The present study develops an artificial neural network (ANN) to model an analysis and a simulation of the correlation between the average corrosion rate carbon steel and the effective parameter Reynolds number (Re), water concentration (Wc) % temperature (T o) with constant of PH 7 . The water, produced fom oil in Kirkuk oil field in Iraq from well no. k184-Depth2200ft., has been used as a corrosive media and specimen area (400 mm2) for the materials that were used as low carbon steel pipe. The pipes are supplied by Doura Refinery . The used flow system is all made of Q.V.F glass, and the circulation of the two –phase (liquid – liquid ) is affected using a Q.V.F pump .The input parameters of the model consists of Reynolds number , water concentration and temperature. The output is average corrosion rate .The performance of the two training algorithms, gradient descent with momentum and Levenberg-Marquardt, are compared to select the most suitable training algorithm for corrosion rate model. The model can be used to calculate the average corrosion rate properties of carbon steel alloy as functions of Reynolds number, water concentration and temperature. Accordingly, the combined influence of these effective parameters and the average corrosion rate is simulated. The results show that the corrosion rate increases with the increase of temperature, Reynolds number and the increase of water concentration.
When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat
... Show MoreForeign direct investment has seen increasing interest worldwide, especially in developing economies. However, statistics have shown that Yemen received fluctuating FDI inflows during the period under study. Against this background, this research seeks to determine the relationship and impact of interest rates on FDI flows. The study also found other determinants that greatly affected FDI inflows in Yemen for the period 1990-2018. Study data collected from the World Bank and International Monetary Fund databases. It also ensured that the time series were made balanced and interconnected, and then the Auto Regressive Distributed Lag method used in the analysis. The results showed that the interest rates and
... Show MoreThe road transportation system is considered as major component of the infrastructure in any country, it affects the developments in economy and social activities. The Asphalt Concrete which is considered as the major pavement material for the road transportation system in Baghdad is subjected to continuous deterioration with time due to traffic loading and environmental conditions, it was felt that implementing a comprehensive pavement maintenance management system (PMMS), which should be capable for preserving the functional and structural conditions of pavement layers, is essential. This work presents the development of PMMS with Visual inspection technique for evaluating the Asphalt Concrete pavement surface condition; common types o
... Show MoreRice is a major staple food for more than two thirds of the world population. Pathogenesis-related proteins-10 (PR10) have a range of 154 to 163 amino acid with molecular weight ~ 17 kDa. They are acidic and generally intracellular and cytosolic proteins accumulate in plants in response to biotic and abiotic stresses. In the present study, a PR10 gene and its corresponding protein were characterized in O. sativa, O. barthii, O. glaberrima, O. glumipatula, O. meridionalis, O. nivara, O. rufipogon and O. punctata. The results revealed a narrow range of variation at both DNA and protein levels in all examined species except O. glumipatula. The latter showed a relatively
... Show MoreIn this work, we have developed a model that describes the relationships between top predators (such as tigers, hyenas, and others), crop raiders (such as baboons, warthogs, and deer), and prey (such as deer) in the coffee forests of southwest Ethiopia. Various potential equilibrium points are identified. Additionally, the model's stability in the vicinity of these equilibrium points is examined. An investigation of the model's Hopf bifurcation is conducted concerning several significant parameters. It is found that prey species may be extinct due to a lower growth rate and consumption by top predators in the absence of human interference in the carrying capacity of prey. It is observed that top predators may be extinct due to human interfe
... Show MoreIn this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha
... Show MoreIn this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func
A theoretical study on corrosion inhibitors was done by quantum calculations includes semi-empirical PM3 and Density Functional Theory (DFT) methods based on B3LYP/6311++G (2d,2P). Benzimidazole derivative (oxo(4- ((phenylcarbamothioyl) carbamoyl)phenyl) ammonio) oxonium (4NBP) and thiourea derivative 2-((4- bromobenzyl)thio) -1H-benzo[d] imidazole (2SB) were used as corrosion inhibitors and an essential quantum chemical parameters correlated with inhibition efficiency, EHOMO (highest occupied molecular orbital energy) and ELUMO (lowest molecular orbital energy). Other parameters are also studied like energy gap [ΔE (HOMO-LUMO)], electron affinity (EA), hardness (Δ), dipole moment (μ), softness (S), ionization potential (IE), absolut
... Show MoreTo produce Zinc Oxide NanoParticles, ZnO-NPs, different methods can be used. However, the utilization of Liquid-Phase Pulsed Laser Ablation, LP-PLA, methodology of three distinct environment of aqueous using pure zinc plate will be one of the approaches for this job. Thus, in this work, concentrates on the influence of the results after employing some changes on the environment in other words, the influence of the NPs size and/or the NPs availability/appearance. Cetyltrimethylammonium Bromide, CTAB, is one of the three surfactants that have been used in the water-based solution. That is, the Sodium Dodecyl Sulfate, SDS, besides the Distilled Water, DW, the three surfactants will be ready when the molarity of the DW is around 10− 3 M. Th
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