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Outage-Aware Online Prediction Control for Securing UAV-Aided Communication

IEEE Transactions on Vehicular Technology 2025年

作者： Sheng, Zhichao Fu, Huabo Huang, Zhiyu Nasir, Ali A. Wu, Qingqing Zeng, Dan Shanghai University Shanghai Institute for Advanced Communication and Data Science Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai200444 China Department of Electrical Engineering and Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Shanghai Jiao Tong University Department of Electronic Engineering Shanghai201210 China

Considering an unmanned aerial vehicle (UAV)-aided multi-user system with multiple mobile eavesdroppers, this paper aims to develop an outage-aware online control strategy to counter the imperfect information of mobile user equipments (UEs) and mobile eavesdroppers. To perform online control effectively, a novel online prediction model is proposed, which models the trajectory of UEs as a stochastic Gauss-Markov mobility model and the trajectory of eavesdroppers as a worst-case model based on the current imperfect location information. The online control problem is formulated as a new multi-stage optimization framework, in which we perform online control by solving per-slot prediction problems and a resource allocation problem for the initial point. In the per-slot prediction problem, we jointly optimize the predictive trajectory and transmit power for the upcoming time slot (TS) by maximizing the minimum achievable secrecy rate (ASR) among all UEs. For resource allocation at the initial TS, we optimize transmit power by maximizing the minimum ASR among all UEs. The per-slot prediction problems and the resources allocation problem are all non-convex problems hence they are intractable in terms of computational complexity. To address them, we propose an efficient inner-approximation methods, by applying successive convex approximation (SCA) methods and S-procedure as well as Bernstein -type inequality. Numerical results demonstrate the superiority of our proposed online control scheme over the conventional benchmark schemes which include existing offline schemes and online scheme without outage based prediction as well as shortest-path navigation scheme. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Smart and Resilient Microgrid for EV Mobility and Commercial Applications with Demand Response

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Transportation Research Procedia 2025年 84卷 362-369页

作者： Mohamed R. Elkadeem Kotb M. Kotb Atif S. Alzahrani Mohammad A. Abido Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) KFUPM Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Hydrogen Technologies and Carbon Management (IRC-HTCM) KFUPM Dhahran 31261 Saudi Arabia Materials Science and Engineering Department KFUPM Dhahran 31261 Saudi Arabia Department of Electrical Engineering KFUPM Dhahran 31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Dhahran 31261 Saudi Arabia

Worldwide, microgrids (MGs) become crucial in enhancing the reliability and cost-effectiveness of energy supply, particularly during significant circumstances in utility grid. Despite their potential, the literature has not extensively explored the resilience benefits of MGs, especially in terms of net energy metering (NEM), demand response (DR), outages, and consequent economic returns. This study fills this gap by optimizing and simulating a smart, resilient solar PV/battery MG tailored for office facilities, with an integration of electric vehicles (EVs) to support e-mobility applications. The impact of NEM and DR on the MG’s performance is assessed, demonstrating that the proposed system can meet the energy demands of both office buildings and EVs during various conditions including normal days, weekends, peak demand periods, outages, and DR events, all while achieving best techno-envrio-economic performance. The annual revenue attained with DR and NEM smart grid mechanism has reduced the energy cost by 6.25% and annual electricity charges by 6.43% compared to the based case without DR and NEM. Also, this proposed MG system cuts the annual greenhouse gas emissions by 4%. The MG effectively utilizes 93.6% of the generated PV electricity on-site, exporting the excess to the grid, which highlights the role of NEM in minimizing power curtailment and enhancing efficiency. In our opinion, the alignment of this study with the objectives of Saudi Vision 2030 in promoting sustainable power and transportation solutions and enhancing energy system resilience highlights the pivotal role of MGs in shaping the future energy landscape.

