The Frmpd3 Protein Regulates Susceptibility to Epilepsy by Combining with GRIP and GluA2 by Yan Jia, Jinqiong Zhan, Pengcheng Huang, Xiaobing Li, Daojun Hong, Xi Lu

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Synergistic Anti-Inflammatory Effects of Pomegranate Peel–Hawthorn Combinations in Ulcerative Colitis: Network Pharmacology Prediction and Experimental Validation by Shouqing Zhang, Quanyuan Qiu, Mengzhen Yuan, Jiajia Yu, Weiwei Gao, Xi Wang, Zhen Liu, Peng Yu, Cen Xiang, Yuou Teng

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Four weeks of slow-paced breathing: a longitudinal, randomized, placebo-controlled study of the autonomic nervous system by Nicolas Bourdillon, Arnaud Rochat, Vanessa Hoop, Grégoire P. Millet

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Characterization of Netrin-1 and Its Receptors UNC5B and Neogenin-1 in a Rat Rotator Cuff Tear Model: Associations with Inflammatory Mediators and Neurite Extension by Kosuke Inoue, Kentaro Uchida, Mitsuyoshi Matsumoto, Ryo Tazawa, Etsuro Ohta, Akito Hattori, Tomonori Kenmoku, Yuka Ito, Yui Uekusa, Gen Inoue, Masashi Takaso

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Contextualization of Training Input in Multi-Level Replication and Scaling-up Approach in EFL Teacher-Training by Hazel Acosta, Diego Cajas, Elizabeth Minchala

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The impact of gut microbiota on mental health by Neska Aleksandra, Kedzierska Ewa, Gibula-Tarlowska Ewa

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Socio-Adaptive Dimension of Students’ Personal Space by Iryna Havryliuk

Enhanced hybrid CNN and transformer network for remote sensing image change detection by Junjie Yang, Haibo Wan, Zhihai Shang

“…Secondly, the frequency component of these features is exploited by a refined module I. Thirdly, an enhanced token mining module based on the Kolmogorov-Arnold Network is utilized to derive semantic information. …”
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Deep Learning-Based Algorithm for Road Defect Detection by Shaoxiang Li, Dexiang Zhang

“…First, it replaces the C2f module in the GD mechanism with the improved C2f module based on RepViTBlock to construct the Rep-GD module. …”
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YOLO-CBD: Classroom Behavior Detection Method Based on Behavior Feature Extraction and Aggregation by Shuyun Peng, Xiaopei Zhang, Luoyu Zhou, Peng Wang

“…Secondly, a novel feature aggregation module is designed for replacing the basic C2f module in the YOLOv10s neck network and enhancing the capability to detect occluded targets effectively. …”
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A lightweight steel surface defect detection network based on YOLOv9 by Tianyi Zheng, Ling Yu, Yongbao Shi, Fanglin Niu

“…Finally, we replace the Fusion module in the CNN-based cross-scale feature fusion (CCFM) module, with the new Fusion-RepNCSPELAN4 module, creating a new feature fusion network, CCFM-YOLO, which replaces the neck network of YOLOv9. …”
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Left ventricular segmentation method based on optimized UNet and improved CBAM: ESV and EDV tracking study. by Kerang Cao, Miao Zhao, Minghui Geng, Shuai Zheng, Hoekyung Jung

“…By integrating the CBAM (Attention module) attention mechanism and a lightweight SimAM (Simple Attention Module) module, we enhance feature selectivity and minimize redundancy. …”
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CGDINet: A Deep Learning-Based Salient Object Detection Algorithm by Chengyu Hu, Jianxin Guo, Hanfei Xie, Qing Zhu, Baoxi Yuan, Yujie Gao, Xiangyang Ma, Jialu Chen, Juan Tian

“…To address these problems, an improved significance object detection network—CGDINet (Coordinate Attention-Group Consensus Aggregation Module-Depth Auxiliary Module-Inverse Saliency Pyramid Reconstruction Network)—is proposed. …”
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Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…The method incorporates a multi-scale temporal attention module and a multi-scale temporal convolution module to extract multi-scale information. …”
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Spatial Shape-Aware Network for Elongated Target Detection by Shaowen Xu, Der-Horng Lee

“…Specifically, we introduce three key modules: a Boundary-Guided Spatial Feature Perception Module (BGSF), a Shape-Sensing Module (SSM), and a Potential Evaluation Module (PEM). …”
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LGFUNet: A Water Extraction Network in SAR Images Based on Multiscale Local Features with Global Information by Xiaowei Bai, Yonghong Zhang, Jujie Wei

“…The LGFUNet model consists of three parts: the encoder–decoder, the DECASPP module, and the LGFF module. In the encoder–decoder, the Swin-Transformer module is used instead of convolution kernels for feature extraction, enhancing the learning of global information and improving the model’s ability to capture the spatial features of continuous water bodies. …”
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MMS-EF: A Multi-Scale Modular Extraction Framework for Enhancing Deep Learning Models in Remote Sensing by Hang Yu, Weidong Song, Bing Zhang, Hongbo Zhu, Jiguang Dai, Jichao Zhang

“…The framework incorporates three key components: (1) a multiscale overlapping segmentation module that captures comprehensive geographical information through multi-channel and multiscale processing, ensuring the integrity of large-scale features; (2) a multiscale feature fusion module that integrates local and global features, facilitating seamless image stitching and improving classification accuracy; and (3) a detail enhancement module that refines the extraction of small-scale features, enriching the semantic information of the imagery. …”
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Spatial–Temporal Semantic and Geographic Correlation Network for SAR Image Change Detection With Limited Training Data by Haolin Li, Bin Zou, Lamei Zhang, Jiang Qin

“…STSGNet consists of three primary modules: the spatial–temporal semantic (STS) module, the spatial–temporal geographic (STG) module, and the dynamic weighted fusion (DWF) module. …”
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Feature Multi-Scale Enhancement and Adaptive Dynamic Fusion Network for Infrared Small Target Detection by Zenghui Xiong, Zhiqiang Sheng, Yao Mao

“…This model is based on a U-Net architecture and incorporates a Residual Multi-Scale Feature Enhancement (RMFE) module and an Adaptive Feature Dynamic Fusion (AFDF) module. …”
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Unpaired Image-to-Image Translation with Diffusion Adversarial Network by Hangyao Tu, Zheng Wang, Yanwei Zhao

“…Specifically, our model consists of two modules: (1) Feature fusion module: In this module, one-dimensional SVD features are transformed into two-dimensional SVD features using the convolutional two-dimensionalization method, enhancing the diversity of the images generated by the network. (2) Network convergence module: In this module, the generator transitions from the U-net model to a superior diffusion model. …”
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