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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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Flexible PDMS-based triboelectric nanogenerator for instantaneous force sensing and human joint movement monitoring

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Science China Materials 2019年第10期62卷 1423-1432页

作者： Junbin Yu Xiaojuan Hou Min Cui Shuzheng Shi Jian He Yawei Sun Chao Wang Xiujian Chou Science and Technology on Electronic Test and Measurement Laboratory School of Instrument and ElectronicsNorth University of ChinaTaiyuan 030051China School of Mechanical Engineering Hebei University of ArchitectureZhangjiakou 075000China North Information Control Research Academy Group Co. LtdNanjing 211100China

Flexible wearable sensors with excellent electric response and self-powered capability have become an appealing hotspot for personal healthcare and human-machine ***,based on triboelectric nanogenerator(TENG),a flexible self-powered tactile sensor composed of micro-frustum-arrays-structured polydimethylsiloxane(PDMS)film/copper(Cu)electrodes,and poly(vinylidenefluoride-trifluoroethylene)(P(VDF-TrFE))nanofibers has been *** TENG-based self-powered tactile sensor can generate electrical signals through the contact-separation process of two triboelectric layers under external mechanical *** to the uniform and controllable micro-frustum-arrays structure fabricated by micro-electro-mechanical system(MEMS)process and the P(VDF-TrFE)nanofibers fabricated by electrostatic spinning,the flexible PDMS-based sensor presents high sensitivity of 2.97 V kPa^-1,stability of 40,000 cycles(no significant decay),response time of 60 ms at 1 Hz,low detection pressure of a water drop(~4 Pa,35 mg)and good linearity of 0.99231 in low pressure *** the PDMS film presents ultra-flexibility and excellent-biocompatibility,the sensor can be comfortably attached on human ***,the tactile sensor can recognize various types of human body movements by the corresponding electrical ***,the as-prepared TENGs are potential on the prospects of gesture detection,health assessment,human-machine interfaces and so on.

关键词： self-powered triboelectric nanogenerator wearable sensor MEMS process personal healthcare

Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification

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arXiv 2020年

作者： Qu, Ying Baghbaderani, Razieh Kaviani Li, Wei Gao, Lianru Zhang, Yuxiang Qi, Hairong The Advanced Imaging and Collaborative Information Processing Group Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Hyperspectral image (HSI) classification is one of the most active research topics and has achieved promising results boosted by the recent development of deep learning. However, most state-of-the-art approaches tend to perform poorly when the training and testing images are on different domains, e.g., source domain and target domain, respectively, due to the spectral variability caused by different acquisition conditions. Transfer learning-based methods address this problem by pre-training in the source domain and fine-tuning on the target domain. Nonetheless, a considerable amount of data on the target domain has to be labeled and non-negligible computational resources are required to retrain the whole network. In this paper, we propose a new transfer learning scheme to bridge the gap between the source and target domains by projecting the HSI data from the source and target domains into a shared abundance space based on their own physical characteristics. In this way, the domain discrepancy would be largely reduced such that the model trained on the source domain could be applied on the target domain without extra efforts for data labeling or network retraining. The proposed method is referred to as physically-constrained transfer learning through shared abundance space (PCTL-SAS). Extensive experimental results demonstrate the superiority of the proposed method as compared to the state-of-the-art. The success of this endeavor would largely facilitate the deployment of HSI classification for real-world sensing scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Outage behaviors of NOMA-based satellite network over shadowed-rician fading channels

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arXiv 2020年

作者： Yue, Xinwei Liu, Yuanwei Yao, Yuanyuan Li, Tian Li, Xuehua Liu, Rongke Nallanathan, Arumugam Key Laboratory of Modern Measurement & Control Technology Ministry of Education School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang Hebei050081 China Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic and Information Engineering Beihang University Beijing100191 China

This paper investigates the application of non-orthogonal multiple access (NOMA) to satellite communication network over Shadowed-Rician fading channels. The impact of imperfect successive interference cancellation (ipSIC) on NOMA-based satellite network is taken into consideration from the perspective of practical scenarios. We first derive new exact expressions of outage probability for the p-th terrestrial user and provide the corresponding asymptotic analysis results. The diversity order of zero and p are achieved by the p-th terrestrial user with ipSIC and perfect successive interference cancellation (pSIC), respectively. Finally, the presented simulation results show that: 1) On the condition of pSIC, the outage behaviors of NOMA-based satellite network are superior to that of orthogonal multiple access;2) With the value of residual interference increasing, the outage performance of terrestrial users with ipSIC is becoming worse seriously;and 3) Infrequent light shadowing of Shadowed-Rician fading brings the better outage probability compared to frequent heavy and average shadowing. Copyright © 2020, The Authors. All rights reserved.

