• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.1 no.1
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• pp.56-78
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• 1998

Objective: Chronic hepatitis B infection (CHB) occurs in 6% to 10% of population in Korea. In ethinic communities where prevalence of chronic infection is high such as Korea, transmission of hepatitis B infection is either vertical (ie, by perinatal infection) or by close family contact (usually from mothers or siblings) during the first 5 years of life. The development of chronic hepatitis B infection is increasingly more common the earlier a person is exposed to the virus, particularly in fetal and neonatal life. And it progress to cirrhosis and hepatocellular carcinoma, especially in severe liver damage and perinatal infection. Histopathology of CHB is important when evaluating the final outcomes. A numerical scoring system which is a semiquantitatively assessed objective reproducible classification of chronic viral hepatitis, is a valuable tool for statistical analysis when predicting the outcome and evaluating antiviral and other therapies. In this study, a numerical scoring system (Ludwig system) was applied and compared with the conventional histological classification of De Groute. And the comparative analysis of cinical findings, family history, serology, and liver function test by histopathological findings in chronic hepatitis B of children was done. Methods: Ninety nine patients [mean age=9 years (range=17 months to 16 years)] with clinical, biochemical, serological and histological patterns of chronic HBV infection included in this study. Five of these children had hepatocelluar carcinoma. They were 83 male and 16 female children. They all underwent liver biopsies and histologic evaluation was performed by one pathologist. The biopsy specimens were classified, according to the standard criteria of De Groute as follows: normal, chronic lobular hepatitis (CLH), chronic persistent hepatitis (CPH), mild to severe chronic active hepatitis (CAH), or active cirrhosis, inactive cirrhosis, hepatocellular carcinoma (HCC). And the biopsy specimens were also assessed and scored semiquantitatively by the numerical scoring Ludwig system. Serum HBsAg, anti-HBs, HBeAg, anti-HBe, anti-HBc (IgG, IgM), and HDV were measured by radioimunoassays. Results: Male predominated in a proportion of 5.2:1 for all patients. Of 99 patients, 2 cases had normal, 2 cases had CLH, 22 cases had CPH, 40 cases had mild CAH, 19 cases had moderate CAH, 1 case had severe CAH, 7 cases had active cirrhosis, 1 case had inactive cirrhosis, and 5 cases had HCC. The mean age, sex distribution, symptoms, signs, and family history did not differ statistically among the different histologic groups. The numerical scoring system was correlated well with the conventional histological classification. The histological activity evaluated by both the conventional classification and the scoring system was more severe as the levels of serum aminotransferases were higher. In contrast, the levels of serum aminotransferases were not useful for predicting the degree of histologic activity because of its wide range overlapping. When the histological activity was more severe and especially the cirrhosis more progressing, the prothrombin time was more prolonged. The histological severity was inversely related with the duration of seroconversion of HBeAg. Conclusions: The histological activity could not be accurately predicted by clinical and biochemical findings, but by the proper histological classification of the numerical scoring system for the biopsy specimen. The numerical scoring system was correlated well with the conventional histological classification, and it seems to be a valuable tool for the statistical analysis when predicting the outcome and evaluating effects of antiviral and other therapies in chronic hepatitis B in children.