Spatial autocorrelation in machine learning for modelling soil organic carbon by Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa

“…This study compares various methods to account for spatial autocorrelation when predicting soil organic carbon (SOC) using random forest models. …”
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SITA: Predicting site-specific immunogenicity for therapeutic antibodies by Yewei Cun, Hao Ding, Tiantian Mao, Yuan Wang, Caicui Wang, Jiajun Li, Zihao Li, Mengdie Hu, Zhiwei Cao, Tianyi Qiu

“…This study introduces Site-specific Immunogenicity for Therapeutic Antibody (SITA), a novel computational framework that predicts B-cell immunogenicity score for not only the overall antibody, but also individual residues, based on a comprehensive set of amino acid descriptors characterizing physicochemical and spatial features for antibody structures. …”
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Urban Expansion Scenario Prediction Model: Combining Multi-Source Big Data, a Graph Attention Network, a Vector Cellular Automata, and an Agent-Based Model by Yunqi Gao, Dongya Liu, Xinqi Zheng, Xiaoli Wang, Gang Ai

“…Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. …”
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Unravelling the importance of spatial and temporal resolutions in modeling urban air pollution using a machine learning approach by Alireza Zhalehdoost, Mohammad Taleai

“…In the spatial phase, emission inventory data are aggregated at three spatial resolutions (500 m, 750 m, and 1000 m) to evaluate their effect on model performance in predicting PM and NOx concentrations. …”
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Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality by Mu Yue, Jingxin Xi

“…In this paper, we consider additive spatial autoregressive model with high-dimensional covariates. …”
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An integrated method of selecting environmental covariates for predictive soil depth mapping by Yuan-yuan LU, Feng LIU, Yu-guo ZHAO, Xiao-dong SONG, Gan-lin ZHANG

“…Environmental covariates are the basis of predictive soil mapping. Their selection determines the performance of soil mapping to a great extent, especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high. …”
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The effect of green credit policy on carbon emissions based on China’s provincial panel data by Xuerui Yang, Lianmei Zhu, Tong Wei

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A digital twin model of urban utility tunnels and its application [version 1; peer review: 2 approved] by Wu Jiansong, Hu Yanzhu, Fan chen, Cai Jitao, Fu Ming, Wang Xin, Zou Xiaofu

“…Subsequently, a natural gas leakage prediction model is developed to enable the efficient prediction of the spatial and temporal distribution in the case of leakage. …”
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Cognitive and Spatial Forecasting Model for Maritime Migratory Incidents: SIFM by Donatien Agbissoh Otote, Antonio Vázquez Hoehne

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Predicting suitable habitats for Asian elephant (Elephas maximus) in Tropical Asia under changing climatic scenarios by Kazi Al Muqtadir Abir, Biplob Dey, Mohammad Redowan, Ashraful Haque, Romel Ahmed

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Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest by Tássia Fraga Belloli, Diniz Carvalho de Arruda, Laurindo Antonio Guasselli, Christhian Santana Cunha, Carina Cristiane Korb

“…This study examined how different sample data treatments and plot sizes impact a random forest model’s performance based on RS for AGB and Corg prediction. …”
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Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Seyedeh Zeinab Shogrkhodaei, Biswajeet Pradhan, Soo-Mi Choi

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SceneDiffusion: Scene Generation Model Embedded with Spatial Constraints by Shanshan Yu, Jiaxin Zhu, Jiaqi Li, Xunchun Li, Kai Wang, Jian Tu, Danhuai Guo

“…The advancement of Geospatial Artificial Intelligence (GeoAI) offers a new technical pathway for the intelligent modeling of spatial scenes. Against this backdrop, we propose SceneDiffusion, a scene generation model embedded with spatial constraints, and construct a geospatial scene dataset incorporating spatial relationship descriptions and geographic semantics, aiming to enhance the understanding and modeling capabilities of GeoAI models for spatial information. …”
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DEBEcoMod: A dynamic energy budget R tool to predict life-history traits of marine organisms across time and space by A. Giacoletti, M. Bosch-Belmar, G. Di Bona, M.C. Mangano, B. Stechele, G. Sarà

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Predicting the geospatial distribution of Chinese rice nutrient element in regional scale for the geographical origin—A case study on the traceability of Japonica rice by Meiling Sheng, Chunlin Li, Weixing Zhang, Jing Nie, Hao Hu, Weidong Lou, Xunfei Deng, Shengzhi Shao, Xiaonan Lyu, Zhouqiao Ren, Karyne M. Rogers, Syed Abdul Wadood, Yongzhi Zhang, Yuwei Yuan

“…In this study, environmental similarity was used to establish a spatial database of rice nutrient element, and then the validity of the database was verified using the back propagation artificial neural networks modeling (BPNN). …”
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Transformer based models with hierarchical graph representations for enhanced climate forecasting by T. Bhargava Ramu, Raviteja Kocherla, G. N. V. G. Sirisha, V. Lakshmi Chetana, P. Vidya Sagar, R. Balamurali, Nanditha Boddu

“…The model integrates three key components: Spatial-Temporal Fusion Module (STFM) to capture spatiotemporal dependencies, Hierarchical Graph Representation and Analysis (HGRA) to model structured climate relationships, and Dynamic Temporal Graph Attention Mechanism (DT-GAM) to enhance temporal feature extraction. …”
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Ultrasonic Experimental Evaluation of the Numerical Model of the Internal Fluid Flow in the Kidney Cooling Jacket by Barbara Gambin, Ilona Korczak-Cegielska, Wojciech Secomski, Eleonora Kruglenko, Andrzej Nowicki

“…By comparing the numerical results with experimental data, the simplified 2D model is shown to be accurate enough to predict the flow distribution of the internal fluid velocity field within the KCJ. …”
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Multi-view fusion of diffusion MRI microstructural models: a preterm birth study by Rosella Trò, Monica Roascio, Domenico Tortora, Mariasavina Severino, Andrea Rossi, Andrea Rossi, Eleftherios Garyfallidis, Gabriele Arnulfo, Gabriele Arnulfo, Marco Massimo Fato, Shreyas Fadnavis

“…Furthermore, we investigated discriminative patterns of preterm birth using multiple analysis methods, drawn from two only seemingly divergent modeling goals, namely inference and prediction. We thus resorted to (i) a traditional univariate voxel-wise inferential method, as the Tract-Based Spatial Statistics (TBSS) approach; (ii) a univariate predictive approach, as the Support Vector Machine (SVM) classification; and (iii) a multivariate predictive Canonical Correlation Analysis (CCA).Main resultsThe TBSS analysis revealed significant differences between preterm and term cohorts in several white matter areas for multiple HARDI features. …”
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<i>Solanum macrocarpon</i> L. Ethanolic Leaf Extract Exhibits Neuroprotective and Anxiolytic Effects in Scopolamine-Induced Amnesic Zebrafish Model by Ion Brinza, Corina Guliev, Ibukun Oluwabukola Oresanya, Hasya Nazli Gok, Ilkay Erdogan Orhan, Lucian Hritcu

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Predicting Forest Evapotranspiration using Remote Sensing and Machine Learning by B. Yadav, L. K. Sharma, B. Bijarniya

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