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FAIR Convergence Matrix:Optimizing the Reuse of Existing FAIR-Related Resources

data Intelligence 2020年第1期2卷 158-170,313页

作者： Hana Pergl Sustkova Kristina Maria Hettne Peter Wittenburg Annika Jacobsen Tobias Kuhn Robert Pergl Jan Slifka Peter McQuilton Barbara Magagna Susanna-Assunta Sansone Markus Stocker Melanie Imming Larry Lannom Mark Musen Erik Schultes GO FAIR International Support and Coordination Office LeidenThe Netherlands Centre for Digital Scholarship Leiden University LibrariesLeidenThe Netherlands Max Planck Computing and Data Facility Gießenbachstraße 285748 GarchingGermany Leiden University Medical Center Leiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK Environment Agency Austria A-1090 ViennaAustria TIB Leibniz Information Centre for Science and Technology HannoverGermany SURF Utrecht 3511 EPThe Netherlands Corporation for National Research Initiatives(CNRI) RestonVirginia 20191USA Stanford Center for Biomedical Informatics Research StanfordCA 94305USA

The FAIR principles articulate the behaviors expected from digital artifacts that are Findable,Accessible,Interoperable and Reusable by machines and by *** by now widely accepted,the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR *** different communities have their own,often well-established implementation preferences and priorities for data reuse,coordinating a broadly accepted,widely used FAIR implementation approach remains a global *** an effort to accelerate broad community convergence on FAIR implementation options,the GO FAIR community has launched the development of the FAIR Convergence *** Matrix is a platform that compiles for any community of practice,an inventory of their self-declared FAIR implementation choices and *** Convergence Matrix is itself a FAIR resource,openly available,and encourages voluntary participation by any self-identified community of practice(not only the GO FAIR Implementation Networks).Based on patterns of use and reuse of existing resources,the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR data and Services.

关键词： FAIR Implementation Choices and Challenges Convergence FAIR Communities

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BuildSenSys: Reusing building sensing data for traffic prediction with cross-domain learning

arXiv

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arXiv 2020年

作者： Fan, Xiaochen Xiang, Chaocan Chen, Chao Yang, Panlong Gong, Liangyi Song, Xudong Nanda, Priyadarsi He, Xiangjian School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China College of Computer Science Chongqing University Chongqing400044 China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230026 China School of Software BNRist Tsinghua University Beijing100084 China

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperf

关键词： Internet of things

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Bayesian data assimilation for estimating epidemic evolution: A COVID-19 study

arXiv

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arXiv 2020年

作者： Yang, Xian Wang, Shuo Xing, Yuting Li, Ling Xu, Richard Yi Da Friston, Karl J. Guo, Yike Department of Computer Science Hong Kong Baptist University Hong Kong Special Administrative Region Hong Kong Data Science Institute Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China School of Computing University of Kent United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Australia Institute of Neurology University College London United Kingdom

The evolution of epidemiological parameters, such as instantaneous reproduction number Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to Rt estimation, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt;the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes;the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in revealing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for accurate and timely estimating transmission dynamics from reported data. Copyright © 2020, The Authors. All rights reserved.

关键词： Cell proliferation

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Preface

EAI/Springer Innovations in Communication and Computing

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EAI/Springer Innovations in Communication and computing 2022年 vii-viii页

作者： Johri, Prashant Diván, Mario José Khanam, Ruqaiya Marciszack, Marcelo Will, Adrián Yamuna Expressway School of Computing Science & Engineering Galgotias University G.B.Nagar Plot No 2 Sector 17 A Greater Noida Uttar Pradesh India Computer Science and Engineering Department SET Sharda University Greater Noida Uttar Pradesh India Information System Department National Technological University-Tucumán Regional Faculty Tucumán Tucumán Argentina Data Science Research Group at Economy School National University of La Pampa Santa Rosa Argentina National Technological University-Córdoba Regional Faculty Córdoba Argentina

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Cell membrane vaccine delivery system: A review of the recent advances against breast cancer

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International journal of pharmaceutics 2025年 681卷 125849页