关键词： Electric vehicles resiliency demand response microgrid renewable energy

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Genomic insights into novel extremotolerant bacteria isolated from the NASA Phoenix mission spacecraft assembly cleanrooms

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Microbiome 2025年第1期13卷 1-27页

作者： Schultz, Júnia Jamil, Tahira Sengupta, Pratyay Sivabalan, Shobhan Karthick Muthamilselvi Rawat, Anamika Patel, Niketan Krishnamurthi, Srinivasan Alam, Intikhab Singh, Nitin K. Raman, Karthik Rosado, Alexandre Soares Venkateswaran, Kasthuri Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal Makkah 23955 Saudi Arabia Department of Biotechnology Bhupat and Jyoti Mehta School of Biosciences Indian Institute of Technology Madras Tamil Nadu Chennai 600036 India Center for Integrative Biology and Systems Medicine (IBSE) Indian Institute of Technology Madras Tamil Nadu Chennai 600036 India Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) Indian Institute of Technology Madras Tamil Nadu Chennai 600036 India Microbial Type Culture Collection and Gene Bank (MTCC) Institute of Microbial Technology Chandigarh 160036 India NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA United States Department of Data Science and AI Wadhwani School of Data Science and AI Indian Institute of Technology Madras Tamil Nadu Chennai 600036 India Bioscience Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Makkah 23955 Saudi Arabia

Background: Human-designed oligotrophic environments, such as cleanrooms, harbor unique microbial communities shaped by selective pressures like temperature, humidity, nutrient availability, cleaning reagents, and radiation. Maintaining the biological cleanliness of NASA’s mission-associated cleanrooms, where spacecraft are assembled and tested, is critical for planetary protection. Even with stringent controls such as regulated airflow, temperature management, and rigorous cleaning, resilient microorganisms can persist in these environments, posing potential risks for space missions. Results: During the Phoenix spacecraft mission, genomes of 215 bacterial isolates were sequenced and based on overall genome-related indices, 53 strains belonging to 26 novel species were recognized. Metagenome mapping indicated less than 0.1% of the reads associated with novel species, suggesting their rarity. Genes responsible for biofilm formation, such as BolA (COG0271) and CvpA (COG1286), were predominantly found in proteobacterial members but were absent in other non-spore-forming and spore-forming species. YqgA (COG1811) was detected in most spore-forming members but was absent in Paenibacillus and non-spore-forming species. Cell fate regulators, COG1774 (YaaT), COG3679 (YlbF, YheA/YmcA), and COG4550 (YmcA, YheA/YmcA), controlling sporulation, competence, and biofilm development processes, were observed in all spore-formers but were missing in non-spore-forming species. COG analyses further revealed resistance-conferring proteins in all spore-formers (n = 13 species) and eight actinobacterial species, responsible for enhanced membrane transport and signaling under radiation (COG3253), transcription regulation under radiation stress (COG1108), and DNA repair and stress responses (COG2318). Additional functional analysis revealed that Agrococcus phoenicis, Microbacterium canaveralium, and Microbacterium jpeli contained biosynthetic gene clusters (BGCs) for ε-poly-L-lysine, benefic

关键词： BGCs Cleanroom Extreme environment Novel species Phoenix mission Phylogenomics

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An effective method for quantification,visualization,and analysis of 3D cell shape during early embryogenesis

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Quantitative Biology 2025年第1期13卷 113-126页

作者： Zelin Li Zhaoke Huang Jianfeng Cao Guoye Guan Zhongying Zhao Hong Yan Department of Electrical Engineering City University of Hong KongHong KongChina Centre for Intelligent Multidimensional Data Analysis Limited Hong KongChina Center for Quantitative Biology Peking UniversityBeijingBeijingChina Department of Systems Biology Harvard Medical SchoolBostonMassachusettsUSA Department of Data Science Dana-Farber Cancer InstituteBostonMassachusettsUSA Department of Biology Hong Kong Baptist UniversityHong KongChina State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist UniversityHong KongChina

Embryogenesis is the most basic process in developmental *** and simply quantifying cell shape is challenging for the complex and dynamic 3D embryonic *** descriptors such as volume,surface area,and mean curvature often fall short,providing only a global view and lacking in local detail and reconstruction *** this,we introduce an effective integrated method,3D Cell Shape Quantification(3DCSQ),for transforming digitized 3D cell shapes into analytical feature vectors,named eigengrid(proposed grid descriptor like eigen value),eigenharmonic,and *** uniquely combine spherical grids,spherical harmonics,and principal component analysis for cell shape *** demonstrate 3DCSQ’s effectiveness in recognizing cellular morphological phenotypes and clustering *** to Caenorhabditis elegans embryos of 29 living embryos from 4-to 350-cell stages,3DCSQ identifies and quantifies biologically reproducible cellular patterns including distinct skin cell *** also provide automatically cell shape lineaging analysis *** method not only systematizes cell shape description and evaluation but also monitors cell differentiation through shape changes,presenting an advancement in biological imaging and analysis.

