The Effect of NaY nano-Zeolite on the Mechanical Properties of Silicon Insoles

Silicon - The chemical composition of the insoles inbounded materials influences some of the mechanical properties of silicon insoles including; tensile strain and modules and moisture adsorption...     

Recovery and Amino Acid Composition of Protein Precipitates Isolated from Rice Starch Processing Liquors

Precipitates containing 47.7% and 30.5% protein were isolated from rice starch steep and sorter liquors respectively by pH adjustment of these liquors. The pH for protein precipitation from the steep liquor was 7.0 and from the sorter liquor was 5.0. The acid hydrolysates of both precipitates contained at least seventeen amino acids, Glutamic acid and arginine constitute the largest proportions of these acids. Nine essential amino acids were found to be present in variable amounts. The industrial application of this method for the preparation of protein precipitates is discussed.     

A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

In cognitive radio networks (CoR), the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability. Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection. However, these methods do not take into account the effect of sample size and its effect on improving CoR performance. In general, a large sample size results in more reliable detection, but takes longer sensing time and increases complexity. Thus, the locally sensed sample size is an optimization problem. Therefore, optimizing the local sample size for each cognitive user helps to improve CoR performance. In this study, two new methods are proposed to find the optimum sample size to achieve objective-based improved (single/double) threshold energy detection, these methods are the optimum sample size N* and neural networks (NN) optimization. Through the evaluation, it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate, detection probability, and throughput.

     

Fixed-time fault-tolerant control of uncertain MIMO switched interconnected systems subject to state delay,unknown control directions,and quantized nonlinear inputs

This paper investigates design of an adaptive fixed-time fault-tolerant decentralized controller for a class of uncertain multi-input multi-output (MIMO) switched large-scale non-strict interconnected systems under arbitrary switching subject to unknown control directions, quantized nonlinear inputs, actuator failures unknown external disturbances, and unmodeled dynamics. In addition to interconnected terms, time-varying delayed interconnected terms have been considered in the system model which makes it more general than previous works in the literature. The proposed controller can handle switched systems with unknown switching signal and different types of input nonlinearities including, saturation, backlash, and dead-zone. The singularity problem in designing the fixed time controller has been solved. The quantizer and actuators fault parameters are assumed to be unknown. The Razumikhin lemma has been used to deal with the delayed interconnected terms. To cope with the system unknown dynamics, neural networks (NNs) have been applied and by updating the maximum norms of the networks weight vectors the computational load has been reduced. The explosion of complexity occurring in the traditional back-stepping technique has been avoided by applying dynamic surface control (DSC). Finally, by defining an appropriate common Lyapunov function (CLF), fixed-time convergence of system outputs and the closed-loop system stability have been established. The effectiveness of the proposed controller has been shown via simulation study.     

Experimental and Simulation Investigations of Mechanical Properties and Gamma Radiation Shielding of Lithium Cadmium Gadolinium Silicate Glasses Doped Erbium Ions

Silicon - Melt-quenching method used to fabricate SiO2-Li2O - CdO – Gd2O3 – Er2O3 glass system. The XRD diffractometer procedure is used to check the status of these samples. The...     

The synergistic effect of texture and surface roughness on electrophoretic deposited bioactive glass coating

To improve the biocompatibility of AISI 316L, bioactive glass (BG) coating of SiO₂–CaO–P₂O₅ which helps bonding with bone implants was used by an electrophoretic deposition method. Before coating deposition, the samples were treated by shot peening, known as an efficient process for metal grain refinement and fatigue properties. The stainless steel 316L was investigated in terms of microstructure, texture, and roughness. This research covers the effects of chosen shot peening parameter on the BG-coating properties on the obtained results. Shot peening was carried using two different sets of parameters as conventional shot peening, and severe shot peening. Wettability, roughness, microstructure, coating thickness, and corrosion behavior of coated sample were investigated in terms of potentiodynamic polarization and electrochemical impedance spectroscopy in simulated body fluid (SBF) solutions at 37°C. The results indicated that the coating thickness decreased from 35.5 ± 10 µm for coated not peened (CNP) to 20 ± 5 and 17 ± 2 µm for coated conventionally shot-peened (CCSP) and coated severely shot-peened (CSSP), respectively. As well as, the water contact angle of CSSP sample was equal to 15.71° which is much lower than CNP (20.7°). The protection ability of the tested samples in the SBF was improved in the following order: CCSP < CNP < CSSP.     

多层鳍线本征模的精确有效和完备求解

对多层鳍线现有的奇异积分方程法进行了如下改进：１）适当选择导体带所在平面切向电场和表面电流的线性组合,以使得导出奇异积分方程的级数具有很快的收敛特性;２）对于附加的边界条件,利用其级数的渐近特性来加速其收敛;３）给出系统计算任意阶特征矩阵元素的解析方法,以避免数值积分或对无穷级数求和;４）为完备地求解本征模的传播常数,构造了一个解析函数,保留特征矩阵行列式的所有零点,但消除其所有奇点．采用本文的奇异积分方程法,首次精确有效完备地求解了鳍线中的大量本征模．     

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.