A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability by Xitailang Cao, Shan Lin, Miao Dong, Quanke Hu, Hong Zheng

“…To address this issue, this study proposes an efficient surrogate modeling approach for the rapid prediction of the factor of safety in slopes while considering the spatial variability of geotechnical parameters. …”
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Light source classification and colour change modelling for understanding and predicting pigments discolouration by Panagiotis Siozos, Letizia Monico, Aldo Romani, Costanza Miliani, Brenda Doherty, Irina Crina Anca Sandu, Hartmut Kutzke, Ingrid M T Flåte, Petros Stavroulakis, Sophia Sotiropoulou

“…This model is experimentally validated by artificial ageing tests on two sets of model samples made of historical pigments (strontium yellow and Prussian blue mixed with lead white) using three white light sources (two WLEDs and a xenon light source). …”
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A deep learning model to predict dose distributions for breast cancer radiotherapy by Xiaorong Hou, Weishi Cheng, Jing Shen, Hui Guan, Yimeng Zhang, Lu Bai, Shaobin Wang, Zhikai Liu

“…Abstract Purpose In this work, we propose to develop a 3D U-Net-based deep learning model that accurately predicts the dose distribution for breast cancer radiotherapy. …”
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Traffic accident severity prediction based on an enhanced MSCPO-XGBoost hybrid model by Fei Chen, Xiang Qun Liu, Jian Jun Yang, Xu Kang Liu, Jing Hui Ma, Jia Chen, Hua Yu Xiao

“…This study proposes a novel severity prediction framework based on a Modified Stochastic Crested Porcupine Optimizer (MSCPO) combined with the XGBoost algorithm. …”
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CKRT coagulation risk prediction and nursing feedback model based on intelligent algorithms by Xianrong Xu, Mou Chen, Lvjing Chen, Kaixing Huang, Shiqi Cao, Wenwen Gao, Kang Liu, Buyun Wu, Huijuan Mao

“…Abstract Objective To construct an intelligent Continuous Kidney Replacement Therapy nursing feedback model to predict extracorporeal coagulation risk and support timely clinical decisions. …”
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Modelling maternal and perinatal risk factors to predict poorly controlled childhood asthma. by Samuel Schäfer, Kevin Wang, Felicia Sundling, Jean Yang, Anthony Liu, Ralph Nanan

“…Applying ROC analysis, the predictive modelling of risk factors for hospital admissions showed an incremental increase with an AUC of 0.84 and 0.75 for girls and boys respectively for >3 hospital admissions. …”
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Pharmacophore Modeling of Janus Kinase Inhibitors: Tools for Drug Discovery and Exposition Prediction by Florian Fischer, Veronika Temml, Daniela Schuster

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Proposing a machine learning-based model for predicting nonreassuring fetal heart by Nasibeh Roozbeh, Farideh Montazeri, Mohammadsadegh Vahidi Farashah, Vahid Mehrnoush, Fatemeh Darsareh

“…Although this study found that the classification tree models performed well in predicting NFH, more research is needed to make a better conclusion on the performance of ML models in predicting NFH.…”
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Predicting DNA Reactions with a Quantum Chemistry‐Based Deep Learning Model by Likun Wang, Na Li, Mengyao Cao, Yun Zhu, Xiewei Xiong, Li Li, Tong Zhu, Hao Pei

“…Abstract In this study, a deep learning model based on quantum chemistry is introduced to enhance the accuracy and efficiency of predicting DNA reaction parameters. …”
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Predicting the potential distribution of Taxus cuspidata in northeastern China based on the ensemble model by Baoliang Chang, Chen Huang, Bingming Chen, Ziwen Wang, Xingyuan He, Wei Chen, Yanqing Huang, Yue Zhang, Shuai Yu

“…In this study, a combined model was employed to predict potentially suitable habitats for T. cuspidata based on extant data of T. cuspidata distributions in northeastern China. …”
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Dynamic Multiobjective Optimization Algorithm Based on Average Distance Linear Prediction Model by Zhiyong Li, Hengyong Chen, Zhaoxin Xie, Chao Chen, Ahmed Sallam

“…The simulation results show that our proposed prediction model outperforms other prediction models for DMOP-TPS.…”
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Nonlinear prediction model of vehicle network traffic management based on the internet of things by Zhijie Peng, Lili Yin

Subjects: “…Nonlinear prediction model…”
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Research on Stock Index Prediction Based on the Spatiotemporal Attention BiLSTM Model by Shengdong Mu, Boyu Liu, Jijian Gu, Chaolung Lien, Nedjah Nadia

“…By comparing it with nine other forecasting models, the experimental results show that the STBL model achieves more accurate predictions of the closing prices for short-term, medium-term, and long-term forecasts of the stock index.…”
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Energy Consumption Prediction Model for Electric Buses Considering Actual Quantifiable Features by Guowei Zhu, Miao Shi, Jia He