关键词： Caenorhabditis elegans(C.elegans) cell shape quantification eigen features(eigengrid eigenharmonic&eigenspectrum) lineage analysis morphological reproducibility spherical harmonics(SPHARM)

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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Fault-tolerant Mobile Service Offloading in Mobile Edge Computing

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IEEE Transactions on Automation science and engineering 2025年

作者： Long, Tingyan Xia, Yunni Zhou, Mengchu Li, Jianqi Ma, Yong Ai-Turki, Yusuf Guizhou University School of Computer Science and Technology Guiyang550025 China Chongqing University College of Computer Science Chongqing400030 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Global energy interconnection research institute Co. Ltd Beijing102209 China Jiangxi Normal University School of Computer and Information Engineering Nanchang China King Abdulaziz University K. A. Care Energy Research and Innovation Center The Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia

Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods. © 2004-2012 IEEE.

关键词： Mobile applications

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Development of Flexible Sendust Composite Sheets with Enhanced Magnetic Permeability for Microwave Absorbers and Antennas

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IEEE Transactions on Magnetics 2025年

作者： Whalen, Matthew P. Plouff, David T. Shevchenko, Nicholas B. Mirotznik, Mark Xiao, John Q. University of Delaware Quantum Science and Engineering Program NewarkDE19716 United States University of Delaware Department of Physics and Astronomy NewarkDE19716 United States University of Delaware Center for Composite Materials NewarkDE19716 United States University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

The demand for high-frequency soft magnetic composites with elevated permeability and flexibility is growing for applications in microwave absorbers and magnetodielectric antennas. However, balancing high permeability with mechanical flexibility remains challenging. This study presents flexible Sendust (FeSiAl) composite sheets, fabricated with a hot-pressing technique that aligns flakes within an acrylic rubber and epoxy resin matrix. The alignment significantly enhances relative permeability at high frequencies, achieving single-layer film properties of μr' = 14.8 and μr"= 20.4 at 1 GHz, alongside high saturation magnetization (118 eμg) and high hesitivity (44.7 kΩ m-1), outperforming commonly used materials such as NiZn ferrite. Our method supports customizable thickness without sacrificing permeability, establishing a versatile platform for advanced conformal microwave absorber and antenna applications. © 2025 IEEE.

关键词： Saturation magnetization

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Erosion Suppression of Zinc Borate Filler in HTV Silicone Rubber under AC Dry-Band Arcing

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IEEE Transactions on Dielectrics and electrical Insulation 2025年

作者： Ozdemir, Idris Uckol, Halil Ibrahim Ilhan, Suat Hadjadj, Yazid Soykan, Gurkan Aydogan, Abdullah Ghunem, Refat Istanbul Technical University Electrical and Electronics Faculty Electrical Engineering Department Maslak Istanbul34469 Turkey National Research Council Canada Metrology Research Center ON OttawaK1A 0R6 Canada Bahçeşehir University Faculty of Engineering and Natural Sciences Energy Systems Engineering Department Besiktas Istanbul Turkey Istanbul Technical University Faculty of Science and Letters Department of Chemistry Maslak Istanbul34469 Turkey

This paper investigates the erosion suppression mechanisms of Zinc Borate in high-temperature vulcanized silicone rubber using the IEC 60587 inclined plane test and simultaneous thermogravimetric-differential thermal analysis. Alumina tri-hydrate is employed in this study as a reference filler for comparison with Zinc Borate filler. The dehydration of Zinc Borate is reported to start around 350°C, whereas alumina tri-hydrate starts dehydration at lower temperatures around 230°C. An insignificant difference is shown in the erosion resistance between the alumina tri-hydrate and Zinc Borate-filled composites. Both fillers are shown viable in preventing the tracking and erosion failure in the IEC 60587 inclined plane test under the critical 4.5kV ACrms voltage. Zinc Borate is found to suppress failure with the formation of residue acting as a shield against the progression of erosion. Whereas, alumina tri-hydrate alleviates surface temperature by promoting an internal oxidation mechanism that suppresses combustion of silicone rubber. This study's findings highlight the potential application of Zinc Borate as a cost-effective filler in high-temperature vulcanized silicone rubber for outdoor insulation, particularly in regions where this filler is readily available. © 1994-2012 IEEE.

