• Korean Journal of Crystallography
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• v.15 no.2
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• pp.59-68
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• 2004

Three crystal structures of dehydrated partially $Co^{2+}-exchanged$ zeolite A treated with 0.6 Torr of K at $300^{\circ}C$ (for 12 hrs, 6 hrs, and 2 hrs) vapor have been determined by single-crystal X-ray diffraction techniques in the cubic space group Pm3m at 21(1)$^{\circ}C(a=12.181(1)\;{\AA},\;a=12.184(1)\;{\AA},\;and\;a=12.215(1)\;{\AA})\;respectively)$. Their structures were refined to the final error indices, R(weight) of 0.090 with 10 reflections, 0.091 with 82 reflections, and 0.090 with 80 reflections, respectively, for which $1>\sigma(I)$. In each structure, all four $Co^{2+}$ and four $Na^+$ ions to be reduced by K atoms. The cobalt and sodium atoms produced are no longer found in the zeolite. K species are found at five different crystallographic sites: three $K^+$ ions lie at the planes of 8-rings, filling that position, ca. 11.5 K^+$ ions lie on threefold axes, ca. 4.0 in the large cavity and ca. 4.0 in the sodalite cavity, and ca. 0.5 $K^+$ ion is found near a 4-ring. ca. three $K^0$ atoms are found deep into the large cavity on threefold axes. In these structures, crystallographic results show that cationic tetrahedral $K_4$ (and/or triangular $K_3$) clusters have formed in the sodalites of zeolite A. The $K_4$ and/or $K_3$ clusters coordinate trigonally to three oxygens of a six-oxygen ring. The partially reduced ions of these clusters interact primarily with oxygen atoms of the zeolite structure rather than with each other. ca. 14.5K species are found per unit cell, more than the twelve $K^+$ ions needed to balance the anionic charge of zeolite framework, indicating that sorption of $K^0$ has occurred. The three $K^0$ atoms in the large cavity are closely associated with three out of four $K^+$ ions in the large cavity to form $K_7^{4+}$ clusters. The $K_7^{4+}$ cluster not interacts primarily with framework oxygens.

• Journal of the Korea Concrete Institute
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• v.14 no.4
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• pp.457-466
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• 2002

An artificial neural network was applied to predict compressive strength, slump value and mix proportion of a concrete. Standard mixed tables were trained and estimated, and the results were compared with those of the experiments. To consider variabilities of material properties, the standard mixed fables from two companies of Ready Mixed Concrete were used. And they were trained with the neural network. In this paper, standard back propagation network was used. The mix proportion factors such as water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate and air entraining admixture were used. For the arrangement on the approval of prediction of mix proportion factor, the standard compressive strength of $180kgf/cm^2{\sim}300kgf/cm^2$, and target slump value of 8 cm, 15 cm were used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of $210kgf/cm^2{\sim}240kgf/cm^2$, and target slump value of 12 cm and 15 cm wore used because these ranges are most frequently used. In results, in the prediction of mix proportion factor, for all of the water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate, air entraining admixture, the predicted values and the values of standard mixed tables were almost the same within the target error of 0.10 and 0.05, regardless of two companies. And in the prediction of compressive strength and slump value, the predicted values were converged well to the values of standard mixed fables within the target error of 0.10, 0.05, 0.001. Finally artificial neural network is successfully applied to the prediction of concrete mixture and compressive strength.

• Journal of the Korean Chemical Society
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• v.40 no.7
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• pp.474-482
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• 1996

