Quantum Threshold Voltage Modeling of Short Channel Quad Gate Silicon Nanowire Transistor

In this paper, a physically based analytical quantum linear threshold voltage model for short channel quad gate MOSFETs is developed. The proposed model, which is suitable for circuit simulation, is based on the analytical solution of 3-D Poisson and 2-D Schrodinger equation. Proposed model is fully validated against the professional numerical device simulator for a wide range of device geometries and also used to analyze the effect of geometry variation on the threshold voltage.

Calcium ionophore, A23187 and its amino acid complexes: spectroscopic and molecular modeling studies.

The circular dichroism, fluorescence, Nuclear Magnetic Resonance and BLM conductance studies indicate that A23187 forms a stable complex with amino acids at low ionophore concentrations (<10(-4)M). However, A23187 prefers to be in a dimeric structure with no significant binding to amino acids, at concentrations higher than 10(-4)M. It was also observed that at lower concentrations, at which the amino acids bind to the ionophore, the affinity for calcium ions was several orders of magnitude lower than that at higher ionophore concentrations. We have also conducted molecular modeling studies to examine the structure of the A23187 dimer and its amino acid complexes. The results of these modeling studies strongly support our experimental results and validate the formation of a hydrogen bonded and energetically stable A23187 dimer and its amino acid complexes.

Multiscale Modeling Approach to Acoustic Emission during Plastic Deformation

We address the long-standing problem of the origin of acoustic emission commonly observed during plastic deformation. We propose a framework to deal with the widely separated time scales of collective dislocation dynamics and elastic degrees of freedom to explain the nature of acoustic emission observed during the Portevin-Le Chatelier effect. The Ananthakrishna model is used as it explains most generic features of the phenomenon. Our results show that while acoustic emission bursts correlated with stress drops are well separated for the type C serrations, these bursts merge to form nearly continuous acoustic signals with overriding bursts for the propagating type A bands.

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.

Modeling and design of a solar thermal system for hybrid cooking application

This paper proposes a hybrid solar cooking system where the solar energy is brought to the kitchen. The energy source is a combination of the solar thermal energy and the Liquefied Petroleum Gas (LPG) that is in common use in kitchens. The solar thermal energy is transferred to the kitchen by means of a circulating fluid. The transfer of solar heat is a twofold process wherein the energy from the collector is transferred first to an intermediate energy storage buffer and the energy is subsequently transferred from the buffer to the cooking load. There are three parameters that are controlled in order to maximize the energy transfer from the collector to the load viz, the fluid flow rate from collector to buffer, fluid flow rate from buffer to load and the diameter of the pipes. This is a complex multi energy domain system comprising energy flow across several domains such as thermal, electrical and hydraulic. The entire system is modeled using the bond graph approach with seamless integration of the power flow in these domains. A method to estimate different parameters of the practical cooking system is also explained. Design and life cycle costing of the system is also discussed. The modeled system is simulated and the results are validated experimentally. (C) 2010 Elsevier Ltd. All rights reserved.

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.

Water-modeling study of the surface disturbances in continuous slab caster

The present work is based on four static molds using nozzles of different port diameter, port angle, and immersion depth. It has been observed that the meniscus is wavy. The wave amplitude shows a parabolic variation with the nozzle exit velocity. The dimensionless amplitude is found to vary linearly with the Froude number. Vortex formation and bubble entrainment by the wave occurs at the meniscus beyond a critical flow rate, depending upon the nozzle configuration, immersion depth, and the mold aspect ratio.

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.

Modeling Of Hydrolytic Polymerization In A Semibatch Nylon 6 Reactor

Hydrolytic polymerization of caprolactam to Nylon 6 in a semibatch reactor is carried out by heating a mixture of water and caprolactam. Evaporation of volatiles caused by heating results in a pressure build-up. After the pressure reaches a predetermined value, vapors are vented to keep the pressure constant for some time, and thereafter, to lower the pressure to a value slightly above atmospheric in a preprogrammed manner. The characteristics of the polymer are determined by the chemical reactions and the vaporization of water and caprolactam. The semibatch operation has been simulated and the predictions have been compared with industria data. The observed temperature and pressure histories were predicted with a fair degree of accuracy. It was found that the predictions of the degree of polymerization however are sensitive to the vapor-liquid equilibrium relations. A comparison with an earlier model, which neglected mass transfer resistance, indicates that simulation using the VLE data of Giori and Hayes and accounting for mass transfer resistance is more reliable.

