An efficient maximum-likelihood-decoding algorithm for linear blockcodes with algebraic decoder

A new soft decoding algorithm for linear block codes is proposed. The decoding algorithm works with any algebraic decoder and its performance is strictly the same as that of maximum-likelihood-decoding (MLD). Since our decoding algorithm generates sets of different candidate codewords corresponding to the received sequence, its decoding complexity depends on the received sequence. We compare our decoding algorithm with Chase (1972) algorithm 2 and the Tanaka-Kakigahara (1983) algorithm in which a similar method for generating candidate codewords is used. Computer simulation results indicate, for some signal-to-noise ratios (SNR), that our decoding algorithm requires less average complexity than those of the other two algorithms, but the performance of ours is always superior to those of the other two     

Augmentation of polysialic acid by valproic acid in early postnatal mouse hippocampus and primary cultured hippocampal neurons

We examined the effects of valproic acid (VPA) on hippocampal neurons. Prenatal VPA exposure significantly increased polysialic acid (PSA) expression in the early postnatal mouse hippocampus. Moreover, VPA treatment significantly enhanced PSA expression in primary cultured hippocampal neurons and stimulated neurite growth. Our results suggest that VPA exposure in ovo affects hippocampal development.     

Analysis of energy consumption of elevator group supervisory control system based on genetic network programming

Elevator Group Supervisory Control System (EGSCS) is a traffic system, which provides the transportation services for passengers in modern buildings. As the elevator systems include uncertainty due to the future arrival of the passengers, it difficult to model, analyze, and optimize the elevator group supervisory control system. Recently, artificial intelligence technology has been used in such complex systems. Genetic Network Programming(GNP), a graph‐based evolutionary method extended from genetic algorithm and genetic programming, has been already applied to EGSCS. On the other hand, since energy consumption is becoming one of the greatest challenges in the society, it should be taken as one of the criteria of the elevator operations. The elevators with maximum energy efficiency are therefore required. In this paper, the GNP is used to solve EGSCS with energy consumption (EC). Moreover, the idle car assignment has been embedded in the proposed method. Finally, the simulations show that some factors should be introduced into GNP in order to deal with the higher EC in the light traffic of the elevator systems. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A portfolio selection model using genetic relation algorithm and genetic network programming

In this paper, new evolutionary computation methods named genetic relation algorithm (GRA) and genetic network programming (GNP) have been applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up the most efficient portfolio, the proposed model considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. Then, the selected portfolio is further optimized by the stock trading model of GNP. In a sense, the proposed model is an integrated intelligent model. A comprehensive analysis of the results is provided, and it is clarified that the proposed model can obtain much higher profits than other traditional methods. © 2011 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.     

Performance of non-destructive evaluation by diffracted SH ultrasonic waves in predicting degree of fatigue in cyclic bending of ferritic steel

The degree of fatigue in ferritic wrought steel during cyclic bending was determined by analysis of diffracted SH ultrasonic waves with the aid of multiple regression analysis. As the degree of fatigue increases, accompanied by residual stress, the incident waves curve to the interior region with positive stress field owing to the acoustoelastic effect. As a result, the propagation time of the launched waves lengthens, leading to a modulation of the received waveform. Multiple regression analysis for the waveform modulation produces a reliable estimation, with correlation coefficient of 0.948, for the degree of fatigue.     

PEI-assisted preparation of Au nanoparticles via reductive crystallization process

Nano-sized gold particles were prepared in a solution containing polyethylenimine (PEI) utilizing a batch reactor. PEI acts as a reducing agent as well as a stabilizing agent of nanoparticles. The effects of initial concentration of PEI on the mean particle diameter, coefficient of variation (C.V.), growth rate, nucleation rate and the number of nuclei were studied. The particle diameter of gold decreased markedly with increasing initial concentration of PEI, from 3.3 μm to a minimum value of about 5 nm. The observed decrease of particle diameter was considered to be caused by the growth-inhibiting effect of PEI, which affects the conclusive number of nuclei.     

A FAST METHOD OF RADIATIVE HEAT TRANSFER ANALYSIS BETWEEN ARBITRARY THREE-DIMENSIONAL BODIES COMPOSED OF SPECULAR AND DIFFUSE SURFACES

The radiation element method by ray emission model (REM²) has been improved by using the law of reciprocity for the specular view factor and the incomplete Cholesky conjugate gradient (ICCG) method to reduce computational time. This improved method was applied to analyze the radiative heat transfer between arbitrary three-dimensional bodies composed of specular and diffuse surfaces. The accuracy of the improved method was evaluated by comparing analytical solutions. And the method was used to calculate radiative heat transfer between machine elements and heater panels. CPU time to calculate the radiative exchange for a model composed of 3193 elements was reduced to 1/430 of that by the previous numerical method using a decomposition method.     

Molecular Features and Clinical Management of Hereditary Gynecological Cancers

Hereditary gynecological cancers are caused by several inherited genes. Tumors that arise in the female reproductive system, such as ovaries and the uterus, overlap with hereditary cancers. Several hereditary cancer-related genes are important because they might lead to therapeutic targets. Treatment of hereditary cancers should be updated in line with the advent of various new methods of evaluation. Next-generation sequencing has led to rapid, economical genetic analyses that have prompted a concomitant and significant paradigm shift with respect to hereditary cancers. Molecular tumor profiling is an epochal method for determining therapeutic targets. Clinical treatment strategies are now being designed based on biomarkers based on tumor profiling. Furthermore, the National Comprehensive Cancer Network (NCCN) guidelines significantly changed the genetic testing process in 2020 to initially consider multi-gene panel (MGP) evaluation. Here, we reviewed the molecular features and clinical management of hereditary gynecological malignancies, such as hereditary breast and ovarian cancer (HBOC), and Lynch, Li–Fraumeni, Cowden, and Peutz–Jeghers syndromes. We also reviewed cancer-susceptible genes revealed by MGP tests.     

Effect of Ultrasonic Irradiation on the Probability of Primary Nucleation of L-Arginine Hydrochloride

Most of the amino acids that are utilized as medical raw materials and food additives show polymorphism. To improve the functionality of amino acid crystals, an effective method of polymorph control is required in the crystallization process. Here, primary nucleation of L-arginine hydrochloride by ultrasonication was investigated. L-Arginine hydrochloride exhibits polymorphism, and it crystallizes into three distinct crystal forms. A cooling crystallization experiment was performed, and nucleation of each polymorph upon ultrasonication was observed. In addition, the nucleation was analyzed using the nucleation probability theory. The results indicate that ultrasonic irradiation would significantly induce the nucleation of a particular polymorph.