Khajeh M.,University of Zabol |
Laurent S.,University of Mons |
Dastafkan K.,University of Zabol
Chemical Reviews | Year: 2013
Nanomaterials possess a series of unique physical and chemical properties. A very important one is that most of the atoms that have high chemical activity and adsorption capacity to many metal ions are on the surface of the nanomaterials. The surface atoms are unsaturated and are thus subject to combination with other element ions by static electricity. Therefore, nanomaterials can strongly adsorb many substances including trace metals and polar organic compounds. Nanomaterials possess key physical characteristics which are dictated by the kind of nano-objects they contain. They are classified into compact materials and nanodispersions. The first type includes so-called 'nanostructured' materials, that is, isotropic materials in their macroscopic composition, and consists of connecting nanometer-sized units that repeat structural elements.
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.
Keshtegar B.,University of Zabol
Computer Methods in Applied Mechanics and Engineering | Year: 2016
The efficiency and robustness are two important issues in First Order Reliability Method (FORM). The Hasofer and Lind–Rackwitz and Fiessler (HL–RF) algorithm is widely used in FORM, but it produces unstable results as periodic and chaotic solutions for highly nonlinear problems. Hence, the stability transformation method (STM) with chaos feedback control was proposed to overcome the numerical instabilities of FORM, but it is inefficient for both concave and convex reliability problems. In this paper, a stability transformation method with chaotic conjugate search direction is proposed to improve both the robustness and efficiency of FORM formula, where a chaos control factor is proposed based on Logistic map, and a transformation matrix is adaptively defined based on the reliability index and the Logistic map at each iteration. Eight nonlinear mathematical and structural/mechanical examples are selected to demonstrate the efficiency and robustness of the proposed chaotic conjugate stability transformation method (CCSTM). Results illustrate that the CCSTM is more robust than the HL–RF and more efficient than other existing reliability methods. © 2016 Elsevier B.V.
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.
Mohammadzadeh Kakhki R.,University of Zabol
Journal of Inclusion Phenomena and Macrocyclic Chemistry | Year: 2013
Recently, much attention has been paid to chromatographic characteristics and applications of crown ethers. These compounds were employed as chiral stationary phase for resolution of various racemic compounds in high performance chromatography and capillary electrochromatography techniques. Crown ethers also used in gas chromatography as the stationary phase. Recently, it has been found that, crown ethers also may be useful in cation chromatographic separation in ion chromatography for the determination of alkali and alkaline-earth cations, ammonium, and amines. In this paper we have an overview on these applications of crown ethers. © 2012 Springer Science+Business Media B.V.
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.
Tavakol H.,University of Zabol
Structural Chemistry | Year: 2011
DFT and MP2 methods were used to calculate structural parameters, vibrational modes, solvent effect, and energetic properties of amidrazones. Amidrazones can be presented by three tautomeric forms and six isomers. All tautomers and transition states were optimized at the B3LYP/6- 311++g** and MP2/6-311++G** levels of theory. The relative stabilities of amidrazone isomers in the gas phase were found to be as 1Z>1E>2E>2Z >3E>3Z>TS(1-2) >TS(1-3). The calculated energy differences between E and Z isomers are very low and between different tautomers are nearly low, but the energy barriers for tautomerism interconversions at the gas phase are high. The kinetic and thermodynamic data in solvents (chloroform, tetrahydrofuran, acetone, and water) are nearly similar to those in the gas phase but their rate constants are slightly less than those in the gas phase. Moreover, equilibrium and rate constants of intermolecular tautomerism in presence of 1-3 molecules of water were calculated. Computed energy barriers show that the barrier energy of water-assisted tautomerism is very lower than that in simple tautomerism and also calculated binding energies show that water can stabilize transition states more than tautomers. Therefore, this water-assisted tautomerism can be performed fast, especially with the assistance of two molecules of water. © Springer Science+Business Media, LLC 2011.
Motalleb G.,University of Zabol
Cell Journal | Year: 2014
Objective: In this study, artificial neural network (ANN) analysis of virotherapy in preclinical breast cancer was investigated. Materials and Methods: In this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. The input parameters of the model were virus dose, week and tamoxifen citrate, while tumor weight was included in the output parameter. Two different training algorithms, namely quick propagation (QP) and Levenberg-Marquardt (LM), were used to train ANN. Results: The results showed that the LM algorithm, with 3-9-1 arrangement is more efficient compared to QP. Using LM algorithm, the coefficient of determination (R2) between the actual and predicted values was determined as 0.897118 for all data. Conclusion: It can be concluded that this ANN model may provide good ability to predict the biometry information of tumor in preclinical breast cancer virotherapy. The results showed that the LM algorithm employed by Neural Power software gave the better performance compared with the QP and virus dose, and it is more important factor compared to tamoxifen and time (week).
Khajeh M.,University of Zabol
Journal of Supercritical Fluids | Year: 2011
The oil and extracts of Satureja hortensis cultivated in Iran were extracted using supercritical carbon dioxide and hydrodistillation method. The oil and extracts were analyzed by GC-FID and GC/MS. The compounds were identified according to their retention indices and mass spectra (EI, 70 eV). The effects of various parameters such as pressure, temperature, percent of modifier (methanol) and extraction time, were investigated by a fractional factorial design (2 4-1) to determine the significant parameters and their interactions. The results showed that the pressure, temperature and percent of modifier are significant (p < 0.05), but the extraction time was found to be insignificant. The response surface methodology (RSM), based on Box-Behnken design was employed to obtain the optimum conditions of the significant parameters (pressure, temperature and percent of modifier). The optimal conditions could be obtained at a pressure of 35.0 MPa, temperature of 72.6 °C, and 8.6% (v/v) for methanol. The main extracted components using SFE were γ-Terpinene (35.5%), Thymol (18.2%) and Carvacrol (29.7%). © 2010 Elsevier B.V. All rights reserved.