Sharif Digital Repository

An artificial neural network model was developed for modeling of the effects of mechanical alloying parameters including milling time, milling speed and ball to powder weight ratio on the characteristics of Al-8 vol%SiC nanocomposite powders. The crystallite size and lattice strain of the aluminum matrix were considered for modeling. This nanostructured nanocomposite powder was synthesized by utilizing planetary high energy ball mill and the required data for training were collected from the experimental results. The characteristics of the particles were determined by X-ray diffraction, scanning and transmission electron microscopy. Two types of neural network architecture, i.e. multi-layer... 

STUDY DESIGN. Cross-cultural translation and psychometric testing were performed. OBJECTIVES. To cross-culturally translate the Oswestry Disability Index (ODI), Roland-Morris Disability Questionnaire (RDQ), and Quebec Back Pain Disability Scale (QDS) into Persian, and then investigate the psychometric properties of the Persian versions produced. SUMMARY OF BACKGROUND DATA. To the authors' knowledge, there is no validated instrument to measure functional status in Persian-speaking patients with low back pain (LBP) in Iran. To our knowledge, the widely used back-specific measures, the ODI, RDQ, and QDS, have not been translated and validated for Persian-speaking patients with LBP. METHODS. The... 

An immersed solvent microextraction (SME) method was successfully developed for the trace enrichment of s-triazine herbicides from aquatic media. A microdrop of butyl acetate was applied as the extraction solvent. After extraction, the microdrop was introduced directly into a gas chromatography-mass spectrometry (GC-MS) injection port. Some important extraction parameters such as type of solvent, extraction time, stirring rate, and temperature were investigated and optimized. The highest possible microdrop volume of 3 μl, a sampling temperature of 60°C, and use of butyl acetate are major parameters to obtain high enrichment factors. The enrichment factor and linearity was studied by... 

Abstract. The linear sequential circuit approximation method for combiners with memory is used to find mutually correlated linear transforms of the input and output sequences in the well-known summation generator with any number of inputs. It is shown that the determined correlation coefficient is large enough for applying a fast correlation attack to the output sequence to reconstruct the initial states of the input linear feedback shift registers. The proposed attack is based on iterative probabilistic decoding and appropriately generated low-weight parity-checks. The required output sequence length and the computational complexity are both derived. Successful experimental results for the... 

Reservoir permeability is an important parameter that its reliable prediction is necessary for reservoir performance assessment and management. Although many empirical formulas are derived regarding permeability and porosity in sandstone reservoirs, these correlations cannot be accurately depicted in carbonate reservoir for the wells that are not cored and for which there are no welltest data. Therefore, having a framework for estimation of these parameters in reservoirs with neither coring samples nor welltest data is crucial. Rock properties are characterized by using different well logs. However, there is no specific petrophysical log for estimating rock permeability; thus, new methods... 

A magnetic polyurethane (PU) nanocomposite was synthesized by an electrospinning technique and applied for isolation and preconcentration of fluoxetine from aquatic and biological samples. The nanocomposite was electrospun using a PU polymer solution containing the dispersed magnetic nanoparticles. The magnetic properties of iron nanoparticles, along with the use of an electrospinning technique, led to the formation of a suitable sorbent toward isolation of fluoxetine. The magnetic PU nanofibers could be subsequently removed from the sample solution by applying a permanent magnet. The scanning electron microscopy (SEM) image of the magnetic PU nanofibers confirms that their diameters are in... 

The objective of the work ahead is presentation of a new carbon paste electrode (CPE) modified by TiO2 nanoparticles (TiO2NPs) and 1,2-bis-[o-aminophenyl thio] ethane (APTE) ligand as a selective cation receptor for determination of the Cd (II) ions using differential pulse anodic stripping voltammetry (DPASV). The electrode shows an excellent tendency to Cd (II) ions in presence of some interfering species. Under the optimum conditions, a linear calibration curve was obtained in the concentration range of 2.9 nM to 4.6 μM with a correlation coefficient of 0.9969 in the anodic potential of 0.54 (V vs. Ag/AgCl). The metal detection limit was 2.0 nM after 10 min preconcentration (S/N = 3). The... 

