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Higher-order moments of the elliptic flow distribution in PbPb collisions at sNN = 5.02 TeV

Journal of High Energy Physics 2024年第2期2024卷 1-45页

作者： Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Paulitsch, P. Pitters, F.M. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Rejeb Sfar, H. Van Mechelen, P. Bols, E.S. D’Hondt, J. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Moortgat, S. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Doninck, W. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Makarenko, I. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Mondal, K. Taliercio, A. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. 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. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Thakur, S. Cheng, T. Javaid, T. Mittal, M. Yuan, L. Ahmad, M. Bauer, G. Hu, Z. Lezki, S. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Milosevic, V. Monti, F. Sharma, R. Tao, J. Thomas-Wilsker Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

The hydrodynamic flow-like behavior of charged hadrons in high-energy lead-lead collisions is studied through multiparticle correlations. The elliptic anisotropy values based on different orders of multiparticle cumulants, v₂{2k}, are measured up to the tenth order (k = 5) as functions of the collision centrality at a nucleon-nucleon center-of-mass energy of s_NN = 5.02 TeV. The data were recorded by the CMS experiment at the LHC and correspond to an integrated luminosity of 0.607 nb⁻¹. A hierarchy is observed between the coefficients, with v₂{2} > v₂{4} ≳ v₂{6} ≳ v₂{8} ≳ v₂{10}. Based on these results, centrality-dependent moments for the fluctuation-driven event-by-event v₂ distribution are determined, including the skewness, kurtosis and, for the first time, superskewness. Assuming a hydrodynamic expansion of the produced medium, these moments directly probe the initial-state geometry in high-energy nucleus-nucleus collisions. © The Author(s) 2024.

关键词： Event-by-Event fluctuation Harmonic Flow Heavy Ion Experiments Relativistic Heavy Ion Physics

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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Biometrics and quality of life of lymphoma patients: A longitudinal mixed-model approach

Biometrics and quality of life of lymphoma patients: A longi...

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作者： Oliveira, Alexandra Silva, Eliana Aguiar, Joyce Faria, Brígida Mónica Reis, Luís Paulo Cardoso, Henrique Gonçalves, Joaquim Oliveira e Sá, Jorge Carvalho, Victor Marques, Herlander LIACC - Artificial Intelligence and Computer Science Laboratory Porto Portugal ESS-IPP - Department of Biomathematics Biostatistics and Bioinformatics School of Health Polytechnic Institute of Porto Porto Portugal DEI-FEUP - Department of Informatics Engineering Faculty of Engineering University of Porto Porto Portugal Centro ALGORITMI – University of Minho Guimarães Portugal LITEC - Lab. de Inovação Tecnológica e Engenharia do Conhecimento Porto Portugal 2Ai - Applied Artificial Intelligence Laboratory School of Technology of Polytechnic Institute of Cávado and Ave Barcelos Portugal CINTESIS - Centro de Investigação em Tecnologias e Serviços de Saúde Porto Portugal Centro Hospitalar de Braga Braga Portugal

Knowledge engineering has become essential in the fields of Medical and Health Care with emphasis for helping citizens to improve their health and quality of life. This includes individual methods and techniques in health-related knowledge acquisition and representation and their application in the construction of intelligent systems capable of using the acquired information to improve the patients' health and/or quality of life. Haemato-oncological diseases can provide significant disability and suffering, with severe symptoms and psychological distress. They can create difficulties in fulfilling professional, family and social roles, affecting an individual's quality of life. Health related quality of life (HRQoL) is a subjective concept but there is also an objective component related to physiological indicators. Some of these physiological indicators can be easily assessed by wearable technology such heart rate variability (HRV). This paper introduces an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability. The system is based on wearable technology and intelligent processing of the patients' biometric information to assess some quality of life related parameters. A longitudinal study was conducted among 16 lymphoma patients using this intelligent system. Mixed-effect regression models were performed to investigate predictors for and time effects on HRQoL. There were no significant changes in all HRQoL domains over time. Some quality of life domains revealed similar time trends as HRV indices. These HRV indices also have a significant effect on the domains of quality of life. © 2020 John Wiley & Sons Ltd

关键词： Regression analysis

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The rank of contextuality

arXiv

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arXiv 2022年

作者： Horodecki, Karol Zhou, Jingfang Stankiewicz, Maciej Salazar, Roberto Horodecki, Pawel Raussendorf, Robert Horodecki, Ryszard Ramanathan, Ravishankar Tyhurst, Emily Institute of Informatics National Quantum Information Centre Faculty of Mathematics Physics and Informatics University of Gdańsk Wita Stwosza 57 Gdańsk80-308 Poland International Centre for Theory of Quantum Technologies University of Gdańsk Wita Stwosza 63 Gdańsk80-308 Poland Department of Applied Physics Graduate School of Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland Faculty of Applied Physics and Mathematics National Quantum Information Centre Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Physics and Astronomy University of British Columbia VancouverBCV6T 1Z1 Canada Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong Department of Physics University of Toronto TorontoON Canada

Quantum contextuality is one of the most recognized resources in quantum communication and computing scenarios. We provide a new quantifier of this resource, the rank of contextuality (RC). We define RC as the minimum number of non-contextual behaviors that are needed to simulate a contextual behavior. We show that the logarithm of RC is a natural contextuality measure satisfying several properties considered in the spirit of the resource-theoretic approach. The properties include faithfulness, monotonicity, and additivity under tensor product. We also give examples of how to construct contextual behaviors with an arbitrary value of RC exhibiting a natural connection between this quantifier and the arboricity of an underlying hypergraph. We also discuss exemplary areas of research in which the new measure appears as a natural quantifier. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum communication

