• The korean journal of orthodontics
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• v.26 no.4
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• pp.414-420
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• 1996

Cephalometric measureements have disadvantage of representing cranio-facial structures in two dimension only and therefore they pose limitations in describing three-dimentional structures of cranio-facial region. More interests have been put on the correlation between the two planes. This study evaluated correlations between facial type score, which allows effects on malocclusion, growth change prediction and establishment of treatment method and prognosis, and measurements from submentovertex view. Cephalometric view and submentovertex view were taken of skeletal Class I adults with optimal profile and correlations between them have been observed. Following results were obtained: 1. To learn about factors that influence average condylar angulation, FACE, INT-CO-ANG, MN-CORPUS, CON-RATIO, GON-RATIO, MN-RATIO were used as variables and underwent multiple regression analysis. As a result, the following equation was obtained : CON-AVE=.l73(FACE)-.322(INT-CO-ANG)+36.34(GON-RATIO) +.420(MN-CORPUS) (($R^2=.85451$) 2. The following equation was obtained concerning facial type score. FACE= .050(CON-ANG)+.023(INT-CO-ANG)-.075(MN-CORPUS)($R^2=.31547$) 3. Among the submentovertex measurements, MN-CORPUS, CON-RATIO, GON-RATIO, MN-RATIO showed close correlations. (P<0.05) 4. Average condylar angualtions were $23.37^{\circ}$ on the right and $20.71^{\circ}$ on left. There was a difference between the two. FACE : facial type soore. CON-ANG: mean value of condylar angulation. CON-AVE: mean value of Rt. Lt condylar angulation. INT-CO-ANG : angle between Rt. Lt condylar axis. MN-CORPUS : angle formed between RT. Lt gonion & pogonion. CON-RATIO: lntercondylar distance/mandibular body length. GON-RATIO : intergonion distanoe/mandibular body length. MN-RATIO: lntermylohyoid distance/mandibular body length. MX-RATIO: intermaxillary tuberosity distance/ANS-PNS distance.

• Analytical Science and Technology
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• v.29 no.1
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• pp.19-28
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• 2016

The objective of this study was to determine the acidification of precipitation in the Jeju area. Precipitation samples were collected from the Jeju area from 2009-2014, and the major ionic species were analyzed. In the regression analysis, through a comparison of ion balance, electric conductivity, and acid fraction, the correlation coefficients showed a good linear relationship within the range of 0.927~0.983. The volume-weighted means of the pH and electric conductivity were 4.9 and 22.7 µS/cm, respectively. The ionic strength of precipitation was 0.27±0.38 mM, indicating about 35.9 % of total precipitation within the pure precipitation criteria. The volume-weighted mean concentrations (ìeq/L) of the ionic species in the precipitation were in the order of Na⁺ > Cl⁻ > nss-SO₄²⁻ > NO₃⁻ > NH₄⁺ > Mg²⁺ > H⁺ > nss-Ca²⁺ > PO₄³⁻ > K⁺ > HCOO⁻ > CH₃COO⁻ > NO₂⁻ > F⁻ > HCO₃⁻ > CH₃SO₃⁻ . The acidification contributions by sulfuric and nitric acids were 54.5 % and 36.5 %, respectively. Meanwhile the acidification contributions by formic and acetic acids were 4.8 % and 4.2 %, respectively. Thus, it was found that the acidification of the precipitation in the Jeju area was mainly due to the inorganic acids. The neutralization factors by NH₃ and CaCO₃ were also 33 % and 20 %, respectively.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.7
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• pp.942-952
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• 2008

