• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.94-99
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• 2019

The objective of this study is to establish the shelf life of non-pasteurized whole egg, egg yolk and egg white liquid. Each sample was stored for two weeks at $5^{\circ}C$, $10^{\circ}C$, $15^{\circ}C$, and $25^{\circ}C$, and then sensory, microbial, and physicochemical tests were performed periodically. The estimation of shelf life was based on the microbial standards of total viable counts and coliforms. The chemical properties highly correlated with the sensory evaluation were also used. Our results showed that the shelf life was the most influenced by microbial properties. Exceptionally, however, whole egg and white liquid stored at $5^{\circ}C$ and $10^{\circ}C$ with limited bacterial growth were affected by chemical property. The shelf life of the three non-pasteurized liquids was calculated to be less than one day at over $15^{\circ}C$. At $5^{\circ}C$ and $10^{\circ}C$, the shelf life was calculated to be 5 d and 1 d for egg yolk liquid, 5 d and 5 d for egg white, and 7 d and 5 d for whole egg, respectively. Therefore, it is advisable to establish reasonable shelf life in the more specific manner based on consideration of these findings.

• Korean Journal of Heritage: History & Science
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• v.42 no.4
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• pp.74-95
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• 2009

Folk villages have higher historic and cultural values than other villages and contain considerably many traditional elements today. In Korea, there are seven folk villages that are under the protection of the Cultural Properties Protection Act. Unlike other kinds of tangible cultural assets individually appointed according to the act, those folk villages are protected by the act in entirety including the houses and auxiliary structures inhabited and used by the villagers. Since the act covers the entire villages, the residents are not allowed to repair or renovate their structures and accordingly suffer from huge limitations in everyday life with housing life under the biggest restrictions. Being appointed as a folk village is positive from the perspective of preserving the village. However, it is negative to the villagers because of the limitations to their housing lives. While common people lead a convenient life by the introduction of high technologies in modern society, they do not get to benefit from such technologies for the cause of preserving the traditional culture. Upon the appointment, they are subject to all sorts of building regulations and under huge direct and indirect influences of those regulations across many different aspects of life including housing life. Thus the residents of folk villages do have many complaints about the act. It is only natural that there occur conflicts between the state, which tries to preserve the traditional culture according to the act, and the residents, who pursue convenience in life. At the same time, it is natural too that the residents have the desire to pursue convenience in daily life. Thus they renovate their houses illegally. The government agencies are aware of that, however, it is not right for them to enforce the act and restrict their daily lives. Their tacit approval of such illegal renovations is the product of compromises between the residents' right to their private property and the state's policies of cultural asset protection. The residents try to renovate their houses within the limit that will not call for legal restrictions from the government agencies. The government allows for renovations as long as they are within the minimum limit. It is the result of efforts for the state and the residents to stitch up and compromise their own complaints.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.199-232
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• 2018

There are increasing needs for understanding and fathoming of business management environment through big data analysis at industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm level analyses using publicly available company disclousre data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels. Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries' topic trend, software and hardware industries are compared in recent 20 years. Also, the changes of management subject at firm level are observed with comparison of two companies in software industry. The changes of topic trends provides lens for identifying decreasing and growing management subjects at industrial and firm level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at firm level in software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades. For suggesting methodology to develop analysis model based on public management data at industrial and corporate level, there may be contributions in terms of making ground of practical methodology to identifying changes of managements subjects. However, there are required further researches to provide microscopic analytical model with regard to relation of technology management strategy between management performance in case of related to various pattern of management topics as of frequent changes of management subject or their momentum. Also more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.1
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• pp.64-68
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• 2019

Purpose In the preparation process for N-13 Ammonia injections, there were radioactive medicines adsorbed on filters remarkably. Hereby, we have compared the adsorption rate and quality test on Millex GS filter and Satorious Minisart filter, both representatively hydrophilic sterilizing filters, also evaluated which filter is more accommodative for N-13 Ammonia injection. Materials and Methods The filters used for sterilization of N-13 Ammonia injections were Millex GS filter($0.22{\mu}m$) mand Satorious Minisart filter ($0.2{\mu}m$), which are generally used to strain aqueous solutions. After the N-13 Ammonia passes through each sterilization filter, the adsorption rate of the filter (n=10) is determined by measuring not only the radioactivity through the filter also the amount of radioactivity remaining in it using a Dose Calibrator. The N-13 Ammonia injections after each filter is tested by the quality control test to conform to the Samsung Medical Center standard. Results The ratio of radioactivity passed through Millex GS indicated $29.0{\pm}17.6%$. Satorious Minisart filters output was $80.9{\pm}3.2%$, respectively. Each ratio of radioactivity adsorbed on the sterile filter was $71.0{\pm}17.6%$ for Millex GS and $19.1{\pm}3.2%$ for the Satorious Minisart filters, respectively. Furthermore, on the ratio of filtered radioactivity, Using Satorious Minisart filter showed about 2.8 times higher than using Millex GS filter. The quality testing of N-13 Ammonia injections through each filter met the Samsung Medical Center standard. Conclusion The Millex GS filter is composed of cellulose acetate and cellulose nitrate, whereas the Satorious Minisart filter if composed only of cellulose acetate. Therefore, the presence of cellulose nitrate in the membrane seems to have made differences. Therefore, the use of Satorious Minisart filter in the preparation of N-13 Ammonia injection solution minimized the loss of radioactive medicines due to filter adsorption, thereby improving the synthesis yield.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.429-439
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• 2019

