Diacylglycerol Kinase Is Stimulated by Arachidonic Acid in Neural Membranes.

The effect of arachidonic acid (AA) on the activity of diacylglycerol (DG) kinase in neural membranes was investigated. When rat brain cortical membranes were incubated with 0.5 mM dipalmitin and [gamma-P-32]ATP, formation of phosphatidic acid (PA) was observed. It was linear up to 5 min, and the initial rate was similar to 1.0 nmol/min/mg of protein. The DG kinase activity was stimulated twofold by 0.25 mM AA. The stimulation was apparent at the earliest time point measured (1 min) and with the lowest concentration of AA tested (62.5 mu M). The stimulation was proportional to the concentration of AA up to 250 mu M. AA was the most potent stimulator of DG kinase, and linolenic acid showed similar to 40% stimulation. Oleic acid showed no effect, whereas linoleic and the saturated fatty acids tested were inhibitory. AA stimulation of DG kinase was observed only with membranes of cerebrum, cerebellum, and myelin and not with brain cytosol or liver membranes. AA also stimulated the formation of PA in the absence of added dipalmitin (endogenous activity) with membranes prepared from whole brain. DG kinase of neural membranes was extracted with 2 M NaCl, which on dialysis yielded a precipitate. Both the precipitate and the supernatant showed DG kinase activity, but only the enzyme in the precipitate was stimulated by AA at concentrations as low as 25 mu M. It is suggested that AA, through its effect on DG kinase, regulates the level of DG in neural membranes, which in turn regulates protein kinase C activity.

Probing the inter-tube transport in aligned and random multiwall carbon nanotubes

The temperature and magnetic field dependence of conductivity has been used to probe the inter-tube transport in multiwall carbon nanotubes (MWNTs). The scanning electron microscopy images show highly aligned and random distribution of MWNTs. The conductivity in aligned carbon nanotube (ACNT) and random carbon nanotube (RCNT) samples at low temperature follows T-1/2 (at T < 8 K) and T-3/4 (at T > 8 K) dependence in accordance with the weak localization and electron-electron (e-e) interaction model. The values of diffusion coefficient in ACNT and RCNT are 0.25 x 10(-2) and 0.71 x 10(-2) cm(2) s(-1), respectively, indicating that larger number of inter-tube junctions in later enhances the bulk transport. The positive magnetoconductance (MC) data in both samples show that the weak localization contribution is dominant. However, the saturation of MC at higher fields and lower temperatures indicate that e-e interaction is quite significant in RCNT. The T-3/4 and T-1/2 dependence of inelastic scattering length (l(in)) in ACNT and RCNT samples show that the inelastic e-e scattering is more important in aligned tubes. (C) 2011 American Institute of Physics. doi:10.1063/1.3552911]

Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A I-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

Analysis of texture evolution in pure magnesium and the magnesium alloy AM30 during rod and tube extrusion

The evolution of microstructure and texture during extrusion of pure magnesium and its single phase alloy AM30 has been studied experimentally as well as by crystal plasticity simulation. Microstructure and micro-texture were characterized by electron back scattered diffraction (EBSD), bulk-texture was measured using X-ray diffraction and deformation texture simulations were carried out using visco-plastic self consistent (VPSC) model. In spite of clear indications of the occurrence of dynamic recrystallization (DRX), simulations were able to reproduce the experimental textures successfully. This was attributed to the fact that the textures were c-type fibers with their axis of rotation parallel to the c-axis and DRX leads to simply rotate the texture around the c-axis. (C) 2011 Elsevier B.V. All rights reserved.

Adaptive optimization of continuous bioreactor using neural network model

An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed.

A novel neural network design for long range prediction of rainfall pattern

The importance of long-range prediction of rainfall pattern for devising and planning agricultural strategies cannot be overemphasized. However, the prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. The conventional methods for prediction of rainfall use either dynamical or statistical modelling. In this article we report the results of a new modelling technique using artificial neural networks. Artificial neural networks are especially useful where the dynamical processes and their interrelations for a given phenomenon are not known with sufficient accuracy. Since conventional neural networks were found to be unsuitable for simulating and predicting rainfall patterns, a generalized structure of a neural network was then explored and found to provide consistent prediction (hindcast) of all-India annual mean rainfall with good accuracy. Performance and consistency of this network are evaluated and compared with those of other (conventional) neural networks. It is shown that the generalized network can make consistently good prediction of annual mean rainfall. Immediate application and potential of such a prediction system are discussed.

