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arXiv 2024年

作者： Tang, Xiaoyu Chen, Xingming Cheng, Jintao Wu, Jin Fan, Rui Zhang, Chengxi Zhou, Zebo The School of Electronic and Information Engineering Faculty of Engineering South China Normal University Guangdong Foshan528225 China The School of Physics South China Normal University Guangdong Guangzhou510000 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The School of Internet of Things Engineering Jiangnan University Wuxi214122 China The School of Aeronautics Astronautics University of Electronic Science and Technology of China and Aircraft swarm intelligent sensing and cooperative control Key Laboratory of Sichuan Province Chengdu610097 China The National Laboratory on Adaptive Optics Chengdu610209 China

In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles to perform various high-altitude detection tasks. Because the field of object detection for antenna interference sources has not been fully explored, this industry lacks dedicated learning samples and detection models for this specific task. In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research. We introduce YOLO-Ant, a lightweight CNN and transformer hybrid detector specifically designed for antenna interference source detection. Specifically, we initially formulated a lightweight design for the network depth and width, ensuring that subsequent investigations were conducted within a lightweight framework. Then, we propose a DSLK-Block module based on depthwise separable convolution and large convolution kernels to enhance the network’s feature extraction ability, effectively improving small object detection. To address challenges such as complex backgrounds and large interclass differences in antenna detection, we construct DSLKVit-Block, a powerful feature extraction module that combines DSLK-Block and transformer structures. Considering both its lightweight design and accuracy, our method not only achieves optimal performance on the antenna dataset but also yields competitive results on public datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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MAL-XSEL: Enhancing Industrial Web Malware Detection with an Explainable Stacking Ensemble Model

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Processes 2025年第5期13卷 1329-1329页

作者： Ezz El-Din Hemdan Samah Alshathri Haitham Elwahsh Osama A. Ghoneim Amged Sayed Structure and Materials Research Lab Prince Sultan University Riyadh 11586 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh 33511 Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Department of Computer Science Faculty of Computers and Information Tanta University Tanta 31527 Egypt Industrial Electronics and Control Engineering Department Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electrical Energy Engineering College of Engineering & Technology Arab Academy for Science Technology & Maritime Transport Smart Village Campus Giza 12577 Egypt

The escalating global incidence of malware presents critical cybersecurity threats to manufacturing, automation, and industrial process control systems. Given the fast-developing web applications and IoT devices in use by industry operations, securing a transparent and effective malware detection mechanism has become imperative to operational resilience and data integrity. Classical methods of malware detection are conventionally opaque “black boxes” with limited transparency, thus eroding trust and hindering deployment in security-sensitive contexts. In this respect, this research proposes MAL-XSEL—a malware detection framework using an explainable stacking ensemble learning approach for performing high-accuracy classification and interpretable decision-making. MAL-XSEL explicates the model predictions through Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), which enable security analysts to validate how the detection logic works and prioritize the features contributing to the most critical threats. Evaluated on two benchmark datasets, MAL-XSEL outperformed conventional machine learning models, achieving top accuracies of 99.62% (ClaMP dataset) and 99.16% (MalwareDataSet). Notably, it surpassed state-of-the-art algorithms such as LightGBM (99.52%), random forest (99.33%), and decision trees (98.89%) across both datasets while maintaining computational efficiency. A unique interaction of ensemble learning and XAI is employed for detection, not only with improved accuracy but also with interpretable insight into the behavior of malware, thereby allowing trust to be substantiated in an automated system. By closing the divide between performance and interpretability, MAL-XSEL enables cybersecurity practitioners to deploy transparent and auditable defenses against an ever-growing resource of threats. This work demonstrates how there can be no compromise on explainability in security-critical applications and, as such, esta

关键词： malware mitigation industrial web applications classification machine learning explainable artificial intelligence (XAI) SHAP (Shapley additive explanations) (LIME) local interpretable model-agnostic explanations

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From sensing to energy savings: A comprehensive survey on integrating emerging technologies for energy efficiency in WBANs

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Digital Communications and Networks 2024年

