• The Journal of Society for e-Business Studies
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• v.14 no.2
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• pp.41-56
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• 2009

The purpose of this paper is to present a statistical model that can predict the rapid shift of users' attention by analyzing the lifecycle patterns of news in a social news aggregation service. Internet news service sites have a distinct characteristic in a sense that users' attention change very quickly in a short period of time. In this research, we propose a regression model for each news category which can model the decay pattern of users' attention and the content promotion policy of a social news aggregator is proven to be a major source of the rapid growth in the popularity of news. The proposed model is expected to be useful for evaluation of the social news aggregation service provider's content promotion policy that attempts to maximize users' attention as well as the diversity of news contents.

• The Zoological Society Korea : Newsletter
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• v.18 no.1
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• pp.10-15
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• 2001

사람의 체세포가 분열수명에 한계가 있다는 것은 1960년대 초기에 보고되었으며 이것은 artifact가 아니며 세포자신이 가지는 프로그램에 의한 것임이 보고된 것은 최근의 일이다. 그러나 이 프로그램 안에 체세포가 실지로 증식을 정지하는 시기, 모든 세포노화의 타이밍이 어떻게 설정되는가\ulcorner 에 대해서는 아직 알 수 없다. 한편 노화세포가 왜 증식할 수 없는가\ulcorner 에 대해서는 세포주기를 조절하는 유전자에 관한 최근의 연구에 의해 상당부분 밝혀졌다. 또한 분열회수를 인식하는 분열시계의 진행은 세포증식을 억제하는 유전자의 발현을 촉진하여 증식을 정지시킨다. 분열시계를 정지시킨 세포는 기본적으로 무한히 분열하는 것이 가능하다. 분열시계를 정지시키는 주역은 telomerase이다. 정상 세포에서는 생식소(生殖巢)에 존재한다. 대부분의 정상체세포, 세포조직에는 없으나 대부분의 암에서 존재한다. 본 논단에서는 전반부에 노화를 후반부에 암에 관해서 분자세포생물학 차원에서 최근 연구성과를 중심으로 살펴보고자 한다.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.