Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting by Na Hu, Dafang Zhang, Kun Xie, Wei Liang, Meng-Yen Hsieh

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Impacts of Climate Change on the Spatial Distribution and Habitat Suitability of <i>Nitraria tangutorum</i> by Lianxing Li, Zhiqing Jia, Lingxianzi He, Dong Han, Qiankun Yang, Jialuo Li, Pingyi Zhou

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Integration of fuzzy logic and geographic weighted regression modeling for enhanced groundwater potential mapping using remote sensing and GIS by Junguang Gao, Amnah A. Alasgah, Imran Ahmad, Faten Nahas, Mithas Ahmad Dar, Martina Zelenakova, Milashu Sisay, Getanew Sewnet Zewdu

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Effectiveness Model of Coastal Forest in Pananjung Nature Reserve, Pangandaran as Tsunami Buffer by Denni Susanto, Lies Rahayu Wijayanti Faida, Sunarto Sunarto

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Fertility, Migration, and Spatial Interaction in Turkey: An Analysis under the Selectivity Hypothesis by Sibel Selim, Derya Bilgin

“…In this context, the study unveils a spatial relationship between migration and fertility in Turkey and, thus, invalidates the selectivity hypothesis.…”
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Influence of the Human Skin Tumor Type in Photodynamic Therapy Analysed by a Predictive Model by I. Salas-García, F. Fanjul-Vélez, J. L. Arce-Diego

“…We employ a predictive PDT model and apply it to different skin tumors. …”
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Mapping and understanding the regional farmland SOC distribution in southern China using a Bayesian spatial model by Bifeng Hu, Yibo Geng, Hanjie Ni, Zhou Shi, Zheng Wang, Nan Wang, Jipeng Luo, Modian Xie, Qian Zou, Thomas Optiz, Hongyi Li

“…Finally, an interpretable machine learning model, the SHapley Additive exPlanation (SHAP), is used to quantify the environmental covariates’ contribution to mapping SOC, as well as mapping spatial varying primary covariates for predicting SOC in the study area. …”
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Collaborative Joint Perception and Prediction for Autonomous Driving by Shunli Ren, Siheng Chen, Wenjun Zhang

“…To achieve effective and communication-efficient information sharing, two novel designs are proposed: (1) a task-oriented spatial–temporal information-refinement model, which filters redundant and noisy multi-frame features into concise representations; (2) a spatial–temporal importance-aware feature-fusion model, which comprehensively fuses features from various agents. …”
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Spatial risk modelling of highly pathogenic avian influenza in France: Fattening duck farm activity matters. by Jean Artois, Timothée Vergne, Lisa Fourtune, Simon Dellicour, Axelle Scoizec, Sophie Le Bouquin, Jean-Luc Guérin, Mathilde C Paul, Claire Guinat

“…In this study, we present a comprehensive analysis of the key spatial risk factors and predictive risk maps for HPAI infection in France, with a focus on the 2016-17 and 2020-21 epidemic waves. …”
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FibroRegNet: A Regression Framework for the Pulmonary Fibrosis Prognosis Prediction Using a Convolutional Spatial Transformer Network by Pardhasaradhi Mittapalli, V. Thanikaiselvan

“…Predicting the growth of idiopathic pulmonary fibrosis (IPF) is crucial for effectively treating patients affected by the disease. …”
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Characteristics of Spatial and Temporal Evolution of Coastal Wetland Landscape Patterns and Prediction Analysis—A Case Study of Panjin Wetland, China by Qian Cheng, Ruixin Chen, Wei Xu, Meiqing Wang

“…For this research, we quantified the landscape type changes in Panjin Wetland from 1992–2022, and analyzed the interaction between the combined PLUS and InVEST models to predict the future evolution of spatial and temporal patterns of habitat quality (HQ) and landscape patterns in Panjin Wetland. …”
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Spatial-temporal Distribution of Carbon Storage in Qilian Mountain National Park Based on InVEST Model by Deng Zhe, Ding Wenguang, Pu Xiaoting, Lyu Yongjie, Wang Yali

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Spatial and temporal distribution of infiltration, curve number and runoff coefficients using TOPMODEL and SCS-CN models by Mohammad Hossein Pishvaei, Shabnam Noroozpour, Touraj Sabzevari, Mostafa Akbari Kheirabadi, Andrea Petroselli

“…Infiltration, the process by which water enters the soil, is intricately intertwined with the attributes of the catchment, including soil composition and vegetation cover, both of which exhibit temporal and spatial variability. Accurate quantification of infiltration rates is imperative for enhancing the predictive capabilities of rainfall-runoff models, especially in regions with limited hydrological monitoring infrastructure, such as many developing countries where a significant portion of catchments remains ungauged. …”
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RETRACTED: Effects of Forestry on Carbon Emissions in China: Evidence From a Dynamic Spatial Durbin Model by Zaijun Li, Zouheir Mighri, Zouheir Mighri, Suleman Sarwar, Chen Wei

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Spatial and Temporal Dynamics of Water Conservation in Xiangjiang River Basin Based on ANUSPLIN and InVEST Model by GUO Binbin, LIU Yuxin

“…This study evaluated the simulation results of the Xiangjiang River basin at different spatial and temporal scales from 1991 to 2020 based on the ANUSPLIN interpolation precipitation data and the InVEST model and explored the spatial and temporal dynamics of water conservation within the basin. …”
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Spatial and Temporal Dynamics of Water Conservation in Xiangjiang River Basin Based on ANUSPLIN and InVEST Model by GUO Binbin, LIU Yuxin

“…This study evaluated the simulation results of the Xiangjiang River Basin at different spatial and temporal scales from 1991 to 2020 based on the ANUSPLIN interpolation precipitation data and the InVEST model and explored the spatial and temporal dynamics of water conservation within the basin. …”
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The Metaverse Is Geospatial: A System Model Architecture Integrating Spatial Computing, Digital Twins, and Virtual Worlds by Theofilos Papadopoulos, Konstantinos Evangelidis, Theodore H. Kaskalis, Georgios Evangelidis

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Predictive Modeling the Turbidity Response in Al-Saray Water Distribution Network in Najaf Governorate/Middle of Iraq, Using PODDS Model by Abed Zahraa H., Jasem Hayder M., Mohammed Hayder S.

“…Reducing water turbidity is one of the main issues the water industry is currently experiencing. The ability to predict the spatial probability and intensity of discoloration events in distribution systems can lead to the adoption and improvement of proactive operation and maintenance strategies to reduce turbidity. …”
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Where to refine spatial data to improve accuracy in crop disease modelling: an analytical approach with examples for cassava by Yevhen F. Suprunenko, Christopher A. Gilligan

“…However, the underlying data on spatial locations of host crops that are susceptible to a pathogen are often incomplete and inaccurate, thus reducing the accuracy of model predictions. …”
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Spatial transcriptomics and scRNA-seq: decoding tumor complexity and constructing prognostic models in colorectal cancer by Wei Song, Yatao Wang, Min Zhou, Fengqin Guo, Yanliang Liu

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