schieck, J.; Schoefbeck, R.; Spanring, M.; Spitzbart, D.; Waltenberger, W.; Wulz, C-E; Zarucki, M.; Drugakov, V; Mossolov, V; Gonzalez, J. Suarez; Darwish, M. R.; De Wolf, E. A.; Di Croce, D.; Janssen, X.; Lelek, A.; Pieters, M.; Sfar, H. Rejeb; Van Haevermaet, H.; Van Mechelen, P.; Van Putte, S.; Van Remortel, N.; Blekman, F.; Bols, E. S.; Chhibra, S. S.; D'Hondt, J.; De Clercq, J.; Lontkovskyi, D.; Lowette, S.; Marchesini, I; Moortgat, S.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Beghin, D.; Bilin, B.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Dorney, B.; Favart, L.; Grebenyuk, A.; Kalsi, A. K.; Popov, A.; Postiau, N.; Starling, E.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Cornelis, T.; Dobur, D.; Khvastunov, I; Niedziela, M.; Roskas, C.; Tytgat, M.; Verbeke, W.; Vermassen, B.; Vit, M.; Bondu, O.; Bruno, G.; Caputo, C.; David, P.; Delaere, C.; Delcourt, M.; Giammanco, A.; Lemaitre, V; Prisciandaro, J.; Saggio, A.; Marono, M. Vidal; Vischia, P.; Zobec, J.; Alves, F. L.; Alves, G. A.; Correia Silva, G.; Hensel, C.; Moraes, A.; Rebello Teles, P.; Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Coelho, E.; Da Costa, E. M.; Da Silveira, G. G.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Huertas Guativa, L. M.; Malbouisson, H.; Martins, J.; Matos Figueiredo, D.; Medina Jaime, M.; Melo De Almeida, M.; Mora Herrera, C.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Sanchez Rosas, L. J.; Santoro, A.; Sznajder, A.; Thiel, M.; Tonelli Manganote, E. J.; Torres Da Silva De Araujo, F.; Vilela Pereira, A.; Bernardes, C. A.; Calligaris, L.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Lemos, D. S.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Aleksandrov, A.; Antchev, G.; Hadjiiska, R.; Iaydjiev, P.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.; Bonchev, M.; Dimitrov, A.; Ivanov, T.; Litov, L.; Pavlov, B.; Petkov, P.; Fang, W.; Gao, X.; Yuan, L.; Chen, G. M.; Chen, H. S.; Chen, M.; Jiang, C. H.; Leggat, D.; Liao, H.; Liu, Z.; Spiezia, A.; Tao, J.; Yazgan, E.; Zhang, H.; Zhang, S.; Zhao, J.; Agapitos, A.; Ban, Y.; Chen, G.; Levin, A.; Li, J.; Li, L.; Li, Q.; Mao, Y.; Qian, S. J.; Wang, D.; Wang, Q.; Ahmad, M.; Hu, Z.; Wang, Y.; Xiao, M.; Avila, C.; Cabrera, A.; Florez, C.; Gonzalez Hernandez, C. F.; Segura Delgado, M. A.; Mejia Guisao, J.; Ruiz Alvarez, J. D.; Salazar Gonzalez, C. 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H.; Teague, D.; Trembath-reichert, S.; Woods, N. | Yerevan Phys Inst, Yerevan, Armenia; Inst Hochenergiephys, Vienna, Austria; Inst Nucl Problems, Minsk, BELARUS; Univ Antwerp, Antwerp, Belgium; Vrije Univ Brussel, Brussels, Belgium; Univ Libre Bruxelles, Brussels, Belgium; Univ Ghent, Ghent, Belgium; Catholic Univ Louvain, Louvain La Neuve, Belgium; Ctr Brasileiro Pesquisas Fis, Rio De Janeiro, Brazil; Univ Estado Rio de Janeiro, Rio De Janeiro, Brazil; Univ Estadual Paulista, Sao Paulo, Brazil; Univ Fed ABC, Sao Paulo, Brazil; Bulgarian Acad Sci, Inst Nucl Res & Nucl Energy, Sofia, Bulgaria; Univ Sofia, Sofia, Bulgaria; Beihang Univ, Beijing, Peoples R China; Inst High Energy Phys, Beijing, Peoples R China; Peking Univ, State Key Lab Nucl Phys & Technol, Beijing, Peoples R China; Tsinghua Univ, Beijing, Peoples R China; Zhejiang Univ, Hangzhou, Peoples R China; Univ Los Andes, Bogota, Colombia; Univ Antioquia, Medellin, Colombia; Univ Split, Fac Elect Engn Mech Engn & Naval Architecture, Split, Croatia; Univ Split, Fac Sci, Split, Croatia; Inst Rudjer Boskovic, Zagreb, Croatia; Univ Cyprus, Nicosia, Cyprus; Charles Univ Prague, Prague, Czech Republic; Escuela Politec Nacl, Quito, Ecuador; Univ San Francisco Quito, Quito, Ecuador; Acad Sci Res & Technol Arab Republ Egypt, Egyptian Network High Energy Phys, Cairo, Egypt; NICPB, Tallinn, Estonia; Univ Helsinki, Dept Phys, Helsinki, Finland; Helsinki Inst Phys, Helsinki, Finland; Lappeenranta Univ Technol, Lappeenranta, Finland; Univ Paris, CEA, IRFU, Gif Sur Yvette, France; Inst Polytech Paris, Lab Leprince Ringuet, Ecole Polytech, IN2P3,CNRS, Paris, France; Univ Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France; Inst Natl Phys Nucl & Phys Particules, Ctr Calcul, IN2P3, CNRS, Villeurbanne, France; Univ Lyon, Univ Claude Bernard Lyon 1, Inst Phys Nucl Lyon, IN2P3,CNRS, Villeurbanne, France; Georgian Tech Univ, Tbilisi, Georgia; Tbilisi State Univ, Tbilisi, Georgia; Rhein Westfal TH Aachen, Phys Inst 1, Aachen, Germany; Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany; Rhein Westfal TH Aachen, Phys Inst B 3, Aachen, Germany; DESY, Hamburg, Germany; Univ Hamburg, Hamburg, Germany; Karlsruher Inst Technol, Karlsruhe, Germany; NCSR Demokritos, Inst Nucl & Particle Phys, Aghia Paraskevi, Greece; Natl & Kapodistrian Univ Athens, Athens, Greece; Natl Tech Univ Athens, Athens, Greece; Univ Ioannina, Ioannina, Greece; Eotvos Lorand Univ, MTA ELTE Lendulet CMS Particle & Nucl Phys Grp, Budapest, Hungary; Wigner Res Ctr Phys, Budapest, Hungary; Inst Nucl Res ATOMKI, Debrecen, Hungary; Univ Debrecen, Inst Phys, Debrecen, Hungary; Eszterhazy Karoly Univ, Karoly Robert Campus, Gyongyos, Hungary; Indian Inst Sci IISc, Bangalore, Karnataka, India; HBNI, Natl Inst Sci Educ & Res, Bhubaneswar, India; Panjab Univ, Chandigarh, India; Univ Delhi, Delhi, India; HBNI, Saha Inst Nucl Phys, Kolkata, India; Indian Inst Technol Madras, Madras, Tamil Nadu, India; Bhabha Atom Res Ctr, Mumbai, Maharashtra, India; Tata Inst Fundamental Res A, Mumbai, Maharashtra, India; Tata Inst Fundamental Res B, Mumbai, Maharashtra, India; Indian Inst Sci Educ & Res, Pune, Maharashtra, India; Inst Res Fundamental Sci IPM, Tehran, Iran; Univ Coll Dublin, Dublin, Ireland; INFN, Sez Bari, Bari, Italy; Univ Bari, Bari, Italy; Politecn Bari, Bari, Italy; INFN, Sez Bologna, Bologna, Italy; Univ Bologna, Bologna, Italy; INFN, Sez Catania, Catania, Italy; Univ Catania, Catania, Italy; INFN, Sez Firenze, Florence, Italy; Univ Firenze, Florence, Italy; INFN, Lab Nazl Frascati, Frascati, Italy; INFN, Sez Genova, Genoa, Italy; Univ Genoa, Genoa, Italy; INFN, Sez Milano Bicocca, Milan, Italy; Univ Milano Bicocca, Milan, Italy; INFN, Sez Napoli, Naples, Italy; Univ Napoli Federico II, Naples, Italy; Univ Basilicata, Potenza, Italy; Univ G Marconi, Rome, Italy; INFN, Sez Padova, Padua, Italy; Univ Padua, Padua, Italy; Univ Trento, Trento, Italy; INFN, Sez Pavia, Pavia, Italy; Univ Pavia, Pavia, Italy; INFN, Sez Perugia, Perugia, Italy; Univ Perugia, Perugia, Italy; INFN, Sez Pisa, Pisa, Italy; Univ Pisa, Pisa, Italy; Scuola Normale Super Pisa, Pisa, Italy; INFN, Sez Roma, Rome, Italy; Sapienza Univ Roma, Rome, Italy; INFN, Sez Torino, Turin, Italy; Univ Torino, Turin, Italy; Univ Piemonte Orientale, Novara, Italy; INFN, Sez Trieste, Trieste, Italy; Univ Trieste, Trieste, Italy; Kyungpook Natl Univ, Daegu, South Korea; Chonnam Natl Univ, Inst Universe & Elementary Particles, Kwangju, South Korea; Hanyang Univ, Seoul, South Korea; Korea Univ, Seoul, South Korea; Kyung Hee Univ, Dept Phys, Seoul, South Korea; Sejong Univ, Seoul, South Korea; Seoul Natl Univ, Seoul, South Korea; Univ Seoul, Seoul, South Korea; Sungkyunkwan Univ, Suwon, South Korea; Riga Tech Univ, Riga, Latvia; Vilnius Univ, Vilnius, Lithuania; Univ Malaya, Natl Ctr Particle Phys, Kuala Lumpur, Malaysia; Univ Sonora UNISON, Hermosillo, Sonora, Mexico; IPN, Ctr Invest & Estudios Avanzados, Mexico City, DF, Mexico; Univ Iberoamer, Mexico City, DF, Mexico; Benemerita Univ Autonoma Puebla, Puebla, Mexico; Univ Autonoma San Luis Potosi, San Luis Potosi, San Luis Potosi, Mexico; Univ Montenegro, Podgorica, Montenegro; Univ Auckland, Auckland, New Zealand; Univ Canterbury, Christchurch, New Zealand; Quaid I Azam Univ, Natl Ctr Phys, Islamabad, Pakistan; AGH Univ Sci & Technol, Fac Comp Sci Elect & Telecommun, Krakow, Poland; Natl Ctr Nucl Res, Otwock, Poland; Univ Warsaw, Fac Phys, Inst Expt Phys, Warsaw, Poland; Lab Instrumentacao & Fis Expt Particulas, Lisbon, Portugal; Joint Inst Nucl Res, Dubna, Russia; Petersburg Nucl Phys Inst, St Petersburg, Russia; Inst Nucl Res, Moscow, Russia; NRC Kurchatov Inst, Inst Theoret & Expt Phys, Moscow, Russia; Moscow Inst Phys & Technol, Moscow, Russia; Natl Res Nucl Univ, Moscow Engn Phys Inst MEPhI, Moscow, Russia; PN Lebedev Phys Inst, Moscow, Russia; Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia; Novosibirsk State Univ, Novosibirsk, Russia; Kurchatov Inst, Inst High Energy Phys, Natl Res Ctr, Protvino, Russia; Natl Res Tomsk Polytech Univ, Tomsk, Russia; Tomsk State Univ, Tomsk, Russia; Univ Belgrade, Fac Phys, Belgrade, Serbia; Univ Belgrade, VINCA Inst Nucl Sci, Belgrade, Serbia; Ctr Invest Energet Medioambientales & Tecnol CIEM, Madrid, Spain; Univ Autonoma Madrid, Madrid, Spain; Univ Oviedo, Inst Univ Ciencias & Tecnol Espaciales Asturias I, Oviedo, Spain; Univ Cantabria, Inst Fis Cantabria IFCA, CSIC, Santander, Spain; Univ Colombo, Colombo, Sri Lanka; Univ Ruhuna, Dept Phys, Matara, Sri Lanka; CERN, European Org Nucl Res, Geneva, Switzerland; Paul Scherrer Inst, Villigen, Switzerland; Swiss Fed Inst Technol, Inst Particle Phys & Astrophys IPA, Zurich, Switzerland; Univ Zurich, Zurich, Switzerland; Natl Cent Univ, Chungli, Taiwan; Natl Taiwan Univ, Taipei, Taiwan; Chulalongkorn Univ, Fac Sci, Dept Phys, Bangkok, Thailand; Cukurova Univ, Sci & Art Fac, Phys Dept, Adana, Turkey; Middle East Tech Univ, Phys Dept, Ankara, Turkey; Bogazici Univ, Istanbul, Turkey; Istanbul Tech Univ, Istanbul, Turkey; Istanbul Univ, Istanbul, Turkey; Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; Kharkov Inst Phys & Technol, Natl Sci Ctr, Kharkov, Ukraine; Univ Bristol, Bristol, Avon, England; 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Univ Wisconsin, Madison, WI USA; Vienna Univ Technol, Vienna, Austria; Univ Paris Saclay, CEA, IRFU, Gif Sur Yvette, France; Univ Estadual Campinas, Campinas, Brazil; Univ Fed Rio Grande do Sul, Porto Alegre, RS, Brazil; Univ Fed Mato Grosso do Sul, Campo Grande, MS, Brazil; Univ Fed Pelotas, Pelotas, RS, Brazil; Univ Chinese Acad Sci, Beijing, Peoples R China; British Univ Egypt, Cairo, Egypt; Suez Univ, Suez, Egypt; Univ Haute Alsace, Mulhouse, France; Erzincan Binali Yildirim Univ, Erzincan, Turkey; Brandenburg Tech Univ Cottbus, Cottbus, Germany; IIT Bhubaneswar, Bhubaneswar, India; Inst Phys, Bhubaneswar, India; Shoolini Univ, Solan, India; Univ Hyderabad, Hyderabad, India; Univ Visva Bharati, Santini Ketan, W Bengal, India; Isfahan Univ Technol, Esfahan, Iran; Univ Bari, Politecn Bari, Sez Bari, INFN, Bari, Italy; Italian Natl Agcy New Technol Energy & Sustainabl, Rome, Italy; Ctr Siciliano Fis Nucl & Struttura Mat, Catania, Italy; Scuola Normale, Pisa, Italy; Sezione Ist Nazl Fis Nucl, Pisa, Italy; MOSTI, Malaysian Nucl Agcy, Kajang, Malaysia; Consejo Nacl Ciencia & Technol, Mexico City, DF, Mexico; Warsaw Univ Technol, Inst Elect Syst, Warsaw, Poland; St Petersburg State Polytech Univ, St Petersburg, Russia; Budker Inst Nucl Phys, Novosibirsk, Russia; Univ Siena, Siena, Italy; Univ Pavia, Sez Pavia, INFN, Pavia, Italy; Stefan Meyer Inst Subat Phys, Vienna, Austria; Burdur Mehmet Akif Ersoy Univ, Burdur, Turkey; Adiyaman Univ, Adiyaman, Turkey; Sirnak Univ, Sirnak, Turkey; Beykent Univ, Istanbul, Turkey; Istanbul Aydin Univ, Istanbul, Turkey; Mersin Univ, Mersin, Turkey; Pin Reis Univ, Istanbul, Turkey; Gaziosmanpasa Univ, Tokat, Turkey; Ozyegin Univ, Istanbul, Turkey; Izmir Inst Technol, Izmir, Turkey; Marmara Univ, Istanbul, Turkey; Kafkas Univ, Kars, Turkey; Istanbul Bilgi Univ, Istanbul, Turkey; Hacettepe Univ, Ankara, Turkey; Univ Southampton, Sch Phys & Astron, Southampton, Hants, England; IPPP Durham Univ, Durham, England; Monash Univ, Fac Sci, Clayton, Vic, Australia; Bethel Univ, Minneapolis, MN USA; Karamanoglu Mehmetbey Univ, Karaman, Turkey; Bingol Univ, Bingol, Turkey; Sinop Univ, Sinop, Turkey; Mimar Sinan Univ, Istanbul, Turkey; Texas A&M Univ Qatar, Doha, Qatar | ; Sacchi, Roberto/AGS-7202-2022; Masciovecchio, Mario/IXD-8018-2023; Gupta, Rajat/GVS-7964-2022; Duarte-Campderrós, Jordi/F-5025-2018; Fallavollita, Francesco/AAL-8850-2020; zhang, shiju/GWV-2688-2022; Kaur, Amandeep/IYJ-2622-2023; Elwood, Adam/GSN-1799-2022; gomez, marina/I-7072-2012; Mercadante, Pedro/K-1918-2012; Kalsi, Amandeep Kaur/JNR-9607-2023; Pérez-Calero Yzquierdo, Antonio/F-2235-2013; IBRAHIM, ZAINOL/C-1121-2010; Dharmaratna, Welathantri/F-6745-2018; Skoro, Goran/F-3642-2010; Flix, Josep/G-5414-2012; Meyer, Manuel/E-2697-2016; Gil Da Silveira, Gustavo/N-7279-2014; cartiglia, nicolo/ABF-1008-2021; Giammanco, Andrea/O-5017-2019; YU, SHIN-SHAN/JPW-8635-2023; Gomez, Gervasio/K-2860-2014; Shchutska, Lesya/M-7052-2017; Ulrich, Ralf/Q-3882-2019; Caspart, Rene/LNQ-1560-2024; Lee, Jeongeun/IYJ-2550-2023; Smirnov, Vitaly/B-5001-2017; Sagir, Sinan/A-5219-2018; Rossi, Alessandro/I-8230-2017; Elgammal, Sherif/KGM-3809-2024; GULER, E/AAH-4748-2019; wang, xiang/IVV-6713-2023; Barria, Patrizia/AAT-3894-2020; Mora