• Proceedings of the Korea Concrete Institute Conference
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• 2004.11a
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• pp.683-686
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• 2004

In recent years, some attention was particularly given to cracking sensitivity of high performance concrete. It has been argued and demonstrated experimentally that such concrete undergoes autogenous shrinkage due to self-desiccation at early age, and, as a result, internal tensile stress may develop, leading to micro cracking and macro cracking. One possible method to reduce cracking due to autogenous shrinkage is the addition of expansive additive. Tests conducted by many researches have shown the beneficial effects of addition of expansive additive for reducing the risk of shrinkage-introduced cracking. However, the research on hydration model of expansion additive has been hardly researched up to now. This paper presents a study of the hydration model of Ettringite-Gypsum type expansive additive. Result of comparing forecast values with experiment value, proposed model is shown to expressible of hydration of expansive additive.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.6
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• pp.2889-2892
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• 2014

Death receptor 4 (TRAIL-R1 or DR4) polymorphisms have been associated with cancer risk, but findings have been inconsistent. To estimate the relationship in detail, a meta-analysis was here performed. A search of PubMed was conducted to investigate the association between DR4 C626G, A683C and A1322G polymorphisms and cancer risk, using odds ratios (ORs) with 95% confidence intervals. The results suggested that DR4 C626G and A683C polymorphisms were indeed associated with cancer risk (for C626G, dominant model, OR 0.991, 95%CI 0.866-1.133, p=0.015; for A683C, additive model, OR=1.140, 95%CI: 0.948-1.370, p=0.028; dominant model, OR=1.156, 95%CI: 0.950-1.406, p=0.080) in the Caucasian subgroup. However, the association was not significant between DR4 polymorphism A1322G with cancer risk in Caucasians (For A1322G, additive model: OR 1.085, 95%CI 0.931-1.289, p=0.217; dominant model: OR 1.379, 95%CI 0.934-2.035, p=0.311; recessive model: OR 1.026, 95%CI 0.831-1.268 p=0.429.). In summary, our finding suggests that DR4 polymorphism C626G and A683 rather than A1322G are associated with cancer risk in Caucasians.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.555-558
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• 2003

The Choquet-Stieltjes integral is defined. It is shown that the Choquet -Stieltjes integral is rep-resented by a Choquet integral. As an application of the theorem above, it is shown that Choquet expected utility model for decision under uncertainty and rank dependent utility model for decision under .risk are respectively same as their simplified version.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9643-9647
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• 2014

Background: Several studies have been performed to investigate the association of the HER2 Ile655Val polymorphism and breast cancer risk. However, the results were inconsistent. To understand the precise relationship, a meta-analysis was here conducted. Materials and Methods: A search of PubMed conducted to investigate links between the HER2 Ile655Val polymorphism and breast cancer, identified a total of 32 studies, of which 29, including 14,926 cases and 15,768 controls, with odds ratios (ORs) with 95% confidence intervals were used to assess any association. Results: In the overall analysis, the HER2 Ile655Val polymorphism was associated with breast cancer in an additive genetic model (OR=1.136, 95% CI 1.043-1.239, p=0.004) and in a dominant genetic (OR=1.118, 95% CI 1.020-1.227, p=0.018), while no association was found in a recessive genetic model. On subgroup analysis, an association with breast cancer was noted in the additive genetic model (OR=1.111, 95% CI: 1.004-1.230, p=0.042) for the Caucasian subgroup. No significant associations were observed in Asians and Africans in any of the genetic models. Conclusions: In summary, our meta-analysis findings suggest that the HER2 Ile655Val polymorphism is marginally associated with breast cancer susceptibility in worldwide populations with additive and dominant models, but not a recessive model.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1821-1825
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• 2015

Background: Associations between the NQO1 C609T polymorphism and hepatocellular carcinoma (HCC) risk are a subject of debate. We therefore performed the present meta-analysis to evaluate links with HCC susceptibility. Materials and Methods: Several major databases (PubMed, EBSCO), the Chinese national knowledge infrastructure (CNKI) and the Wanfang database were searched for eligible studies. Crude odds ratios (ORs) with 95% confidence intervals (CIs) were used to measure the strength of associations. Results: A total of 4 studies including 1,325 patients and 1,367 controls were identified. There was a significant association between NQO1 C609T polymorphism and HCC for all genetic models (allelic model: OR=1.45, 95%CI=1.23-1.72, p<0.01; additive model: OR=1.96, 95%CI=1.57-2.43, p<0.01; dominant model: OR=1.62, 95%CI=1.38-1.91, p<0.01; and recessive model: OR=1.53, 95%CI=1.26-1.84, p<0.01). On subgroup analysis, similarly results were identified in Asians. For Asians, the combined ORs and 95% CIs were (allelic model: OR=1.50, 95%CI=1.24-1.82, p<0.01; additive model: OR=2.11, 95%CI=1.48-3.01, p<0.01; dominant model: OR=1.69, 95%CI=1.42-2.02, p<0.01; and recessive model: OR=1.59, 95%CI=1.16-2.19, p<0.01). Conclusions: The current meta-analysis suggested that the NQO1 C609T polymorphism could be a risk factor for developing HCC, particularly in the Chinese population.