Experimental validation of the 1-D acoustical model for conical concentric tube resonators with moving medium

Variable cross-sectional area ducts are often used for attenuation at lower frequencies (of the order of firing frequency), whereas concentric tube resonators provide attenuation at relatively higher frequencies. In this paper, analysis of one dimensional control volume approach of conical concentric tube resonators is validated experimentally. The effects of mean flow and taper are investigated. The experimental setup is specially designed to measure the pressure transfer function in the form of Level Difference or Noise Reduction across the test muffler. It is shown that there is a reasonably good agreement between the predicted values of the Noise Reduction and the measured ones for incompressible mean flow as well as stationary medium. (C) 2011 Institute of Noise Control Engineering.

Neural network modeling of associative memory: Beyond the Hopfield model

A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

Optimization of Helicopter Rotor Using Polynomial and Neural Network Metamodels

This study aims to determine optimal locations of dual trailing-edge flaps and blade stiffness to achieve minimum hub vibration levels in a helicopter, with low penalty in terms of required trailing-edge flap control power. An aeroelastic analysis based on finite elements in space and time is used in conjunction with an optimal control algorithm to determine the flap time history for vibration minimization. Using the aeroelastic analysis, it is found that the objective functions are highly nonlinear and polynomial response surface approximations cannot describe the objectives adequately. A neural network is then used for approximating the objective functions for optimization. Pareto-optimal points minimizing both helicopter vibration and flap power ale obtained using the response surface and neural network metamodels. The two metamodels give useful improved designs resulting in about 27% reduction in hub vibration and about 45% reduction in flap power. However, the design obtained using response surface is less sensitive to small perturbations in the design variables.

Peristaltic pumping in a circular tube in the presence of an eccentric catheter

The influence of an eccentrically inserted catheter on the peristaltic pumping in a tube is investigated under long wavelength, low Reynolds number assumptions. The radially asymmetric deformation of the wall arising through an eccentrically inserted catheter is taken into consideration by choosing an appropriate bipolar coordinate system. The effect of the position and size of the catheter on pumping characteristics is studied. The best performance of pumping is noticed at a certain position of the catheter. The size of the catheter, when placed eccentrically, alters the pressure signature significantly inside the bolus, unlike the concentric case discussed by Lykoudis and Roos (1971). Further, the maximum pressure rise in one period of the peristaltic wave is observed to decrease with an increase in the eccentricity.

Peristaltic transport of two immiscible viscous fluids in a circular tube

Peristaltic motion of two immiscible viscous incompressible fluids in a circular tube is studied in pumping and copumping ranges under long-wavelength and low-Reynolds-number assumptions. The effect of the peripheral-layer viscosity on the time-averaged flux and the mechanical efficiency is studied. The formation and growth of the trapping zone in the core and the peripheral layer are explained. It is observed that the bolus volume in the peripheral layer increases with an increase in the viscosity ratio. The limits of the time-averaged flux (Q) over bar for trapping in the core are obtained. The trapping observed in the peripheral layer decreases in size with an increase in (Q) over bar but never disappears. The development of the complete trapping of the core fluid by the peripheral-layer fluid with an increase in the time-averaged flux is demonstrated. The effect of peripheral-layer viscosity on the reflux layer is investigated. It is also observed that the reflux occurs in the entire pumping range for all viscosity ratios and it is absent in the entire range of copumping.

