Epidemiology of inherited metabolic disorders in newborn screening: insights from three years of experience in Southern Iran by Leila Salarian, Homa Ilkhanipoor, Anis Amirhakimi, Zhila Afshar, Saman Nahid, Fariba Moradi Ardekani, Nazila Rahimi, Negar Yazdani, Abdolhossein Nikravesh, Zahra Beyzaei, Hossein Moravej

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Detecting depression in patients with coronary heart disease: a diagnostic evaluation of the PHQ-9 and HADS-D in primary care, findings from the UPBEAT-UK study. by Mark Haddad, Paul Walters, Rachel Phillips, Jacqueline Tsakok, Paul Williams, Anthony Mann, André Tylee

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A study on the status of myopia and pre-myopia among primary school students in different regions of Shaanxi Province, China by Dong Wei, Dong Wei, Li Yangbing, Liu Chengfei, Liu Chengfei, Zhang Ye, Zhang Ye, Wang Zihao, Wang Zihao, Li Yanying, Li Yanying, He Xiaokang, He Xiaokang, Fu Kunlin, Du Zhaojiang

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No change in electrocortical measures of performance monitoring in high trait anxious individuals following multi-session attention bias modification training by Joshua M. Carlson, Lin Fang, Jeremy A. Andrzejewski

“…Previous studies have shown that ABM can change the activity in brain regions that are involved in cognitive control and threat detection, such as anterior cingulate cortex, which also plays a critical role in error monitoring and is the main source of the error-related negativity (ERN). …”
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Robust outdoor trajectory mapping using CNN features and loop closure optimization by Kamran Kazi, Arbab Nighat Kalhoro, Farida Memon, Tarique Rafique Memon, Azam Rafique Memon

“…To find a suitable layer to obtain features from the CNN model, 313,746 filters were checked to find a filter that has the least odometry error from three pre-trained CNN models, the ConvNeXtXLarge is leveraged to extract high-level semantic features from monocular images, enabling resilient optical flow estimation even in scenes with transient objects and lighting variations. …”
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A case study of fractional-order varicella virus model to nonlinear dynamics strategy for control and prevalence by Nisar Kottakkaran Sooppy, Farman Muhammad, Ghannam Manal, Hincal Evren, Sambas Aceng

“…Several findings have been discussed by considering various fractal dimensions and arbitrary order. …”
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Fucosidosis: clinical and molecular findings of Turkish patients by Merve Emecen Şanlı, Serap Uysal

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MobNas ensembled model for breast cancer prediction by Tariq Shahzad, Sheikh Muhammad Saqib, Tehseen Mazhar, Muhammad Iqbal, Ahmad Almogren, Yazeed Yasin Ghadi, Mamoon M. Saeed, Habib Hamam

“…From the findings of this research, it is evident that MobNAS can enhance diagnostic accuracy and reduce existing shortcomings in breast cancer detection.…”
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Optimization of IMU-based bending strain solving algorithm and full-scale experimental validation by Tong SHI, Xiaoben LIU, Lin ZHANG, Jun WANG, Rui LI, Ting Xie, Qingshan FENG, Qiyu HUANG

“…Results This study revealed that as pipeline bending strain increased, the errors between IMU detections and true bending strains grew. …”
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Evaluation of the Effectiveness of the National Vision Screening Program in School-Age Children by Mehtap Savar Çağlayan, Betül Dertsiz Kozan

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Rate of Unexpected Findings in Adolescent Lumbar Magnetic Resonance Imagings Ordered by Orthopaedic Surgeons by Bilal S. Siddiq, BS, Anna Rambo, MD, Benjamin Sheffer, MD, Vania Ejiofor, BA, Abu M. Naser, PhD, Trevor McGee, MD, William C. Warner, Jr., MD, Derek M. Kelly, MD

“…Background: The use of advanced imaging in children is increasing and unexpected findings (UFs) are often detected. The present literature lacks studies investigating the rate of UFs in pediatric lumbar spine magnetic resonance imagings (MRIs) and the sequelae of these findings. …”
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On the reliability of published findings using the regression discontinuity design in political science by Drew Stommes, P. M. Aronow, Fredrik Sävje

“…However, researchers tend to use inappropriate methods for inference, rendering standard errors artificially small. A retrospective power analysis reveals that most of these studies were underpowered to detect all but large effects. …”
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Positioning of Lightning Electromagnetic Radiation Sources With Satellite Constellations: Simulation and Preliminary Validation by Xiao Li, Zongxiang Li, Xiaoqiang Li, Xiong Zhang, Yunfen Chang, Kai Zhang, Yongli Wei, Baofeng Cao, Peng Li

“…Furthermore, we verified the detecting and positioning capabilities based on the data from transmitting‐and‐receiving tests utilizing a terrestrial LERS simulator and the on‐orbit satellite SY‐15, and the positioning error is less 3 km under a certain virtual multi‐satellite constellation when accounting for ionospheric delay.…”
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Deepfakes in Visual Art: Differentiating AI-Generated Art From Human Art Using Convolutional Neural Networks (CNN) by Ngonidzashe Tinago, Silas Formunyuy Verkijika, Kelibone Eva Mamabolo

“…This study explores the use of Convolutional Neural Networks (CNNs) to differentiate AI-generated art from human-created art. By employing Error Level Analysis (ELA), an image forensic technique for detecting fake and real images, this study develops a robust CNN classifier. …”
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Hierarchical clustering methods in a task to find abnormal observations based on groups with broken symmetry by A. N. Kislyakov, S. V. Polyakov

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Model Updating of Bridges Using Measured Influence Lines by Doron Hekič, Jan Kalin, Aleš Žnidarič, Peter Češarek, Andrej Anžlin

“…Bridge responses from two calibration vehicles were used to derive strain influence lines (ILs) from mid-span B-WIM strain transducers mounted on the main girders. The error-domain model falsification (EDMF) methodology was applied to perform strain IL-based FEMU and the more conventional frequency-based, MAC-based, and combined frequency and MAC-based FEMU. …”
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PatchDetect: Breast Cancer Detection combining Unet-ResNet-50 and Patch Embedding LSTM by Hadj Ahmed Bouarara, kadda benyahia

“…The model's performance was optimized using various hyperparameters, achieving an accuracy of 94%, recall of 93%, precision of 92%, and F-measure of 92% while maintaining a minimal error rate of 6%. The findings emphasize the importance of integrating pre-trained CNNs with sequential analysis via LSTMs for feature-rich and temporal data like mammographic patches. …”
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Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images by Yi-Ching Cheng, Yi-Chieh Hung, Guan-Hua Huang, Tai-Been Chen, Nan-Han Lu, Kuo-Ying Liu, Kuo-Hsuan Lin

“…However, traditional manual interpretation is often subjective, time-consuming, and prone to errors, leading to inconsistent detection accuracy and poor generalization. …”
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Comparative Model Efficiency Analysis Based on Dissimilar Algorithms for Image Learning and Correction as a Means of Fault-Finding by Joe Benganga, Tshepo Kukuni, Ben Kotze, Lepekola Lenkoe

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On Risk Assessment for Out-of-Distribution Detection by Anton Vasiliuk

“…Finally, an analysis of popular computer vision benchmarks reveals that ID errors often dominate overall risk, highlighting the importance of strong ID performance as a foundation for effective OOD detection. …”
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