Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model by Liwei Zhang, Lisang Liu, Wenwei Chen, Zhihui Lin, Dongwei He, Jian Chen

“…Empirical tests on a PV dataset from an Australian solar power plant show that the proposed CECSVB-LSTM model significantly outperforms traditional single models and combination models with different decomposition methods, improving R² by more than 7.98% and reducing the root mean square error (RMSE) and mean absolute error (MAE) by at least 60% and 55%, respectively.…”
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INFO-RF-based fault diagnosis and analysis method for busbars by Chen Xue, Jian Zhu, Haiou Cao, Yan Gu, Siyu Chen

“…A simulation model of a dual-busbar power system is first established, and key electrical quantities such as differential current, bus tie current, and voltage are extracted to quantify fault features using Root Mean Square (RMS) values. The RF model is then used to predict fault types and fault resistance, with the INFO algorithm iteratively optimizing the hyperparameters of the RF model to further improve prediction accuracy. …”
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Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks by Ihor Blinov, Virginijus Radziukynas, Pavlo Shymaniuk, Artur Dyczko, Kinga Stecuła, Viktoriia Sychova, Volodymyr Miroshnyk, Roman Dychkovskyi

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Research on path planning for coal mine rescue robots by ZHU Hongbo, YIN Hongliang

“…Firstly, an adjustment mechanism of collision constraint function is incorporated into the bidirectional A* algorithm to improve path safety. Next, a correction factor is incorporated into the cost function of the Bidirectional A* algorithm to ensure that the forward and backward search paths intersect, preventing them from diverging. …”
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Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models by Yasmine Gaaloul, Olfa Bel Hadj Brahim Kechiche, Houcine Oudira, Aissa Chouder, Mahmoud Hamouda, Santiago Silvestre, Sofiane Kichou

“…Accurate and reliable fault detection in photovoltaic (PV) systems is essential for optimizing their performance and durability. This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss analysis and predictive models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. …”
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Bond behavior of galvanized iron fiber reinforced concrete with recycled coarse aggregate and model prediction using machine learning by Md. Jahidul Islam, Ummul Wara Labiba, Tasfiah Faisal Chowdhury, Md. Shahjalal, Tanvir Mustafy, Tanvir Hassan Tusher

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Multi-Agent Mapping and Tracking-Based Electrical Vehicles with Unknown Environment Exploration by Chafaa Hamrouni, Aarif Alutaybi, Ghofrane Ouerfelli

“…When rapid deliveries are required, the algorithm prioritizes faster routes, whereas, for flexible schedules, it optimizes energy conservation. …”
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Multi-strategy fusion binary SHO guided by Pearson correlation coefficient for feature selection with cancer gene expression data by Yu-Cai Wang, Hao-Ming Song, Jie-Sheng Wang, Xin-Ru Ma, Yu-Wei Song, Yu-Liang Qi

“…Firstly, the CEC-2022 test functions were used to test the performance of the multi-strategy fusion SHO, from which the best variant TanASSHO was selected, and then compared with other nine swarm intelligent optimization algorithms. Performance tests of various algorithm variants on 18 UCI datasets show that V1PTASSHO is the most effective binary version. …”
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SC-Route: A Scalable Cross-Layer Secure Routing Method for Multi-Hop Inter-Domain Wireless Networks by Yanbing Li, Yang Zhu, Shangpeng Wang

“…Second, we introduce a cross-layer information fusion mechanism that allows nodes to adapt routing costs in real time under heterogeneous network conditions, thereby improving path reliability and load balancing. …”
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Сontrol of Intelligent Transport System in Minsk by D. V. Kapskiy, D. V. Navoy, P. A. Pegin

“…The paper considers algorithms for searching a maximum traffic volume of road vehicles in a traffic light cycle with a distributed intensity pulse and optimization of shifts under coordinated traffic flow control. …”
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Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning by YAN Li, WEN Hu, WANG Zhenping, JIN Yongfei

“…The application results from the 215 working face of the Huangling No. 2 Coal Mine in Shaanxi Province showed that the maximum validity clustering rate (MVCR) and adjusted rand index (ARI) of SSGMM and SSK-Means reached 82.64% and 85.83%, respectively, significantly outperforming conventional clustering methods. After optimization through a dynamic feedback mechanism, boreholes initially classified as "poor" showed an improvement of 5.26% to 5.80% in extraction efficiency, achieving a 100% remediation rate.…”
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Interactive online learning method for students based on artificial intelligence by Cizhang Li, Wenfen Yin

“…This study proposes a novel approach that integrates the Dwarf Mongoose Optimization (DMO) algorithm with a Gated Recurrent Unit (GRU) neural network to develop an AI-powered interactive online learning model. …”
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New method for landslide susceptibility evaluation in alpine valley regions that considers the suitability of InSAR monitoring and introduces deformation rate grading by Dingyi Zhou, Zhifang Zhao, Wenfei Xi, Xin Zhao, Jiangqin Chao

“…Taking the Dongchuan district, the most typical high mountain valley area in southwest China, as the research object, the SAR is quantitatively simulated and analyzed in this study using the improved R-index shadow layover method. …”
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Lossless Compression with Trie-Based Shared Dictionary for Omics Data in Edge–Cloud Frameworks by Rani Adam, Daniel R. Catchpoole, Simeon J. Simoff, Zhonglin Qu, Paul J. Kennedy, Quang Vinh Nguyen

“…The preprocessed data are subsequently transmitted to the cloud, where advanced compression algorithms, such as Zstd, GZIP, Snappy, and LZ4, further compress them. …”
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Robust Framework for PMU Placement and Voltage Estimation of Power Distribution Network by Nida Khanam, Mohd. Rihan, Salman Hameed

“…The framework improves network observability with a minimum of PMUs, lowering deployment costs while retaining excellent estimation accuracy. …”
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SABO-ELM model for remaining life prediction of lithium-ion batteries under multiple health factors by Jiabo LI, Zhonglin SUN, Di TIAN, Zhixuan WANG

“…The SABO algorithm optimizes the weights and bias thresholds of the ELM model, which effectively reduces the risk of local optima and improves its predictive performance and stability. …”
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Electric Vehicle Cluster and Scheduling Strategy Based on Dynamic Game by LI Shuai, DING Xiying, JIANG Hongfa

“…The upper layer takes the peak shaving demand and peak shaving cost of distribution system operator (DSO) as the optimization objectives, and uses an improved multi-objective particle swarm optimization algorithm to obtain the game strategy set of DSO. …”
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Control-Oriented Real-Time Trajectory Planning for Heterogeneous UAV Formations by Weichen Qian, Wenjun Yi, Shusen Yuan, Jun Guan

“…Inspired by Model Predictive Control (MPC), in the trajectory planning stage, the method generates multi-step trajectory points using an improved artificial potential field (APF) method, estimates the actual formation trajectory using the prediction network, and optimizes the trajectory through a multi-objective particle swarm optimization (MOPSO) algorithm after evaluating the planning costs. …”
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Vibration control and noise attenuation strategies via acoustic metamaterial in railway transportation: a state-of-the-art review by Cong Wang, Guifeng Wang, Zhenyu Chen, C. W. Lim, Weiqiu Chen

“…Additionally, integrating optimization algorithms and artificial intelligence (AI) enhances AMM design precision and scalability. …”
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Advanced Machine Learning Methodology for Earthquake Magnitude Forecasting Using Comprehensive Seismic Data by Subhieh El-Salhi, Bashar Igried, Sari Awwad

“…Feature selection was performed using Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing, while ten machine learning models were implemented — ranging from Linear Regression and Decision Trees to Gradient Boosting, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) networks. …”
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