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Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990–2021, for 204 countries and territories: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet Infectious Diseases 2025年第5期25卷 519-536页

作者： Kyu, Hmwe Hmwe Vongpradith, Avina Dominguez, Regina-Mae Villanueva Ma, Jianing Albertson, Samuel B. Novotney, Amanda Khalil, Ibrahim A. Troeger, Christopher E. Doxey, Matthew C. Ledesma, Jorge R. Sirota, Sarah Brooke Bender, Rose Grace Swetschinski, Lucien R. Cunningham, Matthew Spearman, Sandra Abate, Yohannes Habtegiorgis Abd Al Magied, Abdallah H.A. El Hafeez, Samar Abd Abdoun, Meriem Abera, Bayeh Abidi, Hassan Aboagye, Richard Gyan Abtew, Yonas Derso Abualruz, Hasan Abu-Gharbieh, Eman Abukhadijah, Hana J. Aburuz, Salahdein Addo, Isaac Yeboah Adekanmbi, Victor Adetunji, Charles Oluwaseun Oluwaseun Adeyeoluwa, Temitayo Esther Adhikary, Ripon Kumar Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Adzigbli, Leticia Akua Afolabi, Aanuoluwapo Adeyimika Afzal, Muhammad Sohail Afzal, Saira Agampodi, Suneth Buddhika Agide, Feleke Doyore Ahinkorah, Bright Opoku Ahmad, Aqeel Ahmad, Sajjad Ahmed, Ayman Ahmed, Haroon Ahmed, Saeed Akinosoglou, Karolina Akter, Ema Al Awaidy, Salah Alajlani, Muaaz M. Alam, Khurshid Albakri, Almaza Albashtawy, Mohammed Aldhaleei, Wafa A. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Syed Shujait Ali, Waad Alif, Sheikh Mohammad Aljunid, Syed Mohamed Al-Marwani, Sabah Almazan, Joseph Uy Al-Mekhlafi, Hesham M. Almustanyir, Sami Alqahatni, Saleh A. Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Altaf, Awais Altirkawi, Khalid A. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Alyahya, Mohammad Sharif Ibrahim Al-Zyoud, Walid A. Amugsi, Dickson A. Andrei, Catalina Liliana Antoni, Sebastien Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anwar, Saleha Anwari, Palwasha Apostol, Geminn Louis Carace Arabloo, Jalal Arafat, Mosab Aravkin, Aleksandr Y. Areda, Demelash Aregawi, Brhane Berhe Aremu, Abdulfatai Arndt, Michael Benjamin Asgedom, Akeza Awealom Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Ayele, Firayad Azadi, Davood Azhar, Gulrez Shah Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Taghanaki, Pegah Bahrami Bahramian, Saeed Balakrishnan, Senthi Department of Health Metrics Sciences School of Medicine Institute for Health Metrics and Evaluation University of Washington Seattle 98195 WA United States 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 Center for Biostatistics Ohio State University Columbus OH United States Department of Global Health University of Washington Seattle WA United States Urban Indian Health Institute Seattle Indian Health Board Seattle WA United States School of Medicine Yale University New Haven CT United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia College of Pharmacy Ajman University Ajman United Arab Emirates Department of Epidemiology Alexandria University Alexandria Egypt Department of Medicine University of Sétif Algeria Sétif Algeria Department of Health Sétif Algeria Department of Medical Microbiology Bahir Dar University Bahir Dar Ethiopia Department of Biology Ghent University Ghent Belgium Laboratory Technology Sciences Department Yasuj University of Medical Sciences Yasuj Iran Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Department of Biomedical Science Arba Minch University Arba Minch Ethiopia Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center-Academic Health System Hamad Medical Corporation Doha Qatar College of Pharmacy University of Jordan Amman Jordan Department of Therapeutics United Arab Emirates University Al Ain United Arab Emirates School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Syd

