A Review of Soil Constitutive Models for Simulating Dynamic Soil–Structure Interaction Processes Under Impact Loading by Tewodros Y. Yosef, Chen Fang, Ronald K. Faller, Seunghee Kim, Qusai A. Alomari, Mojtaba Atash Bahar, Gnyarienn Selva Kumar

“…Recent advancements in computational methods, particularly the development of large-deformation numerical schemes, such as the multi-material arbitrary Lagrangian–Eulerian (MM-ALE) and smoothed particle hydrodynamics (SPH) approaches, offer viable alternatives for simulating soil behavior under impact loading. These methods have enabled a more realistic representation of granular soil dynamics, particularly that of the Manual for Assessing Safety Hardware (MASH) strong soil, a well-graded gravelly soil commonly used in crash testing of soil-embedded barriers and safety features. …”
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Comparative experimental study of different bearings vibration using FFT and statistical feature extraction by Wafa Abd Soud, Baqer Ihsan A.

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A Super-Resolution-Based Feature Map Compression for Machine-Oriented Video Coding by Jung-Heum Kang, Muhammad Salman Ali, Hye-Won Jeong, Chang-Kyun Choi, Younhee Kim, Se Yoon Jeong, Sung-Ho Bae, Hui Yong Kim

“…Especially, compressing features has advantages in terms of privacy protection and computation off-loading. …”
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Specific features of the principle of the time-temperature superposition in low density polyethylene filled with shungite by N. N. Komova, E. E. Potapov, E. E. Gruskov, G. E. Zaikov

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Machine Learning-Based Seismic Response Prediction for Nuclear Power Plant Structures Considering Aging Deterioration by Hyunsu Kim, Soyeon Lee, Junsu Jang, Sihyeon An

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Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models by Bingbing Tang, Jie Hu, Mei Yang, Chenglong Zhang, Qiang Bai

“…Subsequently, we reconstruct the features and input them into the LSTM for feature extraction. …”
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Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithm by Jianbin WANG, Shuchun WANG, Shangjin LIAO, Shuyuan SHI

“…With the rapid construction of the 5G wireless communication network, the energy consumption pressure of operators, and even the overall communication industry, is simultaneously highlighted.Achieving sustainable development of the industry through energy conservation and consumption reduction has become a new research direction for the current 5G network development.Taking the PRB rate as the load evaluation index, LSTM model was improved by using DCNN to extract the depth feature of the cell’s indicators.A set of DCNN-LSTM deep learning model that could predict the future value of PRB rate was proposed.On the basis of the improved algorithm, the network topology of the current 5G access network was optimized.An additional network element and its working system were designed.An intelligent energy-saving system, which ensured the network experience, of 5G base stations was realized.…”
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Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load by Toan Pham-Bao, Vien Le-Ngoc

“…This paper presents a novel method of assessing structural damage in beams exposed to moving loads via acceleration signals through experimental studies. …”
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Reshaping Load-Dependent Mesh Excitation Waveforms of Spur Gears—An Analytical Framework on Tip Relief Modeling and Design by Xingyuan Zheng, Weidong Zhu, Gang Li, Yumei Hu

“…Tip relief is a critical design feature of modern spur gears, aimed at improving dynamic performance through a typical design strategy involving peak-to-peak minimization of mesh excitations. …”
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Reducing the solubility of the major birch pollen allergen Bet v 1 by particle-loading mitigates Th2 responses by Amin Kraiem, Erica Pelamatti, Sophie Grosse-Kathoefer, Hilal Demir, Ute Vollmann, Caroline Ehgartner, Maria Stigler, Benjamin Punz, Litty Johnson, Nicola Hüsing, Barbara Bohle, Lorenz Aglas

“…Background: Solubility is a common feature of allergens. However, the causative relationship between this protein-intrinsic feature and sensitization capacity of allergens is not fully understood. …”
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Tumor break load quantitates structural variant-associated genomic instability with biological and clinical relevance across cancers by Soufyan Lakbir, Renske de Wit, Ino de Bruijn, Ritika Kundra, Ramyasree Madupuri, Jianjiong Gao, Nikolaus Schultz, Gerrit A. Meijer, Jaap Heringa, Remond J. A. Fijneman, Sanne Abeln

