• Journal of the Society of Cosmetic Scientists of Korea
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• v.49 no.3
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• pp.247-258
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• 2023

This study aims to investigate the enhancement of water resistance and improvement in adhesion to the skin by combining dextrin palmitate and isopropyl titanium triisostearate coating materials with titanium dioxide. Due to the recent increase in consumers who enjoy outdoor activities, the demand for sunscreen with excellent water resistance is increasing. Prior research was conducted with O/W, Pickering, and W/O/W multiple formulations, but there was a limit to water resistance. The purpose of this study is to develop a complex inorganic powder that can improve water resistance and increase adhesion to the skin to solve this problem. First, we combined dextrin palmitate and isopropyl titanium triisostearate coating materials to form a composite with titanium dioxide. The coating of the inorganic powder was confirmed using FE-SEM and FT-IR analysis. The composite exhibited significantly higher in vitro water resistance compared to other formulations. The hydrophobicity of the coated inorganic powder was compared by measuring the contact angles. When the coated inorganic powder was applied to the W/O sunscreen formulation and the non-coated inorganic powder was applied to the W/O sunscreen formulation as a control, the SPF of the sunscreen containing the coated inorganic powder was higher. These results were the same when observed with a UV camera. Finally the adhesion of the coated inorganic powder to the skin was assessed by applying it to a foundation product. In vivo study, it was observed that the product formulated with the coated powder exhibited less smudging compared to the foundation product formulated with the non-coated powder. The developed inorganic powder in this study demonstrated excellent adhesion to the skin, providing a superior sensory experience, as well as enhanced hydrophobicity and remarkable water resistance effects. In the future, the result of this study is expected to help develop various sunscreen products to improve water resistance.

• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.60-75
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• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

• Journal of Life Science
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• v.34 no.2
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• pp.86-93
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• 2024

Since replication of plasmids must be strictly controlled, plasmids that generally perform rolling circle replication generally maintain a constant copy number by strictly controlling the replication initiator Rep at the transcriptional and translational levels. Plasmid pJB01 contains three orfs (copA, repB, repC or repABC) consisting of a single operon. From analysis of amino acid sequence, pJB01 CopA was homologous to the Cops, as a copy number control protein, of other plasmids. When compared with a CopG of pMV158, CopA seems to form the RHH (ribbon-helix-helix) known as a motif of generalized repressor of plasmids. The result of gel mobility shift assay (EMSA) revealed that the purified fusion CopA protein binds to the operator region of the repABC operon. To examine the functional role of CopA on transcriptional level, 3 point mutants were constructed in coding frame of copA such as CopA R16M, K26R and E50V. The repABC mRNA levels of CopA R16M, K26R and E50V mutants increased 1.84, 1.78 and 2.86 folds more than that of CopA wt, respectively. Furthermore, copy numbers owing to mutations in three copA genes also increased 1.86, 1.68 and 2.89 folds more than that of copA wt, respectively. These results suggest that CopA is the transcriptional repressor, and lowers the copy number of pJB01 by reducing repABC mRNA and then RepB, as a replication initiator.

• Horticultural Science & Technology
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• v.31 no.5
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• pp.640-647
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• 2013