The purpose of this research was to evaluate the quality of take-out services at restaurants in Chungbuk Province. A questionnaire survey by 450 customers who had experience in take-out service at the restaurants was conducted and 378 completed questionnaires were available for statistical evaluation. Statistical analyses were made of raw data by SAS V8.2. The scale for analyzing the importance and performance of the service quality was composed of 5-point Likert scales. The main results of this study are as follow: The quality attributes of take-out service were rearranged into four factors in terms of food, sanitation, access and service. The importance score was higher than performance score. IPA showed that 'freshness of food material', 'cleanliness and hygiene in food', 'sanitation of facilities', 'neatness of employees' and 'price in food' was included in 'focus here' area. There was significantly positive correlation between factors such as food, sanitation, access, service and overall customer satisfaction (p<.001); between factors and repurchasing intentions (p<.001); and between customer satisfaction and repurchasing intentions (p<.001). According to multiple regression analysis, 26.27% of the variance in respondents' overall satisfaction score and 9.21% of the variance in respondents' repurchasing intention score could be explained by factors such as food, sanitation, access and service.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.4
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• pp.263-271
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• 2009

This study evaluated the flocs forming characteristics in the mixing zone to increase the coagulation effect in the drinking water plant. As a measuring tool of formed flocs, on-line particle dispersion analyzer (iPDA) was used in Y drinking water plant. To evaluate the forming flocs, many parameters such as poly amine, coagulant dosing amount, raw water turbidity, and pH was applied in this study. During the periods of field test, poly aluminium chloride (PACl) as a coagulant was used. With the increase of the raw water turbidities, poly amine was also added as one of aids for increasing in coagulation efficiency. The turbidity and pH of raw water was ranged from 7 to 9 and from 25 to 140 NTU, respectively. The increasing of raw water turbidity brought the bigger floc sizes accordingly. From a regression analysis, $R^2$ value was 0.8040 as a function of T, raw water turbidity. Floc size index (FSI) was obtained from a correlation equation as follows; FSI = 0.9388logT - 0.3214 Also, polyamine gave the bigger flocs the moment it is added to the coagulated water in the rapid mixing zone. One of parameters influencing the floc sizes was the addition of powdered active carbon(PAC) in the mixing zone. In case of higher turbidity of raw water, $R^2$ value was 0.9050 in the parameters of [PACl] and [PAC]; FSI = $0.0407[T]^{0.324}[PACI]^{0.769}[PAC]^{0.178}$ On-line floc monitor was beneficial to evaluate the flocs sizes depending on the many parameters consisting raw water properties, bring the profitable basic data to control the mixing zone more effectively.

• Journal of Nutrition and Health
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• v.41 no.5
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• pp.414-427
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• 2008

The purpose of the study was to develop an eating habit checklist for screening elementary school children at high risk of energy overintake. Dietary habits, food intake, anthropometric data were collected from 142 children (80 boys and 62 girls) in the 4th to 6th grades of elementary schools. Energy intake, fat intake, and percentage of Estimated Energy Requirement (%EER) were used as indices to detect the risk of energy overintake of the children. Pearson correlation coefficients were calculated between dietary habit scores and energy overintake indices in order to select questions included in the checklist. TV watching during the meal, meal speed, meal amount, overintake frequency, eatingout frequency, snack frequency, frequency of eating Ramyun or fast foods showed significant correlations with energy overintake indices. Stepwise regression analysis was performed to give each item a different weight by prediction strength. To determine the cut-off point of the test score, sensitivity, specificity, and positive predictive values were calculated. The 7-item checklist with test results from 0 to 13 points was developed, and those with equal or higher than 5 points were diagnosed as a risk group of energy overintake. Among our subjects 13.4% was diagnosed as the risk group. Mean energy intake of the subjects in the risk group and the normal group were 2,650 kcal and 1,640 kcal, respectively. However, there were no significant differences of Index of Nutritional Quality (INQ) of the other nutrients except eating fiber between the risk group and the normal group. This checklist will provide a useful screening tool to identify children at high risk of energy overintake.

