• Food Science and Biotechnology
• /
• v.18 no.1
• /
• pp.137-142
• /
• 2009

One of the most important aspects of conducting this microbial risk assessment (MRA) is determining the model in microbial behaviors in food systems. However, to fully these modeling, large expenditures or newly laboratory experiments will be spent to do it. To overcome these problems, it has to be considered to develop the new strategies that can be used data in the published literatures. This study is to show whether or not the data set from the published experimental data has more value for modeling for MRA. To illustrate this suggestion, as example of data set, 4 published Salmonella survival in Cheddar cheese reports were used. Finally, using the GInaFiT tool, survival was modeled by nonlinear polynomial regression model describing the effect of temperature on Weibull model parameters. This model used data in the literatures is useful in describing behavior of Salmonella during different time and temperature conditions of cheese ripening.

• Journal of Environmental Science International
• /
• v.25 no.8
• /
• pp.1107-1114
• /
• 2016

This research, sponsored by the Korean Ministry of Environment in 2014, was the first epidemiological study in Korea that investigated the health impact assessment of radon exposure. Its purpose was to construct a model that calculated the annual mean cumulative radon exposure concentrations, so that reliable conclusions could be drawn from environment-control group research. Radon causes chronic lung cancer. Therefore, the long-term measurement of radon exposure concentration, over one year, is needed in order to develop a health impact assessment for radon. Hence, based on the seasonal correction model suggested by Pinel et al.(1995), a predictive model of annual mean radon concentration was developed using the year-long seasonal measurement data from the National Institute of Environmental Research, the Korea Institute of Nuclear Safety, the Hanyang University Outdoor Radon Concentration Observatory, and the results from a 3-month (one season) survey, which is the official test method for radon measurement designated by the Korean Ministry of Environment. In addition, a model for evaluating the effective annual dose for radon was developed, using dosimetric methods. The model took into account the predictive model for annual mean radon concentrations and the activity characteristics of the residents.

• Asian-Australasian Journal of Animal Sciences
• /
• v.32 no.2
• /
• pp.274-281
• /
• 2019

Objective: The objective of this study was to estimate the risk of Campylobacter jejuni (C. jejuni) infection from various jerky products in Korea. Methods: For the exposure assessment, the prevalence and predictive models of C. jejuni in the jerky and the temperature and time of the distribution and storage were investigated. In addition, the consumption amounts and frequencies of the products were also investigated. The data for C. jejuni for the prevalence, distribution temperature, distribution time, consumption amount, and consumption frequency were fitted with the @RISK fitting program to obtain appropriate probabilistic distributions. Subsequently, the dose-response models for Campylobacter were researched in the literature. Eventually, the distributions, predictive model, and dose-response model were used to make a simulation model with @RISK to estimate the risk of C. jejuni foodborne illness from the intake of jerky. Results: Among 275 jerky samples, there were no C. jejuni positive samples, and thus, the initial contamination level was statistically predicted with the RiskUniform distribution [RiskUniform (-2, 0.48)]. To describe the changes in the C. jejuni cell counts during distribution and storage, the developed predictive models with the Weibull model (primary model) and polynomial model (secondary model) were utilized. The appropriate probabilistic distribution was the BetaGeneral distribution, and it showed that the average jerky consumption was 51.83 g/d with a frequency of 0.61%. The developed simulation model from this data series and the dose-response model (Beta Poisson model) showed that the risk of C. jejuni foodborne illness per day per person from jerky consumption was $1.56{\times}10^{-12}$. Conclusion: This result suggests that the risk of C. jejuni in jerky could be considered low in Korea.

• Journal of the Korean Dietetic Association
• /
• v.25 no.1
• /
• pp.30-43
• /
• 2019

This study compared the predictive models for the growth kinetics of Staphylococcus aureus in ham rice balls. In addition, a semi-quantitative risk assessment of S. aureus on ham rice balls was conducted using FDA-iRISK 4.0. The rice was rounded with chopped ham, which was mixed with mayonnaise (SHM), soy sauce (SHS), or gochujang (SHG), and was contaminated artificially with approximately $2.5{\log}\;CFU{\cdot}g^{-1}$ of S. aureus. The inoculated rice balls were then stored at $7^{\circ}C$, $15^{\circ}C$, and $25^{\circ}C$, and the number of viable S. aureus was counted. The lag phases duration (LPD) and maximum specific growth rate (SGR) were calculated using a Baranyi model as a primary model. The growth parameters were analyzed using the polynomial equation as a function of temperature. The LPD values of S. aureus decreased with increasing temperature in SHS and SHG. On the other hand, those in SHM did not show any trend with increasing temperature. The SGR positively correlated with temperature. Equations for LPD and SGR were developed and validated using $R^2$ values, which ranged from 0.9929 to 0.9999. In addition, the total DALYs (disability adjusted life years) per year in the ham rice balls with soy sauce and gochujang was greater than mayonnaise. These results could be used to calculate the expected number of illnesses, and set the hazard management method taking the DALY value for public health into account.