This study was conducted to provide exportation strategy by surveying on preference of Japanese consumers on cut chrysanthemum exported. The survey was conducted two times by a local survey company in Japan, and the surveys were conducted largely on chrysanthemums for casual flowers and the altar. After departmentalizing Japanese consumers per groups the result were analyzed through conjoint and cluster methods, flower colors and shape were used relatively higher rate for selection criteria of flowers in every group in the case of casual flowers. Group 1 comprised of 60 year-old housewives who reside in a small city with high school diploma and annual income less than 300 million yen, and group 2 of 40 year-old housewives who are small city residents with high school diplomas and annual income of 300 million yen show higher rate of use in flower shape than colors. Another group 3 whose members are 50 year-old housewives, small city residents with high school diplomas and annual income of 600 million yen showed higher rate of use colors than the shape for selection criteria of flowers. The consumption characteristics according to the ages of the consumers showed a pronounced tendency. The 40-50 year-old housewives preferred single flowers packed with other flowers, and the 60 year-old housewives double flowers packed with only chrysanthemums. In flower color, the 50-60 year-old housewives preferred white and yellow flowers, and the 40 year-old housewives pink and yellow flowers. Therefore, there are needs for development strategy of new products considering the consumption characteristics of flower shape and color according to the ages of consumer. After analyzing the chrysanthemums for altar by departmentalization of Japanese consumers, every group showed relative higher rate of use for flower shape for selection criteria of flowers. According to the analysis on the consumption characteristics, group 1 which is comprised of 30-40 year-old housewives who reside in small city with high school diplomas and income less than 300 million yen, and the group 2 of 20 year-old housewives who reside in small city with college diplomas and annual income less than 300 million yen. They are very sensitive to the price of the products while the group 3 of 50 year-old housewives who reside in small city with high school diplomas and annual income less than 300 million yen are insensitive to the price. The 30-50 year-old housewives preferred white and pink flowers, and the 20 year-old housewives yellow and pink flowers. In flower shape, the 50 year-old housewives preferred anemone shape, the 30-40 year-old housewives double shape, and the 20 year-old housewives pompon shapes. Therefore, the white, double flowers for the 30-40 year-old housewives and the yellow, pompon flowers for the 20 year-old housewives are needed to be created at the lowest cost, while the white, anemone flowers are needed to created at higher cost with high quality. In light of these results, it is considered that we should understand the types of purchasing products through consumption characteristics of Japanese consumers. Also we should plan, create market-oriented and consumer-oriented products, and should export them in order to expand more exportation.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Korean Journal of Environmental Agriculture
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• v.13 no.3
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• pp.321-337
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• 1994

The garbage from the dwelling houses was composted in two kinds of small composter in laboratory to investigate the possibility of garbage composting. They were general small composters. One (type 1) was insullated but the other (type 2) was not. Because it was found that type 2 was not available for composting under our meteorological conditions through winter experiment, only type 1 was tested in spring and summer. The experiment was performed for 8 weeks in each season. The seasonal variation of several compounds in compost was evaluated and discussed. The result summarized belows are those taken at the end of the experiment, if the time was not specified. 1) The maximum temperature was $58^{\circ}C$ in spring, $57^{\circ}C$ in summer and $41^{\circ}C$ in winter. This temperature was enough to destroy the pathogen except for winter. 2) The mass was reduced to average 62.5% and the volume reduction was avergae 74%. 3) The density was estimated as 0.7kg/l in spring, 0.8kg/l in summer and 1.1kg/l in winter. 4) The water content was not much changed for composting periods. It had 75.6% in spring and 76.6% in summer and winter. 5) There was a great seasonal difference in pH value. It was reached to pH 6.13 in spring, pH 8.62 in summer and pH 4.75 in winter. 6) The faster organic matter was decomposed, the greater ash content was increased. Cellulose and lignin content were increased, but hemicellulose content was reduced during composting period. 7) Nitrogen contents were in the range of 3.1-5.6% and especially high in summer. After ammonium nitrogen contents were increased at the early stage of composting period, they were decreased. The maximum ammonium nitrogen content was 3,243mg/kg after 2 weeks in winter, 6,053mg/kg after 3 weeks in spring and 30,828mg/kg after 6 weeks in summer. C/N-ratios were not much changed. Nitrification occurred actively in spring and summer. 8) The contents of volatile and higher fatty acids were increased in early stage of composting and reduced after that. The maximum content of total fatty acid was 10.1% after 2 weeks in winter, 5.8% after 2 weeks in spring and 15.7% after 4 weeks in summer. 9) The contents of inorganic compounds were not accumulated as composting was proceeded. They were in the range of 0.9-4.4% $P_2O_5$, 1.6-2.9% $K_2O$, 2.4-4.6% CaO and 0.30-0.80% MgO. 10) CN and heavy metal contents did not show any tendency. They were in the range of 0.11-28.99mg/kg CN, 24-166mg/kg Zn, 5-129mg/kg Cu, 0.8-14.3mg/kg Cd, 7-42mg/kg Pb, ND-30mg/kg Cr and $ND-132.16\;{\mu}g/kg$ Hg.

