Integrating proximal geophysical sensing and machine learning for digital soil mapping: Spatial prediction and model evaluation using a small dataset by Danilo César de Mello, Gustavo Vieira Veloso, Murilo Ferre de Mello, Marcos Guedes de Lana, Isabelle de Angeli Oliveira, Fellipe Alcantara de Oliveira Mello, Rafael Gomes Siqueira, Lucas Carvalho Gomes, Elpídio Inácio Fernandes-Filho, Carlos Ernesto Gonçalves Reynaud Schaefer, Márcio Rocha Francelino, Emilson Pereira Leite, Tiago Osório Ferreira, José Alexandre Melo Demattê

“…In this research, we aimed to model and predict the spatial distribution of soil geophysical properties using parent material and terrain attributes with machine learning algorithms. …”
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SpatConv Enables the Accurate Prediction of Protein Binding Sites by a Pretrained Protein Language Model and an Interpretable Bio-spatial Convolution by Mingming Guan, Jiyun Han, Shizhuo Zhang, Hongyu Zheng, Juntao Liu

“…Traditional protein binding site prediction models usually extract residue features manually and then employ a graph or point-cloud-based architecture borrowed from other fields. …”
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Analysis and Prediction of Spatial and Temporal Land Use Changes in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains by Xiaoxu He, Zhaojin Yan, Yicong Shi, Zhe Wei, Zhijie Liu, Rong He

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Spatial and Temporal Characteristics of Land Use Changes in the Yellow River Basin from 1990 to 2021 and Future Predictions by Yali Cheng, Yangbo Chen

“…Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. …”
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Modeling and mapping spatial distribution of baseline soil organic carbon stock, a case of West Hararghe, Oromia Regional State, Eastern Ethiopia by Martha Kidemu Negassa, Mitiku Haile, Gudina Legesse Feyisa, Lemma Wogi, Feyera Merga

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Revisiting the "satisfaction of spatial restraints" approach of MODELLER for protein homology modeling. by Giacomo Janson, Alessandro Grottesi, Marco Pietrosanto, Gabriele Ausiello, Giulia Guarguaglini, Alessandro Paiardini

“…The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. …”
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Spatial Autoregressive Modeling on Linear Mixed Models for Dependency Between Regions by Timbang Sirait

“…In this study, we are concerned with the spatial lag or SAR models because dependency between variables of interest is easier to predict. …”
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Combining habitat selection, behavioural states, and individual variation to predict fish spatial usage near a barrier by Rachel Mawer, Jelger Elings, Stijn P. Bruneel, Ine S. Pauwels, Eliezer Pickholtz, Renanel Pickholtz, Johan Coeck, Peter L.M. Goethals

“…Model results were explored to assess the benefits of including behavioural state and understand state-specific habitat preferences, then cross-validated and used to develop an individual based model to predict fish spatial usage. …”
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I.S.G.E.: An Integrated Spatial Geotechnical and Geophysical Evaluation Methodology for Subsurface Investigations by Christos Orfanos, Konstantinos Leontarakis, George Apostolopoulos, Ioannis E. Zevgolis, Bojan Brodic

“…The automatically derived 3D models, depicting the spatial distribution of specific geotechnical parameters, provide engineers with an additional interpretation tool for better understanding the subsurface conditions of a survey area.…”
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Spatial analysis and prediction of psittacosis in Zhejiang Province, China, 2019–2024 by Zheyuan Ding, Haocheng Wu, Chen Wu, Kui Liu, Qinbao Lu, Xinyi Wang, Tianying Fu, Junjie Li, Ke Yang, Queping Song, Junfen Lin

“…This study aimed to characterize the epidemiological patterns and spatiotemporal distribution of psittacosis in Zhejiang Province, China, and to identify high-risk clusters through predictive modeling.MethodsWe conducted a comprehensive analysis of reported psittacosis cases in Zhejiang Province from January 2019 to June 2024. …”
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Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province by Zhongxia Yu, Mingcong Zhang, Yingce Zhan, Yongxia Guo, Yuxian Zhang, Xiaoyan Liang, Chen Wang, Yuxin Fan, Mingfen Shan, Haiqing Guo, Wei Zhou

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Prediction Modeling and Driving Factor Analysis of Spatial Distribution of CO₂ Emissions from Urban Land in the Yangtze River Economic Belt, China by Chao Wang, Jianing Wang, Le Ma, Mingming Jia, Jiaying Chen, Zhenfeng Shao, Nengcheng Chen

“…Based on socioeconomic grid data, such as nighttime lights and the population, this study proposes a spatial prediction method for CO₂ emissions from urban land using a Long Short-Term Memory (LSTM) model with added fully connected layers. …”
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STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction by Ruizhe Feng, Shanshan Jiang, Xingyu Liang, Min Xia

“…Additionally, deep learning methods, especially temporal convolution networks and graph attention networks, have been introduced in this area and have achieved significant improvements in both stock price prediction and portfolio optimization. Therefore, this study proposes a Spatial–Temporal Graph Attention Network (STGAT) that integrates STL decomposition components and graph structures to model both temporal patterns and asset correlations. …”
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New multifactor spatial prediction method based on Bayesian maximum entropy by YANG Yong, ZHANG Chutian, HE Liyuan

“…Currently, the spatial distribution of soil properties is usually predicted with classical geostatistics or environmental correlation. …”
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A reliability model to predict failure behaviour of overlying strata in groundwater-rich coal mine by Ruirui Li, Xiaowei Hou, Luwang Chen, Yingxin Wang, Fuyou Huang, Lanting Wang

“…In this study, a reliability model with consideration of spatial variability and uncertainty of strength parameters was proposed to predict the failure behaviour of overlying strata during coal mining in groundwater-rich coalfields. …”
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Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model by Yilin Chen, Haitao Zheng

Subjects: “…house price prediction…”
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Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation by Eve Laroche-Pinel, Vincenzo Cianciola, Khushwinder Singh, Gaetano A. Vivaldi, Luca Brillante

“…The results of the study demonstrate that machine learning is accurate in predicting vine water status spatially within the training measurement dates with low errors (NRMSEΨstem = 2.7 %, NRMSEgs = 16.2 %, NRMSEAN = 11.2 %) and a high degree of accuracy (R2 greater than 0.8 in the prediction of all three measurements) as assessed by block-out cross-validation. …”
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Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation. by Janis Keck, Caswell Barry, Christian F Doeller, Jürgen Jost

“…In spatial cognition, the Successor Representation (SR) from reinforcement learning provides a compelling candidate of how predictive representations are used to encode space. …”
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Enhancing land use planning through integrating landscape analysis and flood inundation prediction Bekasi City’s in 2030 by Novia Fitriyati, Hadi Susilo Arifin, R. L. Kaswanto, Marimin

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Extended Generalized Skew Laplace Random Field: Spatial Autoregressive and Moving Average Model for Prediction of Missing Data in Skew and Heavy Tailed Data by Mohammad Mehdi Saber, Alireza Nematollahi, Mohsen Mohammadzadeh

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