Estimation of the viscosity of naturally occurring petroleum gases is essential to provide more accurate analysis of gas reservoir engineering problems. In this study, a new soft computing approach, namely, least square support vector machine (LSSVM) modeling, optimized with a coupled simulated annealing technique was applied for estimation of the natural gas viscosities at different temperature and pressure conditions. This model was developed based on 2485 viscosity data sets of 22 gas mixtures. The model predictions showed an average absolute relative error of 0.26% and a correlation coefficient of 0.99. The results of the proposed model were also compared with the well-known predictive... 

Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g. sum-of-squared-differences, correlation coefficient, mutual information and correlation ratio) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation among pixel intensities; hence, a perfect image registration cannot be achieved especially in the presence of spatially varying intensity distortions and outlier objects that appear in one image but not in the other. It is supposed here that non-stationary intensity distortion (such as bias field) has a sparse representation in the transformation domain. Based on this... 

In oil and gas fields, it is a common practice to flow liquid and gas mixtures through choke valves. In general, different types of primary valves are employed to control pressure and flow rate when the producing well directs the natural gas to the processing equipment. In this case, the valve normally is affected by elevated levels of flow (or velocity) as well as solid materials suspended in the gas phase (e.g., fine sand and other debris). Both surface and subsurface chokes may be installed to regulate flow rates and to protect the porous medium and surface facilities from unusual pressure instabilities.In this study a reliable, novel, computer based predictive model using Least-Squares... 

In this study, a reliable and predictive model namely, least-squares support vector machine (LS-SVM) was developed to predict dye removal efficiency. Four LS-SVM models have been developed and tested using more than 630 series of experimental data which were obtained from our previous paper. These data consist of adsorbate type, adsorbent dosage, initial dye concentration, salt, absorbance time and dye removal efficiency. Direct Red 31 (DR31), Direct Green 6 (DG6) and Acid Blue (AB92) were used as a model dyes. The results show that the developed model is more accurate and reliable with the average absolute relative deviation of 0.678%, 0.877%, 0.581% and 0.978% for single systems and... 

Recent research works have shown that control statistics based on squared deviation of observations from target have the ability to monitor variability in both univariate and multivariate processes. In the current research, the properties of the control statistic S t that has been proposed by Huwang et al. (J. Quality Technology 2007; 39:258-278) are first reviewed and three new S t-based multivariate schemes are then presented. Extensive simulation experiments are performed to compare the performances of the proposed schemes with those of the multivariate exponentially weighted mean squared deviation (MEWMS) and the L 1-norm distance of the MEWMS deviation from its expected value (MEWMSL 1)... 

Analytical Jacobian, nonlinear least square and three layer artificial neural network models are employed to predict deformation of mouse embryos under needle injection, based on experimental data captured from literature. The Maximum Absolute Error (MAE), coefficient of determination ( R2), Relative Error of Prediction (REP), Root Mean Square Error of Prediction (RMSEP), NashSutcliffe coefficient of efficiency ( Ef) and accuracy factor ( Af) are used as the basis for comparison of these three models. Analytical Jacobian, nonlinear least square and ANN models have yielded the correlation coefficient of 0.9985, 0.9964 and 0.9998, respectively. The REP between the models predicted values and... 

The influence of structural configuration on vibration responses of smart laminated beams under random loading is studied. The effect of laminate configurations and locations of sensors/actuators in the smart system is also investigated. The layer-wise approximation for displacement and electric potential is utilized to construct the finite element model. The closed-loop control response is determined through an optimal control algorithm based on the Linear Quadratic Regulator (LQR). The correlation coefficient between the input random force and the applied actuating voltage for various configurations is also computed. It is revealed that for softer configurations, the correlation... 

The quantification of carbon dioxide (CO2) dissolution in oil is crucial in predicting the potential and long-term behavior of CO2 in reservoir during secondary and tertiary oil recovery. Accurate predicting carbon dioxide molecular diffusion coefficient is a key parameter during carbon dioxide injection into oil reservoirs. In this study a new model based on adaptive neuro-fuzzy inference systems (ANFIS) is designed and developed for accurate prediction of carbon dioxide diffusivity in oils at elevated temperature and pressures. Particle Swarm Optimization (PSO) as population based stochastic search algorithms was applied to obtain the optimal ANFIS model parameters. Furthermore, a simple...