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Methods of the International Tobacco control (ITC) EUREST-PLUS ITC Europe Surveys (vol 30, pg 4, 2020)

EUROPEAN JOURNAL OF PUBLIC HEALTH 2020年第SUPPLEMENT_3期30卷 113-113页

作者： Thompson, M. E. Department of Statistics and Actuarial Science University of Waterloo Waterloo Ontario Canada Department of Psychology University of Waterloo Waterloo Ontario Canada School of Public Health and Health Systems University of Waterloo Waterloo Ontario Canada Kantar Public (TNS) Brussels Belgium Health Promotion Foundation Warsaw Poland European Observatory of Health Inequalities President Stanisław Wojciechowski State University of Applied Sciences Kalisz Poland Maria Sklodowska-Curie Institute – Oncology Center Warsaw Poland Cancer Prevention Unit and WHO Collaborating Centre for Tobacco Control German Cancer Research Center (DKFZ) Heidelberg Germany Smoking or Health Hungarian Foundation (SHHF) Budapest Hungary Center for Health Services Research School of Medicine National and Kapodistrian University of Athens (UoA) Athens Greece University of Medicine and Pharmacy ‘Grigore T. Popa’ Iasi Iasi Romania Aer Pur Romania Bucharest Romania Tobacco Control Unit Catalan Institute of Oncology (ICO) L'Hospitalet de Llobregat Barcelona Catalonia Spain Tobacco Control Research Group Bellvitge Biomedical Research Institute (IDIBELL) L'Hospitalet de Llobregat Barcelona Catalonia Spain School of Medicine and Health Sciences Bellvitge Campus Universitat de Barcelona L'Hospitalet de Llobregat Barcelona Catalonia Spain Consortium for Biomedical Research in Respiratory Diseases (CIBER en Enfermedades Respiratorias CIBERES) Madrid Spain Addictions Department King's College London London UK UK Centre for Tobacco and Alcohol Studies King's College London London UK European Network for Smoking and Tobacco Prevention (ENSP) Brussels Belgium Laboratory of Toxicology School of Medicine University of Crete (UoC) Heraklion Greece Ontario Institute for Cancer Research Toronto Canada

BACKGROUND:The EUREST-PLUS ITC Europe surveys aim to evaluate the impact of the European Union's Tobacco Products Directive (EU TPD) implementation within the context of the WHO FCTC. This article describes the methodology of the 2016 (Wave 1) and 2018 (Wave 2) International Tobacco control 6 European (6E) Country Survey in Germany, Greece, Hungary, Poland, Romania and Spain; the England arm of the 2016 (Wave 1) and 2018 (Wave 2) ITC 4 Country Smoking and Vaping (4CV) Survey; and the 2016 (Wave 10) and 2017 (Wave 11) ITC Netherlands (NL) Survey. All three ITC surveys covering a total of eight countries are prospective cohort studies with nationally representative samples of smokers. METHODS:In the three surveys across the eight countries, the recruited respondents were cigarette smokers who smoked at least monthly, and were aged 18 and older. At each survey wave, eligible cohort members from the previous waves were retained, regardless of smoking status, and dropouts were replaced by a replenishment sample. RESULTS:Retention rates between the two waves of the ITC 6E Survey by country were 70.5% for Germany, 41.3% for Greece, 35.7% for Hungary, 45.6% for Poland, 54.4% for Romania and 71.3% for Spain. The retention rate for England between ITC 4CV1 and ITC 4CV2 was 39.1%; the retention rates for the ITC Netherlands Survey were 76.6% at Wave 10 (2016) and 80.9% at Wave 11 (2017). CONCLUSION:The ITC sampling design and data collection methods in these three ITC surveys allow analyses to examine prospectively the impact of policy environment changes on the use of cigarettes and other tobacco products in each country, to make comparisons across the eight countries.

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Evidential Occupancy Grid Map Augmentation using Deep Learning

Evidential Occupancy Grid Map Augmentation using Deep Learni...

