Suppression and control of unbalanced and misalignment coupler vibration of a double-rotor permanent magnet drive by Yongcun GUO, Xin MA, Shuang WANG, Deyong LI, Tun YANG

“…Multi-physics field finite element simulation is used to model the vibration characteristics of the system under unaligned unbalanced condition. …”
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Integrating polygenic risk scores in the prediction of type 2 diabetes risk and subtypes in British Pakistanis and Bangladeshis: A population-based cohort study. by Sam Hodgson, Qin Qin Huang, Neneh Sallah, Genes & Health Research Team, Chris J Griffiths, William G Newman, Richard C Trembath, John Wright, R Thomas Lumbers, Karoline Kuchenbaecker, David A van Heel, Rohini Mathur, Hilary C Martin, Sarah Finer

“…We constructed a T2D PRS and combined it with a clinical risk instrument (QDiabetes) in a novel, integrated risk tool (IRT) to assess risk of incident diabetes. To assess model performance, we compared categorical net reclassification index (NRI) versus QDiabetes alone. …”
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Quantitative analysis of targeted lipidomics in the hippocampus of APP/PS1 mice employing the UHPLC-MS/MS method by Shiyu Xiao, Xuemeng Wei, Bing Han, Xiaoqian Shi, Congzhen Wei, Rumeng Liang, Jingna Sun, Zheng Zhang, Zhengang Han, Li Shen

“…Statistical method was performed by Student’s t-test and multivariate analysis. …”
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Study on stability control of stope structural in short-wall cemented backfill mining for recovery of room-and-pillar residual coal by Yun ZHANG, Long ZHANG, Xingping LAI, Shenggen CAO, Yanbin YANG, Yongzi LIU, Rui ZHAN, Licheng BAI, Hao YE

“…Finally, mechanical tests were performed to analyze the mechanical properties, failure characteristics, and microstructural features of the cemented backfill, and a complete design method for short-wall cemented backfill mining was proposed based on the actual engineering. …”
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Abnormal traffic detection based on image recognition and attention-residual optimization by Pengfei Wang, Xinpeng Chen, Jinan Shen, Zhenwu Xu, Fang Liang, Qiuyang Du

“…This model converts traffic data into feature images and enhances the feature contrast through histogram equalization. …”
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Genomic and phenomic prediction for soybean seed yield, protein, and oil by Liza Van der Laan, Kyle Parmley, Mojdeh Saadati, Hernan Torres Pacin, Srikanth Panthulugiri, Soumik Sarkar, Baskar Ganapathysubramanian, Aaron Lorenz, Asheesh K. Singh

“…We observed that PP had a higher correlation coefficient than GP for seed yield, while GP had higher correlation coefficients for seed protein and oil contents. VIs with high feature importance were used as covariates in a new GBLUP model, and a new random forest model was trained with the inclusion of selected SNPs. …”
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Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks by Georgios Lekkas, Eleni Vrochidou, George A. Papakostas

“…<b>Results:</b> These top-performing methods achieved accuracies of 99%, 98%, and 95%, respectively, on the minimal 2D CNN and accuracies of 99%, 99%, and 99%, respectively, on the LeNet-5 model for the ECG signals. …”
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Design of an Intervention and Education System for Children with Emotional Disorders Based on Semantic Analysis by Ruifang Yang, Yiqun Wang

“…This paper acquires video data by deploying a common camera acquisition and transforms, annotates, frames, and processes the data with the help of feature engineering methods. In addition, this paper proposes a fine-grained action decomposition strategy to solve the problem of extreme imbalance in the dataset to improve the performance of the model and proposes an iterative sampling data fusion strategy, which aims to integrate and fuse data from multiple sources to make them more effective and further improve the robustness and generalization ability of the model. …”
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Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation by Tareq Mahmod AlZubi, Hamza Mukhtar

“…To address these challenges, we propose the Federated 3D Knowledge Distillation Network (Fed3D-KDNet), a hybrid federated learning (FL) framework that integrates both global and local knowledge distillation mechanisms. Our model adapts the Segment Anything Model (SAM) for volumetric medical imaging by introducing architectural enhancements, including 3D spatial feature adapters and an Auto Prompt Generator (APG), to optimize spatial representation and reduce reliance on manually crafted prompts. …”
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Enhancing Fault Detection and Classification in Wind Farm Power Generation Using Convolutional Neural Networks (CNN) by Leveraging LVRT Embedded in Numerical Relays by Tarek Kandil, Adam Harris, Remon Das

