• Journal of the Korea Safety Management & Science
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• 제23권1호
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• pp.81-94
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• 2021

In SMEs, technological innovation is recognized as an important tool in terms of sustainable growth. This study analyzed the determinants of technological innovation by using the information of the corporate panel DB composed of local SMEs. The internal factors were added with technological innovation capacity and production capacity and the industrial cluster environment was first applied to external factors. Also, whether the industrial cluster environment influences technological innovation through R&D capabilities, the mediating effect was tested with the Sobel Test. Among the internal and external factors, the most important determinant was marketing ability, and a policy was proposed to develop measures to increase R&D capability with mediating effect. Among the technological innovation variables, which are dependent variables, the most determinant factor was the proportion of new product sales. For this, it is considered that additional research such as longitudinal research with the concept of repetition and parallax using the corporate panel DB is necessary.

• The Plant Pathology Journal
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• 제37권4호
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• pp.389-395
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• 2021

Soil is the major source of plant-associated microbes. Several fungal and bacterial species live within plant tissues. Actinomycetes are well known for producing a variety of antibiotics, and they contribute to improving plant health. In our previous report, Streptomyces globisporus SP6C4 colonized plant tissues and was able to move to other tissues from the initially colonized ones. This strain has excellent antifungal and antibacterial activities and provides a suppressive effect upon various plant diseases. Here, we report the genome-wide analysis of antibiotic producing genes in S. globisporus SP6C4. A total of 15 secondary metabolite biosynthetic gene clusters were predicted using antiSMASH. We used the CRISPR/Cas9 mutagenesis system, and each biosynthetic gene was predicted via protein basic local alignment search tool (BLAST) and rapid annotation using subsystems technology (RAST) server. Three gene clusters were shown to exhibit antifungal or antibacterial activity, viz. cluster 16 (lasso peptide), cluster 17 (thiopeptide-lantipeptide), and cluster 20 (lantipeptide). The results of the current study showed that SP6C4 has a variety of antimicrobial activities, and this strain is beneficial in agriculture.

• Journal of the Korean Magnetic Resonance Society
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• 제28권1호
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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.

• The Research Journal of the Costume Culture
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• 제6권4호
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• pp.120-135
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• 1998

The purpose of this study were to develop the taxonomy of apparel buying decision approaches and to identify the relationships between the apparel buying decision approaches and the situational characteristics. Data were collected via a questionnaire developed on the previous studies and the focus interview from 425 female college students living at Pusan, and analyzed by Factor Analysis, Cluster Analysis, Analysis of Variance, and Discriminant Analysis. Results indicated that apparel buying decision approaches consisted of eight dimensions and situational characteristics of affecting a particular apparel buying decision approaches were composed of three or five factors. The four types of apparel buying decision approaches were derived by Cluster Analysis and ANOVA: Recreational Shoppers, Brand Conscious Shoppers, Quality Conscious Shoppers, and Apathetic Shoppers. The findings revealed some patterns that were similar to previous studies and was useful to marketing managers who can view their customer segments in terms of the types in the taxonomy. Further, it provided a tool by which sales representatives can develop adaptive selling approaches based on a small set of buying situation and corresponding apparel buying decision approaches.

• Transactions of the Korean Society of Machine Tool Engineers
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• 제15권1호
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• pp.56-62
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• 2006

An experimental investigation was performed to study the characteristics of ER(Electro-Rheological) fluid flow in a horizontal rectangular tube with or without D.C voltage. To determine some characteristics of the ER flow, 2D PIV(Particle Image Velocimetry) technique is employed for velocity measurement. This research found the mean velocity distribution with 0kV/mm, 1.0kV/mm and 1.5kV/mm for Re = 0, 0.62, 1.29 and 2.26. When the strength of the electric field increased, the cluster of ERF are clearly strong along the test tube and the flow rate decreased. In this study, the rheology of ER fluid stagnating or flowing through a dispersion meter will be investigated by PIV method. And then the ER effect, which appears at the ER valves and their appliance will be visualized.

