Cyber Potential Metaphorical Map Method Based on GMap by Dongyu Si, Bingchuan Jiang, Qing Xia, Tingting Li, Xiao Wang, Jingxu Liu

“…A cyber-geography metaphor model integrates scientific and geographical elements, improving readability. …”
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Real defect partial discharge identification method for power cables joints based on integrated PJS-M and GA-SVM algorithm with multi-source fusion by Ling-Xuan Zhang, Yi-Yang Zhou, Shen-Jiong Yao, Jia-Luo Chai, Ying-Jing Chen, Zhou-Sheng Zhang

“…The proposed model was compared with several state-of-the-art SVM optimization algorithms, including GA-SVM, PCA-SVM, and PSO-SVM. …”
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Chaotic chimp-mountain gazelle optimized FOPID control for frequency regulation in islanded airport microgrids with heterogeneous energy systems by Odelu P., Chandan Kumar Shiva, Sachidananda Sen, Vedik Basetti, Chandra Sekhar Reddy

“…To address these issues, this study proposes a Chaotic Chimp Mountain Gazelle Optimizer (CCMGO) algorithm. The CCMGO algorithm integrates the exploration capabilities of the Chimp Optimization Algorithm (ChOA) with the fast convergence of the Mountain Gazelle Optimizer (MGO), which is further enhanced by chaotic mapping to improve search diversity and avoid local optima. …”
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Neural Network Method of Controllers’ Parametric Optimization with Variable Structure and Semi-Permanent Integration Based on the Computation of Second-Order Sensitivity Functions... by Serhii Vladov, Lukasz Scislo, Nina Szczepanik-Ścisło, Anatoliy Sachenko, Victoria Vysotska

“…The traditional PI controller modification, including a switching function for adaptation to operating conditions, allows for the system’s effective control in real time. The controller optimization algorithm is based on a functional expression with weighting coefficients that take into account control errors and the control action. …”
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Joint Optimization of Item and Pod Storage Assignment Problems with Picking Aisles’ Workload Balance in Robotic Mobile Fulfillment Systems by Jun Zhang, Lingkun Tian, Zijuan Zhou

“…The improved genetic algorithm (IGA) with the decentralized pod storage assignment strategy is designed to solve the J-IPSAP model. …”
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Machine Learning-Driven Optimization of Transport Layers in MAPbI₃ Perovskite Solar Cells for Enhanced Performance by Velpuri Leela Devi, Piyush Kuchhal, Debasis de, Abhinav Sharma, Neeraj Kumar Shukla, Mona Aggarwal

“…In this research work, among those eight ML models, the XGBoost algorithm shows high accuracy for predicting the power conversion efficiency (PCE) of the cell, achieving root mean square error (RMSE) of 0.052 and a coefficient of determination (R2) of 0.999. …”
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A Snow Depth Downscaling Algorithm Based on Deep Learning Fusion of Enhanced Passive Microwave and Cloud-Free Optical Remote Sensing Data in China by Zisheng Zhao, Xiaohua Hao, Donghang Shao, Wenzheng Ji, Tianwen Feng, Qin Zhao, Wenxin He, Liyun Dai, Zhaojun Zheng, Yan Liu

“…When compared with downscaling algorithms utilizing the raw TB data and the traditional random forest model, the CETB data and FT-Transformer model optimize the RMSE by 10.08% and 4.84%, respectively, which demonstrates the superiority of FTSD regarding data sources and regression methods. …”
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Prediction of Students’ Performance Based on the Hybrid IDA-SVR Model by Huan Xu

“…The results show that the IDA algorithm can effectively avoid the local optima and the blindness search and can definitely improve the speed of convergence to the optimal solution.…”
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Toward a linear-ramp QAOA protocol: evidence of a scaling advantage in solving some combinatorial optimization problems by J. A. Montañez-Barrera, Kristel Michielsen

