Spatial and social determinants of tuberculosis in the Brazilian Amazon: a five-year multilevel and cluster-based analysis in Pará state, 2018-2022 by C. Andrade-Sales, P. A. Mendonça-Cavalcante, A. P. Simões-Castro, M. P. Moreira-Sena, A. P. Galvão-Fonseca, A. G. N. Cardoso-Mello, C. A. Abreu-Alberio, J. L. Fernandes-Vieira, L. W. Pereira-Sena

“…The findings underscore the importance of integrating spatial epidemiology with multilevel modeling to uncover both individual and territorial determinants of tuberculosis. …”
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Linear attention based spatiotemporal multi graph GCN for traffic flow prediction by Yanping Zhang, Wenjin Xu, Benjiang Ma, Dan Zhang, Fanli Zeng, Jiayu Yao, Hongning Yang, Zhenzhen Du

“…This study introduces the Linear Attention Based Spatial-Temporal Multi-Graph Convolutional Neural Network (LASTGCN), a novel deep learning model tailored for traffic flow prediction. …”
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Spatial Change of Dominant Baltic Sea Demersal Fish Across Two Decades by Liam MacNeil, Frane Madiraca, Saskia Otto, Marco Scotti

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Reviewing the complexity of endogenous technological learning for energy system modeling by Johannes Behrens, Elisabeth Zeyen, Maximilian Hoffmann, Detlef Stolten, Jann M. Weinand

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Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration by Pin Chen, Xiyue Wang, Zexia Yang, Changfeng Shi

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Spatial prediction and visualization of PM2.5 susceptibility using machine learning optimization in a virtual reality environment by Seyed Vahid Razavi-Termeh, Jalal Safari Bazargani, Abolghasem Sadeghi-Niaraki, X. Angela Yao, Soo-Mi Choi

“…The evaluation results of the VR systems from the Virtual Reality Neuroscience Questionnaire (VRNQ) and System Usability Scale (SUS) for spatial visualization showed that they had high graphics capabilities and equipment for the spatial prediction of PM2.5.…”
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A New Prediction Model of Dam Deformation and Successful Application by Shuangping Li, Bin Zhang, Meng Yang, Senlin Li, Zuqiang Liu

“…In view of the poor accuracy of the monitoring data, which reflect the overall deformation response in the current dam monitoring practices, this paper proposes an innovative solution of ensemble empirical mode decomposition and a wavelet noise reduction method. A high-precision prediction model considering spatial correlation is constructed. …”
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Model of Problem-Based Learning in Geography: Focusing on Societal Dynamics to Enhance Spatial Thinking Skills by Fadjarajani Siti, As’ari Ruli, Putri Anita Eka

“…This article explores how problem-based learning (PBL) can enhance geography education by improving spatial thinking through social dynamics. …”
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The effect of the cube model on visual-spatial intelligence and learning the skill of spiking in volleyball for female students by Alyaa Hussein Farhan, Tahseen Husni Tahseen, Badra Malik Shihab, Maher Amer Jabar, Nahidah Abd Zaid Aldulimey, Suhad Qassem Saeed Al-Mousawi, Nidaa Yasir Farhood

“…The researchers attribute the reason for these differences for the control group to the method followed in the educational units for the members of this group. Conclusions: Using the cube model contributed in a positive and effective way to developing visual-spatial intelligence and learning the skill of spiking the volleyball for the members of the experimental group. …”
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Enhancing Preschool Spatial Skills: A Comprehensive Intervention Using Digital Games and Hands-On Activities by Ashley E. Lewis Presser, Emily Braham, Regan Vidiksis

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Application of Radionuclides Migration Software(MNS) in Radionuclides Diffusion Modeling by Yao LI, Rui-hao LI, Duo ZHOU, Xi CHEN, Yu-wei XU, Bo WANG

“…Pre-processing involves the construction of spatial models and parameter assignment, such as porosity, mineral density, radionuclide diffusion and migration parameters, and water flow velocity. …”
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Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning by Alireza Mohammadi, Bardia Mashhoodi, Ali Shamsoddini, Elahe Pishgar, Robert Bergquist

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Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models. by Michael J Tildesley, Sadie J Ryan

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A Combined Model for Simulating the Spatial Dynamics of Epidemic Spread: Integrating Stochastic Compartmentalization and Cellular Automata Approach by Murad Bashabsheh

“…The model presented in this paper is designed to simulate the spatial distribution of diseases in a spatially structured population. …”
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Predictive Deep Learning for High‐Dimensional Inverse Modeling of Hydraulic Tomography in Gaussian and Non‐Gaussian Fields by Quan Guo, Ming Liu, Jian Luo

“…In this work, we develop a novel method called HT‐INV‐NN, which combines dimensionality reduction techniques with a predictive deep learning (DL) model to estimate high‐dimensional Gaussian and non‐Gaussian channel fields. …”
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A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background by Jianyong Chai, Limin Jia, Jianfeng Liu, Enguang Hou, Zhe Chen

“…The model leverages the strengths of GCNs in capturing spatial correlations and LSTM networks in processing temporal dynamics, offering a robust framework for accurate displacement prediction. …”
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A novel hybrid machine learning approach for δ13C spatial prediction in polish hard-water lakes by Himan Shahabi, Ataollah Shirzadi, Alicja Ustrzycka, Natalia Piotrowska, Janusz Filipiak, Marzieh Hajizadeh Tahan

“…For the first time, this model is used to predict the spatial prediction of a stable isotope in Polish lakes. …”
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Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas by WANG Hanwei, ZENG Linlan, ZHAO Mimi

“… ‍Objective‍　‍To establish and validate a multimodal MRI radiomics model based on intratumoral and peritumoral heterogeneity for prediction of spatial pattern of locally recurrent high-grade gliomas (HGGs). …”
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Spatial-Temporal Distribution Prediction of Charging Load of Electric Truck Cluster Considering Battery Loss Under Path Planning by CUI Jinghao, ZHANG Yi, ZHANG Zhichao, YU Yang

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Spatial Prediction of High-Risk Areas for Asthma in Metropolitan Areas: A Machine Learning Approach Applied to Tehran, Iran by Alireza Mohammadi, Elahe Pishgar, Juan Aguilera

“…Three ensemble machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were applied to model and predict asthma risk. A Negative Binomial Regression Model (NBRM) identified seven key predictors: population density, unemployment rate, particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), neighborhood deprivation index, and road intersection density. …”
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