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arXiv 2020年

作者： Yilmaz, Serife Dudkina, Ekaterina Bin, Michelangelo Crisostomi, Emanuele Ferraro, Pietro Murray-Smith, Roderick Parisini, Thomas Stone, Lewi Shorten, Robert Dyson School of Design Engineering Imperial College London London United Kingdom Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Energy Systems Territory and Constructions Engineering University of Pisa Pisa Italy School of Computing Science University of Glasgow Glasgow United Kingdom George S. Wise Faculty of Life Sciences Tel Aviv University Israel

The Kemeny constant of a graph can be used to identify and analyse bridges between communities in a graph. Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Tracing techniques aim at reconstructing past history of contacts, but can face practical limits in an exponential growth of either testing or overly conservative quarantining. We show how this application of graph theory can be conveniently used to efficiently intercept new virus outbreaks, when they are still in their early stage. Simulations provide promising results in early identification and blocking of possible ‘super-spreader’ links that transmit disease between different communities. Copyright © 2020, The Authors. All rights reserved.

关键词： Viruses

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A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Parisa Khoshvaght Amir Haider Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Ferzat Anka Jan Lansky Mehdi Hosseinzadeh Institute of Research and Development Duy Tan University Da Nang Vietnam School of Engineering & Technology Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of AI and Robotics Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Data Science Application and Research Center (VEBIM) Fatih Sultan Mehmet Vakif University Istanbul Türkiye Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Republic of Korea

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms.

关键词： Double deep Q-learning Latency and energy efficiency Mobile edge computing Spatio-temporal optimization

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The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

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science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

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Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update

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Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 2023年第1期43卷 3-25页

作者： Ronney B Panerai Patrice Brassard Joel S Burma Pedro Castro Jurgen Ahr Claassen Johannes J van Lieshout Jia Liu Samuel Je Lucas Jatinder S Minhas Georgios D Mitsis Ricardo C Nogueira Shigehiko Ogoh Stephen J Payne Caroline A Rickards Andrew D Robertson Gabriel D Rodrigues Jonathan D Smirl David M Simpson Department of Cardiovascular Sciences University of Leicester and NIHR Biomedical Research Centre Leicester UK. Department of Kinesiology Faculty of Medicine and Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec Université Laval Québec QC Canada. Faculty of Kinesiology University of Calgary Calgary AB Canada. Department of Neurology Centro Hospitalar Universitário de São João Faculty of Medicine University of Porto Porto Portugal. Department of Geriatric Medicine and Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen The Netherlands. Department of Internal Medicine Amsterdam UMC The Netherlands and Division of Physiology Pharmacology and Neuroscience School of Life Sciences University of Nottingham Medical School Queen's Medical Centre UK. Institute of Advanced Computing and Digital Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen University Town Shenzhen China. School of Sport Exercise and Rehabilitation Sciences and Centre for Human Brain Health University of Birmingham Birmingham UK. Department of Bioengineering McGill University Montreal Québec QC Canada. Neurology Department School of Medicine Hospital das Clinicas University of São Paulo São Paulo Brazil. Department of Biomedical Engineering Toyo University Kawagoe-Shi Saitama Japan. Institute of Applied Mechanics National Taiwan University Taipei. Department of Physiology & Anatomy University of North Texas Health Science Center Fort Worth Texas USA. Department of Kinesiology and Health Sciences University of Waterloo Waterloo ON Canada. Department of Clinical Sciences and Community Health University of Milan Milan Italy. Institute of Sound and Vibration Research University of Southampton Southampton UK.

Cerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.

关键词： Cerebral hemodynamics consensus guidelines reference values transfer function analysis

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engineering Meaningful computing Education: Programming Learning Experience Model

Engineering Meaningful Computing Education: Programming Lear...

