Traffic anomaly event detection and auxiliary decision-making based on large language models by LI Yanying, WANG Xinyu, WANG Xiao, SUN Changyin

“…The results show that compared with traditional methods, TMGPT significantly improves the accuracy of detection and reduced response time in the detection and assisted decision-making of abnormal traffic events, which demonstrates the application potential of large language models in complex urban traffic management.…”
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High catalytic nickel-platinum nanozyme enhancing colorimetric detection of Salmonella Typhimurium in milk by Jie Zhu, Lingyue Xu, Junlin Zhang, Yuxin Wang, Hongyue Yu, Chuanchuan Hao, Guohui Cheng, Daofeng Liu, Minghui Chen

“…Meanwhile, the antibody employed in this NiPt NP-based NLISA exhibits exceptional capture efficacy, generating a stable immune complex with Salmonella Typhimurium. The NiPt NP-based NLISA demonstrates sensitivity, specificity, convenience, and cost-efficiency for the detection of Salmonella Typhimurium. …”
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Enhancing image quality in circular-view photoacoustic tomography using randomized detection points by Soheil Hakakzadeh, Praveenbalaji Rajendran, Zahra Kavehvash, Manojit Pramanik

“…Circular-view (circular scan) photoacoustic computed tomography (PACT) with low-density detection points (DPs) is an efficient, high-speed, and inexpensive modality with numerous (pre-) clinical applications. …”
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MSDNet: A Multi-Scale Feature Representation Network Model for Tunnel Bolt Detection by Xiufeng Wu, Xueli Li, Guangxing Cao, Chao Guo, Wei Chang, Zhao Wang, Shuliang Liu

“…To address these issues, we introduces a novel multi-scale feature extraction detection network (MSDNet) designed to improve tunnel bolt maintenance by reducing false positives and missed detections. …”
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The Scalable Detection and Resolution of Data Clumps Using a Modular Pipeline with ChatGPT by Nils Baumgartner, Padma Iyenghar, Timo Schoemaker, Elke Pulvermüller

“…This paper explores a modular pipeline architecture that integrates ChatGPT, a Large Language Model (LLM), to automate the detection and refactoring of data clumps—a prevalent type of code smell that complicates software maintainability. …”
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InvMOE: MOEs Based Invariant Representation Learning for Fault Detection in Converter Stations by Hao Sun, Shaosen Li, Hao Li, Jianxiang Huang, Zhuqiao Qiao, Jialei Wang, Xincui Tian

“…Converter stations are pivotal in high-voltage direct current (HVDC) systems, enabling power conversion between an alternating current (AC) and a direct current (DC) while ensuring efficient and stable energy transmission. Fault detection in converter stations is crucial for maintaining their reliability and operational safety. …”
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Radio Frequency Interference Detection Using Swin Transformer Embedding U2-Net by Shengdi Chen, Bo Liang

“…The main encoder enhances the feature representation through the efficient multiscale attention (EMA) mechanism, reorganizes the channel information, captures pixel-level relationships, and improves the detection accuracy in complex backgrounds. …”
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Enhancing Object Detection in Underground Mines: UCM-Net and Self-Supervised Pre-Training by Faguo Zhou, Junchao Zou, Rong Xue, Miao Yu, Xin Wang, Wenhui Xue, Shuyu Yao

“…However, limited computational resources and complex environmental conditions in mine shafts significantly impact the recognition and computational capabilities of detection models. …”
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Accuracy and cost comparison of 3D-printed guides in complex spinal deformity correction: direct vs indirect design by Wei Yang, Wei Yang, Wei Yang, Wei Guo, Wei Guo, Wen-Jun Wu, Rong Ma, Zemin Wang, Honglai Zhang, Wanzhong Yang, Zhaohui Ge, Zhaohui Ge

“…The results indicate that both guide techniques can achieve precise orthopedics, but the indirect guide has advantages in clinical transformation efficiency.ConclusionBoth direct and indirect 3D-printed guides can optimize screw implantation and complex osteotomy procedures, improving the accuracy of pedicle screw placement and osteotomy. …”
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Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar by Pengxiang Li, Mingzhou Bai, Xin Li, Chenyang Liu

“…Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. …”
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Research on detection and tracking methods of unmanned ship water targets based on light vision by LIU Yibo, QIU Xinyu, WANG Tianhao, GAOYAN Xiusong, WANG Yintao

