Carbon nanoparticles are prepared by sonication using carbon black powder. The surface morphology of carbon black (CB) and carbon nanoparticles (CNPs) is investigated using scanning electron microscopy (SEM). The particles size ranges from 100 nm to 400 nm for CB and from 10 nm to 100 nm for CNPs. CNPs and CB are mixed with silicon glue of different ratios of 0.025, 0.2, 0.05, and 0.1 to synthesis films. The optical properties of the prepared films are investigated through reflectance and absorbance analyses. The ratio of 0.05 for CNPs and CB is the best for solar paint because of its higher solar water heater efficiency and is then added to the silicon glue . Temperature of cold water and temperature of hot water in storage tank were ta

Dust storms are among the most important weather phenomena in Middle East. The Shamal dust storms are dominated across Iraq and the whole Middle East, especially in summer. However, frontal type of dust storms is possible in winter and spring. In this research, a comprehensive case study was conducted to a dust storm that occurred on 20 March 2016 from many perspectives: synoptic, satellite imagery, dust concentration analysis, visibility reduction, and aerosol optical depth. The study shows that the dust storm initiated inside Syria and moved eastward with the movement of the front. Dust concentrations and aerosol optical depth were also discussed that simulate the dust storm over Iraq in a reasonable way with some differences. The dust

     Neutron differential-elastic and inelastic scattering cross-sections of Yttrium-89 isotope were calculated at energies 8,10,12,14, and 17 MeV, at angles distributed between 20^o and 180^o in the center of mass frame. The obtained results data were interpreted using a spherical optical potential model and Eikonal approximation, to examine the effect of the first-order Eikonal correction on the effective potential. The real and imaginary parts of optical potential were calculated. It was found that the nominal imaginary potential increase monotonically while the effective imaginary one has a pronounced minimum around r = 6fm and then increases. The analysis of the relative energy of the projectile and reaction

A gliding arc discharge (GAD) with a water spray system was constructed. A non-thermal plasma, generated between two V shaped electrodes in an ambient argon driven by 100 Hz AC voltage, was investigated using optical emission spectroscopy (OES) with different gas flow rates (0.5, 1, 1.5, 2 , 2.5 , 3  1/min). Boltzmann plot method was used to calculate electron temperature (T_e) and electron density (n_e). The electrodes design was spectrally recognized and its T_e value was about 0.588-0.863 eV, while the n_e value of 6.875×10¹⁷-10.938×10¹⁷ cm^-3. The results of the plasma diagnostics generated by gliding arc showed that increasing gas f

In this research, the effect of electrode material on the parameters of the produced DBD plasma was investigated. First, a non-thermal plasma was created by applying a 15 kV AC voltage between two electrodes and using a glass plate as a dielectric barrier in the design Dielectric Barrier Discharge (DBD) plasma system. The obtained plasma spectrum was analyzed using optical emission spectroscopy to calculate plasma parameters by the Boltzmann plot method. Electrodes made of copper, aluminium, and stainless steel were employed in this research. Electron temperature ( ) for copper, aluminium, and stainless steel was found to be (1.398 eV), (1.093 eV) and (1.009 eV), respectively.

This work aims to provide a statistical analysis of metal removal during the Magnetic Abrasive Finishing process (MAF) and find out the mathematical model which describes the relationship between the process parameters and metal removal, also estimate the impact of the parameters on metal removal. In this study, the single point incremental forming was used to form the truncated cone made of low carbon steel (1008-AISI) based on the Z-level tool path. Then the finishing was accomplished using a magnetic abrasive process based on the Box-Behnken design of the experiment using Minitab 17 software was used to finish the surface of the formed truncated cone. The influences of different parameters (feed rate, machining step s

15 sediment samples were collected; 8 samples from the eastern bank, and 7 samples from the western bank of Al-Wind River in Diyala governorate to assess the sediment pollution in some trace elements such as Fe, Ni, Cd, Zr, Zn and Cu in addition to some oxides such as Al₂O₃, CaO, Na₂O and K₂O to find the effect of anthropogenic pollution and the industrial production on the sediment closed especially Naftkhana by using some geochemical pollution indices such as: geoaccumulation factor (I-geo), enrichment factor (EF),contamination factor (CF), pollution loud index (PLI) and to evaluate the degree of weathering by Applying the Chemical Index of Alteration (CIA)in both banks of Al-Wind River. The

Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Mak

     Coronavirus disease (COVID-19), which is caused by SARS-CoV-2, has been announced as a global pandemic by the World Health Organization (WHO), which results in the collapsing of the healthcare systems in several countries around the globe. Machine learning (ML) methods are one of the most utilized approaches in artificial intelligence (AI) to classify COVID-19 images. However, there are many machine-learning methods used to classify COVID-19. The question is: which machine learning method is best over multi-criteria evaluation? Therefore, this research presents benchmarking of COVID-19 machine learning methods, which is recognized as a multi-criteria decision-making (MCDM) problem. In the recent century, the trend of developing