作者： Ayah Rebhi Hilles Syed Mahmood Riyanto Teguh Widodo Ahmad Khusairi Azemi Abu Hassan Nordin Abdin Shakirin Mohamad Norpi Muhammad Fauzi Abd Jalil Zarif Mohamed Sofian Mazlan Mohamed Kamarul Ariffin Hambali Nani Izreen Mohd Sani Roslina Rashid Muhammad Luqman Nordin INHART International Islamic University Malaysia Jalan Gombak 53100 Kuala Lumpur Malaysia. Department of Pharmaceutical Technology Faculty of Pharmacy Universiti Malaya 50603 Kuala Lumpur Malaysia Universiti Malaya Research Centre for Pharmaceuticals and Advanced Therapeutics (UBAT) Department of Pharmacology Faculty of Medicine Universiti Malaya 50603 Kuala Lumpur Malaysia Centre of Advanced Materials (CAM) Faculty of Engineering Universiti Malaya 50603 Kuala Lumpur Malaysia Faculty of Pharmaceutical Sciences Chulalongkorn University Pathum Wan Bangkok 10330 Thailand. Universiti Malaya Research Centre for Pharmaceuticals and Advanced Therapeutics (UBAT) Department of Pharmacology Faculty of Medicine Universiti Malaya 50603 Kuala Lumpur Malaysia. Institute of Climate Change Adaptation and Marine Biotechnology Universiti Malaysia Terengganu 21030 Kuala Terengganu Terengganu Malaysia. Faculty of Applied Sciences Universiti Teknologi MARA (UiTM) Campus Arau 02600 Perlis Malaysia. Faculty of Pharmacy and Health Sciences Universiti Kuala Lumpur Royal College of Medicine Perak Perak 30450 Malaysia. Department of Pharmaceutical Technology Faculty of Pharmacy Universiti Malaya 50603 Kuala Lumpur Malaysia. Faculty of Data Science and Computing Universiti Malaysia Kelantan City Campus Pengkalan Chepa Kota Bharu 16100 Kelantan Malaysia. Faculty of Earth Science Universiti Malaysia Kelantan Jeli 17600 Kelantan Malaysia Animal and Wildlife Research Group Faculty of Earth Science Jeli Campus Universiti Malaysia Kelantan Jeli 17600 Kelantan Malaysia. Faculty of Veterinary Medicine Universiti Malaysia Kelantan Pengkalan Chepa 16100 Kelantan Malaysia. Universiti Malaya Research Centre for Pharmaceuticals and Advanced Therapeutics (UBAT) Department of Pharmacology Faculty of Medicine Universiti Malaya 50603 Kuala Lumpur Malaysia. Electronic address: muhammadluqman@um.edu.my.

Natural cells exhibit unique properties, such as homotypic targeting and prolonged circulation in the bloodstream, making them attractive candidates for breast cancer therapy vaccine delivery systems (VDSs). To improve delivery efficiency, these cells can be integrated with nanocarriers, paving the way for next-generation VDSs in the form of biomimetic nanomedicines. This review highlights recent progress in cell membrane-based vaccine delivery systems (CVDSs) and other types of vaccine delivery systems as a potential for breast cancer vaccine development. Cell membrane sources, including red blood cells (RBCs), cancer cells, and platelets are explored. Additionally, the methods for extracting these membranes and the mechanisms driving the functionality of biomimetic CVDSs are detailed. This review also highlights recent advancements and cutting-edge strategies of cell membrane vaccine delivery systems, with a particular focus on the use of cell-derived biomimetic nanotechnology for breast cancer. It also highlights breast cancer vaccines under development encompass protein-based (e.g., peptide vaccines), nucleic acid-based (DNA and RNA vaccines), immune cell-based (including dendritic cell and autologous tumor cell vaccines), and bacterial/viral vector-based vaccines. Each platform aims to elicit targeted anti-tumor immune responses.

关键词： Biomimetic Breast cancer Cell membrane Nanotechnology Vaccine

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The atomic and thermal performance of CuO nanoparticles/paraffin as phase change materials in a circular tube: Molecular dynamics simulation approach

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Nano-Structures and Nano-Objects 2025年 42卷

作者： Al-Timimy, Sabreen Q. Hassan, Waqed H. Singh, Narinderjit Singh Sawaran Naser, Ghazi Faisal Salahshour, Soheil Sajadi, S. Mohammad Hekmatifar, Maboud University of Misan College of Engineering Department of Materials Engineering Amarah Iraq University of Warith Al-Anbiyaa Kerbala 56001 Iraq Department of Civil Engineering College of Engineering University of Kerbala Kerbala 56001 Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Chemical Engineering Department College of Engineering Al-Muthanna University Iraq College of Engineering Al-Ayen University Thi-Qar Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Department of Chemistry Payam e Noor University Saqqez Branch Kurdistan Saqqez Iran Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Background: Using molecular dynamics simulation, this study investigates the effect of CuO nanoparticle addition on the thermodynamic and atomic properties of an octadecane that was being utilized as a phase change material within a circular tube. Methods: The results indicate that the density (D) was greatest in the vicinity of the tube walls. At its peak, D was 0.0300 atoms per square centimeter. This behavior is due to the increased attractive force that is between the structure's boundaries and its particles. Particle velocity (V) values reached their utmost attainable values in the intermediate regions of the tube, where movement was greatest. At its peak, V was 0.0078 Å/fs. The tube exhibits a maximum temperature (Max T) value of 754.43 K at its midpoint. Significant Findings: Due to the increased particle motion in the intermediate regions, the investigated structure experienced a greater number of collisions in those areas. After 10 ns, the sample's heat flux, thermal conductivity, and thermal stability converged to values of 3.94 W/m², 1.38 W/mK, and 1821 K, respectively. The structure showed charging and discharging times of 6.41 and 7.15 ns, respectively. © 2025 Elsevier B.V.

