The structures of bacteriorhodopsin with different retinal-Schiff base orientations--computer modeling and energy minimization studies

Bacteriorhodopsin has been the subject of intense study in order to understand its photochemical function. The recent atomic model proposed by Henderson and coworkers based on electron cryo-microscopic studies has helped in understanding many of the structural and functional aspects of bacteriorhodopsin. However, the accuracy of the positions of the side chains is not very high since the model is based on low-resolution data. In this study, we have minimized the energy of this structure of bacteriorhodopsin and analyzed various types of interactions such as - intrahelical and interhelical hydrogen bonds and retinal environment. In order to understand the photochemical action, it is necessary to obtain information on the structures adopted at the intermediate states. In this direction, we have generated some intermediate structures taking into account certain experimental data, by computer modeling studies. Various isomers of retinal with 13-cis and/or 15-cis conformations and all possible staggered orientations of Lys-216 side chain were generated. The resultant structures were examined for the distance between Lys-216-schiff base nitrogen and the carboxylate oxygen atoms of Asp-96 - a residue which is known to reprotonate the schiff base at later stages of photocycle. Some of the structures were selected on the basis of suitable retinal orientation and the stability of these structures were tested by energy minimization studies. Further, the minimized structures are analyzed for the hydrogen bond interactions and retinal environment and the results are compared with those of the minimized rest state structure. The importance of functional groups in stabilizing the structure of bacteriorhodopsin and in participating dynamically during the photocycle have been discussed.

Water Modeling Study Of The Jet Characteristics In A Continuous-Casting Mold

The jet characteristics and the fluid flow pattern in a continuous slab caster have been studied using a water model. The fluid jet is studied under free fall and submerged discharge conditions. In the latter case, the jet was followed by dye-injection technique and image analyser was used to find out the effect of nozzle parameters on jet-spread angle, jet-discharge angle and the volume entrainment by the jet. All free-fall jets with nozzle port angle zero and upward are found to be spinning. Some of the free-fall jets with downward nozzle-port angle are found to be spinning and rest are smooth. The spinning direction of the jets are found to change with time. The well depth, port diameter and the inner diameter of the nozzle have a clear effect on the free-fall jets with downward port angle. The jet-spread angle is found to be about 17-degrees for smooth jets. The spread angle for spinning jet increases as the nozzle-port angle is increased from downward 25 to upward 15-degrees. The jet-discharge angle is always downward even when the nozzle-discharge ports are angled upward. The extent of volume entrainment by the spinning jet is higher and it increases as the nozzle-port angle is increased from 25 downward to 15-degrees upward.

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.

Modeling of angiogenin-3 '-NMP complex

Angiogenin belongs to the Ribonuclease superfamily and has a weak enzymatic activity that is crucial for its biological function of stimulating blood vessel growth. Structural studies on ligand bound Angiogenin will go a long way in understanding the mechanism of the protein as well as help in designing drugs against it. In this study we present the first available structure of nucleotide ligand bound Angiogenin obtained by computer modeling. The importance of this study in itself notwithstanding, is a precursor to modeling a full dinucleotide substrate onto Angiogenin. Bovine Angiogenin, the structure of which has been solved at a high resolution, was earlier subjected to Molecular Dynamics simulations for a nanosecond. The MD structures offer better starting points for docking as they offer lesser obstruction than the crystal structure to ligand binding. The MD structure with the least serious short contacts was modeled to obtain a steric free Angiogenin - 3' mononucleotide complex structure. The structures were energetically minimized and subjected to a brief spell of Molecular Dynamics. The results of the simulation show that all the li,ligand-Angiogenin interactions and hydrogen bonds are retained, redeeming the structure and docking procedure. Further, following ligand - protein interactions in the case of the ligands 3'-CMP and 3'-UMP we were able to speculate on how Angiogenin, a predominantly prymidine specific ribonuclease prefers Cytosine to Uracil in the first base position.

