The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on

Aniline and its derivatives are common contaminants in various wastewaters and represent a serious worry for societies health and a challenge to ecologists due to their dangers effects on to the human health.

ZSM-5 zeolite was prepared from locally available materials (kaolin and rice husk) for adsorption of aniline from synthetic wastewater. Characterization of the prepared zsm-5, kinetics and thermodynamic of the adsorption process were investigated.

The characterization results of the prepared zsm-5 zeolite showed that the surface area was 270.1 m²/g and pore volume 0.21828 cm³/g. The silica to alumina ratio (Si/Al) was 166. 47 and the sodium content was 11 wt. %. The atomic force microscope (AFM)

In this work, excess properties (eg excess molar volume (VE), excess viscosity (ȠE), excess Gibbs free energy of activation of viscos flow (ΔG* E) and molar refraction changes (ΔnD) of binary solvent mixtures of tetrahydrofurfuryl alcohol (THFA) with aromatic hydrocarbons (benzene, toluene and p-xylene) have been calculated. This was achieved by determining the physical properties including density ρ, viscosity Ƞ and refraction index nD of liquid mixtures at 298.15 K. Results of the excess parameters and deviation functions for the binary solvent mixtures at 298.15 K have been discussed by molecular interactions that occur in these mixtures. Generally, parameters showed negative values and have been found to fit well to Redlich-Kister

A set of hydro treating experiments are carried out on vacuum gas oil in a trickle bed reactor to study the hydrodesulfurization and hydrodenitrogenation based on two model compounds, carbazole (non-basic nitrogen compound) and acridine (basic nitrogen compound), which are added at 0–200 ppm to the tested oil, and dibenzotiophene is used as a sulfur model compound at 3,000 ppm over commercial CoMo/ Al2O3 and prepared PtMo/Al2O3. The impregnation method is used to prepare (0.5% Pt) PtMo/Al2O3. The basic sites are found to be very small, and the two catalysts exhibit good metal support interaction. In the absence of nitrogen compounds over the tested catalysts in the trickle bed reactor at temperatures of 523 to 573 K, liquid hourly space v

In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respe

Power switches require snubbing networks for driving single – phase industrial heaters. Designing these networks, for controlling the maximum allowable rate of rise of anode current (di/dt) and excessive anode – cathode voltage rise (dv/dt) of power switching devices as thyristors and Triacs, is usually achieved using conventional methods like Time Constant Method (TCM), resonance Method (RM), and Runge-Kutta Method (RKM). In this paper an alternative design methodology using Fuzzy Logic Method (FLM) is proposed for designing the snubber network to control the voltage and current changes. Results of FLM, with fewer rules requirements, show the close similarity with those of conventional design methods in such a network of a Triac drivin

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

The present work investigates the effect of; superficial air velocities of: 1, 3, and 6 cm/s for two types of perforated distributor on hydrodynamic characteristic in a gas-liquid dispersion column of; air-water, and airaqueous-n-propanol solution. Bubble distribution, gas holdup, and power consumption are parameters take in consideration. Experimental work was carried out in perspex column of 8.5 cm inside diameter and 1.5 m height. Two types of bubble generator (perforated plate) were fixed at the bottom of the column; plate A (99 holes of 0.5 mm diameter and free area of 0.34%), plate B (20 holes of 1.5 mm diameter and free area of 0.62%). Photographic technique was used to measure the bubble parameters. The experimental results were