• Radiation Oncology Journal
• /
• v.28 no.4
• /
• pp.231-237
• /
• 2010

Purpose: We investigated the effect of location changes in the inferior border of the belly board (BB) aperture by adding a bladder compression device (BCD). Materials and Methods: We respectively reviewed data from 10 rectal cancer patients with a median age 64 years (range, 45~75) and who underwent computed tomography (CT) simulation with the use of BB to receive pelvic radiotherapy between May and September 2010. A CT simulation was again performed with the addition of BCD since small bowel (SB) within the irradiated volume limited boost irradiation of 5.4 Gy using the cone down technique after 45 Gy. The addition of BCD made the inferior border of BB move from symphysis pubis to the lumbosacral junction (LSJ). Results: Following the addition of BCD, the irradiated volumes of SB and the abdominopelvic cavity (APC) significantly decreased ($174.3{\pm}89.5mL$ vs. $373.3{\pm}145.0mL$, p=0.001, $1282.6{\pm}218.7mL$ vs. $1,571.9{\pm}158mL$, p<0.001, respectively). Bladder volume within the treated volume increased with BCD ($222.9{\pm}117.9mL$ vs. $153.7{\pm}95.5mL$, p<0.001). The ratio of irradiated bladder volume to APC volume with BCD ($33.5{\pm}14.7%$) increased considerably compared to patients without a BCD ($27.5{\pm}13.1%$) (p<0.001), and the ratio of irradiated SB to APC volume decreased significantly with BCD ($13.9{\pm}7.6%$ vs. $24.2{\pm}10.2%$, p<0.001). The ratios of the irradiated SB volume and irradiated bladder volume to APC volume negatively correlated (p=0.001). Conclusion: This study demonstrated that the addition of BCD, which made the inferior border of BB move up to the LSJ, increased the ratio of the bladder to APC volume and as a result, decreased the irradiated volume of SB.

• Journal of the East Asian Society of Dietary Life
• /
• v.13 no.4
• /
• pp.267-283
• /
• 2003

This study reviewed the prisoners' dietary lift status under the world panics and Japanese food shortage based on the data of the 1920~1930's prisons' main dish supplies in Chosun, Shinchu boys' prison in Taiwan, Franue correction center in France and Moabit detention house in Germany. 1. The status of main dish food supply of Chosun prisons in 1920~1930's was as follows: 1) Meals were provided with 12 rates depending on the working activities. There were big differences in energy supply between 1$^{st}$ rate of 6045.0 ㎉ in the Mockpo prison and 12$^{th}$ rate of 1855.8 ㎉ in the Masan prison in accordance with the grain supply ratio and the diet rates. 2) The average ratio of energy provided with protein, fat and carbohydrate(PFC ratio) was 20.0: 20.2: 59.8. The supplies of protein and fat were relatively high because main dish was mostly composed of soybean. The soybean was used in 20 ~60％ of main dish in prisons except Gaesung. 3) It was estimated that PFC ratio(8.3: 8.1 : 83.6) in Gaesung boys' prison was not appropriate for growing boys because the soybean supply was low. 2. The overall comparison of nutrition supply of prisons in Chosun, Taiwan, France and Germany was as follows: 1) The daily supplies of energy in Keongsung prison was 3966.5 ㎉, of which the PFC ratio was 18.9: 16.6: 64.5. This showed that the PFC ratio seemed to be balanced, even though the total amount of energy is too high and the ratios of protein and fat were somewhat high and somewhat low, respectively. 2) The main dish of the Taiwan boys' prison was provided with 6 rates and the side dish in the from of weekly cycle menu. The energy intakes from 1$^{st}$ rate of 2862.9 ㎉ to 6$^{th}$ rate of 1388.9 ㎉ were not quite enough for growing boys. It was estimated that the amounts of protein and fat taken were small but the quality was not that bad because animal protein such as protein small fish and fried tofu were supplied. 3) In the French Frenue correction center and the German Moabit detention house, the daily total amounts of energy were 2771.3 ㎉ and 2678.7 ㎉, respectively, which was estimated as appropriate compared with standard energy amount of 3000 ㎉ at that time and the current energy RDA of 2500 ㎉ for adult. The ratio of PFC was 16.2: 12.0: 71.8 in Frenue correction center and 12.4: 14.3: 73.3 in Moabit detention house, which showed that the amount of fat was slightly lacked. From this study, it was suggested that the prisons in Chosun and Taiwan under the Japanese rule and European prisons after the world panic were making an efforts to supply prisoners the appropriate amount of energy. The only question remains is that this data may be from only the food supply plan not from the data the prisoners took in real.eal.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.12 no.2
• /
• pp.122-136
• /
• 1983

