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.     

A Low-Power Wide-Dynamic-Range Analog VLSI Cochlea

Low-power wide-dynamic-range systems are extremely hard to build. The biological cochlea is one of the most awesome examples of such a system: It can sense sounds over 12 orders of magnitude in intensity, with an estimated power dissipation of only a few tens of microwatts. In this paper, we describe an analog electronic cochlea that processes sounds over 6 orders of magnitude in intensity, and that dissipates 0.5 mW. This 117-stage, 100 Hz to 10 KHz cochlea has the widest dynamic range of any artificial cochlea built to date. The wide dynamic range is attained through the use of a wide-linear-range transconductance amplifier, of a low-noise filter topology, of dynamic gain control (AGC) at each cochlear stage, and of an architecture that we refer to as overlapping cochlear cascades. The operation of the cochlea is made robust through the use of automatic offset-compensation circuitry. A BiCMOS circuit approach helps us to attain nearly scale-invariant behavior and good matching at all frequencies. The synthesis and analysis of our artificial cochlea yields insight into why the human cochlea uses an active traveling-wave mechanism to sense sounds, instead of using bandpass filters. The low power, wide dynamic range, and biological realism make our cochlea well suited as a front end for cochlear implants.     

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.     

A rule-based fuzzy-logic approach for the measurement of manufacturing flexibility

Manufacturing flexibility is a difficult to quantify concept that defies universal definition. This paper presents a novel fuzzy-logic approach for measuring manufacturing flexibility that exploits linguistic variables for quantifying pertinent factors affecting commonly utilized flexibility types. Towards this end, we identify and measure the contribution of specified state variables towards the assumed flexibility types in order to compute an overall flexibility index for a generic manufacturing system. The suggested framework provides a convenient end user approach amenable to software implementation that is exemplified through the development of a prototypical software tool called “Flexibility Evaluator”.     

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.