Herrera, Maria Clemencia/AAD-6466-2021; Amoroso, Simone/AAW-4334-2021; Safronov, Grigory/F-8494-2017; Saka, Halil/GPF-8892-2022; KIM, Tae/P-7848-2015; Kim, Mee/E-7234-2015; Pavlov, Borislav/O-8491-2018; Purohit, Arnab/MSW-1519-2025; Perfilov, Maxim/E-1064-2012; Li, Wenxian/AAJ-5087-2021; Ogul, Hasan/S-7951-2016; Van Remortel, Nick/AGQ-7045-2022; Saoulidou, Niki/AAA-2891-2020; Paganoni, Marco/A-4235-2016; ciocci, maria/I-2153-2015; Dhingra, Nitish/ABA-9507-2020; BAT, Ayse/GOK-2363-2022; Ozturk, Sertac/AGO-2476-2022; Okhotnikov, Vitalii/O-4293-2017; Lannon, Kevin/HGU-5755-2022; Hildreth, Michael/LWK-8035-2024; Moscatelli, Francesco/N-6333-2014; Focardi, Ettore/E-7376-2012; Sen, Sercan/C-6473-2014; Menasce, Dario/A-2168-2016; Orimoto, Toyoko/D-4759-2016; Amapane, Nicola/HDN-9630-2022; Paulini, Manfred/N-7794-2014; Orlando, Nicola/AAL-1723-2021; Puerta Pelayo, Jesus/L-1632-2014; Calderon, Alicia/K-3658-2014; Rolandi, Luigi/E-8563-2013; Naimuddin, Md/JXL-7104-2024; ., MOHD./AAD-2818-2019; Rossi, Biagio/F-4137-2018; Govoni, Pietro/K-9619-2016; Cipriani, Marco/MHU-3780-2025; Borg, Jacqueline/AFS-8793-2022; Rodrigo, Teresa/F-6183-2018; Castilla-Valdez, Heriberto/F-8906-2019; LECOQ, Paul/S-7246-2019; Ganjour, Serguei/D-8853-2011; Grzanka, Leszek/M-9052-2018; Akgun, Bora/AAM-8311-2021; Shah, Aashaq/GYI-8983-2022; Delgado Peris, Antonio/AAA-5165-2019; Cankocak, Kerem/A-1507-2018; Tadel, Matevz/H-3204-2016; Sculac, Toni/AAE-4141-2019; Strologas, John/GWR-2036-2022; Verdini, Piero/J-2839-2012; Cassese, Antonio/R-1713-2016; Konecki, Marcin/G-4164-2015; Pasztor, Gabriella/D-9716-2016; Yazgan, Efe/C-4521-2014; My, Salvatore/I-5160-2015; Triossi, Andrea/K-2813-2017; Wulz, Claudia-Elisabeth/H-5657-2011; Bermudez Martinez, Armando/HHN-4625-2022; Palencia Cortezon, Jose Enrique/F-1059-2017; Rivero, Celso/V-6916-2017; Gerosa, Raffaele/AGL-6079-2022; Montanari, Alessandro/J-2420-2012; SHIRAZI, SHARIF/G-4823-2010; Van Haevermaet, Hans/HOI-0668-2023; Singh, J B/IXD-2130-2023; Mishra, Trilochan/A-7947-2010; Hernandez Calama, Jose Maria/H-9127-2015; Della Ricca, Giuseppe/B-6826-2013; Mundim, Luiz/A-1291-2012; KUMAR, ASHOK/AAB-8729-2019; Primavera, Federica/KUD-4067-2024; Skovpen, Kirill/HKM-3974-2023; Stahl, Achim/E-8846-2011; zhao, jize/AAS-1102-2021; Tsirou, Andromachi/HHC-6869-2022; d'Enterria, David/W-7763-2019; Khvedelidze, Arsen/AAF-5025-2020; Hill, Christopher/B-5371-2012; Radics, Balint/AAN-2721-2020; Puljak, Ivica/D-8917-2017; ozok, ferhat/A-6803-2018; Andreev, Vladimir/M-8665-2015; Viliani, Lorenzo/AAU-3462-2021; Aimè, Chiara/GXG-4131-2022; KÖMÜRCÜ, YILDIRAY/AAC-6166-2020; Obraztsov, Stepan/E-1895-2012; Shukla, Prashant/MZS-4135-2025; Bainbridge, Robert/JRW-6150-2023; Ciulli, Vitaliano/ABC-5440-2020; Bonacorsi, Daniele/F-1505-2014; Kaveh, Hessamoddin/AAI-7230-2021; Paktinat Mehdiabadi, Saeid/AAY-8501-2020; Leonidov, Andrey/P-3197-2014; Misheva, Mihaela/AEK-6084-2022; Cakir, Altan/ABD-4450-2020; Ćirković, Predrag/G-8059-2012; Melo da Costa, Eliza/AAX-2394-2021; Sert, H/ABA-5645-2020; Malik, Sudhir/D-6621-2018; Navarro-Tobar, Alvaro/K-7864-2014; Barreto, Lucas/J-9426-2013; Alves, Gilvan/C-4007-2013; Manisha, Manisha/KVB-3511-2024; Shmatov, Sergei/J-5621-2014; Lander, Roddy/KQU-7736-2024; D'Alessandro, Raffaello/F-5897-2015; Dewanjee, Ram Krishna/AFE-3468-2022; Errico, Filippo/AAD-7755-2019; Wang, Man/AGE-9961-2022; Barnyakov, Alexander/AAZ-8825-2021; Behera, Prafulla/AAK-8686-2020; Finco, Linda/AAH-3618-2019; Wu, Wenjie/KVA-7436-2024; Bourilkov, Dimitri/AAO-4908-2020; Malawski, Maciej/H-9119-2012; Presilla, Matteo/MGW-3532-2025; Dordevic, Milos/HSH-8083-2023; OZOK, FERHAT/A-6803-2018; Chistov, Ruslan/B-4893-2014; Calderon, Dr. Alicia/K-3658-2014; Swain, Subrat/IXW-8971-2023; Cocoros, Alice/AAA-6661-2021; Milosevic, Jelena/ABG-9850-2020; Hoh, Siew Yan/AAM-9562-2021; Dutta, Ipsita/JDW-8207-2023; Mondal, Santu/GSE-1742-2022; Cimmino, Anna/AAA-1673-2020; Loukas, Dimitris/ABF-1478-2020; Dubinin, Mikhail/I-3942-2016; Yalvac, Metin/LYP-1776-2024; Bossini, Edoardo/AAA-4972-2020; Go, Yeonju/HSH-6030-2023; Fernandez Perez Tomei, Thiago Rafael/E-7091-2012; Asawatangtrakuldee, Chayanit/GLS-1465-2022; Sarkar, Debojit/GNW-6585-2022; Flore, Carlo/HZJ-6377-2023; Mousa, Jehad/I-8019-2019; Mazza, Giovanni/ABI-2143-2020; Clements, Emma/HSG-3424-2023; Faccioli, Pietro/AAF-1499-2019; Buchanan, James/ABB-6906-2021; Josa, Isabel/K-5184-2014; Pásztor, Gabriella/D-9716-2016; Ivanov, Andrew/A-7982-2013; Rossi, Andrea/AGH-4546-2022; Duarte, Javier/AAA-5414-2020; Kratochwil, Nicolaus/ACR-4384-2022; Garg, Rocky/AAV-9845-2021; De Iorio, Agostino/HJH-7155-2023; Zhemchugov, Evgeny/C-8885-2016; Jain, Shikha/ADG-6788-2022; Ragazzi, Stefano/D-2463-2009; Heredia de la Cruz, Ivan/L-2610-2018; Mussgiller, Andreas/AAF-6933-2019; Voytishin, Nikolay/IYJ-7234-2023; Csorgo, Tamas/I-4183-2012; Bedoya, Cristina/K-8066-2014; Roy Chowdhury, Suvankar/IXN-7230-2023; Nguyen, Thong/JHS-8911-2023; González Caballero, Isidro/E-7350-2010; Padula, Sandra/AAE-4304-2020; Tinoco Mendes, Andre David/D-4314-2011; Mohammadi Najafabadi, Mojtaba/HLH-5741-2023; Popov, Andrey/E-1052-2012; Raicevic, Natasa/ABD-5889-2020; Zhang, Jixie/A-1461-2016; Ahmad, Muhammad/LBH-2342-2024; Sznajder, Andre/L-1621-2016; fan, jing/KHX-6210-2024; Mrenna, Stephen/KIL-6081-2024; Pompili, Alexis/ISU-8813-2023; Gütay, Levent/C-5352-2009; Rout, Prasanna/N-8012-2019; Sunar Cerci, Deniz/AHE-1706-2022; Demiragli, Zeynep/IZE-1919-2023; Kim, Jeehoon/K-3763-2012; de guio, federico/B-8553-2009; Canelli, Florencia/AAI-1616-2021; Kadastik, Mario/B-7559-2008; Roguljic, Matej/NMJ-5801-2025; Ruhlmann-Kleider, Vanina/AGJ-4460-2022; Arcidiacono, Roberta/ABF-3918-2020; Dewanjee, Dr. Ram Krishna/AFE-3468-2022; Hernández, José/H-9127-2015; Mohammadi Najafabadi, Mohammad/GLR-0213-2022; Gelmi, Amy/D-4456-2012; Josa Mutuberria, Isabel/K-5184-2014; Moraes, Arthur/F-6478-2010; Amendola, Chiara/KCK-3814-2024; Juodagalvis, Andrius/F-6952-2018; Salvini, Paola/G-1670-2018; Mulders, Martijn/AGK-9612-2022; Fasanella, Daniele/ABD-9759-2020; Iorio, Alberto Orso Maria/GXA-0233-2022; Spagnolo, Paolo/G-3401-2017; Hos, ilknur/T-1165-2019; Gonzalez, Bárbara/GRR-9771-2022; Sanchez, Federico/F-5809-2012; Novak, Tibor/JGE-0651-2023; ciocci, maria agnese/I-2153-2015; Martin Perez, Cristina/MEP-9682-2025; Seixas, Joao/F-5441-2013; Chang, Hui/AGD-4270-2022; boccali, tommaso/ACT-7600-2022; Teles, Patricia/ABF-9723-2021; Xie, Si/O-6830-2016; Kirpichnikov, Dmitry/U-9605-2019; Fiorendi, Sara/ABA-3311-2021; Bastos, Daniel/GZG-4209-2022; Ivanchenko, Vladimir/L-5254-2017; Matorras, Francisco/I-4983-2015; Anampa, Kenyi/GPX-8479-2022; Ahmad, Ashfaq/ABJ-8366-2022; Malakhov, Alexander/D-5702-2016; , Luo/AAP-8401-2020; Golubev, Nikolay/L-4131-2017; Legger, Federica/HNC-2568-2023; Calvo Alamillo, Enrique/L-1203-2014; Isildak, Bora/D-8829-2015; Paramatti, Riccardo/IXW-6363-2023; wang, long/IZE-1764-2023; Jeitler, Manfred/H-3106-2012; Cepeda, María/HNO-9314-2023; Chang, Yuan/AFO-2322-2022; Bertacchi, Valerio/KFQ-6990-2024; Civinini, Carlo/AAL-6403-2020; Gennai, Simone/P-2880-2015; Maselli, Silvia/J-1599-2012; de Paula Carvalho, Wagner/V-3343-2019; Amiss, Anna/AAB-2424-2020; Hegeman, Jeroen/HJA-1005-2022; Zaleski, Stéphane/N-8879-2017; Vala, Martin/HZL-1003-2023; xu, lingzhi/JVZ-8748-2024; Obertino, Maria/ABH-1662-2020; Varela, Joao/K-4829-2016; Joo, Changwoo/ABI-4034-2020; Leonardo, Nuno/M-6940-2016; Neumeister, Norbert/JXN-1675-2024; staiano, amedeo/KVB-8364-2024; chen, gang/JRX-1197-2023; Mora Herrera, Clemencia/AAD-6466-2021; Chenarani, Shirin/AAO-8918-2021; Goy Lopez, Silvia/K-9200-2017; Manganote, Edmilson/K-8251-2013; Ferencek, Dinko/G-6411-2015; Belyaev, Alexander/F-6637-2015; Perieanu, Adrian/D-3347-2018; Venditti, Rosamaria/IVH-5947-2023; Venditti, Rosamaria/I-1030-2014; Gregores, Eduardo/F-8702-2012; Kumar, Vijay/G-4290-2011; Goh, Junghwan/Q-3720-2016; Raidal, Martti/F-4436-2012; Gomez, Jaime/P-5242-2018; Poudyal, Nabin/AAA-7981-2020; ASILAR, Ece/ABC-4577-2020; Blekman, Freya/ABD-7916-2020; Wimpenny, Stephen/K-8848-2013; Gorski, Maciej/AAU-1250-2020; Hoorani, Hafeez/D-1791-2013; Kim, Geuk-Nam/IAO-4628-2023; Galati, Giuliana/AAV-8088-2020; Giacomelli, Paolo/B-8076-2009; Kaur, Manjit/MBF-0295-2025; Chen, Yang/KHD-8849-2024; Ozdemir, Kadri/P-8058-2014; Tiras, Emrah/ABG-2354-2020; Khakzad, Mohsen/ABB-1945-2021; Androsov, Konstantin/HJA-2332-2022; Everaerts, Pieter/KIE-3686-2024; My, Salvatore/HTM-1335-2023; Gozalez-Lopez, Oscar/AAH-3533-2019; ZHANG, JIANWEN/JMQ-9363-2023; Hebbeker, Thomas/L-4073-2013; liu, wenli/JRW-0517-2023; Kim, Soon Hee/GXF-6736-2022; Gulmez, Erhan/P-9518-2015; Roy, Ashim/HLV-9248-2023; kangal, evrim/L-8978-2013; Petkov, Peicho/M-2080-2016; Grandi, Claudio/B-5654-2015; Lange, Torben/HOH-4787-2023; Casarsa, Massimo/L-3623-2018; BARRILLON, Stephanie/ABA-6873-2021; Kovac, Marko/D-5817-2017; Battilana, Carlo/AAA-7345-2020; Gonzalez, Barbara/ABG-7021-2020; MAESTRE, JUAN/I-5763-2015; Li, Xuefei/C-3861-2012; Rodríguez, Manel/C-2539-2017; Gribushin, Andrei/J-4225-2012; Cavallo, Nicola/F-8913-2012; Colaleo, Anna/HSG-1637-2023; Marchesini, Ivan/G-7852-2018; Bartosik, Nazar/ABF-2326-2020; Donato, Silvio/GPK-2262-2022; Bunkowski, Karol/KOD-1567-2024; Liu, Tong/JDM-9629-2023; Paganis, Stathes/J-8413-2017; Marin, Jose/K-6412-2014; Selvaggi, Michele/CAJ-2129-2022; Sarica, Ulascan/JXY-2829-2024; TUVE', Cristina/P-3933-2015; Bhatnagar, Vipin/Z-1079-2018; Fedi, Giacomo/IRZ-8694-2023; Marinelli, Nancy/MCJ-3716-2025; Doroba, krzysztof/AAA-3419-2021; Ling, Ting/J-9131-2018; Ince Lezki, Merve/AAG-5283-2021; Osherson, Marc/LWZ-8687-2024; Steggemann, Jan/AAL-5700-2020; Klyukhin, Vyacheslav/D-6850-2012; Pedraza Morales, Maria Isabel/ABC-7289-2021; Bunichev, Viacheslav/E-2285-2012; Reis, Tomas/JHS-4868-2023; Wang, Shaohui/HKO-6774-2023; Missiroli, Marino/AAA-9072-2021; lin, yuan/JXL-9592-2024; Elkafrawy, Tamer/HPF-5873-2023; Dimova, Tatyana/AAQ-9303-2021; Piperov, Stefan/Q-1980-2017; Chang, Philip/AAN-3350-2021; Lau, Kok-Tee/AAX-5361-2020; Malvezzi, Sandra/B-8524-2009; Sen, S./C-6473-2014; Shopova, Mariana/HKW-0149-2023; Lee, Suhyun/AAA-3368-2022; Ligabue, Franco/F-3432-2014; Navarro-Tobar, Álvaro/K-7864-2014; Heredia De La Cruz, Ivan/L-2610-2018; de Jesus Damiao, Dilson/G-6218-2012; Ferro, Fabrizio/HMP-5516-2023; Majumder, Gobinda/MTB-7852-2025; Organtini, Giovanni/D-3990-2009; Babaev, Anton/U-5457-2019; Waltenberger, Wolfgang/H-9330-2018; Zhang, Chao/AAY-5867-2021; Kardapoltsev, Leonid/AAQ-4047-2021; de Souza Sandro, Fonseca/ABB-8505-2020; Terkulov, Adel/M-8581-2015; Guler, Yalcin/AAG-7833-2019; Dudko, Lev/D-7127-2012; Forthomme, Laurent/AFS-8908-2022; Duarte-Campderros, Jordi/F-5025-2018; Brandt, Steven/AGV-6975-2022; SHARMA, RAM/MIO-2784-2025; Karancsi, János/A-9710-2013; CHEN, CHAO/AAG-1555-2021; Malbouisson, H./N-6733-2015; Kratschmer, Ilse/LSK-3076-2024; Mignerey, Alice/D-6623-2011; Lokhtin, Igor/D-7004-2012; Lychkovskaya, Natalia/F-8341-2017; Aushev, Tagir/AAN-9735-2020; Ramos, Juan/AAF-3114-2019; Hall-Wilton, Richard/U-6918-2019; Tytgat, Michael/F-3732-2018; Sola, Valentina/E-7726-2015; Gülmez, Erhan/P-9518-2015; Voevodina, Elena/AAI-4805-2021; Kaspar, Jan/LPP-7624-2024; Creanza, Donato/KIC-8791-2024; Tumasyan, Armen/P-3390-2015; Ghezzi, Alessio/I-9494-2012; Radogna, Raffaella/I-1033-2014; Gonzalez, Jesus David/IAN-5526-2023; Quijada, Javier/AAS-4234-2021; Bornheim, Adi/L-2750-2019; Ahuja, Sudha/I-4097-2014; buotempo, salvatore/B-5210-2012; Tuominen, Eija/A-5288-2017; Meridiani, Paolo/ABH-1655-2020; Benaglia, Andrea/AGF-5495-2022; Calligaris, Luigi/K-2018-2017; SEVINC KAYA, OZLEM/AAE-8069-2020; Fouz, Maria Cruz/AAF-1105-2019; Mahmoud, Mohammed/AAD-1249-2019; Salerno, Roberto/A-7509-2010; Cadamuro, Luca/AAO-8637-2020; García-García, Francisco/B-1929-2014; Gonzalez Caballero, Isidro/E-7350-2010; Lowette, Steven/HKV-3341-2023; Bernardes, Cesar Augusto/D-2408-2015; Arneodo, Michele/ABF-7197-2020; Menichelli, Mauro/AAQ-4026-2020; Spiga, Daniele/ABB-8842-2021; Tuo, Shengquan/ABA-9032-2021; Reyes-Almanza, Rogelio/GQJ-8236-2022; Fiorina, Davide/HKE-5979-2023; Piedra, Jonatan/F-3247-2018; Gavrilov, Gennadii/GRO-6222-2022; SIMSEK, Ali Eren/AFO-3050-2022; Trocino, Daniele/AGI-2155-2022; Lee, Jason/B-9701-2014; Polikarpov, Sergey/C-8939-2016; Heller, Richard/I-6605-2012; Trocsanyi, Zoltan/A-5598-2009; Tomei, Thiago/E-7091-2012; Xu, Qiao/AAM-9242-2020; Gandrajula, Reddy/AFR-4403-2022; Fanò, Livio/K-2460-2015; Kapoor, Aakanksha Rajiv/KAM-5986-2024; Hobson, Peter/C-8919-2016; KUMAR, ASHOK/AAF-4055-2021; De Filippis, Nicola/AAD-6280-2019; Haddad, Yacine/GRX-5022-2022; Tcherniaev, Evgueni/G-3453-2016; Theofilatos, Konstantinos/AAL-9162-2021; La