Digitalization of human resource management: current trends and challenges for Russian enterprises by Mikhail K. Chernyakov, Irina A. Chernyakova

“…The findings of the article can be applied in shaping corporate strategies, government programs to support business digitalization, as well as in educational courses on HR management and IT. EDN: YPKICO…”
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Application of a Dual-Stream Network Collaboratively Based on Wavelet and Spatial-Channel Convolution in the Inpainting of Blank Strips in Marine Electrical Imaging Logging Images:... by Guilan Lin, Sinan Fang, Manxin Li, Hongtao Wu, Chenxi Xue, Zeyu Zhang

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Generalized perception of tree-row with distribution-peak feature in 3D point cloud for various orchards navigation by Yingxing Jiang, Wuhao Li, Jizhan Liu, Muhammad Mahmood ur Rehman, Binbin Xie, Jie Wang

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XFEM for Fracture Analysis of Centrally Cracked Laminated Plates Subjected to Biaxial Loads by Shailesh Palekar, Achchhe Lal

“…The effect of loadings on the crack growth and crack propagation direction and their effects on the MSIFs using global tracking crack growth algorithm is also presented. The results of the present investigation will be useful for accurate prediction of fracture response of cracked composite structures, crack growth and crack propagation behavior which ultimately effects on the structural safety and integrity of the composite structures.…”
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Fault Diagnosis of Industrial Process Based on FDKICA-PCA by ZHANG Jing, ZHU Fei-fei, LIU Jia-xing, WANG Jiang-tao

“…Because the dynamic characteristics of autocorrelation and lag correlation in production process are neglected in fault diagnosis,Kernel Independent Component AnalysisPrincipal Component Analysis (KICAPCA) is very poor in detecting small and gradual faults because of lacking available variable contribution analysis.In this paper, a dynamic kernel independent component analysis (KICAPCA) fault diagnosis method based on wavelet packet filtering is proposed.This method integrates wavelet packet filtering theory and AR model prediction data characteristics into KICAPCA to extract the feature information of process variable autocorrelation and lagrelated .In this paper, KICAPCA algorithm is used to extract the independent components and principal components of process variables to determine the control limits of three monitoring indicators T2, SPE,I2.Nonlinear contribution graph is used for fault diagnosis, and the advantage of FDKICAPCA method is verified by simulation results of Tennessee process.…”
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DS-AdaptNet: An Efficient Retinal Vessel Segmentation Framework With Adaptive Enhancement and Depthwise Separable Convolutions by Shuting Chen, Chengxi Hong, Hong Jia

“…Most importantly, DS-AdaptNet achieves these results with only 1.57M parameters and 44.08 GFLOPs—a 94.9% reduction in parameters and 77.2% reduction in computational operations compared to standard U-Net. …”
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Quantitative analysis of deep learning-based denoising model efficacy on optical coherence tomography images with different noise levels by Furkan Kirik, Farid Iskandarov, Kamile Melis Erturk, Hakan Ozdemir

“…Background: To quantitatively evaluate the effectiveness of the Noise2Noise (N2N) model, a deep learning (DL)-based noise reduction algorithm, on enhanced depth imaging-optical coherence tomography (EDI-OCT) images with different noise levels. …”
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Abnormal sound detection method for coal mine belt conveyors based on convolutional autoencoder by SHEN Long, SHAN Haoran, PEI Wenliang, YANG Guixiang, WANG Yongli

“…Background noise in the signals was filtered using the WebRTC noise reduction algorithm, and Mel-Frequency Cepstral Coefficients (MFCC) were calculated from the denoised signals to obtain audio features of normal operation. …”
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Spot Position Extraction Based on High-resolution Parametric Subspace Method without Eigen decomposition by Azad Raheem Kareem

“…A simulated example is provided to evaluate the proposed method and the simulation results shows that the proposed algorithm has the advantages of reduction the computational load and superior estimation performance. …”
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Real-time classification of EEG signals using Machine Learning deployment by Swati CHOWDHURI, Satadip SAHA, Samadrita KARMAKAR, Ankur CHANDA

“…The prevailing educational methods predominantly rely on traditional classroom instruction or online delivery, often limiting the teachers’ ability to engage effectively with all the students simultaneously. …”
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Classical special functions of matrix arguments by Dmytro Shutiak, Gleb Podkolzin, Victor Bondarenko, Yury Chapovsky

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Evaluation of Machine Learning Applications for the Complex Near-Critical Phase Behavior Modelling of CO₂–Hydrocarbon Systems by Daulet Magzymov, Meruyert Makhatova, Zhasulan Dairov, Murat Syzdykov

“…The objective of this study was to evaluate the capability of machine learning models to accurately predict complex near-critical phase behavior in CO₂–hydrocarbon systems, which are crucial for enhanced oil recovery and carbon storage applications. …”
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AI-driven model for optimized pulse programming of memristive devices by Benjamin Spetzler, Markus Fritscher, Seongae Park, Nayoun Kim, Christian Wenger, Martin Ziegler

“…Here, we present a computationally efficient AI model for predicting the weight update of memristive devices and guiding device programming. …”
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Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning by Brandon K. Phan, Kuan-Hsuan Shen, Rishi Gurnani, Huan Tran, Ryan Lively, Rampi Ramprasad

“…To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce “high-fidelity” experimental data with abundant diverse “low-fidelity” simulation or synthetic data, resulting in predictive models that display a high level of generalizability across novel chemical spaces. …”
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Power System Operation Stability Assessment Method Based on Deep Convolutional Neural Network by Jinman Luo, Yuqing Li, Qile Wang, Liyuan Liu

“…Compared with other algorithm reference models, this model has a higher evaluation accuracy of 98.39%, far exceeding other comparison models. …”
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Small fixed-wing UAV Precision Aerial Drop capability development by Antonić Vuk, Trifković Milenko, Molović Vojimir, Milenković-Babić Miodrag

“…Precision aerial drop software module is presented, focusing on the automated payload drop algorithm with continually calculated impact point prediction (CCIP) and UAV guidance to the continually calculated release point (CCRP). …”
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Forecast-Aided Converter-Based Control for Optimal Microgrid Operation in Industrial Energy Management System (EMS): A Case Study in Vietnam by Yeong-Nam Jeon, Jae-ha Ko

“…The forecasted load data is then used to optimize charge/discharge schedules for energy storage systems (ESS) using a Particle Swarm Optimization (PSO) algorithm. The strategy is validated using real-site data from a Vietnamese industrial complex, where the proposed method demonstrates enhanced load prediction accuracy, cost-effective ESS operation, and multi-microgrid flexibility under weather variability. …”
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Spatial and temporal distribution patterns and factors influencing hepatitis B in China: a geo-epidemiological study by Kang Fang, Na Cheng, Chuang Nie, Wentao Song, Yunkang Zhao, Jie Pan, Qi Yin, Jiwei Zheng, Qinglin Chen, Tianxin Xiang

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A transcriptomic landscape analysis of human necrotizing enterocolitis: Important roles of immune infiltration by Zhuojun Xie, Quan Kang, Yulu Shi, Junbao Du, Hao Jiang

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Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach by Rongshang Chen, Zhiyong Chen

“…Computational efficiency is achieved through a modified reversible-jump MCMC algorithm incorporating adaptive movement steps to accelerate chain convergence. …”
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