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COMMUNICATIONS MEDICINE 2024年第1期4卷 1-10页

作者： Wong, Kerry L. M. Banke-Thomas, Aduragbemi Olubodun, Tope Macharia, Peter M. Stanton, Charlotte Sundararajan, Narayanan Shah, Yash Prasad, Gautam Kansal, Mansi Vispute, Swapnil Shekel, Tomer Ogunyemi, Olakunmi Gwacham-Anisiobi, Uchenna Wang, Jia Abejirinde, Ibukun-Oluwa Omolade Makanga, Prestige Tatenda Afolabi, Bosede B. Benova, Lenka Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London UK School of Human Sciences University of Greenwich London UK Maternal and Reproductive Health Research Collective Lagos Nigeria Department of Obstetrics and Gynaecology College of Medicine of the University of Lagos Lagos Nigeria Department of Community Medicine and Primary Care Federal Medical Centre Abeokuta Abeokuta Ogun Nigeria Department of Public Health Institute of Tropical Medicine Antwerp Belgium Population & Health Impact Surveillance Group Kenya Medical Research Institute-Wellcome Trust Research Programme Nairobi Kenya Centre for Health Informatics Computing and Statistics Lancaster Medical School Lancaster University Lancaster UK Google LLC Mountain View CA USA Lagos State Ministry of Health Ikeja Lagos Nigeria Nuffield Department of Population Health University of Oxford Oxford UK School of Computing & Mathematical Sciences University of Greenwich London UK Dalla Lana School of Public Health University of Toronto Toronto Canada Women’s College Hospital Institute for Health System Solutions and Virtual Care Toronto Canada Surveying and Geomatics Department Midlands State University Faculty of Science and Technology Gweru Midlands Zimbabwe Climate and Health Division Centre for Sexual Health and HIV/AIDS Research Harare Zimbabwe

BackgroundBetter geographical accessibility to comprehensive emergency obstetric care (CEmOC) facilities can significantly improve pregnancy outcomes. However, with other factors, such as affordability critical for care access, it is important to explore accessibility across groups. We assessed CEmOC geographical accessibility by wealth status in the 15 most-populated Nigerian *** mapped city boundaries, verified and geocoded functional CEmOC facilities, and assembled population distribution for women of childbearing age and Meta’s Relative Wealth Index (RWI). We used the Google Maps Platform’s internal Directions Application Programming Interface to obtain driving times to public and private facilities. City-level median travel time (MTT) and number of CEmOC facilities reachable within 60 min were summarised for peak and non-peak hours per wealth quintile. The correlation between RWI and MTT to the nearest public CEmOC was *** show that MTT to the nearest public CEmOC facility is lowest in the wealthiest 20% in all cities, with the largest difference in MTT between the wealthiest 20% and least wealthy 20% seen in Onitsha (26 vs 81 min) and the smallest in Warri (20 vs 30 min). Similarly, the average number of public CEmOC facilities reachable within 60 min varies (11 among the wealthiest 20% and six among the least wealthy in Kano). In five cities, zero facilities are reachable under 60 min for the least wealthy 20%. Those who live in the suburbs particularly have poor accessibility to CEmOC *** findings show that the least wealthy mostly have poor accessibility to care. Interventions addressing CEmOC geographical accessibility targeting poor people are needed to address inequities in urban settings.

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Corrigendum to “Hybridizing flower pollination algorithm with particle swarm optimization for enhancing the performance of IPv6 intrusion detection system” [Alex. Eng. J. 104 (2024) 504–514]

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Alexandria Engineering Journal 2025年 110卷 376-376页

