An Electrochemical Sensor for the Simultaneous Detection of Pb²⁺ and Cd²⁺ in Contaminated Seawater Based on Intelligent Mobile Detection Devices by Zizi Zhao, Wei Qu, Chengjun Qiu, Yuan Zhuang, Kaixuan Chen, Yi Qu, Huili Hao, Wenhao Wang, Haozheng Liu, Jiahua Su

“…In this study, we present an electrochemical sensor based on intelligent mobile detection devices. By combining G-COOH-MWCNTs/ZnO with differential pulse voltammetry, the sensor enables the efficient, simultaneous detection of Pb²⁺ and Cd²⁺ in seawater. …”
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Improving Object Detection in High-Altitude Infrared Thermal Images Using Magnitude-Based Pruning and Non-Maximum Suppression by Yajnaseni Dash, Vinayak Gupta, Ajith Abraham, Swati Chandna

“…We converted dataset annotations from the COCO and PASCAL VOC formats to YOLO’s required format, enabling efficient model training and inference. The results demonstrate the proposed architecture’s superior speed and accuracy, effectively handling thermal signatures and object detection. …”
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Insights into the Silver Camphorimine Complexes Interactions with DNA Based on Cyclic Voltammetry and Docking Studies by Joana P. Costa, Gonçalo C. Justino, Fernanda Marques, M. Fernanda N. N. Carvalho

“…The formation of a light grey product adherent to the Pt electrode in the case of {Ag(OH)} and {Ag₂(µ-O)} complexes further corroborates the interaction of the complexes with CT-DNA detected by CV. …”
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Comprehensive influence evaluation algorithm of complex network nodes based on global-local attributes by Weijin JIANG, Ying YANG, Tiantian LUO, Wenying ZHOU, En LI, Xiaowei ZHANG

“…Mining key nodes in the network plays a great role in the evolution of information dissemination, virus marketing, and public opinion control, etc.The identification of key nodes can effectively help to control network attacks, detect financial risks, suppress the spread of viruses diseases and rumors, and prevent terrorist attacks.In order to break through the limitations of existing node influence assessment methods with high algorithmic complexity and low accuracy, as well as one-sided perspective of assessing the intrinsic action mechanism of evaluation metrics, a comprehensive influence (CI) assessment algorithm for identifying critical nodes was proposed, which simultaneously processes the local and global topology of the network to perform node importance.The global attributes in the algorithm consider the information entropy of neighboring nodes and the shortest distance nodes between nodes to represent the local attributes of nodes, and the weight ratio of global and local attributes was adjusted by a parameter.By using the SIR (susceptible infected recovered) model and Kendall correlation coefficient as evaluation criteria, experimental analysis on real-world networks of different scales shows that the proposed method is superior to some well-known heuristic algorithms such as betweenness centrality (BC), closeness centrality (CC), gravity index centrality(GIC), and global structure model (GSM), and has better ranking monotonicity, more stable metric results, more adaptable to network topologies, and is applicable to most of the real networks with different structure of real networks.…”
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Plant Disease Detection Using an Innovative Swin-Axial Transformer by Ao Zhang, Wei Liu

“…Plant diseases are a significant threat to global agricultural production, and accurate and efficient disease detection is crucial for ensuring food security. …”
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An improved EAE-DETR model for defect detection of server motherboard by Jian Chi, Mingke Zhang, Puhon Zhang, Guowang Niu, Zhihao Zheng

“…On the PKU-Market-PCB dataset, the mAP50 reached 96.1%, and the mAP50:95 reached 65.1%.This model effectively facilitates high-precision and high-efficiency defect detection for server motherboards in complex industrial environments, thereby offering a robust solution for the intelligent manufacturing sector.…”
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Malicious Traffic Detection on Tofino Using Graph Attention Model by Xichang Gao, Lizhuang Tan, Shengpeng Chen, Peiying Zhang, Jian Wang

“…With the surge of malicious traffic in networks, existing detection methods struggle to balance real-time performance and efficiency. …”
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Enhancing Mirror and Glass Detection in Multimodal Images Based on Mathematical and Physical Methods by Jiyuan Qiu, Chen Jiang

