In this paper, some estimators of the unknown shape parameter and reliability function  of Basic Gompertz distribution (BGD) have been obtained, such as MLE, UMVUE, and MINMSE, in addition to estimating Bayesian estimators under Scale invariant squared error loss function assuming informative prior represented by Gamma distribution and non-informative prior by using Jefferys prior. Using Monte Carlo simulation method, these estimators of the shape parameter and R(t), have been compared based on mean squared errors and integrated mean squared, respectively

In this research ,Undoped Nio and 1%Li doped Nio thin films were deposited utilizing chemical spray pyrolysis on the glass substrates heated (450C). The effects of non-thermal plasma on the structural and optical properties were studied. XRD measurement shows that Nio and Nio:1%Li films were found to be polycrystalline and have cubic structure with a preferred orientation (111). Decreased crystal size after exposure especially at (7) sec. AFM data indicate that the surface roughness average and (RMS) values of the prepared doped films are increasing after exposure to plasma, the transmittance increases after doped samples exposure to plasma, it was found that the energy gap value decreased when doped samples exposure to plasma, also, thickn

Features is the description of the image contents which could be corner, blob or edge. Corners are one of the most important feature to describe image, therefore there are many algorithms to detect corners such as Harris, FAST, SUSAN, etc. Harris is a method for corner detection and it is an efficient and accurate feature detection method. Harris corner detection is rotation invariant but it isn’t scale invariant. This paper presents an efficient harris corner detector invariant to scale, this improvement done by using gaussian function with different scales. The experimental results illustrate that it is very useful to use Gaussian linear equation to deal with harris weakness.

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

Nowadays, information systems constitute a crucial part of organizations; by losing security, these organizations will lose plenty of competitive advantages as well. The core point of information security (InfoSecu) is risk management. There are a great deal of research works and standards in security risk management (ISRM) including NIST 800-30 and ISO/IEC 27005. However, only few works of research focus on InfoSecu risk reduction, while the standards explain general principles and guidelines. They do not provide any implementation details regarding ISRM; as such reducing the InfoSecu risks in uncertain environments is painstaking. Thus, this paper applied a genetic algorithm (GA) for InfoSecu risk reduction in uncertainty. Finally, the ef

Necessary and sufficient conditions for the operator equation I AXAX n*, to have a real positive definite solution X are given. Based on these conditions, some properties of the operator A as well as relation between the solutions X andAare given.

Abstract:

Aim: The goal of this research was to study the influence of Er,Cr:YSGG laser at short pulse duration (60 µsec) on the number of streptococcus mutans bacteria in vitro.

Material and Methods:  twenty-eight extracted third molars free of caries, cracks, and other irregularities were used. For the testing of the materials, both the agar well technique and a tooth cavity model were employed. The agar wells of plates that had been inoculated with Streptococcus mutans previously were stuffed with the test materials, in order to conduct the tests. The zones of inhibition were assessed using millimeter measurements, after an incubation period of 48 hours .In order to a

In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function lnL(φ1, θ1) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (φ1, θ1) for the ARMA(1,1) model. Anot