POD-Based Machine Learning Approach for Coupled EM-Thermal Analysis in Microwave Heating by Jeong-Wan Lee, Gyu-Sik Choi, Sung-Jun Yang

“…In this paper, we propose a machine learning-based approach for reduced-order modeling that efficiently predicts the specific absorption rate (SAR) distributions in coupled EM and thermal analyses. …”
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Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN by Ali Erbey, Necaattin Barışçı

“…While LSTM models are effective in processing temporal data, 3DCNN-based models, which can process both spatial and temporal information, achieved higher accuracy in this study. …”
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A novel remote sensing method for monitoring Large-Scale grassland aboveground Biomass: The case study of grassland key belt in the Tibetan Plateau by Juan Wang, Aiwu Zhang, Jiancong Shi, Xiaoyan Kang, Nianpeng He, Xinwang Gao, Haiyang Pang

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Temporal Stability of Plant Species α-Diversity in Alpine Grasslands of the Tibetan Plateau and Their Implications for Biodiversity Conservation by Tianyu Li, Wei Sun, Shaowei Li, Erfu Dai, Gang Fu

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Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…Convolutional Neural Networks (CNNs) have demonstrated potential in this domain but encounter limitations when addressing varying scales, resolutions, and complex spatial dependencies inherent in wildfire datasets. …”
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Variations and impact factors of land use carbon emissions in the Yangtze River Economic Belt from a multiscale perspective by Chong Liu, Xiaoman Wang, Haiyang Li

“…This study focuses on the Yangtze River Economic Belt (YREB), utilizing land use, nighttime light, and energy consumption data to compute LUCE at provincial, prefectural, and county scales, employing spatial autocorrelation, geographic detectors, and the Multiscale Geographically Weighted Regression (MGWR) model to analyze the spatiotemporal dynamics and impact factors of LUCE across different scales. …”
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Urban expansion and the loss of potential crop yield in the North China Plain: implications for regional food security (1980–2020) by Shuangqing Sheng, Shuangqing Sheng, Jinchuan Huang

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Snowdrift‐Permitting Simulations of Seasonal Snowpack Processes Over Large Mountain Extents by Christopher B. Marsh, Zhibang Lv, Vincent Vionnet, Phillip Harder, Raymond J. Spiteri, John W. Pomeroy

“…These simulations show how multiscale modeling can improve snowpack predictions to support prediction of water supply, droughts, and floods.…”
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Spatio-temporal characteristics and analysis of influencing factors of inclusive green growth in China’s oil and gas resource industry by Xiangyu Sun, Yanqiu Wang

“…This study introduces a novel index system for inclusive green growth (IGG) specific to China’s oil and gas resource industry, encompassing dimensions such as industrial development, social equity in opportunities, poverty and income inequality reduction, and green ecology. This research marks the first application of the CRITIC combined weighting-TOPSIS model to assess inclusive green growth in the oil and gas resource industry (IGGOG) across 30 Chinese provinces from 2012 to 2021. …”
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Interpretable machine learning analysis of environmental characteristics on bacillary dysentery in Sichuan Province by Yao Zhang, Qiao-Lin Wang, Wei Peng, Meng-Yuan Zhang, Yao Qin, Lun Zhang, Rong-Jie Wei, Dian-Ju Kang

“…Additionally, precipitation displayed a U-shaped relationship with BD risk in both the Subtropical Semi-Humid and Plateau Cold Climate Zones.ConclusionThis study developed a climate zone-specific predictive model for BD, systematically evaluating the interactions between environmental factors and BD dynamics. …”
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Probabilistic Seismic Hazard Analysis for Yemen by Rakesh Mohindra, Anand K. S. Nair, Sushil Gupta, Ujjwal Sur, Vladimir Sokolov

“…Distribution of horizontal peak ground acceleration (PGA) was calculated for all stochastic events considering epistemic uncertainty in ground-motion modeling using three suitable ground motion-prediction relationships, which were applied with equal weight. …”
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High-resolution Annual Dynamic dataset of Curve Number from 2008 to 2021 over Conterminous United States by Qiong Wu, John J. Ramirez Avila, Jia Yang, Cunxiong Ji, Shanmin Fang

“…Abstract The spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. …”
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Explainable AI-driven assessment of hydro climatic interactions shaping river discharge dynamics in a monsoonal basin by Prashant Parasar, Akhouri Pramod Krishna