Two crystal structures of the vacuum dehydrated $Ag^+$-exchanged zeolite X have been determined by single-crystal X-ray diffraction techniques in the cubic space group Fd3 at 21(1)$^{\circ}C$ (a=24.922(1)${\AA}$ and a=24.901(1)${\AA}$, respectively). Each crystal was ion exchanged in flowing streams of aqueous $AgNO_3$ for three days. The first crystal was dehydrated at 300$^{\circ}C$ and $2{\times}10^{-6$torr for two days. The second crystal was similarly dehydrated at 350$^{\circ}C$. Their structures were refined to the final error indices, $R_1=0.095\;and\;R_2=0.092$ with 227 reflections, and $R_1=0.096\;and\;R_2=0.087$ with 334 reflections, respectively, for which I > 3${\sigma}$(I). In the first crystal, Ag species are found at five different crystallographic sites: sixteen $Ag^+$ ions fill the site I, the center of the double 6-ring, thirty-two Ag0 atoms fill the I' site in the sodalite cavities opposite double six-rings, seventeen $Ag^+$ ions lie at the 32-fold site II' inside the sodalite cavity at the single six-oxygen ring in the supercage, fifteen Ag+ ions lie at the 32-fold site II, in the supercage, and the remaining twelve $Ag^+$ ions lie at site III' in the supercage at a little off two-fold axes. In the second crystal, all Ag species are located similarly as crystal 1; 16 at site I, 28 at site I', 16 at site II, 16 at site II', 6 at site III and 6 at site III'. Total 88 silver species were found per unit cell. The remaining four Ag atoms were migrated out of the zeolite framework to form small silver crystallites on the surface of the zeolite single crystal. In the first structure, the numbers of Ag atoms per unit cell are approximately 32.0 and these may form tetrahedral $Ag_4$ clusters at the centers of the sodalite cavities. The probable four-atom cluster is stabilized by coordination to two $Ag^+$ ions. The Ag-Ag distance in the cluster, ca. 3.05 ${\AA}$, is a little longer than 2.89 ${\AA}$, Ag-Ag distance in silver metal. At least two six-ring $Ag^+$ ions on sodalite cavity (site II') must necessarily approach this cluster and this cluster may be viewed as a distorted octahedral silver cluster, (Ag6)2+.

• Korean Journal of Agricultural Science
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• v.21 no.1
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• pp.45-53
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• 1994

This study was conducted to estimate the genetic and environmental effects on lactation yield and milk compositions in Holstein cows. The data analysis were the records of 159 cows rearing at Nation Animal Breeding Institute from 1990 to 1991. The least square means were estimated on milk and fat yield in lactation yield, and the percent of fat, protein, solids-not-fat and total solid in milk composition. The results obtained are summarized as follows: 1. The average yield of milk and fat in 305 days were $7,26.56{\pm}3,57.24$ and $254.65{\pm}44.94Kg$ and the percent of fat, protein, solids-not-fat and total solid were $3.69{\pm}0.43$, $3.32{\pm}0.41$, $9.15{\pm}0.49$ and $12.75{\pm}0.96$ and the coefficients of variation were 18.68, 17.64, 11.88 and 12.34% for milk yield, fat yield, fat percent and protein percent, respectively. 2. The effect of sires was highly significant at 1% level in milk and fat yield and fat percent, and significant at 5% level in protein and total solid percent. Among the sires, B, L and O sire were superior in milk yield with 7,571.22, 7,499.11 and 7,420.58 Kg, and A, F and K sire were superior in protein percent with 3.75, 3.64 and 3.65, respectively. 3. The effect of parity was highly significant at 1% level in milk yield, and significant at 5% level in fat yield. Among the parities, the 3rd parity was superior in milk and fat yield with 7,634.54 and 274.98 Kg, and the 4th and over was superior in fat and protein percent with 3.90 and 3.50, respectively. 4. The effect of calving seasons was highly significant at 1% level in milk yield, and significant at 5% level in fat yield, and the percent of fat, protein and total solid. Among the calving seasons, spring and winter were superior in milk yield with 7,310.31 and 7,364.57 Kg, also spring and winter were superior in protein percent with 3.68 and 3.52, respectively.

