Radiomics Analysis of Whole-Kidney Non-Contrast CT for Early Identification of Chronic Kidney Disease Stages 1–3 by Guirong Zhang, Pan Zhang, Yuwei Xia, Feng Shi, Yuelang Zhang, Dun Ding

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AI-driven data fusion modeling for enhanced prediction of mixed-mode I/III fracture toughness by Anantaya Timtong, Atthaphon Ariyarit, Wanwanut Boongsood, Prasert Aengchuan, Attasit Wiangkham

“…By employing adaptive boosting and general regression neural network algorithms, the models developed in this research demonstrate a marked improvement in predictive performance compared to traditional primary source models. …”
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RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes by Keda Li, Xiangyue Zheng, Jingxin Bi, Gang Zhang, Yi Cui, Tao Lei

“…Aircraft detection technology plays a vital role in civilian applications, with significant attention being devoted to research on related algorithms in recent years. However, most existing research predominantly focuses on aircraft detection from a single top–down viewpoint, which constrains the applicability of detection technology across diverse scenarios. …”
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Artificial intelligence may affect diversity: architecture and cultural context reflected through ChatGPT, Midjourney, and Google Maps by Ingrid Campo-Ruiz

“…Abstract This study aims to understand how widely used Artificial Intelligence (AI) tools reflect the cultural context through the built environment. This research explores how outputs obtained with ChatGPT-4o, Midjourney’s bot on Discord and Google Maps represent the cultural context of Stockholm, Sweden. …”
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Verifying Measurements of Surface Current Velocities by X-Band Coherent Radar Using Drifter Data by Igor G. Gorbunov, Vladimir I. Veremyev, Vadim D. Shestak, Gleb V. Komarov, Stanislav A. Myslenkov, Ksenia P. Silvestrova

“…Measurements were taken onboard of the research vessel at a low speed and different distances from the shore, near the drifters. …”
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Sleep stages classification based on feature extraction from music of brain by Hamidreza Jalali, Majid Pouladian, Ali Motie Nasrabadi, Azin Movahed

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Performance Analysis of Real-Time Detection Transformer and You Only Look Once Models for Weed Detection in Maize Cultivation by Oscar Leonardo García-Navarrete, Jesús Hernán Camacho-Tamayo, Anibal Bregon Bregon, Jorge Martín-García, Luis Manuel Navas-Gracia

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Adaptive gradient scaling: integrating Adam and landscape modification for protein structure prediction by Vitalii Kapitan, Michael Choi

“…Despite their success, machine learning methods face fundamental limitations in optimizing complex high-dimensional energy landscapes, which motivates research into new methods to improve the robustness and performance of optimization algorithms. …”
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The Changes in Color and Image Parameters and Sensory Attributes of Freeze-Dried Clones and a Cultivar of Red-Fleshed Apples by Ewa Ropelewska, Mariusz Lewandowski

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Development of a Success Prediction Model for Crowdfunding Based on Machine Learning Reflecting ESG Information by Jiyoung Park, Hyung Jong Na, Hyungjoon Kim

“…Incorporating ESG factors into crowdfunding success prediction models is crucial, as these factors represent environmental responsibility, social accountability, and ethical governance, which are increasingly important to investors and sponsors. To achieve the research objectives, this study employed advanced machine learning algorithms, including XGBoost, LightGBM, AdaBoost, CatBoost, and NGBoost, to analyze data from a prominent reward-based crowdfunding platform in Korea, ‘Wadiz.…”
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The Impact of AI-Generated Instructional Videos on Problem-Based Learning in Science Teacher Education by Nikolaos Pellas

“…Employing a within-subjects design, the current study included pre-test, post-test, and transfer assessments to evaluate learning durability and transferability, consistent with design-based research methodology. Moreover, this study compares the effectiveness of two AI-generated instructional video formats: one with an embedded preview feature allowing learners to preview key concepts before detailed instruction (video-with-preview condition) and another without this feature (video-without-preview condition). …”
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DBSANet: A Dual-Branch Semantic Aggregation Network Integrating CNNs and Transformers for Landslide Detection in Remote Sensing Images by Yankui Li, Wu Zhu, Jing Wu, Ruixuan Zhang, Xueyong Xu, Ye Zhou

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Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis? by Mohamed Hassan, Aleksandar Vakanski, Boyu Zhang, Min Xian

“…In recent years, significant research has focused on improving the generalization of deep learning models by regularizing the sharpness of the loss landscape. …”
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Investigating the feasibility of utilising bagasse ash and reclaimed asphalt pavement dust in geopolymer mortar by Sajan Sharma, Shailja Bawa

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Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU by Pascal Riedel, Kaouther Belkilani, Manfred Reichert, Gerd Heilscher, Reinhold von Schwerin

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Spatiotemporal Gap‐Filling of NASA Deep Blue Satellite Aerosol Optical Depth Over the Contiguous United States (CONUS) Using the UNet 3+ Architecture by Jeffrey S. M. Lee, S. Marcela Loría‐Salazar, Heather A. Holmes, Andrew M. Sayer

“…Abstract Due to sensor and algorithmic constraints, satellite aerosol optical depth (AOD) retrievals are spatially incomplete and have gaps caused by clouds and bright surfaces. …”
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Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis by Pradeep Kumar Yadalam, Prabhu Manickam Natarajan, Carlos M. Ardila

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Downscaling Satellite Night-Time Light Imagery While Addressing the Blooming Effect by Nikolaos Tziokas, Ce Zhang, Alexandros Tziokas, Qunming Wang, Peter M. Atkinson

“…In this article, we proposed a spatially nonstationary, geostatistical-based downscaling technique [random forest (RF) area-to-point kriging (ATPK)] to downscale NTL data (from 440 m for Delhi and 430 m for LA to 130 m) while accounting explicitly for the point spread function (PSF), thus, dealing with the blooming effect specific to NTL data. We compared several image fusion algorithms for downscaling while reducing the blooming effect. …”
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Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations by Nguyen Ky Phat, Yoonah Lee, Dinh Hoa Vu, Nguyen Phuoc Long, Seongoh Park

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Comparison of safety of lecanemab and aducanumab: a real-world disproportionality analysis using the FDA adverse event reporting system by Lingti Kong, Lingti Kong, Lingti Kong, Xiaohu Yang, Xiaohu Yang, Jian Xu, Jian Xu

“…This study aims to compare the adverse reaction signals of lecanemab and aducanumab, also exploring the differences between genders.Research design and methodsWe analyzed ADEs reported by patients using lecanemab and aducanumab, using the FDA adverse event reporting system (FAERS). …”
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