• Food Engineering Progress
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• v.15 no.4
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• pp.388-397
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• 2011

The purpose of this study was to determine the optimum ethanol extraction conditions for maximum extraction of functional components such as ferulic acid, oryzanol, and toopherol from black rice bran using Response Surface Methodology (RSM). A central composite design was applied to investigate the effects of the independent variables of solvent ratio ($X_{1}$), extraction temperature ($X_{2}$) and extraction time ($X_{3}$) on the dependent variables such as total phenol components ($Y_{1}$), total flavonoids compounds ($Y_{2}$), electron donating ability ($Y_{3}$), $\gamma$-oryzanol ($Y_{4}$), ferulic acid ($Y_{5}$) and $\alpha$-toopherol components ($Y_{6}$). ANOVA results showed that coefficients of determination (R-square) of estimated models for dependent variables ranged from 0.8939 to 0.9470. It was found that solvent ratio and extraction temperature were the main effective factors in this extraction proess. Particularly, the extraction efficiency of ferulic acid, $\gamma$-oryzanol and $\alpha$-toopherol components were significantly affected by extraction temperature. As a result, optimum extraction conditions were 20.35 mL/g of solvent ratio, 79.4$^{\circ}C$ of extraction temperature and 2.88 hr of extraction time. Predicted values at the optimized conditions were acceptable when compared with experimental values.

• Food Engineering Progress
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• v.15 no.2
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• pp.130-135
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• 2011

Acanthopanax sessiliflorus (A. sessiliflorus) has been known as a traditional medicine having anti-stress, antioxidative and platelet aggregation inhibitory effects. This study was undertaken to investigate the functional properties of water extracts from four parts of A. sessiliflorus. Root, stem, leaf and fruit extracts from A. sessiliflorus were prepared with hot water ($80^{\circ}C$). The contents of functional substances, eleutheroside B and E, polyphenol, antioxidative activity, nitrite scavenging ability and anti-cancer activity of the extracts were determined. The contents of eleutheroside E in stem, root and fruit extracts were 542.50 ${\mu}$g/g, 343.35 ${\mu}$g/g and 30.78 ${\mu}$g/g, respectively. A large part of eleutheroside B was found in fruit (372.01 ${\mu}$g/g) and root (289.33 ${\mu}$g/g) extracts. Root and stem extracts contained 227.21 mg/100g and 131.22 mg/100g of polyphenols, respectively. Antioxidative activities (electron donating ability) of stem and root extracts were 79.87% and 77.27%, respectively. It appears that the antioxidative activities were related to polyphenol contents of the extracts. Most extracts showed 76-81.5% of nitrite scavenging ability at pH 1.2. It reveals that water extract from parts of A. sessiliflorus can inhibit formation of nitrosoamine in food. Effects of the extracts on the growth of normal and cancer cell lines were investigated. Extracts showed no cytotoxicity to normal dendritic cell line (DC2.4). Especially, the root extract promoted the growth of normal cell line. Root and stem extracts had 20-23% of inhibitory effect against stomach cancer cell line (SNU-719) and liver cancer cell line (Hep3B). These result indicated that the extracts from A. sessiliflorus can be used as functional food materials with antioxidative activity and nitrite scavenging ability to eliminate nitrosoamine in food.

• Food Science and Industry
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• v.53 no.1
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• pp.2-32
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• 2020

In recent years, significant importance has been given to chitooligosaccharides (COS) due to its potent notable biological applications. COS can be derived from chitosan which is commonly produced by partially hydrolyzed products from crustacean shells. In order to produce COS, there are several approaches including chemical and enzymatic methods which are the two most common choices. In this regard, several new methods were intended to be promoted which use the enzymatic hydrolysis with a lower cost and desired properties. Hence, the dual reactor system has gained more attention than other newly developed technologies. Enzymatic hydrolysis derived COS possesses important biological activities such as anticancer, antioxidant, anti-hypersentive, anti-dementia (Altzheimer's disease), anti-diabeties, anti-allergy, anti-inflammatory, etc. Results strongly suggest that properties of COS can be potential materials for nutraceutical, pharmaceutical, and cosmeceutical product development.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.12-28
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• 2021

Based on the global smart city promotion trend, in 2018, the "Fourth Industrial Revolution Committee" selected "sustainability" and "people-centered" as keywords in relation to the direction of domestic smart city policy. Accordingly, the Living Lab program, which is an active citizen-centered innovation methodology, is applied to each stage of the domestic smart city construction project. Through the Living Lab program, and in collaboration with the public and experts, the smart city discovers local issues as it focuses on citizens, devises solutions to sustainable urban problems, and formulates a regional development plan that reflects the needs of citizens. However, compared to citizen participation in urban regeneration projects that have been operated for a relatively long time, participation in smart city projects was found to significantly differ in level and sustainability. Therefore, this study conducted a comparative analysis of the characteristics of citizen participation at each stage of an urban regeneration project and, based on Arnstein's "Participation Ladder" model, examined the level of citizen participation activities in the Living Lab program carried out in a smart city commercial area from 2018 to 2019. The results indicated that citizen participation activities in the Living Lab conducted in the smart city project had a great influence on selecting smart city services, which fit the needs of local residents, and on determining the technological level of services appropriate to the region based on a relatively high level of authority, such as selection of smart city services or composition of solutions. However, most of the citizen participation activities were halted after the project's completion due to the one-off recruitment of citizen participation groups for the smart city construction project only. On the other hand, citizens' participation activities in the field of urban regeneration were focused on local communities, and continuous operation and management measures were being drawn from the project planning stage to the operation stage after the project was completed. This study presented a plan to revitalize citizen participation for the realization of a more sustainable smart city through a comparison of the characteristics and an examination of the level of citizen participation in such urban regeneration and smart city projects.

