Local and global searches of approximate optimal designs of regular frames

Methods of local and global searches of approximate optimal designs minimizing total structural volume under stress and displacement constraints are presented for regular frames subjected to static loads. Nonuniqueness of the optimal solution is extensively utilized for local search of approximate optimal solutions, where the search direction is computed from singular value decomposition of the stiffness matrix with respect to the cross‐sectional areas, or the sensitivity matrix of the constraints. The distance between the solutions is then defined, and the approximate optimal solutions are globally and consecutively found so as to maximize the distance from the already found solutions under upper bound constraint on the total structural volume. The effectiveness of the proposed method is demonstrated in application to a plane frame. Copyright © 2005 John Wiley & Sons, Ltd.     

Interactive Evolutionary Computation-Based Hearing Aid Fitting

An interactive evolutionary computation (EC) fitting method is proposed that applies interactive EC to hearing aid fitting and the method is evaluated using a hearing aid simulator with human subjects. The advantages of the method are that it can optimize a hearing aid based on how a user hears and that it realizes whatever+whenever+wherever (W³) fitting. Conventional fitting methods are based on the user's partially measured auditory characteristics, the fitting engineer's experience, and the user's linguistic explanation of his or her hearing. These conventional methods, therefore, suffer from the fundamental problem that no one can experience another person's hearing. However, as interactive EC fitting uses EC to optimize a hearing aid based on the user's evaluation of his or her hearing, this problem is addressed. Moreover, whereas conventional fitting methods must use pure tones and bandpass noise for measuring hearing characteristics, our proposed method has no such restrictions. Evaluating the proposed method using speech sources, we demonstrate that it shows significantly better results than either the conventional method or the unprocessed case in terms of both speech intelligibility and speech quality. We also evaluate our method using musical sources, unusable for evaluation by conventional methods, and demonstrate that its sound quality is preferable to the unprocessed case     

An extended probabilistic model building genetic network programming using both of good and bad individuals

Classical estimation of distribution algorithms (EDAs) generally use truncation selection to estimate the distribution of the good individuals while ignoring the bad ones. However, various researches in evolutionary algorithms (EAs) have reported that the bad individuals may affect and help solving the problem. This paper proposes a new method to use the bad individuals by studying the substructures rather than the entire individual structures to solve reinforcement learning (RL) problems, which generally factorize their entire solutions to the sequences of state–action pairs. This work was studied in a recent graph‐based EDA named probabilistic model building genetic network programming (PMBGNP), which could solve RL problems successfully, to propose an extended PMBGNP. The effectiveness of this work is verified in an RL problem, namely robot control. Compared to other related work, results show that the proposed method can significantly speed up the evolution efficiency. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Acquisition of optimal attribute subset through genetic algorithm using GNP‐based class association rule mining

Attribute selection is a technique to prune less relevant information and discover high‐quality knowledge. It is especially useful for the classification of a large database, because the preprocessing of data increases the possibility that predictor attributes given to the mining algorithm become more relevant to the class attribute. In this paper, a method to acquire the optimal attribute subset for the genetic network programming (GNP) based class association rule mining has been proposed, and this attribute selection process using genetic algorithm (GA) leads to a higher accuracy for classification. Class association rule mining through GNP is conducted with a small subset of data rather than the original large number of attributes; thus simple but important rules are obtained for classification while the local optimal problem is avoided. Simulation results with educational data show that the classification accuracy is largely improved from 52.73 to 74.54%, when classification is made using the optimal attribute subset. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Image denoising using pulse coupled neural network with an adaptive Pareto genetic algorithm

Recently, pulse coupled neural network (PCNN) attracts much attention in image denoising as a nonlinear filtering technique. The PCNN‐based anisotropic diffusion (PCNN‐AD) method has been proposed previously for flicker noise reduction and its effectiveness has been demonstrated. Using the visual characteristics of PCNN, PCNN‐AD has also solved the problem of AD that AD is not able to suppress the isolate noise. However, there are still two drawbacks in PCNN‐AD, that is, time consuming and PCNN parameters' estimation. In order to improve the efficiency and the denoising performance of PCNN‐AD, a PCNN‐based method with an adaptive Pareto genetic algorithm (GA‐PCNN) has been proposed to restrain from additive white Gaussian noise (AWGN) in this paper. GA‐PCNN firstly integrates the PCNN and AD as a parallel system, then, optimizes the parameters of a simplified PCNN by the adaptive Pareto GA. Experimental results indicate that GA‐PCNN has better performances than the previous denoising techniques, i.e. median filter, Wiener filter, AD filter, and PCNN‐AD. The effectiveness of GA‐PCNN on AWGN reduction and edge preservation are shown finally. The results will also contribute to denoising in CMOS image sensors in the future. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Highly anisotropic thermal conductivity of mesogenic epoxy resin film through orientation control

