• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.201-210
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• 2010

In this study, new methodology to estimate the reliability of a water distribution system using HSPDA model is suggested. In general, the reliability of a water distribution system can be determined by estimating either the ratio of the required demand to the available demand or the ratio of the number of nodes with sufficient pressure head to the number of nodes with insufficient pressure head when the abnormal operating condition occurs. To perform this approach, hydraulic analysis under the abnormal operating condition is essential. However, if the Demand-Driven Analysis (DDA) which is dependant on the assumption that the required demand at a demand node is always satisfied regardless of actual nodal pressure head is used to estimate the reliability of a water distribution system, the reliability may be underestimated due to the defect of the DDA. Therefore, it is necessary to apply the Pressure-Driven Analysis (PDA) having a different assumption to the DDA's which is that available nodal demand is proportion to nodal pressure head. However, because previous study used a semi-PDA model and the PDA model which had limited applicability depending on the characteristics of a network, proper estimation of the reliability of a water distribution system was impossible. Thus, in this study, a new methodology is suggested by using HSPDA model which can overcome weak points of existing PDA model and Available Demand Fraction (ADF) index to estimate the reliability. The HSPDA can simulate the hydraulic condition of a water distribution system under abnormal operating condition and based on the hydraulic condition simulated, ADF index at each node is calculated to quantify the reliability of a water distribution system. The suggested model is applied to sample networks and the results are compared with those of existing method to demonstrate its applicability.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.499-506
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• 2017

Consideration of influential factors through division of capital market sector and interest rate sector to find and resolve the problems in current housing market and leasing market will become an important index to prepare measures for stabilization of housing sales market and housing lease market. Furthermore, a guideline will be provide you with preliminary data using Big Data to prepare for sudden price fluctuation because expected economic crisis, stock market situation, and uncertain future financial crisis can be predicted which may help anticipate real estate price index such as housing sales price index and housing lease price index.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.9
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• pp.1303-1308
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• 2003

Samples of eleven brown midrib (ICSU 96031, ICSU 93046, ICSU 96082, ICSU 96078, ICSU 96075, ICSU 95101, ICSU 96034, ICSU 96063, ICSU 45116, ICSA 93-3 and ICSA 3845 X 3816) and nine white midrib genotypes (ICSU 96050, ICSU 96030, ISU 95082, SSG 59-3, FSHI 93-1, FSHI 2219A X 3211, HC 171, ICSA 93-2 and ICSA 93-1) based on their phenotypic appearance were collected at 50 per cent flowering from the sorghum germplasm grown at Research farm of IGFRI, Jhansi. These genotypes were evaluated with respect to crude protein, fiber composition, in-sacco dry matter, OM, cell wall components disappearance/digestibility besides the fodder yield, total phenolic and availability index values. Brown midrib genotypes were lower (p<0.05) in NDF, ADF, cellulose and acid detergent lignin concentration than white midrib genotypes. Mean NDF, ADF, cellulose and lignin contents were 69.4, 42.1, 35.4 and 5.7% in brown mid rib vis-a vis 75.8, 47.5, 39.6 and 7.3% in white mid rib genotypes. Nonsignificant (p>0.05) differences were observed in dry matter, crude protein and organic matter contents between brown midrib and white midrib genotypes. Phenolic contents were significantly (p<0.05) lower in browm mid rib (0.2) than white mid rib (0.3%) sorghum. Brown midrib genotypes exhibited significantly (p<0.05) higher in-sacco DM, OM and CP disappearance than normal (white midrib) genotypes. The mean degradability of DM, OM and CP was 64.1, 62.6 and 79.6% in brown mid rib and 53.1, 54.0 and 76.6% in white mid rib genotypes, respectively. There were no significant (p>0.05) differences between genotypes in extent of fiber fraction degradability though in-sacco NDF and ADF degradability was more by 5 and 4 units, respectively in brown midrib genotypes vis-a-vis white midrib genotypes. Average fodder yield (green and dry g/plant) and availability index (%) values were significantly (p<0.05) higher for brown midrib (474.2, 129.8 and 80.4) genotypes than white midrib (375.0, 104.8 and 69.2) genotypes. Lignin contents had significant negative correlation with DM, OM, NDF and ADF degradability. The results of the study revealed that brown midrib genotypes are superior not only with regard to chemical entities and disappearance of DM and fiber fractions but also better in respect of fodder yield and availability index values. Thus, brown midrib sorghum strains may be useful in increasing digestibility, intake, feed efficiency and animal performance.

