Adaptive reactions of rats after light desynchronosis and physical overwork by A. A. Gostyukhina, T. A. Zamoshchina, K. V. Zaitsev, S. S. Gutor, O. B. Zhukova, M. V. Svetlik, N. G. Abdulkina, A. A. Zaitsev

“…An experimental study was performed on 60 mature male rats of the Wistar breed. …”
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Scalable Detection of Underground Water Leaks in Dense Urban Environments Using L-Band SAR and Machine Learning by E. Ali, E. Ali, L. Xie, A. Sani-Mohammed, W. Xu, T. Zayed

“…A robust leak detection framework was developed using six dual-polarized SAR images (HH and VV modes) alongside historical leak data from the Hong Kong Water Supplies Department (WSD). Features extracted via Gray-Level Co-occurrence Matrix (GLCM) metrics and backscattering coefficients were used to train various machine learning, deep learning, and ensemble learning models, with hyperparameter optimization performed using a grid search algorithm. …”
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LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study by Hengrui Liang, Runchen Wang, Ran Cheng, Zhiming Ye, Na Zhao, Xiaohong Zhao, Ying Huang, Zhanpeng Jiang, Wangzhong Li, Jianqi Zheng, Hongsheng Deng, Yu Jiang, Yuechun Lin, Yun Yan, Lei Song, Jie Li, Xin Xu, Wenhua Liang, Jun Liu, Jianxing He

“…Random forest was used for multitask model construction based on the key proteins. Feature importance was interpreted using Shapley additive explanations (SHAP) algorithm. …”
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Magnetic-Resonance-Imaging Texture Analysis Predicts Early Progression in Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiation by Valerio Nardone, Alfonso Reginelli, Fernando Scala, Salvatore Francesco Carbone, Maria Antonietta Mazzei, Lucio Sebaste, Tommaso Carfagno, Giuseppe Battaglia, Pierpaolo Pastina, Pierpaolo Correale, Paolo Tini, Gianluca Pellino, Salvatore Cappabianca, Luigi Pirtoli

“…The prediction accuracy of the generated model was 82% (AUC=0.853) in the training and 80% (AUC=0.833) in the validation cohort. …”
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An Insight on the Timely Diagnosis of Diabetic Retinopathy Using Traditional and AI-Driven Approaches by Malaika Asif, Fasih Ur Rehman, Zoya Rashid, Altaf Hussain, Alina Mirza, Waqar Shahid Qureshi

“…Artificial intelligence is transforming the detection of diabetic retinopathy, moving away from traditional machine learning models that rely on manually created features to deep learning methods that allow for automatic feature extraction from retinal images. …”
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WHU-RS19 ABZSL: An Attribute-Based Dataset for Remote Sensing Image Understanding by Mattia Balestra, Marina Paolanti, Roberto Pierdicca

“…In particular, EfficientNet-B0 and ViT-B/16 are the top performers in terms of the overall macro F1-score and consistency across attributes, while all models show a consistent decline in performance for infrequent or visually ambiguous categories. …”
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DGCFNet: Dual Global Context Fusion Network for remote sensing image semantic segmentation by Yuan Liao, Tongchi Zhou, Lu Li, Jinming Li, Jiuhao Shen, Askar Hamdulla

“…Therefore, segmentation models need to capture both rich local information and long-distance contextual information to overcome these challenges. …”
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Continuous Joint Kinematics Prediction Using GAT-LSTM Framework Based on Muscle Synergy and Sparse sEMG by Meiju Li, Zijun Wei, Zhi-Qiang Zhang, Shuhao Ma, Sheng Quan Xie

“…The proposed framework integrates GAT for relational feature learning with LSTM networks for temporal dependency modeling, leveraging the strengths of both architectures. …”
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Prognostic Value of B7-H3 and a Novel Scoring System in Localized Renal Cell Carcinoma by Faruk Recep Özalp, Kutsal Yörükoğlu, Eda Çalışkan Yıldırım, Mehmet Uzun, Erkut Demirciler, Hüseyin Salih Semiz

“…<i>Background and Objectives</i>: Renal cell carcinoma (RCC) is a biologically heterogeneous malignancy, and traditional prognostic models often fail to provide accurate risk stratification. …”
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Fine-Grained Extraction of Coastal Aquaculture Ponds From Remote Sensing Images Using an Edge-Supervised Multi-task Neural Network by Jian Qi, Min Ji, Fengxiang Jin, Jianran Xu, Hanyu Ji, Juan Wang

