Origin of the Heavy Fermion Behavior in Ca_{2-x}Sr_{x}RuO_{4}: Roles of Coulomb Interaction and the Rotation of RuO_{6} octahedra

We study the electronic states for Ca_{2-x}Sr_{x}RuO_{4} in $0.5\leq x \leq 2$ within the Gutzwiller approximation (GA) on the basis of the three-orbital Hubbard model for the Ru t_{2g} orbitals. The main effects of the Ca substitution are taken account as the changes of the $dp$ hybridizations between the Ru 4d and O 2p orbitals. Using the numerical minimization of the energy obtained in the GA, we obtain the renormalization factor (RF) of the kinetic energy and total RF, which estimates the inverse of the mass enhancement, for three cases with the effective models of x=2 and 0.5 and a special model. We find that the inverse of the total RF becomes the largest for the case of x=0.5, and that the van Hove singularity, which is located on (below) the Fermi level for the special model (the effective model of x=0.5), plays a secondary role in enhancing the effective mass. Our calculation suggests that the heavy fermion behavior around x=0.5 comes from the cooperative effects between moderately strong Coulomb interaction compared to the total bandwidth and the modification of the electronic structures due to the rotation of RuO_{6} octahedra (i.e., the variation of the $dp\pi$ hybridizations and the downward shift for the $d_{xy}$ orbital). We propose that moderately strong electron correlation and the orbital-dependent modifications of the electronic structures due to the lattice distortions play important roles in the electronic states for Ca_{2-x}Sr_{x}RuO_{4}.Comment: 16 pages, 13 figures, 1 table, accepted for publication in Physical Review B; added the discussions both about the validity of the present treatment and about Hund's metal in this allo

DETC2005-84942 PSO DRIVEN GENETIC RANGE GENETIC ALGORITHMS

ABSTRACT This paper deals with development of Genetic Range Genetic Algorithms (GRGAs). In GRGAs, one of the key is to set a new searching range, it needs to be followed after current searching situations, to be focused on local minute search and to be scattered as widely as possible for global search. However, first two strategies have a possibility of early stage convergence, and random scattering cause vain function calls to produce the range which seems no chance to prosper for a number of generations. In this paper, we propose a new method of setting it by using Particle Swarm Optimization (PSO) to overcome dilemma of the conventional method. INTRODUCTION We have been developing Genetic Range Genetic Algorithm

Global optimization by generalized random tunneling algorithm (5th report, approximate optimization using RBF network)

金沢大学大学院自然科学研究科知的システム創成In practical applications, it is important to reduce the function evaluations in the simulation, and obtain the approximate optimum with high accuracy. To achieve these objectives, the integrative optimization system using the RBF Network (RBFN) and the Generalized Random Tunneling Algorithm (GRTA) is proposed in this paper. This system consists of three parts. (1) Construction of the response surface, (2) Optimization by the GRTA, and (3) Adding the sampling points. The RBFN is used to construct the response surface. The radius on RBFN, which affects the accuracy of response surface, is an important parameter. Firstly new equation for the radius is proposed, based on the examination of existing equation. Secondly a simple sampling strategy to obtain an optimum with high accuracy is also proposed. In general, the objective function and the constraints are approximated, separately. However, the optimum of response surface will often violate the constraints. To avoid such situations, the augmented objective function is utilized in this paper. Then the proposed sampling strategy is applied. Through typical benchmark problems, the validity and effectiveness are examined

Sequential Approximate Optimization using Radial Basis Function network for engineering optimization

金沢大学理工研究域機械工学系This paper presents a Sequential Approximate Optimization (SAO) procedure that uses the Radial Basis Function (RBF) network. If the objective and constraints are not known explicitly but can be evaluated through a computationally intensive numerical simulation, the response surface, which is often called meta-modeling, is an attractive method for finding an approximate global minimum with a small number of function evaluations. An RBF network is used to construct the response surface. The Gaussian function is employed as the basis function in this paper. In order to obtain the response surface with good approximation, the width of this Gaussian function should be adjusted. Therefore, we first examine the width. Through this examination, some sufficient conditions are introduced. Then, a simple method to determine the width of the Gaussian function is proposed. In addition, a new technique called the adaptive scaling technique is also proposed. The sufficient conditions for the width are satisfied by introducing this scaling technique. Second, the SAO algorithm is developed. The optimum of the response surface is taken as a new sampling point for local approximation. In addition, it is necessary to add new sampling points in the sparse region for global approximation. Thus, an important issue for SAO is to determine the sparse region among the sampling points. To achieve this, a new function called the density function is constructed using the RBF network. The global minimum of the density function is taken as the new sampling point. Through the sampling strategy proposed in this paper, the approximate global minimum can be found with a small number of function evaluations. Through numerical examples, the validities of the width and sampling strategy are examined in this paper. © 2010 Springer Science+Business Media, LLC