• Ecology and Resilient Infrastructure
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• v.9 no.1
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• pp.1-14
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• 2022

The total maximum daily load (TMDL) implemented in Korea mainly manages the mainstream considering a single common pollutant and river discharge, and the river system is divided into unit watersheds. Changes in the water quality of managed rivers owing to the water quality management in tributaries and unit watersheds are not considered when implementing the TMDL. In addition, it is difficult to consider the difference in the load of pollutants generated in the tributary depending on the conditions of the water quality change in each unit watershed, even if the target water quality was maintained in the managed water system. Therefore, it is necessary to introduce the total maximum load management at tributaries to manage the pollution load of tributaries with a high degree of pollution. In this study, the HSPF model, a watershed runoff model, was applied to the target areas consisting of 53 sub-watersheds to analyze the effect of water quality changes the in tributaries on the mainstream. Sub-watersheds were selected from the three major areas of the Paldang water system, including the drainage basins of the downstream of the South Han-River, Gyeongan stream, and North Han-River. As a result, BOD ranged from 0.17 mg/L to 4.30 mg/L, and was generally high in tributaries and decreased in the downstream watershed. TP ranged from 0.02 mg/L - 0.22 mg/L, and the watersheds that had a large impact on urbanization and livestock industry were high, and the North Han-River basin was generally low. In addition, a pollution source reduction scenario was selected to analyze the change in water quality by the amount of pollution load discharged at each unit watershed. The reduction rate of BOD and TP according to the scenario changes was simulated higher in the watershed of the downstream of the North Han-River and downstream and midstream of the Gyeongan stream. It was found that the benefits of water quality reduction from each sub-watershed efforts to improve water quality are greatest in the middle and downstream of each main stream, and it is judged that it can be served as basic data for the management of total tributaries.

• Journal of Life Science
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• v.33 no.11
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• pp.897-904
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• 2023

This study was done to develope genetic markers with the unique characteristics of genes according to the genomic information of Bacillus velezensis K10. B. velezensis K10 maintained a total of 4,159,835 bps, which was found to encode 5,136 open reading frames (orfs). B. velezensis K10 was found to have much more gene migration due to external factors overall compared to standard strain B. velezensis JS25R. In order to discover genetic selection markers, orfs on the genome to be easily induced to gene mutation were surveyed such as recombinase, integrase, transposase, and phage-related genes. As a result of the investigation, 9 candidate markers were isolated with high possibility as genetic selection markers. Although a part in the various origin's areas showed specificities in comparison with homology, the selected markers were all existed in phage-related areas because they were relatively lower homologies in phage-related genes. PCR analysis was done on B. licheniformis K12, B. velezensis K10, B. subtilis, and B. cereus to establish them as inter-species candidate selection markers. As a result, it was confirmed that B. velezensis K10-specific PCR products were formed in a total of 6 primer sets such as BV3 and BV5 to 9. On the other hand, analysis at the subspecies level observed the formation of B. velezensis K10-specific PCR products in 4 primer sets such as BV3, 5, 8, and 9. Among them, since BV5 and BV8 were detected by very specific results, we suggest that BV5 and 8 can be used as B. velezensis K10 gene selection markers at the species and sub-species level.

• Journal of the Korea Institute of Building Construction
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• v.24 no.2
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• pp.181-191
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• 2024

In the realm of cement manufacturing, concerted efforts are underway to mitigate the emission of greenhouse gases. A significant portion, approximately 60%, of these emissions during the cement clinker sintering process is attributed to the decarbonation of limestone, which serves as a fundamental ingredient in cement production. Prompted by these environmental concerns, there is an active pursuit of alternative technologies and admixtures for cement that can substitute for limestone. Concurrently, initiatives are being explored to harness technology within the cement industry for the capture of carbon dioxide from industrial emissions, facilitating its conversion into carbonate minerals via chemical processes. Parallel to these technological advances, economic growth has precipitated a surge in construction activities, culminating in a steady escalation of construction waste, notably waste concrete. This study is anchored in the innovative production of calcium silicate cement clinkers, utilizing finely powdered waste concrete, followed by a thorough analysis of their mineral phases. Through X-ray diffraction(XRD) analysis, it was observed that increasing the substitution level of waste concrete powder and the molar ratio of SiO₂ to (CaO+SiO₂) leads to a decrease in Belite and γ-Belite, whereas minerals associated with carbonation, such as wollastonite and rankinite, exhibited an upsurge. Furthermore, the formation of gehlenite in cement clinkers, especially at higher substitution levels of waste concrete powder and the aforementioned molar ratio, is attributed to a synthetic reaction with Al₂O₃ present in the waste concrete powder. Analysis of free-CaO content revealed a decrement with increasing substitution rate of waste concrete powder and the molar ratio of SiO₂/(CaO+SiO₂). The outcomes of this study substantiate the viability of fabricating calcium silicate cement clinkers employing waste concrete powder.

• 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.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• 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.