The objectives of this study were to gauge the extent of the forestry business through establishing the definition of forestry industry from the perspective of economic convergence and to analyze key components that affect each growth phase of a forestry business entity by classifying them. A total of 1,397 "sixth-sector industry" management entities were certified by the Ministry of Agriculture, Food, and Rural Affairs in South Korea from 2012-2017. Of these, 259 (18.5%) were in the forestry sector. In this study, the 259 forestry management entities were further classified into three phases based on sales distribution: entrance, development, and maturity. The entrance phase (<100 million KRW), development phase (>100 million and <1 billion KRW), and maturity phase (>1 billion KRW) constituted 33.2%, 55.4%, and 12.4% of the total 259 entities, respectively. The results showed that most of the management entities were either in the entrance or development phases, and only a small portion was in the maturity phase. To identify the key variables that affect each of the phases, chi-square analysis was used. We designed the "sixth-sector industry" type as an independent variable, whereas selected region, business organization, manager age group, forest product, processing type, and service type were designated as dependent variables. The results of the analysis showed that the processing and service types influenced all three developmental phases. Moreover, as the phase advanced, processing type showed a higher proportion of health-functional ingredients, such as powder or extract from forest products, which enable to develop and produce a variety of products. Service type also changed from simple experience to integrated experience tourism and finally to tourism education. Distribution and sales channel also turned out to be a significant factor during the development phase. This study provides the basic information needed to guide government support in the implementation of a formal forestry business through convergence as well as to increase the efficiency of business management.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.221-238
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• 2019

Recently, companies and public institutions have been actively introducing chatbot services in the field of customer counseling and response. The introduction of the chatbot service not only brings labor cost savings to companies and organizations, but also enables rapid communication with customers. Advances in data analytics and artificial intelligence are driving the growth of these chatbot services. The current chatbot can understand users' questions and offer the most appropriate answers to questions through machine learning and deep learning. The advancement of chatbot core technologies such as NLP, NLU, and NLG has made it possible to understand words, understand paragraphs, understand meanings, and understand emotions. For this reason, the value of chatbots continues to rise. However, technology-oriented chatbots can be inconsistent with what users want inherently, so chatbots need to be addressed in the area of the user experience, not just in the area of technology. The Fourth Industrial Revolution represents the importance of the User Experience as well as the advancement of artificial intelligence, big data, cloud, and IoT technologies. The development of IT technology and the importance of user experience have provided people with a variety of environments and changed lifestyles. This means that experiences in interactions with people, services(products) and the environment become very important. Therefore, it is time to develop a user needs-based services(products) that can provide new experiences and values to people. This study proposes a chatbot development process based on user needs by applying the design thinking approach, a representative methodology in the field of user experience, to chatbot development. The process proposed in this study consists of four steps. The first step is 'setting up knowledge domain' to set up the chatbot's expertise. Accumulating the information corresponding to the configured domain and deriving the insight is the second step, 'Knowledge accumulation and Insight identification'. The third step is 'Opportunity Development and Prototyping'. It is going to start full-scale development at this stage. Finally, the 'User Feedback' step is to receive feedback from users on the developed prototype. This creates a "user needs-based service (product)" that meets the process's objectives. Beginning with the fact gathering through user observation, Perform the process of abstraction to derive insights and explore opportunities. Next, it is expected to develop a chatbot that meets the user's needs through the process of materializing to structure the desired information and providing the function that fits the user's mental model. In this study, we present the actual construction examples for the domestic cosmetics market to confirm the effectiveness of the proposed process. The reason why it chose the domestic cosmetics market as its case is because it shows strong characteristics of users' experiences, so it can quickly understand responses from users. This study has a theoretical implication in that it proposed a new chatbot development process by incorporating the design thinking methodology into the chatbot development process. This research is different from the existing chatbot development research in that it focuses on user experience, not technology. It also has practical implications in that companies or institutions propose realistic methods that can be applied immediately. In particular, the process proposed in this study can be accessed and utilized by anyone, since 'user needs-based chatbots' can be developed even if they are not experts. This study suggests that further studies are needed because only one field of study was conducted. In addition to the cosmetics market, additional research should be conducted in various fields in which the user experience appears, such as the smart phone and the automotive market. Through this, it will be able to be reborn as a general process necessary for 'development of chatbots centered on user experience, not technology centered'.

