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arXiv 2020年

作者： Rahman, Tawsifur Chowdhury, Muhammad E.H. Khandakar, Amith Islam, Khandaker R. Islam, Khandaker F. Mahbub, Zaid B. Kadir, Muhammad A. Kashem, Saad Department of Biomedical Physics & Technology University of Dhaka Dhaka1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Department of Orthodentics Bangabandhu Sheikh Mujib Medical University Dhaka1000 Bangladesh Department of Mathematics and Physics North South University Dhaka1229 Bangladesh Faculty of Robotics and Advanced Computing Qatar Armed Forces-Academic Bridge Program Qatar Foundation Doha24404 Qatar

Pneumonia is a life threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life endangering if not acted upon in the right time and thus early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using the digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201 and SqueezeNet were used for transfer learning. 5247 chest x-ray images consisting of bacterial, viral and normal chest x-rays were used and through preprocessing techniques, the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95% and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster diagnosing pneumonia by the radiologist and can help in the fast airports screening of the pneumonia patients. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Unprecedented anti-inflammatory biscembranoids with 3-hydroperoxy-3-methylcyclohex-1-ene moiety isolated from aquaculture Sarcophyton trocheliophorum

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Aquaculture Reports 2023年 33卷

作者： Peng, Bo-Rong Nguyen, Ngoc Bao An Chen, Lo-Yun El-Shazly, Mohamed Hwang, Tsong-Long Su, Jui-Hsin Lai, Kuei-Hung Graduate Institute of Pharmacognosy College of Pharmacy Taipei Medical University Taipei 11031 Taiwan PhD Program in Clinical Drug Development of Herbal Medicine College of Pharmacy Taipei Medical University Taipei 11031 Taiwan Department of Pharmacognosy Faculty of Pharmacy Ain-Shams University Organization of African Unity Street Cairo Abassia 11566 Egypt Research Center for Chinese Herbal Medicine College of Human Ecology Chang Gung University of Science and Technology Taoyuan 33303 Taiwan Graduate Institute of Health Industry Technology College of Human Ecology Chang Gung University of Science and Technology Taoyuan 33303 Taiwan Department of Anaesthesiology Chang Gung Memorial Hospital Taoyuan 33305 Taiwan Graduate Institute of Natural Products College of Medicine Chang Gung University Taoyuan 33302 Taiwan Department of Chemical Engineering Ming Chi University of Technology New Taipei City 24301 Taiwan National Museum of Marine Biology & Aquarium Pingtung 94450 Taiwan Department of Marine Biotechnology and Resources National Sun Yat-sen University Kaohsiung 80424 Taiwan Traditional Herbal Medicine Research Center Taipei Medical University Hospital Taipei 11031 Taiwan

Recent advancements in the discovery of natural products have made significant progress in rapidly identifying known compounds from complex extracts using advanced spectroscopic technologies. Among these methods, one powerful approach called MS/MS molecular networking (MN) organizes chemical similarity based on MS/MS fragmentation data, serving as a valuable complement to traditional dereplication strategies. In our ongoing research focused on lead discovery from a sustainable medicinal source, an aquaculture soft coral Sarcophyton trocheliophorum, the MN profiling approach revealed the chemical diversity of biscembranoid analogs. By analyzing the MN pattern, biscembranoid derivatives with unique MS/MS fragments were prioritized for further investigation. Two novel biscembranoids, sarcotroxide A (1) and B (2), were successfully obtained through targeted isolation. These novel biscembranes contained an unprecedented 3-hydroperoxy-3-methylcyclohex-1-ene moiety compared with all biscembranoid analogs. The chemical structures of the two new biscembranoids were confirmed through spectroscopic analyses and in silico calculations. Their anti-inflammatory properties were assessed against the generation of superoxide anion (O₂^•−) and elastase release in activated human neutrophils. Sarcotroxides A (1) and B (2) exhibited anti-inflammatory activity by inhibiting O₂^•− accumulation (with 43.67% and 61.62% inhibition) and elastase release (with 38.21% and 61.62% inhibition) at 10 µM. These findings validated the potential of these biscembranoids from the cultured S. trocheliophorum as promising candidates for the future development of anti-inflammatory agents specifically designed to target neutrophils. © 2023 The Authors

