Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea by Tianhong Ding, Zhiqiang Xu, Yunjie Wang, Qinglian Hou, Xiangyong Liu, Fengshuang Ma

“…A novel feature extraction module, ASPC-DSC, combines atrous spatial pyramid convolution with depthwise separable convolution to effectively fuse multi-scale contextual features while improving computational efficiency. …”
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A segmentation-combination data augmentation strategy and dual attention mechanism for accurate Chinese herbal medicine microscopic identification by Xiaoying Zhu, Xiaoying Zhu, Guangyao Pang, Guangyao Pang, Xi He, Xi He, Yue Chen, Yue Chen, Zhenming Yu

“…Additionally, a shallow-deep dual attention module enhances the model's ability to focus on relevant features across different layers. …”
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MACA-Net: Mamba-Driven Adaptive Cross-Layer Attention Network for Multi-Behavior Recognition in Group-Housed Pigs by Zhixiong Zeng, Zaoming Wu, Runtao Xie, Kai Lin, Shenwen Tan, Xinyuan He, Yizhi Luo

“…To further improve detection, a Cross-Layer Feature Pyramid Transformer (CFPT) neck employs non-upsampled feature fusion, mitigating semantic gap issues where small target features are overshadowed by large target features during feature transmission. …”
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A case report on the safety and efficacy of aripiprazole for depression in Brugada syndrome by Monica Fahmy, PharmD, Youssef Bessada, PharmD, Cassandra Ramsey, APRN, Jennifer R. Zigun, MD

“…Separately, web-hosted continuing education platforms indicated that more than 75% of clinicians rated their understanding of these 2 issues as “very high” or “high” upon module completion. …”
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MissPred: A Robust Two-Stage Radar Echo Extrapolation Algorithm for Incomplete Sequences by Ziqi Zhao, Chunxu Duan, Lin Song, Qilin Zhang, Wenda Zhu, Yi Liu

“…Furthermore, a missing spatiotemporal feature fusion module (MSTF) that is absent has been designed for the purpose of extracting fine-grained complete spatiotemporal features. …”
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Application of the program for artificial intelligence analytics of paper text and segmentation by specified parameters in clinical practice by A. A. Komkov, V. P. Mazaev, S. V. Ryazanova, A. A. Kobak, E. V. Bazaeva, D. N. Samochatov, E. V. Koshkina, Е. V. Bushueva, O. M. Drapkina

“…The development of novel technologies using elements of artificial intelligence (AI) in medicine is addressed to practical clinical implementation and provision of key issues, including improvement in the use of routine clinical data, aimed at practical relevance, standardization, confidentiality and patient safety.Aim. …”
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CaLiJD: Camera and LiDAR Joint Contender for 3D Object Detection by Jiahang Lyu, Yongze Qi, Suilian You, Jin Meng, Xin Meng, Sarath Kodagoda, Shifeng Wang

“…To address this issue, this study proposes a 3D object detection network, CaLiJD (Camera and Lidar Joint Contender for 3D object Detection), guided by two-dimensional detection results. …”
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LiDAR point cloud denoising for individual tree extraction based on the Noise4Denoise by Xiangfei Lu, Zongyu Ye, Liyong Fu, Huaiyi Wang, Kaiyu Wang, Yaquan Dou, Dongbo Xie, Xiaodi Zhao

“…The processing of LiDAR point cloud data is of critical importance in the context of forest resource surveys, as well as representing a pivotal element in the realm of forest physiological and ecological studies.Nonetheless, conventional denoising algorithms frequently exhibit deficiencies with regard to adaptability and denoising efficacy, particularly when employed in relation to disparate datasets.To address these issues, this study introduces DEN4, an unsupervised, deep learning-based point cloud denoising algorithm designed to improve the accuracy of single tree segmentation in LiDAR point clouds.DEN4 introduces a multilevel noise separation module that effectively distinguishes between signal and noise, thereby improving the signal-to-noise ratio (SNR) and reducing the error.The experimental results demonstrate that DEN4 significantly outperforms traditional denoising methods in several key metrics, including mean square error (MSE), SNR, Hausdorff distance, and structural similarity index (SSIM).In the 60 sample dataset, DEN4 achieved the best mean and standard deviation on all metrics: Specifically, the MSE mean was found to be 0.0094, with a standard deviation of 0.0008, the SNR mean was 149.1570, with a standard deviation of 0.5628, the Hausdorff mean was 0.8503, with a standard deviation of 0.0947, and the SSIM mean was 0.8399, with a standard deviation of 0.0054. …”
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Automated detection of diabetic retinopathy lesions in ultra-widefield fundus images using an attention-augmented YOLOv8 framework by Lei-Si Hu, Jie Wang, Heng-Ming Zhang, Hai-Yu Huang

