A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others

Precision is one of the main elements that control the quality of a geodetic network, which defines as the measure of the network efficiency in propagation of random errors. This research aims to solve ZOD and FOD problems for a geodetic network using Rosenbrock Method to optimize the geodetic networks by using MATLAB programming language, to find the optimal design of geodetic network with high precision. ZOD problem was applied to a case study network consists of 19 points and 58 designed distances with a priori deviation equal to 5mm, to determine the best points in the network to consider as control points. The results showed that P55 and P73 having the minimum ellipse of error and considered as control points. FOD problem was applie

In this study, the stress-strength model R = P(Y < X < Z)  is discussed as an important parts of reliability system by assuming that the random variables follow Invers Rayleigh Distribution. Some traditional estimation methods are used    to estimate the parameters  namely; Maximum Likelihood, Moment method, and Uniformly Minimum Variance Unbiased estimator and Shrinkage estimator using three types of shrinkage weight factors. As well as, Monte Carlo simulation are used to compare the estimation methods based on mean squared error criteria.  

Background: Alterations in the microhardness and roughness are commonly used to analyze the possible negative effects of bleaching products on restorative materials. This in vitro study evaluated the effect of in-office bleaching (SDI pola office +) on the surface roughness and micro-hardness of four newly developed composite materials (Z350XT –nano-filled, Z250XT-nano-hybrid, Z250-mico-hybrid and Silorane-silorane based). Materials and methods: Eighty circular samples with A3 shading were prepared by using Teflon mold 2mm thickness and 10mm in diameter. 20 samples for each material, 10 samples for base line measurement (surface roughness by using portable profillometer, and micro-hardness by usingDigital Micro Vickers Hardness Test

In this study, mesoporous silica (MPS) is made using the sol-gel method from a cheap source (Na2SiO3) using the surfactant hydroxycetyl hydroxyethyl dimonium chloride as a template. The task is the adsorption-based removal of the medication metoprolol (MP) at concentrations between 10 and 50 ppm. Variables such as: contact time, dose of adsorbent, starting concentration of adsorbate, and adsorption temperature were studied which show the equilibrium time and adsorbent dose are 40 min and 0.05 g respectively. The Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherm models were fitted to the data obtained from the experiments. Comparing the outcomes showed that, of the four investigated isotherm models, the Freundlich equation m

            In this paper the research represents an attempt of expansion in using the parametric and non-parametric estimators to estimate the median effective dose ( ED50 ) in the quintal bioassay and comparing between  these methods . We have Chosen three estimators for Comparison. The first estimator is
( Spearman-Karber )  and the second estimator is ( Moving Average ) and The Third estimator  is ( Extreme Effective Dose ) . We used a minimize Chi-square as a parametric method. We made a Comparison for these estimators by calculating the mean square error of (ED50) for each one of them and comparing it with the optimal the mean square

In the present study, a powder mixture of elements Ti and Ni was mechanically alloyed in a high energy ball mill. Microstructure of the nanosized amorphous milled product in different stages of milling has been characterized by X- ray diffraction, scanning electron microscopy and differential thermal analysis. We found that time of mechanical alloying is more significant to convert all crystalline structure to the amorphous phase. Nanocrystalline phase was achieved as a result of the mechanical alloying process. The results also indicates that the phase transformation and the grain size occurs in these alloys are controlled by ball milling time

In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.

The question of estimation took a great interest in some engineering, statistical applications, various applied, human sciences, the methods provided by it helped to identify and accurately the many random processes.

In this paper, methods were used through which the reliability function, risk function, and estimation of the distribution parameters were used, and the methods are (Moment Method, Maximum Likelihood Method), where an experimental study was conducted using a simulation method for the purpose of comparing the methods to show which of these methods are competent in practical application This is based on the observations generated from the Rayleigh logarithmic distribution (RL) with sample sizes