Activation barriers for d.c. conductivity in ionic glasses: Classical calculations using the small-cluster approximation

A small-cluster approximation has been used to calculate the activation barriers for the d.c. conductivity in ionic glasses. The main emphasis of this approach is on the importance of the hitherto ignored polarization energy contribution to the total activation energy. For the first time it has been demonstrated that the d.c. conductivity activation energy can be calculated by considering ionic migration to a neighbouring vacancy in a smali cluster of ions consisting of face-sharing anion polyhedra. The activation energies from the model calculations have been compared with the experimental values in the case of highly modified lithium thioborate glasses.

Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection

The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread. In this paper, a novel method of estimating such sparse multipath fading channels for OFDM systems is explored. In particular, Sparse Bayesian Learning (SBL) techniques are applied to jointly estimate the sparse channel and its second order statistics, and a new Bayesian Cramer-Rao bound is derived for the SBL algorithm. Further, in the context of OFDM channel estimation, an enhancement to the SBL algorithm is proposed, which uses an Expectation Maximization (EM) framework to jointly estimate the sparse channel, unknown data symbols and the second order statistics of the channel. The EM-SBL algorithm is able to recover the support as well as the channel taps more efficiently, and/or using fewer pilot symbols, than the SBL algorithm. To further improve the performance of the EM-SBL, a threshold-based pruning of the estimated second order statistics that are input to the algorithm is proposed, and its mean square error and symbol error rate performance is illustrated through Monte-Carlo simulations. Thus, the algorithms proposed in this paper are capable of obtaining efficient sparse channel estimates even in the presence of a small number of pilots.

Machine learning modelling for predicting soil liquefaction susceptibility

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Variable Sparsity Kernel Learning

This paper(1) presents novel algorithms and applications for a particular class of mixed-norm regularization based Multiple Kernel Learning (MKL) formulations. The formulations assume that the given kernels are grouped and employ l(1) norm regularization for promoting sparsity within RKHS norms of each group and l(s), s >= 2 norm regularization for promoting non-sparse combinations across groups. Various sparsity levels in combining the kernels can be achieved by varying the grouping of kernels-hence we name the formulations as Variable Sparsity Kernel Learning (VSKL) formulations. While previous attempts have a non-convex formulation, here we present a convex formulation which admits efficient Mirror-Descent (MD) based solving techniques. The proposed MD based algorithm optimizes over product of simplices and has a computational complexity of O (m(2)n(tot) log n(max)/epsilon(2)) where m is no. training data points, n(max), n(tot) are the maximum no. kernels in any group, total no. kernels respectively and epsilon is the error in approximating the objective. A detailed proof of convergence of the algorithm is also presented. Experimental results show that the VSKL formulations are well-suited for multi-modal learning tasks like object categorization. Results also show that the MD based algorithm outperforms state-of-the-art MKL solvers in terms of computational efficiency.

Learning automata in feedforward connectionist systems

This paper analyses the behaviour of a general class of learning automata algorithms for feedforward connectionist systems in an associative reinforcement learning environment. The type of connectionist system considered is also fairly general. The associative reinforcement learning task is first posed as a constrained maximization problem. The algorithm is approximated hy an ordinary differential equation using weak convergence techniques. The equilibrium points of the ordinary differential equation are then compared with the solutions to the constrained maximization problem to show that the algorithm does behave as desired.

Micellar structures of dimeric surfactants with phosphate head groups and wettable spacers: A small-angle neutron scattering study

Dimeric or gemini surfactants consist of two hydrophobic chains and two hydrophilic head groups covalently connected by a hydrophobic or hydrophilic spacer. This paper reports the small-angle neutron scattering (SANS) measurements from aqueous micellar solutions of two different recently developed types of dimeric surfactants: (i) bis-anionic C16H33PO4--(CH2)(m)-PO4-C16H33,2Na(+) dimeric surfactants composed of phosphate head groups and a hydrophobic polymethylene spacer, referred to as 16-m-16,2Na(+), for spacer lengths m = 2, 4, 6, and 10, (ii) bis-cationic C16H33N+(CH3)(2)-CH2-(CH2-O-CH2)(p)-CH2-N+ (CH3)(2)C16H33,2Br(-) dimeric surfactants composed of dimethylammonium head groups and a wettable polyethylene oxide spacer, referred to as 16-CH2-p-CH2-16,2Br(-), for spacer lengths p = 1 - 3. The micellar structures of these surfactants are compared with the earlier studied bis-cationic C16H33N+ (CH3)(2)-(CH2)(m)-N+ (CH3)(2)C16H33,2Br(-) dimeric surfactants composed of dimethylammonium head groups and a hydrophobic polymethylene spacer, referred to as 16-m-16,2Br(-). It is found that 16-m-16,2Na(+), similar to 16-m-16,2Br(-), form various micellar structures depending on the spacer length. Micelles an disklike for rn = 2, rodlike for m = 4, and prolate ellipsoidal fur m = 6 and 10. The micelles of 16-CH2-p-CH2-16,2Br(-) are prolate ellipsoidal for all the values of p = 1 - 3. It is also found that micelles of 16-m-16,2Na(+) and 16-CH2-p-CH2-16,2Br(-) are large in comparison to those of 16-in-16,2Br(-) for similar spacer lengths. This is connected with the fact that both in 16-m-16,2Na(+) and 16-CH2-p-CH2-16,2Br(-), the head group or the spacer is more hydrated as compared to that in the 16-m-16,2Br(-). An increase in the hydration of the spacer or the head group increases the screening of the Coulomb repulsion between the charged head groups. This effect has been found to be more pronounced in the dimeric surfactants having wettable spacers. [S1063-651X(99)00303-7].