Background: Diarrhoeal diseases claim more than 1 million lives annually and are a leading cause of death in children younger than 5 years. Comprehensive global estimates of the diarrhoeal disease burden for specific age groups of children younger than 5 years are scarce, and the burden in children older than 5 years and in adults is also understudied. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to assess the burden of, and trends in, diarrhoeal diseases overall and attributable to 13 pathogens, as well as the contributions of associated risk factors, in children and adults in 204 countries and territories from 1990 to 2021. Methods: We used the Cause of Death Ensemble modelling strategy to analyse vital registration data, verbal autopsy data, mortality surveillance data, and minimally invasive tissue sampling data. We used DisMod-MR (version 2.1), a Bayesian meta-regression tool, to analyse incidence and prevalence data identified via systematic reviews, population-based surveys, and claims and inpatient data. We calculated diarrhoeal disability-adjusted life-years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for each location, year, and age–sex group. For aetiology estimation, we used a counterfactual approach to quantify population-attributable fractions (PAFs). Additionally, we estimated the diarrhoeal disease burden attributable to the independent effects of risk factors using the comparative risk assessment framework. Findings: In 2021, diarrhoeal diseases caused an estimated 1·17 million (95% uncertainty interval 0·793–1·62) deaths globally, representing a 60·3% (50·6–69·0) decrease since 1990 (2·93 million [2·31–3·73] deaths). The most pronounced decline was in children younger than 5 years, with a 79·2% (72·4–84·6) decrease in diarrhoeal deaths. Global YLLs also decreased substantially, from 186 million (147–221) in 1990 to 51·4 million (39·9–65·9) in 2021. In 2021, an e

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The teacher-student chatroom corpus

arXiv

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arXiv 2020年

作者： Caines, Andrew Yannakoudakis, Helen Edmondson, Helena Allen, Helen Pérez-Paredes, Pascual Byrne, Bill Buttery, Paula ALTA Institute & Computer Laboratory University of Cambridge United Kingdom Department of Informatics King’s College London United Kingdom Theoretical & Applied Linguistics University of Cambridge United Kingdom Cambridge Assessment University of Cambridge United Kingdom Faculty of Education University of Cambridge United Kingdom Department of Engineering University of Cambridge United Kingdom

The Teacher-Student Chatroom Corpus (TSCC) is a collection of written conversations captured during one-to-one lessons between teachers and learners of English. The lessons took place in an online chatroom and therefore involve more interactive, immediate and informal language than might be found in asynchronous exchanges such as email correspondence. The fact that the lessons were one-to-one means that the teacher was able to focus exclusively on the linguistic abilities and errors of the student, and to offer personalised exercises, scaffolding and correction. The TSCC contains more than one hundred lessons between two teachers and eight students, amounting to 13.5K conversational turns and 133K words: it is freely available for research use. We describe the corpus design, data collection procedure and annotations added to the text. We perform some preliminary descriptive analyses of the data and consider possible uses of the TSCC. Copyright © 2020, The Authors. All rights reserved.

关键词： Students

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A PSO based optimal EVs Charging utilizing BESSs and PVs in buildings

A PSO based optimal EVs Charging utilizing BESSs and PVs in ...

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Compatibility in Power Electronics (CPE)

作者： Aggelos S. Bouhouras Paschalis A. Gkaidatzis Ioannis Panapakidis Anastasios Tsiakalos Dimitris P. Labridis Georgios C. Christoforidis Department of Electrical Engineering Western Macedonia University of Applied Sciences Kozani Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical Engineering University of Applied Sciences of Thessaly Larissa Greece Dept. of Applied Informatics University of Macedonia Thessaloniki Greece

The penetration of Electric Vehicles (EVs) is rapidly increasing at the Low Voltage (LV) level, thus increasing the electrification rate of transport. Moreover, Photovoltaic (PV) systems at the LV level have been constantly increasing as well. Buildings with PVs on their roof and EVs as the main transport means may well become the norm the following years, considering additionally that all new buildings in the EU should be Nearly Zero Energy Buildings (NZEBs). Such NZEBs should cover the majority of their low energy demand by Renewable Energy Sources, and PV technology is the main candidate to achieve that. Both these facts pose considerable challenges on the LV networks. In this context, this paper examines the potential of efficiently utilizing the stored energy of Battery Energy Storage Systems (BESSs) in order to face the increased load demand by the penetration of EVs in LV Distribution Networks (DNs), with the presence of PVs. A Particle Swarm Optimization (PSO) algorithm is developed in order to perform optimal charging schedule for the EVs under the aim of optimizing the DN voltage profile. The analysis is performed on a real LV DN with real load data and the results indicate that under proper charging schedule both the voltage profile and the energy losses of the DN could be improved.

