• Korean Journal of Childcare and Education
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• v.6 no.3
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• pp.23-44
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• 2010

This study analyzed the relationships mother's discipline style, preschooler's emotional regulation strategies, and preschooler's leadership. The subjects were 180 preschoolers who were 5 years old and their mothers live in Gwang-ju and Jeon-Nam province. Collected data were analyzed by descriptive statistics, Pearson's correlation, and stepwise regression analysis using the program of SPSS. The findings of the study were as follows. (1) there was a difference between the average score of subcategory and the total score of leadership according to the gender of preschooler and mother's employment and her educational background. However, there was no difference from the birth order of preschooler. (2) A correlation was not found between mother's discipline style and preschooler's leadership, but the significant negative correlation showed up between preschooler's emotional regulation strategies and preschooler's leadership. (3) The leadership of preschoolers who use positive coping strategies of a sub-area of emotional regulation strategies appeared strong. And the leadership appeared low with a boy who uses avoidant & supportive strategies, supportive strategies, aggressive strategies and venting strategies more often. Furthermore, the model of explanatory adequacy (R2) of some independent variables from the average scores of subcategory and the total score of leadership was confirmed to be the statistically significant explanatory model.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.395-404
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• 2012

This study was conducted to develop site index equations and to estimate productive areas for major coniferous species in Korea such as Pinus densiflora Sieb. et. Zucc, Pinus densiflora for. erect, Larix leptolepis and Pinus koraiensis using environmental factors. Using the large data set from both a digital forest site map and a climatic map, a total of 43 environmental factors including 15 climatic variables were regressed on site index by tree species to develop site index equations. Six environmental factors by species were selected as independent variables in the final site index equations. The result showed that the coefficients of determination for site index equations by species were ranged from 0.36 to 0.56, which seem to be relatively low but good enough for the estimation of forest stand productivity. The site index equations developed in this study were also verified by three evaluation statistics such as the estimation bias of model, precision of model, and mean square error of measurement. According to the evaluation statistics, it was found that the site index equations by species fitted well to the test data sets with relatively low bias and variation. As a result, it was concluded that the site index equations by species were well capable of estimating site quality. Based on the site index equations, the productive areas by species for all forest areas were estimated by applying GIS technique to the digital forest site map and climate map. In addition, the distribution of productive areas by species was illustrated by using GIS technique.

• Journal of Preventive Medicine and Public Health
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• v.34 no.4
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• pp.337-346
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• 2001

Objective : To determine the clinical risk factors associated with the mode of delivery decision and to compare cesarean section rates after adjusting for risk factors identified among Korean hospitals. Methods Data were collected from 9 general hospitals in two provincial regions by medical record abstraction during February 2000. A total of 3,467 cases were enrolled and analyzed by stepwise logistic regression. Performance of the risk-adjustment model (discrimination and calibration) was evaluated by the C statistic and the Hosmer-Lemeshow test. Crude rates, predicted rates with 95% confidence intervals, and adjusted rates of cesarean section were calculated and compared among the hospitals. Results : The average crude cesarean section rate was 53.2%, ranging from 39.4% to 65.7%. Several risk factors such as maternal age, previous history of cesarean section, placenta previa, placental abruption, malpresentation, amniotic fluid abnormality, gestational anemia, infant body weight, pregnancy-induced hypertension, and chorioamnionitis were found to have statistically significant effects on the mode of delivery. It was confirmed that information about most of these risk factors was able to be collected through the national health insurance claims database in Korea. Performance of the risk-adjustment model was good (c statistic=0.815, Hosmer-Lemeshow test=0.0621). Risk factor adjustment did lead to some change in the rank of hospital cesarean section rates. The crude rates of three hospitals were beyond 95% confidence intervals of the predicted rates. Conclusions : Considering that cesarean section rates in Korean hospitals are too high, it is apparent that some policy interventions need to be introduced. The concept and methodology of risk adjustment should be used in the process of health policy development to lower the cesarean section rate in Korea.

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.368-378
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• 2021

Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required. Materials and Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set. Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%. Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.

• Research in Community and Public Health Nursing
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• v.14 no.2
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• pp.190-199
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• 2003

