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Conformation of the clinical diagnoses of some malnutrition diseases in local and Shammi goats in Baghdad province: Al-Shawi A.F.S .; Al-Sammarraie ,S.A.G.and Al-Judi Abdul,M.H.
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The relationship between the clinical singes and some of the hematological and biochemical values in 126 cases of local breed and Shammi breed goats (from both sexes) diagnosed as cases of malnutrition from 230 goats examined .The clinical examination include (beside the general inspection and case history) body temperature, respiratory and pulse rates, mucus membranes skin and coat. Examination of the blood samples included RBCs count, Hb concentration, PCV % and values of cupper, magnesium, phosphorus, and potassium. The results showed that the mean body temperature of these goats was lower than in normal goats, while the respiratory and pulse rates were higher .Examination of blood revealed decrease in the means of RBCs counts ,Hb concentration and PCV % in the clinically diagnosed cases , and the decrease was more sever in local goats . However the results indicated lower values of cupper, magnesium, phosphorus, and potassium in comparison with normal values in goats. The ratio of cupper deficiency was the highest followed by phosphorus, magnesium and potassium and the ratio of malnutrition was high in Baghdad province. It was in Shammi breed higher than in local breed.

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Publication Date
Wed Apr 25 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Different Estimation Methods for System Reliability Multi-Components model: Exponentiated Weibull Distribution
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        In this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through  Monte Carlo simulation technique were made depend on mean squared error (MSE)  criteria

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