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Pain-Evoked Functional Activation Combining Transcranial Direct Current Stimulation with Brain Imaging in Older Adults with Alzheimer’s Disease and Related Dementias

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Brain Stimulation 2025年第1期18卷 393-394页

作者： Park, Juyoung Montero-Hernandez, Samuel Huff, Allison Lee, Chiyoung Pollonini, Luca Park, Lindsey Lin, Lifeng Galvin, James Ahn, Hyochol The University of Arizona College of Nursing USA University of Birmingham UK The University of Arizona College of Medicine Tucson USA University of Houston Department of Electrical and Computer Engineering USA University of Houston Department of Biomedical Engineering USA The University of Arizona Mel and Enid Zuckerman College of Public Health USA University of Miami Miller School of Medicine USA

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Hazard Resistance-Based Spatiotemporal Risk Analysis for Distribution Network Outages During Hurricanes

arXiv

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arXiv 2024年

作者： Xu, Luo Lin, Ning Xi, Dazhi Feng, Kairui Poor, H. Vincent Department of Civil and Environmental Engineering Princeton University PrincetonNJ08544 United States Center for Policy Research on Energy and the Environment Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Blackouts in recent decades show an increasing prevalence of power outages due to extreme weather events such as hurricanes. Precisely assessing the spatiotemporal outages in distribution networks, the most vulnerable part of power systems, is critical to enhance power system resilience. The Sequential Monte Carlo (SMC) simulation method is widely used for spatiotemporal risk analysis of power systems during extreme weather hazards. However, it is found here that the SMC method can lead to large errors by directly applying the fragility function or failure probability of system components in time-sequential analysis, particularly overestimating damages under evolving hazards with high-frequency sampling. To address this issue, a novel hazard resistance-based spatiotemporal risk analysis (HRSRA) method is proposed. This method converts the time-varying failure probability of a component into a hazard resistance as a time-invariant value during the simulation of evolving hazards. The proposed HRSRA provides an adaptive framework for incorporating high-spatiotemporal-resolution meteorology models into power outage simulations. By leveraging the geographic information system data of the power system and a physics-based hurricane wind field model, the superiority of the proposed method is validated using real-world time-series power outage data from Puerto Rico during Hurricane Fiona 2022. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk analysis

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Optimal Restoration of a Power Distribution System During Extreme Events Considering Load Criticality

Optimal Restoration of a Power Distribution System During Ex...

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Annual North-American Power Symposium

作者： Adithya Melagoda Arun-Kaarthick Manoharan Mohd Abrar Rahman Visvakumar Aravinthan Al Tamimi Department of Electrical & Computer Engineering Wichita State University Wichita KS USA Transmission Planning & Policy Sunflower Electric Power Corporation Wichita KS USA

Recent natural and man-made disasters leading to sustained power outage has urged the system planners and policy makers for improving grid resiliency. This requires innovation across diverse facets ranging from better anticipation of such events to post event response. This work focuses on the problem of supporting the critical loads when the system is undergoing such an event. As a preliminary step towards the big picture, this work aims to develop a load criticality-based model to operable microgrid formation. A new firefly search inspired metaheuristic algorithm is developed in this work for microgrid formation with the objective of maximizing the critical load restoration with minimal switching operation. Numerical analysis is conducted on a modified Iowa state 240-bus distribution system and the results show the scalability and critical load restoration capabilities of the proposed algorithm.

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Constrained Regularization by Denoising with Automatic Parameter Selection

arXiv

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arXiv 2023年

作者： Cascarano, Pasquale Benfenati, Alessandro Kamilov, Ulugbek S. Xu, Xiaojian the Department of the Arts University of Bologna Bologna40123 Italy the Department of Environmental Science and Policy University of Milan Milan20133 Italy the Department of Computer Science & Engineering Department of Electrical & System Engineering Washington University in St. Louis St. LouisMO63130 United States the Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

Regularization by Denoising (RED) is a well-known method for solving image restoration problems by using learned image denoisers as priors. Since the regularization parameter in the traditional RED does not have any physical interpretation, it does not provide an approach for automatic parameter selection. This letter addresses this issue by introducing the Constrained Regularization by Denoising (CRED) method that reformulates RED as a constrained optimization problem where the regularization parameter corresponds directly to the amount of noise in the measurements. The solution to the constrained problem is solved by designing an efficient method based on alternating direction method of multipliers (ADMM). Our experiments show that CRED outperforms the competing methods in terms of stability and robustness, while also achieving competitive performances in terms of image quality. © 2023, CC BY.

