• Korean Journal of Environmental Agriculture
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• v.40 no.4
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• pp.248-259
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• 2021

BACKGROUND: Contaminated water was a major source of food-borne pathogens in various recent fresh produce-related outbreaks. This study was conducted to investigate the microbial contamination level and correlations between the level of sanitary indicator bacteria and the detection ratio of pathogens in agricultural water by logistic regression analysis. METHODS AND RESULTS: Agricultural water was collected from 457 sites including surface water (n=300 sites) and groundwater (n=157 sites) in South Korea from 2018 to 2020. Sanitary indicator bacteria (total coliform, fecal coliform, and Escherichia coli) and food-borne pathogens (pathogenic E. coli, E. coli O157:H7, Salmonella spp., and Listeria monocytogenes) were analyzed. In surface water, the coliform, fecal coliform, and E. coli were 3.27±0.89 log CFU/100 mL, 1.90±1.19 log CFU/100 mL, and 1.39±1.26 log CFU/100 mL, respectively. For groundwater, three kinds of sanitary indicators ranged in the level from 0.09 - 0.57 log CFU/100 mL. Pathogenic E. coli, Salmonella and Listeria monocytogenes were detected from 3%-site, 1.5%- site, and 0.6%-site water samples, respectively. According to the results of correlations between the level of sanitary indicator bacteria and the detection ratio of pathogens by logistic regression analysis, the probability of pathogen detection increased individually by 1.45 and 1.34 times as each total coliform and E. coli concentration increased by 1 log CFU/100mL. The accuracy of the model was 70.4%, and sensitivity and specificity were 81.5% and 51.7%, respectively. CONCLUSION(S): The results indicate the need to manage the microbial risk of agricultural water to enhance the safety of fresh produce. In addition, logistic regression analysis is useful to analyze the correlation between the level of sanitary indicator bacteria and the detection ratio of pathogens in agricultural water.

• International Journal of Reliability and Applications
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• v.13 no.1
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• pp.19-35
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• 2012

This paper presents an optimum design of step-stress partially accelerated life test (PALT) plan which allows the test condition to be changed from use to accelerated condition on the occurrence of fixed number of failures. Various life distribution models such as exponential, Weibull, log-logistic, Burr type-Xii, etc have been used in the literature to analyze the PALT data. The need of different life distribution models is necessitated as in the presence of a limited source of data as typically occurs with modern devices having high reliability, the use of correct life distribution model helps in preventing the choice of unnecessary and expensive planned replacements. Truncated distributions arise when sample selection is not possible in some sub-region of sample space. In this paper it is assumed that the lifetimes of the items follow Truncated Logistic distribution truncated at point zero since time to failure of an item cannot be negative. Optimum step-stress PALT plan that finds the optimal proportion of units failed at normal use condition is determined by using the D-optimality criterion. The method developed has been explained using a numerical example. Sensitivity analysis and comparative study have also been carried out.

• Korean Journal of Food Science and Technology
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• v.42 no.3
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• pp.373-376
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• 2010

The objective of this study was to present the proper probability distribution model based on the data obtained from surveys on domestic refrigerator food storage temperatures in home. Domestic refrigerator temperatures were determined as risk factors in foodborne disease outbreaks for microbial risk assessment (MRA). The temperature was measured by directly visiting 139 homes using a data logger from May to September of 2009. The overall mean temperature for all the refrigerators in the survey was $3.53{\pm}2.96^{\circ}C$, with 23.6% of the refrigerators measuring above $5^{\circ}C$. Probability distributions were also created using @RISK program based on the measured temperature data. Statistical ranking was determined by the goodness of fit (GOF, i.e., the Kolmogorov-Smirnov (KS) or Anderson-Darling (AD) test) to determine the proper probability distribution model. This result showed that the LogLogistic (-10.407, 13.616, 8.6107) distribution was found to be the most appropriate for the MRA model. The results of this study might be directly used as input variables in exposure evaluation for conducting MRA.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9673-9678
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• 2014

Background: Breast cancer is a fatal disease and the most frequently diagnosed cancer in women with an increasing pattern worldwide. The burden is mostly attributed to metastatic cancers that occur in one-third of patients and the treatments are palliative. It is of great interest to determine factors affecting time from cancer diagnosis to secondary metastasis. Materials and Methods: Cure rate models assume a Poisson distribution for the number of unobservable metastatic-component cells that are completely deleted from the non-metastasis patient body but some may remain and result in metastasis. Time to metastasis is defined as a function of the number of these cells and the time for each cell to develop a detectable sign of metastasis. Covariates are introduced to the model via the rate of metastatic-component cells. We used non-mixture cure rate models with Weibull and log-logistic distributions in a Bayesian setting to assess the relationship between metastasis free survival and covariates. Results: The median of metastasis free survival was 76.9 months. Various models showed that from covariates in the study, lymph node involvement ratio and being progesterone receptor positive were significant, with an adverse and a beneficial effect on metastasis free survival, respectively. The estimated fraction of patients cured from metastasis was almost 48%. The Weibull model had a slightly better performance than log-logistic. Conclusions: Cure rate models are popular in survival studies and outperform other models under certain conditions. We explored the prognostic factors of metastatic breast cancer from a different viewpoint. In this study, metastasis sites were analyzed all together. Conducting similar studies in a larger sample of cancer patients as well as evaluating the prognostic value of covariates in metastasis to each site separately are recommended.

