• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.19-32
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• 2020

The purpose of this study was to investigate the influence factors of the university level centering on the entrepreneurial performance of the university students and full-time faculties in the situation of increasing interest in entrepreneurial university. In order to achieve the purpose of the study, a panel data was established from 2015 to 2018 on the basis of the university notification data. The panel data included universities with data on the number of university students and full-time faculty founders for at least two years. Through this, four year data from 154 universities were used for analysis. As an analysis method, frequency analysis and descriptive statistics were conducted to understand the characteristics of the university. Since then, panel negative binomial regression analysis has been conducted in consideration of the longitudinal features and distribution of the data. Also, based on the Hausman test results, the results were interpreted based on random effect model. The results of this study are as follows. First, as a result of the analysis of the entrepreneurial performance and the change trend of the domestic university from 2015 to 2018, the entrepreneurial performance of the university has been steadily increasing in the last four years, and the increase in the number of university student entrepreneurs was relatively higher than the full-time faculties. Second, economic and educational approaches need to be combined to promote university students' start-ups. The university factors that promote the start-up of university students were found to be scholarships, start-up grants, startup lectures, and startup clubs. Third, the openness and regional characteristics of the univeristy can promote the establishment of university students. Fourth, the establishment of a research environment and support for start-ups for full-time faculty members can enhance their start-up performance. The university factors that promote the start-up of full-time faculty were research funds and staffes who support start-up. The conclusions drawn from these findings are as follows. First, overall efforts are needed to develop into an entrepreneurial university. Second, in order to change into an entrepreneurial university, direct support for entrepreneurship is needed. Third, as an entrepreneurial university, it is necessary to find a way to bridge the gap by university according to region and size. Fourth, it is necessary to reinforce the support for linking the research results of universities to start-ups. Fifth, it is necessary to improve the atmosphere for full-time faculty members to be entrepreneur.

• Tuberculosis and Respiratory Diseases
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• v.45 no.6
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• pp.1143-1153
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• 1998

Background : Recent outbreaks of pulmonary disease due to drug-resistant strains of Mycobacterium Tuberculosis have resulted in significant morbidity and mortality in patients worldwide. We reviewed our experience to evaluate the effects of pulmonary resection on the management of multidrug-resistant tuberculosis. Method : A retrospective review was performed of 41 patients undergoing pulmonary resection for multidrug-resistant tuberculosis between January 1993 and December 1997. We divided these into 3 groups according to the radiologic findings : (1) patients who have reasonably localized lesion (Localized Lesion Group ; LLG) (2) patients who have cavitary lesions after pulmonary resection on chest roentgenogram (Remained Cavity Group : RCG) (3) patients who have Remained infiltrative lesions postoperatively (Remained infiltrative group : RIG). We evaluated the negative conversion rate after resection and overall response rate of the groups. Then they were compared with the results of the chemotherapy on the multi drug-resistant tuberculosis which has been outcome by Goble et al. Goble et al reported that negative conversion rate was 65% and overall response rate, 56% over a mean period of 5.1 months. Results : Seventy five point six percent were men and 24.4% women with a median age of 31 years (range, 16 to 60 years). Although the patients were treated preoperatively with multidrug regimens in an effort to reduce the mycobacterial burden, 22 of 41 were still sputum culture positive at the time of surgery. 20 of 22 patients(90.9%, p<0.01) responded which is defined as negative sputum cultures within 2 months postoperative. Of 26 patients with the sufficient follow up data, 19 have Remained sputum culture negative for a mean duration of 25.7 months (73.1%, p<0.05). The bulk of the disease was manifest in one lung, but lesser amounts of contralateral disease were demonstrated in 15, consisted of 8 in RIG and 7 in RCG, of 41. 12 of 12 patients (100%, p<0.01) who were sputum positive at the time of surgery in LLG converted successfully. 14 of 15 patients (93.3%, p<0.05) with the follow up have completed treatment and not relapsed for a mean period of 25. 7 months. The mean length of postoperative drug therapy of LLG was 12.2 months. In RIG, postoperative negative conversion rate was 83.3% which was not significant statistically. There was a statistical significance in overall response rate (100%, p<0.05) of RIG for a mean period of 24.4 months with a mean length of postoperative chemotherapy, 11.8 months. In RCG a statistically lower overall response rate (14.3%, p<0.01) has been revealed for a mean duration of follow up, 24.2 months. A negative conversion rate of RCG was 75% which was not significant statistically. Conclusion : Surgery plays an important role in the management of patients with multidrug-resistant Mycobacterium tuberculosis infection. Aggressive pulmonary resection should be performed for resistant Mycobacterium tuberculosis infection to avoid treatment failure or relapse. Especially all cavitary lesions on preoperative chest roentgenogram should be resected completely. If all of them could not be resected perfectly, you should not open the thorax.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.