关键词： Anti-inflammation Aquaculture soft coral Biscembranoid Neutrophil Sarcophyton trocheliophorum

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Review of the use of mangrove forests in supporting the socio-economic life of fishing communities

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IOP Conference Series: Earth and Environmental Science 2020年第1期575卷

作者： Untari R Darma P Betaubun A A Arief PhD. Student on Agricultural System Graduate School Hasanuddin University South Sulawesi Indonesia Department of Agribusiness Faculty of Agriculture Universitas Musamus Papua Indonesia Agribusiness Study Program Faculty of Agriculture Hasanuddin University South Sulawesi Indonesia Department of civil Engineering Faculty of Engineering Universitas Musamus Papua Indonesia Socio Economic of Fisheries Study Program Faculty of Marine Science and Fisheries Hasanuddin University South Sulawesi Indonesia

Mangrove forests have an important function in human life that cannot be replaced by other functions. The existence of mangrove forests has six main functions, namely biological, ecological, physical, social, economic and chemical functions. This article discusses the description of mangrove forest management in meeting the needs of the economic and social functions of the fishing communities on the coast of Merauke Regency, Papua Province, which were reviewed descriptively using relevant literature studies. In terms of economic function, mangrove forests provide economic value for fishing communities because mangrove forests become habitat for crabs, fish and other marine biota that are captured and sold as the main source of family income. In addition, the use of mangrove forests by fishermen on the coast is used as firewood and building materials. The high utilization of mangrove forests by coastal communities that ignores ecological sustainability causes damage to mangrove forests that threatens the sustainability of ecological functions. Damage to mangrove forests in Merauke in the period 2000-2005 was recorded as wide as 2,416 ha while in the 2005-2010 period damage reached 2,233 ha of the total area of the Merauke mangrove forest which reached 216,001.95 Ha or 6.98% of the Merauke forest area. Based on this, the management of mangrove forest utilization needs to get more attention from various parties so that the ecological function of mangrove forests as a condition of sustainable development, especially for fishing communities on the coast, can be realized in Merauke Regency.

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Observation of topological polaritons and photonic magic angles in twisted van der Waals bi-layers

arXiv

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arXiv 2020年

作者： Alù, Andrea Krasnok, Alex Dai, Zhigao Wu, Jing Qiu, Cheng-Wei Wu, Yingjie Bao, Qiaoliang Si, Guangyuan Ou, Qingdong Hu, Guangwei Mazor, Yarden Zhang, Qing Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Photonics Initiative Advanced Science Research Center City University of New York New YorkNY United States Monash University ClaytonVIC Australia Melbourne Centre for Nanofabrication Victorian Node of the Australian National Fabrication Facility ClaytonVIC Australia Singapore Singapore Faculty of Materials Science and Chemistry China University of Geosciences 388 Lumo Road Wuhan430074 China Department of Electrical and Computer Engineering University of Texas at Austin AustinTX United States Physics Program Graduate Center City University of New York New YorkNY United States

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Performance analysis virtual server VMware Vsphere 5.5 with physical enterprise server 2

Performance analysis virtual server VMware Vsphere 5.5 with ...

