• The Journal of Engineering Geology
• /
• v.15 no.3
• /
• pp.337-348
• /
• 2005

Jurassic granite from Pocheon area were tested to investigate the effect of microcracks on mechanical properties of the granite. Three oriented core specimens were used for uniaxial compressive tests and each core specimen are perpendicular to the axes'R'(rift plane),'c'(grain plane) and'H'(hardway plane), respectively Among vacious elastic constants, the variation of Poisson's ratio as function of the directions was examined. From the related chart between ratio of failure strength and Poisson's ratio, H-specimen shows the highest range in Poisson's ratio and Poisson's ratio decreases in the order of C-specimen and R-specimen. The curve pattern is nearly linear in stage $I\simIII$ but the slope increases abruptly in stage H-3. As shown in the related chart, diverging point of a curve is formed when ratio of failure strength is $0.92\sim0.96$ Stage IV -3 is out of elastic region. The behaviour of rock in the four fracturing stages was analyzed in term of the stress-volumetric strain me. From the stress increment-volumetric strain equations governing the behaviour of rock, characteristic material constants, a, n, Q, m and $\varepsilon_v^{mcf}$, were determined. Among these, inherent microcrack porosity$(a, 10^{-3})$ and compaction exponent(n) in the microcrack closure region(stage I ) show an order of $a^R(3.82)>a^G(3.38)>a^H(2.32)\;and\;n^R(3.69)>n^G(2.79)>n^H(1.99)4, respectively. Especially, critical volumetric microcrack strain($\varepsilon_v^{mcf}$) in the stage W is highest in the H-specimen, normal to the hardway plane. These results indicate a strong correlation between two major sets of microcracks and mechanical properties such as Poisson's ratio and material constants. Correlation of strength anisotropy with microcrack orientation can have important application in rock fracture studies.

• Journal of Korean Society of Forest Science
• /
• v.70 no.1
• /
• pp.103-108
• /
• 1985

This paper is concerned with estimating equations of leaf area(A) obtained from linear measurements - leaf length(L) and leaf width(W) - on the leaves of 13 species composing a natural mixed stand in Mt. Jiri. This method is known to be rapid and non-destructive in estimating leaf area. The equation of A=bLW is frequently used in rough and rapid estimation. Each species in this study has its own coefficient b according to its geometrical leaf shape. The range of coefficients of 13 species was 0.579 to 0.717. This means that the relationship A=2/3LW is suitable to most broad leaf species in a natural mixed stand in Mt. Jiri. When more precise estimation of leaf area is needed, full regression equation is used. In this study, the form of ${\log}A=b_0+b_1{\log}LW$ was the most precise estimation equation in 8 species. In addition to this, the form of $A=b_0+b_1LW$ and $A=b_0+b_1L^2+b_2W^2$ were founded to be suitable for estimation of leaf area. In comparision of these two forms, the determination coefficient were about the same, but the F-value of the former was greater than that of the latter. Therefore, the use of the former seems to be more reliable and practical.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.2 no.4
• /
• pp.71-102
• /
• 2007

There were many comprehensive analyses conducted within the existing research activities wherein factors affecting technology progress including investment in R&D vis-${\Box}$-vis their influences act as the determinants of TFP. Note, however, that there were few comprehensive analysis in the industrial research performed regarding the impact of the economy of scale as it affects TFP; most of these research studies dealt with the analysis of the non -parametric Malmquist productivity index or used the stochastic frontier production function models. No comprehensive analysis on the impacts of individual independent variables affecting TFP was performed. Therefore, this study obtained the TFP increase rate of each industry by analyzing the factors of the existing growth accounting equation and comprehensively analyzed the TFP determinants by constructing a comprehensive analysis model considering the investment in R&D and economy of scale (smallness by industry) as the influencers of TFP by industry. First, for the TFP increase rate of the 15 industries as a whole, the annual average increase rate for 1993${\sim}$ 1997 was approximately 3.8% only; during 1999${\sim}$ 2000 following the foreign exchange crisis, however, the annual increase rate rose to approximately 7.8%. By industry, the annual average increase rate of TFP between 1993 and 2000 stood at 11.6%, the highest in the electrical and electronic equipment manufacturing business and IT manufacturing sector. In contrast, a -0.4% increase rate was recorded in the furniture and other product manufacturing sectors. In the case of the service industry, the TFP increase rate was 7.3% in the transportation, warehousing, and communication sectors. This is much higher than the 2.9% posted in the electricity, water, and gas sectors and -3.7% recorded in the wholesale, food, and hotel businesses. The results of the comprehensive analysis conducted on the determinants of TFP showed that the correlations between R&D and TFP in general were positive (+) correlations whose significance has yet to be validated; in the model where the self-employed and unpaid family workers were used as proxy variables indicating the smallness of industry out of the total number of workers, however, significant negative (-) correlations were noted. On the other hand, the estimation factors of variables surrogating the smallness of scale in each industry showed that a consistently high "smallness of scale" in an industry means a decrease in the increase rate of TFP in the same industry.