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). …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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

Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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%. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text