• Journal of vocational education research
• /
• v.37 no.6
• /
• pp.1-15
• /
• 2018

The objective of this study is to provide the basic data for the efficient operation of industry-academia partnership apprenticeship school, by analyzing the interest in the relevant policy, targeting the interested parties of school and corporation as the two main subjects operating the industry-academia partnership apprenticeship school. Using the Concerns-Based Adoption Model(CBAM) used for understanding the interested parties' interest in the adoption of a certain new changing. In the results of analysis, first, currently, the operating subjects of industry-academia partnership apprenticeship school showed the similar interest with the pattern of nonusers. In other words, currently, based on the curiosity about the relevant policy, they are interested in which roles they should perform for the successful operation. Second, when dividing the operating subjects of industry-academia partnership apprenticeship school into school parties and corporate parties, the results of examining the differences in the interest of each subject are as follows. First, in the stages except for the Stage 0(indifference), the interest of school parties was relatively higher than the one of corporate parties. It might be because the school's role is bigger in the operation of industry-academia partnership apprenticeship school, contrary to the advanced countries. In other words, in case of school parties, the overall and general understanding of the relevant policy is premised, so that their interest of each stage is higher than the one of corporate parties. Especially, the Stage 5(cooperative interest) showed the biggest differences. As the cooperation between industry and academia is the success factor of the relevant policy, it would be necessary to implant the concrete measures for industry-academia cooperation in school parties, and also to implant the importance of industry-academia cooperation in corporate parties. Next, both operating subjects showed the lowest intensity in the Stage 4(consequential interest). It means that the operating subjects' interest in the evaluation of apprenticeship students is relatively low.

• Korean Journal of Environmental Agriculture
• /
• v.37 no.4
• /
• pp.283-290
• /
• 2018

BACKGROUND: Pesticide residue analysis is an essential activity in order to establish the food safety of agricultural products. Analytical approaches to the food safety are required to meet internationally the guideline of Codex (Codex Alimentarius Commission, CAC/GL 40). In this study, we developed a liquid chromatograph-tandem mass spectrometer (LC-MS/MS) method to determine the herbicide clopyralid in food matrixes. METHODS AND RESULTS: Clopyralid was extracted with aqueous acetonitrile containing formic acid and the extracts were mixed in a citrate buffer consisted of magnesium sulfate anhydrous, NaCl, sodium citrate dihydrate and disodium hydrogencitrate sesquihydrate followed by centrifugation. The supernatants were filtered through a nylon membrane filter and used for the analysis of clopyralid. The method was validated by accuracy and precision experiments on the samples fortified at 3 different levels of clopyralid. LC-MS/MS in positive mode was employed to quantitatively determine clopyralid in the food samples. Matrix-matched calibration curves were inearranged from 0.001 to 0.25 mg/kg with r2 > 0.994. The limits of detection and quantification were determined to be 0.001 and 0.01 mg/kg, respectively. There covery values of clopyralid for tified at 0.01 mg/kg in the control samples ranged from approximately 82 to 106% with relative standard deviations below 2 0%. CONCLUSION: The method developed in this study meets successfully the Codex guideline for pesticide residue analysis in food samples. This, the method could be applicable to determine pesticides in foods produced domestically and internationally.

• Korean Journal of Food Science and Technology
• /
• v.50 no.6
• /
• pp.565-571
• /
• 2018

After adding eight different kinds of PAHs to soybean oil and perilla oil samples, changes of PAHs contents after activated carbon treatment with different conditions of activated carbon concentration, temperature, and time were investigated. PAHs contents decreased in both soybean oil and perilla oil with increasing activated carbon concentrations. Neither of the PAHs were detected in the soybean oil samples after the addition of only 0.05% of activated carbon, while the contents of most kinds of PAHs decreased below the limit of quantification in the perilla oil samples after the addition of up to 0.4% of activated carbon. PAHs contents decreased as the temperature of the activated carbon treatment increased, and most PAHs were not detected in the soybean oil samples at temperatures above $80^{\circ}C$. With regards to the activated carbon treatment time, the PAHs contents also decreased as the treatment time increased. In case of soybean oil, four kinds of PAHs were not detected after treatment with 0.05% of activated carbon at $70^{\circ}C$ for 10 min.