• Korean Journal of Crystallography
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• v.6 no.2
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• pp.125-133
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• 1995

The crystal structure of dehydrated, partially Co(II)-exchanged zeolite X, stoichiometry Co2+Na+-X (Co41+Na10Si100Al92O384) per unit cell, has been determined from three-dimensional X-ray diffraction data gathered by counter methods. The structure was solved and refined in the cubic space group Fd3:α=24.544(1)Å at 21(1)℃. The crystal was prepared by ion exchange in a flowing stream using a solution 0.025 M each in Co(NO3)2 and Co(O2CCH3)2. The crystal was then dehydrated at 380℃ and 2×10-6 Torr for two days. The structure was refined to the final error indices, R1=0.059 and R2=0.046 with 211 reflections for which I > 3σ(I). Co2+ ions and Na+ ions are located at the four different crystallographic sites. Co2+ ions are located at two different sites of high occupancies. Sixteen Co2+ ions are located at the center of the double six-ring (site I; Co-O = 2.21(1)Å, O-Co-O = 90.0(4)°) and twenty-five Co2+ ions are located at site II in the supercage. Twenty-five Co2+ ions are recessed 0.09Å into the supercage from its three oxygen plane (Co-O = 2.05(1)Å, O-Co-O = 119.8(7)°). Na+ ions are located at two different sites of occupandies. Seven Na+ ions are located at site II in the supercage (Na-O = 2.29(1)Å, O-Na-O = 102(1)°). Three Na+ ions are statistically distribyted over site III, a 48-fold equipoint in the supercages on twofold axes (Na-O = 2.59(10)Å, O-Na-O = 69.0(3)°). Seven Na+ ions are recessed 1.02Å into the supercage from the three oxygen plane. It appears that Co2+ ions prefer sites I and II in order, and that Na+ ions occupy the remaining sites, II and III.

• Korean Journal of Crystallography
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• v.7 no.1
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• pp.8-19
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• 1996

Cholestryl Methyl and Propyl Carbonate(CH3OCOOC27H45, C3H7OCOOC27H45) are monoclinic, space group P21, with a=17.014(1), b=7.682(1), c=10.612(1)Å, β=103.05(1)°, Z=2, V=1351.16Å3, Dc=1.09 g/cm3 for methyl carbonate, and with a=13.683(1), b=11.864(2), c=18.904(2)Å, β=106.30(1)°, Z=4, V=2945.4Å3, Dc=1.06 g/cm3, Dm=1.06 g/cm3 for propyl carbonate. The intensity data were collected on an Enraf-Nonius CAD-4 diffractometer with a graphite monochromated Cu-Kα radiation. The structure was solved by direct methods and refined by full matrix least-squares methods. The final R factor was 0.051 for 2323 observed reflections for methyl carbonate and 0.074 for 3323 observed reflections for propyl carbonate. Compared with other cholesteryl derivatives, the cholesteryl ring and tail region of the molecules are normal. The molecules are stacked in clearly separated layers. At center of the layer, there are cholesteryl-C(17) side chain interactions. The interface region between layers is occupied by the loosely packed methyl carbonate chains. The structure of cholesteryl propyl carbonates have two propyl carbonates have two molecules(A, B) that are not related by crystal symmetry and have their tetracyclic system almost parallel to each other. Cholesteryl-cholesteryl interactions between symmetry related A-molecules, and cholesteryl-C(17) side chain interactions between symmetry related B-molecules occur at the center of the layers and these molecules stack along 2₁ screw axes. There are also C(17)chain-carbonate chain and C(17)chain-C(17)chain interactions in the interface region between layers. There is efficient packing between cholesteryl ring systems in propyl carbonates. Temperature ranges of cholesteric mesophases of cholesteryl alkyl cargonates are narrow for methyl, pentyl and hexyl carbonates, and rather broader for ethyl and propyl carbonates. Cholesteryl-isotropic transitions change very little with chain length.

