SERPINA3 predicts long-term neurological outcomes and mortality in patients with intracerebral hemorrhage by Pei Zheng, Zhihui Qi, Bin Gao, Yang Yao, Jingshan Chen, Hengri Cong, Yue Huang, Fu-Dong Shi

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TransXLT: A novel ZTD prediction method with SASR-based data reconstruction by Shicheng Xie, Xuexiang Yu, Jiajia Yuan, Xu Yang, Mingfei Zhu, Yuchen Han, Min Wei, Zhongchen Guo

“…Summary: Traditional Zenith Tropospheric Delay (ZTD) models often face difficulties in maintaining prediction accuracy under complex meteorological conditions and data loss. …”
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Predicting Solar Flares Using a Convolutional Neural Network with Extreme-ultraviolet Images by Yun Yang, Yi Wei Ni, P. F. Chen, Xue Shang Feng

“…This information, which cannot be provided by the data from a single active region alone, is demonstrated to be crucial in predicting solar flares. The results show that our prediction model performs well in both short-term and long-term forecasting.…”
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Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit by Hongtai Yang, Chaojing Li, Xuan Li, Jinghai Huo, Yi Wen, Emma G. P. Sexton, Yugang Liu

“…Direct ridership models can predict station-level urban rail transit ridership. …”
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Channel Path Loss Prediction Using Satellite Images: A Deep Learning Approach by Chenlong Wang, Bo Ai, Ruisi He, Mi Yang, Shun Zhou, Long Yu, Yuxin Zhang, Zhicheng Qiu, Zhangdui Zhong, Jianhua Fan

“…This paper proposes a novel deep learning-based path loss prediction model using satellite images. In order to efficiently extract environment features from satellite images, residual structure, attention mechanism, and spatial pyramid pooling layer are developed in the network based on expert knowledge. …”
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Predicting the distribution and abundance of bustards, storks, and harriers in Kenya using citizen science data by Frank Juma Ong’ondo, Frank Juma Ong’ondo, Rosie Trevelyan, Anthony Kuria, Peter Njoroge, Samuel Guchu, Colin Jackson

“…A linear mixed-effect model was used to predict grassland birds’ response. …”
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Hybrid machine learning for flood prediction: comparing CHIRPS satellite and ground station data by Agustina Rachmawardani, Budhy Kurniawan, Sastra Kusuma Wijaya, Ardhasena Sopaheluwakan, Marzuki Sinambela

“…While CHIRPS satellite data offers broader spatial coverage, its limitation in precision and higher susceptibility to errors reduce its effectiveness for accurate flood prediction.…”
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Trends of road traffic mortality in Tehran city during 2012-2021 by Amir Kavousi, Khaled Rahmani, Ali Moradi, Fatemeh Darabi, Mohammad Razeghi

“…Over the years, a pronounced reduction in fatalities has been observed compared to 2012 figures. …”
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The Study of Spatial Heterogeneity and Interregional Relations in the Processes of Attracting Banking Capital to the Russian Economy by I. V. Naumov, S. S. Krasnykh

“…The purpose of the research is to assess the dynamics of changes in spatial heterogeneity in the processes of attracting banking capital in the Russian economy and to model the system of interregional relationships in these processes. …”
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The effect of heterogeneity on invasion in spatial epidemics: from theory to experimental evidence in a model system. by Franco M Neri, Anne Bates, Winnie S Füchtbauer, Francisco J Pérez-Reche, Sergei N Taraskin, Wilfred Otten, Douglas J Bailey, Christopher A Gilligan

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EpiGeoPop: a tool for developing spatially accurate country-level epidemiological models by Lara Herriott, Henriette L. Capel, Isaac Ellmen, Nathan Schofield, Jiayuan Zhu, Ben Lambert, David Gavaghan, Ioana Bouros, Richard Creswell, Kit Gallagher

“…These models have aided pandemic preparedness by predicting outcomes under hypothetical scenarios and identifying weaknesses in existing frameworks; however, their accuracy, utility, and comparability are being scrutinised. …”
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Spatial modelling of soil-transmitted helminth infections in Kenya: a disease control planning tool. by Rachel L Pullan, Peter W Gething, Jennifer L Smith, Charles S Mwandawiro, Hugh J W Sturrock, Caroline W Gitonga, Simon I Hay, Simon Brooker

“…The model estimated that a total of 2.8 million school-age children live in districts which warrant mass treatment.…”
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Impact of Urban Spatial Compactness on Carbon Emissions: Heterogeneity at the County Level in the Beijing–Tianjin–Hebei Area, China by Muhan Li, Minghao Zuo, Saiyi Chen, Shuang Tang, Tian Chen, Jia Liu

“…However, the potential of and variations in spatial compactness in county-level carbon reduction have been largely overlooked. …”
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Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory. by Qi Dai, Xiao-Yan Liu, Fang-Yi Sun, Fang-Rong Ren

“…The study carries out regression analysis and a long-short-term memory model (LSTM) to respectively filter out the factors and predict TCEI. …”
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Facial muscle mapping and expression prediction using a conformal surface-electromyography platform by Hila Man, Paul F. Funk, Dvir Ben-Dov, Chen Bar-Haim, Bara Levit, Orlando Guntinas-Lichius, Yael Hanein

“…Using this foundation, we demonstrated a deep-learning model to predict facial expressions. This approach enables precise, participant-specific monitoring with applications in medical rehabilitation and psychological research.…”
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Exploration of Carbon Emission Reduction Pathways for Urban Residential Buildings at the Provincial Level: A Case Study of Jiangsu Province by Jian Xu, Tao Lei, Milun Yang, Huixuan Xiang, Ronge Miao, Huan Zhou, Ruiqu Ma, Wenlei Ding, Genyu Xu

“…This study examines Jiangsu Province in eastern China—a economic hub with north-south development gradients—to develop an integrated framework for differentiated carbon reduction pathways. The methodology combines spatial autocorrelation analysis, logarithmic mean Divisia index (LMDI) decomposition, system dynamics modeling, and Tapio decoupling analysis to examine urban residential building emissions across three regions from 2016–2022. …”
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Study on Spatial and Temporal Evolution of Carbon Stock in East Coastal Area of Zhejiang Based on InVEST and GIS Modeling by Chen Fang, Zhiyu Wang

“…Global climate change, driven by increasing carbon emissions, poses a significant challenge to sustainable development, yet regional studies on carbon stock dynamics in rapidly urbanizing coastal areas remain limited. Utilizing the InVEST model and GIS spatial analysis methods, this research examines the spatiotemporal dynamics of carbon stock in the eastern coastal regions of Zhejiang from 2000 to 2020. …”
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Multi-spatial urban function modeling: A multi-modal deep network approach for transfer and multi-task learning by Zhaoya Gong, Chenglong Wang, Bin Liu, Binbo Li, Wei Tu, Yuting Chen, Zhicheng Deng, Pengjun Zhao

“…A range of data-driven models based on the representation learning of multiple data sources have focused on extracting spatially explicit characteristics at the feature level for urban function inference. …”
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Estimating the carbon emission reduction potential of using carbon-oriented demand response for data centers: A case study in China by Bojun Du, Hongyang Jia, Yaowang Li, Ershun Du, Ning Zhang, Dong Liang

“…In the planning model, future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service (QoS). …”
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Improving rice yield prediction with multi-modal UAV data: hyperspectral, thermal, and LiDAR integration by Shaofeng Tan, Jie Pei, Yaopeng Zou, Huajun Fang, Tianxing Wang, Jianxi Huang

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