Text Geolocation Prediction via Self-Supervised Learning by Yuxing Wu, Zhuang Zeng, Kaiyue Liu, Zhouzheng Xu, Yaqin Ye, Shunping Zhou, Huangbao Yao, Shengwen Li

“…As the mainstream approach, the deep learning-based methods follow the supervised learning paradigms, which rely heavily on a large amount of labeled samples to train model parameters. To address this limitation, this paper presents a method for text geolocation prediction without labeled samples, namely GeoSG (Geographic Self-Supervised Geolocation) model, which leverages self-supervised learning to improve text geolocation prediction in situations where labeled samples are unavailable. …”
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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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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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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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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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INFORMATION SYSTEM OF FIRE SAFETY IN EDUCATIONAL INSTITUTION by Andrey A. PETRIKIN

“…The information system role in the fire safety structure of the educational institution is considered. A model of spatial fire spread according to Monte Carlo method is offered. …”
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Bolstering spatial learning in the primary classroom: identifying the factors underlying primary teachers’ spatial pedagogical practices by Kelsey Rocha, Catherine M. Lussier, Kinnari Atit

“…Teachers’ cognition and affect can influence their pedagogical choices, which in turn can shape the development of their students’ spatial skills. Here we examined the relations between primary teachers’ spatial cognition, affect, and their inclination for choosing teaching practices, such as models and diagrams, that engage and facilitate spatial learning. …”
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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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Multidimensional assessment of the spatiotemporal evolution, driving mechanisms, and future predictions of urban heat islands in Jinan, China by Lingye Tan, Tiong Lee Kong Robert, Yan Zhang, Siyi Huang, Ziyang Zhang

“…By analyzing the evolution of LST in Jinan city, China, from 2002 to 2022, and forecasts future trends using advanced spatial analysis and predictive modeling techniques. …”
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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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Full-scale Educational Model in Arafa prayer and Educational Principles related to it by mahdiyeh keshani, abazar kafimosavi

“…The aim of the present study was to explain the full-scale educational model in the Arafa prayer and to infer educational principles based on it. …”
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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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