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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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Preface

EAI/Springer Innovations in Communication and Computing

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EAI/Springer Innovations in Communication and Computing 2023年 Part F274卷 vii-viii页

作者： Joseph, Ferdin Joe John Balas, Valentina Emilia Rajest, S. Suman Regin, R. Thai-Nichi Institute of Technology Bangkok Thailand Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad Arad Romania Department of Research Arts and Humanities Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chennai India

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Cyber security after COVID 19: A review

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AIP Conference Proceedings 2023年第1期2839卷

作者： Muthana Alisawi Layth Hammood Alaan Ghazi Sirwan Saber Abdullah Aras Al-Dawoodi Abbas Hussein Ali Ashraf Nabeel Almallah Nidhal Mohsin Hazzaa Yousif Mohammed Wahab Asmaa Yaseen Nawaf Department of Computer science college of computer science and information technology University of Kirkuk. Kirkuk Iraq Computer science informatics institute Gazi university Çankaya Ankara 06690 Turkey Institute of science faculty of technology computer engineering Gazi university Çankaya Ankara 06690 Turkey Department of Computer Technical Engineering Al-Qalam university College Kirkuk Iraq Faculty of Applied and Human Sciences University Malaysia Perlis Perlis Malaysia Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia Information Technology and Communication Department Rafidain Bank Baghdad Iraq Department of Quality Assurance and Academic Performance University of Kirkuk Kirkuk Iraq Department of computer science college of computer and mathematics University of Mosul Mosul Iraq Gazi University - Turkey Çankaya Ankara 06690 Turkey

Nowadays, when talking about cybersecurity, the talk is associated with the most important event in the world during the year 2020, which is COVID-19. Although the digital world exists parallel to the real world, humanity did not have to rely on the Internet to accomplish its tasks. However, the situation changed after the Corona pandemic, as the world found itself in front of one option, which is to work remotely to overcome the impact of the pandemic. Therefore, with this massive and sudden digital transformation on the global level, cyber criminals have many opportunities through cyber vulnerabilities that appear due to the rapid shift to remote work. In this study, we presented an accurate review of the effects that accompanied the occurrence of the Coronavirus pandemic on cybersecurity. Moreover, the world has seen a growing number of cyber-attacks due to these effects, which led to attract the attention to the importance of cyber security to ensure full benefit from digital services without exposure to damage. Also, we indicated in this paper a number of recommendations that contribute to mitigating the damages of cyber-attacks or preventing their occurrence. Furthermore, Cyber security should be a complete approach and not limited to the technical side, where the awareness of the community about cyber risks should contribute to preventing cyber criminals from achieving their goals.

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Erratum to:Influence of structural defects on charge density waves in 1T-TaS_(2)

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Nano Research 2024年第3期17卷 2204-2204页

作者： Iaroslav Lutsyk Karol Szalowski Pawel Krukowski Pawel Dabrowski Maciej Rogala Witold Kozlowski Maxime Le Ster Michal Piskorski Dorota A.Kowalczyk Wojciech Rys Rafal Dunal Aleksandra Nadolska Klaudia Toczek Przemyslaw Przybysz Ewa Lacinska Johannes Binder Andrzej Wysmolek Natalia Olszowska Jacek J.Kolodziej Martin Gmitra Takuma Hattori Yuji Kuwahara Guang Bian Tai-Chang Chiang Pawel J.Kowalczyk Faculty of Physics and Applied Informatics University of LodzPomorska 149/15390-236 LodzPoland Faculty of Physics University of WarsawPasteura 502-093 WarsawPoland Faculty of Physics Astronomyand Applied Computer ScienceJagiellonian UniversityLojasiewicza 1130-348 KrakowPoland National Synchrotron Radiation Centre SOLARIS Jagiellonian UniversityCzerwone Maki 9830-392 KrakówPoland Institute of Physics Faculty of SciencePavol JozefŠafárik University in KošicePark Angelinum 904001 KošiceSlovakia Institute of Experimental Physics Slovak Academy of SciencesWatsonova 4704001 KošiceSlovakia Department of Precision Engineering Graduate School of EngineeringOsaka University2-1 Yamada-okaSuita 565-0871Japan Department of Physics and Astronomy University of MissouriColumbiaMissouri 65211USA Department of Physics and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana-ChampaignUrbanaIllinois 61801-3080USA

The name of the second author in original paper was unfortunately *** should be“Karol Szalowski”,instead of“Karoli Szalowski”.

关键词： structural charge defects

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Cost optimization in CNN for big data of share price

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AIP Conference Proceedings 2023年第1期2587卷

作者： R. Saravanakumar Gokul Talele Ali Hazim Alyamoor Kaushal Kishor Bhatt Prashant Vats Malik Bader Alazzam Dept. of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India Dept. of Data Science IIIT Banglore India Dept. of Accounting Economic and Administration College University of Mosul Ninawa Iraq Dept. of Computer Science and Engineering Birla Institute of Applied Science Bhimtal Nainital Uttarakhand India Dept. of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Faculty of Computer Science and Informatics Amman Arab University Mubis-Amman Jordan

Optimization of share price in order to evaluate whether to invest in specific share or not is subject of study paper. Optimized value has been utilized to enhance the performance and accuracy when dataset is trained using CNN. Review has been given on big cap, mid cap & small cap fund which are accessible in large, medium and small amount for the purpose of investing. It has been found that share in big cap are more dependable but offers limited return. However share in case of mid cap are considered comparatively more hazardous than big cap yet return may be substantial. The suggested study has given mechanism that will make use of optimization mechanism to filter the data set considering PSO and MVO mechanism. The suggested CNN based training model is claimed high performance system with improved accuracy.

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