“…This study explores technical methods based on light vision to address the problem of target detection and tracking by surface unmanned ships in complex environments. …”
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Passive indoor human daily behavior detection method based on channel state information by Xiaochao DANG, Yaning HUANG, Zhanjun HAO, Xiong SI

“…The daily behavior detection of indoor human based on CSI is developing rapidly in the field of WSN.At present,most of the research is still in the environment of 2.4 GHz,so the detection rate,robustness and overall performance still need to be improved.In order to solve this problem,a passive indoor human behavior detection method HDFi (Human Detection with Wi-Fi) based on CSI signal was proposed.The method was used to detect the indoor human daily behavior in a 5 GHz band environment,which was divided into three steps:data acquisition,data processing,feature extraction,online detection.Firstly,the experiment collected typical daily behavioral data in complex laboratory and relatively empty meeting room.Secondly,the amplitude and phase data with more obvious features were extracted and processed by low-pass filtering to obtain a set of stable and noise-free data,and then the fingerprint database was established effectively.Finally,in the real-time detection stage,the collected data features were classified by SVM algorithm to extract more stable eigenvalues,and a classification model of indoor human daily behavior detection was established,and then matched the data in the fingerprint database.The experimental results show that the proposed method has the characteristics of high efficiency,high precision and good robustness,and the method does not need any testing personnel to carry any electronic equipment,so it has high practicability.…”
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Aero-Engine Blade Defect Detection: A Systematic Review of Deep Learning Models by Yusra Abdulrahman, M. A. Mohammed Eltoum, Abdulla Ayyad, Brain Moyo, Yahya Zweiri

“…This is the first systematic review of deep learning models for aero-engine blade defect detection. The findings of this review demonstrate the potential of deep learning in detecting blade defects and improving the accuracy and efficiency of visual inspection. …”
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A novel feature extractor based on constrained cross network for detecting sleep state by Chenlei Tian, Fei Song

“…Most existing methods that utilize wrist-worn devices data for detection rely on heuristic algorithms or traditional machine learning, which suffer from low classification efficiency and insufficient accuracy. …”
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ASSESSMENT OF COLLECTION NURSERY OF WINTER GARLIC ON ECONOMY VALUABLE SIGNS by L. I. Gerasimova, A. F. Agafonov, Т. М. Seredin

“…Results of long-term researches by laboratory selection of onions cultures (VNIISSOK) on studying, assessment and selection exemplars grades of garlic winter, collected from different regions of Russia and the CIS countries, on a complex of signs are presented in article (winter hardiness, efficiency, to quality of production, resistance to wreckers and diseases).…”
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YOLOv8-LSW: A Lightweight Bitter Melon Leaf Disease Detection Model by Shuang Liu, Haobin Xu, Ying Deng, Yixin Cai, Yongjie Wu, Xiaohao Zhong, Jingyuan Zheng, Zhiqiang Lin, Miaohong Ruan, Jianqing Chen, Fengxiang Zhang, Huiying Li, Fenglin Zhong

“…Bitter melon, an important medicinal and edible economic crop, is often threatened by diseases such as downy mildew, powdery mildew, viral diseases, anthracnose, and blight during its growth. Efficient and accurate disease detection is of significant importance for achieving sustainable disease management in bitter melon cultivation. …”
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Detecting Fraudulent Transactions for Different Patterns in Financial Networks Using Layer Weigthed GCN by Shaziya Islam, Gagan Raj Gupta, Apu Chakraborty, Santosh Singh, Anisha Soni, Chhavi Patle

“…Additionally, the flexible architecture of LayerWeighted-GCN enhances its ability to model complex financial relationships, improving fraud detection accuracy and robustness. …”
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Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance. by Hootan Kamran, Dionne Aleman, Chris McIntosh, Tom Purdie

“…By offering clear insights into decision-making processes, our method allows healthcare professionals to quickly identify and address specific deficiencies in RT plans deemed unacceptable, thereby streamlining the QA process and enhancing patient care efficiency and safety.…”
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DSF-YOLO for weld defect detection in X-ray images with dynamic staged fusion by Meng Zhang, Yanzhu Hu, Binbin Xu, Lisha Luo, Song Wang

“…These results establish DSF-YOLO as an efficient and accurate solution addressing critical challenges in industrial weld defect detection with significant practical value.…”
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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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