• Nuclear Engineering and Technology
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• v.40 no.5
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• pp.327-348
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• 2008

A risk-informed regulatory approach implies that risk insights be used as supplement of deterministic information for safety decision-making purposes. In this view, the use of risk assessment techniques is expected to lead to improved safety and a more rational allocation of the limited resources available. On the other hand, it is recognized that uncertainties affect both the deterministic safety analyses and the risk assessments. In order for the risk-informed decision making process to be effective, the adequate representation and treatment of such uncertainties is mandatory. In this paper, the risk-informed regulatory framework is considered under the focus of the uncertainty issue. Traditionally, probability theory has provided the language and mathematics for the representation and treatment of uncertainty. More recently, other mathematical structures have been introduced. In particular, the Dempster-Shafer theory of evidence is here illustrated as a generalized framework encompassing probability theory and possibility theory. The special case of probability theory is only addressed as term of comparison, given that it is a well known subject. On the other hand, the special case of possibility theory is amply illustrated. An example of the combination of probability and possibility for treating the uncertainty in the parameters of an event tree is illustrated.

• Water Engineering Research
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• v.3 no.4
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• pp.215-234
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• 2002

Uncertainty in dam breach flood routing results was analyzed in order to provide the basis fer the investigation of their effects on the flood damage assessments and dam safety risk assessments. The Monte Carlo simulation based on Latin Hypercube Sampling technique was used to generate random values for two uncertain input parameters (i.e., dam breach parameters and Manning's n roughness coefficients) of a dam breach flood routing analysis model. The flood routing results without considering the uncertainty in two input parameters were compared with those with considering the uncertainty. This paper showed that dam breach flood routing results heavily depend on the two uncertain input parameters. This study indicated that the flood damage assessments in the downstream areas can be critical if uncertainty in dam breach flood routing results are considered in a reasonable manner.

• Nuclear Engineering and Technology
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• v.35 no.1
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• pp.64-79
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• 2003

In the risk and reliability analysis of complex technological systems, the primary concern of formal uncertainty analysis is to understand why uncertainties arise, and to evaluate how they impact the results of the analysis. In recent times, many of the uncertainty analyses have focused on parameters of the risk and reliability analysis models, whose values are uncertain in an aleatory or an epistemic way. As the field of parametric uncertainty analysis matures, however, more attention is being paid to the explicit treatment of uncertainties that are addressed in the predictive model itself as well as the accuracy of the predictive model. The essential steps for evaluating impacts of these model uncertainties in the presence of parameter uncertainties are to determine rigorously various sources of uncertainties to be addressed in an underlying model itself and in turn model parameters, based on our state-of-knowledge and relevant evidence. Answering clearly the question of how to characterize and treat explicitly the forgoing different sources of uncertainty is particularly important for practical aspects such as risk and reliability optimization of systems as well as more transparent risk information and decision-making under various uncertainties. The main purpose of this paper is to provide practical guidance for quantitatively treating various model uncertainties that would often be encountered in the risk and reliability modeling process of complex technological systems.

• Journal of Construction Engineering and Project Management
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• v.3 no.2
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• pp.21-34
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• 2013

Various risk factors affect construction projects. Due to the uncertainties created by risk factors, actual activity durations frequently deviate from the estimated durations in either favorable or adverse direction. For this reason, evaluation of schedule uncertainty is required to make decisions accurately when managing construction projects. In this regard, this paper presents a new computer simulation model - the Repetitive Schedule Risk Analysis Model (RSRAM) - to evaluate unit-based repetitive building project schedules under uncertainty when activity durations and risk factors are correlated. The proposed model utilizes Monte Carlo Simulation and a Critical Path Method based repetitive scheduling procedure. This new procedure concurrently provides the utilization of resources without interruption and the maintenance of network logic through successive units. Furthermore, it enables assigning variable production rates to the activities from one unit to another and any kind of relationship type with or without lag time. Details of the model are described and an example application is presented. The findings show that the model produces realistic results regarding the extent of uncertainty inherent in the schedule.

• The Journal of Fisheries Business Administration
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• v.39 no.2
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• pp.109-123
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• 2008

This study is aimed to establish an investment decision model for offshore aquaculture project of rock bream in Korea using a certainty equivalent method (CEM) based on the expected utility theory and to investigate its economic viability under risk and uncertainty. In the analysis with CEM, the effects of risk attitude and risk level on investment and risk premium were examined and the impacts of various risk and uncertainty factors on the investment decision were also assessed. In addition, the outcomes were compared to those evaluated by the traditional net present value (NPV) method. Results show that risk premium grew as the investors became more risk averse and uncertainty level (the variance of NPV) increased. Consequently, the certainty equivalent value was predicted to decrease from the value assessed by the traditional NPV method.

