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

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

This work was conducted to determine the volumetric mass transfer coefficient (Ky.a) infixed bed adsorption using hexane-benzene mixture by adsorption onto a fixed bed of white silica gel. Benzene concentration was measured by gas chromatography. The effect of feed flow rate and initial concentration of benzene in hexane-benzene mixture on the volumetric mass transfer coefficient and on the adsorption capacity of silica gel was investigated.

In general, the volumetric mass transfer coefficient increases with increasing hexane flow rate, and with increasing initial concentration of benzene in the mixture. The ultimate value of (Ky.a) was at 53 ml/min of hexane flow rate with benzene initial concentration of (6.53 wt. %), and it wa

The Early Cenomanian Ahmadi carbonates succession in selected oil-wells in south Iraqi oil fields have undergone; into sequence stratigraphic analysis as new reservoir stratigraphy optimization understanding. The sequence stratigraphy context: has applied on the mentioned carbonate reservoir in selected oil-wells from West-Qurna and Majnoon oil fields, with respect to Arabian-plate (AP) chronosequence stratigraphy and chrono-markers setting.

A meso-genetic buildup has infra-structured the studied Formation based-on; smallest-set of the genetically-related high-frequency lithofacies-cycle and cycles-set modeling. A genetic sequence (meso-sequence one MS1) is described as a well-encountered buildup between the (MFS-K120 of lower Ah

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