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2023 Radiation and Scattering of Electromagnetic Waves, RSEMW 2023

作者： Klimov, Konstantin N. Konov, Kirill I. Epaneshnikova, Irina K. Belevtsev, Andrey M. Kryuchkov, Vasily L. Boldyreff, Anton S. Moscow Aviation Institute National Research University Department 317 Innovation Management Moscow Russia Institute of Radio Engineering Systems and Control Southern Federal University Taganrog Russia

ISBN: (纸本)9798350322378

The numerical analysis in the time domain of the propagating of the short terahertz range radio pulse through the medium with negative permittivity is presented in the paper. This medium simulates the layer of plasma collisionless with a specified frequency dispersion. This model does not take into account the movement of positive ions. The process of signal formation in the time domain for the frequency of 10 THz with pulse duration of 0.1 ps and filling frequency of 10 THz is presented. It was shown that the plasma layer is the high-pass filter. Examples of the given short radio pulse propagation through the plasma layer are presented for frequencies of 10, 15, and 20 THz. The principle of the impedance analogue of electromagnetic space and the advantages of the Tamic Planar Rt-H Analyzer numerical simulation package for the terahertz range is explained. The geometry of the problem is described. The process of passing short terahertz radio pulse through the plasma layer is investigated. The electric field distribution from the coordinate at different time points is given. The spectral density of terahertz pulse after passing through the plasma layer is carried out. It was shown that the plasma layer behaves like a high-pass filter for the terahertz range. Examples of choosing a plasma frequency that allows the desired frequency range of the initial short terahertz radio pulse to pass through the plasma layer are given. © 2023 IEEE.

关键词： Spectral density

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Beyond the network-based multi-omics data integration in COPD: A pathway-centric analysis

Beyond the network-based multi-omics data integration in COP...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Sibilio, Pasquale Conte, Federica Paci, Paola Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Karolinska Institutet Stockholm17177 Sweden

ISBN: (纸本)9798350386226

Chronic obstructive pulmonary disease (COPD) is a lung disease causing hundred thousand of death each year worldwide and defined as a respiratory and airflow impairment majorly due to large and small airways dysfunctions. COPD could be considered as a syndrome that includes disease axes of variable pathological and clinical conditions. Despite recent advances, the comprehension of the molecular mechanisms responsible for COPD disease spectrum is far to be reached. Moreover, we should also consider continuous smoking exposure, which could lead to variability in the disease mechanisms and progression. Network-based multi-omics data integration can help to study the association between molecular determinants on diverse biomolecular layers in a disease context. In a previous study, we leveraged lung RNA-seq and DNA-methylation data of a COPD-control cohort to build a correlation-based integrated network (called coupled network), that helped to unveil genes involved in immune and inflammatory modulation of COPD. Therefore, in our previous study we highlighted the most important genes of the coupled network and inspected their single contribution to disease-related pathways. In this study we aim to overcome this limitation by performing a pathway activity analysis by considering the expression and DNA methylation profiles of coupled network genes. Moreover, we exploit this analysis to study the possible contribution of coupled network genes to the differential disease progression between current and former smokers patients. © 2024 IEEE.

关键词： Lung cancer

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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Differential gene correlation analysis to investigate evolutionary divergence between Cardamine hirsuta and Arabidopsis thaliana

Differential gene correlation analysis to investigate evolut...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： D'Annunzio, Federica Fiscon, Giulia Bertolotti, Gaia Dello Ioio, Raffaele Paci, Paola Sapienza University of Rome Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science 'A. Ruberti' National Research Council of Rome Department of Computer Control and Management Engineering 'A. Ruberti' Rome Italy Sapienza University of Rome Department of Biology and Biotechnology 'Charles Darwin' Rome Italy Department of Computer Control and Management Engineering 'A. Ruberti' Sapienza University of Rome Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Dissecting the intricate regulatory dynamics between genes stands as a critical step towards the development of precise predictive models within biological systems. A highly effective strategy in this pursuit involves an examination of variations in correlation among gene pairs across different conditions trough differential gene correlation analysis. We anticipated that the application of this method to phylogenetically close plant species could reveal powerful insights on how genes' relationships have a fundamental role in evolutionary dynamics. Building upon this principle, our investigation focused primarily on conducting a comprehensive differential correlation analysis of the roots' orthologous genes of two closely related plant species: Cardamine hirsuta and Arabidopsis thaliana. By leveraging their shared phylogenetic proximity, we were able to draw comparisons that shed light on how evolutionary pressures might have shaped their gene regulatory networks. By integrating differential expression analysis, differential correlation analysis and functional enrichment analysis, we offer a robust framework for future studies aiming to unravel the complexities of adaptation and evolution across a wide range of biological systems. © 2024 IEEE.

关键词： Plant diseases

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A network-based bioinformatic analysis for identifying potential repurposable drugs for obesity avoiding hepatic steatosis side-effect

A network-based bioinformatic analysis for identifying poten...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Brunetti, Martina Fiscon, Giulia Costanzo, Alessia Di Arca, Marcello Paci, Paola Sapienza University of Rome Department of Experimental Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science "a. Ruberti" National Research Council of Rome Department of Computer Control and Management Engineering "a. Ruberti" Rome Italy Sapienza University of Rome Policlinico Umberto I Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering "a. Ruberti" Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Obesity is a complex multifactorial disorder characterized by the excess accumulation of body fat that impairs human health due to the risk of developing other diseases, including cardiovascular and hepatic diseases, hypertension, diabetes, hyperlipidemia. Its spread has been progressively accelerating, resulting in an unprecedented epidemic with no significant signs of slowing down any time soon. Drug therapy via the proposal repurposing solutions can represent an actionable treatment strategy, even if the emergence of drug-induced adverse effects can affect the treatment of this pathology. In this study, we propose a network-based analysis to identify a list of drug candidates predicted to be repurposable for obesity that are unlikely to produce specific adverse side-effect, such as hepatic steatosis. © 2024 IEEE.

