• Health Policy and Management
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• v.31 no.2
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• pp.158-172
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• 2021

Background: Metabolic syndrome has been known as a risk of cardiovascular disease. Meanwhile, high sensitivity C-reactive protein (hs-CRP) is used as a predictor of cardiovascular disease. In this paper, we aimed to investigate the association between hs-CRP and metabolic syndrome. Method: A total of 7,633 were chosen as the study population from the 7th Korea National Health and Nutrition Examination Survey dataset (2016-2017). Our dependent variable was whether an individual had metabolic syndrome or not, and the independent variable of interest was hs-CRP which was categorized into three groups. The chi-square tests and hierarchical logistic regression analyses reflecting survey characteristics were conducted. All analyses were stratified by gender. Results: According to the adjusted model with all covariates, compared to individuals having the low risk of hs-CRP, those having its average risk were more likely to have metabolic syndrome in men (odds ratio [OR], 1.41; 95% confidence interval [CI], 1.12-1.76) and women (OR, 1.69; 95% CI, 1.33-2.16). Individuals having the high risk was not significantly different in men; however, they were more likely to have metabolic syndrome in women (OR, 2.03; 95% CI, 1.28-3.23). Conclusion: In an upcoming aging society, it is important to reduce the risk of metabolic syndrome to improve population health. This study suggests that hs-CRP may be used as a marker of the risk of metabolic syndrome in a gender-specific way, thereby contributing to enhancing awareness of the risk of metabolic syndrome among the general public.

• Animal Bioscience
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• v.37 no.6
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• pp.1077-1084
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• 2024

Objective: The present work was conducted to evaluate suitable variables and develop prediction equations using chemical composition and physical characteristics for estimating standardized ileal digestibility (SID) of lysine (Lys) in full-fat soybeans (FFSB). Methods: The chemical composition and physical characteristics were determined including trypsin inhibitor activity (TIA), urease activity (UA), protein solubility in 0.2% potassium hydroxide (KOH), protein dispersibility index (PDI), lysine to crude protein ratio (Lys:CP), reactive Lys:CP ratio, neutral detergent fiber, neutral detergent insoluble nitrogen (NDIN), acid detergent insoluble nitrogen (ADIN), acid detergent fiber, L* (lightness), and a* (redness). Pearson's correlation (r) was computed, and the relationship between variables was determined by linear or quadratic regression. Stepwise multiple regression was performed to develop prediction equations for SID of Lys. Results: Negative correlations (p<0.01) between SID of Lys and protein quality indicators were observed for TIA (r = -0.80), PDI (r = -0.80), and UA (r = -0.76). The SID of Lys also showed a quadratic response (p<0.01) to UA, NDIN, TIA, L*, KOH, a* and Lys:CP. The best-fit model for predicting SID of Lys in FFSB included TIA, UA, NDIN, and ADIN, resulting in the highest coefficient of determination (R² = 0.94). Conclusion: Quadratic regression with one variable indicated the high accuracy for UA, NDIN, TIA, and PDI. The multiple linear regression including TIA, UA, NDIN, and ADIN is an alternative model used to predict SID of Lys in FFSB to improve the accuracy. Therefore, multiple indicators are warranted to assess either insufficient or excessive heat treatment accurately, which can be employed by the feed industry as measures for quality control purposes to predict SID of Lys in FFSB.

• Journal of Korean Neurosurgical Society
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• v.67 no.2
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• pp.177-185
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• 2024

Objective : Delayed cerebral ischemia (DCI) is a major cause of disability in patients who survive aneurysmal subarachnoid hemorrhage (aSAH). Systemic inflammatory markers, such as peripheral leukocyte count and systemic immune-inflammatory index (SII) score, have been considered predictors of DCI in previous studies. This study aims to investigate which systemic biomarkers are significant predictors of DCI. Methods : We conducted a retrospective, observational, single-center study of 170 patients with SAH admitted between May 2018 and March 2022. We analyzed the patients' clinical and laboratory parameters within 1 hour and 3-4 and 5-7 days after admission. The DCI and non-DCI groups were compared. Variables showing statistical significance in the univariate logistic analysis (p<0.05) were entered into a multivariate regression model. Results : Hunt-Hess grade "4-5" at admission, modified Fisher scale grade "3-4" at admission, hydrocephalus, intraventricular hemorrhage, and infection showed statistical significance (p<0.05) on a univariate logistic regression. Lymphocyte and monocyte count at admission, SII scores and C-reactive protein levels on days 3-4, and leukocyte and neutrophil counts on days 5-7 exhibited statistical significance on the univariate logistic regression. Multivariate logistic regression analysis revealed that monocyte count at admission (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.04-2.65; p=0.036) and SII score at days 3-4 (OR, 1.55; 95% CI, 1.02-2.47; p=0.049) were independent predictors of DCI. Conclusion : Monocyte count at admission and SII score 3-4 days after rupture are independent predictors of clinical deterioration caused by DCI after aSAH. Peripheral monocytosis may be the primer for the innate immune reaction, and the SII score at days 3-4 can promptly represent the propagated systemic immune reaction toward DCI.