关键词： Asymptotic analysis

Impact of the antenna cavity on in-body propagation and channel characteristics between capsule endoscope and on-body antenna

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Impact of the antenna cavity on in-body propagation and chan...

International Symposium on Medical information & Communication technology (ISMICT)

作者： Mariella Särestöniemi Carlos Pomalaza Raez Chaïmaâ Kissi Markus Berg Matti Hämäläinen Jari Iinatti Center for Wireless Communications Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland Department of Electrical and Computer Engineering Purdue University Fort Wayne Indiana USA Electronics and Telecommunication Systems Research Group National School of Applied Sciences (ENSA) Ibn Tofail University Kenitra Morocco

ISBN: (数字)9781728166179

ISBN: (纸本)9781728166186

This paper presents a study on the impact of the antenna cavity on the in-body propagation and ultra wideband wireless body area networks (UWB-WBAN) radio channel characteristics between the capsule endoscope and the on-body antenna. The study is conducted with CST Studio Suite simulations and one of its anatomical voxel models. A simplified capsule model and two different on-body antennas designed for low-band UWB in-body communications are used in this study. The first antenna has a smaller cavity while the second antenna has a larger cavity. The radio channel characteristics between a capsule endoscope model and an on-body antenna are evaluated with the cavity and without the cavity in frequency and time domains. Furthermore, 2D power flow representations are studied to get insight how the presence and absence of the cavity changes the signal propagation within the tissues. It is found that with the antenna having a smaller cavity, the cavity impact depends on the on-body antenna location respect to the abdominal muscle layers. Instead, the antenna having a larger cavity enables to capture multipath components from the wider area resulting in stronger signal regardless where the antenna is located.

关键词： Cavity resonators Load flow Endoscopes Dipole antennas Directive antennas Mathematical model

Outliers detection is not so hard: Approximation algorithms for robust clustering problems using local search techniques

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arXiv 2020年

作者： Wang, Yishui Möhring, Rolf H. Wu, Chenchen Xu, Dachuan Zhang, Dongmei School of Mathematics and Physics University of Science and Technology Beijing Beijing100083 China Institute for Applied Optimization Department of Computer Science and Technology Hefei University China Group Institute for Mathematics Technical University of Berlin Germany College of Science Tianjin University of Technology Tianjin300384 China Department of Operations Research and Information Engineering Beijing University of Technology Beijing100124 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China

In this paper, we consider two types of robust models of the k-median/k-means problems: the outlier-version (k-MedO/k-MeaO) and the penalty-version (k-MedP/k-MeaP), in which we can mark some points as outliers and discard them. In k-MedO/k-MeaO, the number of outliers is bounded by a given integer. In k-MedP/k-MeaP, we do not bound the number of outliers, but each outlier will incur a penalty cost. We develop a new technique to analyze the approximation ratio of local search algorithms for these two problems by introducing an adapted cluster that can capture useful information about outliers in the local and the global optimal solution. For k-MeaP, we improve the best known approximation ratio based on local search from 25+Ε to 9+Ε. For k-MedP, we obtain the best known approximation ratio. For k-MedO/k-MeaO, there exists only two bi-criteria approximation algorithms based on local search. One violates the outlier constraint (the constraint on the number of outliers), while the other violates the cardinality constraint (the constraint on the number of clusters). We consider the former algorithm and improve its approximation ratios from 17 + Ε to 3 + Ε for k-MedO, and from 274 + Ε to 9 + Ε for *** Codes 68W25, 90B10, 90B80 Copyright © 2020, The Authors. All rights reserved.

关键词： Statistics

A large deviation principle for block models

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arXiv 2020年

作者： Borgs, Christian Chayes, Jennifer Gaudio, Julia Petti, Samantha Sen, Subhabrata University of California Berkeley United States Massachusetts Institute of Technology United States Harvard University United States Berkeley AI Research Group Department of Electrical Engineering and Computer Science Department of Electrical Engineering and Computer Science Department of Mathematics Department of Statistics School of Information Department of Mathematics NSF-Simons Center for Mathematical and Statistical Analysis of Biology Department of Statistics

We initiate a study of large deviations for block model random graphs in the dense regime. Following [14], we establish an LDP for dense block models, viewed as random graphons. As an application of our result, we study upper tail large deviations for homomorphism densities of regular graphs. We identify the existence of a "symmetric" phase, where the graph, conditioned on the rare event, looks like a block model with the same block sizes as the generating graphon. In specific examples, we also identify the existence of a "symmetry breaking" regime, where the conditional structure is not a block model with compatible dimensions. This identifies a "reentrant phase transition" phenomenon for this problem—analogous to one established for Erdős–Rényi random graphs [13, 14]. Finally, extending the analysis of [33], we identify the precise boundary between the symmetry and symmetry breaking regime for homomorphism densities of regular graphs and the operator norm on Erdős–Rényi bipartite *** Codes 60F10, 05C80, 60C05 Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

