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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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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. Abd ElHafeez, Samar 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, Ali 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 Syed Shujait Ali 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 Anwari, Saleha Anwar 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 Balakrishna 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 esti

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Sparse weighted canonical correlation analysis

arXiv

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arXiv 2017年

作者： Min, Wenwen Liu, Juan Zhang, Shihua State Key Laboratory of Software Engineering School of Computer Wuhan University National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics Sciences University of Chinese Academy of Sciences

Given two data matrices X and Y, Sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Y v. However, classical and sparse CCA models consider the contribution of all the samples of data matrices and thus cannot identify an underlying specific subset of samples. To this end, we propose a novel Sparse weighted canonical correlation analysis (SWCCA), where weights are used for regularizing different samples. We solve the L0-regularized SWCCA (L0-SWCCA) using an alternating iterative algorithm. We apply L0-SWCCA to synthetic data and real-world data to demonstrate its effectiveness and superiority compared to related methods. Lastly, we consider also SWCCA with different penalties like LASSO (Least absolute shrinkage and selection operator) and Group LASSO, and extend it for integrating more than three data matrices. Copyright © 2017, The Authors. All rights reserved.

关键词： Iterative methods

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What industry wants from academia in software testing?: Hearing practitioners' opinions 17

What industry wants from academia in software testing?: Hear...

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Proceedings of the 21st International Conference on Evaluation and Assessment in software engineering

作者： Vahid Garousi Michael Felderer Marco Kuhrmann Kadir Herkiloğlu University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Belval Luxembourg University of Innsbruck Institute of Computer Science Innsbruck Austria Clausthal University of Technology Institute for Applied Software Systems Engineering Goslar Germany HAVELSAN A.Ş. Quality Test and Process Directorate Ankara Turkey

ISBN: (纸本)9781450348041

The level of industry-academia collaboration (IAC) in software engineering in general and in software testing in particular is quite low. Many researchers and practitioners are not collaborating with the "other side" to solve industrial problems. To shed light on the above issue and to characterize precisely what industry wants from academia in software testing, we solicited practitioners' opinions on their challenges in different testing activities and also the particularly relevant topics that they want the research community to work on. This short paper aims to draw the community's attention to the important issue of strengthening IAC with the hope of more IAC in software testing in the areas of most importance to the industry.

关键词： opinion survey industry-academia collaborations industrial challenges industrial needs software testing

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2025 photonics for agrifood roadmap: towards a sustainable and healthier planet

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Journal of Physics: Photonics 2025年第3期7卷 032501-032501页

