Sixteen polycyclic aromatic hydrocarbons (PAHs) concentrations were measured in aerosol samples collected for the period from April 2012 to February 2013 at thermal south power station of Baghdad. Fourty one aerosol sample were extracted with (1:1) dichloromethane and methanol using soxhlet for seventeen hour. The extraction solution was analyzed applying GC/MS. The PAH concentrations outside thermal south power station were higher than those inside it, and higher in summer season than in winter. Naphthalene, pyrene, Anthracene, Indeno [1, 2, 3-cd] pyrene and Phenanthrene were the most abundant PAHs detected in all points at the site sampling. The total polycyclic aromatic hydrocarbon (TPAH) and total suspended particles (TSP) concentrat

The research aims to show the relationship between artificial intelligence in accounting education and its role in achieving sustainable development goals in the Kingdom of Bahrain. The research dealt with the role of artificial intelligence applications in accounting education at the University of Applied Sciences as a model for Bahraini universities to achieve sustainable development goals. The application of artificial intelligence in accounting education achieves seven of the seventeen sustainable development goals. It also concludes that there is an artificial intelligence infrastructure in the Kingdom of Bahrain, as it occupies a leading regional position in digital transformation, as Bahrain ranks first in the Arab world i

The study searches for the possibility of using duckweed Lemna spp. to reduce the concentration of heavy metals (zinc and iron) in the wastewater of Baghdad by culturing two different densities of the plant with a fresh weights 5 and 10 g/l and without the plant under optimum uncontrolled conditions. The result showed that there was a significant differences at the possibility level of (p? 0.05) for the three treatments, as the highest percentages for zinc removal in the second day for the plant treatment of 5 g/l were 66.40%, while the highest percentage of iron removal were in the tenth days for the plant treatment 10 g/l were 80 %, and noticed that the increase of the heavy metals concentrations accumulated in the plant after bei

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% respectively. For

The aim of the research is a techno-economic analysis of the use of concentrated solar energy technologies in the Iraqi city, considering the concentrated solar energy technology is a renewable energy technology that derives its resources from the sun and is replenished at a rate that exceeds its use. It is also inexhaustible and environmentally friendly energy from its environmental footprint, unlike traditional fossil energy which produces greenhouse gases and a major cause of global warming.

This research measures the costs of concentrated solar energy technology to Reduce the effects caused by other energies and work to fill part of the shortfall in the total electricity production, even at a specific percentage, in preparati

In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be

In this paper, the solar surface magnetic flux transport has been simulated by solving the diffusion–advection equation utilizing numerical explicit and implicit methods in 2Dsurface. The simulation was used to study the effect of bipolar tilted angle on the solar flux distribution with time. The results show that the tilted angle controls the magnetic distribution location on the sun’s surface, especially if we know that the sun’s surface velocity distribution is a dependent location. Therefore, the tilted angle parameter has distribution influence.