Spatial environmental impact of immigration in Europe: Mediating roles of energy intensity and concentration by Ladan Ghodrati, Saeed Shouri, Masoud Shirazi

“…Thus, the present study for the first time investigates the effects of immigration on environmental degradation in 19 European countries from 2000 to 2019. For this reason, a spatial panel model is employed to incorporate the geographical dependence of the studied variables. …”
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Spatial correlation guided cross scale feature fusion for age and gender estimation by Shiyi Jiang, Qing Ji, Hukui Shi, Che Chen, Yang Xu

“…Comprehensive experiments demonstrate that SCGNet achieves state-of-the-art performance with minimum Mean Absolute Error (MAE) 4.01% for age estimation on IMDB-Clean (2.9% improvement over VOLO-D1) and highest gender classification accuracy on IMDB-Clean, UTKFace, and Lagenda datasets, showing improvements in cross-scene adaptability compared to VOLO and MiVOLO models respectively. Notably, the method maintains gender discrimination accuracy under complete facial occlusion scenarios, validating the effectiveness of spatial correlation modeling for non-facial feature reasoning, maintaining 97.32% gender accuracy even with complete facial occlusion on Lagenda dataset. …”
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Comparative evaluation of machine learning models for extreme river water level forecasting in Bangladesh: Implications for flood and drought resilience by Md Touhidul Islam, Sujan Chandra Roy, Nusrat Jahan, Al-Mahmud, Md Mazharul Islam, Abdullah Al Ferdaus, Kazunori Fujisawa, A.K.M. Adham

“…This study compares nine machine learning (ML) models for predicting monthly maximum and minimum water levels at three key stations along the Old Brahmaputra River using a 34-year dataset (1990–2024). …”
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Decoding PM_2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM by Minru Chen, Binglin Liu, Mingzhi Liang, Nini Yao

“…The SARIMA model is based on time series prediction theory and performs well in some scenarios, but has limitations in dealing with non-stationary data and spatial heterogeneity. …”
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Forecasting Day-Ahead Electricity Demand in Australia Using a CNN-LSTM Model with an Attention Mechanism by Laial Alsmadi, Gang Lei, Li Li

“…Despite advancements in various prediction models, existing approaches often struggle to capture the complex, nonlinear relationships between temperature variations and electricity consumption. …”
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A Multi-Regional CGE Model for the Optimization of Land Resource Allocation: A Simulation of the Impact of High-Quality Development Policies in China by Luge Wen, Tiyan Shen, Yuran Huang

“…To address these gaps, this study introduces a multi-scale, multi-type China Territorial Spatial Planning Simulation Model (CTSPM). This model integrates cultivated, forest, grassland, and construction land, simulating the land use changes driven by socioeconomic impacts through price mechanisms. …”
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Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III–IV d-MMR/MSI-H colorectal cancer by Xuan Zhang, Zhenhui Li, Yiwen Zhang, Yanli Li, Xi Zhong, Wenjing Jiang, Xiaobo Chen, Zaiyi Liu, Liebin Huang, Caixia Zhang, Lizhu Liu, Ruimin You, Xiaoping Yi

“…This study aimed to develop an interpretable radiomics model guided by immunophenotypes to predict response to preoperative immunotherapy in CRC, with the goal of enabling more precise and personalized treatment strategies.Methods First, we retrospectively collected 108 patients with CRC from the center who underwent preoperative CT and RNA sequencing. …”
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Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation by James G. C. Ball, Katerina Petrova, David A. Coomes, Seth Flaxman

“…We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short‐Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. …”
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RCSAN residual enhanced channel spatial attention network for stock price forecasting by WenJie Sun, Ziyang Liu, ChunHong Yuan, Xiang Zhou, YuTing Pei, Cui Wei

“…Abstract This study proposes a stock price prediction model based on the Residual-enhanced Channel-Spatial Attention Network (R-CSAN), which integrates channel-spatial adaptive attention mechanisms with residual connections to effectively capture the multidimensional complex patterns in financial time series. …”
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Spacecraft measurements constraining the spatial extent of a magnetopause reconnection X line by B. M. Walsh, C. M. Komar, Y. Pfau‐Kempf

