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Theoretical study on adsorption and reaction of polymeric formic acid on the Cu(111) surface

Physical Review Materials 2021年第7期5卷 075801-075801页

作者： Septia Eka Marsha Putra Fahdzi Muttaqien Yuji Hamamoto Kouji Inagaki Akitoshi Shiotari Jun Yoshinobu Yoshitada Morikawa Ikutaro Hamada Department of Precision Engineering Graduate School of Engineering Osaka University 2-1 Yamada-oka Suita Osaka 565-0871 Japan Department of Engineering Physics Institut Teknologi Sumatera Jalan Terusan Ryacudu Way Hui Jati Agung South Lampung 35365 Indonesia Instrumentation and Computational Physics Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung West Java 40132 Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung West Java 40132 Indonesia Elements Strategy Initiative for Catalysts and Batteries (ESICB) Kyoto University Goryo-Ohara Nishikyou-ku Katsura Kyoto 615-8245 Japan Department of Advanced Materials Science The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8561 Japan The Institute for Solid State Physics The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8581 Japan Research Center for Precision Engineering Graduate School of Engineering Osaka University 2-1 Yamada-oka Suita Osaka 565-0871 Japan

We investigate the polymeric adsorption of formic acid (HCOOH) on the Cu(111) surface by means of density functional theory. We present structural models for the polymeric form of HCOOH on Cu(111) and characterize their stability, electronic, and vibrational properties. Based on the energetics and dynamics, as well as simulated scanning tunneling microscopy and atomic force microscopy images, we propose that the α polymorph is likely to be formed on the Cu(111) surface and can explain the experimental findings. We also study the initial step of the catalytic dehydrogenation of HCOOH and find that the O-H bond dissociation at the edge of the polymer is facilitated, rather than that forming hydrogen bonding, agreeing well with the experiments.

关键词： Adsorption Chemical bonding Chemical kinetics, dynamics & catalysis Electronic structure Surface & interfacial phenomena Surfaces Density functional theory First-principles calculations

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Deep-learning-driven event reconstruction applied to simulated data from a single Large-Sized Telescope of CTA 37

Deep-learning-driven event reconstruction applied to simulat...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado, C. Delgado Mengual, J. della Volpe, D. Depaoli, D. Di Pierro, F. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jacquemont, M. Jimenez Martinez, I. Jouvin, L. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mazin, D. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moralejo, A. Morcuende, D. Moretti, E. Morselli, A. Mrakovcic, K. Murase, K. Nagai, A. Nakamori, T. Nickel, L. LISTIC Université Savoie Mont-Blanc Polytech Annecy-Chambéry 5 chemin de bellevue Annecy74940 France Institute for Cosmic Ray Research University of Tokyo Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Italy INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy Aix Marseille Univ CNRS IN2P3 CPPM France The Barcelona Institute of Science and Technology Spain IRFU CEA Université Paris-Saclay France Laboratoire d'Annecy de Physique des Particules Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS LAPP 9 chemin de bellevue Annecy74940 France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan Instituto de Física de Partículas y del Cosmos Departamento de EMFTEL Universidad Complutense de Madrid Madrid Spain INAF Osservatorio di Astrofisica e Scienza Spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Università degli Studi di Padova Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik Germany Department of Physics TU Dortmund University Germany Saha Institute of Nuclear Physics INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezione di Catania Italy Italy University of Geneva Département de Physique Nucléaire et Corpusculaire Switzerland Palacky University Olomouc Faculty of Science Czech Republic CIEMAT Port d'Informació Científica INFN Sezione di Torino Italy Dipartimento di Fisica Universitá degli Studi di Torino Italy INFN Sezione di Bari Università di Bari Italy University of Rijeka Department of Physics Croatia Institute for Theoretical Physics and Astrophysics Universität Würzburg Germany Physics Program Graduate School of Advanced Science and Engin

