• Fisheries and Aquatic Sciences
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• v.13 no.2
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• pp.157-166
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• 2010

We investigated the benthic polychaete assemblage in Gwangyang Bay and Yeosu Sound in February 1997. The sediment was an almost entirely muddy facies. The benthic macrofauna comprised 295 species occurring at a mean density of 875 $indiv./m^2$. Polychaetes were the major faunal component; there were 94 species at mean density 765 $indiv./m^2$. The highest abundance and species richness occurred in the Myodo south and north channels, in the mouth of Gwangyang Bay, and in the Noryang channel mouth. The most abundant polychaete was Tharyx sp. (47.9%), followed in rank order by Heteromastus filiformis (9.6%), Melinna cristata (9.3%), and Lumbrineris longifolia (7.3%). Cluster analysis divided the study area into four station groups based on station similarities in benthic polychaete assemblages: the Glycinde-Prionospio cluster in the western inner bay, the H. filiformis cluster in the middle inner bay, the Melinna-Lumbrineris cluster in the Myodo south-north channel, and the Tharyx cluster in the eastern main channel region. The sediment type of Gwangyang Bay has changed gradually from sandy to muddy. Dominant species have also changed from Chone teres and Lagis bocki to Tharyx sp., which is a potential organic pollution indicator.

• Journal of Technology Innovation
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• v.25 no.4
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• pp.107-143
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• 2017

This study aims to understand the impact of internal and external environments and innovation efforts on a company's performance. First, the relationships and patterns between variables were determined through an exploratory factor analysis. Afterwards, a cluster analysis was conducted, in which the influential factors summarized in the factor analysis were classified. Finally, structural equation modeling was used to carry out an empirical analysis of the structural relationship between innovation efforts and the company's performance in the classified clusters. 7 factors were derived from the exploratory factor analysis of 40 input variables from external and internal environments. 4 clusters (n=1,022) were formed based on the 7 factors. Empirical analysis of the 4 clusters using structural equation modelling showed the following: Only independent technology development had a positive impact on the company's performance for Cluster 1, which is characterized by sensitivity to a technological/competitive environment and innovativeness. Only independent technology development and joint research had positive impacts on the company's performance for Cluster 2, which is characterized by sensitivity to a market environment and internal orientation. Joint research and the mediating variable of government support program utilization had positive impacts, while the introduction of technology had a negative impact on the company's performance for Cluster 3, which is characterized by sensitivity to a competitive environment, innovativeness, and willingness to cooperate with the government and related institutions. Independent technology development as well as the mediating variables of network utilization and government support program utilization had positive impacts on the company's performance for Cluster 4, which is characterized by openness and external cooperation.

• Geomechanics and Engineering
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• v.25 no.2
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• pp.135-142
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• 2021

This study provided a new research perspective for processing and analyzing AE data to evaluate rock failure. Cluster method and information entropy theory were introduced to investigate temporal and spatial correlation of acoustic emission (AE) events during the rock failure process. Laboratory experiments of granite subjected to compression were carried out, accompanied by real-time acoustic emission monitoring. The cumulative length and dip angle curves of single links were fitted by different distribution models and distribution functions of link length and directionality were determined. Spatial scale and directionality of AE event distribution, which are characterized by two parameters, i.e., spatial correlation length and spatial correlation directionality, were studied with the normalized applied stress. The entropies of link length and link directionality were also discussed. The results show that the distribution of accumulative link length and directionality obeys Weibull distribution. Spatial correlation length shows an upward trend preceding rock failure, while there are no remarkable upward or downward trends in spatial correlation directionality. There are obvious downward trends in entropies of link length and directionality. This research could enrich mathematical methods for processing AE data and facilitate the early-warning of rock failure-related geological disasters.

• Journal of the Korean Magnetic Resonance Society
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• v.28 no.1
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• pp.1-5
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• 2024

IscU, the iron-sulfur (Fe-S) cluster scaffold protein, is an essential protein for biogenesis of Fe-S clusters. Previous studies showed that IscU manifests a metamorphic structural feature; at least two structural states, namely the structured state (S-state) and the disordered state (D-state), interconverting in a physiological condition, was observed. Moreover, subsequent studies demonstrated that the metamorphic flexibility of IscU is important for its Fe-S cluster assembly activity as well as for an efficient interaction with various partner proteins. Although solution nuclear magnetic resonance (NMR) spectroscopy has been a useful tool to investigate this protein, the detailed molecular mechanism that sustains the structural heterogeneity of IscU is still unclear. To tackle this issue, we applied a high-pressure NMR (HP-NMR) technique to the IscU variant, IscU(I8K), which shows an increased population of the S-state. We found that the equilibrium between the S- and D-state was significantly perturbed by pressure application, and the specific regions of IscU exhibited more sensitivity to pressure than the other regions. Our results provide novel insights to appreciate the dynamic behaviors of IscU and the related versatile functionality.

