• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.659-668
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• 2006

The uncertainty of the risk of failure of hydraulic structures can be determined by estimating the variance of the risk of failure based on the methods of moments, probability weighted moments, and maximum likelihood assuming that the underlying model is the Gumbel distribution. In this paper, the variance of the risk of failure was derived. Monte Carlo simulation was peformed to verify the characteristics of the derived formulas for various sample size, design life, nonexceedance probability, and variation coefficient. As the results, PWM showed the smallest relative bias and root mean square error than the others while ML showed the smallest ones for relatively large sample siBes regardless of design life and nonexceedance probability. Also, it was found that variation coefficient does not effect on the relative bias and relative root mean square error.

• Journal of the Korean Home Economics Association
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• v.38 no.6
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• pp.149-169
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• 2000

The purpose of this study was to examine the environmental characteristics of adolescents in low-income families, identify the high-risk & protective factors among environmental contexts surrounding adolescents, and investigate the relative importance of high-risk & protective factors to adolescents'psychological and behavioral adjustment separately. The present study was the primary research of developing prevention program for adolescents'deviant behaviors in low-income families. Subjects of this study consisted of 176 adolescents drawn from 8 social-welfare institutions in Chungbuk province. The pilot study was done to examine the applicability of survey instrument. Data were analyzed by the frequency, percentage, Pearson correlation, stepwise regression using SPSS/WIN program. The results were as followings: 1. There was statistically correlated with each other in environmental high-risk and protective factors except an housing environment. The results implies that environmental contexts itself surrounding adolescents in low-income families can be either high-risk factors or protective factors. 2. The adolescents in low-income families perceived that stresses from consumer and school environments were high-risk factors among other environmental contexts. 3. The adolescents in low-income families perceived that resources from friend and school were protective factors among other environmental contexts. 4. The stresses from friend and eating behaviors were significant factors predicting adolescents'relative psychological adjustment. However, the behavioral adjustment was not predicted by environmental contexts. 5. The resources from school, consumer, and eating behaviors were significant factors predicting adolescents'relative psychological adjustment. Also, the resources from school, eating behavior, and family were predictors of behavioral adjustment. This research implies that the findings can be based on the development of prevention program for adolescents deviant behaviors in low-income families.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.2
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• pp.205-216
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• 2003

We have gathered exposure data on ambient air quality level and investigated dose-response slope factors of air pollutants such as fine particle, HAPs (metals, VOCs, PAHs) and dioxins in Seoul. Theoretical mortality incidences were estimated from exposure to these pollutants. From the results, priorities were ranked in the order fine particle, metals, VOCs, dioxins and PAHs by ordinal scale, and the uncertainties relative to those risk estimates were described.

• International Journal of Human Ecology
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• v.2 no.1
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• pp.135-152
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• 2001

The purpose of this study was to investigate the association of protective and risk factors with adolescent delinquency. Using nationally representative data from the National Longitudinal Study of Adolescent Health (Add Health) project, the delinquency level and the influence of individual characteristics, dyadic relationships in the family, school and peer variables on adolescent delinquency were examined by gender. Boys and girls differed in delinquency level with boys showing significantly higher delinquency than girls. The relative influence of protective and risk factors in individual, familial, peer, and school contexts differed by gender as well. More diverse variables influenced the delinquency level of girls than that of boys.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.1090-1094
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• 2017

The determinants of the public's nuclear power acceptance have received considerable attention as decisive factors regarding nuclear power policy. However, the contingency of the relative importance of different determinants has been less explored. Building on the literature of psychological distance between the individual and the object, the present study demonstrates that the relative effects of different types of perceived risks regarding nuclear power generation differ across acceptance targets. Using a sample of Korea, our results show that, regarding national acceptance of nuclear power generation, perceived risk from nuclear power plants exerts a stronger negative effect than that from radioactive waste management; however, the latter exerts a stronger negative effect than the former on local acceptance of a nuclear power plant. This finding provides implications for efficient public communication strategy to raise nuclear power acceptance.

• Safety and Health at Work
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• v.7 no.3
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• pp.251-255
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• 2016

Background: Building demolition can lead to emission of dust into the environment. Exposure to silica dust may be considered as an important hazard in these sites. The objectives of this research were to determine the amount of workers' exposure to crystalline silica dust and assess the relative risk of silicosis and the excess lifetime risk of mortality from lung cancer in demolition workers. Methods: Four sites in the Tehran megacity region were selected. Silica dust was collected using the National Institute for Occupational Safety and Health method 7601 and determined spectrophotometrically. The Mannetje et al and Rice et al models were chosen to examine the rate of silicosis-related mortality and the excess lifetime risk of mortality from lung cancer, respectively. Results: The amount of demolition workers' exposure was in the range of $0.085-0.185mg/m^3$. The range of relative risk of silicosis related mortality was increased from 1 in the workers with the lowest exposure level to 22.64/1,000 in the employees with high exposure level. The range of the excess lifetime risk of mortality from lung cancer was in the range of 32-60/1,000 exposed workers. Conclusion: Geometric and arithmetic mean of exposure was higher than threshold limit value for silica dust in all demolition sites. The risk of silicosis mortality for many demolition workers was higher than 1/1,000 (unacceptable level of risk). Estimating the lifetime lung cancer mortality showed a higher risk of mortality from lung cancer in building demolition workers.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.89-111
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• 2008

