Impact of Leg Position on Measurements Used to Detect Femoral Component Subsidence in THA by Elizabeth S. Kaji, BA, Austin F. Grove, BA, Eva Lehtonen, MD, Kellen L. Mulford, PhD, Pouria Rouzrokh, MD, MPH, MHPE, Charles P. Hannon, MD, MBA, Michael J. Taunton, MD, Cody C. Wyles, MD

“…Conclusions: Minor changes (20° or less) in leg rotation between serial radiographs are unlikely to significantly affect the greater trochanter to tip of the stem measurement, whereas flexion is highly impactful. These findings suggest that the fully automated artificial intelligence–based tool for detecting and quantifying femoral component subsidence is robust against rotational variations but may be susceptible to significant measurement errors if there are considerable changes in leg flexion between comparative radiographs.…”
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Tree Top Detection in UAV Data: Evaluating Accuracy of Different Estimation Techniques by A. Hosingholizade, Y. Erfanifard, Y. Erfanifard, S. K. Alavipanah, V. García Millan, S. Pirasteh, S. Pirasteh, A. N. Arslan, A. N. Arslan

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SSB: Smart Contract Security Detection Tool Suitable for Industrial Control Scenarios by Ci Tao, Shuai He, Xingqiu Shen

“…The framework’s ability to model industrial invariants—covering security, functionality, consistency, time-related, and resource consumption aspects—provides a robust mechanism to prevent critical errors like unauthorized access or premature equipment operation. …”
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fossilbrush: An R package for automated detection and resolution of anomalies in palaeontological occurrence data by Joseph T. Flannery‐Sutherland, Nussaïbah B. Raja, Ádám T. Kocsis, Wolfgang Kiessling

“…There are also no standardised methods to detect and resolve errors despite the development of an ecosystem of cleaning tools fuelled by the concurrent growth of neontological occurrence databases. …”
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Automated concrete exposed reinforcement and corrosion detection and measurement using coloured illumination by Dow Hamish, Darby Andrew, Perry Marcus, Zhang Feng

“…Additionally, the algorithm’s corrosion surface area measurements produced a maximum relative error of negative 8 %. These findings demonstrate how colour-controlled illumination can enhance visual inspections, leading to more efficient monitoring and improved durability of reinforced concrete structures.…”
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Zero-Shot Automated Detection of Fake News: An Innovative Approach (ZS-FND) by Rania Baashirah

“…These findings highlight the potential of ZSL models in providing a more robust and efficient solution for FND.…”
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On the black-box explainability of object detection models for safe and trustworthy industrial applications by Alain Andres, Aitor Martinez-Seras, Ibai Laña, Javier Del Ser

“…However, their adoption in high-risk applications, where errors may cause severe consequences, remains limited. …”
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Quaternion Signal Analysis for Detection of Broken Rotor Fault Degrees in Induction Motors by Jose Luis Contreras-Hernandez, Dora Luz Almanza-Ojeda, Rogelio Castro-Sanchez, Mario Alberto Ibarra-Manzano

“…Fault detection in induction motors is essential for maintaining the reliability of industrial operations. …”
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A Comparison Between Artificial Intelligence and Radiologists’ Ability to Detect Lung Nodules by Cristina Mihaela CIOFIAC, Rossy Vlăduţ TEICĂ, Lucian Mihai FLORESCU, Ioana Andreea GHEONEA

“…Results: Radiologists assisted by AI missed significantly fewer nodules (p = 0.049) and demonstrated an almost perfect correlation (0.999) with expert reference values, reducing the mean absolute error (MAE) from 12.24 to 4.92. Artificial Intelligence also improved detection sensitivity from 80% to 99% and significantly lowered false negatives from 938 to 34, enhancing both diagnostic accuracy and efficiency. …”
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Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data by H. T. Rüdisser, A. Windisch, U. V. Amerstorfer, C. Möstl, T. Amerstorfer, R. L. Bailey, M. A. Reiss

“…Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hr and 56 min, and the end time with a MAE of 3 hr and 20 min. …”
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Integrating ANN and ANFIS for effective fault detection and location in modern power grid by Yadav Goutam Kumar, Kirar Mukesh Kumar, Gupta S.C., Rajender Jatoth

