Hybrid mechanism‐data‐driven iron loss modelling for permanent magnet synchronous motors considering multiphysics coupling effects by Lin Liu, Wenliang Yin, Youguang Guo

“…Purely mechanistic models require detailed theoretical knowledge and exact parameters, often struggling to accurately describe complex systems, while purely data‐driven methods lack interpretability, which are susceptible to data noise and outliers in feature extraction and complicated pattern recognition. …”
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An insulator target detection algorithm based on improved YOLOv5 by Bing Zeng, Zhihao Zhou, Yu Zhou, Dilin He, Zhanpeng Liao, Zihan Jin, Yulu Zhou, Kexin Yi, Yunmin Xie, Wenhua Zhang

“…Firstly, in the backbone and neck networks, a lightweight CSP-SCConv module is employed to replace the original CSP-Darknet53 module, thereby reducing the parameter count and enhancing the feature extraction capabilities. …”
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GPC-YOLO: An Improved Lightweight YOLOv8n Network for the Detection of Tomato Maturity in Unstructured Natural Environments by Yaolin Dong, Jinwei Qiao, Na Liu, Yunze He, Shuzan Li, Xucai Hu, Chengyan Yu, Chengyu Zhang

“…This study proposes a C2f-PC module based on partial convolution (PConv) for less computation, which replaced the original C2f feature extraction module of YOLOv8n. …”
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YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

“…Secondly, within the neck network, multi-scale feature extraction is facilitated through the design of novel composite convolutions, ConvX and MConv, based on a “split–differentiate–concatenate” paradigm. …”
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An enhanced YOLOv8‐based bolt detection algorithm for transmission line by Guoxiang Hua, Huai Zhang, Chen Huang, Moji Pan, Jiyuan Yan, Haisen Zhao

“…Firstly, the C2f module in the feature extraction network is integrated with the self‐calibrated convolution module, and the model is streamlined by reducing spatial and channel redundancies of the network through the SRU and CUR mechanisms in the module. …”
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Study on the Relationship Between Porosity and Mechanical Properties Based on Rock Pore Structure Reconstruction Model by Nan Xiao, Jun-Qing Chen, Xiang Qiu, Fu Huang, Tong-Hua Ling

“…Initially, high-resolution X-ray computed tomography (CT) was utilized to capture three-dimensional geometric features of Sichuan white sandstone microstructures, complemented by mechanical parameter acquisition through standardized testing protocols. …”
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Fault Diagnosis Method of Plunger Pump Based on Meta-Learning and Improved Multi-Channel Convolutional Neural Network Under Small Sample Condition by Xiwang Yang, Jiancheng Ma, Hongjun Hu, Jinying Huang, Licheng Jing

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River floating object detection with transformer model in real time by Chong Zhang, Jie Yue, Jianglong Fu, Shouluan Wu

“…Further innovation is evident in the introduction of the Residual Partial Convolutional Network (RPCN) as the backbone, which selectively applies convolutions to key channels, leveraging the concept of residuals to reduce computational redundancy and enhance accuracy. The enhancement of the RepBlock with Conv3XCBlock, along with the integration of a parameter-free attention mechanism within the convolutional layers, underscores our commitment to efficiency, ensuring that the model prioritizes valuable information while suppressing redundancy. …”
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Research on Vehicle Road Noise Prediction Based on AFW-LSTM by Yan Ma, Ruxue Dai, Tao Liu, Jian Liu, Shukai Yang, Jingjing Wang

“…However, using the traditional TPA (transfer path analysis) method and CAE (Computer-Aided Engineering) method to analyze the road noise problem has the problems of complex transfer path, difficult acquisition of modeling parameters, long duration and high cost. …”
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OptWake-YOLO: a lightweight and efficient ship wake detection model based on optical remote sensing images by Runxi Qiu, Nan Bi, Chaoyue Yin