• Journal of Ginseng Research
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• v.43 no.4
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• pp.600-605
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• 2019

Background: The leaves and roots of Panax ginseng are rich in ginsenosides. However, the chemical compositions of the leaves and roots of P. ginseng differ, resulting in different medicinal functions. In recent years, the aerial parts of members of the Panax genus have received great attention from natural product chemists as producers of bioactive ginsenosides. The aim of this study was the isolation and structural elucidation of novel, minor ginsenosides in the leaves of P. ginseng and evaluation of their antiinflammatory activity in vitro. Methods: Various chromatographic techniques were applied to obtain pure individual compounds, and their structures were determined by nuclear magnetic resonance and high-resolution mass spectrometry, as well as chemical methods. The antiinflammatory effect of the new compounds was evaluated on lipopolysaccharide-stimulated RAW 264.7 cells. Results and conclusions: Two novel, minor triterpenoid saponins, ginsenoside $LS_1$ (1) and 5,6-didehydroginsenoside $Rg_3$ (2), were isolated from the leaves of P. ginseng. The isolated compounds 1 and 2 were assayed for their inhibitory effect on nitric oxide production in LPS-stimulated RAW 264.7 cells, and Compound 2 showed a significant inhibitory effect with $IC_{50}$ of $37.38{\mu}M$ compared with that of NG-monomethyl-L-arginine ($IC_{50}=90.76{\mu}M$). Moreover, Compound 2 significantly decreased secretion of cytokines such as prostaglandin $E_2$ and tumor necrosis factor-${\alpha}$. In addition, Compound 2 significantly suppressed protein expression of inducible nitric oxide synthase and cyclooxygenase-2. These results suggested that Compound 2 could be used as a valuable candidate for medicinal use or functional food, and the mechanism is warranted for further exploration.

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

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.347-358
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• 2019

Lambertian cloud model (Lambertian Cloud Model) is the simplified cloud model which is used to effectively retrieve the vertical ozone distribution of the atmosphere where the clouds exist. By using the Lambertian cloud model, the optical characteristics of clouds required for radiative transfer simulation are parametrized by Optical Centroid Cloud Pressure (OCCP) and Effective Cloud Fraction (ECF), and the accuracy of each parameter greatly affects the radiation simulation accuracy. However, it is very difficult to generalize the vertical ozone error due to the OCCP error because it varies depending on the radiation environment and algorithm setting. In addition, it is also difficult to analyze the effect of OCCP error because it is mixed with other errors that occur in the vertical ozone calculation process. This study analyzed the ozone retrieval error due to OCCP error using two methods. First, we simulated the impact of OCCP error on ozone retrieval based on Optimal Estimation. Using LIDORT radiation model, the radiation error due to the OCCP error is calculated. In order to convert the radiation error to the ozone calculation error, the radiation error is assigned to the conversion equation of the optimal estimation method. The results show that when the OCCP error occurs by 100 hPa, the total ozone is overestimated by 2.7%. Second, a case analysis is carried out to find the ozone retrieval error due to OCCP error. For the case analysis, the ozone retrieval error is simulated assuming OCCP error and compared with the ozone error in the case of PROFOZ 2005-2006, an OMI ozone profile product. In order to define the ozone error in the case, we assumed an ideal assumption. Considering albedo, and the horizontal change of ozone for satisfying the assumption, the 49 cases are selected. As a result, 27 out of 49 cases(about 55%)showed a correlation of 0.5 or more. This result show that the error of OCCP has a significant influence on the accuracy of ozone profile calculation.

• Journal of Food Hygiene and Safety
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• v.34 no.3
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• pp.235-241
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• 2019

An analytical method was developed for the determination of an antibiotic fungicide, kasugamycin, in agricultural products (hulled rice, potato, soybean, mandarin and green pepper) using liquid chromatographytandem mass spectrometry (LC-MS/MS). Samples were extracted with methanol adjusted to pH 13 using 1 N sodium hydroxide, and purified with a HLB (hydrophilic lipophilic balance) cartridge. Linearity of a matrix-matched calibration curve using seven concentration levels, from 0.001 to 0.1 mg/kg, was excellent with a correlation coefficient ($R^2$) of more than 0.9998. The limits of detection (LOD) and quantification (LOQ) of instrument were 0.0005 and $0.001{\mu}g/mL$, respectively, and the LOQ of analytical method calculated as 0.01 mg/kg. The average recoveries at three spiking levels (LOQ, $LOQ{\times}10$, $LOQ{\times}50$, n=5) were in the range of 71.2~95.4% with relative standard deviation of less than 12.1%. The developed method was simple and all optimized results was satisfied with the criteria ranges requested in the Codex guidelines and Food Safety Evaluation Department guidelines. The present study could be served as a reference for the establishment of maximum residue limits (MRL) of kasugamycin and be used as basic data for safety management relative to kasugamycin residues in imported and domestic agricultural products.