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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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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Global prevalence and gender inequalities in at least 60 min of self-reported moderate-to-vigorous physical activity 1 or more days per week:An analysis with 707,616 adolescents

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Journal of Sport and Health science 2024年第5期13卷 709-716页

作者： Raphael H.O.Araujo AndrúO.Werneck Clarice L.Martins Luciana L.Barboza Rafael M.Tassitano Nicolas Aguilar-Farias Gilmar M.Jesus Robinson Ramírez-Véelez Riki Tesler Adewale L.Oyeyemi Ellen C.M.Silva Robert G.Weaver Mark S.Tremblay Javier Brazo-Sayavera Grégore I.Mielke Danilo R.P.Silva Graduation Program in Health Sciences Londrina State University(UEL)Londrina 86057-970Brazil Center for Epidemiological Research in Nutrition and Health Department of NutritionSchool of Public HealthUniversidade de Sao Paulo(USP)Sao Paulo 01246-904Brazil Department of Physical Education Federal University of ParaıbaParaıba 58051-900Brazil Research Centre of Physical Activity Health and LeisureFaculty of SportsLaboratory for Integrative and Translational Research in Population Health(ITR)University of PortoPorto 4500Portugal Postgraduate Program in Physical Education University of BrasıliaBrasılia 70910-900Brazil Department of Kinesiology and Community Health University of Illinois at Urbana-ChampaignChampaignIL 61801-3841USA Department of Physical Education Sports and RecreationUniversidad de La FronteraTemuco 4680000Chile Department of Health State University of Feira de SantanaFeira de Santana 44036-900Brazil Navarrabiomed Hospital Universitario de Navarra(HUN)Navarra Institute for Health Research(IdiSNA)Universidad Publica de Navarra(UPNA)Pamplona 31006Spain Facultad de Ciencias de la Educacion Unidad Central del Valle del Cauca(UCEVA)TuluaValle del Cauca 763022Colombia Health Management Department Faculty of Heath sciencesAriel UniversityAriel 40700Israel College of Health Solutions Arizona State UniversityPhoenixAZ 85004USA Arnold School of Public Health University of South CarolinaColumbiaSC 29208USA Healthy Active Living and Obesity Research Group Children’s Hospital of Eastern Ontario Research InstituteOttawaON K1H 8L1Canada Department of Pediatrics University of OttawaOttawaON K1H 8L1Canada Department of Health Sciences Carleton UniversityOttawaON K1S 5B6Canada Department of Sports and Computer Science Universidad Pablo de Olavide(UPO)Seville 41013Spain Centro Universitario Regional Noreste Universidad de la RepublicaRivera 40000Uruguay School of Public Health The University of QueenslandBrisbaneQLD 4006Australia Department of Physical Education

Background:Guidelines recommend that adolescents should accumulate an average of 60 min per day of moderate-to-vigorous physical activity(MVPA).However,using only this cut-off could hide important *** instance,from a population-level point of view,increasing physical activity for those with no or low physical activity could provide more health benefits than increasing physical activity for those with intermediate ***,including a more sensitive cut-point of≥1 days per week could be an additional strategy for identifying those with low access/opportunities for physical activity ***,the current study aims to estimate the prevalence of≥60 min of MVPA≥1 days per week among adolescents globally,and to describe any relevant gender ***:We used representative datasets from 146 countries/territories collected between 2003 and *** was *** were grouped into younger(≤14 years old)and older(>14 years old)*** Poisson regression models were used to identify the relative differences in≥60 min of MVPA≥1 days per week between boys and girls,and random-effects meta-analysis models were used to identify the pooled *** were stratified by country and ***:Approximately 80%of both younger and older adolescents reported≥60 min of MVPA≥1 days per *** prevalence was≥94%in Europe and Central Asia and North America,while the estimates for the other regions were<77%.The prevalence of≥60 min of MVPA≥1 days per week was higher among boys than girls,with the largest differences occurring among the oldest adolescents(Prevalence ratio_(≤14y)=1.04(95%confidence interval(95%CI)):1.03-1.04)*** ratio_(>14y)=1.09(95%CI:1.08-1.10)).Conclusion:Approximately 8 out of 10 adolescents reported accumulating≥60 min of MVPA≥1 days per week,with notable differences between *** differences were observed in several countries,especially among the oldest *** fo

