Evolutionary trends of the mitochondrial lineage differentiation in species of genera Martes and Mustela

We compared partial sequences (402 bp) of the mitochondrial cytochrome b gene in 68 individuals of martens (Martes), weasels (Mustela) and their relatives from the Northern Hemisphere to identify the modes of geographic differentiation in each species. We then compared complete sequences (1140 bp) of the gene in 17 species of the family Mustelidae to know the spatial and temporal modes of speciation, constructing linearized trees with transversional substitutions for deeper lineage divergences and with transversions and transitions for younger lineages. Our data suggested that these lineages of Martes and Mustela differentiated in a stepwise fashion with five radiation stages from the generic divergences (stage I) to the intraspecific divergences (stage V), during the last 10 or 20 million years as the fossil evidence suggests. In the lineage of Martes, the first offshoots are of Martes flavigula, M. pennanti, and Gulo gulo (stage II), the second is M. foina (stage III), and the third are M. americana, M. martes, M. melampus, and M. zibellina (stage IV). The divergence of the lineages of Mustela is likely to have taken place concurrently with the radiations of the Martes. These divergence processes are attributable in part to the geographic allocation along the two continents, North America and Eurasia, as well as among peripheral insular domains, such as Taiwan and the Japanese Islands. In addition, the Eurasian continent itself was shown to have been involved in the species diversification in the martens and weasels.

A Viscoelastic Constitutive Model with Nonlinear Evolutionary Internal Variables

Based on the internal variable theory, a viscoelastic constitutive model of a highly deformable continuous medium is proposed. A set of second rank tensorial internal state variables corresponding to Biot's strain is introduced, and a nonlinear evolution

The Relationship between Decision Trees and the Scale of Train Data Set

Decision Trees need train samples in the train data set to get classification rules. If the number of train data was too small, the important information might be missed and thus the model could not explain the classification rules of data. While it is not affirmative that large scale of train data set can get well model. This Paper analysis the relationship between decision trees and the train data scale. We use nine decision tree algorithms to experiment the accuracy, complexity and robustness of decision tree algorithms. Some results are demonstrated.

An Integrated Evolutionary Algorithm for Expensive Global Optimization

We propose an integrated algorithm named low dimensional simplex evolution extension (LDSEE) for expensive global optimization in which only a very limited number of function evaluations is allowed. The new algorithm accelerates an existing global optimization, low dimensional simplex evolution (LDSE), by using radial basis function (RBF) interpolation and tabu search. Different from other expensive global optimization methods, LDSEE integrates the RBF interpolation and tabu search with the LDSE algorithm rather than just calling existing global optimization algorithms as subroutines. As a result, it can keep a good balance between the model approximation and the global search. Meanwhile it is self-contained. It does not rely on other GO algorithms and is very easy to use. Numerical results show that it is a competitive alternative for expensive global optimization.