• Journal of The Geomorphological Association of Korea
• /
• v.27 no.3
• /
• pp.87-103
• /
• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• Journal of dental hygiene science
• /
• v.24 no.1
• /
• pp.22-28
• /
• 2024

Background: Particulate matter (PM) has been extensively observed due to its negative association with human health. Previous research revealed the possible negative effect of air pollutant exposure on oral health. However, the predictive model between air pollutant exposure and the prevalence of periodontitis has not been observed yet. Therefore, this study aims to propose a predictive model for the number of patients with periodontitis exposed to PM and atmospheric factors in South Korea using deep learning. Methods: This study is a retrospective cohort study utilizing secondary data from the Korean Statistical Information Service and the Health Insurance Review and Assessment database for air pollution and the number of patients with periodontitis, respectively. Data from 2015 to 2022 were collected and consolidated every month, organized by region. Following data matching and management, the deep neural networks (DNN) model was applied, and the mean absolute percentage error (MAPE) value was calculated to ensure the accuracy of the model. Results: As we evaluated the DNN model with MAPE, the multivariate model of air pollution including exposure to PM_2.5, PM₁₀, and other atmospheric factors predict approximately 85% of the number of patients with periodontitis. The MAPE value ranged from 12.85 to 17.10 (mean±standard deviation=14.12±1.30), indicating a commendable level of accuracy. Conclusion: In this study, the predictive model for the number of patients with periodontitis is developed based on air pollution, including exposure to PM_2.5, PM₁₀, and other atmospheric factors. Additionally, various relevant factors are incorporated into the developed predictive model to elucidate specific causal relationships. It is anticipated that future research will lead to the development of a more accurate model for predicting the number of patients with periodontitis.

• The Journal of Information Technology
• /
• v.2 no.1
• /
• pp.191-207
• /
• 1999

The study is in attempting for reviewing the selection problem of the measurement and the model, concerning a consumer satisfaction model. Therefore, a common model, which measures degree of consumer satisfaction by an arithmetic mean from measurement method including data, which assess compulsively the attribution and the importance to consumers, shows the problems of a field application. There showed a high predictive validity in the model of a singular item using the degree of a general satisfaction rather than a detailed assessment. However, the single model needs the model of consumer satisfaction from the using of plural items, because of the field problems that produce in an alternative application. There showed a high significance level in the model including variables, which are showing a high correlation between purchase intention and predictive validity.

• The Korean Journal of Pain
• /
• v.28 no.4
• /
• pp.244-253
• /
• 2015

Background: Although circumcision for phimosis in children is a minor surgical procedure, it is followed by pain and carries the risk of increased postoperative anxiety. This study examined predictive factors of postoperative pain and anxiety in children undergoing circumcision. Methods: We conducted a prospective cohort study of children scheduled for elective circumcision. Circumcision was performed applying one of the following surgical techniques: sutureless prepuceplasty (SP), preputial plasty technique (PP), and conventional circumcision (CC). Demographics and base-line clinical characteristics were collected, and assessment of the level of preoperative anxiety was performed. Subsequently, a statistical model was designed in order to examine predictive factors of postoperative pain and postoperative anxiety. Assessment of postoperative pain was performed using the Faces Pain Scale (FPS). The Post Hospitalization Behavior Questionnaire study was used to assess negative behavioral manifestations. Results: A total of 301 children with a mean age of $7.56{\pm}2.61$ years were included in the study. Predictive factors of postoperative pain measured with the FPS included a) the type of surgical technique, b) the absence of siblings, and c) the presence of postoperative complications. Predictive factors of postoperative anxiety included a) the type of surgical technique, b) the level of education of mothers, c) the presence of preoperative anxiety, and d) a history of previous surgery. Conclusions: Although our study was not without its limitations, it expands current knowledge by adding new predictive factors of postoperative pain and postoperative anxiety. Clearly, further randomized controlled studies are needed to confirm its results.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.21 no.1
• /
• pp.124-132
• /
• 2018

This paper presents a shelf life assessment for K-1 military gas masks in the Republic of Korea using test data of Chemical Materiels Stockpile Reliability Program(CSRP). For the shelf life assessment, over 2,500 samples between 2006 and 2015 were collected from field tests and analyzed to estimate a probability of proper and improper functionality using Bayesian estimation. For this, three stages were considered; a pre-processing, a processing and an assessment. In the pre-processing, major components which directly influence the shelf life of the mask were statistically analyzed and selected by applying principal component analysis from all test components. In the processing, with the major components chosen in the previous stage, both proper and improper probability of gas masks were computed by applying Bayesian estimation. In the assessment, the probability model of the mask shelf life was analyzed with respect to storage periods between 0 and 29 years resulting in between 66.1 % and 100 % performances in accuracy, sensitivity, positive predictive value, and negative predictive value.

• Food Science of Animal Resources
• /
• v.39 no.4
• /
• pp.565-575
• /
• 2019

This study evaluated Campylobacter jejuni risk in ground meat products. The C. jejuni prevalence in ground meat products was investigated. To develop the predictive model, survival data of C. jejuni were collected at $4^{\circ}C-30^{\circ}C$ during storage, and the data were fitted using the Weibull model. In addition, the storage temperature and time of ground meat products were investigated during distribution. The consumption amount and frequency of ground meat products were investigated by interviewing 1,500 adults. The prevalence, temperature, time, and consumption data were analyzed by @RISK to generate probabilistic distributions. In 224 samples of ground meat products, there were no C. jejuni-contaminated samples. A scenario with a series of probabilistic distributions, a predictive model and a dose-response model was prepared to calculate the probability of illness, and it showed that the probability of foodborne illness caused by C. jejuni per person per day from ground meat products was $5.68{\times}10^{-10}$, which can be considered low risk.