• Nuclear Engineering and Technology
• /
• v.56 no.8
• /
• pp.3155-3166
• /
• 2024

While several nuclear energy newcomer (NEN) countries have shown interest in small modular reactors (SMRs) as a potential energy source, this interest can generate new uncertainties regarding future nuclear weapons proliferation risks. Therefore, this research seeks to determine whether future SMR deployment in NEN countries will contribute to nuclear weapons proliferation, and how the risks can be mitigated. This research uses the Bayesian network statistical approach in conjunction with surveys of experts to assess nuclear proliferation risks when NEN countries deploy SMRs or a large commercial nuclear reactor. The results indicate that an NEN with a strong commitment to the nuclear non-proliferation norms and a stable security environment will experience a lower probability of having higher proliferation risks relative to the United Arab Emirates. Specifically, we demonstrate that experts anticipate a minimal escalation in proliferation risks across different SMR types. Instead, the results show that enrichment or reprocessing (E&R) facilities, if associated with an SMR, exert a substantial influence on proliferation risks. Lastly, implementing a spent nuclear fuel (SNF) retrieval system could serve as an option to mitigate proliferation risks in an NEN country. These findings offer insights for leading nuclear supplier countries to alleviate the potential proliferation risks by NEN countries.

• Asia pacific journal of information systems
• /
• v.22 no.3
• /
• pp.75-97
• /
• 2012

Knowledge residing in the heads of employees has always been regarded as one of the most critical resources within a firm. However, many tries to facilitate knowledge transfer among employees has been unsuccessful because of the motivational and cognitive problems between the knowledge source and the recipient. Social capital, which is defined as "the sum of the actual and potential resources embedded within, available through, derived from the network of relationships possessed by an individual or social unit [Nahapiet and Ghoshal, 1998]," is suggested to resolve these motivational and cognitive problems of knowledge transfer. In Social capital theory, there are two research streams. One insists that social capital strengthens group solidarity and brings up cooperative behaviors among group members, such as voluntary help to colleagues. Therefore, social capital can motivate an expert to transfer his/her knowledge to a colleague in need without any direct reward. The other stream insists that social capital provides an access to various resources that the owner of social capital doesn't possess directly. In knowledge transfer context, an employee with social capital can access and learn much knowledge from his/her colleagues. Therefore, social capital provides benefits to both the knowledge source and the recipient in different ways. However, prior research on knowledge transfer and social capital is mostly limited to either of the research stream of social capital and covered only the knowledge source's or the knowledge recipient's perspective. Social network theory which focuses on the structural dimension of social capital provides clear explanation about the in-depth mechanisms of social capital's two different benefits. 'Strong tie' builds up identification, trust, and emotional attachment between the knowledge source and the recipient; therefore, it motivates the knowledge source to transfer his/her knowledge to the recipient. On the other hand, 'weak tie' easily expands to 'diverse' knowledge sources because it does not take much effort to manage. Therefore, the real value of 'weak tie' comes from the 'diverse network structure,' not the 'weak tie' itself. It implies that the two different perspectives on strength of ties can co-exist. For example, an extroverted employee can manage many 'strong' ties with 'various' colleagues. In this regards, the individual-level structure of one's relationships as well as the dyadic-level relationship should be considered together to provide a holistic view of social capital. In addition, interaction effect between individual-level characteristics and dyadic-level characteristics can be examined, too. Based on these arguments, this study has following research questions. (1) How does the social capital of the knowledge source and the recipient influence knowledge transfer respectively? (2) How does the strength of ties between the knowledge source and the recipient influence knowledge transfer? (3) How does the social capital of the knowledge source and the recipient influence the effect of the strength of ties between the knowledge source and the recipient on knowledge transfer? Based on Social capital theory and Social network theory, a multi-level research model is developed to consider both the individual-level social capital of the knowledge source and the recipient and the dyadic-level strength of relationship between the knowledge source and the recipient. 'Cross-classified random effect model,' one of the multi-level analysis methods, is adopted to analyze the survey responses from 337 R&D employees. The results of analysis provide several findings. First, among three dimensions of the knowledge source's social capital, network centrality (i.e., structural dimension) shows the significant direct effect on knowledge transfer. On the other hand, the knowledge recipient's network centrality is not influential. Instead, it strengthens the influence of the strength of ties between the knowledge source and the recipient on knowledge transfer. It means that the knowledge source's network centrality does not directly increase knowledge transfer. Instead, by providing access to various knowledge sources, the network centrality provides only the context where the strong tie between the knowledge source and the recipient leads to effective knowledge transfer. In short, network centrality has indirect effect on knowledge transfer from the knowledge recipient's perspective, while it has direct effect from the knowledge source's perspective. This is the most important contribution of this research. In addition, contrary to the research hypothesis, company tenure of the knowledge recipient negatively influences knowledge transfer. It means that experienced employees do not look for new knowledge and stick to their own knowledge. This is also an interesting result. One of the possible reasons is the hierarchical culture of Korea, such as a fear of losing face in front of subordinates. In a research methodology perspective, multi-level analysis adopted in this study seems to be very promising in management research area which has a multi-level data structure, such as employee-team-department-company. In addition, social network analysis is also a promising research approach with an exploding availability of online social network data.

