• The korean journal of orthodontics
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• v.39 no.5
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• pp.300-309
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• 2009

Objective: The purpose of this study was to evaluate the correlation between menton deviation and dental compensation in facial asymmetry. Methods: Tooth axis and distance of first molar and canine to the reference plane were investigated by cone-beam computerized tomography. The subjects consisted of 50 patients with asymmetric mandibles (male 21, female 29, mean age 24.3 years). Control groups were also assessed (male 11, female 9, mean age 25.6 years). Nine measurements (5 linear measurements and 4 angular measurements) were measured in order to evaluate the correlation between menton deviation and the linear and angular difference of first molar and canine in the deviated and none-deviated sides using the defined MPR images. The differences between deviated and non-deviated side, according to menton deviation, were statistically analyzed using stepwise multiple regression analysis. Results: From the result, Menton deviation was negatively correlated with mandibular first molar's angular measurement (${\Delta\angle}LM6$-Mn plane (dev.-ndev.)) and positively with maxillary fist molar's angular measurement (${\Delta\angle}UM6$-FH plane (dev.-ndev.)) (p < 0.01). Two angular measurements (${\Delta\angle}LM6$-Mn plane (dev.-ndev.), ${\Delta\angle}UM6$-FH plane (dev.-ndev.)) explained the variability in menton deviation with a significant $r^2$ value of 0.589. Conclusions: This study suggests that the tooth axis of upper and lower first molars leans towards the deviated side of Menton when there is mandibular asymmetry with Menton deviation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.4
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• pp.158-166
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• 2008

The pumpkin fruit fly, Bactrocera depressa (Tephritidae: Diptera), is one of the most important pests in Cucurbitaceae plants. This study was conducted to investigate the basic ecology of B. depressa, and to develop a forecasting model for predicting the time of adult emergence in early season. In green pumpkin producing farms, the oviposition punctures caused by the oviposition of B. depressa occurred first between mid- and late July, peaked in late August, and then decreased in mid-September followed by disappearance of the symptoms in late September, during which oviposition activity of B. depressa is considered active. In full-ripened pumpkin producing farms, damaged fruits abruptly increased from early Auguest, because the decay of pumpkins caused by larval development began from that time. B. depressa produced a mean oviposition puncture of 2.2 per fruit and total 28.8-29.8 eggs per fruit. Adult emergence from overwintering pupae, which was monitored using a ground emergence trap, was first observed between mid- and late May, and peaked during late May to early June. The development times from overwintering pupae to adult emergence decreased with increasing temperature: 59.0 days at $15^{\circ}C$, 39.3 days at $20^{\circ}C$, 25.8 days at$25^{\circ}C$ and 21.4 days at $30^{\circ}C$. The pupae did not develop to adult at $35^{\circ}C$. The lower developmental threshold temperature was calculated as $6.8^{\circ}C$ by linear regression. The thermal constant was 482.3 degree-days. The non-linear model of Gaussian equation well explained the relationship between the development rate and temperature. The Weibull function provided a good fit for the distribution of development times of overwintering pupae. The predicted date of 50% adult emergence by a degree-day model showed one day deviation from the observed actual date. Also, the output estimated by rate summation model, which was consisted of the developmental model and the Weibull function, well pursued the actual pattern of cumulative frequency curve of B. depressa adult emergence. Consequently, it is expected that the present results could be used to establish the management strategy of B. depressa.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.133-149
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• 2022

Existing domestic studies on estimating rice yield were mainly implemented at the level of cities and counties in the entire nation using MODIS satellite images with low spatial resolution. Unlike previous studies, this study tried to estimate rice yield at the level of eup-myon-dong in Gimje-si, Jeollabuk-do using Sentinel-2 satellite images with medium spatial resolution, rainfall and soil data, and then to evaluate its accuracy. Five vegetation indices such as NDVI, LAI, EVI2, MCARI1 and MCARI2 derived from Sentinel-2 images of August 1, 2018 for Gimje-si, Jeollabuk-do, rainfall and paddy soil-type data were aggregated by the level of eup-myon-dong and then rice yield was estimated with gamma generalized linear model, an expanded variant of multi-variate regression analysis to solve the non-normality problem of dependent variable. In the rice yield model finally developed, EVI2, rainfall days in September, and saline soils ratio were used as significant independent variables. The coefficient of determination representing the model fit was 0.68 and the RMSE for showing the model accuracy was 62.29kg/10a. This model estimated the total rice production in Gimje-si in 2018 to be 96,914.6M/T, which was very close to 94,470.3M/T the actual amount specified in the Statistical Yearbook with an error of 0.46%. Also, the rice production per unit area of Gimje-si was amounted to 552kg/10a, which was almost consistent with 550kg/10a of the statistical data. This result is similar to that of the previous studies and it demonstrated that the rice yield can be estimated using Sentinel-2 satellite images at the level of cities and counties or smaller districts in Korea.

