• Korean Journal of Food Science and Technology
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• v.46 no.3
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• pp.352-357
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• 2014

In this study, 100 consumers (men, 50; women, 50; age group, 20-50 years) rated their overall preferences for 24 Korean raspberry wines by using a 9-point hedonic scale. The analysis of variance was constructed to evaluate the effect of gender, age, and samples on the preference scores of the wine products. Significant differences were observed in overall preferences for the 24 samples; however, no interactions based on preferences by age and gender groups were noted. Cluster analysis was performed to determine sample clustering based on the frequencies from the preference data. Three clusters were obtained; these three clusters were well separated based on the mean overall preference scores for the samples. Discriminant analysis based on the three clusters also confirmed the same grouping of samples with 100% accuracy.

• The Korean Journal of Franchise Management
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• v.7 no.1
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• pp.21-29
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• 2016

Purpose - The primary purpose of this study is to employ effective marketing methods using market segmentation of coffee shops by determining how motivations to visit coffee shops have different impacts on demographic profile of visitors and characteristics of coffee shop visits, so as to draw out a better understanding of customers of coffee market. Research design, data, and methodology - Data were collected using surveys of self-administered questionnaires toward coffee shop users in Daejeon, Korea. A number of samples used in data analysis were 253 excluding unusable responses. The data were analyzed through frequency, reliability, and factor analysis using SPSS 20.0. Factor analysis was conducted through the principal component analysis and varimax rotation method to derive factors of one or more eigen values. In addition, the cluster analysis, multivariate ANOVA, and cross-tab analysis were used for the market segmentation based on the types of motivation for coffee shop visits. The process of the cluster analysis is as follows. Four clusters were derived through hierarchical clustering, and k-means cluster analysis was then carried out using mean value of the four clusters as the initial seed value. Result - The factor analysis delineated four dimensions of motivation to visit coffee shops: ostentation motivation, hedonic motivation, esthetic motivation, utility motivation. The cluster analysis yielded four clusters: utility and esthetic seekers, hedonic seekers, utility seekers, ostentation seekers. In order to further specify the profile of four clusters, each cluster was cross tabulated with socio-demographics and characteristics of coffee shop visits. Four clusters are significantly different from each other by four types of motivations for coffee shop visits. Conclusions - This study has empirically examined the difference in demographic profile of visitors and characteristics of coffee shop visits by motivation to visit coffee shops. There are significant differences according to age, education background, marital status, occupation and monthly income. In addition, coffee shops use pattern characterization in frequency of visits to coffee shops, relationships with companion, purpose of visit, information sources, brand type, average expense per visit, important elements of selection attribute were significantly different depending on motivations for coffee shop visits.

• The Journal of Korean Academic Society of Nursing Education
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• v.30 no.2
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• pp.182-191
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• 2024

Purpose: The purpose of this study was to explore the characteristics of social networks among registered nurses in acute nursing care units. Methods: This study used a survey design. Four nursing units from two acute hospitals were selected using a convenience method, and 83 nurses from those nursing units participated in the study in July 2022. The positive influences among nurses included friendship, collaboration, advice, and referent networks, and the negative influences included avoidance and bullying networks. Using the NetMiner program, the k-means clustering technique was applied to create groups of nodes with similar characteristics. The general characteristics of the participants were analyzed by mean, standard deviation, frequency, and ANOVA or chi-squared test. Results: As a result of dividing the 83 nurse participants into four clusters, positive influencers, silent peers, unwelcome peers, and active bullies were identified. Positive influence group nurses were frequently mentioned in the friendship, collaboration, advice, and referent networks. On the other hand, nurses in the unwelcome group and the active bullying group were frequently mentioned in the avoidance and bullying networks. Conclusion: Social networks that have a positive or negative impact on nursing performance are created through different relationships between nurses. Nurse managers can use the findings to create a more supportive and collaborative environment. Further research is needed to develop intervention programs to improve interactions and relationships between fellow nurses.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.5
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• pp.27-39
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• 2004

