Multiple studies support a role for inflammation in the pathogenesis of coronary atherosclerosis and unstable cardiac syndromes. However, of the known pro-inflammatory cytokines, only elevated plasma levels of interleukin-6(IL-6) have been linked to Unstable Angina. We sought to examine the plasma levels of other major proinflammatory cytokines in similar clinical settings patients with unstable angina and acute myocardial infarction and the relationship extent between them. This study aimed to investigate and compare the level of IL-1 in Unstable Angina and Acute Myocardial Infarction patients. Thirty patients with unstable angina and thirty patients with Acute Myocardial Infarction, also thirty healthy individual as control were included

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

The objective of the research is to uncover the effect of the strategy of Quranic verses in the collection of science and systemic intelligence for second-grade students. The research sample consisted of (48) students of second grade students in the middle of Al Rasheed Boys School of the second Karkh Directorate, Distribution in the two divisions, Division of (b) and experimental group that studied strategy of Quranic verses, and the Division (a) control group which studied the regular way, and results indicated a statistically significant differences for the experimental group students studied using the strategy Verses in systemic intelligence collection.

Abstract                                                         

The issue of the protection of the environment is a shared responsibility between several destinations and sectors, and constitutes a main subject in which they can achieve sustainable development. In the sectors of government programs can be set up towards the establishment of the government sector to the green environment, so to be the implementati

Asset management involves efficient planning of economic and technical performance characteristics of infrastructure systems. Managing a sewer network requires various types of activities so the network can be able to achieve a certain level of performance. During the lifetime of the network various components will start to deteriorate leading to bad performance and can damage the infrastructure. The main objective of this research is to develop deterioration models to provide an assessment tool for determining the serviceability of the sewer networks in Baghdad city the Zeppelin line was selected as a case study, as well as to give top management authorities the appropriate decision making. Different modeling techniques

In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col