Search

The emergency of a new computing pattern must be supported by powerful mathematical theory, weak theoretical supported ideology does not have a strong vitality, and which will become vulnerable. As being a new theory, the DNA computing treated as a new computational pattern also need a lot of basic theoretical research system. This article describes the nature of DNA computing, given the equivalence of DNA computing pattern with traditional computer under the support of automata theory system, further analysis and understanding the DNA computing pattern.

In 2012, Wang Zuofei built up granularity-function and applied it to the measure of attribute importance and attribute reduction. On this basis, granularity-function based upon pessimistic and optimistic multi-granularity rough set is constructed. It is applied to the calculation of attribute importance and attribute reduction. According to the experimental results, the method can reduce the dimension of features and obviously improve the classification accuracy and efficiency.

KNN classifier is a simple, non-parametric, high efficiency algorithm. But it has the defect that the classification efficiency will increase as the enlargement of data scale. This paper put forward a new KNN classification method based on rough set on the research of algorithms presented by Yu Ying and Heping Gou. The innovation of this algorithm is that the kernel, negative domain and boundary region of variable precision rough set are used to be the indexes of measuring the intra-class, extra-class and the class boundary of training sample set. The samples of intra-class, extra-class and the class boundary that are to be classed have differential treatment when judging the category. In this way, the scale of training sample set is effectively reduced and the efficiency and precision of classification are improved. At last, the category function is improved to reflect the category of samples to be classed. The experimental results show that the method can obviously improve the classification efficiency and accuracy.

A two-year (2011-2012 and 2012-2013) field experiment was conducted on one winter wheat cultivar supplied with two levels, of nitrogen (180 and 240 kg N · hm(-2)) under three plant densities (135 x 10(4), 270 x 10(4), and 405 x 10(4) plants · hm(-2)) . The 15N-labeled urea was injected into 20, 60 and 100 cm soil depths, respectively, aiming to investigate the effect of nitrogen and plant density and their interaction on the N uptake, utilization and nitrate nitrogen contents at different soil depths. The results showed that increasing the plant density from 135 x 10(4) to 405 x 10(4) plants · hm(-2) significantly increased the 15N uptake at depths of 20, 60 and 100 cm averagely by 1.86, 2.28 and 2.51 kg · hm(-2), respectively, and increased the above ground N uptake (AGN) , N uptake efficiency (UPE) averagely by 12.6% and 12.6%, respectively, but decreased the N utilization efficiency (UTE) by 5.4%. Compared to the N input of 240 kg N · hm(-2) the 180 kg N · hm(-2) significantly reduced the 15N uptake at depths of 20 and 60 cm averagely by 4. 11 and 1.21 kg · hm(-2), respectively, and significantly increased the 15N uptake at depths of 100 cm averagely by 1.02 kg · hm(-2). Reducing the N input decreased the AGN averagely by 13.5%, but significantly increased the UPE and UTE by 9.4% and 12.2%, respectively. Equivalent grain yield was observed among N input of 180 kg N · hm(-2) with plant density of 405 x 10(4) plants · hm(-2) and N input of 240 kg N · hm(-2) with plant densities of 270 x 10(4) and 405 x 10(4) plants · hm(-2). Increasing the plant density or reducing the N input could encourage the N uptake at deep soil profile and increased UPE and UTE by 13.4% and 11.9%, respectively. Meanwhile, both the nitrate nitrogen contents in 0-200 cm soil layers at maturity and the ratio of the nitrate nitrogen in 100-200 cm soil layers to that in -200 cm were significantly decreased. Therefore, properly decreasing the N input with increasing the plant density of winter wheat was efficient in absorbing N at deep soil, synergistically obtaining high grain yield, UPE and UTE, and reducing the pollution of residual soil nitrate.