Global foot-and-mouth disease risk assessment based on multiple spatial analysis and ecological niche model by Qi An, Yiyang Lv, Yuepeng Li, Zhuo Sun, Xiang Gao, Hongbin Wang

“…A multi-algorithm ensemble model considering climatic, geographic, and social factors was developed to predict the suitability area for FMDV, and then risk maps of FMD for each species of livestock were generated in combination with the distribution of livestock. …”
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Multi-scenario modelling of urban spatial growth under water resources and aquatic ecological environmental constraints by Ran Xu, Lu Liu, Yaliang Liu, Xin Yi, Hui Qiu

“…A logistic model based on spatial autocorrelation can explain the driving factors of land use in the study area. …”
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Numerical modeling of electromagnetic wave propagation in spatially-varying evaporation duct conditions via 3D parabolic equation method by Hanjie Ji, Hanjie Ji, Lixin Guo, Yan Zhang, Tianhang Nie, Yiwen Wei, Jinpeng Zhang, Qingliang Li, Xiangming Guo, Yusheng Zhang

“…Conventional two-dimensional (2D) models assume homogeneous refractive index distribution along the cross-range dimension in a single propagation plane, limiting their ability to capture the 3D spatial heterogeneities present in real-world scenarios. …”
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Learning behavior aware features across spaces for improved 3D human motion prediction by Ruiya Ji, Chengjie Lu, Zhao Huang, Jianqi Zhong

“…Additionally, we design an Euclidean Kinematic-Aware Extractor utilizing temporal-wise Kinematic-Aware Attention and spatial-wise Kinematic-Aware Feature Extraction. These two modules enhance and complement each other, leading to effective human motion prediction. …”
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Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…Abstract This study presents a comprehensive hybrid modeling framework that integrates computational fluid dynamics (CFD) with machine learning (ML) techniques to predict chemical concentration distributions during the adsorption of organic compounds onto porous materials. …”
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An improved machine-learning model for lightning-ignited wildfire prediction in Texas by Qi Zhang, Cong Gao, Chunming Shi

“…Using this dataset, we developed an eXtreme gradient boosting-based machine learning model that integrates meteorological, soil, vegetative, lightning, topographic, and human activity variables to predict LIW probability. …”
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Spatial Modeling of the Impact of Human and Public Capital on Employment Convergence between Regions in Morocco: Time Period from 2010 to 2023. by Marmad Toufik, Ritahi Oussama

“…This article explores spatial convergence in Morocco to assess whether the benefits of development are equitably distributed across regions, recognizing that growth in one region can influence its neighbors. …”
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Using Structural Class Pairing to Address the Spatial Mismatch Between GEDI Measurements and NFI Plots by Nikola Besic, Sylvie Durrieu, Anouk Schleich, Cedric Vega

“…Beginning with the prediction of profile structural classes and shapes on NFI plots, the proposed method ultimately projects actual measurements onto the NFI plot sites through profile pairing within the predicted structural classes. …”
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Predictive and spatial analysis for estimating the impact of sociodemographic factors on contraceptive use among women living with HIV/AIDS (WLWHA) in Kenya: Implications for polic... by Menkeoma Laura Okoli, Samuel Alao, Somtochukwu Ojukwu, Nnadozie C Emechebe, Asuelimen Ikhuoria, Kevin E Kip

“…Spatial autocorrelation revealed significant positive clusters with weak clustering tendencies of non-contraceptive use among different levels of wealth index and education within different regions of Kenya.Conclusion These findings underscores the need for intervention programmes to further target socially disadvantaged WLWHA, which is necessary for achieving the SDGs.…”
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A lightweight hybrid model for accurate ammonia prediction in pig houses by Jacqueline Musabimana, Qiuju Xie, Hong Zhou, Ping Zheng, Honggui Liu, Tiemin Ma, Jiming Liu

“…The model improves accuracy compared to other state-of-the-art and ability for NH3 prediction.…”
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Design and development of an efficient RLNet prediction model for deepfake video detection by Varad Bhandarkawthekar, T. M. Navamani, Rishabh Sharma, K. Shyamala

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Identifying key aspects to enhance predictive modeling for early identification of schistosomiasis hotspots to guide mass drug administration. by Yewen Chen, Fangzhi Luo, Leonardo Martinez, Susan Jiang, Ye Shen

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Adaptive dynamic prediction model of mining subsidence aided by measured data by Yuanfei Chen, Jianfeng Zha, Lei Wang

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Advanced Nanopharmaceutical Intervention for the Reduction of Inflammatory Responses and the Enhancement of Behavioral Outcomes in APP/PS1 Transgenic Mouse Models by Jun Li, Dongqing Huang, Wanchen Liao, Yulin Wang, Yibiao Liu, Ping Luan

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An Interpretable Implicit-Based Approach for Modeling Local Spatial Effects: A Case Study of Global Gross Primary Productivity Estimation by S. Du, H. Huang, K. Shen, Z. Liu, S. Tang

“…In geographic machine learning tasks, conventional statistical learning methods often struggle to capture spatial heterogeneity, leading to unsatisfactory prediction accuracy and unreliable interpretability. …”
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Spatial clustering analysis combined with ensemble modeling identified potential coastal conservation hotspots of White-eyed gulls in the Red Sea by Mohanad Abdelgadir, Monif AlRashidi, Randa Alharbi, Abdulaziz S. Alatawi

“…In this study, we used a spatial clustering analysis combined with an ensemble modeling approach to predict the coastal distribution and identify potential hotspots for the White-eyed gull. …”
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Application of fluid dynamics in modeling the spatial spread of infectious diseases with low mortality rate: A study using MUSCL scheme by Nnaji Daniel Ugochukwu, Kiogora Phineas Roy, Onah Ifeanyi Sunday, Mung’atu Joseph, Aguegboh Nnaemeka Stanley

“…This study presents a comprehensive mathematical framework that applies fluid dynamics to model the spatial spread of infectious diseases with low mortality rates. …”
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Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model by Kai Ren, Yongze Song, Linchao Li, Francesco Mancini, Zhuoyao Xiao, Xueyuan Zhang, Rui Qu, Qiang Yu

“…Next, the geographically optimal zones-based heterogeneity (GOZH) model, an integration of spatial stratified heterogeneity and decision tree learning models, is used to identify determinants and their interactions on spatial disparities of wheat production. …”
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Monthly Arctic Sea‐Ice Prediction With a Linear Inverse Model by M. Kathleen Brennan, Gregory J. Hakim, Edward Blanchard‐Wrigglesworth

“…Abstract We evaluate Linear Inverse Models (LIMs) trained on last millennium model data to predict Arctic sea‐ice concentration, thickness, and other atmospheric and oceanic variables on monthly timescales. …”
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Hybrid approaches enhance hydrological model usability for local streamflow prediction by Yiheng Du, Ilias G. Pechlivanidis

“…Abstract Hydrological models are essential for predicting water flux dynamics, including extremes, and managing water resources, yet traditional process-based large-scale models often struggle with accuracy and process understanding due to their inability to represent complex, non-linear hydrometeorological processes, limiting their effectiveness in local conditions. …”
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