Modes of binding of 2'-AMP to RNase T1. A computer modeling study

The modes of binding of adenosine 2'-monophosphate (2'-AMP) to the enzyme ribonuclease (RNase) T1 were determined by computer modelling studies. The phosphate moiety of 2'-AMP binds at the primary phosphate binding site. However, adenine can occupy two distinct sites--(1) The primary base binding site where the guanine of 2'-GMP binds and (2) The subsite close to the N1 subsite for the base on the 3'-side of guanine in a guanyl dinucleotide. The minimum energy conformers corresponding to the two modes of binding of 2'-AMP to RNase T1 were found to be of nearly the same energy implying that in solution 2'-AMP binds to the enzyme in both modes. The conformation of the inhibitor and the predicted hydrogen bonding scheme for the RNase T1-2'-AMP complex in the second binding mode (S) agrees well with the reported x-ray crystallographic study. The existence of the first mode of binding explains the experimental observations that RNase T1 catalyses the hydrolysis of phosphodiester bonds adjacent to adenosine at high enzyme concentrations. A comparison of the interactions of 2'-AMP and 2'-GMP with RNase T1 reveals that Glu58 and Asn98 at the phosphate binding site and Glu46 at the base binding site preferentially stabilise the enzyme-2'-GMP complex.

Image reconstruction from incomplete projections data: A multiresolution modeling approach

Computerized tomography is an imaging technique which produces cross sectional map of an object from its line integrals. Image reconstruction algorithms require collection of line integrals covering the whole measurement range. However, in many practical situations part of projection data is inaccurately measured or not measured at all. In such incomplete projection data situations, conventional image reconstruction algorithms like the convolution back projection algorithm (CBP) and the Fourier reconstruction algorithm, assuming the projection data to be complete, produce degraded images. In this paper, a multiresolution multiscale modeling using the wavelet transform coefficients of projections is proposed for projection completion. The missing coefficients are then predicted based on these models at each scale followed by inverse wavelet transform to obtain the estimated projection data.

DockYard-a repository to assist modeling of protein-protein docking

In the absence of interlogs, building docking models is a time intensive task, involving generation of a large pool of docking decoys followed by refinement and screening to identify near native docking solutions. This limits the researcher interested in building docking methods with the choice of benchmarking only a limited number of protein complexes. We have created a repository called dockYard (http://pallab.serc.iisc.ernet.in/dockYard), that allows modelers interested in protein-protein interaction to access large volume of information on protein dimers and their interlogs, and also download decoys for their work if they are interested in building modeling methods. dockYard currently offers four categories of docking decoys derived from: Bound (native dimer co-crystallized), Unbound (individual subunits are crystallized, as well as the target dimer), Variants (match the previous two categories in at least one subunit with 100% sequence identity), and Interlogs (match the previous categories in at least one subunit with >= 90% or >= 50% sequence identity). The web service offers options for full or selective download based on search parameters. Our portal also serves as a repository to modelers who may want to share their decoy sets with the community.

Modeling of a reacting nanofilm on a composite substrate

This article provides a detailed computational analysis of the reaction of dense nanofilms and the heat transfer characteristics on a composite substrate. Although traditional energetic compounds based on organic materials have similar energy per unit weight, non-organic material in nanofilm configuration offers much higher energy density and higher flame speed. The reaction of a multilayer thin film of aluminum and copper oxide has been studied by varying the substrate material and thicknesses. The numerical analysis of the thermal transport of the reacting film deposited on the substrate combined a hybrid approach in which a traditional two-dimensional black box theory was used in conjunction with the sandwich model to estimate the appropriate heat flux on the substrate accounting for the heat loss to the surroundings. A procedure to estimate this heat flux using stoichiometric calculations is provided. This work highlights two important findings. One is that there is very little difference in the temperature profiles between a single substrate of silica and a composite substrate of silicon silica. Secondly, with increase in substrate thickness, the quenching effect is progressively diminished at a given speed. These findings show that the composite substrate is effective and that the average speed and quenching of flames depend on the thickness of the silica substrate, and can be controlled by a careful choice of the substrate configuration. (C) 2011 Elsevier Ltd. All rights reserved.

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.