• Proceedings of the Korea Contents Association Conference
• /
• 2006.05a
• /
• pp.223-226
• /
• 2006

The development of Semantic Web basically requires knowledge induced from the formalization and semantization of information, and thus ontology should be introduced as a knowledgization tool. URI(Universal Resource Identifier) is an indispensible scheme to uniquely indicate individuals on ontology. However, it is difficult to find the use cases of URI in real data including science & technology publications. This paper describes the method to construct human resources based on URI which are the critical components on national R&D reference information ontology. We use co-authors, e-mails, publication date, and affiliation for discriminating authors with the same strings. HRST is referred to acquire human resource URIs. We expect the human resources would be adopted to outcome analysis applications such as researcher network analysis and outcome statistics.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.06a
• /
• pp.304-305
• /
• 2022

Recently, as digital communication technology is widely applied to the maritime field, large amounts of maritime data are being accumulated. Accordingly, attempts to create new value by applying data science and Artificial Intelligence(AI) technologies are emerging. Typically, Ministry of Oceans and Fisheries has been providing korean e-Navigation service since 2021 based on LTE-Maritime communication network, as well as R&D for creating value-added service through analyzing huge-sized maritime traffic data is underway. By the way, to do any data-based research, legal system, as a research infra, that researchers can get the data whenever they need is essential. This paper looked at types of data in maritime fields, checked related legal system about scientific analysis and utilization. It is confirmed that there are some legal factors which restrict its scientific analysis and utilization, and suggested ways of improvement to boost R&D using maritime data as a conclusion.

• IMMUNE NETWORK
• /
• v.21 no.6
• /
• pp.42.1-42.20
• /
• 2021

Eosinophils play critical roles in the maintenance of homeostasis in innate and adaptive immunity. Although primarily known for their roles in parasitic infections and the development of Th2 cell responses, eosinophils also play complex roles in other immune responses ranging from anti-inflammation to defense against viral and bacterial infections. However, the contributions of pattern recognition receptors in general, and NOD-like receptors (NLRs) in particular, to eosinophil involvement in these immune responses remain relatively underappreciated. Our in vivo studies demonstrated that NLRC4 deficient mice had a decreased number of eosinophils and impaired Th2 responses after induction of an allergic airway disease model. Our in vitro data, utilizing human eosinophilic EoL-1 cells, suggested that TLR2 induction markedly induced pro-inflammatory responses and inflammasome forming NLRC4 and NLRP3. Moreover, activation by their specific ligands resulted in caspase-1 cleavage and mature IL-1β secretion. Interestingly, Th2 responses such as secretion of IL-5 and IL-13 decreased after transfection of EoL-1 cells with short interfering RNAs targeting human NLRC4. Specific induction of NLRC4 with PAM3CSK4 and flagellin upregulated the expression of IL-5 receptor and expression of Fc epsilon receptors (FcεR1α, FcεR2). Strikingly, activation of the NLRC4 inflammasome also promoted expression of the costimulatory receptor CD80 as well as expression of immunoregulatory receptors PD-L1 and Siglec-8. Concomitant with NLRC4 upregulation, we found an increase in expression and activation of matrix metalloproteinase (MMP)-9, but not MMP-2. Collectively, our results present new potential roles of NLRC4 in mediating a variety of eosinopilic functions.