• Korean Journal of Environmental Agriculture
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• v.14 no.2
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• pp.232-245
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• 1995

The garbage from the dwelling house was composted in two kinds of small composter in the laboratory, and the possibility of garbage composting was examined. The composters were general small. One (type 3) was constructed with the double layer walls and the other (type 4) was the same as the first except for being insulated. Because it was found that type 3 was not available for composting under our meteorological conditions through the winter experiment, only type 4 was tested in spring and summer. The experiment was performed for 8 weeks in each season. The seasonal variation of several components in the compost was evaluated and discussed. The results summarized below were those obtained at the end of the experiment, if the time was not specified. 1) The maximum temperature was $43^{\circ}C$ in winter, $55^{\circ}C$ in spring and $56^{\circ}C$ in summer. 2) The mass was reduced to an average of 63% and the volume reduction was an average of 78%. 3) The density was estimated as 1.5 kg/l in winter and 0.8 kg/l in spring and summer. 4) The water content was not much changed during the composting periods. It was 79.3% in winter, 75.0% in spring and 70.0% in summer. 5) After pH value increased during the first week, it decreased until the second week and increased again continuously thereafter. It reached pH 6.19 in winter, pH 7.59 in spring and pH 8.69 in summer. 6) The faster the organic matter was decomposed, the greater the ash content increased. The contents of cellulose and lignin increased, but that of hemicellulose decreased during the composting period. 7) Nitrogen contents were in the range of 3.3-6.8% and especially high in summer. After ammonium contents increased at the early stage of the composting period, they decreased. The maximum ammonium-nitrogen content was 2,404mg/kg after 8 weeks in winter, 12,400mg/kg after 3 weeks in spring and 20,718mg/kg after 3 weeks in summer. C/N-ratios decreased with the lapse of composting time, but they were not much changed. Nitrification occurred actively in summer. 8) The contents of volatile and higher fatty acids increased at the early stage of composting and reduced after that. The maximum content of total fatty acid was 9.7% after 6 weeks in winter, 14.8% after 6 weeks in spring and 15.8% after 2 weeks in summer. 9) The contents of inorganic components were not accumulated as composting proceeded. They were in the range of 0.9-4.4% $P_2O_5$, 1.6-2.4% $K_2O$, 2.2-5.4% CaO and 0.30-0.61% MgO. 10) CN and heavy metal contents did not show any tendency. They were in the range of 0.21-14.55mg/kg CN, 11-166mg/kg Zn, 5-65mg/kg Cu, 0.5-10.8mg/kg Cd, 6- 35mg/kg Pb, ND-33 mg/kg Cr and ND-302.04 g/kg Hg.

• Journal of Global Scholars of Marketing Science
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• v.20 no.3
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• pp.239-248
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• 2010

