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An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems

Scientific Reports 2025年第1期15卷 1-17页

作者： Sasikumar, A. Ravi, Logesh Devarajan, Malathi Almazyad, Abdulaziz S. De, Shuvodeep Xiong, Guojiang Mousavirad, Seyed Jalaleddin Mohamed, Ali Wagdy Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Centre for Advanced Data Science Tamil Nadu Chennai 600127 India School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai 600127 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Virginia Tech Blacksburg United States Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang 550025 China Department of Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza 12613 Egypt Applied Science Research Center Applied Science Private University Amman 11931 Jordan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take advantage of multiuser heterogeneity and acquire maximum gain in systems where the total number of recipients exceeds the number of transmitting antennas. Due to their high computational cost, many user selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable for MU-MIMO systems. A suitable scheduling mechanism is essential for the various users in an MU-MIMO system to utilise bandwidth and enhance the system’s total rate effectively. In this article, we proposed a user and antenna scheduling with a population-based meta-heuristic approach, namely the binary salp swarm algorithm (binary SSA), to increase the system sum rate with low computing complexity. We specifically used a population-based meta-heuristics optimisation technique to simulate the user scheduling problem in MU-MIMO systems, characterising complicated issues with binary decisions. Additionally, binary SSA significantly outperforms existing population-based models, such as the binary bat algorithm (binary BA), PSO, SSA, FPA and binary flower pollination algorithm (binary FPA), regarding system throughput/sum rate. The proposed binary SSA technique also effectively achieves a system sum rate compared to a random search scheme and other existing suboptimal scheduling methods. Compared to binary BA and binary FPA approaches, the binary SSA has a higher convergence rate and superior searching capabilities. The simulation outcomes show the proposed binary SSA-based scheduling scheme delivers noticeable performance benefits. © The Author(s) 2025.

关键词： Antenna Binary salp swarm algorithm Metaheuristics optimization Multiuser MIMO User scheduling

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THE-TAFL: Transforming Healthcare Edge with Transformer-based Adaptive Federated Learning and Learning Rate Optimization

Internet of Things (The Netherlands)

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Internet of Things (The Netherlands) 2025年 32卷

作者： Ullah, Farhan Mohammad, Nazeeruddin Mostarda, Leonardo Cacciagrano, Diletta Ullah, Shamsher Zhao, Yue Cybersecurity Center Prince Mohammad Bin Fahd University Khobar 34754 Saudi Arabia Department of Mathematics and Computer Science University of Perugia Perugia 06123 Italy Division of Computer Science University of Camerino Camerino 62032 Italy National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060 China Department of Computer Science College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou 325060 China

The healthcare industry is becoming more vulnerable to privacy violations and cybercrime due to the pervasive dissemination and sensitivity of medical data. Advanced data security systems are needed to protect privacy, data integrity, and dependability as confidentiality breaches increase across industries. Decentralized healthcare networks face challenges in feature extraction during local training, hindering effective federated averaging and learning rate optimization, which affects data processing and model training efficiency. This paper introduces a novel approach of Transforming Healthcare Edge with Transformer-based Adaptive Federated Learning (THE-TAFL) and Learning Rate Optimization. In this paper, we combine Transformer-based Adaptive Federated Learning (TAFL) with learning rate optimization to improve the privacy and security of healthcare information on edge devices. We used data augmentation approaches that generate robust and generalized input datasets for deep learning models. Next, we use the Vision Transformer (ViT) model for local training, generating Local Model Weights (LMUs) that enhance feature extraction and learning. We designed a training optimization method that improves model performance and stability by combining a loss function with weight decay for regularization, learning rate scheduling, and gradient clipping. This ensures effective training across decentralized clients in a Federated Learning (FL) framework. The FL server receives LMUs from many clients and aggregates them. The aggregation procedure utilizes adaptive federated averaging to aggregate the LMUs based on the performance of each client. This adaptive method ensures that high-performing clients contribute more to the Global Model Update (GMU). Following aggregation, clients receive the GMU to continue training with the updated parameters, ensuring collaborative and dynamic learning. The proposed method provides better performance on two standard datasets using various numb

关键词： Adaptive Federated Learning Edge computing Healthcare Privacy & Security Medical imaging Transforming Healthcare Edge Vision transformer

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Wetland key habitat functional areas in China informed by flagship waterbirds: Past changes, present status and future trend with modeling scenarios