关键词： Nutrition

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Optimizing Maximum Profit for Electric Vehicle Users in a Bipolar DC Distribution Grid with Probabilistic EV Load Demand and V2G Integration 12

Optimizing Maximum Profit for Electric Vehicle Users in a Bi...

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12th International Electrical engineering Congress, iEECON 2024

作者： Sakulphaisan, Guntinan Son-In, Sirirat Junhuathon, Nitikorn Pathumwan Institute Of Technology Industrial Technology And Innovation Management Faculty Of Science And Technology Bangkok Thailand Bansomdejchaopraya Rajabhat University Electrical Engineering And Automation Control Systems Faculty Of Engineering And Industrial Technology Bangkok Thailand Rajamangala University Of Technology Thanyaburi Faculty Of Engineering Department Of Electrical Engineering Pathumthani Thailand

ISBN: (纸本)9798350383591

This article delves into the synergistic potential of integrating Bipolar DC distribution grids, Probabilistic EV load demand forecasting, and Vehicle-to-Grid (V2G) technology. It offers a blueprint for electric vehicle (EV) users to smartly improve their financial results. Through the amalgamation of these components, individuals who possess electric vehicles have the potential to mitigate their energy expenditures while concurrently generating additional income. The study reveals that the integration of EV into the distribution system can lead to a substantial increase in daily energy loss, reaching up to 25.91%. However, when V2G technology is employed within the distribution grid, it has the remarkable capability to mitigate this increase in daily energy loss significantly, achieving a reduction of up to 64.12%. © 2024 IEEE.

关键词： Integration

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Parallel Driving with Big Models and Foundation Intelligence in Cyber-hysical-ocial Spaces

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Research 2024年第2期2024卷 1-17页

作者： Xiao Wang Jun Huang Yonglin Tian Chen Sun Lie Yang Shanhe Lou Chen Lv Changyin Sun Fei-Yue Wang School of Artificial Intelligence Anhui UniversityHefeiChina Macao University of Science and Technology MacaoChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina MVSLab Department of Mechanical and Mechatronics EngineeringUniversity of Waterloo200 University Ave WestWaterlooON N2L3G1Canada School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.

关键词： breakthrough integrate corners

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Image Semantic Segmentation Used in Automatics

Image Semantic Segmentation Used in Automatics

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International Conference on engineering of Modern Electric systems (EMES)

作者： Viorica Spoiala Control Systems Engineering and Management Department University of Oradea Oradea Romania

This paper presents the importance of segmentation methods used in the object analysis in computer vision and image proccessing field, in the automated processes. Image segmentation is a crucial stage of image processing in the context of industrial automation. The detection of objects is one of the main purposes of image segmentation, and in order to do this, it is necessary to perform the segmentation and extraction of the image's significant elements in advance. Putting pixels into semantic classifications is the foundation of semantic segmentation, this being a deep learning method. In order to apply segmentation in computer vision of automated processes the first step could be to label the objects and then to train a neural network to apply this labels to all the similar objects detected in similar images. In this paper some images were segmented by using labels and deep learning methods. After the theoretical aspects are presented, then some examples are made in Matlab environment.

关键词：

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Comparative Effectiveness of Classification Algorithms in Predicting Diabetes 16

Comparative Effectiveness of Classification Algorithms in Pr...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Dael, Fares A. Mareyev, D. Shayea, Ibraheem Kulniyazova Korlan, S. Abitova, Gulnara İzmir Bakirçay University Management Information Systems Izmir Turkey Astana It University Department of Computational and Data Science Astana Kazakhstan Istanbul Technical University Electronic and Communication Engineering Istanbul Turkey L.N. Gumilyov Eurasian National University Department of Systems Analysis and Control Astana Kazakhstan Astana It University Department of Intelligent Systems and Cybersecurity Astana Kazakhstan

ISBN: (纸本)9798331505264

Diabetes mellitus poses a significant global health challenge, with increasing prevalence, particularly in low socioeconomic regions. Accurate and early diagnosis is crucial to prevent the severe long-term complications associated with diabetes. This study conducts a comprehensive comparison of six prominent machine learning algorithms-K-Nearest Neighbors (K-NN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forest, and Logistic Regression-in predicting diabetes using a dataset of 768 individuals with diverse diabetic indicators from Kaggle. Each algorithm is rigorously evaluated based on precision, recall, and F1-score to determine the most effective method for diabetes diagnosis. The results indicate that Logistic Regression outperforms the other algorithms, achieving an accuracy of 81%. This superior performance is attributed to Logistic Regression's ability to effectively delineate linear separations, which is crucial for distinguishing between diabetic and non-diabetic individuals. The study underscores the importance of feature selection and model tuning in enhancing predictive performance. The findings suggest that integrating Logistic Regression into clinical settings can significantly improve the accuracy and timeliness of diabetes diagnosis, potentially leading to better patient outcomes and reduced healthcare costs. © 2024 IEEE.

关键词： Support vector regression

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Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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