Rosa, Alessandro/I-1856-2013; Lohan, Manisha/KVB-3511-2024; Langenegger, Urs/A-4578-2008; Gokbulut, Gul/G-2141-2018; Stojanović, Mirjana/AAM-4320-2021; Kumar, Parveen/I-3259-2015; Iaydjiev, Plamen/AEB-8785-2022; Simsek, Ali Eren/AFO-3050-2022; Ciangottini, Diego/HJH-5914-2023; Scodellaro, Luca/K-9091-2014; Seidel, Marcus/AAA-4799-2022; Fienga, Francesco/HKE-1204-2023; Watson, Ian/IAM-9344-2023; Troshin, Sergey/AAR-8556-2020; Hlushchenko, Olena/AAC-8972-2022; Vischia, Pietro/AAB-7811-2021; Doğangün, Oktay/AAC-1579-2022; Pastrone, Nadia/JRZ-0528-2023; yagil, avi/I-3278-2018; Chen, Fang/JZE-4446-2024; Nandan, Saswati/JVY-9235-2024; Bilki, Burak/GRF-5072-2022; Ozkorucuklu, Suat/AAE-7535-2020; Maier, Benedikt/AAN-1929-2021; D'Hondt, Jorgen/ABD-7930-2020; Ceccarelli, Rudy/JTU-5218-2023; Ranieri, Antonio/E-9545-2015; Kim, Dongwon/KHV-7759-2024; Azarkin, Maxim/N-2578-2015; Zorbilmez, Caglar/ABH-1905-2020; kaya, mithat/D-8062-2011; Snoeys, Walter/K-8259-2015; Torres, Felipe/ABA-9751-2020; Kim, Junghwan/AAQ-9204-2021; Pinna, Daniele/AAF-4490-2021; Hong, Byungsik/O-8359-2018; TUVE, Cristina/P-3933-2015; Gleyzer, Sergei/AAE-6726-2020; Fiori, Francesco/H-1454-2018; Hollar, Jonathan/KZU-8312-2024; Janssen, Xavier/E-1915-2013; Hall, Jeter/F-6108-2013; Csanad, Mate/D-5960-2012; Rabady, Dinyar/GSN-9210-2022; Meijers, Femke/IUM-7952-2023; Voevodina, Elena/G-7314-2016; Dallavalle, Gaetano Marco/AFW-4654-2022; Luukka, Panja-Riina/S-8956-2018; Geralis, Theodoros/I-6467-2016; Garutti, Erika/NRY-4813-2025; Wang, Dayong/LRC-9881-2024; Schwarz, Dennis/GYA-4479-2022; Di Florio, Adriano/ABA-3631-2020; ABDULLAH, WAN/B-5439-2010; Venturi, Andrea/J-1877-2012; Vilela Pereira, Antonio/L-4142-2016; del Arbol, Pablo/Z-4358-2019; Bouhali, Othmane/JXM-3572-2024; Cerri, Olmo/Z-3579-2019; Popov, Vitaliy/C-9925-2016; Demiroglu, Zuhal Seyma/A-5168-2016; Dai, Yuxiang/JGC-6867-2023; celik, Ali/K-6506-2019; Verwilligen, Piet/AAA-8819-2019; Sguazzoni, Giacomo/J-4620-2015; Belyaev, Andrey/E-1540-2012; Padula, Sandra S./G-3560-2012; de la Cruz, Begona/K-7552-2014; Benussi, Luigi/O-9684-2014; Maravin, Yurii/N-9259-2018; Titov, Maxim/JAC-6742-2023; Kirakosyan, Martin/N-2701-2015; Soares, Mara/GZM-6676-2022; Fouz Iglesias, Maria Cruz/AAF-1105-2019; Soffi, Livia/HSC-0774-2023; Dremin, Igor/K-8053-2015; Ristic, Branko/J-9948-2017; Ma, Xiaodong/JAN-7473-2023; Camporesi, Tiziano/F-8307-2013; Fernandez Menendez, Javier/B-6550-2014; chengwei, zhang/GPC-8987-2022; KARA, Ozgun/MCY-5689-2025; Majumder, Devdatta/X-2542-2019; Novaes, Sergio/D-3532-2012; Da Silveira, Gustavo Gil/N-7279-2014; Palla, Fabrizio/F-4727-2012; Ryutin, Roman/P-3615-2015; Khvedelidze, Arsen/GYE-2017-2022; Padhi, Saswat/LTF-2060-2024; Marlow, Daniel/C-9132-2014; Chinellato, Jose/I-7972-2012; Azzi, Patrizia/H-5404-2012; Aarrestad, Thea/GLV-3031-2022; Salvatico, Riccardo/JAO-1069-2023; Cardini, Andrea/KUF-0984-2024; Choudhury, Seema/LEM-7962-2024; Asavapibhop, Burin/X-1477-2019; Yetkin, E. Asli/AAQ-8732-2020; Zhang, Shuangshuang/GVS-0109-2022; Kim, Hyun-Jong/X-3662-2019; Petrucciani, Giovanni/AAM-8482-2020; Cavallari, Francesca/AAE-8571-2019; Bilican, Fuat/AGD-3089-2022; Wang, Hui/GLT-7990-2022; Chernyavskaya, Nadezda/F-2161-2015; Abbrescia, Marcello/HTN-5367-2023; Grynyov, Borys/D-1714-2010; Zolkapli, Zukhaimira/AAA-4039-2019; Bortignon, Pierluigi/AAF-6561-2020; Boimska, Bozena/Z-6088-2019; Minafra, Nicola/AAZ-6781-2020; Perez-Calero Yzquierdo, Antonio/F-2235-2013; Romero, Luciano/B-8917-2018; Li, Ruixi/AAE-7585-2022; Montagna, Paolo Maria/AAY-8669-2021; Boimska, Bożena/Z-6088-2019; Pinna Angioni, Gian Luca/AAZ-6834-2021; Kodolova, Olga/IQV-0056-2023; Sanchez-Hernandez, Alberto/L-4955-2017; Kumar, Saroj/AAU-5615-2021; Wadud, Dr. Mohammad A./AAR-2288-2020; Aguilar-Benitez, Manuel/L-4277-2017; Kim, Tae Jeong/P-7848-2015; Martinez Rivero, Celso/V-6916-2017; Dozen, Candan/IQS-4597-2023; HAJAHMAD (Wael HAJ AHMAD), Vael/E-6738-2016; Mejia Guisao, Jhovanny/ABG-3627-2021; Jafari, Abideh/JVP-2320-2024; Simone, Federica/HDM-3686-2022; Di Crescenzo, Antonia/GVS-4144-2022; Felcini, Marta/S-2684-2019; Khurana, Raman/IUN-3205-2023; Boos, Eduard/D-9748-2012; Zalewski, Piotr/H-7335-2013; Di Mattia, Alessandro/HNQ-0365-2023; Bagliesi, Giuseppe/C-2230-2013; Vai, Ilaria/AAT-5836-2020; Palladino, Vito/AAA-5328-2020; De La Cruz Burelo, Eduard/AAR-9103-2021; kasem, Ashraf/GMX-4925-2022; Sandro, Fonseca de Souza/M-8731-2014; Singha, Soumya/AAS-5837-2021; Ruiz Jimeno, Alberto/E-4473-2011; tosi, mia/J-5777-2012; Moon, Chang-Seong/J-3619-2014; Květoň, Antonín/AAA-9178-2020; KAYİS TOPAKSU, Aysel/B-8910-2018; Krücker, Dirk/X-1844-2019; chen, ying/HHS-8254-2022; Wittich, Peter/HOH-5761-2023; candelise, vieri/H-2195-2015; KHAN, Masood/AAA-7384-2020; Dorigo, Tommaso/AAY-6673-2020; Lezki, Samet/AAG-8277-2019; Savina, Maria/HDO-6544-2022; Hoh, Siewyan/AAM-9562-2021; SAHOO, DILLIP/AAA-4724-2022; Dermenev, Alexander/M-4979-2013; tsamalaidze, zviadi/U-6949-2019; Veckalns, Viesturs/AAZ-3105-2020; Morovic, Srecko/T-9019-2019; 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24376473600; 56682068800; 55134092500; 7005485775; 36170873500; 56924477700; 55603522400; 55496142400; 7102931559; 57216998160; 37051309800; 16240181000; 7004188390; 57220064533; 35274582500; 57202522344; 57219648360; 57216596346; 35227365100; 57220064314; 35227503800; 35227736500; 56073436300; 57220064351; 57220064259; 57220064396; 57203514331; 57199996011; 35222476000; 57206353679; 22996761700; 16226119300; 55951354400; 35356070900; 57201370087; 56681988200; 56414432900; 35271695700; 55868435500; 57210751238; 35308730700; 8984617000; 57189693933; 7006404363; 57210312265; 7201657574; 7003879901; 57212375663; 56501874000; 56681978600; 36123824900; 57215610762; 35227990900; 57193206437; 56648200300; 56181581800 | PHYSICAL REVIEW LETTERS | PHYS REV LETT | 0031-9007 | 1079-7114 | 124 | 4 | SCIE | PHYSICS, MULTIDISCIPLINARY | 2020 | 9.161 | 7.6 | 1.01 | 2025-06-25 | 5 | 22 | CO-ANNIHILATION REGION; E(+)E(-) COLLISIONS; SCALAR LEPTONS | Fighter aircraft; Germanium alloys; Germanium compounds; Mass spectrometry; Tellurium compounds; CMS detectors; Dark matter; Initial-state radiations; Integrated luminosity; Mass spectra; Neutralinos; Proton proton collisions; Supersymmetric models; Supersymmetries; Transverse momenta; article; lepton; mass spectrometry; prediction; Supersymmetry | English | 2020 | 2020-01-29 | 10.1103/physrevlett.124.041803 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Studies of Charm Quark Diffusion inside Jets Using Pb-Pb and pp Collisions at √sNN=5.02 TeV | The first study of charm quark diffusion with respect to the jet axis in heavy ion collisions is presented. The measurement is performed using jets with p(T)(jet) > 60 GeV/c and D-0 mesons with p(T)(D) > 4 GeV/c in lead-lead (Pb-Pb) and proton-proton (pp) collisions at a nucleon-nucleon center-of-mass energy of root s(NN) = 5.02 TeV, recorded by the CMS detector at the LHC. The radial distribution of D-0 mesons with respect to the jet axis is sensitive to the production mechanisms of the meson, as well as to the energy loss and diffusion processes undergone by its parent parton inside the strongly interacting medium produced in Pb-Pb collisions. When compared to Monte Carlo event generators, the radial distribution in pp collisions is found to be well described by PYTHIA, while the slope of the distribution predicted by SHERPA is steeper than that of the data. In Pb-Pb collisions, compared to the pp results, the D-0 meson distribution for 4 < p(T)(D) < 20 GeV/c hints at a larger distance on average with respect to the jet axis, reflecting a diffusion of charm quarks in the medium created in heavy ion collisions. At higher p(T)(D), the Pb-Pb and pp radial distributions are found to be similar. | Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Ambrogi, F.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Dragicevic, M.; Ero, J.; Del Valle, A. Escalante; Flechl, M.; Fruhwirth, R.; Ghete, V. M.; Hrubec, J.; Jeitler, M.; Krammer, N.; Kratschmer, I; Liko, D.; Madlener, T.; Mikulec, I; Rad, N.; Rohringer, H.; Schieck, J.; Schofbeck, R.; Spanring, M.; Spitzbart, D.; Taurok, A.; Waltenberger, W.; Wittmann, J.; Wulz, C-E; Zarucki, M.; Chekhovsky, V; Mossolov, V; Gonzalez, J. Suarez; De Wolf, E. A.; Di Croce, D.; Janssen, X.; Lauwers, J.; Pieters, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; De Bruyn, I; De Clercq, J.; Deroover, K.; Flouris, G.; Lontkovskyi, D.; Lowette, S.; Marchesini, I; Moortgat, S.; Moreels, L.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I; Beghin, D.; Bilin, B.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Dorney, B.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Kalsi, A. K.; Lenzi, T.; Luetic, J.; Postiau, N.; Starling, E.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Wang, Q.; Cornelis, T.; Dobur, D.; Fagot, A.; Gul, M.; Khvastunov, I; Poyraz, D.; Roskas, C.; Trocino, D.; Tytgat, M.; Verbeke, W.; Vermassen, B.; Vit, M.; Zaganidis, N.; Bakhshiansohi, H.; Bondu, O.; Brochet, S.; Bruno, G.; Caputo, C.; David, P.; Delaere, C.; Delcourt, M.; Giammanco, A.; Krintiras, G.; Lemaitre, V; Magitteri, A.; Mertens, A.; Musich, M.; Piotrzkowski, K.; Saggio, A.; Vidal Marono, M.; Wertz, S.; Zobec, J.; Alves, F. L.; Alves, G. A.; Martins Junior, M. Correa, Jr.; Correia Silva, G.; Hensel, C.; Moraes, A.; Pol, M. E.; Teles, P. Rebello; Batista Das Chagas, E. Belchior; Carvalho, W.; Chinellato, J.; Coelho, E.; Da Costa, E. M.; Da Silveira, G. G.; Damiao, D. De Jesus; Martins, C. De Oliveira; De Souza, S. Fonseca; Malbouisson, H.; Figueiredo, D. Matos; De Almeida, M. Melo; Herrera, C. Mora; Mundim, L.; Nogima, H.; Da Silva, W. L. Prado; Rosas, L. J. Sanchez; Santoro, A.; Sznajder, A.; Thiel, M.; Tonelli Manganote, E. J.; Da Silva De Araujo, F. Torres; Pereira, A. Vilela; Ahuja, S.; Bernardes, C. A.; Calligaris, L.; Perez Tomei, T. R. Fernandez; Gregores, E. M.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Marinov, A.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.; Dimitrov, A.; Litov, L.; Pavlov, B.; Petkov, P.; Fang, W.; Gao, X.; Yuan, L.; Wang, Y.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Jiang, C. H.; Leggat, D.; Liao, H.; Liu, Z.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, Z.; Yazgan, E.; Zhang, H.; Zhang, S.; Zhao, J.; Ban, Y.; Chen, G.; Levin, A.; Li, J.; Li, L.; Li, Q.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Avila, C.; Cabrera, A.; Carrillo Montoya, C. A.; Chaparro Sierra, L. F.; C Florez; Hernandez, C. F. Gonzalez; Segura Delgado, M. A.; Courbon, B.; Godinovic, N.; Lelas, D.; Puljak, I; Sculac, T.; Antunovic, Z.; Kovac, M.; Brigljevic, V; Ferencek, D.; Kadija, K.; Mesic, B.; Starodumov, A.; Susa, T.; Ather, M. W.; Attikis, A.; Kolosova, M.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Finger, M.; Finger, M., Jr.; Ayala, E.; Jarrin, E. Carrera; Kamel, A. Ellithi; Mahmoud, M. A.; Mohammed, Y.; Bhowmik, S.; De Oliveira, A. Carvalho Antunes; Dewanjee, R. K.; Ehataht, K.; Kadastik, M.; Raidal, M.; Veelken, C.; Eerola, P.; Kirschenmann, H.; Pekkanen, J.; Voutilainen, M.; Havukainen, J.; Heikkila, J. K.; Jarvinen, T.; Karimaki, V; Kinnunen, R.; Lampen, T.; Lassila-Perini, K.; Laurila, S.; Lehti, S.; Linden, T.; Luukka, P.; Maenpaa, T.; Siikonen, H.; Tuominen, E.; Tuominiemi, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; de Monchenault, G. Hamel; Jarry, P.; Leloup, C.; Locci, E.; Malcles, J.; Negro, G.; Rander, J.; Rosowsky, A.; Sahin, M. O.; Titov, M.; Abdulsalam, A.; Amendola, C.; Antropov, I; Beaudette, F.; Busson, P.; Charlot, C.; de Cassagnac, R. Granier; Kucher, I; Lobanov, A.; Blanco, J. Martin; Perez, C. Martin; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Rembser, J.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Leiton, A. G. Stahl; Zabi, A.; Zghiche, A.; Agram, J-L; Andrea, J.; Bloch, D.; Brom, J-M; Chabert, E. C.; Cherepanov, V; Collard, C.; Conte, E.; Fontaine, J-C; Gele, D.; Goerlach, U.; Jansova, M.; Le Bihan, A-C; Tonon, N.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Chanon, N.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Finco, L.; Gascon, S.; Gouzevitch, M.; Grenier, G.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lattaud, H.; Lethuillier, M.; Mirabito, L.; Perries, S.; Popov, A.; Sordini, V; Touquet, G.; Vander Donckt, M.; Viret, S.; Toriashvili, T.; Tsamalaidze, Z.; Autermann, C.; Feld, L.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Preuten, M.; Rauch, M. P.; Schomakers, C.; Schulz, J.; Teroerde, M.; Wittmer, B.; Zhukov, V; Albert, A.; Duchardt, D.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Ghosh, S.; Guth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Keller, H.; Mastrolorenzo, L.