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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future

The Lancet 2021年第10311期398卷 1619-1662页

作者： Romanello, Marina McGushin, Alice Di Napoli, Claudia Drummond, Paul Hughes, Nick Jamart, Louis Kennard, Harry Lampard, Pete Solano Rodriguez, Baltazar Arnell, Nigel Ayeb-Karlsson, Sonja Belesova, Kristine Cai, Wenjia Campbell-Lendrum, Diarmid Capstick, Stuart Chambers, Jonathan Chu, Lingzhi Ciampi, Luisa Dalin, Carole Dasandi, Niheer Dasgupta, Shouro Davies, Michael Dominguez-Salas, Paula Dubrow, Robert Ebi, Kristie L Eckelman, Matthew Ekins, Paul Escobar, Luis E Georgeson, Lucien Grace, Delia Graham, Hilary Gunther, Samuel H Hartinger, Stella He, Kehan Heaviside, Clare Hess, Jeremy Hsu, Shih-Che Jankin, Slava Jimenez, Marcia P Kelman, Ilan Kiesewetter, Gregor Kinney, Patrick L Kjellstrom, Tord Kniveton, Dominic Lee, Jason K W Lemke, Bruno Liu, Yang Liu, Zhao Lott, Melissa Lowe, Rachel Martinez-Urtaza, Jaime Maslin, Mark McAllister, Lucy McMichael, Celia Mi, Zhifu Milner, James Minor, Kelton Mohajeri, Nahid Moradi-Lakeh, Maziar Morrissey, Karyn Munzert, Simon Murray, Kris A Neville, Tara Nilsson, Maria Obradovich, Nick Sewe, Maquins Odhiambo Oreszczyn, Tadj Otto, Matthias Owfi, Fereidoon Pearman, Olivia Pencheon, David Rabbaniha, Mahnaz Robinson, Elizabeth Rocklöv, Joacim Salas, Renee N Semenza, Jan C Sherman, Jodi Shi, Liuhua Springmann, Marco Tabatabaei, Meisam Taylor, Jonathon Trinanes, Joaquin Shumake-Guillemot, Joy Vu, Bryan Wagner, Fabian Wilkinson, Paul Winning, Matthew Yglesias, Marisol Zhang, Shihui Gong, Peng Montgomery, Hugh Costello, Anthony Hamilton, Ian Institute for Global Health University College London London UK United Kingdom Institute for Sustainable Resources University College London London UK United Kingdom UCL Energy Institute University College London London UK United Kingdom Institute for Environmental Design and Engineering University College London London UK United Kingdom Department of Geography University College London London UK United Kingdom The Bartlett School of Sustainable Construction University College London London UK United Kingdom Centre for Human Health and Performance University College London London UK United Kingdom School of Agriculture Policy and Development University of Reading Reading United Kingdom Department of Meteorology University of Reading Reading United Kingdom The Walker Institute University of Reading Reading United Kingdom Department of Health Sciences University of York York United Kingdom Institute for Environment and Human Security United Nations University Bonn Germany Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK United Kingdom Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Earth System Science Tsinghua University Beijing China Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute for Environmental Sciences World Health Organization Geneva Switzerland Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff United Kingdom Yale Center on Climate Change and Health Yale University New Haven CT United States School of Government University of Birmingham Birmingham United Kingdom Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatic

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Predicting complex user behavior from CDR based social networks

arXiv

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arXiv 2019年

作者： Doyle, Casey Herga, Zala Dipple, Stephen Szymanski, Boleslaw K. Korniss, Gyorgy Mladenić, Dunja Network Science and Technology Center Rensselaer Polytechnic Institute 110 8th Street TroyNY12180 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute 110 8th Street TroyNY12180 United States Artificial Intelligence Laboratory Jožef Stefan Institute Jamova 39 Ljubljana Slovenia Jožef Stefan International Postgraduate School Jamova 39 Ljubljana Slovenia Department of Computer Science Rensselaer Polytechnic Institute 110 8th Street TroyNY12180 United States Faculty of Computer Science and Management Wroclaw University of Science and Technology Wroclaw Poland

