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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Prediction of litchi flower induction in South China region based on the CMIP6 climate model by HOU Wei, ZHANG Liuhong, ZHANG Lei, LUAN Lan, ZHANG Mingjie, WANG Xiuzhen, ZHANG Hui

“…Additionally, we selected the average ensemble of four climate models (CanESM5, FGOALS-g3, GFDL-CM4, and IPSL-CM6A-LR) from CMIP6 to assess the spatial and temporal evolution characteristics of the commercial cultivation limits and flower formation induction of litchi under two climate scenarios, comparing the base period with future projections in the South China region. …”
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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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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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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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Improving GNSS precise point positioning with tropospheric constraints from data-driven numerical weather prediction model by Yuanfan Deng, Wu Chen, Junsheng Ding, Ahmed El-Mowafy, Duojie Weng, Long Tang, Lei Bai, Xiaolong Mi

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Impacts of global climate change on the spatial range of insects distributed across the Pacific Ocean by Ziheng Ma, Haoyu Liu, Yuxia Yang

“…In this study, we focus on this issue with a small yet ancient group of soldier beetles, Trypherus, as a case study. Through spatial analysis and ecological niche modeling approaches, we analyze the geographical distribution pattern and habitat suitability for Trypherus, and further predict its potential changes in suitable habitats under different future climate scenarios. …”
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A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer by Yuan Huang, Feilong Han, Qimeng Feng

“…To enhance the model’s predictive performance, we designed a new Transformer architecture named FFPformer, which incorporates the Fast Fourier Transform into the Transformer framework. …”
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A Fluid Flow‐Based Deep Learning (FFDL) Architecture for Subsurface Flow Systems With Application to Geologic CO2 Storage by Zhen Qin, Yingxiang Liu, Fangning Zheng, Behnam Jafarpour

“…Abstract Prediction of the spatial‐temporal dynamics of the fluid flow in complex subsurface systems, such as geologic CO2 storage, is typically performed using advanced numerical simulation methods that solve the underlying governing physical equations. …”
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Spatial inference of Culex pipiens abundance and biting activity distribution in the Netherlands using citizen science by Ayat Abourashed, Catuxa Cerecedo-Iglesias, Martha Dellar, John R. B. Palmer, Frederic Bartumeus

“…Methods Our study utilized data from the Mosquito Alert mobile app to model the spatial distribution and abundance of Cx. pipiens and mosquito bites during the summer of 2021 in the Netherlands. …”
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A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space by Fantong Meng, Jinhua Wei, Qianyi Feng, Zhigang Dong, Renke Kang, Dongming Guo, Jiankun Yang

“…A spatial-interpolation-based unbiased estimation method with joint weights optimization is proposed for robot errors prediction, and the particle filter method is utilized to search for the optimal joint weights, enhancing the anisotropic characteristics of the prediction model. …”
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Digital Mapping of Soil Equivalent Calcium Carbonate Using Landsat 8 Satellite Images and Environmental Data by Machine Learning Models in Badr Watershed, Kurdistan Province by M. Zarinibahador

“…In the first case, artificial neural network models, decision tree analysis, random forest, and the K-nearest neighbor model were used for prediction. …”
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Predicting cortical bone resorption in the mouse tibia under disuse conditions caused by transient muscle paralysis by Himanshu Shekhar, Sanjay Singh, Jitendra Prasad

“…Dissipation energy density induced by loading, based on interstitial fluid flow, has been adopted as the mechanotransduction-triggering stimulus. The developed model uses the loss of stimulus due to the disuse of bone as an input and predicts the quantity of bone loss with spatial accuracy. …”
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