Recently, in the asset management industry, the smart beta strategy, which has an intermediate nature between passive and active strategies, is attracting attention. In this smart beta strategy, value, momentum, low volatility, and quality index are widely used. In this study, we analyzed the quality index which is not clear and complicated to calculate. According to the MSCI methodology, the quality index was calculated using three variables: return on equity, debt to equity, and earnings variability. In addition, we use the index using only return on equity variable, the index using only two variables of return on equity and debt to equity, and the KOSPI index as comparison targets for the quality index. In order to evaluate the performance of the indices used in the analysis, the arithmetic mean return, the coefficient of variation, and the geometric mean return were used. In addition, Fama and French (1993) model, which is widely used in related studies, was used as a pricing model to test whether abnormal returns in each index are occurring. The results of the empirical analysis are as follows. First, in all period analysis, quality index was the best in terms of holding period returns. Second, the quality index performed best in the currency crisis and the global financial crisis. Third, abnormal returns were not found in all indices before the global financial crisis. Fourth, in the period after the global financial crisis, the quality index has the highest abnormal return.
Park, Jin-Hyeog;Lee, Geun-Sang;Yang, Jeong-Seok;Kim, Sea-Won
Spatial Information Research
/
v.20
no.3
/
pp.39-50
/
2012
The objective of this study is the quantitative analysis of climate change effects by performing several statistical analyses with hydrometeorological data sets for past 30 years in Geum river watershed. Temperature, precipitation, relative humidity data sets were collected from eight observation stations for 37 years(1973~2009) in Geum river watershed. River level data was collected from Gongju and Gyuam gauge stations for 36 years(1973~2008) considering rating curve credibility problems and future long-term runoff modeling. Annual and seasonal year-to-year variation of hydrometeorological components were analyzed by calculating the average, standard deviation, skewness, and coefficient of variation. The results show precipitation has the strongest variability. Run test, Turning point test, and Anderson Exact test were performed to check if there is randomness in the data sets. Temperature and precipitation data have randomness and relative humidity and river level data have regularity. Groundwater level data has both aspects(randomness and regularity). Linear regression and Mann-Kendal test were performed for trend test. Temperature is increasing yearly and seasonally and precipitation is increasing in summer. Relative humidity is obviously decreasing. The results of this study can be used for the evaluation of the effects of climate change on water resources and the establishment of future water resources management technique development plan.
Lee, Jae Sun;Park, Young Uk;Jeong, Jae Hyun;Kwon, Young Hee;Chang, Who Bong;Lee, Hee Du
Korean Journal of Plant Resources
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v.34
no.1
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pp.79-88
/
2021
This research was conducted to investigate the genetic diversity and select useful accession with agronomic characteristics of garlic (Allium sativum L.). germplasms at Garlic Research Institute in Chungbuk Agricultural Research and Extension Service. Morphological diversity and relationships among 160 germplasms collected from 26 countries were assessed by methods of clustering and principal component analysis. Among 11 types of leaves and bulbs characteristics, emergence days of leaf showed the highest variation with coefficient of variation of 84.8%, and the bulb weight and the number of scales showed higher variability with 24.3%. Correlation analysis based on 11 quantitative traits showed that bulb weight and bulb length have very high positive correlation with bulb quantity. Plant height, leaf length, and number of leaves showed positive correlation with bulb weight as collections with better performance in growth produced large bulb with higher quality. The cluster analysis based on 5 principal components generated 6 clusters with an average distance of 1.6 among clusters. Domestic genetic resources were the largest with 36 species (22.5%) in group II.
This study was carried out to estimate average inbreeding coefficients, relatedness and effective population size of breeding bulls and to suggest optimal alternatives on problems of current Hanwoo improvement system. Data on proven and young bulls were obtained from 1,128 heads of Livestock Improvement Main Center from 1983 to 2008. Pedigree information on proven and young bulls was obtained from 3,760 heads of Korea Animal Improvement Association. Average inbreeding coefficients and average relatedness of proven and young bulls were estimated at the range of 0.04-0.07%, 0.10-6.82%, respectively. Effective population size was estimated for 220 heads from the average rate of inbreeding of last 26 years. Average inbreeding coefficient is rising rapidly for the last two years as well as average relatedness. Effective population size was estimated for 47 heads for the last five years. These results suggest that selection criteria of proven bulls should include not only genetic evaluation of carcass performance from progeny-test, but also inbreeding and relationship coefficients, in order to maintain genetic variability of Hanwoo. In addition, effective population size should be increased by increasing the number of proven bulls.
In South Korea, ongoing incidents related to drinking water quality have eroded consumer trust. Specifically, beyond quality incidents, there have been complaints about taste, odor, and other issues stemming from the presence of chlorine. To address this, water service operators are employing various management strategies from both temporal (scheduling) and spatial (rechlorination) perspectives to ensure uniform and safe distribution of chlorine residuals. In this study, we focus on the optimal monthly management of chlorine residuals, based on water distribution network analysis. Water quality reaction coefficients, including bulk fluid and wall reaction coefficients, were estimated through lab-scale tests and EPANET water quality simulations, respectively, accounting for temperature variations in a large-scale water distribution network. Utilizing these estimated coefficients, we examined the monthly variations in chlorine residual distribution under different chlorine injection conditions. The results indicate that the efficient concentration for chlorine injection, which satisfies the residual chlorine limit range, varies with temperature changes. Consequently, it is imperative to establish a specific and quantitative chlorine injection plan that considers the accurate spatial distribution of monthly chlorine residuals.
