Background: Glass ionomer restorations are widely employed in the field of pediatric dentistry. There is a constant demand for a durable restoration that remains functional until exfoliation. This study aimed to measure and compare the effect of a novel coating material (EQUIA Forte Coat) on the microleakage of glass hybrid restoration (EQUIA Forte HT) in primary teeth. Material and method: Thirty cavitated (class-II) primary molars were allocated randomly into two groups based on the coat application; uncoated (control) and coated group (experimental). Cavities were prepared by the use of a ceramic bur (CeraBur) and restored with EQUIA Forte HT with or without applying a protective coat (EQUIA Forte Coat). Samples went through the

Background: Glass ionomer restorations are widely employed in the field of pediatric dentistry. There is a constant demand for a durable restoration that remains functional until exfoliation. This study aimed to measure and compare the effect of a novel coating material (EQUIA Forte Coat) on the microleakage of glass hybrid restoration (EQUIA Forte HT) in primary teeth. Material and method: Thirty cavitated (class-II) primary molars were allocated randomly into two groups based on the coat application; uncoated (control) and coated group (experimental). Cavities were prepared by the use of a ceramic bur (CeraBur) and restored with EQUIA Forte HT with or without applying a protective coat (EQUIA Forte Coat). Samples went through the therm

Estimation of the tail index parameter of a one - parameter Pareto model has wide important by the researchers because it has awide application in the econometrics science and reliability theorem.

Here we introduce anew estimator of ^"generalized median" type and compare it with the methods of Moments and Maximum likelihood by using the criteria, mean square error.

 The estimator of generalized median type performing best over all.

The aim of this paper to find Bayes estimator under new loss function assemble between symmetric and asymmetric loss functions, namely, proposed entropy loss function, where this function that merge between entropy loss function and the squared Log error Loss function, which is quite asymmetric in nature. then comparison a the Bayes estimators of exponential distribution under the proposed function, whoever, loss functions ingredient for the proposed function the using a standard mean square error (MSE) and Bias quantity (M_bias), where the generation of the random data using the simulation for estimate exponential distribution parameters different sample sizes (n=10,50,100) and (N=1000), taking initial

In this study, we used Bayesian method to estimate scale parameter for the normal distribution. By considering three different prior distributions such as the square root inverted gamma (SRIG) distribution and the non-informative prior distribution and the natural conjugate family of priors. The Bayesian estimation based on squared error loss function, and compared it with the classical estimation methods to estimate the scale parameter for the normal distribution, such as the maximum likelihood estimation and th

In this paper an estimator of reliability function for the pareto dist. Of the first kind has been derived and then a simulation approach by Monte-Calro method was made to compare the Bayers estimator of reliability function and the maximum likelihood estimator for this function. It has been found that the Bayes. estimator was better than maximum likelihood estimator for all sample sizes using Integral mean square error(IMSE).

This paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model. The prior information is described as linear stochastic restrictions. We study the properties and the performances of these estimators compared to other common estimators using the mean squared error as a criterion for the goodness of fit. A numerical example and a simulation study are proposed to explain the performance of the estimators.