• Journal of Conservation Science
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• v.29 no.1
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• pp.47-54
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• 2013

The following study tests for the effects of mordants on the production of restoration papers during the conservation process of damaged paper artifacts. For this, four different types of synthetic mordants that are being marketed currently ($K_2CO_3$, $AlK(SO_4)_2{\cdot}12H_2O$, $Cu_2SO_4{\cdot}5H_2O$, $FeSO_4$) were selected to produce samples for measuring variable properties through artificial degradation. The research conducts tests for changes in color, tensile index, and pH level (degree of acidity). The results for changes in color have shown that the value of ${\Delta}E^*ab$ of $K_2CO_3$ mordant sample was the highest, and the tests for tensile index have shown that the strength of dyeing sample was decreased in accordance with the aging time, but Changes according to the mordant was not evident. Finally, the findings for pH level have shown that samples with $Cu_2SO_4{\cdot}5H_2O$ and $FeSO_4$ have pH levels drop below 6. As a result, the research have concluded that mordants used for dyeing restoration paper were identified to have an affect in the dyeing and aging characteristics of the paper.

• Journal of Life Science
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• v.21 no.7
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• pp.1060-1066
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• 2011

In order to investigate the variations on the ratio of mannose to galactose in the seeds of Phaseolus angularis, 17 local strains (Yangyang, Pyeongchang, Ganghwa, Pocheon, Geumsan, Seocheon, Jincheon, Danyang, Tongyeong, Sancheong, Gumneung, Wolseong, Wando, Gokseong, Okgu, Jangsu, Bukjeju), which are located from $33^{\circ}15'N$ to $38^{\circ}11'N$, were selected according to their latitudes and geographical distances. The seeds of these strains were collected and their contents of mannose and galactose were analyzed. Mannose contents in the seeds were variable, ranging from 17.071 mg/g at its highest (Jangsu) and 6.488 mg/g at its lowest (Geumsan). The contents of galactose also showed remarkable differences, ranging from 9.477 mg/g (Wolseong) to 19.877 mg/g (Jangsu). The local strains were classified into 3 variation types - coastal type I (Wando, Okgu, Bukjeju), the inland type (Jangsu, Weolseong, Danyang, Geumneung, Pyeongchang, Sancheong) and coastal type II (Ganghwa, Seocheon, Tongyeong, Jincheon), as well as 4 strange strains (Gokseong, Yangyang, Pocheon, Geumsan) according to the geographical climatic type and the ratio of mannose to galactose, which indicate the hardness of seeds in Leguminosae and ranged from 0.64 to 1.22. The variation types are very significant genecologically as evidence for microevolution related to natural and artificial selection in cultivated plants.

• Korean Journal of Microbiology
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• v.39 no.3
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• pp.128-134
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• 2003

Transformation-associated recombination (TAR) cloning is based on co-penetration into yeast spheroplasts of genomic DNA along with TAR vector DNA that contains 5'- and 3'-sequences (hooks) specific for a gene of interest, followed by recombination between the vector and the human genomic DNA to establish a circular YAC. Typically, the frequency of recombinant insert capture is 0.01-1% for single-copy genes by TAR cloning. To further refine the TAR cloning technology, we determined the effect of GC content on target hooks required for gene isolation utilizing the $Tg\cdot\AC$ mouse transgene as the targeted region. For this purpose, a set of vectors containing a B1 repeated hook and Tg AC-specific hooks of variable GC content (from 18 to 45%) was constructed and checked for efficiency of transgene isolation by radial TAR cloning. Efficiency of cloning decreased approximately 2-fold when the TAR vector contained a hook with a GC content ～${\leq}23$% versus ～40%. Thus, the optimal GC content of hook sequences required for gene isolation by TAR is approximately 40%. We also analyzed how the distribution of high GC content (65%) within the hook affects gene capture, but no dramatic differences for gene capturing were observed.

• Korean Journal of Ichthyology
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• v.33 no.2
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• pp.107-116
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• 2021

Two stone loaches (Nemacheilidae, Cypriniformes), Barbatula toni (Dybowski, 1869) and B. nuda (Bleeker, 1864), have been recognized in the Korean waters to date. Recently, due to indiscriminate artificial introduction as well as the change of their habitats induced by natural disasters, it seems to be concerned about the damage of species-specific geographic boundaries. We examined the genetic difference of two Korean Barbatula species by the haplotype network based on the Cytochrome b sequences of mitochondrial DNA and the phylogenetic relationships among them including Barbatula fishes occurring around the Korean peninsula. As a result, three and 29 haplotypes were obtained from B. toni and B. nuda, respectively, and totally three clades comprising "toni group", "nuda hangang group", and "nuda donghae group" were identified. The sequence variable sites among them was 10~24%, showing a difference of interspecific level. Phylogenetic relationships of the latter group, especially, forms an independent cluster discriminating with other two groups as well as the Chinese, Japanese, Russian, and European Barbatula species, suggesting the possibility of the specific level divergence.

