A noval RUL prediction method for rolling bearing: TcLstmNet-CBAM by Qiang Liu, Zhengwei Dai, Hongxi Lai, Minghao Chen, Huiyuan Huang, Jiahui Fu, Mingxin Hou, Xiaoming Xu, Guangbin Wang, Jin Yan

“…Additionally, a Convolutional Block Attention Module (CBAM) is incorporated to perform multi-dimensional weighting on the extracted features, assigning greater importance to critical features. …”
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Fire and Smoke Detection Based on Improved YOLOV11 by Zhipeng Xue, Lingyun Kong, Haiyang Wu, Jiale Chen

“…However, complex backgrounds, large environmental changes, and data requirements pose great challenges to high-precision outdoor smoke detection. To address these issues, this paper proposes an improved model, YOLOV11-DH3, based on YOLOV11. …”
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Nondestructive freshness recognition of chicken breast meat based on deep learning by Rui Jian, Guangbo Li, Xie Jun, Guolong Shi

“…Traditional methods for chicken breast freshness recognition suffer from issues such as high cost, difficulty in recognition, and low efficiency. …”
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Integrating Pose Features and Cross-Relationship Learning for Human–Object Interaction Detection by Lang Wu, Jie Li, Shuqin Li, Yu Ding, Meng Zhou, Yuntao Shi

“…Methods: In this paper, we design a Pose-Aware Interaction Network (PAIN) based on transformer architecture and human posture to address these issues through two innovations: A new feature fusion method is proposed, which fuses human pose features and image features early before the encoder to improve the feature expression ability, and the individual motion-related features are additionally strengthened by adding to the human branch; the Cross-Attention Relationship fusion Module (CARM) better fuses the three-branch output and captures the detailed relationship information of HOI. …”
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Research on Foreign Object Intrusion Detection for Railway Tracks Utilizing Risk Assessment and YOLO Detection by Shanping Ning, Rui Guo, Pengfei Guo, Lu Xiong, Bangbang Chen

“…Furthermore, a BiFPN-Lite module is introduced to fuse more target features without increasing complexity, with YOLO Head utilized to output the type information of foreign objects on the track surface. …”
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Research on Abnormal Ship Brightness Temperature Detection Based on Infrared Image Edge-Enhanced Segmentation Network by Xiaobin Hong, Guanqiao Chen, Yuanming Chen, Ruimou Cai

“…The Sobel operator is used to obtain edge feature maps, and the Convolutional Block Attention Module (CBAM) extracts key feature information. In the Fusion Unit, edge features guide the extraction of infrared ship features in the backbone network, resulting in feature maps rich in edge information. …”
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SDMSEAF-YOLOv8: a framework to significantly improve the detection performance of unmanned aerial vehicle images by Linxuan Li, Xiaoyu Liu, Xuan Chen, Fengjuan Yin, Bin Chen, Yufeng Wang, Fanbin Meng

“…A Multi-Separated and Enhancement Attention module enhances the feature learning ability of the occluded target region, thus reducing missed and false detections. …”
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EMPIRICAL ASPECTS OF THE INFLUENCE OF THE COOPERATION OF FISCAL INSTITUTIONS ON PROPERTY TAX ADMINISTRATION by Oksana Desyatnyuk, Andriy Krysovatyy, Bohdana Shuliuk, Fedir Tkachyk, Vadym Maslii

“…The article addresses current issues concerning the interaction between government and local authorities in enhancing property tax administration procedures. …”
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BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images by Wei Zhang, Jinsong Li, Shuaipeng Wang, Jianhua Wan

“…Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. …”
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DMF-YOLO: Dynamic Multi-Scale Feature Fusion Network-Driven Small Target Detection in UAV Aerial Images by Xiaojia Yan, Shiyan Sun, Huimin Zhu, Qingping Hu, Wenjian Ying, Yinglei Li