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IEEE Symposium on Intelligent Vehicle

作者： Sascha Wirges Christoph Stiller Felix Hartenbach Mobile Perception Systems Group FZI Research Center for Information Technology Karlsruhe Germany Institute of Measurement and Control Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

A detailed environment representation is a crucial component of automated vehicles. Using single range sensor scans, data is often too sparse and subject to occlusions. Therefore, we present a method to augment occupancy grid maps from single views to be similar to evidential occupancy maps acquired from different views using Deep Learning. To accomplish this, we estimate motion between subsequent range sensor measurements and create an evidential 3D voxel map in an extensive post-processing step. Within this voxel map, we explicitly model uncertainty using evidence theory and create a 2D projection using combination rules. As input for our neural networks, we use a multi-layer grid map consisting of the three features detections, transmissions and intensity, each for ground and non-ground measurements. Finally, we perform a quantitative and qualitative evaluation which shows that different network architectures accurately infer evidential measures in real-time.

关键词： Three-dimensional displays Training Robot sensing systems Machine learning Two dimensional displays Neural networks Estimation

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Transforming heat transfer with thermal metamaterials and devices

arXiv

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arXiv 2020年

作者： Li, Ying Li, Wei Han, Tiancheng Zheng, Xu Li, Jiaxin Li, Baowen Fan, Shanhui Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China Department of Electrical Engineering Ginzton Laboratory Stanford University StanfordCA94305 United States National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China Department of Physics University of Colorado BoulderCO80309 United States School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China Paul M. Rady Department of Mechanical Engineering University of Colorado BoulderCO80309 United States

| The demand for sophisticated tools and approaches in heat management and control has triggered fast development of emerging fields including conductive thermal metamaterials, nanophononics, far-field and near-field radiative thermal management, etc. In this review, we cast a unified perspective on the control of heat transfer, based on which the related studies can be considered as complementary paradigms toward manipulating physical parameters and realizing unprecedented phenomena in heat transfer using artificial structures, such as thermal conductivity in heat conduction, thermal emissivity in radiation, and properties related to multi-physical effects. The review is divided into three parts that focus on the three main categories of heat flow control, respectively. Thermal conduction and radiation are emphasized in the first and second parts at both macro- and micro-scale. The third part discusses the efforts to actively introduce heat sources or tune the material parameters with multi-physical effects in both conduction and radiation, including works using thermal convection. We conclude the review with challenges in this research topic and new possibilities about topological thermal effects, heat waves, and quantum thermal effects. Copyright © 2020, The Authors. All rights reserved.

关键词： Metamaterials

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Development and test verification of air temperature model for Chinese solar and Spainish Almeria-type greenhouses

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International Journal of Agricultural and Biological Engineering 2017年第4期10卷 66-76页

作者： Jorge Antonio Sanchez-Molina Li Ming Francisco Rodriguez Jose Luis Guzman Wang Hui Yang Xinting The Automatic Control Robotics and Mechatronics Research GroupDepartment of InformaticsceiA3CIESOLUniversity of AlmeriaAlmeria 04120Spain Beijing Research Center for Information Technology in Agriculture/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability/Key Laboratory for Information Technologies in Agriculture Ministry of AgricultureBeijing 100097China

Growth can be defined as an increment in biomass or an increment in weight or height of the organs of the plant influenced by physiological *** of these processes have their limits genetically determined,but climate and irrigation play an important *** of its importance,microclimate has been extensively studied in the modeling as a surrounding condition which is imposed by the exterior *** main objective of this work was to develop a temperature model based on the energy balance dynamics at two different greenhouse locations-South-eastern Spain and Northern China,and the traditional structures of Chinese solar greenhouse and Almería-type multi-span greenhouse were taken into *** final model was developed by combining the external conditions,the actuator influence and the crop growth,where the temperature is influenced by soil,crop,cover,actuators,back wall and greenhouse *** model took into account the energy lost by convective and conductive fluxes,as well as the energy supplied by solar radiation and heating *** soil and the back wall are the main media for energy *** temperature dynamic was determined by a physical model,which considered the energy balance from a holistic point of view-as a sub-model for a customizable interface among the external climate,the plant and the greenhouse *** influences of different subsystems included in the temperature model were analyzed and *** results showed a high R^(2)value of 0.94 for Beijing and 0.95 for Almeria,and the average error was low,of which the MAE and RMSE were 0.71 and 1.365 for Almeria and 0.62 and 1.102 for Beijing,***,the model can be considered as a powerful tool for control design purposes in microclimate systems.

