Development of a composite material with high magnetic permeability and low loss factor for high frequency application

A flexible composite suitable for MHz frequency application has been developed by combining Fe3O4 and polyvinyl alcohol (PVA). The loss factor and the permeability have been evaluated. At an optimum weight percentage of Fe3O4 in the PVA matrix, the frequency at which the loss factor gives a minimum shifts to the MHz region. The loss factor has been found to be lower by one order of magnitude at 70 MHz compared to the presently used nickel zinc ferrite. The Henkel plot and the Cole-Cole plot have been obtained for the understanding of the high magnetic permeability and the low loss factor. (C) 2012 American Institute of Physics. doi:10.1063/1.3672867]

AdFT: An Adaptive Framework for Fault Tolerance on Large Scale Systems using Application Malleability

Exascale systems of the future are predicted to have mean time between failures (MTBF) of less than one hour. Malleable applications, where the number of processors on which the applications execute can be changed during executions, can make use of their malleability to better tolerate high failure rates. We present AdFT, an adaptive fault tolerance framework for long running malleable applications to maximize application performance in the presence of failures. AdFT framework includes cost models for evaluating the benefits of various fault tolerance actions including checkpointing, live-migration and rescheduling, and runtime decisions for dynamically selecting the fault tolerance actions at different points of application execution to maximize performance. Simulations with real and synthetic failure traces show that our approach outperforms existing fault tolerance mechanisms for malleable applications yielding up to 23% improvement in application performance, and is effective even for petascale systems and beyond.

Large improvements in application throughput of long-running multi-component applications using batch grids

Computational grids with multiple batch systems (batch grids) can be powerful infrastructures for executing long-running multi-component parallel applications. In this paper, we evaluate the potential improvements in throughput of long-running multi-component applications when the different components of the applications are executed on multiple batch systems of batch grids. We compare the multiple batch executions with executions of the components on a single batch system without increasing the number of processors used for executions. We perform our analysis with a foremost long-running multi-component application for climate modeling, the Community Climate System Model (CCSM). We have built a robust simulator that models the characteristics of both the multi-component application and the batch systems. By conducting large number of simulations with different workload characteristics and queuing policies of the systems, processor allocations to components of the application, distributions of the components to the batch systems and inter-cluster bandwidths, we show that multiple batch executions lead to 55% average increase in throughput over single batch executions for long-running CCSM. We also conducted real experiments with a practical middleware infrastructure and showed that multi-site executions lead to effective utilization of batch systems for executions of CCSM and give higher simulation throughput than single-site executions. Copyright (c) 2011 John Wiley & Sons, Ltd.

Completely random nanoporous Cu4O3-CuO-C composite thin films for potential application as multiple channel photonic band gap based filter in the telecommunication wavelengths

In 2003, Babin et al. theoretically predicted (J. Appl. Phys. 94:4244, 2003) that fabrication of organic-inorganic hybrid materials would probably be required to implement structures with multiple photonic band gaps. In tune with their prediction, we report synthesis of such an inorganic-organic nanocomposite, comprising Cu4O3-CuO-C thin films that experimentally exhibit the highest (of any known material) number (as many as eleven) of photonic band gaps in the near infrared. On contrary to the report by Wang et al. (Appl. Phys. Lett. 84:1629, 2004) that photonic crystals with multiple stop gaps require highly correlated structural arrangement such as multilayers of variable thicknesses, we demonstrate experimental realization of multiple stop gaps in completely randomized structures comprising inorganic oxide nanocrystals (Cu4O3 and CuO) randomly embedded in a randomly porous carbonaceous matrix. We report one step synthesis of such nanostructured films through the metalorganic chemical vapor deposition technique using a single source metalorganic precursor, Cu-4(deaH)(dea)(oAc)(5) a <...aEuro parts per thousand(CH3)(2)CO. The films displaying multiple (4/9/11) photonic band gaps with equal transmission losses in the infrared are promising materials to find applications as multiple channel photonic band gap based filter for WDM technology.

