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2024 IEEE/ACS 21st International Conference on computer Systems and Applications, AICCSA 2024

作者： Frikha, Ghassen Lorca, Xavier Pingaud, Herve Taweel, Adel Bortolaso, Christophe Borgiel, Katarzyna Lamine, Elyes IMT Mines Albi Industrial Engineering Center University of Toulouse Albi France Champollion National University Institute University of Toulouse CNRS-LGC Albi France Birzeit University Department of Computer Science Birzeit Palestine Research and Innovation Division Berger-Levrault Labège France ISIS Champollion National University Institute University of Toulouse Castres France

ISBN: (纸本)9798331518240

The growing elderly population in developed countries highlights the critical need for preventing frailty, which poses significant challenges to health systems due to increased risks of severe health issues. This paper introduces Senselife, a framework that provides explainable service recommendations specifically tailored for frailty prevention. It begins by outlining the medical context and challenges associated with aging, followed by an overview of existing recommender systems with similar objectives. We detail the integration of three key resources-ROR, RNA, and Data Laregion-within the Senselife framework to represent service supply. The paper explains how service supply is structured and the transformation of available data for use within our recommender engine. We introduce the concept of operational activities derived from the ROR and leverage the capabilities of LLMs to incorporate RNA data into Senselife. Additionally, we illustrate how these services are ultimately compiled into recommended service packages. Finally, the paper concludes by summarizing key findings and suggesting potential directions for future research. © 2024 IEEE.

关键词： Electronic health record

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A Mixture Model Parameters Estimation Algorithm for Inter-Contact Times in Internet of Vehicles

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computers, Materials & Continua 2021年第11期69卷 2445-2457页

作者： Cheng Gong Xinzhu Yang Wei Huangfu Qinghua Lu School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Advanced Innovation Center for Materials Genome Engineering School of Computer and Communication EngineeringUniversity of Science and Technology Beijing100083China Shunde Graduate School of University of Science and Technology Beijing Foshan City528300GuangdongChina Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services University of Science and Technology BeijingBeijing100083China Commonwealth Scientific and Industrial Research Organization Canberra2600Australia

Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal *** calculating the expectation of hidden variables in vehicle communication,Expectation Maximization(EM)algorithm solves the maximum likelihood estimation of parameters,and then obtains the mixture model of vehicle communication ***,the EM algorithm requires multiple iterations and each iteration needs to process all the *** its computational complexity is high.A parameter estimation algorithm with low computational complexity based on Bin Count(BC)and Differential Evolution(DE)(PEBCDE)is *** overcomes the disadvantages of the EM algorithm in solving mixture models for big *** order to reduce the computational complexity of the mixture models in the IoV,massive data are divided into relatively few time intervals and then *** to these few counted values,the parameters of the mixture model are obtained by using DE *** modeling and analysis of simulation data and instance data,the PEBCDE algorithm is verified and discussed from two aspects,i.e.,accuracy and *** numerical solution of the probability distribution parameters is obtained,which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.

关键词： Internet of vehicles opportunistic networks inter-contact times mixture model parameters estimation

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Performance Evaluation of District Energy Microgrids Planning Tool for Non-Technical Users

Performance Evaluation of District Energy Microgrids Plannin...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Fatima Ezzahra El Aidos Simon Abongmbo Safae Bourhnane Driss Benhaddou Fan Lei Lennart Johnsson Carlos Gamarra Tianrun Zhang Muhammad Anan Mohamed Riduan Abid Dept. of Computer Science University of Houston Houston TX USA Dept. of Electrical and Computer Engineering University of Houston Houston TX USA School of Science and Engineering Al Akhawayn University in Ifrane Ifrane Morocco Technology Division University of Houston Houston TX USA Houston Advanced Research Center Houston TX USA College of Engineering Al Fayssal University Riyadh Saudi Arabia TSYS School of Computer Science Columbus State University Columbus GA USA

Community Microgrids are increasingly gaining popularity worldwide for their efficiency, cost-effectiveness, and local resilience improvement. Microgrid planning tools play a crucial role in their deployment. In the process, tentative designs of the microgrid are simulated, analyzed, and optimized. Due to the complexity of the problem, planning tools must carefully balance computational efficiency while seeking the most optimal solutions. This paper investigates the impact of algorithm selection on CPU and memory utilization of a Community Microgrid planning tool that is specifically designed for non-technical users. We compare two version of the code with two alternatives for Community Microgrid planning tools: a Greedy Algorithm, and a Linear Programming Approach. This comparison examines scenarios spanning from 2 to 50 buildings. Our findings revealed a significant difference in performance between the two algorithms, underscoring the critical role of algorithm selection in optimizing the efficiency of Community Microgrid Planning Tools.

