Multiphase Transport Network Optimization: Mathematical Framework Integrating Resilience Quantification and Dynamic Algorithm Coupling by Linghao Ren, Xinyue Li, Renjie Song, Yuning Wang, Meiyun Gui, Bo Tang

“…Next, we create a dynamic adaptive public transit optimization model using an entropy weight-TOPSIS decision framework coupled with an improved simulated annealing algorithm (ISA-TS), achieving coordinated suburban–urban network optimization while maintaining 92.3% solution stability under simulated node failure conditions. …”
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Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning by Yihan Zheng, Zhuanji Fang, Xizhu Wu, Huale Zhang, Pengming Sun

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Electric Vehicle Charging Load Forecasting Method Based on Improved Long Short-Term Memory Model with Particle Swarm Optimization by Xiaomeng Yang, Lidong Zhang, Xiangyun Han

“…In the spring, the MAE is 3.910, showing a 2.71% improvement over the LSTM model and a 7.32% reduction compared to the GRU model.…”
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Stochastic sizing and energy management of a hybrid energy system using cloud model and improved Walrus optimizer for China regions by Wenjun Liao, Qing Xiong, Zilong Chen, Jinhui Tan, Pingfei Li, Hadi Gharoei

“…Additionally, the cloud model findings demonstrate how uncertainty distributions impact the system’s operation, with the variation in cloud model droplets on both sides of the expected value reflecting the effects of renewable generation and demand uncertainties. …”
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Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model by Yu Xia, Ao Shen, Tianci Che, Wenbo Liu, Jie Kang, Wei Tang

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Flexible Job Shop Dynamic Scheduling and Fault Maintenance Personnel Cooperative Scheduling Optimization Based on the ACODDQN Algorithm by Jiansha Lu, Jiarui Zhang, Jun Cao, Xuesong Xu, Yiping Shao, Zhenbo Cheng

“…To this end, a self-learning Ant Colony Algorithm based on deep reinforcement learning (ACODDQN) is designed in this paper. …”
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SMEA-YOLOv8n: A Sheep Facial Expression Recognition Method Based on an Improved YOLOv8n Model by Wenbo Yu, Xiang Yang, Yongqi Liu, Chuanzhong Xuan, Ruoya Xie, Chuanjiu Wang

“…Additionally, the EfficiCIoU loss function replaces the original Complete IoU(CIoU) loss function, thereby improving bounding box localization accuracy and accelerating model convergence. …”
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An Ensemble Model for Predicting Cardiovascular Disease utilizing Nature Inspired Optimization by Annwesha Banerjee Majumder, Somsubhra Gupta, Sourav Majumder, Dharmpal Singh

“… This paper represents an efficient model for heart disease prediction model utilizing an ensemble mechanism optimized through BAT algorithm. …”
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Fractional order modelling and optimal control of dual active bridge converters by Zengbo Dong, Peng Yang, Qian Li, Mingxiao Zhang, Yujian Chang, Shuohe Wang

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Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model by Gonglin Xu, Mei Zhang, Wanli Chen, Zhihui Wang

“…The final step involves optimizing the Light Gradient Boosting Machine (LightGBM) model using IAOS algorithm for transformer fault classification; this model is an ensemble learning model. …”
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Dissolved Oxygen Prediction Based on SOA-SVM and SOA-BP Models by ZHANG Xuekun

