The aim of this study is to understand the effect of addition carbon types on aluminum electrical conductivity which used three fillers of carbon reinforced aluminum at different weight fractions. The experimental results showed that electrical conductivity of aluminum was decreased by the addition all carbon types, also at low weight fraction of carbon black; it reached (4.53S/cm), whereas it was appeared highly increasing for each carbon fiber and synthetic graphite. At (45%) weight fraction the electrical conductivity was decreased to (4.36Scm) and (4.27Scm) for each carbon fiber and synthetic graphite, respectively. While it was reached to maximum value with carbon black. Hybrid composites were investigated also; the results exhibit tha

The influence of different thickness (500,750, and 1000) nm on the structure properties electrical conductivity and hall effect measurements have been investigated on the films of copper indium selenide CuInSe2 (CIS) the films were prepared by thermal evaporation technique on glass substrates at RT from compound alloy. The XRD pattern show that the film have poly crystalline structure a, the grain size increasing with as a function the thickness. Electrical conductivity (σ), the activation energies (Ea1,Ea2), hall mobility and the carrier concentration are investigated as function of thickness. All films contain two types of transport mechanisms of free carriers increase films thickness. The electrical conductivity increase with thickness

The cross section evaluation for (α,n) reaction was calculated according to the available International Atomic Energy Agency (IAEA) and other experimental published data . These cross section are the most recent data , while the well known international libraries like ENDF , JENDL , JEFF , etc. We considered an energy range from threshold to 25 M eV in interval (1 MeV). The average weighted cross sections for all available experimental and theoretical(JENDL) data and for all the considered isotopes was calculated . The cross section of the element is then calculated according to the cross sections of the isotopes of that element taking into account their abundance . A mathematical representative equation for each of the element

Artificial Neural Network (ANN) model's application is widely increased for wastewater treatment plant (WWTP) variables prediction and forecasting which can enable the operators to take appropriate action and maintaining the norms. It is much easier modeling tool for dealing with complex nature WWTP modeling comparing with other traditional mathematical models. ANN technique significance has been considered at present study for the prediction of sequencing batch reactor (SBR) performance based on effluent's (BOD5/COD) ratio after collecting the required historical daily SBR data for two years operation (2015-2016) from Baghdad Mayoralty and Al-Rustamiya WWTP office, Iraq. The prediction was gotten by the application of a feed-forwa

 Researchers have increased interest in recent years in determining the optimum sample size to obtain sufficient accuracy and estimation and to obtain high-precision parameters in order to evaluate a large number of tests in the field of diagnosis at the same time. In this research, two methods were used to determine the optimum sample size to estimate the parameters of high-dimensional data. These methods are the Bennett inequality method and the regression method. The nonlinear logistic regression model is estimated by the size of each sampling method in high-dimensional data using artificial intelligence, which is the method of artificial neural network (ANN) as it gives a high-precision estimate commensurate with the dat

Abstract\

In this research, estimated the reliability of water system network in Baghdad was done. to assess its performance during a specific period. a fault tree through static and dynamic gates was belt and these gates represent logical relationships between the main events in the network and analyzed using dynamic Bayesian networks . As it has been applied Dynamic Bayesian networks estimate reliability by translating dynamic fault tree to Dynamic Bayesian networks and reliability of the system appreciated. As was the potential for the expense of each phase of the network for each gate . Because there are two parts to the Dynamic Bayesian networks and two part of gate (AND), which includes the three basic units of the

In this article, the casting method was used to prepare poly(methyl methacrylate)/hydroxyapatite (PMMA/HA) nanocomposite films incorporated with different contents (0.5, 1, and 1.5 wt%) of graphene nanoplatelets (Gnp). The chemical properties and surface morphology of the PMMA/HA blend and PMMA/HA/Gnp nanocomposite were characterized using FTIR, and SEM analysis. Besides, the thermal conductivity, dielectric and electrical properties at (1–107 Hz) of the PMMA/HA blend and PMMA/HA/Gnp composites were investigated. The structural analysis showed that the synthesized composites had a low agglomerated state, with multiple wrinkles of graphene flakes in the PMMA/HA blend. The thermal conductivity was improved by more than 35-fold its value for

The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).