• Korean Journal of Community Nutrition
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• v.24 no.4
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• pp.300-308
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• 2019

Objectives: This research was conducted to identify the consumers' food choice factors that affect the consumers' replacement of soft drinks with carbonated water. Methods: The present study used secondary data from a consumer panel survey conducted by the Rural Development Administration of Korea, and the data included the panel members' purchase records based on their monthly spending receipts. The survey asked the participants about their food choice factors and their personal responsibility for their health. This survey included independent variables for the consumers' food purchase factors. As a dependent variable, two types of groups were defined. The replacement group included those people who increased their purchase of carbonated water and decreased their purchase of soft drinks. The non-replacement group included those people who did not change their purchase patterns or they increased their purchase of soft drinks and they decreased their purchase of carbonated water. Logistic regression analysis was conducted to determine the consumers' food choice factors that were associated with replacing soft drinks with carbonated water. Results: The replacement group was significantly associated with (1) a younger age (OR=0.953), (2) being a housewife (OR=2.03), (3) higher income (OR=1.001) and (4) less concern about price (OR=0.819) when purchasing food. This group also showed (5) higher enjoyment (OR=1.328) when choosing food and (6) they took greater responsibly for their personal health (OR=1.233). Conclusions: This research is the first study to mainly focus on soft drinks and carbonated water. The result of this research showed that young, health-conscious consumers with a higher income and who are more interested in food have more possibilities to replace soft drinks with carbonated water. These research findings may be applied to consumers who have characteristics that are similar to the young health-conscious consumers and the results can help to suggest ways to reduce sugar intake and improve public health. However, this research has a limitation due to the application of secondary data. Therefore, a future study is needed to develop detailed survey questions about food choice factors and to extend these factors to all beverages, including soft drinks made with sugar substitutes, so as to reflect the growth of alternative industries that use artificial sweeteners or different types of sugar to make commercially available drinks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.67-74
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• 2021

Ground Operations Command (GOC) has studied various methods using artificial intelligence (AI) in order to accomplish ground missions more effectively and to strongly respond to variable strategic situations with advancements in fourth industrial revolution technology. As the result of various literature reviews, PEST-7S-SWOT is considered the most appropriate methodology for promoting strategies and for task development. These procedures consist of three stages. Phase 1 is analysis of external environmental factors from applying PEST procedures. We analyzed external environmental factors to determine opportunities and risk factors. Phase 2 is the analysis of internal environmental factors from applying 7S strategies. We analyzed the current state of an organization to find strengths and weaknesses. Phase 3 is SWOT analysis. It is based on the opportunities and risk factors from Phase 1 and the strength and weakness factors from Phase 2. We derive promotional strategies and tasks through SWOT analysis. In this study, four strategies and 11 tasks were derived for GOC AI systems. Those are promotion of policies and systems, reinforcing organizations, building an AI base, increasing expertise and capabilities, and validating PEST-7S-SWOT methodologies.

• The Journal of Korean Philosophical History
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• no.52
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• pp.341-364
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• 2017

This thesis is to reflect prevalently stereo-typed community ideas and find alternative ones that have interconnected, relational, and autonomous acting system for our lives. Probably community is the collective space in which 'I' as the subject in the world meet others and achieve the desirable objects each other. By the way the community spaces could be nation state, societies, and people of nation or ones that deal local problems, environments, and ecology and clubs ect, which are variable according to our concerns. In a sense community pay attention to not societies such as nations or people but lives of individuals, preparing for the territories where men feel convenient in their bodies and mentalities without artificial manipulation. In such a community the participant's vital energy can be stretched actively and relationally, and even if the leader be, there is the politics of doing nothingness not to be the obstacle in the way mens' will goes. In those communities they can live their lives at their nature and realize their dreams without barriers to their way. If we find these ideas of communities which are alternative for our period, we should gaze at Asiatic ones that may be scattered in classics of Confucianism, Daoim and ect. With these concepts and concerns, this paper was drawn up.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.33-45
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• 2020

This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.