Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning by Mingyi Kuang, Fei Fu, Fangzhou Tian, Liwei Lin, Can Du, Yuesong Zhang

Subjects: “…metro station spatial growth prediction…”
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Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan by Ahmed Emara, Sameh A. Kantoush, Mohamed Saber, Tetsuya Sumi, Vahid Nourani, Emad Mabrouk

“…Results indicate that the XGBoost model effectively predicts 2D spatial abrasions in SBTs, achieving an overall accuracy of 0.864, exceeding 0.9 in some sections. …”
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Spatial navigation entropy suggests allocentric dysfunction in PPPD by Felipe Faúndez, Camilo Arévalo-Romero, Karen Villarroel, Claudio Lavín, Kevin Alarcón, Kevin Alarcón, Gustavo Vial, Francisco Artus, Pablo Billeke, Paul H. Delano, Paul H. Delano, Paul H. Delano, Hayo A. Breinbauer, Hayo A. Breinbauer

“…VR intolerance was highest in PPPD patients, followed by vestibular controls, with healthy volunteers showing the lowest discomfort.DiscussionOur findings suggest that PPPD involves deficits in allocentric spatial navigation, likely due to predictive coding errors and impaired internal model updating, rather than sensory input dysfunction. …”
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A mineral-strength conversion model based on LIBS technology and rapid batch testing and application of uniaxial compressive strength by Qinghe ZHANG, Xiaorui WANG, Chuanbing WANG, Weiguo LI, Fengqiang GONG, Xinsheng ZHANG, Jingguo LI

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Transformer based models with hierarchical graph representations for enhanced climate forecasting by T. Bhargava Ramu, Raviteja Kocherla, G. N. V. G. Sirisha, V. Lakshmi Chetana, P. Vidya Sagar, R. Balamurali, Nanditha Boddu

“…The model integrates three key components: Spatial-Temporal Fusion Module (STFM) to capture spatiotemporal dependencies, Hierarchical Graph Representation and Analysis (HGRA) to model structured climate relationships, and Dynamic Temporal Graph Attention Mechanism (DT-GAM) to enhance temporal feature extraction. …”
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Predicting climate-driven shift of the East Mediterranean endemic Cynara cornigera Lindl by Heba Bedair, Heba Bedair, Yehia Hazzazi, Asmaa Abo Hatab, Marwa Waseem A. Halmy, Mohammed A. Dakhil, Mohammed A. Dakhil, Mubaraka S. Alghariani, Mubaraka S. Alghariani, Mari Sumayli, A. El-Shabasy, Mohamed M. El-Khalafy

“…Furthermore, our models predicted that the distribution range of C. cornigera would drop by more than 25% during the next few decades. …”
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Predicting Multi-Scenario Land Use Changes and Soil Erosion in the Huaihe River Basin Based on Coupled PLUS-CSLE Model by GUO Weiling, XU Liuyang, JIA Jiang, GAO Chang, XIA Xiaolin, WANG Bangwen, ZHANG Jingyu, CHEN Lei, CHEN Yingjian

“…[Methods] Based on the PLUS model and the Chinese Soil Loss Equation (CSLE), the land use patterns in the Huaihe River Basin under three scenarios—natural development, ecological protection, and rapid development—for the year 2030 were simulated, and the future soil erosion patterns in the basin under these three scenarios were predicted. …”
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A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems by Tsikai S. Chinembiri, Onisimo Mutanga, Timothy Dube

“…Despite a multi-source data prediction approach to the modeling of C stock in a managed plantation forest ecosystem set-up, the issues of scale still play a major role in modeling spatial variability of natural resource variables. …”
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High-resolution soil temperature and soil moisture patterns in space, depth and time: An interpretable machine learning modelling approach by Maiken Baumberger, Bettina Haas, Sindhu Sivakumar, Marvin Ludwig, Nele Meyer, Hanna Meyer

“…Current gridded products typically have a low resolution, either spatially or temporally. Here, we aim at modelling and explaining high-resolution soil temperature and soil moisture patterns in 4D for a 400 km2 study area in a heterogeneous landscape. …”
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A Model for Predicting Short-Term Operating Speeds of Compact Passenger Vehicles on Interchange Ramps Within Urban Expressway Networks by Tingyu Liu, Lanfang Zhang, Genze Li, Yating Wu, Zhenyu Zhao

