An Examination of Temporal and Spatial Trends of Growing Degree Days in Turkey by İlhami Doğan, Murat Karabulut

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A spatial transcriptomic atlas of the host response to oropharyngeal candidiasis by Sunna Nabeela, Hayden McSwiggin, Rubens Daniel Miserani Magalhaes, Eliciane Cevolani Mattos, Mohammad Mannan, John T. Dillon, Ashley Barbarino, Eman G. Youssef, Shakti Singh, Wei Yan, Ashraf S. Ibrahim, Heather R. Conti, Priya Uppuluri

“…We employed 10× Visium Spatial Transcriptomics, a next-generation technology that preserves the spatial integrity of infected tongue tissues, enabling high-resolution mapping of the host microenvironment during Candida infection in a murine OPC model. …”
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Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin by Qingqin Bai, Lei Wang, Yangzong Cidan

“…This paper utilizes daily precipitation data from 123 grid points in the Niyang River Basin, spanning from 2008 to 2016, to calculate rainfall erosivity using a straightforward algorithmic model. Ordinary Kriging was used to examine the spatial and temporal variations in rainfall erosivity, while Spearman’s correlation analysis was employed to examine the relationships between annual rainfall erosivity and various factors, including multi-year average precipitation, erosive rainfall, dry-season rainfall, wet-season rainfall, temperature, and elevation. …”
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Classificação numérica e modelo digital de elevação na caracterização espacial de atributos dos solos Numerical classification and digital elevation model to spatial characterizati... by João F. da Silva Júnior, Diego S. Siqueira, José Marques Júnior, Gener T. Pereira

“…<br>One of needs of modern agriculture is the prediction of spatial variability of soil properties at more detailed scales for sustainable management and optimization of management practices. …”
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Urban morphology and climate vulnerability assessment in Kuwait: A spatio-temporal predictive analysis utilizing deep neural network-enhanced markov chain models for 2050 and 2100. by Walid Al-Shaar, Xavier Lehmann, Noha Saad, Gremina Elmazi, Mohamad Al-Shaar, Christelle Tohme

“…Utilizing historical LULC data from 1985, 2005, and 2022, along with various spatial drivers, the research predicts urban expansion patterns for 2050 and 2100. …”
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Prediction, Prevention, and Control of “Overall–Local” Coal Burst of Isolated Working Faces Prior to Mining by Ming Zhang, Shiji Yang

“…Numerical simulations are used to validate the effectiveness of borehole stress relief, while field monitoring further confirms the accuracy of the proposed model, leading to the development of the “overall–local” coal burst prediction method. …”
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LinU-Mamba: Visual Mamba U-Net with Linear Attention to Predict Wildfire Spread by Henintsoa S. Andrianarivony, Moulay A. Akhloufi

“…In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. …”
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Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model by Jinghang Cai, Hui Chi, Nan Lu, Jin Bian, Hanqing Chen, Junkeng Yu, Suqin Yang

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GreenNav: Spatiotemporal Prediction of CO₂ Emissions in Paris Road Traffic Using a Hybrid CNN-LSTM Model by Youssef Mekouar, Imad Saleh, Mohammed Karim

“…By merging their outputs, we leverage both spatial and temporal dependencies, ensuring more accurate predictions. …”
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A hybrid deep learning model for predicting atmospheric corrosion in steel energy structures under maritime conditions based on time-series data by Mohamed El Amine Seghier Ben, Tam T. Truong, Christian Feiler, Daniel Höche

“…By leveraging both the feature extraction strengths of Convolutional layers, which capture spatial hierarchies from input, and the ability of Gated Recurrent Unit (GRU) layers to learn long-term dependencies, the proposed CGRU model can capture both spatial and temporal features of atmospheric corrosion data within time-series signals, resulting in precise predictions. …”
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Combination of the Improved Diffraction Nonlocal Boundary Condition and Three-Dimensional Wide-Angle Parabolic Equation Decomposition Model for Predicting Radio Wave Propagation by Ruidong Wang, Guizhen Lu, Rongshu Zhang, Weizhang Xu