关键词： Electric vehicles Battery powered vehicles Electric vehicle charging Energy storage Particle swarm optimization Photovoltaic systems

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A Survey on Dragonfly Algorithm and its Applications in engineering

TechRxiv

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TechRxiv 2020年

作者： Rahman, Chnoor M. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mirjalili, Seyedali Applied Computer Department College of Health and Applied Sciences Charmo University Sulaimany Iraq Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Charles Sturt University Sydney Australia Singidunum University Belgrade Serbia Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq Centre for Artificial Intelligence Research and Optimisation Torrens University Adelaide Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of University of Kurdistan Hewler Iraq

The dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance,

关键词： Multiobjective optimization

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Model -Free Adaptive Control With Fuzzy Component for Tower Crane Systems

Model -Free Adaptive Control With Fuzzy Component for Tower ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Raul-Cristian Roman Radu-Emil Precup Emil M. Petriu Elena-Lorena Hedrea Claudia-Adina Bojan-Dragos Mircea-Bogdan Radac Department of Automation and Applied Informatics Politehnica University of Timisoara Timisoara Romania School of Electrical Engineering and Computer Science University of Ottawa Otawa Canada

The objective of the current paper is to improve the performance of data-driven Model-Free Adaptive Control (MFAC) by adding a Proportional-Derivative Takagi-Sugeno fuzzy controller (PDTSFC) component. This MFAC improvement is called MFAC-Proportional-Derivative Takagi-Sugeno Fuzzy Controller (MFAC-PDTSFC). MFAC-PDTSFC is applied to a Multi Input-Multi Output control structure of a mathematical model that describes the behavior of a nonlinear tower crane system (TCS) laboratory equipment. The MFAC-PDTSFC parameters are tuned in a model-based manner using metaheuristic Grey Wolf optimizer algorithm. The performances of the new MFAC-PDTSFC are compared with those of MFAC and Proportional-Derivative Takagi-Sugeno Fuzzy Controller (PDTSFC).

关键词： PD control Mathematical model Cranes Poles and towers Automation informatics Takagi-Sugeno model

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Building a frame-based cyber security learning game 1

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1st International Symposium on Mobile Internet Security, MobiSec 2016

作者： Wang, Yuh-Jye Tseng, Shian-Shyong Yang, Tsung-Yu Weng, Jui-Feng Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Applied Informatics and Multimedia Asia University Taichung Taiwan

ISBN: (数字)9789811078507

ISBN: (纸本)9789811078491

With the rapid growth of the Internet-based smart mobile phone applications, a variety of sophisticated threats or attacks have been developed on the Internet recently. Therefore, how to enhance the cyber-security literacy education to prevent or mitigate Internet crimes becomes a very important and challenging issue. In this paper, the frame structure of the knowledge base for the card game called iMonsters has been designed to help students to easily understand the attack or defense strategies and concepts in cyber-security domain, and the ontology crystallization method will be applied to refine the existing ontology. We then propose a rule generation algorithm to generate new rules based upon learning from examples approach, and the generated new rules can be used as the scaffoldings to help the users to learn the knowledge obtained from the real cases. 53 children have participated in the iMonster card game playing in 2015. Based upon the experimental results, we can find that the card game playing can assist students to learn the cyber-security knowledge. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Ontology

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GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows in Medical Imaging

arXiv

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arXiv 2021年

作者： Pati, Sarthak Thakur, Siddhesh P. Hamamcı, İbrahim Ethem Baid, Ujjwal Baheti, Bhakti Bhalerao, Megh Güley, Orhun Mouchtaris, Sofia Lang, David Thermos, Spyridon Gotkowski, Karol González, Camila Grenko, Caleb Getka, Alexander Edwards, Brandon Sheller, Micah Wu, Junwen Karkada, Deepthi Panchumarthy, Ravi Ahluwalia, Vinayak Zou, Chunrui Bashyam, Vishnu Li, Yuemeng Haghighi, Babak Chitalia, Rhea Abousamra, Shahira Kurc, Tahsin M. Gastounioti, Aimilia Er, Sezgin Bergman, Mark Saltz, Joel H. Fan, Yong Shah, Prashant Mukhopadhyay, Anirban Tsaftaris, Sotirios A. Menze, Bjoern Davatzikos, Christos Kontos, Despina Karargyris, Alexandros Umeton, Renato Mattson, Peter Bakas, Spyridon MLCommons Medical Working Group San FranciscoCA United States University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany International School of Medicine Istanbul Medipol University Marmara Istanbul Turkey Department of Bioengineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Mathematics School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Institute for Digital Communications School of Engineering The University of Edinburgh Scotland United Kingdom Department of Computer Science Technical University of Darmstadt Hesse Darmstadt Germany Intel Corporation Santa ClaraCA United States Department of Computer Science Stony Brook University Stony Brook New YorkNY United States Department of Biomedical Informatics Stony Brook University Stony Brook New YorkNY United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Institute of Image-Guided Surgery of Strasbourg Strasbourg France Department of Informatics & Analytics Dana-Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine Weill Cornell Medicine New YorkNY United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Department of Biological Engineering Department of Mechanical Engineering Massachusetts Institute of Technology BostonMA United States Google Menlo ParkCA United States

Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility, translation, and deployment. Here we present the community-driven Generally Nuanced Deep Learning Framework (GaNDLF), with the goal of lowering these barriers. GaNDLF makes the mechanism of DL development, training, and inference more stable, reproducible, interpretable, and scalable, without requiring an extensive technical background. GaNDLF aims to provide an end-to-end solution for all DL-related tasks in computational precision medicine. We demonstrate the ability of GaNDLF to analyze both radiology and histology images, with built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes. Our quantitative performance evaluation on numerous use cases, anatomies, and computational tasks supports GaNDLF as a robust application framework for deployment in clinical workflows. © 2021, CC BY.

关键词： Medical imaging

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IoT sensors in sea water environment: Ahoy! Experiences from a short summer trial

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Electronic Notes in Theoretical computer Science 2019年 343卷 117-130页

作者： Chrysanthi Tziortzioti Dimitrios Amaxilatis Irene Mavrommati Ioannis Chatzigiannakis Hellenic Open University School of Applied Arts Patras Greece Computer Technology Institute and Press “Diophantus” & Computer Engineering and Informatics Department University of Patras Patras Greece Hellenic Open University School of Applied Arts & Computer Technology Institute and Press “Diophantus” Patras Greece Sapienza University of Rome Italy Computer Technology Institute and Press “Diophantus” Patras Greece

IoT sensors for measuring various sea water parameters, are explored here, aiming towards an educational context, in order to lead to a deeper understanding of the use of aquatic environments as natural resources, and towards the adoption of environmentally friendly behaviors. Sea-water sensing via IoT has not been extensively explored, due to practical difficulties in deployment, and the same applies to devising appropriate scenaria for understanding aquatic parameters in STEM education. A short hands-on IoT sensing trial, that has been conducted in various location of the Aegean sea, is reported in this paper. This research set out to gain insight into real data sets on which to base observations for devising realistic educational scenaria pertaining aquatic parameters. The results of this experiment are meant to guide research further, by shedding light into the IoT sensing issues that are involved in an educational scientific context. The goal is conducting broader research in the area of IoT water sensing towards its further utilization in STEM education.

关键词： IoT water sensors Science Technology engineering and Mathematics education (STEM) crowd sensing

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Balanced-imbalanced transitions in indirect reciprocity dynamics on networks

arXiv

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arXiv 2021年

作者： Oishi, Koji Miyano, Shuhei Kaski, Kimmo Shimada, Takashi Department of International Politics Aoyama Gakuin University Tokyo Japan Japan Society for the Promotion of Science 5-3-1 Kojimachi Chiyoda-ku Tokyo102-8471 Japan Department of Applied Physics Graduate School of Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Computer Science Aalto University School of Science PO Box 15500 Espoo Finland Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United Kingdom Mathematics and Informatics Center Department of Systems Innovation Graduate School of Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

Here we investigate the dynamics of indirect reciprocity on networks, a type of social dynamics in which the attitude of individuals, either cooperative or antagonistic, toward other individuals changes over time by their actions and mutual monitoring. We observe an absorbing state phase transition as we change the network's link or edge density. When the edge density is either small or large enough, opinions quickly reach an absorbing state, from which opinions never change anymore once reached. In contrast, if the edge density is in the middle range the absorbing state is not reached and the state keeps changing thus being active. The result shows a novel effect of social networks on spontaneous group formation. © 2021, CC BY.

关键词： Dynamics

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Dragonfly algorithm and its applications in applied science - Survey

arXiv

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arXiv 2019年

作者： Rahman, Chnoor M. Rashid, Tarik A. Technical College of Informatics Sulaimany Polytechnic University Sulaimany Iraq Applied Computer Department College of Health and Applied Sciences Charmo University Sulaimany Iraq School of Science and Engineering Science and Engineering Department University of Kurdistan Hewler Erbil Iraq

One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized dragonfly algorithm in applied science are offered in the following area: Machine Learning, Image Processing, Wireless, and Networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than other algorithms in the literature, such as PSO and GA. In general, in this survey the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm's weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Multiobjective optimization

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