Local demodulation of holograms using the Riesz transform with application to microscopy

We propose a Riesz transform approach to the demodulation of digital holograms. The Riesz transform is a higher-dimensional extension of the Hilbert transform and is steerable to a desired orientation. Accurate demodulation of the hologram requires a reliable methodology by which quadrature-phase functions (or simply, quadratures) can be constructed. The Riesz transform, by itself, does not yield quadratures. However, one can start with the Riesz transform and construct the so-called vortex operator by employing the notion of quasi-eigenfunctions, and this approach results in accurate quadratures. The key advantage of using the vortex operator is that it effectively handles nonplanar fringes (interference patterns) and has the ability to compensate for the local orientation. Therefore, this method results in aberration-free holographic imaging even in the case when the wavefronts are not planar. We calibrate the method by estimating the orientation from a reference hologram, measured with an empty field of view. Demodulation results on synthesized planar as well as nonplanar fringe patterns show that the accuracy of demodulation is high. We also perform validation on real experimental measurements of Caenorhabditis elegans acquired with a digital holographic microscope. (c) 2012 Optical Society of America

Naphthylhydrazone based selective and sensitive chemosensors for Cu2+ and their application in bioimaging

Two new hydroxynaphthyl-hydrazone based fluorogenic chemosensors R-1 and R-2 have been synthesized by Schiff base condensation of Tris(4-formylphenyl)amine with 1-hydroxynaphthalene-2-hydrazide and 1-hydroxynaphthalene-2-carbohydrazone, respectively. They are examined as highly selective and sensitive receptors for Cu2+ ions in aqueous medium. Electronic absorption as well as fluorescence titration studies of receptors R-1 and R-2 with different metal cations in H2O/CH3CN medium showed highly selective and very rapid (< 2 min) binding affinity towards Cu2+ ions even in the presence of other commonly coexisting metal ions such as Na+, K+, Mg2+, Ca2+, Mn2+, Fe2+, Co2+, Ni2+, Zn2+, Cd2+ and Hg2+. Quantification of the fluorescence titration analysis indicated that these newly synthesized receptors (R-1 and R-2) can indicate the presence of Cu2+ ions even at very low concentrations of 598 and 676 ppt, respectively. In addition, the propensity of these receptors as bio-imaging fluorescent probes to detect Cu2+ ions in human cervical HeLa cancer cell lines and their cytotoxicity against HeLa cells have been investigated.

Application of a Class of Nano fluids to improve the Loadability of Power Transformers

It is generally known that addition of conducting or insulating particles to mineral transformer oil, lowers its breakdown strength, E-d. However, if the particulates are of molecular dimensions, or nanoparticles, (NPs), as they are called, the breakdown strength is seen to increase considerably. Recent experiments by the authors on oil cooled power equipment such as transformers showed that, nanofluids comprising NPs of selected oxides of iron, such as Fe(3)o(4), called magnetite, added to transformer oil increased the breakdown voltage of the virgin oil and more importantly a remarkable enhancement in the thermal conductivity and the viscosity and hence an increased loadability of the transformer for a given top oil temperature (TOT).

A phenanthrene based highly selective fluorogenic and visual sensor for Cu2+ ion with nanomolar detection limit and its application in live cell imaging

A new phenanthrene based chemosensor has been synthesized and investigated to act as highly selective fluorescence and visual sensor for Cu2+ ion with very low detection limit of 1.58 nM: this has also been used to image Cu2+ in human cervical HeLa cancer cells. (C) 2012 Elsevier B.V. All rights reserved.

A novel gas flow sensing application using piezoelectric ZnO thin films deposited on Phynox alloy

We report on the novel flow sensing application of piezoelectric ZnO thin film deposited on Phynox alloy sensing element. Characterization of piezoelectric ZnO films deposited on Phynox (Elgiloy) substrate at different RF powers is discussed. ZnO films deposited at RF power of 100W were found to have fine c-axis orientation, possesses excellent surface morphology with lower rms surface roughness of 1.87 nm and maximum d(31) coefficient value 4.7 pm V-1. The thin cantilever strip of Phynox alloy with ZnO film as a sensing layer for flow sensing has been tested for flow rates ranging from 2 to 18 L min(-1). A detailed theoretical analysis of the experimental set-up showing the relationship between output voltage and force at a particular flow rate has been discussed. The sensitivity of now sensing element is similar to 18 mV/(L min(-1)) and typical response time is of the order of 20 m s. The sensing element is calibrated using in-house developed testing set-up. (C) 2012 Elsevier B.V. All rights reserved.

Active Sequential Hypothesis Testing with Application to a Visual Search Problem

We consider a visual search problem studied by Sripati and Olson where the objective is to identify an oddball image embedded among multiple distractor images as quickly as possible. We model this visual search task as an active sequential hypothesis testing problem (ASHT problem). Chernoff in 1959 proposed a policy in which the expected delay to decision is asymptotically optimal. The asymptotics is under vanishing error probabilities. We first prove a stronger property on the moments of the delay until a decision, under the same asymptotics. Applying the result to the visual search problem, we then propose a ``neuronal metric'' on the measured neuronal responses that captures the discriminability between images. From empirical study we obtain a remarkable correlation (r = 0.90) between the proposed neuronal metric and speed of discrimination between the images. Although this correlation is lower than with the L-1 metric used by Sripati and Olson, this metric has the advantage of being firmly grounded in formal decision theory.

