• Journal of the Korea Institute of Building Construction
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• v.22 no.5
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• pp.473-483
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• 2022

BIM(Building Information Modeling) is a technology that can manage information throughout the entire life cycle of the construction industry and serves as a platform for improving productivity and integrating the entire construction industry. Currently, BIM is actively applied in developed countries, and its use at various overseas construction sites is increasing This is unclear. due to air shortening and budget savings. However, there is still a lack of institutional basis and technical limitations in the domestic construction sector, which have led to the lack of utilization of BIM. Various activation measures and institutional frameworks will need to be established for the early establishment of these productive BIMs in Korea. Therefore, as part of the research for the domestic settlement and revitalization of BIM, this study derived a number of key factors necessary for the development of the construction industry through brainstorming and expert surveys using AHP techniques and analyzed the relative importance of each factor. In addition, prior surveys by a group of experts resulted in 1, 3 items in level, 2, 9 items in level, and 3, 27 items in level, and priorities analysis was performed through pairwise comparisons. As a result of the AHP analysis, it was found that the relative importance weight of policy aspects was highest in level 1, and the policy factors in level 2 and the cost-based and incentive system introduction factors were considered most important in level 3. These findings show that the importance of the policy guidance or institutions underlying the activation of BIM rather than research and development or corporate innovation is relatively high, and that the preparation of policy plans by public institutions should be the first priority. Therefore, it is considered that the development of a policy system or guideline must be prioritized before it can be advanced to the next activation stage. The use of BIM technologies will not only contribute to improving the productivity of the construction industry, but also to the overall development of the industry and the growth of the construction industry. It is expected that the results of this study can provide as useful information when establishing policies for activating BIM in central government, relevant local governments, and related public institutions.

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.1
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• pp.61-70
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• 2019

"Jungmo2510", a rye cultivar, Secale cereal L., was developed by National Institute of Crop Science, RDA in 2015. It was developed from open pollination from within 10 rye cultivars or lines including "Chochun" in 1995. The line "SR95POP-S1-523-1-5-5-4-7-3-B-16-3-19" was selected for its excellent agronomic appearance and was placed in yield trials for two years from 2011 to 2012. The line was designated "Homil55" and was placed in regional yield trials at the four locations around Korea from 2013 to 2015, during which time the name "Jungmo2510" was given. This cultivar is an erect plant type and leaves of short and broad size with a green color, a yellow colored, medium-diameter culm, and a yellowish brown-colored, medium-size grain. The heading date of "Jungmo2510" was April 16, which were 2 days earlier than that of "Gogu". "Jungmo2510" also showed similar to winter hardiness and greater resistance to lodging compared to those of the check cultivar. Over three years, the average dry matter yield of "Jungmo2510" was 802 kg 10a^-1 , which was harvested in late April and was lower than that of the check cultivar "Gogu" (825 kg). The seed productivity of "Jungmo2510" was approximately 481 kg 10a^-1 , which was 2.4% less than that of the check. "Jungmo2510" was higher to than "Gogu" in term of protein content (9.1% and 8.0%, respectively), total digestible nutrients(TDN)(57.5% and 55.5%, respectively), and TDN yield 10a^-1(419 kg and 392 kg, respectively). This cultivar is recommended as a fall sowing crop in areas where the average daily minimum-mean temperatures are higher than -12 ℃ in January, and as a winter crop for whole-crop forage before the planting of rice or green manure around Korea.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.13 no.2
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• pp.121-128
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• 2008

As an aquatic ecotoxicity test method, a bioassay using the inhibition of sporualtion of the green macroalga, Ulva pertusa, has been developed. Optimal test conditions determined for photon irradiance, pH, salinity and temperature were $100\;{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, $7{\sim}9$, $25{\sim}35\;psu$ and $15{\sim}20^{\circ}C$, respectively. The validity of the test endpoint was evaluated by assessing the toxicity of four metals (Cd, Cu, Pb, Zn) and elutriates of sewage or waste sludge collected from 9 different locations. When the metals were assayed, the $EC_{50}$ values indicated the following toxicity rankings: Cu ($0.062\;mg{\cdot}L^{-1}$) > Cd ($0.208\;mg{\cdot}L^{-1}$) > Pb ($0.718\;mg{\cdot}L^{-1}$) > Zn ($0.776\;mg{\cdot}L^{-1}$). When compared with other commonly used bioassays of metal pollution listed on US ECOTOX database, the sporualtion test proved to be the most sensitive. Ulva sporulation was significantly inhibited in all elutriates with the greatest and least effects observed in elutriates of sludge from industrial waste ($EC_{50}=6.78%$) and filtration bed ($EC_{50}=15.0%$), respectively. The results of the Spearman rank correlation analysis for $EC_{50}$ data versus the concentrations of toxicants in the sludge presented a significant correlation between toxicity and four heavy metals(Cd, Cu, Pb, Zn). The method described here is sensitive to toxicants, simple to use, easy to interpret and economical. It is also easy to procure samples and maintain cultures. The present method would therefore probably make a useful assessment of aquatic toxicity of a wide range of toxicants. In addition, the genus Ulva has a wide geographical distribution and species have similar reproductive processes, so the test method would have a potential application worldwide.

