Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…To address these limitations, the integration of digital technologies, such as computational modeling and machine learning (ML), has become increasingly important.MethodsThis paper proposes a hybrid multiscale modeling framework that integrates molecular dynamics (MD) simulations, finite element methods (FEM) from continuum mechanics, and supervised ML algorithms—including deep neural networks and gradient boosting regressors—to enable accurate and efficient prediction of material properties across scales. …”
Get full text

Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP by Shuang Liu, Enxiang Qu, Chun LV, Xueyuan Zhang

“…Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value.…”
Get full text

Small Scale Multi-Object Segmentation in Mid-Infrared Image Using the Image Timing Features–Gaussian Mixture Model and Convolutional-UNet by Meng Lv, Haoting Liu, Mengmeng Wang, Dongyang Wang, Haiguang Li, Xiaofei Lu, Zhenhui Guo, Qing Li

“…The approach integrates the Image Timing Features–Gaussian Mixture Model (ITF-GMM) and Convolutional-UNet (Con-UNet) to improve the accuracy of target detection. …”
Get full text

Multi-Scale Spatiotemporal Feature Enhancement and Recursive Motion Compensation for Satellite Video Geographic Registration by Yu Geng, Jingguo Lv, Shuwei Huang, Boyu Wang

“…Based on the SuperGlue matching algorithm, the method achieves automatic matching of inter-frame image points by introducing the multi-scale dilated attention (MSDA) to enhance the feature extraction and adopting a joint multi-frame optimization strategy (MFMO), designing a recursive motion compensation model (RMCM) to eliminate the cumulative effect of the orbit error and improve the accuracy of the inter-frame image point matching, and using a rational function model to establish the geometrical mapping between the video and the ground points to realize the georeferencing of satellite video. …”
Get full text

Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD by Xukang Yang, Anqi Jiang, Wanlu Jiang, Yonghui Zhao, Enyu Tang, Zhiqian Qi

“…This model uses a multi-scale one-dimensional residual neural network (M1D_ResNet) for feature extraction and a genetic algorithm (GA)-optimized support vector data description (SVDD). …”
Get full text

Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models by Emrah ASLAN, Yıldırım ÖZÜPAK

“…In order to improve the performance of the machine learning models, hyperparameter tuning was performed by Grid Search Optimization method. …”
Get full text

Model‐Free Deep Reinforcement Learning with Multiple Line‐of‐Sight Guidance Laws for Autonomous Underwater Vehicles Full‐Attitude and Velocity Control by Chengren Yuan, Changgeng Shuai, Zhanshuo Zhang, Jianguo Ma, Yuan Fang, YuChen Sun

Get full text

Artificial Intelligence Models for Predicting Stock Returns Using Fundamental, Technical, and Entropy-Based Strategies: A Semantic-Augmented Hybrid Approach by Gil Cohen, Avishay Aiche, Ron Eichel

“…This study examines the effectiveness of combining semantic intelligence drawn from large language models (LLMs) such as ChatGPT-4o with traditional machine-learning (ML) algorithms to develop predictive portfolio strategies for NASDAQ-100 stocks over the 2020–2025 period. …”
Get full text

Three-Dimensional Shape Reconstruction from Digital Freehand Design Sketching Based on Deep Learning Techniques by Ding Zhou, Guohua Wei, Xiaojun Yuan

Get full text

Full-chain comprehensive assessment and multi-scenario simulation of geological disaster vulnerability based on the VSD framework: a case study of Yunnan province in China by Li Xu, Shucheng Tan, Runyang Li

“…Furthermore, the Ordered Weighted Averaging (OWA) algorithm and the Partical Swarm Optimization-Support Vector Machine (PSO-SVM) model were combined to simulate future GDV scenarios for 2030–2050 under three development preferences: environment oriented, status quo, and economically oriented. …”
Get full text

Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions by Oğuzhan Timur, Bayram Kaan Uzundağ

“…The partial shading of PV modules is one of the most crucial factors that causes the performance degradation of PV systems. …”
Get full text

