Detection of disease on nasal breath sound by new lightweight architecture: Using COVID-19 as an example by Jiayuan She, Lin Shi, Peiqi Li, Ziling Dong, Renxing Li, Shengkai Li, Liping Gu, Zhao Tong, Zhuochang Yang, Yajie Ji, Liang Feng, Jiangang Chen

“…Although many countries have reduced or stopped large-scale testing measures, the detection of such diseases remains a propriety. Objective This study aims to develop a novel, lightweight deep neural network for efficient, accurate, and cost-effective detection of COVID-19 using a nasal breathing audio data collected via smartphones. …”
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Multi-frequency EEG and multi-functional connectivity graph convolutional network based detection method of patients with Alzheimer’s disease by Yujian Liu, Libing An, Haiqiang Yang, Shuzhi Sam Ge

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Robust Target Detection and Estimation for Airborne STAP Radar With Arbitrary Array Errors and Target Uncertainty by Jianxin Wu, Yang Zhao, Changxian Li, Peng Shen

“…Finally, target angle estimation using subspace coefficients, which implements the ML estimator in a reduced-dimensional version, is presented to reduce the computational complexity of the ML estimator. …”
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Ionic Liquid-Based Centrifuge-Less Cloud Point Extraction of a Copper(II)–4-Nitrocatechol Complex and Its Analytical Application by Denitsa Kiradzhiyska, Nikolina Milcheva, Miglena Ruzmanova, Fatma Genç, Petya Racheva, Kiril Gavazov

“…The extracted ternary ion-association complex, identified as (A336⁺)₂[Cu(4NC)₂], exhibits a maximum absorbance at 451 nm, with a molar absorption coefficient of 8.9 × 10⁴ M⁻¹ cm⁻¹ and a Sandell’s sensitivity of 0.71 ng cm⁻². …”
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MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection by Hantao Ding, Shengfeng Lou, Hairong Ye, Yanbing Chen

“…Video anomaly detection (VAD) faces significant challenges in multimodal semantic alignment and long-term temporal modeling within open surveillance scenarios. …”
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Anomaly Detection of Pipeline Leakage Based on Electric Field Component Imaging Using Ground Penetrating Radar by Yang Yang, Zhiyuan Du, Yanan Li, Tianyu Zhang, Lu Liu, Ibrar Iqbal, Sanxi Peng

“…Ground penetrating radar (GPR) is an effective method that can be applied in pipeline leakage detection. However, when the distribution of the underground medium is complex, it becomes difficult to accurately identify the abnormal leakage area directly from the GPR data profile. …”
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DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment by Goda Srinivasa Rao, P. Santosh Kumar Patra, V.A. Narayana, Avala Raji Reddy, G.N.V. Vibhav Reddy, D. Eshwar

“…The Internet of Things (IoT) network infrastructures are becoming more susceptible to distributed denial of service (DDoS) attacks because of the proliferation of IoT devices. Detecting and predicting such attacks in this complex and dynamic environment requires specialized techniques. …”
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Research on Concrete Crack and Depression Detection Method Based on Multi-Level Defect Fusion Segmentation Network by Zhaochen Yao, Yanjuan Li, Hao Fu, Jun Tian, Yang Zhou, Chee-Loong Chin, Chau-Khun Ma

“…Joint detection loss and segmentation loss are used for task co-optimization. …”
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A hierarchical simulation framework incorporating full-link physical response for short-range infrared detection by Mingze Gao, Lixin Xu, Shiyuan Hu, Xiaolong Shi, Jiaming Gao, Yanjiang Wu, Huimin Chen

“…Missile-borne short-range infrared detection (SIRD) technology is commonly used in military ground target detection. …”
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Quality and reliability of IFC/BIM models for public educational facilities construction projects via clash detection by Michał Juszczyk, Mantas Vaišnoras, Robertas Kontrimovičius, Tomáš Hanák, Hanna Łukaszewska, Leonas Ustinovichius

“…A mixed-methods approach was employed to analyse the number, types, and geometric characteristics of detected clashes. The research introduces innovative metrics, such as the Relative Quality Coefficient (RQC), Relative Uncertainty Coefficient (RUC), and Modified Relative Quality Coefficient (MRQC), to assess model quality and reliability quantitatively. …”
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Deep learning-based dual monitoring system for power forecasting and fault detection in nuclear power applications by Mingzhe Lyu, Helin Gong, Zhang Chen, Jiangyu Wang, Mingxiao Zhong, Zhiyong Wang, Qing Li, Zefei Pan