Modeling of precipitation in reverse micellar systems

A model of the precipitation process in reverse micelles has been developed to calculate the size of fine particles obtained therein. While the method shares several features of particle nucleation and growth common to precipitation in large systems, complexities arise in describing the processes of nucleation, due to the extremely small size of a micelle and of particle growth caused by fusion among the micelles. Occupancy of micelles by solubilized molecules is governed by Poisson statistics, implying most of them are empty and cannot nucleate of its own. The model therefore specifies the minimum number of solubilized molecules required to form a nucleus which is used to calculate the homogeneous nucleation rate. Simultaneously, interaction between micelles is assumed to occur by Brownian collision and instantaneous fusion. Analysis of time scales of various events shows growth of particles to be very fast compared to other phenomena occurring. This implies that nonempty micelles either are supersaturated or contain a single precipitated particle and allows application of deterministic population balance equations to describe the evolution of the system with time. The model successfully predicts the experimental measurements of Kandori ct al.(3) on the size of precipitated CaCO3 particles, obtained by carbonation of reverse micelles containing aqueous Ca(OH)(2) solution.

Modeling of the cooling process on the runout table of a hot strip mill - A parallel approach

This paper deals with the development of a new model for the cooling process on the runout table of hot strip mills, The suitability of different numerical methods for the solution of the proposed model equation from the point of view of accuracy and computation time are studied, Parallel solutions for the model equation are proposed.

On the problem of spurious patterns in neural associative memory models

The problem of spurious patterns in neural associative memory models is discussed, Some suggestions to solve this problem from the literature are reviewed and their inadequacies are pointed out, A solution based on the notion of neural self-interaction with a suitably chosen magnitude is presented for the Hebb learning rule. For an optimal learning rule based on linear programming, asymmetric dilution of synaptic connections is presented as another solution to the problem of spurious patterns, With varying percentages of asymmetric dilution it is demonstrated numerically that this optimal learning rule leads to near total suppression of spurious patterns. For practical usage of neural associative memory networks a combination of the two solutions with the optimal learning rule is recommended to be the best proposition.

Petri net-based modeling of a class of complex digital systems

We present through the use of Petri Nets, modeling techniques for digital systems realizable using FPGAs. These Petri Net models are used for logic validation at the logic design phase. The technique is illustrated by modeling practical circuits. Further, the utility of the technique with respect to timing analysis of the modeled digital systems is considered. Copyright (C) 1997 Elsevier Science Ltd

Analysis, Design, Modeling, and Implementation of an Active Clamp HF Link Converter

This paper deals with the system oriented analysis, design, modeling, and implementation of active clamp HF link three phase converter. The main advantage of the topology is reduced size, weight, and cost of the isolation transformer. However, violation of basic power conversion rules due to presence of the leakage inductance in the HF transformer causes over voltage stresses across the cycloconverter devices. It makes use of the snubber circuit necessary in such topologies. The conventional RCD snubbers are dissipative in nature and hence inefficient. The efficiency of the system is greatly improved by using regenerative snubber or active clamp circuit. It consists of an active switching device with an anti-parallel diode and one capacitor to absorb the energy stored in the leakage inductance of the isolation transformer and to regenerate the same without affecting circuit performance. The turn on instant and duration of the active device are selected such that it requires simple commutation requirements. The time domain expressions for circuit dynamics, design criteria of the snubber capacitor with two conflicting constrains (over voltage stress across the devices and the resonating current duration), the simulation results based on generalized circuit model and the experimental results based on laboratory prototype are presented.

