In this paper, a new third kind Chebyshev wavelets operational matrix of derivative is presented, then the operational matrix of derivative is applied for solving optimal control problems using, third kind Chebyshev wavelets expansions. The proposed method consists of reducing the linear system of optimal control problem into a system of algebraic equations, by expanding the state variables, as a series in terms of third kind Chebyshev wavelets with unknown coefficients. Example to illustrate the effectiveness of the method has been presented.

A high Tc superconductor with a nominal composition
(Bi1-xPbx)2(Sr1-yBay)2Ca2Cu3O10+δ for (0 £ x £ 0.5) and (0 £ y £ 0.5) was prepared by
a solid state reaction method. The effect of the substitution of Pb for Bi and Ba for Sr and
quenching temperature on the superconductivity has been investigated to obtain the
optimum conditions for the formation and stabilization of the high Tc phase (2223).
The results showed that the optimum sintering temperature for the pure composition is
equal to 875°C and the sintering time is equal to 240h with heating and cooling rate of
60°C/h . Our results indicated that a small amount of (Ba = 0.1) could raise the transition
temperature (Tc), but enhancing Ba to 0.4 has raised

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

The purpose of this article is to improve and minimize noise from the signal by studying wavelet transforms and showing how to use the most effective ones for processing and analysis. As both the Discrete Wavelet Transformation method was used, we will outline some transformation techniques along with the methodology for applying them to remove noise from the signal. Proceeds based on the threshold value and the threshold functions Lifting Transformation, Wavelet Transformation, and Packet Discrete Wavelet Transformation. Using AMSE, A comparison was made between them , and the best was selected. When the aforementioned techniques were applied to actual data that was represented by each of the prices, it became evident that the lift