Analysis of risk factors affecting delay of high-speed railway in China based on Bayesian network modeling

2021 ◽  
pp. 1-22
Author(s):  
Jing Wang ◽  
Yichuan Peng ◽  
Jian Lu ◽  
Yuming Jiang
Keyword(s):  
Risk Factors ◽  
Bayesian Network ◽  
High Speed ◽  
Network Modeling ◽  
High Speed Railway ◽  
Factors Affecting ◽  
Bayesian Network Modeling

Bayesian Network Modeling in Discovering Risk Factors of Dental Caries in Three-Year-Old Children

2019 ◽  
Vol 1 ◽  
pp. 118-125
Author(s):  
W. Łaguna ◽  
J. Bagińska ◽  
A. Oniśko
Keyword(s):  
Risk Factors ◽  
Dental Caries ◽  
Bayesian Network ◽  
Network Model ◽  
Graphical Models ◽  
Network Modeling ◽  
Significant Risk ◽  
Probabilistic Graphical Models ◽  
Bayesian Network Model ◽  
Bayesian Network Modeling

<br/><b>Purpose</b> - The aim of this study was to use probabilistic graphical models to determine dental caries risk factors in three-year-old children. The analysis was conducted on the basis of the questionnaire data and resulted in building probabilistic graphical models to investigate dependencies among the features gathered in the surveys on dental caries. <br/><b>Materials and Methods</b> - The data available in this analysis came from dental examinations conducted in children and from a questionnaire survey of their parents or guardians. The data represented 255 children aged between 36 and 48 months. Self-administered questionnaires contained 34 questions of socioeconomic and medical nature such as nutritional habits, wealth, or the level of education. The data included also the results of oral examination by a dentist. We applied the Bayesian network modeling to construct a model by learning it from the collected data. The process of Bayesian network model building was assisted by a dental expert. <br/><b>Results</b> - The model allows to identify probabilistic relationships among the variables and to indicate the most significant risk factors of dental caries in three-year-old children. The Bayesian network model analysis illustrates that cleaning teeth and falling asleep with a bottle are the most significant risk factors of dental caries development in three-year-old children, whereas socioeconomic factors have no significant impact on the condition of teeth. <br/><b>Conclusions</b> - Our analysis results suggest that dietary and oral hygiene habits have the most significant impact on the occurrence of dental caries in three-year-olds.

scholarly journals Bayesian Network Modeling to Improve Water Pricing Practices in Northwest China

Water ◽  
2015 ◽  
Vol 7 (10) ◽  
pp. 5617-5637 ◽  
Author(s):  
Yusuyunjiang Mamitimin ◽  
Til Feike ◽  
Reiner Doluschitz
Keyword(s):  
Bayesian Network ◽  
Network Modeling ◽  
Northwest China ◽  
Water Pricing ◽  
Bayesian Network Modeling

scholarly journals Examining partial proportional odds model in analyzing severity of high-speed railway accident

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Wang ◽  
Yinghan Wang ◽  
Yichuan Peng ◽  
Jian John Lu
Keyword(s):  
High Speed ◽  
Proportional Odds Model ◽  
Impact Factors ◽  
Proportional Odds ◽  
High Speed Railway ◽  
Severity Level ◽  
Content Type ◽  
Factors Affecting ◽  
Partial Proportional Odds Model ◽  
The Impact

Purpose The operation safety of the high-speed railway has been widely concerned. Due to the joint influence of the environment, equipment, personnel and other factors, accidents are inevitable in the operation process. However, few studies focused on identifying contributing factors affecting the severity of high-speed railway accidents because of the difficulty in obtaining field data. This study aims to investigate the impact factors affecting the severity of the general high-speed railway. Design/methodology/approach A total of 14 potential factors were examined from 475 data. The severity level is categorized into four levels by delay time and the number of subsequent trains that are affected by the accident. The partial proportional odds model was constructed to relax the constraint of the parallel line assumption. Findings The results show that 10 factors are found to significantly affect accident severity. Moreover, the factors including automation train protection (ATP) system fault, platform screen door and train door fault, traction converter fault and railway clearance intrusion by objects have an effect on reducing the severity level. On the contrary, the accidents caused by objects hanging on the catenary, pantograph fault, passenger misconducting or sudden illness, personnel intrusion of railway clearance, driving on heavy rain or snow and train collision against objects tend to be more severe. Originality/value The research results are very useful for mitigating the consequences of high-speed rail accidents.

Bayesian Network Modeling of Transcription Factor Binding Sites

2007 ◽  
pp. 300-318
Author(s):  
Vipin Narang ◽  
Rajesh Chowdhary ◽  
Ankush Mittal ◽  
Wing-Kin Sung
Keyword(s):  
Transcription Factor ◽  
Parameter Estimation ◽  
Bayesian Network ◽  
Network Modeling ◽  
Network Models ◽  
Transcription Factor Binding Sites ◽  
Physical Problem ◽  
Network Applications ◽  
Bayesian Network Modeling ◽  
Bayesian Network Models

A predicament that engineers who wish to employ Bayesian networks to solve practical problems often face is the depth of study required in order to obtain a workable understanding of this tool. This chapter is intended as a tutorial material to assist the reader in efficiently understanding the fundamental concepts involved in Bayesian network applications. It presents a complete step by step solution of a bioinformatics problem using Bayesian network models, with detailed illustration of modeling, parameter estimation, and inference mechanisms. Considerations in determining an appropriate Bayesian network model representation of a physical problem are also discussed.

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