Spatiotemporal Distribution Characteristics and Influencing Factors of Negative Air Ion (NAI) Concentrations in Yunnan by Xiaodong Dou, Shengfang Hou, Yinghua Shen, Qiyang Peng, Huajun Liu, Qi Yi, Yujie Liu

“…Stepwise linear regression and random forest models were employed to construct NAI concentration estimation models suitable for Yunnan Province and compare the model predictive performance, revealed the NAI concentration distribution characteristics of Yunnan Province from the temporal and spatial scales for the first time. …”
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Extending isolation by resistance to predict genetic connectivity by Robert J. Fletcher Jr, Jorge A. Sefair, Nicholas Kortessis, Roldolfo Jaffe, Robert D. Holt, Ellen P. Robertson, Sarah I. Duncan, Andrew J. Marx, James D. Austin

“…This framework extends isolation‐by‐resistance modelling to account for some common processes that can impact gene flow, which can improve predicting genetic connectivity across complex landscapes.…”
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Enhancing Hybrid Exoskeleton Performance With Spatially Distributed Asynchronous Stimulation and Ultrasound Imaging-Based Fatigue Measurements by Ashwin Iyer, Vidisha Ganesh, Mayank Singh, William Filer, Christine Cleveland, Nitin Sharma

“…The study focuses on modulating stimulation intensity in multiple distributed electrodes through a phenomenological model of SDAS to increase muscle power output. A model predictive control (MPC) approach is used to design optimal SDAS intensity profiles for knee extension tracking. …”
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Unraveling global malaria incidence and mortality using machine learning and artificial intelligence–driven spatial analysis by Md. Siddikur Rahman, Md. Abu Bokkor Shiddik

“…To inform efforts aimed at alleviating the global malaria burden, this study utilized spatial analysis, advanced machine learning (ML), and explainable AI (XAI) to identify high-risk areas, uncover key determinants, predict disease outcomes, and establish causal relationships. …”
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Evaluation of Spatial Interpolation Methods for Wind Speed and Direction: A Case Study in Split-Dalmatia County by M. Radić, L. Šerić, M. Bugarić

“…These variables are crucial for urban and spatial planning, agriculture and crop management, sports activity planning, aerial navigation, air pollution modeling and fire management. …”
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Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data—a case study of Southwest basin by Zehui Zhou, Jiaxin Jin, Bin Yong, Weidong Huang, Lei Yu, Peiqi Yang, Dianchen Sun

“…Spatially, the model’s predictions aligned closely with observed values, particularly in the middle and lower reaches of the Yarlung Zangbo River. …”
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Choosing blocks for spatial cross-validation: lessons from a marine remote sensing case study by Andy Stock, Andy Stock

“…Furthermore, even the best choice of blocks reduced but did not eliminate a bias to select too complex models. These results 1) yield practical lessons for testing spatial predictive models in remote sensing and other applications, 2) highlight the limitations of model testing by splitting a single data set, even when following elaborate and theoretically sound splitting strategies; and 3) help explain contradictions between past studies evaluating cross-validation methods and model transferability in remote sensing and other spatial applications of supervised learning.…”
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Spatial Variation and Stock Estimation of Soil Organic Carbon in Cropland in the Black Soil Region of Northeast China by Wenwen Li, Zhen Yang, Jie Jiang, Guoxin Sun

“…In addition, the Random Forest (RF) model outperformed the Cubist model in predicting the spatial distribution of SOC contents. …”
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A Criterion of Reduction Quality of Femoral Neck Fractures Based on Spatial Residual Displacements of Femoral Head by Shuang‐Shuang Cui, Li‐Kun Zhao, Jing‐Bo Yu, Jian‐Xiong Ma, Xin‐Long Ma

“…The purpose of this study is to construct a reduction quality criterion based on spatial residual displacements of 3D reconstruction and to predict the occurrence of ONFH. …”
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Computational Modelling of NF-κB Activation by IL-1RI and Its Co-Receptor TILRR, Predicts a Role for Cytoskeletal Sequestration of IκBα in Inflammatory Signalling. by David M Rhodes, Sarah A Smith, Mike Holcombe, Eva E Qwarnstrom