关键词： Global school-based Student Health Survey Health Behaviour Among school-aged Children survey Inequality Surveillance Youth

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The utilization of search engines by students of the Library and Information science program at Universitas Padjadjaran

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Nuning Kurniasih Nanik Kurniawati Yulianti Robbi Rahim Sujito Afiful Ikhwan Hasrat A. Aimang Farid Haluti Lusi Dwi Putri Darmawan Napitupulu Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia The National Archive of The Republic of Indonesia (ANRI) Jakarta Indonesia Sekolah Dasar Negeri 3 Rajapolah Tasikmalaya Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia Department of English Language Education IAIN Surakarta Surakarta Indonesia Postgraduate Universitas Muhammadiyah Ponorogo Ponorogo Indonesia Universitas Muhammadiyah Luwuk Luwuk Banggai Indonesia Universitas Lancang Kuning Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Indonesia

Search engines have become an integral part of student learning activities. This study aims to see how the use of search engines by students, especially by students of the Library and Information science program, Faculty of Communication sciences, Universitas Padjadjaran. This study uses the mixed method. The research data was obtained through questionnaires, in-depth interviews, observations and literature studies. The results of the study showed that in learning activities, the respondents used search engines to find the references for college assignments and thesis writing material. The students also use search engines to meet daily information needs, solve problems, increase knowledge, reduce doubts, clarify things, entertain, fulfill curiosity about others, etc. The retrieval technique used by students is by using keywords/queries, Boolean logic, query strategies in the form of a brief search and using the advanced search feature. This research is expected to contribute to the university in supporting online-based learning facilities for students.

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"So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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International Journal of Information Management 2023年 71卷

作者： Dwivedi, Yogesh K. Kshetri, Nir Hughes, Laurie Slade, Emma Louise Jeyaraj, Anand Kar, Arpan Kumar Baabdullah, Abdullah M. Koohang, Alex Raghavan, Vishnupriya Ahuja, Manju Albanna, Hanaa Albashrawi, Mousa Ahmad Al-Busaidi, Adil S. Balakrishnan, Janarthanan Barlette, Yves Basu, Sriparna Bose, Indranil Brooks, Laurence Buhalis, Dimitrios Carter, Lemuria Chowdhury, Soumyadeb Crick, Tom Cunningham, Scott W. Davies, Gareth H. Davison, Robert M. Dé, Rahul Dennehy, Denis Duan, Yanqing Dubey, Rameshwar Dwivedi, Rohita Edwards, John S. Flavián, Carlos Gauld, Robin Grover, Varun Hu, Mei-Chih Janssen, Marijn Jones, Paul Junglas, Iris Khorana, Sangeeta Kraus, Sascha Larsen, Kai R. Latreille, Paul Laumer, Sven Malik, F. Tegwen Mardani, Abbas Mariani, Marcello Mithas, Sunil Mogaji, Emmanuel Nord, Jeretta Horn O'Connor, Siobhan Okumus, Fevzi Pagani, Margherita Pandey, Neeraj Papagiannidis, Savvas Pappas, Ilias O. Pathak, Nishith Pries-Heje, Jan Raman, Ramakrishnan Rana, Nripendra P. Rehm, Sven-Volker Ribeiro-Navarrete, Samuel Richter, Alexander Rowe, Frantz Sarker, Suprateek Stahl, Bernd Carsten Tiwari, Manoj Kumar van der Aalst, Wil Venkatesh, Viswanath Viglia, Giampaolo Wade, Michael Walton, Paul Wirtz, Jochen Wright, Ryan Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Fabian Bay Wales Swansea United Kingdom Maharashtra Pune India Bryan School of Business and Economics University of North Carolina at Greensboro United States University of Bristol Business School University of Bristol BS8 1SD United Kingdom Professor of Information Systems Raj Soin College of Business Wright State University 3640 Colonel Glenn Highway DaytonOH45435 United States School of Artificial Intelligence Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Studies Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia School of Computing Middle Georgia State University MaconGA United States Client Advisory and Transformation Stackroute NIIT Limited India Department of Information Systems Analytics and Operations College of Business University of Louisville United States Northumbria University London United Kingdom IRC-FDE KFUPM Saudi Arabia ISOM KFUPM Business School KFUPM Saudi Arabia Innovation and Technology Transfer Center Sultan Qaboos University Oman Department of Business Communication Sultan Qaboos University Oman Department of Management Studies National Institute of Technology Tiruchirappalli India Montpellier France FORE School of Management New Delhi India Indian Institute of Management Ahmedabad Vastrapur Ahmedabad380015 India Information School University of Sheffield United Kingdom Bournemouth University Business School Poole United Kingdom School of Information Systems and Technology Management University of New South Wales Sydney Australia Information Operations and Management Sciences Department TBS Business School 1 Place Alphonse Jourdain Toulouse31068 France Department of Education & Childhood Studies Swansea Univers