关键词： air temperature model Chinese solar greenhouse Spanish Almería-type greenhouse energy balance dynamics microclimate

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CPSS-based Signal Forwarding Method at Relays for Full-duplex Cooperative Vehicular Networks

CPSS-based Signal Forwarding Method at Relays for Full-duple...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Han, Shuangshuang Ma, Houxue Wang, Xiao Liu, Hong Cao, Dongpu Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Shandong266000 China Qingdao Huituo Intelligent Machine Company Qingdao China JIANGSU XCMG CONSTRUCTION MACHINERY RESEARCH INSTITUTE LTD Xuzhou Jiangsu221004 China Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue Wes WaterlooONN2L 3G1 Canada Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781538644522

With increasing popularity of Internet of Vehicles (IoV), concerns for reliable and low complexity communication techniques are proposed due to the requirements of signal reliability and transmission delay for vehicles. Meanwhile, the explosive and pervasive use of social network applications further adds drivers' social relationships and behavioural characteristics into it, and makes it a cyber-physical-social system (CPSS). This paper proposes and analyzes an improved forward scheme for full deplex cooperative vehicular networks in terms of its CPSS features. The proposed CPSS-based forwarding (CPSS-F) strategy forwards a soft estimate of the received signal based on social historic data at the relay node (vehicle/infrastructure) to the destination node (vehicle/infrastructure), which achieves improved reliability than the two conventional strategies in cooperative networks, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, the proposed CPSS-F relay achieves performance gains and complexity reduction compared to the conventional AF and DF. Experimental results further confirm the advantages of the proposed CPSS-F for cooperative vehicular networks. The proposed CPSS-F approach is easily extended to other cooperative vehicular social networks, for example, multi-way, half-duplex, or large-antenna networks. © 2018 IEEE.

关键词： Complex networks

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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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Approximated unbiased minimum-variance simultaneous states and parameters estimation for nonlinear systems

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IFAC-PapersOnLine 2017年第1期50卷 1607-1612页

作者： Ishihara S. Yamakita M. Hitachi Ltd. Research & Development Group Center for Technology Innovation Mechanical Engineering Ibaraki Japan Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

This paper addresses joint state and parameter estimation problem for nonlinear systems. We linearize the nonlinear system by unscented statistical linearization (USL) and decouple unknown parameter vector of state vector. Then, we treat unknown parameter vector as unknown inputs to the approximated linear model. Furthermore, we applied unbiased minimum variance estimation (UMVE) method for the approximated linear system with unknown input. Our proposed method also offers an easy way to apply the UMVE to nonlinear systems. We confirm the validity of the proposed methods by Monte Carlo simulations. © 2017

关键词： Nonlinear filters Nonlinear sysytems Parameter estimation State estimation Uncertain dynamic systems

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A Novel Magnetic Multiple Spur Gear for High Speed Motor System 10

A Novel Magnetic Multiple Spur Gear for High Speed Motor Sys...

引用

10th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2018

作者： Aiso, Kohei Akatsu, Kan Aoyama, Yasuaki Functional Control Systems Shibaura Institute of Technology 3-7-5 Toyosu Koto-ku Tokyo Japan Department of Electrical Engineering Shibaura Institute of Technology 3-7-5 Toyosu Koto-ku Tokyo Japan Research Development Group Center for Technology Innovation - Materials Hitachi Ltd. 1-1 Omika-cho 7-chome Hitachi-shi Ibaraki319-1292 Japan

ISBN: (纸本)9781479973118

Magnetic gears achieve the system with maintenance-free, low noise and low vibration characteristics by contact-less power transformation. However, conventional flux modulation type magnetic gears are not suitable system for high speed motors because the mechanical strength is weak due to permanent magnets of the high speed rotor. Adding that, the gear efficiency much decreases by increased the magnet eddy current loss and core loss in high speed region. To overcome these problems of the flux modulation type magnetic gears in high speed region, this paper presents a novel magnetic gear which based on the direct flux transmission method to achieve the high efficiency and to make downsizing the system. It is shown that the proposed magnetic gear of the direct flux transmission type realizes the high performances such as high torque density, high efficiency, and downsizing of gear. © 2018 IEEE.

关键词： Speed

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