• Journal of Korean Society of societal Security
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• v.1 no.3
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• pp.69-76
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• 2008

The object of this study is to develop a tool for quantifying risks related to the rail transportation of hazardous commodities and to present mitigation measures. In this study, the Quantitative Risk Assessment (QRA) is used as a risk analysis tool. Based on the previous explosion history (Iri explosion) and consideration of its high risk, Iksan-si is selected as a model city. The result, expressed as average individual risk for exposed people with various distance, indicates that the model city is considered to be safe according to the nuclear energy standard. Also, the mitigation measures are provided since Societal risk of Iksan-si is set within ALARP. Risk reduction measures include rail car design, rail transportation operation, demage spread control as well as derail prevention and alternative routes for reducing accident frequencies. Finally, it is expected to achieve high level of public safety by appling the risk reduction measures.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7577-7581
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• 2013

The interleukin-18 promoter -607C>A gene polymorphism may be related to nasopharyngeal carcinoma (NPC) risk but the results of individual studies remain conflicting. A meta-analysis including 1,886 subjects from five individual studies was therefore performed to provide a more accurate estimation. Pooled odds ratios (ORs) and their corresponding 95% confidence intervals (95% CIs) were evaluated by fixed- or random-effects models. A significant relationship between interleukin-18 promoter -607C>A gene polymorphism and NPC was found in a dominant genetic model (OR: 1.351, 95% CI: 1.089-1.676, P=0.006, $P_{heterogeneity}$=0.904), a homozygote model (OR: 1.338, 95% CI: 1.023-1.751, P=0.034, $P_{heterogeneity}$=0.863), and a heterozygote model (OR: 1.357, 95% CI: 1.080-1.704, P=0.009, $P_{heterogeneity}$=0.824). No significant association was detected in either an allelic genetic model (OR: 1.077, 95% CI: 0.960-1.207, 0.207, $P_{heterogeneity}$=0.844) or a recessive genetic model (OR: 1.093, 95% CI: 0.878-1.361, P=0.425, $P_{heterogeneity}$=0.707). In conclusion, a significant association was found between interleukin-18 promoter -607C>A gene polymorphism and NPC risk. Individuals with the C allele of interleukin-18 promoter -607C>A gene polymorphism have a higher risk of NPC development.

• The Korean Journal of Community Living Science
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• v.22 no.4
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• pp.655-668
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• 2011

Although consumer ethical behavior related with illegal copies of digital software has been considered to be an important issue, not many studies have attempted to examine the issue. Firstly, this study attemped to explain the moral judgment and intention to make illegal copies of smart phone applications for college students. Secondly, psychological factors such as moral intensity and perceived risk related to making illegal copies were tested to be significantly different in individual characteristics such as experience of ethical education and past experience of making illegal copies of software, sex, age and household income. Thirdly, the effect of related factors such as psychological factors and individual characteristics was estimated to significantly influence moral judgment and intention to make illegal copies. Two step method(using LIMDEP program) was applied to estimate the model as a structural equation model. According to the results of this study, magnitude of consequences, financial risk and performance risk were found to be significantly different in income groups(less than middle class vs more than middle class). Prosecution risk was found to be significantly different in gender groups(female vs male). In addition, social consensus, financial risk, performance risk and prosecution risk were found to be significantly different in ethical education groups(experience vs no experience). Furthermore, moral judgment for making illegal copies of smart phone applications was found to be significantly influenced by income, ethical education, magnitude of consequences, temporal immediacy and social consensus. And intention to make illegal copies of smart phone applications was found to be significantly influenced by moral judgment, age, financial risk, performance risk and prosecution risk.

• The Journal of Information Systems
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• v.16 no.1
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• pp.135-158
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• 2007

The purpose of this research is to identify multidimensional motivation factors that determine the piracy of copyrighted digital music. The model is based on TPB(theory of planned behavior) as well as other models in consumer behavior. An empirical study resulted in the following findings. first Both individual's attitude toward music piracy and individual's perceived behavior control have positive impacts on the individual's behavioral intention of piracy. It turned out that perceived behavior control has a stronger impact on behavioral intention than attitude does. Second, the level of individual's moral judgment has negative impacts on both the attitude and behavioral intention toward music piracy. Third, individual's experience in music piracy positively affects the attitude, but does not directly or indirectly affect the behavior intention. Fourth, an economic gain from music piracy is not a significant factor in determining both attitude and behavioral intention. Fifth, the risk of being prosecuted for music piracy is a major factor in determining one's attitude, although the risk is not significant enough to change one's behavioral intention. This research found that individuals' intention to pirate digital music is mainly affected by the moral and ethical standards of the individuals and by the extra resources and abilities they possess. Such factors as economic gain and law enforcement were not significant enough to alter one's behavioral intention. This research is significant in that it established a behavioral model to understand the piracy of copyrighted digital music and that it empirically tested the model with Internet users in Korea. This is one of the first empirical studies in Korea to touch such ethically and perhaps politically sensitive issues as online music piracy.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.339-350
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• 2021