• Annals of Occupational and Environmental Medicine
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• v.34
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• pp.35.1-35.12
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• 2022

Background: Shift work is known to be associated with cardiovascular disease (CVD). It has been found that inflammatory reactions are involved in the onset and progression of CVD. Therefore, the purpose of this study was to investigate the association between shift work and inflammatory markers. Methods: Among workers at an electronics manufacturing company, 2,329 workers who had a health checkup from January 2019 to December 2019 were targeted. The general and biochemical characteristics of daytime workers and shift workers were compared through the Independent-test and the χ² test. Through multiple linear regression analysis, the association with shift work and inflammatory markers was investigated. Through multiple logistic regression analysis, the association with shift work and high inflammatory markers Results: The mean total leukocytes, neutrophils, monocytes, lymphocytes of shift workers were significantly higher than those of daytime worker. The mean high-sensitivity C-reactive protein (hs-CRP) of shift workers was also higher than that of daytime workers but not significantly. In multiple linear regression, shift work was associated with increase of total leukocyte count (β = 0.367, p < 0.001) and hs-CRP (β = 0.140, p = 0.005) after adjusting for all variables. In multiple logistic regression analysis, shift work showed 2.27 times risk of high leukocyte count and 1.8 times risk of high hs-CRP level compared to daytime work after adjusting for all variables. Conclusions: This study confirmed that shift work is associated with high inflammatory markers. Considering that high inflammatory markers is independent indicator of CVD, the association between shift work and high inflammatory markers may help to understand the CVD risk of shift workers.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.05a
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• pp.244-245
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• 2003

고체나 액체 추진로켓에 비하여 하이브리드 추진 시스템은 작동조건의 안정성과 안전함등의 많은 장점을 가지고 있다. HTPB와 같은 고체연료는 제작 및 저장, 운송 그리고 장착상의 안정성을 가지고 있으며 하이브리드 로켓의 고체연료로의 산화제의 유입을 제어하면서 추력의 변화와 엔진내부의 연소중단과 재 점화를 용이하게 할 수 있다. 이러한 이유로 인하여 하이브리드 엔진은 좀 더 경제적인 장치로 기대를 모으고 있다. 그러나, 기존의 하이브리드 로켓 엔진은 고체 추진 로켓에 비하여 낮은 연료 regression 율과 연소효율을 가지는 단점이 있다. 이러한 단점을 해결하고 요구되어지는 추력값과 연료유량을 증가시키기 위하여 고체연료의 표면적을 증가시킬 필요가 있다. 기존의 하이브리드 엔진에서는 연료 그레인에 다수의 연소포트를 만들어 표면적을 증가시켰으나 이는 비 활용 공간의 증가와 추진제의 질량 및 체적분율의 상당한 감소를 초래한다. 지난 수십년간에 걸쳐 하이브리드 엔진에서 연료의 regression 특성 및 엔진 성능 향상을 위한 연구가 계속되어 왔으며 최근에 엔진의 체적 규제를 경감시키고 연료의 regression율을 향상시키기 위하여 선회유동을 이용하는 하이브리드 로켓 엔진들이 제안되고 있다. 이러한 선회유동을 가지는 하이브리드 로켓은 고체연료 그레인에 대하여 평행하게 유입되는 기존의 하이브리드 로켓에 비하여 고체연료 벽면에서의 대류열전달이 현저하게 증가하게 되어 아주 높은 고체연료의 regression율을 얻을 수 있는 이점이 있다. 선회유동 하이브리드 로켓의 연소과정은 고체 연료의 열분해과정, 대류 열전달, 난류 혼합, 난류와 화학반응의 상호작용, soot의 생성 및 산화과정, soot 입자 및 연소가스에 의한 복사 열전달, 연소장과 음향장의 상호작용 등의 복잡한 물리적 과정을 포함하고 있다. 이러한 물리적 과정 중 난류연소, 고체연료 벽면 근방에서의 대류 열전달 및 연소과정에서 생성되는 soot 입자로부터의 복사 열전달, 그리고 고체연료 열 분해시 표면반응들은 고체연료의 regression율에 큰 영향을 미친다. 특히 고체연료의 난류화염면의 위치와 폭, 그리고 비 예혼합 난류화염장에서 생성되는 soot의 체적분율의 예측은 난류연소모델, 열전달 모델, 그리고 regression율 모델에 의해 크게 영향을 받기 때문에 수치모델의 예측 능력 향상시키기 위하여 이러한 물리적 과정을 정확히 모델링해야 할 필요가 있다. 특히 vortex hybrid rocket내의 난류연소과정은 아래와 같은 Laminar Flamelet Model에 의해 모델링 하였다. 상세 화학반응 과정을 고려한 혼합분율 공간에서의 화염편의 화학종 및 에너지 보존 방정식은 다음과 같다. 화염편 방정식과 혼합분률과 scalar dissipation rate의 관계식을 이용하여 혼합분률과 scalar dissipation rate에 따른 모든 reactive scalar들을 구하게 된다. 이러한 화염편 방정식들을 mixture fraction space에서 이산화시켜서 얻은 비선형 대수방정식은 TWOPNT(Grcar, 1992)로 계산돼 flamelet Library에 저장되게 된다. 저장된 laminar flamelet library를 이용하여 난류화염장의 열역학 상태량 평균치는 presumed PDF approach에 의해 구해진다. 본 연구에서는 강한 선회유동을 가지는 Hybrid Rocket 연소장내의 난류와 화학반응의 상호작용을 분석하기 위하여 Laminar Flamelet Model, 화학평형모델, 그리고 Eddy Dissipation Model을 이용한 수치해석결과를 체계적으로 비교하였다. 또한 Laminar Flamelet Model과 state-of-art 물리모델들을 이용하여 선회 유동을 갖는 하이브리드 로켓 엔진의 연소 및 Soot 생성 및 산화과정을 살펴보았으며 복사 열전달이 고체 연료 표면의 regression율에 미치는 영향도 살펴보았다. 특히 swirl강도, 산화제의 유입위치 그리고 선회유동의 형성방식이 하이브리드 로켓의 연소특성 및 regression rate에 미치는 영향을 상세히 해석하였다.

