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SSRN 2023年

作者： Cerna, Fernando Vladimir Dantas, Jamile T. Naderi, Ehsan Contreras, Javier Department of Electrical Engineering Federal University of Roraima Boa Vista69310-000 Brazil Postgraduate Program in Natural Resources Research Area in Management and Dynamics of Natural Resources – Renewable Energies Federal University of Roraima Av. Nova Iorque – Aeroporto Boa Vista69310-000 Brazil School of Electrical Computer and Biomedical Engineering Southern Illinois University CarbondaleIL62901 United States School of Industrial Engineering University of Castilla-La Mancha Ciudad Real13071 Spain

In recent decades, the ever-increasing demand for electricity in large cities is leading the electricity distribution network (EDN) to an operational state of fatigue, especially at peak load times. Thus, the lifetime of feeders, transformers, circuit breakers, among others, can be compromised. In this context, load congestion, technical losses, and voltage deviations across the EDN can contribute to the high waste of electricity as well as to the worsening of the supply service. To face this problem, this paper proposes a mixed integer linear programming (MILP) model that aims to reshape the demand profile (i.e., load reduction in peak periods without load rebound in the remaining periods of the day) of total consumers while the voltage deviations and technical losses are minimized. In the proposed model, the demand profile reshaping is carried out through direct (DC) and indirect (IC) bulk control of residential, commercial, and industrial loads, while the load factor (LF), i.e., indicator related to the efficient use of electricity, is improved. Due to the scheduling of these loads at certain times of the day, the occurrence of voltage deviations and technical losses can be mitigated by the efficient control of capacitor banks (CBs). © 2023, The Authors. All rights reserved.

关键词： Electric circuit breakers

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Load Factor Improvement of the Electricity Grid Considering Distributed Resources Operation and Regulation of Peak Load

SSRN

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SSRN 2022年

作者： Cerna, Fernando Vladimir Naderi, Ehsan Marzband, Mousa Contreras, Javier Coêlho, John K. Fantesia, Maurício P. Department of Electrical Engineering Federal University of Roraima Boa Vista69310-000 Brazil Postgraduate Program in Natural Resources Research Area in Management and Dynamics of Natural Resources – Renewable Energies Federal University of Roraima Av. Nova Iorque – Aeroporto Boa Vista69310-000 Brazil School of Electrical Computer and Biomedical Engineering Southern Illinois University CarbondaleIL62901 United States Department of Mathematics Physics and Electrical Engineering Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom School of Industrial Engineering University of Castilla-La Mancha Ciudad Real13071 Spain

The load factor (LF) of an electricity network (EN) is an indicator related to the efficient electricity usage within a time horizon (e.g., day, month, year, etc.). LF improvement can be achieved through an efficient operational scheme that combines several strategies. Among them, peak load regulation in the demand profile of all the consumers together with the operation of distributed resources (DREs), such as renewable sources (RSs) and capacitor banks (CBs) represents a promising alternative. To address this efficient scheme, this work proposes a mixed-integer linear programming (MILP) model that aims to increase the LF through the bulk scheduling of residential, commercial, and industrial electrical appliances during the day, considering a baseline demand and an hourly rate. At the same time, voltage fluctuations and technical losses are mitigated by the presence of DREs in the EN. Operational constraints related to the performance of EN, RSs, and CBs are considered. Uncertainties in the habitual demand profiles and solar radiation incidence during the day are represent through simulation algorithms. In order to evaluate our proposal, a modified 14-node IEEE EN was used. The results corroborate the applicability of this proposed MILP model, reducing electricity waste and ensuring the efficient EN operation. © 2022, The Authors. All rights reserved.

关键词： Integer programming

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Load Factor Improvement of the Electricity Grid Considering Distributed Resources Operation and Regulation of Peak Load

SSRN

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SSRN 2022年

关键词： Integer programming

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SMALL SHIPS, ADVANCED TECHNOLOGY AND WARFIGHTING PERFORMANCE

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NAVAL ENGINEERS JOURNAL 1991年第3期103卷 30-45页

作者： SKOLNICK, DH SKOLNICK, A David H. Skolnickhas practiced naval engineering in both government and industry. He has supported the Military Sealift Command and the Naval Sea Systems Command Ship Design Group and Amphibious Ship Acquisition Program Office participating in the design and assessment of ship structure evaluation of intact and damaged stability and arrangements during design and construction phases of acquisition conversion and overhaul. He is currently involved in systems engineering and integration. Recent responsibilities have included requirements analyses and feasibility studies interface analyses and computer aided analyses. He received his B.S. in naval architecture and marine engineering from Webb Institute of Naval Architecture in 1982 (as an ASNE scholar) and is currently an M.S. candidate in systems engineering at the University of Virginia. Alfred Skolnickserved over 30 years as an engineering duty officer and retired from the Navy with the rank of captain in 1983. His early assignments included tactical missile engineering shipboard duty and Polaris submarine inertial navigation. He later served in the Deep Submergence Systems Project was project director surface effect ships (SES) David Taylor Model Basin director of technology Joint Navy-Commerce SES Program director combat systems Naval Sea Systems Command and project manager directed energy weapons. His awards include the Navy League's Parsons Award in 1979 for scientific and technical progress ASNE's Gold Medal in 1981 for high energy laser development the Navy Legion of Merit in 1983 National Capital Engineer of the Year in 1986 and the American Defense Preparedness Association Gold Medal in 1988 for contributions to strategic defense. He was president of ASNE from 1985–1989. He received his B.S. in mathematics from Queens College his M.A. in mathematics from Columbia University his M.S. in electrical engineering from U.S. Naval Postgraduate School and his Ph.D. in electrical engineering/applied mathematics from Polytechnic University. He w