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics;digital transformation of organisations and societies;and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI;examining biases of generative AI attributable to training datasets and processes;exploring business and societal contexts best suited for generative AI implementation;determining optimal combinations of human and generative AI for various tasks;identifying ways to assess accuracy of text produced by generative AI;and uncovering the ethical and legal issues in using generative AI across different contexts. © 2023 The Authors

关键词： Marketing

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BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series

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Global Ecology and Biogeography 2025年第5期34卷

作者： Maria Dornelas Laura H. Antão Amanda E. Bates Viviana Brambilla Jonathan M. Chase Cher F. Y. Chow Ada Fontrodona-Eslava Anne E. Magurran Inês S. Martins Faye Moyes Alban Sagouis Samuel Adu-Acheampong Daniel Acquah-Lamptey Dušan Adam Penelope A. Ajani Aitor Albaina Pablo Almaraz Jeongseop An Roger Sigismund Anderson Madelaine Jean Robertson Anderson Alexsander Z. Antunes Ivan Arismendi Linda Armbrecht Pedro Aros-Mardones Sreejith Kalpuzha Ashtamoorthy Narayanan Ayyappan Gal Badihi Joseph J. Bailey Andrew H. Baird Mark Edward Baird Sreekumar Vadakkethil Balakrishnan José António L. Barão-Nóbrega Adi Barash Miguel Barbosa Jos Barlow Claus Bässler Matthieu Beaumont Natalie Beenaerts Tiago Octavio Begot Wallace Beiroz Ricardo Beldade David M. Bell Alecia Bellgrove Jonathan Belmaker Lisandro Benedetti-Cecchi Cassandra E. Benkwitt Pamela Medina-van Berkum Brandon T. Bestelmeyer Matthew G. Betts Maxwell Kelvin Billah Anne D. Bjorkman Magdalena Błażewicz Christopher P. Bloch Shane A. Blowes Antonio Bode Juliano A. Bogoni Thomas Bolger Timothy C. Bonebrake Erik Bonsdorff Roberta Bottarin Luke N. Brokensha Rob W. Brooker Andrew J. Brooks Helge Bruelheide Thiago Almeida Bueno Claire Laguionie Mariana Lopes Campagnoli James Cant Erica Pellegrini Caramaschi Alexandre Caron Tadhg Carroll Tancredi Caruso Juan Carvajal-Quintero Giuseppe Castaldelli Edward Castañeda-Moya Pedro V. Castilho Sonia Zanini Cechin Shahar Chaikin Uchangi Manjunatha Chandrashekara Tory J. Chase Chaolun Allen Chen Jorge José Cherem Sei-Woong Choi Erica M. Christensen Alexander V. Christianini Jackson Wing Four Chu Peter Coad Carl Van Colen Lise Comte Elisabeth J. Cooper J. Hans C. Cornelissen Eddy Cosson Unai Cotano Luc Crevecoeur Shannan Kyle Crow Graeme S. Cumming Vanessa S. Daga Gabriella Damasceno Gergana N. Daskalova Claire H. Davies Robert A. Davis Frank P. Day Sussy De-La-Zerda Amy Elizabeth Deacon Indradatta de Castro-Arrazola Steven Degraer Kharran Deonarinesingh Juan C. Diaz-Ricaurte Christopher R. Dickman Tara Dirilgen Ciaran John Dolan J. Emmet Centre for Biological Diversity School of Biology University of St Andrews Fife UK MARE—Centro de Ciências do Mar e do Ambiente Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Research Centre for Ecological Change Organismal and Evolutionary Biology Research Programme Faculty of Biological and Environmental Sciences University of Helsinki Helsinki Finland Department of Biology University of Victoria Victoria British Columbia Canada German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Leipzig Germany Department of Computer Science Martin Luther University Halle-Wittenberg Halle (Saale) Germany Leverhulme Centre for Anthropocene Biodiversity Department of Biology University of York York UK Department of Animal Biology and