• Journal of Yeungnam Medical Science
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• v.6 no.2
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• pp.173-181
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• 1989

The effect of interpregnancy interval on birthweight of the subsequent child was investigated for the 1,347 womens of 25 to 40 years old age who visited OBGY and Pediatric department of the general hospital in Taegu city. Questions in designed questionnaire were asked by student interviewers who were trained in nursing school. Mean birth weight by interpregnancy intervals were compared by the intervals of 6 months. Mean birth weight increased from 3,250 grams for intervals of 6 months to 3,357 grams for intervals of 25-30 months, hut the difference was not statistically significant(=0.47). Correlations between the continuous variables which were suspected as con founders and interpregnancy interval and birth weight were investigated. The coefficient of correlation between maternal age and interpregnancy interval was 0.39, between gestational period and birth weight 0.30 and between prepregnant weight and birth weight 0.16 and between birth weight of first baby and birth weight(of second baby) 0.44. But maternal age, gestational period and prepregnant weight were not considered as confounder, because they were not correlated simultaneously with birth interval and birth weight. Associations between the discrete variables which were suspected as confounders, and interpregnancy interval were investigated by Chi-square test. Associations between interpregnancy interval and educational level of mothers, types of husband's occupation, types of medical security, sex were not significant(P-values were 0.59, 0.75, 0.75, 0.82 respectively), so we did not considered these variables as confounding variables. In multiple regression analysis of birth weight, significant variables were birth weight of first baby, gestational period, sex of neonate and prepregnancy body weight of mother. Of the 1,347 births, the rate of low birth weight was 2% (27 birth). The rate for interpregnancy interval 7-12 months was highest as 3.6% and that for 13-18 months was lowest as 0.6%, but there was no regular tendency related with interpregnancy interval.

• Korean Journal of Adult Nursing
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• v.12 no.1
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• pp.99-111
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• 2000

The purpose of this study was to identify risk factors related to sexual function of women with coronary artery disease, and to determine the predictors of sexual function. The study design, a descriptive correlational study, was done through structural questionnaire and interview. A total of 50 subjects from C University Hospital at Kwang-ju city who have undergone coronary angiography at department of cardiology were observed and interviewed from Feb. 22, 1999 to March. 23, 1999. The number of affected vessels, the level of total serum cholesterol, and the ejection fraction of 2-D echo cardiography were analyzed to evaluate the severity of coronary artery disease. And also type A behavior pattern, health behavior, Brief Index of Sexual Functioning for Women (BISF-W) were measured. The data obtained were analyzed using percentage, mean and standard deviation, t-test, ANOVA, Pearson's correlation coefficient, and stepwise multiple regression analysis via SPSS PC+. The results of this study were as follows: 1. The mean age of the subjects were 58.1 and 72.0% of those have been married over 30 years. Seventy two percentage were unemployed and monthly family income of 56.6% was less than 1,000,000 won (approximately $ 840). Eighty percent were in their postmenopausal state, and the frequency of sexual intercourse of 84.0% were two to three times per month. 2. The scores of type A behavior pattern were from 16 to 38(mean 24.94) and health behavior ranged from 21 to 43(mean 31.2). Abstinence from smoking, alcohol, and caffeine were best compliant factors and weight control and exercise were least abided ones. The result of 2D-ECHO EF showed that the half of the subjects were abnormal, and 24% had more than 240mg/dl of total serum cholesterol. The coronary angiography showed that 64% of the subjects had more than one affected vessels. 3. The predictors to explain the factor score of 'orgasm' were number of health examination, the pre- or post-menopausal state, protestant, number of coronary vessel affected, level of serum total cholesterol, and comorbid group of hypertension and diabetes, and it's total variance accounted for 52.4%. The predictors to explain the factor score of 'sexual activity' were comorbid group of hypertension and diabetes and type A behavior pattern, which accounted for 22.4% of total variance. The predictors to explain the factor score of 'sexual satisfaction' were type A behavior pattern, no religion, exercise, level of serum total cholesterol, and pre or post menopausal state, which accounted for 52.1%. The predictors to explain the factor score of 'sexual desire' were the period of marriage, type A behavior, employment or unemployment, and weight control, which accounted for 43.2%. The predictors to explain the factor score of 'external force of sexual functioning' were physical overload and exercise, which accounted for 41.1%. The predictors to explain the factor score of 'sexual activity' were family monthly income, catholics, and exercise, and which accounted for 35.4%. Above results lead us to some consensus that sexual function of women with coronary artery disease is related to various factors including vasogenic factors such as total serum cholesterol level, number of coronary vessel affected, an endocrinal factor such as menopausal state, and type A behavior pattern as a sociopshychological factor. And also health behaviors such as fitness care, overwork, weight control, and emotional tension are contributed to sexual function.

