Beyond financial incentives: a quantitative study on spatial stigma and Puerto Rican physician migration to the United States by Nelson Varas-Díaz, Sheilla R. Madera, Mark Padilla, Torsten B. Neilands, John Vertovec, Kariela Rivera-Bustelo, Adrián J. Santiago-Santiago, Claudia A. Mercado Ríos, Genevieve Reid, Kevin Grove, Alíxida Ramos Pibernus

“…This study quantitatively examines the role of push factors, pull factors, and spatial stigmatisation in physician migration from Puerto Rico. …”
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Object based Markov random field model for hierarchical semantic segmentation of remote sensing imagery by Jun Wang, Chen Zheng, Haoyu Fu, Yili Zhao, Qinling Dai, Xin Huang, Junfeng Xie, Leiguang Wang

“…Traditional object-based methods face inherent limitations in modeling these complex relationships. To overcome these limitations, we proposed a novel object-based Markov random field (OMRF) model for hierarchical semantic segmentation. …”
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A method integrating a non-stationary random field for constrained inversion of CSAMT data by Qianwei DAI, Luyao GUO, Yun WU, Zhexian XIONG, Dan DUAN, Zhonglin BAO, Hongfei WU, Fengyun HAO

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Statistical Analysis of Tract-Tracing Experiments Demonstrates a Dense, Complex Cortical Network in the Mouse. by Rolf J F Ypma, Edward T Bullmore

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Coupled intelligent prediction model for medium- to long-term runoff based on teleconnection factors selection and spatial-temporal analysis. by Jintao Li, Ping Ai, Chuansheng Xiong, Yanhong Song

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Multi-environment trials data analysis: linear mixed model-based approaches using spatial and factor analytic models by Tarekegn Argaw, Berhanu Amsalu Fenta, Habtemariam Zegeye, Girum Azmach, Assefa Funga

“…The results clearly demonstrate that linear mixed model-based approaches, especially the spatial + G × E analysis excel in capturing complex spatial plot variation and G × E effects in MET data by effectively integrating spatial and FA models. …”
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Spatial transcriptomics reveals prognostically LYZ + fibroblasts and colocalization with FN1 + macrophages in diffuse large B-cell lymphoma by Liyuan Dai, Ning Lou, Liling Huang, Lin Li, Le Tang, Yuankai Shi, Xiaohong Han

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How to improve forest biodiversity management by comparing broad-scale stands' structural spatial heterogeneity between two forests by Elias Milios, Kyriachi Kitikidou

“…The approach provides a statistically sound and flexible tool for comparing structural spatial heterogeneity across different forests, potentially guiding practices aimed at enhancing stand complexity and ecological resilience. …”
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Comprehensive Analysis of a Parcel-Level Crop Mapping (PARCM) Framework From the Perspective of Spatial Heterogeneity and Spatiotemporal Transferability by Chen Wang, Jiaqi Chen, Cheng Cai, Mengyu Liu, Ziming Wang, Haoyu Zhang, Huanxue Zhang

“…Our framework can maintain the spatial consistency of crops and parcels while adapting to varying spatiotemporal complexities, providing a strong solution for parcel-level crop mapping.…”
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Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression by Qile Ding, Yiren Wang, Yu Zheng, Fengyang Wang, Shudong Zhou, Donghui Pan, Yuchun Xiong, Yi Zhang

“…The findings suggest that the proposed model effectively captures complex subsurface spatial relationships, offering a reliable and precise solution for performing spatial interpolation tasks.…”
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The evaluation model of engineering practice teaching with complex network analytic hierarchy process based on deep learning by Xianlong Han, Xiaohui Chen

“…RNN is used to capture time series information, and CNN is used to extract spatial features. Through the hierarchical analysis of complex network, the relationship between different teaching elements is revealed and the hierarchical structure is constructed. …”
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Attention Mechanism with Spatial-Temporal Joint Deep Learning Model for the Forecasting of Short-Term Passenger Flow Distribution at the Railway Station by Zhicheng Dai, Dewei Li, Shiqing Feng

“…However, due to the complexity and randomness of passenger flow and the unclear spatial-temporal correlation between functional areas within the station, predicting the spatiotemporal distribution dynamics of inflow and future short-term distribution trends is challenging. …”
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Hidden origami in Trypanosoma cruzi nuclei highlights its non-random 3D genomic organization by Natália Karla Bellini, Pedro Leonardo Carvalho de Lima, David da Silva Pires, Julia Pinheiro Chagas da Cunha

“…Our data indicate 3D clustering of tRNA loci, likely optimizing transcription by RNA polymerase III, and a complex interaction between spliced leader RNA and 18S rRNA loci, suggesting a link between RNA polymerase I and II machineries. …”
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High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia by I Gede Nyoman Mindra Jaya, Henk Folmer

“…It uses the MSTS-generated PM_2.5 predictions for the sampled spatiotemporal units and observations of the covariate’s altitude, population density, and rainfall for sampled and non-samples spatiotemporal units. For the spatially correlated random effects, we apply a first-order random walk process. …”
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GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data. by Borbala Mifsud, Inigo Martincorena, Elodie Darbo, Robert Sugar, Stefan Schoenfelder, Peter Fraser, Nicholas M Luscombe

“…Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. …”
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The Sloping Mire Soil-Landscape of Southern Ecuador: Influence of Predictor Resolution and Model Tuning on Random Forest Predictions by Mareike Ließ, Martin Hitziger, Bernd Huwe

“…The recursive partitioning algorithm Random Forest was used to predict the spatial water stagnation pattern and the thickness of the organic layer from terrain attributes. …”
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Random Undersampled Digital Elevation Model Super-Resolution Based on Terrain Feature-Aware Deep Learning Network by Ziqiang Huo, Meng Xi, Jingyi He, Zhengjian Li, Jiabao Wen

“…However, due to the limitation of measurement cost and complex terrain, the collected DEMs often have randomly missing undersampled points and low sampling density. …”
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Enhancing flood susceptibility mapping in Sana’a, Yemen with random forest and eXtreme gradient boosting algorithms by Yahia Alwathaf, Ahmed M. Al-Areeq, Yousef A. Al-Masnay, Ali R. Al-Aizari, Nabil M. Al-Areeq

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Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation by Wenzhong Li, Chengshuai Liu, Caihong Hu, Chaojie Niu, Runxi Li, Ming Li, Yingying Xu, Lu Tian

“…Abstract Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. …”
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A High-Granularity, Machine Learning Informed Spatial Predictive Model for Epidemic Monitoring: The Case of COVID-19 in Lombardy Region, Italy by Lorenzo Gianquintieri, Andrea Pagliosa, Rodolfo Bonora, Enrico Gianluca Caiani

“…The model integrates spatial filtering and machine learning (random forest classifier) to categorize municipalities into five epidemic scenarios: from no diffusion to active spread with increasing trends. …”
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