作者： Adnan Hasan Bdair AIghuraibawi Selvakumar Manickam Zaid Abdi Alkareem Alyasseri Rosni Abdullah Ayman Khallel Riyadh Rahef Nuiaa Al Ogaili Fahd N. Al-Wesabi Abdulsamad Ebrahim Yahya National Advanced IPv6 Centre (NAv6) Universiti Sains Malaysia Penang 11800 Malaysia Baghdad College of Economic Sciences University Baghdad Iraq Information Technology Research and Development Center University of Kufa Najaf Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq School of Computer Sciences Universiti Sains Malays Penang 11800 Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia Department of Cybersecurity Engineering Technology Al Hikma University College Baghdad Iraq Department of Computer Science/ College of Computer Science and Information Technology Wasit University Kut Wasit 52001 Iraq Department of Computer Science College of Science & Art at Mahayil King Khalid University Saudi Arabia Department of Information Technology College of Computing and Information Technology Northern Border University Arar Saudi Arabia

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A Quantum Beta Distributed Multi-Objective Particle Swarm Optimization Algorithm for Twitter Fake Accounts Detection

TechRxiv

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TechRxiv 2022年

作者： Aboud, Ahlem Rokbani, Nizar Mirjalili, Seyedali Hussain, Amir Chabchoub, Habib Alimi, Adel M. University of Sousse ISITCom Sousse4011 Tunisia BP 1173 Sfax3038 Tunisia High Institute of Applied Science Technolgy of Sousse University of Sousse Tunisia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Brisbane Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom College of Business Al Ain University of Science and Technology Abu Dhabi United Arab Emirates Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa University of Sousse Tunisia

Fake account detection is a topical issue when many Online Social Networks encounter several issues caused by the growing number of unethical online activities. This study presents a new Quantum Beta-behaved Multi-Objective Particle Swarm Optimization (QB-MOPSO) algorithm for machine learning based Twitter fake accounts detection. The proposed approach aims to improve the learning process of deep neural networks, random forest, through minimizing simultaneously the feature dimensionality and the classification error rate. The main contribution consists in proposing a quantum beta MOPSO to handle the training phase of neural and deep architectures. The QB-MOPSO is used to perform a multi-objective training of the random forest algorithm. The QB-MOPSO has two optimization profiles: the first one uses a quantum-behaved equation for improving the exploratory behaviour of PSO, while the second one uses a beta function to enhance PSO’s exploitation. An extensive experimental study is carried out using two open Twitter datasets with 1982 and 928 accounts. The new proposal is a random forest QB-MOPSO. Results showed that random forest QB-MOPSO accuracy is about 99.19% and 97.52% accounts on datasets 1 and 2. Comparative analysis of the prosed architecture toward the original architecture showed that the use of QB-MOPSO for learning enhances the random forest algorithm which perform then the original ones. © 2022, CC BY-NC-SA.

关键词： Social networking (online)

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SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

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Brain Informatics 2021年第1期8卷 14页

作者： Fabietti, Marcos Mahmud, Mufti Lotfi, Ahmad Kaiser, M. Shamim Averna, Alberto Guggenmos, David J. Nudo, Randolph J. Chiappalone, Michela Chen, Jianhui Department of Computer Science Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh Department of Health Sciences University of Milan Via di Rudinì 8 Milan20142 Italy Department of Physical Medicine and Rehabilitation University of Kansas Medical Center 3901 Rainbow Blvd Kansas City66160 United States Department of informatics Bioengineering Robotics and System Engineering-DIBRIS University of Genova Via All’Opera Pia 13 Genoa16145 Italy Faculty of Information Technology International WIC Institute Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials. © 2021, The Author(s).

关键词： Neural networks

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Preface

SpringerBriefs in Applied Sciences and Technology

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SpringerBriefs in Applied sciences and technology 2025年 Part F127卷 v页

作者： Sambhav, Saurabh Singh, Deepak Kumar Pandey, Ashok Kumar Ismail, Azman Zulkipli, Fatin Nur Öchsner, Andreas Amity University Patna Bihar Patna India Madhya Pradesh Bhopal India Maritime Engineering Technology and Centre for Women Advancement and Leadership Universiti Kuala Lumpur Malaysian Institute of Marine Engineering Technology Perak Lumut Malaysia Information Science Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA Machang Kelantan Malaysia Faculty of Mechanical Engineering Esslingen University of Applied Sciences Baden-Württemberg Esslingen Germany

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Geographical accessibility to functional emergency obstetric care facilities in urban Nigeria using closer-to-reality travel time estimates: a population-based spatial analysis

