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Showing 4 results for A. Rezaei

M. Mohamadi Bazargani, B. E. S. Tabatabaei, A. Rezaei, C. Ghobadi,
Volume 8, Issue 2 (summer 2004)
Abstract

Optimizing regeneration of cotton plant in two variety (Sahel and Varamin) via shoot apex was done in order to Agrobacterium-mediated transformation. In this reaserch shoot apexes of two varieties were isolated from four or five day seedling and were placed on a special medium of shooting (modified MS without hormon).In order to select the best rooting media, The Statistical Analysis explants that produce shoot and leaves in a CRBD with 4 replicates and 4 rooting treatments: 1) modified MS without hormon, 2) ½ MS with 0.1 mg/lit IBA, 3) ½ MS with 0.1 mg/lit NAA, 4) ½ MS with 0.1 mg/lit IAA. The statistical analysis indicated that the best for both varieties, was medium with 0.1 mg/lit IBA and rooting percentage of Varamin is better than sahel in all of media.
B. M. Ashour, A. Arzani, A. Rezaei, S. A. M. Mirmohammady Maibody,
Volume 9, Issue 4 (winter 2006)
Abstract

The Genetic basis of grain yield and related characteristics were studied by a generation mean analysis in five crosses of winter wheat (Triticum aestivum L.). “Roshan”, “Mahdavi”, “Inia”, “Atila” and “Goscoyin” cultivars along with their F1, F2, BC1 and BC2 populations were evaluated by a split-plot design with crosses as the whole plot in a randomized complete block design with two replications and generations were applied as the subplots. Analysis of variance revealed significant differences among generations for studied characteristics including the grain yield per plant, the number of spikelet per plant, the number of spikelet per spike and grain weight per spike. For the majority of traits and crosses, F/DH1/2 was less than one, indicating that the sign and magnitude of gene actions were different. Estimates of broadsense and narrow sense heritabilities were low for the grain yield per plant compared with other traits, rating from 28.5% to 58.6% and 24% to 48,5% for the five crosses, respectively. Genetic components of generation means were calculated by fitting different models and choosing the best model indicated that the impact of additive, dominance and epistasis genetic components in controlling the traits depend on the cross and the trait under study.
M. Kabiri, S. A. M. Mirmohammady Maibody, A. Shakib, A. Rezaei,
Volume 9, Issue 4 (winter 2006)
Abstract

To obtain a suitable explant and efficient culture medium for plant regeneration in spinach, two cultivars of Melody and Karaj local seedlings were chosen. A hypocotyl and cotyledon segments as well as shoot tip explants were dissected from seedlings. The explants were then cultured on MS medium supplemented with IAA, GA3, NAA, and BAP and their response to this media was studied. A completely randomized design with different replicates was used to conduct the experiments. Callus was formed at the base of the hypocotyl explants on the medium containing 15 mg.l-1 IAA, and 3.4 mg.l-1 GA3. Calli capable of regeneration was obtained after subculturing on the medium containing 2 mg.l-1 IAA, and 3.4 mg.l-1 GA3 at the rate of 38 percent. The vitrified plantlets were abnormally glassy, and translucent which might have high water content. It was a physiological disorder which was overcome when an improved agar medium raising to 9 gl-1 was used. Callus has been obtained only from the hypocotyl explants, while regeneration has been obtained from shoot tip cultured on the medium containing 0.02 mgl-1 BAP at the rate of 80 percent.
A. Rezaei, M. Mahdavi, K. Luxe, S. Feiznia, M. H. Mahdian,
Volume 11, Issue 1 (spring 2007)
Abstract

The model in this research was created based on the Artificial Neural Network (ANN) and calibrated in the Sefid-rood dam basin (excluding Khazar zone). This research was done by gathering and selecting peak flows of hydrographs from 12 sub basins, the concentration time of which was equal to or less than 24 hours and was caused only by rainfall. From all the selected sub basins, totally 661 hydrographs were prepared and their peak flows data wes used to make prediction model. The input variables of the model consisted of the depth of daily flooding rainfalls, and so the five days before rainfall of every peak flow, the area of sub basins, the main stream length, the slope of 10-85 percent of main stream, the median height of sub basins, the area of geological formations and rock units, classified at three hydrological groups of I, II, III, the base flow, and output variable was only peak flow. By using Feed Forward Artificial Neural Network with training method of back propagation error the function approximation of inputs to output was created by passing the three processes of training (learning), testing and validation. So based on that data and variables, the Multivariable Linear Regression model was created. The comparison of observed peak flows, based on validation data package, showed that the statistical parameters of (R2) coefficient and Fisher’s test parameter coefficient (F) for ANN model and MLR respectively were 0.84, 33.66 and 0.33, 3.60, indicating the superiority of ANN to traditional methods.

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