• Journal of the Economic Geographical Society of Korea
• /
• v.9 no.1
• /
• pp.81-96
• /
• 2006

The Lorraine region of France is one of the regions in the core of Europe that suffered most from the decline of its traditional industries. Since the 1970s, various levels of governments have attempted to solve the economic and social problems originating from de-industrialization with policies of regional innovation, e.g., establishing technopole, the creation of the technology transfer network, the launch of the RTP project, and business incubators to promote start-ups, technology transfers, and networks between governments, businesses, universities, public research institutes, and the public. In this context, this paper attempts to analyse governance of regional innovation policies of the Lorraine region, based on an analytical framework developed by a groups of researchers, i.e.. Bae et at (2006). The paper concludes that the roles of governments are important in creating and implementing regional innovation policies of the Lorraine region; the policies heavily rely on the R&D capacity of universities and public research institutes; and various levels of governments have different roles to play.

• Nuclear Engineering and Technology
• /
• v.56 no.3
• /
• pp.993-1001
• /
• 2024

The TANDEM project is a European initiative funded under the EURATOM program. The project started on September 2022 and has a duration of 36 months. TANDEM stands for Small Modular ReacTor for a European sAfe aNd Decarbonized Energy Mix. Small Modular Reactors (SMRs) can be hybridized with other energy sources, storage systems and energy conversion applications to provide electricity, heat and hydrogen. Hybrid energy systems have the potential to strongly contribute to the energy decarbonization targeting carbon-neutrality in Europe by 2050. However, the integration of nuclear reactors, particularly SMRs, in hybrid energy systems, is a new R&D topic to be investigated. In this context, the TANDEM project aims to develop assessments and tools to facilitate the safe and efficient integration of SMRs into low-carbon hybrid energy systems. An open-source "TANDEM" model library of hybrid system components will be developed in Modelica language which, by coupling, will extend the capabilities of existing tools implemented in the project. The project proposes to specifically address the safety issues of SMRs related to their integration into hybrid energy systems, involving specific interactions between SMRs and the rest of the hybrid systems; new initiating events may have to be considered in the safety approach. TANDEM will study two hybrid systems covering the main trends of the European energy policy and market evolution at 2035's horizon: a district heating network and power supply in a large urban area, and an energy hub serving energy conversion systems, including hydrogen production; the energy hub is inspired from a harbor-like infrastructure. TANDEM will provide assessments on SMR safety, hybrid system operationality and techno-economics. Societal considerations will also be encased by analyzing European citizen engagement in SMR technology safety.

• IMMUNE NETWORK
• /
• v.21 no.5
• /
• pp.32.1-32.14
• /
• 2021

Over two hundred twenty-eight million cases of coronavirus disease 2019 (COVID-19) in the world have been reported until the 21^st of September 2021 after the first rise in December 2019. The virus caused the disease called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Over 4 million deaths blame COVID-19 during the last one year and 8 months in the world. Currently, four SARS-CoV-2 variants of concern are mainly focused by pandemic studies with limited experiments to translate the infectivity and pathogenicity of each variant. The SARS-CoV-2 α, β, γ, and δ variant of concern was originated from United Kingdom, South Africa, Brazil/Japan, and India, respectively. The classification of SARS-CoV-2 variant is based on the mutation in spike (S) gene on the envelop of SARS-CoV-2. This review describes four SARS-CoV-2 α, β, γ, and δ variants of concern including SARS-CoV-2 ε, ζ, η, ι, κ, and B.1.617.3 variants of interest and alert. Recently, SARS-CoV-2 δ variant prevails over different countries that have 3 unique mutation sites: E156del/R158G in the N-terminal domain and T478K in a crucial receptor binding domain. A particular mutation in the functional domain of the S gene is probably associated with the infectivity and pathogenesis of the SARS-CoV-2 variant.

• Smart Structures and Systems
• /
• v.29 no.4
• /
• pp.625-640
• /
• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• The Journal of Society for e-Business Studies
• /
• v.25 no.3
• /
• pp.95-108
• /
• 2020

The development of 5G technology, which is a next-generation communication technology capable of processing large amounts of data in real-time and solving delays, is drawing attention. Not only in the United States but also Korea, 5G is focused on supporting R&D as a national strategic technology. The strategy for the smart factory, one of the core services of the 5G, aims to increase the flexibility of manufacturing production lines. The existing wired communications devices can be replaced into wireless ones with the ultra-low-delay and ultra-high-speed characteristics of 5G. For the efficient development of 5G technology, it is necessary to keep abreast of the status and trend. In this study, based on the collected data of 1517 Korea patents and 1928 US patents, 5G technologies trend was analyzed and key technologies were identified by network analysis and topic modeling. We expect that it will be used for decision making for policy establishment and technology strategy of related industries to provide the trends of technology development related to the introduction of 5G technology to smart factories.