• Fire Science and Engineering
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• v.29 no.2
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• pp.25-32
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• 2015

The improved thermal stability and anti-oxidation properties of lyocell fiber were studied based on flame retardant treatment by using $Na_3PO_4$ solution. The optimized conditions of flame retardant treatment were studied on various concentrations of $Na_3PO_4$ and the mechanism was proposed through experimental results of thermal stability and anti-oxidation. The integral procedural decomposition temperature (IPDT), limiting oxygen index (LOI) and activation energy ($E_a$) increased 30, 160% respectively via flame retardant treatment. It is noted that thermal stability and anti-oxidation improved based on char and carbon layer formation by dehydrogenation and dissociation of C-C bond resulting the hindrance of oxygen and heat energy into polymer resin. The optimized conditions for efficient flame retardant property of lyocell fiber were provided using $Na_3PO_4$ solution and the mechanism was also studied based on experimental results such as initial decomposition temperature (IDT), IPDT, LOI and $E_a$.

• International Area Studies Review
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• v.15 no.2
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• pp.77-100
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• 2011

The Korean economy have experienced the remarkable decreases in factor prices such as bond yields, real wage since the IMF foreign exchange crisis. This paper investigates the effects of the price changes in the factor markets on determining the level and cyclicality of industrial markups in the manufacturing industry. For this purpose, we construct a markup equation in the small open economy based on the production function including foreign intermediate goods and assuming constant returns to scale technology and AR(1) process of technological coefficient. Empirical results are summarized as the followings. The empirical results shows that the increased markups after the IMF crisis can be explained by the price decreases in the factor markets which result in lowering marginal costs. And we also observed counter cyclicality of markup, labor share and interest rates while real wages, technical coefficients, and production price index proved to be pro-cyclical. In conclusion, the price changes in factor market have contributed to the stickiness in markup fluctuation in the manufacturing industry.

• Journal of Pharmacopuncture
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• v.22 no.3
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• pp.166-170
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• 2019

Objectives: Attentional and memory functions are important aspects of neural plasticity that, theoretically, should be amenable to pharmacopuncture treatments. A previous study from our laboratory suggested that quantitative electroencephalographic (qEEG) measurements of theta/beta ratio (TBR), an index of attentional control, may be indicative of academic performance in a first-semester medical school course. The present study expands our prior report by extracting and analyzing data on frontal theta and beta asymmetries. We test the hypothesis that the amount of frontal theta and beta asymmetries (fTA, fBA), are correlated with TBR and academic performance, thereby providing novel targets for pharmacopuncture treatments to improve cognitive performance. Methods: Ten healthy male volunteers were subjected to 5-10 min of qEEG measurements under eyes-closed conditions. The qEEG measurements were performed 3 days before each of first two block examinations in anatomy-physiology, separated by five weeks. Amplitudes of the theta and beta waveforms, expressed in ${\mu}V$, were used to compute TBR, fTA and fBA. Significance of changes in theta and beta EEG wave amplitude was assessed by ANOVA with post-hoc t-testing. Correlations between TBR, fTA, fBA and the raw examination scores were evaluated by Pearson's product-moment coefficients and linear regression analysis. Results: fTA and fBA were found to be negatively correlated with TBR (P<0.03, P<0.05, respectively) and were positively correlated with the second examination score (P<0.03, P=0.1, respectively). Conclusion: Smaller fTA and fBA were associated with lower academic performance in the second of two first-semester medical school anatomy-physiology block examination. Future studies should determine whether these qEEG metrics are useful for monitoring changes associated with the brain's cognitive adaptations to academic challenges, for predicting academic performance and for targeting phamacopuncture treatments to improve cognitive performance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.63-75
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• 2023

This study conducted empirical analysis on the factors affecting the investment performance of 205 domestic venture funds (with a total liquidation amount of 7.25 trillion KRW) newly formed from 2007 to 2017 and completely liquidated as of the end of 2022. Due to the nature of private equity funds, obtaining empirical data is extremely challenging, especially for data post-COVID-19 era liquidations. Nevertheless, despite these challenges, it is meaningful to analyze the impact on the investment returns of domestic venture funds using the most recent data available from the past 10 years. This study categorized the factors influencing venture fund performance into external environmental factors and internal factors. External environmental factors included "economic cycles," "stock markets," "venture markets," and "exit markets," while internal factors included the fund management company's capabilities in terms of "experience," "professional personnel," and "assets under management (AUM)." The fund structure was also categorized into "fund size" and "fund length" for comparative analysis. In summary, the analysis yielded the following results: First, the 3-year government bond yield, which represents economic cycles well, was found to have a significant impact on fund performance. Second, the average 3-month KOSDAQ index return after fund formation had a statistically significant positive effect on fund performance. Third, the number of IPOs, indicating the competition intensity at the time of venture fund liquidation, was shown to have a negative effect on fund performance. Fourth, it was observed that the larger the AUM of the fund management company, the better the fund's returns. Finally, venture fund returns showed variations depending on the year of formation (Vintage). Therefore, when individuals consider investing in venture funds, it is considered a highly effective investment strategy to construct an investment portfolio taking into account not only external environmental factors and internal fund factors but also the vintage year.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.