• Clinical and Experimental Pediatrics
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• v.52 no.10
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• pp.1140-1146
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• 2009

Purpose:The pathophysiology of magnesium, the second highest common compound in humans, is still unclear, especially in preterm babies. We accessed the association between total magnesium (tMg), ionized Mg (iMg), and gestational age (GA) and that between serum magnesium (sMg) and intraventricular hemorrhage (IVH) in preterm babies. Methods:In all, 119 inborn preterm infants admitted between July 2006 and February 2008 were divided into the IVH group (19) and the control group (100) and were prospectively analyzed. TMg, iMg, pH, total calcium (tCa), and ionized Ca (iCa) levels were determined immediately after delivery or within 3 hours after birth, and their correlation with GA were investigated. Results:TMg was not correlated with GA, tCa, iCa, and pH. IMg was correlated with tMg (r=0.288, P=0.002) and iCa (r=0.212, P=0.021); however, it was not correlated with GA and pH. Mean GA and birth weight were significantly lower (P=0.002) and smaller (P=0.030) in the IVH group. Mean sMg was higher in the IVH group ($2.5{\pm}0.9mg/dL$) than in the control group ($2.1{\pm}0.6mg/dL$) (P=0.021). SMg was a risk factor even after logistic regression analysis (OR, 2.798; 95% C.I., 1.265-6.192; P=0.011). Conclusion:In less than 37-week-old preterm babies, tMg and iMg were similar, regardless of GA. High sMg may be a risk factor for IVH in premature babies, regardless of their exposure to antenatal magnesium.

• Journal of Nutrition and Health
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• v.42 no.1
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• pp.38-47
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• 2009

The purpose of the study was to develop an eating habit checklist for screening elementary school children at risk of inadequate micronutrient intake. Eating habits, food intake, and anthropometric data were collected from 142 children (80 boys and 62 girls) in the $4^{th}$ to $6^{th}$ grades of elementary schools. Percentage of Recommended Intakes (RI) and Mean Adequacy Ratio (MAR) of six micronutrients; vitamin A, riboflavin, vitamin C, calcium, iron, zinc, and the number of nutrients the children consumed below EAR among the six nutrients were used as indices to detect the risk of inadequate micronutrient intake. Pearson correlation coefficients were calculated between eating habit scores and inadequate micronutrient intake indices in order to select questions included in the checklist. Meal frequency, enough time for breakfast, regularity of dinner, appetite, eating frequencies of Kimchi, milk, fruits and beans showed significant correlations with indices of inadequate micronutrient intake. Stepwise regression analysis was performed to give each item a different weight by prediction strength. To determine the cut-off point of the test score, sensitivity, specificity, and positive predictive values were calculated. The 8-item checklist with test results from 0 to 12 points was developed, and those with equal or higher than 6 points were diagnosed as high-risk group of inadequate micronutrient intake, and those with 4 or 5 points were diagnosed as moderate-risk group. Among our subjects 14.1% was diagnosed as high-risk group, and 30.3% as moderate-risk group. The proportions of the subjects who consumed below EAR of all micronutrients but vitamin C were highest in the high-risk group, and there were significant differences in the proportions of the subjects with intake below EAR of all micronutrients except vitamin B6 among the three groups. This checklist will provide a useful screening tool to identify children at risk of inadequate micronutrient intake.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.