• Tuberculosis and Respiratory Diseases
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• v.43 no.6
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• pp.945-953
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• 1996

Background : Many acute and chronic lung diseases including pneumoconiosis are characterized by the presence of increased numbers of activated macrophages. These macrophages generate several inflammatory cell chemoattractants, by which neutrophil migrate from vascular compartment to the alveolar space. Recruited neutrophils secrete toxic oxygen radicals or proteolytic enzymes and induce inflammatory response. Continuing inflammatory response results in alteration of the pulmonary structure and irreversible fibrosis. Recently, a polypeptide with specific neutrophil chemotactic activity, interleukin-8(IL-8), has been cloned and isolated from a number of cells including : monocytes, macrophages and fibroblasts. IL-1 and/or TNF-${\alpha}$ preceded for the synthesis of IL-8, and we already observed high level of IL-1 and TNF-${\alpha}$ in the pneumoconioses. So we hypothesized that IL-8 may be a central role in the pathogenesis of pneumoconiosis. In order to evaluate the clinical utility of IL-8 as a biomarker in the early diagnosis of pneumoconiosis, we investigated the increase of IL-8 in the pneumoconiotic patient and the correlation between IL-8 level and progression of pneumoconiosis. Method : We measured IL-8 in the serum of 48 patients with pneumoconiosis and 16 persons without dust exposure history as a control group. Pneumoconiotic cases were divided into 3 groups according to ILO Classification : suspicious group(n=16), small opacity group(n=16) and large opacity group(n=16). IL-8 was measured by a sandwich enzytne immunoassay technique. All data were expressed as the $mean{\pm}standard$ deviation. Results: 1) The mean value of age was higher in the small opacity and large opacity group than comparison group, but smoking history was even. Duration of dust exposure was not different among 3 pneumoconiosis groups. 2) IL-8 level was $70.50{\pm}53.63pg/m{\ell}$ in the suspicious group, $107.50{\pm}45.88pg/m{\ell}$ in the small opacity group, $132.50{\pm}73.47pg/m{\ell}$ in the large opacity group and $17.85{\pm}33.85pg/m{\ell}$ in the comparison group. IL-8 concentration in all pneumoconiosis group was significant higher than that in the comparison group(p<0.001). 3) IL-8 level tended to increase with the progression of pneumoconiosis. Multiple comparison test using Anova/Scheffe analysis showed a significant difference between suspicious group and large opacity group(p<0.05). 4) The level of IL-8 was correlated with the progression of pneumoconiosis(r=0.4199, p<0.05). Conclusion : IL-8 is thought to be a good biomarker for the early diagnosis of pneumoconiosis.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Korean Journal of Poultry Science
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• v.42 no.1
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• pp.15-26
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• 2015