“…The proposed model performs well in fault detection and classification, introducing a new feature of numerical relays.…”
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Robotics Classification of Domain Knowledge Based on a Knowledge Graph for Home Service Robot Applications by Yiqun Wang, Rihui Yao, Keqing Zhao, Peiliang Wu, Wenbai Chen

“…The lightweight network MobileNetV3 is used to pre-train the model, and a lightweight convolution method with good feature extraction performance is selected. …”
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A novel approach for the detection of brain tumor and its classification via end-to-end vision transformer - CNN architecture by K. Chandraprabha, L. Ganesan, K. Baskaran

“…The experiments are performed on a dataset of 4,855 image featuring various tumor classes. …”
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Temporal Community Detection and Analysis with Network Embeddings by Limengzi Yuan, Xuanming Zhang, Yuxian Ke, Zhexuan Lu, Xiaoming Li, Changzheng Liu

“…Extensive experiments on synthetic and real-world social networks, including email and phone call networks, demonstrate the superior performance of our model in community detection and tracking temporal network evolution. …”
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Lightweight Pepper Disease Detection Based on Improved YOLOv8n by Yuzhu Wu, Junjie Huang, Siji Wang, Yujian Bao, Yizhe Wang, Jia Song, Wenwu Liu

“…Finally, we use Large Separable Kernel Attention (LSKA) to improve the SPPF module (Spatial Pyramid Pooling Fast) to enhance the performance of multi-scale feature fusion. The experimental results show that the accuracy, recall, and average precision of the DD-YOLO model are 91.6%, 88.9%, and 94.4%, respectively. …”
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Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques by Antonietta Eliana Barrasso, Claudio Perone, Roberto Romaniello

“…Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. …”
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Individual and integrated indexes of inflammation predicting the risks of mental disorders - statistical analysis and artificial neural network by Shu-Min Huang, Fu-Hsing Wu, Kai-Jie Ma, Jong-Yi Wang

“…When the same variables were considered, statistical analysis and the deep neural network had similar results. After feature extraction was incorporated, the performance of the deep neural network model improved (excellent, area under the curve = 0.9162) and could effectively predict the risk of mental illness. …”
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High-throughput phenotyping tools for blueberry count, weight, and size estimation based on modified YOLOv5s by Xingjian Li, Sushan Ru, Zixuan He, James D. Spiers, Lirong Xiang

“…The second pipeline deployed YOLOv5 models with additional modifications using the Ghost module and bi-Feature Pyramid Network (biFPN). …”
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The control effect of the pore and fracture characteristics of tectonic coal assisted by the triaxial stress permeability system on permeability by Shi wenfang, Ru zhongliang, Zhao binbin, Lu guoju

“…Moreover, the proposed ShuffleNet V2-Bidirectional Gated Recurrent Unit-Graph Attention Mechanism model demonstrates superior performance compared to existing models, achieving an accuracy of 95.62%, a root mean square error of 4.19, and a mean absolute error of 8.13. …”
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A New Classification Method in Ultrasound Images of Benign and Malignant Thyroid Nodules Based on Transfer Learning and Deep Convolutional Neural Network by Weibin Chen, Zhiyang Gu, Zhimin Liu, Yaoyao Fu, Zhipeng Ye, Xin Zhang, Lei Xiao

“…With data augmentation as a training set, transfer learning with the trained GoogLeNet convolutional neural network was performed to extract image features. Finally, joint training and secondary transfer learning were performed to improve the classification accuracy, based on the thyroid images from open source data sets and the thyroid images collected from local hospitals. …”
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Elevated risk of possible sarcopenia and weak muscle strength with higher dietary inflammatory index in Iranian breast cancer survivors: a cross-sectional study by Kianaz Shirinyfard Pilehrood, Gholamreza Askari, Mehran Sharifi, Mehdi Kargarfard, Sahar Saraf-Bank

“…In addition, a decreasing linear trend was observed between higher DII score and 0.059 and 0.349- units lower in appendicular skeletal muscle mass index and hand grip strength variables in the crude Model (P-value < 0.05). Conclusion Diets with more pro-inflammatory features might be associated with increased risk of possible sarcopenia, as well as its components especially muscle mass and strength in women recovering from breast cancer.…”
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