When very-high-energy gamma rays interact high in the Earth's atmosphere, they produce cascades of particles that induce flashes of Cherenkov light. Imaging Atmospheric Cherenkov Telescopes (IACTs) detect these flashes and convert them into shower images that can be analyzed to extract the properties of the primary gamma ray. The dominant background for IACTs is comprised of air shower images produced by cosmic hadrons, with typical noise-to-signal ratios of several orders of magnitude. The standard technique adopted to differentiate between images initiated by gamma rays and those initiated by hadrons is based on classical machine learning algorithms, such as Random Forests, that operate on a set of handcrafted parameters extracted from the images. Likewise, the inference of the energy and the arrival direction of the primary gamma ray is performed using those parameters. State-of-the-art deep learning techniques based on convolutional neural networks (CNNs) have the potential to enhance the event reconstruction performance, since they are able to autonomously extract features from raw images, exploiting the pixel-wise information washed out during the parametrization process. Here we present the results obtained by applying deep learning techniques to the reconstruction of Monte Carlo simulated events from a single, next-generation IACT, the Large-Sized Telescope (LST) of the Cherenkov Telescope Array (CTA). We use CNNs to separate the gamma-ray-induced events from hadronic events and to reconstruct the properties of the former, comparing their performance to the standard reconstruction technique. Three independent implementations of CNN-based event reconstruction models have been utilized in this work, producing consistent results. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Hadrons

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Systematic study of flow vector fluctuations in sNN=5.02 TeV Pb-Pb collisions

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Physical Review C 2024年第6期109卷 065202-065202页

作者： S. Acharya D. Adamová A. Agarwal G. Aglieri Rinella L. Aglietta M. Agnello N. Agrawal Z. Ahammed S. Ahmad S. U. Ahn I. Ahuja A. Akindinov V. Akishina M. Al-Turany D. Aleksandrov B. Alessandro H. M. Alfanda R. Alfaro Molina B. Ali A. Alici N. Alizadehvandchali A. Alkin J. Alme G. Alocco T. Alt A. R. Altamura I. Altsybeev J. R. Alvarado M. N. Anaam C. Andrei N. Andreou A. Andronic E. Andronov V. Anguelov F. Antinori P. Antonioli N. Apadula L. Aphecetche H. Appelshäuser C. Arata S. Arcelli M. Aresti R. Arnaldi J. G. M. C. A. Arneiro I. C. Arsene M. Arslandok A. Augustinus R. Averbeck M. D. Azmi H. Baba A. Badalà J. Bae Y. W. Baek X. Bai R. Bailhache Y. Bailung R. Bala A. Balbino A. Baldisseri B. Balis D. Banerjee Z. Banoo V. Barbasova F. Barile L. Barioglio M. Barlou B. Barman G. G. Barnaföldi L. S. Barnby E. Barreau V. Barret L. Barreto C. Bartels K. Barth E. Bartsch N. Bastid S. Basu G. Batigne D. Battistini B. Batyunya D. Bauri J. L. Bazo Alba I. G. Bearden C. Beattie P. Becht D. Behera I. Belikov A. D. C. Bell Hechavarria F. Bellini R. Bellwied S. Belokurova L. G. E. Beltran Y. A. V. Beltran G. Bencedi A. Bensaoula S. Beole Y. Berdnikov A. Berdnikova L. Bergmann M. G. Besoiu L. Betev P. P. Bhaduri A. Bhasin M. A. Bhat B. Bhattacharjee L. Bianchi N. Bianchi J. Bielčík J. Bielčíková A. P. Bigot A. Bilandzic G. Biro S. Biswas N. Bize J. T. Blair D. Blau M. B. Blidaru N. Bluhme C. Blume G. Boca F. Bock T. Bodova J. Bok L. Boldizsár M. Bombara P. M. Bond G. Bonomi H. Borel A. Borissov A. G. Borquez Carcamo H. Bossi E. Botta Y. E. M. Bouziani L. Bratrud P. Braun-Munzinger M. Bregant M. Broz G. E. Bruno M. D. Buckland D. Budnikov H. Buesching S. Bufalino P. Buhler N. Burmasov Z. Buthelezi A. Bylinkin S. A. Bysiak J. C. Cabanillas Noris M. F. T. Cabrera M. Cai H. Caines A. Caliva E. Calvo Villar J. M. M. Camacho P. Camerini F. D. M. Canedo S. L. Cantway M. Carabas A. A. Carballo F. Carnesecchi R. Caron L. A. D. Carvalho J. Castillo Castellanos M. Castoldi F. Catalano S. Cattaruzzi C. Ceballos Sanchez R. Cerri I. Chakabe CNRS/IN2P3 LPC Clermont-Ferrand France Nuclear Physics Institute of the Czech Academy of Sciences Husinec-Řež Czech Republic Homi Bhabha National Institute Kolkata India Geneva Switzerland and Sezione INFN Turin Italy and Sezione INFN Bologna Italy Department of Physics Daejeon Republic of Korea Faculty of Science Affiliated with an institute covered by a cooperation agreement with CERN Frankfurt Institute for Advanced Studies Research Division and ExtreMe Matter Institute EMMI Turin Italy Wuhan China Instituto de Física Houston Texas USA Sungkyunkwan University Suwon City Republic of Korea Department of Physics and Technology Cagliari Italy Institut für Kernphysik Bari Italy Physik Department Technische Universität München Munich Germany High Energy Physics Group Bucharest Romania University of Derby Derby United Kingdom Institut für Kernphysik Münster Germany Physikalisches Institut Padova Italy Bologna Italy Berkeley California USA SUBATECH Université Grenoble-Alpes CNRS-IN2P3 Grenoble France and Sezione INFN Cagliari Italy São Paulo Brazil New Haven Connecticut USA Tokyo Japan Catania Italy Gangneung Republic of Korea Hefei China Indore India Physics Department Université Paris-Saclay Centre d'Etudes de Saclay (CEA) IRFU Départment de Physique Nucléaire (DPhN) Saclay France Cracow Poland Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS) Kolkata India and Sezione INFN Bari Italy School of Science Department of Physics Athens Greece Department of Physics Guwahati India Budapest Hungary Liverpool United Kingdom Department of Physics Division of Particle Physics Lund Sweden Affiliated with an international laboratory covered by a cooperation agreement with CERN Mumbai India Sección Física Departamento de Ciencias Niels Bohr Institute CNRS IPHC UMR 7178 F-67000 Strasbourg France Strasbourg France Universidad Autónoma de Sinaloa Culiacá