The effect of dietary casein, calcium, magnesium and some vegetable oils such as seasme, perilla and soybean oil on the serum cholesterol level in the rabbit were studied for a period of 5 weeks using isocalories and isonitrogenous as basal diets. The experimental rabbits fed the following basal diets containing crude protein 68.47%, carbohydrates 13.35%. fats 16.18% and vegetable oil 10%. casein 10%, calcium and magnesium according to experimental plan making. In order to calculate the feeding efficiency, protein efficiency and calorie efficiency during period, the body weight gains were measured at the same time using same balance, respectively. The results are summarized as follows. Body weight gains per week of the group fed perilla oil, calcium and basal diet were the higher than any other groups. And body weights gains per week of the group fed basal diet, vegetable oil were the lower than any other groups. In the case of efficiency of reed, protein and calorie, the efficiency ratios of the group fed perilla oil were the higher than any other groups. Especially, perilla oil and calcium diet effect on body weight gain in rabbit. In the case of serum protein, the total proteins in serum were almost same value for all the groups. Serum albumin of group fed basal diet. vegetable oil and casein were the higher than any other groups. The ${\alpha}$-globulin of the groups, fed basal diet and calcium was the lower than any other grosps. The ${\beta}$-globulins of the groups fed basad diet, perilla oil and casein were the highest value. In serum lipoprotein, lipalbumin was almost same value for the groups fed vegetable oil, but fed vegetable oil and calcium diet was the lowest value. The ${\beta}$-lipoprotein in high cholesterol level group was increased some degree, the group fed perilla oil added was lower. The ratio of ${\beta}$-lipoprotein per lipalbumin was from 0.11 to 0.26. The ratio of lipalbumin per total lipoprotein was high in calcium and soybean oil added diet. In serum triglyceride, the level of triglyceride of groups fed seasme oil or perilla oil was the higher than any othe groups, but in the group fed casein and calcium or magnesium, the level of triglyceride level was decreased. Calcium and magnesium effect on triglyceridge level lowering action. In serum total cholesterol, the group does fed vegetable oil with basal diet and casein added more, total cholesterol level increased as much as triglyceride level increased. But the group does fed perilla oil and magnesium shows total cholesterol level decreased remarkably. In the group fed basal diet with calium, the amount of serum calcium was increased, but of serum magnesium was decreased. In the case of blood glucose, the group fed basal diet and vegetable oil was decreased. According to the regression and correlation coefficient in blood components in rabbit, there are positive correlation $${\gamma}{\sim_=}1$$ between serum cholesterol and triglyceride, ${\beta}$-lipoprotein, ${\alpha}$-globulin, calcium and magnesium according to diet composition. From the above results, the serum cholesterol level lowering factors in rabbit, was the amount of triglyceride and ${\beta}$-lipoprotein which was decreased in perilla oil fed. It assumes that serum cholesterol and triglyceride level lowering factors are not only unsaturation degree of fatty acid but the amount of calcium and magnesium and the ratio calcium per magnesium 2:1 in the diet.

• Asian Journal of Turfgrass Science
• /
• v.18 no.4
• /
• pp.179-191
• /
• 2004

This study was conducted to find out the effect of sodding and seeding time and rate of seed mixtures on the establishment of cool-season turfgrasses by evaluating the turf coverage rates for two years. In fall planting, the required establishment period of full coverage($100\%$) was 1.5 months with a rolled turf sodding(Kentucky bluegrass $100\%$, Kentucky bluegrass $80\%$+perennial ryegrass $20\%$). The $100\%$ turf establishment was achieved in 7 months with Perennial ryegrass $100\%$, and 7.5 months by seeding with Kentucky bluegrass $100\%$(KB 100), Kentucky bluegrass $80\%$+perennial ryegrass $20\%$(KB80+PR20), Kentucky bluegrass $70\%$+perennial ryegrass $30\%$(KB70+PR30). In spring planting, the establishment periods far sod with KB 100 or KB80+PR20 were taken one month. However, in the case of seeding, the establishment periods were 3 months, 3.5 months, 3.5 months and 4 months with PR100, KB80+PR20, KB70+PR30, and KB 100, respectively Comparing the turf establishment vigor between fall and spring planting, the vigor was higher In spring planting than in fall planting in both sodding and . seeding. In the case of spring planting, the most proper time for turf establishment was tested on April, May, and June trials. The effect was significant in establishment vigor. The result showed highest on April planting. On May and June trials, establishment vigors were decreased gradually As the mixture rate of PR increased, ryegrass, establishment vigor was decreased with the rates. These results indicated that perennial ryegrass has relatively less tolerant to summer heat than Kentucky bluegrass. Number of shoots in 95 days after seeding was higher in KB100 by 16,600 per $m^2$ than in PR100 by 12,400 per $m^2$, while the lowest number showed in KB50+PR50 by 3,300 per $m^2$. Those in KB80:PR20, KB70:PR30 were 6,700 and 4,900 per $m^2$, respectively. The ratios of tillers according to mixture rates between Kentucky bluegrass and perennial ryegrass were KB80:PR20=87:13, KB70:PR30=78:22, and KB50:PR50=48:52. According to results in this study, Ideal seeding time might be spring (April) than in fall (September), and proper mixture rate was $80\%$ of Kentucky bluegrass with $20\%$ of perennial ryegrass.

