Edge Computing-Based Video Action Recognition Method and Its Application in Online Physical Education Teaching by Jinzhu Han, Jinjin Zhao, Yan Yue, Xinrui Che

“…Due to the impact of COVID-19, online physical education (PE) teaching has garnered increasing attention. …”
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Predicting bone metastasis risk of colorectal tumors using radiomics and deep learning ViT model by Guanfeng Chen, Wenxi Liu, Yingmin Lin, Jie Zhang, Risheng Huang, Deqiu Ye, Jing Huang, Jieyun Chen

“…The ViT model’s strength lies in its ability to capture complex spatial relationships and long-range dependencies within the imaging data, which are often missed by traditional models. …”
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Insights into the spatial epidemiology of hepatitis C infection: systematic synthesis of area-level determinants and spatiotemporal analyses by Jacob Mugoya Gizamba, Brian Karl Finch, Siqin Wang, Jeffrey D. Klausner

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Enhanced real-time Parkinson’s disease monitoring and severity prediction using a multi-faceted deep learning approach by Marreddy Naga Sabari, Deepak Ch

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The impact of spatiotemporal variability of environmental conditions on wheat yield forecasting using remote sensing data and machine learning by Keltoum Khechba, Mariana Belgiu, Ahmed Laamrani, Alfred Stein, Abdelhakim Amazirh, Abdelghani Chehbouni

“…This study aims to assess the impact of spatial and temporal heterogeneity of environmental conditions on wheat yield forecasting using machine learning models. …”
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Testing spatial transferability of species distribution models reveals differing habitat preferences for an endangered delphinid (Cephalorhynchus hectori) in Aotearoa, New Zealand by Steph Bennington, Peter W. Dillingham, Scott D. Bourke, Stephen M. Dawson, Elisabeth Slooten, William J. Rayment

“…Abstract Species distribution models (SDMs) can be used to predict distributions in novel times or space (termed transferability) and fill knowledge gaps for areas that are data poor. …”
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Unveiling Hidden Dynamics in Air Traffic Networks: An Additional-Symmetry-Inspired Framework for Flight Delay Prediction by Chao Yin, Xinke Du, Jianyu Duan, Qiang Tang, Li Shen

“…To address this challenge, this study proposes a novel hybrid predictive framework named DenseNet-LSTM-FBLS. The framework first employs a DenseNet-LSTM module for deep spatio-temporal feature extraction, where DenseNet captures the intricate spatial correlations between airports, and LSTM models the temporal evolution of delays and meteorological conditions. …”
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Temporal-spatial evolution analysis of carbon emission efficiency in the logistics industry of coastal provinces in China based on the super-efficiency SBM model by Beilei Wang, Meiling Liu, Shan Gao

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Analysis of Drought Evolution Characteristics in Haihe River Basin Based on Sub-Period Prediction Model by HAN Dongmei, JIANG Shanshan

“…In order to reduce the prediction uncertainty of future extreme climate events,a sub-period prediction model was constructed based on the daily observed precipitation data of 0.5°×0.5° provided by the China Meteorological Data Service Center and the simulated data of five global climate models (GCMs) from CMIP5.Meanwhile,the spatio-temporal evolution of drought in the Haihe River Basin (HRB) during 2020—2050 was predicted.Results show that both single GCM and multi-model ensemble average can better reproduce changes in annual average precipitation in HRB,but a large error in extreme precipitation simulation exists.The sub-period prediction model was constructed by the regression relationship at the monthly scale between the five GCMs and actually observed precipitation,and the test results show that the model has significantly improved the simulation ability of extreme precipitation in HRB.In the future,HRB tends to be humid,with moderate drought mainly appearing.Spatially,the frequency and degree of drought increase from west to east.This study aims to provide a reference for improving the ability of GCMs in simulating extreme climate events and offer ideas for decision-making for future droughts in HRB.…”
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Assessing the impact of land use and land cover change on the Densu Delta wetland using Markov chain modeling and artificial neural networks by Cynthia Laar, Kevin Buah Kofi Annan, Abass Gibrilla, Zenobia Kusi-Afrakoma, Owusu Korkor-Asante, Michael Saah-Hayford, Courage Egbi, Dickson Abdul-Wahab, Julliet Attah, Geophrey Anornu

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Predicting potential biomass production by geospatial modelling: The case study of citrus in a Mediterranean area by G.A. Catalano, P.R. D'Urso, C. Arcidiacono

“…The methodology combines Geographic Information System (GIS) tools, for data interpolation and map overlays, with Software for Assisted Habitat Modelling (SAHM) for local level simulations.The results of the different models showed accurate and spatially coherent predictions, with AUC values ranging from 0.85 to 0.90, and highest potentialities in the northern and eastern regions of the study area. …”
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Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems by Hui Wu, Zhijun Jin, Su Jiang, Hewei Tang, Joseph P. Morris, Jinjiang Zhang, Bo Zhang

“…Abstract A major challenge in the inversion of subsurface parameters is the ill‐posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. …”
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Spatiotemporal heterogeneity and its influencing factors: a perspective on the carbon emissions in China’s beef cattle industry by Yumeng Sun, Mingli Wang

“…The western region remains the core of high emissions. (3) Carbon emission dynamics indicate a trend of spreading from high-emission regions to peripheral areas, with medium- and small-scale farming regions having greater potential for emission reduction. (4) Improvements in environmental governance, mechanization, and education significantly reduce carbon emissions per unit of beef, driving emission reductions in neighboring regions through spatial spillover effects. …”
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A Meteorological Data-Driven eLoran Signal Propagation Delay Prediction Model: BP Neural Network Modeling for Long-Distance Scenarios by Tao Jin, Shiyao Liu, Baorong Yan, Wei Guo, Changjiang Huang, Yu Hua, Shougang Zhang, Xiaohui Li, Lu Xu

“…A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. …”
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Importance Sampling for Cost-Optimized Estimation of Burn Probability Maps in Wildfire Monte Carlo Simulations by Valentin Waeselynck, David Saah

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FOREST FIRE ANALYSIS FROM PERSPECTIVE OF SPATIAL-TEMPORAL USING GSTAR (p;λ_1,λ_2,…,λ_p) MODEL by Hesty Pratiwi, Nurfitri Imro'ah, Nur'ainul Miftahul Huda

“…The research process consists of the following stages: data preparation, stationarity testing, calculation of the Queen Contiguity spatial weight matrix, identification of model orders based on STACF and STPACF plots, and estimation of model parameters to predict hotspot confidence levels. …”
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MetaBiome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities by Javad Aminian-Dehkordi, Andrew Dickson, Amin Valiei, Mohammad R. K. Mofrad

“…Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes in E. coli. …”
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Stressor-driven changes in freshwater biological indicators inform spatial management strategies using expert knowledge, observational data, and hierarchical models by Sean C. Emmons, Matthew J. Cashman, Rosemary M. Fanelli, Greg J. Pond, Gregory B. Noe, Taylor E. Woods, Kelly O. Maloney

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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

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Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy by Mauro Francesco Pio Maiorano, Gennaro Cormio, Vera Loizzi, Brigida Anna Maiorano

“…Pooled AUCs indicated strong predictive performance for genomics-based (0.78), radiomics-based (0.88), and immunotherapy-based (0.77) models. …”
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