In the first part of this study, five homogeneous regions in view of topographical and geographically homogeneous aspects except Jeju and Ulreung islands in Korea were accomplished by K-means clustering method. A total of 57 rain gauges were used for the regional frequency analysis with minimum rainfall series for the consecutive durations. Generalized Extreme Value distribution was confirmed as an optimal one among applied distributions. Drought rainfalls following the return periods were estimated by at-site and regional frequency analysis using L-moments method. It was confirmed that the design drought rainfalls estimated by the regional frequency analysis were shown to be more appropriate than those by the at-site frequency analysis. In the second part of this study, LH-moment ratio diagram and the Kolmogorov-Smirnov test on the Gumbel (GUM), Generalized Extreme Value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) distributions were accomplished to get optimal probability distribution. Design drought rainfalls were estimated by both at-site and regional frequency analysis using LH-moments and GEV distribution, which was confirmed as an optimal one among applied distributions. Design rainfalls were estimated by at-site and regional frequency analysis using LH-moments, the observed and simulated data resulted from Monte Carlotechniques. Design drought rainfalls derived by regional frequency analysis using L1, L2, L3 and L4-moments (LH-moments) method have shown higher reliability than those of at-site frequency analysis in view of RRMSE (Relative Root-Mean-Square Error), RBIAS (Relative Bias) and RR (Relative Reduction) for the estimated design drought rainfalls. Relative efficiency were calculated for the judgment of relative merits and demerits for the design drought rainfalls derived by regional frequency analysis using L-moments and L1, L2, L3 and L4-moments applied in the first report and second report of this study, respectively. Consequently, design drought rainfalls derived by regional frequency analysis using L-moments were shown as more reliable than those using LH-moments. Finally, design drought rainfalls for the classified five homogeneous regions following the various consecutive durations were derived by regional frequency analysis using L-moments, which was confirmed as a more reliable method through this study. Maps for the design drought rainfalls for the classified five homogeneous regions following the various consecutive durations were accomplished by the method of inverse distance weight and Arc-View, which is one of GIS techniques.

• Korean Journal of Radiology
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• v.24 no.3
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• pp.235-246
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• 2023

Objective: It is difficult to predict the treatment response of tissue after stereotactic radiosurgery (SRS) because radiation necrosis (RN) and tumor recurrence can coexist. Our study aimed to predict tumor recurrence, including the recurrence site, after SRS of brain metastasis by performing a longitudinal tumor habitat analysis. Materials and Methods: Two consecutive multiparametric MRI examinations were performed for 83 adults (mean age, 59.0 years; range, 27-82 years; 44 male and 39 female) with 103 SRS-treated brain metastases. Tumor habitats based on contrast-enhanced T1- and T2-weighted images (structural habitats) and those based on the apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) images (physiological habitats) were defined using k-means voxel-wise clustering. The reference standard was based on the pathology or Response Assessment in Neuro-Oncologycriteria for brain metastases (RANO-BM). The association between parameters of single-time or longitudinal tumor habitat and the time to recurrence and the site of recurrence were evaluated using the Cox proportional hazards regression analysis and Dice similarity coefficient, respectively. Results: The mean interval between the two MRI examinations was 99 days. The longitudinal analysis showed that an increase in the hypovascular cellular habitat (low ADC and low CBV) was associated with the risk of recurrence (hazard ratio [HR], 2.68; 95% confidence interval [CI], 1.46-4.91; P = 0.001). During the single-time analysis, a solid low-enhancing habitat (low T2 and low contrast-enhanced T1 signal) was associated with the risk of recurrence (HR, 1.54; 95% CI, 1.01-2.35; P = 0.045). A hypovascular cellular habitat was indicative of the future recurrence site (Dice similarity coefficient = 0.423). Conclusion: After SRS of brain metastases, an increased hypovascular cellular habitat observed using a longitudinal MRI analysis was associated with the risk of recurrence (i.e., treatment resistance) and was indicative of recurrence site. A tumor habitat analysis may help guide future treatments for patients with brain metastases.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.7
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• pp.926-931
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• 2014

Pork from Jeju black pig (population J) and Berkshire (population B) has a unique market share in Korea because of their high meat quality. Due to the high demand of this pork, traceability of the pork to its origin is becoming an important part of the consumer demand. To examine the feasibility of such a system, we aim to provide basic genetic information of the two black pig populations and assess the possibility of genetically distinguishing between the two breeds. Muscle samples were collected from slaughter houses in Jeju Island and Namwon, Chonbuk province, Korea, for populations J and B, respectively. In total 800 Jeju black pigs and 351 Berkshires were genotyped at thirteen microsatellite (MS) markers. Analyses on the genetic diversity of the two populations were carried out in the programs MS toolkit and FSTAT. The population structure of the two breeds was determined by a Bayesian clustering method implemented in structure and by a phylogenetic analysis in Phylip. Population J exhibited higher mean number of alleles, expected heterozygosity and observed heterozygosity value, and polymorphism information content, compared to population B. The $F_{IS}$ values of population J and population B were 0.03 and -0.005, respectively, indicating that little or no inbreeding has occurred. In addition, genetic structure analysis revealed the possibility of gene flow from population B to population J. The expected probability of identify value of the 13 MS markers was $9.87{\times}10^{-14}$ in population J, $3.17{\times}10^{-9}$ in population B, and $1.03{\times}10^{-12}$ in the two populations. The results of this study are useful in distinguishing between the two black pig breeds and can be used as a foundation for further development of DNA markers.

