• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1654-1666
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• 2024

The goal of this research is to propose a way to maximize small modular reactor (SMR) utilization to gain better market feasibility in support of carbon neutrality. For that purpose, a comprehensive tool was developed, combining off-design thermohydraulic models, economic objective models (levelized cost of electricity, annual profit), non-economic models (saved CO2), a parameter input sampling method (Latin hypercube sampling, LHS), and a multi-objective evolutionary algorithm (Non-dominated Sorting Algorithm-2, NSGA2 method) for optimizing a SMR-combined heat and power cycle (CHP) system design. Considering multiple objectives, it was shown that NSGA2+LHS method can find better optimal solution sets with similar computational costs compared to a conventional weighted sum (WS) method. Out of multiple multi-objective optimal design configurations for a 105 MWe design generation rating, a chosen reference SMR-CHP system resulted in its levelized cost of electricity (LCOE) below $60/MWh for various heat prices, showing economic competitiveness for energy market conditions similar to South Korea. Examined economic feasibility may vary significantly based on CHP heat prices, and extensive consideration of the regional heat market may be required for SMR-CHP regional optimization. Nonetheless, with reasonable heat market prices (e.g. district heating prices comparable to those in Europe and Korea), SMR can still become highly competitive in the energy market if coupled with a CHP system.

• Journal of Korea Technology Innovation Society
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• v.21 no.1
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• pp.64-91
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• 2018

This study explores the interactive pattern among social issue, academic research, and governmental policy on science and technology during the last 20 years. In particular, we try understand wether the science and technology policy research and governmental policy meets social needs appropriately. In order to do this, we have collected text data from news articles, papers, and governmental documents. Based on these data, social network analysis and cluster analysis has been carried out. According to the results, we have found that science and technology policy researches tend to focus on fragmented technological innovation meeting urgent practical needs at the initial stage. However, recently, the main characteristics of science and technology policy research shows co-evolutionary patterns responding to society. Furthermore, time lag also has been observed in the process of interaction among the three bodies. Based on these results, we put forward some suggestions for upcoming researches in science and technology policy. Firstly, analysis levels are needed to be shifted from micro level to mezo or macro level. Secondly, more research efforts are required to be focused on policy process in science technology and its public management. Finally, we have to enhance the sensitiveness to social issues through studies on agenda setting in science and technology policy.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.347-356
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• 2008

High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.

• 한국균학회소식:학술대회논문집
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• 2015.05a
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• pp.61-61
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• 2015

Rice blast, caused by the fungal pathogen Magnaporthe oryzae, is one of the most destructive diseases of rice worldwide. The rice - M. oryzae pathosystem has become a model in the study of plant - fungal interactions due to its economic importance and accumulating knowledge. During the evolutionary arms race with M. oryzae, rice plants evolved a repertoire of Resistance (R) genes to protect themselves from diseases in a gene-for-gene fashion. M. oryzae secretes a battery of small effector proteins to manipulate host functions for its successful infection, and some of them are recognized by host R proteins as avirulence effectors (AVR), which turns on strong immunity. Therefore, the analysis of interactions between AVRs and their cognate R proteins provide crucial insights into the molecular basis of plant - fungal interactions. Rice blast resistance genes Pik, Pia, Pii comprise pairs of protein-coding ORFs, Pik-1 and Pik-2, RGA4 and RGA5, Pii-1 and Pii-2, respectively. In all three cases, the paired genes are tightly linked and oriented to the opposite directions. In the AVR-Pik/Pik interaction, it has been unraveled that AVR-Pik binds to the N-terminal coiled-coil domain of Pik-1. RGA4 and RGA5 are necessary and sufficient to mediate Pia resistance and recognize the M. oryzae effectors AVR-Pia and AVR1-CO39. A domain at the C-terminus of RGA5 characterized by a heavy metal associated domain was identified as the AVR-binding domain of RGA5. Similarly, physical interactions among Pii-1, Pii-2 and AVR-Pii are being analyzed.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.292-300
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• 2020

According to the development of artificial intelligence technology, attempts have been made to interpret phenomena in various fields of the real world such as economic, social, and scientific fields through computer simulations using virtual artificial agents. In the existing artificial agent-based bargaining game analysis, there was a problem that did not reflect the cost incurred when the stage progresses in the real-world bargaining game and the depreciation of the bargaining target over time. This study intends to observe the effect on the bargaining game by adding the cost incurred in the bargaining stage and depreciation of the bargaining target over time (bargaining cost) to the previous artificial agent-based bargaining game model. As a result of the experiment, it was observed that as the cost incurred in the bargaining stage increased, the two artificial agents participating in the game had a share close to half the ratio and tried to conclude the negotiation in the early stage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2067-2077
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• 2011