Robust machine learning models for calculating the carbon dioxide desublimation point within natural gas mixtures at low temperature conditions

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Journal of CO2 Utilization 2025年 99卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Dharmesh Sur Menon Soumya V Prabhat Kumar Sahu Kamred Udham Singh R. Sivaranjani Rohit Chauhan Siya Singla Fereydoon Ranjbar Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of pharmacy the Islamic University Najaf Iraq College of pharmacy the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi University Research Center Department of Chemical Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Department of Chemistry and Biochemistry School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Information Technology Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India School of Computing Graphic Era Hill University Dehradun India Centre for Promotion of Research Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India Department of CSE Raghu Engineering College Visakhapatnam Andhra Pradesh 531162 India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab 140307 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India Department of Chemistry Islamic Azad University Najafabad Branch Iran

Desublimation at low temperatures offers an efficient method for removing CO 2 from gas streams. Accurate prediction of the carbon dioxide desublimation temperature (CDDT) is essential for applying this method in natural gas processing. This investigation aimed to develop predictive tools utilizing machine learning approaches to estimate CDDT within natural gas mixtures. To reach this target, a large data set comprising 430 measurements obtained from published sources, was prepared. These data points cover the CDDT in binary and ternary natural gas mixtures under different pressures and gas fractions. In addition to black-box tools such as Decision Tree (DT), Gaussian Process Method (GPM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods, a mathematical equation was developed via Genetic Programming (GP) technique for CDDT calculation. The performances of the designed models were rigorously evaluated through various visual inspections and statistical indices. While all models demonstrated excellent predictive accuracy, the GPM model provided superior results among black-box tools, exhibiting a mean absolute percentage error (MAPE) of 0.99 %. Furthermore, the GP equation achieved an overall MAPE of 0.65 % for the CDDT data. The intelligent models also performed well in predicting the data pertinent to both binary and ternary mixtures. A series of simulations based on the models’ outcomes were carried out to depict the CDDT variations in response to operational parameters, and the findings showed full consistency with previous experimental results. Ultimately, a sensitivity analysis was conducted to pinpoint the dominant factors affecting the CO 2 desublimation. Overall, the outcomes of this research enhance the understanding of CDDT behavior and provide valuable information for optimizing low-temperature CO 2 capture processes used in natural gas purification.

关键词： Carbon dioxide desublimation temperature (CDDT) Carbon capture Natural gas Modeling Intelligent methods

Taking the pulse of COVID-19:a spatiotemporal perspective

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International Journal of Digital Earth 2020年第10期13卷 1186-1211页

作者： Chaowei Yang Dexuan Sha Qian Liu Yun Li Hai Lan Weihe Wendy Guan Tao Hu Zhenlong Li Zhiran Zhang John Hoot Thompson Zifu Wang David Wong Shiyang Ruan Manzhu Yu Douglas Richardson Luyao Zhang Ruizhi Hou You Zhoua Cheng Zhong Yifei Tian Fayez Beaini Kyla Carte Colin Flynn Wei Liu Dieter Pfoser Shuming Bao Mei Li Haoyuan Zhang Chunbo Liu Jie Jiang Shihong Du Liang Zhao Mingyue Lu Lin Li Huan Zhou Andrew Ding NSF Spatiotemporal Innovation Center George Mason Univ.FairfaxVAUSA Department of Geography and GeoInformation Science George Mason Univ.FairfaxVAUSA Department of Geographical Sciences University of MarylandCollege ParkMDUSA Center for Geographic Analysis Harvard UniversityCambridgeMAUSA Geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Chinese Academy of Surveying and Mapping BeijingPeople’s Republic of China School of Resource and Environmental Sciences Wuhan UniversityWuhanPeople’s Republic of China Cloud Computeand Storage OperationsInformation Technology ServicesGeorge Mason UniversityFairfaxVAUSA Department of Geography Pennsylvania State UniversityState CollegePAUSA School of Business Management East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences East China Normal UniversityShanghaiPeople’s Republic of China Department of Psychology George Mason UniversityFairfaxVAUSA mDepartment of Computer Science George Mason UniversityFairfaxVAUSA Department of Information Technology George Mason UniversityFairfaxVAUSA Department of Biology George Mason UniversityFairfaxVAUSA College of Land Science and Technology China Agricultural UniversityBeijingPeople’s Republic of China China Data Institute Ann ArborMIUSA Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjingPeople’s Republic of China School of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China wFaculty of Environment University of WaterlooWaterlooCanada

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the *** transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and *** response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global *** perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

关键词： Big Data Earth system emergency geospatial sciences epidemics applications