Differential evolution as the global optimization technique and its application to structural optimization

金沢大学理工研究域機械工学系In this paper, the basic characteristics of the differential evolution (DE) are examined. Thus, one is the meta-heuristics, and the other is the global optimization technique. It is said that DE is the global optimization technique, and also belongs to the meta-heuristics. Indeed, DE can find the global minimum through numerical experiments. However, there are no proofs and useful investigations with regard to such comments. In this paper, the DE is compared with the generalized random tunneling algorithm (GRTA) and the particle swarm optimization (PSO) that are the global optimization techniques for continuous design variables. Through the examinations, some common characteristics as the global optimization technique are clarified in this paper. Through benchmark test problems including structural optimization problems, the search ability of DE as the global optimization technique is examined. © 2011 Elsevier B.V. All rights reserved

Multi-objective Particle Swarm Optimization considering the diversity of the inferior solutions

In this paper, a simple method for the multi-objective optimization problems by the Particle Swarm Optimization (PSO) is proposed. The objectives of the Multi Objective Evolutionary Algorithms (MOEA) are summarized as follows : (1) To find the pareto optimal solutions, (2) To find the pareto optimal solutions as diverse as possible. To achieve these objectives by the PSO for the single objective problems, we propose how to define the g-best in this swarm without introducing some new parameters. That is, one particle among the non-inferior solutions is selected as the g-best to achieve the diversity among the non inferior solutions. The relative distance in the objective space is utilized to select the gbest among the non-inferior solutions. Additionally, some particles among the non inferior solutions are also selected as the gbest of the inferior solutions to find the pareto optimal solutions. The absolute distance in the objective space is utilized to select the g-best of the inferior solutions. We also show the geometric interpretation about the movement of particles. The validity of proposed approach is examined through typical numerical examples

Proposal of adaptive range particle swarm optimization

金沢大学大学院自然科学研究科知的システム創成金沢大学工学部This paper proposes a new method which is called as Adaptive Range Particle Swarm Optimization (ARPSO), based on Adaptive Range Genetic Algorithm. That is, the active search domain is determined by using the mean and standard deviation of each design variable. In general, multipoints methods are utilized in the field of evolutionary computation. At the initial search stage it is preferable to explore the search domain widely, and is also preferable to explore the smaller search domain as the search goes on. To achieve this objective, new parameter which determines the active search domain is introduced. This new parameter gradually increases as the search goes on. Finally it is possible to shrink the search domain. The way to determine the maximum value of this new parameter is also shown in this paper. The optimum solution with high accuracy and a. little number of function calls is obtained by proposed method in compared with original Particle Swarm Optimization. Through numerical examples, the effectiveness and validity of proposed method are examined

A single Gly114Arg mutation stabilizes the hexameric subunit assembly and changes the substrate specificity of halo-archaeal nucleoside diphosphate kinase

AbstractNucleoside diphosphate kinase from extremely halophilic archaeon (HsNDK) requires above 2M NaCl concentration for in vitro refolding. Here an attempt was made to isolate mutations that allow HsNDK to refold in low salt media. Such a screening resulted in isolation of an HsNDK mutant, Gly114Arg, which efficiently refolded in the presence of 1M NaCl. This mutant, unlike the wild type enzyme, was expressed in Escherichia coli as an active form. The residue 114 is in close proximity to Glu155 of the neighboring subunit in the three dimensional hexameric structure of the HsNDK. It is thus possible that the attractive electrostatic interactions occur between Arg114 and Glu155 in the mutant HsNDK, stabilizing the hexameric subunit assembly

Activation of the chicken Ig-β locus by the collaboration of scattered regulatory regions through changes in chromatin structure

A total of 10 B-lymphocyte-specific DNase I hypersensitive sites located in the chicken Ig-β locus were divided into four regions and combinations of deletions of these regions were carried out. A decrease in transcription of the Ig-β gene to <3% was demonstrated in cells with deletions in all four regions. The Ig-β chromatin was resistant to DNase I digestion in these cells. Thus, the collaboration is shown to convert the Ig-β chromatin from the condensed state to a relaxed state. H3 and H4 acetylation decreased to <8% but H3K4 hypermethylation was observed at the Ig-β promoter and exon 3. The collaboration of four regions had virtually no effect on CG hypomethylation in the region upstream the transcriptional start site. Accordingly, neither the DNase I general sensitive state in the Ig-β chromatin nor hyperacetylation of H3 and H4 histones in the promoter proximal region causes H3K4 di-methylation or CG hypomethylation in the promoter. From these analyses, a chromatin situation was found in which both an active state, such as enhanced H3K4 methylation, or CG hypomethylation, and an inactive state, such as DNase I resistance in the Ig-β chromatin or hypoacetylation of H3 and H4 histones in the Ig-β locus, coexist