Call Detail Record (CDR) datasets provide enough information about personal interactions of cell phone service customers to enable building detailed social networks. We take one such dataset and create a realistic social network to predict which customer will default on payments for the phone services, a complex behavior combining social, economic, and legal considerations. After extracting a large feature set from this network, we find that each feature poorly correlates with the default status. Hence, we develop a sophisticated model to enable reliable predictions. Our main contribution is a methodology for building complex behavior models from very large sets of diverse features and using different methods to choose those features that perform best for the final model. This approach enables us to identify the most efficient features for our problem which, unexpectedly, are based on the number of unique users with whom the given user communicates around the Christmas and New Year’s Eve holidays. In general, features based on the number of close ties maintained by a user perform better than others. Our resulting models significantly outperform the methods Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Characteristics and Generation of Household Hazardous Waste (HHW) in Semarang City Indonesia 2

Characteristics and Generation of Household Hazardous Waste ...

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2nd International Conference on Energy, Environmental and Information System, ICENIS 2017

作者： Fikri, Elanda Purwanto, Purwanto Sunoko, Henna Rya Department of Environmental Health Bandung Health Polytechnic Cimahi Utara Bandung Indonesia Doctoral Program of Environmental Science School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Diponegoro Semarang Indonesia Faculty of Medicine Universitas Diponegoro Semarang Indonesia

Most of Household Hazardous Waste (HHW) is currently mixed with domestics waste. So that, it can impact human health and environmental quality. One important aspect in the management strategy is to determine the quantity generated and characteristics of HHW. The method used to determine the characteristics HHW refers to SNI 19-2454-2002, while the HHW generation refers to the SNI 19-3694-1994 calculated based on weight and volume. Research was conducted in four districts of Semarang. The samples used in this study were 400 families calculated based on the proportion of Slovin Formula. The characteristic of HHW in Semarang City is mainly infectious (79%), then poisonous (13%), combustible (6%) and corrosive materials (2%). The quantity HHW generated is 0.01 kg/person/day equivalent with 5.1% of municipal solid waste (MSW) in Semarang (linear equations: Y=1,278x+82,00 (volume), y=0,216x+13,89 (weight). © 2018 The Authors, published by EDP sciences.

关键词： Municipal solid waste

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Comparison of Data Mining Algorithm: PSO-KNN, PSO-RF, and PSO-DT to Measure Attack Detection Accuracy Levels on Intrusion Detection System

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Journal of Physics: Conference Series 2020年第1期1471卷

作者： Sularso Budilaksono Andri Agung Riyadi Lukman Azhari Dedi Dwi Saputra M. Anno Suwarno I Gede Agus Suwartane Jupriyanto Andika Ramadhan Agus Prasetyo Utomo Achmad Fauzi University of Persada Indonesia YAI Jakarta Indonesia Master of Computer Science-Postgraduate Program STMIK Nusa Mandiri Jakarta Indonesia University of Muhammadiyah Tangerang Indonesia University of Persada Indonesia YAI Jakarta Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Jakarta Indonesia University of Muhammadiyah Tangerang Indonesia University of Stikubank Semarang Indonesia

Nowadays, computer networks are widely used to exchange valuable and confidential data information between servers to computers or cellular devices. Access to user control and use of software or hardware as a firewall often experience security problems. Unauthorized access to information through computer networks continues to occur and tends to increase. This study examines the attack detection mechanism by using three data mining algorithms based on particle swarm optimization (PSO), namely PSO-K Nearest Neighbor, PSO-Random Forest, and PSO-Decision Tree in the Canadian Institute for Cybersecurity Dataset (CICIDS2017). The initial experiment showed that the approach using the PSO-RF method was able to produce the highest accuracy of attack detection. Accuracy values generated using the PSO-RF algorithm with a combination of the number of trees and maximal depth = 20 in the CICIDS2017 dataset are intact higher than other proposed algorithms. The highest accuracy of attack detection in the CICIDS2017 dataset is intact, which is 99.76%. In the CICIDS2017 dataset 50% Benign and 50% Attack it turns out that the PSO-RF algorithm with a combination of the number of trees and maximal depth = 20 also gets the highest accuracy value of 99.67%.