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Schmidt, A.; Teyssier, D.; Thueer, S.; Fluegge, G.; Hlushchenko, O.; Kress, T.; Kuensken, A.; Mueller, T.; Nehrkorn, A.; Nowack, A.; Pistone, C.; Pooth, O.; Roy, D.; Sert, H.; Stahl, A.; Martin, M. Aldaya; Arndt, T.; Asawatangtrakuldee, C.; Babounikau, I; Beernaert, K.; Behnke, O.; Behrens, U.; Martinez, A. Bermudez; Bertsche, D.; Bin Anuar, A. 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G.; Barone, L.; Cavallari, F.; Cipriani, M.; Del Re, D.; Di Marco, E.; Diemoz, M.; Gelli, S.; Longo, E.; Marzocchi, B.; Meridiani, P.; Organtini, G.; Pandolfi, F.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Biino, C.; Cartiglia, N.; Cenna, F.; Cometti, S.; Costa, M.; Covarelli, R.; Demaria, N.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V; Monteil, E.; Monteno, M.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Angioni, G. L. Pinna; Romero, A.; Ruspa, M.; Sacchi, R.; Shchelina, K.; Sola, V; Solano, A.; Soldi, D.; Staiano, A.; Belforte, S.; Candelise, V; Casarsa, M.; Cossutti, F.; Da Rold, A.; Della Ricca, G.; Vazzoler, F.; Zanetti, A.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Lee, J.; Lee, S.; Lee, S. W.; Moon, C. S.; Oh, Y. D.; Pak, S., I; Sekmen, S.; Son, D. C.; Yang, Y. C.; Kim, H.; Moon, D. H.; Oh, G.; Goh, J.; Kim, T. 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T.; Luo, J.; Marlow, D.; Mei, K.; Ojalvo, I; Olsen, J.; Palmer, C.; Piroue, P.; Salfeld-Nebgen, J.; Stickland, D.; Tully, C.; Malik, S.; Norberg, S.; Barker, A.; Barnes, V. E.; Das, S.; Gutay, L.; Jones, M.; Jung, A. W.; Khatiwada, A.; Mahakud, B.; Miller, D. H.; Neumeister, N.; Peng, C. C.; Piperov, S.; Qiu, H.; Schulte, J. F.; Sun, J.; Wang, F.; Xiao, R.; Xie, W.; Cheng, T.; Dolen, J.; Parashar, N.; Chen, Z.; Ecklund, K. M.; Freed, S.; Geurts, F. J. M.; Kilpatrick, M.; Li, W.; Padley, B. P.; Redjimi, R.; Roberts, J.; Rorie, J.; Shi, W.; Tu, Z.; Zabel, J.; Zhang, A.; Bodek, A.; de Barbaro, P.; Demina, R.; Duh, Y. T.; Dulemba, J. L.; Fallon, C.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Hindrichs, O.; Khukhunaishvili, A.; Tan, P.; Taus, R.; Agapitos, A.; Chou, J. P.; Gershtein, Y.; Halkiadakis, E.; Heindl, M.; Hughes, E.; Kaplan, S.; Elayavalli, R. 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Texas A&M Univ Qatar, Doha, Qatar | ; Aarrestad, Thea/GLV-3031-2022; Ahmad, Ashfaq/ABJ-8366-2022; ., MOHD./AAD-2818-2019; Mejia Guisao, Jhovanny/ABG-3627-2021; Amendola, Chiara/KCK-3814-2024; bolun, zhang/ADT-6343-2022; Goy Lopez, Silvia/K-9200-2017; Arcidiacono, Roberta/ABF-3918-2020; Palla, Fabrizio/F-4727-2012; Josa, Isabel/K-5184-2014; Dewanjee, Dr. Ram Krishna/AFE-3468-2022; Karancsi, János/A-9710-2013; Mignerey, Alice/D-6623-2011; Abbrescia, Marcello/HTN-5367-2023; da Cruz e Silva, Cristóvão/KVA-3786-2024; Wang, Zhong/ABA-8164-2021; Verma, Rahul/W-7692-2019; Sarica, Ulascan/JXY-2829-2024; Hoh, Siew Yan/AAM-9562-2021; Venditti, Rosamaria/IVH-5947-2023; chen, gang/JRX-1197-2023; Bhatnagar, Vipin/Z-1079-2018; Steggemann, Jan/AAL-5700-2020; Abdulsalam, Abdulla/B-3386-2019; Gandrajula, Reddy/AFR-4403-2022; De La Cruz Burelo, Eduard/AAR-9103-2021; Ozdemir, Kadri/P-8058-2014; Pompili, Alexis/ISU-8813-2023; Dimova, Tatyana/AAQ-9303-2021; Wadud, Dr. Mohammad A./AAR-2288-2020; TUVE, Cristina/P-3933-2015; Katkov, Igor/E-2627-2012; Hoh, Siewyan/AAM-9562-2021; Juodagalvis, Andrius/F-6952-2018; SHIRAZI, SHARIF/G-4823-2010; Zalewski, Piotr/H-7335-2013; Aushev, Tagir/AAN-9735-2020; Gütay, Levent/C-5352-2009; Kovac, Marko/D-5817-2017; Obertino, Maria/ABH-1662-2020; Barreto, Lucas/J-9426-2013; Heredia De La Cruz, Ivan/L-2610-2018; Guler, Yalcin/AAG-7833-2019; Tomei, Thiago/E-7091-2012; Galati, Giuliana/AAV-8088-2020; Ling, Ting/J-9131-2018; Soares, Mara/GZM-6676-2022; Garg, Rocky/AAV-9845-2021; Marin, Jose/K-6412-2014; De Filippis, Nicola/AAD-6280-2019; Cadamuro, Luca/AAO-8637-2020; Chadeeva, Marina/M-9644-2019; Luo, Wuming/J-4263-2018; Krammer, Manfred/A-6508-2010; Azarkin, Maxim/N-2578-2015; Kumar, Vijay/G-4290-2011; Rodrigo, Teresa/F-6183-2018; Suarez, Rebeca/L-6128-2014; Lannon, Kevin/HGU-5755-2022; Trocino, Daniele/AGI-2155-2022; Kirpichnikov, Dmitry/U-9605-2019; Duarte, Javier/AAA-5414-2020; Gonzalez Caballero, Isidro/E-7350-2010; Gomez, Jaime/P-5242-2018; Zhang, Jixie/A-1461-2016; Hoorani, Hafeez/D-1791-2013; KUMAR, ASHOK/AAB-8729-2019; Blekman, Freya/ABD-7916-2020; Csanad, Mate/D-5960-2012; Chernyavskaya, Nadezda/F-2161-2015; Bunkowski, Karol/KOD-1567-2024; Gennai, Simone/P-2880-2015; Gregores, Eduardo/F-8702-2012; cartiglia, nicolo/ABF-1008-2021; Dordevic, Milos/HSH-8083-2023; GULER, E/AAH-4748-2019; Creanza, Donato/KIC-8791-2024; Fasanella, Daniele/ABD-9759-2020; Raidal, Martti/F-4436-2012; Lowette, Steven/HKV-3341-2023; de la Cruz, Begona/K-7552-2014; Daskalakis, Georgios/M-4480-2013; Puljak, Ivica/D-8917-2017; buotempo, salvatore/B-5210-2012; Liu, Tong/JDM-9629-2023; Jeitler, Manfred/H-3106-2012; gomez, marina/I-7072-2012; Pavlov, Borislav/O-8491-2018; Sun, Mingkang/NGS-8788-2025; Tuominen, Eija/A-5288-2017; Pérez-Calero Yzquierdo, Antonio/F-2235-2013; Perieanu, Adrian/D-3347-2018; Piedra, Jonatan/F-3247-2018; Gonzalez, Jesus David/IAN-5526-2023; Poudyal, Nabin/AAA-7981-2020; Hill, Christopher/B-5371-2012; Cocoros, Alice/AAA-6661-2021; Bornheim, Adi/L-2750-2019; Radogna, Raffaella/I-1033-2014; Kaur, Manjit/MBF-0295-2025; Hollar, Jonathan/KZU-8312-2024; My, Salvatore/HTM-1335-2023; Lezki, Samet/AAG-8277-2019; Paktinat Mehdiabadi, Saeid/AAY-8501-2020; Nguyen, Thong/JHS-8911-2023; Petrucciani, Giovanni/AAM-8482-2020; Pásztor, Gabriella/D-9716-2016; Misheva, Mihaela/AEK-6084-2022; Rout, Prasanna/N-8012-2019; Demianov, Andrei/E-4565-2012; Donato, Silvio/GPK-2262-2022; Mohammadi Najafabadi, Mohammad/GLR-0213-2022; Salvatico, Riccardo/JAO-1069-2023; Bilican, Fuat/AGD-3089-2022; Varela, Joao/K-4829-2016; Bonacorsi, Daniele/F-1505-2014; Camporesi, Tiziano/F-8307-2013; Cavallari, Francesca/AAE-8571-2019; Al-bataineh, Ayman/AAK-1197-2021; Kadastik, Mario/B-7559-2008; Dai, Yuxiang/JGC-6867-2023; Li, Ruixi/AAE-7585-2022; Lee, Suhyun/AAA-3368-2022; Strologas, John/GWR-2036-2022; Dermenev, Alexander/M-4979-2013; Belyaev, Alexander/F-6637-2015; Mrenna, Stephen/KIL-6081-2024; Sert, H/ABA-5645-2020; Gil Da Silveira, Gustavo/N-7279-2014; Arneodo, Michele/ABF-7197-2020; Montagna, Paolo Maria/AAY-8669-2021; Calvo Alamillo, Enrique/L-1203-2014; Fouz Iglesias, Maria Cruz/AAF-1105-2019; de Paula Carvalho, Wagner/V-3343-2019; Anampa, Kenyi/GPX-8479-2022; Ramos, Juan/AAF-3114-2019; Calderon, Dr. Alicia/K-3658-2014; Meijers, Femke/IUM-7952-2023; Zhukov, Valery/K-3615-2013; Yang, YiChen/KEI-0140-2024; , Luo/AAP-8401-2020; Pantaleo, Francesca/AAV-4919-2020; Casarsa, Massimo/L-3623-2018; Kirakosyan, Martin/N-2701-2015; Shchutska, Lesya/M-7052-2017; KHAN, Masood/AAA-7384-2020; Duarte-Campderrós, Jordi/F-5025-2018; Leonardo, Nuno/M-6940-2016; Theofilatos, Konstantinos/AAL-9162-2021; Asawatangtrakuldee, Chayanit/GLS-1465-2022; Zaleski, Stéphane/N-8879-2017; Mora Herrera, Clemencia/AAD-6466-2021; Rolandi, Luigi/E-8563-2013; Okhotnikov, Vitalii/O-4293-2017; Chang, Yuan/AFO-2322-2022; Chistov, Ruslan/B-4893-2014; kaya, mithat/D-8062-2011; Janssen, Xavier/E-1915-2013; Hobson, Peter/C-8919-2016; Majumder, Devdatta/X-2542-2019; Soffi, Livia/HSC-0774-2023; Castilla-Valdez, Heriberto/F-8906-2019; Sguazzoni, Giacomo/J-4620-2015; Jo, Hyon-Suk/HGC-7070-2022; Piotrzkowski, Krzysztof/IST-1292-2023; Majumder, Gobinda/MTB-7852-2025; Rossi, Andrea/AGH-4546-2022; Ogul, Hasan/S-7951-2016; Selvaggi, Michele/CAJ-2129-2022; De Iorio, Agostino/HJH-7155-2023; Ulrich, Ralf/Q-3882-2019; Voevodina, Elena/G-7314-2016; Field, Robert/B-6850-2008; Kumar, Ramandeep/AAY-6388-2020; Shmatov, Sergei/J-5621-2014; Dozen, Candan/IQS-4597-2023; Vilar Cortabitarte, Rocio/D-7454-2014; Battilana, Carlo/AAA-7345-2020; Rivero, Celso/V-6916-2017; Menasce, Dario/A-2168-2016; OZOK, FERHAT/A-6803-2018; SIMSEK, Ali Eren/AFO-3050-2022; Yalvac, Metin/LYP-1776-2024; Seidel, Marcus/AAA-4799-2022; boccali, tommaso/ACT-7600-2022; Rovelli, Tiziano/K-4432-2015; Schwarz, Dennis/GYA-4479-2022; González-Calatrava, Isidro/S-4410-2018; KÖMÜRCÜ, YILDIRAY/AAC-6166-2020; Gupta, Rajat/GVS-7964-2022; Voevodina, Elena/AAI-4805-2021; Joo, Changwoo/ABI-4034-2020; D'Alessandro, Raffaello/F-5897-2015; Manisha, Manisha/KVB-3511-2024; Mussgiller, Andreas/AAF-6933-2019; Ganjour, Serguei/D-8853-2011; Viliani, Lorenzo/AAU-3462-2021; Venditti, Rosamaria/I-1030-2014; Navarria, Francesco Luigi/ACP-7823-2022; Snoeys, Walter/K-8259-2015; Da Silveira, Gustavo Gil/N-7279-2014; Moraes, Arthur/F-6478-2010; Ince Lezki, Merve/AAG-5283-2021; Della Ricca, Giuseppe/B-6826-2013; Bilki, Burak/GRF-5072-2022; Behera, Prafulla/AAK-8686-2020; Venturi, Andrea/J-1877-2012; Tinoco Mendes, Andre David/D-4314-2011; Lu, Lu/JPE-5187-2023; Van Haevermaet, Hans/HOI-0668-2023; Tytgat, Michael/F-3732-2018; Polikarpov, Sergey/C-8939-2016; de guio, federico/B-8553-2009; Mundim, Luiz/A-1291-2012; Iorio, Alberto Orso Maria/GXA-0233-2022; Chenarani, Shirin/AAO-8918-2021; Organtini, Giovanni/D-3990-2009; Leonidov, Andrey/P-3197-2014; BAT, Ayse/GOK-2363-2022; Luukka, Panja-Riina/S-8956-2018; Xu, Qiao/AAM-9242-2020; Seixas, Joao/F-5441-2013; Kim, Victor/L-1648-2013; Piperov, Stefan/Q-1980-2017; González Caballero, Isidro/E-7350-2010; Demiragli, Zeynep/IZE-1919-2023; Scodellaro, Luca/K-9091-2014; Paramatti, Riccardo/IXW-6363-2023; de Jesus Damiao, Dilson/G-6218-2012; Naimuddin, Md/JXL-7104-2024; Palladino, Vito/AAA-5328-2020; Kodolova, Olga/IQV-0056-2023; Bourilkov, Dimitri/AAO-4908-2020; LI, WEI/ISS-1208-2023; celik, Ali/A-3563-2019; Wang, Shaohui/HKO-6774-2023; Sznajder, Andre/L-1621-2016; Ćirković, Predrag/G-8059-2012; Hernández Calama, José María/AAW-6394-2021; Grandi, Claudio/B-5654-2015; Palencia Cortezon, Jose Enrique/F-1059-2017; yagil, avi/I-3278-2018; Swain, Subrat/IXW-8971-2023; Di Mattia, Alessandro/HNQ-0365-2023; Calligaris, Luigi/K-2018-2017; Maier, Benedikt/AAN-1929-2021; Verwilligen, Piet/AAA-8819-2019; YU, SHIN-SHAN/JPW-8635-2023; Hall-Wilton, Richard/U-6918-2019; Ranieri, Antonio/E-9545-2015; Seo, Seon-Hee/M-1189-2019; celik, Ali/K-6506-2019; Jafari, Abideh/JVP-2320-2024; Norjoharuddeen, Nurfikri/S-3109-2018; Gülmez, Erhan/P-9518-2015; Neumeister, Norbert/JXN-1675-2024; Hong, Byungsik/O-8359-2018; Dremin, Igor/K-8053-2015; Sharma, Vandana/MDT-7486-2025; D'Alessandro, Raffaello/P-4138-2019; Parygin, Pavel/C-8908-2016; Gozalez-Lopez, Oscar/AAH-3533-2019; Duarte-Campderros, Jordi/F-5025-2018; Melo da Costa, Eliza/AAX-2394-2021; Missiroli, Marino/AAA-9072-2021; Di Florio, Adriano/ABA-3631-2020; Fallavollita, Francesco/AAL-8850-2020; Tcherniaev, Evgueni/G-3453-2016; Tadel, Matevz/H-3204-2016; Masciovecchio, Mario/IXD-8018-2023; Hos, ilknur/T-1165-2019; Sarkar, Debojit/GNW-6585-2022; Danilov, Mikhail/C-5380-2014; Bermudez Martinez, Armando/HHN-4625-2022; Lohan, Manisha/KVB-3511-2024; Borg, Jacqueline/AFS-8793-2022; Jain, Shikha/ADG-6788-2022; Felcini, Marta/S-2684-2019; Polatoz, Ayse/AAF-6722-2021; Cimmino, Anna/AAA-1673-2020; Albataineh, Ayman Ahmad/AAK-1197-2021; ABDULLAH, WAN/B-5439-2010; Salvini, Paola/G-1670-2018; Fernandez Perez Tomei, Thiago Rafael/E-7091-2012; Meyer, Manuel/E-2697-2016; Eskut, Eda/B-8905-2018; Sen, S./C-6473-2014; Maravin, Yurii/N-9259-2018; Malakhov, Alexander/D-5702-2016; Gokbulut, Gul/G-2141-2018; Fernandez Menendez, Javier/B-6550-2014; Azzi, Patrizia/H-5404-2012; Ligabue, Franco/F-3432-2014; Roy, Ashim/HLV-9248-2023; Ivanov, Andrew/A-7982-2013; Buchanan, James/ABB-6906-2021; Fiorendi, Sara/ABA-3311-2021; Iaydjiev, Plamen/AEB-8785-2022; Garutti, Erika/NRY-4813-2025; Obraztsov, Stepan/E-1895-2012; Romero, Luciano/B-8917-2018; Finco, Linda/AAH-3618-2019; Hsu, En-Chi/AAH-4589-2021; Go, Yeonju/HSH-6030-2023; Konecki, Marcin/G-4164-2015; Kraetschmer, Ilse/LSK-3076-2024; Marlow, Daniel/C-9132-2014; Caspart, Rene/LNQ-1560-2024; Zorbilmez, Caglar/ABH-1905-2020; Spiga, Daniele/ABB-8842-2021; Manganote, Edmilson/K-8251-2013; Benaglia, Andrea/AGF-5495-2022; Terkulov, Adel/M-8581-2015; Focardi, Ettore/E-7376-2012; Kapoor, Aakanksha Rajiv/KAM-5986-2024; Martinelli, Maurizio/AAC-8282-2021; Safronov, Grigory/F-8494-2017; Veckalns, Viesturs/AAZ-3105-2020; Barria, Patrizia/AAT-3894-2020; Langenegger, Urs/A-4578-2008; Dewanjee, Ram Krishna/AFE-3468-2022; Luukka, Panja/S-8956-2018; Geralis, Theodoros/I-6467-2016; candelise, vieri/H-2195-2015; Barnyakov, Alexander/AAZ-8825-2021; Delgado Peris, Antonio/AAA-5165-2019; Cipriani, Marco/MHU-3780-2025; Lista, Luca/M-2912-2019; Meridiani, Paolo/ABH-1655-2020; Trocsanyi, Zoltan/A-5598-2009; Hildreth, Michael/LWK-8035-2024; Ozkorucuklu, Suat/AAE-7535-2020; Cakir, Altan/ABD-4450-2020; Govoni, Pietro/K-9619-2016; Elkafrawy, Tamer/HPF-5873-2023; HAJAHMAD (Wael HAJ AHMAD), Vael/E-6738-2016; Minafra, Nicola/AAZ-6781-2020; Di