• Korean Journal of Poultry Science
• /
• v.16 no.2
• /
• pp.61-71
• /
• 1989

The purposes of this study were to investigate the genetic variations by backcrossing in commercial chickens. Backcrossing was carried out successively back to parent stock (P.S). Heritabilities and genetic correlation coefficients were estimated to verify the genetic variations. The data obtained from a breeding programme with commercial chickens (I strain) were collected from 1955 to 1987 at Poultry Breeding Farm, Seoul National University. Data came from a total of 1230 female offspring. The results obtained are summarized as follows： 1. The general performance ($Mean\pmStandard deviation$) of each trait was $663.94\pm87.11$g for 8 weeks body weight, $1579.1\pm155.43$g for 20 weeks body weight, $2124.1\pm215.3$g for 40 weeks body weight, $2269.1\pm242.94$g for 60 weeks body weight, $168.43\pm12.94$ day for a9e at sexual maturity (SM), $214.52\pm29.82$ eggs , for total egg number to 60 weeks of age (TEN), $61.45\pm3.48$ g for average weight (AEW), $13180.7\pm1823.22$ g for total egg mass to 60 weeks of age(TEM). All traits, except 10 weeks body weight and AEW, were significant for the degrees of backcross (p＜0.01). 2. The pooled estimates of heritabilities derived from the sire, dam and combined variance components were 0.47~0.52 for age at sexual maturity (SM), 0.07~0.37 for total egg number (TEN), 0.40~0.54 for average egg weight (AEW), 0.18~0.27 for total egg mass (TEM). High heritability estimates were found for SM and AEW. TEN and TEM were estimated to be a lowly heritable traits. Heritability estimates from dam components were higher than those from sire components. These differences might be due to non-additive genetic effect and maternal effect. 3. The estimates of heritabilities and standard errors derived from combined variance components for different degrees of backcross were $0.47\pm0.11$ (BCO), $0.42\pm0.16$ (BC1), $0.51\pm0.29$ (BC2) for TEN, $0.59\pm0.20$ (BCO), $0.43\pm0.17$ (BC1), $0.35\pm0.18$ (BC2) for AEW, $0.28\pm0.12$(BC0), $0.20\pm0.11$(BC1), $0.18\pm0.14$ (BC2) for TEM. Heritability estimates for AEW and TEM were decreased by backcrossing while those for SM and TEN remained constant. Since backcrossing contributes to increased homozygosity, the genetic variation of the traits (AEW and TEM) decreased . 4. The pooled estimates of genetic correlation coefficients were -0.55 between SM and TEN, 0.20 between SM and AEW, -0.29 between TEN and AEW, 0.82 between TEM and TEN, 0.31 between TEM and AEW, -0.42 between TEM and SM. The genetic correlation between TEM and TEN was higher than that between TEM and AEW, and it was suggested that egg mass was strongly affected by egg number. Also, age at sexual maturity(SM) contributes to egg mass(TEM). 5. When backcrossing was carried out successively, the genetic correlation between TEM and TEN increased (BC0：0.79, BC1：0.82, BC2：0.91) but those between TEM and SM decreased (BC0：-0.54, BC1：-0.36, BC2：-0.09) with successive backcrosses.

• Journal of the Korean Chemical Society
• /
• v.37 no.2
• /
• pp.191-198
• /
• 1993

The crystal structures of $Cd_{6-}A$ evacuated at $2{\times}10^{-6}$ torr and $750^{\circ}C$ (a = 12.204(1) $\AA$) and dehydrated $Cd_{6-}A$ reacted with 0.1 torr of Cs vapor at $250^{\circ}C$ for 12 hours (a = 12.279(1) $\AA$) have been determined by single crystal X-ray diffraction techniques in the cubic space group Pm3m at $21(1)^{\circ}C.$ Their structures were refined to final error indices, $R_1=$ 0.081 and $R_2=$ 0.091 with 151 reflections and $R_1=$ 0.095 and $R_2=$ 0.089 with 82 reflections, respectively, for which I > $3\sigma(I).$ In vacuum dehydrated $Cd_{6-}A$, six $Cd^{2+}$ ions occupy threefold-axis positions near 6-ring, recessed 0.460(3) $\AA$ into the sodalite cavity from the (111) plane at O(3) : Cd-O(3) = 2.18(2) $\AA$ and O(3)-Cd-O(3) = $115.7(4)^{\circ}.$ Upon treating it with 0.1 torr of Cs vapor at $250^{\circ}C$, all 6 $Cd^{2+}$ ions in dehydrated $Cd_{6-}A$ are reduced by Cs vapor and Cs species are found at 4 crystallographic sites : 3.0 $Cs^+$ ions lie at the centers of the 8-rings at sites of $D_{4h}$ symmetry; ca. 9.0 Cs+ ions lie on the threefold axes of unit cell, ca. 7 in the large cavity and ca. 2 in the sodalite cavity; ca. 0.5 $Cs^+$ ion is found near a 4-ring. In this structure, ca. 12.5 Cs species are found per unit cell, more than the twelve $Cs^+$ ions needed to balance the anionic charge of zeolite framework, indicating that sorption of Cs0 has occurred. The occupancies observed are simply explained by two unit cell arrangements, $Cs_{12}-A$ and $Cs_{13}-A$. About 50% of unit cells may have two $Cs^+$ ions in sodalite unit near opposite 6-rings, six in the large cavity near 6-ring and one in the large cavity near a 4-ring. The remaining 50% of unit cells may have two Cs species in the sodalite unit which are closely associated with two out of 8 $Cs^+$ ions in the large cavity to form linear $(Cs_4)^{3+}$ clusters. These clusters lie on threefold axes and extend through the centers of sodalite units. In all unit cells, three $Cs^+$ ions fill equipoints of symmetry $D_{4h}$ at the centers of 8-rings.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.17-35
• /
• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_2
• /
• pp.1565-1576
• /
• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.11 no.4
• /
• pp.1775-1782
• /
• 1969

