Quantitative risk assessment for skin sensitisation: Consideration of a simplified approach for hair dye ingredients

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.

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GEOSTATISTICAL ANALYSIS OF DISSOLUTIONING AND VOIDS IN THE FOUNDATION OF MOSUL DAM, IRAQ TO CHARACTERIZE SUBSURFACE CONDITIONS AND FACILITATE SEMI-QUANTITATIVE RISK ASSESSMENT

10.1130/abs/2018am-318094 ◽

2018 ◽

Author(s):

Laurel E. Blackman ◽

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Baron M. Worsham ◽

Nathan D. Williams ◽

Steven J. Jirousek ◽

...

Keyword(s):

Risk Assessment ◽

Geostatistical Analysis ◽

Quantitative Risk Assessment

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

The Review of Socionetwork Strategies ◽

10.1007/s12626-021-00086-5 ◽

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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