• Journal of the korean academy of Pediatric Dentistry
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• v.27 no.4
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• pp.540-548
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• 2000

The aim of this study was to evaluate the timing of sequence of tooth calcification in current Korean growing children. The Calcification stage of permanent teeth of Korean children was investigated by classifying them into 10 stages by the criteria of Nolla, using the panoramic radiographs of 258 healthy Korean children, 149 males and 109 females, between the ages of 4 years and 13 years, with normal growing tendency and no orthodontic treatment experience. The obtained results were as follows: 1. Timing of calcification of permanent teeth by Nolla stage was established with mean values. Among the mean value, results of Nolla stage 7 were as follows: Calcification timing of male in the maxilla was 6 year 9 month on central incisor, 7 year 4 month on lateral incisor,7 year 9 month on canine, 8 year 8 month on the first premolar, 9 year 4 month on the second premolar, 6 years 3 month on the first molar and 10 year 8 month on the second molar, calcification timing of male in the mandible was 5 year 11 month on central incisor, 6 year 4 month on lateral incisor, 7 year 5 month on canine, 8 year 1 month on the first premolar, 8 year 6 month on the second premolar 5 years 6 month on the first molar and 10 year 3 month on the second molar. Calcification timing of female in the maxilla was 6 year 2 month on central incisor, 6 year 7 month on lateral incisor, 6 year 11 month on canine, 8 year 1 month on the first premolar, 8 year 5 month on the second premolar, 5 years 10 month on the first molar and 9 year 10 month on the second molar, calcification timing of male in the mandible was 5 year 6 month on central incisor, 5 year 9 month on lateral incisor, 6 year 8 month on canine, 7 year 6 month on the first premolar, 8 year 4 month on the second premolar, 5 years 3 month on the first molar and 9 year 7 month on the second molar. 2. The sequence of calcification at Nolla stage 7 was in consequence to the first molar, central incisor, lateral incisor, canine, the first premolar, the second premolar and second molar. 3. While the sequence of root completion of maxilla was in consequence to the first molar, central incisor, lateral incisor, that of mandible was in order of central incisor, first molar and lateral incisor. 4 the calcification timing of permanent teeth was earlier in female than in male (p<0.05). According to above data, the result of this study is applicable for diagnosis and routine clinical practice for children.

• Journal of Soil and Groundwater Environment
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• v.10 no.4
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• pp.1-12
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• 2005

Policies regarding to soil quality of the United States, the United Kingdom, the Netherlands, Germany, and Demark were analyzed to suggest Korean policy for improving soil quality concept and it's implementation. All countries met four criteria: I) Soil quality levels of contaminants are indebt to concept of contaminant risk to recipients (human and ecosystem); ii) Any soil quality value can't be a magic number to determine whether a site is contaminated or not. To determine risk of sites, risk assessment of the sites should be followed; iii) Concentrations of contaminants of sites are not always significantly certain to risk of human and ecosystem of the sites; and iv) Soil quality levels are adopted based on land uses and plans. Considering our rooms to improve policies and analysis of the other country reports on their legislations about soil quality levels, our policy implementation could be approached from these directions: i) Our concept for soil quality levels needs to develop in scientific and rational. ii) Soil quality levels and risk assessment should be implemented as determining tools of site contamination in parallel, and iii) Soil quality levels depending on land uses and plans should be developed in debt with rational and scientific concept of risk. Increasing efficacy of Korea policy regarding the soil quality levels would be in dept to applying concepts of SCL (Soil Contamination Level) and SRL (Soil Regulatory Level) developed, implementing soil quality levels and risk assessment of contaminated sites in conjunction, and classifying three distinctions of land uses based on sensitiveness of recipients (human and ecosystem) to contaminants in soil in this research.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.