• Title/Summary/Keyword: $F_2$-hybrid

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Studies on Ecological Variation and Inheritance for Agronomical Characters of Sweet Sorghum Varieties (Sorghum vulgare PERS) in Korea (단수수(Sorghum vulgare PERS) 품종의 생태변이 및 유용형질의 유전에 관한 연구)

  • Se-Ho Son
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.10
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    • pp.1-43
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    • 1971
  • Experiment I: The objective of this study was to know variation in some selected agronomic characters of sweet sorghum when planted in several growing seasons. The 17 different sweet sorghum varieties having various maturities, and plant, syrup and sugar types were used in this study which had been carried out for the period of two years from 1968 to 1969 at Industrial Crops Division of Crop Experiment Station in Suwon. These varieties were planted at an interval of 20 days from April 5 to August 25 both in 1968 and 1969. The experimental results could be summarized as follows: 1. As planting was made early, the number of days from sowing to germination was getting prolonged while germination took place early when planted at the later date of which air temperature was relatively higher. However, such a tendency was not observed beyond the planting on August 25. In general, a significant negative correlation was found between the number of days from sowing to germination and the average daily temperature but a positive correlation was found between the former and the total accumulated average temperature during the growth period. 2. The period from sowing to heading was generally shortened as planting was getting delayed. The average varietal difference in number of days from sowing to heading was as much as 30.2 days. All the varieties were grouped into early-, medium and late-maturing groups based upon a difference of 10 days in heading. The average number of days from sowing to heading was 78.5$\pm$4.5 days in the early-maturing varieties, 88.5$\pm$4.5 days in the medium varieties and 98.5$\pm$4.5 days in the late-maturing varieties, respectively. The early-maturing varieties had the shortest period to heading when planted from July 15 to August 5, the medium varieties did when planted before July 15 and the late-maturing varieties did when planted before June 5. 3. The relationship between the sowing date (x) and number of days from sowing to heading could be expressed in an equation of y=a+bx. A highly positive correlation was found between the coefficient of the equation(shortening rate in heading time) and the average number of days from sowing to heading. 4. The number of days from sowing to heading was shortened as the daily average temperature during the growth period was getting higher. Early-maturing varieties had the shortest period to heading at a temperature of 24.2$^{\circ}C$, medium varieties at 23.8$^{\circ}C$ and late-maturing varieties at 22.9$^{\circ}C$, respectively. In other words, the number of days from sowing to heading was shortened rapidly in case that the average temperature for 30 days before heading was 22$^{\circ}C$ to $25^{\circ}C$. It prolonged relatively when the temperature was lower than 21$^{\circ}C$. 5. There was a little difference in plant height among varieties. In case of early planting, no noticeable difference in the height was observed. The plant height shortened generally as planting season was delayed. Elongation of plant height was remarkably accelerated as planting was delayed. This tendency was more pronounced in case of early-maturing varieties rather than late-maturing varieties. As a result, the difference in plant height between the maximum and the minimum was greater in late-maturing varieties than in early-maturing varieties. 6. Diameter of the stalk was getting thicker as planted earlier in late-maturing varieties. On the other hand, medium or early-maturing varieties had he thickest diameter when they were planted on April 25. 7. In general, a higher stalk yield was obtained when planted from April 25 to May 15. However, the planting time for the maximum stalk yield varied from one variety to another depending upon maturity of variety. Ear]y-maturing varieties produced the maximum yield when planted about April 25, medium varieties from April 25 to May 15 and late-maturing varieties did when planted from April 5 to May 15 respectively. The yield decreased linearly when they were planted later than the above dates. 8. A varietal difference in Brix % was also observed. The Brix % decreased linearly when the varieties were planted later than May 15. Therefore, a highly negative relationship between planting date(x) and Brix %(y) was detected. 9. The Brix % during 40 to 45 days after leading was the highest at the 1st to the 3rd internodes from the top while it decreased gradually from the 4th internode. It increased again somewhat at the 2nd internode from the ground level. However, it showed a reverse relationship between the Brix % and position of internode before heading. 