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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Temporal Vine Water Status Modeling Through Machine Learning Ensemble Technique and Sentinel-2 Multispectral Images Under Semi-Arid Conditions by Vincenzo Giannico, Simone Pietro Garofalo, Luca Brillante, Pietro Sciusco, Mario Elia, Giuseppe Lopriore, Salvatore Camposeo, Raffaele Lafortezza, Giovanni Sanesi, Gaetano Alessandro Vivaldi

“…In this study, the integration of machine learning and satellite remote sensing (Sentinel-2) was investigated to obtain a model able to predict the stem water potential in viticulture using multispectral imagery. …”
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Modelling the Spatial Dependence of Multi‐Species Point Patterns by Chathuri L. Samarasekara, Ian Flint, Yan Wang

“…Using synthetic and real datasets, we assessed both models based on their predictive accuracy of the empirical K function. …”
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Predicting spatio-temporal dynamics of dengue using INLA (integrated nested laplace approximation) in Yogyakarta, Indonesia by Marko Ferdian Salim, Tri Baskoro Tunggul Satoto, Danardono

“…Its incidence fluctuates due to spatial and temporal factors, necessitating robust modeling approaches for prediction and risk mapping. …”
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A novel ST-iTransformer model for spatio-temporal ambient air pollution forecasting by Rui Zhang, Norhashidah Awang

“…Additionally, ablation experiments confirm that the spatio-temporal embedding, the inclusion of spatial data, and the addition of meteorological data all improve the prediction accuracy of the model. …”
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Using extreme gradient boosting for predictive urban expansion analysis in Rustenburg, South Africa from 2000 to 2030 by Paidamwoyo Mhangara, Eskinder Gidey, Bruce Steadman Mayise

“…This study modeled the spatial extent of urban growth in Rustenburg from 1994 to 2022 using Extreme Gradient Boosting (XGB) and predicted future urban expansion from 2022 to 2030 through the Cellular Automata Simulation in the MOLUSCE plugin. …”
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Evaluating logistic regression and geographically weighted logistic regression models for predicting orange-fleshed sweet potato adoption intention in Benin by Idrissou Ahoudou, Nicodeme V. Fassinou Hotegni, Charlotte O. A. Adjé, Tania L. I. Akponikpè, Dêêdi E. O. Sogbohossou, Nadia Fanou Fogny, Françoise Assogba Komlan, Ismail Moumouni-Moussa, Enoch G. Achigan-Dako

“…The GWLR model excels in elucidating the spatial nuances of diverse factors, offering a promising avenue for more reliable predictions for OFSP adoption.…”
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Construction of a predictive model for the efficacy of anti-VEGF therapy in macular edema patients based on OCT imaging: a retrospective study by Tingting Song, Boyang Zang, Chui Kong, Xifang Zhang, Huihui Luo, Wenbin Wei, Zheqing Li

“…Therefore, it is crucial to develop automated and efficient methods for predicting therapeutic outcomes.MethodsWe have developed a predictive model for the surgical efficacy in ME patients based on deep learning and optical coherence tomography (OCT) imaging, aimed at predicting the treatment outcomes at different time points. …”
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<strong>Hybrid neural network with genetic algorithms for predicting distribution pattern of <em>Tetranychus urticae</em> (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Ir... by Alireza Shabaninejad, Bahram Tafaghodinia, Nooshin Zandi Sohani

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Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data by Yifan Liu, Huabiao Jin, Xiangguo Yang, Telu Tang, Qijia Song, Yuelin Chen, Lin Liu, Shoude Jiang

“…Furthermore, the fault prediction method based on the iTransformer model is introduced to forecast variations in battery cluster voltages. …”
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Potential Distribution Prediction of Terminalia chebula Retz. in China Under Current and Future Climate Scenarios by Zhang‐Hong Dong, Hua Jiang, Wei Zhang, Jianhua Wu, Yanping Yang, Taoming Yang, Jiangping Zhao, Cunzhen Luo, Xiaoxia Yang, Guilin Li

“…Utilizing GIS and MaxEnt model, we predicted the spatial distribution of Terminalia chebula Retz. in China for the current and for the future (2050s and 2070s) under the RCP4.5 and RCP8.5 representative concentration pathways. …”
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Using Earth observation to support the predictive accuracy of species distribution models in ecological restoration: a case study of Poland beavers (Castor fiber) by H. A. Cheriyanda Raveendra, L. Picco, F. Pirotti, F. Pirotti

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Study and Prediction of Land Use/Cover Change in Small and Medium-Sized River Basins in Typical Urbanization Areas by LI Liujie, LIU Bingjun, WEI Qiuying

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