In order to determine whether growth performance could be improved by hybridizing full-sib families of Manila clam （Ruditapes philippinarum）, crosses between two full-sib families including self and reciprocal cross...In order to determine whether growth performance could be improved by hybridizing full-sib families of Manila clam （Ruditapes philippinarum）, crosses between two full-sib families including self and reciprocal crosses were carried out. The effects of heterosis, combining ability and interaction on the growth of shell length were estimated. The results showed that the growth of hybrid larvae was intermediate between parents on days 6 and 9. Heterosis on shell length was observed, which varied at juvenile stage. The cross of ♂A×♀B （Hp varied between 10.41% and 68.27%） displayed larger heterosis than ♂A×♀B （Hp varied between 1.89% and 32.33%） did, suggesting that ♂A×♀B was an ideal hatchery method of improving the growth performance of Manila clam. The variances of general combining ability （GCA）, special combining ability （SCA） and interaction （I） were significant in shell length （P〈 0.05）, indicating that both additive and non-additive genetic factors were important contributors to the growth of larvae and juveniles. The GCA for shell length of ♂A×♀B was higher than that of ♂A×♀B at both larval and juvenile stages. This con- firmed that the cross between ♂A and ♀B showed great growth in shell length. In summary, the growth of Manila clam seeds could be improved by hybridizing selected parents from large numbers of full-sib families.展开更多
Hybrid Machine Learning (HML) is a kind of advanced algorithm in the field of intelligent information process. It combines the induced learning based-on decision-making tree with the blocking neural network. And it pr...Hybrid Machine Learning (HML) is a kind of advanced algorithm in the field of intelligent information process. It combines the induced learning based-on decision-making tree with the blocking neural network. And it provides a useful intelligent knowledge-based data mining technique. Its core algorithm is ID3 and Field Theory based ART (FTART). The paper introduces the principals of hybrid machine learning firstly, and then applies it into analyzing family apparel expenditures and their influencing factors systematically. Finally, compared with those from the traditional statistic methods, the results from HML is more friendly and easily to be understood. Besides, the forecasting by HML is more correct than by the traditional ways.展开更多
基金supported by the earmarked fund for Modern Agro-industry Technology Research System (CARS-48)grants from the ‘863’ Project of China (2012AA10AA400)
文摘In order to determine whether growth performance could be improved by hybridizing full-sib families of Manila clam （Ruditapes philippinarum）, crosses between two full-sib families including self and reciprocal crosses were carried out. The effects of heterosis, combining ability and interaction on the growth of shell length were estimated. The results showed that the growth of hybrid larvae was intermediate between parents on days 6 and 9. Heterosis on shell length was observed, which varied at juvenile stage. The cross of ♂A×♀B （Hp varied between 10.41% and 68.27%） displayed larger heterosis than ♂A×♀B （Hp varied between 1.89% and 32.33%） did, suggesting that ♂A×♀B was an ideal hatchery method of improving the growth performance of Manila clam. The variances of general combining ability （GCA）, special combining ability （SCA） and interaction （I） were significant in shell length （P〈 0.05）, indicating that both additive and non-additive genetic factors were important contributors to the growth of larvae and juveniles. The GCA for shell length of ♂A×♀B was higher than that of ♂A×♀B at both larval and juvenile stages. This con- firmed that the cross between ♂A and ♀B showed great growth in shell length. In summary, the growth of Manila clam seeds could be improved by hybridizing selected parents from large numbers of full-sib families.
文摘Hybrid Machine Learning (HML) is a kind of advanced algorithm in the field of intelligent information process. It combines the induced learning based-on decision-making tree with the blocking neural network. And it provides a useful intelligent knowledge-based data mining technique. Its core algorithm is ID3 and Field Theory based ART (FTART). The paper introduces the principals of hybrid machine learning firstly, and then applies it into analyzing family apparel expenditures and their influencing factors systematically. Finally, compared with those from the traditional statistic methods, the results from HML is more friendly and easily to be understood. Besides, the forecasting by HML is more correct than by the traditional ways.