This study was conducted to obtain the basic informations on the selection for the early and high-yielding lines in $F_2$ populations of wheat. Two combinations such as Olmil-Saemil cross and Naphal-Cheonggemil cross and their eight populations were used and genetic parameters of genetic variances and path-coefficients for agronomic traits mainly in $F_2$ populations were analyzed. Genetic coefficient of variability in Olmil-Saemil cross was higher in harvest index, grain number, grain yield and plant height in decreasing order, but in Naphal-Cheonggemil cross harvest index, plant height, and 1,000-grain weight were higher in order. Heading and maturing dates, plant height, and harvest index showed high hertability (0.525-0.808) in both crosses, but very low heritabilities were observed in grain numbers per panicle and yield. In both crosses, genetic advances(%) was higher in heading and maturing stage, but lowest in yield. High genetic advance was obtained in 1,000-grain weight of Naphal-Cheonggemil cross. The highly significant correlations between yield and panicle number, yield and grain number per panicle were obtained, and the highest direct effect on yield was obtained in panicle number followed by grain number per panicle number.
Jeong, Young Ae;Jang, Dong Cheol;Kwon, Jin Kyung;Kim, Dae Hyun;Choi, Eun Young
Journal of Bio-Environment Control
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v.31
no.3
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pp.221-229
/
2022
This study was aimed to investigate spatial and vertical characteristics of greenhouse environments according to the location of the environmental sensors, and to investigate the correlations between temperature, light intensity, and carbon dioxide (CO2) concentration according to the type of greenhouse. Temperature, relative humidity (RH), CO2, and light sensors were installed in the four-different vertical positions of the whole canopy as well as ground and roof space at the five spatial locations of the Venlo greenhouse. Also, correlations between temperature, light intensity, and CO2 concentration in Venlo and semi-closed greenhouses were analyzed using the Curve Expert Professional program. The deviations among the spatial locations were larger in the CO2 concentration than other environmental factors in the Venlo greenhouse. The average CO2 concentration ranged from 465 to 761 µmol·mol-1 with the highest value (646 µmol·mol-1) at the Middle End (4ME) close to the main pipe (50Ø) of the liquefied CO2 gas supply and lowest (436 µmol·mol-1) at the Left Middle (5LM). The deviation among the vertical positions was greater in temperature and relative humidity than other environments. The time zone with the largest deviation in average temperature was 2 p.m. with the highest temperature (26.51℃) at the Upper Air (UA) and the lowest temperature (25.62℃) at the Lower Canopy (LC). The time zone with the largest deviation in average RH was 1 p.m. with the highest RH (76.90%) at the LC and the lowest RH (71.74%) at the UA. The highest average CO2 concentration at each hour was Roof Air (RF) and Ground (GD). The coefficient of correlations between temperature, light intensity, and CO2 concentration were 0.07 for semi-closed greenhouse and 0.66 for Venlo greenhouse. All the results indicate that while the CO2 concentration in the greenhouse needs to be analyzed in the spatial locations, temperature and humidity needs to be analyzed in the vertical positions of canopy. The target CO2 fertilization concentration for the semi-closed greenhouse with low ventilation rate should be different from that of general greenhouses.
Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.
The variability in the phosphorus concentrations and the decomposition rates of organic phosphorus were measured in two rivers, the Youngsan River and the Sumjin River through four surveys in June, August and December of 2006 and February of 2007. Water samples were incubated for 20 days in a dark incubator and the change of forms of phosphorus (POP, DOP, DIP) were analyzed. By fitting the change to four types of models the decomposition rates of organic phosphorus were determined. The mean total organic phosphorus (TOP) decomposition rate coefficients in the Youngsan River and the Sumjin River were $0.036day^{-1}$ and $0.035day^{-1}$, respectively. In POP$\rightarrow$DIP model, the average decomposition rate coefficients in the Youngsan River and the Sumjin River were 0.049 and $0.035day^{-1}$, respectively. The average POP decomposition rate coefficients of POP$\rightarrow$DOP$\rightarrow$DIP model were $0.042day^{-1}$ and $0.038day^{-1}$ in the Youngsan River and Sumjin River respectively while the mean DOP decomposition rate coefficients were $0.255day^{-1}$ and $0.244day^{-1}$, respectively. In the Youngsan River, the mean POP$\rightarrow$DOP decomposition rate coefficient and POP$\rightarrow$DIP decomposition rate coefficient of POP$\rightarrow$DOP$\rightarrow$DIP, POP$\rightarrow$DIP model were $0.039day^{-1}$ and $0.007day^{-1}$, respectively. And in the Sumjin River, the above decomposition rate coefficients were $0.031day^{-1}$ and $0.004day^{-1}$, respectively. The decomposition rate coefficients measured in this study might be applicable for modeling of river water quality.
With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.
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