• The Journal of Korean Academy of Prosthodontics
• /
• v.37 no.2
• /
• pp.200-211
• /
• 1999

For many years permanent soft denture liners has been widely used in dental practice directly or indirectly because of its function in absorbing and distributing the impact force. However, it reveals problems such as lack of permanency and decreased bond strength in long term use. The purpose of this study is to measure the bond strength and failure between denture base resin and several permanent liners. Lucitone 199 was used as denture base resin with soft acrylic liners (Triad, Tokuso Rebase) and silicone elastomers (Tokuyama, Ufi Gel C) bonded to measure the tensile strength before and after thermocycling. The thermocycling was done in 2000 cycles at $5^{\circ}C,\;26^{\circ}C\;and\;55^{\circ}C$ and the measured tensile strength values before and after thermocycling were compared. The mode of failure was investigated in the separated specimens. The results are as follows. 1. As to tensile strength, the strongest material is Tokuso Rebase followed by Triad, Tokuyama, Ufi Gel C in before thermocycling and the order of Triad, Tokuso Rebase, Tokuyama, Ufi Gel C in after thermocycling state. There was significant difference between the values of Triad, Tokuso Rebase and Tokuyama, Ufi Gel C(p<0.05). 2. As to degree of displacement, Ufi Gel C showed most displacement with or without thermo-cycling treatment and also the difference was significant with the other materials(p<0.05). 3. As to comparisons before and after thermocycling, Tokuso Rebase and Tokuyama showed significant difference in bond strength, whereas Triad and Tokuso Rebase showed significant difference in the degree of displacement(p<0.05). 4. In debonded specimens, Triad and Ufi Gel C showed adhesion failure and Tokuyama showed cohesion failure. Both failures were observed in Tokuso Rebase with adhesion failure up to 70%. The results of this study showed that degree of bond strength between permanent soft denture liner and denture base resin were variable. There was a significant difference between soft acrylics and silicone elastomers with regard to bond strength. Further research in improving bond strength of widely used silicone elastomers and in developing the method of measuring bond strength between denture base resin and the lining materials is needed.

• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.59-77
• /
• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.