Nature and strength of C-H center dot center dot center dot O interactions involving formyl hydrogen atoms: computational and experimental studies of small aldehydes

Structural and electronic properties of C-H center dot center dot center dot O contacts in compounds containing a formyl group are investigated from the perspective of both hydrogen bonding and dipole-dipole interactions, in a systematic and graded approach. The effects of a-substitution and self-association on the nature of the formyl H-atom are studied with the NBO and AIM methodologies. The relative dipole-dipole contributions in formyl C-H center dot center dot center dot O interactions are obtained for aldehyde dimers. The stabilities and energies of aldehyde clusters (dimer through octamer) have been examined computationally. Such studies have an implication in crystallization mechanisms. Experimental X-ray crystal structures of formaldehyde, acrolein and N-methylformamide have been determined in order to ascertain the role of C-H center dot center dot center dot O interactions in the crystal packing of formyl compounds.

Single-machine scheduling with past-sequence-dependent setup times and learning effects: a parametric analysis

In this article, we consider the single-machine scheduling problem with past-sequence-dependent (p-s-d) setup times and a learning effect. The setup times are proportional to the length of jobs that are already scheduled; i.e. p-s-d setup times. The learning effect reduces the actual processing time of a job because the workers are involved in doing the same job or activity repeatedly. Hence, the processing time of a job depends on its position in the sequence. In this study, we consider the total absolute difference in completion times (TADC) as the objective function. This problem is denoted as 1/LE, (Spsd)/TADC in Kuo and Yang (2007) ('Single Machine Scheduling with Past-sequence-dependent Setup Times and Learning Effects', Information Processing Letters, 102, 22-26). There are two parameters a and b denoting constant learning index and normalising index, respectively. A parametric analysis of b on the 1/LE, (Spsd)/TADC problem for a given value of a is applied in this study. In addition, a computational algorithm is also developed to obtain the number of optimal sequences and the range of b in which each of the sequences is optimal, for a given value of a. We derive two bounds b* for the normalising constant b and a* for the learning index a. We also show that, when a < a* or b > b*, the optimal sequence is obtained by arranging the longest job in the first position and the rest of the jobs in short processing time order.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

Inhibition of the Interaction Between NS3 Protease and HCV IRES With a Small Peptide: A Novel Therapeutic Strategy

Recently, we have demonstrated that the protease domain of NS3 alone can bind specifically to hepatitis C virus (HCV) internal ribosome entry site (IRES) near the initiator AUG, dislodges human La protein and inhibits translation in favor of viral RNA replication. Here, by using a computational approach, the contact points of the protease on the HCV IRES were putatively mapped. A 30-mer NS3 peptide was designed from the predicted RNA-binding region that retained RNA-binding ability and also inhibited IRES-mediated translation. This peptide was truncated to 15 mer and this also demonstrated ability to inhibit HCV RNA-directed translation as well as replication. More importantly, its activity was tested in an in vivo mouse model by encapsulating the peptide in Sendai virus virosomes followed by intravenous delivery. The study demonstrates for the first time that the HCV NS3-IRES RNA interaction can be selectively inhibited using a small peptide and reports a strategy to deliver the peptide into the liver.

Selective excitation and detection of maximum quantum coherence of a group of scalar coupled protons in chiral molecules: an NMR experiment for enantiodiscrimination

The H-1 NMR spectroscopic discrimination of enantiomers in the solution state and the measurement of enantiomeric composition is most often hindered due to either very small chemical shift differences between the discriminated peaks or severe overlap of transitions from other chemically non-equivalent protons. In addition the use of chiral auxiliaries such as, crown ether and chiral lanthanide shift reagent may often cause enormous line broadening or give little degree of discrimination beyond the crown ether substrate ratio, hampering the discrimination. In circumventing such problems we are proposing the utilization of the difference in the additive values of all the chemical shifts of a scalar coupled spin system. The excitation and detection of appropriate highest quantum coherence yields the measurable difference in the frequencies between two transitions, one pertaining to each enantiomer in the maximum quantum dimension permitting their discrimination and the F-2 cross section at each of these frequencies yields an enantiopure spectrum. The advantage of the utility of the proposed method is demonstrated on several chiral compounds where the conventional one dimensional H-1 NMR spectra fail to differentiate the enantiomers.

