Enhanced magnetoelectric effect in M-type hexaferrites by Co substitution into trigonal bi-pyramidal sites

The magnetoelectric effect in M-type Ti-Co doped strontium hexaferrite has been studied using a combination of magnetometry and element specific soft X-ray spectroscopies. A large increase (>×30) in the magnetoelectric coefficient is found when Co2+ enters the trigonal bi-pyramidal site. The 5-fold trigonal bi-pyramidal site has been shown to provide an unusual mechanism for electric polarization based on the displacement of magnetic transition metal (TM) ions. For Co entering this site, an off-centre displacement of the cation may induce a large local electric dipole as well as providing an increased magnetostriction enhancing the magnetoelectric effect

Enhanced magnetoelectric effect in M-type hexaferrites by Co substitution into trigonal bi-pyramidal sites

The magnetoelectric effect in M-type Ti-Co doped strontium hexaferrite has been studied using a combination of magnetometry and element specific soft X-ray spectroscopies. A large increase (>×30) in the magnetoelectric coefficient is found when Co2+ enters the trigonal bi-pyramidal site. The 5-fold trigonal bi-pyramidal site has been shown to provide an unusual mechanism for electric polarization based on the displacement of magnetic transition metal (TM) ions. For Co entering this site, an off-centre displacement of the cation may induce a large local electric dipole as well as providing an increased magnetostriction enhancing the magnetoelectric effect

Software Systems Clustering Using Estimation of Distribution Approach

Software clustering is usually used for program understanding. Since the software clustering is a NP-complete problem, a number of Genetic Algorithms (GAs) are proposed for solving this problem. In literature, there are two wellknown GAs for software clustering, namely, Bunch and DAGC, that use the genetic operators such as crossover and mutation to better search the solution space and generating better solutions during genetic algorithm evolutionary process. The major drawbacks of these operators are (1) the difficulty of defining operators, (2) the difficulty of determining the probability rate of these operators, and (3) do not guarantee to maintain building blocks. Estimation of Distribution (EDA) based approaches, by removing crossover and mutation operators and maintaining building blocks, can be used to solve the problems of genetic algorithms. This approach creates the probabilistic models from individuals to generate new population during evolutionary process, aiming to achieve more success in solving the problems. The aim of this paper is to recast EDA for software clustering problems, which can overcome the existing genetic operators’ limitations. For achieving this aim, we propose a new distribution probability function and a new EDA based algorithm for software clustering. To the best knowledge of the authors, EDA has not been investigated to solve the software clustering problem. The proposed EDA has been compared with two well-known genetic algorithms on twelve benchmarks. Experimental results show that the proposed approach provides more accurate results, improves the speed of convergence and provides better stability when compared against existing genetic algorithms such as Bunch and DAGC

A Learning based Evolutionary Approach for Minimization of Matrix Bandwidth Problem

Nowadays, graphs and matrix have been used extensively in computing. In this paper an evolutionary approach to solve a problem related with the matrix called minimization of bandwidth problem is proposed. Due to difficulties to solve of this problem, using of evolutionary processing and especially genetic algorithm is efficient. In this paper by adding learning concepts such as penalties and rewards (Guidance) to the genetic algorithm, we obtained an efficient method for solving minimization of matrix bandwidth problem; so that in search process, the speed of finding the answer to the significantly increased. Obtained results of experiments on twenty sample matrices show the efficiency and speed of suggested method in comparison with other method

Application of Wilmink’s Exponential Function in Genetic Analysis of 305-d Milk Production and Lactation Persistency in Holstein Cows of Razavi Khorasan

To estimate heritability and genetic trend for 305-d milk production and lactation persistency, a total of 130,668 monthly test day milk yields belonging to 15,183 first lactation Holstein cows in 131 herds and calved from 2000 to 2009 were used. To calculate 305-d milk yield as well as lactation persistency, estimated parameters of Wilmink’s exponential function were applied. The parameters of the function were estimated by SAS software. Genetic and environmental variance components and heritability of the traits were estimated by single trait animal model using DMU software. Genetic trend was estimated based upon weighted simple linear regression of average breeding values on calving year. Heritability estimate of 305-d milk yield and lactation persistency were found to be 0.184 and 0.05, respectively. A positive significant phenotypic trend (166.11 kg per year) was observed for 305-d milk yield while a non-significant genetic trend (-2.0107) was revealed for the trait. For the lactation persistency, there were no significant phenotypic (0.054 % per year) and genetic (0.003 % per year) trends over the period of time