• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.10
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• pp.1545-1554
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• 2010

Korean government will set up the nationwide food safety system with strict control of hazardous nutrients like sugar, fatty acids and sodium as well as advanced nutrition education system. In addition, almost one hundred percent of school food service rate forced the government to consider more effective ways to upgrade the nutritional status of school meals. The object of our study was to provide the data on content and consumption of sugar in school meal for the nationwide project. For this purpose, we surveyed the sugar content of 842 school meal menus and their intake level for 154 days in 8 schools in Daejeon and Chungcheong Province. Sugar contents, the sum of the quantity of 5 sugars commonly detected in food, were analysed with HPLC-RID (Refractive Index Detector). Sugar intakes were calculated by multiplying the intake of each menu to the sugar content of that menu. The sugar content was highest in the desserts, which include fruit juices, dairy products and fruits. Sugar content of side dish was high in sauces and braised foods. Sugar intake from one dish is high in beverage and dairy product, and one dish meals contribute greatly to sugar intake because of their large amount of meal intake. The average lunch meal intakes of second grade and fifth grade elementary school students were 244 g/meal and 304 g/meal, respectively. The meal intake of middle school student was 401 g/meal. The average sugar intake from one day school lunch was 4.22 g (4.03 g on elementary and 5.31 g on middle school student), which is less than 10% of daily sugar reference value for Koreans. The result of this study provides exact data of sugar intake pattern based on the content of sugar which is matched directly to the meals consumed by the students.

• Radiation Oncology Journal
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• v.19 no.3
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• pp.205-210
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• 2001

Purpose : This study tried to evaluate the effectiveness of combined treatment using radiation therapy and concurrent cisplatin as a radiosensitizer in the management of locally advanced head and neck cancer. Materials and methods : From January 1995 to August 1998, 29 evaluable patients with locally advanced head & neck cancels (AJCC stage $II\~IV$) were received curative radiation therapy $(total\;70\~75.6\;Gy/35\~42\;fractions,\;1.8\~2\;Gy/fraction)$ and concurrent cisplatin chemotherapy ($100\;mg/m^2$, D1, D22, D43). The neck dissections were peformed for residual lymphadenopathy. Follow-up ranged from 5 to 55 months (median 24 months). Results : Twenty-one $(72.4\%)$ patients achieved clinical complete responses. The partial response and minimal response rates were $17.2\%\;and\;10.4\%$, respectively. Locoregional failure rate was $27.6\%$, and included 6 patients with local failures, 4 patients with regional failures, and 2 patients with combined local and regional failures. Four of 29 patients $(13.8\%)$ developed distant metastasis. The disease free survival rate at 3 years was $60\%$. Nasopharyngeal primary tumors or complete responders showed significantly higher disease free survival rate. The grade 3 mucositis and nausea/vomiting was noted in $34.5\%$, respectively. Major prolongation of radiation therapy duration was inevitable in three patients. Twenty-one patients $(72.4\%)$ completed 3 courses of cisplatin and 5 patients received 2 courses of cisplatin. Three patients received only one course of cisplatin due to nephrotoxicity and neurotoxicity, and then changed to 5-FU regimen. Conclusions : Concurrent cisplatin-radiation therapy in locally advanced head and neck cancer showed high response rate, reasonable locoregional control, and survival rate. As expected, acute toxicities were increased, but compliance to treatment was acceptable. Assessment of the effect of the combination in this setting requires further accrual and follow-up.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• (The)Study of the Eastern Classic
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• no.73
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• pp.445-473
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• 2018

