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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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

“…This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies, enhancing SOH prediction accuracy. …”
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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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A Global Irradiance Prediction Model Using Convolutional Neural Networks, Wavelet Neural Networks, and Masked Multi-Head Attention Mechanism by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Salah Hannechi

“…However, traditional models struggle to capture the complex spatial and temporal dependencies in irradiance data, limiting prediction accuracy under varying weather conditions. …”
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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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A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention by Huifeng Wang, Jianchuan Yin, Jianchuan Yin, Nini Wang, Lijun Wang, Lijun Wang

“…As such, the study proposes a combined prediction model. This model integrates data decomposition, dimensionality reduction, deep learning, and optimization techniques.MethodsThe model uses the variational mode decomposition (VMD) method to break down the ship’s roll motion data into components at different scales. …”
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A data-driven reduced-order model for fast prediction of resonant acoustic flow under vertical vibration based on secondary decomposition by Yuqi Gao, Ning Ma, Shifu Zhu, Pengchao Zhang, Hongxing Liu, Zhongyuan Xie

“…Accurate dimensionality reduction models are crucial for constructing real-time computational digital twin systems for process equipment. …”
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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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Prediction of Land Use Change and Carbon Storage in Lijiang River Basin Based on InVEST-PLUS Model and SSP-RCP Scenario by Jing Jing, Feili Wei, Hong Jiang, Zhantu Chen, Shuang Lv, Tengfang Li, Weiwei Li, Yi Tang

“…Previous studies have not combined different climate scenarios and land use patterns to predict carbon storage. Using scenarios from both the InVEST-PLUS model and SSP-RCP, combined with multi-source remote sensing data, this study takes the Lijiang River Basin as the study area to explore the dynamic changes in land use and carbon storage under different climate scenarios. …”
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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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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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