Multi-Source Reinforced Selective Domain Adaptation for Cross-Subject and Cross-Session EEG-Based Emotion Recognition by Xin Deng, Xinyi Hong, LiJiao Ai, Xingchen Li, Chenhui Li

“…The MSRSDA model comprises three components: a data augmentation module, a data selector based on the actor-critic framework, and a domain adaptation module. …”
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TEBS: Temperature–Emissivity–Driven band selection for thermal infrared hyperspectral image classification with structured State-Space model and gated attention by Enyu Zhao, Nianxin Qu, Yulei Wang, Caixia Gao, Jian Zeng

“…Finally, a band evaluation (BE) module is employed to assess the band selection results and optimize the model parameters. …”
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Enhancing students performance through dynamic personalized learning path using ant colony and item response theory (ACOIRT) by Imamah, Umi Laili Yuhana, Arif Djunaidy, Mauridhi Hery Purnomo

“…The parameters used to create the PLP include the initial module, targeted module, knowledge level, pretest score, and difficulty level. …”
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SP-IGAN: An Improved GAN Framework for Effective Utilization of Semantic Priors in Real-World Image Super-Resolution by Meng Wang, Zhengnan Li, Haipeng Liu, Zhaoyu Chen, Kewei Cai

“…This information is injected into the RRDB module through Spatial Feature Transform (SFT) layers, generating more accurate and semantically consistent texture details. …”
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Research on Point Cloud Registration and Stitching Fusion Algorithm Based on GCN-PRFNet by Wenhao Zeng, Gongbing Su, Zixuan Su, Rui Li, Jun Chen

“…The network has a feature extraction module, a point cloud registration module, and a point cloud splicing and fusion module. …”
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Mixed multi-branch feature fusion model for efficient automatic building extraction from high-resolution remote sensing images by Yaohui Liu, Shuzhe Zhang, Xinkai Wang, Taoyi Chen, Hu Jiang, Fei Su

“…Firstly, we designed a Mixed Multi-Branch Feature Fusion (MMFF) module, which performs multi-dimensional weighted fusion on the feature information captured by the Transformer. …”
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Sonar-based object detection for autonomous underwater vehicles in marine environments by Zhen Wang, Zhen Wang, Jianxin Guo, Shanwen Zhang, Yucheng Zhang

“…To tackle the issue of object scale variation, we designed a multi-scale feature refinement module (MFRM) to improve both classification accuracy and positional precision by refining the feature representations of objects at different scales. …”
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MDIGCNet: Multidirectional Information-Guided Contextual Network for Infrared Small Target Detection by Luping Zhang, Junhai Luo, Yian Huang, Fengyi Wu, Xingye Cui, Zhenming Peng

“…The primary structure of this network adopts the U-Net architecture. To address the issue of lacking texture and structural information in the target images, we employ an integrated differential convolution (IDConv) module to extract richer image features during both the encoding and decoding stages. …”
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PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion by Ying Qi, Qiushi Li, Zhaoyuan Huang, Jian Li, Chenyang Wang, Teng Wan, Qiang Zhang

“…This approach treats alignment quality as a learnable, spatially-varying confidence and materializes this concept through three synergistic modules. The Progressive Deformable Feature Alignment (PDFA) module achieves robust feature extraction through a two-stage deformable convolution and exposure-aware modulation. …”
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Modular gardens by James C. Rose – A 1946 experiment for Ladies’ Home Journal by Anna-Maria Visilia

“… The ‘module’ is a design tool that is profoundly intertwined with design concepts such as proportions and geometry as well as globalization, sustainability, and equity. …”
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Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ by HU Chengxi, TAN Lixin, WANG Wenyin, SONG Min

“…Traditional neural network methods suffer from issues such as large model size, long training times, and difficulties in dealing with complex scenes. …”
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Review of Research on Foundation Scour of River-Crossing Bridges by WANG Lu, LIU Hong-wei, WEI Kai, Bruce Melville, NIE Rui-hua

“…In particular, multi-module multilayer perceptrons (multi-module MLPs) can construct hybrid neural networks incorporating physical scour mechanisms, showing great potential in addressing the challenges of predicting scour under complex boundary conditions. …”
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SMILE: Semi-supervised multi-view classification based on dynamical fusion. by Hui Yang, Linyan Kang, Xun Che

“…This approach leverages a high-level semantic mapping module to extract discriminative features from each view, reducing redundancy features. …”
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SOC Equalization Control Method Considering SOH in DC–DC Converter Cascaded Energy Storage Systems by Shixian Bai, Xiangqian Tong, Xin Ma, Jie Zhou

“…The “barrel effect” diminishes the effective capacity of the energy storage system. To mitigate this issue, a DC–DC converter cascaded energy storage system has been developed, incorporating precise charge and discharge management for each battery module within a cluster. …”
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AFMF: adaptive fusion of multi-hop neighborhood features in graph convolutional network by Kang Liu, Xinyu Li, Lian Liu, Zhihao Xv, Aohang Pei, Runshi Ji

“…Then, we propose a feature fusion weight generation module (FFWG), which can adaptively generate fusion weights based on the above aggregated node features. …”
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DiffMamba: semantic diffusion guided feature modeling network for semantic segmentation of remote sensing images by Zhen Wang, Nan Xu, Zhuhong You, Shanwen Zhang

“…DiffMamba uses a hybrid CNNs-Transformer as the encoder structure, and is equipped with the efficient phase sensing module (EPSM), the multi-view transformer module (MVTrans), the semantic diffusion alignment module (SDAM), and the coordinate state space model (CAMamba). …”
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TSAS—YOLOv8: An Optimization Detection Model for Capturing Small Target Features and Processing Key Information by Yongbo Yuan, Linlin Cao

“…This not only reduces the detection accuracy but also undermines the model’s generalization performance. To address this issue, we propose the TSAS-YOLOv8 method. This method defines the T-CMUNeXt module and introduces a new Backbone structure based on this module, leveraging its multi-scale feature fusion advantage to capture the subtle features of small targets. …”
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A Vehicle–Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion by Jianhu Liu, Ping Wang, Xia Wu

“…For feature fusion at each scale, we introduce the Multi-Source Dynamic Interaction Module (MSDI) and the Per-Point Self-Attention Module (PPSA). …”
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FD-YOLO: A YOLO Network Optimized for Fall Detection by Hoseong Hwang, Donghyun Kim, Hochul Kim

“…First, a global attention module (GAM) based on the Convolutional Block Attention Module (CBAM) was employed to improve detection performance. …”
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Semi-Supervised Remote Sensing Building Change Detection with Joint Perturbation and Feature Complementation by Zhanlong Chen, Rui Wang, Yongyang Xu

“…Among them, the network facilitates the generation of multi-scale change features, integrates features, and captures multi-scale change targets through the temporal difference guidance module, the full-scale feature fusion module, and the depth feature guidance fusion module. …”
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