Development of a machine learning-based surrogate model for friction prediction in textured journal bearings by Yujun Wang, Georg Jacobs, Shuo Zhang, Benjamin Klinghart, Florian König

“…This enhancement is achieved through an architecture design based on cross-validation and further optimization utilizing the genetic algorithm. Eventually, the average prediction accuracy is improved to 98.81% from 95.89%, with the maximum error reduced to 3.25% from 13.17%. …”
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Power Dispatch Stability Technology Based on Multi-Energy Complementary Alliances by Yiming Zhao, Chengjun Zhang, Changsheng Wan, Dong Du, Jing Huang, Weite Li

“…Particularly in handling intermittent power resources such as solar and wind energy, the proposed model effectively reduces peak shaving time and improves the overall network energy efficiency. Compared with the preference relationship based on selfish and Pareto sequence, the PGG-TS algorithm based on BMBT has an average utility of 10.2% and 25.3% in terms of load, respectively. …”
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ASSESSMENT OF THE RURAL POPULATION ECONOMIC ACTIVITY IN THE SYSTEM OF UNITED TERRITORIAL COMMUNITIES DEVELOPMENT: A CASE STUDY OF VOLYN REGION, UKRAINE by Oksana APOSTOLYUK, Tetiana SHMATKOVSKA, Inna CHYKALO1, Andrew HUSAK

“…Peculiarities of the settlement network in Volyn region are that the dominant segment of rural employment is agricultural enterprises. …”
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Research on CTSA-DeepLabV3+ Urban Green Space Classification Model Based on GF-2 Images by Ruotong Li, Jian Zhao, Yanguo Fan

“…The experimental results showed that the overall classification accuracy of the CTSA-DeepLabV3+ model is 96.21%, and the average intersection ratio, precision, recall, and F1-score reach 89.22%, 92.56%, 90.12%, and 91.23%, respectively, which is better than DeepLabV3+, Fully Convolutional Networks (FCNs), U-Net (UNet), the Pyramid Scene Parseing Network (PSPNet), UperNet-Swin Transformer, and other mainstream models. …”
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Residential Building Renovation Considering Energy, Carbon Emissions, and Cost: An Approach Integrating Machine Learning and Evolutionary Generation by Rudai Shan, Wanyu Lai, Huan Tang, Xiangyu Leng, Wei Gu

“…Using the heuristic optimization algorithm and entropy-weighted method, the framework achieved average energy savings of 56.62%, a reduction in carbon emissions of 51.60%, and a 24.27% decrease in life-cycle costs. …”
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Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago by Yuxiao Fan, Xiaofeng Hu, Jinming Hu

“…Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. …”
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From Simulation to Implementation: A Systems Model for Electric Bus Fleet Deployment in Metropolitan Areas by Ludger Heide, Shuyao Guo, Dietmar Göhlich

“…Applied to Berlin’s bus network—Germany’s largest—three scenarios were evaluated: maintaining existing blocks with electrification, exclusive depot charging, and small batteries with extensive terminus charging. …”
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A Novel Approach to Enhancing the Accuracy of Prediction in Ship Fuel Consumption by Tianrui Zhou, Jinggai Wang, Qinyou Hu, Zhihui Hu

“…The results indicate the following: (1) at a 95% confidence level, the proposed method achieves a prediction interval coverage probability of 0.98 and a prediction interval normalized average width of 0.123, which are significantly better than those of the existing backpropagation neural network (BPNN) and gradient boosting decision tree (GBDT) quantile regression models; (2) the prediction accuracy of the proposed method is 92% for point forecasts; and (3) the proposed method is applicable to main datasets, including both noon report and sensor datasets. …”
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Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs Model by Zhiyong Yang, Zhen Fang, Shengze Yang, Yuhong Xiong, Daode Zhang

“…Simulation analysis using Simulink–CoppeliaSim evaluates the robot’s obstacle-crossing ability and the optimization of deflection joint signal noise. The results indicate a 55.70% increase in the robot’s average speed during cable traversal, a 57.53% reduction in deflection joint noise disturbance, and successful crossing of the vibration damper. …”
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Multiview 2D/3D image registration in minimally invasive pelvic surgery navigation by Fujiao Ju, Ya Wang, Jingxin Zhao, Mingjie Dong

“…The key code is available at https://github.com/TUYaYa1/Points-of-Interest-tracking-network.…”
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Link-disjoint routing algorithm under multiple additive QoS constraints by XIONG Ke1, QIU Zheng-ding1, ZHANG Yu1, ZHANG Hong-ke3

