Automatic training sample collection utilizing multi-source land cover products and time-series Sentinel-2 images by Yanzhao Wang, Yonghua Sun, Xuyue Cao, Yihan Wang, Wangkuan Zhang, Xinglu Cheng, Ruozeng Wang, Jinkun Zong

“…This article proposes an automatic training sample collection approach (ATSC) that utilizes multi-source LC products and time-series Sentinel-2 images. …”
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Prediction Model for the Environmental Noise Distribution of High-Speed Maglev Trains Using a Segmented Line Source Approach by Shiquan Cheng, Jianmin Ge, Longhua Ju, Yuhao Chen

Subjects: “…high-speed Maglev train…”
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LKJ Data Preparation Technology and Application Research Based on Train Diagram Data by LEI Liping, YANG Shaobin, ZHU Chengjian, DENG Yong

Subjects: “…train diagram data…”
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Body composition changes in resistance-trained adults Following 8-Weeks of supplementation with a combination of plant protein and creatine monohydrate by Joesi M. Krieger, Kevin F. Holley, Alex C. Schrautemeier, James L. Tice, Joshua Iannotti, Anthony M. Hagele, Connor J. Gaige, Wyatt B. McLaughlin, Ralf Jäger, Chad M. Kerksick

“…Introduction Adequate dietary protein supports body composition changes while resistance training. Due to its complete amino acid profile, whey protein has routinely outperformed plant-based options. …”
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Correction: A study on the impact of open source metaverse immersive teaching method on emergency skills training for medical undergraduate students by Huanliang Huang, Jiayi Yin, Fa Lv, Yuqi Lin, Jun Zou

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Day-ahead energy optimization and scheduling strategy of "source-network-train-storage" coordinated power supply system for electrified railways by LIAO Haizhu, HU Haitao, HUANG Yi, GE Yinbo, GENG Anqi, WANG Ke

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Content Sources of Anti-Corruption Education in Russian Pedagogy by Grigory S. Bukharin

Exploration of computational method for far-field aerodynamic noise from bogie zone of high-speed train by ZHANG Hongjun, WANG Zhonghui, XU Yumin, GAO Yang, WANG Yigang, ZHU Langxian

Subjects: “…high-speed train…”
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Source-Free Domain Adaptation Framework for Rotary Machine Fault Diagnosis by Hoejun Jeong, Seungha Kim, Donghyun Seo, Jangwoo Kwon

“…Intelligent fault diagnosis for rotary machinery often suffers performance degradation under domain shifts between training and deployment environments. To address this, we propose a robust fault diagnosis framework incorporating three key components: (1) an order-frequency-based preprocessing method to normalize rotational variations, (2) a U-Net variational autoencoder (U-NetVAE) to enhance adaptation through reconstruction learning, and (3) a test-time training (TTT) strategy enabling unsupervised target domain adaptation without access to source data. …”
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MoTiF: a self-supervised model for multi-source forecasting with application to tropical cyclones by Clément Dauvilliers, Claire Monteleoni

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A Novel Curriculum Learning Training Strategy for Pomegranate Growth Stage Classification Using YOLO Models on Multi-Source Datasets for Precision Agriculture by N. Shobha Rani, K. R. Bhavya, A. Vadivel, T. Vasudev, Raghavendra M. Devadas, Vani Hiremani

“…The proposed training strategy proves to show improvement over state-of-the-art work Zhao et al. (2024) in achieving higher detection accuracies towards all five classes. …”
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Generative-assisted multi-stage integrated network: Tackling extreme noise in image denoisingThe source code and trained models are made publicly available at: by Taeyong Park, Muhammad Sohail Ibrahim, Minseok Kim, Zahyun Ku, Yunsang Kwak

“…These findings underscore the potential of GainNet for real-world applications in aerospace imaging, autonomous navigation, and medical diagnostics. The source code and trained models are made publicly available at https://github.com/AVIP-laboratory/Generative-assisted_multi-stage_integrated_network.…”
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Influence of Vortex Source Density on Performance of Rotatory Eddy Current Brake Device by Zhiding YING, Jiamin CHEN

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A low-cost, multiplatform, open-source, and automatic system for lever-press timing tasks with rodents by Tércio Apolinário-Souza, Samuel Penna Wanner, Lucas Bicalho, Guilherme Menezes Lage

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Aux sources des inégalités face au numérique, le rapport des enseignants du secondaire aux technologies numériques : retrait, appropriation ou partage ? by Isabelle Danic, Thierry Lefort, Laurent Mell

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MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models by Feng Wang, Jinming Chu, Liyan Shen, Shan Chang

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SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI by Ami Tsuchida, Martin Goubet, Philippe Boutinaud, Iana Astafeva, Victor Nozais, Pierre-Yves Hervé, Thomas Tourdias, Stéphanie Debette, Marc Joliot

“…Yet, the lack of open sharing of pre-trained models hampers the practical application and evaluation of these methods beyond specific data sources used in each study. …”
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Key Technologies for Intelligent Control of Heavy-Haul Trains Focusing on Safe and Efficient Operation by WANG Qingyuan, WEI Mi, HU Yunqing, WANG Jianhua, JIANG Fan, ZHANG Zhengfang, WANG Kaiyun

Subjects: “…heavy-haul train…”
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A SWIN-based vision transformer for high-fidelity and high-speed imaging experiments at light sources by Songyuan Tang, Tekin Bicer, Kamel Fezzaa, Samuel Clark

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The ZTF Source Classification Project. III. A Catalog of Variable Sources by Brian F. Healy, Michael W. Coughlin, Ashish A. Mahabal, Theophile Jegou du Laz, Andrew Drake, Matthew J. Graham, Lynne A. Hillenbrand, Jan van Roestel, Paula Szkody, LeighAnna Zielske, Mohammed Guiga, Muhammad Yusuf Hassan, Jill L. Hughes, Guy Nir, Saagar Parikh, Sungmin Park, Palak Purohit, Umaa Rebbapragada, Draco Reed, Daniel Warshofsky, Avery Wold, Joshua S. Bloom, Frank J. Masci, Reed Riddle, Roger Smith

“…Building on previous work, this paper reports the results of the ZTF Source Classification Project ( SCoPe ), which trains neural network and XGBoost (XGB) machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. …”
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