作者： Lien Smeesters Francesca Venturini Stefan Paulus Anne-Katrin Mahlein David Perpetuini Daniela Cardone Arcangelo Merla Benjamin Gac Stephane Perrin Denis Trégoat Luiz Poffo Antoine Fournier Giuseppe Bonifazi Silvia Serranti Wenzhi Liao Noureddin Osseiran Mathias Vanwolleghem Romain Peretti Aparajita Bandyopadhyay Karl Bertling Krishnan Parameswaran Aleksandar D Rákic Amartya Sengupta Sune Svanberg Małgorzata Kujawinska Daniel Cozzolino Dolores Pérez Marín José A Entrenas de León Irina Torres Rodríguez Marietta Fodor Eszter Benes Anna Matkovits Zsuzsa Jókai Simona M Cristescu Amir Khodabakhsh Khalil Jahromi Mohammadreza Nematollahi Frans H Harren Eugene Rokx Ewa Sikorska Kay Sowoidnich Martin Maiwald Bernd Sumpf Matthieu Roussey Kai-Erik Peiponen Antoni Femenias Polina Fomina Vjekoslav Kokoric Robert Stach Boris Mizaikoff Maria Konstantaki Stavros Pissadakis Umberto Michelucci Sławomir Paśko Anna Pakuła Richard A Crocombe Ellen V Miseo Andreas C Liapis Faisal Ahmed Zhipei Sun Chrysoula Chandrinou Marianneza Chatzipetrou Ioanna Zergioti Ulrich Trog Anja Haase Martin Smolka Lex Oosterveld Chris Van Hoof André Müller Michael Hlavatsch Diellza Bajrami Ernesto Olvera-Gonzalez Nivia Escalante-Garcia Arturo B Soro Koenraad Van Hoorde Brijesh K Tiwari Thomas Westerhoff Michael Kneissl Anna Grazia Mignani Department of Applied Physics and Photonics Vrije Universiteit Brussel and Flanders Make Brussels Photonics (B-PHOT) Pleinlaan 2 B-1050 Brussels Belgium School of Engineering ZHAW Zurich University of Applied Sciences 8401 Winterthur Switzerland TOELT LLC Research and Development 8600 Duebendorf Switzerland Institute of Sugar Beet Research Holtenser Landstr. 77 37079 Göttingen Germany Department of Engineering and Geology University ‘D’Annunzio’ of Chieti-Pescara Pescara 65127 Italy Arvalis 45 Voie Romaine 41240 Beauce la Romaine France Université de Rennes CNRS Institut FOTON—UMR 6082 F-22305 Lannion France Photonics Bretagne 4 Rue Louis de Broglie 22300 Lannion France University of Limoges XLIM UMR CNRS 7252 Limoges France Department of Chemical Engineering Materials and Environment Sapienza—University of Rome via Eudossiana 18 00184 Rome Italy Flanders Make 8500 Kortrijk Belgium Department of Telecommunications and Information Processing Ghent University 9000 Ghent Belgium Institut d’Electronique de Microélectronique et de Nanotechnologie (IEMN) CNRS–UMR 8520 Université de Lille 59652 Villeneuve d’Ascq France DRDO-Industry-Academia Centre of Excellence (DIA-CoE) Indian Institute of Technology Delhi New Delhi 110016 India School of Electrical Engineering and Computer Science University of Queensland Brisbane QLD 4072 Australia Cambridge Consultants Boston MA United States of America Department of Physics Indian Institute of Technology Delhi New Delhi 110016 India Physics Department Lund University SE-221 00 Lund Sweden Institute of Micromechanics and Photonics Warsaw University of Technology 8 Sw. Andrzeja Boboli St 02-525 Warsaw Poland Central Office of Measures 2 Elektoralna St. 00-139 Warsaw Poland Centre for Nutrition and Food Sciences Queensland Alliance for Agriculture and Food Innovation (QAAFI) The University of Queensland St.Lucia Brisbane QLD 4072 Australia Non-Destructive Spectral Sensors Unit Department of Animal Production Fa

Photonics technologies play a crucial role in driving technological advancements within the agrifood industry, aiming to deliver a sustainable food and agriculture, and offering healthy, nutritious and safe food for all of us. Particularly, optical sensors and imaging systems, together with machine-learning processing and advanced lighting, play a pivotal role in monitoring crop and soil health with unprecedented precision, while safeguarding the food supply chain. This roadmap aims to provide an overview of the state-of-the-art photonics technologies benefitting agrifood applications, including a view on their current limitations, challenges and future potential, while addressing practical case studies. Future trends towards multimodal sensors and sensor fusion, artificial intelligence and digital twins, miniaturization and controlled farming are highlighted. The revolutionizing capabilities of the photonics technologies are indicated, inspiring future applications and developments, and paving the way towards optimized resource utilization, increased crop yields, stopping land degradation and reduction of food waste.

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Fusing R Features and Local Features with Context-Aware Kernels for Action Recognition

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International Journal of computer Vision 2016年第2期118卷 151-171页

作者： Yuan, Chunfeng Wu, Baoxin Li, Xi Hu, Weiming Maybank, Stephen Wang, Fangshi National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Information Systems Birkbeck College London United Kingdom School of Software Engineering Beijing Jiaotong University Beijing China