Due to the development of digital technology, studies regarding smart wear integrating daily life have rapidly increased. However, consumer research about perception and attitude toward smart clothing hardly could find. The purpose of this study was to identify innovative characteristics and perceived risk of smart clothing and to analyze the influences of theses factors on product attitudes and intention to adopt. Specifically, five hypotheses were established. H1: Perceived attributes of smart clothing except for complexity would have positive relations to product attitude or purchase intention, while complexity would be opposite. H2: Product attitude would have positive relation to purchase intention. H3: Product attitude would have a mediating effect between perceived attributes and purchase intention. H4: Perceived risks of smart clothing would have negative relations to perceived attributes except for complexity, and positive relations to complexity. H5: Product attitude would have a mediating effect between perceived risks and purchase intention. A self-administered questionnaire was developed based on previous studies. After pretest, the data were collected during September, 2006, from university students in Korea who were relatively sensitive to innovative products. A total of 300 final useful questionnaire were analyzed by SPSS 13.0 program. About 60.3% were male with the mean age of 21.3 years old. About 59.3% reported that they were aware of smart clothing, but only 9 respondents purchased it. The mean of attitudes toward smart clothing and purchase intention was 2.96 (SD=.56) and 2.63 (SD=.65) respectively. Factor analysis using principal components with varimax rotation was conducted to identify perceived attribute and perceived risk dimensions. Perceived attributes of smart wear were categorized into relative advantage (including compatibility), observability (including triability), and complexity. Perceived risks were identified into physical/performance risk, social psychological risk, time loss risk, and economic risk. Regression analysis was conducted to test five hypotheses. Relative advantage and observability were significant predictors of product attitude (adj $R^2$=.223) and purchase intention (adj $R^2$=.221). Complexity showed negative influence on product attitude. Product attitude presented significant relation to purchase intention (adj $R^2$=.692) and partial mediating effect between perceived attributes and purchase intention (adj $R^2$=.698). Therefore hypothesis one to three were accepted. In order to test hypothesis four, four dimensions of perceived risk and demographic variables (age, gender, monthly household income, awareness of smart clothing, and purchase experience) were entered as independent variables in the regression models. Social psychological risk, economic risk, and gender (female) were significant to predict relative advantage (adj $R^2$=.276). When perceived observability was a dependent variable, social psychological risk, time loss risk, physical/performance risk, and age (younger) were significant in order (adj $R^2$=.144). However, physical/performance risk was positively related to observability. The more Koreans seemed to be observable of smart clothing, the more increased the probability of physical harm or performance problems received. Complexity was predicted by product awareness, social psychological risk, economic risk, and purchase experience in order (adj $R^2$=.114). Product awareness was negatively related to complexity, meaning high level of product awareness would reduce complexity of smart clothing. However, purchase experience presented positive relation with complexity. It appears that consumers can perceive high level of complexity when they are actually consuming smart clothing in real life. Risk variables were positively related with complexity. That is, in order to decrease complexity, it is also necessary to consider minimizing anxiety factors about social psychological wound or loss of money. Thus, hypothesis 4 was partially accepted. Finally, in testing hypothesis 5, social psychological risk and economic risk were significant predictors for product attitude (adj $R^2$=.122) and purchase intention (adj $R^2$=.099) respectively. When attitude variable was included with risk variables as independent variables in the regression model to predict purchase intention, only attitude variable was significant (adj $R^2$=.691). Thus attitude variable presented full mediating effect between perceived risks and purchase intention, and hypothesis 5 was accepted. Findings would provide guidelines for fashion and electronic businesses who aim to create and strengthen positive attitude toward smart clothing. Marketers need to consider not only functional feature of smart clothing, but also practical and aesthetic attributes, since appropriateness for social norm or self image would reduce uncertainty of psychological or social risk, which increase relative advantage of smart clothing. Actually social psychological risk was significantly associated to relative advantage. Economic risk is negatively associated with product attitudes as well as purchase intention, suggesting that smart-wear developers have to reflect on price ranges of potential adopters. It will be effective to utilize the findings associated with complexity when marketers in US plan communication strategy.

• Proceedings of KOSOMES biannual meeting
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• 1999.03a
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• pp.43-51
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• 1999

The objective of this research is to ensure that the ports and waterways management system can establish and maintain a reasonable level of safety level during high density passenger operations. The research model was developed included computer based model that could be used that can be used to measure and monitor risk and evolved overtime. The research methodology provides model for assessing relative risk and evaluating risk reduction measures. The risk analysis based on expert judgement was refined overtime. They provide a basis for risk reduction and risk management policies and strategies. The evaluation and validation of risk model and development of data, methods, tools required to measure, monitor and evaluate ports and waterway risk was implemented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.151-161
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• 1999

For many decades, Deterministic DCF approach has been widely used to evaluate investment opportunities. Under new manufacturing conditions involving uncertainty and risk, the DCF approach is not appropriate. In DCF, Risk is incorporated in two ways: certainty equivalent method, risk adjusted discount rate. This paper proposes a determination method of the Risk Adjusted Discount Rate for economically decision making advanced manufacturing technologies. Conventional DCF techniques typically use discount rate which do not consider the difference in risk of differential investment options and periods. Due to their relative efficiency, advanced manufacturing technologies have different degree of risk. The risk differential of investments is included using $\beta$ coefficient of capital asset pricing model. The comparison between existing and proposed method investigated. The DCF model using proposed risk adjusted discount rate enable more reasonable evaluation of advanced manufacturing technologies.