The results of the study on the consumptine use of irrigated water in paddy fields during the growing season of rice plants are summarized as follows. 1. Transpiration and evaporation from water surface. 1) Amount of transpiration of rice plant increases gradually after transplantation and suddenly increases in the head swelling period and reaches the peak between the end of the head swelling poriod and early period of heading and flowering. (the sixth period for early maturing variety, the seventh period for medium or late maturing varieties), then it decreases gradually after that, for early, medium and late maturing varieties. 2) In the transpiration of rice plants there is hardly any difference among varieties up to the fifth period, but the early maturing variety is the most vigorous in the sixth period, and the late maturing variety is more vigorous than others continuously after the seventh period. 3) The amount of transpiration of the sixth period for early maturing variety of the seventh period for medium and late maturing variety in which transpiration is the most vigorous, is 15% or 16% of the total amount of transpiration through all periods. 4) Transpiration of rice plants must be determined by using transpiration intensity as the standard coefficient of computation of amount of transpiration, because it originates in the physiological action.(Table 7) 5) Transpiration ratio of rice plants is approximately 450 to 480 6) Equations which are able to compute amount of transpiration of each variety up th the heading-flowering peried, in which the amount of transpiration of rice plants is the maximum in this study are as follows: Early maturing variety ; Y=0.658+1.088X Medium maturing variety ; Y=0.780+1.050X Late maturing variety ; Y=0.646+1.091X Y=amount of transpiration ; X=number of period. 7) As we know from figure 1 and 2, correlation between the amount evaporation from water surface in paddy fields and amount of transpiration shows high negative. 8) It is possible to calculate the amount of evaporation from the water surface in the paddy field for varieties used in this study on the base of ratio of it to amount of evaporation by atmometer(Table 11) and Table 10. Also the amount of evaporation from the water surface in the paddy field is to be computed by the following equations until the period in which it is the minimum quantity the sixth period for early maturing variety and the seventh period for medium or late maturing varieties. Early maturing variety ; Y=4.67-0.58X Medium maturing variety ; Y=4.70-0.59X Late maturing variety ; Y=4.71-0.59X Y=amount of evaporation from water surface in the paddy field X=number of period. 9) Changes in the amount of evapo-transpiration of each growing period have the same tendency as transpiration, and the maximum quantity of early maturing variety is in the sixth period and medium or late maturing varieties are in the seventh period. 10) The amount of evapo-transpiration can be calculated on the base of the evapo-transpiration intensity (Table 14) and Tablet 12, for varieties used in this study. Also, it is possible to compute it according to the following equations with in the period of maximum quantity. Early maturing variety ; Y=5.36+0.503X Medium maturing variety ; Y=5.41+0.456X Late maturing variety ; Y=5.80+0.494X Y=amount of evapo-transpiration. X=number of period. 11) Ratios of the total amount of evapo-transpiration to the total amount of evaporation by atmometer through all growing periods, are 1.23 for early maturing variety, 1.25 for medium maturing variety, 1.27 for late maturing variety, respectively. 12) Only air temperature shows high correlation in relation between amount of evapo-transpiration and climatic conditions from the viewpoint of Korean climatic conditions through all growing periods of rice plants. 2. Amount of percolation 1) The amount of percolation for computation of planning water requirment ought to depend on water holding dates. 3. Available rainfall 1) The available rainfall and its coefficient of each period during the growing season of paddy fields are shown in Table 8. 2) The ratio (available coefficient) of available rainfall to the amount of rainfall during the growing season of paddy fields seems to be from 65% to 75% as the standard in Korea. 3) Available rainfall during the growing season of paddy fields in the common year is estimated to be about 550 millimeters. 4. Effects to be influenced upon percolation by transpiration of rice plants. 1) The stronger absorbtive action is, the more the amount of percolation decreases, because absorbtive action of rice plant roots influence upon percolation(Table 21, Table 22) 2) In case of planting of rice plants, there are several entirely different changes in the amount of percolation in the forenoon, at night and in the afternoon during the growing season, that is, is the morning and at night, the amount of percolation increases gradually after transplantation to the peak in the end of July or the early part of August (wast or soil temperature is the highest), and it decreases gradually after that, neverthless, in the afternoon, it decreases gradually after transplantation to be at the minimum in the middle of August, and it increases gradually after that. 3) In spite of the increasing amount of transpiration, the amount of daytime percolation decreases gadually after transplantation and appears to suddenly decrease about head swelling dates or heading-flowering period, but it begins to increase suddenly at the end of August again. 4) Changs of amount of percolation during all growing periods show some variable phenomena, that is, amount of percolation decreases after the end of July, and it increases in end August again, also it decreases after that once more. This phenomena may be influenced complexly from water or soil temperature(night time and forenoon) as absorbtive action of rice plant roots. 5) Correlation between the amount of daytime percolation and the amount of transpiration shows high negative, amount of night percolation is influenced by water or soil temperature, but there is little no influence by transpiration. It is estimated that the amount of a daily percolation is more influenced by of other causes than transpiration. 6) Correlation between the amount of night percoe, lation and water or soil temp tureshows high positive, but there is not any correlation between the amount of forenoon percolation or afternoon percolation and water of soil temperature. 7) There is high positive correlation which is r=+0.8382 between the amount of daily percolation of planting pot of rice plant and amount and amount of daily percolation of non-planting pot. 8) The total amount of percolation through all growin. periods of rice plants may be influenced more from specific permeability of soil, water of soil temperature, and otheres than transpiration of rice plants.