The present work aims to study the efficiency of using aluminum refuse, which is available locally (after dissolving it in sodium hydroxide), with different coagulants like alum [Al2 (SO4)3.18H2O], Ferric chloride FeCl3 and polyaluminum chloride (PACl) to improve the quality of water. The results showed that using this coagulant in the flocculation process gave high results in the removal of turbidity as well as improving the quality of water by precipitating a great deal of ions causing hardness. From the experimental results of the Jar test, the optimum alum dosages are (25, 50 and 70 ppm), ferric chloride dosages are (15, 40 and 60 ppm) and polyaluminum chloride dosages were (10, 35 and 55 ppm) for initial water turbidity (100, 500 and 1000 NTU) respectively. While, adding sodium aluminate with the coagulants (Alum, FeCl3 and PACl), the optimum dose of 50 ppm was enough for the reduction of turbidity and hardness of water.
This study conducted an analytical investigation on the behavior of concrete beams with openings reinforced by glass-fiber-reinforced polymer (GFRP) bars. In this study, five proposed beams reinforced by GFRP bars as flexural and shear reinforcement with openings were numerically examined. The variables were the opening orientation (vertical and horizontal) and the number of openings. These openings were located within the flexural zone of the proposed beams. The result shows that the vertical openings had a significant effect over the horizontal openings on reducing the ultimate load and increasing the mid-span deflection compared with the control beam. Moreover, the results showed t
Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the
... Show MoreBackground:-Osteoarthritis (OA) is the most common form of arthritis and the leading source of physical disability in elderly people. The Prevalence of OA is increasing and will continue to do so as the population gets older. The OA is predominantly managed in primary care centers by primary health care physicians and much can be done to alleviate symptoms from osteoarthritis by combinations of therapeutic options including pharmacological and non-pharmacological treatments.
Objectives of study :- To assess the knowledge, attitude and practice of Iraqi PHCC physicians in Baghdad, AL-Rusafa, regarding the management of osteoarthritis patient, and it's association with sociodemogra
... Show MoreKE Sharquie, AA Noaimi, MA Al-Shukri, Journal of Cosmetics, Dermatological Sciences and Applications, 2015 - Cited by 3
This study was conducted in the poultry field of the College of Agricultural Engineering Sciences / University of Baghdad for the period from 10/15/2021 to 11/25/2021 in order to show the effect of adding different levels of Ganoderma lucidum to broiler diets on physiological traits and indicators of fat oxidation in meat. In it, 200 unsexed (Ross 308) chicks of one-day-old breed were used, with a starting weight of (40) g. The chicks were distributed and randomly divided into four treatments, with 50 birds for each treatment. One treatment included five replicates (10 birds/repeat) and the experiment treatments were T1, T2, T3, and T4. The percentages of adding reishi mushrooms were 0, 0.5, 1, and 1.5 g/kg of feed, respectively. Th
... Show MorePrecise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables
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