The drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the
There has been a growing interest in the use of chaotic techniques for enabling secure communication in recent years. This need has been motivated by the emergence of a number of wireless services which require the channel to provide very low bit error rates (BER) along with information security. As more and more information is transacted over wireless media, there has been increasing criminal activity directed against such systems. This paper investigates the feasibility of using chaotic communications over Multiple-Input-Multiple-Output (MIMO) channels. We have studied the performance of differential chaos shift keying (DCSK) with 2×2 Alamouti scheme and 2×1 Alamouti scheme for different chaotic maps over additive white Gaussian noise (
... Show MoreUnmanned aerial vehicles (UAVs) can provide valuable spatial information products for many projects across a wide range of applications. One of the major challenges in this discipline is the quality of positioning accuracy of the resulting mapping products in professional photogrammetric projects. This is especially true when using low-cost UAV systems equipped with GNSS receivers for navigation. In this study, the influence of UAV flight direction and camera orientation on positioning accuracy in an urban area on the west bank of the Euphrates river in Iraq was investigated. Positioning accuracy was tested in this study with different flight directions and camera orientation settings using a UAV autopilot app (Pix4Dcapture software
... Show MorePhase change materials are extensively studied for use in low-, mid-, and high-temperature applications due to their melting and solidification temperatures, latent heat, and thermophysical properties. This work aims to explore the energy stored, or released and their duration for the energy storage unit formed of a phase change material surrounding a tube within which a hot or cold, single or Two-Phase fluid flows, serving as a heat source or sink. The 3D axial transient thermal analysis of the energy storage unit is performed using the finite element method via a MATLAB-developed computer program. The effects of single- or Two-Phase fluid flow on temperature distribution, solidification, melting duration, and energy stored within phase ch
... Show MoreCD40 is a type 1 transmembrane protein composed of 277 amino acids, and it belongs to the tumor necrosis factor receptor (TNFR) superfamily. It is expressed in a variety of cell types, including normal B cells, macrophages, dendritic cells, and endothelial cells, as a costimulatory molecule. This study aims to summarize the CD40 polymorphism effect and its susceptibility to immune-related disorders. The CD40 gene polymorphisms showed a significant association with different immune-related disorders and act as a risk factor for increased susceptibility to these diseases.
The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par
... Show MoreIn this paper, Pentacene based-organic field effect transistors (OFETs) by using different layers (monolayer, bilayer and trilayer) for three different gate insulators (ZrO2, PVA and CYEPL) were studied its current–voltage (I-V) characteristics by using the gradual-channel approximation model. The device exhibits a typical output curve of a field-effect transistor (FET). Source-drain voltage (Vds) was also investigated to study the effects of gate dielectric on electrical performance for OFET. The effect of capacitancesemiconductor in performance OFETs was considered. The values of current and transconductance which calculated using MATLAB simulation. It exhibited a value of current increase with increasing source-drain voltage.
This paper aims to study the chemical degradation of Brilliant Green in water via photo-Fenton (H2O2/Fe2+/UV) and Fenton (H2O2/Fe2+) reaction. Fe- B nano particles are applied as incrustation in the inner wall surface of reactor. The data form X- Ray diffraction (XRD) analysis that Fe- B nanocomposite catalyst consist mainly of SiO2 (quartz) and Fe2O3 (hematite) crystallites. B.G dye degradation is estimated to discover the catalytic action of Fe- B synthesized surface in the presence of UVC light and hydrogen peroxide. B.G dye solution with 10 ppm primary concentration is reduced by 99.9% under the later parameter 2ml H2O2, pH= 7, temperature =25°C within 10 min. It is clear that pH of the solution affects the photo- catalytic degradation
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