• Journal of The Korean Society of Grassland and Forage Science
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• v.29 no.1
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• pp.31-36
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• 2009

This study was conducted to find out an alternative way of rapid and accurate analysis of forage quality. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the possibility of forage analysis and collect 258 samples such as barley for whole crop silage, forage corn and sudangrass from 2002 to 2007. The samples were analyzed for CP (crude protein), CF (crude fiber), ADF (acid detergent fiber), NDF (neutral detergent fiber) and IVTD (in vitro true digestibility), and also scanned using NIRSystem with wavelength from $400{\sim}2,400nm$. Multiple linear regression was used with wet analysis data for developing the calibration model and validate unknown samples. The important index In this experiment was SEC and SEP $r^2$ for CF, CP, NDF, ADF and IVTD in calibration set were 0.70, 0.86, 0.94, 0.94 and 0.89, also 0.47, 0.39, 0.89, 0.90 and 0.61 in validation sample, respectively. The results of this experiment indicates that NIRS was reliable analytical method to assess forage quality, specially in CF, ADF and IVTD, sample should be included for respective forage samples to get accurate result. More robust calibrations can be made to cover every forage samples if added representative sample set.

• Journal of The Korean Society of Grassland and Forage Science
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• v.31 no.4
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• pp.409-414
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• 2011

This study was conducted to find out an alternative way of rapid and accurate analysis of forage quality. Near Infrared Reflectance Spectroscopy (NIRS) was used to evaluate the possibility of forage analysis. 175 samples consisted of Italian ryegrass, whole crop barley and pea seeded spring in 2009 were collected. The samples were analyzed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF), and neutral detergent fiber (NDF), and also scanned using NIRSystem with wavelength from 400~2,500 nm. Multiple linear regression was used with wet analysis data for developing the calibration model and validated unknown samples. The important index in this experiment were SEC, SEP. The r2 value for moisture, CP, CA, ADF, and NDF in calibration set was 0.65, 0.97, 0.93, 0.99, and 0.97 and also was 0.15, 0.94, 0.96, 0.98 and 0.98 in validation set, respectively. The results of this experiment indicates that NIRS was reliable analytical method to assess forage quality for CP, CA ADF and NDF except moisture content in forage when proper samples incorporated into the equation development.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4B
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• pp.355-362
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• 2006

Several techniques of MK test, Spearman's Rho test, Linear Regression test, CUSUM test, Cumulative Deviation, Worsley Likelihood Ratio test, Rank Sum test, and Students' t test were applied to detect the trends of slope and shift which exist in hydrologic and climate time series. The time series of annual rainfall, inflow, tree ring index, and southern oscillation index (SOI) were used and the trends of these series were compared in the study. From the results, it can be found that the data could be classified into two categories such as linear trend and shift. 4 series data of 8 rainfall series which reveal the trend show the shift and 8 series data of 18 tree ring index and March and April series of monthly SOI data show shift. Moreover, ADF test and BDS test were used to test stationarity and non-linearity of the data. In conclusion, through the study, various trend analysis techniques were compared and 6 kinds of characteristics which can exist in hydrologic time series were identified.

