Spatial Effects and Mechanisms of the Digital Economy on Energy Efficiency: Empirical Findings from China by Rongbin Zhang, Xiaoxiao Wu, Yifei Chen

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Assessment of Geostatistical Approach to Spatial Distribution of Soil Fertility Characteristics of the Southwest of Shiraz in Different Land Uses by H.R. Owliaie, A.R. Salehi, Gh.R. Zareian

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SFSCDNet: A Deep Learning Model With Spatial Flow-Based Semantic Change Detection From Bi-Temporal Satellite Images by K. S. Basavaraju, N. Sravya, Vibha Damodara Kevala, Shilpa Suresh, Shyam Lal

“…Existing deep learning-based methods, particularly those relying on triple-branch architectures, often struggle to accurately localize and predict changes in complex spatial environments characterized by diverse land-cover types. …”
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Spatial disparities of antenatal care utilization among pregnant women in sub-Saharan Africa—Bayesian geo-additive modelling approach by Denekew Bitew Belay, Denekew Bitew Belay, Haile Mekonnen Fenta, Haile Mekonnen Fenta, Haile Mekonnen Fenta, Nigussie Adam Birhan, Najmeh Nakhaei Rad, Ding-Geng Chen, Ding-Geng Chen

“…A Bayesian geo-additive model with Besag-York-Mollié (BYM) mixed effect was found to be the best model to assess the spatial dependencies and the non-linear effects of the factors on ANC utilization among women of reproductive age. …”
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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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Prediction of Spatiotemporal Distribution of Electric Vehicle Charging Load Based on Multi-Source Information by WANG Qiang, BI Yuhao, GAO Chao, SONG Duoyang

“…The proposed charging demand gravity model optimizes users' charging station selection behavior by integrating factors such as charging station size， electricity price， and user time cost， resulting in a more reasonable spatial and temporal distribution of the charging load[Conclusions] This study constructed a spatial and temporal distribution prediction model for electric vehicle charging loads by integrating information from multiple sources. …”
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Prevalence, associated risk factors and satellite imagery analysis in predicting soil-transmitted helminth infection in Nakhon Si Thammarat Province, Thailand by Jarawadee Muenjak, Jutarat Thongrod, Chanakan Choodamdee, Pongphan Pongpanitanont, Manachai Yingklang, Tongjit Thanchomnang, Sakhone Laymanivong, Penchom Janwan

“…This study aimed to assess the prevalence and associated factors of STH infections among schoolchildren in Thasala District, Nakhon Si Thammarat Province, Thailand, and to develop a predictive model for identifying high-risk areas using satellite imagery data. …”
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Attention Mechanism with Spatial-Temporal Joint Deep Learning Model for the Forecasting of Short-Term Passenger Flow Distribution at the Railway Station by Zhicheng Dai, Dewei Li, Shiqing Feng

“…Emerging deep learning models offer valuable insights for accurately predicting passenger flow distribution. …”
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Modelling of spatially correlated weather-based electricity forecasting using combined frequency-based signal decomposition with optimized boosting approach by Indra A. Aditya, Didit Adytia

“…The primary contribution is a spatially correlation-driven feature selection technique to choose ideal weather input sites, coupled with the extraction of predominant frequency components from the load signal to enhance model input. …”
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Nonlocal fractional MGT-non-Fourier photothermal model with spatial and temporal nonlocality for controlling the behavior of semiconductor materials with spherical cavities by Mofareh Alhazmi, Ahmed E. Abouelregal

“…In this work, we present the nonlocal Moore-Gibson-Thompson photothermal (NMGTPT) theory, a novel framework that integrates spatial and temporal nonlocality to address limitations in both traditional and advanced thermoelastic models. …”
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Modelling the Spatial Distribution of <i>Dosidicus gigas</i> in the Southeast Pacific Ocean at Multiple Temporal Scales Based on Deep Learning by Mingyang Xie, Bin Liu, Xinjun Chen, Wei Yu, Jintao Wang, Jiawen Xu

“…With the advent of the big data era in ocean remote sensing and fisheries, there is a growing demand for finer temporal scales to predict spatial distribution of the jumbo flying squid (<i>Dosidicus gigas</i>). …”
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High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau by Zhenghao Li, Qiangqiang Yuan, Xin Su

“…As a key climate variable, soil moisture plays a crucial role in drought detection, flood warning, and crop yield prediction. In recent years, the demand for high-spatial-resolution soil moisture has increased, particularly in environmental management. …”
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Assessing spatial soil erosion under climate change using SWAT model in a semi-arid watershed of northeastern Tunisia by Fathia Jarray, Taoufik Hermassi, Haykel Sellami, Mohamed Ali Ben Abdallah, Mohamed Mechergui

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An EG-Tree Model Incorporating Spatial Heterogeneity for Analyzing Multifactorial Coupling Effects on Carbon Emissions Across Industries and Regions in China by Jinrui Zang, Xin Hu, Kun Qie, Zian Zhang, Shi Zhang

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Efficient room-level heat load prediction in buildings using spatiotemporal distribution characteristics by Xin Tan, Kaixuan Xu, Yahui Wang, Qihui Yu, Yongheng Yu, Guoxin Sun

“…A thermodynamic model built with DesignBuilder and a ResGRU neural network enables overall heat load prediction, with spatiotemporal matrix decomposition ensuring rapid room-level estimations. …”
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Geospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, Nigeria by Joel Efiong, Eme Efiong, Oluyemi Akintoye, Ebin Inah, Obianuju Awan, Francis Ogban

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Survival Prediction of Esophageal Cancer Using 3D CT Imaging: A Context-Aware Approach With Non-Local Feature Aggregation and Graph-Based Spatial Interaction by Fuce Guo, Chen Huang, Shengmei Lin, Yongmei Dai, Qianshun Chen, Shu Zhang, Xunyu XU

“…In the current study, we aimed to develop an effective EC survival risk prediction using only 3D computed tomography (CT) images.The proposed model consists of two essential components: 1) non-local feature aggregation module(NFAM) that integrates visual features from tumor and lymph nodes at both local and global scales, 2) graph-based spatial interaction module(GSIM) that explores the latent contextual interactions between tumors and lymph nodes.The experimental results demonstrate that our model achieves superior performance compared to state-of-the-art survival prediction methods, emphasizing its robust predictive capability. …”
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Measuring and modelling functional moat area in perennially ice-covered Lake Fryxell, Antarctica by Michael S. Stone, Mark R. Salvatore, Hilary A. Dugan, Madeline E. Myers, Peter T. Doran

“…Finally, we developed a predictive model based on readily available climate data, allowing moat area to be predicted beyond the limits of the satellite-based records. …”
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Modeling the effects of land use change on agricultural carrying capacity and food security by R. Harini, R. Rijanta, E.H. Pangaribowo, R.F. Putri, I. Sukri

“…Predictions of spatial land changes will reveal changes in land function, carrying capacity and food security between regions.METHODS: Land changes were studied using remote sensing imagery-based mapping methods and spatial simulations using the cellular automata approach. …”
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The Application of Principal Component Analysis and the Wilson Model in Urban Economics by Yiwei Chen, Congbin Guo, Junhao Fu

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