“…It notably enhances performance in complex environments and significantly boosts generalization capabilities by learning global structural features. …”
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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System by Safdar Hussain Javed, Maaz Bin Ahmad, Muhammad Asif, Waseem Akram, Khalid Mahmood, Ashok Kumar Das, Sachin Shetty

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Single-cell Analysis Highlights Anti-apoptotic Subpopulation Promoting Malignant Progression and Predicting Prognosis in Bladder Cancer by Linhuan Chen, Yangyang Hao, Tianzhang Zhai, Fan Yang, Shuqiu Chen, Xue Lin, Jian Li

“…Co-expression analysis identified co-expression modules of the anti-apoptotic subpopulation. A prognostic model was constructed using hub genes and Cox regression, and nomogram analysis was performed. …”
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Single-Shot Wavefront Sensing in Focal Plane Imaging Using Transformer Networks by Hangning Kou, Jingliang Gu, Jiang You, Min Wan, Zixun Ye, Zhengjiao Xiang, Xian Yue

“…Experimental results in both simulated and real-world conditions indicate that our method achieves a 4.5% reduction in normalized wavefront error (NWE) compared to ResNet34, suggesting improved performance over conventional deep learning models. …”
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Background-Masked Lightweight Approach for Pear Leaf Disease Recognition by Romiyal George, Selvarajah Thuseethan, Roshan G. Ragel, Sutharshan Rajasegarar, Mamoun Alazab, Hamish Campbell, John Yearwood

“…Using various lightweight neural networks as backbones for the classification task, we validated the efficiency of our approach, which outperformed the baseline in terms of performance. Specifically, our proposed background masking model achieved an IoU of 93.93% for background masking. …”
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MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer by Van-Nhan Tran, Hoanh-Su Le, Piljoo Choi, Suk-Hwan Lee, Ki-Ryong Kwon

“…With the rapid advances in deep generative models, the accessibility and sophistication of such manipulation technologies are increasing, making it more challenging to detect fake content. …”
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Over-the-air federated learning: Status quo, open challenges, and future directions by Bingnan Xiao, Xichen Yu, Wei Ni, Xin Wang, H. Vincent Poor

“…Over-the-air federated learning (OTA-FL), leveraging the superposition feature of multi-access channels, enables users at the network edge to share spectrum resources and achieves efficient and low-latency global model aggregation. …”
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Unsupervised detection of high-frequency oscillations in intracranial electroencephalogram: promoting a valuable automated diagnostic tool for epilepsy by Wenjing Chen, Tongzhou Kang, Md Belal Bin Heyat, Jamal E. Fatima, Yuanning Xu, Dakun Lai

“…Candidate HFOs are identified using STE and transformed into time-frequency maps using the continuous wavelet transform (CWT). The CVAE model is trained for dimensionality reduction and feature reconstruction, followed by clustering of the reconstructed maps using the K-means algorithm for automated HFOs detection.ResultsEvaluation of the proposed unsupervised method on clinical iEEG data demonstrates its superior performance compared to traditional supervised models. …”
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A study on improved random forest-based anomaly detection of regional tariff data under distributed photovoltaic access by Shujun Ji, Kai Liu, Bo Ling, Jiadong Li, Jinteng Wang, Xun Ma

“…Test results demonstrate the method's strong application performance, with waveform entropy results remaining below 0.11, centroid results exceeding 0.907, and effective feature extraction outcomes. …”
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Numerical investigation of pile foundation systems employing an enhanced embedded finite element by Andreas-Nizar Granitzer, Haris Felic, Johannes Leo, Alexander Stastny, Alexander Stastny, Franz Tschuchnigg

“…The present work expands this validation scope to large-scale boundary value problems involving multiple piles and investigates the model performance based on three different case studies. …”
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RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays by Hanan Aljuaid, Hessa Albalahad, Walaa Alshuaibi, Shahad Almutairi, Tahani Hamad Aljohani, Nazar Hussain, Farah Mohammad

“…This work addresses the challenge of chest X-ray interpretation by fine tuning the following three advanced deep learning models: Feature-selective and Spatial Receptive Fields Network (FSRFNet50), ResNext50, and ResNet50. …”
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