This paper is concerned with preliminary test double stage shrinkage estimators to estimate the variance (s²) of normal distribution when a prior estimate  of the actual value (s²) is a available when the mean is unknown  , using specifying shrinkage weight factors y(×) in addition to pre-test region (R).

      Expressions for the Bias, Mean squared error [MSE (×)], Relative Efficiency [R.EFF (×)], Expected sample size [E(n/s²)] and percentage of overall sample saved of proposed estimator were derived. Numerical results (using MathCAD program) and conclusions are drawn about selection of different constants including in the me

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

In drilling processes, the rheological properties pointed to the nature of the run-off and the composition of the drilling mud. Drilling mud performance can be assessed for solving the problems of the hole cleaning, fluid management, and hydraulics controls. The rheology factors are typically termed through the following parameters: Yield Point (Yp) and Plastic Viscosity (μp). The relation of (YP/ μp) is used for measuring of levelling for flow. High YP/ μp percentages are responsible for well cuttings transportation through laminar flow. The adequate values of (YP/ μp) are between 0 to 1 for the rheological models which used in drilling. This is what appeared in most of the models that were used in this study. The pressure loss

A numerical computation for determination transmission coefficient and resonant tunneling energies of multibarriers heterostructure has been investigated. Also, we have considered GaN/Al_0.3Ga_0.7N superlattice system to estimate the probability of resonance at specific energy values, which are less than the potential barrier height. The transmission coefficient is determined by using the transfer matrix method and accordingly the resonant energies are obtained from the T(E) relation. The effects of both well width and number of barriers (N) are observed and discussed. The numbers of resonant tunneling peaks are generally increasing and they become sharper with the increasing of N. The resonant tunneling levels are sh

A numerical computation for determination transmission coefficient and resonant tunneling energies of multibarriers heterostructure has been investigated. Also, we have considered GaN/Al0.3Ga0.7N superlattice system to estimate the probability of resonance at specific energy values, which are less than the potential barrier height. The transmission coefficient is determined by using the transfer matrix method and accordingly the resonant energies are obtained from the T(E) relation. The effects of both well width and number of barriers (N) are observed and discussed. The numbers of resonant tunneling peaks are generally increasing and they become sharper with the increasing of N. The resonant tunneling levels are shifted inside the well by

The utilization of artificial intelligence techniques has garnered significant interest in recent research due to their pivotal role in enhancing the quality of educational offerings. This study investigated the impact of employing artificial intelligence techniques on improving the quality of educational services, as perceived by students enrolled in the College of Pharmacy at the University of Baghdad. The study sample comprised 379 male and female students. A descriptive-analytical approach was used, with a questionnaire as the primary tool for data collection. The findings indicated that the application of artificial intelligence methods was highly effective, and the educational services provided to students were of exceptional

In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.

The cytotoxic effect of catechol was examined in two human cancer cell lines, Epidermoid larynx carcinoma (Hep- 2), Cerebral glioblastoma multiforme (AMGM-5) and Murine mammary adenocarcinomacell (AMN3) treated with half concentrations of catechol (1000, 500, 250, 125, 62.5 and 32.25 μM) for 72 hr. The get hold of results showed catechol have a toxic effect of the cell viability of three types of cell lines after 72h of exposure, the toxicity was dependent on catechol concentrations and/or autoxidation for quinines formation, there were a marked decreased of cell viability in a dose dependent manner in all cell line types. Inhibition concentration of catechol for 50% of cell viability (IC50) were calculated, they were at 581.5 μM, 478 μM

The limitations of wireless sensor nodes are power, computational capabilities, and memory. This paper suggests a method to reduce the power consumption by a sensor node. This work is based on the analogy of the routing problem to distribute an electrical field in a physical media with a given density of charges. From this analogy a set of partial differential equations (Poisson's equation) is obtained. A finite difference method is utilized to solve this set numerically. Then a parallel implementation is presented. The parallel implementation is based on domain decomposition, where the original calculation domain is decomposed into several blocks, each of which given to a processing element. All nodes then execute computations in parall