Conservation Science University of Ghana Accra Ghana Philipps Universität Marburg Marburg Germany Department of Forest Ecology Silva Tarouca Research Institute Brno Czech Republic School of Life Sciences University of Technology Sydney Sydney New South Wales Australia Zoology and Animal Cell Biology Department University of the Basque Country (UPV/EHU) Leioa Spain Grupo de Oceanografía de Ecosistemas Instituto de Ciencias Marinas de Andalucía CSIC Campus Universitario de Puerto Real Puerto Real Spain National Institute of Ecology Seocheon Republic of Korea African Regional Postgraduate Programme in Insect Science University of Ghana Accra Ghana Département de Biologie Université de Sherbrooke Sherbrooke Quebec Canada Núcleo de Conservação da Biodiversidade Instituto de Pesquisas Ambientais Secretaria de Meio Ambiente Infraestrutura e Logística do Estado de São Paulo São Paulo Brazil Department of Fisheries Wildlife and Conservation Sciences Oregon State University Corvallis Oregon USA Institute for Marine and Antarctic Studies Ecology & Biodiversity Centre University of Tasmania Hobart Tasmania Australia Graduate Program in Oceanography Department of Oceanography Faculty of Natural Scienc

Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.

关键词： assemblage biodiversity community database ecoinformatics species taxa time

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MASTER ORDNANCE REPAIR APPLIED - STANDARD ITEM 009-67

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NAVAL ENGINEERS JOURNAL 1986年第3期98卷 35-42页

作者： STIMSON, WA MARSH, MT UTTICH, RM William A. Stimsonreceived his B.S. degree in mathematics from the University of Texas at El Paso in 1964 and his M.S. degree in engineering from the University of Santa Clara in 1971. He served in the U.S. Army Artillery during the Korean Conflict and subsequently was employed at IBM Huntsville Alabama until 1968 where he worked in the design of automatic control systems of the Saturn vehicle. From 1968 until 1971 he was employed at Ames Research Center Moffett Field in the design of nonlinear control systems for sounding rockets and pencil-shaped spacecraft. Following this Mr. Stimson worked at Hewlett Packard Sunnyvale California as a test engineer in automatic test systems. Since 1973 Mr. Stimson has been employed at the Naval Ship Weapon Systems Engineering Station Port Hueneme. He was a ship qualification trials project supervisor for many years and is now serving as master ordnance repair deputy program manager. Mr. Stimson is a member of the American Society of Naval Engineers and is program chairman of the Channel Islands Section. Cdr. Michael T. Marsh USNreceived a B.S. in mathematics from the University of Nebraska and was commissioned via the NESEP program in 1970. He holds an M.S. in computer science from the U.S. Navy Postgraduate School and an MBA from the State University of New York. Cdr. Marsh has served in the weapons department of USSFrancis Hammond (FF-1067) and of USSJohn S. McCain (DDG-36). He was weapons officer aboard USSSampson (DDG-10). As an engineering duty officer Cdr. Marsh was the technical design officer for PMS-399 at the FFG-7 Class Combat System Test Center from 1978 to 1982. He is presently combat system officer at SupShip Jacksonville and has been active in the MOR program since its inception. Cdr. Marsh is also the vice chairman of the Jacksonville Section of ASNE. LCdr. Richard M. Uttich USNholds B.S. and M.S. degrees in mechanical engineering from Stanford University. He enlisted in the Navy in 1965 serving as an electronics technician aboard USSNereus (A