“…A Shared Lightweight Object Detection Head (SLODH) using parameter sharing and Group Normalization.ResultsExperiments on the SWIM dataset show OptWake-YOLO improves mAP50 by 1.5% (to 93.2%) and mAP50-95 by 2.9% (to 66.5%) compared to YOLOv11n, while reducing parameters by 40.7% (to 1.6M) and computation by 25.8% (to 4.9 GFLOPs), maintaining 303 FPS speed.DiscussionThe model demonstrates superior performance in complex maritime conditions through: RCEA's multi-branch feature extraction. …”
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Lightweight multiscale information aggregation network for land cover land use semantic segmentation from remote sensing images by Yahia Said, Oumaima Saidani, Ali Delham Algarni, Mohammad H. Algarni, Ayman Flah

“…Traditional models often face limitations in real-time processing and deployment on resource-constrained devices due to their high computational requirements. This paper presents a lightweight neural network designed to address these challenges by integrating dense dilated convolutions with pyramid depthwise convolutions for multiscale feature extraction. …”
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GPU Acceleration for FHEW/TFHE Bootstrapping by Yu Xiao, Feng-Hao Liu, Yu-Te Ku, Ming-Chien Ho, Chih-Fan Hsu, Ming-Ching Chang, Shih-Hao Hung, Wei-Chao Chen

“…To address this challenge, hardware acceleration has emerged as a promising approach, aiming to achieve real-time computation across a wider range of scenarios. In line with this, our research focuses on designing and implementing a Graphic Processing Unit (GPU)-based accelerator for the third generation FHEW/TFHE bootstrapping scheme, which features smaller parameters and bootstrapping keys particularly suitable for GPU architectures compared to the other generations. …”
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An enhanced lightweight model for apple leaf disease detection in complex orchard environments by Ge Wang, Wenjie Sang, Fangqian Xu, Yuteng Gao, Yue Han, Qiang Liu

“…Experimental results demonstrate ELM-YOLOv8n’s effectiveness, achieving 94.0% of F1 value and 96.7% of mAP50 value—a significant improvement over YOLOv8n. Furthermore, the parameter count and computational load are reduced by 44.8% and 39.5%, respectively. …”
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Machine Learning-Based Image Pattern Recognition Using Histogram of Oriented Gradient for Islanding Detection by Kumaresh Pal, Kumari Namrata, Ashok Kumar Akella, Manoj Gupta, Pannee Suanpang, Aziz Nanthaamornphong

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Voice biomarkers as prognostic indicators for Parkinson’s disease using machine learning techniques by Ifrah Naeem, Allah Ditta, Tehseen Mazhar, Muhammad Anwar, Mamoon M. Saeed, Habib Hamam

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Conditional Diffusion-Flow models for generating 3D cosmic density fields: applications to f(R) cosmologies by Julieth K Riveros, Paola A Saavedra, Héctor J Hortúa, Jorge Enrique García-Farieta, Ivan Olier

“…To improve conditional generation, we introduce a novel multi-output model to develop feature representations of the cosmological parameters. …”
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A Poisson Equation-Based Method for 3D Reconstruction of Animated Images by Ziang Lei

“…The calibration theory is used to calibrate the multivisual animated images, obtain the internal and external parameters of the camera calibration module, extract the feature points from the animated images of each viewpoint by using the corner point detection operator, then match and correct the extracted feature points by using the least square median method, and complete the 3D reconstruction of the multivisual animated images. …”
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Geometrical and structural design development of an active-bending structure from natural fibre pultruded profiles: The LightPRO shell by Evgenia Spyridonos, Yanan Guo, Marta Gil Pérez, Hanaa Dahy

“…The paper focuses on the geometrical and structural design development of the structure employing computational design tools for optimisation, ensuring design parameters and performance requirements were met. …”
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An enhanced tabu search algorithm for resource-constrained project scheduling with a flexible project structure by Chunlai Yu, Xiaoming Wang, Qingxin Chen

“…The parameters of the algorithm are calibrated using orthogonal experiments, and its efficacy is evaluated through extensive computational experiments conducted on multiple benchmark datasets. …”
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D³-YOLOv10: Improved YOLOv10-Based Lightweight Tomato Detection Algorithm Under Facility Scenario by Ao Li, Chunrui Wang, Tongtong Ji, Qiyang Wang, Tianxue Zhang

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