scholarly journals Advance Liquid Metal Reactor Discrete Dynamic Event Tree/Bayesian Network Analysis and Incident Management Guidelines (Risk Management for Sodium Fast Reactors)

2015 ◽  
Author(s):  
Matthew R. Denman ◽  
Katrina M. Groth ◽  
Jeffrey N. Cardoni ◽  
Timothy A. Wheeler
Keyword(s):  
Risk Management ◽  
Network Analysis ◽  
Liquid Metal ◽  
Bayesian Network ◽  
Incident Management ◽  
Event Tree ◽  
Fast Reactors ◽  
Management Guidelines ◽  
Dynamic Event ◽  
Liquid Metal Reactor

How influential factors affect aviation networks: A Bayesian network analysis

2021 ◽  
Vol 91 ◽  
pp. 101995
Author(s):  
Yue Wang ◽  
Collin Wai Hung Wong ◽  
Tommy King-Yin Cheung ◽  
Edmund Yangming Wu
Keyword(s):  
Network Analysis ◽  
Bayesian Network ◽  
Influential Factors

scholarly journals Bayesian Network Structure and Predictability of Autistic Traits

2020 ◽  
pp. 003329412097815
Author(s):  
Giovanni Briganti ◽  
Donald R. Williams ◽  
Joris Mulder ◽  
Paul Linkowski
Keyword(s):  
Sex Differences ◽  
Network Analysis ◽  
Bayesian Network ◽  
Network Structure ◽  
Network Connectivity ◽  
Autistic Traits ◽  
Data Set ◽  
Conditional Dependence ◽  
Bayesian Network Structure ◽  
Male Subjects

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

A Bayesian network analysis of workplace accidents caused by falls from a height

2009 ◽  
Vol 47 (2) ◽  
pp. 206-214 ◽  
Author(s):  
J.E. Martín ◽  
T. Rivas ◽  
J.M. Matías ◽  
J. Taboada ◽  
A. Argüelles
Keyword(s):  
Network Analysis ◽  
Bayesian Network ◽  
Workplace Accidents

Suitability of Eutectic Field’s Metal for Use in an Electromagnetohydrodynamically-Enhanced Experimental Two-Phase Flow Loop

2012 ◽  
Author(s):  
A. Lipchitz ◽  
Lilian Laurent ◽  
G. D. Harvel
Keyword(s):  
Liquid Metal ◽  
Two Phase Flow ◽  
Nuclear Reactors ◽  
Liquid Metals ◽  
Metal Flow ◽  
Phase Flow ◽  
Laboratory Setting ◽  
Flow Loop ◽  
Fast Reactors ◽  
Two Phase

Several Generation IV nuclear reactors, such as sodium fast reactors and lead-bismuth fast reactors, use liquid metal as a coolant. In order to better understand and improve the thermal hydraulics of liquid metal cooled GEN IV nuclear reactors liquid metal flow needs to be studied in experimental circulation loops. Experimental circulation loops are often located in a laboratory setting. However, studying liquid metal two phase flow in laboratory settings can be difficult due to the high temperatures and safety hazards involved with traditional liquid metals such as sodium and lead-bismuth. One solution is to use a low melt metal alloy that is as benign as reasonably achievable. Field’s metal is a eutectic alloy of 51% Indium, 32.5% Bismuth and 16.5% Tin by weight and has a melting point of 335K making it ideal for use in a laboratory setting. A study is undertaken to determine its suitability to use in a two-phase experimental flow loop enhanced by magnetohydrodynamic forces. The study investigated its reactivity with air and water, its ability to be influenced by magnetic fields, its ability to flow, and its ease of manufacture. The experiments melted reference samples of Field’s metal and observed its behaviour in a glass beaker, submerged in water and an inclined stainless steel pipe. Then Field’s metal was manufactured in the laboratory and compared to the sample using the same set of experiments and standards. To determine Field’s metal degree of magnetism permanent neodymium magnets were used. Their strength was determined using a Gaussmeter. All experiments were recorded using a COHU digital camera. Image analysis was then performed on the video to determine any movements initiated by the magnetic field forces. In conclusion, Field’s metal is more than suitable for use in experimental settings as it is non-reactive, non-toxic, simple to manufacture, easy to use, and responds to a magnetic force.

Traffic Incident Duration Estimation Based on a Dual-Learning Bayesian Network Model

2018 ◽  
Vol 2672 (45) ◽  
pp. 196-209 ◽  
Author(s):  
Haozhe Cong ◽  
Cong Chen ◽  
Pei-Sung Lin ◽  
Guohui Zhang ◽  
John Milton ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Expert Knowledge ◽  
Transportation Systems ◽  
Practical Significance ◽  
Incident Management ◽  
Highway Traffic ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Incident Duration ◽  
Traffic Incident Management

Highway traffic incidents induce a significant loss of life, economy, and productivity through injuries and fatalities, extended travel time and delay, and excessive energy consumption and air pollution. Traffic emergency management during incident conditions is the core element of active traffic management, and it is of practical significance to accurately understand the duration time distribution for typical traffic incident types and the factors that influence incident duration. This study proposes a dual-learning Bayesian network (BN) model to estimate traffic incident duration and to examine the influence of heterogeneous factors on the length of duration based on expert knowledge of traffic incident management and highway incident data collected in Zhejiang Province, China. Fifteen variables related to three aspects of traffic incidents, including incident information, incident consequences, and rescue resources, were included in the analysis. The trained BN model achieves favorable performance in several areas, including classification accuracy, the receiver operating characteristic (ROC) curve, and the area under curve (AUC) value. A classification matrix, and significant variables and their heterogeneous influences are identified accordingly. The research findings from this study provide beneficial reference to the understanding of decision-making in traffic incident response and process, active traffic incident management, and intelligent transportation systems.

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