The performance of action recognition in video sequences depends significantly on the representation of actions and the similarity measurement between the representations. In this paper, we combine two kinds of features extracted from the spatio-temporal interest points with context-aware kernels for action recognition. For the action representation, local cuboid features extracted around interest points are very popular using a Bag of Visual Words (BOVW) model. Such representations, however, ignore potentially valuable information about the global spatio-temporal distribution of interest points. We propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the 3D R transform which is defined as an extended 3D discrete Radon transform, followed by the application of a two-directional two-dimensional principal component analysis. For the similarity measurement, we model a video set as an optimized probabilistic hypergraph and propose a context-aware kernel to measure high order relationships among videos. The context-aware kernel is more robust to the noise and outliers in the data than the traditional context-free kernel which just considers the pairwise relationships between videos. The hyperedges of the hypergraph are constructed based on a learnt Mahalanobis distance metric. Any disturbing information from other classes is excluded from each hyperedge. Finally, a multiple kernel learning algorithm is designed by integrating the l2 norm regularization into a linear SVM classifier to fuse the R feature and the BOVW representation for action recognition. Experimental results on several datasets demonstrate the effectiveness of the proposed approach for action recognition. © 2015, Springer Science+Business Media New York.

关键词： Principal component analysis

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Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension

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Journal of Ambient Intelligence and Humanized Computing 2017年 1-11页

作者： Wang, Shui-Hua Muhammad, Khan Phillips, Preetha Dong, Zhengchao Zhang, Yu-Dong Jiangsu Key Laboratory of Advanced Manufacturing Technology HuaiyinJiangsu223003 China School of Computer Science and Technology Henan Polytechnic University JiaozuoHenan454000 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China College of Software Convergence Sejong University Seoul Korea Republic of Department of Informatics University of Leicester LeicesterLE1 7RH United Kingdom West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Translational Imaging Division and MRI Unit Columbia University and New York State Psychiatric Institute New YorkNY10032 United States

Ductal carcinoma in situ (DCIS) is a severe breast disease. It generates little symptom and may be neglected in prodromal stage. In this study, we developed a novel DCIS detection method based on breast thermography, which can provide earlier alert than other exams. We created a 40 breast-thermogram dataset. We used six statistical measures, and we used fractal dimension to describe the texture measure. The extreme learning machine was used as the classifier. Our developed system yielded a sensitivity of 93.0 ± 2.6%, a specificity of 92.5 ± 2.6%, and an accuracy of 92.8 ± 1.8%. The extreme learning machine was better than support vector machine, artificial neural network, decision tree, and weighted k-nearest neighbors. Besides, our developed system was superior to six state-of-the-art approaches. © 2017 Springer-Verlag GmbH Germany, part of Springer Nature

关键词： Fractal dimension

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How sustainable are model software artifacts in the context of model driven software engineering 3

How sustainable are model software artifacts in the context ...

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3rd International Workshop on Measurement and Metrics for Green and Sustainable software systems, MeGSuS 2016

作者： Torre, Damiano Calero, Coral Department of Systems and Computer Engineering Software Quality Engineering Laboratory Carleton University 1125 Colonel by Drive OttawaONK1S5B6 Canada ALARCOS Research Group Department of Technologies and Information Systems University of Castilla-La Mancha Paseo de la Universidad 4 Ciudad Real13071 Spain

Today, software is pervasive in our daily lives and its sustainability and environmental impact have become major factors in the development of software systems. Due to increasing of software complexity, it is widely acknowledged that it is impossible to test every aspect of system software. One method of increasing understanding between the software developer and the customer, of deepening the understanding of how software works, and ultimately of reducing the complexity of software, is through the use of models. Model driven software engineering (MDSE) is an established approach for developing complex software systems. This work presents a first attempt at reshaping existing basic dimensions for software sustainability in terms of MDSE. Moreover, we describe 8 quality aspects and their relationships with each other. We also present a set of guidelines to identify the 8 quality aspects, and the paths to measure sustainability in terms of MDSE. Finally, we describe an example with a set of hypothetical scenarios where we show how our quality aspects could be helpful to measure and analyze the degree of sustainability in the context of MDSE.