关键词： Image reconstruction

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Wireless,multimodal sensors for continuous measurement of pressure,temperature,and hydration of patients in wheelchair

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npj Flexible Electronics 2023年第1期7卷 479-493页

作者： Seokjoo Cho Hyeonseok Han Hyunwoo Park Sung-Uk Lee Jae-Hwan Kim Sung Woo Jeon Mengqiu Wang Raudel Avila Zhaoqian Xi Kabseok Ko Minsu Park Jungyup Lee Myungwoo Choi Je-Sang Lee Weon Gi Min Byeong-Ju Lee Soyeong Lee Jungrak Choi Jimin Gu Jaeho Park Min Seong Kim Junseong Ahn Osman Gul Chankyu Han Gihun Lee Seunghwan Kim Kyuyoung Kim Jeonghyun Kim Chang-Mo Kang Jahyun Koo Sung Soo Kwak Sungbong Kim Dong Yun Choi Seokwoo Jeon Hyung Jin Sung Yong Bae Park Minkyu Je Young Tae Cho Yong Suk Oh Inkyu Park Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Advanced 3D Printing Technology Development Division Korea Atomic Energy Research InstituteDaejeon 34057Republic of Korea Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian 116023P.R.China Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA DUT-BSU Joint Institute Dalian University of TechnologyDalian 116023China Ningbo Institute of Dalian University of Technology Ningbo 315016China Department of Electronics Engineering Kangwon National UniversityChuncheon 24341Republic of Korea Querrey-Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Sibel Health Inc NilesIL 60714USA Department of Materials Science and Engineering KAIST Institute for The Nanocentury(KINC)Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of planning and development Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of rehabilitation medicine Pusan national university hospitalBusan 49241Republic of Korea Department of AI Convergence Network Ajou UniversitySuwon 16499Republic of Korea Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897Republic of Korea Korea Photonics Technology Institute Gwangju 61007Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02841Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02841Republic of Korea Center

Individuals who are unable to walk independently spend most of the day in a *** population is at high risk for developing pressure injuries caused by ***,early diagnosis and prevention of these injuries still remain ***,we introduce battery-free,wireless,multimodal sensors and a movable system for continuous measurement of pressure,temperature,and hydration at skin *** device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity,a temperature sensor for measuring skin temperature,and a galvanic skin response sensor for measuring skin hydration *** movable system enables power harvesting,and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body *** evaluations and numerical simulations of the devices,together with clinical trials for wheelchair patients,demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

关键词： injuries modal mounted

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Network-Based Epidemic Control Through Optimal Travel and Quarantine Management

arXiv

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arXiv 2024年

作者： Talaei, Mahtab Rikos, Apostolos I. Olshevsky, Alex White, Laura F. Paschalidis, Ioannis Ch. The Department of Electrical and Computer Engineering the Division of Systems Engineering Boston University BostonMA United States I.Ch. Pascha The Department of Biomedical Engineering the Faculty for Computing & Data Sciences Boston University BostonMA United States The School of Public Health Boston University BostonMA United States

Motivated by the swift global transmission of infectious diseases, we present a comprehensive framework for network-based epidemic control. Our aim is to curb epidemics using two different approaches. In the first approach, we introduce an optimization strategy that optimally reduces travel rates. We analyze the convergence of this strategy and show that it hinges on the network structure to minimize infection spread. In the second approach, we expand the classic SIR model by incorporating and optimizing quarantined states to strategically contain the epidemic. We show that this problem reduces to the problem of matrix balancing. We establish a link between optimization constraints and the epidemic’s reproduction number, highlighting the relationship between network structure and disease dynamics. We demonstrate that applying augmented primal-dual gradient dynamics to the optimal quarantine problem ensures exponential convergence to the KKT point. We conclude by validating our approaches using simulation studies that leverage public data from counties in the state of Massachusetts. © 2024, CC BY.

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Cold ironing techno-economical study for a port with photovoltaic plants

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e-Prime - Advances in electrical engineering, Electronics and Energy 2024年 8卷

作者： Lamprinidi, Eleni P. Tsekouras, George J. Orfanos-Pepainas, Stamatios Kanellos, Fotios D. Prousalidis, John M. Vlachos, Andreas Tsirekis, Constantinos D. School of Electrical & Computer Engineering National Technical University of Athens Greece Department of Electrical and Electronics Engineering Faculty of Engineering University of West Attica Greece School of Electrical & Computer Engineering Technical University of Crete Greece School of Naval Architecture & Marine Engineering National Technical University of Athens Greece Department of Long Term Energy Planning Renewable Energy Sources & Environmental Policy Regulatory Authority of Energy Greece Strategy & System Planning Department Hellenic Independent Power Transmission Operator Greece

According to European regulation 2023/1804/EU, all member states shall ensure the availability of cold ironing (CI) in large ports for at least 90 % of the total number of port calls of seagoing ships above 5000 gross tonnes, by the end of 2029. This is a measure expected to improve air quality, especially in the vicinity of the port's urban environment. Preliminary studies show that there are financial viability issues related to CI installations, under the current electricity pricing policies and the high investment cost for CI infrastructure. In this paper, the financial viability of the penetration of photovoltaic (PV) units optimized from spatial and power produced point of view is investigated. The proposed method is applied to various ship types, indicatively for the Thessaloniki Port, taking into consideration the connection with the Medium Voltage (MV) electricity distribution grid, as well as the construction, operation, and maintenance costs, under modified economic criterion of benefit-cost ratio (BCR). © 2024 The Authors

关键词： Cold ironing Feasibility study Photovoltaic plant Shore connection

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Performance Comparison of Deep Learning Algorithms in Twitter Bot Detection

Performance Comparison of Deep Learning Algorithms in Twitte...