“…We introduce tumor break load (TBL), defined as the sum of unbalanced SVs, as a measure for SV-associated genomic instability. …”
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Load Forecasting Using BiLSTM with Quantile Granger Causality: Insights from Geographic–Climatic Coupling Mechanisms by Xianan Huang, Lin Liu, Nuo Xu, Yantao Chen, Xiaofei Wang, Zhenzhi Lin

“…Furthermore, the BiLSTM model is then constructed using the selected factors to generate load forecasts. Using real data from Fujian, China, we demonstrate that QGCT-based feature screening reduces forecasting errors by an average of 34.96%, where the RMSE, MAE and MAPE are 29.19%, 30.06% and 45.63%, respectively, thereby validating the necessity of causal factor selection. …”
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Research on Electric Vehicle Charging Load Prediction Methods Combining Signal Noise Reduction and Time Series Modeling by Liyun Liu, Xiaomei Xu, Jinsong Zhang, Dong Li

“…The BiGRU is then utilized to analyze both long- and short-term temporal dependencies inherent in the load data features, facilitating enhanced feature extraction. …”
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Assessment of Knot-Induced Degradation in Timber Beams: Probabilistic Modeling and Data-Driven Prediction of Load Capacity Loss by Peixuan Wang, Guoming Liu, Fanrong Li, Shengcai Li, Gabriele Milani, Donato Abruzzese

“…Finally, a predictive model based on a fully connected neural network is developed; feature analysis indicates that the longitudinal position of knots exerts a stronger nonlinear influence on load capacity than radial depth or diameter. …”
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An Improved IoT Based Hybrid Predictive Load Forecasting Model for a Greenhouse Integrated With Demand Side Management by Soumya Ranjan Biswal, Tanmoy Roy Choudhury, Babita Panda, Subhrajyoti Mishra

“…Greenhouse farming enhances agricultural productivity but remains highly energy-intensive, requiring advanced energy management strategies to ensure sustainability. Traditional load forecasting and demand-side management (DSM) methods often fall short in adapting to the dynamic and highly variable environmental conditions within greenhouses. …”
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Medium and short-term load forecasting based on NPMA-LSSVM algorithm in the case of unbalance and minority sample data by YANG Qiuyu, KUANG Shusen, ZHENG Xiaogang, YE Guoqi, ZHANG Zhongxin

“…In order to eliminate the features with repeated information, principal component analysis (PCA) is used to extract the main features of power load. …”
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REST Network: An Ensemble Deep Learning Approach for EV Charging Load Forecasting in Artificial Port Supply Chains by Elham Abdullah Alghamdi, Monagi Hassan Alkinani, Abdulwahab Ali Almazroi, Mohammed Alqarni, Eman Ali Aldhahri, Nasir Ayub

“…REST outperforms standard methods with a MAPE of 6.5%, an RMSE of 9.8 kW, and strong results across several custom metrics, including Energy Efficiency Error (3.2%), Load Variability Deviation (4.1%), Cost of Prediction Error (12.50), and Time-Aware Accuracy (92.3%). …”
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Graph-based fault diagnosis for rotating machinery: Adaptive segmentation and structural feature integration by Moirangthem Tiken Singh

“…Evaluated on the Case Western Reserve University (CWRU) bearing dataset across 0–3 HP loads and the Southeast University (SU) gearbox dataset under varied load conditions, the framework outperformed traditional and deep learning approaches. …”
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Series-arc-fault diagnosis using feature fusion-based deep learning model by Won-Kyu Choi, Se-Han Kim, Ji-Hoon Bae

“…Experimental results show that the proposed model achieves an accuracy of 99.99% in classifying series arc faults for five different loads. Hence, a perfor-mance improvement of approximately 1.7% in classification accuracy is reached compared with a feature fusion model that does not incorporate TL-based model transfer and the attention mechanism.…”
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Demand response potential evaluation based on feature fusion with expert knowledge and multi‐image by Jiale Liu, Xinlei Cai, Zijie Meng, Xin Jin, Zhangying Cheng, Tingzhe Pan

“…First, typical load profiles are extracted by the proposed procedure. …”
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