• Korean Journal of Medicinal Crop Science
• /
• v.1 no.2
• /
• pp.109-114
• /
• 1993

This study was carried out to determine the optimum planting time for in vitromultiplied tuber of Pinellia ternata(Thunb.) Breit. The tubers were planted on April 20, May 20, June 20, July 20, August 20 and September 20 in 1990. Emergence ratios were 68 to 87% in any planting time except planting on July 20. The number of tubers per $m^2$ at harvest in plantings on May 20 and June 20 were significantly higher with 1,110 and 1,021, respectively, while in plantings after July 20, those were drastically decreased. As compared with fresh yield of planting on April 20(352kg /10a), that of May 20 was 109% and June 20 was 103%, while those of after July 20 were from 41% to 19%. There was a highly positive correlation between dry tuber yield and the number of tubers per $m^2(r=0.991^{**})$. Tuber yields for commercial use(diameter over 7.1mm) were high in planting on May 20(322kg /10a) and on June 20(299kg /10a). It was suggested that optimum field planting time for in vitro multiplied tuber of Pinellia ternata(Thunb.) Breit was from May 20 to June May 20.

• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.35-48
• /
• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.105-129
• /
• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Korean Journal of Veterinary Research
• /
• v.35 no.3
• /
• pp.543-552
• /
• 1995

This study was undertaken to investigate the eftects of Cd administered ad libitum for 6 weeks on the cellular immune responses of Balb/c mice. The results were summarized as follows; 1. The mice fed 25, 50 and 100ppm Cd drank as much as control, but the mice fed 200ppm Cd drank significantly less water after Cd exposure than did control. Increasing rates of body weight of Cd-fed mice for 6 weeks were as this, control group 27.0%, Cd administered groups(25, 50, 100 and 200ppm) 28.54%, 28.31%, 20.49% and 18.04%, respectively. 2. Absolute spleen to body weight(mg/g) of control, 25, 50, 100 and 200ppm Cd administered groups were $4.34{\pm}0.23$, $4.20{\pm}0.54$, $4.80{\pm}0.87$, $4.25{\pm}0.32$ and $4.40{\pm}0.32$, respectively. Splenic cellularity(${\times}10^7$) of control was $24.29{\pm}5.98$ but increased to $27.72{\pm}5.48$, $32.96{\pm}8.44$, $28.32{\pm}8.76$ and $29.64{\pm}4.08$ in 25, 50, 100 and 200ppm Cd-fed groups, respectively. 3. Total $CD_4{^+}$ cells(${\times}10^7$) of control, 25, 50, 100 and 200ppm Cd-fed groups were $9.15{\pm}2.24$, $10.40{\pm}2.04$, $12.04{\pm}3.08$, $10.20{\pm}3.16$ and $10.80{\pm}1.48$, respectively and total $CD_8{^+}$ cells(${\times}10^7$) of these groups were $2.32{\pm}0.56$, $2.54{\pm}0.27$, $3.12{\pm}0.80$, $2.25{\pm}0.70$ and $2.24{\pm}0.28$, in order. On the other hand, $CD_4{^+}/CD_8{^+}$ ratios in total cells were increased significantly except for 50ppm Cd-fed group($3.88{\pm}0.01$). And that of control was $3.97{\pm}0.02$, but those of 25, 100 and 200ppm were $4.35{\pm}0.01$, $4.54{\pm}0.03$ and $4.81{\pm}0.03$. 4. Phagocytosis rates of peritoneal macrophages were increased significantly in 25 and 50ppm Cd groups($36.34{\pm}9.45$ and $37.15{\pm}9.22$, respectively), but 100 and 200ppm groups showed similar rates($18.20{\pm}3.04$ and $19.48{\pm}3.22$ respectively) to that of control($21.43{\pm}3.62$). 5. In mitogen-induced splenocyte proliferation, various concentraions of $CdCl_2(10^{-4}-10^{-7}M)$ were added to mitogen-stimulated culture in vitro. Splenocyte proliferation induced by LPS was decreased dose dependently, but proliferation by Con-A was increased slightly in concentrations of $10^{-7}-10^{-6}M$. 6. Significant cytotoxicity of splenocytes with $CdCl_2$ were shown at $10^{-4}M$ treated group, especially at 24 hrs. From these results, it could be concluded that Cd might modulate the immune responses by modifying a distribution of T cell subpopulations.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.1-32
• /
• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.21 no.2
• /
• pp.281-313
• /
• 1976

