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

The performance of sewage pumps stations affected by many factors through its work time which produce undesired transportation efficiency. This paper is focus on the use of artificial neural network and multiple linear regression (MLR) models for prediction the major sewage pump station in Baghdad city. The data used in this work were obtained from Al-Habibia sewage pump station during specified records- three years in Al-Karkh district, Baghdad. Pumping capability of the stations was recognized by considering the influent input importance of discharge, total suspended solids (TSS) and biological oxygen demand (BOD).  In addition, the chemical oxygen demands (COD), pH and chloride (Cl). The proposed model performanc

The research aims at recognition of The rate of performing tasks done by the scientific committees in the scientific departments in the University of Baghdad, within the evaluative perspective of the departments’ Heads. To find any statistically significant differences in the responses of the research sample to the rates performance of the scientific committees in scientific departments in the University of Baghdad, within the evaluative perspective of the departments’ Heads, according to the two variables of the social gender; male and females, and field of specialization of Scientific and Humanities. The research sample consisted if (107) heads of the scientific department in the University of Baghdad. The researchers designed a

Many countries are very important in their interest not only in diversifying foreign reserves, but in determining and planning their volume in accordance with the goals set, namely facing potential external shocks, as the research aims to determine the extent of the strength of foreign reserves in the possession of the Central Bank in relation to every influential variable in the Iraqi economy. , in order to determine the minimum level of reserves that requires reconsideration of the exchange rate, as the research adopted the inductive analytical method in analyzing real (Quantitative data) for the research variables in the years of study, as the research adopted a set of analytical indicators approved by the International Moneta

Background: The cells of periodontium contain many intracellular enzymes like (alkaline phosphatase ALP) that are released outside into the saliva and gingival crevicular fluid (GCF) after destruction of periodontal tissue. The aim of study was to determine the activity of this enzyme in saliva and its relation to the salivary flow rate, PH and clinical periodontal parameters in patients with chronic periodontitis. Subject, Materials and methods: Sample population consist of 75 individuals ;divided into four groups , the first group (15):control subject, the second group (20):mild chronic periodontitis, the third group(20) moderate chronic periodontitis and the fourth group (20) sever chronic periodontitis, Measurements of plaque index (PL

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra