2(n)-SEMA-a robust solid state nuclear magnetic resonance experiment for measuring heteronuclear dipolar couplings in static oriented systems using effective transverse spin-lock

Separated local field (SLF) spectroscopy is a powerful technique to measure heteronuclear dipolar couplings. The method provides site-specific dipolar couplings for oriented samples such as membrane proteins oriented in lipid bilayers and liquid crystals. A majority of the SLF techniques utilize the well-known Polarization Inversion Spin Exchange at Magic Angle (PISEMA) pulse scheme which employs spin exchange at the magic angle under Hartmann-Hahn match. Though PISEMA provides a relatively large scaling factor for the heteronuclear dipolar coupling and a better resolution along the dipolar dimension, it has a few shortcomings. One of the major problems with PISEMA is that the sequence is very much sensitive to proton carrier offset and the measured dipolar coupling changes dramatically with the change in the carrier frequency. The study presented here focuses on modified PISEMA sequences which are relatively insensitive to proton offsets over a large range. In the proposed sequences, the proton magnetization is cycled through two quadrants while the effective field is cycled through either two or four quadrants. The modified sequences have been named as 2(n)-SEMA where n represents the number of quadrants the effective field is cycled through. Experiments carried out on a liquid crystal and a single crystal of a model peptide demonstrate the usefulness of the modified sequences. A systematic study under various offsets and Hartmann-Hahn mismatch conditions has been carried out and the performance is compared with PISEMA under similar conditions.

Thermal sresses in afinite solid cylinder due to steady temperature variation along the curved and end surface

An exact solution for determining the thermal stresses in a finite short cylinder due to an axisymmetric steady temperature field along the curved surface has been given. It is shown that a part of the solution obtained for this problem can be used to determine the thermal stresses in a finite solid cylinder heated over the end surfaces. Numerical results for a finite cylinder symmetrically heated over a portion on the curved surface and heated over the complete end surfaces have been given.

Ring loading of a long, thin, circular cylindrical shell enclosing a soft, solid core: A recalculation by the love function method of elasticity.

The problem is solved using the Love function and Flügge shell theory. Numerical work has been done with a computer for various values of shell geometry parameters and elastic constants.

Thermal stresses in a finite solid cylinder due to an axisymmetric temperature field at the end surface.

A three-dimensional rigorous solution for determining thermal stresses in a finite solid cylinder due to a steady state axisymmetric temperature field over one of its end surfaces is given. Numerical results for a solid cylinder having a length to diameter ratio equal to one and subjected to a symmetric temperature variation over half the radius of the cylinder at the end surfaces are included. These results have been compared with the results of the approximate solution given by W. Nowacki.

Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.

Prediction of solid block masonry prism compressive strength using FE model

Masonry strength is dependent upon characteristics of the masonry unit,the mortar and the bond between them. Empirical formulae as well as analytical and finite element (FE) models have been developed to predict structural behaviour of masonry. This paper is focused on developing a three dimensional non-linear FE model based on micro-modelling approach to predict masonry prism compressive strength and crack pattern. The proposed FE model uses multi-linear stress-strain relationships to model the non-linear behaviour of solid masonry unit and the mortar. Willam-Warnke's five parameter failure theory developed for modelling the tri-axial behaviour of concrete has been adopted to model the failure of masonry materials. The post failure regime has been modelled by applying orthotropic constitutive equations based on the smeared crack approach. Compressive strength of the masonry prism predicted by the proposed FE model has been compared with experimental values as well as the values predicted by other failure theories and Eurocode formula. The crack pattern predicted by the FE model shows vertical splitting cracks in the prism. The FE model predicts the ultimate failure compressive stress close to 85 of the mean experimental compressive strength value.

Vapour phase synthesis of salol over solid acids via transesterification

The transesterification of methyl salicylate with phenol has been studied in vapour phase over solid acid catalysts such as ZrO2, MoO3 and SO42- or Mo(VI) ions modified zirconia. The catalytic materials were prepared and characterized for their total surface acidity, BET surface area and powder XRD patterns. The effect of mole-ratio of the reactants, catalyst bed temperature, catalyst weight, flow-rate of reactants, WHSV and time-on-stream on the conversion (%) of phenol and selectivity (%) of salol has been investigated. A good yield (up to 70%) of salol with 90% selectivity was observed when the reactions were carried out at a catalyst bed temperature of 200 degrees C and flow-rate of 10 mL/h in presence of Mo(VI)/ZrO2 as catalyst. The results have been interpreted based on the variation of acidic properties and powder XRD phases of zirconia on incorporation of SO42- or Mo(VI) ions. The effect of poisoning of acid sites of SO42- or Mo(VI) ions modified zirconia on total surface acidity, powder XRD phases and catalytic activity was also studied. Possible reaction mechanisms for the formation of salol and diphenyl ether over acid sites are proposed.

New Solid State Forms of the Anti-HIV Drug Efavirenz. Conformational Flexibility and High Z ` Issues

Structural information on the solid forms of efavirenz, a non-nucleoside reverse transcriptase inhibitor, is limited, although various polymorphic forms of this drug have been patented. We report here structural studies of four new crystal forms a pure form, a cyclohexane solvate, and cocrystals with 1,4-cyclohexanedione and 4,4'-bipyridine. Temperature dependent single-crystal to single-crystal phase transitions are observed for the pure form and for the cyclohexane solvate with an increase in the number of symmetry independent molecules, Z', upon a lowering of temperature. Other issues related to these solid forms, such as thermal stability, conformational flexibility, and high Z' occurrences, are addressed by using a combined experimental and computational approach.

Decision support requirements for intelligent operation of southern grid in India

Control centers (CC) play a very important role in power system operation. An overall view of the system with information about all existing resources and needs is implemented through SCADA (Supervisory control and data acquisition system) and an EMS (energy management system). As advanced technologies have made their way into the utility environment, the operators are flooded with huge amount of data. The last decade has seen extensive applications of AI techniques, knowledge-based systems, Artificial Neural Networks in this area. This paper focuses on the need for development of an intelligent decision support system to assist the operator in making proper decisions. The requirements for realization of such a system are recognized for the effective operation and energy management of the southern grid in India The application of Petri nets leading to decision support system has been illustrated considering 24 bus system that is a part of southern grid.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Minimum enclosing spheres formulations for support vector ordinal regression

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.

NMR relaxation study of disorder in condensed matter: solid solutions of betaine phosphate and phosphite

Proton NMR relaxation measurements have been carried out in anti-ferroelectric Betaine phosphate (BP), ferroelectric Betaine phosphite (BPI) and the mixed system BPI(1-x)BPx, at 11.4MHz and 23.3MHz from 300K to 80K for x=0.0, 0.25, 0.45, 0.85, and 1.0. The temperature dependence of spin lattice relaxation time T, exhibits two minima as expected from the BPP model in BP and BPI. The Larmor frequency dependence of T, in the mixed system is rather unusual and exhibits different slopes for the low temperature wings at the two frequencies, which is a clear experimental evidence of the presence of different methyl groups with different activation energies (E-a) indicating disorder.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.