Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model by Weijie Liu, Jie Hu, Jin Qi

“…Key innovations include using high-confidence anchor boxes from the RPN network to locate welding spots, using the SmoothL1 loss function, and applying Fast R-CNN to classify detected defects. …”
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Multimodal fusion image enhancement technique and CFEC-YOLOv7 for underwater target detection algorithm research by Xiaorong Qiu, Yingzhong Shi

“…The underwater environment is more complex than that on land, resulting in severe static and dynamic blurring in underwater images, reducing the recognition accuracy of underwater targets and failing to meet the needs of underwater environment detection. …”
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FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. by Junjie Lu, Yuchen Zheng, Liwei Guan, Bing Lin, Wenzao Shi, Junyan Zhang, Yunping Wu

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. Firstly, the FasterNext module is proposed to reduce computational cost and enhance detection precision through lightweight design. …”
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Heart failure with preserved ejection fraction: the role of diastolic stress test in diagnostic algorithms by E. N. Pavlyukova, D. A. Kuzhel

“…Despite the preserved left ventricular (LV) and a moderate increase in natriuretic peptide, patients  with HFpEF have the same out-of-hospital mortality as those with HF with reduced ejection fraction (HFrEF). Diagnosis of HFpEF is difficult due to nonspecific symptoms, expensive blood tests, and questionable rest echocardiographic data on diastolic function. …”
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CASES OF DIFFUSE LARGE B-CELL LYMPHOMA WITH FUNCTIONAL INTRON MUTATIONS IN THE TP53 GENE by E. N. Voropaeva, M. I. Voevoda, T. I. Pospelova, V. N. Maksimov

“…Mutations in the TP53 gene are the driver of the tumor process, serve not only as a marker of aggressive tumor progression, but also as an independent predictor of reduced sensitivity to treatment. the presented clinical cases show that an in-depth analysis of the results of the TP53 sequencing in tumors and functional assessment of all detected changes, including changes in the introns of the gene and involving in silico analysis techniques, are necessary.…”
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Functional brain changes in vascular cognitive impairment: a whole brain ALE meta-analysis by Chunyang Zhang, Mingchen Xue, Han Zhang, Juan Li, Mingli He

“…BackgroundVascular cognitive impairment (VCI) is a prevalent form of cognitive dysfunction. Resting-state functional magnetic resonance imaging (rs-fMRI) could serve as a potential biomarker for early detection. …”
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Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One-Class Support Vector Machine by Jiusheng Chen, Xingkai Xu, Xiaoyu Zhang

“…Recently, the support vector machine (SVM) with kernel function is the most popular technique for monitoring nonlinear processes, which can better handle the nonlinear representation of fault detection of turbine engine disk. …”
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Enhancing Frequency Event Detection in Power Systems Using Two Optimization Methods with Variable Weighted Metrics by Hussain A. Alghamdi, Midrar A. Adham, Umar Farooq, Robert B. Bass

“…Unlike conventional approaches that apply equally weighted metrics within the objective function, this work implements variable weighted metrics that prioritize specificity, thereby strengthening detection accuracy by minimizing false-positive events. …”
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A Crowd Density Detection Algorithm for Tourist Attractions Based on Monitoring Video Dynamic Information Analysis by Lina Li

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YOLO-SDLUWD: YOLOv7-based small target detection network for infrared images in complex backgrounds by Jinxiu Zhu, Chao Qin, Dongmin Choi

“…“YOLO-SDLUWD” aims to reduce detection accuracy when the maximum pooling downsampling layer in the backbone network loses important feature information, support the interaction and fusion of high-dimensional and low-dimensional feature information, and overcome the false alarm predictions induced by noise in small target images. …”
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Affinity Tag for Protein Purification and Detection Based on the Disulfide-Linked Complex of InaD and NorpA by Michelle E. Kimple, John Sondek