Stability of the viscous flow of a fluid through a flexible tube

The stability of the Hagen-Poiseuille flow of a Newtonian fluid in a tube of radius R surrounded by an incompressible viscoelastic medium of radius R < r < HR is analysed in the high Reynolds number regime. The dimensionless numbers that affect the fluid flow are the Reynolds number Re = (rho VR/eta), the ratio of the viscosities of the wall and fluid eta(r) = (eta(s)/eta), the ratio of radii H and the dimensionless velocity Gamma = (rho V-2/G)(1/2). Here rho is the density of the fluid, G is the coefficient of elasticity of the wall and V is the maximum fluid velocity at the centre of the tube. In the high Reynolds number regime, an asymptotic expansion in the small parameter epsilon = (1/Re) is employed. In the leading approximation, the viscous effects are neglected and there is a balance between the inertial stresses in the fluid and the elastic stresses in the medium. There are multiple solutions for the leading-order growth rate s((0)), all of which are imaginary, indicating that the fluctuations are neutrally stable, since there is no viscous dissipation of energy or transfer of energy from the mean flow to the fluctuations due to the Reynolds stress. There is an O(epsilon(1/2)) correction to the growth rate, s((1)), due to the presence of a wall layer of thickness epsilon(1/2)R where the viscous stresses are O(epsilon(1/2)) smaller than the inertial stresses. An energy balance analysis indicates that the transfer of energy from the mean flow to the fluctuations due to the Reynolds stress in the wall layer is exactly cancelled by an opposite transfer of equal magnitude due to the deformation work done at the interface, and there is no net transfer from the mean flow to the fluctuations. Consequently, the fluctuations are stabilized by the viscous dissipation in the wall layer, and the real part of s(1) is negative. However, there are certain values of Gamma and wavenumber k where s((1)) = 0. At these points, the wall layer amplitude becomes zero because the tangential velocity boundary condition is identically satisfied by the inviscid flow solution. The real part of the O(epsilon) correction to the growth rate s((2)) turns out to be negative at these points, indicating a small stabilizing effect due to the dissipation in the bulk of the fluid and the wall material. It is found that the minimum value of s((2)) increases proportional to (H-1)(-2) for (H-1) much less than 1 (thickness of wall much less than the tube radius), and decreases proportional to H-4 for H much greater than 1. The damping rate for the inviscid modes is smaller than that for the viscous wall and centre modes in a rigid tube, which have been determined previously using a singular perturbation analysis. Therefore, these are the most unstable modes in the flow through a flexible tube

Stability of the viscous flow of a fluid through a flexible tube

The stability of Hagen-Poiseuille flow of a Newtonian fluid of viscosity eta in a tube of radius R surrounded by a viscoelastic medium of elasticity G and viscosity eta(s) occupying the annulus R < r < HR is determined using a linear stability analysis. The inertia of the fluid and the medium are neglected, and the mass and momentum conservation equations for the fluid and wall are linear. The only coupling between the mean flow and fluctuations enters via an additional term in the boundary condition for the tangential velocity at the interface, due to the discontinuity in the strain rate in the mean flow at the surface. This additional term is responsible for destabilizing the surface when the mean velocity increases beyond a transition value, and the physical mechanism driving the instability is the transfer of energy from the mean flow to the fluctuations due to the work done by the mean flow at the interface. The transition velocity Gamma(t) for the presence of surface instabilities depends on the wavenumber k and three dimensionless parameters: the ratio of the solid and fluid viscosities eta(r) = (eta(s)/eta), the capillary number Lambda = (T/GR) and the ratio of radii H, where T is the surface tension of the interface. For eta(r) = 0 and Lambda = 0, the transition velocity Gamma(t) diverges in the limits k much less than 1 and k much greater than 1, and has a minimum for finite k. The qualitative behaviour of the transition velocity is the same for Lambda > 0 and eta(r) = 0, though there is an increase in Gamma(t) in the limit k much greater than 1. When the viscosity of the surface is non-zero (eta(r) > 0), however, there is a qualitative change in the Gamma(t) vs. k curves. For eta(r) < 1, the transition velocity Gamma(t) is finite only when k is greater than a minimum value k(min), while perturbations with wavenumber k < k(min) are stable even for Gamma--> infinity. For eta(r) > 1, Gamma(t) is finite only for k(min) < k < k(max), while perturbations with wavenumber k < k(min) or k > k(max) are stable in the limit Gamma--> infinity. As H decreases or eta(r) increases, the difference k(max)- k(min) decreases. At minimum value H = H-min, which is a function of eta(r), the difference k(max)-k(min) = 0, and for H < H-min, perturbations of all wavenumbers are stable even in the limit Gamma--> infinity. The calculations indicate that H-min shows a strong divergence proportional to exp (0.0832 eta(r)(2)) for eta(r) much greater than 1.

Mathematical modelling of neurons and neural networks

The basic concepts and techniques involved in the development and analysis of mathematical models for individual neurons and networks of neurons are reviewed. Some of the interesting results obtained from recent work in this field are described. The current status of research in this field in India is discussed

Vibration Signature Analysis Using Artificial Neural Networks

Damage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data.