Bio-inspired route for the synthesis of spherical shaped MgO:Fe3+ nanoparticles: Structural, photoluminescence and photocatalytic investigation

MgO:Fe3+ (0.1-5 mol%) nanoparticles (NPs) were synthesized via eco-friendly, inexpensive and simple low temperature solution combustion route using Aloe vera gel as fuel. The final products were characterized by SEM, TEM and HRTEM. PXRD data and Rietveld analysis revealed the formation of cubic system. The influence of Fe3+ ion concentration on the structure morphology, UV absorption, PL emission and photocatalytic activity of MgO:Fe3+ NPs were investigated. The yellow emission with CIE chromaticity coordinates (0.44, 0.52) and average correlated color temperature value was found to be 3540 K which corresponds to warm light of NPs. The control of Fe3+. on MgO matrix influences the photocatalytic decolorization of methylene blue (MB) under UV light. The enhanced photocatalytic activity of MgO:Fe3+ (4 mol%) was attributed to dopant concentration, effective crystallite size, textural properties, decreased band gap and capability for reducing the electron hole pair recombination. Further, the trends of inhibitory effect in the presence of different radical scavengers were explored. These findings open up new avenues for the exploration of Fe-doped MgO in eco-friendly water applications and in the process of display devices. (C) 2015 Elsevier B.V. All rights reserved.

Multiobjective fuzzy optimization for sustainable groundwater management using particle swarm optimization and analytic element method

Groundwater management involves conflicting objectives as maximization of discharge contradicts the criteria of minimum pumping cost and minimum piping cost. In addition, available data contains uncertainties such as market fluctuations, variations in water levels of wells and variations of ground water policies. A fuzzy model is to be evolved to tackle the uncertainties, and a multiobjective optimization is to be conducted to simultaneously satisfy the contradicting objectives. Towards this end, a multiobjective fuzzy optimization model is evolved. To get at the upper and lower bounds of the individual objectives, particle Swarm optimization (PSO) is adopted. The analytic element method (AEM) is employed to obtain the operating potentio metric head. In this study, a multiobjective fuzzy optimization model considering three conflicting objectives is developed using PSO and AEM methods for obtaining a sustainable groundwater management policy. The developed model is applied to a case study, and it is demonstrated that the compromise solution satisfies all the objectives with adequate levels of satisfaction. Sensitivity analysis is carried out by varying the parameters, and it is shown that the effect of any such variation is quite significant. Copyright (c) 2015 John Wiley & Sons, Ltd.

Lower-Bound Axisymmetric Formulation for Geomechanics Problems Using Nonlinear Optimization

A lower-bound limit analysis formulation, by using two-dimensional finite elements, the three-dimensional Mohr-Coulomb yield criterion, and nonlinear optimization, has been given to deal with an axisymmetric geomechanics stability problem. The optimization was performed using an interior point method based on the logarithmic barrier function. The yield surface was smoothened (1) by removing the tip singularity at the apex of the pyramid in the meridian plane and (2) by eliminating the stress discontinuities at the corners of the yield hexagon in the pi-plane. The circumferential stress (sigma(theta)) need not be assumed. With the proposed methodology, for a circular footing, the bearing-capacity factors N-c, N-q, and N-gamma for different values of phi have been computed. For phi = 0, the variation of N-c with changes in the factor m, which accounts for a linear increase of cohesion with depth, has been evaluated. Failure patterns for a few cases have also been drawn. The results from the formulation provide a good match with the solutions available from the literature. (C) 2014 American Society of Civil Engineers.

Effect of Sm3+-Gd3+ on structural, electrical and magnetic properties of Mn-Zn ferrites synthesized via combustion route

Nanocrystalline Mn0.4Zn0.6SmxGdyFe2-(x+y)O4 (x = y = 0.01, 0.02, 0.03, 0.04 and 0.05) were synthesized by combustion route. The detailed structural studies were carried out through X-ray diffractometer (XRD), Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM). The results confirms the formation of mixed spine phase with cubic structure due to the distortion created with co-dopants substitution at Fe site in Mn-Zn ferrite lattice. Further, the crystallite size increases with an increase of Sm3+-Gd3+ ions concentration while lattice parameter and lattice strain decreases. Furthermore, the effect of Sm-Gd co-doping in Mn-Zn ferrite on the room temperature electrical (dielectric studies) studies were carried out in the wide frequency range 1 GHz-5 GHz. The magnetic studies were carried out using vibrating sample magnetometer (VSM) under applied magnetic field of 1.5T and also room temperature electron paramagnetic resonance (EPR) spectra's were recorded. From the results of dielectric studies, it shows that the real and imaginary part of permittivities are increasing with variation of Gd3+ and Sm3+ concentration. The magnetic studies reveal the decrease of remnant, saturation magnetization and coercivity with increasing of Sm3+-Gd3+ ion concentration. The g-value, peak-to-peak line width and spin concentration evaluated from EPR spectra correlated with cations occupancy. The electromagnetic properties clearly indicate that these materials are the good candidates which are useful at L and C band frequency. (C) 2015 Elsevier B.V. All rights reserved.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Ultrasonic Guided Wave Sensor Network Optimization for Monitoring Rivet Line

