• Journal of Food Hygiene and Safety
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• v.14 no.4
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• pp.319-326
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• 1999

Recently, it is continuously rising to concern about the health risk being induced by microorganisms in food such as Escherichia coli O157:H7 and Listeria monocytogenes. Various organizations and regulatory agencies including U.S.FPA, U.S.DA and FAO/WHO are preparing the methodology building to apply microbial quantitative risk assessment to risk-based food safety program. Microbial risks are primarily the result of single exposure and its health impacts are immediate and serious. Therefore, the methodology of risk assessment differs from that of chemical risk assessment. Microbial quantitative risk assessment consists of tow steps; hazard identification, exposure assessment, dose-response assessment and risk characterization. Hazard identification is accomplished by observing and defining the types of adverse health effects in humans associated with exposure to foodborne agents. Epidemiological evidence which links the various disease with the particular exposure route is an important component of this identification. Exposure assessment includes the quantification of microbial exposure regarding the dynamics of microbial growth in food processing, transport, packaging and specific time-temperature conditions at various points from animal production to consumption. Dose-response assessment is the process characterizing dose-response correlation between microbial exposure and disease incidence. Unlike chemical carcinogens, the dose-response assessment for microbial pathogens has not focused on animal models for extrapolation to humans. Risk characterization links the exposure assessment and dose-response assessment and involve uncertainty analysis. The methodology of microbial dose-response assessment is classified as nonthreshold and thresh-old approach. The nonthreshold model have assumption that one organism is capable of producing an infection if it arrives at an appropriate site and organism have independence. Recently, the Exponential, Beta-poission, Gompertz, and Gamma-weibull models are using as nonthreshold model. The Log-normal and Log-logistic models are using as threshold model. The threshold has the assumption that a toxicant is produce by interaction of organisms. In this study, it was reviewed detailed process including risk value using model parameter and microbial exposure dose. Also this study suggested model application methodology in field of exposure assessment using assumed food microbial data(NaCl, water activity, temperature, pH, etc.) and the commercially used Food MicroModel. We recognized that human volunteer data to the healthy man are preferred rather than epidemiological data fur obtaining exact dose-response data. But, the foreign agencies are studying the characterization of correlation between human and animal. For the comparison of differences to the population sensitivity: it must be executed domestic study such as the establishment of dose-response data to the Korean volunteer by each microbial and microbial exposure assessment in food.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.10
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• pp.997-1004
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• 2017

The life of elevator wire ropes is one of the most important characteristics of an elevator, which is closely related to the safety of users and its maintenance policy. It is not cost effective to measure the lifetime of elevator wire ropes during their use. In this study, the life estimation of elevator wire ropes (8x19W-IWRC) is considered using accelerated degradation test data. A bending fatigue tester is used to perform the accelerated degradation tests, incorporating the acceleration factor of tensile force. Assuming that the life of wire ropes is log-normally distributed, two life estimation methods are suggested and their results are compared. The first method estimates the life of wire ropes utilizing the accelerated life model with pseudo lives obtained from a linear regression model. The second method estimates the life using a logistic model based on failure probability.

• Korean Chemical Engineering Research
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• v.62 no.1
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• pp.53-69
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• 2024

The solubility of Cloxacillin sodium in ethanol, 1-propanol, isopropanol, and acetone solutions was measured at different temperatures. The melting property was also tested by using a differential scanning calorimeter (DSC). Then, the solubility data were fitted using Apelblat equation and λh equation, respectively. The Wilson model and NRTL model were not utilized to correlate the test data, since Cloxacillin sodium will decompose directly after melting. For comparison purposes, the four empirical models, i.e., Apelblat equation, λh equation, Wilson model and NRTL Model, were evaluated by using 1155 solubility curves of 103 solutes tested under different monosolvents and temperatures. The comparison results indicate that the Apelblat equation is superior to the others. Furthermore, a new method (named the calculation method) for determining the Apelblat equation using only three data points was proposed to solve the problem that there may not be enough solute in the determination of solubility. The log-logistic distribution function was used to further capture the trend of the correlation and to make better quantitative comparison between predicted data and the experimental ones for the Apelblat equation determined by different methods (fitting method or calculation method). It is found that the proposed calculation method not only greatly reduces the number of test data points, but also has satisfactory prediction accuracy.

