Zervamicins, a structurally characterised peptide model for membrane ion channels

Voltage dependent membrane channels are formed by the zervamicins, a group of α-aminoisobutyric acid containing peptides. The role of polar residues like Thr, Gln and Hyp in promoting helical bundle formation is established by dramatically reduced channel lifetimes for a synthetic apolar analog. Crystal structures of Leu1-zervamicin reveal association of bent helices. Polar contacts between convex faces result in an ‘hour glass’ like arrangement of an aqueous channel with a central constriction. The structure suggests that gating mechanisms may involve movement of the Gln11 carboxamide group. Gln3 may play a role in modulating the size of the channel mouth.

Design of first- and second-order sliding mode observers for induction motors using a stator-flux model

We extend current research in the area of 'sensorless' control of induction motors by presenting two observers based on first- and second-order sliding mode control theories for the simultaneous estimation of flux and speed. We base the observers on the stator-flux model of the motor instead of the usual rotor-flux model mainly because of the uncertain rotor resistance that plays a significant role in the latter. By designing the observers as if they are sliding mode controllers, we lend the properties of parameter insensitive closed-loop dynamics and finite time convergence to the stator flux and speed estimation schemes. We also present simulation and experimental results to validate the operation of the observers.

Structural studies of model peptides containing beta-, gamma- and delta-amino acids

The crystal structures of five model peptides Piv-Pro-Gly-NHMe (1), Piv-Pro-beta Gly-NHMe (2), Piv-Pro-beta Gly-OMe (3), Piv-Pro-delta Ava-OMe (4) and Boc-Pro-gamma Abu-OH (5) are described (Piv:pivaloyl; NHMe: N-methylamide; beta Gly:beta-glycine; OMe:O-methyl ester; delta Ava:delta-aminovaleric acid; gamma Abu:gamma-aminobutyric acid). A comparison of the structures of peptides 1 and 2 illustrates the dramatic consequences upon backbone homologation in short sequences. 1 adopts a type II beta-turn conformation in the solid state, while in 2, the molecule adopts an open conformation with the beta-residue being fully extended. Piv-Pro-beta Gly-OMe (3), which differs from 2 by replacement of the C-terminal NH group by an O-atom, adopts an almost identical molecular conformation and packing arrangement in the solid state. In peptide 4, the observed conformation resembles that determined for 2 and 3, with the delta Ava residue being fully extended. In peptide 5, the molecule undergoes a chain reversal, revealing a beta-turn mimetic structure stabilized by a C-H center dot center dot center dot O hydrogen bond.

Double time window targeting technique: Real-time DMRG dynamics in the Pariser-Parr-Pople model

We present a generalized adaptive time-dependent density matrix renormalization-group (DMRG) scheme, called the double time window targeting (DTWT) technique, which gives accurate results with nominal computational resources, within reasonable computational time. This procedure originates from the amalgamation of the features of pace keeping DMRG algorithm, first proposed by Luo et al. [Phys. Rev. Lett. 91, 049701 (2003)] and the time-step targeting algorithm by Feiguin and White [Phys. Rev. B 72, 020404 (2005)]. Using the DTWT technique, we study the phenomena of spin-charge separation in conjugated polymers (materials for molecular electronics an spintronics), which have long-range electron-electron interactions and belong to the class of strongly correlated low-dimensional many-body systems. The issue of real-time dynamics within the Pariser-Parr-Pople (PPP) model which includes long-range electron correlations has not been addressed in the literature so far. The present study on PPP chains has revealed that, (i) long-range electron correlations enable both the charge and spin degree of freedom of the electron, to propagate faster in the PPP model compared to Hubbard model, (ii) for standard parameters of the PPP model as applied to conjugated polymers, the charge velocity is almost twice that of the spin velocity, and (iii) the simplistic interpretation of long-range correlations by merely renormalizing the U value of the Hubbard model fails to explain the dynamics of doped holes/electrons in the PPP model.