“…Due to their reflective and transparent nature, these surfaces are often difficult to distinguish from their surrounding environments, posing substantial challenges even for advanced deep learning models tasked with performing such detection. Current research primarily relies on complex network models that learn and fuse different modalities of images, such as RGB, depth, and thermal, to achieve mirror and glass detection. …”
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An advanced three stage lightweight model for underwater human detection by Zichen Liao, Kai Hu, Yuancheng Meng, Shuai Shen

“…Abstract This study presents StarEye, a lightweight deep learning model designed for underwater human body detection (UHBD) that addresses the challenges of complex underwater environments. …”
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Underwater Object Detection Algorithm Based on an Improved YOLOv8 by Fubin Zhang, Weiye Cao, Jian Gao, Shubing Liu, Chenyang Li, Kun Song, Hongwei Wang

“…Due to the complexity and diversity of underwater environments, traditional object detection algorithms face challenges in maintaining robustness and detection accuracy when applied underwater. …”
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Enhanced YOLO and Scanning Portal System for Vehicle Component Detection by Feng Ye, Mingzhe Yuan, Chen Luo, Shuo Li, Duotao Pan, Wenhong Wang, Feidao Cao, Diwen Chen

“…In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. …”
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Research on Surface Defect Detection of Rare-Earth Magnetic Materials Based on Improved SSD by Bin Zhang, Shuqi Fang, Zhixi Li

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YOLO-Dynamic: A Detection Algorithm for Spaceborne Dynamic Objects by Haiying Zhang, Zhengyang Li, Chunyan Wang

“…This paper presents YOLO-Dynamic, a novel detection algorithm aimed at addressing the limitations of existing models, particularly in complex environments and small-object detection. …”
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MGL-YOLO: A Lightweight Barcode Target Detection Algorithm by Yuanhao Qu, Fengshou Zhang

“…Due to the critical importance of one-dimensional barcode detection in logistics, retail, and manufacturing, which has become a key issue affecting operational efficiency, researchers have shown increasing interest in this area. …”
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YOLOv11-HRS: An Improved Model for Strawberry Ripeness Detection by Jianhua Liu, Jing Guo, Suxin Zhang

“…To address these problems, an efficient strawberry ripeness detection model, YOLOv11-HRS, is proposed. …”
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Multi-granularity Android malware fast detection based on opcode by Xuetao ZHANG, Meng SUN, Jinshuang WANG

“…The detection method based on opcode is widely used in Android malware detection,but it still contains some problems such as complex feature extraction method and low efficiency.In order to solve these problems,a multi-granularity fast detection method based on opcode for Android malware was proposed.Multi-granularity refers to the feature based on the bag of words model,and with the function as basic unit to extract features.By step-by-level aggregation feature,the APK multi-level information is obtained.The log length characterizes the scale of the function.And feature can be compressed and mapped to improve the efficiency and construct the corresponding classification model based on the semantic similarity of the Dalvik instruction set.Tests show that the proposed method has obvious advantages in performance and efficiency.…”
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Method for Detecting Tiny Defects on Machined Surfaces of Mechanical Parts Based on Object Recognition by Haotian Li, Zhen Wang, Lipeng Qiu, Xichu Wei

“…Practical results demonstrate that this method outperforms traditional approaches in terms of missed detection rates and detection efficiency, effectively addressing the challenge of detecting complex machining surface defects, and providing a high-precision, high-efficiency defect detection solution for the mechanical part machining field.…”
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Unsupervised selective labeling for semi-supervised industrial defect detection by Jian Ge, Qin Qin, Shaojing Song, Jinhua Jiang, Zhiwei Shen

“…This has motivated a shift towards semi-supervised learning (SSL), which leverages labeled and unlabeled data to improve learning efficiency and reduce annotation costs. This work proposes the unsupervised spectral clustering labeling (USCL) method to optimize SSL for industrial challenges like defect variability, rarity, and complex distributions. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This study pioneers an innovative framework, using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, combined with a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, to enhance the detection of DR. Inspired by principles of randomization-based learning, our approach incorporates elements of stochastic modeling within the CNN-RBF architecture to optimize feature extraction and classification, mirroring the efficiency of non-iterative training processes. …”
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DCFE-YOLO: A novel fabric defect detection method. by Lei Zhou, Bingya Ma, Yanyan Dong, Zhewen Yin, Fan Lu

“…However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. …”
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