ON PRESENTATION OF LINEAR OPERATORS COMMUTING WITH DIFFERENTIATION IN SIMPLY-CONNECTED DOMAIN by A. V. Bratishchev

“…It is known that a linear complex convolution operator is generated by a one - variable analytic function, a multivalued one in general. …”
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Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation by Xu Zhang, Gaoquan Gu

“…Experimental results using variable working condition datasets demonstrate that the proposed method consistently achieves diagnostic accuracies exceeding 95%, substantiating its feasibility and effectiveness.…”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
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DLI: A Deep Learning-Based Granger Causality Inference by Wei Peng

“…And the DLI performs a superior prediction accuracy by integrating variables that have causalities with the target variable into the prediction process.…”
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Multi-Attribute Data-Driven Flight Departure Delay Prediction for Airport System Using Deep Learning Method by Yujie Yuan, Yantao Wang, Chun Sing Lai

“…The model is based on a 3D convolutional neural network (3D-CNN), graph convolutional network (GCN) and long short-term memory networks (LSTM) model. …”
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Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study by Xiangkui Jiang, Bingquan Wang

“…MethodsIn this study, we analyzed data from 1948 patients with heart failure in a hospital in Sichuan Province between 2016 and 2019. By applying 3 variable selection strategies, 29 relevant variables were identified. …”
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Research on Long-Distance Snow Depth Measurement Method Based on Improved YOLOv8 by Jia-Wen Wang, Yu Cao, Zong-Kai Guo, Cheng Xu

“…Second, the introduction of the variable kernel convolution (AKConv) module improves the adaptability of convolutional operations, boosting the model’s performance in snow depth detection. …”
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ON PRESENTATION OF GELFOND—LEONTIEV OPERATORS OF GENERALIZED DIFFERENTIATION IN SIMPLY CONNECTED REGION by Alexander Vasilyevich Bratishchev

“…It is known to be presented as an operator of general complex convolution. The convolution kernel is generated by the many-valued function of one variable. …”
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Prediction of Grain Yield in Henan Province Based on Grey BP Neural Network Model by Bingjun Li, Yifan Zhang, Shuhua Zhang, Wenyan Li

“…BP neural network (BPNN) is widely used due to its good generalization and robustness, but the model has the defect that it cannot automatically optimize the input variables. In response to this problem, this study uses the grey relational analysis method to rank the importance of input variables, obtains the key variables and the best BPNN model structure through multiple training and learning for the BPNN models, and proposes a variable optimization selection algorithm combining grey relational analysis and BP neural network. …”
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A Quality Soft Sensing Method Designed for Complex Multi-process Manufacturing Procedures by Kaixiang PENG, Xin QIN, Jiahao WANG, Hui YANG

“…Objective Accurately perceiving key quality variables in complex manufacturing processes is essential for achieving system optimization control and ensuring safe and stable operation. …”
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Short-term photovoltaic power forecasting based on a new hybrid deep learning model incorporating transfer learning strategy by Tiandong Ma, Feng Li, Renlong Gao, Siyu Hu, Wenwen Ma

“…First, the processed data are input into the DCNN layer, and the dilation convolution mechanism captures the spatial features of the wide sensory field of the input data. …”
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Video Visualization Technology and Its Application in Health Statistics Teaching for College Students by Chengfei Li, Yuan Xie, Shuanbao Li

“…The results show that the external model load difference between each explicit variable and latent variable is statistically significant. …”
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On Symmetrical Sonin Kernels in Terms of Hypergeometric-Type Functions by Yuri Luchko

“…In this paper, a new class of kernels of integral transforms of the Laplace convolution type that we named symmetrical Sonin kernels is introduced and investigated. …”
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Extending the forecasting horizon of daily new COVID-19 cases using non-pharmaceutical measures and the effective reproduction number (Rt): A deep learning-based framework by Tuga Mauritsius

“…The inclusion of additional variables was found to diminish the predictive accuracy of DL algorithms.…”
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Spatiotemporal information conversion machine for time-series forecasting by Hao Peng, Pei Chen, Rui Liu, Luonan Chen

“…STICM combines the advantages of both the STI equation and the temporal convolutional network, which maps the high-dimensional/spatial data to the future temporal values of a target variable, thus naturally providing the forecasting of the target variable. …”
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Weed Detection Algorithms in Rice Fields Based on Improved YOLOv10n by Yan Li, Zhonghui Guo, Yan Sun, Xiaoan Chen, Yingli Cao

“…Accurate weed detection is vital for implementing variable spraying with unmanned aerial vehicles (UAV) for weed control. …”
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Time–frequency ensemble network for wind turbine mechanical fault diagnosis by Haiyu Guo, Xingzheng Guo, Xiaoguang Zhang, Fanfan Lu, Chuang Liang

“…Wind turbines typically operate under variable speed conditions, so the collected vibration signals are affected by non-linearity and information mixing, while also containing a large amount of noise interference. …”
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MSVMD-Informer: A Multi-Variate Multi-Scale Method to Wind Power Prediction by Zhijian Liu, Jikai Chen, Hang Dong, Zizhuo Wang

“…Existing prediction methods demonstrate insufficient integration of multi-variate features, such as wind speed, temperature, and humidity, along with inadequate extraction of correlations between variables. This paper proposes a novel multi-variate multi-scale wind power prediction method named multi-scale variational mode decomposition informer (MSVMD-Informer). …”
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Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

“…The GNN component captures spatial dependencies by representing the data as a graph, where nodes correspond to system variables, and edges encode their relationships. Temporal dependencies are modeled using temporal convolutional layers and recurrent neural networks (RNNs), enabling the framework to learn both short-term variations and long-term trends. …”
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KERNEL DETERMINATION PROBLEM FOR ONE PARABOLIC EQUATION WITH MEMORY by Durdimurod K. Durdiev, Javlon Z. Nuriddinov

“…This paper studies the inverse problem of determining a multidimensional kernel function of an integral term which depends on the time variable \(t\) and \((n-1)\)-dimensional space variable \(x'= \left(x_1,\ldots, x_ {n-1}\right)\) in the \(n\)-dimensional diffusion equation with a time-variable coefficient at the Laplacian of a direct problem solution. …”
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