Prevalence of hepatitis B and C virus infections among haemodialysis patients in Pune (western India)

Vascular injuries may occur as complications of elbow dislocation and usually involve the brachial artery. A case report is presented in which only the radial artery was compromised as a result of the dislocation.     

A new method to estimate parameters of linear compartmental models using artificial neural networks

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.     

Calculation of shift of avalanche transit time phase delay due tooptically injected carriers in indium phosphide avalanche diodes

A computer method for the calculation of the phase shift due to optically injected carriers in an InP avalanche transit time diode has been suggested using the numerically simulated negative resistance profiles in the depletion layer of the diode. The results show that the phase shift due to hole injection is larger than that due to electron injection which explains the pronounced effect of photogenerated hole leakage current in modulating the microwave properties of InP diodes     

Modeling the tensile properties in β-processed α/β Ti alloys

The development of a set of computational tools that permit microstructurally based predictions for the tensile properties of commercially important titanium alloys, such as Ti-6Al-4V, is a valuable step toward the accelerated maturation of materials. This paper will discuss the development of neural network models based on a Bayesian framework to predict the yield and ultimate tensile strengths of Ti-6Al-4V at room temperature. The development of such rules-based model requires the population of extensive databases, which in the present case are microstructurally based. The steps involved in database development include producing controlled variations of the microstructure using novel approaches to heat treatments, the use of standardized stereology protocols to characterize and quantify microstructural features rapidly, and mechanical testing of the heat-treated specimens. These databases have been used to train and test neural network models for prediction of tensile properties. In addition, these models have been used to identify the influence of individual microstructural features on the tensile properties, consequently guiding the efforts toward development of more robust mechanistically based models. Based on the neural network model, it is possible to investigate the influence of individual microstructural features on the tensile properties, and in certain cases these dependencies can point toward unrecognized phenomena. For example, the apparently unexpected trend of increase in tensile strength with increasing prior β-grain size has led to the determination of the pronounced role of the basketweave microstructure in strengthening these alloys, especially in case of larger prior β grains. This article is based on a presentation made in the symposium “Computational Aspects of Mechanical Properties of Materials,” which occurred at the 2005 TMS Annual Meeting, February 13–17, 2005, in San Francisco, CA, under the auspices of the MPMD-Computational Materials Science & Engineering (Jt. ASM-MSCTS) Committee.     

Coupled bending–torsional dynamic stiffness matrix for axially loaded beam elements

Analytical expressions for the coupled bending–torsional dynamic stiffness matrix elements of an axially loaded uniform beam element are derived in an exact sense by solving the governing differential equations of motion of the beam. The influence of axial force on the coupled bending–torsional frequencies of a cantilever and hinged–hinged beam of thin-walled section is demonstrated by numerical results. Application of the developed theory includes coupled bending–torsional frequency and mode calculations of helicopter, turbine and propeller blades, plane and space frames, and grillages consisting of axially loaded beam elements with non-coincident mass centre and shear centre.     

A comparison between isoparametric Lagrangian elements in 2D BEM

A comparison between a family of isoparametric C°-continucus elements in the context of two-dimensional elastostatics using the direct boundary element method is presented. The elements studied use Lagrangian shape functions of orders quadratic, cubic and quartic. The relative efficiencies of these elements from the viewpoint of accuracy of solution as well as cost effectiveness are examined through certain selected problems. It emerges from the present study that the quartic element is more reliable in accuracy than the lower members of the family without being necessarily more expensive.     

A Model for Flooding Prediction in Circular Tubes

Flooding phenomenon limits the stability and the flow of a liquid film falling along the walls of a channel in which a gas is flowing upwards.As known, the entrainment effect can completely prevent the liquid to fall from its natural flow. The present work proposes a new mechanistic model for the prediction of the onset of flooding in vertical and inclined pipes in the presence of obstructions, as well as taking into account the viscosity effect. The good performance of the model in the different geometrical conditions and for variable viscosities of the liquid component assesses the validity of the hypothesis that the instability of a wavelike disturbance limits the countercurrent flow in a channel.     

Studies on jute–asphalt composites

The tensile properties of jute make it a suitable raw material for asphalt overlay (A/O) fabric. In this study, the thermal stability of jute under conditions of asphalt overlaying process was investigated and the compatibility of jute with asphalt was assessed through experimentation on jute–asphalt composites under mechanical and hygral loads. Fourier transform infra red (FTIR) spectroscopic study revealed probable chemical bonding between jute and asphalt. The test for ascertaining the capability of asphalt encasement for protecting jute against biodegradation under enzymatic attack was found positive. A 6‐month hygral treatment, of the jute–asphalt composite, showed significant increase in modulus of the material. The results indicate that although the strength of jute deteriorates by about 10% under asphalt overlaying condition, the overall tensile behavior of jute fabric–asphalt composite material is considerably superior to that of the pure jute fabric, even under biological and extended hygral loading conditions. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci 2008