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Geography and Sustainability 2025年第4期6卷

作者： Xiang, Hengxing Mao, Dehua Wang, Ming Wang, Yeqiao Choi, Chi-Yeung Wang, Wenjuan Wu, Haitao Feng, Kaidong Wang, Zongming State Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun 130102 China Department of Natural Resources Sciences University of Rhode Island Kingston 02881 RI United States School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China National Earth System Science Data Center Beijing 100101 China

Considering the crucial role of wetland conservation in China for the sustainability of biodiversity, it is imperative to identify key habitat functional areas (KHFAs), which are suitable for sustaining waterbirds and ensuring landscape connectivity, to optimize wetland management. This study identifies the past changes, present status, and future patterns of wetland KHFAs in China by using the Zonation model with comprehensive data inputs, including wetland distribution, key bird distribution areas (such as Ramsar sites and Important Bird Areas), and flagship waterbird species. Results show that the current wetland KHFAs in China is 41,613.5 km², mainly in the Sanjiang Plain (SJP), Songnen Plain (SNP), middle and lower reaches of the Yangtze River, and the Qinghai-Xizang Plateau (QXP) regions. The area of wetland KHFAs has been declining since 1990, especially in 2000, mainly due to anthropogenic impacts such as urbanization and agricultural expansion. The future projections suggest a continued decline in the area of wetland KHFAs, although the trend is expected to be slowed. The conservation gap analysis indicates that prioritizing wetland reserves in KHFAs areas, such as the SJP, SNP, and QXP, can significantly enhance the protection of wetland flagship species and their habitats. The results of this study establish conservation priorities that align with national goals of a 55 % wetland protection rate and the global biodiversity framework in protected areas and biodiversity, indicating that the spatial conservation optimization approach is an effective method for identifying wetland KHFAs. © 2025

关键词： China Conservation prioritization Waterbird key habitat Wetland Zonation model

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Adaptive multiscale convolutional neural network model for chemical process fault diagnosis

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Chinese Journal of Chemical engineering 2022年第10期50卷 398-411页

作者： Ruoshi Qin Jinsong Zhao State Key Laboratory of Chemical Engineering Department of Chemical EngineeringTsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua UniversityBeijing 100084China

Intelligent fault recognition techniques are essential to ensure the long-term reliability of *** to the variations in material,equipment and environment,the process variables monitored by sensors contain diverse data characteristics at different time scales or in multiple operating *** much progress in statistical learning and deep learning for fault recognition,most models are constrained by abundant diagnostic expertise,inefficient multiscale feature extraction and unruly multimode *** overcome the above issues,a novel fault diagnosis model called adaptive multiscale convolutional neural network(AMCNN)is developed in this paper.A new multiscale convolutional learning structure is designed to automatically mine multiple-scale features from time-series data,embedding the adaptive attention module to adjust the selection of relevant fault pattern *** triplet loss optimization is adopted to increase the discrimination capability of the model under the multimode *** benchmarks CSTR simulation and Tennessee Eastman process are utilized to verify and illustrate the feasibility and efficiency of the proposed *** with other common models,AMCNN shows its outstanding fault diagnosis performance and great generalization ability.

关键词： Neural networks Multiscale Adaptive attentionmodule Triplet lossoptimization Fault diagnosis Chemical processes

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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A Geneformer-Based Model for Identifying Early Gastric Cancer Therapeutic Targets

A Geneformer-Based Model for Identifying Early Gastric Cance...

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2025 2nd International Conference on Algorithms, Software engineering and Network Security (ASENS)