To develop insulating materials with a high thermally conductive anisotropy, planarly aligned mesogenic epoxy (ME) resin film was fabricated by uniaxial coating on a hydrophobic polyethylene terephthalate substrate. Grazing incidence small-angle X-ray scattering (GISAXS) and transmission SAXS measurements exhibited that the films spontaneously formed uniaxially aligned monodomain-like smectic structures by curing on the hydrophobic substrate. Then, an in- and out-of-plane thermal conductivity of 10 and 0.048 W m⁻¹ K⁻¹ and outstanding thermal conductivity anisotropy of 208 have been confirmed, respectively. The ME resin films with high thermal conductivity can be applied as insulating materials for multiple-layer electrical and electronic devices.     

Links between Immune Cells from the Periphery and the Brain in the Pathogenesis of Epilepsy: A Narrative Review

Accumulating evidence has demonstrated that the pathogenesis of epilepsy is linked to neuroinflammation and cerebrovascular dysfunction. Peripheral immune cell invasion into the brain, along with these responses, is implicitly involved in epilepsy. This review explored the current literature on the association between the peripheral and central nervous systems in the pathogenesis of epilepsy, and highlights novel research directions for therapeutic interventions targeting these reactions. Previous experimental and human studies have demonstrated the activation of the innate and adaptive immune responses in the brain. The time required for monocytes (responsible for innate immunity) and T cells (involved in acquired immunity) to invade the central nervous system after a seizure varies. Moreover, the time between the leakage associated with blood–brain barrier (BBB) failure and the infiltration of these cells varies. This suggests that cell infiltration is not merely a secondary disruptive event associated with BBB failure, but also a non-disruptive event facilitated by various mediators produced by the neurovascular unit consisting of neurons, perivascular astrocytes, microglia, pericytes, and endothelial cells. Moreover, genetic manipulation has enabled the differentiation between peripheral monocytes and resident microglia, which was previously considered difficult. Thus, the evidence suggests that peripheral monocytes may contribute to the pathogenesis of seizures.     

Controlled radical polymerization with polyolefin macroinitiator: a convenient and versatile approach to polyolefin-based block and graft copolymers

This paper introduces the new synthetic methodology of polyolefin-based block and graft copolymers with polar segments [e.g., polystyrene and poly(meth)acrylates]. Various brominated polyolefins were prepared by bromination of polyolefins with N-bromosuccinimide. The resulting brominated polyolefins were able to initiate the controlled radical polymerization of polar monomers, such as methyl methacrylate, ethyl acrylate, t-butyl acrylate, styrene and 2-(dimethylamino)ethyl acrylate, using a CuBr/N,N,N′,N″,N″-pentamethyldiethylenetriamine catalyst system, leading to a variety of polyolefin-based copolymers with a different content of the corresponding polar segment. Because of the accessible synthesis of polyolefin macroinitiators, this synthetic methodology is expected to result in the preparation of a wide range of polyolefin-based block and graft copolymers.     

The effects of amino-acid mutations on specific interactions between urokinase-type plasminogen activator and its receptor: Ab initio molecular orbital calculations

During cancer invasion, the binding of urokinase-type plasminogen activator (uPA) to its receptor (uPAR) on the surface of a cancer cell is considered a trigger for invasion. Here, we present a stable structure of the solvated complex formed between uPA and uPAR (uPA-uPAR) and investigate the specific interactions between uPA and uPAR by ab initio fragment molecular orbital (FMO) calculations. The result indicates that the electrostatic interactions between the charged amino acid residues existing in both uPA and uPAR make a large contribution to the binding between uPA and uPAR. In particular, Lys23, Lys46, Lys98 and Lys61 of uPA are found to have strong attractive interactions with uPAR. To elucidate the effect of these residues on the interactions between uPA and uPAR, we substituted each of them with the uncharged amino acid Leu and investigated the interactions between the mutated uPA and wild-type uPAR. The interaction energies indicate that Lys46 and Lys98, which bind uPA to the rim of the central ligand-binding cavity of uPAR, make greater contributions to the binding between uPA and uPAR than Lys23, which is positioned at the bottom of the ligand-binding cavity of uPAR. The effect of hydrating water molecules located between uPA and uPAR is also investigated to be significant for the specific interactions between uPA and uPAR. These results are expected to be informative for developing new peptide antagonists that block the binding of uPA to uPAR.