“…Each model predicts a continuous X line passing close to the two spacecraft, suggesting both would observe reconnection, if active. …”
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Spatial Quality Control Method for Surface Temperature Observations Based on Multiple Elements by Xiaoling Ye, Xing Yang, Xiong Xiong, Shuai Yang, Yang Chen

“…The results show that using PCA to analyze the elemental composition and select elements with high correlation factors, as well as applying the Random Forest algorithm, can effectively reduce the run time and keep the accuracy of the model. The training sample dependence, model prediction accuracy and error detection rate of the PCA-RF model are superior to those of the Spatial Regression method. …”
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Spatial Dynamics and High Risk Transmission Pathways of Poliovirus in Nigeria 2001-2013. by Tara D Mangal, R Bruce Aylward, Faisal Shuaib, Michael Mwanza, Muhammed A Pate, Emmanuel Abanida, Nicholas C Grassly

“…The best-fitting spatial model was the radiation model, outperforming the simple distance and gravity models (likelihood ratio test P < 0.05), under which the number of people estimated to move from an infected LGA to an uninfected LGA was strongly associated with the incidence of poliomyelitis in that LGA. …”
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SPADE: A spatial information assisted collision distance estimator for robotic arm by Jiakang Zhou, Yue Cao, Yu-Xuan Ren, Steve Feng Shu

“…With evaluation in both static and dynamic environments, our model shows higher prediction accuracy than multiple baselines, and higher accuracy can be corroborated by experiment with our model under the premise of equal inference efficiency. …”
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Spatial Accuracy Evaluation for Mobile Phone Location Data With Consideration of Geographical Context by Xiaoqing Song, Yi Long, Ling Zhang, David G. Rossiter, Fengyuan Liu, Wei Jiang

“…The RF model can estimate the spatial accuracy of the MPL data within narrow margins of error. …”
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MSLKSTNet: Multi-Scale Large Kernel Spatiotemporal Prediction Neural Network for Air Temperature Prediction by Feng Gao, Jiaen Fei, Yuankang Ye, Chang Liu

“…However, statistical analysis reveals that temperature evolution varies across temporal and spatial scales due to factors like terrain, leading to a lack of existing temperature prediction models that can simultaneously learn both large-scale global features and small to medium-scale local features over time. …”
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Aquifer vulnerability assessment in data-scarce areas: a spatially explicit assessment by Changhyun Jun, Dongkyun Kim, Sayed M. Bateni, Meghdad Biyari, Ely Salwana, Farzaneh Sajedi Hosseini, Amir Mosavi, Hao-Ting Pai, Bahram Choubin

“…Consequently, this study investigates and predicts spatial variations in groundwater quality and vulnerability within a data-scarce area by applying efficient machine learning methods that compensate for the limited availability of quality groundwater data. …”
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Element Nucleosynthetic Origins from Abundance Spatial Distributions beyond the Milky Way by Zefeng Li, Mark R. Krumholz, Anna F. McLeod, A. Mark Swinbank, Emily Wisnioski, J. Trevor Mendel, Francesco Belfiore, Giovanni Cresci, Giacomo Venturi, Jia-Lai Kang

“…These findings both open a new avenue to test nucleosynthetic models and make predictions for the structure of stellar chemical abundance distributions.…”
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Spatial co-distribution of tuberculosis prevalence and low BCG vaccination coverage in Ethiopia by Haileab Fekadu Wolde, Archie C. A. Clements, Beth Gilmour, Kefyalew Addis Alene

“…A Bayesian geostatistical model was built to identify the drivers for the spatial distribution of TB prevalence and low BCG vaccination coverage. …”
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Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information by Yiqiong Yang, Yitian Yuan, Baoxing Ren, Ye Wu, Yanqiu Feng, Xinyuan Zhang

“…This is largely due to their reliance on local information to predict long-range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the latter modeled via a Transformer-decoder. …”
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Spatial distribution of population and forest cover in extractive reserves in the Amazon biome, Brazil by Álvaro de Oliveira D’Antona, Julia Correa Côrtes, José Diego Gobbo Alves, Guilherme Pelegrina, Leonardo Tomazeli Duarte

“…Abstract This study relates spatial measures of forest cover with measures of the spatial distribution of the population in the 31 Extractive Reserves (ERs) within the Amazon biome, Brazil, in 2010. …”
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