Intermittent hypoxia-induced enhancements in corticospinal excitability predict gains in motor learning and metabolic efficiency by Alysha T. Bogard, Thomas G. Hembree, Aviva K. Pollet, Andrew C. Smith, Stephanie C. Ryder, George E. Marzloff, Andrew Q. Tan

“…However, the functional relevance of this plasticity remains unexplored, and it is unclear whether similar plasticity can be induced for lower limb motor areas. …”
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A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection by Yizhe Li, Yidong Xie, Hu He

“…After preprocessing, the images are fed into a deep learning network incorporated with a multi-scale feature pyramid structure, which effectively enhances defect recognition accuracy by integrating high-level semantic information with location details. …”
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Cross-Subject EEG-Based Emotion Recognition Using Deep Metric Learning and Adversarial Training by Hawraa Razzaq Abed Alameer, Pedram Salehpour, Seyyed Hadi Aghdasi, Mohammad-Reza Feizi-Derakhshi

“…Also, we integrate the deep similarity learning module with adversarial learning, which helps to learn a subject-invariant representation of EEG signals in an end-to-end training paradigm. …”
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Integrating Inverse Data Envelopment Analysis and Machine Learning for Enhanced Road Transport Safety in Iran by Seyyed Mojtaba Tabatabai, Fatemeh Al-Sadat Tabatabai

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Machine learning developed immune-related exosome signature for prognosis and immunotherapy benefit in bladder cancer by Xiaoting Luo, Yi Luo

“…A lower level of immune-activated cells and immune-related function, higher tumor immune dysfunction and exclusion score, higher immune escape score, higher intratumor heterogeneity score and lower PD1&CTLA4 immunophenoscore, and lower tumor mutational burden score were obtained in bladder cancer with high IES score, suggesting less immunotherapy benefits. …”
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A Hybrid Machine Learning and Optimization Model to Minimize the Total Cost of BRT Brake Components by Saeed Najafi-Zangeneh, Naser Shams Gharneh, Ali Arjomandi-Nezhad, Erfan Hassannayebi

“…The failure rate is modeled as a linear function of ETE and the quality of brake system subcomponents using a Lasso regression model. …”
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Effects of liver-specific LCMT1 gene knockout on glycolipid metabolism, learning and memory in mice by CHEN Sihong, TANG Tingting, CAI Haiqing, WANG Xinhang, WANG Minghong, PENG Yang, ZHANG Minhua, WEI Meifang, ZHU Congrui, ZHANG Jing, LI Xiaoying, LU Cailing, TANG Shen, LI Xiyi

“…Objective To investigate the effects of liver-specific leucine carboxyl methyltransferase 1 (LCMT1) gene knockout on glycolipid metabolism, learning and memory in mice and the possible mechanism. …”
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Contextual antecedents and motivational mediators in EFL summary-writing and future CLIL course choices by Ming-Chia Lin

Subjects: “…Cognitive discourse function…”
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Improvement teaching-learning-based optimization algorithm for solar cell parameter extraction in photovoltaic systems by H. Khaterchi, M. H. Moulahi, A. Jeridi, R. Ben Messaoud, A. Zaafouri

“…The work aims to improve the Teaching-Learning-Based Optimization (TLBO) algorithm to enhance the accuracy of parameter extraction in PV models. …”
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Knowledge Extraction via Machine Learning Guides a Topology‐Based Permeability Prediction Model by Jia Zhang, Gang Ma, Zhibing Yang, Jiangzhou Mei, Daren Zhang, Wei Zhou, Xiaolin Chang

“…Commonly used empirical formulas neglect its microscopic and topological characteristics, thus lacking accuracy and adaptability. While machine learning (ML) and deep learning (DL) models demonstrate promising performance, but encounter challenges of data availability, computational cost, and model interpretability. …”
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Optimization of Table Tennis Swing Action Supported by the Temporal Convolutional Network Algorithm in Deep Learning by Shaoxuan Sun, Hongyu Zheng, Zhixin Lin

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Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning by Pierre Gouton, Kacoutchy Jean Ayikpa, Diarra Mamadou

“…Finally, we evaluate the images’ quality using metrics such as loss function, PSNR, and SSIM. The model evaluation revealed that DRCN outperforms others in crucial performance. …”
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Residual channel attention based sample adaptation few-shot learning for hyperspectral image classification by Yuefeng Zhao, Jingqi Sun, Nannan Hu, Chengmin Zai, Yanwei Han

“…Abstract Few-shot learning (FSL) uses prior knowledge and supervised experience to effectively classify hyperspectral images (HSIs), thereby reducing the cost of large numbers of labeled samples. …”
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Predicting soil organic carbon with ensemble learning techniques by using satellite images for precision farming by Shyamal Mundada, Pooja Jain

“…The goal of the research is to create a system for evaluating soil organic carbon based on topographic features and soil properties incorporating machine learning algorithms. A group of covariates has been chosen to function as potential predictor factors for soil properties, including four topographical variables, two soil-related remote sensing indices, and four climate variables which were retrieved from satellite images. …”
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A Scale-Aware and Discriminative Feature Learning Network for Fine-Grained Rigid Object Recognition by Yangte Gao, Chenwei Deng, Liang Chen, Zicong Zhu

“…It consists of a fused label learning process based on probability distribution function (PDF) and a DFE branch. …”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…Rolling Element Bearings perform a vital function by ensuring the reliable and efficient operation of machinery in modern industries. …”
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Learning Distributed Parameters of Land Surface Hydrologic Models Using a Generative Adversarial Network by Ruochen Sun, Baoxiang Pan, Qingyun Duan

“…However, calibrating distributed land surface hydrologic models presents a great challenge, often resulting in uneven spatial performance due to the compression of information inherent in model outputs and observations into a single‐value objective function. To address this problem, we propose a novel Generative Adversarial Network‐based Parameter Optimization (GAN‐PO) method. …”
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A Mutual Information Constrained Multitask Learning Method for Very High-Resolution Building Segmentation by Yongchuang Wu, Yingchun Wang, Hui Yang, Peng Zhang, Yanlan Wu, Biao Wang

“…The MIMNet simultaneously segments building mask and boundary utilizing the MTL strategy, and employs a mutual information loss function to enhance information exchange between the two tasks. …”
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Machine learning based method for forecasting short-term passenger flow in urban rail stations by HU Mingwei, SHI Xiaolong, WU Wenlin, HE Guoqing

“…Compared with a single model, LightGBM-LSTM model equipped with merits of multiple models, fulfills a better function in forecasting and a better adaptability to different types of urban rail transit stations.…”
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Estimating Energy Consumption During Soil Cultivation Using Geophysical Scanning and Machine Learning Methods by Jasper Tembeck Mbah, Katarzyna Pentoś, Krzysztof S. Pieczarka, Tomasz Wojciechowski

“…These data, along with soil texture, served as inputs for predicting fuel consumption and field productivity. Three machine learning algorithms were tested: support vector machines (SVMs), multilayer perceptron (MLP), and radial basis function (RBF) neural networks. …”
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