关键词： Atomic and thermal behavior CuO nanoparticles Molecular dynamics simulation Phase change material

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RETRACTED ARTICLE: SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System

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IETE Journal of Research 2023年第11期69卷 XLIV-LVII页

作者： M. Kavitha Margret E. Golden Julie Y. Harold Robinson D. Vijayanandh S. Vimal Seifidine Kadry Ahmed M. El-Sherbeeny Mohammed A. El-Meligy 1 Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore India 2 Department of Computer Science and Engineering Anna University Regional Campus Tirunelveli India 3 School of Information Technology and Engineering Vellore Institute of Technology Vellore India 4 Department of Electrical and Electronics Engineering Hindustan College of Engineering and Technology Coimbatore India 5 Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamilnadu India 6 Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway 7 Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:M. Kavitha Margret, E. Golden Julie, Y. Harold Robinson, D. Vijayanandh, S. Vimal, Seifidine Kadry, Ahmed M. El-Sherbeeny & Mohammed A. El-Meligy (2021) SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System, IETE Journal of Research, DOI: 10.1080/03772063.2021.1994041Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

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A Review of Breast Cancer Histopathological Image Classification

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Journal of Physics: Conference Series 2021年第1期2129卷

作者： Lei Huang Azlan Mohd Zain Kai-Qing Zhou Chang-Feng Chen School of Computing Faculty of Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru Johor Malaysia. UTM Big Data Centre School of Computing Universiti Teknologi Malaysia UTM Skudai Johor Bahru Johor Malaysia. College of Information Science and Engineering Jishou University Jishou Hunan China.

Breast Cancer (BC) is the most common malignant tumor for women in the world. Histopathological examination serves as basis for breast cancer diagnosis. Due to the low accuracy of histopathological images through manual judgment, the classification of histopathological images of breast cancer has become a research hotspot in the field of medical image processing. Accurate classification of images can help doctors to properly diagnoses and improve the survival rate of patients. This paper reviews the existing works on histopathological image classification of breast cancer and analysis the advantages and disadvantages of related algorithms. Findings of the histopathological image classification of the Breast Cancer study are drawn, and the possible future directions are also discussed.

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Knowledge-Based Expert System for Orthopedic Injury Management With Integrated Transcutaneous Electrical Nerve Simulation Therapy

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International Journal of Telemedicine and Applications 2025年第1期2025卷

作者： Haneen Banjar Shahad Almalki Lama Almehmadi Amjad Alshahrani Ali Chaudhary Reda Ghoname Computer Science Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia kau.edu.sa Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia kau.edu.sa Institute of Genomic Medicine Sciences King Abdulaziz University Jeddah Saudi Arabia kau.edu.sa Centre of Artificial Intelligence in Precision Medicines King Abdulaziz University Jeddah Saudi Arabia kau.edu.sa John Hopkins Aramco Healthcare Dhahran Saudi Arabia *** Computer and Electrical Engineering Department King Abdulaziz University Jeddah Saudi Arabia kau.edu.sa

Orthotic treatment, particularly for ankle injuries, often relies on devices such as ankle–foot orthoses (AFOs) to support and stabilize the foot and ankle. Given the high prevalence of lower extremity injuries, especially in sports, and the diagnostic challenges faced by clinicians, there is a pressing need for intelligent systems that aid in accurate assessment and personalized management. Misdiagnosis and suboptimal treatment of ankle conditions, including fractures and sprains, can lead to long-term complications and hinder recovery. This study presents TeleOrtho , a knowledge-based expert system designed to provide personalized diagnosis, treatment recommendations, and recovery plans for ankle injuries, integrating transcutaneous electrical nerve stimulation (TENS) therapy to enhance rehabilitation. The system was implemented using Flutter for cross-platform deployment and Python for expert rule modeling, and it features a user-friendly interface for both clinicians and patients. Diagnostic accuracy and treatment pathways were validated through two published case scenarios, confirming the system’s alignment with medical guidelines. Static and dynamic testing demonstrated consistent functionality, and initial usability assessment indicated high user satisfaction (average SUS score: 85.3/100). The expert system reliably identified injury types based on structured symptom input and suggested individualized recovery plans. These results affirm the potential of TeleOrtho to reduce diagnostic errors, improve therapy adherence, and enhance clinical decision-making. Future work will focus on integrating imaging analysis, expanding the knowledge base to include additional orthopedic conditions, and conducting clinical trials for broader validation.

关键词： ankle injuries ankle–foot orthoses (AFOs) knowledge-based expert system personalized treatment TENS therapy

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Explainable AI: Definition and attributes of a good explanation for health AI

arXiv

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arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

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