As asphalt concrete wearing course (ACWC) is the top layer in the pavement structure, the material should be able to sustain stresses caused by direct traffic loading. The objective of this study is to evaluate the influence of aggregate gradation and mineral filler type on Marshall Properties.  A detailed laboratory study is carried out by preparing asphalt mixtures specimens using locally available materials including asphalt binder (40-50) penetration grade, two types of aggregate gradation representing SCRB and ROAD NOTE 31 specifications and two types of  mineral filler including limestone dust and coal fly ash. Four types of mixtures were prepared and tested. The first type included SCRB specification and

Bulk polycrystalline samples have been prepared by the two-step solid state reaction process. It has been observed that as grown Tl2-xHgxSr2Ca2Cu3O10+δ (with x = 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1) corresponds to the 2223 phase. It has been found that Tc varies with Hg content .The optimum Tc is about 120K for the composition Tl1.6Hg0.4Sr2Ca2Cu3O10+δ.The microstructure for Tl1.6Hg0.4Sr2Ca2Cu3O10+δ observed to be most dense and this phase exhibits the highest stability.

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

          Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discrimina

The Tigris River is one of the main rivers and an important resource for the population of Iraq. The present study aimed to quantify the concentration of surface-active substances in the Tigris River and to investigate the dynamics of this ecosystem. Five sampling sites were selected along the river within Baghdad city (Al-Muthanna Bridge, Al-Greaat Bridge, Al-Sarrafia Bridge, Al-Jadriyah Bridge, and Al-Za'franiya Area) for the period from July 2020 to April 2021. The study examined the relationship between the concentrations of surface-active materials (surfactants, including anionic and nonionic types) and their potential interaction with nutrients—nitrate (NO₃⁻), phosphate (PO₄), and sulfate (SO₄²⁻)—as well as the

Background: This in vitro study measure and compare the effect of light curing tip distance on the depth of cure by measuring vickers microhardness value on two recently launched bulk fill resin based composites Tetric EvoCeram Bulk Fill and Surefil SDR Flow with 4 mm thickness in comparison to Filtek Z250 Universal Restorative with 2 mm thickness. In addition, measure and compare the bottom to top microhardness ratio with different light curing tip distances. Materials and Method: One hundred fifty composite specimens were obtained from two cylindrical plastic molds the first one for bulk fill composites (Tetric EvoCeram Bulk Fill and Surefil SDR Flow) with 4 mm diameter and 4 mm depth, the second one for Filtek Z250 Universal Restorative

In this paper, the behavior of structural concrete linear bar members was studied using numerical model implemented in a computer program written in MATLAB. The numerical model is based on the modified version of the procedure developed by Oukaili. The model is based on real stress-strain diagrams of concrete and steel and their secant modulus of elasticity at different loading stages. The behavior presented by normal force-axial strain and bending moment-curvature relationships is studied by calculating the secant sectional stiffness of the member. Based on secant methods, this methodology can be easily implemented using an iterative procedure to solve non-linear equations. A comparison between numerical and experimental data, illustrated