Measurements of the pT-dependent flow vector fluctuations in Pb–Pb collisions at sNN=5.02TeV using azimuthal correlations with the ALICE experiment at the Large Hadron Collider are presented. A four-particle correlation approach [ALICE Collaboration, Phys. Rev. C 107, L051901 (2023)] is used to quantify the effects of flow angle and magnitude fluctuations separately. This paper extends previous studies to additional centrality intervals and provides measurements of the pT-dependent flow vector fluctuations at sNN=5.02TeV with two-particle correlations. Significant pT-dependent fluctuations of the V⃗2 flow vector in Pb–Pb collisions are found across different centrality ranges, with the largest fluctuations of up to ∼15% being present in the 5% most central collisions. In parallel, no evidence of significant pT-dependent fluctuations of V⃗3 or V⃗4 is found. Additionally, evidence of flow angle and magnitude fluctuations is observed with more than 5σ significance in central collisions. These observations in Pb–Pb collisions indicate where the classical picture of hydrodynamic modeling with a common symmetry plane breaks down. This has implications for hard probes at high pT, which might be biased by pT-dependent flow angle fluctuations of at least 23% in central collisions. Given the presented results, existing theoretical models should be reexamined to improve our understanding of initial conditions, quark–gluon plasma properties, and the dynamic evolution of the created system.

关键词： Particle correlations & fluctuations Quark-gluon plasma Relativistic heavy-ion collisions

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Measurements of jet quenching using semi-inclusive hadron+jet distributions in pp and central Pb-Pb collisions at sNN=5.02 TeV

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Physical Review C 2024年第1期110卷 014906-014906页