• Journal of Korean Neurosurgical Society
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• v.63 no.5
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• pp.640-648
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• 2020

Objective : This study aimed to assess the relationship between increased intracranial pressure (ICP) and mastoid effusions (ME). Methods : Between January 2015 and October 2018, patients who underwent intracranial surgery and had ICP monitoring catheters placed were enrolled. ICP was recorded hourly for at least 3 days. ME was determined by the emergence of opacification in mastoid air cells on follow-up brain imaging. C-reactive protein (CRP) levels, presence of endotracheal tube (ETT) and nasogastric tube (NGT), duration of intensive care unit (ICU) stay, duration of mechanical ventilator application, diagnosis, surgical modalities, and presence of sinusitis were recorded. Each factor's effect on the occurrence of ME was analyzed by binary logistic regression analyses. To analyze the independent effects of ICP as a predictor of ME a multivariable logistic regression analysis was performed. Results : Total of 61 (53%) out of 115 patients had ME. Among the patients who had unilateral brain lesions, 94% of subject (43/50) revealed the ipsilateral development of ME. ME developed at a mean of 11.1±6.2 days. The variables including mean ICP, peak ICP, age, trauma, CRP, ICU stays, application of mechanical ventilators and presence of ETT and NGT showed statistically significant difference between ME groups and non-ME groups in univariate analysis. Sex and the occurrence of sinusitis did not differ between two groups. Adding the ICP variables significantly improved the prediction of ME in multivariable logistic regression analysis. Conclusion : While multiple factors affect ME, this study demonstrates that ICP and ME are probably related. Further studies are needed to determine the mechanistic relationship between ICP and middle ear pressure.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.153-160
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• 2020

Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Computers and Concrete
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• v.31 no.2
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• pp.85-95
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• 2023

Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO₂ and SiO₂+Al₂O₃+Fe₂O₃ contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R²), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes.

• Bulletin of the Korean Chemical Society
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• v.33 no.2
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• pp.613-619
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• 2012

We applied several machine learning methods for developing QSAR models for prediction of acute toxicity to fathead minnow. The multiple linear regression (MLR) and artificial neural network (ANN) method were applied to predict 96 h $LC_{50}$ (median lethal concentration) of 555 chemical compounds. Molecular descriptors based on 2D chemical structure were calculated by PreADMET program. The recursive partitioning (RP) model was used for grouping of mode of actions as reactive or narcosis, followed by MLR method of chemicals within the same mode of action. The MLR, ANN, and two RP-MLR models possessed correlation coefficients ($R^2$) as 0.553, 0.618, 0.632, and 0.605 on test set, respectively. The consensus model of ANN and two RP-MLR models was used as the best model on training set and showed good predictivity ($R^2$=0.663) on the test set.

• Elastomers and Composites
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• v.22 no.4
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• pp.333-339
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• 1987

A modern kinetic evaluation method for nonisothermal reactions measured with Differential Scanning Calorimetry(DSC) is presented. It is based on multiple linear regression analysis using a number of curve points in a selectable range of conversion. The obtained kinetic data are the basis to compute a reaction process under any condition e.g. isothermal or adiabatic. The DSC measurements was performed on a Mettler TA3000 SYSTEM with the built in evaluation software. Mainly the following reactions are discussed: vulcanization of natural rubber compounds containing vulcanizing accelerators. The purpose of this work is to analyse the vulcanization kinetics of typical NR vulcanization systems using DSC. These systems were chosen because they are typically reactive elastomer and are commercially important.