“…Additionally, the system exhibits a strong capability in pinpointing fault locations with minimal error margins. This research underscores the potential of ANFIS as a powerful tool for improving the consistency and competence of fault management in power systems. …”
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Research on plasma arc flame length detection technology based on region of interest. by Jie Li, Wei Jiang, Jian Lei, Xiaoxiao Xing

“…The corrected plasma arc length averages around 40mm, with a detection error of less than 1mm when compared to the desired controlled plasma arc length. …”
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Impacts of Spatial and Temporal Resolution on Remotely Sensed Corn and Soybean Emergence Detection by Feng Gao, Martha Anderson, Rasmus Houborg

“…However, the 30 m HLS lacked sub-field details in fields with mixed cropping systems. The findings from this study could benefit remotely sensed crop emergence detection from local to regional scales.…”
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Robust Image Steganography Approach Based on Edge Detection Combined With CNN Algorithm by Rana Al-Rawashdeh, Md Mahfuzur Rahman, Mahmood Niazi

“…The main problem in image steganography is to find the right balance between the maximum amount of secret data that can be transmitted or stored in the cover image and the quality of the stego image to make the secret data un-noticeable to human senses or other detection mechanisms. …”
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Advanced deep transfer learning techniques for efficient detection of cotton plant diseases by Prashant Johri, SeongKi Kim, Kumud Dixit, Prakhar Sharma, Barkha Kakkar, Yogesh Kumar, Jana Shafi, Muhammad Fazal Ijaz

“…After pre-processing the images of the dataset, their region of interest is obtained by applying feature extraction techniques such as the generation of the biggest contour, identification of extreme points, cropping of relevant regions, and segmenting the objects using adaptive thresholding.Results and DiscussionDuring experimentation, it is found that the EfficientNetB3 model outperforms in accuracy, loss, as well as root mean square error by obtaining 99.96%, 0.149, and 0.386 respectively. …”
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Adaptive path planning for efficient object search by UAVs in agricultural fields by Rick van Essen, Eldert van Henten, Lammert Kooistra, Gert Kootstra

“…We evaluated the effect of different detection certainty measures, optimized the path planning parameters, investigated the effects of localization errors, and different numbers of objects in the field. …”
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AI-enabled driver assistance: monitoring head and gaze movements for enhanced safety by Sayyed Mudassar Shah, Gan Zengkang, Zhaoyun Sun, Tariq Hussain, Khalid Zaman, Abdullah Alwabli, Amar Y. Jaffar, Farman Ali

“…The proposed system achieves superior precision, with a weighted accuracy of 95.02% and Root Mean Square Errors of 2.23 and 1.68 for vertical and horizontal gaze estimation, respectively, validated on the MPII-Gaze and DG-Unicamp datasets. …”
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Research on the optimization of grouting flow detection sensor layouts based on laser ranging technology by Lichang Wang, Wei Long, Zhanghui Fei, Zhongli Yang, Meng Xu, Lu Wang

“…Optimal sensor placement in the stable zone reduced measurement errors to 0.83% (power-function-distributed sensors) and 1.12% (uniformly spaced sensors), significantly outperforming electromagnetic flowmeters (up to 20% error). …”
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The Advanced Reasoning Capabilities of Large Language Models for Detecting Contraindicated Options in Medical Exams by Yuichiro Yano, Mizuki Ohashi, Taiju Miyagami, Hirotake Mori, Yuji Nishizaki, Hiroyuki Daida, Toshio Naito

“… AbstractEnhancing clinical reasoning and reducing diagnostic errors are essential in medical practice; OpenAI-o1, with advanced reasoning capabilities, performed better than GPT-4 on 15 Japanese National Medical Licensing Examination questions (accuracy: 100% vs 80%; contraindicated option detection: 87% vs 73%), though findings are preliminary due to the small sample size.…”
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Feasibility study of texture-based machine learning approach for early detection of neonatal jaundice by Nanthida Phattraprayoon, Teerapat Ungtrakul, Patiparn Kummanee, Sunisa Tavaen, Tanin Pirunnet, Todsaporn Fuangrod

“…Blind testing demonstrated good performance in bilirubin level prediction (mean absolute error: 1.675 mg/dL; root mean square error: 2.192 mg/dL), with moderate correlation between predicted and measured values (r = 0.644, p < 0.001). …”
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