• 한국사회복지학회:학술대회논문집
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• 2003.10a
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• pp.185-216
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• 2003

This study was aimed at investigating important factors influencing career success among regular workers. The current researcher scrutinized the degree to which variables and factors affect the career success and occupational turnover rates of the research participants. At the same tune, two hypothetical path models established by the researcher were examined using linear multiple regression methods and the LISREL. After examining the differences among the factors of career success, a comparison was made between the disabled worker group and the non-disabled worker group. A questionnaire using the 5-point Likert scale was distributed to a group of 374 workers with disabilities and 463 workers without disabilities. For the data analysis purpose, the structural equation model, factor analysis, correlation analysis, and multiple regression analysis were carried out. The results of this study ran be summarized as follows. First, the results of factor analysis showed important categories of conceptual themes of career success. The initial conceptual factor model did not accord with the empirical one. A three-factorial model revealed categories of personal, family, and organizational factor respectively. The personal factor was composed of the self-esteem and self-efficiency. The family factor was consisted of the multi-roles stress and the number of children. Finally, the organizational factor was composed of the capacity for utilizing resources, networking, and the frequency of mentoring. In addition, the total 10 sub areas of career success were divided by two important aspects; the subjective career success and the objective career success. Second, both research participant groups seemed to be influenced by their occupational types. However, all predictive variables excluding the wage rate and the average length of work years had significant impact on job success for the disabled work group, while all the variables excluding the frequency of advice and length of working years had significant impact on job success for the non-disabled worker group. Third, the turnover rate was significantly influenced by the age and the experience of turnover of the research participants. However, the number of co-workers was the strongest predictive variable for the worker group with disabilities, but the occupation choice variable for the worker group without disabilities. For the disabled worker group, the turnover rate was differently influenced by the type of occupation, the length of working years, while multi-role stress and the average working years at the time of turnover for the worker group without disabilities. Fifth, as a result of verifying the hypothetical path model, it showed that the first model was somewhat proper and could predict the career success on both research participant groups. In the second model, the Chi-square, the degree of freedom (($x^2=64.950$, df=61, P=0.341), and the adjusted Goodness of Fit Index (AGFI) were .964, and the Comparative Fit Index (CFI) were .997, and the Root Mean Squared Residual (RMR) was respectively. .038. The model was best fitted and could predict the career success more highly because the goodness of fit index in the whole models was within the allowed range. In conclusion, the following research implications can be suggested. First, the occupational type of research participants was one of the most important variables to predict the career success for both research participant groups. It means that people with disabilities require human development services including education. They need to improve themselves in this knowledge-based society. Furthermore, for maintaining the career success, people with disabilities should be approached by considering the subjective career success aspects including wages and the promotion opportunities than the objective career success aspects.

• Journal of Chest Surgery
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• v.41 no.1
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• pp.41-48
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• 2008