“…Affinity tags are not only used for the expression and purification of recombinant proteins but also for the detection of protein-protein interactions. Common problems with many affinity tags are excessive length, which may interfere with the structure and function of tagged proteins, and low affinity and/or specificity for primary detection and purification agents. …”
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Ginkgo Biloba Extract Reduces Cardiac and Brain Inflammation in Rats Fed a HFD and Exposed to Chronic Mental Stress through NF-κB Inhibition by Lijun Zhang, Guo Li, Shuhui Tao, Pengyan Xia, Naveed Chaudhry, Shawn Kaura, Sara Saymuah Stone, Meiyan Liu

“…Cortex tissues were used for detecting 8-iso-prostaglandinF2α (8-iso-PGF2α) by ELISA. …”
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Evaluating functional C1INH with multiple laboratory methods across Hereditary Angioedema types by Maine Luellah Demaret Bardou, Rosemeire Navickas Constantino-Silva, Maria Luiza Oliva Alonso, Ana Júlia Ribeiro Teixeira, Pedro Francisco Giavina-Bianchi, Eli Mansour, João Bosco Pesquero, Solange Oliveira Rodrigues Valle, Anete Sevciovic Grumach

“…Among patients with HAE-FXII, fC1INH levels were reduced by 36.5% by ELISA-FXIIa (19/52), 19.1% by DBS (9/47), and 3.8% by ELISA-PKa (2/52), and no alterations were detected by the chromogenic assay. …”
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LPCF-YOLO: A YOLO-Based Lightweight Algorithm for Pedestrian Anomaly Detection with Parallel Cross-Fusion by Peiyi Jia, Hu Sheng, Shijie Jia

“…To address the issue of high complexity in current pedestrian anomaly detection network models, which hinders real-world deployment, this paper proposes a lightweight anomaly detection network called LPCF-YOLO (Lightweight Parallel Cross-Fusion YOLO) based on the YOLOv8n model. …”
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Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection by Sofia Biju Francis, Sofia Biju Francis, Jai Prakash Verma

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A Lightweight Citrus Ripeness Detection Algorithm Based on Visual Saliency Priors and Improved RT-DETR by Yutong Huang, Xianyao Wang, Xinyao Liu, Liping Cai, Xuefei Feng, Xiaoyan Chen

“…However, accurately and efficiently detecting citrus ripeness in complex orchard environments for selective robotic harvesting remains a challenge. …”
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Integrated Machine Learning and Region Growing Algorithms for Enhanced Concrete Crack Detection: A Novel Approach by Wenxuan Yao, Hui Li, Yanlin Li

“…During structural repair, crack detection is the most critical step. Automatic detection significantly reduces the engineering cost and human factor error compared with manual detection. …”
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YOLO-SUMAS: Improved Printed Circuit Board Defect Detection and Identification Research Based on YOLOv8 by Ying Tang, Runhao Liu, Sheng Wang

“…The model introduces the SCSA attention mechanism, which improves the feature expression capability through spatial and channel synergistic attention; adopts the Unified-IoU loss function, combined with the dynamic bounding box scaling and bi-directional weight allocation strategy, to optimize the accuracy of high-quality target localization; integrates the MobileNetV4 lightweight architecture and its MobileMQA attention module, which reduces the computational complexity and improves the inference speed; and combines ASF-SDI Neck structure with weighted bi-directional feature pyramid and multi-level semantic detail fusion to strengthen small target detection capability. …”
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A Coverage-Based Cooperative Detection Method for CDUAV: Insights from Prediction Error Pipeline Modeling by Jiong Li, Xianhai Feng, Yangchao He, Lei Shao

“…This framework incorporates the target HPR and the seeker detection FOV models, with an objective function defined for coverage optimization. …”
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YOLOv10-kiwi: a YOLOv10-based lightweight kiwifruit detection model in trellised orchards by Jie Ren, Wendong Wang, Yuan Tian, Jinrong He

“…In addition, a MPDIoU loss function is introduced to overcome the limitations of the traditional CIoU in terms of aspect ratio mismatch and bounding box regression, accelerating convergence and improving detection accuracy. …”
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