Sharif Digital Repository

Background: Gold nanoparticles (GNPs) have been shown as a good radiosensitizer. In combination with radiotherapy, several studies with orthovoltage X-rays have shown considerable dose enhancement effects. This paper reports the dose enhancement factor (DEF) due to GNPs in 18 megavoltage (MV) beams. Materials and Methods: Different geometrical 50-nm GNPs configurations at a concentration of 5 mg/ml were used by both experimental and Monte Carlo (MC) simulation in a deep-seated tumor-like insertion within a phantom. Using MCNP repeated structure capability; a large number of gold nanospheres with a semi-random distribution were applied to simulate this phantom based study. Thermoluminescence... 

Dual-phase steels with different morphology and volume fraction of martensite were deformed between 2% and 8% at a high-temperature range of 150-450 °C. Room-temperature tensile properties showed that both yield and tensile stresses depend on the amount of pre-strain, deformation temperature, volume fraction and morphology of martensite. Results show that both YS and UTS increase with increasing the amount of pre-strain at a given temperature. © 2005 Elsevier B.V. All rights reserved  

An age-hardening model has been developed to predict the evolution of the hardness of Al-Mg-Si alloys during non-isothermal ageing before peak age. The concurrent precipitation and dissolution have been considered in the structural model. Then the structural model has been combined with strengthening model to predict the precipitation-hardening behavior of the alloy AA6061. The results indicate that the developed model can be used as a predictive tool to model the mechanical properties evolution of Al-Mg-Si alloys during non-isothermal heat treatment. © 2008 Elsevier B.V. All rights reserved  

In this investigation heat treatment optimization of 17-4PH stainless steel has been carried out by a genetic algorithm. The optimum technique of heat treatment, adaptive to 17-4PH stainless steel, was obtained from the initial data set by the use of genetic algorithms based on modeling with artificial neural network. The results strongly indicate that the presented model has the great ability for heat treatment optimization of 17-4PH stainless steel to yield the highest strength levels in different working temperatures. © 2006 Elsevier Ltd. All rights reserved  

In this investigation a theoretical model based on artificial neural network (ANN) has been developed to predict porosity percent and correlate the chemical composition and cooling rate to the amount of porosity in Al-Si casting alloys. In addition, the sensivity analysis was performed to investigate the importance of the effects of different alloying elements, composition, grain refiner, modifier and cooling rate on porosity formation behavior of Al-Si casting alloys. By comparing the predicted values with the experimental data, it is demonstrated that the well-trained feed forward back propagation ANN model with eight nodes in hidden layer is a powerful tool for prediction of porosity... 

In this investigation, a neural network model was used to predict mechanical properties of dual phase (DP) steels and sensivity analysis was performed to investigate the importance of the effects of pre-strain, deformation temperature, volume fraction and morphology of martensite on room temperature mechanical behavior of these steels. In order to train the neural network, dual-phase (DP) steels with different morphology and volume fractions of martensite were deformed between 2 and 8%, at high temperature range of 150-450 °C. The results of this investigation show that there is a good agreement between experimental and predicted values and the well-trained neural network has a great... 

In this paper, an artificial neural network (ANN) model has been developed to predict the yield and tensile strengths of hot pressed NiAl intermetallic compound based on the experimental data from Albiter et al. [A. Albiter, M. Salazar, E. Bedolla, R.A.L. Drew, R. Perez, Mater. Sci. Eng. A 347 (2003) 154]. The predicted results, with a correlation relation between 0.9791 and 0.9921, show a very good agreement with the experimental values. Furthermore, the sensitivity analysis was performed to investigate the importance of the effects of chemical composition and temperature on the mechanical behavior of hot pressed NiAl intermetallic compound. © 2008 Elsevier B.V. All rights reserved  

Artificial neural network (ANN) and genetic algorithm were used in this study to obtain a relatively high flow stress in compression tests for 304 stainless steel. Cold and warm compression were carried out in a temperature range from 20 to 600 °C, strain-rate from 0.001 to 100 S-1 and a strain range from 0.1 to 0.5. Optimum conditions for each case were obtained experimentally and were evaluated by the ANN model. The ANN model was used as fitness function for genetic algorithm. The results indicated that this combined algorithm offers an effective condition for 304 stainless steel, which avoids flow localization, dynamic strain aging, adiabatic shear deformation and void generation. © 2005... 

The purpose of this investigation is to develop a model for prediction of topologically closed-packed (TCP) phases formation in superalloys. In this study, artificial neural networks (ANN), using several different network architectures, were used to investigate the complex relationships between TCP phases and chemical composition of superalloys. In order to develop an optimum ANN structure, more than 200 experimental data were used to train and test the neural network. The results of this investigation shows that a multilayer perceptron (MLP) form of the neural networks with one hidden layer and 10 nodes in the hidden layer has the lowest mean absolute error (MAE) and can be accurately used... 

In this study, the prediction of flow stress in 304 stainless steel using artificial neural networks (ANN) has been investigated. Experimental data earlier deduced-by [S. Venugopal et al., Optimization of cold and warm workability in 304 stainless steel using instability maps, Metall. Trans. A 27A (1996) 126-199]-were collected to obtain training and test data. Temperature, strain-rate and strain were used as input layer, while the output was flow stress. The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. The results of this investigation shows that the R2 values for the test and training data set are about... 