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Recent Trends in Information Technology (CSCTIT), College of Science International Conference of

作者： Samer Emad Neama Al-ibadi Mesut Cevik Department of Electrical and Computer Engineering Altinbas University Istanbul Turkey Ministry of Communications Information & Telecom Public Company Baghdad Iraq Electrical and Computer Engineering Department Altinbas University Istanbul Turkey Istanbul Turkey

Difficulty in detecting social media bots - software-controlled accounts that seem as human - has serious consequences. Bots have been used to affect the financial market, political elections, and spreading conspiracy theories. Most approaches detect bots at the account level by studying social media posts and using network structure, system dynamics, content analytics, etc. In this study, we compare the performance of the recurrent neural network (RNN), convolutional neural network (CNN), and feed forward neural network (FNN) in detecting Twitter bots by evaluating the textual content of 10,416,240 tweets from humans and bots. Deep learning models utilize sample data to gauge performance. The CNN model identified recent social bot samples most accurately (97.67 %). In comparison, the RNN model achieved great accuracy (97.04 %) with a longer training time. RNN and CNN algorithms perform well when compared to other deep learning classifiers.

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A Road Map of Prompt engineering for ChatGPT in Healthcare: A Perspective Study

A Road Map of Prompt Engineering for ChatGPT in Healthcare: ...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Abhari, Shahabeddin Fatahi, Somayeh Saragadam, Ashish Chumachenko, Dmytro Pelegrini Morita, Plinio School of Public Health Sciences University of Waterloo WaterlooON Canada Department of Computer Science University of Saskatchewan SaskatoonSK Canada Department of Mathematical Modelling and Artificial Intelligence National Aerospace University "Kharkiv Aviation Institute" Kharkiv Ukraine Department of Systems Design Engineering University of Waterloo WaterlooON Canada Research Institute for Aging University of Waterloo WaterlooON Canada Centre for Digital Therapeutics Techna Institute University Health Network TorontoON Canada Institute of Health Policy Management and Evaluation Dalla Lana School of Public Health University of Toronto TorontoON Canada

ISBN: (纸本)9781643685335

Generative AI models, such as ChatGPT, have significantly impacted healthcare through the strategic use of prompts to enhance precision, relevance, and ethical standards. This perspective explores the application of prompt engineering to tailor outputs specifically for healthcare stakeholders: patients, providers, policymakers, and researchers. A nine-stage process for prompt engineering in healthcare is proposed, encompassing identifying applications, understanding stakeholder needs, designing tailored prompts, iterative testing and refinement, ethical considerations, collaborative feedback, documentation, training, and continuous updates. A literature review focused on "Generative AI" or "ChatGPT," prompts, and healthcare informed this study, identifying key prompts through qualitative analysis and expert input. This systematic approach ensures that AI-generated prompts align with stakeholder requirements, offering valuable insights into symptoms, treatments, and prevention, thereby supporting informed decision-making among patients. © 2024 The Authors.

关键词： Decision making

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Recursive Feature Elimination with Cross Validation for Alzheimer's Disease Classification using Cognitive Exam Scores 1

Recursive Feature Elimination with Cross Validation for Alzh...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Freytes, Christian Yaphet Perry Mayrand, Robin Sawada, Luana Okino Yan Liang, Thony Curiel Cid, Rosie E. Burke, Shanna Loewenstein, David Duara, Ranjan Adjouadi, Malek Florida International University Electrical & Computer Engineering MiamiFL United States Florida International University Computing & Information Sciences MiamiFL United States University of Miami Department of Neuropsychology MiamiFL United States Florida International University Public Health and Social Work Miami United States University of Miami Cognitive Neurosciences and Aging MiamiFL United States Mt Sinai Medical Center Wien Center for AD & MD MiamiFL United States Florida International University Electrical & Computer Engineering Miami United States

ISBN: (纸本)9798350335569

Prodromal detection of Alzheimer's Disease(AD) is a substantial challenge in the research community. Among the tools used in AD diagnosis, cognitive exams are standard in most procedures. However, the barrage of cognitive examinations is both time and resource consuming. With the use of Machine Learning, Feature Elimination (FE) can be combined with classification algorithms to determine which cognitive exams are best suited for diagnosis. Using the results of FE, it can be determined if subsections of different composite scores can be combined to create a new enhanced and exhaustive exam. This paper implements a Recursive Feature Elimination with Cross Validation (RFECV) machine learning algorithm to determine which cognitive exams perform best for AD classification tasks. Out of 119 features, an average of 16 features were selected as optimal. These optimal features average 75% Accuracy, 70% Precision, and 75% Recall and an F1 Weighted score of 71% in classification. © 2023 IEEE.

关键词： Decision trees

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