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The Lancet Global Health 2024年第5期12卷 e848-e858页

作者： Banke-Thomas, Aduragbemi Wong, Kerry L M Olubodun, Tope Macharia, Peter M Sundararajan, Narayanan Shah, Yash Prasad, Gautam Kansal, Mansi Vispute, Swapnil Shekel, Tomer Ogunyemi, Olakunmi Gwacham-Anisiobi, Uchenna Wang, Jia Abejirinde, Ibukun-Oluwa Omolade Makanga, Prestige Tatenda Azodoh, Ngozi Nzelu, Charles Afolabi, Bosede B Stanton, Charlotte Beňová, Lenka Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London United Kingdom School of Human Sciences University of Greenwich London United Kingdom Maternal and Reproductive Health Research Collective Lagos Nigeria Department of Community Medicine and Primary Care Federal Medical Centre Abeokuta Ogun Abeokuta Nigeria Department of Public Health Institute of Tropical Medicine Antwerp Belgium Population & Health Impact Surveillance Group Kenya Medical Research Institute–Wellcome Trust Research Programme Nairobi Kenya Centre for Health Informatics Computing and Statistics Lancaster Medical School Lancaster University Lancaster United Kingdom Google Mountain View CA United States Lagos State Ministry of Health Lagos Ikeja Nigeria Nuffield Department of Population Health University of Oxford Oxford United Kingdom School of Computing & Mathematical Sciences University of Greenwich London United Kingdom Dalla Lana School of Public Health University of Toronto Toronto ON Canada Women's College Hospital Institute for Health System Solutions and Virtual Care Toronto ON Canada Surveying and Geomatics Department Midlands State University Faculty of Science and Technology Gweru Zimbabwe Climate and Health Division Centre for Sexual Health and HIV/AIDS Research Zimbabwe Department of Health Planning Research and Statistics Federal Ministry of Health Abuja Nigeria Department of Obstetrics and Gynaecology College of Medicine of the University of Lagos Lagos Nigeria

Background: Better accessibility for emergency obstetric care facilities can substantially reduce maternal and perinatal deaths. However, pregnant women and girls living in urban settings face additional complex challenges travelling to facilities. We aimed to assess the geographical accessibility of the three nearest functional public and private comprehensive emergency obstetric care facilities in the 15 largest Nigerian cities via a novel approach that uses closer-to-reality travel time estimates than traditional model-based approaches. Methods: In this population-based spatial analysis, we mapped city boundaries, verified and geocoded functional comprehensive emergency obstetric care facilities, and mapped the population distribution for girls and women aged 15–49 years (ie, of childbearing age). We used the Google Maps Platform's internal Directions Application Programming Interface to derive driving times to public and private facilities. Median travel time and the percentage of women aged 15–49 years able to reach care were summarised for eight traffic scenarios (peak and non-peak hours on weekdays and weekends) by city and within city under different travel time thresholds (≤15 min, ≤30 min, ≤60 min). Findings: As of 2022, there were 11·5 million girls and women aged 15–49 years living in the 15 studied cities, and we identified the location and functionality of 2020 comprehensive emergency obstetric care facilities. City-level median travel time to the nearest comprehensive emergency obstetric care facility ranged from 18 min in Maiduguri to 46 min in Kaduna. Median travel time varied by location within a city. The between-ward IQR of median travel time to the nearest public comprehensive emergency obstetric care varied from the narrowest in Maiduguri (10 min) to the widest in Benin City (41 min). Informal settlements and peripheral areas tended to be worse off compared to the inner city. The percentages of girls and women aged 15–49 years within 60 min of

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Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

arXiv

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arXiv 2021年

作者： Wang, Shen Qureshi, M. Atif Miralles-Pechuán, Luis Huynh-The, Thien Gadekallu, Thippa Reddy Liyanage, Madhusanka The School of Computer Science University College Dublin Ireland ADAPT Centre eXplainable Analytics Group Faculty of Business Technological University Dublin Ireland The School of Computing Technological University Dublin Central Quad Grangegorman Dublin Ireland The Department of Computer and Communication Engineering Ho Chi Minh City University of Technology and Education Viet Nam The School of Information Technology and Engineering Vellore Institute of Technology India The Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made in real-time. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed, data-intensive AI decision-making beyond designers' and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This paper surveys the application of XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for 6G in the near future. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

