检索结果-内蒙古大学图书馆

Parallel plasma loops and the energization of the solar corona

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Peter, Hardi Chitta, Lakshmi Pradeep Chen, Feng Pontin, David I. Winebarger, Amy R. Golub, Leon Savage, Sabrina L. Rachmeler, Laurel A. Kobayashi, Ken Brooks, David H. Cirtain, Jonathan W. De Pontieu, Bart McKenzie, David E. Morton, Richard J. Testa, Paola Tiwari, Sanjiv K. Walsh, Robert W. Warren, Harry P. Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 GöttingenD-37077 Germany Laboratory for Atmospheric and Space Physics University of Colorado Boulder Boulder CO80303 United States School of Information and Physical Sciences University of Newcastle CallaghanNSW2308 Australia School of Science and Engineering University of Dundee Nethergate DundeeDD1 4HN United Kingdom NASA/Marshall Space Flight Center HuntsvilleAL35812 United States Harvard-Smithsonian Center for Astrophysics 60 Garden St. CambridgeMA02138 United States Boulder CO80305 United States College of Science George Mason University 4400 University Drive FairfaxVA22030 United States BWX Technologies Inc. 800 Main St #400 LynchburgVA24504 United States Lockheed Martin Solar and Astrophysics Laboratory Palo AltoCA94304 United States Rosseland Centre for Solar Physics University of Oslo P.O. Box 1029 Blindern OsloNO-0315 Norway Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern OsloNO-0315 Norway Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Bay Area Environmental Research Institute NASA Research Park Moffett FieldCA94035 United States Jeremiah Horrocks Institute University of Central Lancashire PrestonPR1 2HE United Kingdom Space Science Division Naval Research Laboratory WashingtonDC20375 United States

关键词： Magnetohydrodynamics

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

学校读者我要写书评

暂无评论

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

Join the Fold: Video Games, Science Fiction, and the Refolding of Citizen Science

学校读者我要写书评

暂无评论

DESIGN ISSUES 2023年第1期39卷 70-87页

作者： Milburn, Colin Buse, Katherine Dhaliwal, Ranjodh Singh Wills, Melissa Aldosari, Raida Camarador, Patrick Miller, Josh Aaron Siegel, Justin Colin Milburn is Gary Snyder Chair in Science and the Humanities and a professor of Science and Technology Studies English and Cinema and Digital Media at the University of California Davis. His research focuses on science literature and media technologies. His books include Nanovision: Engineering the Future (2008) Mondo Nano: Fun and Games in the World of Digital Matter (2015) and Respawn: Gamers Hackers and Technogenic Life (2015). Katherine Buse is an assistant professor of Media Studies at the University of Chicago. Her research focuses on digital media the environmental humanities and the cultures of science and technology. Her current book project Speculative Planetology: Science Culture and the Building of Model Worlds tells the story of how the sciences and the arts co-constructed a toolkit for thinking about planets as whole systems. Ranjodh Singh Dhaliwal is Ruth and Paul Idzik College Chair in Digital Scholarship and an assistant professor of English and Film Television and Theatre at the University of Notre Dame. His work spans media theory science and technology studies and literary criticism. His current book project Rendering: A Political Diagrammatology of Computation shows how our cultural politico-economic and epistemic formulations crystallize into hardware and software architectures. Melissa Wills is a visiting scholar in the Department of Science and Technology Studies at the University of California Davis. She completed her PhD in English at UC Davis in 2021. Her research addresses the relationship between the life sciences and science fiction. Her current book project Ruptures: Life without Germs in the Microbiome Era examines microbiology's debt to the imagination of germ-free worlds. Raida Aldosari is the lead artist on the Foldit: First Contact development team. She received her BS in Nutrition Science from the University of California Davis in 2018. She has experience as a freelance graphic designer and as a project manager for nutrition training and courses.

This article explores the value of science fiction narratives in games for citizen science. Focusing on the protein-folding game Foldit, it describes the process of modifying and redesigning the game to feature a framing narrative and other alterations to the main tutorial campaign. The campaign narrative, Foldit: First Contact, situates the practices of citizen science in an expanded context of meanings and ethical implications, promoting critical self-reflection on the relations of science and civic values. A study of player responses to Foldit: First Contact suggests the significance of science fiction and critical game design for attuning citizen scientists to the collective responsibilities of experimentation and innovation, drawing attention to the intersecting social, technical, and environmental domains in which gamers may contribute to scientific research.