“…In this study we combine 3-D predictive protein modelling and in vitro experiments with in silico simulations to determine the role of the cytoskeleton in regulation of NF-κB. …”
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Shifting sands to sustainable soils: Spatial dynamics of soil water and salinity in a desert oasis ecotone by Qiqi Cao, Tao Liu, Jiangbao Xia, Junran Li, Sujith Ravi, Zhiming Xin, Huijie Xiao

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Environmental Data Analytics for Smart Cities: A Machine Learning and Statistical Approach by Ali Suliman AlSalehy, Mike Bailey

“…This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022) of data from ten monitoring stations, combined with meteorological variables. …”
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Assessing the Robustness of Multispectral Satellite Imagery with LiDAR Topographic Attributes and Ancillary Data to Predict Vertical Structure in a Wet Eucalypt Forest by Bechu K. V. Yadav, Arko Lucieer, Gregory J. Jordan, Susan C. Baker

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Site Selection of Elderly Care Facilities Based on Multi-Source Spatial Big Data and Integrated Learning by Yin Zhang, Junhong Zhu, Fangyi Li, Yingjie Wang

“…It combines topographic conditions, population distribution, economic development status, and other multi-source spatial big data at a 500 m grid scale; constructs a prediction model for the suitability of sites for elderly care facilities based on integrated learning; and carries out a comprehensive evaluation and feature importance analysis. …”
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Probabilistic analysis of active earth pressures in spatially variable soils using machine learning and confidence intervals by Tran Vu-Hoang, Tan Nguyen, Jim Shiau, Duy Ly-Khuong, Hung-Thinh Pham-Tran

“…To improve computational efficiency and prediction accuracy, machine learning models, such as Multivariate Adaptive Regression Splines (MARS), are utilized to predict failure probabilities based on key spatial variability parameters. …”
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Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects by Xiang Chen, Paula Moraga

“…With the country’s diverse climate and geographical spread, effective dengue prediction requires models that can account for both climate variations and spatial dynamics. …”
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Winnow-KAN: single-cell RNA-seq location recovery with small-gene-set spatial transcriptomics by Yuyang Zhang, Qihuang Zhang

“…In this study, we demonstrate the feasibility of leveraging a novel model architecture to address the redundancy in scRNA-seq datasets and facilitate prediction with a much smaller set of genes. …”
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Multi-sensor proximal remote sensing for cover crop biomass estimation at high and moderate spatial resolutions by Jyoti S. Jennewein, Brian W. Davis, Sarah Seehaver-Eagen, Jordan Nicolette, Josh Pittman, W. Dean Hively, Avi Goldsmith, Chris Hidalgo, Chris Reberg-Horton, Steven B. Mirsky

“…We assessed 11 species from three functional groups – grasses (n = 797), legumes (n = 264), and brassicas (n = 181) – using Random Forest (RF) models and four cross-validation strategies. The ACS-214 showed moderate to strong prediction accuracy for grasses (R2 = 0.51 – 0.64) and legumes (R2 = 0.44 – 0.76), though performance declined in leave-one-region-out analyses (R2 = 0.06 – 0.46), indicating limited spatial generalizability. …”
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Spatial Information of Somatosensory Stimuli in the Brain: Multivariate Pattern Analysis of Functional Magnetic Resonance Imaging Data by In-Seon Lee, Won-mo Jung, Hi-Joon Park, Younbyoung Chae

“…We hypothesized that performance of brain-based prediction models may vary across the types of stimuli, and neural patterns of voxels in the SI and parietal cortex would significantly contribute to the prediction of stimulated locations. …”
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A survey on spatial and temporal variations of Agricultural water quality in Gorganrood River using fuzzy rules by Ali Heshmatpour, Seyed Javad Sajjadi

“…Objective:in this research, the development of a fuzzy model is proposed to enhance the Wilcox classification system. …”
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