Comparison of stochastic and deterministic models for gambiense sleeping sickness at different spatial scales: A health area analysis in the DRC. by Christopher N Davis, Ronald E Crump, Samuel A Sutherland, Simon E F Spencer, Alice Corbella, Shampa Chansy, Junior Lebuki, Erick Mwamba Miaka, Kat S Rock

“…The spatial heterogeneity in cases is reflected in modelling results, where we predict that under the current intervention strategies, the health area of Kinzamba II, which has approximately one third of the health zone's cases, will have the latest expected year for EoT. …”
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AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis. by Michael Alexander Ramirez Sierra, Thomas R Sokolowski

“…In this study, we present a multi-scale, spatial-stochastic simulation framework for mouse embryogenesis, focusing on inner cell mass (ICM) differentiation into epiblast (EPI) and primitive endoderm (PRE) at the blastocyst stage. …”
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Within-Field Temporal and Spatial Variability in Crop Productivity for Diverse Crops—A 30-Year Model-Based Assessment by Ixchel Manuela Hernández-Ochoa, Thomas Gaiser, Kathrin Grahmann, Anna Maria Engels, Frank Ewert

“…The results revealed that the spatial variability in crop yield was higher than the temporal variability for most crops, except for sunflower. …”
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Analysis of Spatial Suitable Habitats of Four Subspecies of <i>Hippophae rhamnoides</i> in China Based on the MaxEnt Model by Mengyao He, Fanyan Ma, Junjie Ding, Panxin Niu, Cunkai Luo, Mei Wang, Ping Jiang

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Evaluation of Spatial Matching Between Water and Soil Resources in Shiyang River Basin Based on FLUS-InVEST Model by HOU Hui-min, WANG Hui, WANG Peng-quan, CAO Jin-jun

“…[Methods] Using the FLUS model, this study simulated the spatial patterns of land use of the Shiyang River Basin in 2035 under three scenarios: cropland protection, natural development, and ecological conservation. …”
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Spatial and Temporal Variability of Chlorophyll-a and the Modeling of High-Productivity Zones Based on Environmental Parameters: a Case Study for the European Arctic Corridor by Kuzmina Sofia, Lobanova Polina, Chepikova Svetlana Sergeevna

“…Our study aims to create models that predict the position of high chlorophyll-a concentration (Chl-a) zones in the European Arctic Corridor (the Barents, Norwegian and Greenland Seas) to monitor these changes. …”
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Modeling land cover changes using an enhanced Markov-future land use simulation model with spatial distribution considerations: a case study in the Yellow River Basin by Jianchen Zhang, Heying Li, Hanwen Zhang, Jiayao Wang, Guangxia Wang, JianWei Xu, Haohua Zheng, HuiLing Ma

“…The traditional Markov-future land use simulation (FLUS) model for land use prediction primarily emphasizes the quantity changes and spatial distribution of land use types, but it neglects the influence of their inherent spatial characteristics on the prediction precision. …”
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Evaluation of the relationships between physico-chemical parameters and the abundance of Vibrio spp. in blue crabs (Callinectes sapidus) and seawater from the Maryland Coastal Bays by Jasmine Smalls, John Jacobs, Howard Townsend, Paulinus Chigbu, Salina Parveen

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Approaches to Proxy Modeling of Gas Reservoirs by Alexander Perepelkin, Anar Sharifov, Daniil Titov, Zakhar Shandrygolov, Denis Derkach, Shamil Islamov

“…On average, the ST-GNN method reduces computational time by a factor of 4.3 compared to traditional hydrodynamic models, with a median predictive error not exceeding 10% across diverse datasets, despite variability in specific scenarios. …”
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Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods by Shiyang Cheng, Qihang Zhang, Hao Min, Wenhui Jiang, Jueting Liu, Chunsheng Liu, Zehua Wang

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Temporal and Spatial Dynamics of Rodent Species Habitats in the Ordos Desert Steppe, China by Rui Hua, Qin Su, Jinfu Fan, Liqing Wang, Linbo Xu, Yuchuang Hui, Miaomiao Huang, Bobo Du, Yanjun Tian, Yuheng Zhao, Manduriwa

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Predicting the Distribution of Mesophotic Coral Ecosystems in the Chagos Archipelago by Clara Diaz, Kerry L. Howell, Kyran P. Graves, Adam Bolton, Phil Hosegood, Edward Robinson, Nicola L. Foster

“…The goals of this study are to (1) predict the spatial distribution and extent of distinct benthic communities and MCEs in the Chagos Archipelago, central Indian Ocean, (2) test the effectiveness of a range of environmental and topography derived variables to predict the location of MCEs around Egmont Atoll and the Archipelago, and (3) independently validate the models produced. …”
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Study on Key Influencing Factors of Carbon Emissions from Farmland Resource Utilization in Northeast China Under the Background of Energy Conservation and Emission Reduction by Mulin Sun, Yuhao Fu, Mingyao Sun, Run Huang, Yun Teng

“…A gray prediction model is constructed to predict the carbon emissions from the utilization of farmland resources in the next 10 years, and the logarithmic mean Divisia index model is used to analyze the effects of the various influencing factors on the carbon emissions from farmland utilization. …”
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Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

“…In this study, we propose a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Spatial Attention Mechanism (SAM) hybrid deep learning model designed for spatial landslide susceptibility prediction. …”
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Spatial Distribution Pattern of Aromia bungii Within China and Its Potential Distribution Under Climate Change and Human Activity by Liang Zhang, Ping Wang, Guanglin Xie, Wenkai Wang

“…Hot spot distribution areas were identified using Getis‐Ord Gi*. An optimized MaxEnt model was used to predict the potential distribution areas of A. bungii within China under four shared economic pathways by combining multivariate environmental data: (1) prediction of natural environmental variables predicted under current climate models; (2) prediction of natural environmental variables + human activities under current climate models; and (3) prediction of natural environmental variables under the future climate models (2050s and 2070s). …”
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Improved digital mapping of soil texture using the kernel temperature–vegetation dryness index and adaptive boosting by Xu Zhai, Yuzhong Liu, Yuanyuan Hong, Yunjie Yang, Pengju Wang, Zhicheng Ye, Xiaoyan Liu, Tianlong She, Lihui Wang, Chen Xu, Lili Zhang, Qiang Wang

“…In this study, we collected 399 soil samples collected from Mingguang City in southeast China and made spatial predictions of soil texture based on remote sensing indices such as the kernel normalized difference vegetation index computed from Landsat8 data and topographic attributes computed via digital elevation model as environmental covariates. …”
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Alzheimer’s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models by Sameer Abbas, Mustafa Yeniad, Javad Rahebi

“…The selected features were classified using a CNN-LSTM model, capturing both spatial and temporal patterns. …”
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Optimizing Clinical Management of COVID-19: A Predictive Model for Unvaccinated Patients Admitted to ICU by Ahmed Farhan, Khouloud Ayed, Sara Zayati, Rym Akrout, Akram Dlala, Amal Abouda, Nesrine Zoghlami, Halima Mahjoubi, Iheb Labbane, Asma Gati

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Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model by Yiqiang Liu, Luming Shen, Xinghui Zhu, Yangfan Xie, Shaofang He

“…This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. …”
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Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation by Yue Hu, Yitong Ding, Wenjing Jiang

“…This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications.…”
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