Long-Term Secrecy Throughput and Fairness Tradeoff for Wireless Powered Secure IoT Networks by Lei Zheng, Xiaolong Lan, Juanjuan Ren, Yong Liu, Qingchun Chen

“…In order to ensure network fairness, we construct a virtual fairness queue for each device to characterize the gap between its averaged secrecy rate and the expected secrecy min-rate, and incorporate its status into the transmission design. …”
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Combined Dynamic Route Guidance and Signal Timing Optimization for Urban Traffic Congestion Caused by Accidents by He Zhang, Shanshan Guo, Xuzhi Long, Yuanyuan Hao

“…This model is solved with a particle swarm optimization algorithm and simulated to the actual road network with VISSIM. …”
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Live video transmission optimization mechanism based on edge intelligence in high client-density environment by Xiaodan GU, Wenjia WU, Zhen LING

“…The traditional live video transmission optimization mechanism is deployed on the server side, which cannot quickly respond to the dynamic changes of the user’s wireless network environment.To address this problem, a live video transmission optimization mechanism based on edge intelligence called S-Edge was proposed.It was deployed on the OpenWrt-based wireless access point, and comprehensively utilized the wireless channel state information such as airtime utilization and signal-to-noise ratio to make intelligent decisions on terminal priority and transmission rate based on fuzzy logic theory.Furthermore, the active queue management with hierarchical token bucket and service demand-driven wireless transmission rate adaptive control technologies were introduced to realize the real-time scheduling of live video data.In order to verify the effectiveness and performance of the proposed mechanism, a high client-density environment was built through user clusters based on multi-radio interfaces in the real-world scenario.Experimental results show that S-Edge can significantly reduce the average delay and packet loss rate, which meets QoS requirements of live video transmission services in the high client-density environment.…”
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Analysis of Spatial-Temporal Characteristics of Operations in Public Transport Networks Based on Multisource Data by Hui Zhang, Yanjun Liu, Baiying Shi, Jianmin Jia, Wei Wang, Xiang Zhao

“…In addition, the relationships among average headway of stations, boarding passenger number, bus number, and complex network indicators are revealed. …”
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Enhancing Power Distribution Efficiency in Central Kalimantan through Renewable Energy Integration and Grid Optimization by Iqbal Asad Kutsuro, Faiz Husnayain

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Adaptive RFID Data Scheduling Using Proximal Policy Optimization for Reducing Data Processing Latency by Guowei Guo, Xinsen Yang, Ziwei Liang, Zeli Xi, Ximei Zhan, Peisong Li

“…This paper presents a novel approach for dynamically offloading data using deep reinforcement learning, specifically employing the Proximal Policy Optimization (PPO) algorithm. The proposed method utilizes a central controller equipped with the PPO model to make intelligent, real-time reader selection decisions based on environmental factors such as reader load, tag mobility, and network conditions. …”
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Deep neural network application in real-time economic dispatch and frequency control of microgrids by Liu Jun

“…This study explores the application of an intelligent dynamic programming algorithm based on the deep neural network algorithm, combined with adaptive dynamic programming. …”
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Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids by Sungmin Lim, Jaekyu Lee, Sangyub Lee

“…This paper presents a model predictive control (MPC)-based energy management system (EMS) for optimizing cooperative operation of networked microgrids (MGs). …”
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An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning by L. Ravi Kumar, Ravi Kumar Tata, T. R. Mahesh, Endris Mohammed Ali

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Non-destructive detection of early wheat germination via deep learning-optimized terahertz imaging by Guangming Li, Hongyi Ge, Yuying Jiang, Yuan Zhang, Xi Jin

“…Subsequently, we introduce the EfficientViT-based YOLO V8 classification model, incorporating a Depthwise Separable Attention (C2F-DSA) module, and further optimize the model using the Gazelle Optimization Algorithm (GOA). …”
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Multi-period superstructure optimization for CCS source-sink matching in South Sumatra industrial clusters by Osaliana Budiarto, Widodo Wahyu Purwanto, Alin Halimatussadiah, Dara Ayuda Maharsi