This article deals with the reason for translating Korean Chinese written classics and the image of the person performing the translation. The scope of the research was restricted to South Korea and the translation value of translating the Korean classical texts from the 1960s to the 2018. In the 1960s and 1970s, the discourse of national culture and the Classical Sinology(漢學) discourse centered around the Minjokmunwhachujinhwe(民族文化推進會, National Culture Promotion Association). The discourse of the national culture was paired with the modernization, and the discourse of Classical Sinology(漢學) discourse was a certain antagonism to the discourse of modernization. The translator stereotype in this periods was close to a Classical Sinology(漢學) who could wright Korean letters. The discourse of the national culture led to the establishment of The Academy of Korean Studies by pairing with the discourse of the spiritual culture, and then changed into Korean study discourse in the 1980s. Since the mid 80s, the theory of translation has been introduced byo Kim Yong-ok. The translation of the Chosun dynasty annals, which started in the 70s, made the classical translation discourse in the classical translation field into the national project efficiency discourse. To the Early achievement of state-led gigantic project through group translation, they emphasized coherence and efficiency. On the contrary, the individuality of the translators and aspects of in-depth research have weakened. This discourse also influenced until the early 2000s. These large translation projects were produced by professional translator group. With the establishment of the Institute for the Translation of Korean Classics(Hankuk Kojon Bunyukwon) in 2007, he foundation for the stability of the classical translation business was established, and the classical translation discourse was shifted to the academic discourse centered on classical translation sudies. This discussion was expanded to the request of the establishment of an academic institution called the Classical Translation Graduate School, with a discussion on the academic identities of classical translation studies. The imagies of translators, paired with the academic discourse of this period, and that the classical translators must be classical scholars and translators, are begun to be requested. Thus, the classical translation value discourse changed with the passage of time, and the imagies of classical translators have been changed accordingly.

• Journal of Life Science
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• v.29 no.7
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• pp.800-808
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• 2019

Charcot-Marie-Tooth disease (CMT) is a group of rare peripheral neuropathies characterized by progressive muscle weakness and atrophy and areflexia in the upper and lower extremities. The most common subtype of CMT is CMT1A, which is caused by a tandem duplication of the PMP22 gene in the 17p12 region. Patients with CMT1A show a loose genotype-phenotype correlation, which suggests the existence of secondary genetic or association factors. Recently, polymorphisms of rs71428439 (n.83A>G) and rs2292832 (n.86T>C) in the MIR149 have been reported to be associated with late onset and mild phenotypic CMT1A severity. The aim of this study was to examine the intrafamilial heterogeneities of clinical phenotypes according to the genotypes of these two SNPs in MIR149. For this study, we selected 6 large CMT1A families who showed a wide range of phenotypic variation. This study suggested that both SNPs were related to the onset age and severity in the dominant model. In particular, the AG+GG (n.83A>G) and TC+CC genotypes (n.86T>C) were associated to late onset and mild symptoms. Motor nerve conduction velocity (MNCV) was not related to the MIR149 genotypes. These results were consistent with the previous studies. Therefore, we suggest that the rs71428439 and rs2292832 variants in MIR149 may serve as genetic modifiers of CMT1A intrafamilial phenotypic heterogeneity, as they have a role in the unrelated patients. This is the first study to show an association using large families with variable clinical CMT1A phenotypes. The results will be helpful in the molecular diagnosis and treatment of patients with CMT1A.

• Journal of Life Science
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• v.28 no.11
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• pp.1301-1315
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• 2018

We purified an antimicrobial peptide from the acidified hemocyte extract of Mytilus coruscus by $C_{18}$ reversed-phase high-performance liquid chromatography (RP-HPLC). The peptide was 4041.866 Da based on matrix-assisted laser desorption ionization time-of-flight mass spectrophotometer (MALDI-TOF/MS) and the 25 amino acids of the N-terminus sequence were identified. Comparison of this sequence of the purified peptide with the N-terminus sequences of other antimicrobial peptides revealed 100% identity with the mytilin B precursor of Mytilus coruscus. We also identified a 312 bp open-reading frame (ORF) encoding 103 amino acids based on the obtained amino acid residues. The nucleotide sequence of this ORF and the amino acid sequence also revealed 100% identity with the mytilin B precursor of Mytilus coruscus. We synthesized two antimicrobial peptides with an alanine residue in the C-terminus, and designated them mytilin B1 and B2. These two antimicrobial peptides showed antimicrobial activity against gram-positive bacteria, including Bacillus cereus and Streptococcus parauberis (minimal effective concentration, MECs $41.6-89.7{\mu}g/ml$), gram-negative bacteria, including Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Providencia stuartii, Pseudomonas aeruginosa, and Vibrio ichthyoenteri (MECs $7.4-39.5{\mu}g/ml$), and the fungus Candida albicans (MECs $26.0-31.8{\mu}g/ml$). This antimicrobial activity was stable under heat and salt conditions. Furthermore, the peptides did not exhibit significant hemolytic activity or cytotoxic effects. These results suggest that mytilin B could be applied as alternative antibiotic agent, and they add to the understanding of the innate immunity of hard-shelled mussels.

