• Journal of Korean Society for Atmospheric Environment
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• v.29 no.1
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• pp.74-85
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• 2013

Since the underground transportation system is a closed environment, indoor air quality problems may seriously affect many passengers' health. The purpose of this study was to understand $PM_{10}$ characteristics in the underground air environment and further to quantitatively estimate $PM_{10}$ source contributions in a Seoul Metropolitan subway station. The $PM_{10}$ was intensively collected on various filters with $PM_{10}$ aerosol samplers to obtain sufficient samples for its chemical analysis. Sampling was carried out in the M station on the Line-4 from April 21 to 28, July 13 to 21, and October 11 to 19 in the year of 2010 and January 11 to 17 in the year of 2011. The aerosol filter samples were then analyzed for metals, water soluble ions, and carbon components. The 29 chemical species (OC1, OC2, OC3, OC4, CC, PC, EC, Ag, Al, Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Si, Ti, V, Zn, $Cl^-$, $NO_3{^-}$, $SO_4{^{2-}}$, $Na^+$, $NH_4{^+}$, $K^+$, $Mg^{2+}$, $Ca^{2+}$) were analyzed by using ICP-AES, IC, and TOR after proper pretreatments of each sample filter. Based on the chemical information, positive matrix factorization (PMF) model was applied to identify the $PM_{10}$ sources and then six sources such as biomass burning, outdoor, vehicle, soil and road dust, secondary aerosol, ferrous, and brakewear related source were classified. The contributions rate of their sources in tunnel are 4.0%, 5.8%, 1.6%, 17.9%, 13.8% and 56.9% in order.

• Journal of Technology Innovation
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• v.20 no.1
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• pp.235-262
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• 2012

Although a commercialization of developed technology is an important factor for firm's competitiveness, the success rate in technology commercialization is significantly low. This fact raises a need of an analysis on factors affecting success in technology commercialization. Thus, in this study, in order to determine the success factors of technology commercialization, statistical analysis is done on 4 different elements of Korean automobile industry firms: managerial group attitude, market orientation, technology quality, and government support; and developed a causal-relationship model of the above elements and commercialization. In the developed model, two moderating variables, corporate ability and industry classification, are added to determine the level of correlations respect to two moderating variables. As a result of hypothesis tests, market orientation, managerial attitude as an antecedent variable; and government support, technology quality as an antecedent variable, both have significant correlation with technology commercialization. For moderating variables, a corporate ability has moderating effects on the connections of managerial attitude, market orientation and technology commercialization; but an industry classification has a moderating effect only on the link between technology quality and government support. The results of this research serve a contribution to the development of R&D efficiency improvement by providing government with direction in science & technology policy.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.277-307
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• 2016

Since 2011, DCF(Discounted Cash Flow) method has been used initiatively for valuating R&D technology assets in the agricultural food industry and recently technology valuation based on royalties comparison among technology transfer transactions has been also carried out in parallel when evaluating the technology assets such as new seed development technologies. Since the DCF method which has been known until now has many input variables to be estimated, sophisticated estimation has been demanded at the time of technology valuation. In addition, considering more similar trading cases when applying sales transaction comparison or industry norm method based on information of technology transfer royalty, it is an important issue that should be taken into account in the same way in the Agri-Food industry. The main input variables used for technology valuation in the Agri-Food industry are life cycle of technology asset, the financial information related to the Agri-Food industry, discount rate, and technology contribution rate. The latest infrastructure building and data updating related to technology valuation has been carried out on a regular basis in the evaluation organization of the Agri-Food segment. This study verifies the key variables that give the most important impact on the results for the existing technology valuation in the Agri-Food industry and clarifies the difference between the existing valuation result and the outcome by referring the support information that is derived through the latest input information applied in DCF method. In addition, while presenting the scheme to complement fragment information which the latest input data just influence result of technology valuation, we tried to perform comparative analysis between the existing valuation results and the evaluated outcome after the latest of reference data for making a decision the input values to be estimated in DCF. To perform these analyzes, it was first selected the representative cases evaluated past in the Agri-Food industry, applied a sensitivity analysis for input variables based on these selected cases, and then executed a simulation analysis utilizing the key input variables derived from sensitivity analysis. The results of this study is to provide the information which there are the need for modernization of the data related to the input variables that are utilized during valuating technology assets in the Agri-Food sector and for building the infrastructure of the key input variables in DCF. Therefore it is expected to provide more fruitful information about the results of valuation.

• Journal of Animal Science and Technology
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• v.51 no.5
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• pp.353-360
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• 2009

When 14 microsatellite (MS) markers were applied in the identifying test for 480 Hanwoo, the discriminating power was estimated as $3.43{\times}10^{-27}$ based on the assumption of a random mating group (PI). This rate is 1,000 times higher than that of 60 single nucleotide polymorphism (SNP) markers. On the other hand, the power of the 60 SNP markers was estimated as $4.69{\times}10^{-20}$ and $8.02{\times}10^{-12}$ on the assumption of a half-sib mating group ($PI_{half-sibs}$) and a full-sib mating group ($PI_{sibs}$), respectively. These powers were 10 times and 10,000 times higher than those of the 14 MS markers. The results indicated that the total number of alleles (MS vs SNP = 146 vs 120) acted as a key factor for the discriminating power in a random mating population, and the total number of markers (MS vs SNP = 14 vs 60) was a dominant influence on the power in half-sib and full-sib populations. In the Hanwoo population, in which it was assumed that the entire population is the enormous half-sib group formed by the absolute genetic contribution of a few nuclear bulls, there will be only a 10 times difference in the discriminating power between the 14 MS markers and the 60 SNP makers. However, the probability of not excluding a candidate parent pair from the parentage of an arbitrary offspring, given that only the genotype of the offspring ($PNE_{pp}$) was 1,000 times higher as shown by the 14 MS markers than that by the 60 SNP markers. The strong points of SNP makers are the stability of the variation (low mutation rate) and automation of high-throughput genotyping. In order to apply these merits for the practical and constant Hanwoo identity test, research and development are required to set a cost-effective platform and produce a homemade apparatus for SNP genotyping.