“…Three models are established: a short-term operating speed model based on a generalized linear model (GLM), a GLM incorporating for spatial correlation (GLMS), and a deep neural network model considering spatial correlation (DNNS). …”
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CropSTS: A Remote Sensing Foundation Model for Cropland Classification with Decoupled Spatiotemporal Attention by Jian Yan, Xingfa Gu, Yuxing Chen

“…To efficiently pre-train the model under limited labeled data, we employ a hybrid framework combining joint-embedding predictive architecture with knowledge distillation from web-scale foundation models. …”
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A novel telomere-associated genes signature for the prediction of prognosis and treatment responsiveness of hepatocellular carcinoma by Kuo Kang, Hui Nie, Weilu Kuang, Xuanxuan Li, Yangying Zhou

“…Conclusion In this study, we developed a novel prognostic model comprising 18 TRGs for HCC, which exhibited remarkable accuracy in predicting HCC patients’ prognosis. …”
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Aggregated Housing Price Predictions with No Information About Structural Attributes—Hedonic Models: Linear Regression and a Machine Learning Approach by Joanna Jaroszewicz, Hubert Horynek

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A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks by Vikram S. Ingole, Ujwala A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna, Roshan Kumar

“…TCNs can capture long-range temporal dependencies well, while the GCN model has complex spatial relationships and enhanced the features for making yield predictions. …”
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Development of a Weighted Average Ensemble Model for Predicting Officially Assessed Land Prices Using Grid Map Data and SHAP by Surin Im, Kangmin Kim, Geunhee Lee, Hoi-Jeong Lim

“…This study proposes a weighted average ensemble model to predict the Officially Assessed Land Price in Sejong City, South Korea, using 500m <inline-formula> <tex-math notation="LaTeX">$\times 500$ </tex-math></inline-formula>m grid-based spatial data. …”
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Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data by Chunbo Jiang, Xiaoshuai Guo, Yongfu Li, Ning Lai, Lei Peng, Qinglong Geng

“…Furthermore, although the models exhibited comparable prediction accuracy (RMSE: 0.27–0.33 t/ha; R²: 0.61–0.69 across test datasets), their yield prediction spatial distributions varied significantly (e.g., Model 9 predicted a mean yield of 3.88 t/ha with a range of 2.51–4.89 t/ha, versus Model 18 at 3.74 t/ha and 2.33–4.76 t/ha), suggesting the need for further evaluation of spatial stability. …”
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Leveraging Satellite Data for Predicting PM10 Concentration with Machine Learning Models: A Study in the Plains of North Bengal, India by Ayan Das, Manoranjan Sahu

“…To assess model transferability, all five models were utilized to predict PM10 concentrations in the Jalpaiguri region, referencing National Air Quality Monitoring Programme (NAMP) data. …”
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Prediction of spatiotemporal evolution and zoning of ecological sensitivity in the upper reaches of Minjiang river, sichuan, China by Lingfan Ju, Yan Liu, Shunduo Liu, Qing Xiang, Wenkai Hu, Peng Yu

“…In this study, an ecological sensitivity index system is established to quantitatively analyze the interrelationships of ecological factors. The CA-MC model and center of gravity migration are used to investigate the spatial and temporal evolution of ecological sensitivity in the upper Minjiang River basin from 2000 to 2020 and to predict the ecological sensitivity in 2040. …”
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Multi-Task Trajectory Prediction Using a Vehicle-Lane Disentangled Conditional Variational Autoencoder by Haoyang Chen, Na Li, Hangguan Shan, Eryun Liu, Zhiyu Xiang

“…Trajectory prediction under multimodal information is critical for autonomous driving, necessitating the integration of dynamic vehicle states and static high-definition (HD) maps to model complex agent–scene interactions effectively. …”
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Measurement-guided therapeutic-dose prediction using multi-level gated modality-fusion model for volumetric-modulated arc radiotherapy by Changfei Gong, Changfei Gong, Changfei Gong, Yuling Huang, Yuling Huang, Yuling Huang, Junming Jian, Junming Jian, Junming Jian, Wenheng Zheng, Wenheng Zheng, Wenheng Zheng, Xiaoping Wang, Xiaoping Wang, Xiaoping Wang, Shenggou Ding, Shenggou Ding, Shenggou Ding, Yun Zhang, Yun Zhang, Yun Zhang

“…Furthermore, the existing models simply take advantage of low-dimensional dosimetry information, meaning that the spatial features about the complex dose distribution may be lost and limiting the predictive power of the models. …”
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