• The Journal of Korean Association of Computer Education
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• v.17 no.1
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• pp.1-12
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• 2014

Since students' behaviors during e-learning are automatically stored in LMS(Learning Management System), the LMS log data convey the valuable information of students' engagement. The purpose of this study is to develop a prediction model of e-learning course dropout by utilizing LMS log data. Log data of 578 college students who registered e-learning courses in a traditional university were used for the logistic regression analysis. The results showed that attendance and study time were significant to predict dropout, and the model classified between dropouts and completers of e-learning courses with 96% accuracy. Furthermore, the feasibility of early detection of dropouts by utilizing the model were discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1277-1286
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• 2018

In this paper, we provide flexible scalability of computing resources in cloud environment and Apache Hadoop based cloud environment for analysis of public medical information big data. In fact, it includes the ability to quickly and flexibly extend storage, memory, and other resources in a situation where log data accumulates or grows over time. In addition, when real-time analysis of accumulated unstructured log data is required, the system adopts Hadoop-based analysis module to overcome the processing limit of existing analysis tools. Therefore, it provides a function to perform parallel distributed processing of a large amount of log data quickly and reliably. Perform frequency analysis and chi-square test for big data analysis. In addition, multivariate logistic regression analysis of significance level 0.05 and multivariate logistic regression analysis of meaningful variables (p<0.05) were performed. Multivariate logistic regression analysis was performed for each model 3.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7923-7927
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• 2015

Background: The Cox PH model is one of the most significant statistical models in studying survival of patients. But, in the case of patients with long-term survival, it may not be the most appropriate. In such cases, a cure rate model seems more suitable. The purpose of this study was to determine clinical factors associated with cure rate of patients with breast cancer. Materials and Methods: In order to find factors affecting cure rate (response), a non-mixed cure rate model with negative binomial distribution for latent variable was used. Variables selected were recurrence cancer, status for HER2, estrogen receptor (ER) and progesterone receptor (PR), size of tumor, grade of cancer, stage of cancer, type of surgery, age at the diagnosis time and number of removed positive lymph nodes. All analyses were performed using PROC MCMC processes in the SAS 9.2 program. Results: The mean (SD) age of patients was equal to 48.9 (11.1) months. For these patients, 1, 5 and 10-year survival rates were 95, 79 and 50 percent respectively. All of the mentioned variables were effective in cure fraction. Kaplan-Meier curve showed cure model's use competence. Conclusions: Unlike other variables, existence of ER and PR positivity will increase probability of cure in patients. In the present study, Weibull distribution was used for the purpose of analysing survival times. Model fitness with other distributions such as log-N and log-logistic and other distributions for latent variable is recommended.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6751-6755
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• 2013

Background: Research on cancers with a high rate of mortality such as those occurring in the stomach requires using models which can provide a closer examination of disease processes and provide researchers with more accurate data. Various models have been designed based on this issue and the present study aimed at evaluating such models. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. Cox-Snell Residuals and Akaike Information Criterion were used to compare parametric and semi-parametric Cox models in modeling transition rates among different states of a multi-state model. R 2.15.1 software was used for all data analyses. Results: Analysis of Cox-Snell Residuals and Akaike Information Criterion for all probable transitions among different states revealed that parametric models represented a better fitness. Log-logistic, Gompertz and Log-normal models were good choices for modeling transition rate for relapse hazard (state $1{\rightarrow}state$ 2), death hazard without a relapse (state $1{\rightarrow}state$ 3) and death hazard with a relapse (state $2{\rightarrow}state$ 3), respectively. Conclusions: Although the semi-parametric Cox model is often used by most cancer researchers in modeling transition rates of multistate models, parametric models in similar situations- as they do not need proportional hazards assumption and consider a specific statistical distribution for time to occurrence of next state in case this assumption is not made - are more credible alternatives.

• Korean Management Science Review
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• v.24 no.2
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• pp.115-126
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• 2007

Weekly minima of daily log returns of Korean composite stock price index 200 and its five industry-based business divisions over the period from January 1990 to December 2005 are fitted using two block-based extreme distributions: Generalized Extreme Value(GEV) and Generalized Logistic(GLO). Parameters are estimated using the probability weighted moments. Applicability of two distributions is investigated using the Monte Carlo simulation based empirical p-values of Anderson Darling test. Our empirical results indicate that both the GLO and GEV models seem to be comparably applicable to the weekly minima. These findings are against the evidences in Gettinby et al.[7], who claimed that the GEV model was not valid in many cases, and supported the significant superiority of the GLO model.

• Clinical and Experimental Pediatrics
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• v.63 no.9
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• pp.361-367
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• 2020

Background: Length of stay is a significant indicator of care effectiveness and hospital performance. Owing to the limited number of healthcare centers and facilities, it is important to optimize length of stay and associated factors. Purpose: The present study aimed to investigate factors associated with neonatal length of stay in the neonatal intensive care unit (NICU) using parametric and semiparametric models and compare model fitness according to Akaike information criterion (AIC) between 2016 and 2018. Methods: This retrospective cohort study reviewed 600 medical records of infants admitted to the NICU of Bandar Abbas Hospital. Samples were identified using census sampling. Factors associated with NICU length of stay were investigated based on semiparametric Cox model and 4 parametric models including Weibull, exponential, log-logistic, and log-normal to determine the best fitted model. The data analysis was conducted using R software. The significance level was set at 0.05. Results: The study findings suggest that breastfeeding, phototherapy, acute renal failure, presence of mechanical ventilation, and availability of central venous catheter were commonly identified as factors associated with NICU length of stay in all 5 models (P<0.05). Parametric models showed better fitness than the Cox model in this study. Conclusion: Breastfeeding and availability of central venous catheter had protective effects against length of stay, whereas phototherapy, acute renal failure, and mechanical ventilation increased length of stay in NICU. Therefore, the identification of factors associated with NICU length of stay can help establish effective interventions aimed at decreasing the length of stay among infants.