Colistin broth disk elution for the detection of Colistin resistance among Carbapenem-resistant Klebsiella pneumoniae by Phu Thien Truong, Ngoc Bich Tran, Hoang Huy Pham, Van Thi Hue Tran, Hoang Thanh Phan, Binh Thai Pham, Lam Thi Ngoc Tran, Truc Thi Thanh Nguyen, Mai Le Phuong, Dat Quoc Ngo

“…The CA between the two methods was 100 %, and the EA was 91.47 %, with no major errors (ME) or very major errors (VME) detected. Conclusion: The CBDE method has shown a high level of agreement with the reference BMD method, making it a reliable alternative for the routine detection of colistin resistance in microbiology laboratories. …”
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Novel Fault Detection Method for Rolling Bearings Based on Improved Variational Modal Decomposition Method by Xiaoli Huang, Haifeng Xu, Junying Cui

“…Finally, the wavelet packet decomposition is adopted to filter noise and yield the Hilbert envelope spectrum of the de-noised signal to ascertain the bearing’s health status based on the extraction of characteristic fault frequencies and harmonics. The experimental findings illustrate that the enhanced variational mode decomposition technique not only escalates the inner ring signal’s signal-to-noise ratio from −10.844 dB to 8.4471 dB and the outer ring signal’s ratio from −4.5852 dB to 3.0997 dB but also reduces the error of outer ring fault detection from 3.14% to 0.37%, and improves the frequency of inner ring fault detection from a feature extraction inability to an error frequency of 0.45%.…”
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Deception mechanisms of FDA‒AWACS against passive monopulse angle measurements by Bo Wang, Gang Wang, Yonglin Li, Rennong Yang, Yu Zhao

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Detecting Exercise-Induced Temperature Distribution Changes at the Knee Using a Wearable Array of Thermistors by Benito L. Pugliese, Virginia Civeriati, Adam S. Tenforde, Danilo Demarchi, Paolo Bonato

“…The mean HDI error for the thermistor array was 0.20±0.36∘C. …”
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Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context. by Thomas W Willis, Chris Wallace

“…We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context.…”
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Dissociable effects of fatigue on performance and metacognition from automatic target cuing in undersea threat detection by Max Kailler Smith, Amelia R. Kracinovich, Brandon J. Schrom, Timothy L. Dunn

“…This study investigated whether ATC enhances UTD performance under low fatigue conditions and protects against errors when operators are fatigued, as is common during fleet operations. …”
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Analysis of the status of leprosy symptom surveillance in Guangdong province: an exploring effort to promote early detection by Lei Chen, Quan Luo, Haoze Xue, Jing Wang, Xiaohua Wang, Cheng Wang

“…The delayed discovery time decreased from (36.2 ± 2.0) months to (31.8 ± 3.1) months, and the patients delayed discovery time decreased from (27.7 ± 1.8) months to (20.8 ± 2.5) months, both showing a statistically significant difference (p < 0.05). Discussion Our findings indicate that the leprosy symptom surveillance program in Guangdong Province has effectively reduced delayed discovery time, particularly patients delayed discovery time in seeking medical care, thereby facilitating early detection of leprosy cases.…”
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The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection by Tarek Berghout

“…Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 research papers over past half-decade (2019–2024), this review fills that gap, exploring the latest methods and paradigms, summarizing key concepts, challenges, datasets, and offering insights into future directions for brain tumor detection using deep learning. …”
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A novel small-scale wind-turbine blade failure detection according to monitored-data by A. Aranizadeh, H. Shad, B. Vahidi, A. Khorsandi

“…Additionally, strain gauge signals demonstrate superior accuracy in identifying failure modes over accelerometer signals. These findings provide valuable insights into mode-specific impacts and the effectiveness of monitoring techniques for accurate fault detection.…”
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A robust dataset for surgical instrument detection to aid autonomous robotic surgeryMendeley Data by Ganesh Kumar Chellamani, Aishwarya N, Bhavesh Kumar P, Palle Sravan Kumar Reddy, Rakesh Thoppaen Suresh Babu