Successful data recovery from oscillation photographs containing strong polycrystalline diffraction rings from an unknown small-molecule contaminant: preliminary structure solution of Salmonella typhimurium pyridoxal kinase (PdxK)

Pyridoxal kinase (PdxK; EC 2.7.1.35) belongs to the phosphotransferase family of enzymes and catalyzes the conversion of the three active forms of vitamin B-6, pyridoxine, pyridoxal and pyridoxamine, to their phosphorylated forms and thereby plays a key role in pyridoxal 5 `-phosphate salvage. In the present study, pyridoxal kinase from Salmonella typhimurium was cloned and overexpressed in Escherichia coli, purified using Ni-NTA affinity chromatography and crystallized. X-ray diffraction data were collected to 2.6 angstrom resolution at 100 K. The crystal belonged to the primitive orthorhombic space group P2(1)2(1)2(1), with unitcell parameters a = 65.11, b = 72.89, c = 107.52 angstrom. The data quality obtained by routine processing was poor owing to the presence of strong diffraction rings caused by a polycrystalline material of an unknown small molecule in all oscillation images. Excluding the reflections close to powder/polycrystalline rings provided data of sufficient quality for structure determination. A preliminary structure solution has been obtained by molecular replacement with the Phaser program in the CCP4 suite using E. coli pyridoxal kinase (PDB entry 2ddm) as the phasing model. Further refinement and analysis of the structure are likely to provide valuable insights into catalysis by pyridoxal kinases.

Small molecule inhibitors of HCV replication from Pomegranate

Hepatitis C virus (HCV) is the causative agent of end-stage liver disease. Recent advances in the last decade in anti HCV treatment strategies have dramatically increased the viral clearance rate. However, several limitations are still associated, which warrant a great need of novel, safe and selective drugs against HCV infection. Towards this objective, we explored highly potent and selective small molecule inhibitors, the ellagitannins, from the crude extract of Pomegranate (Punica granatum) fruit peel. The pure compounds, punicalagin, punicalin, and ellagic acid isolated from the extract specifically blocked the HCV NS3/4A protease activity in vitro. Structural analysis using computational approach also showed that ligand molecules interact with the catalytic and substrate binding residues of NS3/4A protease, leading to inhibition of the enzyme activity. Further, punicalagin and punicalin significantly reduced the HCV replication in cell culture system. More importantly, these compounds are well tolerated ex vivo and `no observed adverse effect level' (NOAEL) was established upto an acute dose of 5000 mg/kg in BALB/c mice. Additionally, pharmacokinetics study showed that the compounds are bioavailable. Taken together, our study provides a proof-of-concept approach for the potential use of antiviral and non-toxic principle ellagitannins from pomegranate in prevention and control of HCV induced complications.

Digestive ripening and green synthesis of ultra-small (r < 2 nm) stable ZnO quantum dots

Given the recent reports pertaining to novel optical properties of ultra-small quantum dots (QDs) (r <2 nm), this nanomaterial is of relevance to both technology and science. However it is well known that in these size regimes most chalocogenide QD dispersions are unstable. Since applications often require use of QD dispersions (e.g. for deployment on a substrate), stabilizing these ultra-small particles is of practical relevance. In this work we demonstrate a facile, green, solution approach for synthesis of stable, ultra-small ZnO QDs having radius less than 2 nm. The particle size is calculated using Brits' equation and confirmed by transmission electron micrographs. ZnO QDs reported remain stable for > 120 days in ethanol (at similar to 298-303 K). We report digestive ripening (DR) in TEA capped ZnO QDs; this occurs rapidly over a short duration of 5 min. To explain this observation we propose a suitable mechanism based on the Lee's theory, which correlates the tendency of DR with the observed zeta potentials of the dispersed medium. To the best of our knowledge this is the (i) first report on DR in oxide QDs, as well as the first direct experimental verification of Lee's theory, and (ii) most rapid DR reported so far. The facile nature of the method presented here makes ultra-small ZnO readily accessible for fundamental exploration and technologically relevant applications. (C) 2014 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

l(1)-K-SVD: A robust dictionary learning algorithm with simultaneous update

We develop a new dictionary learning algorithm called the l(1)-K-svp, by minimizing the l(1) distortion on the data term. The proposed formulation corresponds to maximum a posteriori estimation assuming a Laplacian prior on the coefficient matrix and additive noise, and is, in general, robust to non-Gaussian noise. The l(1) distortion is minimized by employing the iteratively reweighted least-squares algorithm. The dictionary atoms and the corresponding sparse coefficients are simultaneously estimated in the dictionary update step. Experimental results show that l(1)-K-SVD results in noise-robustness, faster convergence, and higher atom recovery rate than the method of optimal directions, K-SVD, and the robust dictionary learning algorithm (RDL), in Gaussian as well as non-Gaussian noise. For a fixed value of sparsity, number of dictionary atoms, and data dimension, l(1)-K-SVD outperforms K-SVD and RDL on small training sets. We also consider the generalized l(p), 0 < p < 1, data metric to tackle heavy-tailed/impulsive noise. In an image denoising application, l(1)-K-SVD was found to result in higher peak signal-to-noise ratio (PSNR) over K-SVD for Laplacian noise. The structural similarity index increases by 0.1 for low input PSNR, which is significant and demonstrates the efficacy of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.