• Journal of Food Hygiene and Safety
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• v.27 no.3
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• pp.312-316
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• 2012

This study was to present the proper probability distribution models that based on the data for surveys of food cold storage temperatures as the input variables to the further MRA (Microbial risk assessment). The temperature was measured by directly visiting 7 food plants. The overall mean temperature for food cold storages in the survey was $2.55{\pm}3.55^{\circ}C$, with 2.5% of above $10^{\circ}C$, $-3.2^{\circ}C$ and $14.9^{\circ}C$ as a minimum and maximum. Temperature distributions by space-locations was $0.80{\pm}1.69^{\circ}C$, $0.59{\pm}1.68^{\circ}C$, and $0.65{\pm}1.46^{\circ}C$ as an upper (2.4~4 m), middle (1.5~2.4 m), and lower (0.7~1.5 m), respectively. Probability distributions were also created using @RISK program based on the measured temperature data. Statistical ranking was determined by the goodness of fit (GOF) to determine the proper probability distribution model. This result showed that the LogLogistic (-4.189, 5.9098, 3.2565) distribution models was found to be the most appropriate for relative MRA conduction.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.311-316
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• 2016

Breast cancer is one of the most common causes of cancer mortality in Iran. Social determinants of health are among the key factors affecting the pathogenesis of diseases. This cross-sectional study aimed to determine the social determinants of breast cancer survival time with parametric and semi-parametric regression models. It was conducted on male and female patients diagnosed with breast cancer presenting to the Cancer Research Center of Shohada-E-Tajrish Hospital from 2006 to 2010. The Cox proportional hazard model and parametric models including the Weibull, log normal and log-logistic models were applied to determine the social determinants of survival time of breast cancer patients. The Akaike information criterion (AIC) was used to assess the best fit. Statistical analysis was performed with STATA (version 11) software. This study was performed on 797 breast cancer patients, aged 25-93 years with a mean age of 54.7 (${\pm}11.9$) years. In both semi-parametric and parametric models, the three-year survival was related to level of education and municipal district of residence (P<0.05). The AIC suggested that log normal distribution was the best fit for the three-year survival time of breast cancer patients. Social determinants of health such as level of education and municipal district of residence affect the survival of breast cancer cases. Future studies must focus on the effect of childhood social class on the survival times of cancers, which have hitherto only been paid limited attention.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.43-60
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• 2018

This paper considers the use of classical and Bayesian inference methods to analyze data generated by variables whose natural behavior can be modeled using asymmetric distributions in the presence of left censoring. Our approach used a $L{\grave{e}}vy$ distribution in the presence of left censored data and covariates. This distribution could be a good alternative to model data with asymmetric behavior in many applications as lifetime data for instance, especially in engineering applications and health research, when some observations are large in comparison to other ones and standard distributions commonly used to model asymmetry data like the exponential, Weibull or log-logistic are not appropriate to be fitted by the data. Inferences for the parameters of the proposed model under a classical inference approach are obtained using a maximum likelihood estimators (MLEs) approach and usual asymptotical normality for MLEs based on the Fisher information measure. Under a Bayesian approach, the posterior summaries of interest are obtained using standard Markov chain Monte Carlo simulation methods and available software like SAS. A numerical illustration is presented considering data of thyroglobulin levels present in a group of individuals with differentiated cancer of thyroid.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.151-151
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• 2019

Because drought is a complex and stochastic phenomenon in nature, statistical approaches for drought assessment receive great attention for water resource planning and management. Generally drought characteristics such as severity, duration and intensity are modelled separately. This study aims to develop a relationship between drought characteristics using a bivariate copula model. To achieve the objective, we calculated the Standardized Precipitation Index (SPI) using rainfall data at 6 rain gauge stations for the period of 1961-1999 in Jehlum River Basin, Pakistan, and investigated the drought characteristics. Since there is a significant correlation between drought severity and duration, they are usually modeled using different marginal distributions and joint distribution function. Using exponential distribution for drought severity and log-logistic distribution for drought duration, the Galambos copula was recognized as best copula to model joint distribution of drought severity and duration based on the KS-statistic. Various return periods of drought were calculated to identify time interval of repeated drought events. The result of this study can provide useful information for effective water resource management and shows superiority against univariate drought analysis.