To obtain fundamental informations on cotton breeding efficiences for Korea, individual genetic relationships and interrelationships between the agronomic characteristics of Upland cotton were investigated. These experiments were couducted at the Mokpo Branch Station $(34^{\circ}48'N, $ $126^{\circ}23'E$ and altitude of 10m above sea level) from 1969 through 1972. Heterosis, combining ability, dominance and recessive gene action, genetic variance, and phenotypic and genotypic correlation were investigated by $F_1'S$ from an 11-parent partial diallel cross and the segregating $F_2$ and $F_3$ populations of the cross Paymaster times Heujueusseo Trice. The following points resulted from this study, 1. Heteroses for number of bolls per plant and lint yield were significant at 27, 84% and 37.26%, respectively. No other character had significant heteroses. 2. The GCA estimates for all studied characteristics were higher than the SCA estimates. Varieties with high GCA effects were Suwon 1 for earliness, Paymaster and Arijona for high lint percent, and Arijona for long fiber, etc, 3. SCA estimates for lint yield varied widely in crosses with Mokpo 4, Mokpo 6 and Heujueusseo Trice. Those crosses with the highest SCA effects were combinations with large characteristics differences, Example of these crosses are Mokpo 4 times Acala 1517W, Mokpo 4 times D. P. L. and Heujueusseo Trice aud Paymaster. 4. Early-maturing varieties were completely dominant to late-maturing varieties in some combinations while other crosses gave intermediate phenotypes. These results suggest additive genetic action by multi-genes. Heujueusseo Trice, Mokpo 6, and Suwon 1 showed highest degree of dominance for earliness. 5. There were no significant trends for inheritance of weight of boll and 100 seeds weight. 6. Long staple was partially to completely dominant to short staple. Though there were single gene ratios the rate of dominance decreased in the $F_2$ and $F_3$ populations in the cross between the long staple variety Paymaster and the short staple variety Heujueusseo Trice. Diallel cross $F_1$ hybrids showed complicated allelic gene action for staple length. Various dominance degree were shown by varieties. 7. Number of bolls per plant indicated strong over-dominance and small non-allelic additive gene action. 8. Lint Yield was characterized by over-dominance and by multiple non-allelic-gene action. High-yielding varieties were dominant to low-yielding ones. However, the low-yielding variety Heujueusseo Trice showed over-dominance, indicating different reactions according to the varieties and combinations. 9. Broad sense heritability for days to flowering was 34-39% while narrow sense heritability was 11%. Large variations of individual plants caused by Korean climatic conditions cause this situation. Heritability estimates for weight of boll was 30% for broad sense and 22% for narrow sense. 10. Heritability estimates for staple length and lint percent were very high suggesting strong selection effects. 11. Narrow sense heritability estimates for number of bolls per plant was 30% in the diallel cross $F_1$ hybrids and 36% in the $F_2$ population of the special cross. Broad sense heritability was estimated at 67% suggesting that. 12. Heritability estimates for lint yield was low due to high over-dominance in the diallel cross $F_1$ hybrids. Heritability estimates for yield was low in the $F_1$ hybrids but high in the $F_2$ and $F_3$ populations. 13. Phenotypic and genotypic correlations between lint percent and days to flowering and between staple length and days to flowering were high in the $F_1, $ $F_2$ and $F_3$ populations. Late-maturing varieties and individuals had long staple and high lint percent in general. As the correlation between days to flowering and lint yield was extremely low, the two traits were considered independent of each other. Days to flowering and number of bolls per plant were negatively correlated in the $F_3$ population, indicating early-maturing individual plants with many bolls may be readily selected. 14. Phenotypic and genotypic correlations between lint percent and staple length were high in $F_1, $ $F_2$ and $F_3$ populations. Accordingly, long staple varieties were high in lint percent. It was recognized that lint yield and lint percent were positively correlated in the diallel cross $F_1$ hybrids, and lint percent and staple length were positively correlated in the $F_2$ population, indicating that lint percent and staple length affect lint yield. 15. Lint yield was significantly and positively phenotypically correlated with number of bolls per plant in $F_1, $ $F_2$ and $F_3$ populations. A high genotypic correlation was also noted indicating a close genetic relationship. The selection efficiencies for a high-yielding variety can be increased when individual plants with many bolls are selected in later generations. The selection efficiencies for good fiber quality can be enhanced when individuals with long staple and high lint percent are selected in early generations.