Correlation Between Involute Profile Waviness and Gear Transmission Performance by Zhou Yuansheng, Yue Huijun, Wu Xiangkai, Shi Zhaoyao

“…The finite element model of involute tooth surface with waviness is established, and the correlation between wavelength, amplitude, phase of waviness and contact stress in gear transmission is simulated and analyzed. …”
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Plant-derived exosome-like nanoparticles: characteristics and their potential for use in dermatological and cosmetological practice by T. V. Doronina, M. V. Koshkina, D. I. Znatdinov

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Light Field Angular Super-Resolution via Spatial-Angular Correlation Extracted by Deformable Convolutional Network by Daichuan Li, Rui Zhong, Yungang Yang

“…To solve this problem, we introduce Deformable Convolutional Network (DCN), which adaptively changes the position of limited sampling point using offsets, so as to extract SAC from distant pixels. …”
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Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling by Younghyun Koo, Maryam Rahnemoonfar

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Adaptive Weighted CNN Features Integration for Correlation Filter Tracking by Chunbao Li, Bo Yang

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Research on rolling bearing compound fault diagnosis based on AMOMCKD and convolutional neural network by Runfang Hao, Yunpeng Bai, Kun Yang, Yongqiang Cheng, Shengjun Chang

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Ultrasonographic Screening of Dairy Cows with Normal Uterine Involution or Developing Postpartum Uterine Disease Using B-Mode, Color, and Spectral Doppler by Mariana Henao-Gonzalez, María S. Ferrer, Claudia Jiménez-Escobar, Luis G. Palacio-Baena, Juan G. Maldonado-Estrada

“…Finally, no correlation was found between Doppler spectral parameters and uterine involution (P > 0.05). …”
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Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method by Dianli Wang, Yu Zhang, Nasser Yousefi

“…The results show a strong correlation between the simulated and observed data, with a correlation coefficient of 0.87 for water consumption and 0.91 for energy consumption, indicating a high level of agreement between the simulated and real-world data. …”
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Improved Convolutional Neural Networks for Course Teaching Quality Assessment by Yun Liu

“…The cultivation of innovative talents is closely related to the quality of course teaching, and there is a correlation between facial expressions and the effectiveness of classroom teaching. …”
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Integrating Graph Convolution and Attention Mechanism for Kinase Inhibition Prediction by Hamza Zahid, Kil To Chong, Hilal Tayara

“…Kinase is an enzyme responsible for cell signaling and other complex processes. Mutations or changes in kinase can cause cancer and other diseases in humans, including leukemia, neuroblastomas, glioblastomas, and more. …”
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Improved Hierarchical Convolutional Features for Robust Visual Object Tracking by Jinping Sun

“…Thus, to improve the tracking performance and robustness, an improved hierarchical convolutional features model is proposed into a correlation filter framework for visual object tracking. …”
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Comparative convolutional neural networks for perovskite solar cell PCE predictions by Milan Harth, D. Kishore Kumar, Said Kassou, Kenza El Idrissi, Ritesh Kant Gupta, Yonatan Daniel, Ofry Makdasi, Iris Visoly-Fisher, Alessio Gagliardi

“…The approach predicts relative changes in PCE by comparing images of the same device in different states (e.g., before and after encapsulation) or against a reference image. …”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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STFGCN: Spatio-Temporal Fusion Graph Convolutional Networks for Subway Traffic Prediction by Xiaoxi Zhang, Zhanwei Tian, Yan Shi, Qingwen Guan, Yan Lu, Yujie Pan

“…Furthermore, we introduce trend similarity-aware attention to capture the evolutionary trends of time series and adopt a dynamic correlation graph convolutional network to dynamically adjust spatial correlation strengths based on changes in different time periods. …”
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Multistation Wind Speed Forecasting Based on Dynamic Spatiotemporal Graph Convolutional Networks by Jianhong Gan, Runqing Kang, Xun Deng, Chentao Mao, Zhibin Li, Peiyang Wei, Chunjiang Wu, Tongli He

“…However, spatiotemporal correlations of wind speed are dynamically influenced by weather conditions, seasonal variations, and diurnal fluctuations, resulting in constantly changing spatiotemporal patterns. …”
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Temperature Forecasting via Convolutional Recurrent Neural Networks Based on Time-Series Data by Zao Zhang, Yuan Dong

“…The model can learn the time correlation and space correlation of temperature changes from historical data through neural networks. …”
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Evaluation of retinal pigment epithelium changes in serous pigment epithelial detachment using synthesized multi-contrast polarization-sensitive optical coherence tomography by Kosei Yanagida, Masahiro Miura, Hidetaka Noma, Toshihiro Mino, Shinnosuke Azuma, Thitiya Seesan, Shuichi Makita, Yoshiaki Yasuno

“…To overcome this limitation, we utilized a convolutional neural network to generate DOPU-like images from standard OCT images and used these to calculate a synthesized RPE-melanin thickness map. …”
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2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction by Cheng Fang, Li Wang

“…Initially, temporal characteristics of traffic flow between OD pairs are captured using a 1D convolution neural network (1D-CNNs). Subsequently, a 2D hypergraph convolutional network is introduced to uncover spatial correlations in OD flow patterns. …”
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Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting by Na Hu, Dafang Zhang, Kun Xie, Wei Liang, Meng-Yen Hsieh

“…To save training time and computation resources, we adopt dilated causal convolution networks with a gating mechanism to capture long-term temporal correlations in traffic data. …”
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