Performance Prediction and Evaluation of Biogas-Fed Solid Oxide Fuel Cell Plants Based on the Spatial and Temporal Fusion Model under Fluctuation Fuel Types and Processing Options by Shuai Ma, Run Jia, Yanan Li, Changyan Liu, Hao Guo

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A data-driven supervised machine learning approach to estimating global ambient air pollution concentrations with associated prediction intervals by Liam Jordan Berrisford, Hugo Barbosa, Ronaldo Menezes

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Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model by Dengke Xu, Ruiqin Tian, Ying Lu

“…This study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional covariate. …”
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Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models by Carlo Grillenzoni

“…Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. …”
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The role of host plants, land cover and bioclimate in predicting the invasiveness of Aromia bungii on a global scale by Enrico Ruzzier, Seunghyun Lee, Pietro Tirozzi, Valerio Orioli, Andrea Di Giulio, Olivia Dondina, Luciano Bani

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Modified STARIMA model for space-time data by Laura Šaltytė

“… In this paper we propose spatial time series model. ARIMA model class is considered for each location. …”
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A novel approach to skin disease segmentation using a visual selective state spatial model with integrated spatial constraints by Yu Bai, Hai Zhou, Hongjie Zhu, Shimin Wen, Binbin Hu, Haotian Li, Huazhang Wang, Daji Ergu, Fangyao Liu

“…Additionally, we introduce a spatially-constrained loss function that mitigates gradient stability issues by considering the distance between label and prediction boundaries. …”
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Integrative spatial and single-cell transcriptomics elucidate programmed cell death-driven tumor microenvironment dynamics in hepatocellular carcinoma by Kai Lei, Kai Lei, Yutong Zhao, Shumin Li, Jiawei Liu, Wenhao Chen, Caihong Zhou, Yi Zhang, Jinmei Tan, Jian Wu, Qi Zhou, Qi Zhou, Jiehui Tan

“…This study aims to develop a PCD scores prediction model to evaluate the prognosis of hepatocellular carcinoma (HCC) and elucidate the tumor microenvironment differences.MethodsWe analyzed transcriptomic data from 363 HCC patients in the TCGA database and 221 patients in the GEO database to develop a PCD prediction model. …”
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Numerical simulation for spray spatial distribution of swirl nozzle and its target dustfall area prediction by Cuicui Xu, Xinyu Jia, Jingjing Jing, Junpeng Wang, Qingyi Mi, Gang Zhou, Lirong Wu

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Spatial modelling of vector-borne diseases: Where? When? How many? by Mr Cedric Marsboom

“…Avia-GIS R&D team has an extensive expertise in the spatial modeling of vector-borne diseases (VBDs) to address critical concerns regarding the epidemiology and control of VBDs. …”
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A model of spatially restricted transcription in opposing gradients of activators and repressors by Michael A White, Davis S Parker, Scott Barolo, Barak A Cohen

“…This model quantitatively predicts the boundaries of gene expression within OARGs. …”
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Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models by Mohsen Mohammadzadeh, Leyla Salehi

“…When discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. …”
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Latent spectral-spatial diffusion model for single hyperspectral super-resolution by Yingsong Cheng, Yong Ma, Fan Fan, Jiayi Ma, Yuan Yao, Xiaoguang Mei

“…To address these issues, we propose a novel latent spectral-spatial diffusion model (LSDiff) for single hyperspectral SR. …”
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Mechanisms and spatial spillover effects of science and technology financial ecosystem on carbon emission reduction from multiple perspectives: evidence from China by Jinyue Zhang, Zhenglin Sun

“…Based on the panel data of 284 prefecture-level cities from 2011 to 2020, the dynamic spatial Durbin model is adopted to examine the spatial spillover effect of the STFE on carbon emission reduction and its influencing mechanism. …”
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Predictive modeling of building energy consumption and thermal comfort for decarbonization in construction and retrofitting by Sameer Algburi, Aymen Mohammed, Ibrahim Abdullah, Talib Munshid Hanoon, Hassan Falah Fakhruldeen, Otabek Mukhitdinov, Feryal Ibrahim Jabbar, Qusay Hassan, Ali Khudhair, David Kato

“…This study introduces an integrated predictive modeling framework for assessing building energy consumption and indoor thermal comfort, with a focus on supporting decarbonization efforts in both new construction and retrofit scenarios. …”
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Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor by Ran Yang, Ran Yang, Lulu Xie, Rui Wang, Yi Li, Yifei Lu, Baihui Liu, Shuyang Dai, Shan Zheng, Shan Zheng, Kuiran Dong, Kuiran Dong, Rui Dong, Rui Dong

“…A prognostic ensemble machine learning model was constructed based on the Scissor+ tumor signature to accurately predict patient RFS. …”
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Modeling of spatial spread of COVID-19 pandemic waves in Russia using a kinetic-advection model by V. V. Aristov, A. V. Stroganov, A. D. Yastrebov

“…This paper studies the development of epidemic events in Russia, starting from the third and including the most recent fifth and sixth waves. Our twoparameter model is based on a kinetic equation. The investigated possibility of predicting the spatial spread of the virus according to the time lag of reaching the peak of infections in Russia as a whole as compared to Moscow is connected with geographical features: in Russia, as in some other countries, the main source of infection can be identified. …”
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Application of a land use regression (LUR) model to the spatial modelling of air pollutants in Esfahan city by Maryam Sharifi Sadeh, Mozhgan Ahmadi Nadoushan

“…Since the air pollution is in relation to factors such as population, traffic, land use, height, road length and public transportation as the most effective factors in producing these pollutants have prepared using ArcGIS 10.2 and modeled by LUR method. The regression model was run using SPSS 19.  …”
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Spatiotemporal data modeling and prediction algorithms in intelligent management systems by Xin Cao, Chunxiao Mei, Zhiyong Song, Hao Li, Jingtao Chang, Zhihao Feng

“…The author first makes a preliminary analysis of the wireless network data (mainly the data of cellular mobile networks) obtained by Internet service providers, reveals that the data of adjacent base stations have temporal and spatial correlations, then establishes a hybrid deep learning model for spatio-temporal prediction, and proposes a new spatial model training algorithm. …”
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Multimodal neural network for enhanced protein stability prediction by integration of contact scores and spatial maps by G Gladstone Sigamani, P.M. Durai Raj Vincent

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