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Improved Particle Swarm Optimization Algorithm for EIT Image Reconstruction 20

Improved Particle Swarm Optimization Algorithm for EIT Image...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Kahouli, Oumayma Hafsa, Mariem Hellara, Hiba Bennour, Imed Eddine Essoukri Ben Amarax, Najoua Kanoun, Olfa University of Sousse National Engineering School of Sousse Department of Computer Science Sousse4023 Tunisia National Engineering School of Sousse Laboratory of Advanced Technology and Intelligent Systems Tunisia Technische Universität Chemnitz Laboratory of Measurement and Sensor Technology Chemnitz Germany University of Sfax Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Center of Sfax Tunisia

ISBN: (纸本)9798350332568

Electrical Impedance Tomography (EIT) is a clinical imaging technique that gained a lot of attention because it is a non-invasive and radiation-free method. To obtain an inner image of the domain under study, a specific number of electrodes is attached to the surface of the object, then through a pair of electrodes, a low alternative current is injected. Potentials produced by this injection are measured with the remaining electrodes. Based on these boundary voltages measurements, the inner conductivity can be inferred, thus the image is reconstructed. The focus of this paper is to overcome the major weakness of the EIT image reconstruction technique, which is the low spatial resolution of the reconstructed images caused by the non-linearity of EIT inverse problem. This paper considers this problem, an optimization problem that is solved using the Particle Swarm Optimization (PSO) meta-heuristic. A variant of the standard PSO is proposed and validated, first on a circular mesh composed of 686 triangles then on a lung mesh composed of 2707 triangles. © 2023 IEEE.

关键词： Electrodes

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Spatial variations of nutrient and trace metal concentrations in macroalgae across blue carbon habitats of the Saudi Arabian Red Sea

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science of the Total Environment 2024年 956卷 177197页

作者： Cai, Chunzhi Anton, Andrea Duarte, Carlos M. Agusti, Susana Ocean College Zhejiang University Zhoushan316000 China Ocean Research Center of Zhoushan Zhejiang University Zhoushan316021 China Marine Science Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Mediterranean Institute for Advanced Studies Illes Balears Esporles07190 Spain

Macroalgae play a crucial role in blue carbon ecosystems, yet their elemental compositions in the Red Sea are not well documented. This study examined the concentrations of 22 elements in 161 macroalgae blade samples from 19 species (5 unidentified) across 3 phyla in the Saudi Arabian Red Sea. Macroalgae blades collected from coral reef habitats exhibited higher concentrations of K, As, and Sr compared to those from seagrass meadows, but had lower levels of total nitrogen (TN), Na, Mg, Al, P, S, Cr, Mn, Fe, and Zn. These differences may be attributed to the limited or absent sediment in coral reef habitats, as sediment in seagrass meadows acts as a trap for heavy metals, influencing element accumulation. In the Southern Red Sea, macroalgae blades showed lower Sr levels but higher total organic carbon (TOC), TN, P, and Cd than those in the North, a trend influenced by nutrient inflows from the Indian Ocean and the semi-enclosed structure of the Red Sea. The study found that macroalgae blades had lower mean TOC and TN content than seagrass and mangrove leaves from the same research cruises but higher levels of Al, Cr, Ni, Cu, Zn, As, Mo, Cd, and Pb. Among the surveyed red, green, and brown macroalgae blades, Halymenia sp., Caulerpa taxifolia, and Dictyota sp. had the highest TOC, TN, and P contents, while Amphiroa fragilissima, Udotea flabellum, and Padina sp. had higher trace metal contents. Notably, some macroalgae blades, including red macroalgae A. fragilissima, green macroalgae Halimeda tuna, U. flabellum, and brown macroalgae Padina pavonica, Turbinaria ornata, collected from Al Birk, Al Lith, Thuwal, Yanbu, and Al Wajh exhibited concerning levels of Cr (~10.9 mg kg−1) and Ni (~27.2 mg kg−1), surpassing toxicity thresholds. These findings emphasize the urgent need for effective pollution mitigation measures to protect these marine ecosystems. © 2024 Elsevier B.V.

