Process-driven strength enhancement and progressive damage analysis of three-dimensional woven composites traction rods by Hao Huang, Zhongde Shan, Yanming Xing, Zitong Guo, Chunguang Yang, Jianhua Liu, Zheng Sun, Xiaohui Ao, Dong Wang, Chenchen Tan, Weihao Wang, Juncheng Luo

“…The surrogate model achieved high accuracy across multiple metrics, and the predictive capabilities of the damage model showed a relative error of 13.5% for stiffness and 10.7% for strength. …”
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Does fiscal decentralization foster renewable electricity generation? A panel data study of OECD countries by Serhiy Lyeonov, Oksana Okhrimenko, Artem Artyukhov, Mariia Saiensus, Iuliia Myroshnychenko, Yuliia Yehorova, Oleksii Havrylenko

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A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors by Seren Smith, Theodore Trefonides, Anusha Srirenganathan Malarvizhi, Shyra LaGarde, Jiakang Liu, Xiaoguo Jia, Zifu Wang, Jacob Cain, Thomas Huang, Mohammad Pourhomayoun, Grace Llewellyn, Wai Phyo, Sina Hasheminassab, Joe Roberts, Kevin Marlis, Daniel Q. Duffy, Chaowei Yang

“…Long Short-Term Memory (LSTM) models trained in RStudio and TensorFlow excelled, with high R² scores of 0.856 and 0.857 and low Root Mean Squared Errors (RMSEs) of 4.25 µg/m³ and 4.26 µg/m³, respectively. …”
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Enhancing image security via chaotic maps, Fibonacci, Tribonacci transformations, and DWT diffusion: a robust data encryption approach by Mohammad Mazyad Hazzazi, Mujeeb Ur Rehman, Arslan Shafique, Amer Aljaedi, Zaid Bassfar, Aminu Bello Usman

“…Abstract In recent years, numerous image encryption schemes have been developed that demonstrate different levels of effectiveness in terms of robust security and real-time applications. While a few of them outperform in terms of robust security, others perform well for real-time applications where less processing time is required. …”
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An Efficient Intersection Over Union Algorithm for 3D Object Detection by Sazan Ali Kamal Mohammed, Mohd Zulhakimi Ab Razak, Abdul Hadi Abd Rahman, Maria Abu Bakar

“…Quite recently, the combination of improved accuracy, flexibility, available datasets, state-of-the-art architectures, and diverse applications has contributed to the widespread adoption and popularity of deep learning-based object detection techniques, particularly for many applications in computer vision and emerging applications. …”
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The Utility of Virtual Reality in Ophthalmology: A Review by Ahuja AS, Paredes III AA, Eisel ML, Ahuja SA, Wagner IV, Vasu P, Dorairaj S, Miller D, Abubaker Y

“…VR training simulators have decreased surgical error rates and improved technique in cataract surgery. …”
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Machine learning enhanced ultra-high vacuum system for predicting field emission performance in graphene reinforced aluminium based metal matrix composites by Sunil Kumar Pradhan, Subhayu Kabiraj, Shivin Kumar Gupta, Abhishek Singh, Padmakar G. Chavan, Shubham S. Patil, Trilok Nath Pandey

“…Model evaluation was conducted based on R²(R-squared), RMSE (Root Mean Squared Error), and Adjusted R² scores. In stage 2, the top models were further refined using advanced techniques, including Gradient-Based Methods and Ensemble Methods. …”
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Robust confinement state classification with uncertainty quantification through ensembled data-driven methods by Yoeri Poels, Cristina Venturini, Alessandro Pau, Olivier Sauter, Vlado Menkovski, the TCV Team, the WPTE Team

“…To enable wide-range applicability, we develop methods for confinement state classification with uncertainty quantification and model robustness . …”
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Validation of Global Moderate-Resolution FAPAR Products over Boreal Forests in North America Using Harmonized Landsat and Sentinel-2 Data by Yinghui Zhang, Hongliang Fang, Zhongwen Hu, Yao Wang, Sijia Li, Guofeng Wu

“…The reference FAPAR maps revealed a strong agreement with the in situ FAPAR from AmeriFlux (correlation coefficient (R) = 0.91; root mean square error (RMSE) = 0.06). The results revealed that global FAPAR products show similar uncertainties (RMSE: 0.16 ± 0.04) and moderate agreement with the reference FAPAR (R = 0.75 ± 0.10). …”
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Metaheuristic-Based Model Optimization of a Steam-Filled Chamber by Hubert Guzowski, Roman Senkerik, Maciej Smolka, Frantisek Gazdos, Miroslav Palka, Libor Pekar, Michal Pluhacek, Adam Viktorin, Tomas Kadavy, Aleksander Byrski, Zuzana Kominkova Oplatkova, Radek Matusu, Janusz Kacprzyk