作者： S. Acharya D. Adamová G. Aglieri Rinella M. Agnello N. Agrawal Z. Ahammed S. Ahmad S. U. Ahn I. Ahuja A. Akindinov M. Al-Turany D. Aleksandrov B. Alessandro H. M. Alfanda R. Alfaro Molina B. Ali A. Alici N. Alizadehvandchali A. Alkin J. Alme G. Alocco T. Alt A. R. Altamura I. Altsybeev J. R. Alvarado M. N. Anaam C. Andrei N. Andreou A. Andronic V. Anguelov F. Antinori P. Antonioli N. Apadula L. Aphecetche H. Appelshäuser C. Arata S. Arcelli M. Aresti R. Arnaldi J. G. M. C. A. Arneiro I. C. Arsene M. Arslandok A. Augustinus R. Averbeck M. D. Azmi H. Baba A. Badalà J. Bae Y. W. Baek X. Bai R. Bailhache Y. Bailung A. Balbino A. Baldisseri B. Balis D. Banerjee Z. Banoo R. Barbera F. Barile L. Barioglio M. Barlou B. Barman G. G. Barnaföldi L. S. Barnby V. Barret L. Barreto C. Bartels K. Barth E. Bartsch N. Bastid S. Basu G. Batigne D. Battistini B. Batyunya D. Bauri J. L. Bazo Alba I. G. Bearden C. Beattie P. Becht D. Behera I. Belikov A. D. C. Bell Hechavarria F. Bellini R. Bellwied S. Belokurova Y. A. V. Beltran G. Bencedi S. Beole Y. Berdnikov A. Berdnikova L. Bergmann M. G. Besoiu L. Betev P. P. Bhaduri A. Bhasin M. A. Bhat B. Bhattacharjee L. Bianchi N. Bianchi J. Bielčík J. Bielčíková J. Biernat A. P. Bigot A. Bilandzic G. Biro S. Biswas N. Bize J. T. Blair D. Blau M. B. Blidaru N. Bluhme C. Blume G. Boca F. Bock T. Bodova A. Bogdanov S. Boi J. Bok L. Boldizsár M. Bombara P. M. Bond G. Bonomi H. Borel A. Borissov A. G. Borquez Carcamo H. Bossi E. Botta Y. E. M. Bouziani L. Bratrud P. Braun-Munzinger M. Bregant M. Broz G. E. Bruno M. D. Buckland D. Budnikov H. Buesching S. Bufalino P. Buhler N. Burmasov Z. Buthelezi A. Bylinkin S. A. Bysiak M. Cai H. Caines A. Caliva E. Calvo Villar J. M. M. Camacho P. Camerini F. D. M. Canedo S. L. Cantway M. Carabas A. A. Carballo F. Carnesecchi R. Caron L. A. D. Carvalho J. Castillo Castellanos F. Catalano C. Ceballos Sanchez I. Chakaberia P. Chakraborty S. Chandra S. Chapeland M. Chartier S. Chattopadhyay T. Cheng C. Cheshkov B. Cheynis V. Chibante Barroso D. D. Chinellato E. CNRS/IN2P3 LPC Clermont-Ferrand France Nuclear Physics Institute of the Czech Academy of Sciences Husinec-Řež Czech Republic Geneva Switzerland Turin Italy Bologna Italy Homi Bhabha National Institute Kolkata India Department of Physics Daejeon Republic of Korea Faculty of Science Affiliated with an institute covered by a cooperation agreement with CERN Research Division and ExtreMe Matter Institute EMMI Wuhan China Instituto de Física and Sezione INFN Bologna Italy Houston Texas USA Department of Physics and Technology Cagliari Italy Institut für Kernphysik Bari Italy Physik Department Technische Universität München Munich Germany High Energy Physics Group Bucharest Romania University of Derby Derby United Kingdom Institut für Kernphysik Münster Germany Physikalisches Institut Padova Italy Berkeley California USA SUBATECH IMT Atlantique Université Grenoble-Alpes CNRS-IN2P3 Grenoble France and Sezione INFN Cagliari Italy São Paulo Brazil New Haven Connecticut USA Tokyo Japan Catania Italy Sungkyunkwan University Suwon City Republic of Korea Gangneung Republic of Korea Hefei China Indore India Université Paris-Saclay Centre d'Etudes de Saclay (CEA) IRFU Départment de Physique Nucléaire (DPhN) Saclay France Cracow Poland Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS) Kolkata India Physics Department and Sezione INFN Catania Italy and Sezione INFN Bari Italy School of Science Department of Physics Athens Greece Department of Physics Guwahati India Budapest Hungary Nuclear Physics Group STFC Liverpool United Kingdom Department of Physics Division of Particle Physics Lund Sweden Affiliated with an international laboratory covered by a cooperation agreement with CERN Mumbai India Sección Física Departamento de Ciencias Niels Bohr Institute CNRS IPHC UMR 7178 F-67000 Strasbourg France Strasbourg France and Sezione INFN Turin Italy INFN

The ALICE Collaboration reports measurements of the semi-inclusive distribution of charged-particle jets recoiling from a high transverse momentum (high pT) charged hadron, in pp and central Pb-Pb collisions at center-of-mass energy per nucleon–nucleon collision sNN=5.02 TeV. The large uncorrelated background in central Pb-Pb collisions is corrected using a data-driven statistical approach which enables precise measurement of recoil jet distributions over a broad range in pT,chjet and jet resolution parameter R. Recoil jet yields are reported for R=0.2, 0.4, and 0.5 in the range 7nuclear collisions. Comparison of pT,chjet distributions from pp and central Pb-Pb collisions probes medium-induced jet energy loss and intra-jet broadening, while comparison of their acoplanarity distributions explores in-medium jet scattering and medium response. The measurements are compared to theoretical calculations incorporating jet quenching.

