In this study, the plasma formed by the preparation of Se and Tin (Sn) using a Nd: YAG laser with a wavelength of 1064 nm in air, which was then studied using the technique of optical emission spectrum, was presented (OES).The laser-induced plasma parameters such an electron temperature (Te) were identified using two-ratio methods, using Stark broadening methods to determine the density of electrons (n_e). According to the findings, there is a correlation between the amount of laser energy that is applied and the increase in the emission intensity of the spectral lines. In the case of Se plasma, an increase in laser energy causes a rise in the temperature of the electrons. While increasing the temperature of the elec

This paper deals with, Bayesian estimation of the parameters of Gamma distribution under Generalized Weighted loss function, based on Gamma and Exponential priors for the shape and scale parameters, respectively. Moment, Maximum likelihood estimators and Lindley’s approximation have been used effectively in Bayesian estimation. Based on Monte Carlo simulation method, those estimators are compared in terms of the mean squared errors (MSE’s).

        In this paper, an estimate has been made for parameters and the reliability function for Transmuted power function (TPF) distribution through using some estimation methods as proposed new technique for white, percentile, least square, weighted least square and modification moment methods. A simulation was used to generate random data that follow the (TPF) distribution on three experiments (E₁ , E₂ , E₃)  of the real values of the parameters, and with sample size (n=10,25,50 and 100) and iteration samples (N=1000), and taking reliability times (0< t < 0) . Comparisons have been made between the obtained results from the estimators using mean square error (MSE). The results showed the

An optimization analysis of drilling process constitutes a powerful tool for operating under desired pressure levels and simultaneously maximizing the penetration rate, which reduces costs and time thus increases the profit.
In this study, a composite drilling model (Young-Bourgyen model) of eight functions was used to determine the optimum drilling mechanical parameters (Weight on bit and rotary speed) for an Iraqi oil field. These functions model the effect of most drilling parameters such as formation strength, mud density, formation compaction, weight on bit, rotary speed, tooth dullness, and bit hydraulic on drilling rate. Data are extracted from bit record and drilling report of well BUZ-20 for calculation of eight exponents of

     In this paper, suggested formula as well a conventional method for estimating the twoparameters (shape and scale) of the Generalized Rayleigh Distribution was proposed. For different sample sizes (small, medium, and large) and assumed several contrasts for the two parameters a percentile estimator was been used. Mean Square Error was implemented as an indicator of performance and comparisons of the performance have been carried out through data analysis and computer simulation between the suggested formulas versus the studied formula according to the applied indicator. It was observed from the results that the suggested method which was performed for the first time (as far as we know), had highly advantage than t

In this paper, Bayes estimators for the shape and scale parameters of Weibull distribution have been obtained using the generalized weighted loss function, based on Exponential priors. Lindley’s approximation has been used effectively in Bayesian estimation. Based on theMonte Carlo simulation method, those estimators are compared depending on the mean squared errors (MSE’s).

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Methods of estimating statistical distribution have attracted many researchers when it comes to fitting a specific distribution to data. However, when the data belong to more than one component, a popular distribution cannot be fitted to such data. To tackle this issue, mixture models are fitted by choosing the correct number of components that represent the data. This can be obvious in lifetime processes that are involved in a wide range of engineering applications as well as biological systems. In this paper, we introduce an application of estimating a finite mixture of Inverse Rayleigh distribution by the use of the Bayesian framework when considering the model as Markov chain Monte Carlo (MCMC). We employed the Gibbs sampler and