In this investigation a theoretical model based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to optimize effective parameters on porosity formation in Al-Si casting alloys. The ANN theory was used to correlate the chemical composition and cooling rate to the amount of porosity. The GA and ANN were incorporated to find the optimal conditions for achieving the minimum porosity percent. By comparing the predicted values with the experimental data - earlier deduced by Dash et al. - it is demonstrated that the combined GA-ANN model is a useful and efficient method to find the optimal conditions for casting of Al-Si alloys associated with the minimum porosity... 

In this study, effects of tungsten on wear resistance of high chromium white cast iron with and without tungsten in erosion-corrosion condition have been investigated. At the same time, the comparison between wear resistance of this grade of cast iron and low alloy steels with various contents of Cr which are used in industrial condition (in Sarcheshme Company, the greatest copper production company in the Middle East and with more than 4000 years historical cupper production background) was studied, while, copper concentrates have used for erosion particles. Results show that, because of higher hardness of matrix due to the tungsten, the wear resistance of high chromium cast iron increases.... 

Micro motion stages are one of the essential components in field of micro robotics and ultra fine positioning systems. This research presents the optimum design of a 3-DOF micro motion stage and its position control using FEM simulation. This stage to be studied uses a 3 RRR flexure hinge base compliant mechanism driven by three piezoelectric stack actuators to provide micro scale planar motion. First of all parametric modeling of the stage will be fulfilled in ANSYS environment utilizing a commercial piezostack and different types of flexure hinges. Hence the Jacobian matrix will be achieved for each case. The optimum selection of the hinge form will be achieved upon results of the previous... 

The chaotic dynamics of roto-translatory motion of a triaxial Kelvin-type gyrostat satellite under gravity gradient perturbations is considered. The Hamiltonian approach is used for modelling of the coupled spin-orbit equations of motion. The complex Hamiltonian of the system is reduced via the extended Deprit canonical transformation using the Serret- Andoyer variables. Therefore, this reduction leads to the derivation of the perturbation form of the Hamiltonian that can be used in the Ricci curvature criterion based on the Riemannian manifold geometry for the analysis of chaos phenomenon. The results obtained from Ricci method as well as the values from the Lyapunov exponent demonstrate... 

IT Governance provides a business focus to enable alignment between business and IT objectives at high level COBIT framework and focused on IT operational levels, ITIL standard. COBIT and ITIL are not mutually exclusive and can be combined to provide a powerful IT governance, control and best-practice framework in IT service management. So ITIL business-IT strategic alignment perspective could be improved using COBIT framework. Focusing on COBIT processes which support (primarily and secondarily) strategic alignment, in this paper, first, we map COBIT 4.1 to ITIL v3 to identify how ITIL cover COBIT control objectives. Furthermore, based on control objectives which are not completely... 

Quasi-periodic and chaotic behavior, along with the control of chaos for a Gyrostat satellite (GS), is investigated in this work. The quaternion-based dynamical model of the GS is first derived, and then the influences of the reaction wheels in the GS structure, under the gravity gradient perturbation that causes a route to chaos through quasi-periodicity mechanism, is investigated. For the suppression of chaos in the system, a chaos control system with the quaternion feedback is designed for the GS based on the extension of the Ott-Grebogi-Yorke (OGY) method using the linearization of the Poincaré map. In the extended OGY controller, the Poincaré map is estimated using the Least Square... 

This article presents a study on the capability of the time domain OMA method, NExT-ERA, to identify closely spaced structural dynamic modes. A survey in the literature reveals that few experimental studies have been conducted on the effectiveness of the NExT-ERA methodology in case of closely spaced modes specifically. In this paper we present the formulation for NExT-ERA. This formulation is then implemented in an algorithm and a code, developed in house to identify the modal parameters of different systems using their generated time history data. Some numerical models are firstly investigated to validate the code. Two different case studies involving a plate with closely spaced modes and... 

Micro motion stages are one of the essential components in field of micro robotics and ultra fine positioning systems. This research presents the optimum design of a 3-DOF micro motion stage and its position control using FEM simulation. This stage to be studied uses a 3 RRR flexure hinge base compliant mechanism driven by three piezoelectric stack actuators to provide micro scale planar motion. First of all parametric modeling of the stage will be fulfilled in ANSYS environment utilizing a commercial piezostack and different types of flexure hinges. Hence the Jacobian matrix will be achieved for each case. The optimum selection of the hinge form will be achieved upon results of the previous... 

Vivid banding pattern of the chromosome image is a crucial part for diagnosis in karyotype medical test. Furthermore, thriving computer aided segmentation and classification depend on the initial image quality. In this paper, we propose an adaptive and iterative histogram matching algorithm for chromosome contrast enhancement especially in banding patterns which is one of the most important information laid in chromosome image. Objective histogram, with which the initial image needs to be matched, is created based on processes on the initial image histogram. Calculation of statistical moments of image histogram and determination of parameters in each step of iteration based on these moments... 

Vivid banding patterns in medical images of chromosomes are a vital feature for karyotyping and chromosome classification. The chromosome image quality may be degraded by many phenomenon such as staining, sample defectness and imaging conditions. Thus, an image enhancement processing algorithm is needed before classification of chromosomes. In this paper, we propose an adaptive and iterative histogram matching (AIHM) algorithm for chromosome contrast enhancement especially in banding patterns. The reference histogram, with which the initial image needs to be matched, is created based on some processes on the initial image histogram. Usage of raw information in the histogram of initial image...