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Showing 4 results for Parsaei
The Effect of Endophytic Fungi on Physiological Characteristics and Cold Tolerance of Two Species of Meadow Fescue and Tall Fescue
M. Parsaeian, A. F. Mirlohi, A. M. Rezaie, M. Khayyam Nekoie,
Volume 10, Issue 4 (winter 2007)
Abstract
To determine the role of endophytes in conferring valuable physiological characteristics on and induction inducing cold tolerance in two species of festuca, an experiment was done at Isfahan University of Technology in 2002. Endophyte-infected and non-infected clones from two genotypes of tall fescue and one meadow fescue were prepared and coded 75,83 and 60 respectively. The clones were exposed to cold treatments at 6, -2 and –10C and compared with control treatment at 20C. After three weeks of cold treatments, relative water content of leaf and crown, cell membrane stability (electrolyte leakage), percentage of membrane damage and finally proline content of leaf were measured. The presence of endophyt caused an slight increase in relative water content of leaf and crown. There was considerably higher proline in endophyte-infected plants compared with non-infected ones in both stress and non-stress conditions. Endophytic fungi had strong effects on maintenance of membrane stability and on the decrease of electrolyte leakage at all temperature levels. Among plant genotypes, 83 preformed better for some characteristics, specially in the presence of endophyte, and showed higher cold tolerance.
Effects of Phosphorous Fertilizers and Arbuscular Mycorrhiza Fungi Symbiosis with Sunflower on Pb Availability in a Contaminated Soil
M. Amouzegar, A. Abbaspour, Sh. Shahsavani, H. R. Asghari , M. Parsaeiyan,
Volume 19, Issue 74 (Winter 2016)
Abstract

Soil contamination by Pb leads to a reduction in the quality and quantity of crop yield, because it is highly toxic in soluble ionic forms. The availability of this element for plant roots can reduce by the formation of compounds with low solubility and their sedimentation by phosphorous amendments.. Root symbiosis with mycorrhizal fungi can also increase plant resistance against heavy metals. This study was carried out as a factorial experiment in a randomized complete block design asa greenhouse experiment on sunflower plant at Shahrood University. Treatments included mycorrhizal fungi with two levels of inoculation, (with and without inoculation), organic and inorganic phosphorous fertilizers such as humic acid, diammonium phosphate, bone meal and bone meal+humic acid. The results showed that inoculation with mycorrhizal fungi resulted in a significant increase (P&ge0.05) in percentage of mycorrhizal colonization and an increase in soil EC,shootdry weight and phosphous uptake by the plant. Phosphorus fertilizers significantly increased the available phosphorus in soil, dry weight and uptake of phosphorus by the shoots. The interaction effects of mycorrhiza and phosphorus fertilizers on soil exchange able Pbwere significant. The application of diammonium phosphate and mycorrhiza had the greatest impacton the reduction of Pb (by 25.48percent) in the soil exchange. Mycorrhizal plants had a lower rate of lead concentrations in shoots, which was equal to 78/14%, and also the application of phosphorus fertilizers significantly reduced Pb in plant shoots.


Modeling and Estimating the Dimensions of Stable Alluvial Channels using Soft Calculations
B. Shahinejad, A. Parsaei, A. Haghizadeh, A. Arshia, Z. Shamsi,
Volume 26, Issue 3 (Fall 2022)
Abstract
In this research, soft computational models including multiple adaptive spline regression model (MARS) and data group classification model (GMDH) were used to estimate the geometric dimensions of stable alluvial channels including channel surface width (w), flow depth (h), and longitudinal slope (S) and the results of the developed models were compared with the multilayer neural network (MLP) model. To develop the models, the flow rate parameters (Q), the average particle size in the floor and body (d50) as well as the shear stress (t) as input and the parameters of water surface width (w), flow depth (h), and longitudinal slope (S) were used as output parameters. Soft computing models were developed in two scenarios based on raw parameters and dimensionless form independent and dependent parameters. The results showed that the statistical characteristics in estimating w, the best performance is related to the MARS model, whose statistical indicators of accuracy in the training stage are R2 = 0.902, RMSE=1.666 and in the test phase is R2 = 0.844, RMSE=2.317. In estimating the channel depth, the performance of both GMDH and MARS models is approximately equal, both of which were developed based on the dimensionless form of flow rate as the input variable. The statistical indicators of both models in the training stage are R2 » 0.90, RMSE » 8.15 and in the test phase is R2 » 0.90, RMSE = 7.40. The best performance of the developed models in estimating the longitudinal slope of the channel was related to both MARS and GMDH models, although, in part, the accuracy of the GMDH model with statistical indicators R2 = 0.942, RMSE = 0.0011 in the training phase and R2 = 0.925, RMSE = 0.0014 in the experimental stage is more than the MARS model.

Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models
B. Shahinejad, A. Parsaei, H. Yonesi, Z. Shamsi, A. Arshia,
Volume 26, Issue 4 (Winiter 2023)
Abstract
In the present study, the flow rate in flues containing lateral semi-cylinders (SMBF) was simulated and estimated under free and submerged conditions using back vector machine models (SVM), spin multivariate adaptive regression (MARS), and multilayer artificial neural network (MLPNN) model. In free flow mode, the dimensionless parameters extracted from the dimensional analysis include the ratio of upstream flow to throat width and contraction ratio (throat width to channel width), and in the submerged state, in addition to these two parameters, the depth-to-throat width, and bottom-depth parameters upstream depth were used as input and the two-dimensional form of flow rate was used as the output of the models. The results showed that in free flow mode in the validation stage, the MARS model with statistical indices of R2 = 0.985, RMSE = 0.008, MAPE = 0.87%, and the SVM model with statistical indices of  R2 = 0.971, RMSE = 0.0012, MAPE =1.376%, and MLPNN model with statistical indices of R2 = 0.973,  RMSE = 0.011, MAPE = 1.304% have modeled and predicted the flow rate. In the submerged state, the statistical indices of the developed MARS model were R2 = 0.978, RMSE = 0.018, MAPE = 3.6%, and the statistical indices of the SVM model were R2 = 0.988, RMSE = 0.014, 2%. MAPE = 4, and the statistical indicators of the MLPNN model were R2 = 0.966, RMSE = 0.022, and MAPE = 5.7%. In the development of SVM and MLPNN models, radial kernel and hyperbolic tangent functions were used, respectively.

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