作者： Xuan Liu Guohua Li Yanshen Liao Zishao Zhong Hang Wei School of Medical Information Engineering Guangzhou University of Chinese Medicine Guangzhou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangzhou China Syndrome/Department of Gastroenterology State Key Laboratory of Traditional Chinese Medicine The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou China Guangdong Provincial Hospital of Chinese Medicine Guangdong Provincial Academy of Chinese Medical Sciences/State Key Laboratory of Dampness Syndrome of Chinese Medicine Guangdong Provincial Key Laboratory of Chinese Medicine for Prevention and Treatment of Refractory Chronic Diseases Guangzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Gastric cancer is a major global health issue characterized by high incidence and mortality rates. Early gastric cancer has a more favorable prognosis, yet most patients are diagnosed at an advanced stage due to subtle symptoms and limited diagnostic methods. Currently, the screening for therapeutic targets in early gastric cancer primarily relies on CRISPR-Cas9, next-generation sequencing, and advanced bioinformatics analysis, facing challenges such as off-target effects, high costs, complex data interpretation, and the elucidation of causal relationships. In this study, the Geneformer model was pre-trained on a large-scale corpus and fine-tuned on an early gastric cancer dataset to identify early gastric cancer cells. Potential therapeutic targets were identified through simulated gene perturbations and disease treatments, and their effectiveness was validated using KEGG pathway enrichment analysis and biological experimental results. After rigorous quality control and preprocessing of the data, the Geneformer model achieved excellent performance in the cell state annotation task, with an accuracy of 93.47% and an F1 score of 0.9187. Additionally, the model identified 445 potential therapeutic targets for early gastric cancer, such as TFF1, TFF2, and GKN2, which were enriched in gastric cancer-related pathways, with some targets validated by biological experiments. In summary, the proposed methodologies are capable of effectively identifying early gastric cancer cells and discovering potential therapeutic targets.

关键词： Sequential analysis Costs Annotations Biological system modeling Microprocessors Computer architecture data models Bioinformatics Biological cells Cancer

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A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

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Chinese Journal of Chemical engineering 2023年第3期55卷 266-276页

作者： Yiming Bai Shuaiyu Xiang Feifan Cheng Jinsong Zhao Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua UniversityBeijing 100084China Sinopec Engineering Incorporation Beijing 100084China

With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data, this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis(DiPCA) and long short-term memory(LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance, which can provide the basis for fault prognosis and health management.

关键词： Fault prognosis Process systems Safety Prediction Principal component analysis Long short term memory

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Survival Analysis of Kasturi Tobacco Plants in Jember District using the Stratified Cox and Extended Cox Models

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MethodsX 2025年

作者： Angga Iryanto Pratama Dwi Rantini Ratih Ardiati Ningrum Arip Ramadan Fazidah Othman Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Research Group of Data-Driven Decision Support System Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

In 2022, tobacco was the top export commodity, generating $106.3 million in sales. East Java Province contributes significantly to Indonesia's tobacco production, with an annual output of 188.6 thousand tons. Jember Regency is the area in East Java Province which is a Kasturi tobacco cultivation area. The productivity of Kasturi tobacco continues to decrease due to several factors that can affect the survival of the tobacco. This study aims to analyze the survival of Kasturi tobacco by creating stratified Cox and extended Cox models in order to handle factors that cannot fulfill the proportional hazard assumption. Based on the results of the analysis, the stratified Cox model is the best model with AIC and BIC values of 798.108 and 805.5748 respectively, while the extended Cox has AIC and BIC values of 840.2186 and 850.1732 respectively. Variables that can significantly affect the survival of Kasturi tobacco are the variable concentration of ZA fertilizer, pesticide concentration. The addition of ZA fertilizer must be appropriate, because if excessive it can cause poisoning of tobacco plants. Likewise with pesticides, if excessive it will cause damage to the leaves. This policy can increase the productivity of Kasturi tobacco. Then, Jember Regency contributes greater export capacity. • This paper aims to determine the factors that affect the survival of Kasturi tobacco plants in Jember • By using the extended Cox model, the best one is obtained using the Heaviside function with AIC and BIC values of 840.2186 and 850.1732, respectively • The stratified Cox model is better than the extended Cox model, which has AIC and BIC values of 798.108 and 805.5748, respectively

关键词： Productive Agriculture Cox Proportional Hazard Stratified Cox Extended Cox Kasturi Tobacco

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Refactoring administrative services module platform software

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BELL LABS TECHNICAL JOURNAL 2003年第3期8卷 105-110页

作者： Nguyen, AG Sohn, RL Member of Technical staff SFUS--System Data Development & Evolution Lucent Technologies Naperville Illinois Consulting member of Technical staff Softswitch System Engineering and Architecture Department Lucent Technologies Naperville Illinois Member IEEE

A well-defined, fully backward-compatible application programming interface (API) allowed Lucent Technologies' administrative services module software to be transformed over six releases to incorporate architectural changes driven by market needs not envisioned in its original conception. We describe this software, the changing demands placed on it, how it was refactored, and our guiding strategies. (C) 2003 Lucent Technologies Inc.

关键词： APPLICATION program interfaces (Computer software) APPLICATION software COMPUTER software

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