In this investigation, the mechanical properties and microstructure of Metal Matrix Composites (MMCs) of Al.6061 alloy reinforced by ceramic materials SiC and Al₂O₃ with different additive percentages 2.5, 5, 7.5, and 10 wt.% for the particle size of 53 µ_m are studied. Metal matrix composites were prepared by stir casting using vortex technique and then treated thermally by solution heat treatment at 530 ⁰C for 1 hr. and followed by aging at 175 ⁰C with different periods. Mechanical tests were done for the samples before and after heat treatment, such as impact test, hardness test, and tensile test. Also, the microstructure of the metal matrix composites was examine

An experimental study was performed to estimate the forced convection heat transfer performance and the pressure drop of a single layer graphene (GNPs) based DI-water nanofluid in a circular tube under a laminar flow and a uniform heat flux boundary conditions. The viscosity and thermal conductivity of nanofluid at weight concentrations of (0.1 to 1 wt%) were measured. The effects of the velocity of flow, heat flux and nanoparticle weight concentrations on the  enhancement of the heat transfer are examined. The Nusselt number of the GNPs nanofluid was enhanced as the heat flux and the velocity of flow rate  increased, and the maximum Nusselt number  ratio (Nu nanofluid/ Nu base fluid)   and thermal performance factor

The research aims to test the effect of the behavioral factors (intangible) represented by an explanatory variable represented by organizational silence and a responsive variable of quality of Function life. The problem was the negative effects of the organizational silence on the morale of the employees and consequently their performance and the quality of function life. To collect the data and information needed to measure the two variables of research conducted in the health center / Sulaikh by taking a sample of (40) employees to test the hypotheses of research through the survey of their views, using statistical tools non parametric using the program. The most important recommendations were the establishment of training workshops fo

Isolated Bacteria from the roots of barley were studied; two stages of processes Isolated and screening were applied in order to find the best bacteria to remove kerosene from soil. The active bacteria are isolated for kerosene degradation process. It has been found that Klebsiella pneumoniae sp. have the highest kerosene degradation which is 88.5%. The optimum conditions of kerosene degradation by Klebsiella pneumonia sp. are pH5, 48hr incubation period, 35°C temperature and 10000ppm the best kerosene concentration. The results 10000ppm showed that the maximum kerosene degradation can reach 99.58% after 48 h of incubation. Higher Kerosene degradation which was 99.83% was obtained at pH5. Kerosene degradation was found to be maximum at 3

Isolated Bacteria from the roots of barley were studied; two stages of processes Isolated and screening were applied in order to nd the best bacteria to remove kerosene from soil. The acve bacteria are isolated for kerosene degradaon process. It has been found that Klebsiella pneumoniae sp. have the highest kerosene degradaon which is 88.5%. The opmum condions of kerosene degradaon by Klebsiella pneumonia sp. are pH5, 48hr incubaon period, 35°C temperature and 10000ppm the best kerosene concentraon. The results 10000ppm showed that the maximum kerosene degradaon can reach 99.58% aer 48 h of incubaon. Higher Kerosene degradaon which was 99.83% was obtained at pH5. Kerosene degradaon was found

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.