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Stochastic interpretation for a single server retrial queue with Bernoulli feedback and negative customers

Applied Mathematics(A Journal of Chinese Universities) 2025年第1期40卷 1-19页

作者： Mohamed Boualem Amina Angelika Bouchentouf Aicha Bareche Mouloud Cherfaoui Research Unit LaMOS(Modeling and Optimization of Systems) Faculty of TechnologyUniversity of Bejaia06000 BejaiaAlgeria Laboratory of Mathematics Djillali Liabes University of Sidi Bel AbbesB.P.89Sidi Bel Abbes 22000Algeria Research Unit LaMOS(Modeling and Optimization of Systems) University of Bejaia06000 BejaiaAlgeria

In this paper,we introduce a qualitative analysis in order to study the monotonicity and comparability properties of a single-server retrial queueing model with Bernoulli feedback and negative customers,relative to stochastic *** measures of such a system are available explicitly,while their forms are cumbersome(these formulas include integrals of Laplace transform,solutions of functional equations,etc.).Therefore,they are not exploitable from the application point of *** overcome these difficulties,we present stochastic comparison methods in order to get qualitative estimates of these *** particular,we prove the monotonicity of the transition operator of the embedded Markov *** addition,we establish conditions for which transition operators as well as stationary probabilities,associated with two embedded Markov chains,having the same structure but with different parameters,are comparable relative to the given stochastic ***,numerical examples are carried out to illustrate the theoretical results.

关键词： retrial queueing models negative arrivals stochastic orderings monotonicity simulation

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Agricultural consumer Internet of Things devices: Methods for optimizing data aggregation

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Alexandria Engineering Journal 2025年 125卷 692-699页

作者： Mengash, Hanan Abdullah Mahgoub, Hany Alshuhail, Asma Darem, Abdulbasit A. Majdoubi, Jihen Yafoz, Ayman Alsini, Raed Alghushairy, Omar Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Engineering and Technology Unit Applied College Majmaah University Al Majmaah11952 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah21589 Saudi Arabia

With the advent of state-of-the-art computer and digital technology, modern civilisation has been immensely facilitated and optimised. The Internet of Things (IoT) has grown in importance in recent years, allowing us to monitor our physical environments and broadening our horizons. The "practice, science, or art" of farming is defined as tending to land, growing crops with the use of different tools and techniques, and then selling the harvested food. If farmers optimise their operations with the help of a Wireless Sensor Network (WSN), they will be able to work much more efficiently and effectively. Data aggregation involves collecting information from multiple sensors. The data aggregation process is optimised by applying metaheuristic techniques. A Genetic Algorithm (GA) is a method for modelling evolution that uses mutation, crossover, and natural selection as its building blocks. The key benefit of the Artificial Bee Colony (ABC) approach is that it simultaneously considers local and global search, and it doesn't get trapped calculating its local minima. Naturalistic algorithms like ALO model their hunting behaviour after that of ant-lions and doodlebugs. It manages to find a happy medium between exploration and exploitation with just one operator. Experimental evidences show that the proposed metaheuristic technique, ABC-ALO, which combines the best elements of Artificial Bee Colony and Ant Lion Optimisation, is superior to existing metaheuristic approaches in terms of lifetime computation, or the number of alive nodes at different round counts. © 2025

关键词： Genetic algorithms

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Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing

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IEEE Transactions on Signal and information Processing over Networks 2025年 11卷 388-400页

作者： Lari, Ehsan Arablouei, Reza Gogineni, Vinay Chakravarthi Werner, Stefan Commonwealth Scientific and Industrial Research Organisation PullenvaleQLD4069 Australia University of Southern Denmark Applied AI and Data Science Unit The Maersk Mc-Kinney Moller Institute Faculty of Engineering Odense5230 Denmark Norwegian University of Science and Technology Department of Electronic Systems Trondheim7034 Norway Aalto University Department of Information and Communications Engineering Espoo02150 Finland

Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicious clients tamper with their local models to manipulate the global model. In this work, we investigate the resilience of the partial-sharing online FL (PSO-Fed) algorithm against such attacks. PSO-Fed reduces communication overhead by allowing clients to share only a fraction of their model updates with the server. We demonstrate that this partial sharing mechanism has the added advantage of enhancing PSO-Fed's robustness to model-poisoning attacks. Through theoretical analysis, we show that PSO-Fed maintains convergence even under Byzantine attacks, where malicious clients inject noise into their updates. Furthermore, we derive a formula for PSO-Fed's mean square error, considering factors like stepsize, attack probability, and the number of malicious clients. Interestingly, we find a non-trivial optimal stepsize that maximizes PSO-Fed's resistance to these attacks. Extensive numerical experiments confirm our theoretical findings and showcase PSO-Fed's superior performance against model-poisoning attacks compared to other leading FL algorithms. © 2015 IEEE.