作者： Shumaila Javaid Hamza Fahim Sherali Zeadally Bin He Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai 201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai 201210 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai 201210 China Department of Computer Science Faculty of Computer Sciences Ilma University Karachi 75190 Pakistan College of Communication and Information University of Kentucky Lexington KY USA Academy of Computer Science & Software Engineering University of Johannesburg Johannesburg South Africa

Energy is essential for human existence, and its high consumption is a growing concern in today's technology-driven society. Global initiatives aim to reduce energy consumption and pollution by developing and deploying energy-efficient sensing technologies for long-term monitoring, control, automation, security, and interactions. Wireless Body Area Networks (WBANs) benefit a lot from the continuous monitoring capabilities of these sensing devices, which include medical sensors worn on or implanted in the human body for healthcare monitoring. Despite significant advancements, achieving energy efficiency in WBANs remains a significant challenge. A deep understanding of the WBAN architecture is essential to identify the causes of its energy inefficiency and develop novel energy-efficient solutions. We investigate energy efficiency issues specific to WBANs. We discuss the transformative impact that artificial intelligence and Machine Learning (ML) can have on achieving the energy efficiency of WBANs. Additionally, we explore the potential of emerging technologies such as quantum computing, nano-technology, biocompatible energy harvesting, and Simultaneous Wireless Information and Power Transfer (SWIPT) in enabling energy efficiency in WBANs. We focus on WBANs' architecture, hardware, and software components to identify key factors responsible for energy consumption in the WBAN environment. Based on our comprehensive review, we introduce an innovative, energy-efficient three-tier architecture for WBANs that employs ML and edge computing to overcome the limitations inherent in existing energy-efficient solutions. Finally, we summarize the lessons learned and highlight future research directions that will enable the development of energy-efficient solutions for WBANs.

关键词： Artificial intelligence Energy Healthcare Sensors Wireless body area networks

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Productivity analysis and non-linear gain scheduling approach for multi-species bioprocesses with product inhibition

Productivity analysis and non-linear gain scheduling approac...

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IEEE Annual Conference on Decision and control

作者： Piotr Skupin Alain Rapaport Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland MISTEA Univ Montpellier INRA France

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Bioprocesses with product inhibition are known to allow species coexistence. In this work, we first study the productivity of the different possible equilibria, depending on the operating conditions, and show that single species offers the best performances. Then, we propose a control strategy to stabilize the dynamics about the desired equilibria, in presence of instability. Based on output feedback linearization, we propose a family of controllers and a gain-scheduling approach to adapt the controller. Finally, we illustrate our approach on numerical simulations, showing that the attraction basin of the closed-loop system is improved by considering the gain-scheduling approach.

关键词： Productivity Silicon compounds Steady-state Mathematical model Stability analysis Closed loop systems Inhibitors

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How does bisphenol A damage cells? Studies on the freshwater shrimpNeocaridina davidi

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Bollettino di zoologia 2025年第1期92卷

作者： H. Gorol A. Ostróżka G. Wilczek Ł. Chajec S. Student A. Aleksa M. Rost-Roszkowska 1 Institute of Biology Biotechnology and Environmental Protection University of Silesia in Katowice Katowice Poland 2 Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland3 Biotechnology Center Silesian University of Technology Gliwice Poland 1 Institute of Biology Biotechnology and Environmental Protection University of Silesia in Katowice Katowice Poland4 SPIN-Lab Centre for Microscopic Studies on Matter University of Silesia in Katowice Chorzów Poland

