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Showing 2 results for Azimi
Genetic Variation of Onion Local Varieties in Iran
M. Azimi, S. Massiha, M. Moghaddam, M. Valizadeh,
Volume 3, Issue 4 (winter 2000)
Abstract

In order to study the genetic variation among local varieties of onion in Iran, an experiment was conducted in the Research Center, Faculty of Agriculture, Tabriz University. Sixteen populations were evaluated for agronomic characteristics and also total seed proteins via SDS-PAGE. Cluster analysis and principal component analysis were used to group the onion populations under study.

 Analysis of variance showed significant differences among varieties for leaf color, leaf length, texture tightness, onion yield per plant, and number of edible layers. No significant differences were observed for the number of twin onions, bulb diameter, and onion dry weight. Hamadan (98-148), Arak (98-95, 98-96, 98-97, 98-103), and Zanjan (98-223) populations acquired the highest onion yield per plant. The significant differences between populations for the majority of characteristics proved the existence of genetic variation in the Iranian onion germplasm. The results from cluster analysis for agronomic characteristics were the same as those from the cluster analysis for the onion yield per plant. The 16 populations were divided into 4 groups. Cluster analysis for the electrophoresis banding pattern resulted in two groups, which was not similar to the dendrogram of agronomic traits. Using principal component analysis, the first principal components determined 97.57% of the total variation. Onion yield per plant was the most important trait in the first principal component and onion dry weight was the second trait in the rank.


Simulation of Scour Pattern Around Cross-Vane Structures Using Outlier Robust Extreme Learning Machine
A.h. Azimi, S Shabanlou, F. Yosefvand, A. Rajabi, B. Yaghoubi,
Volume 25, Issue 4 (Winiter 2022)
Abstract
In this research, the scour hole depth at the downstream of cross-vane structures with different shapes (i.e., J, I, U, and W) was simulated utilizing a modern artificial intelligence method entitled "Outlier Robust Extreme Learning Machine (ORELM)". The observational data were divided into two groups: training (70%) and test (30%). Then, using the input parameters including the ratio of the structure length to the channel width (b/B), the densimetric Froude number (Fd), the ratio of the difference between the downstream and upstream depths to the structure height (Δy/hst), and the structure shape factor (φ), eleven different ORELM models were developed for estimating the scour depth. Subsequently, the superior model and also the most effective input parameters were identified through the conduction of uncertainty analysis. The superior model simulated the scour values by the dimensionless parameters b/B, Fd, Δy/hst. For this model, the values of the correlation coefficient (R), the variance accounted for (VAF), and the Nash-Sutcliffe efficiency (NSC) for the superior model in the test mode were obtained 0.956, 91.378, and 0.908, respectively. Also, the dimensionless parameters b/B and Δy/hst were detected as the most effective input parameters. Furthermore, the results of the superior model were compared with the extreme learning machine model and it was concluded that the ORELM model was more accurate. Moreover, an uncertainty analysis exhibited that the ORELM model had an overestimated performance. Besides, a partial derivative sensitivity analysis (PDSA) model was performed for the superior model.

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