Background: Accurate assessment of the preload and the fluid responsiveness is of great importance for optimizing cardiac output, especially in those patients with coronary artery occlusive disease (CAOD). In this study, we evaluated the relationship between the parameters of preload with the changes in the stroke volume index (SVI) after fluid loading in patients who were undergoing coronary artery bypass grafting (CABG). The purpose of this study was to find the predictors of fluid responsiveness in order to assess the feasibility of using. certain parameters of preload as a guide to fluid therapy. Material and Method: We studied 96 patients who were undergoing CABG. After induction of anesthesia, the hemodynamic parameters were measured before (T1) and 10 min after volume replacement (T2) by an infusion of 6% hydroxyethyl starch 130/0.4 (10 mL/kg) over 20 min. Result: The right ventricular end-diastolic volume index (RVEDVI), as well as the central venous pressure (CVP) and pulmonary capillary wedge pressure (PCWP), failed to demonstrate significant correlation with the changes in the SVI (%). Only the right ventricular ejection fraction (RVEF) measured at T1 showed significant correlation. with the changes of the SVI by linear regression (r=0.272, p=0.017). However, when the area under the curve of receiver operating characteristics (ROC) was evaluated, none of the parameters were over 0.7. The volume-induced increase in the SVI was 10% or greater in 31 patients (responders) and under 10% in 65 patients (non-responders). None of the parameters of preload measured at T1 showed a significant difference between the responders and non-responders, except for the RVEF. Conclusion: The conventional parameters measured with a volumetric pulmonary artery catheter failed to predict the response of SVI following fluid administration in patients suffering with CAOD.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.191-204
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• 2015

According to the '2013 Performance Assessment Report on the Financial Program' from the National Assembly Budget Office, the unfilled recruitment ratio of Software(SW) Developers in South Korea was 25% in the 2012 fiscal year. Moreover, the unfilled recruitment ratio of highly-qualified SW developers reaches almost 80%. This phenomenon is intensified in small and medium enterprises consisting of less than 300 employees. Young job-seekers in South Korea are increasingly avoiding becoming a SW developer and even the current SW developers want to change careers, which hinders the national development of IT industries. The Korean government has recently realized the problem and implemented policies to foster young SW developers. Due to this effort, it has become easier to find young SW developers at the beginning-level. However, it is still hard to recruit highly-qualified SW developers for many IT companies. This is because in order to become a SW developing expert, having a long term experiences are important. Thus, improving job continuity intentions of current SW developers is more important than fostering new SW developers. Therefore, this study surveyed the job continuity intentions of SW developers and analyzed the factors associated with them. As a method, we carried out a survey from September 2014 to October 2014, which was targeted on 130 SW developers who were working in IT industries in South Korea. We gathered the demographic information and characteristics of the respondents, work environments of a SW industry, and social positions for SW developers. Afterward, a regression analysis and a decision tree method were performed to analyze the data. These two methods are widely used data mining techniques, which have explanation ability and are mutually complementary. We first performed a linear regression method to find the important factors assaociated with a job continuity intension of SW developers. The result showed that an 'expected age' to work as a SW developer were the most significant factor associated with the job continuity intention. We supposed that the major cause of this phenomenon is the structural problem of IT industries in South Korea, which requires SW developers to change the work field from developing area to management as they are promoted. Also, a 'motivation' to become a SW developer and a 'personality (introverted tendency)' of a SW developer are highly importantly factors associated with the job continuity intention. Next, the decision tree method was performed to extract the characteristics of highly motivated developers and the low motivated ones. We used well-known C4.5 algorithm for decision tree analysis. The results showed that 'motivation', 'personality', and 'expected age' were also important factors influencing the job continuity intentions, which was similar to the results of the regression analysis. In addition to that, the 'ability to learn' new technology was a crucial factor for the decision rules of job continuity. In other words, a person with high ability to learn new technology tends to work as a SW developer for a longer period of time. The decision rule also showed that a 'social position' of SW developers and a 'prospect' of SW industry were minor factors influencing job continuity intensions. On the other hand, 'type of an employment (regular position/ non-regular position)' and 'type of company (ordering company/ service providing company)' did not affect the job continuity intension in both methods. In this research, we demonstrated the job continuity intentions of SW developers, who were actually working at IT companies in South Korea, and we analyzed the factors associated with them. These results can be used for human resource management in many IT companies when recruiting or fostering highly-qualified SW experts. It can also help to build SW developer fostering policy and to solve the problem of unfilled recruitment of SW Developers in South Korea.

