Distributed Coordinated Dispatch Model for Multi-area Interconnected Integrated Energy Systems Based on Sequential Cone Programming by Yujie REN, Yuhan HUANG, Zhenbo WEI

“…The solution of the distributed algorithm based on ATC is close to the global optimal solution of the distributed algorithm. …”
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Matching heterogeneous ontologies with adaptive evolutionary algorithm by Xingsi Xue, Haolin Wang, Xin Zhou, Guojun Mao, Hai Zhu

“…Ontology matching technique uses the similarity measure to determine the correspondences between two heterogeneous ontology entities. In order to improve the quality of ontology alignment, it is necessary to combine different kinds of similarity measures, and how to optimize the aggregating weights is called the ontology meta-matching problem. …”
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Risk-Aware Lane Change and Trajectory Planning for Connected Autonomous Vehicles Based on a Potential Field Model by Tao Wang, Dayi Qu, Kedong Wang, Chuanbao Wei, Aodi Li

“…These candidate trajectories are further assessed using a minimum safety distance model derived from potential field theory, with optimization focusing on safety, comfort, and efficiency. …”
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Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The integration of these models within an AdaBoost framework, coupled with advanced hyperparameter tuning via the Firefly Algorithm (FA), demonstrates a novel strategy for improving predictive accuracy and model robustness. …”
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Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models. by Yubo Zhao, Mo Chen

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”
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Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions by YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying

“…[Methods] Based on historical precipitation, temperature, and runoff sequences from the Yulongkashi River, a Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (CNN-BiGRU-Attention) model was developed. An Improved Particle Swarm Optimization (IPSO) algorithm was used to optimize this model, forming the IPSO-CNN-BiGRU-Attention hybrid model. …”
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Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment by Abuduaini Abudureheman, Yan Zhao, Aishanjiang Nilupaer

“…However, the diversity of commodities poses challenges such as data imbalance, model overfitting, and underfitting. To address these issues, this paper presents an improved generative adversarial network model that integrates the strengths of Conditional Generative Adversarial Nets and the Wasserstein Generative Adversarial Network. …”
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A High-Feasibility Real-Time Trajectory-Planning Method for Parafoils Based on a Flexible Dynamic Model by Jiaming Yu, Hao Sun, Qinglin Sun, Mingwei Sun, Zengqiang Chen

“…To address these issues, this paper proposes a flexible 8-degree-of-freedom (8-DOF) dynamic model based on the FSI method, utilizing the actual aerodynamic parameters of the canopy to achieve improved consistency with the behavior of the actual system. …”
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Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC by Muhammad Ricky Perdana Putra, Ema Utami

“…The use of ensemble techniques to build models can improve performance, but previous research has not reviewed the most optimal ensemble technique for this case study. …”
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Advanced biomedical imaging for identifying blood cell type: Integrating segmentation, feature extraction, and GraphSAGE model by Nur Mohammad Fahad, Mohaimenul Azam Khan Raiaan, Arefin Ittesafun Abian, Ripon Kumar Debnath, Sidratul Montaha, Mirjam Jonkman, Sami Azam

“…An improved GraphSAGE model is constructed to classify blood cells. …”
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RBCKF-Based Vehicle State Estimation by Adaptive Weighted Fusion Strategy Considering Composite-State Tire Model by Xi Chen, Xinlong Cheng

“…Meanwhile, considering the adaptive and dynamic adjustment capabilities of the observation system in complex state-change scenarios, an estimation strategy based on adaptive weight fusion and a model-based estimator is proposed. The results confirm that the robust bias compensation Kalman filter can ensure estimation exactitude and robustness when the vehicle state fluctuates greatly, and the proposed fusion strategy can ensure that the vehicle maintains optimal estimation performance during operating condition switching.…”
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A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis by Lei You, Wenjie Fan, Zongwen Li, Ying Liang, Miao Fang, Jin Wang

“…Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. …”
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Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph. by Lin Jun, Zhou Chenliang

“…It can not only meet the demand of users and realize the optimal allocation of resources, but also improve the safety, economy and reliability of power supply, it has become a major trend in the future development of electric power industry. …”
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Research on High Arch Dam Deformation Monitoring Model with Deep Capturing Related Features in Factor-time Dimensions by XUE Jianghan, ZHANG Pengtao, TIAN Jichen, LU Xiang, CHEN Jiankang, Guo Yinju

“…However, at the present stage, the dam prediction model based on machine learning mostly adopts the means of data preprocessing, using optimization algorithm, and using the model's characteristics to stack multiple models, lacking in in-depth consideration of the physical mechanism of dam deformation. …”
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Cooperative routing algorithm based on game theory by Kun XIE, Shen-lin DUAN, Ji-gang WEN, Shi-ming HE

“…VMIMO routing among groups was modeled as a repeated routing game. To improve the data delivery ratio, a fit function was proposed to evaluate the nodes' credit for participating in packet for-warding. …”
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Cooperative routing algorithm based on game theory by Kun XIE, Shen-lin DUAN, Ji-gang WEN, Shi-ming HE

“…VMIMO routing among groups was modeled as a repeated routing game. To improve the data delivery ratio, a fit function was proposed to evaluate the nodes' credit for participating in packet for-warding. …”
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A Novel Time Delay Nonsingular Fast Terminal Sliding Mode Control for Robot Manipulators with Input Saturation by Thanh Nguyen Truong, Anh Tuan Vo, Hee-Jun Kang

“…Manipulator systems are increasingly deployed across various industries to perform complex, repetitive, and hazardous tasks, necessitating high-precision control for optimal performance. However, the design of effective control algorithms is challenged by nonlinearities, uncertain dynamics, disturbances, and varying real-world conditions. …”
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Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System by Yi Sun, Xinke Liu

“…To address the issues of low efficiency in manual processing and lack of accuracy in judgment within traditional mine gas safety inspections, this paper designs and implements the Intelligent Mine Gas State Decision-Making System based on large language models (LLMs) and a multi-agent system. The system aims to enhance the accuracy of gas over-limit alarms and improve the efficiency of generating judgment reports. …”
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Integrated export instream coefficient model for accurate nonpoint source pollution estimation and management in the Yellow River Basin by Xueting Wang, Lei Wu, Yongkun Luo, Yimu Liu, Ruowen Wang

“…Future research can further explore the impact of improving temporal resolution, future climate change and combining hydrodynamic models on the ability to simulate the amount of pollutants entering the river.…”
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Intelligent Methods of Operational Response to Accidents in Urban Water Supply Systems Based on LSTM Neural Network Models by Aliaksey A. Kapanski, Nadezeya V. Hruntovich, Roman V. Klyuev, Aleksandr E. Boltrushevich, Svetlana N. Sorokova, Egor A. Efremenkov, Anton Y. Demin, Nikita V. Martyushev

“…The incorporation of seasonal parameters improved prediction accuracy. The model training time increased significantly with the number of layers and neurons, but this did not always result in improved forecast accuracy. …”
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