Bisphenol A (BPA) is an organic compound used in the production of numerous plastics, which poses a real threat to health and life. Although BPA is poorly soluble in water, its presence in marine and freshwater environments has been recorded at quite high levels. Numerous studies are being conducted on the impact of this xenobiotic on the population size, fertility, and reproduction of various animals. A freshwater shrimpNeocaridina davidi(Crustacea) originates from Asia and enjoys great interest among breeders and researchers studying the influence of various xenobiotics on the functioning of freshwater animals. Therefore, we selected this species to analyze the cytotoxicity of BPA and possible changes in cells leading to their protection. The study aimed to determine whether BPA only causes degenerative changes in the midgut epithelium of freshwater crustaceans or whether cell and tissue protection processes (e.g. stem cell proliferation) will be visible at the ultrastructural level. The most important goals were to establish whether the cytotoxicity of BPA is connected with its concentration and the period of animal exposure and whether it proceeds in the same manner in different organs. Thus, we used different concentrations of BPA, corresponding to its concentrations in different waters and different times of the animals’ breeding in a contaminated environment. Shrimps were exposed to three concentrations of BPA: 1, 5, and 10 mg/L for 24, 48, and 72 h. Two organs that form the midgut were isolated – the intestine and hepatopancreas. The results revealed that the concentration and exposure time of freshwater animals affect the functioning of stem cells. However, autophagy and lipid digestion play an important role in protecting cells against the effects of BPA, which is also related to the increased energy demand of cells. Further studies connected with cell death processes and antioxidative mechanisms are needed.

关键词： Bisphenol A midgut stem cells cycle cycle cytotoxicity

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Observability in Kubernetes Cluster: automatic Anomalies Detection using Prometheus

Observability in Kubernetes Cluster: Automatic Anomalies Det...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Octavian Mart Catalin Negru Florin Pop Aniello Castiglione Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Romania National Institute for Research and Development in Informatics – ICI Bucharest Romania Dipartimento di Scienze e Tecnologie University of Naples Parthenope Italy

ISBN: (纸本)9781728176505

Kubernetes is a portable, extensible, open-source platform for managing containers. It comes with features such as automatic scaling, service discovery, load balancing, fault tolerance, etc. Being such a complex system, which has a lot of internal services and with the ability to manage a lot more user services, Kubernetes comes with a monitoring system, which provides metrics and logs for every service in the cluster. However, most of the time, the monitoring system needs human intervention for detection and troubleshooting defects. Human intervention usually occurs when it is too late, when a defect appears. We think that detecting anomalies in metrics provided by the monitoring system will help to prevent defects. In this paper, we analyze current solutions for automatic anomaly detection and alerting, and also we propose a new solution that will help system administrators to catch and predict anomalies earlier, which may lead to defects. Our solution, which is a technical one, is developed around Prometheus, an open-source monitoring system for metrics.

关键词： Measurement Fault tolerance High performance computing Fault tolerant systems Containers Load management Observability

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Metabolomics reveals that PS-NPs promote lung injury by regulating prostaglandin B1 through the cGAS-STING pathway

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Chemosphere 2023年 342卷 140108页

作者： Xuan, Lihui Wang, Yin Qu, Can Yan, Yuhui Yi, Wensen Yang, Jingjing Skonieczna, Magdalena Chen, Cuimei Miszczyk, Justyna Ivanov, Dmitry S. Zakaly, Hesham M.H. Markovic, Vladimir Huang, Ruixue Department of Occupational and Environmental Health Xiangya School of Public Health Central South University Hunan Province Changsha410078 China Department of Systems Biology and Engineering Silesian University of Technology Institute of Automatic Control Akademicka 16 Gliwice44-100 Poland Biotechnology Centre Silesian University of Technology Krzywoustego 8 Gliwice44-100 Poland School of Public Health Xiang Nan University Hunan Chenzhou423000 China Department of Medical Physics Cyclotron Centre Bronowice Institute of Nuclear Physics Polish Academy of Sciences KrakowPL-31342 Poland Quantum Electronics Division Lebedev Physical Institute Moscow119991 Russia Institute of Physics and Technology Ural Federal University Yekaterinburg620002 Russia Physics Department Faculty of Science Al-Azhar University Assuit71524 Egypt Faculty of Sciences University of Kragujevac Kragujevac34000 Serbia

