This paper implements a derivative free optimization method called "Genetic Algorithm" to estimate the parameters of a four-wheel, three degrees of freedom vehicle handling model. At first the model is developed containing a non-linear tire model called "Fiala". Then, an error function is defined and the "Genetic Algorithm" optimization method is introduced and applied to minimize the error. Finally, verification of parameter estimation is checked.

Records of 3257 lambs of Zandi sheep breed, collected from 1991to 2003 in Khojir rearing and breeding station of Tehran province were used to estimate the genetic parameters for growth traits (birth weight, weaning weight, body weight at 6, 9, and 12 months of ages) along with composite reproduction traits (total weight of lamb weaned per ewe joined through three consecutive parities. Based on univariate analysis, the best model for birth and weaning weights was model 3 (including direct and maternal additive genetic effects). Modell (including direct additive genetic effect) was the most appropriate model for body weight at 6, 9 and 12 months of ages. According to this model, the direct heritability estimates for growth traits (from birth weight to body weight at 12 months of age) were 0.08±0.04, 0.20±0.05, 0.29±0.06, 0.25±0.06 and 0.35±0.07, respectively. Maternal heritabilities of 0.17±0.02 and 0.06±0.03 wares obtained for birth and weaning weights, respectively. Genetic parameters for composite reproduction traits were estimated using model 1. Heritability estimates of 0.06±0.08, 0.03±0.09, and 0.08±0.11 were obtained from first to third parity, respectively. Covariance components and correlations between growth and composite reproduction traits were estimated on the basis of multivariate analysis, using the most appropriate model in univariate analysis. The highest additive genetic correlation among growth and composite reproduction traits was obtained between weaning weight and TWW2/EJ (0.99). The corresponding estimate for additive genetic correlation among growth traits was 0.99 (between weaning and body weights at 6 months of age). The results of this study indicated that selection on the basis of weaning weight can improve the post weaning and composite reproduction traits.

Genetic evaluation of sport horses using various methods is commonly carried out in many countries, except for Iran. Accurate estimation of genetic parameters for functional traits in horses has always been a subject of interest among livestock breeders. The aim of this study was to investigate the genetic parameters of athletic performance traits in Iranian jumping horses. Traits include race completion time (RCT), rank at the end of the competition (REC), and number of errors in the competition (NEC). The study were conducted based on Equestrian Federation of Iran (FEI) athletic performance records of 1498 horses (10–23 years old). The genetic parameters were estimated by using Restricted Maximum Likelihood (REML) procedures and airemf90 software. The statistical model included fixed effects of birth year, sex, age, breed, height of obstacles, date/city, level of difficulty of the event and random effects of rider, permanent environment and an additive genetic effect. Heritability of RCT, REC and NEC traits were estimated 0.03, 0.11 and 0.09 and their repeatability were 0.16, 0.25 and 0.13, respectively. Genetic and phenotypic trends of RCT, REC and NEC traits were estimated -0.002, 0.0091, 0.0003 and -0.7057, 0.2575, -0.0807, respectively. According to this result, the performance traits are highly affected by environmental factors, especially the effect of the rider, but among the studied traits, selection based on rank traits can lead to more genetic progress. It can be concluded that we can apply these parameters to improve the genetic potential of show jumping performance records and design the appropriate breeding strategies for horse jumping population in Iran.

This study is focused on two parameter estimation methods of Ordinary Least Square (OLS) and binary code Genetic Algorithm (GA) for estimating the k, n Nash conceptual model parameters. The efficiency of these methods is compared by applying the calibrated models in simulating seven rainfall-runoff events in Heng-Chi watershed in north of Taiwan. The results of the goodness of fit criteria indicate that GA method has better performance in terms of coefficient of efficiency and has reduced the coefficient of variation of error in simulated discharge and error in peak discharge.

The ionospheric plasma bubbles cause unpredictable changes in the ionospheric electron density. These variations in the ionospheric layer can cause a phenomenon known as the ionospheric scintillation. Ionospheric scintillation could affect the phase and amplitude of the radio signals traveling through this medium. This phenomenon occurs frequently around the magnetic equator and in low latitudes, mid as well as high latitude regions. ionospheric scintillation is a very complex phenomenon to be modeled. Patterns of ionospheric scintillation occurrence are depended on spatial and temporal ionospheric variabilities. Neural Network (NN) is a data-dependent method, that its performance improves with the sample size. According to the advantages of NN for large datasets and noisy data, the NN model has been implemented for predicting the occurrences of amplitude scintillations. In this paper, the GA technique was considered to obtain primary weights in the NN model in order to identify appropriate S4 values for GUAM GPS station in Guam country (latitude: 144. 8683, Longitude: 13. 5893). The modeling was carried out for the whole month of June 2017, while this model along with ionospheric physical data was used for predicting ionospheric scintillation at the first day of July 2017, the day after the modeling. The designed model has the ability to predict daily ionospheric scintillation with the accuracy of about 78%.

This study aimed to perform a meta-analysis to combine genetic parameters for economically important traits of Iranian indigenous chickens. A data set of information related to different growth, reproduction, and egg quality traits including 336 heritability estimates and 433 genetic correlations from 45 articles published between 2007 and 2019 were used. Meta-analysis was performed based on a random-effects model to calculate the effect size for genetic parameters. Also, statistic and Q test were used to measure the degree of heterogeneity among studies. The mean heritability for growth traits ranged from 0.222 (body weight at hatch) to 0.34 (body weight at 12 weeks of age). The lowest and highest estimates of the heritability for reproductive traits were 0.181 (number of eggs produced) and 0.449 (age at sexual maturity), respectively. The mean heritability estimate for egg internal quality traits varied from 0.211 (yolk weight) to 0.355 (albumin weight) and for external quality traits of eggs in the range from 0.261 (shell strength) up to 0.332 (Shell weight). Also, the mean genetic correlation estimates between growth traits, and between reproductive traits ranged from 0.297 to 0.878 and -0.678 to 0.788, respectively. Also, the genetic correlation between internal and external quality traits of eggs ranged from -0.069 to -0.979 and -0.012 to -0.856, respectively. The estimates reported in the present study are appropriate to be used in breeding programs when reliable genetic parameter estimates are not accessible for economically important traits in native fowls.