“…However, the speed of computation is low because of the time-consuming spatial convolution integrals. To solve this problem, we introduce the recursive convolution (RC) with vector fitting (VF) method to accelerate the computational speed. …”
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Difference Equation Model-Based PM2.5 Prediction considering the Spatiotemporal Propagation: A Case Study of Bohai Rim Region, China by Ceyu Lei, Xiaoling Han, Chenghua Gao

“…On this basis, we propose a special difference equation model, especially the use of nonlinear diffusion equations to characterize the temporal and spatial dynamic characteristics of PM2.5 propagation between and within clusters for real-time prediction. …”
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Assessing past, present, and simulated future prediction of land use land cover changes using CA-Markov chain models with Satellite data by Sajjad Hussain, Saeed Ahmad Qaisrani, Aqil Tariq, Muhammad Mubeen, Sajid Ullah

“…Our findings indicated significant LULCC changes over the study period, including urban expansion and agricultural encroachment. CA–Markov model is calibrated and validated using observed data, ensuring accuracy in predicting spatial shifts and magnitudes of land cover alterations. …”
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Research on a hybrid deep learning model based on two-stage decomposition and an improved whale optimization algorithm for air quality index prediction by Hangyu Zhou, Yongquan Yan

“…A hybrid deep learning model is developed for AQI prediction, incorporating two-stage decomposition and hyperparameter optimization. …”
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Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data by Cesilia Mambile, Shubi Kaijage, Judith Leo

“…Leveraging this diverse, high-dimensional dataset, the ConvLSTM model engineered to capture intricate spatial and temporal relationships delivered superior performance, achieving an AUROC of 0.9785 and Accuracy 98.08%, surpassing the LSTM and CNN models. …”
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Response of Ecosystem Service Value to LULC Under Multi-Scenario Simulation Considering Policy Spatial Constraints: A Case Study of an Ecological Barrier Region in China by Chen Zhang, Zhanqi Wang, Hanwen Du, Haiyang Li

“…Policy constraints were incorporated into the scenario simulations, and an improved equivalent factor method, Markov-PLUS model, global spatial autocorrelation, and the Getis-Ord Gi* method were applied to predict and analyze LULC and ESVs under different scenarios for 2030. …”
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Convolutional Neural Networks—Long Short-Term Memory—Attention: A Novel Model for Wear State Prediction Based on Oil Monitoring Data by Ying Du, Hui Wei, Tao Shao, Shishuai Chen, Jianlei Wang, Chunguo Zhou, Yanchao Zhang

“…However, the complexity of lubricating oil monitoring data often poses challenges in extracting discriminative features, limiting the accuracy of wear state prediction. To address this, a CNN–LSTM–Attention network is specially constructed for predicting wear state, which hierarchically integrates convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal dynamics modeling, and self-attention mechanisms for adaptive feature refinement. …”
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High-Resolution Daily XCH₄ Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data by Mohamad M. Awad, Saeid Homayouni

“…To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. …”
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MaxEnt Modeling of Future Habitat Shifts of <i>Itea yunnanensis</i> in China Under Climate Change Scenarios by Jinxin Zhang, Xiaoju Li, Suhang Li, Qiong Yang, Yuan Li, Yangzhou Xiang, Bin Yao

“…The optimized model (RM = 3.0, FC = QHPT) significantly reduced overfitting risk (ΔAICc = 0) and achieved high prediction accuracy (AUC = 0.968). …”
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Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors by Qing Wang, Ke Shao, Zhibo Cai, Yingpu Che, Haochong Chen, Shunfu Xiao, Ruili Wang, Yaling Liu, Baoguo Li, Yuntao Ma

“…However, traditional methods are constrained by reliance on empirical knowledge, time-consuming processes, resource intensiveness, and spatial-temporal variability in prediction accuracy. …”
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