Changing threat requirements and radical budget shifts imply that Navy operational needs will broaden and engineering solutions will face tougher constraints. Existing and emerging technology promise increased combat capability in smaller packages;space-based assets will allow operator orchestration of widely dispersed naval units via connectivity attributes previously unavailable. Tactical data relay by downlink may permit reallocation of responsibilities among several platforms, space, air, or seaborne, so ships can be outfitted for custom-use (sensing, unique data processing, high-firepower) and optimized to meet specific mission needs. These evolving capabilities demand a fresh look at ship concepts and prospective force structures consistent with global and fiscal realities. Warfighting performance formerly unknown in small ship design may offer a very effective solution to the intricate, interacting issues of falling defense budgets, diverse operational requirements and complex national priorities. Multimission ships which take advantage of new or current technology to reduce ship size, manning and cost could be affordable in sufficient numbers to meet our continuing worldwide obligations, complement our larger ships' force structure, and produce a balanced fleet. These same ships could satisfy U.S. maritime needs beyond the Navy and improve export trade through foreign military sales (FMS).

关键词： Naval Warfare

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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NTDS - A PAGE IN NAVAL HISTORY

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NAVAL ENGINEERS JOURNAL 1988年第3期100卷 53-61页

作者： SWENSON, EN MAHINSKE, EB STOUTENBURGH, JS Capt. Erick N. Swenson USNR (Ret.):is a project manager for special projects in the Surface Ship Systems Division Hughes Aircraft Company Fullerton Calif where he has been employed since his retirement from the U.S. Navy in 1975. Originally trained as an electronics technician during WWII in the Captain Eddy program he later received a BS degree in electrical engineering from the University of Rochester Rochester N. Y. in 1950. Subsequent engineering education was received at the University of Pittsburgh Pittsburgh Penn. and the Naval Postgraduate School Monterey Calif. After commissioning he was ordered to duty as the electronics division officer on the USSMissouri(BB-63) and electronics ships superintendent at Hunters Point Naval Shipyard San Francisco Calif. When the design of the Naval Tactical Data System began in the mid-1950s Lt. (j.g.) Swenson was ordered to the Bureau of Ships Navy Department Washington D.C. as the junior engineering duty only officer assigned to the project. From 1962 to 1965 LCdr. Swenson was assigned as the BuShips technical representative on the program at Remington Rand Univac St. Paul Minn. For the next ten years he returned to BuShips/NavSea/NAVSEC as the NTDS project officer. During this time the project expanded considerably foreign military sales were heavily involved and interoperability with other services and countries were established. His final effort on active duty was to instigate the redesign of the previousSpruanceclass destroyers into the newerAdmiral Kiddclass improvement program. He is a registered professional electrical engineer in the State of California listed inWho's Who in the Worldis a life member of ASNE and chairman of the Long Beach/Greater LA Section. Capt. Edmund B. Mahinske USN (Ret.):is an alumnus of the U.S. Naval Academy the Massachusetts Institute of Technology and the Harvard Business School. His technical background is in electronics and he specialized in the management of programs involving the application of comp

A little over thirty years ago, a group of naval engineers were assembled by the Bureau of Ships to develop a new system approach to the combat information center (CIC). The CIC of World War II, with its “grease pencil” plots and voice telling of tactical information from sensors and other ships, could no longer provide the timely, coordinated reaction to postwar threats. This project group led the Navy into the new world of large-scale, high-speed digital electronics and into a new mode of conducting naval warfare as well. There were no off-the-shelf computers of the requisite capability, size and reliability; what were available were monstrous vacuum tube computers. There were no display equipments that were “conversant” in both the digital language of the computer and the analog language of the sensors and the weapon systems. Who ever heard, at that time, of a computer running a tactical communication net automatically? It was hard enough to find sufficient numbers of engineers who knew what a digital computer was. This paper, by three naval engineers in the implementing engineering office, depicts the evolvement of the Naval Tactical Data Systems (NTDS) as they saw it. It discusses the problems that stemmed from the transition from the old world of analog into the new digital world, the system concepts that steered the development; the key decisions that were made; new electronic equipment and processes that became necessary; and the need of the mangagement to face the real world of deadlines, ship schedules and operational requirements.

关键词： NAVAL WARFARE

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

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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