Crescenzo, Antonia/GVS-4144-2022; Sacchi, Roberto/AGS-7202-2022; Ragazzi, Stefano/D-2463-2009; Nandan, Saswati/JVY-9235-2024; Kim, Junghwan/AAQ-9204-2021; Asavapibhop, Burin/X-1477-2019; Tsirou, Andromachi/HHC-6869-2022; Malvezzi, Sandra/B-8524-2009; Colaleo, Anna/HSG-1637-2023; Andreev, Vladimir/M-8665-2015; Kumar, Abhishek/KCL-4676-2024; Bainbridge, Robert/JRW-6150-2023; Androsov, Konstantin/HJA-2332-2022; Kim, Myeong/AAS-1488-2021; Kumar, Saroj/AAU-5615-2021; Spagnolo, Paolo/G-3401-2017; Tiras, Emrah/ABG-2354-2020; Yetkin, E. Asli/AAQ-8732-2020; kangal, evrim/L-8978-2013; ozok, ferhat/A-6803-2018; Loukas, Dimitris/ABF-1478-2020; Mercadante, Pedro/K-1918-2012; IBRAHIM, ZAINOL/C-1121-2010; Pedraza Morales, Maria Isabel/ABC-7289-2021; Golubev, Nikolay/L-4131-2017; Cankocak, Kerem/A-1507-2018; Shah, Aashaq/GYI-8983-2022; Errico, Filippo/AAD-7755-2019; Sanchez-Hernandez, Alberto/L-4955-2017; Chaparro Sierra, Luisa Fernanda/AHD-3083-2022; Gomez, Gervasio/K-2860-2014; Akgun, Bora/AAM-8311-2021; Biino, Cristina/AAF-1585-2020; Gómez-Vargas, Juan Carlos/S-5253-2016; Kim, Geuk-Nam/IAO-4628-2023; Padhi, Saswat/LTF-2060-2024; Kardapoltsev, Leonid/AAQ-4047-2021; Teles, Patricia/ABF-9723-2021; Paulini, Manfred/N-7794-2014; Elgammal, Sherif/KGM-3809-2024; Kim, Jeehoon/K-3763-2012; Menichelli, Mauro/AAQ-4026-2020; Benussi, Luigi/O-9684-2014; Elwood, Adam/GSN-1799-2022; Flix, Josep/G-5414-2012; Fabbri, Fabio/Q-9443-2019; Colafranceschi, Stefano/M-1807-2016; Perez-Calero Yzquierdo, Antonio/F-2235-2013; Rossi, Alessandro/I-8230-2017; Vilela Pereira, Antonio/L-4142-2016; GURPINAR GULER, EMINE/V-2170-2018; Trejo, Raul/F-7924-2019; Primavera, Federica/KUD-4067-2024; Wimpenny, Stephen/K-8848-2013; Krücker, Dirk/X-1844-2019; Bedoya, Cristina/K-8066-2014; de Souza Sandro, Fonseca/ABB-8505-2020; zhao, jize/AAS-1102-2021; Grynyov, Borys/D-1714-2010; Kharlamova, Tatyana/AAQ-5430-2020; Boos, Eduard/D-9748-2012; Radics, Balint/AAN-2721-2020; Mulders, Martijn/AGK-9612-2022; Zeyrek, Mehmet/ABA-7235-2020; Pinna Angioni, Gian Luca/AAZ-6834-2021; Bartosik, Nazar/ABF-2326-2020; Torassa, Ezio/I-1788-2012; Ferencek, Dinko/G-6411-2015; Ciulli, Vitaliano/ABC-5440-2020; Popov, Vitaliy/C-9925-2016; Vai, Ilaria/AAT-5836-2020; Puerta Pelayo, Jesus/L-1632-2014; SAHOO, DILLIP/AAA-4724-2022; Martinez Rivero, Celso/V-6916-2017; Cepeda, María/HNO-9314-2023; Triossi, Andrea/K-2813-2017; Lokhtin, Igor/D-7004-2012; chen, yue/JEF-2824-2023; Bernardes, Cesar Augusto/D-2408-2015; Wang, Yifan/KDO-8319-2024; Hernandez Calama, Jose Maria/H-9127-2015; Kim, Jaehyun/LNR-5242-2024; D'Hondt, Jorgen/ABD-7930-2020; Chang, Philip/AAN-3350-2021; Clements, Emma/HSG-3424-2023; Sanchez Rosas, Luis Junior/IXW-7054-2023; ciocci, maria agnese/I-2153-2015; walia, genius/ACL-5617-2022; MAESTRE, JUAN/I-5763-2015; Kalsi, Amandeep Kaur/JNR-9607-2023; Pastrone, Nadia/JRZ-0528-2023; Malbouisson, H./N-6733-2015; Giammanco, Andrea/O-5017-2019; Traczyk, Piotr/ABA-8929-2020; Boimska, Bożena/Z-6088-2019; Liu, Jianglai/P-2587-2015; Martin Perez, Cristina/MEP-9682-2025; Mesyats, Gennady/L-3138-2015; Sculac, Toni/AAE-4141-2019; Agaras, Merve/AAB-5221-2021; Gelmi, Amy/D-4456-2012; Belyaev, Andrey/E-1540-2012; Navarria, Francesco/ACP-7823-2022; fan, jing/KHX-6210-2024; tosi, mia/J-5777-2012; Shopova, Mariana/HKW-0149-2023; Pinna, Daniele/AAF-4490-2021; Everaerts, Pieter/KIE-3686-2024; Ghezzi, Alessio/I-9494-2012; Reyes-Almanza, Rogelio/GQJ-8236-2022; Tumasyan, Armen/P-3390-2015; Hegeman, Jeroen/HJA-1005-2022; Zolkapli, Zukhaimira/AAA-4039-2019; Bortignon, Pierluigi/AAF-6561-2020; Amiss, Anna/AAB-2424-2020; Waltenberger, Wolfgang/H-9330-2018; KUMAR, ASHOK/AAF-4055-2021; Liu, huihui/HME-1734-2023; Matorras, Francisco/I-4983-2015; Flore, Carlo/HZJ-6377-2023; Khakzad, Mohsen/ABB-1945-2021; Verdini, Piero/J-2839-2012; Sagir, Sinan/A-5219-2018; Isildak, Bora/D-8829-2015; d'Enterria, David/W-7763-2019; Bouhali, Othmane/JXM-3572-2024; Reis, Tomas/JHS-4868-2023; Petkov, Peicho/M-2080-2016; Demiroglu, Zuhal Seyma/A-5168-2016; Faccioli, Pietro/AAF-1499-2019; Lu, Wang/JVO-0416-2024; Rabady, Dinyar/GSN-9210-2022; Salerno, Roberto/A-7509-2010; Skoro, Goran/F-3642-2010; Voytishin, Nikolay/IYJ-7234-2023; Roy Chowdhury, Suvankar/IXN-7230-2023; Gribushin, Andrei/J-4225-2012; Fienga, Francesco/HKE-1204-2023; Gleyzer, Sergei/AAE-6726-2020; Amoroso, Simone/AAW-4334-2021; Doğangün, Oktay/AAC-1579-2022; Dhingra, Nitish/ABA-9507-2020; ASILAR, Ece/ABC-4577-2020; Brandt, Steven/AGV-6975-2022; Gerosa, Raffaele/AGL-6079-2022; Amapane, Nicola/HDN-9630-2022; Chaparro Sierra, Luisa/AHD-3083-2022; Van Remortel, Nick/AGQ-7045-2022; Cavallo, Nicola/F-8913-2012; ciocci, maria/I-2153-2015; Chen, Fang/JZE-4446-2024; Stahl, Achim/E-8846-2011; Fiori, Francesco/H-1454-2018; BARRILLON, Stephanie/ABA-6873-2021; Saka, Halil/GPF-8892-2022; Korenkov, Vladimir/H-7665-2017; Ryutin, Roman/P-3615-2015; LECOQ, Paul/S-7246-2019; Kumar, Parveen/I-3259-2015; Hall, Jeter/F-6108-2013; Ahuja, Sudha/I-4097-2014; Guth, Adrien/JVM-7643-2024; Lander, Roddy/KQU-7736-2024; Choudhury, Seema/LEM-7962-2024; Marco de Lucas, Jesus/B-8735-2008; TUVE', Cristina/P-3933-2015; Mousa, Jehad/I-8019-2019; Moscatelli, Francesco/N-6333-2014; Goh, Junghwan/Q-3720-2016; staiano, amedeo/KVB-8364-2024; Ferro, Fabrizio/HMP-5516-2023; KAYİS TOPAKSU, Aysel/B-8910-2018; Wang, Dayong/LRC-9881-2024; Wulz, Claudia-Elisabeth/H-5657-2011; Moon, Chang-Seong/J-3619-2014; Boimska, Bozena/Z-6088-2019; Skovpen, Kirill/HKM-3974-2023; Doroba, krzysztof/AAA-3419-2021; Dutta, Ipsita/JDW-8207-2023; Aimè, Chiara/GXG-4131-2022; Singh, J B/IXD-2130-2023; mohammed, yasser/P-1462-2016; 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57220064375; 57216597785; 56592245400; 57220064259; 57220064396; 57203514331; 57199996011; 35222476000; 57206353679; 57194775056; 36443322800; 16226119300; 55951354400; 35356070900; 57201370087; 56414432900; 47960970300; 35313880900; 35308730700; 8984617000; 57189693933; 7006404363; 7201657574; 7003879901; 56681978600; 36123824900; 58601492400; 35227990900; 56181581800 | PHYSICAL REVIEW LETTERS | PHYS REV LETT | 0031-9007 | 1079-7114 | 125 | 10 | SCIE | PHYSICS, MULTIDISCIPLINARY | 2020 | 9.161 | 7.6 | 0.54 | 2025-06-25 | 15 | 16 | Binary alloys; Bosons; Colliding beam accelerators; Energy dissipation; Germanium alloys; Hadrons; Heavy ions; Ion sources; Center-of-mass energies; Centre-of-mass energies; Charm quarks; CMS detectors; Heavy-ion collisions; Jet axis; Nucleon-nucleon; Pb-Pb collisions; Proton proton collisions; Radial distributions; Diffusion | English | 2020 | 2020-09-03 | 10.1103/physrevlett.125.102001 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||||
| ○ | ○ | Article | Unusual Pressure-Induced Quantum Phase Transition from Superconducting to Charge-Density Wave State in Rare-Earth-Based Heusler LuPd2In Compound | We investigate the pressure effects on the electronic structures and phonon properties of rare-earth-based cubic-Heusler compound LuPd2In, on the basis of ab initio density functional theory. We find the occurrence of intriguing phase transition from the superconducting (SC) to charge-density wave (CDW) state under pressure (P), which is quite unusual in that the pressure is detrimental to the CDWstate in usual systems. The SC transition temperature T-C of LuPd2In increases first with increasing pressure, up to P-C approximate to 28 GPa, above which a quantum phase transition into the CDW state takes place. This extraordinary transition originates from the occurrence of phonon softening instability at a special q = M in the Brillouin zone. We thus propose that LuPd2 In is a quite unique material, in which the CDW quantum critical point is realized under the SC dome by applying the pressure. | Kim, Heejung; Shim, J. H.; Kim, Sooran; Park, Jae-Hoon; Kim, Kyoo; Min, B., I | POSTECH, Dept Phys, Pohang 37673, South Korea; Max Planck POSTECH Korea Res Initiat, MPPHC CPM, Pohang 37673, South Korea; POSTECH, Dept Chem, Pohang 37673, South Korea; Kyungpook Natl Univ, Dept Phys Educ, Daegu 41566, South Korea; Korea Atom Energy Res Inst KAERI, 111 Daedeok Daero, Daejeon 34057, South Korea; POSTECH, Div Adv Mat Sci, Pohang 37673, South Korea | ; Park, Jae/A-1275-2018; Kim, Kwang Ho/CAH-2165-2022; MIN, BYUNG IL/U-3364-2017 | 56640022300; 7201856274; 55146733200; 15036184900; 57214859153; 7202932118 | kyoo@kaeri.re.kr;bimin@postech.ac.kr; | PHYSICAL REVIEW LETTERS | PHYS REV LETT | 0031-9007 | 1079-7114 | 125 | 15 | SCIE | PHYSICS, MULTIDISCIPLINARY | 2020 | 9.161 | 7.6 | 0.27 | 2025-06-25 | 8 | 8 | CRITICAL-POINT; TEMPERATURE; CRITICALITY; BENEATH; SYSTEM | Charge density; Charge density waves; Density functional theory; Electronic structure; Phase transitions; Phonons; Pressure effects; Rare earths; Ternary alloys; Ab initio density functional theories (DFT); Brillouin zones; Charge density wave state; Heusler compound; Phonon properties; Phonon softening; Quantum critical points; Quantum phase transitions; Quantum theory | English | 2020 | 2020-10-05 | 10.1103/physrevlett.125.157001 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||
| ○ | ○ | Article | A test-suite of non-convex constrained optimization problems from the real-world and some baseline results | Real-world optimization problems have been comparatively difficult to solve due to the complex nature of the objective function with a substantial number of constraints. To deal with such problems, several metaheuristics as well as constraint handling approaches have been suggested. To validate the effectiveness and strength, performance of a newly designed approach should be benchmarked by using some complex real-world problems, instead of only the toy problems with synthetic objective functions, mostly arising from the area of numerical analysis. A list of standard real-life problems appears to be the need of the time for benchmarking new algorithms in an efficient and unbiased manner. In this study, a set of 57 real-world Constrained Optimization Problems (COPs) are described and presented as a benchmark suite to validate the COPs. These problems are shown to capture a wide range of difficulties and challenges that arise from the real life optimization scenarios. Three state-of-the-art constrained optimization methods are exhaustively tested on these problems to analyze their hardness. The experimental outcomes reveal that the selected problems are indeed challenging to these algorithms, which have been shown to solve many synthetic benchmark problems easily. | Kumar, Abhishek; Wu, Guohua; Ali, Mostafa Z.; Mallipeddi, Rammohan; Suganthan, Ponnuthurai Nagaratnam; Das, Swagatam | Indian Inst Technol BHU, Dept Elect Engn, Varanasi 221005, Uttar Pradesh, India; Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Peoples R China; Jordan Univ Sci & Technol, Sch Comp Informat Syst, Irbid 22110, Jordan; Kyungpook Natl Univ, Sch Elect Engn, Daegu 41566, South Korea; Nanyang Technol Univ, Sch Elect Elect Engn, Singapore 639798, Singapore; Indian Stat Inst, Elect & Commun Sci Unit, Kolkata, India | Kumar, Abhishek/ABA-5251-2021; Das, Swagatam/AAG-6753-2019; Mallipeddi, Rammohan/AAL-5306-2020; Wu, Guohua/JMA-8934-2023; Ali, Mostafa/AEA-4964-2022; Suganthan, Ponnuthurai/A-5023-2011 | 57206266703; 57188928924; 55470910800; 25639919900; 7003996538; 24729258600 | epnsugan@ntu.edu.sg; | SWARM AND EVOLUTIONARY COMPUTATION | SWARM EVOL COMPUT | 2210-6502 | 2210-6510 | 56 | SCIE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;COMPUTER SCIENCE, THEORY & METHODS | 2020 | 7.177 | 7.7 | 30.61 | 2025-06-25 | 345 | 387 | Real-world optimization problem; Metaheuristics; Constraint handling technique; Benchmark suite | OPTIMAL PULSEWIDTH MODULATION; DIFFERENTIAL EVOLUTION; STRUCTURAL OPTIMIZATION; GLOBAL OPTIMIZATION; DESIGN; ALGORITHM; INTEGER; STRATEGY | Benchmark suite; Constraint handling technique; Metaheuristics; Real-world optimization problem | Benchmarking; Constrained optimi-zation problems; Constrained optimization methods; Constraint handling; Objective functions; Real-life problems; Real-world optimization; Real-world problem; Synthetic benchmark; Constrained optimization | English | 2020 | 2020-08 | 10.1016/j.swevo.2020.100693 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Analyses and numerical evaluation of integrated time-series monitoring datasets including CO2 concentration and fluxes at controlled CO2 release site in South Korea | In Eumseong, Republic of Korea, a controlled CO2 release site was designed by Korea CO2 Storage Environmental Management (K-COSEM) Research Center to evaluate properly integrated CO2 monitoring tools at the soil surface. CO2 was released from the 55 m-length PVC-pipe buried at 2.5 m-depth, and then, CO2 concentration and flux were monitored from August 1, 2015 to March 21, 2018 (965 days). Influencing environmental factors such as meteorological data, soil moisture and temperature at 5 different depths were monitored concurrently. The measured soil moisture content decreased rapidly when the