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

Films of pure Poly (methyl methacrylate) PMMA and Iron chromate doped PMMA have been prepared using casting method. Transmission and absorptance spectra have been recorded in the wavelength range (300-900) nm, in order to calculate, single oscillator energy, dispersion energy proposed by Wemple - DiDomenico model, average oscillator strength, average oscillator wavelength. The refractive index data at infinite wavelength which was found to obey single oscillator model which was found to increase from 2.27-2.56 as the doping percentage increase. The decreasing in the optical energy gap which was found according to Tauc model were (3.74-3.63) eV , is in good agreement with that obtained by wimple-DiDomenico model. The inverse behavior comp

Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin

This work presents a simple method for determination of the neutron reflection coefficient (n) as a function of different neutron reflector materials.A laboratory neutron source (Am-Be) with activity of 16 ci is employed with a (BF3) neutron detector. Am-BeThree types of reflector materials are used as samples, the thickness of each sample is (5cm).It is found that( ?7) is: -For polyethlyene = 0.818

This paper deals with the problem of the mechanics of the operation of cinematography in the development of museum exhibition halls. In the first chapter, the researcher dealt with the problem and presented it to reach the goals and purpose of the research, which was represented in using and developing the methods and mechanisms of the presentation to keep pace with what is happening in the world of technology and access to the presented model to new formula and vision declares aesthetical and cognitive measure, thus the search constitutes an importance in absorbing Scenography dimensions in the theater and moved to the idea of the museum and the development of the display models and using them in drawing and representation of perception

The problem of the paper focused on the role of the learning organization in the crisis management strategy, and the extent of the actual interest in both the learning organization and the crisis management and aimed at diagnosing and analyzing that and surrounding questions. The Statistical Package for the Social Sciences (SPSS) program was used to calculate the results and the correlation coefficient between the two main variables. The methodology was descriptive and analytical. The case study was followed by a questionnaire that was distributed to a sample of 31 teachers. The paper adopted a seven-dimensional model of systemic thinking that encourages questioning, empowerment, provision of advanced technologies, and strategic lea

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To