关键词： Heavy metals

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Decoupling the orientation dependence of deformation-induced martensitic transformation via single-crystal micropillar compression

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Journal of Materials science & Technology 2026年 244卷 80-87页

作者： Guosheng Sun Shilong Lv Yunfei Zhang Lirong Xiao Yong Peng Yuntian Zhu Kehong Wang Jizi Liu School of Materials Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Center of Analytical Facilities Nanjing University of Science and Technology Nanjing 210094 China Department of Materials Science and Engineering Hong Kong Institute for Advanced Study College of Science and Engineering City University of Hong Kong Kowloon 999077 Hong Kong China Mechanical Behavior Division of Shenyang National Laboratory for Materials Science City University of Hong Kong Kowloon 999077 Hong Kong China Nanjing Research Center for Biomedical Electron Microscopy (NRC-BEM) Nanjing Drum Tower Hospital Medical School Nanjing University Nanjing 210093 China

This study explores the significant orientation dependence of deformation-induced martensitic transformation. By employing single crystalline micropillar compression, three typical low-index crystallographic orientations were selected to examine their influence on the martensitic transformation of metastable austenitic stainless steel. Results demonstrate that the [011] orientation significantly inhibits the formation of α'-martensite, whereas the [001] and [111] orientations more effectively facilitate phase transformation, which is closely related to the transformation mechanisms. Specifically, the [001] orientation, which is prone to stacking fault formation, promotes martensitic transformation significantly with the aid of ε-martensite and the decreased effective stacking fault energy during compression. Meanwhile, the [111] oriented pillars demonstrate the highest yield strength, with shear deformation responsible for the relatively high fraction of α'-martensite. Additionally, the non-Schmid behavior occurred in the [011]- and [111]- oriented micropillars were found and discussed. This research advances the understanding of orientation-dependent martensitic transformation and provides an optional way to tailor the Transformation-induced plasticity effect via grain orientation engineering.

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Melanoma Detection using advanced Deep Neural Network

Melanoma Detection using Advanced Deep Neural Network

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Pallabi Sharma Anmol Gautam Rajashree Nayak Bunil Kumar Balabantaray Computer Science and Engineering National Institute of Technology Meghalaya Shillong India Computer Science and Engineering NIT Meghalaya Shillong India Center for Data Science JIS Institute of Advanced Studies and Research Kolkata

Melanoma is a type of skin cancer that starts in the cells (melanocytes) that govern the color of your skin. Melanoma is the most lethal one among all other skin diseases and the only reason for 77% deaths due to skin cancer. The best way to reduce these deaths is to detect cancer at its early stages so it can be treated and cured with minor treatment or surgeries. To speed up and improve the process of early detection, we propose an automatic classification method for melanoma cancer using an advanced deep neural network. Deep learning models require a large dataset to work efficiently, but due to limited time and the heavy workload of doctors, there is a lack of annotated skin can-cer images. Therefore, the proposed model introduces adversarial training for achieving better accuracy even with a small amount of data. The model removes unnecessary details and noise from the image and amplifies the depth and gradient in the dimensions and the shade of the image. This proposed adversarial method uses the gradients of the loss with respect to the input image to create a new adversarial example image that maximizes the loss for an input image. The synthetically generated images are used in the classification system for training and testing purposes. A comparative analysis of training with an adversarial approach and without an adversarial approach on different pre-trained models, namely VGG16, VGG19, Densenet121, and Resnet101, is also introduced in this work. ResNet101 with adversarial training has shown a state-of-the-art accuracy performance of 84.77% for melanoma classification. Therefore, the proposed approach can be considered an efficient method for classifying benign and malignant melanoma.

