Meso-Structured Silica-Nafion Hybrid Membranes for Direct Methanol Fuel Cells

A series of novel organic-inorganic hybrid membranes have been prepared employing Nafion and acid-functionalized meso-structured molecular sieves (MMS) with varying structures and surface area. Acid-functionalized silica nanopowder of surface area 60 m(2)/g, silica meso-structured cellular foam (MSU-F) of surface area 470 m(2)/g and silica meso-structured hexagonal frame network (MCM-41) of surface area 900 m(2)/g have been employed as potential filler materials to form hybrid membranes with Nafion framework. The structural behavior, water uptake, proton conductivity and methanol permeability of these hybrid membranes have been investigated. DMFCs employing Nafion-silica MSU-F and Nafion-silica MCM-41 hybrid membranes deliver peak-power densities of 127 mW/cm(2) and 100 mW/cm(2), respectively; while a peak-power density of only 48 mW/cm(2) is obtained with the DMFC employing pristine recast Nafion membrane under identical operating conditions. The aforesaid characteristics of the hybrid membranes could be exclusively attributed to the presence of pendant sulfonic acid groups in the filler, which provide fairly continuous proton-conducting pathways between filler and matrix in the hybrid membranes facilitating proton transport without any trade-off between its proton conductivity and methanol crossover. (C) 2012 The Electrochemical Society. DOI: 10.1149/2.036211jes] All rights reserved.

Hybrid molecules of carvacrol and benzoyl urea/thiourea with potential applications in agriculture and medicine

Benzoyl phenyl urea, a class of insect growth regulator's acts by inhibiting chitin synthesis. Carvacrol, a naturally occurring monoterpenoid is an effective antifungal agent. We have structurally modified carvacrol (2-methyl-5-1-methylethyl] phenol) by introducing benzoylphenyl urea linkage. Two series of benzoylcarvacryl thiourea (BCTU, 4a-f) and benzoylcarvacryl urea (BCU, 5a-f) derivatives were prepared and characterized by elemental analysis, IR, H-1 and C-13 NMR and Mass spectroscopy. Derivatives 4b, 4d, 4e, 4f and 5d, 5f showed comparable insecticidal activity with the standard BPU lufenuron against Dysdercus koenigii. BCTU derivatives 4c, 4e and BCU 5c showed good antifungal activity against phytopathogenic fungi viz. Magnaporthe grisae, Fusarium oxysporum, Dreschlera oryzae; food spoilage yeasts viz. Debaromyces hansenii, Pichia membranifaciens; and human pathogens viz. Candida albicans and Cryptococcus neoformans. Compounds 5d, 5e and 5f showed potent activity against human pathogens. Moderate and selective activity was observed for other compounds. All the synthesized compounds were non-haemolytic. These compounds have potential application in agriculture and medicine. (C) 2012 Elsevier Ltd. All rights reserved.

Metal cholate hydrogels: versatile supramolecular systems for nanoparticle embedded soft hybrid materials

This work describes the formation of hydrogels from sodium cholate solution in the presence of a variety of metal ions (Ca2+, Cu2+, Co2+, Zn2+, Cd2+, Hg2+ and Ag+). Morphological studies of the xerogels by electron microscopy reveal the presence of helical nanofibres. The rigid helical framework in the calcium cholate hydrogel was utilised to synthesize hybrid materials (AuNPs and AgNPs). Doping of transition metal salts into the calcium cholate hydrogel brings out the possibility of synthesising metal sulphide nano-architectures keeping the hydrogel network intact. These novel gel-nanoparticle hybrid materials have encouraging application potentials.