• Health Policy and Management
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• v.17 no.3
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• pp.50-67
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• 2007

This study investigated income elasticity of household health expenditures and differences by income level from 1998 through 2003. Data from Korean Labor and Income Panel Study was used for empirical analyses. To estimate the income effects on health expenditure, the two-part model was employed: a logistic regression for any health expenditure-first part-and a Ordinary Least Square regression for health expenditure conditional on any spending-second part. To estimate income elasticity, both health expenditure and income were log transformed in the second part. In addition, the random effects(RE) model was used for a longitudinal panel which was continuously followed from 1998 through 2003 to estimate income effects on health expenditures controlling for within and between unobservable household characteristics. Furthermore, difference in income effects on health expenditure across income level was investigated. Although income slightly increased odds of any health expenditure, there was not no table differences across income level. Income significantly increased health expenditures during study period(overall income elasticity: about 0.2) and the highest 20% income group presented higher income elasticity than the lowest 20% income group.

• Journal of Korean Neurosurgical Society
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• v.50 no.4
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• pp.293-298
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• 2011

Objective : The extracellular matrix (ECM) and cell adhesion molecules play crucial roles in angiogenesis, apoptosis, thrombosis, and inflammation, and also contribute to the pathogenesis of stroke. Integrin, alpha 6 (ITGA6) is a member of ECM adhesion receptors. We investigated whether two single nucleotide polymorphisms (SNPs) (rs11895564, Ala380Thr; rs2293649, Asp694Asp) of ITGA6 were associated with the development and clinical phenotypes of intracerebral hemorrhage (ICH) and ischemic stroke (IS). Methods : We enrolled 199 stroke (78 ICH and 121 IS) and 291 control subjects. Stroke patients were divided into subgroups according to the scores of the National Institutes of Health Stroke Survey (NIHSS, <6 and ${\geq}6$) and Modified Barthel Index (MBI, <60 and ${\geq}60$). SNPStats, SNPAnalyzer, and Helixtree programs were used to calculate odds ratios, 95% confidence intervals, and p values. Multiple logistic regression models were used to analyze genetic data. Results : A missense SNP rs11895564 was associated with the development of ICH (p=0.026 in codominant2, p=0.013 in recessive, p=0.02 in log-additive models; p=0.041 in allele distributions). The A allele frequency of rs11895564 was higher in the ICH group (13.5%) than in the control group (8.1%). In the clinical phenotypes, rs11895564 and rs2293649 showed significant associations in the MBI scores of IS (p=0.014 in codominant1 model; p=0.02 in allele distributions) and NIHSS scores of ICH (p=0.017 in codominant2, p=0.035 in recessive, p=0.035 in log-additive models), respectively. Conclusion : These results suggest that ITGA6 may be associated with the development and clinical phenotypes of stroke in Korean population.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.469-476
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• 2021

The purpose of this study is to explore the predictors of academic probation in college. Especially, this study focused on student engagement variables among the predictors of academic probation in college. Student engagement variables include hours of absence from class and numbers of log to LMS(Learning Management System) and in extracurricular program system during four weeks after the opening of a course and the numbers of faculty counseling. GPA(grade Point Average) is a dependent variable and GPA of prior semester is a control variable in this study. 17,261 student data were collected for the study. Linear regression model and logistic regression model analyses were conducted in the study. The finding showed that the hours of absence from class and numbers of log in extracurricular program system during four weeks after the opening of a course predicted academic achievement of college students. The result also indicated that hours of absence from class and numbers of log-ins to LMS(Learning Management System) and in extracurricular program system during four weeks after the opening of a course were the predictors of academic probation in college. This study will contribute to investigate indicators of students with low academic performance and to provide proper support for underachievers.