Combining chromosome conformation capture and exome sequencing for simultaneous detection of structural and single-nucleotide variants by Maria Gridina, Timofey Lagunov, Polina Belokopytova, Nikita Torgunakov, Miroslav Nuriddinov, Artem Nurislamov, Lyudmila P. Nazarenko, Anna A. Kashevarova, Maria E. Lopatkina, Stanislav Vasilyev, Andrey Zuev, Elena O. Belyaeva, Olga A. Salyukova, Aleksandr D. Cheremnykh, Natalia N. Sukhanova, Marina E. Minzhenkova, Zhanna G. Markova, Nina A. Demina, Yana Stepanchuk, Anna Khabarova, Alexandra Yan, Emil Valeev, Galina Koksharova, Elena V. Grigor’eva, Natalia Kokh, Tatiana Lukjanova, Yulia Maximova, Elizaveta Musatova, Elena Shabanova, Andrey Kechin, Evgeniy Khrapov, Uliana Boyarskih, Oxana Ryzhkova, Maria Suntsova, Alina Matrosova, Mikhail Karoli, Andrey Manakhov, Maxim Filipenko, Evgeny Rogaev, Nadezhda V. Shilova, Igor N. Lebedev, Veniamin Fishman

“…Enhanced with targeted long-read sequencing, Exo-C evolves into a cost-efficient solution capable of resolving complex SVs at base-pair accuracy. …”
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Machine learning-based model for acute asthma exacerbation detection using routine blood parameters by Youpeng Chen, Junquan Sun, Yabang Chen, Enzhong Li, Jiancai Lu, Huanhua Tang, Yifei Xie, Jiana Zhang, Lesi Peng, Haojie Wu, Zhangkai J. Cheng, Baoqing Sun

“…Conclusions: This machine learning model provides an efficient and practical tool for detecting AAE using routine blood parameters, offering potential value in clinical practice, especially in resource-limited settings. …”
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Technical note: Flow cytometry assays for the detection, counting and cell sorting of polyphosphate-accumulating bacteria by C. Bouquet, H. Billard, H. Billard, C. C. Bidaud, J. Colombet, J. Colombet, Y.-T. Chang, J. Artigas, I. Batisson, K. Benzerara, F. Skouri-Panet, E. Duprat, A.-C. Lehours

“…In this study, we evaluated the potential of this approach by testing various factors that could affect the efficiency and specificity of polyphosphate labeling. …”
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A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection by Yizhe Li, Yidong Xie, Hu He

“…Additionally, we introduced an optimized Convolutional Block Attention Module (CBAM) to further enhance network efficiency. Furthermore, we employed the genetic K-means algorithm to optimize prior region selection, and a lightweight Ghost model to reduce network complexity by 14.3%, demonstrating the superior performance of the Ghost model in terms of loss function optimization during training and validation as well as in terms of detection accuracy, speed, and stability. …”
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Quantifying solid volume of stacked eucalypt timber using detection-segmentation and diameter distribution models by Gianmarco Goycochea Casas, Zool Hilmi Ismail, Mathaus Messias Coimbra Limeira, Carlos Pedro Boechat Soares, José Marinaldo Gleriani, Daniel Henrique Brada Binoti, Carlos Alberto Araújo Júnior, Mohd Ibrahim Shapiai, Leonardo Ippolito Rodrigues, Tassius Menezes Araújo, Helio Garcia Leite

“…This procedure, which integrates automatic log detection with diameter distribution models, offers a scalable solution applicable to large and complex timber stacks. …”
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Cross-modal interactive and global awareness fusion network for RGB-D salient object detection. by Runqing Li, Ling Yu, Zijian Jiang, Fanglin Niu

“…However, the existing RGB-D detection model still encounters difficulties in accurately recognising and highlighting salient objects when facing complex scenes containing multiple or small objects. …”
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A Deep Learning Enabled Chest X-Ray Abnormality Detection Model for Radiology Assistance. by Ssempeebwa, Phillip, Ainembabazi, Patience

“…Therefore, there is a critical need for innovative solutions to improve the efficiency and accuracy of chest X-ray diagnosis. The main objective of this study is to develop and implement a Deep Learning-Enabled Chest X-ray Abnormality Detection model with automated report generation. …”
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Advancing Software Vulnerability Detection with Reasoning LLMs: DeepSeek-R1′s Performance and Insights by Wenting Qin, Lijie Suo, Liangchen Li, Fan Yang