关键词： Federated learning

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Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

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An Efficient Collaborative Task Offloading Approach Based on Multi-Objective Algorithm in MEC-Assisted Vehicular Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Chen, Shuaijie Li, Wenfeng Sun, Jingtao Pace, Pasquale He, Lijun Fortino, Giancarlo Wuhan University of Technology State Key Laboratory of Maritime Technology and Safety School of Transportation and Logistics Engineering Hubei Wuhan430063 China National Institute of Informatics Information Systems Architecture Research Division Tokyo Japan University of Calabria Department of Informatics Modeling Electronics and Systems Rende87036 Italy

Mobile edge computing assisting vehicular networks to achieve collaborative computing is a significant research area, as it can effectively expand the on-board computational resources, thereby improving the responsiveness of vehicular applications and reducing energy consumption. However, the existing task offloading schemes still have various gaps and face challenges that should be addressed because task offloading problems are usually multi-objective optimization problems (MOOP) and NP-hard problems. In this respect, we formulate a new multi-objective model for vehicle-centered task offloading tacking into account the queuing and computing processes of tasks. The minimization of the average delay time, average energy consumption, and average payment cost for each vehicle is defined as a MOOP. We propose an efficient collaborative offloading approach based on task triage strategy and multi-objective optimization to address the model. Specifically, this approach can be decomposed into two stages. First, a task triage strategy is developed based on a comprehensive analysis of task urgency and task criticality to make offloading decisions for triaged tasks with extreme characteristics. Then, we propose a novel multi-objective optimization framework for the MOOP. Finally, we develop a multi-objective hybrid genetic algorithm that integrates the associative learning immediate memory strategy and genetic operations to solve the MOOP. Extensive experiments and performance evaluations based on real-world traces of taxis demonstrate the effectiveness of the proposed approach. The results indicate that the approach outperforms six other well-known multi-objective algorithms in solving the MOOP. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Gate teleportation-assisted routing for quantum algorithms

arXiv

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arXiv 2025年

作者： Babu, Aravind Plathanam Kerppo, Oskari Moller, Andrés Muñoz Haghparast, Majid Silveri, Matti Nano and Molecular Systems Research Unit University of Oulu Finland Faculty of Information Technology University of Jyväskylä Finland Quanscient Oy Tampere Finland

The limited qubit connectivity of quantum processors poses a significant challenge in deploying practical algorithms and logical gates, necessitating efficient qubit mapping and routing strategies. When implementing a gate that requires additional connectivity beyond the native connectivity, the qubit state must be moved to a nearby connected qubit to execute the desired gate locally. This is typically achieved using a series of SWAP gates creating a SWAP path. However, routing methods relying on SWAP gates often lead to increased circuit depth and gate count, motivating the need for alternative approaches. This work explores the potential of teleported gates to improve qubit routing efficiency, focusing on implementation within specific hardware topologies and benchmark quantum algorithms. We propose a routing method that is assisted by gate teleportation. It establishes additional connectivity using gate teleportation paths through available unused qubits, termed auxiliary qubits, within the topology. To optimize this approach, we have developed an algorithm to identify the best gate teleportation connections, considering their potential to reduce the depth of the circuit and address possible errors that may arise from the teleportation paths. Finally, we demonstrate depth reduction with gate teleportation-assisted routing in various benchmark algorithms, including case studies on the compilation of the Deutsch-Jozsa algorithm and the Quantum Approximation Optimization Algorithm (QAOA) for heavy-hexagon topology used in IBM 127-qubit Eagle r3 processors. Our benchmark results show a 10−20% depth reduction in the routing of selected algorithms compared to regular routing without using the teleported gate. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

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SN Computer Science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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Optimized Energy Management Strategy for Non-Autonomous Microgrids Using Crow Search Optimization

Optimized Energy Management Strategy for Non-Autonomous Micr...