• The Korean Journal of Mycology
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• v.42 no.2
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• pp.159-164
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• 2014

For development of a method for differentiation of Pleurotus eryngii cultivars, simple sequence repeats (SSR) from whole genomic DNA sequence analysis was used for genotyping and two multiplex-SSR primer sets were developed. These SSR primer sets were employed to distinguish 12 cultivars and strains. Five polymorphic markers were selected based on the genotyping results. PCR using each primer produced one to four distinct bands ranging in size from 200 to 300 bp. Polymorphism information content (PIC) values of the five markers were in the range of 0.6627 to 0.6848 with an average of 0.6775. Unweighted pairgroup method with arithmetic mean clustering analysis based on genetic distances using five SSR markers classified 12 cultivars into two clusters. Cluster I and II were comprised of four and eight cultivars, respectively. Two multiplex sets, Multi-1 (SSR312 and SSR366) and Multi-2 (SSR178 and SSR277) completely discriminated 12 cultivars and strains with 21 alleles and a PIC value of 0.9090. These results might be useful in providing an efficient method for the identification of P. eryngii cultivars with separate PCR reactions.

• Journal of Yeungnam Medical Science
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• v.22 no.2
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• pp.221-240
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• 2005

Background: Doxorubicin has proved to be a useful chemotherapeutic agent especially for osteogenic sarcoma. It induces cancer cell death via apoptosis. Materials and Methods: To explore and analyze the changes of gene expression during doxorubicin induced apoptosis on human osteogenic sarcoma, Saos-2 cell, cDNA microarray was performed. After treatment with doxorubicin, total RNA was purified and expressed genes were investigated with a 17k human cDNA microarray. Results: For analysis of the cDNA microarray, the genes were filtered using the sum of the median value of Cy3 and Cy5 signal intensity of greater than 800. Expression of 264 genes was changed by more than 2 fold, and the expression of 35 genes was changed more than 3 fold after treatment with doxorubicin. The genes were primarily related to cell death, cell growth and maintenance, signal transduction, cellular component, transport, and metabolism. Conclusion: Treatment with doxorubicin induced expressional change of many genes. Some of the genes might be related with apoptosis directly or indirectly. Further study is now needed to characterize these genes.

• Korean Journal of Ecology and Environment
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• v.46 no.4
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• pp.581-587
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• 2013

The amounts of carbon (C), nitrogen (N) and phosphorus (P) in relation to dry weight (D.W.) were measured in zooplankton from the large four rivers (Han R., Geum R., Yeongsan R. and Seomjin R.) during 2004~2008. The stoichiometry of total zooplankton in four river systems was highly variable. The ranges of average C, N and P-contents were $70{\sim}620mgC\;mg^{-1}$ D.W., $7.1{\sim}85.5{\mu}gN\;mg^{-1}$ D.W. and $2.5{\sim}7.4{\mu}gP\;mg^{-1}$ D.W., respectively. The mean C :N: P atomic ratios reflected large spatial differences. The C : P and N : P ratios of the zooplankton community ranged from 38 to 392 : 1 and from 4 to 65 : 1 in all sampling sites. Self-Organizing Map (SOM) was applied to the survey data, and the study sites were clearly classified into 3 clusters. Clustering was largely affected by the distribution pattern of C, N, P-contents, which is related with characteristics of river systems on the basis of stoichiometry.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.713-723
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• 2017

Since the panoramic image can overcome the limitation of the viewing angle of the camera and have a wide field of view, it has been studied effectively in the fields of computer vision and stereo camera. In order to generate a panoramic image, stitching images taken by a plurality of general cameras instead of using a wide-angle camera, which is distorted, is widely used because it can reduce image distortion. The image stitching technique creates descriptors of feature points extracted from multiple images, compares the similarities of feature points, and links them together into one image. Each feature point has several hundreds of dimensions of information, and data processing time increases as more images are stitched. In particular, when a panorama is generated on the basis of an image photographed by a plurality of unspecified cameras with respect to an object, the extraction processing time of the overlapping feature points for similar images becomes longer. In this paper, we propose a preprocessing process to efficiently process stitching based on an image obtained from a number of unspecified cameras for one object or environment. In this way, the data processing time can be reduced by pre-grouping images based on camera sensor information and reducing the number of images to be stitched at one time. Later, stitching is done hierarchically to create one large panorama. Through the grouping preprocessing proposed in this paper, we confirmed that the stitching time for a large number of images is greatly reduced by experimental results.