关键词： citizen science science fiction video games narratives

Quantum technologies in space

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Kaltenbaek, Rainer Acin, Antonio Bacsardi, Laszlo Bianco, Paolo Bouyer, Philippe Diamanti, Eleni Marquardt, Christoph Omar, Yasser Pruneri, Valerio Rasel, Ernst Sang, Bernhard Seidel, Stephan Ulbricht, Hendrik Ursin, Rupert Villoresi, Paolo van den Bossche, Mathias von Klitzing, Wolf Zbinden, Hugo Paternostro, Mauro Bassi, Angelo Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia Institute for Quantum Optics and Quantum Information Vienna Austria Spain ICREA-Institucio Catalana de Recerca i Estudis Avançats Barcelona Spain Department of Networked Systems and Services Budapest University of Technology and Economics Budapest Hungary Airbus Defence and Space Ltd Portsmouth United Kingdom LP2N Laboratoire Photonique Numérique et Nanosciences Université Bordeaux IOGS CNRS UMR5298 Talence France LIP6 Sorbonne Université CNRS Paris France Max Planck Institute for the Science of Light Erlangen Germany Instituto Superior Técnico Universidade de Lisboa Portugal Instituto de Telecomunicações Lisbon Portugal Y Quantum - Why Quantum Technologies Ltd. Portugal Institute for Quantum Optics Leibniz University Hannover Hannover Germany OHB System AG Weßling Germany Airbus Defence and Space GmbH Taufkirchen Germany Department of Physics and Astronomy University of Southampton Southampton United Kingdom Department of Information and Engineering University of Padua Italy Padua Quantum Technologies Research Center University of Padua Italy Thales Alenia Space Toulouse France Institute of Electronic Structure and Laser Foundation for Research and Technology - Hellas Heraklion Greece University of Geneva Geneva Switzerland Centre for Theoretical Atomic Molecular and Optical Physics Queen's University Belfast Belfast United Kingdom Department of Physics University of Trieste Trieste Italy Istituto Nazionale di Fisica Nucleare Trieste Section Trieste Italy

Recently, the European Commission supported by many European countries has announced large investments towards the commercialization of quantum technology (QT) to address and mitigate some of the biggest challenges facing today's digital era - e.g. secure communication and computing power. For more than two decades the QT community has been working on the development of QTs, which promise landmark breakthroughs leading to commercialization in various areas. The ambitious goals of the QT community and expectations of EU authorities cannot be met solely by individual initiatives of single countries, and therefore, require a combined European effort of large and unprecedented dimensions comparable only to the Galileo or Copernicus programs. Strong international competition calls for a coordinated European effort towards the development of QT in and for space, including research and development of technology in the areas of communication and sensing. Here, we aim at summarizing the state of the art in the development of quantum technologies which have an impact in the field of space applications. Our goal is to outline a complete framework for the design, development, implementation, and exploitation of quantum technology in space. Copyright © 2021, The Authors. All rights reserved.

关键词： Space applications

Initial ASEAN digital security and interoperability standard proposal

学校读者我要写书评

暂无评论

AIP Conference Proceedings 2023年第1期2739卷

作者： Sangsuree Vasupongayya Gunawan Hutagalung Obrina Candra Chalee Vorakulpipat Doni Purnomo Parinda Wattanasri Eko Supriyanto Shukor Abd Razak Othman Ibrahim Anazida Zainal Sri Devi Ravana Hiep Cong Pham Tharith Sriv Daw Khin Myo Su Mora Hertanto Agus Winarno Prabaswari Raden Muhammad Taufik Yuniantoro Badrus Zaman Fatmawati Retna Apsari Yutika Amelia Effendi Norjihan Abdul Ghani Zarina Shukur Hamidah Ibrahim Nur Izura Udzir Marnel Peradilla Jude Michael Teves Nestor Michael Tiglao Cenon Alfonso Pg Emeroylariffion Abas Mueen Uddin Agustin Kusumayati Brian Sri Prahastuti Department of Computer Engineering Faculty of Engineering Prince of Songkla University Hatyai Songkhla 90110 THAILAND Directorate General of Post and Information Technology Ministry of Communication and Information Technology Jakarta INDONESIA Politeknik Siber dan Sandi Negara National Cyber & Crypto Agency (BSSN) Depok West Java INDONESIA Information Security Research Team at National Electronics and Computer Technology Center Bangkok THAILAND National Standardization Agency of Indonesia Jakarta INDONESIA Department of Disease Control Ministry of Public Health Mueang Nonthaburi Nonthaburi THAILAND School of Biomedical Engineering and Health Sciences Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA IJN-UTM Cardiovascular Engineering Center Institute of Human Centered Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur MALAYSIA RMIT University Vietnam Ho Chi Minh City VIETNAM Royal University of Phnom Penh Phnom Penh CAMBODIA Yangon University of Economics Yangon MYANMAR Agency for the Assessment and Application of Technology (BPPT) Jakarta INDONESIA Faculty of Science and Technology Universitas Airlangga Surabaya East Java INDONESIA Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya East Java INDONESIA Centre for Cyber Security Universiti Kebangsaan Malaysia Bangi Selangor MALAYSIA Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor MALAYSIA Computer Technology Department College of Computer Studies De La Salle University Manila PHILIPPINES Software Technology Department College of Computer Studies De La Salle University Manila PHILIPPINES Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City P