“…ObjectiveTo enhance the automatic detection precision of diabetic retinopathy (DR) lesions, this study introduces an improved YOLOv8 model specifically designed for the precise identification of DR lesions.MethodThis study integrated two attention mechanisms, convolutional exponential moving average (convEMA) and convolutional simple attention module (convSimAM), into the backbone of the YOLOv8 model. …”
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UAV-FAENet: Frequency-Aware and Attention-Enhanced Network for Remote Sensing Semantic Segmentation of UAV Imagery by Dongbo Zhou, Huan Wang, Shiyan Pang, Di Chen, Huang Yao, Jie Yu

“…UAV-FAENet adopts an encoder-decoder architecture, integrating a high-low frequency adaptive enhancement module (HLAEM) and a decoder based on a state space modeling mechanism (CSRA-VSS decoder). …”
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Enhancing VINS with Smart Feature Grading: Overcoming Cautious and Excessive Removal of Dynamic Features for Robust Urban Localization by M. Adham, M. Adham, W. Chen, A. Mansour, A. Mansour, M. Mahmoud, M. Mahmoud, Y. Li

“…Deep learning (DL)-based methods, which directly remove potential dynamic objects, often degrade accuracy in low-texture scenes and overlook the resulting uneven feature distribution, further impacting state estimation. To address these issues, we propose a novel VINS method that combines visual and inertial information with a smart feature grading module to overcome cautious and excessive dynamic feature removal, effectively handling the complexities of dominant and ambiguous dynamic objects beyond the limitations of traditional DL and vision-based methods. …”
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MARSWIN: A Cross-Shot Swin Transformer Network for Super-Resolution on Mars MoRIC Image by Junjie Li, Weikun Lv, Linhui Wei, Yu Liu, Yumei Wang, Zhi Liu, Zhi Zeng

“…In addition, to ensure the alignment of overlapping regions, we design an adaptive cross-shot attention module to utilize the interimage correlations and reconstruct the image region obscured by dust. …”
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The improvement programs for preparation general practitioners on rendering urgent medical aid by V. O. Vishnevskyi

“…A final form of control of knowledge is module control on a traumatology and military field surgery, that must be plugged in state examinations, which includes test control, check of practical skills and interview. …”
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E-CLIP: An Enhanced CLIP-Based Visual Language Model for Fruit Detection and Recognition by Yi Zhang, Yang Shao, Chen Tang, Zhenqing Liu, Zhengda Li, Ruifang Zhai, Hui Peng, Peng Song

“…This problem stems from their reliance on unimodal visual data, which creates a semantic gap between image features and contextual understanding. To solve these issues, this study proposes a multi-modal fruit detection and recognition framework based on visual language models (VLMs). …”
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Improving diagnostic precision in thyroid nodule segmentation from ultrasound images with a self-attention mechanism-based Swin U-Net model by Changan Yang, Muhammad Awais Ashraf, Mudassar Riaz, Pascal Umwanzavugaye, Kavimbi Chipusu, Kavimbi Chipusu, Hongyuan Huang, Yueqin Xu

“…The model integrates residual and multiscale convolutional structures in the encoder path, with long skip connections feeding into an attention module to improve edge preservation and feature extraction. …”
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A Novel Dual-Modal Deep Learning Network for Soil Salinization Mapping in the Keriya Oasis Using GF-3 and Sentinel-2 Imagery by Ilyas Nurmemet, Yang Xiang, Aihepa Aihaiti, Yu Qin, Yilizhati Aili, Hengrui Tang, Ling Li

“…DMSSNet incorporates self-attention mechanisms and a Convolutional Block Attention Module (CBAM) within a hierarchical fusion framework, enabling the model to capture both intra-modal and cross-modal dependencies and to improve spatial feature representation. …”
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Attention residual network for medical ultrasound image segmentation by Honghua Liu, Peiqin Zhang, Jiamin Hu, Yini Huang, Shanshan Zuo, Lu Li, Mailan Liu, Chang She

“…Additionally, a spatial hybrid convolution module is integrated to augment the model’s ability to extract global information and deepen the vertical architecture of the network. …”
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TongueNet: a multi-modal fusion and multi-label classification model for traditional Chinese Medicine tongue diagnosis by Lijuan Yang, Lijuan Yang, Qiumei Dong, Da Lin, Xinliang Lü

“…The model utilizes a Hierarchical Aggregation Network (HAN) and a Feature Space Projection Module to efficiently extract and fuse features while introducing consistency and complementarity constraints to optimize multimodal information fusion. …”
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Physical violence and its associations: Insights from nationally representative data in India by Monisha Mary P, Ankeeta Menona Jacob, Avinash K Shetty

“…Methods: Data from NFHS-5, focusing on women aged 15–49 who completed the domestic violence module, were analyzed. Women’s empowerment was measured through employment, asset ownership, and decision-making autonomy. …”
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Editorial by Bulent Cavas

“…The ninth article, “Investigating the Effects of Socio-Scientific Issues-Based Learning Module in Enhancing Students’ Physics Knowledge,” by Badeo et al. …”
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