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.235-242
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• 2018

BACKGROUND: About 81% of nitrous oxide ($N_2O$) emissions from agricultural land to the atmosphere is due to nitrogen (N) fertilizer application. Mitigation of $N_2O$ emissions can be more effective in controlling biochemical processes such as nitrification and denitrification in the soil rather than decreasing fertilizer application. The use of urease inhibitors is an effective way to improve N fertilizer efficiency and reduce $N_2O$ emissions. Several compounds act as urease inhibitors, but N-(n-butyl) thiophosphoric triamide (NBPT) has been used worldwide. METHODS AND RESULTS: Hot pepper and chinese cabbage were cultivated in five treatments: standard fertilizer of nitrogen-phosphorus-potassium(N-P-K, $N-P_2O_5-K_2O$: 22.5-11.2-14.9 kg/ha for hot pepper and $N-P_2O_5-K_2O$: 32.0-7.8-19.8 kg/ha for chinese cabbage), no fertilizer, and NBPT-treated fertilizer of 0.5, 1.0, and 2.0 times of nitrogen basal application rate of the standard fertilizer, respectively in Gyeonggi-do Hwaseong-si for 2 years(2015-2016). According to application of NBPT-treated fertilizer in hot pepper and chinese cabbage, $N_2O$ emission decreased by 19-20% compared to that of the standard fertilizer plot. CONCLUSION: NBPT-treated fertilizer proved that $N_2O$ emissions decreased statistically significant in the same growth conditions as the standard fertilization in the hot pepper and chinese cabbage cultivated fields. It means that NBPT-treated fertilizer can be applied for N fertilizer efficiency and $N_2O$ emissions reduction.

• Korean Journal of Environmental Agriculture
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• v.38 no.1
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• pp.38-46
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• 2019

BACKGROUND: Salinity is one of the main environmental stresses deteriorating qualities as well as yields of food crops. This study was conducted to identify the effects of salt stress on dry matter ratio, glucose content, and mineral content and composition in potatoes (Solanum tuberosum L.). METHODS AND RESULTS: Four potato varieties were grown in plastic pots (diameter 20 cm and height 25 cm) with three salinity levels (EC: 1.0, 4.0, and 8.0 dS/m) in a glasshouse. Dry matter ratio, specific gravity, starch, and glucose content in tubers harvested at 90 days after sowing were analyzed. Also, mineral contents (T-N, T-C, $P_2O_5$, $K^+$, $Ca^{2+}$, $Mg^{2+}$, $Na^+$) in stem, leaf, and tuber were investigated and statistically analyzed for analysis of variance (ANOVA). Dry matter ratio, specific gravity, and starch content in tubers were reduced in all varieties as the salt concentration increased. Glucose content tended to decrease according to the salt concentration. In ANOVA analysis of mineral contents, there were significant differences in $K^+$ and $Mg^{2+}$ of leaf and stem, in $Na^+$ of leaf and tuber, and also in $Ca^{2+}$ of leaf by the interactions of variety and salinity. In the case of $K^+/Na^+$ and $Ca^{2+}/Na^+$, the stem was more sensitively influenced by the salt treatment than the leaf or the tuber. The $K^+/Na^+$ and $Ca^{2+}/Na^+$ decreased in leaf, stem, and tuber of four varieties, as the salt concentration became higher. The decreasing level varied according to the varieties. 'Kroda' and 'Duback' maintained relatively higher $K^+/Na^+$ and $Ca^{2+}/Na^+$ than 'Atlantic' or 'Goun' under the salt stress conditions. CONCLUSION: The composition and accumulation of minerals in potato plant as well as dry matter ratio, starch, and glucose contents were significantly influenced by salt stress. The respond patterns were different depending on the varieties and it was related to the salt tolerance among varieties.