The 600-ship United States Navy offers private shipyards an unprecedented opportunity for overhaul of surface combatants with complex combat systems. Recognizing the new challenge associated with the overhaul of high technology combat systems in the private sector, the Navy in 1983 established the master ordnance repair (MOR) program. This program, a joint effort of the Naval Sea Systems Command (NAVSEA) and the Shipbuilders Council of America (SCA), was designed to identify and qualify those companies and private shipyards technically capable of managing combat systems work and conducting combat system testing. Standard Item 009–67 describes the role of the MOR company in combat system overhaul. It defines terms that are important to understanding the item itself, and imposes upon the prime contractor an obligation to utilize the MOR subcontractor in a managerial capacity. Specific tasks are assigned to the MOR company in planning, production, and testing. Finally, this standard item describes to the Navy planner how to estimate the size of the MOR team appropriate to the work package, a feature that will ensure that combat system bids are tailored to a specific availability.

关键词： WARSHIPS

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Spectroscopic Confirmation of a Population of Isolated, Intermediate-Mass YSOs

arXiv

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arXiv 2022年

作者： Kuhn, Michael A. Saber, Ramzi Povich, Matthew S. de Souza, Rafael S. Krone-Martins, Alberto Ishida, Emille E.O. Zucker, Catherine Benjamin, Robert A. Hillenbrand, Lynne A. Castro-Ginard, Alfred Zhou, Xingyu Department of Astronomy California Institute of Technology 1216 East California Boulevard PasadenaCA91125 United States Department of Physics and Astronomy California State Polytechnic University 3801 West Temple Ave PomonaCA91768 United States Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences 80 Nandan Rd. Shanghai200030 China Donald Bren School of Information and Computer Sciences University of California IrvineCA92697 United States CENTRA/SIM Faculdade de Ciências Universidade de Lisboa Ed. C8 Campo Grande Lisboa1749-016 Portugal Université Clermont Auvergne CNRS IN2P3 LPC Clermont-FerrandF-63000 France Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States NASA Hubble Fellowship Program United States Department of Physics University of Wisconsin-Whitewater 800 W Main St WhitewaterWI53190 United States Leiden Observatory Leiden University Niels Bohrweg 2 Leiden2333 CA Netherlands Kavli Institute for Astronomy and Astrophysics Peking University Yiheyuan 5 Haidian Qu Beijing100871 China Department of Astronomy Peking University Yiheyuan 5 Haidian Qu Beijing100871 China

Wide-field searches for young stellar objects (YSOs) can place useful constraints on the prevalence of clustered versus distributed star formation. The Spitzer/IRAC Candidate YSO (SPICY) catalog is one of the largest compilations of such objects (∼120,000 candidates in the Galactic midplane). Many SPICY candidates are spatially clustered, but, perhaps surprisingly, approximately half the candidates appear spatially distributed. To better characterize this unexpected population and confirm its nature, we obtained Palomar/DBSP spectroscopy for 26 of the optically-bright (G ☉, and the estimated accretion rates, ranging from 3 × 10−8 to 3 × 10−7 M☉ yr−1, are typical for YSOs in this mass range. The 3D spatial distribution of these stars, based on Gaia astrometry, reveals that the "isolated" YSOs are not evenly distributed in the Solar neighborhood but are concentrated in kpc-scale dusty Galactic structures that also contain the majority of the SPICY YSO clusters. Thus, the processes that produce large Galactic star-forming structures may yield nearly as many distributed as clustered YSOs. Copyright © 2022, The Authors. All rights reserved.

