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36th Annual Conference on Neural Information Processing Systems - Competition Track, NeurIPS 2022

作者： Yip, Kai Hou Changeat, Quentin Waldmann, Ingo Unlu, Eyup B. Forestano, Roy T. Roman, Alexander Matcheva, Katia Matchev, Konstantin T. Stefanov, Stefan Podsztavek, Ondřej Morvan, Mario Nikolaou, Nikolaos Al-Refaie, Ahmed Jenner, Clare Johnson, Chris Tsiaras, Angelos Edwards, Billy de Oliveira, Catarina Alves Thiyagalingam, Jeyan Lagage, Pierre-Olivier Cho, James Tinetti, Giovanna Department of Physics and Astronomy University College London Gower Street United Kingdom 3700 San Martin Drive BaltimoreMD21218 United States Physics Department University of Florida GainesvilleFL32611 United States Faculty of Information Technology Czech Technical University in Prague Thákurova 9 Prague 6160 00 Czech Republic EPCC The University of Edinburgh United Kingdom INAF - Osservatorio Astrofisico di Arcetri Florence Italy Leiden Netherlands European Space Agency ESAC Villanueva de la Cãnada MadridE-28692 Spain STFC Scientific Computing Rutherford Appleton Laboratory Science and Technology Facilities Council Harwell Campus OX11 0QX United Kingdom Centre Energie Atomique Saclay France Martin A. Fisher School of Physics Brandeis University 415 South Street WalthamMA02453 United States

Exo-atmospheric studies, i.e. the study of exoplanetary atmospheres, is an emerging frontier in Planetary science. To understand the physical properties of hundreds of exoplanets, astronomers have traditionally relied on sampling-based methods. However, with the growing number of exoplanet detections (i.e. increased data quantity) and advancements in technology from telescopes such as JWST and Ariel (i.e. improved data quality), there is a need for more scalable data analysis techniques. The Ariel Data Challenge 2022 aims to find interdisciplinary solutions from the NeurIPS community. Results from the challenge indicate that machine learning (ML) models have the potential to provide quick insights for thousands of planets and millions of atmospheric models. However, the machine learning models are not immune to data drifts, and future research should investigate ways to quantify and mitigate their negative impact. © 2023 K.H. Yip et al.

关键词： Physical properties

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Miniaturized Biomedical Wearable Patch Antenna design with Low SAR Content

Miniaturized Biomedical Wearable Patch Antenna design with L...

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Advances in Electrical and Communication Technologies (ICAECOT), International Conference on

作者： Seifeddine Maouni Aissa Salhi Issa Elfergani Chemseddine Zebiri Jonathan Rodriguez Felipe Gil-Castiñeira Younes Alfitorey Mousa Atul Varshney EVOTEL Informatica Lugo Spain DocTIC Escola de Enxeñaría de Telecomunicación Universidade de Vigo Vigo España Instituto de Telecomunicações Campus Universitário de Santiago Aveiro Portugal Dept. of Electronic Ferhat LEPCI Laboratory Abbas University Setif Algeria Faculty of Computing Engineering and Science University of South Wales Pontypridd UK AtlanTTic Research Centre for Telecommunications Technologies University of Vigo Vigo Spain Technical University of Kosice Letna 1/9 Košice Slovakia AECE Department FET Gurukula Kangri (Deemed to be University) Haridwar Uttarakhand India

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

This paper presents the design and simulation of a compact textile antenna for biomedical and wearable applications. With dimensions of $15 \times 15 \times 2.06 \mathrm{~mm}^{\mathbf{3}}\left(\mathbf{0. 1 2} \lambda_{0} \times\right.$ $0.12 \lambda_{0} \times 0.01 \lambda_{0}$), where $\lambda_{0}$ is the free space wavelength at 5.8 GHz), the antenna operates at 5 GHz and is suitable for WLAN, WBAN, and ISM band uses. Made from felt and Zelt, the antenna delivers acceptable performance despite its small size, featuring an omnidirectional radiation pattern and effective integration with a human body model. The specific absorption rate (SAR) meets (FCC) limits, and simulations in both free space and a tissue phantom show good agreement. This work advances textile antenna technology.