“…For fault diagnosis, the dual-monitoring system reduces the false negative rate to 18.73% and improves recall to 81.27%, demonstrating strong anomaly detection under complex conditions. By combining short-term fluctuation sensitivity with long-term trend stability, the proposed approach offers a robust and generalizable solution for intelligent monitoring. …”
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Inter-observational analysis of computed tomography parameters to predict nonobvious posterior ligament complex injury in neurologically intact patients with thoracolumbar trauma by Joana Araújo de Azevedo, Carolina Garcez Martins, Nuno Oliveira, Pedro Varanda, Bruno Direito-Santos

“…Introduction: Assessing the integrity of the posterior ligament complex (PLC), as a key element in the characterization of an unstable Thoracolumbar fracture (TLF), is challenging, but crucial in the choice of treatment. …”
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Fractional Artificial Protozoa Optimization Enabled Deep Learning for Intrusion Detection and Mitigation in Cyber-Physical Systems by Shaik Abdul Rahim, Arun Manoharan

“…Thereafter, intrusion detection is carried out with Spiking VGG-16. Lastly, attack classification is accomplished utilizing Spiking VGG-16 trained by FAPO, and then attack mitigation is detected. …”
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Design and implementation of a 100 kVA transformer monitoring and fault detection system for distribution network by Charity M. Nkinyam, Leo Tanyam Encho, Nfah Eustace Mbaka

“…Abstract The increasing complexity of today’s power distribution networks requires reliable and intelligent monitoring and maintenance systems, especially as the demand for electricity increases due to technological and population growth. …”
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Detection, Isolation, and Identification of Multiplicative Faults in a DC Motor and Amplifier Using Parameter Estimation Techniques by Sanja Antić, Marko Rosić, Branko Koprivica, Alenka Milovanović, Milentije Luković

“…The increasing complexity of modern control systems highlights the need for reliable and robust fault detection, isolation, and identification (FDII) methods, particularly in safety-critical and industrial applications. …”
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Continuous Flow Injection Analysis Method for the Determination of a Drug Diphenhydramine Hydrochloride by Using Phosphomolybdic Acid by Muntadhar M. Jabbar, Elham N. Mezaal

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Aerial image segmentation of embankment dams based on multispectral remote sensing: a case study in the Belo Monte Hydroelectric Complex, Pará, Brazil by Carlos André de Mattos Teixeira, Thabatta Moreira Alves de Araujo, Evelin Cardoso, Marcos Antonio Costantin Filho, João Weyl Costa, Carlos Renato Lisboa Frances

“…Recently, multispectral remote sensing data and machine learning techniques have been applied to develop methodologies that enable automatic vegetation analysis and anomaly detection based on computer vision. As a first step toward this automation, this study introduces a methodology for land cover segmentation of earth-rock embankment dam structures within the Belo Monte Hydroelectric Complex, located in the state of Pará, northern Brazil. …”
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Assessing Temperature-Dependent Deltamethrin Toxicity in Various <i>kdr</i> Genotypes of <i>Aedes aegypti</i> Mosquitoes by Joshua Kalmouni, Brook M. Jensen, Joshua Ain, Krijn P. Paaijmans, Silvie Huijben

“…Insecticide resistance surveillance systems for vector-borne diseases are crucial for early detection of resistance and the implementation of evidence-based resistance management strategies. …”
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Evaluation of Smartphones Equipped with Light Detection and Ranging Technology for Circumferential and Volumetric Measurements in Lower Extremity Lymphedema by Masato Tsuchiya, Kanako Abe, Satoshi Kubo, Ryuichi Azuma

“…Smartphones with light detection and ranging (LiDAR) technology may offer fast and efficient alternatives for three-dimensional imaging and measurement. …”
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Approach for enhancing the accuracy of semantic segmentation of chest X-ray images by edge detection and deep learning integration by Lesia Mochurad

“…These results consistently outperform the baseline U-net model without edge enhancement.DiscussionThe integration of contour detection methods with the U-net model significantly improves the segmentation quality of complex anatomical regions in chest X-rays. …”
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