Deviation from 3-D Ising critical behaviour in aqueous electrolyte solutions

The near-critical behaviour in complex fluids, comprising electrolyte solutions, polymer solutions and amphiphilic systems, reveals a marked departure from the 3-D Ising behaviour. This departure manifests itself either in terms of a crossover from Ising to mean-field (or classical) critical behaviour, when moving away from a given critical point (Tc), or by the persistence of only mean-field region in the surprisingly close vicinity of Tc. The ilo,non-Ising features of the osmotic compressibility (chi(T,p)) in solutions of electrolytes, that exhibit orle or many liquid-liquid transitions, will be presented. The underlying cause of the breakdown of the anticipated 3-D Ising behaviour in aqueous electrolyte solutions is traced to the structuring induced by the electrolytes. New evidence constituting, measurements of small-angle X-ray scattering (SAXS) and the excess molar volume, is advanced to support the thesis of the close relationship, between the structuring and the deviation from the 3-D Ising critical behaviour in aqueous electrolyte solutions.

Chemical and shifted mechanical thresholds in Al-Ge-Te glass system

We report the results of the electrical switching studies performed on the bulk Al20GexTe80-x (2.5 less than or equal to x less than or equal to 15) chalcogenide glasses. The well known topological features, mechanical and chemical thresholds are observed. Mechanical threshold is seen at a mean coordination number of atoms, < r > = 2.50 (x = 5) a clear shift rom the mean field value of < r > = 2.4 whereas the chemical threshold is observed at < r > = 2.65 (x = 12.5) as predicted by the chemically ordered covalent network model These experiments are a sequel to similar experiments on Al20AsxTe80-x glasses in which mechanical threshold was seen at < r > = 2.60 and no chemical threshold was observed These results am well understood by a chemical bond picture developed in this article.

Unusual dependence of raman mode frequency on excitation wavelength in graphite and single walled carbon nanotubes

In this paper we report resonance Raman scattering from graphite covering excitation energies in the range 2.4 eV to 6 eV. The Raman excitation profile shows a maximum at 4.94 eV (lambda = 251nm) for the G - band (1582 cm(-1)). The D-band at similar to 1350 cm(-1), attributed to disorder activated Raman scattering, does not show up in Raman spectra recorded with excitation wavelengths smaller than 257.3 nm, revealing that the resonance enhancements of the G and D-modes are widely different. Earlier Raman measurements in carbon materials have also revealed a very large and unusual dependence of the D - mode frequency on excitation laser wavelength. This phenomenon is also observed in carbon nanotubes. In this paper we show for the first time that the above unusual dependence arises from the disorder - induced double resonance mechanism.

Application of kinetic schemes to all types of meshes

Kinetic schemes as pursued in CFD Centre are obtained by taking suitable moments of upwind schemes for Boltzmann equation without collision term. The primary ones among these are KFVS, LSKUM, KFMG and these have been applied successfully to a variety of flow problems using various meshes. These schemes have been found to be very robust.

Tractable theories for the synthesis of neural networks

The Radius of Direct attraction of a discrete neural network is a measure of stability of the network. it is known that Hopfield networks designed using Hebb's Rule have a radius of direct attraction of Omega(n/p) where n is the size of the input patterns and p is the number of them. This lower bound is tight if p is no larger than 4. We construct a family of such networks with radius of direct attraction Omega(n/root plog p), for any p greater than or equal to 5. The techniques used to prove the result led us to the first polynomial-time algorithm for designing a neural network with maximum radius of direct attraction around arbitrary input patterns. The optimal synaptic matrix is computed using the ellipsoid method of linear programming in conjunction with an efficient separation oracle. Restrictions of symmetry and non-negative diagonal entries in the synaptic matrix can be accommodated within this scheme.

Evaluation of DNA binding, antioxidant and cytotoxic activity of mononuclear Co(III) complexes of 2-oxo-1,2-dihydrobenzoh]quinoline-3-carbaldehyde thiosemicarbazones

Four new 2-oxo-1,2-dihydrobenzoh]quinoline-3-carbaldehyde N-substituted thiosemicarbazone ligands (H-2-LR, where R = H, Me, Et or Ph) and their corresponding new cobalt(III) complexes have been synthesized and characterized. The structures of the complexes 2 and 3 were determined by single crystal X-ray diffraction analysis. The interactions of the new complexes with DNA were investigated by absorption, emission and viscosity studies which indicated that the complexes bind to DNA via intercalation. Antioxidant studies of the new complexes showed that the significant antioxidant activity against DPPH radical. In addition, the in vitro cytotoxicity of complexes 1-4 against A549 cell line was assayed which showed higher cytotoxic activity with lower IC50 values indicating their efficiency in killing the cancer cells even at very low concentrations. (C) 2012 Elsevier Masson SAS. All rights reserved.

Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

Lattice Reduction Aided Detection in Large-MIMO Systems

Lattice reduction (LR) aided detection algorithms are known to achieve the same diversity order as that of maximum-likelihood (ML) detection at low complexity. However, they suffer SNR loss compared to ML performance. The SNR loss is mainly due to imperfect orthogonalization and imperfect nearest neighbor quantization. In this paper, we propose an improved LR-aided (ILR) detection algorithm, where we specifically target to reduce the effects of both imperfect orthogonalization and imperfect nearest neighbor quantization. The proposed ILR detection algorithm is shown to achieve near-ML performance in large-MIMO systems and outperform other LR-aided detection algorithms in the literature. Specifically, the SNR loss incurred by the proposed ILR algorithm compared to ML performance is just 0.1 dB for 4-QAM and < 0.5 dB for 16-QAM in 16 x 16 V-BLAST MIMO system. This performance is superior compared to those of other LR-aided detection algorithms, whose SNR losses are in the 2 dB to 9 dB range.