Flow Generated By Pitched Blade Turbines Ii: Simulation Using Κ-Ε Model

Experimental data on average velocity and turbulence intensity generated by pitched blade downflow turbines (PTD) were presented in Part I of this paper. Part II presents the results of the simulation of flow generated by PTD The standard κ-ε model along with the boundary conditions developed in the Part 1 have been employed to predict the flow generated by PTD in cylindrical baffled vessel. This part describes the new software FIAT (Flow In Agitated Tanks) for the prediction of three dimensional flow in stirred tanks. The basis of this software has been described adequately. The influence of grid size, impeller boundary conditions and values of model parameters on the predicted flow have been analysed. The model predictions successfully reproduce the three dimensionality and the other essential characteristics of the flow. The model can be used to improve the overall understanding about the relative distribution of turbulence by PTD in the agitated tank

Comparative study on damage identification from lso-Eigen-Value-Change contours and smeared damage model

The paper proposes two methodologies for damage identification from measured natural frequencies of a contiguously damaged reinforced concrete beam, idealised with distributed damage model. The first method identifies damage from Iso-Eigen-Value-Change contours, plotted between pairs of different frequencies. The performance of the method is checked for a wide variation of damage positions and extents. The method is also extended to a discrete structure in the form of a five-storied shear building and the simplicity of the method is demonstrated. The second method is through smeared damage model, where the damage is assumed constant for different segments of the beam and the lengths and centres of these segments are the known inputs. First-order perturbation method is used to derive the relevant expressions. Both these methods are based on distributed damage models and have been checked with experimental program on simply supported reinforced concrete beams, subjected to different stages of symmetric and un-symmetric damages. The results of the experiments are encouraging and show that both the methods can be adopted together in a damage identification scenario.

A denotational semantics for the generalized ER model and a simple ER algebra

The recent spurt of research activities in Entity-Relationship Approach to databases calls for a close scrutiny of the semantics of the underlying Entity-Relationship models, data manipulation languages, data definition languages, etc. For reasons well known, it is very desirable and sometimes imperative to give formal description of the semantics. In this paper, we consider a specific ER model, the generalized Entity-Relationship model (without attributes on relationships) and give denotational semantics for the model as well as a simple ER algebra based on the model. Our formalism is based on the Vienna Development Method—the meta language (VDM). We also discuss the salient features of the given semantics in detail and suggest directions for further work.

Spatially dependent correlation functions in the Anderson model

We report results from a first principles calculation of spatially dependent correlation functions around a magnetic impurity in metals described by the nondegenerate Anderson model. Our computations are based on a combination of perturbative scaling theory and numerical renormalization group methods. Results for the conduction election charge density around the impurity and correlation functions involving the conduction electron and impurity charge and spin densities will be presented. The behavior in various regimes including the mixed valent regime will be explored.

Shell-model calculation of defect energies in alkali halides employing crystal-independent interionic potential parameters

Shell model calculation of defect energies in alkali halides have been carried out using the ion-dependent, crystal-independent potential parameters of Sangster and Atwood (1978). Results indicate that appreciable differences exist between barrier heights for migration of cations and anions. While barrier heights for cations are generally lower than for anions in alkali halides of NaCl structure, the opposite is true in alkali halides of CsCl structure.

A Series of Cu-II-Azide Polymers of Cu-6 Building Units and the Role of Chelating Diamine in Controlling their Dimensionality: Synthesis, Structures, and Magnetic Behavior