关键词： Differential thermal analysis

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Stabilizing a Class of Periodical Time-Delay Milling systems by Adaptive Active Control Method

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IEEE Transactions on Industrial Informatics 2025年第6期21卷 4457-4465页

作者： Wu, Yue Zhang, Hai-Tao Xu, Bowen Ren, Gui-Ping Shi, Yang Chen, Guanrong Beijing Forestry University Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation School of Technology State Key Laboratory of Efficient Production of Forest Resources Beijing100083 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation The Institute of Artificial Intelligence The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Dongguan523808 China Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization Dongguan523808 China Northwestern Polytechnical University School of Artificial Intelligence Optics and Electronics Xi'an710072 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada City University of Hong Kong Department of Electrical Engineering Hong Kong999077 Hong Kong

Periodical time-delay scenario is often encountered in industrial manufacturing processes. However, the presence of time delays and periodical coefficients brings challenges to controller design and system analysis, which thereby hinders the performance improvement of such systems. In this work, the dynamics of milling systems are transformed into a time-invariant finite-dimensional uncertain model described by Fourier series and Padé approximation. An adaptive active control law is accordingly designed to stabilize such complex dynamics. With the assistance of LaSalle-Yoshizawa theorem, conditions are derived to guarantee sufficiently large stability regions of the corresponding closed-loop system. A numerical case study is conducted on a standard two degrees of freedom milling perturbation system to substantiate the superiority of the proposed adaptive active control technique in terms of enlarged stable operational regions. © 2025 IEEE. All rights reserved.

关键词： Milling (machining)

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Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs

Heart Rhythm O2

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Heart Rhythm O2 2025年第5期6卷 678.0-686.0页

作者： Wang, Zhen Li, Mingxiao Xia, Peng Jiang, Chao Shen, Ting Ma, Jiaming Bai, Yu Zhang, Suhui Lai, Yiwei Li, Sitong Xu, Hui Xu, Yang Ma, Tong Ju, Lie He, Liu Dong, Li Sang, Caihua Long, Deyong Chen, Yuzhong Du, Xin Ge, Zongyuan Dong, Jianzeng Wei, Wen-Bin Ma, Changsheng Department of Cardiology Beijing Anzhen Hospital Capital Medical University Beijing China Engineering Research Center of Medical Devices for Cardiovascular Diseases Ministry of Education Capital Medical University Beijing China National Clinical Research Center for Cardiovascular Diseases Beijing China Beijing Airdoc Technology Co. Ltd. Beijing China Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton Australia Yale School of Medicine New Haven Connecticut United States School of Clinical Medicine Peking Union Medical College and Chinese Academy of Medical Science Beijing China Beijing Tongren Eye Center Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology Beijing Tongren Hospital Capital Medical University Beijing China Monash e-Research Center Monash University Clayton Australia

Background: Patients with atrial fibrillation (AF) have a higher risk of cognitive impairment (CI). However, complexity of CI diagnosis and lack of simple screening approaches limited early screening and intervention of CI in AF patients. Objective: Our study aimed to develop deep learning models based on fundus photographs for easy screening of CI in AF patients. Methods: From May 2021 to April 2023, patients who completed fundus examination and cognitive function evaluation in the Chinese Atrial Fibrillation Registry Study were included. The training and validation sets were randomly split at an 8:2 ratio. Participants from the Beijing Eye Study served as the external validation set. Different deep learning models were trained, and their CI detection ability was validated. Results: A total of 899 patients in the Chinese Atrial Fibrillation Registry Study were included. In the validation set, the vision-ensemble model based on fundus images alone had an area under the receiver-operating characteristic curve (AUROC) of 0.855 (95% confidence interval 0.816–0.894) for CI screening. The multimodal model (AUROC 0.861, 95% confidence interval 0.823–0.898), based on fundus photographs and 4 clinical variables, performed comparably to the vision-ensemble model. The AUROC of the vision-ensemble model for CI screening achieved 0.773 (95% confidence interval 0.709–0.837) in the external test set. In the saliency map, the vision-ensemble model focused on areas around retinal vessels and the optic disc. Conclusion: A vision-ensemble model based on fundus images might be practical for preliminary screening of CI in AF patients. © 2025 Heart Rhythm Society

关键词： Artificial intelligence Atrial fibrillation Cognitive impairment Deep learning Fundus photography

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