Nanoplastics have been widely studied as environmental pollutants, which can accumulate in the human body through the food chain or direct contact. Research has shown that nanoplastics can affect the immune system and mitochondrial function, but the underlying mechanisms are unclear. Lungs and macrophages have important immune and metabolic functions. This study explored the effects of 100 nm PS-NPs on innate immunity, mitochondrial function, and cellular metabolism-related pathways in lung (BEAS-2B) cells and macrophages (RAW264.7). The results had shown that PS-NPs exposure caused a decrease in mitochondrial membrane potential, intracellular ROS accumulation, and Ca2+ overload, and activated the cGAS-STING signaling pathway related to innate immunity. These changes had been observed at concentrations of PS-NPs as low as 60 μg/mL, which might have been comparable to environmental levels. Non-target metabolomics and Western Blotting results confirmed that PS-NPs regulated prostaglandin B1 and other metabolites to cause cell damage through the cGAS-STING pathway. Supplementation of prostaglandin B1 alleviated the immune activation and metabolic disturbance caused by PS-NPs exposure. This study identified PS-NPs-induced innate immune activation, mitochondrial dysfunction, and metabolic toxicity pathways, providing new insights into the potential for adverse outcomes of NPs in human life. © 2023 Elsevier Ltd

关键词： Toxicity

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UAVs fleet mission planning robust to changing weather conditions

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IFAC-PapersOnLine 2020年第2期53卷 10518-10524页

作者： Grzegorz Bocewicz Grzegorz Radzki Izabela Nielsen Marcin Witczak Banaszak Zbigniew Faculty of Electronics and Computer Science Koszalin University of Technology Poland Dept. of Materials and Production Aalborg University Aalborg Denmark Inst. Control and Comp. Eng. University of Zielona Góra Poland

A fleet of homogeneous UAVs fly in a 2D plane matching a distribution network to service customers in a collision-free manner. Limited UAVs’ battery capacity and UAVs’ weight reduction during they traveling along planned routes and goods delivery as well as changing weather conditions are also taken into account. The goal is to determine a set of routings covering all delivery points so that the total distance traveled by the UAVs fleet is constrained by a battery capacity limit. All customers’ demands are realized within a given time horizon and take into account forecasted weather constraints, focusing on changes in a wind speed and direction. In this context, the main objective is to propose a declarative model allowing one to prototype proactive routings of UAVs fleet mission. Computational experiments assessing alternative strategies of UAVs fleet mission robust to forecast weather conditions are discussed.

关键词： UAVs fleet routing scheduling UAVs fleet mission planning robustness to changing weather conditions

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Correction to: Utilizing social media and machine learning for personality and emotion recognition using PERS

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Neural Computing and Applications 2023年第10期36卷 5649-5649页

作者： Talaat, Fatma M. El-Gendy, Eman M. Saafan, Mahmoud M. Gamel, Samah A. Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa Egypt Nile Higher Institute for Engineering and Technology Mansoura Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Electronics and Communication Engineering Department Faculty of Engineering Horus University Damietta Egypt

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Interval type-3 fuzzy Wiener model for nonlinear dynamic systems: Application to continuous stirred tank reactor

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Chaos, Solitons & Fractals 2025年 199卷

作者： Tarek R. Khalifa Xian Yu Amin Sharafian Xiaopin Zhong Zongze Wu College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 Guangdong China Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Menoufia Egypt Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen 518060 Guangdong China

This paper proposes a novel interval type-3 Takagi–Sugeno–Kang fuzzy Wiener model (IT3TSK-FWM) to address the challenge of modeling nonlinear dynamic systems with complex uncertainties. The proposed model adopts a Wiener structure, where the linear dynamic part is represented by an autoregressive moving average (ARMA) system and the nonlinear static part is represented by an IT3TSK fuzzy system. First, we introduce a novel rule firing strength-based algorithm to eliminate the dependency on predefined structures, allowing the proposed IT3TSK-FWM to adapt its structure online. Then, we introduce a novel Lyapunov-based learning algorithm to ensure convergence of the estimation error to zero. Furthermore, we employ a direct defuzzification method to enhance computational efficiency. Finally, the effectiveness of the IT3TSK-FWM is demonstrated through its application to a continuous stirred tank reactor system, validating the model’s robustness and performance in handling nonlinear dynamics.

关键词： Continuous stirred tank reactor (CSTR) Interval type-3 fuzzy systems Nonlinear dynamic systems Parameter learning Structure learning

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