关键词： Wireless communication Wireless LAN Patch antennas Phantoms Body area networks Specific absorption rate Wearable antennas Textile antennas Textiles Antenna radiation patterns

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Retraction notice to "The investigation of energy management and atomic interaction between coronavirus structure in the vicinity of aqueous environment of H2O molecules via molecular dynamics approach" [J. Mol. Liq. 341 (2021) 117430] (Journal of Molecular Liquids (2021) 341, (S0167732221021541), (10.1016/***.2021.117430))

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Journal of Molecular Liquids 2025年 428卷

作者： Guo, Hui-Hui Bajuri, Mohd Yazid Alrabaiah, Hussam Muhammad, Taseer Mohammad Sajadi, S. Ghaemi, Ferial Baleanu, Dumitru Karimipour, Arash Zhejiang Provincial Key Laboratory of Media Biology and Pathogenic Control Central Laboratory The First Affiliated Hospital of Huzhou University Zhejiang Huzhou313000 China Kuala Lumpur Malaysia College of Engineering Al Ain University Al Ain United Arab Emirates Department of Mathematics College of Science Tafila Technical University Tafila Jordan Department of Mathematics College of Sciences King Khalid University Abha61413 Saudi Arabia Department of Nutrition Cihan University-Erbil Kurdistan Region Iraq Department of Phytochemistry SRC Soran University KRG Iraq Selangor Bangi43600 Malaysia Department of Mathematics Faculty of Arts and Sciences Cankaya University Ankara Turkey Institute of Space Sciences Magurele Romania Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Department of Mechanical Engineering Sapienza University of Rome Rome Italy

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/about/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. Post-publication, the editor discovered suspicious changes in authorship between the original submission and the revised version of this paper. During revision one author was removed. The author Hussam Rabai'ah was added to the revised paper without explanation and without the exceptional approval by the journal editor, which is contrary to the journal policy on changes to authorship. In addition, there are serious concerns about images, specifically: Figures 2,3, 5 and 6 and the relevancy of some of the citations. The editor reached out to the authors for an explanation, but they failed to provide a satisfactory explanation to these issues. Post-publication, an investigation conducted on behalf of the journal by Elsevier's Research Integrity & Publishing Ethics team also discovered that acceptance of this article was solely based upon the positive advice of a reviewer who was closely linked to one of the authors, Arash Karimipour. This compromised the editorial process and breached the journal's policies. The Ethics team has also determined that the authors were requested by one of the reviewers to insert redundant references to their papers during the peer-review process. The investigation also discovered suspicious email addresses used by the authors during submission that were not associated with legitimate researcher accounts. Overall, the editor has determined that the authorship and the findings of the article cannot be relied upon, and has decided to retract the article. The authors disagree with the retraction of the article and dispute the grounds for it. © 2025 Elsevier B.V.

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Thresholding-based approach for segmentation of melanocytic skin lesion in dermoscopic images

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AIP Conference Proceedings 2024年第1期2991卷

作者： Dilovan Asaad Zebari Yusliza Yusoff Adel Al-Zebari Habibollah Haron Nechirvan Asaad Zebari Department of Computer Science College of Science Nawroz University Duhok 42001 Kurdistan Region Iraq Faculty of Computing Universiti Teknologi Malaysia Johor Bahru 81310 Johor Malaysia Department of Information Technology Technical College of Informatics Akre Duhok Polytechnic University Duhok 42001 Kurdistan Region Iraq Department of Computer Engineering Harran University 63300 Sanliurafa Turkey

The early and effective identification of skin lesion relies heavily on skin lesion segmentation. In contrast, automatic lesion segmentation in dermoscopic images is a difficult task due to several issues such as artifacts (hairs, gel bubbles, and ruler markers), indistinct boundaries, low contrast, and differences in sizes and lesion shapes. Nevertheless, this task can be accomplished. This work attempts to segment skin lesions from normal lesions based on the thresholding technique. The proposed work improved the segmented lesion by removing unwanted objects from an image using morphological operation. This work can effectively segment the lesion region from the normal skin. The segmentation accuracy provided by the system is 91.21%, and comparative analysis has been carried out with thresholding techniques. Based on the findings of the investigation, it has been determined that this work can segment lesions effectively.

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Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies

arXiv

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arXiv 2024年

作者： Huang, Jiahao Wu, Yinzhe Wang, Fanwen Fang, Yingying Nan, Yang Alkan, Cagan Abraham, Daniel Liao, Congyu Xu, Lei Gao, Zhifan Wu, Weiwen Zhu, Lei Chen, Zhaolin Lally, Peter Bangerter, Neal Setsompop, Kawin Guo, Yike Rueckert, Daniel Wang, Ge Yang, Guang Department of Bioengineering Faculty of Engineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Imperial-X and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Department of Electrical Engineering Stanford University CA94305 United States Department of Radiology Beijing Anzhen Hospital Capital Medical University 2nd Anzhen Road Chaoyang District Beijing100029 China School of Biomedical Engineering Sun Yat-sen University Guangdong Guangzhou510006 China Hong Kong University of Science and Technology Hongkong Hong Kong Department of Data Science and AI Faculty of Information Technology Monash University Wellington Rd ClaytonVIC3800 Australia Electrical and Computer Engineering Department Boise State University ID83725 United States Department of Radiology Stanford University CA94305 United States Department of Computing Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Technical University of Munich Munich80333 Germany New York United States

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction. © 2024, CC BY.

