• KOREAN JOURNAL OF CROP SCIENCE
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• v.15
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• pp.1-31
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• 1974

Introducing genes for earliness of wheat varieties is important to develop early varieties in winter wheat. In oder to obtain basic informations on the response of heading to the different day length and temperature treatments and on the inheritance of heading dates, experiments were conducted at the field and greenhouse of the Crop Experiment Station, Suwon. Varieties used in this experiments were, early variety Yecora F70, medium varieties Suke #169, Parker and Yukseung #3, and late varieties Changkwang, Bezostaia, Sturdy and Blueboy. The parents and F$_1$s of partial diallel crosses of above eight varieties were subjected the following four different treatments; 1. high temperature and long day, 2. high temperature and short day, 3. low temperature and long day, and 4. low temperature and short day. The same materials were grown also in field condition. Parents, F$_1$ and F$_2$ generation were grown also in both greenhouse under high temperature and short day and in field. The results obtained were summarized as follow: 1. No effects of temperature and daylength on the number of leaves on the main stem were found when -varieties were vernalized. The number of main stem leaves were fewer for spring type of varieties than for winter type of varieties. 2. The effects of temperature and daylength on the days to flag leaf opening were dependent on the speed of leaf emergence. The speed of leaf emergence were faster for lower leaves than for upper leaves. 3. The response to short day and long day (earliness of narrow sense) of varieties were found to be direct factor responsible to physiology of heading dates in vernalized varieties. Great difference of varieties to heading date was found in high temperature and short day treatment, but less differences were found in high temperature and long day, low temperature and long day and low temperature and short day treatments respectively. The least varietal difference for heading dates was found in the field condition. 4. Changkwang and Parker were found to be the most sensitive to short day treatment (photosensitive) and the heading of these varieties were delayed by short day treatment. No great varietal differences were found among other varieties. 5. Varietal differences of heading dates due to daylength were greater in high temperature than in low temperature. 6. Varietal differences of heading dates due to temperature were not great. but in general the heading dates of varieties were faster under high temperature than under low temperature. 7. Earliness of heading dates was due to partial dominance effect of genes involved in any condition. The degree of dominance was greater under short day than under long day treatment. 8. The varietal differences of heading date under high temperature and long day were due to earliness or narrow sense (response to long day) of varieties. The degree of dominance was greater for Yecora F70, spring type than for other winter type of varieties. No differences or less differences of degree of dominance was found among winter type of varieties. The estimated number of effective factor concerned in the earliness of narrow sense was one pair of allele with minor genes. 9. The insensitivity of varieties to short day treatment in heading dates was due to single dominant gene effect. Under the low temperature the sensitivity of varieties to short day treatment was less apparent. 10. The earliness of short day and long day (earliness of narrow sense) sensitivities of varieties appearea to be due to partial dominance of earliness over lateness. In strict sense, the degree of the dominance should be distinguished. 11. Dominant gene effects were found for the thermo-sensitivity of varieties, and the effect was less, significant than the earliness in narrow sense. 12. One pair of allele, ee and EE, for photosensitivity was responsible for the difference in the heading dates between Changkwang and Suke #169. Two pairs of alleles, ee, enen and EE, EnEn. appeared to be responsible for the difference between Changkwang and Yecora F70. The effects of EE and EnEn were, additive to the earliness and the effects of EE were greater than EnEn under short day. However, the effects of EE were not evident in long day but the effects of EnEn were observed in long day. 13. Two pairs of dominant alleles for the earliness were estimated from the analysis of F$_1$ diallels in the field but the effects of these alleles in F$_2$ were not apparent due to low temperature and short day treatment in early part of growth and high temperature and long day treatment in later part of growth. The F$_2$ population shows continuous variation due to environmental effects and due to other minor gene effects. 14. The heritabilities for heading dates were ranged from 0.51 to 0.72, indicating that the selection in early generation might be effective. The extent of heritability for heading dates varied with environments; higher magnitude of heritability was obtained in short day treatment and high temperature compared with long day and low temperature treatments. The heritabilities of heading date due to response to short day were 0.86 in high temperature and 0.76 in low temperature. The heritabilities of heading date due to temperature were not significantly high. 15. The correlation coefficients of heading dates to the number of grains per spike, weight of 1, 000 grains. and grain yield were positive and high, indicating the difficulties of selections of high yielding lines from early population. But no significant correlation coefficient was obtained between the earliness and the number of spikes, indicating the effective selection for high tillering from early varieties for high yielding.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.