This study was conducted to determine the factors affecting job satisfaction in workers by using the Job Stress Model proposed by the National Institute for Occupational Safety and Health (NIOSH). Data were collected from December 1 to December 30, 1999. The subjects were 2,133 workers employed at 155 work sites, who were examined using NIOSH Job Stress questionnaire translated by the Korea Occupational Safety ${\pounds}|$ Health Academy and Occupational Safety ${\pounds}|$ Health Research Institute. SAS/PC program was used for statistical analysis using descriptive analysis. Pearson's correlation coefficient, ANOVA, and Stepwise multiple regression analysis. The results of this study were as follows. 1. According to general characteristics of the subjects, job satisfaction was high in those with less number of children. 2. By work condition, job satisfaction was higher in those who were working in a permanent job position, were working with regular time basis than with shift basis, were working in regular shift hours than in changing shift hours, were working for a short period, and were working less hours and overtime works per week. 3. In terms of physical work environment, job satisfaction was significantly related to 10 physical environmental factors. In other words, job satisfaction was high in workers who were working in an environment with no noise, bright light, temperature adjusted to an appropriate level during summer and winter, humidity adjusted to an appropriate level. well ventilation, clean air, no exposure to hazardous substance during work hour, overall pleasant work environment and not crowded work space. 4. By work-related factors, job satisfaction was high in those with less ambiguity about future job and role, high job control/autonomy, and less workload. On the other hand, job satisfaction was low in those with little utilization of competencies, and much role conflict at work and workload. 5. As for the relationships between job satisfaction and the non-work related factors, job satisfaction was high in workers who were volunteering at different organizations or active in religious activities for 5-10 hours per week. 6. In the relationships between job satisfaction and buffering factors, significantly positive correlations were found between job satisfaction and factors such as support by direct superior, support by peers, and support by spouse, friend and family. 7. There were nine factors that affected job satisfaction in the workers: age, number of children, work hours per week, noise, temperature at the work site during summer, uncomfortable physical environment, role ambiguity, role conflict, ambiguity in job future, work load, no utilization of competencies and social support from direct supervisor. These nine factors accounted for 26% of the total variance in the multiple regression analysis. In conclusion. the following are proposed based on the results of this study. 1. The most important physical environmental factors affecting job satisfaction in workers were noise, role ambiguity, and work load, suggesting a need to develop strategies or programs to manage these factors at work sites. 2. A support system that could promote job satisfaction is needed by emphasizing the roles of occupational health nurses who may be stationed at work sites and manage the factors that could generate job stress. 3. Job satisfaction is one of the three acute responses to stress proposed in NIOSH job stress model (job satisfaction. physical discomfort and industrial accidents). Therefore, further studies need to be conducted on the other two issues.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.373-380
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• 2013

The study aimed to develop prediction models of primal cut yield using body measurements of Hanwoo steers in Korea. The progeny of 874 steers at Hanwoo Improvement Main Center from 2008 to 2010 were recorded. Pearson's correlation coefficients for primal cuts and other traits were estimated. Primal cuts were adjusted for slaughter date and age using the SAS GLM procedure. Afterwards, a stepwise regression was performed on each primal cut by fitting body measurement traits. An independent covariable was selected at the highest coefficient of determination with the greater fitness model using Mallows's Cp statistic. Results showed that primal cuts were significantly influenced by slaughter date (P<0.01). The age at slaughter, however, was only significant for the top round (P<0.05). There was a moderate to high correlation between chest girth and tenderloin (0.54), loin (0.74), and rib (0.80). Most primal cut percentages were negatively related to BFT. Similar negative to low positive correlations were observed for primal cut percentage and body size traits. In addition, a correlation of 0.21 was observed between rib percentage and chest girth. The regression of body measurements on the adjusted primal cuts were significant for later traits. Regression estimates revealed that wither height, body length, rump length, hip bone width, and chest girth are important for primal cut weight and percentage determination. In particular, chest girth was always important for primal cut weight estimates.

• Journal of Distribution Research
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• v.17 no.5
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• pp.59-83
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• 2012

Agriculture is a primary industry that influenced by the weather or meterological factors more than other industry. Global warming and worldwide climate changes, and unusual weather phenomena are fatal in agricultural industry and human life. Therefore, many previous studies have been made to find the relationship between weather and the productivity of agriculture. Meterological factors also influence on the distribution of agricultural product. For example, price of agricultural product is determined in the market, and also influenced by the weather of the market. However, there is only a few study was made to find this link. The objective of this study is to investigate the effects of meterological factors on the distribution of agricultural products, focusing on the distribution of chinese cabbages. Chinese cabbage is a main ingredient of Kimchi, and basic essential vegetable in Korean dinner table. However, the production of chinese cabbages is influenced by weather and very fluctuating so that the variation of its price is so unstable. Therefore, both consumers and farmers do not feel comfortable at the unstable price of chinese cabbages. In this study, we analyze the real transaction data of chinese cabbage in wholesale markets and meterological factors depending on the variety and geography. We collect and analyze data of meterological factors such as temperatures, humidity, cloudiness, rainfall, snowfall, wind speed, insolation, sunshine duration in producing and consuming region of chinese cabbages. The result of this study shows that the meterological factors such as temperature and humidity significantly influence on the volume and price of chinese cabbage transaction in wholesale market. Especially, the weather of consuming region has greater correlation effects on transaction than that of producing region in all types of chinese cabbages. Among the whole agricultural lifecycle of chinese cabbages, 'seeding - harvest - shipment - wholesale', meterological factors such as temperature and rainfall in shipment and wholesale period are significantly correlated with transaction volume and price of crops. Based on the result of correlation analysis, we make a regression analysis to verify the meterological factors' effects on the volume and price of chines cabbage transaction in wholesale market. The results of stepwise regression analysis are shown in