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

Thermodynamic modeling to analyse composition of carbonaceous coatings of MnO and other oxides of manganese grown by MOCVD

Equilibrium thermodynamic analysis has been applied to the low-pressure MOCVD process using manganese acetylacetonate as the precursor. ``CVD phase stability diagrams'' have been constructed separately for the processes carried out in argon and oxygen ambient, depicting the compositions of the resulting films as functions of CVD parameters. For the process conduced in argon ambient, the analysis predicts the simultaneous deposition of MnO and elemental carbon in 1: 3 molar proportion, over a range of temperatures. The analysis predicts also that, if CVD is carried out in oxygen ambient, even a very low flow of oxygen leads to the complete absence of carbon in the film deposited oxygen, with greater oxygen flow resulting in the simultaneous deposition of two different manganese oxides under certain conditions. The results of thermodynamic modeling have been verified quantitatively for low-pressure CVD conducted in argon ambient. Indeed, the large excess of carbon in the deposit is found to constitute a MnO/C nanocomposite, the associated cauliflower-like morphology making it a promising candidate for electrode material in supercapacitors. CVD carried out in oxygen flow, under specific conditions, leads to the deposition of more than one manganese oxide, as expected from thermodynamic analysis ( and forming an oxide-oxide nanocomposite). These results together demonstrate that thermodynamic analysis of the MOCVD process can be employed to synthesize thin films in a predictive manner, thus avoiding the inefficient trial-and-error method usually associated with MOCVD process development. The prospect of developing thin films of novel compositions and characteristics in a predictive manner, through the appropriate choice of CVD precursors and process conditions, emerges from the present work.

Micromechanical modeling of granular materials: effect of confining pressure on mechanical behavior

This paper reports the effect of confining pressure on the mechanical behavior of granular materials from micromechanical considerations starting from the grain scale level, based on the results of numerically simulated tests on disc assemblages using discrete element modeling (DEM). The two macro parameters which are influenced by the increase in confining pressure are stiffness (increases) and volume change (decreases). The lateral strain coefficient (Poisson's ratio) at the beginning of the test is more or less constant. The angle of internal friction slightly decreases with increase in confining pressure. The numerical results of disc assemblages indicate very clearly a non-linear Mohr-Coulomb failure envelope with increase in confining pressure. The increase in average coordination number and accompanying decrease of fabric anisotropy reduce the shear strength at higher confining pressures. Micromechanical explanations of the macroscopic behavior are presented in terms of the force and fabric anisotropy coefficients. (C) 1999 Elsevier Science Ltd. AII rights reserved.

Modeling and control of unified power flow controller for transient stability

FACTS controllers are emerging as viable and economic solutions to the problems of large interconnected ne networks, which can endanger the system security. These devices are characterized by their fast response, absence of inertia, and minimum maintenance requirements. Thyristor controlled equipment like Thyristor Controlled Series Capacitor (TCSC), Static Var Compensator (SVC), Thyristor Controlled Phase angle Regulator (TCPR) etc. which involve passive elements result in devices of large sizes with substantial cost and significant labour for installation. An all solid-state device using GTOs leads to reduction in equipment size and has improved performance. The Unified Power Flow Controller (UPFC) is a versatile controller which can be used to control the active and reactive power in the Line independently. The concept of UPFC makes it possible to handle practically all power flow control and transmission line compensation problems, using solid-state controllers, which provide functional flexibility, generally not attainable by conventional thyristor controlled systems. In this paper, we present the development of a control scheme for the series injected voltage of the UPFC to damp the power oscillations and improve transient stability in a power system. (C) 1998 Elsevier Science Ltd. All rights reserved.

Stochastic dynamics modeling of the protein sequence length distribution in genomes: implications for microbial evolution

In this paper, we report an analysis of the protein sequence length distribution for 13 bacteria, four archaea and one eukaryote whose genomes have been completely sequenced, The frequency distribution of protein sequence length for all the 18 organisms are remarkably similar, independent of genome size and can be described in terms of a lognormal probability distribution function. A simple stochastic model based on multiplicative processes has been proposed to explain the sequence length distribution. The stochastic model supports the random-origin hypothesis of protein sequences in genomes. Distributions of large proteins deviate from the overall lognormal behavior. Their cumulative distribution follows a power-law analogous to Pareto's law used to describe the income distribution of the wealthy. The protein sequence length distribution in genomes of organisms has important implications for microbial evolution and applications. (C) 1999 Elsevier Science B.V. All rights reserved.