Efficient Algorithms for Linear Summed Error Structural SVMs

Structural Support Vector Machines (SSVMs) have become a popular tool in machine learning for predicting structured objects like parse trees, Part-of-Speech (POS) label sequences and image segments. Various efficient algorithmic techniques have been proposed for training SSVMs for large datasets. The typical SSVM formulation contains a regularizer term and a composite loss term. The loss term is usually composed of the Linear Maximum Error (LME) associated with the training examples. Other alternatives for the loss term are yet to be explored for SSVMs. We formulate a new SSVM with Linear Summed Error (LSE) loss term and propose efficient algorithms to train the new SSVM formulation using primal cutting-plane method and sequential dual coordinate descent method. Numerical experiments on benchmark datasets demonstrate that the sequential dual coordinate descent method is faster than the cutting-plane method and reaches the steady-state generalization performance faster. It is thus a useful alternative for training SSVMs when linear summed error is used.

MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures

This paper presents a decentralized/peer-to-peer architecture-based parallel version of the vector evaluated particle swarm optimization (VEPSO) algorithm for multi-objective design optimization of laminated composite plates using message passing interface (MPI). The design optimization of laminated composite plates being a combinatorially explosive constrained non-linear optimization problem (CNOP), with many design variables and a vast solution space, warrants the use of non-parametric and heuristic optimization algorithms like PSO. Optimization requires minimizing both the weight and cost of these composite plates, simultaneously, which renders the problem multi-objective. Hence VEPSO, a multi-objective variant of the PSO algorithm, is used. Despite the use of such a heuristic, the application problem, being computationally intensive, suffers from long execution times due to sequential computation. Hence, a parallel version of the PSO algorithm for the problem has been developed to run on several nodes of an IBM P720 cluster. The proposed parallel algorithm, using MPI's collective communication directives, establishes a peer-to-peer relationship between the constituent parallel processes, deviating from the more common master-slave approach, in achieving reduction of computation time by factor of up to 10. Finally we show the effectiveness of the proposed parallel algorithm by comparing it with a serial implementation of VEPSO and a parallel implementation of the vector evaluated genetic algorithm (VEGA) for the same design problem. (c) 2012 Elsevier Ltd. All rights reserved.

Space-vector-based hybrid pulse width modulation technique to reduce line current distortion in induction motor drives

Pulse width modulation (PWM) techniques involving different switching sequences are used in space vector-based PWM generation for reducing line current ripple in induction motor drives. This study proposes a hybrid PWM technique employing five switching sequences. The proposed technique is a combination of continuous PWM, discontinuous PWM (DPWM) and advanced bus clamping PWM methods. Performance of the proposed PWM technique is evaluated and compared with those of the existing techniques on a constant volts per hertz induction motor drive. In terms of total harmonic distortion in the line current, the proposed method is shown to be superior to both conventional space vector PWM (CSVPWM) and DPWM over a fundamental frequency range of 32-50 Hz at a given average switching frequency. The reduction in harmonic distortion is about 42% over CSVPWM at the rated speed of the drive.

q-Gaussian based Smoothed Functional Algorithms for Stochastic Optimization

The q-Gaussian distribution results from maximizing certain generalizations of Shannon entropy under some constraints. The importance of q-Gaussian distributions stems from the fact that they exhibit power-law behavior, and also generalize Gaussian distributions. In this paper, we propose a Smoothed Functional (SF) scheme for gradient estimation using q-Gaussian distribution, and also propose an algorithm for optimization based on the above scheme. Convergence results of the algorithm are presented. Performance of the proposed algorithm is shown by simulation results on a queuing model.

A Hybrid Five-Level Inverter With Common-Mode Voltage Elimination Having Single Voltage Source for IM Drive Applications

A new hybrid five-level inverter topology with common-mode voltage (CMV) elimination for induction motor drive is proposed in this paper. This topology has only one dc source, and different voltage levels are generated by using this voltage source along with floating capacitors charged to asymmetrical voltage levels. The pulsewidth modulation (PWM) scheme employed in this topology balances the capacitor voltages at the required levels at any power factor and modulation index while eliminating the CMV. This inverter has good fault-tolerant capability as it can be operated in three-or two-level mode with CMV elimination, in case of any failure in the H-bridges. More voltage levels with CMV elimination can be realized from this topology but only in a limited range of modulation index and power factor. Extensive simulation is done to validate the PWM technique for CMV elimination and balancing of the capacitor voltages. The experimental verification of the proposed inverter-fed induction motor is carried out in the linear modulation and overmodulation regions. The steady-state and transient operations of the drive are verified. The dynamics of the capacitor voltage balancing is also tested. The experimental results demonstrate that the proposed topology can be considered for industrial drive applications.