In this work, the result of the initial attempt to propose an Association of Southeast Asian Nations (ASEAN) digital security and interoperability standard to support the mobility among ASEAN countries is presented. Literature reviews on existing coronavirus disease 2019 (COVID-19) related digital technologies of six ASEAN member countries are conducted. Several concerns related to creating/implementing the ASEAN digital security and interoperability standard are discussed including the technology accessing concern, the data security and privacy concern, and the data interoperability and availability concern. The proposed solution toward creating an ASEAN digital security and interoperability standard is then presented. To evaluate the proposed solution, a survey has been conducted on representatives of nine ASEAN countries on four main issues including the security standards and requirements, the interoperability standard and requirements, the certifications, and the implementation challenges. According to the survey and the discussion, seven recommendations were proposed toward creating an ASEAN digital security and interoperability standard. First, each country collects and maintains the data of its own citizens and also provides a method to authenticate and access the data by authorized requests. Second, QR code standard or ASEAN QR code standard must be agreed upon so that it can be authenticated and verified by ASEAN countries. Third, health risk assessment must be agreed upon to be used. Forth, the security standard, the data, the data format and the exchange protocol must be agreed upon at the ASEAN policy level while adoption of international standards as an implementation guideline will ease of the process and neutralize different requirements from various countries. Fifth, any accountability of incidents must be agreed upon at the ASEAN policy level. Sixth, certification might be extended to certification of risk assessment for organizations, products or

关键词：

A regularization approach for instance-based superset label learning

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Gong, Chen Liu, Tongliang Tang, Yuanyan Yang, Jian Yang, Jie Tao, Dacheng School of Computer Science and Engineering Nanjing University of Science and Technology Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University School of Software Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia Faculty of Science and Technology University of Macau Macau999078 China College of Computer Science Chongqing University Chongqing400000 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney J12/318 Cleveland St DarlingtonNSW2008 Australia

Different from the traditional supervised learning in which each training example has only one explicit label, Superset Label Learning (SLL) refers to the problem that a training example can be associated with a set of candidate labels, and only one of them is correct. Existing SLL methods are either regularization-based or instance-based, and the latter of which has achieved state-of-the-art performance. This is because the latest instance-based methods contain an explicit disambiguation operation that accurately picks up the groundtruth label of each training example from its ambiguous candidate labels. However, such disambiguation operation does not fully consider the mutually exclusive relationship among different candidate labels, so the disambiguated labels are usually generated in a nondiscriminative way, which is unfavorable for the instance-based methods to obtain satisfactory performance. To address this defect, we develop a novel Regularization approach for Instancebased Superset Label (RegISL) learning so that our instancebased method also inherits the good discriminative ability possessed by the regularization scheme. Specifically, we employ a graph to represent the training set, and require the examples that are adjacent on the graph to obtain similar labels. More importantly, a discrimination term is proposed to enlarge the gap of values between possible labels and unlikely labels for every training example. As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms. The experimental results on various tasks convincingly demonstrate the superiority of our RegISL to other typical SLL methods in terms of both training accuracy and test accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Progressive LiDAR adaptation for road detection

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Chen, Zhe Zhang, Jing Tao, Dacheng UBTECH Sydney Artificial Intelligence Centre School of Computer Science Faculty of Engineering and Information Technologies University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia School of Software and Advanced Analytics Institute University of Technology Sydney 15 Broadway UltimoNSW2007 Australia School of Automation Hangzhou Dianzi University

Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection, because LiDAR data is less susceptible to visual noises. However, the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR Adaptation-aided Road Detection (PLARD) approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1) data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation;and 2) feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-the-art road detection models and is currently top of the publicly accessible benchmark leader-board. Copyright © 2019, The Authors. All rights reserved.