• Journal of Distribution Research
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• v.17 no.2
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• pp.93-115
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• 2012

This study empirically examines the impact of changes in market shares among retailing types on the price index. The retailing type is classified into 6 groups: department store, big mart, super market, convenient store, specialty merchant, and on-line store. The market shares of retailing types are calculated by the ratio of each retailing type monthly sales to total monthly retailing sales in which total retailing sales is the sum of each retailing type sales. We employed several price indices: consumer price index (CPI), CPI for living necessaries, and fresh food price index. In addition, this study used fundamental price indices based on 25 product families as well as 42 representative products. The empirical model also included several variables in order to control for the macroeconomic effects and those variables are the exchange rate, M1, an oil price, and the industrial production index. The data is monthly time-series data spanning over the period from January 2000 to December 2010. In order to test for the stability of data series, we conducted ADF test and PP test in which the model and length of lag were determined by the relevant previous literature and based on the AIC. The empirical results indicate that changes in market shares among retailing types have impacts on the price index. Table A shows that impacts differ as to which price index to use and which product families and products to use. For department store, it lowers the price of food and non-alcoholic beverages, home appliances, fresh food, fresh and vegetables, but it keeps the price high for fresh fruit. The big mart retailing type has a positive impact on the price of food, nut has a negative effect on clothing and foot wear, non-food, and fresh fruit. For super market, it has a positive impact on food and non-alcoholic beverages, fresh food, fresh shellfishes, but increases the price of CPI for living necessaries and non-food. The specialty merchant retailing type increases the price level of CPI for living necessaries and fresh fruit. For on-line store type, it keeps the price high for CPI for living necessaries and non-food as well as fresh fruit. For the analysis based on 25 product families shows that changes in market shares among retailing types also have different effects on the price index. Table B summarizes the different results. The 42 representative product level analysis is summerized in Table C and it indicates that changes in market shares among retailing types have different effects on the price index. The study offers the theoretical and practical implication to these findings and also suggests the direction for the further analysis.

• Journal of Distribution Science
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• v.11 no.10
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• pp.63-72
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• 2013

Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.165-170
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• 2021

This paper examines the long-run relationship between financial development and economic growth. The effective function of financial development is crucial to promote the economic development of the country. To achieve the objective, this study used Gross Domestic Product as a dependent variable and Credit to The Private Sector, Ratio of the Gross Fixed Capital Formation to GDP, Trade, Consumer Price Index and Labour Force as an independent variable. Augmented Dickey-Fuller test statistic (ADF) to check the stationary. Bounds test for cointegration and Auto-Regressive Distributed Lag Models (ARDL) are used to check cointegrating relationship amongst the variables and causality between financial development and economic growth. Moreover, the Model selection method is Akaike Info Criterion (AIC). This result demonstrates that the labor force and trade hold a significantly negative relationship with economic growth. Nevertheless, inflation, Credit to The Private Sector, and Ratio of the Gross Fixed Capital Formation to GDP show a significantly positive relationship with economic growth. Therefore, there is a statistically significant relationship between Financial Development and Economic growth in Sri Lanka and the Sri Lankan government should reform its trade policies.

• Journal of The Korean Society of Grassland and Forage Science
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• v.14 no.2
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• pp.120-124
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• 1994

Browse from oriental white oak(Quercus aliena Blume) was analysed for nutritive value and biomass estimation. The content of cmde protein was high on August and September. NDF, ADF and lignin contents were increased with progressing the seasons but IVDMD, TDN, DE and ME levels were not different from the variation of seasons. Tannin content was high on May and June. Basal stem diameter $\times$ height of tress was highly correlated with browse dry weights(r=0.80**) and the linear regression equation are the follows; Y = 8.23 + O.57X(Y = browse dry weight(g), X= basal stem diameter $\times$ height(cm)). Using the traditional cutting method, the amounts of browse was determined as 823 g dry weight per tree, while the new method by the above equation gave a similar results, i. e., 793 g dry weight per tree. The possibility of browse biomass estimation of oriental white oak by the basal stem diameter $\times$ height index was found.