关键词： Sustainable development

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Rating protocol design for extortion and cooperation in the crowdsourcing contest dilemma

arXiv

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arXiv 2017年

作者： Lu, Jianfeng Xin, Yun Zhang, Zhao Tang, Shaojie Yan, Songyuan Tang, Changbing Department of Computer Science and Engineering Zhejiang Normal University Jinhua Zhejiang China State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Department of Information Systems University of Texas at Dallas RichardsonTX75080 United States

Crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks. However, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Hence, incentive mechanisms are of great importance to overcome the inefficiency of the socially undesirable equilibrium. Existing incentive mechanisms are not effective in providing incentives for cooperation in crowdsourcing competitions due to the following features: heterogeneous workers compete against each other in a crowdsourcing platform with imperfect monitoring. In this paper, we take these features into consideration, and develop a novel game-theoretic design of rating protocols, which integrates binary rating labels with differential pricing to maximize the requester's utility, by extorting selfish workers and enforcing cooperation among them. By quantifying necessary and sufficient conditions for the sustainable social norm, we formulate the problem of maximizing the revenue of the requester among all sustainable rating protocols, provide design guidelines for optimal rating protocols, and design a low-complexity algorithm to select optimal design parameters which are related to differential punishments and pricing schemes. Simulation results demonstrate how intrinsic parameters impact on design parameters, as well as the performance gain of the proposed rating protocol. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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Guest editorial: The 2016 capstone design conference

Guest editorial: The 2016 capstone design conference

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作者： Brackin, Patsy Howe, Susannah Rogers, Peter Stanfill, R. Keith Beyerlein, Steven Kanai, Junichi Vallino, Jim Palo, Scott Department of Mechanical Engineering Rose-Hulman Institute of Technology 5500 Wabash Avenue CM 165 Terre HauteIN47803 United States Picker Engineering Program Smith College 151 Ford Hall NorthamptonMA01063 United States Department of Engineering Education Ohio State University 3070 Smith Laboratory 174 W. 18th Avenue ColumbusOH43210 United States Engineering Innovation Institute University of Florida P.O. Box 116000 GainesvilleFL32611-6000 United States Department of Mechanical Engineering University of Idaho 875 Perimeter Drive MoscowID83844-0902 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Software Engineering Rochester Institute of Technology 134 Lomb Memorial Drive RochesterNY14623-5608 United States Department of Aerospace Engineering Sciences University of Colorado 429 UCB BoulderCO80309 United States

The goal of the 2016 Capstone Design Conference held in Columbus, OH was to build upon the success of previous conferences (2007 and 2010 in Boulder, CO, 2012 in Champaign, IL, and 2014 in Columbus, OH) and expand the community of educators, students, and industry members engaged in discussing, analyzing, and improving capstone design education. Sessions at the 2016 Capstone Design Conference were designed for vibrant sharing of ideas and experiences across the capstone community via interactive panel sessions, poster session socials, hands-on workshops, and other formal and informal networking activities. This editorial discusses conference planning, structure, and feedback. Technical papers that follow in this issue document scholarship surrounding noteworthy capstone course innovations. Most of these began as four page peer-reviewed papers included in the conference proceedings. © 2017 TEMPUS Publications.

关键词： Teaching

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Heterogeneous defect prediction via exploiting correlation subspace 28

Heterogeneous defect prediction via exploiting correlation s...

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28th International Conference on software engineering and Knowledge engineering, SEKE 2016

作者： Cheng, Ming Wu, Guoqing Jiang, Min Wan, Hongyan You, Guoan Yuan, Mengting State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China Department of Cognitive Science and Technology Fujian Key Laboratory of Brain-Like Intelligent Systems Xiamen University Xiamen361005 China

ISBN: (纸本)189170639X

software defect prediction generally builds models from intra-project data. Lack of training data at the early stage of software testing limits the efficiency of prediction in practice. Thereby researchers proposed cross-project defect prediction using the data from other projects. Most previous efforts assumed the cross-project defect data have the same metrics set which means the metrics used and size of metrics set are same in the data of projects. However, in real scenarios, this assumption may not hold. In addition, software defect datasets have the class imbalance problem increasing the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis for deriving a joint feature space for associating crossproject data and propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. Experiments on public heterogeneous datasets from different projects show that our method is more effective, compared to state-of-the-art methods. Copyright © 2016 by KSI Research Inc. and Knowledge systems Institute Graduate School.

关键词： Support vector machines

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