Regularity and predictability of human mobility in personal space. by Daniel Austin, Robin M Cross, Tamara Hayes, Jeffrey Kaye

“…Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity) is uncovered when explicitly accounting for contextual data in a model of in-home mobility. …”
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Predicting changes in maximum temperatures in the mid-future period in Sistan and Baluchestan under SSP scenarios by abdolreza kashki, ghorban jafari

“…IDW interpolation in GIS was used to map spatial temperature changes. Paired-sample t-tests evaluated differences between baseline and mid-future periods.Finding: The CanESM5 model performed best in predicting temperature changes. …”
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Modeling the Effects of Spatial Heterogeneity and Seasonality on Guinea Worm Disease Transmission by Anthony A. E. Losio, Steady Mushayabasa

“…The model incorporates seasonal variations, educational campaigns, and spatial heterogeneity. …”
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Extending isolation by resistance to predict genetic connectivity by Robert J. Fletcher Jr, Jorge A. Sefair, Nicholas Kortessis, Roldolfo Jaffe, Robert D. Holt, Ellen P. Robertson, Sarah I. Duncan, Andrew J. Marx, James D. Austin

“…This framework extends isolation‐by‐resistance modelling to account for some common processes that can impact gene flow, which can improve predicting genetic connectivity across complex landscapes.…”
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Constraint-incorporated deep learning model for predicting heat transfer in porous media under diverse external heat fluxes by Ziling Guo, Hui Wang, Huangyi Zhu, Zhiguo Qu

“…The temperature field within porous media is considerably affected by different boundary conditions, and effective thermal conductivity varies with spatial structure morphologies. At present, traditional prediction methods for the temperature field are expensive and time consuming, particularly for large structures and dimensions, whereas deep learning surrogate models have limitations related to constant boundary conditions and two-dimensional input slices, lacking the three-dimensional topology and spatial correlations. …”
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Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model Datasets by Mengxin Bai, Peng Zhang, Pei Xing, Wupeng Du, Zhixin Hao, Hui Zhang, Yifan Shi, Lulu Liu

“…Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. …”
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Post-Disaster Recovery Effectiveness: Assessment and Prediction of Coordinated Development in the Wenchuan Earthquake-Stricken Areas by Liang Zhao, Chunmiao Zhang, Xia Zhou

“…By constructing a framework to assess post-disaster coordinated development, this study utilized the entropy weight method and mean-variance method for the comprehensive weighting of evaluation indicators. The gray system prediction model G(1,1) was used to forecast the coordinated development levels of the three cities from 2019 to 2025. …”
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Prediction of Energetic Electrons in the Inner Radiation Belt and Slot Region With a Double‐Layer LSTM Neural Network Model by Ling Yang, Liuyuan Li, Jinbin Cao

“…Here, we trained a double‐layer long short‐term memory (LSTM) neural network model and successfully predicted the spatial and temporal variations of the 108–749 keV electrons in the inner radiation belt (L ∼ 1.2–2.2) and slot region (L ∼ 2.2–3.2). …”
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A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models by Unurnyam Jugnee, Le Jiao, Sainbayar Dalantai, Lili Huo, Yi An, Bayartungalag Batsaikhan, Undrakhtsetseg Tsogtbaatar, Munguntuul Ulziibaatar, Boldbaatar Natsagdorj

“…Current researches about heavy metal pollution mainly focus on source apportionment, while robust and accurate predictions on its spatial distribution and driving mechanisms is still lacking. …”
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An equivalent and simplified approach for acoustic noise prediction in a PM synchronous motor based on the semi‐analytical‐FEM model by Armin Saki, Arash Kiyoumarsi, Alireza Ariaei

“…Based on this approach, the simplest and most adequate semi‐analytical‐FEM model for noise prediction in PMSMs is proposed. …”
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Global distribution prediction and ecological conservation of basking shark (Cetorhinus maximus) under integrated impacts by Runlong Sun, Kaiyu Liu, Wenhao Huang, Xiao Wang, Hongfei Zhuang, Zongling Wang, Zhaohui Zhang, Linlin Zhao

“…This study employs various environmental variables and distribution data to construct a global species distribution model for basking sharks, predicting their distribution patterns under current and future climate scenarios. …”
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SD-LSTM: A Dynamic Time Series Model for Predicting the Coupling Coordination of Smart Agro-Rural Development in China by Chunlin Xiong, Yilin Zhang, Weijie Wang

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Temperature Prediction and Fault Warning of High-Speed Shaft of Wind Turbine Gearbox Based on Hybrid Deep Learning Model by Min Zhang, Jijie Wei, Zhenli Sui, Kun Xu, Wenyong Yuan

“…Compared to the long short-term memory (LSTM) and convolutional neural network and LSTM hybrid models, the STA architecture reduces the root mean square error of the prediction by approximately 37% and 13%, respectively. …”
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High-fidelity surrogate modelling for geometric deviation prediction in laser powder bed fusion using in-process monitoring data by Zhengrui Tao, Mirko Sinico, Bey Vrancken, Wim Dewulf

“…This study targets actual-to-nominal errors within dimensional tolerance, proposing a high-fidelity surrogate model to predict deviations using melt pool monitoring data. …”
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A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions. by Mohammed H Cherkaoui-Rbati, Stuart W Paine, Peter Littlewood, Cyril Rauch

“…The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. …”
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Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions by Saiara Samira Sajid, Zahra Khalilzadeh, Lizhi Wang, Guiping Hu

“…We developed a yield prediction model capable of determining field-level outputs based on comprehensive data inputs, including genotype, spatial, temporal, environmental, and management factors. …”
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Prediction Model and Knowledge Discovery for Roof Stress in Mined-Out Areas Integrating 3D Scanning Image Features by Yong Yang, Kepeng Hou, Huafen Sun, Linning Guo, Yalei Zhe

“…However, existing study methods often overlook the increasingly available image data and fail to balance the model predictive capability with interpretability. …”
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Enhancing Emergency Response in Road Accidents: A Severity Prediction Framework Using RF-RFE and Deep Learning Model by Chaimaa Chaoura, Hajar Lazar, Zahi Jarir

“…The attention mechanism further refines predictions by emphasizing critical features. This deep learning model significantly outperforms traditional machine learning methods, achieving accuracy score of 94.99%. …”
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Measuring and modelling functional moat area in perennially ice-covered Lake Fryxell, Antarctica by Michael S. Stone, Mark R. Salvatore, Hilary A. Dugan, Madeline E. Myers, Peter T. Doran

“…Finally, we developed a predictive model based on readily available climate data, allowing moat area to be predicted beyond the limits of the satellite-based records. …”
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Daily soil moisture prediction during winter wheat growth season using an SCSSA-CNN-BiLSTM model by CUI Song, WU Jin, ZHANG Naifeng, LIU Meng, HU Yongsheng, HE Yanan, GU Yue, LONG Xinya, WANG Zhenlong

“…【Conclusion】The SCSSA-CNN- BiLSTM model is accurate for predicting soil moisture in the 0-20 cm root zone of winter wheat. …”
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