ScTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material by Zihua Chen, Runmei Zhang, Zhong Chen, Yu Zheng, Shunxiang Zhang

“…The existing methods are weak in loading measurement because they ignore the symbol characteristics of loading and adjusting features. …”
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Plant Disease Detection Using an Innovative Swin-Axial Transformer by Ao Zhang, Wei Liu

“…With the rapid development of deep learning technology, Transformer architectures have demonstrated strong global feature extraction capabilities in image classification and detection tasks. …”
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Landscape-ecological features of the transboundary Guton mountain junction in the East Caucasus and the problems of regional nature management by Z. V. Ataev, A. Z. Magomedova

“…In the USSR period the territory of Guton mountain junction had serious recreation loadings. The paper shows the features of the landscape structure which aggravated some conflicts in the region. …”
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Research on Space Targets Simulation Modulation Algorithm Combined Global–Local Multi-Spectral Radiation Features by Yu Zhang, Songzhou Yang, Zhipeng Wei, Jian Zhang, Bin Zhao, Dianwu Ren, Jingrui Sun, Lu Wang, Taiyang Ren, Dongpeng Yang, Guoyu Zhang

“…To solve the international problem of global–local radiation features simulation of multi-spectral space targets, this paper proposes a multi-spectral space target simulation modulation algorithm that can combine global–local spectral radiation features. …”
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Analysis of Electrodermal Signal Features as Indicators of Cognitive and Emotional Reactions—Comparison of the Effectiveness of Selected Statistical Measures by Marcin Jukiewicz, Joanna Marcinkowska

“…The aim of this study is to answer the following research question: Which statistical features of the electrodermal activity signal most effectively indicate changes induced by cognitive and emotional reactions, and are there such significant similarities (high correlations) among these features that some of them can be considered redundant? …”
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Clinical-laboratory features and prognostic factors of adverse clinical outcome in patients with cryptococcal meningoencephalitis and HIV infection by D. H. Zhyvytsia, D. V. Myroshnychenko

“…Clinical and laboratory features of a patient with CM and HIV infection in Ukraine are young age, normal body mass index, sexual route of infection, lack of experience in ART, severe immunosuppression and high levels of HIV viral load. …”
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Physiologically Based Control Laws Featuring Antagonistic Muscle Co-Activation for Stable Compliant Joint Drives by Salvatore Annunziata, Axel Schneider

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Numerical Study of the Hydrodynamic Performance of Rim-Driven Propulsion Featuring a Modified 19A Duct by Masoumpour Meysam, Ghassemi Hassan, Hossein Mousavizadegan S., He Guanghua

“…Notably, the thrust coefficient of the duct at low advance coefficients is high, indicating that the duct can operate efficiently under heavy load conditions. The study then focuses on the RDP, which uses the same propeller but features a distinct duct design due to its rim-driven configuration. …”
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Features of using solar power sources for well drilling and ensuring sustainable development of subsoil use in Ukraine by D. V. Bondarenko, A. A. Tolkunov

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Assessing CO2 separation performances of IL/ZIF-8 composites using molecular features of ILs by Hasan Can Gulbalkan, Alper Uzun, Seda Keskin

“…We examined 1322 different types of IL/ZIF-8 composites, covering the largest variety of ILs studied to date (8 cations and 35 anions) at various loadings, for flue gas separation and natural gas purification. …”
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Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features by Zhen Wang, Yingzhe Song, Lei Pang, Shanjun Li, Gang Sun

“…The baseline CNN-LSTM reached <i>R</i>² = 0.946, outperforming a plain LSTM (<i>R</i>² = 0.926) thanks to stronger local spatio-temporal feature extraction. Introducing a spatial attention mechanism raised accuracy further (<i>R</i>² = 0.962), whereas temporal attention reduced it (<i>R</i>² = 0.930), indicating that attention success depends on how well the attended features align with exercise dynamics. …”
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Features of minimizing total electrical losses in high-voltage electromechanical systems for stationary industrial fan installations by A.V. Synetskyi, I.M. Kotsur

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Unsupervised Segmentation and Classification of Waveform-Distortion Data Using Non-Active Current by Andrea Mariscotti, Rafael S. Salles, Sarah K. Rönnberg

“…The method can be applied to load identification and classification for non-intrusive load monitoring, with the aim of implementing automatic and unsupervised assessment of load behavior, including transient detection, power-quality issues and improvement in energy efficiency.…”
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Investigation on the Effect of Dynamic Frequency on Fracture Evolution in Preflawed Rock under Multistage Cyclic Loads: Insight from Acoustic Emission Monitoring by H. J. Meng, Y. Wang, B. Zhang, S. H. Gao

“…It is shown that large-scaled cracks are easy to be formed for rock subjected to high-frequency loads, reflected as the deceasing of AE signals with high-frequency–high-amplitude signal feature. …”
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Quarter-Hourly Power Load Forecasting Based on a Hybrid CNN-BiLSTM-Attention Model with CEEMDAN, K-Means, and VMD by Xiaoyu Liu, Jiangfeng Song, Hai Tao, Peng Wang, Haihua Mo, Wenjie Du

“…Given the temporal and nonlinear features of power load, this paper proposes a hybrid load-forecasting model using attention mechanisms, CNN, and BiLSTM. …”
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Self-Adaptive Deep Learning Framework for Non-Intrusive Load Monitoring: Addressing Aging Appliance Challenges With Transfer Learning and Pseudo Labeling by W. A. Yasodya, S. M. L. Arampola, M. S. K. Nisakya, V. Logeeshan, S. Kumarawadu, Chathura Wanigasekara

“…Efficient energy management practices are recognized as crucial for optimizing energy utilization. Non-intrusive load monitoring (NILM) has been identified as a promising solution, particularly with the use of deep learning techniques. …”
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TSB-Forecast: A Short-Term Load Forecasting Model in Smart Cities for Integrating Time Series Embeddings and Large Language Models by Mohamed Mahmoud Hasan, Neamat El-Tazi, Ramadan Moawad, Amany H. B. Eissa

“…This work presents a novel load forecasting model, TSB-Forecast (Time Series BERT), a hybrid machine learning model aiming to improve short-term electrical demand forecasting by integrating structured and unstructured data. …”
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Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction by Quota Alief Sias, Rahma Gantassi, Yonghoon Choi

“…The proposed model will also be compared with the fundamental bidirectional models of the GRU and LSTM models. For load prediction, the variables used are all energy supply data and periodic features. …”
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Modeling of Tribological Processes for Abrasive Wear of Soil-Cutting Part by V. I. Myalenko

“…(Materials and methods) This study is based on the simulated loading method and the method of comparing the force equivalents of simulated and real loadings of the friction surfaces of soil-cutting parts using the example of loading flat diagrams. …”
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Fatigue and Ultimate Strength Evaluation of GFRP-Reinforced, Laterally-Restrained, Full-Depth Precast Deck Panels with Developed UHPFRC-Filled Transverse Closure Strips by Mahmoud Sayed Ahmed, Khaled Sennah, Hamdy M. Afefy

“…Two types of fatigue test were performed: incremental variable amplitude fatigue (VAF) loading and constant amplitude fatigue (CAF) loading, followed by monotonically loading the slab ultimate-to-collapse. …”
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