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IEEE International Conference on Industrial engineering and engineering Management

作者： S. B. Ho S. L. Chean I. Chai C. H. Tan Faculty of Computing and Informatics Multimedia University Selangor Malaysia Lee Kong Chian Faculty Engineering and Science Universiti Tunku Abdul Rahman Selangor Malaysia Faculty of Management Multimedia University Selangor Malaysia

An e-learning system provides a platform for communication between educators and students. It facilitates the sharing of information and learning content in a form which can be easily accessed by the users. The scope of the research revolves around the idea that an automated assessment in an e-learning platform would be able to help programming learners understand defects in their program and independently learn how to program. This research would investigate the effects on college or university students. This research analyzes the effectiveness of various ways to learn a programming language. It is believed that automated assessment tools would be able to monitor and improve students' programming skills over time and reduce the effort for both instructors and students.

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Deep Learning Model for the Image Fusion and Accurate Classification of Remote Sensing Images

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Computational intelligence and neuroscience 2022年第1期2022卷 2668567页

作者： S Roselin Mary Sunita Pachar Prabhat Kumar Srivastava Medhavi Malik Avani Sharma Tariq G Almutiri Zabihullah Atal Department of CSE Anand Institute of Higher Technology Kalasalingam Nagar Kazhipattur OMR Chennai 603103 India. President Vasundhara Blessing Foundation Academician GLA University Chaumuhan India. Computer Science and Engineering Department IMS Engineering College Ghaziabad UP India. School of Computing Science and Engineering Galgotias University Noida India. Department of Information Technology Manipal University Jaipur Jaipur India. Department of Computer Science Community College King Saud University Riyadh 11437 Saudi Arabia. Department of Computer Science Faculty of Computer Science Kardan University Afghanistan.

Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote sensing image. A trained depth belief network (DBN) processes and divides fusion images, while a Softmax classifier determines the land type. As tested, the proposed approach can categorise all types of land. Traditional methods of detecting distant sensing photographs have limitations that can be overcome by using convolutional neural networks (CNN). Traditional techniques are incapable of combining deep learning elements while doing badly in classification. After PCA decreases data dimensionality, deep learning is applied to generate effective features that employ deep learning after PCA has reduced the dimensionality of the data. Principal component analysis is commonly used because of its effectiveness in attaining linear dimension reduction. It may be used on its own or as a starting point for further study into various different dimensionality reduction approaches. Data can be altered by remapping onto a new set of orthogonal axes using a process known as projection-based principal component analysis. Following remote sensing of land resources, the pictures were classified using a support vector machine. Euroset satellite images are used to assess the suggested approach. Accuracy and kappa have both increased. It was accurate and within 95.83 % of the planned figures. The classification findings' kappa value and reasoning time were 95.87 % and 128 milliseconds, respectively. Both the model's performance and the classification effect are excellent.

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Head-down tilt lithotomy position and well-leg compartment syndrome: An international survey of current practice

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Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland 2025年第6期27卷 e70134页

作者： Chukwuemeka C Uzoma Anthony I Shepherd Zoe L Saynor Jim S Khan Guglielmo Niccolò Piozzi Rauand Duhoky Christopher Askew M Mahir Ozmen Thierry R F Middleton Shamsul Masum Maria Perissiou Clinical Health and Rehabilitation Team School of Psychology Sport and Health Sciences University of Portsmouth Portsmouth UK. Department of Faculty Surgery RUDN University Moscow Russia. School of Health Sciences Faculty of Environmental and Life Sciences University of Southampton Southampton UK. Department of Colorectal Surgery Portsmouth Hospitals University NHS Trust Portsmouth UK. Faculty of Science and Health University of Portsmouth Portsmouth UK. School of Computing Faculty of Technology University of Portsmouth Portsmouth UK. VasoActive Research Group School of Health University of the Sunshine Coast Sunshine Coast Queensland Australia. Mahir Ozmen Clinic (MOC) Ankara Turkey. Sapienza University of Rome Medical School Rome Italy. School of Electrical and Mechanical Engineering University of Portsmouth Portsmouth UK.

