• Animal Bioscience
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• v.35 no.9
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• pp.1314-1326
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• 2022

Objective: Alongside the rise of animal-protection awareness in Taiwan, the public has been paying more attention to dog genetic deficiencies due to inbreeding in the pet market. The goal of this study was to isolate novel microsatellite markers for monitoring the genetic structure of domestic dog populations in Taiwan. Methods: A total of 113 DNA samples from three dog breeds-beagles (BEs), bichons (BIs), and schnauzers (SCs)-were used in subsequent polymorphic tests applying the 14 novel microsatellite markers that were isolated in this study. Results: The results showed that the high level of genetic diversity observed in these novel microsatellite markers provided strong discriminatory power. The estimated probability of identity (P_(ID)) and the probability of identity among sibs (P_(ID)sib) for the 14 novel microsatellite markers were 1.7×10^-12 and 1.6×10^-5, respectively. Furthermore, the power of exclusion for the 14 novel microsatellite markers was 99.98%. The neighbor-joining trees constructed among the three breeds indicated that the 14 sets of novel microsatellite markers were sufficient to correctly cluster the BEs, BIs, and SCs. The principal coordinate analysis plot showed that the dogs could be accurately separated by these 14 loci based on different breeds; moreover, the Beagles from different sources were also distinguished. The first, the second, and the third principal coordinates could be used to explain 44.15%, 26.35%, and 19.97% of the genetic variation. Conclusion: The results of this study could enable powerful monitoring of the genetic structure of domestic dog populations in Taiwan.

• Fashion & Textile Research Journal
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• v.25 no.1
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• pp.52-61
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• 2023

This study aimed to present data for the development of a shoe which is suitable for plus-size men (BMI 25kg/m² or higher) and to analyze the characteristics of each type of foot. The results of the study are as follows. To assess foot shape in relation to weight gain, participants were categorized into normal weight, overweight, and obese weight groups, according to their BMI indexes. Those in the normal weight group exhibited a smaller value than the overweight or obese weight group in all items. As a result of the cluster analysis, by type of foot, of the obese men category, men were classified into four BMI index groups: members of the type 1 group had a BMI index of 157 (18.4% of total sample), while for type 2 the figure was 213 (25.0% of sample), for type 3 it was 259 (30.4%), and for type 4 it stood at 224 (26.3% of total sample). Those from the type 1 group had thin ankles with narrow toes and flattened sides. Type 2 group members had thick ankles with well-developed outer feet and thick sides. Those within the type 3 group had medium-thick ankles with narrow feet but wide inner feet. Finally, those in the type 4 group had feet with a slanted side, as well as thick ankles, wide feet, and flat sides. Among these categories, the type 3 group members indicated the highest distribution.

• Fisheries and Aquatic Sciences
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• v.26 no.7
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• pp.437-446
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• 2023

Cyprinids are popular species for aquaculture worldwide, with Asia being a significant contributor to their production. In Korea, common carp (Cyprinus carpio), koi carp (Cyprinus rubrofuscus), and goldfish (Carassius auratus) are cultivated domestically and imported for ornamental or human consumption purposes. Among the viruses that infect cyprinids, cyprinid herpesvirus-2 (CyHV-2), koi herpesvirus (KHV, also known as cyprinid herpesvirus-3), and carp edema virus (CEV) are of particular concern as they cause substantial economic losses to the aquaculture industry. In this study, we investigated these viruses in both of domestic and imported cyprinids. Our results revealed that CyHV-2 was only detected in imported goldfish from Thailand. To further investigate the genetic characteristics of them, the marker A region was analyzed. Despite belonging to the same cluster with isolates from China, France, Poland, and Israel, CyHV-2 detected in this study showed distinct differences in their repetitive sequence sizes. Furthermore, two different forms of KHV/CEV coinfection were identified from domestic koi carp, both of which exhibited typical symptoms. Phylogenetic analysis showed that one KHV isolate (ScKc-2105-K) was of the Asian type and closely related to isolates from Japan, Indonesia, Belgium, Taiwan, and China. Two CEV isolates (ScKc-2105-CE and GhKc-2207-CE) be- longed to the IIa type and showed high similarity with isolates from the USA, France, and Korea. Notably, koi carp injected with cultured KHV (ScKc-2105-K) showed 78.0% cumulative mortality within 14 days post-injection (dpi). Our findings support the importance of regular surveillance of viral diseases in cyprinids.

