Efficient Gearbox Fault Diagnosis Based on Improved Multi-Scale CNN with Lightweight Convolutional Attention by Bin Yuan, Yaoqi Li, Suifan Chen

“…In this paper, we propose an intelligent diagnosis framework based on Empirical Mode Decomposition and multimodal feature co-optimization and innovatively construct a fault diagnosis model by fusing a multi-scale convolutional neural network and a lightweight convolutional attention model. …”
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Thermal Evaluation of Biocomposites Made from Poly(Lactic Acid) and Cottonseed Byproducts by Zhongqi He, Sunghyun Nam, Sourabh Kulkarni, Mohammad Bagheri Kashani, Ramaswamy Nagarajan

“…Generally, the low activation energy values of biocomposites other than PLA under nitrogen conditions implied that these cottonseed byproduct constituents promote the thermal decomposition of these biocomposites. However, the presence of oxygen would confound the thermal decomposition of the biocomposites, as shown by variable activation energy curves with higher values under air conditions. …”
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Reliability analysis of landing gear operation based on improved support vector machine-based decomposed-coordinated by ZHANG Lei, CHEN Junyu, FENG Jun, FENG Yunwen, LIU Lei

“…An improved support vector machine-based decomposed-coordinated (ISVM-DC) is proposed to improve the accuracy and efficiency of landing gear operation reliability analysis. The decomposition and coordination strategy decompose the composite failure problem into multiple mathematical models, and then builds the relationship between the total output response and the variable parameters according to the hierarchical relationship. …”
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A hybrid machine learning and ied-based fault detection scheme for microgrids by Hamid Radmanesh, Abolfazl Hadadi

“…This paper proposes a new intelligent fault detection approach that leverages advanced signal processing techniques, including modified Variable Mode Decomposition (MVMD) for feature extraction, combined with a hybrid machine learning (ML) model. …”
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Frequency-Dependent Scaling Factors to Estimate Multiple Time-Period Global Mass Changes Observed by GRACE/GRACE-FO by Zhenran Peng, Linsong Wang, Qing Liang, Chao Chen

“…The scaling factor method is commonly used to restore near-true time-variable gravity from gravity recovery and climate experiment/follow-on (GRACE/GRACE-FO). …”
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Optimal Allocation of Hybrid Energy Storage Capacity for Microgrids Considering Electric Vehicles by Yvhua Bian, Haizheng Wang, Jinhua Zhang, Jie Yan, Xiaoke He

“…Additionally, this paper utilizes a modified Subtraction-Average-Based Optimizer (SABO) algorithm to optimize variable mode decomposition (VMD) in addressing the power allocation problem of HESS. …”
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Use of granular computing for resource optimization in IoT networks by Jaime Govea, Rommel Gutierrez, William Villegas-Ch

“…Existing optimization methods often fail to adapt in real-time or scale adequately under variable conditions, exposing a critical gap in resource management strategies for dense deployments. …”
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Euro-area labour markets: Different reaction to shocks? by Jan Bruha, Beatrice Pierluigi, Roberta Serafini

“…The short run dynamics is governed by a VAR model including six shocks. The state-space framework is convenient for the decomposition of endogenous variables in trends and cycles, for shock decomposition, for incorporating external judgment, and for running conditional projections. …”
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A spatial bearing fault classification method based on improved APSMOTE-WKMFA by CHEN Chao, YANG Chenhao, XU Haosen, WAN Ouying, HAN Liling

“…Finally, the <italic>k</italic>-nearest neighbor classifier algorithm was used to train the classification model on the transformed low-dimensional features. …”
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Multistage power generation planning approach for new power systems with high renewable energy integration considering flexibility constraints by Danyang Li, Hongpeng Liu, Jingwei Zhang, Xu Han, Shuxin Zhang

“…A flexibility-constrained multi-stage generation planning model is formulated, which resolves nonlinearity through auxiliary variable linearization and Benders decomposition algorithm. …”
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An integrative framework for AI-supported coastal hydrodynamics monitoring and analysis by Ruo-Qian Wang, Gustavo Pacheco-Crosetti, Christian Villalta Calderon, Joel Cohen, Emily Smyth