• Clinical and Experimental Pediatrics
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• v.50 no.6
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• pp.549-555
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• 2007

Purpose : Pulse wave velocity (PWV) and ankle brachial index (ABI) are simple, non-invasive methods to assess arterial stiffness. These parameters are also known to be closely related to cardiovascular risk factors and diseases. The purposes of this study were to measure blood pressure, PWV, ABI in healthy Korean adolescents, set up their normal values and assess their correlations. Methods : Three hundred ninety two healthy adolescents (213 boys and 179 girls) underwent measurement of brachial ankle pulse wave velocity (baPWV), ABI, body mass index(BMI) and blood pressure from four extremities. Linear regression analysis was performed to reveal the correlations between PWV, ABI and independent variables. Results : Blood pressure and PWV were significantly higher in all extremities in males compared to females. Blood pressure of both brachial and ankle showed positive correlation with body weight, height, and BMI, whereas ABI showed no correlation with any of these indices. Conclusion : Blood pressure increases as body weight, height and BMI increases. PWV shows positive correlation with blood pressure. It will be helpful to predict the risks of cardiovascular diseases in adolescents.

• Radiation Oncology Journal
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• v.19 no.2
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• pp.163-170
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• 2001

Purpose : The measurement of radiation survival using a clonogenic assay, the established standard, can be difficult and time consuming. In this study, We have used the MTT assay, based on the reduction of a tetrazolium salt to a purple formazan precipitate by living cells, as a substitution for clonogenic assay and have examined the optimal condition for performing this assay in determination of radiation sensitivity. Materials and Methods : Four human cancer cell lines - PCI-1, SNU-1066, NCI-H630 and RKO cells have been used. For each cell line, a clonogenic assay and a MTT assay using Premix WST-1 solution, which is one of the tetrazolium salts and does not require washing or solubilization of the precipitate were carried out after irradiation of 0, 2, 4, 6, 8, 10 Gy. For clonogenic assay, cells in $25\;cm^2$ flasks were irradiated after overnight incubation and the resultant colonies containing more than 50 cells were scored after culturing the cells for $10\~14$ days. For MTT assay, the relationship between absorbance and cell number, optimal seeding cell number, and optimal timing of assay was determined. Then, MTT assay was performed when the irradiated cells had regained exponential growth or when the non-irradiated cells had undergone four or more doubling times. Results : There was minimal variation in the values gained from these two methods with the standard deviation generally less than $5\%$, and there were no statistically significant differences between two methods according to t-test in low radiation dose (below 6 Gy). The regression analyses showed high linear correlation with the $R^2$ value of $0.975\~0.992$ between data from the two different methods. The optimal cell numbers for MTT assay were found to be dependent on plating efficiency of used cell line. Less than 300 cells/well were appropriate for cells with high plating efficiency (more than $30\%$). For cells with low plating efficiency (less than $30\%$), 500 cells/well or more were appropriate for assay. The optimal time for MTT assay was after 6 doubling times for the results compatible with those of clonogenic assay, at least after 4 doubling times was required for valid results. In consideration of practical limits of assay (12 days, in this study) cells with doubling time more than 3 days were inappropriate for application. Conclusion : In conclusion, it is found that MTT assay can successfully replace clonogenic assay of tested cancer cell lines after irradiation only if MTT assay was undertaken with optimal assay conditions that included plating efficiency of each cell line and doubling time at least.

• Journal of agricultural medicine and community health
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• v.26 no.2
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• pp.97-109
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• 2001

To investigate the total cholesterol level and its related factors in the rural and sea-board, 2,840 adults who participated voluntarily were examined during the study period December 1999 to February 2000. The height, weight, and fasting serum total cholesterol were measured. Body mass index was calculated. Information on age, gender, smoking, alcohol, and menopausal status in women were collected using a questionnaire by interviewing method. The mean value of total cholesterol was 191.6mg/ dl in sea- board and 173.6mg/ dl in rural men, respectively, and 206.9mg/ dl and 186.9mg/ dl in sea-board and in rural women. By simple analysis, in men, area, BMI and smoking were significant risk factors(p<0.01). Area(p<0.01), age(p<0.01), BMI(p<0.01), smoking(p<0.05), and menopausal status (p<0.01) were significant. In multiple linear regression analysis, the significant factors for total cholesterol in men were area(sea-board versus rural area; p<0.01), body mass index(the more obese; p<0.01), and smoking (non-smoker versus smoker ; p<0.05). Those in women were area(sea-board versus rural area; p<0.01), body mass index(the more obese; p<0.01), and menopausal status(menopause versus normal; p<0.01). Thus, in both gender, the significant factors related with total cholesterol were area and body mass index, and in addition to those, menopausal status was proved as a significant risk factor in women.