“…The achieved models reflect real data with high accuracy, with mean squared errors as low as 0.0138 on output values ranging from 0.0 to 20.0, representing less than 0.1% of the output range. …”
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A new high-resolution hydrodynamic model for the coastal Beaufort Sea in the Arctic Ocean: model construction and evaluation by J. Blake Clark, J. Blake Clark, Wesley Moses, Ahmed El-Habashi, Maria Tzortziou, Kyle J. Turner, Hisatomo Waga, Hisatomo Waga, Steven Ackelson, Jonathan Sherman, Jonathan Sherman

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Soil Organic Carbon Estimation and Transfer Framework in Agricultural Areas Based on Spatiotemporal Constraint Strategy Combined with Active and Passive Remote Sensing by Jiaxin Qian, Jie Yang, Weidong Sun, Lingli Zhao, Lei Shi, Hongtao Shi, Lu Liao, Chaoya Dang, Qi Dou

“…When SAR, MS, or TB data are used individually in modeling, the estimation errors (RMSE) for SOC are 0.637 g/kg, 0.492 g/kg, and 0.229 g/kg for the SMAPVEX12 sampling campaign, and 0.706 g/kg, 0.454 g/kg, and 0.474 g/kg for the SMAPVEX16-MB sampling campaign, respectively. …”
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Physics-informed and data-driven thermal modeling and temperature field prediction for thermal field-assisted direct ink writing with PDMS by Xinhua Yao, Yuhan Jiang, Lixin Tian, Xiang Li, Yong He

“…The results showed that the average relative error between simulation and experiments was below 3 %. …”
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Enhancing Aerosol Vertical Distribution Retrieval With Combined LSTM and Transformer Model From OCO-2 O2 A-Band Observations by YuXuan Wang, RuFang Ti, ZhenHai Liu, Xiao Liu, HaiXiao Yu, YiChen Wei, YiZhe Fan, YuYao Wang, HongLian Huang, XiaoBing Sun

“…The model's high retrieval accuracy is validated using two metrics: Earth mover's distance and mean-squared error. By integrating advanced machine learning techniques into remote sensing, this study achieves a significant improvement in retrieval accuracy over previous methods. …”
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Zero‐shot insect detection via weak language supervision by Benjamin Feuer, Ameya Joshi, Minsu Cho, Shivani Chiranjeevi, Zi Kang Deng, Aditya Balu, Asheesh K. Singh, Soumik Sarkar, Nirav Merchant, Arti Singh, Baskar Ganapathysubramanian, Chinmay Hegde

“…However, traditional supervised learning methods require labeled data, and manual annotation of these raw datasets with useful labels (such as bounding boxes or segmentation masks) can be extremely laborious, expensive, and error‐prone. In this paper, we demonstrate the power of zero‐shot computer vision methods—a new family of approaches that require (almost) no manual supervision—for plant phenomics applications. …”
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Study on the Thermal Performance of Inner and Outer Wall Surfaces Based on FDM-ATSCM by Li Honglian, Cao Wenhui, Jiang Suwan, Sun Yinghao, Zhu Xinrong

“…Many existing approaches rely on overly simplified assumptions, resulting in significant errors and inefficient strategies. For large-scale or high-performance buildings, even slight temperature deviations can have major impacts on energy consumption and indoor conditions. …”
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A Vision-Based End-to-End Reinforcement Learning Framework for Drone Target Tracking by Xun Zhao, Xinjian Huang, Jianheng Cheng, Zhendong Xia, Zhiheng Tu

“…The experimental results indicate that our proposed VTD3 reinforcement learning algorithm substantially outperforms conventional PD controllers in drone target tracking applications. Across various target trajectories, the VTD3 algorithm demonstrates a significant reduction in average tracking errors along the X-axis and Y-axis of up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>34.35</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>45.36</mn><mo>%</mo></mrow></semantics></math></inline-formula>, respectively. …”
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Fuzzy Hypothesis Testing for Radar Detection: A Statistical Approach for Reducing False Alarm and Miss Probabilities by Ahmed K. Elsherif, Hanan Haj Ahmad, Mohamed Aboshady, Basma Mostafa

“…The findings confirm the applicability of fuzzy hypothesis testing for modern radar systems in both civilian and military contexts, providing a statistically sound and operationally applicable approach for reducing detection errors and optimizing system performance.…”
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Assessing the Feasibility of Persistent Scatterer Data for Operational Dam Monitoring in Germany: A Case Study by Jonas Ziemer, Jannik Jänichen, Carolin Wicker, Daniel Klöpper, Katja Last, Andre Kalia, Thomas Lege, Christiane Schmullius, Clémence Dubois

“…This index is universally applicable and can be transferred to other types of infrastructure. …”
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A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset by Ziyue Zhu, Runze Zhang, Bin Fang, Hyunglok Kim, Hoang Hai Nguyen, Venkataraman Lakshmi

“…When validated against SMAP TB data, this dataset showed a solid correlation (R2 = 0.921) and a low root mean square error (RMSE = 4.254 K), making it a useful resource for fine-scale SM monitoring. …”
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