Transforming the common normal distribution through the generated Kummer Beta model to the Kummer Beta Generalized Normal Distribution (KBGND) had been achieved. Then, estimating the distribution parameters and hazard function using the MLE method, and improving these estimations by employing the genetic algorithm. Simulation is used by assuming a number of models and different sample sizes. The main finding was that the common maximum likelihood (MLE) method is the best in estimating the parameters of the Kummer Beta Generalized Normal Distribution (KBGND) compared to the common maximum likelihood according to Mean Squares Error (MSE) and Mean squares Error Integral (IMSE) criteria in estimating the hazard function. While the practical side showed that the hazard function is increasing, i.e. the increment in staying the teachers in the service, they will be exposed to a greater failure rate as a result of the staying period which decreases in its turn.
In this research is estimated the function of reliability dynamic of multi state systems and their compounds and for three types of systems (serial, parallel, 2-out-of-3) and about two states (Failure and repair) depending on calculating the structur function allow to describing the behavior of
In this paper, we prove some coincidence and common fixed point theorems for a pair of discontinuous weakly compatible self mappings satisfying generalized contractive condition in the setting of Cone-b- metric space under assumption that the Cone which is used is nonnormal. Our results are generalizations of some recent results.
This paper is focused on studying the effect of cutting parameters (spindle speed, feed and depth of cut) on the response (temperature and tool life) during turning process. The inserts used in this study are carbide inserts coated with TiAlN (Titanum, Aluminium and Nitride) for machining a shaft of stainless steel 316L. Finite difference method was used to find the temperature distribution. The experimental results were done using infrared camera while the simulation process was performed using Matlab software package. The results showed that the maximum difference between the experimental and simulation results was equal to 19.3 , so, a good agreement between the experimental and simulation results was achieved. Tool life w
... Show MoreA 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
... Show MoreThis study discussed a biased estimator of the Negative Binomial Regression model known as (Liu Estimator), This estimate was used to reduce variance and overcome the problem Multicollinearity between explanatory variables, Some estimates were used such as Ridge Regression and Maximum Likelihood Estimators, This research aims at the theoretical comparisons between the new estimator (Liu Estimator) and the estimators
This experiment was conducted to examine the effects of injected gibberellic acid GA3 in subcutaneous of hens neck and supplemental vitamin D3 to control basal diet on productive performance and egg shell thickness ,relative weight of egg shell of aged laying hens. Two hundred and seventy Lohmann Brown laying hens at 73 weeks of age were randomly assigned to three treatments groups. Each treatment consist of three replicates (30 hens / replicate). The treatments were : T1 control were injected subcutaneous with 0.2 ml / kg of body weight of ethanol: sesame oil solution, T2 and T3 were injected subcutaneous with 0.2 ml / kg of body weight of ethanol: sesame oil solution which contained 400 μg GA3/ kg of body weight /week during 8 weeks (tre
... Show MoreArtificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett’s method, and Durbin
... Show MoreAbstractThe objective of the present study was measured of several oxidative stresses and liver function parameters in workers occupationally exposed to cement dust in Kufa Cement Factory, in order to test the hypothesis that cement dust exposure may perturb these parameters. Assessment of oxidative stress and liver function parameters were performed in 63 workers occupationally, in different departments of Kufa Cement Factory, exposed to cement dust (range of the exposure time was 5-38 years) and 36 matched unexposed controls. The study results illustrated an increasing in the oxidative stress parameters, moreover; liver function parameters showed abnormal results in the exposed workers compared to the unexposed. An increase in theses para
... Show MoreThe multiple linear regression model is an important regression model that has attracted many researchers in different fields including applied mathematics, business, medicine, and social sciences , Linear regression models involving a large number of independent variables are poorly performing due to large variation and lead to inaccurate conclusions , One of the most important problems in the regression analysis is the multicollinearity Problem, which is considered one of the most important problems that has become known to many researchers , As well as their effects on the multiple linear regression model, In addition to multicollinearity, the problem of outliers in data is one of the difficulties in constructing the reg
... Show MoreIn recent years, the attention of researchers has increased of semi-parametric regression models, because it is possible to integrate the parametric and non-parametric regression models in one and then form a regression model has the potential to deal with the cruse of dimensionality in non-parametric models that occurs through the increasing of explanatory variables. Involved in the analysis and then decreasing the accuracy of the estimation. As well as the privilege of this type of model with flexibility in the application field compared to the parametric models which comply with certain conditions such as knowledge of the distribution of errors or the parametric models may
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