• Journal of Veterinary Science
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• v.25 no.1
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• pp.4.1-4.14
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• 2024

Background: Lawsonia intracellularis is the causative agent of proliferative enteropathy and is associated with several outbreaks, causing substantial economic loss to the porcine industry. Objectives: In this study, we focused on demonstrating the protective effect in the mouse model through the immunological bases of two vaccine strains against porcine proliferative enteritis. Methods: We used live-attenuated Salmonella Typhimurium (ST) secreting two selected immunogenic LI antigens (Lawsonia autotransporter A epitopes and flagellin [FliC]-peptidoglycan-associated lipoprotein-FliC) as the vaccine carrier. The constructs were cloned into a Salmonella expression vector (pJHL65) and transformed into the ST strain (JOL912). The expression of immunogenic proteins within Salmonella was evaluated via immunoblotting. Results: Immunizing BALB/c mice orally and subcutaneously induced high levels of LI-specific systemic immunoglobulin G and mucosal secretory immunoglobulin A. In immunized mice, there was significant upregulation of interferon-γ and interleukin-4 cytokine mRNA and an increase in the subpopulations of cluster of differentiation (CD) 4⁺ and CD 8⁺ T lymphocytes upon splenocytes re-stimulation with LI antigens. We observed significant protection in C57BL/6 mice against challenge with 10^6.9 times the median tissue culture infectious dose of LI or 2 × 10⁹ colony-forming units of the virulent ST strain. Immunizing mice with either individual vaccine strains or co-mixture inhibited bacterial proliferation, with a marked reduction in the percentage of mice shedding Lawsonia in their feces. Conclusions: Salmonella-mediated LI gene delivery induces robust humoral and cellular immune reactions, leading to significant protection against LI and salmonellosis.

• Industry Promotion Research
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• v.9 no.2
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• pp.45-58
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• 2024

This study modelled the social network structure characteristics between Innopolis Start-ups located in Daejeon and Innopolis Start-ups' customers scattered across the country as a tendency of regional clustering among homogeneous technologies, and the observed values were included within the 95% confidence interval of the ERGM(Exponential Random Graph Model) analysis model. If both the research institute and the customer company are located in Yuseong-gu, Daejeon, the probability of being connected is about 13 times higher than if they are located in other administrative districts, and there is a strong tendency of connection between firms with the same technology with a negative value of assortment and homogeneity (0.1904), especially among the six technology sectors, with a P value of 0.035. There was a negative value (-0.0035) among firms not located in Yuseong-gu, with less clustering tendency. This confirms that Yuseong-gu, Daejeon, where the Daedeok Innopolis is located, is forming the centre of an innovation cluster.

• Asia Marketing Journal
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• v.12 no.4
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• pp.79-111
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• 2011