“…The increasing complexity of software systems has heightened the need for efficient and accurate vulnerability detection. …”
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Evaluation of Intrusion Detection Systems in Cyber Security using Fuzzy OffLogic and MCDM Approach by Zhengrui Yang

“…In this study, thirteen widely used IDS models are assessed using a multi-criteria evaluation framework across four key dimensions: detection accuracy, resource efficiency, scalability, and false positive rate. …”
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Optimizing concrete crack detection an echo state network approach with improved fish migration optimization by Zhichun Fang, Xiuhong Wang, Jiaojiao Gao, Behrooz Eskandarpour

“…However, there are many models available for concrete crack detection, an efficient approach is needed because the existing methods often have flaws like overfitting, high computational complexity, and noise sensitivity, which can lead to accurate crack detection and classification. …”
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The application of deep learning technology in smart agriculture: Lightweight apple leaf disease detection model by Luo Man

“…These advancements make it robust, efficient, and practical for real-time disease detection in intelligent agricultural environments.…”
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DECISION TREE WITH HILL CLIMBING ALGORITHM BASED SPECTRUM HOLE DETECTION IN COGNITIVE RADIO NETWORK by N Suganthi, R Meenakshi, A Sairam, M Parvathi

“…This paper proposes a novel hybrid technique, termed Decision Tree with Hill Climbing (DTHC), for efficient spectrum hole detection in CRNs. The objective of the DTHC method is to improve detection accuracy while minimizing false alarm rates. …”
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Visual Detection on Aircraft Wing Icing Process Using a Lightweight Deep Learning Model by Yang Yan, Chao Tang, Jirong Huang, Zhixiong Cen, Zonghong Xie

“…High transparency and blurred edges of icing areas in wing images pose challenges to wing icing detection by machine vision. To address these challenges, this study proposes a detection model, Wing Icing Detection DeeplabV3+ (WID-DeeplabV3+), for efficient and precise aircraft wing leading edge icing detection under natural lighting conditions. …”
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LArcNet: Lightweight Neural Network for Real-Time Series AC Arc Fault Detection by Kamal Chandra Paul, Chen Chen, Yao Wang, Tiefu Zhao

“…Detecting series ac arc faults in diverse residential loads is challenging due to variations in load characteristics and noise. …”
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Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media by Jiao-huang Luo, Ai-hua Yang

“…Therefore, the post-marketing detection of ADRs in a timely and accurate manner becomes imperative. …”
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LiDAR-based Road Intrusion Detection Technology forAutonomous-rail Rapid Tram by LONG Teng, LIU Sisi, HU Yunqing, LI Xiaoguang, YUAN Xiwen, PAN Wenbo, LUO Yiping, YU Wentian

“…In order to ensure the operation safety of autonomous-rail rapid tram and improve the efficiency and robustness of road intrusion detection, a road intrusion detection technology based on multi-LiDAR was proposed in this paper. …”
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PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets by Xue Xia, Ning Zhang, Zhibin Guan, Xin Chai, Shixin Ma, Xiujuan Chai, Tan Sun

“…This study introduced a novel hybrid model, PAB-Mamba-YOLO, integrating the principles of Mamba and YOLO for efficient visual detection of weaned piglets' aggressive behaviors, including climbing body, nose hitting, biting tail and biting ear. …”
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A novel machine learning model for perimeter intrusion detection using intrusion image dataset. by Shahneela Pitafi, Toni Anwar, I Dewa Made Widia, Zubair Sharif, Boonsit Yimwadsana

“…When handling high-dimensional data, the existing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm faces efficiency issues due to its complexity and varying densities. …”
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Advanced fault detection methodologies and communication protocols for DC micro grid -A technical review by Athira Earath Shivadasan, Prabhakar Karthikeyan Shanmugam

“…This critical study provides valuable information for researchers and professionals aiming to refine fault detection and isolation methods and improve the efficiency of DC microgrid systems.…”
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RT-DETR-Pro: An Enhanced RT-DETR Model for Visual Detection of Photovoltaic Module Defects by Yunliang Chen, Mingtao Shan, Wei Zhou, Bing Ma, Xun Mei

“…An enhanced RT-DETR (Real-Time Detection Transformer) model, RT-DETR-Pro, is proposed to address the problem of low recognition rates for visual detection of PV (photovoltaic) module defects by traditional object detection methods. …”
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