“…Employing a blend of traditional methods and cutting-edge technologies, including the Segment Anything Model (SAM) for high-resolution image segmentation and Dynamic Mode Decomposition (DMD) for pattern recognition, we provide a comprehensive assessment of coastal water dynamics. …”
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Centralized and Decentralized Signal Control with Short-Term Origin-Destination Demand for Network Traffic by Can Zhang, Tony Z. Qiu, Amy Kim

“…We also investigate the impact of network decomposition strategy on signal control system performance. …”
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Full cycle rice growth monitoring with dual-pol SAR data and interpretable deep learning by Ji Ge, Hong Zhang, Lu Xu, Wenjiang Huang, Jingling Jiang, Mingyang Song, Zihuan Guo, Chao Wang

“…Finally, an interpretable DL architecture incorporating a feature selection explainer and a feature-aware enhanced segmentation model is introduced. This architecture incorporates feature-wise variable learning networks, interprets the importance of individual features, and optimizes the feature composition, significantly enhancing the interpretability of the DL model. …”
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GLLR-HAD: Global-local low-rank integration for hyperspectral image anomaly detection by Yu Bai, Yanling Zhang, Lili Zhang, Tan Zhao

“…However, existing methods often struggle to simultaneously model global structures and local discriminative features, which hampers their ability to detect structural anomalies and adapt to spectral variability in complex scenarios. …”
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Improving Electrical Distance Accuracy for Cable Fault Location Based on the Prony Method by Xiang Ren, Zhiqiang Feng, Kai Zhou, Xueming Zhou, Jianjun Yuan, Yefei Xu, Li Zhang

“…By establishing a frequency-variable coupling model containing the transfer function of the cable with double joint structure, studying the mapping relationship between the cable attenuation constant and the electromagnetic wave velocity, and adopting the Prony estimation method combined with the singular value decomposition noise reduction algorithm to achieve the extraction of the attenuation constant and the decoupling of the segmented wave velocity, thus improving the electrical accuracy of fault location. …”
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Mechanisms Behind the Soil Organic Carbon Response to Temperature Elevations by Yonglin Wu, Haitao Li, Xinran Liang, Ming Jiang, Siteng He, Yongmei He

“…At the same time, soil aggregates, clay minerals, and iron and aluminum oxides protect the SOC, making it difficult to be utilized by microbial decomposition. The systematic clarification of the mechanism behind the SOC response to higher temperatures is crucial for accurately predicting and modeling global carbon cycles and effectively responding to the loss of SOC pools due to global temperature elevations.…”
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A Methodology for Electricity Demand Forecasting Using a Hybrid Approach by Fanidhar Dewangan, Monalisa Biswal, Nand Kishor

“…In this work, an integrated LF model is proposed that uses modified combined ensemble empirical mode decomposition with adaptive noise (MCEEMDAN), Shannon entropy (SE), and long short-term memory (LSTM) techniques. …”
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Assessing the Impact of the Real Exchange Rate on Okun’s Misery Index in Mexico by Fernando Sánchez, Ericka Judith Arias Guzmán

“…The exchange rate is among the main variables determining foreign trade, as it affects the prices of both exports and imports. …”
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Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion by Jin Wang, Yan Wang, Junhui Yu, Qingping Li, Hailin Wang, Xinzhi Zhou

“…In real-world scenarios, the rotational speed of bearings is variable. Due to changes in operating conditions, the feature distribution of bearing vibration data becomes inconsistent, which leads to the inability to directly apply the training model built under one operating condition (source domain) to another condition (target domain). …”
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Optimized CNN-Bi-LSTM–Based BCI System for Imagined Speech Recognition Using FOA-DWT by Meenakshi Bisla, Radhey Shyam Anand

“…To achieve optimal performance, the CNN-Bi-LSTM model was optimized using the Keras tuner library. …”
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