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A MgNO Method for Multiphase Flow in Porous Media

arXiv

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arXiv 2024年

作者： Liu, Xinliang Yang, Xia Zhang, Chen-Song Zhang, Lian Zhao, Li Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Xiangtan University Hunan China LSEC ICMSEC Academy of Mathematics and System Sciences Chinese Academy of Sciences University of Chinese Academy of Sciences China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

This research investigates the application of Multigrid Neural Operator (MgNO), a neural operator architecture inspired by multigrid methods, in the simulation for multiphase flow within porous media. The architecture is adjusted to manage a variety of crucial factors, such as permeability and porosity heterogeneity. The study extendes MgNO to time-dependent porous media flow problems and validate its accuracy in predicting essential aspects of multiphase flows. Furthermore, the research provides a detailed comparison between MgNO and Fourier Neural Opeartor (FNO), which is one of the most popular neural operator methods, on their performance regarding prediction error accumulation over time. This aspect provides valuable insights into the models' long-term predictive stability and reliability. The study demonstrates MgNO's capability to effectively simulate multiphase flow problems, offering considerable time savings compared to traditional simulation methods, marking an advancement in integrating data-driven methodologies in geoscience applications. © 2024, CC BY.

关键词： Porous materials

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The Transition of Threshold Voltage Shift of Al2O3/Si3N4AlGaN/GaN MIS-HEMTs Under Negative Gate Bias Stress From DC to AC

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IEEE Transactions on Electron Devices 2024年第3期71卷 1792-1797页

作者： Lee, Ya-Huan Chang, Kai-Chun Tai, Mao-Chou Wang, Yu-Xuan Lin, Hsin-Ni Yeh, Yu-Hsuan Kuo, Hung-Ming Tsai, Xin-Ying Lee, Jason Huang, I-Yu Chang, Ting-Chang Sze, Simon National Sun Yat-sen University Department of Physics Kaohsiung80424 Taiwan National Sun Yat-sen University Department of Photonics Kaohsiung80424 Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Electronics National Yang Ming Chiao Tung University Department of Materials and Optoelectronic Science Hsinchu30010 Taiwan Boston University Department of Computer Science BostonMA02215 United States National Sun Yat-sen University College of Semiconductor and Advanced Technology Research Kaohsiung80424 Taiwan National Sun Yat-sen University Department of Physics Center of Crystal Research Kaohsiung80424 Taiwan National Chiao-Tung University Department of Electronics Engineering Institute of Electronics Hsinchu30010 Taiwan

In this study, the reliability issues are discussed under dc and ac negative gate bias stress (ac-NGBS) in Al2O3/Si3N4 metal insulator semiconductor high electron mobility transistors (MIS-HEMTs). Converse degradation between these two stress conditions is discovered. The holes generated by trap-assisted thermionic field emission (TA-TFE) are trapped into the Al2O3 layer, so that the threshold voltage (Vth) decreases under dc negative gate bias stress (dc-NGBS). Vth increases because of the hot electrons injected into the GaN layer, while the device is turned on quickly under ac-NGBS. In addition, the degradation mechanisms under dc-NGBS and ac-NGBS are confirmed by the OFF-state gate and drain leakages, respectively. Silvaco TCAD is used to validate the degradation mechanism under ac-NGBS. Finally, the characteristics of the Vth shift transition from dc to ac NGBS are discussed. © 1963-2012 IEEE.

关键词： Threshold voltage

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Stability Criteria for Neural Networks with Stochastic Impulsive Intensity and Impulsive Density

Stability Criteria for Neural Networks with Stochastic Impul...

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Neuromorphic Computing (ICNC), International Conference on

作者： Hao Deng Jingxin Liu Jun Peng Yingxu Wang Fei Chang School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China Center for Applied Mathematics of Guangxi Guangxi Minzu University Nanning China School of Mathematics and Physics Sciences Chongqing University of Science and Technology Chongqing China Chongqing Research Institute of Intelligent Mathematics and Autonomous AI (RI-IM·AI*) Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

This paper studies stability conditions for neural networks with stochastic impulsive intensity and impulsive density. Firstly, the pth moment exponential stability conditions are given, where the impulsive intensities of impulsive neural networks are independent and identically distributed, and the impulsive sequence is defined by impulsive density. Then, taking a linear time-varying impulsive system as an example, it is shown that impulsive sequence defined by average impulsive interval is sometime insufficient in terms of stabilizing such linear time-varying systems. Finally, two examples are presented to verify that impulsive density method is more effective and universal than average impulsive interval method when dealing with time-varying systems.

关键词： Sufficient conditions Stability Neuromorphic engineering Neural networks Stability criteria Control theory Time-varying systems Numerical stability

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