10. Sugar content in stalk decreased gradually as planting was getting delayed though one variety differed from another. It seemed that sweet sorghum which planted later than June had no value as a sugar crop at all. 11. The Brix % and sugar content in stalk increased from heading and reached the maximum 40 to 45 days after heading. The percentage of purity showed the same tendency as the mentioned characters. Accordingly, a highly positive correlation was observed between. percentage of purity and Brix % or sugar content in stalk. 12. The highest refinable sugar yield was obtained from the planting on April 25 in late-maturing varieties and from that on May 15 in early-maturing varieties. The yield rapidly decreased when planted later than those dates. Such a negative correlation between planting date(x) and refinable sugar yield(y) was highly significant at 1% level. 13. Negative correlations or linear regressions between delayed planting and the number of days from sowing to germination. accumulated temperature during germination period, number of days to heading, accumulated temperature to heading, plant height, stem diameter, stalk weight, Brix %. sugar content, refinable sugar yield or Purity % were obtained. On the other hand, highly positive correlations between the number of days from sowing to heading(x) and Brix %, sugar content, purity %, refinable sugar yield, plant height or stalk yield, between Brix %(x) and purity %, refinable sugar yield or stalk yield, between sugar content(x) and purity% or refinable sugar yield(y), between purity %(x) and refinable sugar yield and between daylength at heading(x) and Brix %. number of days from sowing to heading, sugar content, purity % or refinable sugar yield (y), were found, respectively. Experiment II: The 11 varieties were selected out of the varieties used in Experiment I from ecological and genetic viewpoints. Complete diallel cross were made among them and the heading date, stalk length, stalk yield, Brix %, syrup yield, combining ability and genetic behavior of F$_1$ plants and their parental varieties were investigated. The results could be summarized as follows: 1. In general, number of days to heading showed a partial dominance over earliness or late maturity or had a mid-value, though there were some specific combinations showing a complete dominance or transgressive segregation in maturity. Some combinations showed relatively high general or specific combining abilities in maturity. Therefore, a 50 to 50 segregation ratio in heading date could be estimated in this study and it might be positive to have a selection in early generation since heritability of the character was relatively high. 2. A vigorous hybrid vigor was observed in stalk length. A complete or partial dominant effect of long stalk was obtained. The general combining ability and specific combining ability of stalk length were generally high. Long and short stalks segregated in a ratio of 50:50 and its heritability was relatively low. 3. Except for several specific combinations, high stalk yield seemed to be partial dominant over the low yield. Some varieties demonstrated relatively high general as well as specific combining abilities. It was assumed that several recessive genes were involved in expression of this character. The interaction among regulating recessive genes was also obtained. Accordingly, the heritability of stalk yield seemed to be rather low. 4. The Brix % of hybrid plants located around mid-parental value though some of them showed much higher or lower percentage. It could be explained by the fact that such behavior might be due to partial dominance of Brix %. The varieties with, relatively higher Brix % were high both in general. and specific combining abilities. Therefore, it could be recommended to use the varieties having higher sugar content in order to develop higher-sugar varieties. 5. The syrup yield seemed to be transgressively segregated or completely dominant over low yield. Hybrid vigor of syrup yield was relatively high. No-consistent relationship between general combining ability and specific combining ability was observed. However, some cases demonstrated that the varieties with relatively higher general combining ability had relatively lower specific combining ability. It was assumed that the frequencies of dominant and recessive alleles were almost same.

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Incorporating Social Relationship discovered from User's Behavior into Collaborative Filtering (사용자 행동 기반의 사회적 관계를 결합한 사용자 협업적 여과 방법)

  • Thay, Setha;Ha, Inay;Jo, Geun-Sik
    • Journal of Intelligence and Information Systems
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    • v.19 no.2
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    • pp.1-20
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    • 2013
  • Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.