• Journal of Food Hygiene and Safety
• /
• v.34 no.1
• /
• pp.13-21
• /
• 2019

A method was developed for the simultaneous detection of an antibiotic fungicide, streptomycin, and its metabolite (dihydrostreptomycin) in agricultural products using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The samples were extracted using methanol adjusted to pH 3 using formic acid, and purified with a HLB (Hydrophilic lipophilic balance) cartridge. The matrix-matched calibration curves were constructed using seven concentration levels, from 0.001 to 0.1 mg/kg, and linearity of five agricultural products (hulled rice, potato, soybean, mandarin, green pepper), with coefficients of determination $(R^2){\geq}0.9906$, for streptomycin and dihydrostreptomycin. The mean recoveries at three fortification levels (LOQ, $LOQ{\times}10$, $LOQ{\times}50$, n = 5) were from 72.0~116.5% and from 72.1~116.0%, and relative standard deviations were less than 12.3% and 12.5%, respectively. The limits of quantification (LOQ) were 0.01 mg/kg, which are satisfactory for quantification levels corresponding with the Positive List System. All optimized results satisfied the criteria ranges requested in the Codex guidelines and the Food Safety Evaluation Department guidelines. The present study could serve as a reference for the establishment of maximum residue limits and be used as basic data for detection of streptomycin and dihydrostreptomycin in food.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.89-115
• /
• 2019

This study was conducted to present the stylistic differences between Arthur Conan Doyle and Agatha Christie, famous as writers of classical mystery novels, through data analysis, and further to present the analytical methodology of the study of style based on text mining. The reason why we chose mystery novels for our research is because the unique devices that exist in classical mystery novels have strong stylistic characteristics, and furthermore, by choosing Arthur Conan Doyle and Agatha Christie, who are also famous to the general reader, as subjects of analysis, so that people who are unfamiliar with the research can be familiar with them. The primary objective of this study is to identify how the differences exist within the text and to interpret the effects of these differences on the reader. Accordingly, in addition to events and characters, which are key elements of mystery novels, the writer's grammatical style of writing was defined in style and attempted to analyze it. Two series and four books were selected by each writer, and the text was divided into sentences to secure data. After measuring and granting the emotional score according to each sentence, the emotions of the page progress were visualized as a graph, and the trend of the event progress in the novel was identified under eight themes by applying Topic modeling according to the page. By organizing co-occurrence matrices and performing network analysis, we were able to visually see changes in relationships between people as events progressed. In addition, the entire sentence was divided into a grammatical system based on a total of six types of writing style to identify differences between writers and between works. This enabled us to identify not only the general grammatical writing style of the author, but also the inherent stylistic characteristics in their unconsciousness, and to interpret the effects of these characteristics on the reader. This series of research processes can help to understand the context of the entire text based on a defined understanding of the style, and furthermore, by integrating previously individually conducted stylistic studies. This prior understanding can also contribute to discovering and clarifying the existence of text in unstructured data, including online text. This could help enable more accurate recognition of emotions and delivery of commands on an interactive artificial intelligence platform that currently converts voice into natural language. In the face of increasing attempts to analyze online texts, including New Media, in many ways and discover social phenomena and managerial values, it is expected to contribute to more meaningful online text analysis and semantic interpretation through the links to these studies. However, the fact that the analysis data used in this study are two or four books by author can be considered as a limitation in that the data analysis was not attempted in sufficient quantities. The application of the writing characteristics applied to the Korean text even though it was an English text also could be limitation. The more diverse stylistic characteristics were limited to six, and the less likely interpretation was also considered as a limitation. In addition, it is also regrettable that the research was conducted by analyzing classical mystery novels rather than text that is commonly used today, and that various classical mystery novel writers were not compared. Subsequent research will attempt to increase the diversity of interpretations by taking into account a wider variety of grammatical systems and stylistic structures and will also be applied to the current frequently used online text analysis to assess the potential for interpretation. It is expected that this will enable the interpretation and definition of the specific structure of the style and that various usability can be considered.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.201-220
• /
• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.139-161
• /
• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• The Journal of Korean Philosophical History
• /
• no.25
• /
• pp.381-410
• /
• 2009

