Quantitative risk assessment for ammonia ship‐to‐ship bunkering based on Bayesian network

2021 ◽  
Author(s):  
Hongjun Fan ◽  
Hossein Enshaei ◽  
Shantha Gamini Jayasinghe ◽  
Sock Hua Tan ◽  
Chunchang Zhang
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Quantitative Risk Assessment

Quantitative Risk Assessment of Seafarers’ Nonfatal Injuries Due to Occupational Accidents Based on Bayesian Network Modeling

Risk Analysis ◽  
2019 ◽  
Vol 40 (1) ◽  
pp. 8-23
Author(s):  
Guizhen Zhang ◽  
Vinh V. Thai ◽  
Adrian Wing‐Keung Law ◽  
Kum Fai Yuen ◽  
Hui Shan Loh ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Network Modeling ◽  
Quantitative Risk Assessment ◽  
Occupational Accidents ◽  
Bayesian Network Modeling ◽  
Nonfatal Injuries

scholarly journals Bayesian network modelling of an offshore electrical generation system for applications within an asset integrity case for normally unattended offshore installations

2017 ◽  
Vol 232 (4) ◽  
pp. 402-420 ◽  
Author(s):  
Sean Loughney ◽  
Jin Wang
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Graphical Representation ◽  
Pairwise Comparison ◽  
Analysis Data ◽  
Quantitative Risk Assessment ◽  
Network Modelling ◽  
Failure Data ◽  
Electrical Generation ◽  
The Impact

This article proposes the initial stages of the application of Bayesian networks in conducting quantitative risk assessment of the integrity of an offshore system. The main focus is the construction of a Bayesian network model that demonstrates the interactions of multiple offshore safety critical elements to analyse asset integrity. The majority of the data required to complete the Bayesian network was gathered from various databases and past risk assessment experiments and projects. However, where data were incomplete or non-existent, expert judgement was applied through pairwise comparison, analytical hierarchy process and a symmetric method to fill these data gaps and to complete larger conditional probability tables. A normally unattended installation–Integrity Case will enable the user to determine the impact of deficiencies in asset integrity and demonstrate that integrity is being managed to ensure safe operations in situations whereby physical human-to-machine interaction is not occurring. The Integrity Case can be said to be dynamic as it shall be continually updated for an installation as the quantitative risk analysis data are recorded. This allows for the integrity of the various systems and components of an offshore installation to be continually monitored. The Bayesian network allows cause and effect relationships to be modelled through clear graphical representation. The model accommodates for continual updating of failure data.

A semi-quantitative risk assessment model for the outbreak of grass carp hemorrhage

2013 ◽  
Vol 19 (3) ◽  
pp. 521-527 ◽  
Author(s):  
Song YANG ◽  
Shuqin WU ◽  
Ningqiu LI ◽  
Cunbin SHI ◽  
Guocheng DENG ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Grass Carp ◽  
Assessment Model ◽  
Quantitative Risk Assessment ◽  
Risk Assessment Model

Risk assessment of Campylobacter species in shellfish: identifying the unknown

1997 ◽  
Vol 35 (11-12) ◽  
pp. 29-34 ◽  
Author(s):  
P. Teunis ◽  
A. Havelaar ◽  
J. Vliegenthart ◽  
G. Roessink
Keyword(s):  
Risk Assessment ◽  
Quantitative Risk Assessment ◽  
Contamination Level ◽  
Risk Of Infection ◽  
Worst Case ◽  
Considerable Uncertainty ◽  
Risk Estimates ◽  
Consumption Data ◽  
Campylobacter Infection ◽  
Campylobacter Spp

Shellfish are frequently contaminated by Campylobacter spp, presumably originating from faeces from gulls feeding in the growing or relaying waters. The possible health effects of eating contaminated shellfish were estimated by quantitative risk assessment. A paucity of data was encountered necessitating many assumptions to complete the risk estimate. The level of Campylobacter spp in shellfish meat was calculated on the basis of a five-tube, single dilution MPN and was strongly season-dependent. The contamination level of mussels (<1/g) appeared to be higher than in oysters. The usual steaming process of mussels was found to completely inactivate Campylobacter spp so that risks are restricted to raw/undercooked shellfish. Consumption data were estimated on the basis of the usual size of a portion of raw shellfish and the weight of meat/individual animal. Using these data, season-dependent dose-distributions could be estimated. The dominant species in Dutch shellfish is C. lari but little is known on its infectivity for man. As a worst case assumption, it was assumed that the infectivity was similar to C. jejuni. A published dose-response model for Campylobacter-infection of volunteers is available but with considerable uncertainty in the low dose region. Using Monte Carlo simulation, risk estimates were constructed. The consumption of a single portion of raw shellfish resulted in a risk of infection of 5–20% for mussels (depending on season; 95% CI 0.01–60%). Repeated (e.g. monthly) exposures throughout a year resulted in an infection risk of 60% (95% CI 7–99%). Risks for oysters were slightly lower than for mussels. It can be concluded that, under the assumptions made, the risk of infection with Campylobacter spp by eating of raw shellfish is substantial. Quantitative risk estimates are highly demanding for the availability and quality of experimental data, and many research needs were identified.

scholarly journals Epistemological Equation for Analysing Uncontrollable States in Complex Systems: Quantifying Cyber Risks from the Internet of Things

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Pete Burnap ◽  
Omar Santos
Keyword(s):  
Risk Assessment ◽  
Internet Of Things ◽  
Empirical Analysis ◽  
Quantitative Risk Assessment ◽  
The Internet ◽  
Self Assessment ◽  
Target State ◽  
Epistemological Analysis ◽  
Cyber Risk ◽  
The Internet Of Things

AbstractThe Internet-of-Things (IoT) triggers data protection questions and new types of cyber risks. Cyber risk regulations for the IoT, however, are still in their infancy. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. At present, there are no self-assessment methods for quantifying IoT cyber risk posture. It is considered that IoT represent a complex system with too many uncontrollable risk states for quantitative risk assessment. To enable quantitative risk assessment of uncontrollable risk states in complex and coupled IoT systems, a new epistemological equation is designed and tested though comparative and empirical analysis. The comparative analysis is conducted on national digital strategies, followed by an empirical analysis of cyber risk assessment approaches. The results from the analysis present the current and a target state for IoT systems, followed by a transformation roadmap, describing how IoT systems can achieve the target state with a new epistemological analysis model. The new epistemological analysis approach enables the assessment of uncontrollable risk states in complex IoT systems—which begin to resemble artificial intelligence—and can be used for a quantitative self-assessment of IoT cyber risk posture.

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