AIM:Well-leg compartment syndrome (WLCS) is a serious complication of prolonged surgery in the head-down tilt lithotomy (HDTL) position associated with increased postoperative morbidity and mortality. However, there is a lack of awareness and clinical guidance regarding prevention of WLCS. The aim of this study was to assess current HDTL-related practices and occurrence of WLCS among a global cohort of clinicians. METHOD:An international online survey of clinicians was conducted between July and December 2023. Data analysis involved descriptive statistics, machine learning techniques and qualitative content analysis. RESULTS:A total of 595 clinicians from 71 countries and 14 specialities participated. Most (98%) reported routine use of HDTL, 27% of whom did not implement any preventive strategies. 'Leg rest' was the most reported preventive measure (41%), commonly initiated after 2 or 3 h of HDTL (79%), for 10-15 min (56%). Overall, 170 cases of WLCS were reported by 21% of respondents. The majority reported unilateral WLCS (81%) following a laparoscopic procedure (63%) performed in HDTL (64%). Only 28% of respondents discussed WLCS during consent for operations in HDTL. Machine learning identified 'duration of uninterrupted HDTL' as a positive predictor of the occurrence of WLCS (p < 0.001). Content analysis demonstrated that clinician perspectives and practices regarding WLCS are significantly influenced by personal experience, mostly due to a poor evidence base and lack of standardized institutional policies. CONCLUSION:Perioperative practices during procedures in HDTL vary substantially, and are primarily informed by clinician experience and preferences. There is a need for evidence-based consensus on best practices to enhance safety during procedures in HDTL.

关键词： Lloyd‐Davies compartment syndrome global survey perioperative practices prevention and management robotic surgery

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Roadmap for Sustainable Mixed Ionic-Electronic Conducting Membranes

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Advanced Functional Materials 2022年第6期32卷 2105702-2105702页

作者： Chen, Guoxing Feldhoff, Armin Weidenkaff, Anke Li, Claudia Liu, Shaomin Zhu, Xuefeng Sunarso, Jaka Huang, Kevin Wu, Xiao-Yu Ghoniem, Ahmed F. Yang, Weishen Xue, Jian Wang, Haihui Shao, Zongping Duffy, Jack H. Brinkman, Kyle S. Tan, Xiaoyao Zhang, Yan Jiang, Heqing Costa, Rémi Friedrich, Kaspar Andreas Kriegel, Ralf Fraunhofer Research Institution for Materials Recycling and Resource Strategies IWKS Brentanostraße 2a Alzenau63755 Germany Institute of Physical Chemistry and Electrochemistry Leibniz University Hannover Callinstr. 3A Hannover30167 Germany Department of Materials and Earth Sciences Technical University Darmstadt Alarich-Weiss-Str. 2 Darmstadt64287 Germany Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia State Key Laboratory of Organic-Inorganic Composites College of Chemical Engineering Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences 457 Zhongshan Road Dalian116023 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical Engineering University of South Carolina ColumbiaSC29208 United States Department of Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada Department of Mechanical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States School of Chemistry and Chemical Engineering Guangdong Provincial Key Lab of Green Chemical Product Technology South China University of Technology Guangzhou510640 China Department of Chemical Engineering Beijing Key Laboratory of Membrane Materials and Engineering Tsinghua University Beijing100084 China Western Australia School of Mines: Minerals Energy and Chemical Engineering Curtin University PerthWA6102 Australia College of Chemical Engineering Nanjing Tech University Nanjing210009 China Department of Materials Science and Engineering Clemson University ClemsonSC29634 United States State Key Laboratory of Separation Membranes and Membrane Processes Department of Chemical Engineering Tianjin Polytechnic University Tianjin300

