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RISS 인기검색어
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- 저자 : M.S. Al-Haddad (검색결과 2 건)
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Haddad, S.G.,Kridli, R.T.,Al-Wadi, D.M. Asian Australasian Association of Animal Productio 2005 Animal Bioscience Vol.18 No.5
The objective of this study was to evaluate the effect of dietary undegradable protein (UP) level on body weight change, nutrient intake, milk production and postpartum reproductive performance of Awassi ewes. Twenty-seven multiparous Awassi ewes (initial body weight = 53.3${\pm}$1.6 kg) were randomly assigned to three dietary treatments (9 ewes/treatment) for 62 days using a completely randomized design. Experimental diets were isonitrogenous (15.5% CP), isocaloric, and were formulated to contain 17.9 (LUP), 27.1 (MUP), and 34.0% (HUP) of the dietary CP as UP. On day 10${\pm}$3 (day 0 = parturition) ewes were housed in individual pens for 5 weeks. Feed offered and refused was recorded daily. At the end of this period, animals were removed from their pens and combined into 3 separate groups (LUP, MUP and HUP). One fertile, harnessed ram was allowed with each group for 34 days. Rams were rotated every 2 days among the three groups. Each group was offered the corresponding experimental diet. Organic matter, CP, UP and metabolizable energy intakes were higher (p<0.05) for ewes fed the HUP diet compared with ewes fed the LUP and MUP diets. Ewes fed the HUP diet gained more (p<0.05) weight compared with ewes fed the MUP diet (7.3 vs. 2.1 kg), while ewes fed the LUP diet lost an average of 2.1 kg. Pregnancy rate of ewes fed the HUP diet was 100%, compared with 66 and 33% for ewes fed the MUP and LUP diets, respectively. Lambing rate was greater (p<0.05) for ewes fed HUP (8/9) diet compared with ewes fed the MUP (4/9) and LUP (3/9) diets. These results indicate that Awassi ewes receiving adequate dietary UP level consume more feed and are capable of returning to estrus shortly after parturition and are capable of producing two lamb crops per year.
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Prediction of compressive strength of concrete using neural networks
Yousef A. Al-Salloum,Abid A. Shah,Saleh H. Alsayed,Tarek H. Almusallam,M.S. Al-Haddad,H. Abbas 사단법인 한국계산역학회 2012 Computers and Concrete, An International Journal Vol.10 No.2
This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.
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