The recent trend in technology development is characterized as technology convergence, mainly between IT, BT and NT and also more and more industries are starting to use several technologies simultaneously or in a combined way theses days. As a result, the needs on technology interaction analysis is increasing for strategic technology management and policy-making. Responding to the needs, this research deals with technology innovation process in terms of technology competition, particularly focusing on the 17 new growth drivers in 3 areas, which has been announced by the Korean government as a new growth vision for Korean economy, and analyzing their co-evolutionary process. For the analysis, patent data, a representative data on technology innovation, is adopted. Then, Lotka-Volterra Competition model, a model frequently used to describe the dynamism of competitive innovation is applied to the data. The research results are expected to support strategic decision-makings such as effect policy-making or R&D priority-setting, by analyzing the relationship between the 3 areas, the 17 new growth drivers, or the particular technologies in the drivers.

• Journal of Microbiology and Biotechnology
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• v.21 no.4
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• pp.391-399
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• 2011

Free fatty acids (FFAs) are known to have bacteriocidal activity and are important components of the innate immune system. Many FFAs are naturally present in human and animal skin, breast milk, and in the bloodstream. Here, the therapeutic potential of FFAs against methicillin-resistant Staphylococcus aureus (MRSA) is demonstrated in cultures and in mice. Among a series of FFAs, only oleic acid (OA) (C18:1, cis-9) can effectively eliminate Staphylococcus aureus (S. aureus) through cell wall disruption. Lauric acid (LA, C12:0) and palmitic acid (PA, C16:0) do not have this ability. OA can inhibit growth of a number of Gram-positive bacteria, including hospital and community-associated MRSA at a dose that did not show any toxicity to human sebocytes. The bacteriocidal activities of FFAs were also demonstrated in vivo through injection of OA into mouse skin lesions previously infected with a strain of MRSA. In conclusion, our results suggest a promising therapeutic approach against MRSA through boosting the bacteriocidal activities of native FFAs, which may have been co-evolved during the interactions between microbes and their hosts.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.24-36
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• 2008

Architecture-based competition has become a very important issue in many industries. As companies seek lower cost, fast development, and more customizability at the same time, modular architecture of products and processes seem to be an inevitable choice. Existing literature, however, has only focused on the basic contents of architecture-based competition. Different competitive environments and technological competencies of incumbent companies influence the evolutionary dynamics of dominant architecture of industries. In this paper we suggest a new theoretical framework to deal with the complex co-adaptation process of architecture-based competition. We first explore the emerging modular architecture in Chinese commercial vehicle industry, and then compare it with the architecture strategies of Korean companies. Based on the explorative case study, we propose new hypotheses relating the market demand, technological competencies of major players and dominant architecture in an indus-try.

• BMB Reports
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• v.48 no.9
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• pp.513-518
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• 2015

Factors that modulate cholesterol levels have major impacts on cardiovascular disease. Niemann-Pick C1-like 1 (NPC1L1) functions as a sterol transporter mediating intestinal cholesterol absorption and counter-balancing hepatobiliary cholesterol excretion. The liver receptor homolog 1 (LRH-1) had been shown to regulate genes involved in hepatic lipid metabolism and reverse cholesterol transport. To study whether human NPC1L1 gene is regulated transcriptionally by LRH-1, we have analyzed evolutionary conserved regions (ECRs) in HepG2 cells. One ECR was found to be responsive to the LRH-1. Through deletion studies, LRH-1 response element was identified and the binding of LRH-1 was demonstrated by EMSA and ChIP assays. When SREBP2, one of several transcription factors which had been shown to regulate NPC1L1 gene, was co-expressed with LRH-1, synergistic transcriptional activation resulted. In conclusion, we have identified LRH-1 response elements in NPC1L1 gene and propose that LRH-1 and SREBP may play important roles in regulating NPC1L1 gene. [BMB Reports 2015; 48(9): 513-518]

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.5
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• pp.29-44
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• 2000

Adaptation of operator probabilities is one of the most important and promising issues in evolutionary computation areas. This is because the setting of appropriate probabilities is not only very tedious and difficult but very important to the performance improvement of genetic algorithms. Many researchers have introduced their algorithms for setting or adapting operator probabilities. Experimental results in most previous works, however, have not been satisfiable. Moreover, Tuson have insisted that “the adaptation is not necessarily a good thing” in his papers［$^1$$^2$］. In this paper, we propose a self-tuning scheme for adapting operator probabilities in genetic algorithms. Our scheme was extensively tested on four function optimization problems and one combinational problem； and compared to simple genetic algorithms with constant probabilities and adaptive genetic algorithm proposed by Srinivas et al［$^3$］. Experimental results showed that our scheme was superior to the others. Our scheme compared with previous works has three advantages： less computational efforts, co-evolution without additional operations for evolution of probabilities, and no need of additional parameters.