• Genomics & Informatics
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• v.17 no.4
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• pp.47.1-47.12
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• 2019

The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.

• Journal of Preventive Medicine and Public Health
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• v.57 no.2
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• pp.108-119
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• 2024

Objectives: Abrupt changes in air pollution levels associated with the coronavirus disease 2019 (COVID-19) outbreak present a unique opportunity to evaluate the effects of air pollution on influenza risk, at a time when emission sources were less active and personal hygiene practices were more rigorous. Methods: This time-series study examined the relationship between influenza cases (n=22 874) and air pollutant concentrations from 2018 to 2021, comparing the timeframes before and during the COVID-19 pandemic in and around Thailand's Khon Kaen province. Poisson generalized additive modeling was employed to estimate the relative risk of hospitalization for influenza associated with air pollutant levels. Results: Before the COVID-19 outbreak, both the average daily number of influenza hospitalizations and particulate matter with an aerodynamic diameter of 2.5 ㎛ or less (PM_2.5) concentration exceeded those later observed during the pandemic (p<0.001). In single-pollutant models, a 10 ㎍/m³ increase in PM_2.5 before COVID-19 was significantly associated with increased influenza risk upon exposure to cumulative-day lags, specifically lags 0-5 and 0-6 (p<0.01). After adjustment for co-pollutants, PM_2.5 demonstrated the strongest effects at lags 0 and 4, with elevated risk found across all cumulative-day lags (0-1, 0-2, 0-3, 0-4, 0-5, and 0-6) and significantly greater risk in the winter and summer at lag 0-5 (p<0.01). However, the PM_2.5 level was not significantly associated with influenza risk during the COVID-19 outbreak. Conclusions: Lockdown measures implemented during the COVID-19 pandemic could mitigate the risk of PM_2.5-induced influenza. Effective regulatory actions in the context of COVID-19 may decrease PM_2.5 emissions and improve hygiene practices, thereby reducing influenza hospitalizations.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.587-595
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• 2011

Credit scoring is an objective and automatic system to assess the credit risk of each customer. The logistic regression model is one of the popular methods of credit scoring to predict the default probability; however, it may not detect possible nonlinear features of predictors despite the advantages of interpretability and low computation cost. In this paper, we propose to use a generalized partially linear model as an alternative to logistic regression. We also introduce modern ensemble technologies such as bagging, boosting and random forests. We compare these methods via a simulation study and illustrate them through a German credit dataset.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.61-71
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• 1996

Kim and Song(1995)과 Kim and Lee(1996)는 하나의 이지공변량(binary covariate)을 갖는 가산위험모형(additive risk model)의 적합도검정법(goodness-of-fit test)을 제안했다. 전자는 모수의 가중추정량들의 차에 기초한 검정법이며 후자는 마팅게일잔차(martingale residual)에 기초한 검정법이다. 본 논문에서는 모의실험을 통하여 두 검정법을 비교하였다.

• Journal of Radiation Protection and Research
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• v.46 no.3
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• pp.83-97
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• 2021

Background: The lifetime risk of lung cancer incidence due to radiation for nonsmokers is overestimated because of the use of the average cancer baseline risk among a mixed population, including smokers. In recent years, the generalized multiplicative (GM)-excess relative risk (ERR) model has been developed in the life span study of atomic bomb survivors to consider the joint effect of radiation and smoking. Based on this background, this paper discusses the issues of radiation risk assessment considering smoking in two parts. Materials and Methods: In Part 1, we proposed a simple method of estimating the baseline risk for nonsmokers using current smoking data. We performed sensitivity analysis on baseline risk estimation to discuss the birth cohort effects. In Part 2, we applied the GM-ERR model for Japanese smokers to calculate lifetime attributable risk (LAR). We also performed a sensitivity analysis using other ERR models (e.g., simple additive (SA)-ERR model). Results and Discussion: In Part 1, the lifetime baseline risk from mixed population including smokers to nonsmokers decreased by 54% (44%-60%) for males and 24% (18%-29%) for females. In Part 2, comparison of LAR between SA- and GM-ERR models showed that if the radiation dose was ≤200 mGy or less, the difference between these ERR models was within the standard deviation of LAR due to the uncertainty of smoking information. Conclusion: The use of mixed population for baseline risk assessment overestimates the risk for lung cancer due to low-dose radiation exposure in Japanese males.