The Experts below are selected from a list of 60 Experts worldwide ranked by ideXlab platform
Yan Yang - One of the best experts on this subject based on the ideXlab platform.
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A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry
Energies, 2017Co-Authors: Yang Tang, Jiajia Jing, Zhidong Zhang, Yan YangAbstract:The high hazard mechanical system (HHMS) has three characteristics in the petroleum and petrochemical industry (PPI): high Risk, high cost, and high technology requirements. For a HHMS, part, component, and subsystem failures will Result in varying degrees and various types of Risk consequences, including unexpected downtime, production losses, economic costs, safety accidents, and environmental pollution. Thus, obtaining the quantitative Risk level and distribution in a HHMS to control major Risk accidents and ensure safe production is of vital importance. However, the structure of the HHMS is more complex than some other systems, making the quantitative Risk analysis process more difficult. Additionally, a variety of uncertain Risk data hinder the realization of quantitative Risk analysis. A few quantitative Risk analysis techniques and studies for HHMS exist, especially in the PPI. Therefore, a study on the quantitative Risk analysis method for HHMS was completed to obtain the Risk level and distribution of high-Risk objects. Firstly, Fuzzy Set Theory (FST) was applied to address the uncertain Risk data for the occurrence probability (OP) and consequence severity (CS) in the Risk analysis process. Secondly, a fuzzy fault tree analysis (FFTA) and a fuzzy event tree analysis (FETA) were used to achieve quantitative Risk analysis and calculation. Thirdly, a fuzzy bow-tie model (FBTM) was established to obtain a quantitative Risk Assessment Result according to the analysis Results of the FFTA and FETA. Finally, the feasibility and practicability of the method were verified with a case study on the quantitative Risk analysis of one reciprocating pump system (RPS). The quantitative Risk analysis method for HHMS can provide more accurate and scientific data support for the development of Asset Integrity Management (AIM) systems in the PPI.
Bas G.h. Bokkers - One of the best experts on this subject based on the ideXlab platform.
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Exploring the Uncertainties in Cancer Risk Assessment Using the Integrated Probabilistic Risk Assessment (IPRA) Approach
Risk Analysis, 2014Co-Authors: Wout Slob, Bakker Mi, Jan Dirk Te Biesebeek, Bas G.h. BokkersAbstract:Current methods for cancer Risk Assessment Result in single values, without any quantitative information on the uncertainties in these values. Therefore, single Risk values could easily be overinterpreted. In this study, we discuss a full probabilistic cancer Risk Assessment approach in which all the generally recognized uncertainties in both exposure and hazard Assessment are quantitatively characterized and probabilistically evaluated, Resulting in a confidence interval for the final Risk estimate. The methodology is applied to three example chemicals (aflatoxin, N†nitrosodimethylamine, and methyleugenol). These examples illustrate that the uncertainty in a cancer Risk estimate may be huge, making single value estimates of cancer Risk meaningless. Further, a Risk based on linear extrapolation tends to be lower than the upper 95% confidence limit of a probabilistic Risk estimate, and in that sense it is not conservative. Our conceptual analysis showed that there are two possible basic approaches for cancer Risk Assessment, depending on the interpretation of the dose†incidence data measured in animals. However, it remains unclear which of the two interpretations is the more adequate one, adding an additional uncertainty to the already huge confidence intervals for cancer Risk estimates.
Alexandra Murray - One of the best experts on this subject based on the ideXlab platform.
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i have always believed i was at high Risk the role of expectation in emotional responses to the receipt of an average moderate or high cancer genetic Risk Assessment Result a thematic analysis of free text questionnaire comments
Familial Cancer, 2010Co-Authors: Jennifer Hilgart, Ceri Phelps, Paul Bennett, Kerenza Hood, Katherine Emma Brain, Alexandra MurrayAbstract:It is well-recognised that receipt of cancer genetic Risk information can evoke a mix of both positive and negative emotional responses. Objective Risk itself is not necessarily predictive of emotional response to receipt of Risk information and the Cue Adaptive Reasoning Account (CARA; Renner, 2004) suggests that that the degree to which level of Risk is consistent with expectations may influence emotional responses. This paper reports a thematic analysis of the free-text data structured around responses to the three Risk labels: average, moderate or high. Data is reported from both 123 women and 15 men, including those with a past or current cancer diagnosis. Reactions to Risk information appear to be dependent upon participants’ pre-conceived expectations about their level of cancer Risk. Many average Risk respondents questioned the accuracy of their Result, whereas high Risk information was often expected. Findings are discussed in relation to the CARA model and clinical implications.
Chen Chang-yu - One of the best experts on this subject based on the ideXlab platform.
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Survey of patients with severe mental disorder causing troubles and/or accidents in Rizhao of Shandong Province
The Journal of Clinical Psychiatry, 2012Co-Authors: Chen Chang-yuAbstract:Objective:To investigate management of and treatment with patients with severe mental disorders who caused troubles and/or accidents in Rizhao of Shandong Province.Method:Data of registered patients with severe mental disorder in Rizhao were retrospectively analyzed.The patients'treatment and causing troubles and/or accidents behaviors were assessed.Results:Of the patients,26.34% untreated,34.31% treated in outpatient department,39.35% treated in hospital.82.37% caused troubles and/or accidents most of whom were schizophrenic patients.The Risk Assessment Result indicated that 53.30% patients were at high-Risk.Conclusion:Patients with severe mental disorder are not enough treated and most of them causes troubles and/or accidents.We should strengthen the management and treatment of this special population.
Yang Tang - One of the best experts on this subject based on the ideXlab platform.
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A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry
Energies, 2017Co-Authors: Yang Tang, Jiajia Jing, Zhidong Zhang, Yan YangAbstract:The high hazard mechanical system (HHMS) has three characteristics in the petroleum and petrochemical industry (PPI): high Risk, high cost, and high technology requirements. For a HHMS, part, component, and subsystem failures will Result in varying degrees and various types of Risk consequences, including unexpected downtime, production losses, economic costs, safety accidents, and environmental pollution. Thus, obtaining the quantitative Risk level and distribution in a HHMS to control major Risk accidents and ensure safe production is of vital importance. However, the structure of the HHMS is more complex than some other systems, making the quantitative Risk analysis process more difficult. Additionally, a variety of uncertain Risk data hinder the realization of quantitative Risk analysis. A few quantitative Risk analysis techniques and studies for HHMS exist, especially in the PPI. Therefore, a study on the quantitative Risk analysis method for HHMS was completed to obtain the Risk level and distribution of high-Risk objects. Firstly, Fuzzy Set Theory (FST) was applied to address the uncertain Risk data for the occurrence probability (OP) and consequence severity (CS) in the Risk analysis process. Secondly, a fuzzy fault tree analysis (FFTA) and a fuzzy event tree analysis (FETA) were used to achieve quantitative Risk analysis and calculation. Thirdly, a fuzzy bow-tie model (FBTM) was established to obtain a quantitative Risk Assessment Result according to the analysis Results of the FFTA and FETA. Finally, the feasibility and practicability of the method were verified with a case study on the quantitative Risk analysis of one reciprocating pump system (RPS). The quantitative Risk analysis method for HHMS can provide more accurate and scientific data support for the development of Asset Integrity Management (AIM) systems in the PPI.