Study on debris flow vulnerability of ensemble learning model based on spy technology A case study of upper Minjiang river basin by Yutao Chen, Ning Li, Fucheng Xing, Han Xiang, Zilong Chen

“…In this paper, a debris flow susceptibility assessment model is constructed based on RF (Random Forest) and XGBoost (Extreme Gradient Boosting) models with Stacking ensmble learning method, and SPY technique is introduced to optimize the negative sample selection. …”
Get full text

Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches by Burcu Ramazanli, Oyku Yagmur, Efe Cesur Sarioglu, Huseyin Enes Salman

“…Research on abdominal aortic aneurysms (AAAs) primarily focuses on developing a clear understanding of the initiation, progression, and treatment of AAA through improved model accuracy. High-fidelity hemodynamic and biomechanical predictions are essential for clinicians to optimize preoperative planning and minimize therapeutic risks. …”
Get full text

PolSAR Forest Height Estimation Enhancement With Polarimetric Rotation Domain Features and Multivariate Sensitivity Analysis by Fu-Gen Jiang, Ming-Dian Li, Si-Wei Chen

“…Then, we propose a Bayesian-optimized ensemble learning algorithm to improve the accuracy of forest height estimation. …”
Get full text

Machine Learning in the National Economy by Azamjon A. Usmonov

“…Methods of cleaning, normalization, and data transformation were used for data processing to improve model accuracy. The practical part of the study included the development of machine learning algorithms for predicting economic indexes. …”
Get full text

Two-sided Energy Storage Cooperative Scheduling Method for Transmission and Distribution Network Based on Multi-agent Attention-deep Reinforcement Learning by CHEN Shi, ZHU Yujie, LIU Yihong, XU Liuchao, TANG Guodeng

“…The attention mechanism is introduced into the evaluation network to capture interdependencies among agents, enabling potential intent recognition and cooperative behavior perception, thereby improving algorithm convergence. Additionally, noise is added to expand the exploration space, enhancing training stability.Results and Discussions　Using a modified IEEE transmission-distribution joint system as an example, the modeling and solution demonstrate that the multi-agent attention mechanism can strengthen the focus among collaborators, thereby balancing the interests of both parties. …”
Get full text

Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines by Pengxiang Li, Aiying Gao, Mingzhou Bai, Shumao Qiu

“…The WOE (Weight of Evidence) model was selected as the base evaluation model, and the BP-GA algorithm was applied to optimize the weights of evaluation indicators. …”
Get full text

Research on Fault Diagnosis of Traction Power Supply System Based on PSO-LSSVM by Lei ZHANG

“…According to the working principle and characteristics of the train power supply system, the relationship between the fault phenomenon and the origin was analyzed, and the characteristic signals used for fault diagnosis were extracted. A fault diagnosis model based on PSO optimized least squares support vector machine was established, and PCA algorithm was used to extract data characteristics as input of fault diagnosis model, and reduce input dimension. …”
Get full text

Development and validation of an interpretable multi-task model to predict outcomes in patients with rhabdomyolysis: a multicenter retrospective cohort studyResearch in context by Chunli Liu, Jie Shi, Fengjuan Wang, Duo Li, Yu Luo, Bofan Yang, Yunlong Zhao, Li Zhang, Dingwei Yang, Heng Jin, Jie Song, Xiaoqin Guo, Haojun Fan, Qi Lv

“…Twenty-two clinical features available within the first 24 h of admission were selected to develop the prediction models. Ten machine learning (ML) algorithms were applied to construct multi-task prediction models. …”
Get full text

Research on Key Technologies of Virtual Coupling Control System for Autonomous-rail Rapid Tram by HUANG Qiang, YUAN Xiwen, HU Yunqing, LI Cheng, HUANG Ruipeng, TANG Xiang

“…A safe braking model of autonomous-rail rapid tram is initiatively used to derive the minimum space headway for operation safety between coupled formations, and the collaborative planning and MPC-based collaborative control algorithms utilizing an optimal control approach are applied to guarantee the safe, punctual, comfortable, and efficient operation of coupled formations. …”
Get full text