• Tuberculosis and Respiratory Diseases
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• v.42 no.6
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• pp.913-922
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• 1995

Background: When we define the pressure of pulmonary vasculature in which a recruitment of blood flow occurs as $P_I$ and the proportion of change in pulmonary artery to that in cardiac output as IR and then we compare PI and IR with pulmonary vascular resistance, we would find some problems in pulmonary vascular resistance. In other words, it is the theory that, IR should be increased mainly in pulmonary embolism in which decreases the cross sectional area of pulmonary vasculature. But there are many contradictory reports resulted from various researches and the fact is known widely that any difference exists between PVR and PI, IR. For this reason, the purpose of this study is to observe how PI and IR change at the time of the outbreak and during treatment of the pulmonary embolism, and to find out the meaning of these new indicators and the difference from the pulmonary vascular resistance used generally when we subdivide the pulmonary vascular resistance into PI and IR. Method: After making AV fistula in experimental dog, we controlled cardiac output at the intervals of 15 minute in case of three kinds(all AV fistula are obstructed, only one of fistula is open and all of fistula is open), and after evoking massive pulmonary embolism with radioactive autologous blood clots, we measured the mean pulmonary artery pressure, and calculated PI and IR. We observed the pattern of change in PI and IR, without giving the control group any specific treatment and with injecting intravenously rtPA in the Group 1 and Group 2 at the dose of 1mg per kg, for 15 minutes fot the former and 3 hours for the latter. Result: 1) Pulmonary vascular resistance showed a change similar to that of pulmonary artery pressure and in all three group, PVR increased significantly, but group 1 and group 2 showed tendency that PVR keeps on decreasing after treatment, and the rate of decrease in group 1 is more rapid than group 2 significantly. 2) Both intersection(PI) and degree(IR) are proved statistically significant, in view of the straight line relationship between cardiac output and pulmonary artery pressure, calculated by minimal regression method. 3) PI changed similarly to pulmonary vascular resistance, while in the IR which is theoretically more similar to PVR, there was no significant difference or change after rtPA infusion. Conclusion: In the pulmonary embolism, Both change in IR which means real resistance of pulmonary vasculature and PI which was developed due to secondary vasoconstriction by pulmonary embolism are reflected same time.

• Journal of the Korean Chemical Society
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• v.37 no.3
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• pp.344-350
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• 1993

25,26,27,28-Tetraacetoxycalix[4]arene·monohydrate is orthorhombic, space group Pbca with a = 14.979(4), b = 15.154(4), c = 27.890(3) ${\AA}$, Z = 8, V = 6330.6 ${\AA}^{-3}$, D$_c$ = 1.28 $g{\cdot}cm^{-3}$, (Mo K${\alpha}$) = 0.71069 ${\AA}$, ${\mu}$ = 0.86 cm$^{-1}$, F(000) = 2600, and R = 0.069 for 3376 unique observed reflections with I > 1.0 ${\sigma}$(I). The structure was solved by direct methods and refined by cascade diagonal least-squares refinement. All the C-H bond lengths(= 0.96 ${\AA}$), the methyl groups and the methylene groups are fixed and refined as the rigid groups with ideal geometry. The macrocycle exists in the 1,3 alternate conformation (by Conforth) making the angles of 110.7, 684, 113.7 and 68.8$^{\circ}$ between the benzene rings and the methylenic mean plane, and four each acetoxy groups are twisted away from their own benzene rings with the angles of 68.2, 97.6, 78.9 and 71.3$^{\circ}$, respectively. The relative dihedral angles between two opposite side of the benzene rings are 135.6$^{\circ}$ for the rings (1) and (3) and 135.2$^{\circ}$ for (2) and (4). A water molecule which has nearly the same height of the methylenic plane of the macrocycle in the c-axis, is located within the distances of 2.942(5) ${\AA}$ from the O(8) atom of the carbonyl group and 2.901 ${\AA}$ from, another O(2)(1/2-x, -1/2＋y, z). The shortest contact between the molecule is 3.193 ${\AA}$ from the O(4) to the C(3)(1/2＋x, 1/2-y,-z).