A 12 V Substrate-Integrated PbO2-Activated Carbon Asymmetric Hybrid Ultracapacitor with Silica-Gel-Based Inorganic-Polymer Electrolyte

A 12 V Substrate-Integrated PbO2-Activated Carbon hybrid ultracapacitor (SI-PbO2-AC HUCs) with silica-gel sulfuric acid electrolyte is developed and performance tested. The performance of the silica-gel based hybrid ultracapacitor is compared with flooded and AGM-based HUCs. These HUCs comprise substrate-integrated PbO2 (SI-PbO2) as positive electrodes and high surface-area activated carbon with dense graphite-sheet substrate as negative electrodes. 12 V SI-PbO2-AC HUCs with flooded, AGM and gel electrolytes are found to have capacitance values of 308 F, 184 F, and 269 F at C-rate and can be pulse charged and discharged for 100,000 cycles with only a nominal decrease in their capacitance values. The best performance is exhibited by gel-electrolyte HUCs.

A Hybrid Multilevel Inverter System Based on Dodecagonal Space Vectors for Medium Voltage IM Drives

Dodecagonal (12-sided) space vector pulsewidth modulation (PWM) schemes are characterized by the complete absence of (6n +/- 1)th-order harmonics (for odd n) in the phase voltages, within the linear modulation range and beyond, including over-modulation. This paper presents a new topology suitable for the realization of such multilevel inverter schemes for induction motor (IM) drives, by cascading two-level inverters with flying-capacitor-inverter fed floating H-bridge cells. Now, any standard IM may be used to get the dodecagonal operation which hitherto was possible only with open-end winding IM. To minimize the current total harmonic distortion (THD), a strategy for synchronous PWM is also proposed. It is shown that the proposed method is capable of obtaining better THD figures, compared to conventional dodecagonal schemes. The topology and the PWM strategy are validated through analysis and subsequently verified experimentally.

Smoothed functional and Quasi-Newton algorithms for routing in multi-stage queueing network with constraints

We consider the problem of optimal routing in a multi-stage network of queues with constraints on queue lengths. We develop three algorithms for probabilistic routing for this problem using only the total end-to-end delays. These algorithms use the smoothed functional (SF) approach to optimize the routing probabilities. In our model all the queues are assumed to have constraints on the average queue length. We also propose a novel quasi-Newton based SF algorithm. Policies like Join Shortest Queue or Least Work Left work only for unconstrained routing. Besides assuming knowledge of the queue length at all the queues. If the only information available is the expected end-to-end delay as with our case such policies cannot be used. We also give simulation results showing the performance of the SF algorithms for this problem.

Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.

Operator-splitting finite element algorithms for computations of high-dimensional parabolic problems

An operator-splitting finite element method for solving high-dimensional parabolic equations is presented. The stability and the error estimates are derived for the proposed numerical scheme. Furthermore, two variants of fully-practical operator-splitting finite element algorithms based on the quadrature points and the nodal points, respectively, are presented. Both the quadrature and the nodal point based operator-splitting algorithms are validated using a three-dimensional (3D) test problem. The numerical results obtained with the full 3D computations and the operator-split 2D + 1D computations are found to be in a good agreement with the analytical solution. Further, the optimal order of convergence is obtained in both variants of the operator-splitting algorithms. (C) 2012 Elsevier Inc. All rights reserved.

Novel algorithms for distributed sequential hypothesis testing

This paper considers sequential hypothesis testing in a decentralized framework. We start with two simple decentralized sequential hypothesis testing algorithms. One of which is later proved to be asymptotically Bayes optimal. We also consider composite versions of decentralized sequential hypothesis testing. A novel nonparametric version for decentralized sequential hypothesis testing using universal source coding theory is developed. Finally we design a simple decentralized multihypothesis sequential detection algorithm.