“…The problem of finding link-disjoint paths under multiple additive QoS constraints was studied.Since the existing algorithms depended on network’s structure and could not guarantee to find feasible solutions for arbitrary net-works,a novel algorithm called multiple constrained link-disjoint path routing algorithm（MCLPRA） was proposed.MCLPRA was based on SAMCRA and didn’t rely on the network’s structure.By introducing the parameter to control its search depth,dividing the solution space into different classes and performing searching according to the classes respectively,MCLPRA was able to obtain the feasible solutions for arbitrary networks.Theoretic analysis shows that MCLPRA can get the feasible and optimal solutions when traditional schemes can not.Comprehensive simulations also show that MCLPRA has better performances than existing algorithms in terms of higher average successful rate of getting feasible solutions with shorter average total length of the obtained path pair.…”
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Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. …”
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Real-time Detection of Imperfect Wheat Grains on Wheat Pile Surface Based on IDS-YOLO by FAN Jiawei, WU Lan, YAN Jingjing

“…To address the high missed detection rate of imperfect grains in target detection algorithms and to enhance the model detection speed, this study optimized the lightweight network model YOLOV4-Tiny. …”
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Dynamic energy consumption monitoring and scheduling for green buildings: A comprehensive approach by Hua Zheng, Pengming Wang

“…A three-month comparative experiment is conducted, and the method in this paper is effective in improving the energy efficiency of green buildings, reducing energy consumption, and optimizing system coordination. Experimental results demonstrate that the average energy consumption reduction rate is 4.63%, the comfort retention rate is improved, and the system coordination efficiency and response speed are significantly improved. …”
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EEG emotion recognition based on parallel separable convolution and label smoothing regularization by Yong ZHANG, Jikui LIU, Wenlong KE

“…In recent years, emotion recognition methods based on deep learning and electroencephalogram (EEG) have achieved good results.However, existing methods still have issues such as incomplete extraction of emotional features from EEG and significant impact from artificially mislabeled emotional labels.A parallel separable convolution and label smoothing regularization (PSC-LSR) network model was proposed.Firstly, through the attention mechanism, EEG important time points and important channels were given greater weight to obtain shallow emotional features of EEG.Secondly, a parallel separable convolution module was used to comprehensively extract EEG emotional information and obtain deep emotional features.Finally, the emotion label smoothing regularization method was used to optimize the model parameters, which increased model’s fault tolerance probability for incorrect labels, enhanced the generalization and robustness of the network model, and improved accuracy of EEG emotion recognition.The proposed method has been validated in two datasets, in which the average accuracy rates of arousal and valence dimensions in the DEAP dataset reaches 99.23% and 99.13%, respectively.In the Dreamer dataset, the average accuracy rates for both arousal and valence dimensions reaches 97.33% and 97.25%.…”
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Zero-shot performance analysis of large language models in sumrate maximization. by Ali Abir Shuvro, Md Shahriar Islam Bhuiyan, Faisal Hussain, Md Sakhawat Hossen

“…While Sumrate maximization has been a crucial factor for resource optimization in the networking domain, the optimal or sub-optimal algorithms it requires can be cumbersome to comprehend and implement. …”
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Systematic protection and restoration of water bodies in collapse areas based on surface runoff regulation by Shaogang LEI, Yufan XU, Mian ZHANG, Dongxing CHEN, Xia HUA, Dong GUO

“…After connection, the maximum connection distance from east to west in the study area increased by 5 times, and the overall connectivity of the watershed was improved. The average water velocity in the corridors reached 0.067 m/s, significantly improving the hydrodynamic conditions in the mining area; ② By using the pollutant interception technology based on the spatial optimization layout of vegetation buffer zones in key areas, with water connected corridors and ecological source areas as key areas, the Phillips hydrological model was used to calculate that the required vegetation buffer zone width in the Yanzhou mining area is mainly concentrated between 15～35 m, with a total area of 5.59 km2, of which 18 km2 is already covered by vegetation and 41 km2 is missing vegetation buffer zone to be constructed; ③ The results of SWAT scenario simulation analysis further verified that the model has a significant effect on the protection and restoration of water bodies in mining areas. …”
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The Development of a Lightweight DE-YOLO Model for Detecting Impurities and Broken Rice Grains by Zhenwei Liang, Xingyue Xu, Deyong Yang, Yanbin Liu

“…The loss problem of class imbalance is optimized using the Focal Loss function. The experimental results demonstrate that the DE-YOLO model has an average accuracy (mAP) of 97.55% for detecting rice impurity crushing targets, which is 2.9% higher than the average accuracy of the original YOLOX algorithm. …”
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Distributed Dynamic Traffic Modeling and Implementation Oriented Different Levels of Induced Travelers by Yan Liu, Yao Yu

“…The numerical results show that the proposed model not only can improve the running efficiency of road network but also can significantly decrease the average travel time.…”
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ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11 by Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia

“…Experimental data on the optimized KITTI and BDD100K datasets show that the detection accuracy of the ZZ-YOLO algorithm is 70.9%, the mAP (mean Average Precision) @0.5 is 58%, the model-parameter quantity is 14.1GFLOPs compared to the original algorithm, the detection accuracy is increased by 5.7%, and the average precision is increased by 2.3%. …”
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