关键词： Computer vision

学校读者我要写书评

暂无评论

Preface

CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

Coronal Heating Rate in the Slow Solar Wind

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Telloni, Daniele Romoli, Marco Velli, Marco Zank, Gary P. Adhikari, Laxman Downs, Cooper Burtovoi, Aleksandr Susino, Roberto Spadaro, Daniele Zhao, Lingling Liberatore, Alessandro Shi, Chen De Leo, Yara Abbo, Lucia Frassati, Federica Jerse, Giovanna Landini, Federico Nicolini, Gianalfredo Pancrazzi, Maurizio Russano, Giuliana Sasso, Clementina Andretta, Vincenzo Deppo, Vania Da Fineschi, Silvano Grimani, Catia Heinzel, Petr Moses, John D. Naletto, Giampiero Stangalini, Marco Teriaca, Luca Uslenghi, Michela Berlicki, Arkadiusz Bruno, Roberto Capobianco, Gerardo Capuano, Giuseppe E. Casini, Chiara Casti, Marta Chioetto, Paolo Corso, Alain J. D’Amicis, Raffaella Fabi, Michele Frassetto, Fabio Giarrusso, Marina Giordano, Silvio Guglielmino, Salvo L. Magli, Enrico Massone, Giuseppe Messerotti, Mauro Nisticò, Giuseppe Pelizzo, Maria G. Reale, Fabio Romano, Paolo Schühle, Udo Solanki, Sami K. Straus, Thomas Ventura, Rita Volpicelli, Cosimo A. Zangrilli, Luca Zimbardo, Gaetano Zuppella, Paola Bale, Stuart D. Kasper, Justin C. National Institute for Astrophysics Astrophysical Observatory of Torino Via Osservatorio 20 Pino TorineseI-10025 Italy University of Florence Department of Physics and Astronomy Via Giovanni Sansone 1 Sesto FiorentinoI-50019 Italy Earth Planetary and Space Sciences University of California Los AngelesCA90095 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35805 United States Department of Space Science University of Alabama in Huntsville HuntsvilleAL35805 United States Predictive Science Inc. San DiegoCA92121 United States National Institute for Astrophysics Astrophysical Observatory of Arcetri Largo Enrico Fermi 5 FirenzeI-50125 Italy National Institute for Astrophysics Astrophysical Observatory of Catania Via Santa Sofia 78 CataniaI-95123 Italy Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 GöttingenD-37077 Germany University of Catania Department of Physics and Astronomy Via Santa Sofia 64 CataniaI-95123 Italy National Institute for Astrophysics Astronomical Observatory of Trieste Località Basovizza 302 TriesteI-34149 Italy National Institute for Astrophysics Astronomical Observatory of Capodimonte Salita Moiariello 16 NapoliI-80131 Italy National Research Council Institute for Photonics and Nanotechnologies Via Trasea 7 PadovaI-35131 Italy University of Urbino Carlo Bo Department of Pure and Applied Sciences Via Santa Chiara 27 UrbinoI-61029 Italy National Institute for Nuclear Physics Section in Florence Via Bruno Rossi 1 Sesto FiorentinoI-50019 Italy Czech Academy of Sciences Astronomical Institute Fričova 298 OndřejovCZ-25165 Czech Republic University of Wroclaw Centre of Scientific Excellence – Solar and Stellar Activity ul. Kopernika 11 WroclawPL-51-622 Poland National Aeronautics and Space Administration Headquarters WashingtonDC20546 Un

This Letter reports the first observational estimate of the heating rate in the slowly expanding solar corona. The analysis exploits the simultaneous remote and local observations of the same coronal plasma volume with the Solar Orbiter/Metis and the Parker Solar Probe instruments, respectively, and relies on the basic solar wind magnetohydrodynamic equations. As expected, energy losses are a minor fraction of the solar wind energy flux, since most of the energy dissipation that feeds the heating and acceleration of the coronal flow occurs much closer to the Sun than the heights probed in the present study, which range from 6.3 to 13.3 R☉. The energy deposited to the supersonic wind is then used to explain the observed slight residual wind acceleration and to maintain the plasma in a non-adiabatic state. As derived in the Wentzel-Kramers-Brillouin limit, the present energy transfer rate estimates provide a lower limit, which can be very useful in refining the turbulence-based modeling of coronal heating and subsequent solar wind acceleration. © 2023, CC BY.

关键词： Magnetohydrodynamics