The most common cause of death is cardiovascular disease (CVD), with ischemic heart disease being the most notable type.  There is a propensity to raise the sensitivity of methods in contemporary laboratory for diagnosing of CVD, and assessing key as CVD bio-indicators. The urgent task is to seek for different indicators as a hopeful tool for early detection and monitoring of myocardial infarction in blood samples only. This study comprised 117 Volunteers, recorded with both genders in the age range of 32-64 years old. The volunteers were categorized into two groups: 67patients of myocardial infarction, other group embraced 50 healthy individuals. The samples of blood were collected and directed for biochemical analysis to evaluate estr

Tafsir, ‘interpretation’ is one of the most important sciences the Muslims need. Because Allah, Almighty, revealed the Qur’an to be a way of life for the Muslims in which the healing of what is in the chest, including their goodness and their peasant. Allah says:
“O people, an admonition has indeed come to you from your Lord and a healing for what is in the hearts; and a guidance and a mercy for the believers.”
In order to achieve the goal for which God revealed the Quran must be understood and managed verses:” This is a Book that We have revealed to you abounding in good, that they may ponder over its verses, and that those who have understanding may be mindful.”
The Prophet (PBUH) and his companions understood t

One of the recent significant but challenging research studies in computational biology and bioinformatics is to unveil protein complexes from protein-protein interaction networks (PPINs). However, the development of a reliable algorithm to detect more complexes with high quality is still ongoing in many studies. The main contribution of this paper is to improve the effectiveness of the well-known modularity density ( ) model when used as a single objective optimization function in the framework of the canonical evolutionary algorithm (EA). To this end, the design of the EA is modified with a gene ontology-based mutation operator, where the aim is to make a positive collaboration between the modularity density model and the proposed

It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis and artificial neural networks, to determine the rheological

High performance work systems and general industrial enterprise performance