Method for Detecting Tiny Defects on Machined Surfaces of Mechanical Parts Based on Object Recognition by Haotian Li, Zhen Wang, Lipeng Qiu, Xichu Wei

“…Initially, referencing the network architectures of Resnet and Yolo, an image detection network was designed featuring a shared encoder, a classification decoder, and a localization decoder. …”
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A magnetic epitope-imprinted microsphere used for selective separation and rapid detection of SHV-type β-lactamases in bacteria: a novel strategy of antimicrobial resistance detect... by Yusun Zhou, Kunqi Wang, Lele Li, Hui Li, Qingwu Tian, Baosheng Ge, Yuanyuan Chi, Xiaotong Xu, Shuhui Liu, Meng Han, Tingting Zhou, Yuanqi Zhu, Qing Wang, Bing Yu

“…Based on this, an initial drug option scheme can be quickly formulated for antimicrobial therapy. …”
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Residual stresses in cold-formed steel sections: An overview of influences and measurement techniques by Ayad Mutafi, J.M. Irwan, Noorfaizal Yidris, Abdullah Faisal Alshalif, Yazid Saif, Hamdi Abdulrahman, Ala Mutaafi, Yasser Yahya Al-Ashmori, Mugahed Amran, Nelson Maureira-Carsalade, Siva Avudaiappan

“…This paper reviews current design methods for CFS, focusing on the impact of initial imperfections. It also examines various techniques for measuring residual stress in CFS sections, including analytical, experimental, and numerical approaches. …”
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Dominant Symmetry Plane Detection for Point-Based 3D Models by Chen He, Lei Wang, Yonghui Zhang, Chunmeng Wang

“…In this paper, a symmetry detection algorithm for three-dimensional point cloud model based on weighted principal component analysis (PCA) is proposed. …”
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Hyperspectral Anomaly Detection Based on Intrinsic Image Decomposition and Background Subtraction by Jiao Jiao, Longlong Xiao, Chonglei Wang

“…Finally, the weight map is fused with the initial anomaly detection map to obtain the final anomaly detection result. …”
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A Method for Community Detection of Complex Networks Based on Hierarchical Clustering by Chuantao Yin, Shuaibing Zhu, Hui Chen, Bingxue Zhang, Bertrand David

“…Finding out community structure helps to extract useful information in complex networks, so the research on community detection is becoming a hotspot in recent years. There are two remarkable problems in detecting communities. …”
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Exploring LLM Embedding Potential for Dementia Detection Using Audio Transcripts by Brandon Alejandro Llaca-Sánchez, Luis Roberto García-Noguez, Marco Antonio Aceves-Fernández, Andras Takacs, Saúl Tovar-Arriaga

“…To compare the performance of the five approaches, a stratified 5-fold cross-validation was conducted; the best results were obtained with BERT embeddings (84.73% accuracy) closely followed by the simpler Tf–Idf approach (83.73% accuracy) and the state-of-the-art model Linq-Embed-Mistral (83.54% accuracy), while Gemma-2B and GloVe embeddings yielded slightly lower performances (80.91% and 78.11% accuracy, respectively). Contrary to initial expectations—that richer semantic and contextual embeddings would substantially outperform simpler frequency-based methods—the competitive accuracy of Tf–Idf suggests that the choice and frequency of the words used might be more important than semantic or contextual information in Alzheimer’s detection. …”
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Fuzzy clustering method based on genetic algorithm in intrusion detection study by HUANG Min-ming LIN Bo-gang

“…Regarding the problem that fuzzy c-means algorithm（FCM） was sensitive to the initial value and converging to the local infinitesimal point easily, applies genetic algorithm to optimization of the FCM algorithm.Firstly, the results of FCM will be sent to the genetic algorithm for optimization, then the new results again used in FCM to obtain the most advantage of the overall situation.The experimental result shows that the algorithm can effectively detect anomaly intrusions behavior of special target and be better than FCM algorithm, and have a strong global optimization and faster convergence speed.…”
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Development of transient ischemic attack risk prediction model suitable for initializing a learning health system unit using electronic medical records by Jian Wen, Tianmei Zhang, Shangrong Ye, Cheng Li, Ruobing Han, Ran Huang, Bairong Shen, Anjun Chen, Qinghua Li

“…This model is essential for initiating the first ML-enabled learning health system (LHS) unit designed for routine and equitable TIA screening and early detection across broad populations. …”
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Computational analysis for wax detection in deepwater pipelines using nuclear techniques by Nalber Miranda Leite, Carlos Alberto Brayner de Oliveira Lira, Abel Gámez Rodriguez

“…To solve it, one must monitor the wax formation in its initial stage. In this light, nuclear techniques are an effective alternative solution, as they can detect characteristics of materials or substances in an indirect and non-invasive manner. …”
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Active learning for deep object detection by fully exploiting unlabeled data by Feixiang Tan, Guansheng Zheng

“…Object detection is a challenging task that requires a large amount of labeled data to train high-performance models. …”
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Evaluating the effectiveness of participatory science dog teams to detect devitalized Spotted Lanternfly (Lycorma delicatula) egg masses by Sally Dickinson, Mizuho Nita, Edgar O. Aviles-Rosa, Nathan Hall, Erica N. Feuerbacher

“…The spotted lanternfly (Lycorma delicatula, SLF) is an invasive planthopper first detected in the United States in 2014, with initial sightings in Pennsylvania. …”
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Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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Optimized molecule detection in localization microscopy with selected false positive probability by Miroslav Hekrdla, David Roesel, Niklas Hansen, Soumya Frederick, Khalilullah Umar, Vladimíra Petráková

“…Abstract Single-molecule localization microscopy (SMLM) allows imaging beyond the diffraction limit. Detection of molecules is a crucial initial step in SMLM. …”
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Application of Sinusoidal Function in Financial Crisis Early Warning and Detection System by Xueyin Wang

“…Therefore, this paper studies the application of sine signal function in financial crisis early warning and detection system. According to the principle of “different frequencies are uncorrelated,” the derivation process of a single sinusoidal signal with noise in the crisis warning period is also applicable to the case of multiple sinusoidal signals, so the program can also be used to detect multiple sinusoidal signals in the crisis warning period at the same time. …”
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Three-dimensional Orbital Architectures and Detectability of Adjacent Companions to Hot Jupiters by Thomas MacLean, Juliette Becker

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Implementation of a Methodology for the Detection of Biochemical Markers in Type 1 Tyrosinemia by Iovana Fuentes Cortés, Beliany Pacheco Suárez, Dulce María Charón Savón

“…<strong><br />Objective:</strong> to implement a work methodology for the detection of marker metabolites of type 1 tyrosinemia. …”
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Vulnerable plaques and lipid changes over statin therapy in patients with acute coronary syndrome. Initial data from the Combi-LLT ACS trial by G. R. Bikbaeva, A. N. Kovalskaya, K. V. Kuznetsova, P. D. Duplyakova, A. A. Tukhbatova, E. M. Sukhinina, T. V. Pavlova, D. V. Duplyakov, E. V. Savinova

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Enhancing credit card fraud detection: highly imbalanced data case by Dalia Breskuvienė, Gintautas Dzemyda

“…Given the inherent imbalance in fraud detection data, feature selection must be done with an enhanced focus. …”
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