• Korean Journal of Biological Psychiatry
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• v.21 no.3
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• pp.87-92
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• 2014

Objectives Risk taking has been implicated in the development of various psychiatric disorders. Previous studies have indicated that risk taking behavior is associated with high levels of impulsiveness. Risk taking entail uncertain situation that outcome probability is unknown. This study tested impulsivity, intolerance of uncertainty and risk taking behavior. Methods A total of 73 participants completed a test battery comprised of the UPPS-P scale as a psychometric measurement of five dimensions of impulsivity, Intolerance of Uncertainty Scale, and Balloon Analog Risk Task (BART) as a behavioral measure of risk taking. The Pearson correlation analysis was used. Results The sensation seeking factor was positively correlated with BART measure (r = 0.27, p = 0.02). Specifically, the relationship between sensation seeking and BART was significant in females. Conclusions Among the five factors of UPPS-P, only the sensation seeking factor predicts risk taking propensity.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.1-11
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• 2022

The study incorporates model uncertainty into the private equity (PE) valuation model (SWY model) (Sorensen et al., 2014) to evaluate how model uncertainty distorts the leverage and valuations of PE funds. This study applies a continuous-time model to PE project valuation, modeling the LPs' goal as multiplier preferences provided by Anderson et al. (2003), and assuming that LPs' aversion to model uncertainty causes endogenous belief distortions with entropy as a measure of model discrepancies. Concerns regarding model uncertainty, according to the theoretical model, have an unclear effect on LPs' risk attitude and GPs' decision, which is based on the value of the PE asset. It also demonstrates that model uncertainty lowers the certainty-equivalent valuation of the LPs. Finally, we compare the outcomes of the Full-spanning risk model with the Non-spanned risk model, and they match the intuitive economic reasoning. The most important implication is that model uncertainty will have negative effects on the LPs' certainty-equivalent valuation but has ambiguous effects on the portfolio allocation choice of liquid wealth. Our works contribute to two literature streams. The first is the literature that models the PE funds. The second is the literature introduces model uncertainty into standard finance models.

• Journal of muscle and joint health
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• v.23 no.2
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• pp.114-124
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• 2016

Purpose: The purpose of this study was to investigate the level of uncertainty, physiological risk factors, self-efficacy, and self-management among stroke patients and to identify factors influencing their self-management. Methods: A descriptive correlational design was used for this study. A convenience sample of 149 patients with stroke were enrolled at C national university hospital from February to April in 2016. Data were collected using a structured questionnaire and electronic medical record. Collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlations, and multiple regression analysis with the SPSS/WIN 21.0 program. Results: There were significant negative correlations between uncertainty and self-efficacy (r=-.56, p<.001); between uncertainty and self-management (r=-.56, p<.001); and between total cholesterol and self-management (r=-.23, p=.005). There were significant positive correlations between self-efficacy and self-management (r=.78, p<.001); between uncertainty and total cholesterol (r=.24, p=.003). The significant factors influencing self-management were uncertainty and self-efficacy. Theses variables explained 62.7% of the variance in self-management. Conclusion: The results suggest that intervention programs to reduce the level of uncertainty and to increase the level of self-efficacy among patients would improve the self-management of stroke patients.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.272-288
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• 1990

The uncertainty quantification process in probabilistic Risk Assessment usually involves a specification of the uncertainty in the input data and the propagation of this uncertainty to the final risk results. The distributional sensitivity analysis is to study the impact of the various assumptions made during the quantification of input parameter uncertainties on the final output uncertainty. The uncertainty importance of input parameters, in this case, should reflect the degree of changes in the whole output distribution and not just in a point estimate value. A measure of the uncertainty importance is proposed in the present paper. The measure is called the distributional sensitivity measure(DSM) and explicitly derived from the definition of the Kullback's discrimination information. The DSM is applied to three typical discrimination information. The DSM is applied to three typical cases of input distributional changes: 1) Uncertainty is completely eliminated, 2) Uncertainty range is increased by a factor of 10, and 3) Type of distribution is changed. For all three cases of application, the DSM-based importance ranking agrees very well with the observed changes of output distribution while other statistical parameters are shown to be insensitive.

• Women's Health Nursing
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• v.26 no.2
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• pp.180-190
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• 2020

Purpose: The purpose of this study was to identify the effects of a supportive program on uncertainty, anxiety, and maternal-fetal attachment in high-risk pregnant women. Methods: The participants were 59 high-risk pregnant women admitted to the maternal-fetal intensive care unit. The control group (n=30) received usual treatment and antenatal care, while the experimental group (n=29) received an additional supportive program. Uncertainty, anxiety, and maternal-fetal attachment were measured in both groups prior to the intervention and at 3 days and 10 days after the intervention (or at discharge). Data were analyzed with the t-test, chi-square test, repeated-measures analysis of covariance, and the Greenhouse-Geisser correction in SPSS version 23.0. Results: A supportive program including information provision, nutritional care, emotional care, and exercise care was developed from the literature. All variables except women's length of stay were found to be homogeneous the between experimental and control groups in the pre-test. Length of stay was calculated as a covariate for testing hypotheses. There was a significant difference in state anxiety over time between the two groups, while there were no differences in uncertainty or maternal-fetal attachment. Conclusion: This supportive program was identified as an effective nursing intervention on state anxiety in high-risk pregnant women during their stay in the maternal-fetal intensive care unit. It is suggested that nurses could apply this program to alleviate high-risk pregnant women's state anxiety, and that this program could be modified to be more effective on uncertainty and maternal-fetal attachment in high-risk pregnant women.