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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Performance study of strongly coupled magnetic resonance

arXiv

引用

arXiv 2019年

作者： Wijerathna Basnayaka, Chathuranga M. Jayakody, Dushantha Nalin K. Sharma, Abhishek Wang, Hwang-Cheng Muthuchidambaranathan, P. Kaur, Kuljeet School of Postgraduate Studies and Research SRI LANKA Technological Campus Sri Lanka School of Computer Science and Robotics National Research Tomsk Polytechnic University Russia Dept. of Electronics and Communication Engineering LNM Institute of Information and Technology Jaipur India Department of Electronic Engineering National Ilan University Ilan Taiwan Dept. of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India Ecole de Technologie Superieure Universite du Quebec Montreal Canada

Strongly Coupled Magnetic Resonance (SCMR) uses electromagnetic resonance in order to efficiently transfer power wirelessly over mid-range distances. Since the energy exchange capability of resonant objects higher than non-resonant objects, strongly coupled systems are able to achieve more efficient energy transfer than other wireless power transfer systems. The paper presents detailed experimental and simulated analysis of the performance of the SCMR system. A prototype of the SCMR system was implemented and experiments were conducted to analyze the performance of the system. Finally, the resonant frequency of the system was experimentally verified and the factors influencing the wireless power transfer were also studied. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy transfer

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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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MedShare: Medical Resource Sharing among Autonomous Healthcare Providers

arXiv

引用

arXiv 2018年

作者： Yang, Yilong Li, Xiaoshan Qamar, Nafees Ke, Wei Liu, Zhiming Faculty of Science and Technology University of Macau China Department of Computer Science State University of New York Biomedical and Health Informatics Program United States Macau Polytechnic Institute China School of Computer and Information Science SouthwestUniversity Chongqing China

Legacy Electronic Health Records (EHRs) systems were not developed with the level of connectivity expected from them nowadays. Therefore, interoperability weakness inherent in the legacy systems can result in poor patient care and waste of financial resources. Large hospitals are less likely to share their data with external hospitals due to economic and political reasons. Motivated by these facts, we aim to provide a set of software implementation guidelines, i.e., MedShare to deal with interoperability issues among disconnected healthcare systems. The proposed integrated architecture includes: 1) a data extractor to fetch legacy medical data from a hemodialysis center, 2) converting it to a common data model, 3) indexing patient information using the HashMap technique, and 4) a set of services and tools that can be installed as a coherent environment on top of stand-alone EHRs systems. Our work enabled three cooperating but autonomous hospitals to mutually exchange medical data and helped them develop a common reference architecture. It lets stakeholders retain control over their patient data, winning the trust and confidence much needed towards a successful deployment of MedShare. Security concerns were effectively addressed that also included patient consent in the data exchange process. Thereby, the implemented toolset offered a collaborative environment to share EHRs by the healthcare providers. Copyright © 2018, The Authors. All rights reserved.

关键词： Hospitals

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Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM And NB Methods

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Journal of Physics: Conference Series 2019年第1期1201卷

作者： Bryan Pratama Dedi Dwi Saputra Deny Novianti Endah Putri Purnamasari Antonius Yadi Kuntoro Hermanto Windu Gata Nia K Wardhani Sfenrianto Sfenrianto Sularso Budilaksono Master of Computer Science-Postgraduate Program STMIK Nusa Mandiri Jakarta Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Jakarta Indonesia Master Faculty of Computer Science-Postgraduate Program Universitas Mercubuana Jakarta Indonesia Information Systems Management Department BINUS Graduate Program–Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Graduate School Master's Degree Computer Science Universitas Persada Indonesia YAI. Jakarta Indonesia

The Brimob Corps is a special police force, just like the special military detachments held by the TNI such as Paskhas and so on. At present brigade corps police national is busy being discussed in the real world and cyberspace, especially on social media twitter. Many opinions about the brigade corps police national so there are positive and negative opinions. Social media twitter is now one places to spread information about brigade corps police national. This study cases uses text mining techniques with support vector machine (SVM) method which aims to classify public sentiments towards brigade corps police national on twitter. The dataset used is tweet in Indonesian with keyword 'Brimob' with a total dataset of 1000 tweets. Text mining, transform, tokenize, stemming, and classification, etc. techniques are useful for building classification and analysis of sentiment. RapidMiner and Gataframework are also used to help create sentiment analysis to measure classification values. Accuracy values obtained with support vector machine (SVM) approach 86,96%, with precision values of 86,96%, and recall values of 86,96%.

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