Application of the One Health Surveillance (OHS) Matrix to Evaluate the Disease Surveillance Systems in Gujarat, India: A Policy Content Analysis by Sandul Yasobant, Ravina Tadvi, Walter Bruchhausen, Deepak B. Saxena

“…A veterinarian is placed on establishing cross-sectoral collaborations between these systems, but the joint collaborative activities and their effect on the early warning response are least understood. …”
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Detection of Staphylococcus Enterotoxin sea and seb in Milk Samples by Duplex Droplet Digital PCR by Xiaoyue Wei, Wenzhou Wang, Tong Wang, Yuhua Yang, Yahui Guo, Lijin Long, Jiaming Fan, Fanliang Meng, Wentao Liu, Wanting Wang, Yakun Zhao, Jianling Chen, Fei Zhao, Jianzhong Zhang, Xiaomei Yan

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Coherent Separation Detection for Orbital Angular Momentum Multiplexing in Free-Space Optical Communications by Xiaoke Zhang, Yanliang He, Yao Cai, Mingyang Su, Xinxing Zhou, Yu Chen, Shuqing Chen, Yuanjiang Xiang, Lin Chen, Chenliang Su, Ying Li, Dianyuan Fan

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Research on Robot Force Compensation and Collision Detection Based on Six-Dimensional Force Sensor by Yunyi Wang, Zhijun Wang, Yongli Feng, Yanghuan Xu

“…Firstly, a collision detection model for a joint-type collaborative robot is presented. …”
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Differentiation of high risk prostate cancer with a facile urinary exosome detection workflow by Shuai Qiu, Yue Li, Zheng Zhang, Chunchang Li, Haoyu Wang, Ao Chen, Zhi Yan, Yang Liu, Zifei Li, Hua Huang, Yi Liu, Yiqi Seow, Ruibing Chen, Jinhong Guo, Simeng Wen, Jing Tian, Hongtuan Zhang, Ranlu Liu, Gang Han, Baolong Wang, Yong Wang, Yuanjie Niu, HaiFang Yin

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Detection of Low-Flying Target under the Sea Clutter Background Based on Volterra Filter by Hongyan Xing, Yan Yan

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A Unified Method for Detecting Phylogenetic Signals in Continuous, Discrete, and Multiple Trait Combinations by Liang Yao, Ye Yuan

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CB-MTE: Social Bot Detection via Multi-Source Heterogeneous Feature Fusion by Meng Cheng, Yuzhi Xiao, Tao Huang, Chao Lei, Chuang Zhang

“…Social bots increasingly mimic real users and collaborate in large-scale influence campaigns, distorting public perception and making their detection both critical and challenging. …”
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User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection by Zepu Yi, Chenxu Tang, Songfeng Lu

“…Through the cross-modal adaptive collaborative attention mechanism, UC-CMAF achieves deep semantic alignment between news text and images and uses social signals from user comments to build an enhanced credibility evaluation path, offering a new paradigm for interpretable fake information detection. …”
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Recent process in organic near-infrared fluorescent probes for detecting hypochlorous acid/hypochlorite by Zhongchang Wang, Chenzheng Guan, Wenxu Lu, Liangchao Yuan, Man Chao, Hai-Liang Zhu, Tingwu Liu

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Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI by Haitian Gui, Han Jiao, Li Li, Xinhua Jiang, Tao Su, Zhiyong Pang

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A Dynamic Cascade Cross-Modal Coassisted Network for AAV Image Object Detection by Shu Tian, Li Wang, Lin Cao, Lihong Kang, Xian Sun, Jing Tian, Xiangwei Xing, Bo Shen, Chunzhuo Fan, Kangning Du, Chong Fu, Ye Zhang

“…Meanwhile, to maintain the spatial correlation of multimodal cross-scale features and improve the quality of feature fusion, we derive a global collaborative enhancement module into the feature pyramid network for enhancing the detection accuracy across multiple scales. …”
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Principle and Application of Trans-Cleavage CRISPR/Cas Technology for the Detection of Foodborne Pathogenic Microorganisms by DAI Yongjin, SUN Jing, ZHANG Moran, LIU Yujuan, CHEN Hongzhou, LU Yingjian

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LSTM-JSO framework for privacy preserving adaptive intrusion detection in federated IoT networks by Shaymaa E. Sorour, Mohammed Aljaafari, Amany M. Shaker, Ahmed E. Amin

“…This research introduces a novel framework that enhances intrusion detection in IoT systems through a decentralized approach. …”
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APFed: Adaptive personalized federated learning for intrusion detection in maritime meteorological sensor networks by Xin Su, Guifu Zhang

“…This paper proposes an Adaptive Personalized Federated learning (APFed) framework that allows multiple MMSN owners to engage in collaborative training. By employing an adaptive personalized update and a shared global classifier, the adverse effects of imbalanced, Non-Independent and Identically Distributed (Non-IID) data are mitigated, enabling the intrusion detection model to possess personalized capabilities and good global generalization. …”
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Contextual Attribution Maps-Guided Transferable Adversarial Attack for 3D Object Detection by Mumuxin Cai, Xupeng Wang, Ferdous Sohel, Hang Lei

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A Garbage Detection and Classification Method Based on Visual Scene Understanding in the Home Environment by Yuezhong Wu, Xuehao Shen, Qiang Liu, Falong Xiao, Changyun Li

“…The test results show that, compared with the original YOLOv5 model, the detection and classification accuracy of the proposed model is higher, and the real-time performance can also meet the actual use requirements. …”
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Spatial-Temporal Cooperative In-Vehicle Network Intrusion Detection Method Based on Federated Learning by Liu Tao, Zhao Xiyang

“…This paper proposes a spatial-temporal collaborative intrusion detection method for IVN based on federated learning (FL), aiming to address the limitations of traditional intrusion detection methods in data privacy protection, temporal modeling, and computational efficiency. …”
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A transformer based multi task learning approach to multimodal hate speech detection by Prashant Kapil, Asif Ekbal

“…Multimodal hate speech identification is an inherently difficult and open question. It necessitates collaborative language, visual perception, and multimodal reasoning. …”
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YOLOv8n-SMMP: A Lightweight YOLO Forest Fire Detection Model by Nianzu Zhou, Demin Gao, Zhengli Zhu

“…Global warming has driven a marked increase in forest fire occurrences, underscoring the critical need for timely and accurate detection to mitigate fire-related losses. Existing forest fire detection algorithms face limitations in capturing flame and smoke features in complex natural environments, coupled with high computational complexity and inadequate lightweight design for practical deployment. …”
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