soil temperature dropped below 0 degrees C. When CO2 release began, both surface CO2 flux and soil CO2 concentration increased with a time lag. However, with occurrence of precipitation events, CO2 flux decreased to almost 0 cm(3) cm(-2) day(-1) but soil CO2 concentration increased rapidly. The HYDRUS-SOILCO2 predicted soil moisture, temperature and natural background CO2 flux compared to measured data (R-2 = 0.74, 0.89, and 0.85). The 10 scenarios were constructed with different soil characteristic curves. Unlike soil moisture content, the simulated natural CO2 flux did not depend heavily on soil characteristics. The mass of released CO2 was estimated by the difference between the measured CO2 flux data during the release test and natural CO2 prediction model. At the 2nd CO2 release test, there was 1.5 times difference between the actual released CO2 mass and calculated CO2 mass. | Seo, Do Hyun; Han, Weon Shik; Park, Eungyu; Jeong, Jina; Oh, Yun-Yeong; Kim, Hyun-Jun; Yoo, Gayoung; Jun, Seong-Chun; Yun, Seong-Taek | Yonsei Univ, Dept Earth Syst Sci, Seoul, South Korea; Kyungpook Natl Univ, Dept Geol, Daegu, South Korea; Korea Univ, Dept Earth & Environm Sci, Seoul, South Korea; Korea Univ, K COSEM Res Ctr, Seoul, South Korea; Chonnam Natl Univ, Dept Forest Resources, Gwangju, South Korea; Kyung Hee Univ, Dept Environm Sci & Engn, Yongin, South Korea; GeoGreen21 Co Ltd, Seoul, South Korea | Yoo, Gayoung/AAK-7174-2020; Kim, Hyuk Soon/IQW-9348-2023; Han, Weon Shik/KCY-0126-2024 | 57217417353; 57226420125; 23995577700; 55488558800; 55886779300; 55822964539; 14051329400; 57195726847; 57221362072 | hanw@yonsei.ac.kr; | JOURNAL OF HYDROLOGY | J HYDROL | 0022-1694 | 1879-2707 | 590 | SCIE | ENGINEERING, CIVIL;GEOSCIENCES, MULTIDISCIPLINARY;WATER RESOURCES | 2020 | 5.722 | 7.7 | 0.61 | 2025-06-25 | 9 | 9 | CO2 flux; CO2 release; Soil moisture content; Meteorological; CO2 mass | CARBON-DIOXIDE TRANSPORT; SOIL; LEAKAGE; CAPTURE; SEQUESTRATION; TEMPERATURE; STORAGE; SIMULATION; INJECTION; CHEMISTRY | CO<sub>2</sub> flux; CO<sub>2</sub> mass; CO<sub>2</sub> release; Meteorological; Soil moisture content | South Korea; Digital storage; Environmental management; Meteorology; Moisture determination; Polyvinyl chlorides; Soil moisture; Soil surveys; Time series analysis; CO2 concentration; Environmental factors; Meteorological data; Monitoring tools; Natural backgrounds; Precipitation events; Republic of Korea; Soil characteristics; carbon dioxide; carbon storage; concentration (composition); design; environmental factor; moisture content; precipitation (climatology); prediction; soil carbon; soil moisture; soil temperature; Carbon dioxide | English | 2020 | 2020-11 | 10.1016/j.jhydrol.2020.125213 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Deep-learning-based retrieval of piping component catalogs for plant 3D CAD model reconstruction | In process plants, 3D computer-aided design (CAD) plant models are frequently generated via reverse engineering using point cloud data. Generating a 3D model from scan data consists of point cloud collection, preprocessing, and modeling. The process of 3D modeling to create a plant 3D CAD model from a registered point cloud consists of grouping similar point clouds into several segmented point clouds, identifying the components represented by each segment, selecting catalogs for the components, and placing them into 3D design space. The core of a 3D modeling process is to identify components represented by the segmented point clouds. This study proposes a deep learning-based method to retrieve catalogs for piping components to support the reconstruction of a plant 3D CAD model from point clouds. A prototype catalog retrieval system is implemented based on the proposed method, and the retrieval system is evaluated experimentally using point clouds obtained from a process plant. The results demonstrate that, in terms of accuracy, the proposed method outperformed a conventional shape descriptor-based retrieval method. (c) 2020 Elsevier B.V. All rights reserved. | Kim, Hyungki; Yeo, Changmo; Lee, Inhwan Dennis; Mun, Duhwan | Jeonbuk Natl Univ, Div Comp Sci & Engn, 567 Baekje Daero, Jeonju 54896, Jeollabuk Do, South Korea; Kyungpook Natl Univ, Dept Precis Mech Engn, 2559 Gyeongsang Daero, Sangju 37224, Gyeongsangbuk D, South Korea; Korea Adv Inst Sci & Technol, Dept Mech Engn, 291 Daehak Ro, Daejeon 34141, South Korea; Korea Univ, Sch Mech Engn, 145 Anam Ro, Seoul 02841, South Korea | Mun, Duhwan/AAC-5360-2020 | 55822427500; 57219221804; 57216809619; 23019305700 | hk.kim@jbnu.ac.kr;ycm2420@gmail.com;inhwanlee@kaist.ac.kr;dhmun@korea.ac.kr; | COMPUTERS IN INDUSTRY | COMPUT IND | 0166-3615 | 1872-6194 | 123 | SCIE | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | 2020 | 7.635 | 7.7 | 2.98 | 2025-06-25 | 35 | 46 | Catalog retrieval; Component type identification; Deep learning; Plant 3D CAD model; Point cloud; Reconstruction | POINT CLOUD; EQUIPMENT; CONSTRUCTION; RECOGNITION | Catalog retrieval; Component type identification; Deep learning; Plant 3D CAD model; Point cloud; Reconstruction | 3D modeling; Computer aided design; Deep learning; Image reconstruction; Information retrieval; Learning systems; Reverse engineering; 3D CAD Modeling; Learning-based methods; Piping components; Point cloud data; Process plants; Retrieval methods; Retrieval systems; Shape descriptors; Three dimensional computer graphics | English | 2020 | 2020-12 | 10.1016/j.compind.2020.103320 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Development of an efficient data-driven method to estimate the hydraulic properties of aquifers from groundwater level fluctuation pattern features | A method to develop a data-driven model able to estimate hydraulic properties based on groundwater level (GWL) fluctuation patterns is proposed. In particular, a preprocessing method using a denoising autoencoder (DAE) is incorporated into the proposed method to improve the performance of the developed method. DAE is applied to prepare the input variable of the estimation model by extracting informative low-dimensional features from the original high-dimensional GWL data. Before applying the proposed DAE to this study, the reliability of applying the DAE is validated. First, the ability to reduce the noise of GWL data is validated by observing that an average of 71% of the noise is reduced. Additionally, the performances of the extracted principal characteristics of the GWL data is confirmed by reasonable matches in the extracted features to the corresponding hydraulics of the aquifers. In this case, both synthetic data and actual data acquired over South Korea are applied. Based on the validated DAE results, models to estimate two types of hydraulic properties are constructed. The estimation performances of the models are quantitatively validated using the correlation coefficient between the estimated and actual hydraulic properties. Overall, the constructed models for k and alpha/n show an appropriate estimation accuracy with a high correlation coefficient between the actual result and estimate (0.8663 and 0.7207, respectively). Therefore, using the proposed method, the hydraulic properties of an un-informed aquifer can be effectively inferred given GWL data without conducting field experiments (e.g., pumping tests). The proposed method is promising for efficient evaluations of the physical hydraulics of un-informed aquifers and, therefore, can be used as an effective tool to manage groundwater resources. | Jeong, Jiho; Jeong, Jina; Park, Eungyu; Lee, Byung Sun; Song, Sung-Ho; Han, Weon Shik; Chung, Sungwook | Kyungpook Natl Univ, Dept Geol, Daegu, South Korea; Korea Rural Community Corp, Rural Res Inst, Ansan, South Korea; Yonsei Univ, Dept Earth Syst Sci, Seoul, South Korea; Korea Basic Sci Inst, Div Earth & Environm Sci, Choengju, South Korea | ; Han, Weon Shik/KCY-0126-2024 | 57218684286; 55488558800; 23995577700; 55933858800; 16022930500; 57226420125; 57218684196 | jeong.j@knu.ac.kr; | JOURNAL OF HYDROLOGY | J HYDROL | 0022-1694 | 1879-2707 | 590 | SCIE | ENGINEERING, CIVIL;GEOSCIENCES, MULTIDISCIPLINARY;WATER RESOURCES | 2020 | 5.722 | 7.7 | 0.26 | 2025-06-25 | 4 | 4 | Hydraulic property estimation; Groundwater level fluctuation patterns; Principal feature extraction; Non-linear data dimensionality reduction; Denoising autoencoder | WATER-TABLE FLUCTUATIONS; MODEL; RECHARGE; ZONE | Denoising autoencoder; Groundwater level fluctuation patterns; Hydraulic property estimation; Non-linear data dimensionality reduction; Principal feature extraction | South Korea; Aquifers; Data mining; Hydraulics; Hydrogeology; Correlation coefficient; Data-driven methods; Data-driven model; Estimation models; Estimation performance; Groundwater level fluctuation; Hydraulic properties; Pre-processing method; aquifer characterization; data assimilation; data envelopment analysis; data interpretation; groundwater resource; hydraulic conductivity; pumping; signal-to-noise ratio; water level; Groundwater resources | English | 2020 | 2020-11 | 10.1016/j.jhydrol.2020.125453 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Estimation of groundwater level based on the robust training of recurrent neural networks using corrupted data | In the present study, a cost function is developed for the robust training of recurrent neural-network models using groundwater-level data that are corrupted by outliers and noise. The optimal cost function in this study utilizes least trimmed squares (LTS) with asymmetric weighting (AW) and the Whittaker smoother (WS), which have different outlier- or noise-rejecting mechanisms. The developed cost function is benchmarked with other cost functions in the training of a long short-term memory (ISTM) model using data from the Gangjin-Seongjeon and Pohang-Gibuk monitoring wells in South Korea, the results of which are then compared to the validation data. Based on comparisons of the validation results, it is confirmed that the optimal cost function is the most successful in rejecting the influence of outliers during the training process when applied to data from the Ganglin-Seongjeon monitoring well. It is also demonstrated that the estimation results based on this optimal cost function can effectively identify outliers in groundwater-level data. For the Pohang-Gibuk monitoring well data, the optimal cost function without AW exhibits superior regularizing performance by generating the lowest mean estimation error. Using this cost function, the influence of persistent noise is mostly canceled out, and the estimation results reflect the regular changes in the water table level of a shallow aquifer over time. The developed robust cost function can potentially be employed in many hydrogeological applications, such as the monitoring of groundwater resources, the prediction and analysis of water table levels, and the identification of changes in aquifer processes. The cost function is also expected to be useful for many other field applications in which the data are susceptible to external influences. | Jeong, Jina; Park, Eungyu; Chen, Huali; Kim, Kue-Young; Han, Weon Shik; Suk, Heejun | Kyungpook Natl Univ, Dept Geol, 80 Daehak Ro, Daegu 41566, South Korea; Zhejiang Gongshang Univ, Sch Environm Sci & Engn, Hangzhou 310018, Zhejiang, Peoples R China; Korea Inst Geosci & Mineral Resources, 124 Gwahak Ro, Daejeon 34132, South Korea; Yonsei Univ, Dept Earth Syst Sci, 50 Yonsei Ro, Seoul 03722, South Korea | suk, heejun/A-3212-2015; Han, Weon Shik/KCY-0126-2024 | 55488558800; 23995577700; 56553745900; 15029920800; 57226420125; 23996203400 | jeong.j@knu.ac.kr;egpark@knu.ac.kr;kykim@kigam.re.kr;hanw@yonsei.ac.kr;sxh60@kigam.re.kr; | JOURNAL OF HYDROLOGY | J HYDROL | 0022-1694 | 1879-2707 | 582 | SCIE | ENGINEERING, CIVIL;GEOSCIENCES, MULTIDISCIPLINARY;WATER RESOURCES | 2020 | 5.722 | 7.7 | 3.14 | 2025-06-25 | 48 | 47 | Groundwater level; Robust training; Outliers; Least trimmed squares; Whittaker smoother; Asymmetric weighting | WATER-TABLE DEPTH; LINE-CORRECTION; PRECIPITATION; OUTLIERS; MODEL | Asymmetric weighting; Groundwater level; Least trimmed squares; Outliers; Robust training; Whittaker smoother | North Kyongsang; Pohang; South Korea; Aquifers; Cost estimating; Cost functions; Groundwater; Groundwater resources; Long short-term memory; Statistics; Wells; Asymmetric weighting; Least trimmed squares; Outliers; Robust trainings; Whittaker smoother; aquifer characterization; artificial neural network; data interpretation; groundwater resource; least squares method; smoothing; training; water level; water table; Cost benefit analysis | English | 2020 | 2020-03 | 10.1016/j.jhydrol.2019.124512 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Improved rainfall nowcasting using Burgers' equation | Nowcasting of surface precipitation from radar data typically relies on algorithms that calculate advection, such as the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE). This method offers high spatial and temporal resolution but it cannot represent the growth-decay of precipitation and non-stationary advection vector fields. In this study, we propose some nowcasting rainfall models based on advection-diffusion equation with non-stationary motion vectors. The diffusion term of this equation gives to smoother rainfall predictions for lead times and increased skill scores. The motion vectors are updated in each time step by solving a system of two-dimensional (2D) Burgers' equation. The proposed forecasting models use the following three steps. First, an initial motion vector field is approximated using the Variational Echo Tracking (VET) algorithm. Second, a forecast is obtained for each time step by solving a time-dependent advection or advection-diffusion equation. In this step, the motion vectors are updated by solving Burgers' equation. Lastly, forecasts are evaluated with lead times from 2.5 min to 3 h, and forecasts are compared with rain rate observations for six events over a 250 x 250 km(2) region in southeastern South Korea. To observe the effects of the diffusion term and Burgers' equation, four variants of the proposed modeling methods are considered, depending on the equations: advection equation (Type 1), advection and Burgers' equations (Type 2), advection-diffusion equation (Type 3), and combination of the advection-diffusion and Burgers' equations (Type 4). The forecasts from the Type 1 method are very similar to those of MAPLE. The other models (Type 2-4) yielded clearly better skill scores and correlation on average, with up to 3 h' lead time. Models that use Burgers' equation (Type 2 and Type 4) give much better scores than other methods using fixed motion vectors when the temporal variation of the motion vectors is large. | Ryu, Soorok; Lyu, Geunsu; Do, Younghae; Lee, GyuWon | Kyungpook Natl Univ, Ctr Atmospher REmote Sensing CARE, Daegu 41566, South Korea; Kyungpook Natl Univ, Dept Astron & Atmospher Sci, Res & Training Team Future Creat Astrophys & Cosm, Daegu 41566, South Korea; Kyungpook Natl Univ, Dept Math, KNU Ctr Nonlinear Dynam, Daegu 41566, South Korea | Do, Younghae/G-3522-2011 | 35759778300; 57204505581; 7103101109; 7404852271 | gyuwon@knu.ac.kr; | JOURNAL OF HYDROLOGY | J HYDROL | 0022-1694 | 1879-2707 | 581 | SCIE | ENGINEERING, CIVIL;GEOSCIENCES, MULTIDISCIPLINARY;WATER RESOURCES | 2020 | 5.722 | 7.7 | 1.66 | 2025-06-25 | 28 | 29 | Nowcasting; MAPLE; Advection-diffusion; Burgers' equation; Motion vectors | CONTINENTAL RADAR IMAGES; PRECIPITATION; PREDICTABILITY; TRACKING; GROWTH | Advection-diffusion; Burgers’ equation; MAPLE; Motion vectors; Nowcasting | South Korea; Diffusion; Forecasting; Partial differential equations; Rain; Vectors; Advection - Diffusion; Advection-diffusion equation; MAPLE; Motion Vectors; Nowcasting; Spatial and temporal resolutions; Time-dependent advection; Two Dimensional (2 D); advection-diffusion equation; correlation; data assimilation; nowcasting; rainfall; spatial analysis; temporal analysis; two-dimensional modeling; Advection | English | 2020 | 2020-02 | 10.1016/j.jhydrol.2019.124140 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Multi-objective differential evolution algorithm with fuzzy inference-based adaptive mutation factor for Pareto optimum design of suspension system | In this paper, a multi-objective differential evolution with fuzzy inference-based dynamic adaptive mutation factor (MODE-FM) is proposed for Pareto optimization of problems using a combination of non-dominated sorting and crowding distance. In the proposed algorithm, fuzzy inference is employed to dynamically tune the mutation factor for a better exploration and exploitation ability. In the proposed work, to adapt the mutation factor, the generation count and population diversity in each generation are provided as inputs to fuzzy inference system and the mutation factor is obtained as an output. Performance of the suggested approach is first tested on popular benchmark functions adopted from IEEE CEC 2009. Secondly, vehicle vibration model with five degrees of freedom is selected to be optimally designed by the aforesaid proposed approach. Comparison of the obtained results of this work with those in the literature has confirmed the superiority of the proposed method. | Jamali, A.; Mallipeddi, Rammohan; Salehpour, M.; Bagheri, A. | Univ Guilan, Fac Mech Engn, Rasht, Iran; Kyungpook Natl Univ, Sch Elect Engn, Daegu 41566, South Korea; Islamic Azad Univ, Dept Mech Engn, Bandar Anzali Branch, Bandar Anzali, Iran | Salehpour, Mohammad/AAN-4014-2021; Jamali, Ali/AAX-5841-2020; Mallipeddi, Rammohan/AAL-5306-2020 | 13805822900; 25639919900; 35847889400; 57200842580 | mallipeddi.ram@gmail.com; | SWARM AND EVOLUTIONARY COMPUTATION | SWARM EVOL COMPUT | 2210-6502 | 2210-6510 | 54 | SCIE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;COMPUTER SCIENCE, THEORY & METHODS | 2020 | 7.177 | 7.7 | 3.47 | 2025-06-25 | 26 | 28 | Multi-objective optimization; Differential evolution; Fuzzy logic; Mutation factor; Population diversity; Vehicle vibration model | PERFORMANCE ASSESSMENT; GENETIC ALGORITHM; OPTIMIZATION ALGORITHM; LOCAL SEARCH; ADAPTATION; PARAMETERS | Differential evolution; Fuzzy logic; Multi-objective optimization; Mutation factor; Population diversity; Vehicle vibration model | Automobile suspensions; Benchmarking; Damping; Degrees of freedom (mechanics); Evolutionary algorithms; Fuzzy logic; Inference engines; Multiobjective optimization; Pareto principle; Suspensions (components); Vibration analysis; Differential Evolution; Exploration and exploitation; Fuzzy inference systems; Multi-objective differential evolution algorithms; Multi-objective differential evolutions; Mutation factor; Population diversity; Vehicle vibrations; Fuzzy inference | English | 2020 | 2020-05 | 10.1016/j.swevo.2020.100666 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Atrial Fibrillation Prediction With Residual Network Using Sensitivity and Orthogonality Constraints | Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia. The atrial beat is irregular during AF, which causes blood flow hardly. This may cause blood clot formation and cardioembolic strokes. Computer-aided devices may assist cardiologists in diagnosing heart rhythm disorders better. From this viewpoint, we attempt to identify the premature atrial complexes (PACs) to predict the occurrence of AF by using electrocardiogram (ECG) spectrograms. Convolutional neural networks (CNN) models such as ResNet and Wide-ResNet are used to predict the prelude of AF. Regularization constraints are used to deal with the imbalanced and small number of samples in the minority premature AF class. Sensitivity regularization investigates small variations in premature AF samples. It highlights more representative features that distinguish the PACs from the normal rhythm. On the other hand, orthogonality regularization removes the interference between negatively correlated feature weights. It places constraints on capturing similar patterns with slight differences. This constraint allows convergence to a better feature representation with fewer weight redundancies. We propose a combination of sensitivity and orthogonality penalty terms to the cost function of ResNet to decrease the overfitting and obtain a superior representation. The re-sampling class distribution method is also utilized to mitigate the issue of imbalanced data. The proposed method shows better AF prediction for highly imbalanced data with a small number of samples. | Jalali, Amin; Lee, Minho | Kyungpook Natl Univ, Sch Elect Engn, Daegu 41566, South Korea | Jalali, Amin/AAH-6921-2019; Lee, Min-Ho/ABE-5735-2021 | 57022190400; 57191730119 | max.jalali@gmail.com;mholee@gmail.com; | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | IEEE J BIOMED HEALTH | 2168-2194 | 2168-2208 | 24 | 2 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS;MATHEMATICAL & COMPUTATIONAL BIOLOGY;MEDICAL INFORMATICS | 2020 | 5.772 | 7.8 | 1.49 | 2025-06-25 | 36 | 45 | Cardiac arrhythmia; atrial fibrillation prediction; deep neural structures; sensitivity and orthogonality regularizers; feature decorrelations; wide-residual networks; imbalanced data resampler; premature atrial complexes; redundancy removal | ONSET | atrial fibrillation prediction; Cardiac arrhythmia; deep neural structures; feature decorrelations; imbalanced data resampler; premature atrial complexes; redundancy removal; sensitivity and orthogonality regularizers; wide-residual networks | Algorithms; Atrial Fibrillation; Electrocardiography; Humans; Neural Networks, Computer; Sensitivity and Specificity; Blood; Complex networks; Cost functions; Deep neural networks; Diseases; Electrocardiography; Forecasting; Redundancy; Sensitivity analysis; Atrial fibrillation; Cardiac arrhythmia; Decorrelations; Imbalanced data; Neural structures; Orthogonality; Premature Atrial Complexes; Redundancy removal; Article; atrial fibrillation; clinical article; controlled study; convolutional neural network; electrocardiography; heart arrhythmia; heart atrium flutter; heart failure; heart rate; human; mathematical model; measurement accuracy; paroxysmal atrial fibrillation; prediction; QRS complex; sensitivity and specificity; sinus rhythm; supraventricular premature beat; algorithm; atrial fibrillation; pathophysiology; Convolutional neural networks | English | 2020 | 2020-02 | 10.1109/jbhi.2019.2957809 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |
| ○ | ○ | Article | Investigation of the dynamic characteristics of a carbon -fiber -reinforced epoxy with adhesive -jointed structure | Structural adhesives are used extensively in the automotive industry, especially in lightweight composite struc- tures where conventional welding is not possible. Nevertheless, in finite element modeling, adhesive modeling introduces errors in the analysis results. In this paper, the authors propose the concept of modeling composite structures assembled with a structural adhesive for structural dynamic analysis. Several models of a double - flanged hat -plate adhesive -bonded joint structure were virtually constructed from carbon -fiber reinforced epoxy, and their performances were compared in a parametric study. All performance criteria were linked to the computational cost, permitting an efficient structural dynamic analysis. To smooth any pre -assembly uncertainties, the individual components were updated by a sensitivity and optimization app (MSC Nastran SOL 200). The dynamic characteristics of the structures were obtained in impact tests under free -free boundary conditions. To assess the accuracies of the FE models of the adhesive -jointed structures, the natural frequencies and mode shapes of each structure were compared with their experimental counterparts. Different types of fea- sible element connectors for the structural adhesive were also investigated. The proposed ADHSV-based ele- ment model was found to represent the structural adhesives both accurately and efficiently. | Basri, Ahmad Burhani Ahmad; Chae, Dong Wook; Lee, Hyeongill | Kyungpook Natl Univ, Sch Automot Engn, 2559 Gyeongsang Daero, Sangju 37224, Gyeongsangbuk D, South Korea; Kyungpook Natl Univ, Dept Text Engn, 2559 Gyeongsang Daero, Sangju 37224, Gyeongsangbuk D, South Korea | Ahmad Basri, Ahmad Burhani/AAX-5302-2021 | 57217176021; 7006246752; 8261483800 | hilee@knu.ac.kr; | COMPOSITE STRUCTURES | COMPOS STRUCT | 0263-8223 | 1879-1085 | 247 | SCIE | MATERIALS SCIENCE, COMPOSITES;MECHANICS | 2020 | 5.407 | 7.8 | 0.62 | 2025-06-25 | 11 | 11 | Adhesive modeling; Model updating; Finite element model; Modal analysis; Composite | BOLTED JOINTS; FATIGUE; SELECTION; BEHAVIOR; FAILURE; DAMAGE | Adhesive modeling; Composite; Finite element model; Modal analysis; Model updating | Adhesive joints; Automotive industry; Reinforcement; Sols; Structural analysis; Structural dynamics; Structure (composition); Adhesive bonded joints; Carbon fiber reinforced; Dynamic characteristics; Free boundary conditions; Individual components; Lightweight composites; Natural frequencies and modes; Performance criterion; Adhesives | English | 2020 | 2020-09-01 | 10.1016/j.compstruct.2020.112499 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels | Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation. | Wu, Dufan; Gong, Kuang; Arru, Chiara Daniela; Homayounieh, Fatemeh; Bizzo, Bernardo; Buch, Varun; Ren, Hui; Kim, Kyungsang; Neumark, Nir; Xu, Pengcheng; Liu, Zhiyuan; Fang, Wei; Xie, Nuobei; Tak, Won Young; Park, Soo Young; Lee, Yu Rim; Kang, Min Kyu; Park, Jung