Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling. by Yolanda R Hill, Nick Child, Ben Hanson, Mikael Wallman, Ruben Coronel, Gernot Plank, Christopher A Rinaldi, Jaswinder Gill, Nicolas P Smith, Peter Taggart, Martin J Bishop

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Ultra-short-term prediction of spatio-temporal wind speed based on a hybrid deep learning model by Zixuan Chen, Zixuan Chen, Zixuan Chen, Jinman Zhang, Jinman Zhang, Shuang Zhou, Zengbao Zhao, Zengbao Zhao, Yushan Liu

“…This study develops a spatio-temporal forecasting model for predicting wind speeds across the Beijing-Tianjin-Hebei region over a 4-h horizon. …”
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Prediction models show differences in highly pathogenic avian influenza outbreaks in Japan and South Korea compared to Europe by Lene Jung Kjær, Carsten Thure Kirkeby, Anette Ella Boklund, Charlotte Kristiane Hjulsager, Anthony D. Fox, Michael P. Ward

“…Using data on H5 HPAI virus (HPAIV) occurrence from the World Organization for Animal Health and the Food and Agriculture Organization, we employed a spatial time-series modelling framework to predict occurrences in Japan and South Korea, 2020–2024. …”
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Developing a Prediction Model for Real-Time Incident Detection Leveraging User-Oriented Participatory Sensing Data by Md Tufajjal Hossain, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic, Lazar Spasovic

“…Additionally, multiple machine learning-based predictive models were developed and evaluated to forecast in real time whether Waze alerts correspond to actual incidents. …”
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From Patterns to Predictions: Spatiotemporal Mobile Traffic Forecasting Using AutoML, TimeGPT and Traditional Models by Hassan Ayaz, Kashif Sultan, Muhammad Sheraz, Teong Chee Chuah

“…By merging machine learning techniques with advanced temporal modeling, this study provides a strong framework for scalable and intelligent mobile traffic prediction. …”
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A Study of Landslide Susceptibility Assessment and Trend Prediction Using a Rule-Based Discrete Grid Model by Yanjun Duan, Xiaotong Zhang, Wenbo Zhao, Xinpei Han, Lingfeng Lv, Yunjun Yao, Kun Jia, Qiao Wang

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Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City by Afsaneh Ghasemi, Jamil Amanollahi

“…Lightning is one of the most severe weather hazards that will cause significant economic, social and environmental damage each year. The prediction of a lightning is a very difficult task due to the spatial and temporal expansion of weather either physically or dynamically. …”
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Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data by DongHyeon Yoon, Ha-Eun Yu, Euiho Hwang, Ki-mook Kang, Gibeom Nam, Jin-Gyeom Kim

“…Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R2 core of 0.981 on the test set. …”
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The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction by Chenyue Zhang, Qinxin Wang, Yiyang Li, Anqi Teng, Gang Hu, Qiqige Wuyun, Wei Zheng

“…Recent breakthroughs in AI, particularly in protein and nucleic acid structure prediction, rely heavily on the accuracy and efficiency of MSAs to enhance remote homology detection and guide spatial restraints. …”
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Prediction of difficulty in cryoballoon ablation with a three‐dimensional deep learning model using polygonal mesh representation by Kazutaka Nakasone, Makoto Nishimori, Masakazu Shinohara, Kunihiko Kiuchi, Mitsuru Takami, Kimitake Imamura, Yu Izawa, Toshihiro Nakamura, Yusuke Sonoda, Hiroyuki Takahara, Kyoko Yamamoto, Yuya Suzuki, Kenichi Tani, Hidehiro Iwai, Yusuke Nakanishi, Ken‐ichi Hirata, Koji Fukuzawa

“…This study aimed to develop a three‐dimensional (3D) deep learning (DL) model to predict CBA difficulty and compare its accuracy with conventional manual measurement. …”
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Spatial Autoregressive Modeling on Linear Mixed Models for Dependency Between Regions by Timbang Sirait

“…In this study, we are concerned with the spatial lag or SAR models because dependency between variables of interest is easier to predict. …”
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Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma by Jie Sun, Xuewei Wu, Xiao Zhang, Weiyuan Huang, Xi Zhong, Xueyan Li, Kaiming Xue, Shuyi Liu, Xianjie Chen, Wenzhu Li, Xin Liu, Hui Shen, Jingjing You, Wenle He, Zhe Jin, Lijuan Yu, Yuange Li, Shuixing Zhang, Bin Zhang

“…We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. …”
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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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Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement by Qingmeng Shen, Yuming Wu, Limin Wan, Qian Chen, Yue Li, Zichao Liao, Wenbo Wang, Feng Li, Tao Li, Jiajun Shu

“…The settlement values of subway tunnels during the construction period exhibit significant nonlinear and spatial–temporal variation characteristics. To overcome the problems of historical data interference and spatiotemporal characteristics in tunnel settlement prediction models, this paper proposes a tunnel settlement prediction method based on data decomposition, reconstruction, and optimization. …”
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Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model by Junying WU, Xin LU, Hong LIU, Bin ZHANG, Shouliang CHAI, Yunchun LIU, Jianan WANG

“…To improve the prediction accuracy of multi-region power load, an ultra-short-term multi-region power load forecasting model based on Spearman-GCN-GRU is proposed with focus on the spatial-temporal correlation analysis of multi-region power data. …”
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4D trajectory lightweight prediction algorithm based on knowledge distillation technique by Weizhen Tang, Jie Dai, Zhousheng Huang, Boyang Hao, Weizheng Xie

“…In the distillation process, soft labels from the teacher and hard labels from actual observations jointly guide student trainingResultsIn multi-step prediction experiments, the distilled RCBAM–TCN–LSTM model achieved average reductions of 40%–60% in MAE, RMSE, and MAPE compared with the original RCBAM and TCN–LSTM models, while improving R² by 4%–6%. …”
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Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features by Abdullah Baktash, Yashar Sarbaz, Saeed Meshgini, Reza Afrouzian

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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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Sensitivity Analysis of Soil Hydraulic Parameters for Improved Flow Predictions in an Atlantic Forest Watershed Using the MOHID-Land Platform by Dhiego da Silva Sales, Jader Lugon Junior, David de Andrade Costa, Renata Silva Barreto Sales, Ramiro Joaquim Neves, Antonio José da Silva Neto

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Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach by Huyong Yan, Asad Khan, Ahsan Jamil, Belkendil Abdeldjalil, Taoufik Saidani, Nazih Y. Rebouh

“…The complex, nonlinear interconnections of environmental and human elements cause terrain instability and challenge conventional prediction methods. In this work, we offer a DeepLabV3+-based deep learning framework coupled with geographic information systems and multicriteria decision making methods for spatial prediction of landslide risk, over the Dubai coastal and urban region (covering approximately 4000 km²). …”
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