This research aims to investigate the thermal performance of different thermal composite insulators, wrapped around a closed-loop copper pipe (CLP). To achieve this aim a system was designed and manufactured. It is consisted of closed water tank insulated by Rock Wool, and supplied with two electric heaters, two thermostat, a flow meter, a water pump, digital temperature scales, and four series of (CLP).
Six insulators were prepared namely; composites of Impregnated Fiberglass with Elastoclad and foaming Rubber (FER), Impregnated Fiberglass with Elastoclad resin and Polymeric Membrane (FEM), Impregnated Fiberglass with Polyurethane thermoset resin and Foaming Rubber (FUR), Impregnated Fiberglass with Polyurethane thermoset resin and P

This paper deal with the estimation of the shape parameter (a) of Generalized Exponential (GE) distribution when the scale parameter (l) is known via preliminary test single stage shrinkage estimator (SSSE) when a prior knowledge (a₀) a vailable about the shape parameter as initial value due past experiences as well as suitable region (R) for testing this prior knowledge.

The Expression for the Bias, Mean squared error [MSE] and Relative Efficiency [R.Eff(×)] for the proposed estimator are derived. Numerical results about beha

The two parameters of Exponential-Rayleigh distribution were estimated using the maximum likelihood estimation method (MLE) for progressively censoring data. To find estimated values for these two scale parameters using real data for COVID-19 which was taken from the Iraqi Ministry of Health and Environment, AL-Karkh General Hospital. Then the Chi-square test was utilized to determine if the sample (data) corresponded with the Exponential-Rayleigh distribution (ER). Employing the nonlinear membership function (s-function) to find fuzzy numbers for these parameters estimators. Then utilizing the ranking function transforms the fuzzy numbers into crisp numbers. Finally, using mean square error (MSE) to compare the outcomes of the survival

In 2020 one of the researchers in this paper, in his first research, tried to find out the Modified Weighted Pareto Distribution of Type I by using the Azzalini method for weighted distributions, which contain three parameters, two of them for scale while the third for shape.This research compared the distribution with two other distributions from the same family; the Standard Pareto Distribution of Type I and the Generalized Pareto Distribution by using the Maximum likelihood estimator which was derived by the researchers for Modified Weighted Pareto Distribution of Type I, then the Mont Carlo method was used–that is one of the simulation manners for generating random samples data in different sizes ( n= 10,30,50), and in di

This paper presents stochastic analysis using the perturbation method to model the structure of a container to verify the distributions of probability of maximum and minimum axial forces reactions in piles. The proposed simulation of a container port terminal under 11 scenarios of load combinations was presented. The probability distributions for live loads are assigned according to the input parameters of simulation data. Part of the load itself is implicitly combined such as vertical live load which includes the weight of equipment and containers and wind load. The structural model was simulated in the software STAAD Pro., while the statistical analyses were performed with MATLAB. The results demonstrated that, the most significant extern

An intelligent software defined network (ISDN) based on an intelligent controller can manage and control the network in a remarkable way. In this article, a methodology is proposed to estimate the packet flow at the sensing plane in the software defined network-Internet of Things based on a partial recurrent spike neural network (PRSNN) congestion controller, to predict the next step ahead of packet flow and thus, reduce the congestion that may occur. That is, the proposed model (spike ISDN-IoT) is enhanced with a congestion controller. This controller works as a proactive controller in the proposed model. In addition, we propose another intelligent clustering controller based on an artificial neural network, which operates as a reactive co