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Landscape of global urban environmental resistome and its association with local socioeconomic and medical status

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science China(Life sciences) 2024年第6期67卷 1292-1301页

作者： Jun Wu Yige Hu Michael H Perlin David Danko Jun Lu Manuela Oliveira Johannes Werner Maria M Zambrano Maria A Sierra Olayinka O Osuolale Paweł Łabaj Nicolás Rascovan Nur Hazlin Hazrin-Chong Soojin Jang Haruo Suzuki Marina Nieto-Caballero Bharath Prithiviraj Patrick K H Lee Agnieszka Chmielarczyk Anna Różańska Yongxiang Zhao Lan Wang Christopher E Mason Tieliu Shi Center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEast China Normal UniversityShanghai 200241China Department of Biology Program on Disease EvolutionUniversity of LouisvilleLouisville 40292USA Weill Cornell Medicine New York 10065USA The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York 10065USA Department of Pulmonary Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200025China i3S-Instituto de Investiga玢o e Inova玢o em Saúde Universidade do PortoPorto 4050-290Portugal Ipatimup-Instituto de Patologia e Imunologia Molecular da Universidade do Porto Porto 4200-465Portugal Departamento de Biologia Faculdade de CiênciasUniversidade do PortoPorto 4050-290Portugal High Performance and Cloud Computing Group Zentrum für Datenverarbeitung(ZDV)Eberhard Karls University of TübingenW鋍hterstra遝7672074TübingenGermany Corporación Corpogen Research Center Bogotá110311Colombia Applied Environmental Metagenomics and Infectious Diseases Research(AEMIDR) Department of Biological SciencesElizade UniversityIlara-Mokin 340271Nigeria Maopolska Centre of Biotechnology Jagiellonian UniversityKraków 30-005Poland Aix-Marseille Université Mediterranean Institute of OceanologyUniversitéde ToulonCNRSIRDUM 110Marseille 83041France Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 UKM BangiSelangor Institut Pasteur Korea Seoul 13488Republic of Korea Faculty of Environment and Information Studies Keio UniversityFujisawaKanagawa 252-0882Japan University of Colorado at Boulder CivilEnvironmental and Architectural DepartmentBoulder 80303USA Brooklyn College of the City University of New York Brooklyn 11210USA School of Energy and Environment City University of Hong KongHong Kong 999077China Department of Microbiology Faculty of MedicineJagiellonian UniversityKraków 30-005Poland Biological Targeting Diagnosis and The

Antimicrobial resistance(AMR)poses a critical threat to global health and development,with environmental factors—particularly in urban areas—contributing significantly to the spread of antibiotic resistance genes(ARGs).However,most research to date has been conducted at a local level,leaving significant gaps in our understanding of the global status of antibiotic resistance in urban *** address this issue,we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples,which were collected by the Meta SUB International Consortium involving diverse urban environments in 60 cities of 27 countries,utilizing a deep-learning based *** findings demonstrated the strong geographical specificity of urban environmental resistome,and their correlation with various local socioeconomic and medical *** also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters(BGCs)across different countries,and discovered that the urban environment represents a rich source of novel *** study provides a comprehensive overview of the global urban environmental resistome,and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

关键词： urban environment metagenome antibiotic resistance resistance diversity biosynthetic gene clusters

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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Corrections to “The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic”

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IEEE Access 2025年 13卷 5436-5436页

作者： M. Poongodi Mohit Malviya Mounir Hamdi Hafiz Tayyab Rauf Seifedine Kadry Orawit Thinnukool College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Department of CTO 5G Wipro Ltd. Bengaluru India Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent U.K. Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Research Group of Embedded Systems and Mobile Application in Health Science College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

Presents corrections to the paper, (Corrections to “The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic”).

关键词： COVID-19 Pandemics

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