关键词： Jet quenching Quark & gluon jets Quark-gluon plasma Relativistic heavy-ion collisions Strong interaction

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Physics Performance of the Large-Sized Telescope prototype of the Cherenkov Telescope Array 37

Physics Performance of the Large-Sized Telescope prototype o...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado, C. Delgado Mengual, J. della Volpe, D. Depaoli, D. Di Pierro, F. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jacquemont, M. Jimenez Martinez, I. Jouvin, L. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mazin, D. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moralejo, A. Morcuende, D. Moretti, E. Morselli, A. Mrakovcic, K. Murase, K. Nagai, A. Nakamori, T. Nickel, L. Nieto, D. Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Kashiwa Chiba277-8582 Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Granada18080 Spain INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy Aix Marseille Univ CNRS IN2P3 CPPM France 08193 Spain IRFU CEA Université Paris-Saclay France LAPP Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 9 Chemin de Bellevue - BP 110 AnnecyCedex 74941 France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan EMFTEL Department IPARCOS Universidad Complutense de Madrid MadridE-28040 Spain INAF Osservatorio di Astrofisica e Scienza Spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Sezione di Padova PadovaI-35131 Italy Università degli Studi di Padova Via Marzolo 8 Padova35131 Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik München80805 Germany Department of Physics TU Dortmund University Otto-Hahn-Str. 4 Dortmund44221 Germany Saha Institute of Nuclear Physics INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezione di Catania Italy Italy University of Geneva Département de Physique Nucléaire et Corpusculaire Switzerland Palacky University Olomouc Faculty of Science Czech Republic CIEMAT Port d'Informació Científica Spain INFN Sezione di Torino via P. Giuria 1 Torino10125 Italy Dipartimento di Fisica Universitá degli Studi di Torino Italy INFN Sezione di Bari Università di Bari Italy University of Rijeka Department of Physics Croatia Institute for Theoretical Physics and Astrophysics Universität Würzburg Germany Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Japan INFN Sezione di Bari Politecnico di B

The Large-Sized Telescope (LST) prototype of the future Cherenkov Telescope Array (CTA) is located at the Northern site of CTA, on the Canary Island of La Palma. It is designed to provide optimal performance in the lowest part of the energy range covered by CTA, observing gamma rays down to energies of tens of GeV. The LST prototype started performing astronomical observations in November 2019 during the commissioning of the telescope and it has been taking data since then. In this contribution, we will present the tuning of the characteristics of the telescope in the Monte Carlo (MC) simulations to describe the data obtained, the estimation of its angular and energy resolution, and an evaluation of its sensitivity, both with simulations and with observations of the Crab Nebula. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Telescopes

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Thermodynamic modeling of the LiCl-KCl-LaCl 3 system with Bayesian model selection and uncertainty quantification

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Journal of Molecular Liquids 2025年 432卷

作者： Rushi Gong Shun-Li Shang Vitaliy G. Goncharov Cillian Cockrell Kostya Trachenko Paul A. Bingham Xiaofeng Guo Zi-Kui Liu Department of Materials Science and Engineering The Pennsylvania State University University Park PA 16802 United States Department of Chemistry Washington State University Pullman Washington 99164 United States Materials Science and Engineering Program Washington State University Pullman Washington 99164 United States Nuclear Futures Institute Bangor University Bangor LL57 1UT United Kingdom School of Physical and Chemical Sciences Queen Mary University of London Mile End Road London E1 4NS United Kingdom Materials and Engineering Research Institute Sheffield Hallam University Sheffield S1 1WB United Kingdom

Chloride molten salts are increasingly used in pyroprocessing techniques for the separation of lanthanides. Understanding their thermodynamic properties is essential for predicting their critical characteristics and optimizing the separation process. Various thermodynamic models, including the associate model, the two-sublattice ionic model, and the modified quasichemical model with quadruplet approximation (MQMQA), have been utilized to capture the complexity of molten salts. In the present work, the Bayes factor is used to guide the model selection process for the thermodynamic modeling of the KCl-LaCl 3 system and provide a statistical comparison of different models. The results indicate that the MQMQA model is the most favorable based on the available data. The LiCl-KCl-LaCl 3 system is further modeled with uncertainty quantification (UQ) using the MQMQA model. The thermodynamic properties of compounds in KCl-LaCl 3 are predicted by the quasiharmonic approach in terms of first-principles phonon calculations as a function of temperature. The calculated phase stability shows excellent agreement with experimental data, indicating that an appropriate thermodynamic model is important for accurately predicting the critical characteristics of complex molten salts.

