Mir N.,University of Zabol |
Salavati-Niasari M.,University of Kashan
Materials Research Bulletin | Year: 2013
The effect of different tripodal tetraamine ligands was investigated on the particle size, agglomeration level, optical and photovoltaic properties of TiO2 nanoparticle prepared via a two-step sol-gel method. The products were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), transmission electron spectroscopy (TEM), scanning electron microscopy (SEM), and UV-vis spectroscopy. The results showed that the symmetry of ligands has a crucial effect on the size and agglomeration level of the products. The optical and photovoltaic properties of the products were studied, as well. The reflectivity property of the samples due to different agglomeration sizes is shown to be very important factor in increasing conversion efficiency of DSSC. © 2013 Elsevier Ltd.
Khajeh M.,University of Zabol
Food Chemistry | Year: 2011
In this study, a new method called miniaturised homogenous liquid-liquid extraction, followed by graphite furnace atomic absorption spectrometry, was developed for the extraction and determination of lead from food samples. The procedure was based on the fast extraction of lead from an acetic acid sample solution into 0.5 mL chloroform, as an extraction solvent. After adding water into the mixture, the extracting solvent phase immediately formed a distinct water-immiscible phase below the vial, which could easily be separated, evaporated and re-dissolved in 1.0 mL nitric acid 0.1 mol L-1 for further analysis. The effects of various experimental parameters in extraction step were studied using two optimisation methods, one variable at a time and Box-Behnken design. The results showed that the amount of salt and extraction time did not have effect on the extraction efficiency. Therefore, a three-level Box-Behnken experimental design with three factors, which combined the response surface modelling, was used to optimise lead extraction. Three independent variables, including pH of solution (ranging from 6.5 to 10.5), concentration of dithizone as chelating agent (ranging from 0.05 to 0.5 μg L-1) and extracting solvent volume (ranging from 300 to 900 μL) were respectively coded as pH, D and V at three different levels (-1, 0 and 1). In this study, the optimum condition was determined at pH 8.4, a volume of chloroform at 0.45 mL, and concentration of dithizone at 0.5 μg L-1. Under the optimum condition, the limit of detection (LOD) was 0.05 μg L-1. Furthermore, the relative standard deviation of the ten replicate was <5.0%. The developed procedure was applied to the extraction and determination of lead in the food samples. © 2011 Elsevier Ltd. All rights reserved.
Development of artificial neural network models based on experimental data of response surface methodology to establish the nutritional requirements of digestible lysine, methionine, and threonine in broiler chicks
Mehri M.,University of Zabol
Poultry Science | Year: 2012
An artificial neural network (ANN) approach was used to develop feed-forward multilayer perceptron models to estimate the nutritional requirements of digestible lysine (dLys), methionine (dMet), and threonine (dThr) in broiler chicks. Sixty data lines representing response of the broiler chicks during 3 to 16 d of age to dietary levels of dLys (0.88-1.32%), dMet (0.42-0.58%), and dThr (0.53-0.87%) were obtained from literature and used to train the networks. The prediction values of ANN were compared with those of response surface methodology to evaluate the fitness of these 2 methods. The models were tested using R2, mean absolute deviation, mean absolute percentage error, and absolute average deviation. The random search algorithm was used to optimize the developed ANN models to estimate the optimal values of dietary dLys, dMet, and dThr. The ANN models were used to assess the relative importance of each dietary input on the bird performance using sensitivity analysis. The statistical evaluations revealed the higher accuracy of ANN to predict the bird performance compared with response surface methodology models. The optimization results showed that the maximum BW gain may be obtained with dietary levels of 1.11, 0.51, and 0.78% of dLys, dMet, and dThr, respectively. Minimum feed conversion ratio may be achieved with dietary levels of 1.13, 0.54, 0.78% of dLys, dMet, and dThr, respectively. The sensitivity analysis on the models indicated that dietary Lys is the most important variable in the growth performance of the broiler chicks, followed by dietary Thr and Met. The results of this research revealed that the experimental data of a response-surface-methodology design could be successfully used to develop the well-designed ANN for pattern recognition of bird growth and optimization of nutritional requirements. The comparison between the 2 methods also showed that the statistical methods may have little effect on the ideal ratios of dMet and dThr to dLys in broiler chicks using multivariate optimization. © 2012 Poultry Science Association Inc.
Mehri M.,University of Zabol
Poultry Science | Year: 2013
Application of appropriate models to approximate the performance function warrants more precise prediction and helps to make the best decisions in the poultry industry. This study reevaluated the factors affecting hatchability in laying hens from 29 to 56 wk of age. Twenty-eight data lines representing 4 inputs consisting of egg weight, eggshell thickness, egg sphericity, and yolk/albumin ratio and 1 output, hatchability, were obtained from the literature and used to train an artificial neural network (ANN). The prediction ability of ANN was compared with that of fuzzy logic to evaluate the fitness of these 2 methods. The models were compared using R2, mean absolute deviation (MAD), mean squared error (MSE), mean absolute percentage error (MAPE), and bias. The developed model was used to assess the relative importance of each variable on the hatchability by calculating the variable sensitivity ratio. The statistical evaluations showed that the ANN-based model predicted hatchability more accurately than fuzzy logic. The ANNbased model had a higher determination of coefficient (R2 = 0.99) and lower residual distribution (MAD = 0.005; MSE = 0.00004; MAPE = 0.732; bias = 0.0012) than fuzzy logic (R2 = 0.87; MAD = 0.014; MSE = 0.0004; MAPE = 2.095; bias = 0.0046). The sensitivity analysis revealed that the most important variable in the ANN-based model of hatchability was egg weight (variable sensitivity ratio, VSR = 283.11), followed by yolk/albumin ratio (VSR = 113.16), eggshell thickness (VSR = 16.23), and egg sphericity (VSR = 3.63). The results of this research showed that the universal approximation capability of ANN made it a powerful tool to approximate complex functions such as hatchability in the incubation process. © 2013 Poultry Science Association Inc.
Ghazizadeh-Ahsaee M.,University of Zabol
IEEE Transactions on Power Delivery | Year: 2013
In this paper, an accurate fault locator is proposed for nonlinear high impedance faults (NHIFs) based on unsynchronized data gathered from both ends of the transmission line. This method is independent of the fault type, impedance, configuration, and two-end Thevenin's equivalent impedances of the external networks. The distributed parameter line model in the time domain is considered in the new algorithm which accurately models the transmission line. The location of fault and synchronization time are calculated by solving an optimization problem. The performance of the algorithm is tested for different synchronization times and various fault conditions. The performed evaluation shows the validity of the developed fault locator and its accuracy while NHIFs occur. © 1986-2012 IEEE.