Four new neutral copper-azido polymers Cu-6(N-3)(12)(aem)(2)](n)(1), Cu-6(N-3)(12)(dmeen)(2)(H2O)(2)](n) (2), Cu-6(N-3)(12)(N,N'-dmen)(2)](n) (3), and Cu-6(N-3)(12)(hmpz)(2)](n) (4) aem = 4-(2-aminoethyl)morpholine; dmeen = N,N-dimethyl-N'-ethylethylenediamine; N,N'-dmen = N,N'-dimethylethylenediamine and hmpz = homopiperazine] have been synthesized by using 0.33 mol equiv of the chelating diamine ligands with Cu(NO3)(2)center dot 3H(2)O/CuCl2 center dot 2H(2)O and an excess of NaN3. Single crystal X-ray structures show that the basic unit of these complexes, especially 1-3, contains very similar Cu-6(II) building blocks. But the overall structures of these complexes vary widely in dimensionality. While 1 is three-dimensional (3D) in nature, 2 and 3 have a two-dimensional (2D) arrangement (with different connectivity) and 4 has a one-dimensional (1D) structure. Cryomagnetic susceptibility measurements over a wide range of temperature exhibit dominant ferromagnetic behavior in all the four complexes. The experimental susceptibility data have been analyzed by some theoretical model equations.

Some experiments on human memory and a new neural model

An associative memory with parallel architecture is presented. The neurons are modelled by perceptrons having only binary, rather than continuous valued input. To store m elements each having n features, m neurons each with n connections are needed. The n features are coded as an n-bit binary vector. The weights of the n connections that store the n features of an element has only two values -1 and 1 corresponding to the absence or presence of a feature. This makes the learning very simple and straightforward. For an input corrupted by binary noise, the associative memory indicates the element that is closest (in terms of Hamming distance) to the noisy input. In the case where the noisy input is equidistant from two or more stored vectors, the associative memory indicates two or more elements simultaneously. From some simple experiments performed on the human memory and also on the associative memory, it can be concluded that the associative memory presented in this paper is in some respect more akin to a human memory than a Hopfield model.

A simplified kinetic model for oxidative dehydrogenation of ethylbenzene over Pd-NaBr/Al2O3 catalyst

The oxidative dehydrogenation of ethylbenzene is gaining considerable importance in recent years as a promising alternative for styrene production. This vapour phase reaction has been studied over Pd-NaBr/Al2O3 catalyst in the temperature range 623-793 K in a fixed bed reactor. Kinetic analysis of this reaction has been done using a recursion procedure developed in this work from first principles. The advantage of this method is the absence of any restriction on the conversion level as it uses an integrated rate equation. The rate of styrene formation was found to follow a linear relationship with concentration of ethylbenzene and shows a Langmuir type dependence on the concentration of oxygen.

Metathesis of carbon dioxide and phenyl isocyanate catalysed by group(IV) metal alkoxides: An experimental and computational study

The insertion reactions of zirconium(IV) n-butoxide and titanium(IV) n-butoxide with a heterocumulene like carbodiimide, carbon dioxide or phenyl isocyanate are compared. Both give an intermediate which carries out metathesis at elevated temperatures by inserting a second heterocumulene in a head-to-head fashion. The intermediate metallacycle extrudes a new heterocumulene, different from the two that have inserted leading to metathesis. As the reaction is reversible, catalytic metathesis is feasible. In stoichiometric reactions heterocumulene insertion, metathesis and metathesis cum insertion products are observed. However, catalytic amounts of the metal alkoxide primarily led to metathesis products. It is shown that zirconium alkoxides promote catalytic metathesis (isocyanates, carbon dioxide) more efficiently than the corresponding titanium alkoxide. The difference in the metathetic activity of these alkoxides has been explained by a computational study using model complexes Ti(OMe)(4) (1bTi) and Zr(OMe)(4) (1bZr). The computation was carried out at the B3LYP/LANL2DZ level of theory.

Supersolid Phase in One-Dimensional Bose-Hubbard Model With Extended Range Interactions: DMRG And Field Theoretic Study at Different Densities

We use the Density Matrix Renormalization Group and the Abelian bosonization method to study the effect of density on quantum phases of one-dimensional extended Bose-Hubbard model. We predict the existence of supersolid phase and also other quantum phases for this system. We have analyzed the role of extended range interaction parameters on solitonic phase near half-filling. We discuss the effects of dimerization in nearest neighbor hopping and interaction as well as next nearest neighbor interaction on the plateau phase at half-filling.

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.