The aim of this work is to detect the best operating conditions that effect on the removal of Cu²⁺, Zn²⁺, and Ni²⁺ ions from aqueous solution using date pits in the batch adsorption experiments. The results have shown that the Al-zahdi Iraqi date pits demonstrated more efficient at certain values of operating conditions of adsorbent doses of 0.12 g/ml of aqueous solution, adsorption time 72 h, pH solution 5.5 ±0.2, shaking speed  300 rpm, and smallest adsorbent particle size needed for removal of metals.  At the same time the particle size of date pits has a little effect on the adsorption at low initial concentration of heavy metals. The adsorption of metals increases with increas

Predicting peterophysical parameters and doing accurate geological modeling which are an active research area in petroleum industry cannot be done accurately unless the reservoir formations are classified into sub-groups. Also, getting core samples from all wells and characterize them by geologists are very expensive way; therefore, we used the Electro-Facies characterization which is a simple and cost-effective approach to classify one of Iraqi heterogeneous carbonate reservoirs using commonly available well logs.

The main goal of this work is to identify the optimum E-Facies units based on principal components analysis (PCA) and model based cluster analysis(MC

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

     أجري البحث لمعرفة الملوثات الميكروبية الموجودة على قشرة وفي صفار وبياض بيض الدجاج المحلي ، السوري ، التركي ، الاوكراني ، حيث جمعت العينات بمعدل 10 بيضة من الاسواق الشعبية في محافظة  بغداد . أظهرت النتائج أن بكتريا السالمونيلا واضحة في صفار البيض التركي .  وفي قشرة وبياض وصفار البيض الاوكراني . كما أظهرت نتائج فحص البيض

     In the present investigation two different types of fiber reinforced polymer composites were prepared by hand lay-up method using three different parameters (curing temperature, pressing load and fiber volume fraction). These composites were prepared from the polyester resin as the matrix material reinforced with glass fibers as first group of samples and mat Kevlar fibers as the second group, both with different volume fractions (4%, 8%, and 12%) of fibers. They were then tested by tensile strength and impact strength. The main objective in this study is to use Taguchi method for predicting the better parameters that give the better tensile and impact strength to the composites, and then preparing composites at

China is moving towards building the largest economic power in the world, so what does this mean economically and politically for Europe, America, the Middle East and the Arab world? This project is called the expansionist Chinese Marshall Plan which is a revival of the old Silk Road in a new way. It represents the aspirations of the Chinese people and their president, Xi Jinping, to build a transport route from Asia to Europe and a railway plan to transport goods. The purpose of this is to develop the economic growth model, expand into new markets, promote the Chinese currency, and expand economic and political influence in the areas covered by the Chinese initiative. Equal opportunities in distributing investments to these countries, a

Czerwi’nski et al. introduced Lucky labeling in 2009 and Akbari et al and A.Nellai Murugan et al studied it further. Czerwi’nski defined Lucky Number of graph as follows: A labeling of vertices of a graph G is called a Lucky labeling if  for every pair of adjacent vertices u and v in G where . A graph G may admit any number of lucky labelings. The least integer k for which a graph G has a lucky labeling from the set 1, 2, k is the lucky number of G denoted by η(G). This paper aims to determine the lucky number of Complete graph K_n, Complete bipartite graph K_m,n and Complete tripartite graph K_l,m,n. It has also been studied how the lucky number changes whi