Digital economy has grown rapidly so that the new business area called 'Internet business' has been dramatically extended as time goes on. However, in the case of Internet business, market shares of individual companies seem to fluctuate very extremely. Thus marketing managers who operate the Internet sites have seriously observed the competition structure of the Internet business market and carefully analyzed the competitors' behavior in order to achieve their own business goals in the market. The newly created Internet business might differ from the offline ones in management styles, because it has totally different business circumstances when compared with the existing offline businesses. Thus, there should be a lot of researches for finding the solutions about what the features of Internet business are and how the management style of those Internet business companies should be changed. Most marketing literatures related to the Internet business have focused on individual business markets. Specifically, many researchers have studied the Internet portal sites and the Internet shopping mall sites, which are the most general forms of Internet business. On the other hand, this study focuses on the entire Internet business industry to understand the competitive circumstance of online market. This approach makes it possible not only to have a broader view to comprehend overall e-business industry, but also to understand the differences in competition structures among Internet business markets. We used time-series data of Internet connection rates by consumers as the basic data to figure out the competition patterns in the Internet business markets. Specifically, the data for this research was obtained from one of Internet ranking sites, 'Fian'. The Internet business ranking data is obtained based on web surfing record of some pre-selected sample group where the possibility of double-count for page-views is controlled by method of same IP check. The ranking site offers several data which are very useful for comparison and analysis of competitive sites. The Fian site divides the Internet business areas into 34 area and offers market shares of big 5 sites which are on high rank in each category daily. We collected the daily market share data about Internet sites on each area from April 22, 2008 to August 5, 2008, where some errors of data was found and 30 business area data were finally used for our research after the data purification. This study performed several empirical analyses in focusing on market shares of each site to understand the competition among sites in Internet business of Korea. We tried to perform more statistically precise analysis for looking into business fields with similar competitive structures by applying the cluster analysis to the data. The research results are as follows. First, the leading sites in each area were classified into three groups based on averages and standard deviations of daily market shares. The first group includes the sites with the lowest market shares, which give more increased convenience to consumers by offering the Internet sites as complimentary services for existing offline services. The second group includes sites with medium level of market shares, where the site users are limited to specific small group. The third group includes sites with the highest market shares, which usually require online registration in advance and have difficulty in switching to another site. Second, we analyzed the second place sites in each business area because it may help us understand the competitive power of the strongest competitor against the leading site. The second place sites in each business area were classified into four groups based on averages and standard deviations of daily market shares. The four groups are the sites showing consistent inferiority compared to the leading sites, the sites with relatively high volatility and medium level of shares, the sites with relatively low volatility and medium level of shares, the sites with relatively low volatility and high level of shares whose gaps are not big compared to the leading sites. Except 'web agency' area, these second place sites show relatively stable shares below 0.1 point of standard deviation. Third, we also classified the types of relative strength between leading sites and the second place sites by applying the cluster analysis to the gap values of market shares between two sites. They were also classified into four groups, the sites with the relatively lowest gaps even though the values of standard deviation are various, the sites with under the average level of gaps, the sites with over the average level of gaps, the sites with the relatively higher gaps and lower volatility. Then we also found that while the areas with relatively bigger gap values usually have smaller standard deviation values, the areas with very small differences between the first and the second sites have a wider range of standard deviation values. The practical and theoretical implications of this study are as follows. First, the result of this study might provide the current market participants with the useful information to understand the competitive circumstance of the market and build the effective new business strategy for the market success. Also it might be useful to help new potential companies find a new business area and set up successful competitive strategies. Second, it might help Internet marketing researchers take a macro view of the overall Internet market so that make possible to begin the new studies on overall Internet market beyond individual Internet market studies.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of Korean Society of Transportation
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• v.36 no.2
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• pp.155-168
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• 2018

In this study, we studied the directional pattern of entering the intersection from the intersection upstream link prior to predicting short future (such as 5 or 10 minutes) intersection direction traffic volume on the interrupted flow, and examined the possibility of traffic volume prediction using traffic assignment model. The analysis method of this study is to investigate the similarity of patterns by performing cluster analysis with the ratio of traffic volume by intersection direction divided by 2 hours using taxi DTG (Digital Tachograph) data (1 week). Also, for linking with the result of the traffic assignment model, this study compares the impact area of 5 minutes or 10 minutes from the center of the intersection with the analysis result of taxi DTG data. To do this, we have developed an algorithm to set the impact area of intersection, using the taxi DTG data and traffic assignment model. As a result of the analysis, the intersection entry pattern of the taxi is grouped into 12, and the Cubic Clustering Criterion indicating the confidence level of clustering is 6.92. As a result of correlation analysis with the impact area of the traffic assignment model, the correlation coefficient for the impact area of 5 minutes was analyzed as 0.86, and significant results were obtained. However, it was analyzed that the correlation coefficient is slightly lowered to 0.69 in the impact area of 10 minutes from the center of the intersection, but this was due to insufficient accuracy of O/D (Origin/Destination) travel and network data. In future, if accuracy of traffic network and accuracy of O/D traffic by time are improved, it is expected that it will be able to utilize traffic volume data calculated from traffic assignment model when controlling traffic signals at intersections.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of the Korean association of regional geographers
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• v.13 no.1
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• pp.104-117
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• 2007

According to the evolution of knowledge based economy, the expansion of significance of high-tech and technical innovation, in recent, many of local government of Korea have concern on the establishment and promotion of new growth power such as technopolis, science park, and innovative clusters for regional development. This study focuses on the establishment strategy for R&D special district in Gwangju area. Gwangju area have many potentials in several dimensions; comfortable physical environment, new agglomeration of photonics and household electric industry, the existence of excellent research related manpower, the strong networking with universities, laboratories, and firms. In addition, the establishment of R&D special district in Gwangju area will be provide positive effects in the increase of competitiveness of state, balanced development between regions, revitalization and development of southwestern area, and establishment of innovative clusters for regional development. However, in order to promote and establish the R&D special district of Gwangju area, central and local governments have to concern with some improvements such as the construction of R&D related infrastructure, the strengthening of research activities of research institutes, the building of cluster of strategic industries of Gwangju area, the supporting system for the revitalization of R&D special district.