关键词： Bayesian model selection CALPHAD modeling DFT Lanthanides Molten salts Phonon

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A Geoelectric Approach for Karst Groundwater Analysis

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Amallia Ferhat Taufik Seizarsyah Mohammad Prasanto Bimantio Nanda Satya Nugraha Dian Pratama Putra Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agribusiness Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Geophysical Engineering Department Universitas Pembangunan Nasional 'Veteran' Yogyakarta Indonesia Agricultural Crop Technology Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Forestry Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Artesian well is one of the major efforts to obtain water source for fulfilling water demands in Eroniti Conservation park, Ponjong, Yogyakarta by drilling deep into several soil layers. Vertical Electrical Sounding (VES) method is the most common approach to determine the existence of groundwater. The method probes layers of rock below the ground surface based on their electrical properties, since the electrical resistivity of each layer is theoretically different. This study aims to utilize the VES approach for investigating the properties of the rock layers in the area to explore groundwater sources. We install six different VES points in the observation area using Schlumberger electrode configuration. The methodology of the work consists of three steps: the planning of the placement of the sounding points, the measurement step performed by a unit of IRIS SYSCAL Resistivity meter, and the data analysis and interpretation. Employing the resistivity analysis, we conclude that the observed region consists of Mediterranean soil as a result of weathering of limestone as well as lithology of the limestone. Furthermore, there are two types of aquifer found in the area: a perched aquifer found 27 meters below the ground surface of VES point 1 and a confined aquifer 166 meters below the ground surface of VES point 2 and 6.

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Hybrid Cryptography on Wireless Sensor Network: A Systematic Literature Review 3

Hybrid Cryptography on Wireless Sensor Network: A Systematic...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Gandara, R. Benny Wang, Gunawan Utama, Ditdit Nugeraha Mereu Buana University Master Program of Electrical Engineering Jakarta Indonesia Information Systems Management Department Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

Wireless technology gradual growth in the latest years has facilitated it to bridge its applications operated extensively in many types of industry. Wireless sensor network (WSN) technology brings several benefits over traditional wired technology by reason of the lower cost, immense flexibility escalation, and scalability which is able to be obtained from the old-style one. However, in its application WSN still experience some constraints on resource limitations in the form: processor, memory and energy, and any more challenges that related to availability, reliability and security. Based on its benefits, limitation, and challenges, and also three systematic stages, the review study was performed. The result of study was portrayed in this paper. It is regarding investigation result of hybrid cryptography (HC, as one of defense methods) realized for WSN to methodically increase the security level and decrease the resources usage. The result concluded that HC was proven in escalating the security defense level and downgrading the resources use on WSN compared to symmetric and asymmetric algorithms. © 2018 IEEE.

关键词： Wireless sensor networks

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Vibration-based classification of road damages: Gyroscope data and a simple neural network model 2

Vibration-based classification of road damages: Gyroscope da...

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2nd International Conference on Eco engineering Development 2018, ICEED 2018

作者： Gunawan, Fergyanto E. Herriyandi Soewito, Benfano Mauritsius, Tuga Surantha, Nico Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

A system for automatic detection of road damages is essential for logistics management. At the core of the system lies an algorithm for classification of damages. This work intends to establish such an algorithm. In the current proposal, the algorithm receives data of the probe vehicle rate of rotations, extracts statistics descriptive from the data to produce a feature vector, and finally, feeds the vector to a simple-three-layer neural network to classify the damages. The types of damages are limited to four cases: normal, pothole, speed bump, and expansion joint. For the development, a probe vehicle is used to produce 400 empirical data involving those four damage cases. By using Monte Carlo approach, the established neural network model is evaluated. The results show that the approach is able to classify the cases with 85% accuracy. The study also finds that the rates of pitch and roll to be the determining factors. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Neural network models

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