• Journal of the Mineralogical Society of Korea
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• v.6 no.1
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• pp.1-16
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• 1993

The chemical and optical absorption spectroscopic characters of pink and colorless tourmalines from San Diego mine in California, U.S.A., blue/green tourmalines from anonymous mine, Brazil, and brownis black tourmalines from Uncheon and Haksan mines in Korea have been studied using X-ray diffractometer, electron microprobe, optical absorption spectroscopy, and heat treatment. Least-squares refinements give unit cell diminsions : a = 15.96-16.01 ${\AA}$, c = 7.15-7.16 ${\AA}$ for the brownish black tourmalines, a = 15.82 - 15.87 ${\AA}$, c = 7.09 - 7.10 ${\AA}$ for pink tourmalines, and a = 15.88 - 15.94 ${\AA}$, c = 7.12 - 7.15 ${\AA}$ for blue green tourmalines. The colors of tourmalines are responsible for the transition elements. The pink color is attributed to the $Mn^{3+}$ ions, the blue-green to $Fe^{2+}$ and $Mn^{2+}$, bluish green to $Cu^{2+}$, and the brownish black to $Fe^{2+}$, $Fe^{2+}$ - $Fe^{3+}$, and $Fe^{2+}$ - $Ti^{4+}$. The $Mn^{3+}$ ions of pink color tourmalines are stabilized in the Y sites compressed along the O(1)H-O(3)H axis by Jahn-Teller distortion. Heating removes the pink or red component from tourmalines, producing the colorless stones from the pink and red ones. The bluish green samples change into the greenish blue ones and a certain yellowish green samples change into the light green ones by heat treatment. In the elbaite-schorl series, the concentration of Fe and Mn are variable depending on the color zones. The green zone is characterrized by the high content of Fe and Mn are variable depending on the color zones. The green zone is characterized by the high content of Fe, whereas the pink zone by the high content of Mn. Mn increases in deep yellow zone compared with yellow or colorless zones.

• KDI Journal of Economic Policy
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• v.12 no.2
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• pp.51-79
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• 1990

Research on technical efficiency, an important dimension of market performance, had received little attention until recently by most industrial organization empiricists, the reason being that traditional microeconomic theory simply assumed away any form of inefficiency in production. Recently, however, an increasing number of research efforts have been conducted to answer questions such as: To what extent do technical ineffciencies exist in the production activities of firms and plants? What are the factors accounting for the level of inefficiency found and those explaining the interindustry difference in technical inefficiency? Are there any significant international differences in the levels of technical efficiency and, if so, how can we reconcile these results with the observed pattern of international trade, etc? As the first in a series of studies on the technical efficiency of Korea's manufacturing industries, this paper attempts to answer some of these questions. Since the estimation of technical efficiency requires the use of plant-level data for each of the five-digit KSIC industries available from the Census of Manufactures, one may consture the findings of this paper as empirical evidence of technical efficiency in Korea's manufacturing industries at the most disaggregated level. We start by clarifying the relationship among the various concepts of efficiency-allocative effciency, factor-price efficiency, technical efficiency, Leibenstein's X-efficiency, and scale efficiency. It then becomes clear that unless certain ceteris paribus assumptions are satisfied, our estimates of technical inefficiency are in fact related to factor price inefficiency as well. The empirical model employed is, what is called, a stochastic frontier production function which divides the stochastic term into two different components-one with a symmetric distribution for pure white noise and the other for technical inefficiency with an asymmetric distribution. A translog production function is assumed for the functional relationship between inputs and output, and was estimated by the corrected ordinary least squares method. The second and third sample moments of the regression residuals are then used to yield estimates of four different types of measures for technical (in) efficiency. The entire range of manufacturing industries can be divided into two groups, depending on whether or not the distribution of estimated regression residuals allows a successful estimation of technical efficiency. The regression equation employing value added as the dependent variable gives a greater number of "successful" industries than the one using gross output. The correlation among estimates of the different measures of efficiency appears to be high, while the estimates of efficiency based on different regression equations seem almost uncorrelated. Thus, in the subsequent analysis of the determinants of interindustry variations in technical efficiency, the choice of the regression equation in the previous stage will affect the outcome significantly.