Mixed ionic-electronic conducting (MIEC) membranes have gained growing interest recently for various promising environmental and energy applications, such as H2 and O2 production, CO2 reduction, O2 and H2 separation, CO2 separation, membrane reactors for production of chemicals, cathode development for solid oxide fuel cells, solar-driven evaporation and energy-saving regeneration as well as electrolyzer cells for power-to-X technologies. The purpose of this roadmap, written by international specialists in their fields, is to present a snapshot of the state-of-the-art, and provide opinions on the future challenges and opportunities in this complex multidisciplinary research field. As the fundamentals of using MIEC membranes for various applications become increasingly challenging tasks, particularly in view of the growing interdisciplinary nature of this field, a better understanding of the underlying physical and chemical processes is also crucial to enable the career advancement of the next generation of researchers. As an integrated and combined article, it is hoped that this roadmap, covering all these aspects, will be informative to support further progress in academics as well as in the industry-oriented research toward commercialization of MIEC membranes for different applications. © 2021 The Authors. Advanced Functional Materials published by Wiley-VCH GmbH

关键词： Carbon dioxide

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Measurement of Ultra-High-Energy Diffuse Gamma-Ray Emission of the Galactic Plane from 10 TeV to 1 PeV with LHAASO-KM2A

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Physical Review Letters 2023年第15期131卷 151001-151001页

作者： Zhen Cao F. Aharonian Q. An Axikegu Y. X. Bai Y. W. Bao D. Bastieri X. J. Bi Y. J. Bi J. T. Cai Q. Cao W. Y. Cao Zhe Cao J. Chang J. F. Chang A. M. Chen E. S. Chen Liang Chen Lin Chen Long Chen M. J. Chen M. L. Chen Q. H. Chen S. H. Chen S. Z. Chen T. L. Chen Y. Chen N. Cheng Y. D. Cheng M. Y. Cui S. W. Cui X. H. Cui Y. D. Cui B. Z. Dai H. L. Dai Z. G. Dai Danzengluobu D. della Volpe X. Q. Dong K. K. Duan J. H. Fan Y. Z. Fan J. Fang K. Fang C. F. Feng L. Feng S. H. Feng X. T. Feng Y. L. Feng S. Gabici B. Gao C. D. Gao L. Q. Gao Q. Gao W. Gao W. K. Gao M. M. Ge L. S. Geng G. Giacinti G. H. Gong Q. B. Gou M. H. Gu F. L. Guo X. L. Guo Y. Q. Guo Y. Y. Guo Y. A. Han H. H. He H. N. He J. Y. He X. B. He Y. He M. Heller Y. K. Hor B. W. Hou C. Hou X. Hou H. B. Hu Q. Hu S. C. Hu D. H. Huang T. Q. Huang W. J. Huang X. T. Huang X. Y. Huang Y. Huang Z. C. Huang X. L. Ji H. Y. Jia K. Jia K. Jiang X. W. Jiang Z. J. Jiang M. Jin M. M. Kang T. Ke D. Kuleshov K. Kurinov B. B. Li Cheng Li Cong Li D. Li F. Li H. B. Li H. C. Li H. Y. Li J. Li Jian Li Jie Li K. Li W. L. Li X. R. Li Xin Li Y. Z. Li Zhe Li Zhuo Li E. W. Liang Y. F. Liang S. J. Lin B. Liu C. Liu D. Liu H. Liu H. D. Liu J. Liu J. L. Liu J. Y. Liu M. Y. Liu R. Y. Liu S. M. Liu W. Liu Y. Liu Y. N. Liu R. Lu Q. Luo H. K. Lv B. Q. Ma L. L. Ma X. H. Ma J. R. Mao Z. Min W. Mitthumsiri H. J. Mu Y. C. Nan A. Neronov Z. W. Ou B. Y. Pang P. Pattarakijwanich Z. Y. Pei M. Y. Qi Y. Q. Qi B. Q. Qiao J. J. Qin D. Ruffolo A. Sáiz D. Semikoz C. Y. Shao L. Shao O. Shchegolev X. D. Sheng F. W. Shu H. C. Song Yu. V. Stenkin V. Stepanov Y. Su Q. N. Sun X. N. Sun Z. B. Sun P. H. T. Tam Q. W. Tang Z. B. Tang W. W. Tian C. Wang C. B. Wang G. W. Wang H. G. Wang H. H. Wang J. C. Wang K. Wang L. P. Wang L. Y. Wang P. H. Wang R. Wang W. Wang X. G. Wang X. Y. Wang Y. Wang Y. D. Wang Y. J. Wang Z. H. Wang Z. X. Wang Zhen Wang Zheng Wang D. M. Wei J. J. Wei Y. J. Wei T. Wen C. Y. Wu H. R. Wu S. Wu X. F. Wu Y. S. Wu S. Q. Xi J. Xia J. J. Xia G. M. Xiang D. X. Xiao G. Xiao G. G. Xin Y. L. Xin Y. Xing Z. Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences 100049 Beijing China University of Chinese Academy of Sciences 100049 Beijing China Tianfu Cosmic Ray Research Center 610000 Chengdu Sichuan China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 69029 Heidelberg Germany State Key Laboratory of Particle Detection and Electronics 230026 Hefei China University of Science and Technology of China 230026 Hefei Anhui China School of Physical Science and Technology & School of Information Science and Technology Southwest Jiaotong University 610031 Chengdu Sichuan China School of Astronomy and Space Science Nanjing University 210023 Nanjing Jiangsu China Center for Astrophysics Guangzhou University 510006 Guangzhou Guangdong China Hebei Normal University 050024 Shijiazhuang Hebei China Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences 210023 Nanjing Jiangsu China Tsung-Dao Lee Institute & School of Physics and Astronomy Shanghai Jiao Tong University 200240 Shanghai China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences 200030 Shanghai China Key Laboratory of Cosmic Rays (Tibet University) Ministry of Education 850000 Lhasa Tibet China National Astronomical Observatories Chinese Academy of Sciences 100101 Beijing China School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai) Sun Yat-sen University 519000 Zhuhai & 510275 Guangzhou Guangdong China School of Physics and Astronomy Yunnan University 650091 Kunming Yunnan China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet

