Using benchmark dose modeling for the quantitative risk assessment: Carbon nanotubes, asbestos, glyphosate

2020 ◽  
Vol 41 (1) ◽  
pp. 148-160
Author(s):  
Andrey Korchevskiy
Keyword(s):  
Risk Assessment ◽  
Carbon Nanotubes ◽  
Benchmark Dose ◽  
Quantitative Risk Assessment

Uses of benchmark dose methodology in quantitative risk assessment

2005 ◽  
Vol 42 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Thomas B. Starr ◽  
Jay I. Goodman ◽  
David G. Hoel
Keyword(s):  
Risk Assessment ◽  
Benchmark Dose ◽  
Quantitative Risk Assessment

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.

scholarly journals Quantitative Risk Assessment for Deep Tunnel Failure Based on Normal Cloud Model: A Case Study at the ASHELE Copper Mine, China

2021 ◽  
Vol 11 (11) ◽  
pp. 5208
Author(s):  
Jianpo Liu ◽  
Hongxu Shi ◽  
Ren Wang ◽  
Yingtao Si ◽  
Dengcheng Wei ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Large Scale ◽  
Cloud Model ◽  
Quantitative Risk Assessment ◽  
Geological Condition ◽  
Deep Tunnel ◽  
Entropy Weight ◽  
Copper Mine ◽  
Evaluation Indexes ◽  
Metal Mines

The spatial and temporal distribution of tunnel failure is very complex due to geologic heterogeneity and variability in both mining processes and tunnel arrangement in deep metal mines. In this paper, the quantitative risk assessment for deep tunnel failure was performed using a normal cloud model at the Ashele copper mine, China. This was completed by considering the evaluation indexes of geological condition, mining process, and microseismic data. A weighted distribution of evaluation indexes was determined by implementation of an entropy weight method to reveal the primary parameters controlling tunnel failure. Additionally, the damage levels of the tunnel were quantitatively assigned by computing the degree of membership that different damage levels had, based on the expectation normalization method. The methods of maximum membership principle, comprehensive evaluation value, and fuzzy entropy were considered to determine the tunnel damage levels and risk of occurrence. The application of this method at the Ashele copper mine demonstrates that it meets the requirement of risk assessment for deep tunnel failure and can provide a basis for large-scale regional tunnel failure control in deep metal mines.

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