In this study, genotyping was executed by using 27 microsatellite markers for genetic diversity of 469 Korean Native Chickens [20 population, each population is 24 samples but Hanhyup A line is 13 samples). in total 469 samples were collected from National Institute of Animal Science (Korean Native Chicken (NR, NY, NG, NL and NW), Ogye (NO), Leghorn F,K (NF and NK), Black and Brown cormish (NH and NS), Rhode Island Red C, D (NC and ND), Total is 12 populations] and Hanhyup [H line (HH), F line (HF), G line (HG), V line (HV), S line (HS), W line (HW), Y line (HY), A line (HA), total is 8 populations]. [The allele number were observed 5 (ADL0268) to 20 (MCW0127) each markers. Observed heterozygostiy ($H_{obs}$), expected heterozygosity ($H_{exp}$), polymorphism Information Content (PIC) were observed 0.359 to 0.677, 0.668 to 0.881 and 0.646 to 0.869, respectively. Using these markers, the calculated the heterozygote deficit within chicken line ($F_{is}$) value each population from mean 0.117. Phylogenetic tree showing the genetic relationship among 20 population using standard genetic distance calculated from 27 microsatellite markers. genetic distances revealed the closest (0.175) between NC and ND. on the other hand, Farthest genetic distances (0.710) revealed between NF and HV. STRUCTURE analysis and Principal Components Analysis (PCA) showed that results of similar phylogenetic tree. The expected probability of identity values on random individuals (Total population and only Hanhyup line) was estimated at $8.80{\times}10^{-83}$ and $3.87{\times}10^{-117}$, respectively. In conclusion, This study shows the useful data that be utilized as a basic data of Korean Native Chicken breeding and development for commercial chicken industry to meet the consumer's demand.

• Korean Journal of Poultry Science
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• v.45 no.3
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• pp.229-236
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• 2018

The aim of this study was to evaluate the genetic diversity and relationships of Ogye populations in Korea. A total of 243 genomic DNA samples from 6 Ogye population (Yeonsan Ogye; YSO, Animal Genetic Resources Research Center Ogye; ARO, Chungbuk Ogye; CBO, Chungnam Ogye; CNO, Gyeongbuk Ogye; GBO, Seoul National University Ogye; SUO) and 3 introduced chicken breeds (Rhode Island Red; RIR, White Leghorn; LG, Cornish; CN) were used. Sizes of 25 microsatellite markers were decided using GeneMapper Software(v 5.0) after analyzing ABI 3130XL. A total of 153 alleles were observed and the range was 2 to 10 per each locus. The mean of expected and observed heterozygosity and PIC (Polymorphism Information Content) value was 0.53, 0.50, 0.46 respectively. The lowest genetic distance (0.073) was observed between YSO and SUO, and the highest distance (0.937) between the RIR and CBO. The results of clustering analysis suggested 3 clusters (${\Delta}K=7.96$). Excluding GBO population, 5 Ogye populations (YSO, ARO, CBO, CNO, SUO) were grouped in same cluster with high genetic uniformity (0.990, 0.979, 0.989, 0.994, 0.985 respectively). But GBO population was grouped in cluster 1 with low genetic uniformity (0.340). The results of this study can be use to basic data for the genetic evaluation and management of Ogye populations in Korea.

• Journal of the Korean Society of Food Science and Nutrition
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• v.33 no.3
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• pp.480-486
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• 2004

Despite many therapeutic advances in the understanding of the processes in carcinogenesis, overall mortality statistics are unlikely to change until there is reorientation of the concepts for the use of natural products as new anticarcinogenic agents. In this study, we investigated the anticarcinogenic activity, antioxidant and DPPH scavenging activity of Sargassum fulvellum (SF). SF was extracted with methanol, which was further fractionated into five different types: hexane (SFMH), ethylether (SFMEE), ethyl acetate (SFMEA), butanol (SFMB) and aqueous (SFMA) partition layers. We determined the cytotoxic effect of these layers on human cancer cells by MTT assay. Among various partition layers of SF, at starting concentration of 100 $\mu\textrm{g}$/mL, SFMEE showed very high cytotoxicity which were 92, 90 and 84% and kept high throughout 5 concentration levels sparsed by 100 $\mu\textrm{g}$/mL against all three human cancer cell lines: HepG2, HT-29 and HeLa. SFMEA showed a low cytotoxicity at the beginning concentration level, but as the concentration became denser, growth inhibition effect of cancer cell lines started to increase and at 500 $\mu\textrm{g}$/mL, it hit the highest, which were 91, 96 and 98% against the same three cell lines as above. We observed QR induced effect in all fraction layers of SF. SFMEE showed similar tendensy of QR induced effect as did against cytotoxicity. The QR induced effect of SFMEE on HepG2 cells at 25 $\mu\textrm{g}$/mL concentration indicated 3 times higher than the control value of 1.0 and SFMH tended to be concentration-dependent on HepG2 cells. At 100 $\mu\textrm{g}$/mL, the QR induced effects resulted a ratio, which was 2.5 times higher than the control value. In search for antioxidation effects of SF extract and partition layer, the reducing activity on the 1, 1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging potential was sequentially screened. The SFM has similar antioxidant activity as to BHT and vitamin C groups.