“…The main findings of this study are (1) KAN demonstrated high predictive performance with root mean squared error (RMSE) values ranging from 42.7 to 58.3 m3/s, Nash–Sutcliffe efficiency (NSE) between 0.80 and 0.87, mean absolute error (MAE) between 28.9 to 52.7 and R2 values between 0.84 and 0.90 across stations. (2) SHAP based feature contribution analysis identified Relative humidity (hurs), specific humidity (huss), and temperature (tas) as key predictors, while (pr) showed limited contribution due to spatial inherent inconsistencies in GCM precipitation data. (3) The bootstrapped SHAP distributions highlighted substantial variability in feature importance, particularly for humidity variables, revealing station specific uncertainty patterns in model interpretation. (4) The KAN framework results indicate strong temporal alignment and physical realism, confirming KAN’s robustness in capturing seasonal discharge dynamics and extreme flow events under monsoon influence environments. (5) In this study KAN with SHAP (SHapley additive exPlanations) is implemented for hydrological modeling under monsoon-influenced and data-limited regions such as SRB, offering improved accuracy, functional precision and efficiency compared to traditional models. …”
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Decision support system for Lespedeza cuneata production and quality evaluation: a WebGIS dashboard approach to precision agriculture by Sudhanshu S. Panda, Aftab Siddique, Thomas H. Terrill, Ajit K. Mahapatra, Eric Morgan, Andres A. Pech-Cervantes, Jan A. van Wyk

“…The model incorporates multi-criteria environmental parameters, including soil characteristics, topography, and climate variability, to generate spatially explicit recommendations. …”
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Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review by Mahmoud Saleh Al-Khafaji, Layth Abdulameer, Muthanna M. A. AL-Shammari, Najah M. L. Al Maimuri, Anmar Dulaimi, Dhiya Al‑Jumeily

“…This systematic review addresses these gaps by evaluating the transformative role of artificial intelligence (AI) in revolutionizing monitoring practices through two novel mechanisms: (1) enhanced multivariate data fidelity via Internet of Things (IoT)-sensor networks and satellite remote sensing, and (2) predictive modeling precision using machine learning (ML) algorithms. …”
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Effect of fibroblast heterogeneity on prognosis and drug resistance in high-grade serous ovarian cancer by Tingjie Wang, Lingxi Tian, Bing Wei, Jun Li, Cuiyun Zhang, Ruitao Long, Xiaofei Zhu, Yougai Zhang, Bo Wang, Guangbo Tang, Jun Yang, Yongjun Guo

“…Finally, we constructed a panel of 24 genes through statistical modeling that correlate with CXCL12-positive fibroblasts and can predict both prognosis and the response to chemotherapy in HGSOC patients. …”
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AI-Driven Particulate Matter Estimation Using Urban CCTV: A Comparative Analysis Under Various Experimental Conditions by Woochul Choi, Hongki Sung, Kyusoo Chong

“…The classification model was better than the ResNet regression model, and the hybrid DL-ML model with the post-processed XGBoost was better than the single ResNet152 model regarding AI prediction of PM. …”
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Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam by Praveen Kumar, Thanh H. Nguyen, Phong V.V. Le, Jinhui Yan, Lei Zhao, Brian F. Allan, Andrew W. Taylor-Robinson

“…Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. …”
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Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery by Yakun Zhang, Mengxin Guan, Libo Wang, Xiahua Cui, Yafei Wang, Peng Li, Shaukat Ali, Fu Zhang

“…To verify the applicability of prediction models for soybean canopy nitrogen content, a spatial distribution map of soybean canopy nitrogen content at the regional scale was drawn based on unmanned aerial vehicle (UAV) hyperspectral imaging data at the flowering and seed filling stages of soybean in the experimental area, and the spatial distribution of soybean nitrogen content was statistically analyzed. …”
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Spatiotemporal analysis of schistosomiasis and soil-transmitted helminth distribution in three highly endemic provinces in Angola. by Adam W Bartlett, Tatiana Proboste, Elsa P Mendes, Marta S Palmeirim, Ana Direito, Ricardo J Soares Magalhaes, Sergio Lopes, Susana Vaz Nery

“…Risk prediction maps were then developed using either non-spatial or spatial (using the Matérn covariance) geostatistical models depending on the presence of residual spatial autocorrelation.…”
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