“…To improve the accuracy of dissolved oxygen prediction,this paper researches and proposes a prediction method that combines seagull optimization algorithm (SOA) with support vector machine (SVM) and BP neural network,prepares four prediction schemes based on the monthly dissolved oxygen monitoring data of the Jinghong Power Station in Xishuangbanna,a national important water supply source in Yunnan Province,from January 2009 to September 2020,optimizes the key parameters of SVM and weight threshold of BP neural network by SOA to construct SOA-SVM and SOA-BP models,predicts the dissolved oxygen of Jinghong Power Station based on the models,and compares the prediction results with those of SVM and BP models.The results show that:The absolute values of the average relative errors of the SOA-SVM and SOA-BP models for the 4 schemes of dissolved oxygen prediction are between 4.07%~4.98% and 3.85%~4.83%,and that of the average absolute errors are 0.309~0.374 mg/L and 0.294~0.371 mg/L,respectively.With better prediction accuracy than SVM and BP models,they have good prediction accuracy and generalization ability.SOA can effectively optimize the key parameters of SVM and weight threshold of BP neural network.SOA-SVM and SOA-BP models are feasible for dissolved oxygen prediction,which can provide references for related prediction research.…”
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Robust Photovoltaic Power Forecasting Model Under Complex Meteorological Conditions by Yuxiang Guo, Qiang Han, Tan Li, Huichu Fu, Meng Liang, Siwei Zhang

“…To effectively mitigate these limitations, this work proposes a dual-stage feature extraction method based on Variational Mode Decomposition (VMD) and Principal Component Analysis (PCA), enhancing multi-scale modeling and noise reduction capabilities. Additionally, the Whale Optimization Algorithm is adopted to efficiently optimize the hyperparameters of iTransformer for the framework, improving parameter adaptability and convergence efficiency. …”
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Time Series Prediction Method of Clean Coal Ash Content in Dense Medium Separation Based on the Improved EMD-LSTM Model by Kai Cheng, Xiaokang Zhang, Keping Zhou, Chenao Zhou, Jielin Li, Chun Yang, Yurong Guo, Ranfeng Wang

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Mechanism- and data-driven algorithms of electrical energy consumption accounting and prediction for medium and heavy plate rolling by Qiang Guo, Zimeng Zhou, Jie Li, Fengwei Jing

“…Furthermore, a random forest regression model of additional power consumption based on data was established, and then a prediction algorithm for the comprehensive power consumption of billet rolling was given. …”
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A Method for Quantifying Mung Bean Field Planting Layouts Using UAV Images and an Improved YOLOv8-obb Model by Kun Yang, Xiaohua Sun, Ruofan Li, Zhenxue He, Xinxin Wang, Chao Wang, Bin Wang, Fushun Wang, Hongquan Liu

“…Compared with YOLOv8, YOLOv8-obb, YOLOv9, and YOLOv10, the YOLOv8-obb-p2 model improved precision by 1.6%, 0.1%, 0.3%, and 2%, respectively, and F1 scores improved by 2.8%, 0.5%, 0.5%, and 3%, respectively. …”
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Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice by G. Colas, V. Masson, F. Bouttier, L. Bouilloud, L. Pavan, V. Karsisto

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Improving Fall Classification Accuracy of Multi-Input Models Using Three-Axis Accelerometer and Heart Rate Variability Data by Seunghui Kim, Jae Eun Ko, Seungbin Baek, Daechang Kim, Sungmin Kim

“…Compared to the classification model using conventional HRV and ACC, a higher accuracy was achieved in the multi-input model using ACC-HRV data, and a precision, recall, and F1 score of 0.91 was measured, indicating improved performance. …”
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Technique of Performance Improvement of the Microprocessor-Based Protection Measuring Element by F. A. Romaniuk, V. Yu. Rumiantsev, I. A. Novash, Yu. V. Rumiantsev

“…In the MatLab-Simulink dynamic modeling environment a mathematical model of the developed measuring element has been implemented, as well as a model of the elements of the power system. …”
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Improved Set-point Tracking Control of an Unmanned Aerodynamic MIMO System Using Hybrid Neural Networks by Oduetse Matsebe, David Mohammed Ezekiel, Ravi Samikannu

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DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework by Arshak Rezvani, Nasrin Ravansalar, Mohammad Ali Akhaee, Andrew J. Greenshaw, Russell Greiner, Maryam S. Mirian, Muhammad Yousefnezhad, Martin J. McKeown

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