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2nd Nommensen International Conference on Technology and engineering, NICTE 2018

作者： Kurniawan, Dwi Ely Nashrullah, Muhammad Kurniasih, Nuning Achmad Daengs, G.S. Kurniawan, Citra Department of Informatics Engineering Politeknik Negeri Batam Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Management University of 45 Surabaya Indonesia Department of Electrical Engineering Sekolah Tinggi Teknik Malang Indonesia

The increasing number of servers used to support all the activities that assist the production of a company. Usually, it will be a waste, because a physical server cannot share resources such as CPU, RAM, and network. It will force the company to buy a new server each time to implement a new system. Therefore, we examined and analyzed the comparison of performance on a physical server with a virtual server that is built as an IP camera server to answer the needs of the company. After testing and getting the data, we conclude that in the testing of a turn on, restart and shut down servers, virtual servers have very much superior performance compared to physical servers. While in terms of performance of the CPU, RAM, and network, both physical servers and virtual servers do not have a significant difference. However, the important notes are in virtualization, it can perform resource sharing, and this technology is needed by companies in this era. © Published under licence by IOP Publishing Ltd.

关键词： Plant shutdowns

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基于大语言模型的糖尿病管理:潜力与展望

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Science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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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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Correction: Solid-state nitrogen-doped carbon nanoparticles with tunable emission prepared by a microwave-assisted method

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RSC advances 2022年第5期12卷 2622页

作者： Fitri Aulia Permatasari Fitriyanti Nakul Tirta Rona Mayangsari Akfiny Hasdi Aimon Bebeh Wahid Nuryadin Satria Zulkarnaen Bisri Takashi Ogi Ferry Iskandar Department of Physics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung West Java Indonesia 40132. Department of Electrical Engineering Politeknik Negeri Batam Batam Indonesia. Department of Chemistry Universitas Pertamina Jakarta 12220 Indonesia. Department of Physics Faculty of Science and Technology UIN Sunan Gunung Djati Bandung Jl. A. H. Nasution 105 Bandung Indonesia 40614. RIKEN Center for Emergent Matter Science 2-1 Hirosawa Wako Saitama 351-0198 Japan. Chemical Engineering Program Department of Advanced Science and Engineering Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashihiroshima 739-8527 Japan. Research Center for Nanoscience and Nanotechnology Institut Teknologi Bandung Jalan Ganesha 10 Bandung Indonesia 40132 ferry@fi.itb.ac.id.

[This corrects the article DOI: 10.1039/D1RA07290K.].

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Band-gap engineering, magnetic behavior and Dirac-semimetal character in the MoSi2N4 nanoribbon with armchair and zigzag edges

arXiv

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arXiv 2021年

作者： Bafekry, A. Faraji, M. Stampfl, C. Sarsari, I. Abdolhosseini Ziabari, A. Abdollahzadeh Hieu, N.N. Karbasizadeh, S. Ghergherehchi, M. Department of Physics University of Antwerp Groenenborgerlaan 171 AntwerpB-2020 Belgium Micro and Nanotechnology Graduate Program TOBB University of Economics and Technology Sogutozu Caddesi No 43 Sogutozu Ankara06560 Turkey School of Physics University of Sydney NSW2006 Australia Department of Physics Isfahan University of Technology Isfahan84156-83111 Iran Nano Research Lab Lahijan Branch Islamic Azad University Lahijan Iran Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Faculty of Natural Sciences Duy Tan University Da Nang550000 Viet Nam College of Electronic and Electrical Engineering Sungkyunkwan University Suwon Korea Republic of

Motivated by the recent successful formation of the MoSi2N4 monolayer [Hong et al., Sci. 369, 670 (2020)], the structural, electronic and magnetic properties of MoSi2N4 nanoribbons (NRs) is investigated for the first time . The band structure calculations showed spin-polarization in zigzag edges and a non-magnetic semiconducting character in armchair edges. For armchair-edges, we identify an indirect to direct band gap shift compared to the MoSi2N4 monolayer, and its energy gap increases with increasing NR width. Anisotropic electrical and magnetic behavior is observed via band structure calculations in the zigzag and armchair edges, where, surprisingly, for the one type of zigzag-edges configuration, we identify a Dirac-semimetal character. The appearance of magnetism and Dirac-semimetal in MoSi2N4 ribbon can give rise to novel physical properties, which could be useful in applications for next-generation electronic devices. © 2021, CC BY.

关键词： Nanoribbons

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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 7

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

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