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International Renewable Energy Congress

作者： Marwen Bjaoui K. Khemiri L. Khemissi R. Djebali A. Sellami LAPLACE université de Toulouse CNRS INPT UPS Toulouse France UR22ES12: Modeling Optimization and Augmented Engineering ISLAIB University of Jendouba Beja Tunisia Laboratory of Nanomaterials and Systems for Energy (LaNSER) Research and Technology Centre of Energy (CRTEn) Borj Cedria Tunisia Research unit: LISIER National Higher Engineering School of Tunis Tunis Tunisia

ISBN: (数字)9798331516772

ISBN: (纸本)9798331516789

The need for renewable energy sources is growing because of climate change and rising fossil fuel prices. However, the variability of these sources necessitates an additional energy management unit to ensure smooth operation and a reliable power supply, prompting the development of an effective energy management strategy (EMS). This study introduces an innovative EMS for a grid-connected DC microgrid (MG) that utilizes the crow search optimization (CSO) algorithm. The hybrid energy system consists of a battery energy storage system (BESS), wind turbine (WT), and photovoltaic generator (GPV), with a focus on maximizing the use of renewables to enhance economic efficiency. A proportional-integral (PI) controller regulates source-side converters to optimize energy captured from the wind and PV sources while managing battery current to align with a reference generated by the CSO. The controller also oversees the grid-side inverter. In addition to minimizing various costs associated with energy consumption, battery degradation, and hourly grid energy usage, the CSO algorithm seeks to maintain the battery's state of charge (SOC) between 20% and 80%, with a target of 50%. It computes the overall energy costs by considering the battery's SOC, real-time renewable energy generation, and actual energy demand to determine instantly the optimal battery power. The performance of the CSO is assessed in the MATLAB/Simulink environment and compared to a rules-based EMS. The suggested CSO was highly promising for possible hybrid renewable energy management systems applications based on the obtained performances.

关键词： Renewable energy sources Energy consumption Costs Heuristic algorithms Microgrids Batteries State of charge Energy management MATLAB Optimization

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Fractional exhaled nitric oxide (FeNO) among elementary school children in Stockholm: Associations with asthma, allergies, and home and school environment

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Science of the Total Environment 2025年 972卷 179113页

作者： Bloom, Erica Taubel, Martin Saeidyfar, Gulli Wieslander, Gunilla Wang, Chengju Sacco, Francesco Norbäck, Dan RISE-Research Institutes of Sweden Division of Built Environment-Building Technology Stockholm Sweden Living Environments and Lifestyles Unit Kuopio Finland Mid Sweden University Department of Communication Quality Management and Information Systems Östersund Sweden Uppsala University Department of Medical Science Occupational and Environmental Medicine University Hospital Uppsala75185 Sweden RISE–Research Institutes of Sweden Division of Energy and Resources-efficient Cities Lund Sweden Aalto University Department of Civil Engineering Espoo Finland

Objective: Few studies have investigated links between fraction exhaled nitric oxide (FeNO), the home and the school environment. FeNO is a biomarker of T helper 2 (Th2) airway inflammation. We investigated associations between FeNO and airway symptoms, allergies, household and classroom exposure among pupils in ten primary schools in Stockholm (N = 415). Methods: information on health and household environment was obtained by a questionnaire. FeNO was measured at school. Particle mass (PM1, PM2.5, PM10), carbon dioxide (CO2), temperature, and relative air humidity (RH) were measured in the classrooms. Microbial DNA and 3-hydroxy fatty acids (3-OHs) from endotoxin in Gram-negative bacteria were analysed in vacuumed dust from floors and upper surfaces. Three-level linear mixed models were used to analyse associations. Results: In total, 9.7 % of the pupils had elevated FeNO (>20 ppb), 15.2 % doctor diagnosed asthma, 10.7 % current asthma, 17.8 % reported allergy and 9.6 % doctor diagnosed allergy. Reported allergy (p = 0.02), diagnosed allergy (p = 0.002), and current asthma (p = 0.007) were associated with elevated FeNO. Children living in single-family houses with basement had higher FeNO than those in self-owned apartments (p = 0.001). In the classrooms, PM10 (p = 0.008), RH (p = 0.004) and DNA copies from Gram-negative bacteria in vacuumed floor dust (p = 0.008) were associated with higher FeNO. C16 3-OH in floor dust (p = 0.046) and C10 (p = 0.02) and C11 3-OHs (p = 0.04) in upper surface dust were associated with higher FeNO. The association between Gram-negative bacteria at school and FeNO was stronger among girls. Children with parental asthma and allergy, and among those with dampness and mould at home and in single-family houses with basement. Conclusions: FeNO is associated with reported asthma and allergy. In the school environment, PM10 and exposure to some subpopulations of Gram-negative bacteria can increase FeNO. Sex, parental asthma or allergy, dampn

关键词： Allergies

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