“…The findings highlight the critical role of superstructure-based designs in optimizing the interactions among sources, sinks, and transportation networks. …”
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An Optimized GWO-BPNN Model for Predicting Corrosion Fatigue Performance of Stay Cables in Coastal Environments by Liping Zhou, Guowen Yao

“…In response to the critical need for accurate fatigue life prediction of high-strength steel wires under corrosive conditions, this study proposes an innovative prediction model that combines Grey Wolf Optimization (GWO) with a Backpropagation Neural Network (BPNN). …”
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MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy by Entao Luo, Li Shi, Jiyan Liu, Zheng Wu, Guoyun Duan, Lingxuan Zeng, Tangsen Huang

“…In this paper, we propose a Metabolic Neural Network Prediction Model (MNPM) for carbon emissions. …”
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Eliciting Fairness in N-Player Network Games through Degree-Based Role Assignment by Andreia Sofia Teixeira, Francisco C. Santos, Alexandre P. Francisco, Fernando P. Santos

“…We further show that low-degree Proposer assignment is efficient, in optimizing not only individuals’ offers but also the average payoff level in the population. …”
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Regression analysis and artificial neural networks for predicting pine species volume in community forests by Wenceslao Santiago-García

“…These variables were explicitly incorporated into the ANN structure, enabling the model to learn complex, non-linear interactions. The ANN was optimized using L1 regularization and the Adam optimizer. …”
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Municipal solid waste supply chain optimization for value-added product development under uncertainty by Muazzam Mukhtar, Muhammad Rizwan, Atta Ullah, Ali Elkamel, Salman Raza Naqvi, Muhammad Zaman

“…This study focuses on addressing the MSW supply chain problem by exploring the optimal location for the waste treatment. The supply chain network encompasses MSW transfer stations, treatment facilities, and markets with product demands. …”
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Enhanced prediction of corrosion rates of pipeline steels using simulated annealing-optimized ANFIS models by Ali Hussein Khalaf, Bing Lin, Ahmed N. Abdalla, Zhongzhi Han, Ying Xiao, Junlei Tang

“…This study presents a novel approach integrating Simulated Annealing (SA) with an Adaptive Network-based Fuzzy Inference System (ANFIS) to improve corrosion rate predictions for pipeline steels. …”
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Warehouse Network Design With Fortification Considerations: A Mixed-Integer Nonlinear Modeling Approach by Tareq Oshan

“…This study presents a mathematical model for optimizing warehouse-location decisions in supply-chain network design. …”
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Hybrid channel allocation strategy with group switching and performance evaluation in cognitive radio network by Shun-fu JIN, Hong-ting XIE, Yuan ZHAO

“…Considering the non-real-time traffic in cognitive radio networks and two groups of licensed channels,com-bing the methods of random back-off and control center schedule,a novel hybrid channe allocation strategy with channel group switching mechanism was proposed.Taking into account the total number of the cognitive packets and the numbers of the primary packets on each group channels,a three-dimensional Markov model was constructed,some cogniti user measures in terms of the blocking ratio,the packet loss ratio and the average delay were derived accordingly.Numerical results with analysis and simulation show that,as the back-off time changes,a tradeoff exists between different perfor-mance measures.Therefore,by minimizing the system cost,the back-off time can be optimized.…”
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Prediction of properties of electroless nickel plating with diamond powder based on artificial neural network by Lili FANG, Han LIU, Yufei JIANG

“…The absolute relative error between the predicted coating performance values and experimental values of the GRNN model was less than 10.00%, with an average absolute relative error of 5.07%. In predicting the performance of electroless nickel plating on diamond micro powders, the predictive performance of GRNN is superior to that of BP neural network.ConclusionsThe prediction error values of BP neural network and GRNN for the chemical plating performance of diamond micropowder are both less than 10.00%, which proves that they can be used to predict the relevant results and reduce the number of experiments to obtain optimal process parameters. …”
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