Synthesis of zero valent iron nanoparticles and application to removal of arsenic(III) and arsenic(V) from water

Recently nano scale zero valent iron particles (nZVI) have been considered as smart adsorbent for environmental and groundwater remediation. Although several synthetic methods are available for the preparation of nZVI, air stable nZVI are not available for remediation works. Further, challenges demand synthesis of nZVI without stabilizers and capping agents. A modified methodology for the synthesis of air stable nZVI has been developed without any capping agents and characterized by powder X-Ray Diffraction (XRD), Scanning Electron Microscopy Energy-dispersive X-Ray (SEM-EDS), Transmission Electron Microscopy (TEM) and X-Ray Photoelectron Spectroscopy (XPS). The results of the present study suggest that the synthetic nZVI are air-stable over a period of one year and consists of particles of 30-40 nm in diameter. Although a layer of less than 3 am thick oxide/hydroxide is observed by TEM and XPS, it appears to be due to oxidation of outer surface during analysis. Adsorption study has shown that the synthetic nZVI are more effective adsorbent than the commercial nZVI and can remove simultaneously arsenite As-III] and arsenate As-V] from water without prior reduction of As-V to As-III. The removal process is adsorptive rather than precipitative and the removal of As-III is greater than that of As-V.

AN ITERATIVE ALGORITHM FOR PHASE RETRIEVAL WITH SPARSITY CONSTRAINTS: APPLICATION TO FREQUENCY DOMAIN OPTICAL COHERENCE TOMOGRAPHY

We address the problem of phase retrieval, which is frequently encountered in optical imaging. The measured quantity is the magnitude of the Fourier spectrum of a function (in optics, the function is also referred to as an object). The goal is to recover the object based on the magnitude measurements. In doing so, the standard assumptions are that the object is compactly supported and positive. In this paper, we consider objects that admit a sparse representation in some orthonormal basis. We develop a variant of the Fienup algorithm to incorporate the condition of sparsity and to successively estimate and refine the phase starting from the magnitude measurements. We show that the proposed iterative algorithm possesses Cauchy convergence properties. As far as the modality is concerned, we work with measurements obtained using a frequency-domain optical-coherence tomography experimental setup. The experimental results on real measured data show that the proposed technique exhibits good reconstruction performance even with fewer coefficients taken into account for reconstruction. It also suppresses the autocorrelation artifacts to a significant extent since it estimates the phase accurately.

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.

Incentive Compatible Influence Maximization in Social Networks with Application to Viral Marketing

Information diffusion and influence maximization are important and extensively studied problems in social networks. Various models and algorithms have been proposed in the literature in the context of the influence maximization problem. A crucial assumption in all these studies is that the influence probabilities are known to the social planner. This assumption is unrealistic since the influence probabilities are usually private information of the individual agents and strategic agents may not reveal them truthfully. Moreover, the influence probabilities could vary significantly with the type of the information flowing in the network and the time at which the information is propagating in the network. In this paper, we use a mechanism design approach to elicit influence probabilities truthfully from the agents. Our main contribution is to design a scoring rule based mechanism in the context of the influencer-influencee model. In particular, we show the incentive compatibility of the mechanisms and propose a reverse weighted scoring rule based mechanism as an appropriate mechanism to use.

Review of trend detection methods and their application to detect temperature changes in India

Present study performs the spatial and temporal trend analysis of annual, monthly and seasonal maximum and minimum temperatures (t(max), t(min)) in India. Recent trends in annual, monthly, winter, pre-monsoon, monsoon and post-monsoon extreme temperatures (t(max), t(min)) have been analyzed for three time slots viz. 1901-2003,1948-2003 and 1970-2003. For this purpose, time series of extreme temperatures of India as a whole and seven homogeneous regions, viz. Western Himalaya (WH), Northwest (NW), Northeast (NE), North Central (NC), East coast (EC), West coast (WC) and Interior Peninsula (IP) are considered. Rigorous trend detection analysis has been exercised using variety of non-parametric methods which consider the effect of serial correlation during analysis. During the last three decades minimum temperature trend is present in All India as well as in all temperature homogeneous regions of India either at annual or at any seasonal level (winter, pre-monsoon, monsoon, post-monsoon). Results agree with the earlier observation that the trend in minimum temperature is significant in the last three decades over India (Kothawale et al., 2010). Sequential MK test reveals that most of the trend both in maximum and minimum temperature began after 1970 either in annual or seasonal levels. (C) 2012 Elsevier B.V. All rights reserved.