“…Abstract The quantum approximate optimization algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters $${\{{\gamma }_{i},{\beta }_{i}\}}_{i = 0}^{p-1}$$ { γ i , β i } i = 0 p − 1 . …”
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Progress and perspectives on genomic selection models for crop breeding by Dongfeng Zhang, Feng Yang, Jinlong Li, Zhongqiang Liu, Yanyun Han, Qiusi Zhang, Shouhui Pan, Xiangyu Zhao, Kaiyi Wang

“…A series of algorithms have been proposed to improve the prediction accuracy of genomic selection. …”
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Optimization of Electric Vehicle Charging and Discharging Strategies Considering Battery Health State: A Safe Reinforcement Learning Approach by Shuifu Gu, Kejun Qian, Yongbiao Yang

“…With the widespread adoption of electric vehicles (EVs), optimizing their charging and discharging strategies to improve energy efficiency and extend battery life has become a focal point of current research. …”
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Enhancing molecular property prediction with quantized GNN models by Areen Rasool, Jamshaid Ul Rahman, Rongin Uwitije

“…Therefore, optimizing storage, reducing resource consumption, and improving inference speed are crucial. …”
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Customer Clustering and Marketing Optimization in Hospitality: A Hybrid Data Mining and Decision-Making Approach from an Emerging Economy by Maryam Deldadehasl, Houra Hajian Karahroodi, Pouya Haddadian Nekah

“…Using a hybrid approach that integrates data mining with multi-criteria decision-making techniques, this study aims to identify valuable customer segments and optimize marketing strategies. This research applies the K-means clustering algorithm to classify customers from a hotel in Iran based on RMD attributes. …”
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Co-Optimization Operation of Distribution Network-Containing Shared Energy Storage Multi-Microgrids Based on Multi-Body Game by Hao Wu, Ge Cao, Rong Jia, Yan Liang

“…The method is modeled and solved in two stages. In the first stage, a multi-objective optimization configuration model for shared energy storage among multi-microgrids is established, with optimization objectives balancing the randomness of renewable energy fluctuations and the economics of each microgrid undertaking shared energy storage. …”
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CFD-APSO Co-Optimization for Enhanced Heat Dissipation in a <i>Camellia oleifera</i> Harvester Engine Compartment by Wenfu Tong, Kai Liao, Lefeng Zhou, Haifei Chen, Hong Luo, Jichao Liang

“…Based on these findings, the component arrangement was reconfigured to improve thermal performance. Subsequently, an “engine compartment cover parameters–temperature” correlation model was established, and the dimensional parameters of the engine compartment cover were optimized using the APSO algorithm. …”
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Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections by Xin Liu, Guojing Shi, Changbo Yang, Enyong Xu, Yanmei Meng

“…In the lower layer, an energy management model is constructed using the twin delayed deep deterministic policy gradient (TD3) algorithm to achieve optimal allocation of demanded power through the interaction of the TD3 agent with the truck environment. …”
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An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid by Mohib Hussain, Du Lin, Hassan Waqas, Qasem M. Al-Mdallal

“…However, conventional methods for modelling and optimizing these frameworks frequently encounter challenges owing to their intricacy and the multitude of interconnected variables. …”
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Multi-USV Task Assignment Based on NSGA II-MC by Yonghao Zhang, Xueman Fan, Zhuo Cheng, Changyou Xue

“…An improved task allocation optimization algorithm, NSGA II-MC (Non-dominated Sorting Genetic Algorithm II-Monte Carlo), has been proposed. …”
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Research on Joint Stiffness Identification and Error Compensation of the Serial Six DOF Robot by Ping Rui, Guifang Qiao, Xiulan Wen, Ying Zhang, Dongxia Wang

“…Secondly，in order to improve the identification accuracy and efficiency of joint stiffness parameters, the BP neural network is applied to fit the stiffness error model to optimize the initial population fitness of genetic algorithm. …”
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Peak-Valley difference based pricing strategy and optimization for PV-storage electric vehicle charging stations through aggregators by Qin Yan, Jinxin Wang, Tao Lin, Archie James Johnston

“…Simulation results show that the proposed model improves grid resilience, enhances economic benefits for both aggregators and users, and optimizes the system’s performance. …”
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