Coupling coordination between agricultural carbon emission efficiency and food security in China: The spatial-temporal evolution and prediction. by Xixian Zheng, Wenmei Liao

“…Additionally, a Combination Forecasting Model predicts CCD trends through 2030. The findings indicate positive trends in both ACEE and FS, albeit with significant regional disparities and a notable lag of FS behind ACEE improvement. …”
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Prediction of the daily spatial variation of stem water potential in cherry orchards using weather and Sentinel-2 data by Francisco Zambrano, Abel Herrera, Mauricio Olguín, Miro Miranda, Jesica Garrido, Andrea Miyasaka Almeida

“…The primary goal of this work is to predict the daily spatial variation of Ψs using machine learning models. …”
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Spatial-temporal Differentiation and Prediction of Extreme Weather Events and Vegetation Cover in Guizhou Province Under Climate Change by LI Xi'nan, HE Linhong, XUE Lianqing

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Spatial predictions of soil moisture across a longitudinal gradient in semiarid ecosystems using UAV and RGB sensors by Alexander Hernandez, Efrain Duarte, Peter Porter, Holden Brecht

“…Texture metrics (‘mean’ and ‘entropy’), and the Excess Green (ExG) index had high predictive power while RGB bands performed poorly. Unlike Idaho and Montana, the spatial predictions for Utah and California showed high reliability (α < 0.01). …”
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A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing by Lurui Wang, Mehdi Jadidi, Ali Dolatabadi

“…Stage 1 applies sampling and a K-nearest-neighbor kernel-density-estimation algorithm that predicts the spatial distribution of impacting particles and re-allocates weights in regions of under-estimation. …”
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Predicting Rural Industrial Transformation via Coupling Coordination Between Polder-Based Spatial Features and Industrial Development by Wenzhu Zhou, Dawei Wang, Yiwen Zhang, Hanjing Xu

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Predicting the Distribution of Mesophotic Coral Ecosystems in the Chagos Archipelago by Clara Diaz, Kerry L. Howell, Kyran P. Graves, Adam Bolton, Phil Hosegood, Edward Robinson, Nicola L. Foster

“…The goals of this study are to (1) predict the spatial distribution and extent of distinct benthic communities and MCEs in the Chagos Archipelago, central Indian Ocean, (2) test the effectiveness of a range of environmental and topography derived variables to predict the location of MCEs around Egmont Atoll and the Archipelago, and (3) independently validate the models produced. …”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics by Lele Ling, Bingrong Li, Boliang Ke, Yinjie Hu, Kaiyong Zhang, Siwen Li, Te Liu, Peng Liu, Bimeng Zhang

“…The MRG-based prognostic model was further utilized for functional analysis of the model gene set, pan-cancer analysis of genomic variations, spatial transcriptomics analysis, as well as GO and KEGG enrichment analysis. …”
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Ruxolitinib-dependent reduction of seizure load and duration is accompanied by spatial memory improvement in the rat pilocarpine model of temporal lobe epilepsy by Andrew Carrel, Eleonora Napoli, Kathryn Hixson, Jessica Carlsen, Yasmin Cruz Del Angel, Dana Strode, Nicolas Busquet, Vijay Kumar, Michael F. Wempe, Shelley J. Russek, Amy R. Brooks-Kayal

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Influence of the Human Skin Tumor Type in Photodynamic Therapy Analysed by a Predictive Model by I. Salas-García, F. Fanjul-Vélez, J. L. Arce-Diego

“…We employ a predictive PDT model and apply it to different skin tumors. …”
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Tuberculosis disease burden in China: a spatio-temporal clustering and prediction study by Jingzhe Guo, Ce Liu, Fang Liu, Erkai Zhou, Runxue Ma, Ling Zhang, Bin Luo

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Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature Prediction by Zhenxiang Bai, Zhengya Sun, Bojie Fan, An-An Liu, Zhiqiang Wei, Bo Yin

“…Deep learning has shown preliminary success in modeling the dynamic spatial-temporal dependencies within SST signals, yet it remains challenging to obtain precise SSTs due to the inherent variabilities across multiple temporal and spatial scales, driven by distinct physical processes. …”
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Predictive Deep Learning for High‐Dimensional Inverse Modeling of Hydraulic Tomography in Gaussian and Non‐Gaussian Fields by Quan Guo, Ming Liu, Jian Luo

“…In this work, we develop a novel method called HT‐INV‐NN, which combines dimensionality reduction techniques with a predictive deep learning (DL) model to estimate high‐dimensional Gaussian and non‐Gaussian channel fields. …”
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Evolution and Predictive Analysis of Spatiotemporal Patterns of Habitat Quality in the Turpan–Hami Basin by Yaqian Li, Yongqiang Liu, Yan Qin, Kun Zhang, Reifat Enwer, Weiping Wang, Shuai Yuan

“…The expansion of urban areas and unsustainable land use associated with human activities have brought about a decline in habitat quality (HQ), especially in arid regions with fragile ecosystems. A precise prediction of land use and habitat quality changes across different scenarios is crucial for the sustainable maintenance of ecological diversity. …”
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Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning by Aggrey Muhebwa, Colin J. Gleason, Dongmei Feng, Jay Taneja

“…Abstract Current machine learning methods for discharge prediction often employ aggregated basin‐wide hydrometeorological data (lumped modeling) for parametric and non‐parametric training. …”
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MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics by Brett Addison Emery, Xin Hu, Diana Klütsch, Shahrukh Khanzada, Ludvig Larsson, Ionut Dumitru, Jonas Frisén, Joakim Lundeberg, Gerd Kempermann, Hayder Amin

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Bifurcation Branch in a Spatial Heterogeneous Predator–Prey Model with a Nonlinear Growth Rate for the Predator by Lei Kong

“…A strongly coupled predator–prey model in a spatially heterogeneous environment with a Holling type-II functional response and a nonlinear growth rate for the predator is considered. …”
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From maps to models: Key concepts in Geographic Information Systems by Yohannes Shifera Daka, Kassaye Hussein, Ashenafi Yimam

“…These models help predict and analyze spatial dynamics across time by simulating real-world phenomena. …”
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