In medical research, the risk factors associated with human diseases need to be identified to predict the incidence rate and determine the treatment plan. Logistic regression analysis is primarily used in order to select risk factors. However, individuals who are unfamiliar with statistics outcomes have trouble using these methods. In this study, we develop a nomogram that graphically represents the numerical association between the disease and risk factors in order to identify the risk factors for delirium and to interpret and use the results more effectively. By using the logistic regression model, we identify risk factors related to delirium, construct a nomogram and predict incidence rates. Additionally, we verify the developed nomogram using a receiver operation characteristics (ROC) curve and calibration plot. Nursing home, stroke/epilepsy, metabolic abnormality, hemodynamic instability, and analgesics were selected as risk factors. The validation results of the nomogram, built with the factors of training set and the test set of the AUC showed a statistically significant determination of 0.893 and 0.717, respectively. As a result of drawing the calibration plot, the coefficient of determination was 0.820. By using the nomogram developed in this paper, health professionals can easily predict the incidence rate of delirium for individual patients. Based on this information, the nomogram could be used as a useful tool to establish an individual's treatment plan.

• Journal of Preventive Medicine and Public Health
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• v.45 no.1
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• pp.14-20
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• 2012

Objectives: The aim of this study was to evaluate and quantify the risk of both individual and combined health behaviors on premature mortality in middle aged men in Korea. Methods: In total, 14 533 male subjects 40 to 59 years of age were recruited. At enrollment, subjects completed a baseline questionnaire, which included information about socio-demographic factors, past medical history, and life style. During the follow-up period from 1993 to 2008, we identified 990 all-cause premature deaths using national death certificates. A Cox proportional hazard regression model was used to estimate the hazard ratio (HR) of each health risk behavior, which included smoking, drinking, physical inactivity, and lack of sleep hours. Using the Cox model, each health behavior was assigned a risk score proportional to its regression coefficient value. Health risk scores were calculated for each patient and the HR of all-cause premature mortality was calculated according to risk score. Results: Current smoking and drinking, high body mass index, less sleep hours, and less education were significantly associated with all-cause premature mortality, while regular exercise was associated with a reduced risk. When combined by health risk score, there was a strong trend for increased mortality risk with increased score (p-trend < 0.01). When compared with the 1-9 score group, HRs of the 10-19 and 20-28 score groups were 2.58 (95% confidence intervals [CIs], 2.19 to 3.03) and 7.09 (95% CIs, 5.21 to 9.66), respectively. Conclusions: Modifiable risk factors, such as smoking, drinking, and regular exercise, have considerable impact on premature mortality and should be assessed in combination.

• Journal of the Korean Society of Safety
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• v.12 no.1
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• pp.51-59
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• 1997

These days leakage incidents of toxic materials cause serious effects on the nearby residents as well as the workers around the accidents accompanying massive material losses and human damages through widening influential areas. The risk measure through adequate quantitative analysis as well as the qualitative analysis of the leakage incidents of toxic materials becomes an urgent issue. The damage of the leakage incident on the surrounding area of the dangerous toxic material facilities was calculated quantitatively by adopting several models in this research. First, the calculations of the leakage velocity from the factories were performed by using source model for the assessment of the influential area, and the damages on the nearly residents were calculated by using the dispersion model and the effort model. The probability of the Incidents was computed based on "The manual for classification and priorization of major incidents" published by IAEA( International Atomic Energy Agency ). Above calculated damage area and incident probability were further adopted in this study to induce the individual and societal risk, quantitatively. The calculated data of the real Incident of the toxic material leakage showed reasonable agreements to the actual damage of the incidents, which showed a validity of this study. The result of this study might be a helpful measure for predicting damages and preparing safety systems for similar kinds of incidents.incidents.

• The Journal of the Korean dental association
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• v.52 no.8
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• pp.472-477
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• 2014

The new paradigm of dentistry require the detection of caries in their earlier stages. To achieve this, a high technology detection device and systematic and organized caries management system are needed. Caries management by risk assessment (CAMBRA) model is representative caries management system that satisfied new paradigm. Dental caries prevention and treatment according to CAMBRA model is patient-centered, risk-based, evidence-based practice. Therefore, individual caries management such as CAMBRA should be performed through accurate assessment of caries disease indicators and comprehensive assessment of caries risk factors and protective factors. Based on the CAMBRA better effectiveness of comprehensive dental caries management including non-surgical treatment will be accomplished.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.287-294
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• 2021

Cardiotoxicity assessment of all drugs has been performed according to the ICH guidelines since 2005. Non-clinical evaluation S7B has focused on the hERG assay, which has a low specificity problem. The comprehensive in vitro proarrhythmia assay (CiPA) project was initiated to correct this problem, which presented a model for classifying the Torsade de pointes (TdP)-induced risk of drugs as biomarkers calculated through an in silico ventricular model. In this study, we propose a TdP-induced risk group classifier of artificial neural network (ANN)-based. The model was trained with 12 drugs and tested with 16 drugs. The ANN model was performed according to nine features, seven features, five features as an individual ANN model input, and the model with the highest performance was selected and compared with the classification performance of the qNet input logistic regression model. When the five features model was used, the results were AUC 0.93 in the high-risk group, AUC 0.73 in the intermediate-risk group, and 0.92 in the low-risk group. The model's performance using qNet was lower than the ANN model in the high-risk group by 17.6% and in the low-risk group by 29.5%. This study was able to express performance in the three risk groups, and it is a model that solved the problem of low specificity, which is the problem of hERG assay.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.