Gil; Carriero, Alessandro; Saba, Luca; Masjedi, Mahsa; Talari, Hamidreza; Babaei, Rosa; Mobin, Hadi Karimi; Ebrahimian, Shadi; Dayan, Ittai; Kalra, Mannudeep K.; Li, Quanzheng | Massachusetts Gen Hosp, Dept Radiol, Boston, MA 02114 USA; MGH & BWH Ctr Clin Data Sci, Boston, MA 02114 USA; Kyungpook Natl Univ, Dept Internal Med, Sch Med, Daegu 41944, South Korea; Yeungnam Univ, Dept Internal Med, Coll Med, Daegu 41944, South Korea; Azienda Osped Univ Maggiore Carita, Radiol, I-28100 Novara, Italy; Azienda Osped Univ Policlin Cagliari, Radiol, I-09124 Cagliari, Italy; Shahid Beheshti Hosp, Dept Radiol, Kashan 00000, Iran; Iran Univ Med Sci, Firoozgar Hosp, Dept Radiol, Tehran, Iran | CARRIERO, Alessandro/AAL-3790-2020; Bizzo, Bernardo/AAF-1702-2019; Park, Jung/AAK-5167-2020; Gong, Kuang/W-6203-2019; Wu, Dufan/AFZ-1956-2022; Ebrahimian, Shadi/AAF-7064-2019; Kang, Min/U-8050-2018; Xie, Nuobei/AAG-9180-2021; Kalra, Mannudeep/LXW-4237-2024; Liu, Zhiyuan/I-2233-2014; Fang, Wei/CAA-1842-2022 | 55286668100; 57146819700; 57217417231; 57204089426; 37050694500; 57200946317; 57214672137; 44861409500; 57212374506; 57220762089; 57220759427; 57211655268; 57205364565; 7004074582; 59280453500; 57194094753; 59142854300; 57216816399; 55168579500; 16234937700; 57201113680; 36624428400; 57219048516; 57219239294; 55313895400; 57219308459; 7007035549; 7405862484 | dwu6@mgh.harvard.edu;kgong@mgh.harvard.edu;carru@mgh.harvard.edu;fhomayounieh@mgh.harvard.edu;bbizzo@mgh.harvard.edu;varun.buch@mgh.harvard.edu;hren2@mgh.harvard.edu;kkim24@mgh.harvard.edu;nir.neumark@mgh.harvard.edu;pxu3@mgh.harvard.edu;zliu40@mgh.harvard.edu;wfang3@mgh.harvard.edu;nxie@mgh.harvard.edu;wytak@knu.ac.kr;psy@knu.ac.kr;deblue00@naver.com;kmggood111@naver.com;jgpark@ynu.ac.kr;profcarriero@virgilio.it;lucasabamd@gmail.com;mahsami141@gmail.com;rosa.babaei@gmail.com;hadi.karimimobin@gmail.com;sebrahimian@mgh.harvard.edu;idayan@partners.org;mkalra@mgh.harvard.edu;quanzheng@mgh.harvard.edu; | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | IEEE J BIOMED HEALTH | 2168-2194 | 2168-2208 | 24 | 12 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS;MATHEMATICAL & COMPUTATIONAL BIOLOGY;MEDICAL INFORMATICS | 2020 | 5.772 | 7.8 | 1.92 | 2025-06-25 | 25 | 37 | Computed tomography; COVID-19; Lung; Image segmentation; Training; Semantics; Computed tomography; COVID-19; segmentation; lung; deep learning; severity; consolidation; weak label | SEGMENTATION | Computed tomography; consolidation; COVID-19; deep learning; lung; segmentation; severity; weak label | Algorithms; COVID-19; Deep Learning; Female; Humans; Male; Retrospective Studies; SARS-CoV-2; Severity of Illness Index; Tomography, X-Ray Computed; Computerized tomography; Diagnosis; Image segmentation; Learning systems; Maximum principle; Opacity; Semantics; Disease severity; Expectation-maximization algorithms; Good correlations; Human annotations; Learning methods; Prognosis prediction; Semantic segmentation; Type information; Article; computer assisted tomography; coronavirus disease 2019; deep learning; diagnostic test accuracy study; disease severity; expectation-maximization algorithm; false positive result; human; lung consolidation; lung disease; major clinical study; mathematical analysis; multidetector computed tomography; receiver operating characteristic; reverse transcription polymerase chain reaction; semantic web; algorithm; diagnostic imaging; female; isolation and purification; male; procedures; retrospective study; severity of illness index; virology; x-ray computed tomography; Deep learning | English | 2020 | 2020-12 | 10.1109/jbhi.2020.3030224 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |
| ○ | ○ | Article | Vaginal compared with intramuscular progestogen for preventing preterm birth in high-risk pregnant women (VICTORIA study): a multicentre, open-label randomised trial and meta-analysis | Objective To compare the efficacy of two types of progestogen therapy for preventing preterm birth (PTB) and to review the relevant literature. Design A multicentre, randomised, open-label, equivalence trial and a meta-analysis. Setting Tertiary referral hospitals in South Korea. Population Pregnant women with a history of spontaneous PTB or short cervical length (<25 mm). Methods Eligible women were screened and randomised at 16-22 weeks of gestation to receive either 200 mg of vaginal micronised progesterone daily (vaginal group) or an intramuscular injection of 250 mg 17 alpha-hydroxyprogesterone caproate weekly (IM group). Stratified randomisation was carried out according to participating centres and indications for progestogen therapy. This trial was registered at (NCT02304237). Main outcome measure Preterm birth (PTB) before 37 weeks of gestation. Results A total of 266 women were randomly assigned and a total of 247 women (119 and 128 women in the vaginal and IM groups, respectively) were available for the intention-to-treat analysis. Risks of PTB before 37 weeks of gestation did not significantly differ between the two groups (22.7 versus 25.8%,P = 0.571). The difference in PTB risk between the two groups was 3.1% (95% CI -7.6 to 13.8%), which was within the equivalence margin of 15%. The meta-analysis results showed no significant differences in the risk of PTB between the vaginal and IM progestogen treatments. Conclusion Compared with vaginal progesterone, treatment with intramuscular progestin might increase the risk of PTB before 37 weeks of gestation by as much as 13.8%, or reduce the risk by as much as 7.6%, in women with a history of spontaneous PTB or with short cervical length. Tweetable abstract Vaginal and intramuscular progestogen showed equivalent efficacy for preventing preterm birth before 37 weeks of gestation. | Choi, S-J; Kwak, D. W.; Kil, K.; Kim, S-C; Kwon, J-Y; Kim, Y. H.; Na, S.; Bae, J-G; Cha, H-H; Shim, J-Y; Oh, K. Y.; Lee, K. A.; Kim, S. M.; Cho, I. A.; Lee, S. M.; Cho, G. J.; Jo, Y. S.; Choi, G. Y.; Choi, S. K.; Hur, S. E.; Hwang, H. S.; Kim, Y. J. | Sungkyunkwan Univ, Samsung Med Ctr, Sch Med, Seoul, South Korea; Ajou Univ, Sch Med, Suwon, South Korea; Catholic Univ Korea, Yeouido St Marys Hosp, Seoul, South Korea; Pusan Natl Univ, Coll Med, Pusan, South Korea; Yonsei Univ, Inst Womens Life Med Sci, Coll Med, Seoul, South Korea; Chonnam Natl Univ, Med Sch, Gwangju, South Korea; Kangwon Natl Univ, Kangwon Natl Univ Hosp, Sch Med, Chunchon, South Korea; Keimyung Univ, Sch Med, Daegu, South Korea; Kyungpook Natl Univ, Kyungpook Natl Univ Hosp, Sch Med, Daegu, South Korea; Univ Ulsan, Asan Med Ctr, Coll Med, Seoul, South Korea; Eulji Univ, Sch Med, Daejeon, South Korea; Kyung Hee Univ, Sch Med, Seoul, South Korea; Seoul Natl Univ, Seoul Metropolitan Govt, Boramae Med Ctr, Coll Med, Seoul, South Korea; Gyeongsang Natl Univ, Gyeongsang Natl Univ Hosp, Sch Med, Jinju, South Korea; Seoul Natl Univ, Coll Med, Seoul, South Korea; Korea Univ, Coll Med, Seoul, South Korea; Catholic Univ Korea, St Vincents Hosp, Coll Med, Suwon, South Korea; Soonchunhyang Univ, Seoul Hosp, Coll Med, Seoul, South Korea; Catholic Univ Korea, Coll Med, Seoul, South Korea; Konyang Univ Hosp, Daejeon, South Korea; Konkuk Univ, Res Inst Med Sci, Sch Med, Seoul, South Korea; Ewha Womans Univ, Coll Med, 1071 AnYangCheon Ro, Seoul, South Korea | Lee, Soong/J-5584-2012; Na, Sunghun/AAE-3929-2022; Shim, Jae-Yoon/C-5526-2012 | 55601869400; 56480108900; 54382004500; 57484521500; 8656241100; 36066373700; 55489142900; 57215844401; 37033517800; 7201856356; 34979039700; 57203464136; 56981848500; 59436357300; 50262091500; 12545334500; 26658951500; 59782556700; 36924484600; 57217990429; 17345595900; 55801681000 | kkyj@ewha.ac.kr; | BJOG-AN INTERNATIONAL JOURNAL OF OBSTETRICS AND GYNAECOLOGY | BJOG-INT J OBSTET GY | 1470-0328 | 1471-0528 | 127 | 13 | SCIE | OBSTETRICS & GYNECOLOGY | 2020 | 6.531 | 7.8 | 0.6 | 2025-06-25 | 6 | 8 | Preterm birth; prevention; progestogen; short cervical length | 17-ALPHA-HYDROXYPROGESTERONE CAPROATE; DOUBLE-BLIND; SHORT CERVIX; MICRONIZED PROGESTERONE; RECURRENCE RISK; SUPPOSITORY; PREDICTION | Preterm birth; prevention; progestogen; short cervical length | Administration, Intravaginal; Adult; Female; Humans; Injections, Intramuscular; Meta-Analysis as Topic; Pregnancy; Pregnancy, High-Risk; Premature Birth; Progestins; hydroxyprogesterone caproate; progesterone; gestagen; Article; cervical length; drug efficacy; drug indication; drug safety; equivalence trial (topic); gestational age; high risk pregnancy; human; intention to treat analysis; meta analysis; multicenter study (topic); open study; pregnant woman; premature labor; priority journal; randomized controlled trial (topic); risk assessment; risk benefit analysis; risk reduction; tertiary care center; adult; clinical trial; comparative study; controlled study; female; high risk pregnancy; intramuscular drug administration; intravaginal drug administration; meta analysis (topic); multicenter study; pregnancy; prematurity; randomized controlled trial | English | 2020 | 2020-12 | 10.1111/1471-0528.16365 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |
| ○ | ○ | Article | Antioxidant and Antidiabetic Activities of Flavonoid Derivatives from the Outer Skins of Allium cepa L. | The onion, known as the bulb onion or common onion, is not only a key ingredient in many tasty and healthy vegetarian meals but also many traditional medicines. Nine new flavonoids [cepaflavas A, B (5, 6), cepadials A-D (7-9 and 14), and cepabiflas A-C (10-12)] and six known compounds (1-4, 13, 15) were obtained from the outer skins of Allium cepa L. Among them, compounds 5, 6, and 9 might be artificial products formed during extraction and isolation. New compounds were structurally elucidated using various spectroscopy/spectrometry techniques, including NMR and HRMS, and computational methods. Their absolute configurations were determined using time-dependent density functional theory calculations, combined with ECD spectroscopy, optical rotation calculation, and statistical procedures (CP3 and DP4 analysis). The free radical scavenging assays revealed that the new compounds 10-12 possessed considerable antioxidant activities with IC50 values of 4.25-8.88 and 7.12-8.14 mu M against DPPH and ABTS(center dot+), respectively. Compounds 13-15 showed substantial inhibitory activities against both alpha-glucosidase and protein tyrosine phosphatase 1B (PTP1B), with IC50 values of 0.89-6.80 and 1.13-6.82 mu M, respectively. On the basis of molecular docking studies, 13 and 15 were predicted to have high binding capacity and strong affinity toward the active site of PTP1B. | Ngoc Khanh Vu; Kim, Chung Sub; Manh Tuan Ha; Quynh-Mai Thi Ngo; Park, Se Eun; Kwon, Haeun; Lee, Dongho; Choi, Jae Sue; Kim, Jeong Ah; Min, Byung Sun | Kyungpook Natl Univ, Coll Pharm, Res Inst Pharmaceut Sci, Daegu 41566, South Korea; Daegu Catholic Univ, Coll Pharm, Drug Res & Dev Ctr, Gyeongbuk 38430, South Korea; Sungkyunkwan Univ, Sch Pharm, Suwon 16419, South Korea; Hai Phong Univ Med & Pharm, Coll Pharm, Hai Phong 180000, Vietnam; Pukyong Natl Univ, Dept Food & Life Sci, Busan 48513, South Korea; Korea Univ, Coll Life Sci & Biotechnol, Dept Biosyst & Biotechnol, Seoul 136713, South Korea | ; Tuan, Ha/AAW-5444-2021; khanh, vu/JXL-3187-2024; Kim, Chung Sub/GLN-7192-2022 | 57217172502; 12142324700; 57189040434; 57189343515; 57214081935; 57215901708; 55636321013; 25654629500; 24722570900; 55663638200 | jkim6923@knu.ac.kr;bsmin@cu.ac.kr; | JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY | J AGR FOOD CHEM | 0021-8561 | 1520-5118 | 68 | 33 | SCIE | AGRICULTURE, MULTIDISCIPLINARY;CHEMISTRY, APPLIED;FOOD SCIENCE & TECHNOLOGY | 2020 | 5.279 | 7.9 | 3.05 | 2025-06-25 | 47 | 49 | Allium cepa skins; antioxidant; alpha-glucosidase; PTP1B; kinetic; molecular docking | CIRCULAR-DICHROISM; GLYCOSIDES; ONION; PTP1B; PHOSPHATASES; FLOWERS; LEAVES | Allium cepa skins; antioxidant; kinetic; molecular docking; PTP1B; α-glucosidase | alpha-Glucosidases; Antioxidants; Enzyme Inhibitors; Flavonoids; Hypoglycemic Agents; Molecular Docking Simulation; Molecular Structure; Onions; Plant Extracts; Protein Tyrosine Phosphatase, Non-Receptor Type 1; Amino acids; Antioxidants; Computation theory; Free radicals; alpha glucosidase; antidiabetic agent; antioxidant; enzyme inhibitor; flavonoid; plant extract; protein tyrosine phosphatase 1B; Absolute configuration; Anti-oxidant activities; Antidiabetic activity; Binding capacities; Free radical scavenging; Inhibitory activity; Protein-tyrosine phosphatase 1B; Time dependent density functional theory calculations; chemical structure; chemistry; molecular docking; onion; Density functional theory | English | 2020 | 2020-08-19 | 10.1021/acs.jafc.0c02122 | 바로가기 | 바로가기 | 바로가기 | 바로가기 |
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