Comparison of the STANDARD M10 C. difficile, Xpert C. difficile, and BD MAX Cdiff assays as confirmatory tests in a two-step algorithm for diagnosing Clostridioides difficile infec... by Hyunseul Choi, Minhee Kang, Sun Ae Yun, Hui-Jin Yu, Eunsang Suh, Tae Yeul Kim, Hee Jae Huh, Nam Yong Lee

“…This algorithm starts with enzyme immunoassay (EIA) for detecting glutamate dehydrogenase (GDH) and toxins A/B, followed by nucleic acid amplification test (NAAT) for GDH-positive but toxin-negative cases. …”
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Intelligent library shelf management system for open-access environments: A CNN-based approach with enhanced image recognition and disorder detection by Xueqi Zhang

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Distinguishing Difficulty Imbalances in Strawberry Ripeness Instances in a Complex Farmland Environment by Yang Gan, Xuefeng Ren, Huan Liu, Yongming Chen, Ping Lin

“…The existing strawberry ripeness detection algorithm has the problems of a low precision and a high missing rate in real complex scenes. …”
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Role of low-frequency integrase strand transfer inhibitor resistance mutations on virological outcomes in antiretroviral therapy-naïve individuals initiating second-generation inte... by Daniele Armenia, Greta Marchegiani, Daniele Spalletta, Luca Carioti, Alessandro Tavelli, Maria Concetta Bellocchi, Vincenzo Spagnuolo, Valentina Mazzotta, Eugenia Quiros-Roldan, Valeria Bono, Stefania Carrara, Sergio Lo Caputo, Antonella D’Arminio Monforte, Francesca Ceccherini-Silberstein, Stefano Rusconi, Maria Mercedes Santoro

“…Objectives: This study investigated the role of low-frequency integrase strand transfer inhibitor (INSTI) resistance mutations, detectable by next-generation sequencing (NGS), at predicting virological rebound (VR) among people with HIV (PWH) starting second-generation INSTI-based first-line regimens. …”
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Analysis of the Impact of Rain on Perception in Automated Vehicle Applications by Tim Brophy, Darragh Mullins, Ashkan Parsi, Jonathan Horgan, Enda Ward, Patrick Denny, Ciaran Eising, Brian Deegan, Martin Glavin, Edward Jones

“…This study investigates the performance of object detection under rain conditions, focusing on algorithm performance and low-level object characteristics. …”
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DRR-YOLO: A Study of Small Target Multi-Modal Defect Detection for Multiple Types of Insulators Based on Large Convolution Kernel by Mingming Hu, Jun Liu, Junfu Liu

“…The existing insulator defect detection algorithms are mainly characterized by their ability to identify only a single type of defect, accompanied by relatively low accuracy, a Dilated Re-parameterized Residual-YOLO (DRR-YOLO) algorithm is proposed, which is capable of identifying four defects of each of the four insulator types. …”
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Efficient deep learning based rail fastener screw detection method for fastener screw maintenance robot under complex lighting conditions by Yijie Cai, Ming He, Bin Chen

“…Abstract For the rail fastener replacement operation at night in the wilderness, the lighting conditions on the rail fastener screws are complex, due to the multiple illuminants like headlamps and flashlights at the site, making some parts of the objects appear dark or low light status in the camera. These complex lighting conditions (CLCs) interfere with the fastener recognition ability of the fastener screw detection algorithm since it can hardly maintain fixed and optimized lighting conditions of the fastener screw. …”
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Performance of the Oncuria-Detect bladder cancer test for evaluating patients presenting with haematuria: results from a real-world clinical setting by Ian Pagano, Zhen Zhang, Michael Luu, Sergei Tikhonenkov, Florence Le Calvez-Kelm, Steve Goodison, Toru Sakatani, Kaoru Murakami, Takashi Kobayashi, Patrice Avogbe, Howard Kim, Riko Lee, Arnaud Manel, Emmanuel Vian, Charles J. Rosser, Hideki Furuya