The diffuse Galactic γ-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this Letter, we report the measurements of diffuse γ rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner (15°10 TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of −2.99±0.04, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of ∼3 than the prediction. A similar spectrum with an index of −2.99±0.07 is found in the outer Galaxy region, and the absolute flux for 10≲E≲60 TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations.

关键词： Cosmic rays & astroparticles Gamma ray astronomy

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Sp(2N) Yang-Mills theories on the lattice: Scale setting and topology

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Physical Review D 2022年第9期106卷 094503-094503页

作者： Ed Bennett Deog Ki Hong Jong-Wan Lee C.-J. David Lin Biagio Lucini Maurizio Piai Davide Vadacchino Swansea Academy of Advanced Computing Swansea University Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Physics Pusan National University Busan 46241 Republic of Korea Institute for Extreme Physics Pusan National University Busan 46241 Republic of Korea Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Center for High Energy Physics Chung-Yuan Christian University Chung-Li 32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu 30010 Taiwan Physics Division National Centre for Theoretical Sciences Taipei 10617 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park SA2 8PP Swansea Wales United Kingdom School of Mathematics and Hamilton Mathematics Institute Trinity College Dublin 2 Ireland Centre for Mathematical Sciences University of Plymouth PL4 8AA Plymouth United Kingdom

We study Yang-Mills lattice theories with Sp(Nc) gauge group, with Nc=2N, for N=1,…,4. We show that if we divide the renormalized couplings appearing in the Wilson flow by the quadratic Casimir C2(F) of the Sp(Nc) group, then the resulting quantities display a good agreement among all values of Nc considered, over a finite interval in flow time. We use this scaled version of the Wilson flow as a scale-setting procedure, compute the topological susceptibility of the Sp(Nc) theories, and extrapolate the results to the continuum limit for each Nc.

关键词： Large-N expansion in field theory Lattice field theory Nonperturbative effects in field theory Quantum field theory Strong interaction Topology

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