“…Second, we construct a Multi-scale Feature Aggregation Module (MFAM) that integrates dual-branch spatial attention mechanisms to achieve efficient cross-layer feature fusion, mitigating information conflicts between shallow details and deep semantics. …”
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LH-YOLO: A Lightweight and High-Precision SAR Ship Detection Model Based on the Improved YOLOv8n by Qi Cao, Hang Chen, Shang Wang, Yongqiang Wang, Haisheng Fu, Zhenjiao Chen, Feng Liang

“…However, detecting multi-scale ship targets in complex backgrounds leads to issues of false positives and missed detections, posing challenges for lightweight and high-precision algorithms. …”
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Urban informal settlements interpretation via a novel multi-modal Kolmogorov–Arnold fusion network by exploring hierarchical features from remote sensing and street view images by Hongyang Niu, Runyu Fan, Jiajun Chen, Zijian Xu, Ruyi Feng

“…The proposed KANFusion model employs the Kolmogorov–Arnold Network (KAN) instead of the conventional MLP structure to enhance the model-fitting capability of heterogeneous modality-specific features and uses a novel Multi-level Feature Fusion Module with KAN block (MFF) to fuse the hierarchical modality-specific and modality-fusion features from remote sensing and street view images for better UIS interpretation performance. …”
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An efficient parallel runoff forecasting model for capturing global and local feature information by Yang-hao Hong, Dong-mei Xu, Wen-chuan Wang, Hong-fei Zang, Xiao-xue Hu, Yan-wei Zhao

“…The proposed PCPFN model, during parallel computation, not only utilizes the intrinsic features of sequences and efficiently handles the distribution of local and global features but also shares predictive information in the output module, achieving accurate runoff forecasting and providing crucial references for timely warning and forecasting.…”
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Multi-View Attention Network With Iterative Feature Refinement and Boundary Awareness for Endoscopic Image Segmentation by Dongzhi He, Rui Zhang, Yu Liang, Jiaping Chen, Yunqi Li

“…However, achieving accurate segmentation is challenging due to issues such as boundary blurring, low contrast, and small lesion sizes. …”
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Intelligent Monitoring of BECS Conveyors via Vision and the IoT for Safety and Separation Efficiency by Shohreh Kia, Benjamin Leiding

“…Simultaneously, a machine vision module autonomously detects and corrects belt misalignment, eliminating the need for manual intervention and reducing the risk of worker exposure to moving mechanical parts. …”
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Progressive Guided Fusion Network With Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection by Jiajia Wu, Guangliang Han, Haining Wang, Hang Yang, Qingqing Li, Dongxu Liu, Fangjian Ye, Peixun Liu

“…Moreover, a residual prediction module (RPM) is applied to further suppress background elements. …”
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A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection by Sheng Deng, Yaping Wan

“…To tackle the issues of small target sizes, missed detections, and false alarms in aerial drone imagery, alongside the constraints posed by limited hardware resources during model deployment, a streamlined object detection approach is proposed to enhance the performance of YOLOv8s. …”
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Autonomous slip control inspired by human physiology for improved shared control strategy by Joana Matos, Patricia Capsi-Morales, Cristina Piazza

“…These constraints result in a limited number of control inputs and a lack of sensory feedback. To address these issues, various semi-autonomous control strategies have been proposed, which integrate sensing technologies to complement traditional myoelectric control. …”
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PISCFF-LNet: A Method for Autonomous Flight of UAVs Based on Lightweight Road Extraction by Yuanxu Zhu, Tianze Zhang, Aiying Wu, Gang Shi

“…Furthermore, many existing autonomous-flight-control algorithms for UAVs are computationally demanding, which limits their deployment on embedded devices with constrained memory and processing power, thereby affecting both operational efficiency and the safety of UAV missions. To address these issues, we propose PISCFF-LNet, a lightweight road-extraction network that integrates prior knowledge and spatial contextual features. …”
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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images by Yuhang Zhang, Wuxia Zhang, Songtao Ding, Siyuan Wu, Xiaoqiang Lu

“…The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multilevel Feature Interaction module (TML-FI), and a Multilevel Feature Fusion Decoder (ML-FFD). …”
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