• 한국경영정보학회:학술대회논문집
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• 한국경영정보학회 2008년도 춘계학술대회
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• pp.153-170
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• 2008

Customers' needs change every moment. Profitability of stores can't be increased anymore with an existing standardized chain store management. Accordingly, a personalized store management tool needs through prediction of customers' preference. In this study, we propose a recommending procedure using dynamic customers' preference by analyzing the transaction database. We utilize self-organizing map algorithm and association rule mining which are applied to cluster the chain stores and explore purchase sequence of customers. We demonstrate that the proposed methodology makes an effect on recommendation of products in the market which is characterized by a fast fashion and a short product life cycle.

• Journal of the Korean Society of Clothing and Textiles
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• 제21권2호
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• pp.430-441
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• 1997

Journal of the Korean Society of Clothing and Textiles Vol. 21, No. 2 (1997) p. 430∼441 Clothing fitness is strongly required in the apparel industry, and draping is an effective tool to increase fitness to the wearers. A more sophisticated and systematic information of the somatotype, accordingly, is necessary for better dress form design. This study was performed to provide fundamental data on middle-aged women's somatotypes for dress form designers by classifying the torso somatotype and analyzing the characteristics of their somatotype. The subjects were directly measured anthropometrically and indirectly analyzed photo- graphically. Data were analyzed by factor analysis, cluster analysis and analysis of variance. On the basis of the cluster analysis, using 7 factors cores the subjects were classified into four groups and four dress forms for middle-aged women were constructed. 8y the analysis of moire topography of proposed dress forms that were constructed according to the characteristics and silhouettes of front and lateral views for each somatotype of subjects, three-dimensional characteristics of somatotype and overlapped crosssection diagrams were analyzed.

• Journal of The Korean Society of Agricultural Engineers
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• 제54권1호
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• pp.73-81
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• 2012

For planning and developing rural regions, it is very important to understand and utilize regional characteristics including social, demographic, and economic aspects. The purpose of this study is to find effective analysis techniques and provide a procedure design for mining regional characteristics in South Korea through reviewing and analyzing 41 related studies. The engaged research methods can be classified into five categories (PCA+CA, PCA, CA, GIS, and PCA+GIS) with the combination of three methodologies: principal component analysis (PCA), cluster analysis (CA), and geographical information system (GIS). The combination of PCA and CA occupied about 40 % of research methods used in related studies. The analysis tool of Korean Rural Information Supporting System (KRISS) is designed based on the outcomes of this study and applied to classify the regional capacity of agriculture using agricultural census data (2000) for evaluating its applicability.

• Proceedings of the Technology Innovation Conference
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• 기술경영경제학회 2002년도 제21회 하계학술발표회 논문집
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• pp.238-256
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• 2002

Competitiveness of a nation can be explained by the concept of national innovation systems(NIS). As components of NIS, industry clusters become the issue in analysing innovative activity of an economy. Innovative clusters can be identified by the innovation survey or other economic activity data. Input-output Table was used widely as a tool for quantitative analysis, This paper classifies seven clusters in Korean industry based on inter-industries trade of intermediary goods and services, Maximizing procedure method is used in analysing input-output table. Identified clusters are Textiles/chemicals, Construction/Material, Instrument/Equipment, Automobile, Services, Energy, and Agriculture/Food cluster, Among these clusters, some different characteristics in R&D portfolios are detected. R&D investment characteristics of each cluster give us significant implications in understanding innovative dynamics of Korean industry.

• Genomics & Informatics
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• 제1권1호
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• pp.32-39
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• 2003

We present a latent variable model-based approach to the analysis of gene expression patterns, coupled with topographic clustering. Aspect model, a latent variable model for dyadic data, is applied to extract latent patterns underlying complex variations of gene expression levels. Then a topographic clustering is performed to find coherent groups of genes, based on the extracted latent patterns as well as individual gene expression behaviors. Applied to cell cycle­regulated genes of the yeast Saccharomyces cerevisiae, the proposed method could discover biologically meaningful patterns related with characteristic expression behavior in particular cell cycle phases. In addition, the display of the variation in the composition of these latent patterns on the cluster map provided more facilitated interpretation of the resulting cluster structure. From this, we argue that latent variable models, coupled with topographic clustering, are a promising tool for explorative analysis of gene expression data.