This paper describes a collaborative project between academia and industry which focused on improving the marketing and product development strategies for two private label apparel brands of a large regional department store chain in the southeastern United States. The goal of the project was to revitalize product lines of the two brands by incorporating student ideas for new solutions, thereby giving the students practical experience with a real-life industry situation. There were a number of key players involved in the project. A privately-owned department store chain based in the southeastern United States which was seeking an academic partner had recognized a need to update two existing private label brands. They targeted middle-aged consumers looking for casual, moderately priced merchandise. The company was seeking to change direction with both packaging and presentation, and possibly product design. The branding and product development divisions of the company contacted professors in an academic department of a large southeastern state university. Two of the professors agreed that the task would be a good fit for their classes - one was a junior-level Intermediate Brand Management class; the other was a senior-level Fashion Product Development class. The professors felt that by working collaboratively on the project, students would be exposed to a real world scenario, within the security of an academic learning environment. Collaboration within an interdisciplinary team has the advantage of providing experiences and resources beyond the capabilities of a single student and adds "brainpower" to problem-solving processes (Lowman 2000). This goal of improving the capabilities of students directed the instructors in each class to form interdisciplinary teams between the Branding and Product Development classes. In addition, many universities are employing industry partnerships in research and teaching, where collaboration within temporal (semester) and physical (classroom/lab) constraints help to increase students' knowledge and experience of a real-world situation. At the University of Tennessee, the Center of Industrial Services and UT-Knoxville's College of Engineering worked with a company to develop design improvements in its U.S. operations. In this study, Because should be lower case b with a private label retail brand, Wickett, Gaskill and Damhorst's (1999) revised Retail Apparel Product Development Model was used by the product development and brand management teams. This framework was chosen because it addresses apparel product development from the concept to the retail stage. Two classes were involved in this project: a junior level Brand Management class and a senior level Fashion Product Development class. Seven teams were formed which included four students from Brand Management and two students from Product Development. The classes were taught the same semester, but not at the same time. At the beginning of the semester, each class was introduced to the industry partner and given the problem. Half the teams were assigned to the men's brand and half to the women's brand. The teams were responsible for devising approaches to the problem, formulating a timeline for their work, staying in touch with industry representatives and making sure that each member of the team contributed in a positive way. The objective for the teams was to plan, develop, and present a product line using merchandising processes (following the Wickett, Gaskill and Damhorst model) and develop new branding strategies for the proposed lines. The teams performed trend, color, fabrication and target market research; developed sketches for a line; edited the sketches and presented their line plans; wrote specifications; fitted prototypes on fit models, and developed final production samples for presentation to industry. The branding students developed a SWOT analysis, a Brand Measurement report, a mind-map for the brands and a fully integrated Marketing Report which was presented alongside the ideas for the new lines. In future if the opportunity arises to work in this collaborative way with an existing company who wishes to look both at branding and product development strategies, classes will be scheduled at the same time so that students have more time to meet and discuss timelines and assigned tasks. As it was, student groups had to meet outside of each class time and this proved to be a challenging though not uncommon part of teamwork (Pfaff and Huddleston, 2003). Although the logistics of this exercise were time-consuming to set up and administer, professors felt that the benefits to students were multiple. The most important benefit, according to student feedback from both classes, was the opportunity to work with industry professionals, follow their process, and see the results of their work evaluated by the people who made the decisions at the company level. Faculty members were grateful to have a "real-world" case to work with in the classroom to provide focus. Creative ideas and strategies were traded as plans were made, extending and strengthening the departmental links be tween the branding and product development areas. By working not only with students coming from a different knowledge base, but also having to keep in contact with the industry partner and follow the framework and timeline of industry practice, student teams were challenged to produce excellent and innovative work under new circumstances. Working on the product development and branding for "real-life" brands that are struggling gave students an opportunity to see how closely their coursework ties in with the real-world and how creativity, collaboration and flexibility are necessary components of both the design and business aspects of company operations. Industry personnel were impressed by (a) the level and depth of knowledge and execution in the student projects, and (b) the creativity of new ideas for the brands.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Journal of Animal Science and Technology
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• v.45 no.6
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• pp.939-948
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• 2003

The objective of this study was to identify the quantitative traits loci(QTL) for economically important traits such as growth, carcass and meat quality on pig chromosome 6. A three generation resource population was constructed from cross between Korean native boars and Landrace sows. A total of 240 F$_2$ animals were produced using intercross between 10 boars and 31 sows of F$_1$ animals. Phenotypic data including body weight at 3 weeks, backfat thickness, muscle pH, shear force and crude protein level were collected from F$_2$ animals. Animals including grandparents(F$_0$), parents(F$_1$) and offspring(F$_2$) were genotyped for 29 microsatellite markers and PCR-RFLP marker on chromosome 6. The linkage analysis was performed using CRI-MAP software version 2.4(Green et al., 1990) with FIXED option to obtain the map distances. The total length of SSC6 linkage map estimated in this study was 169.3cM. The average distance between adjacent markers was 6.05cM. For mapping of QTL, we used F$_2$ QTL Analysis Servlet of QTL express, a web-based QTL mapping tool(http://qtl.cap.ed.ac.uk). Five QTLs were detected at 5% chromosome-wide level for body weight of 3 weeks of age, shear force, meat pH at 24 hours after slaughtering, backfat thickness and crude protein level on SSC6.