关键词： Spatial distribution

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Operational systems, logistics engineering and technology insertion

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NAVAL ENGINEERS JOURNAL 1997年第3期109卷 205-220页

作者： Grubb, MJ Skolnick, A Michael J. Grubb:has perfomzed naval engineering at the Crane Division Naval Surface Warfare Center (NSWC) for more than twenty-five years. He currently the program manager for the Sustainable Hardware and Affordable Readiness Practice Program (SHARP). is a Navy-wide logistics research and development prgram aimed at the successful transiton of proved technologies inot the fleet. SHARP focuses on reducing acquisition performance capability reliability maintainability and readiness of these systems. Mr. Grubb has served as a department director for the past eleven years. His most recent assignment was as director of the Tactical Computer Resources and Test Equipment Department. This organization provided acquisition and in-service engineering support of the Navy's tectical embedded computers priphirals displays and mass memory storage devices. The organization also provided a complete range of product engineering and metrology engineerig services for SSP NevSeaSysCom and the Trident Program. Prior to this appointment Mr. Grubb was deptuy director psysical security programs department and site responsible for program management and system integration of ashore integrated security systems. Mr. Grubb holds a B.S. degree in industrial engineering from Iowa State Universtiy an M.S. degree in industrial engineering from Purdue University and has copleted the course work (Dec.'96 for a master's degree in public and environment affairs at Indiana University-Purdue Univeristy Indianapolis. Dr. Alfred Skolnick operates System Science Consultants (SSC) specilizing in strategic planning technical program definiton technology assessment and engineering analyses on selected matters of national interest. He has taught physics mathematics and management sciences at University of Virginia and Marymount University and is currently adjunct professor of mathematics at Northern Virginia Community College. Form 1985 to 1989 he was president of the American Society of Noval Engineers. Dr. Skolnick served at Applied Physic

In an era of fiscal austerity, downsizing and unforgiving pressure upon human and economic capital, it is an Augean task to identify resources for fresh and creative work. The realities of the day and the practical demands of more immediate fleet needs can often dictate higher priorities. Yet, the Navy must avoid eating its seed corn. Exercising both technical insight and management foresight, the fleet, the R&D community, the Office of the Chief of Naval Operations (OpNav) and the product engineering expertise of the Naval Surface Warfare Center (NSWC) are joined and underway with integrated efforts to marry new, fully demonstrated technologies and operational urgencies. Defense funding today cannot sponsor all work that can be mission-justified over the long term because budgets are insufficient to support product maturation within the classical development cycle. However, by rigorous technical filtering and astute engineering of both marketplace capabilities and currently available components, it is possible in a few select cases to compress and, in effect, integrate advanced development (6.3), engineering development (6.4), weapon procurement (WPN), ship construction (SCN), operation and maintenance (O&M,N) budgetary categories when fleet criticalities and technology opportunities can happily meet. In short, 6.3 funds can be applied directly to ''ripe gateways'' so modern technology is inserted into existing troubled or aging systems, sidestepping the lengthy, traditional development cycle and accelerating practical payoffs to recurrent fleet problems. To produce such constructive results has required a remarkable convergence of sponsor prescience and engineering workforce excellence. The paper describes, extensively, the philosophy of approach, transition strategy, polling of fleet needs, technology assessment, and management team requirements. The process for culling and selecting specific candidate tasks for SHARP sponsorship (matching operational need with t

关键词： Naval vessels

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