“…On an average, the models have attained 99.3% of mean Average Precision (mAP) and 99.2% F1-score, demonstrating the quality of SID-RAS dataset for surgical tool detection. These findings contribute to the preliminary development of AI-driven robotic surgical assistance systems, which can be extended to various types of surgeries.…”
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A Novel Machine Learning-based Diagnostic Algorithm for Detection of Onychomycosis through Nail Appearance by Serkan Düzayak, Muhammed Kürşad Uçar

“…Today, new technologies are needed to detect onychomycosis via AI-based ML to reduce the clinician and laboratory-induced error rate and increase diagnostic sensitivity and reliability. …”
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ChaMTeC: CHAnnel Mixing and TEmporal Convolution Network for Time-Series Anomaly Detection by Ibrahim Delibasoglu, Deniz Balta, Musa Balta

“…This paper introduces ChaMTeC (CHAnnel Mixing and TEmporal Convolution Network), a novel deep learning framework designed for time-series anomaly detection. ChaMTeC integrates an inverted embedding strategy, multi-layer temporal encoding, and a Mean Squared Error (MSE)-based feedback mechanism with dynamic thresholding to enhance anomaly detection performance. …”
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A Comparative Study of Deep Audio Models for Spectrogram- and Waveform-Based SingFake Detection by Minh Nguyen-Duc, Luong Vuong Nguyen, Huy Nguyen-Ho-Nhat, Tri-Hai Nguyen, O-Joun Lee

“…Our findings contribute to developing more effective deepfake singing detection methods, with implications for security, media authentication, and digital content protection.…”
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Advancing Wildlife Protection: Mask R-CNN for Rail Track Identification and Unwanted Object Detection by Istiak Mahmud, Md. Mohsin Kabir, Jungpil Shin, Chayan Mistry, Yoichi Tomioka, M. F. Mridha

“…Since the detection depends on the driver or human, there is a possibility of occasionally making an error in honking the horn at the right moment, leading to the accident. …”
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Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning by Muhammad Umair, Jawad Ahmad, Nada Alasbali, Oumaima Saidani, Muhammad Hanif, Aizaz Ahmad Khattak, Muhammad Shahbaz Khan

“…However, the inherent complexity of EEG signals along with the human error in interpreting these readings requires the need for more reliable, automated methods of detection.MethodsThis study utilizes EEG signals to classify MDD and healthy individuals through a combination of machine learning, deep learning, and split learning approaches. …”
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Early detection of Alzheimer’s disease progression stages using hybrid of CNN and transformer encoder models by Hassan Almalki, Alaa O. Khadidos, Nawaf Alhebaishi, Ebrahim Mohammed Senan

“…Manual diagnosis is prone to human error, often leading to misdiagnosis or delayed detection. …”
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Penetrating Barriers: Microwave-Based Remote Sensing and Reconstruction of Audio Signals Through Walls by Kobi Aflalo, Zeev Zalevsky

“…This study investigate the remote detection and reconstruction of audio signals using Radio Frequency (RF) emissions, focusing on the implications for eavesdropping detection and prevention. …”
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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

“…Monitoring key parameters in nuclear power plant control rooms is critical, as human errors can result in severe safety and operational consequences. …”
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Stuck Pipe Detection in Oil and Gas Drilling Operations Using Deep Learning Autoencoder for Anomaly Diagnosis by Hasan N. Al-Mamoori, Jialin Tian, Haifeng Ma

“…Stuck pipe events remain a critical challenge in oil and gas drilling operations, leading to increased non-productive time and substantial financial losses. Traditional detection methods rely on manual monitoring and expert judgment, which are prone to delays and human error. …”
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Precise detection of differential RNA editing sites across varied biological conditions using the CADRES pipeline by Jun Sun, Chi Zhang, Xiuling Li

“…Abstract RNA editing is an important post-transcriptional modification for gene regulation and protein diversity. Detecting these modifications, especially Differential Variants on RNA (DVRs), presents significant challenges due to interference from sequencing errors and genetic variants. …”
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