أن المساهمة الأساسية لهذا البحث هي وصف كيفية تحليل الأنظمة الخدمية المعقدة ذات خصائص الطابور الموجودة في مستشفى بغداد التعليمي العام باستخدام تقنيات شبكية وهي تقنية أسلوب (Q – GERT ) وهي اختصار من الكلمات : Queuing theory _ Graphical Evaluation and Review Technique أي أسلوب التقييم والمراجعة البياني حيث سوف يتم معرفة حركة انسيابية المرضى داخل النظام وبعد استخدام هذا المدخل سيتم تمثيل النظام على هيئة مخطط شبكي احتمالي وتحل

     In this research, several estimators concerning the estimation are introduced. These estimators are closely related to the hazard function by using one of the nonparametric methods namely the kernel function for censored data type with varying bandwidth and kernel boundary. Two types of bandwidth are used:  local bandwidth and global bandwidth. Moreover, four types of boundary kernel are used namely: Rectangle, Epanechnikov, Biquadratic and Triquadratic and the proposed function was employed with all kernel functions. Two different simulation techniques are also used for two experiments to compare these estimators. In most of the cases, the results have proved that the local bandwidth is the best for all the

Background: Difficulties arise when attempting to imagine the interpupillary line and comparing it with the Fox plane guide and not more difficult than holding any instrument over the movable pupils just to demonstrate the interpupillary line. The aim of this study was to introduce ear lobes as alternative landmarks for the interpupillary line during orientation of the occlusal plane. Also, the other aim was to compare the ear lobes with the pupils of the eyes to verify that they were indifferent as anatomical landmarks. Materials & methods: The alternative landmarks, ear lobes, were presented and the method for orienting the occlusal plane with these landmarks was introduced. Digital pictures of 30 subjects, who participated in the study,

Abstract

Characterized by the Ordinary Least Squares (OLS) on Maximum Likelihood for the greatest possible way that the exact moments are known , which means that it can be found, while the other method they are unknown, but approximations to their biases correct to 0(n^-1) can be obtained by standard methods. In our research expressions for approximations to the biases of the ML estimators (the regression coefficients and scale parameter) for linear (type 1) Extreme Value Regression Model for Largest Values are presented by using the advanced approach depends on finding the first derivative, second and third.