• Horticultural Science & Technology
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• v.29 no.4
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• pp.382-387
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• 2011

The chemical composition of essential oils obtained from aerial parts in spearmint, apple mint and chocolate mint, was investigated by gas chromatography/mass spectrometry analyses. (-)-Carvone (33.0%) was quantitatively major compound in spearmint, followed by R-(+)-limonene (11.7%) and ${\beta}$-phellandrene (9.7%); (-)-carvone (37.4%) and germacrene D (11.9%) in apple mint; and (-)-menthol (34.3%), p-menthone (18.4%) and menthofuran (9.8%) in chocolate mint. Hierarchical cluster analysis and principle components analysis showed the clear difference in chemical composition of the three mint oils.

• Journal of the Korean Operations Research and Management Science Society
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• v.8 no.2
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• pp.27-43
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• 1983

The purpose of this paper is to analyze the structure and properties of the patterns by extracting the general patterns on socio-economic development from huge data by statistical analysis. We collected data concerning socio-logical, economical and technological aspects. Indicators used for this study amounted to a total of 136, and among them 39 were on science & technology. What is more, these indicators were set up with the resent data for the first half of the 1970's mainly, and 141 nations were selected as the sample. Some rinkage patterns to the total indicators were abstracted by cluster analysis based on the correlation matrix. And some rinkage patterns to the total countries were educed by applying cluster analysis of centroid method to the respective indicators.

• The Journal of Korean Association of Computer Education
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• v.17 no.1
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• pp.117-130
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• 2014

The purpose of this study is to provide useful knowledge for improving indicators that represent competitiveness and educational competency of the university by deriving a new pattern or the meaningful results from the data of information disclosure of universities using statistical analysis and data mining techniques. To achieve this, a model of decision tree was made and various factors that affect education/research performance such as employment rate, the number of technology transfer and papers per full-time faculty were explored. In addition to this, the cluster analysis of universities was conducted using attributes related to evaluation of university. According to the analysis, common factors affecting higher education/research performance are following indicators ; incoming student recruitment rate, enrollment rate, and the number of students per full-time faculty. In the cluster analysis, when performed by the entire university, the size, location of the university respectively, clusters are mainly formed by well-known universities, art physical non-science and engineering religious leaders training universities, and others. The main influencing factors of this cluster are higher education/research performance indicators such as employment rate and the number of technology transfer.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.761-776
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• 2022

For fostering the biomedical industry, the formation of a high-tech-based innovation cluster is continuously being promoted. Accordingly, studies dealing with domestic clusters are being conducted based on case studies on major overseas clusters, but they are limited to a single case. In this paper, content analysis was used based on the literature about innovation clusters and bio-medical industry to comprehensively summarize the factors to be considered for the creation and activation of bio-medical clusters. Subsequently, the factors derived through content analysis were applied to the case of the Hongreung Innotown. The requirements for the successful creation of the Hongreung Innotown, it is required to improve settlement conditions, prepare systems to create start-up culture, and revitalize translational research, attract investment, and cooperate and connect with local clusters.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.12-24
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• 2022

This study attempted to investigate changes in collaboration structure for each stage of national Research and Development (R&D) in the artificial intelligence (AI) field through analysis of a co-author network for papers written under national R&D projects. For this, author information was extracted from national R&D outcomes in AI from 2014 to 2019. For such R&D outcomes, NTIS (National Science & Technology Information Service) information from the KISTI (Korea Institute of Science and Technology Information) was utilized. In research collaboration in AI, power function structure, in which research efforts are led by some influential researchers, is found. In other words, less than 30 percent is linked to the largest cluster, and a segmented network pattern in which small groups are primarily developed is observed. This means a large research group with high connectivity and a small group are connected with each other, and a sporadic link is found. However, the largest cluster grew larger and denser over time, which means that as research became more intensified, new researchers joined a mainstream network, expanding a scope of collaboration. Such research intensification has expanded the scale of a collaborative researcher group and increased the number of large studies. Instead of maintaining conventional collaborative relationships, in addition, the number of new researchers has risen, forming new relationships over time.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.65-76
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• 2023

This paper identifies the aspects of living environment elements (PM^2.5, PM¹⁰, Noise) throughout Seoul and the urban characteristics that affect them by utilizing the big data of the S-Dot sensors in Seoul, which has recently become a hot topic. In other words, it proposes a big data based urban computing research methodology and research direction to confirm the relationship between urban characteristics and living environments that directly affect citizens. The temporal range is from 2020 to 2021, which is the available range of time series data for S-Dot sensors, and the spatial range is throughout Seoul by 500mX500m GRID. First of all, as part of analyzing specific living environment patterns, simple trends through EDA are identified, and cluster analysis is conducted based on the trends. After that, in order to derive specific urban planning factors of each cluster, basic statistical analysis such as ANOVA, OLS and MNL analysis were conducted to confirm more specific characteristics. As a result of this study, cluster patterns of environment elements(PM^2.5, PM¹⁰, Noise) and urban factors that affect them are identified, and there are areas with relatively high or low long-term living environment values compared to other regions. The results of this study are believed to be a reference for urban planning management measures for vulnerable areas of living environment, and it is expected to be an exploratory study that can provide directions to urban computing field, especially related to environmental data in the future.