“…The performance of Oncuria was similar for both low-grade/low-stage and high-grade/high-stage. Conclusions The multiplex Oncuria assay identified bladder cancer with high sensitivity and NPV. …”
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Using LandTrendr to analyze forest disturbance, recovery, and attribution in Hunan province from 2001 to 2024 by Panlin Cao, Zhuo Zang, Meng Zhang, Xu Wang, Xian Tang, Jiahong Xiang, Shu Tang, Jing Wang, Yanan Zhang

“…This study uses Google Earth Engine and Landsat data (2001–2024) with the LandTrendr algorithm to detect spatiotemporal forest disturbances and recovery in Hunan Province, applying a random forest classifier to identify disturbance types. …”
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Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists by Vinay Sehgal, Avi Rosenfeld, David G. Graham, Gideon Lipman, Raf Bisschops, Krish Ragunath, Manuel Rodriguez-Justo, Marco Novelli, Matthew R. Banks, Rehan J. Haidry, Laurence B. Lovat

“…These generate a simple algorithm to accurately predict dysplasia. Once taught to non-experts, the algorithm significantly improves their rate of dysplasia detection. …”
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Enhanced Detection of Mitochondrial Heteroplasmy and DNA Hypomethylation in Adipose-Derived Mesenchymal Stem Cells Using a Novel Adaptive Sampling Protocol by Antonina Gospodinova, Yuliia Mariienko, Diana Pendicheva-Duhlenska, Soren Hayrabedyan, Krassimira Todorova

“…In mtDNA, direct sequencing showed extensive hypomethylation, and low levels of non-CpG methylation were detected regardless of sequencing coverage depth. …”
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Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Abdul Hameed Kalifullah, Arife Tugsan Isiacik Colak, Md Redzuan Zoolfakar

“…By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. …”
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Research on real time measurement model and measurement system for gas concentration in extraction drilling by Dayang Yu, Junhao Zhu, Xing Li, An He, Huaiqian Liu, Changjiang Chen, Yong Liu

“…This study develops an enhanced TDLAS-based detection system that integrates wavelength modulation spectroscopy (WMS) with a centroid-weighted Lagrange interpolation algorithm. …”
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Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models by Emrah ASLAN, Yıldırım ÖZÜPAK

“…In the study, high, medium and low levels of contamination are considered and both machine learning and deep learning techniques are applied for the detection of these failure types. …”
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Characteristic Analysis and Optimal Regulation of Primary Frequency Regulation Condition in Low Water Head Area Based on Hydraulic-Mechanical-Electrical Coupling Model of Pumped St... by Cong Lv, Yanhe Xu, Xin Wu, Qing Zhang

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QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity by Nan Xu, Behnaz Yousefi, Nmachi Anumba, Theodore J. LaGrow, Xiaodi Zhang, Shella Keilholz

“…Quasi-periodic patterns (QPPs) are prominent spatiotemporal brain dynamics observed in functional neuroimaging data, reflecting the alternation of high and low activity across brain regions and their propagation along cortical gradients. …”
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Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis by Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad Alturise

“…Abstract Recent innovations in medical imaging have markedly improved brain tumor identification, surpassing conventional diagnostic approaches that suffer from low resolution, radiation exposure, and limited contrast. …”
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PermQRDroid: Android malware detection with novel attention layered mini-ResNet architecture over effective permission information image by Kazım Kılıç, İbrahim Alper Doğru, Sinan Toklu

“…The proposed architecture has a low number of parameters and memory consumption despite adding the residual layer and the weighting operations in the attention layer. …”
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AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on a Cue-Masked Paradigm by Keren Shi, Xu Liu, Xue Yuan, Haijie Shang, Ruiting Dai, Hanbin Wang, Yunfa Fu, Ning Jiang, Jiayuan He

“…Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical applications. …”
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Tiny dLIF: a dendritic spiking neural network enabling a time-domain energy-efficient seizure detection system by Luis Fernando Herbozo Contreras, Leping Yu, Zhaojing Huang, Ziyao Zhang, Armin Nikpour, Omid Kavehei

“…However, these techniques often rely on feature extraction techniques such as short time Fourier transform (STFT) for efficiency in seizure detection. Drawing inspiration from brain architecture, we investigate biologically plausible algorithms, specifically emphasizing time-domain inputs with low computational overhead. …”
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