“…Meanwhile, to address the problem that the current electric bus energy consumption prediction model is not conducive to realistic application, this paper proposes an energy consumption prediction model that considers actual electric bus operation data to predict trip energy consumption. …”
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Using the TSA-LSTM two-stage model to predict cancer incidence and mortality. by Rabnawaz Khan, Wang Jie

“…As a result, the model's natural learning trend and prediction quality are enhanced. …”
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Simulation and Prediction of Spring Snow Cover in Northern Hemisphere by CMIP6 Model by Xulei WANG, Hui SUN, Hui GUO, Chula SA, Fanhao MENG, Min LUO

“…As one of the most sensitive natural elements in response to climate change， snow cover has a significant effect on the Earth's surface radiation balance and water cycle.The global snow cover area is approximately 46×106 km2 and 98% of the snow cover distributed in the Northern Hemisphere.Due to its distinctive radiative properties （high surface albedo） and thermal characteristics （low thermal conductivity）， changes in snow cover play a crucial role in the energy balance and water cycle between land and the atmosphere.In the context of global warming， the snow cover in the Northern Hemisphere has been decreasing in recent decades， especially in the spring.Therefore， the capabilities of CMIP6 （Coupled Model Intercomparison Project Phase 6） data to simulate the snow cover area were evaluated based on observational data and the future changes in snow cover were also assessed using a multi-model average in this study.By using the snow cover products from the National Oceanic and Atmospheric Administration/National Climatic Data Center （NOAA/NCDC） as reference data， the Taylor skill scoring， relative deviation， and other methods were applied to evaluate the spring snow cover （SCF） data in the Northern Hemisphere from the International Coupled Model Comparison Project Phase 6 （CMIP6） during 1982 -2014.The ensemble average of the top three models was further selected to predict the spatiotemporal variation characteristics of SCF under different emission scenarios from 2015 to 2099， providing insights into the modeling capabilities of CMIP6 and future changes in SCF.During the historical period （1982 -2014）， SCF was characterized by high coverage at high latitudes and low coverage at low latitudes， with high-altitude regions such as Tibetan Plateau and eastern Asia having higher snow coverage than those at the same latitudes.Overall， 68.37% of the regions in the Northern Hemisphere showed a decreasing trend in SCF， while 31.63% of the regions showed an increasing trend in SCF.Most CMIP6 models overestimated SCF in the Tibetan Plateau region compared to the reference data.In addition， most models simulated larger areas with a decreasing trend in SCF than those evaluated by the reference data and underestimated SCF in March， April， and May.Various models exhibited differing abilities to simulate SCF， with NorESM2-MM， CESM2， BBC-CSM2-MR， NorESM2-LM， and CESM2-WACCM demonstrating superior capabilities.The Multi-Model Ensemble Mean （MME） consistently outperformed individual models， closely aligning with observational data.There were significant differences in the ability of the CMIP6 models to simulate the spatial distribution， inter-annual variation trends， and intra-annual variations of SCF in the Northern Hemisphere.At the end of the 21st-century （2067 -2099）， SCF in the Northern Hemisphere exhibited a decreasing trend in most areas， which intensifies with increasing emission intensity.The changes in SCF were relatively consistent under different emission scenarios before 2040.SCF maintains a steady state under the SSP1-2.6 scenario， showed a slight decreasing trend under the SSP2-4.5 scenario， and showed a significant decreasing trend under the SSP5-8.5 scenario after 2040.…”
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Uncertainty-Based Model Averaging for Prediction of Corrosion Ratio of Reinforcement Embedded in Concrete by Siqing Zeng, Fulin Yang, Zengwei Guo, Ruiqi Guo, Guowen Yao

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Machine learning model for postpancreaticoduodenectomy haemorrhage prediction: an international multicentre cohort study by Zhe Zhang, Xiaowei Wang, Chengfeng Wang, Jianwei Zhang, Xueping Zhao, Minjie Shang, Qiuran Xu, Zongting Gu

“…Decision curve analysis confirmed net clinical benefit, and SHapley Additive exPlanations values highlighted HCT and operative time as top contributors. The model was deployed as an interactive application for real-time risk assessment.Conclusions This novel machine learning model for PPH prediction integrates interpretable risk stratification and demonstrates robust performance across international cohorts. …”
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Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease by Sudharshan Putha, Swaroop Reddy Gayam, Bhavani Prasad Kasaraneni, Krishna Kanth Kondapaka, Sateesh Kumar Nallamala, Praveen Thuniki

“…Subsequently, these variables were integrated into a visualized nomogram model to facilitate early prediction of cognitive impairment (CI) risk. …”
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Maximum entropy model-based spatial sinkhole occurrence prediction in Karapınar, Turkey by Fatih Sarı, Mustafa YALÇIN

“…The Maximum Entropy (MaxEnt) model is applied for sinkhole susceptibility mapping by evaluating 17 variables affecting sinkhole occurrence in meteorological, topographic, environmental, and geological aspects. …”
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