It is a very important book about painting theory, that "Xuanhehuapu"(宣和畵譜) was wrote by Emperor Huizhong(徽宗) in Song Dynasty. Fundamental discussions and studies in the relation of socio-economical base in Song Dynasty are still more needed. And accordingly, it is necessary to advent upgraded aesthetical articles. Li xue(理?) deeply influenced upon paintings and its theories in Song Dynasty. Similarly, Taoism(道家) and Zen Buddhism(?宗) also did. But some people who have not found "Xuanhehuapu" important meaning and rich and complicated aesthetic thought, gave low and even negative valuation to it. There is rich aesthetic in "Xuanhehuapu", which is not as simple and narrow as some people imagined. It was deeply influenced by the aesthetic thought of Confucianism(Lixue 理學), Taoism(Zhuangzi 莊子) and "Zhouyi"({周易}). I will be analytical in a few aspects "Xuanhehuapu" of aesthetics thought. 1. The calligraphy and painting is one flesh. 2. learn a good lesson from painting. 3. The handicrafts(Art, 藝) and Tao(道) unify. 4. It is a Art taxology. 5. It use a new art criticism methods.

• Korean Journal of Environmental Biology
• /
• v.38 no.4
• /
• pp.634-649
• /
• 2020

Recently, stream longitudinal connectivity has been a topic of investigation due to the frequent disconnections and the impact of aquatic ecosystems caused by the construction of small and medium-sized weirs and various artificial structures (fishways) directly influencing the stream ecosystem health. In this study, the international and domestic research trends of the longitudinal connectivity in aquatic ecosystems were evaluated and the applicability of fish-based longitudinal connectivity models used in developed countries was analyzed. For these purposes, we analyzed the current status of research on longitudinal connectivity and structural problems, fish monitoring methodology, monitoring approaches, longitudinal disconnectivity of fish movement, and biodiversity. In addition, we analyzed the current status and some technical limitations of physical habitat suitability evaluation, ecology-based water flow, eco-hydrological modeling for fish habitat connectivity, and the s/w program development for agent-based model. Numerous references, data, and various reports were examined to identify worldwide longitudinal stream connectivity evaluation models in European and non-European countries. The international approaches to longitudinal connectivity evaluations were categorized into five phases including 1) an approach integrating fish community and artificial structure surveys (two types input variables), 2) field monitoring approaches, 3) a stream geomorphological approach, 4) an artificial structure-based DB analytical approach, and 5) other approaches. the overall evaluation of survey methodologies and applicability for longitudinal stream connectivity suggested that the ICE model (Information sur la Continuite Ecologique) and the ICF model (Index de Connectivitat Fluvial), widely used in European countries, were appropriate for the application of longitudinal connectivity evaluations in Korean streams.

• Journal of Applied Biological Chemistry
• /
• v.64 no.2
• /
• pp.113-119
• /
• 2021

Olive (Olea europaea L.) leaves, a raw material for health functional foods and cosmetics have abundant polyphenols including oleuropein (major bioactive compound) with various biological activities: antioxidant, antibacterial, antiviral, anticancer activity, and inhibit platelet activation. Oleuropein has been reported as skin protectant, antioxidant, anti-ageing, anti-cancer, anti-inflammation, anti-atherogenic, anti-viral, and anti-microbial activity. Despite oleuropein is the important compound in olive leaves, there is still no quantitative approach to reveal oleuropein content in commercial products. Therefore, a validated method of analysis has to develop for oleuropein. In this study, the components and oleuropein content in 10 types of products were analyzed using a developed method with ultra-performance liquid chromatography to quadrupole time-of-flight mass spectrometry, charge of aerosol detector, and photodiode array. The total of 18 compounds including iridoids (1, 3, 4, 14, and 16-18), coumarin (2), phenylethanoids (5, 9, and 11), flavonoids (6-8, 10, 12, and 13), lignan (15), were tentatively identified in the leaves extract based high resolution mass spectrometry data, and the content of oleuropein in each product was almost identical between two detection methods. The oleuropein in three commercial product (A, G, H) was contained more over the suggested content, and it of five products (B, E, H, I, J) were analyzed within 5-10% error range. However, the two products (C, D) were found far lower than suggested contents. This study provides that analytical results of oleuropein could be a potential information for the quality control of leaf extract for a manufactured functional food.