• Asia pacific journal of information systems
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• v.23 no.3
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.

• Journal of the Korean Geosynthetics Society
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• v.12 no.4
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• pp.67-76
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• 2013

Landfill require special care due to the dangers of nearby surface water and underground water pollution caused by leakage of leachate. The leachate does not leak due to the installation of the geomembrane but sharp wastes or landfill equipment can damage the geomembrane and therefore a means of protecting the geomembrane is required. In Korea, in accordance with the waste control act being modified in 1999, protecting the geosynthetics liner on top of the slope of landfill and installing a drainage layer to fluently drain leachate became mandatory, and technologies are being researched to both protect the geomembrane and quickly drain leachate simultaneously. Therefore, this research has its purpose in studying the drainage functions of leachate and protection functions of the geomembrane in order to examine the application possibilities of Geo-Multicell-Composite (GMC) as a Leachate Collection Removal and Protection System (LCRPs) at the slope on top of the geomembrane of landfill by observing methods of inserting filler with high-quality water permeability at the drainage net. GMC's horizontal permeability coefficient is $8.0{\times}10^{-4}m^2/s$ to legal standards satisfeid. Also crash gravel used as filler respected by vertical permeability is 5.0 cm/s, embroidering puncture strength 140.2 kgf. A result of storm drain using artificial rain in GMC model facility, maxinum flow rate of 1,120 L/hr even spray without surface runoff was about 92~97% penetration. Further study, instead of crash gravel used as a filler, such as using recycled aggregate utilization increases and the resulting construction cost is expected to savings.

• Radiation Oncology Journal
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• v.18 no.2
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• pp.138-149
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• 2000

Purpose : It was studied that the relationship between radiation dose, dose rate and the frequency of chromosomal aberrations in peripheral lymphocytes. Methods and Materials : Peripheral lymphocytes were irradiated in vitro with 6 MeV X-ray at dose ranges from 50 cGy to 800 cGy. The variations of the frequency of chromosomal aberrations were observed according to different radiation dose rate from 20 cGy/min to 400 cGy/min at constant total dose of 400 cGy which it was considered as factor to correct biological radiation dose measurement. Results : The yields of lymphocytes with chromosomal aberrations (dicentric chromosome, ring chromosome, acentric fragment pairs) are 0% at 50 cGy, 9% at 100 cGy, 20% at 200 cGy, 27% at 300 cGy, 55% at 400 cGy, 88% at 600 cGy, and 100% at 800 cGy. The value of Ydr is 0.000 at 50 cGy, 0.093 at 100 cGy, 0.200 at 200 cGy, 0.354 at 300 cGy, 0.612 at 400 cGy, 2.040 at 600 cGy, and 2.846 at 800 cGy. The relationship between radiation (D) and the frequency of dicentrlc chromosomes and ring Chromosomes (Ydr) can be expressed as Ydr=0.188${\times}$10$^{-2}$ D/Gy+0.422${\times}$10$^{-4}$/Gy$^{2}$${\times}$D$^{2}$ The Value of Qdr is 0.000 at 50 cGy, 1.000 at 100 cGy, 1.000 at 200 cGy, 1.333 at 300 cGy, 1.118 at 400 cGy, 2.318 at 600 cGy, and 2.846 at 800 cGy. When 400 cGy is irradiated with different dose rate each of 20, 40, 60, 80, 100, 160, 240, 320, and 400 cGy/min, Ydr is each of 0.982, 0.837, 0.860, 0.732, 0.763, 0.966, 0.909, 1.006, and 0.806, and Qdr is each of 1.839, 1.555, 1.654, 1.333, 1.381, 1.750, 1.6000, 1.710, and 1.318. Conclusion : There are not the significant variations of Ydr and Qdr values according to different dose rate. And so radiation damage is influenced by total exposed radiation doses and is influenced least of all by different dose rate when it is acute single exposure.