关键词： Magnetic resonance imaging

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Progress on Feynman Integrals for 2 → 3 scattering at NNLO 38

Progress on Feynman Integrals for 2 → 3 scattering at NNLO

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38th Conference on Recent Developments in High Energy Physics and Cosmology, HEP 2021

作者： Canko, D.D. Kardos, A. Papadopoulos, C.G. Smirnov, A.V. Syrrakos, N. Wever, C. Institute of Nuclear and Particle Physics NCSR ‘Demokritos’ Agia Paraskevi15310 Greece Department of Physics University of Athens Zographou15784 Greece Physics Division National Technical University of Athens Athens15780 Greece University of Debrecen Faculty of Science and Technology Department of Experimental Physics PO Box 105 Debrecen4010 Hungary Research Computing Center Moscow State University Moscow119991 Russia Moscow Center for Fundamental and Applied Mathematics Moscow119992 Russia Physik-Department T31 Technische Universitat Munchen James-Franck Strasse 1 GarchingD-85748 Germany

We present recent results on two-loop five-point Feynman Integrals with one off-shell leg that are relevant to the study of many 2 → 3 scattering processes of interest at the LHC, especially for the leading-colour W + 2 jets production. The calculation is based on the Simplified Differential Equations approach. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Differential equations

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Revisiting and modeling power-law distributions in empirical outage data of power systems

arXiv

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arXiv 2023年

作者： Hartmann, Bálint Deng, Shengfeng Ódor, Géza Kelling, Jeffrey Institute of Energy Security and Environmental Safety Center for Energy Research P.O. Box 49 BudapestH-1525 Hungary Institute of Technical Physics and Materials Science Center for Energy Research P.O. Box 49 BudapestH-1525 Hungary Faculty of Natural Sciences Chemnitz University of Technology Straße der Nationen 62 Chemnitz09111 Germany Department of Information Services and Computing Helmholtz-Zentrum Dresden-Rossendorf P.O.Box 51 01 19 Dresden01314 Germany

The size distribution of planned and forced outages and following restoration times in power systems have been studied for almost two decades and has drawn great interest as they display heavy tails. Understanding of this phenomenon has been done by various threshold models, which are self-tuned at their critical points, but as many papers pointed out, explanations are intuitive, and more empirical data is needed to support hypotheses. In this paper, the authors analyze outage data collected from various public sources to calculate the outage energy and outage duration exponents of possible power-law fits. Temporal thresholds are applied to identify crossovers from initial short-time behavior to power-law tails. We revisit and add to the possible explanations of the uniformness of these exponents. By performing power spectral analyses on the outage event time series and the outage duration time series, it is found that, on the one hand, while being overwhelmed by white noise, outage events show traits of self-organized criticality (SOC), which may be modeled by a crossover from random percolation to directed percolation branching process with dissipation, coupled to a conserved density. On the other hand, in responses to outages, the heavy tails in outage duration distributions could be a consequence of the highly optimized tolerance (HOT) mechanism, based on the optimized allocation of maintenance resources. Copyright © 2023, The Authors. All rights reserved.

关键词： Spectrum analysis

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Initial Study on Implementation of Smoothing Filters for the Purpose of Bioimpedance Spectroscopy Parameters Classification

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IFAC-PapersOnLine 2022年第4期55卷 393-398页

作者： Aleksandra Kawala-Sterniuk Amir F. Al-Bakri Mariusz Pelc Katarzyna Cichoń Wojciech Chlewicki Stepan Ozana Radek Martinek Jakub Możaryn Volodymyr Khoma Halyna Kenyo Edward Jacek Gorzelańczyk Michał Podpora Jarosław Zygarlicki Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics 45-758 Opole Poland Department of Biomedical Engineering College of Engineering University of Babylon 51001 Babylon Iraq University of Greenwich School of Computing and Mathematical Sciences SE10 9LS London UK West Pomeranian University of Technology in Szczecin Faculty of Electrical Engineering 70-313 Szczecin Poland Faculty of Mechatronics Warsaw University of Technology 02-525 Warsaw Poland Lviv Polytechnic National University Institute of Computer Technologies Automation and Metrology Lviv Ukraine Institute of Philosophy Kazimierz Wielki University 85-092 Bydgoszcz Poland Faculty of Mathematics and Computer Science Adam Mickiewicz University in Poznań 61-614 Poznań Poland

Bioimpedance is a commonly used method for various conditions monitoring. In this paper, the authors carried out some research where they implemented various smoothing filters to enable identification of the bioimpedance spectroscopy parameters. The proposed filtering methods may also be used for application on embedded systems, which have smaller computing power but have become recently very popular. The obtained results with the implementation of smoothing filters were promising, however, some of the obtained results were unsatisfactory. This work also contains a brief introduction to bioimpedance spectroscopy and smoothing filters.

关键词： Bioimpedance Spectroscopy smoothing-filtering Savitzky-Golay Filter Median Filtering Moving-Average Filters

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