Guided waves using piezo-electric wafer active sensors (PWAS) is one of the useful techniques of damage detection. Sensor network optimization with minimal network hardware footprint and maximal area of coverage remains a challenging problem. PWAS sensors are placed at discrete locations in order to inspect damages in plates and the idea has the potential to be extended to assembled structures. Various actuator-sensor configurations are possible within the network in order to identify and locate damages. In this paper we present a correlation based approach to monitor cracks emanating from rivet line using a simulated guided wave signal whose sensor is operating in pulse echo mode. Discussions regarding the identification of phase change due to reflections from the crack are also discussed in this paper.

Investigations into the structural and down-shifting and up-conversion luminescence properties of Ba2Na1-3xErxNb5O15 (0 <= x <= 0.06) nanocrystalline phosphor synthesized via sol-gel route

The present work deals with the structural and efficient down-shifting (DS) and up-conversion (UC) luminescence properties of erbium ion (Er3+) doped nanocrystalline barium sodium niobate (Ba2Na1-3xErxNb5O15, where x = 0, 0.02, 0.04 and 0.06) powders synthesized via novel citrate-based sol-gel route. The monophasic nature of the title compound was confirmed via x-ray powder diffraction followed by FT-IR studies. High-resolution transmission electron microscopy (HRTEM) facilitated the establishment of the nanocrystalline phase and the morphology of the crystallites. The Kubelka-Munk function, based on diffused reflectance studies and carried out on nano-sized crystallites, was employed to obtain the optical band-gap. The synthesized nanophosphor showed efficient DS/PL-photoluminescence and UC luminescence properties, which have not yet been reported so far in this material. The material emits intense DS green emission on excitation with 378 nm radiation. Interestingly, the material gives intense UC emission in the visible region dominated by green emission and relatively weak red emission on 976 nm excitation (NIR laser excitation). Such a dual-mode emitting nanophosphor could be very useful in display devices and for many other applications.

PolyMage: Automatic Optimization for Image Processing Pipelines

This paper presents the design and implementation of PolyMage, a domain-specific language and compiler for image processing pipelines. An image processing pipeline can be viewed as a graph of interconnected stages which process images successively. Each stage typically performs one of point-wise, stencil, reduction or data-dependent operations on image pixels. Individual stages in a pipeline typically exhibit abundant data parallelism that can be exploited with relative ease. However, the stages also require high memory bandwidth preventing effective utilization of parallelism available on modern architectures. For applications that demand high performance, the traditional options are to use optimized libraries like OpenCV or to optimize manually. While using libraries precludes optimization across library routines, manual optimization accounting for both parallelism and locality is very tedious. The focus of our system, PolyMage, is on automatically generating high-performance implementations of image processing pipelines expressed in a high-level declarative language. Our optimization approach primarily relies on the transformation and code generation capabilities of the polyhedral compiler framework. To the best of our knowledge, this is the first model-driven compiler for image processing pipelines that performs complex fusion, tiling, and storage optimization automatically. Experimental results on a modern multicore system show that the performance achieved by our automatic approach is up to 1.81x better than that achieved through manual tuning in Halide, a state-of-the-art language and compiler for image processing pipelines. For a camera raw image processing pipeline, our performance is comparable to that of a hand-tuned implementation.

Conversion of plastic waste into CNTs using Ni/Mo/MgO catalystAn optimization approach by mixture experiment

A combustion technique is used to study the synthesis of carbon nano tubes from waste plastic as a precursor and Ni/Mo/MgO as a catalyst. The catalytic activity of three components Ni, Mo, MgO is measured in terms of amount of carbon product obtained. Different proportions of metal ions are optimized using mixture experiment in Design expert software. D-optimal design technique is adopted due to nonsimplex region and presence of constraints in the mixture experiment. The activity of the components is observed to be interdependent and the component Ni is found to be more effective. The catalyst containing Ni0.8Mo0.1MgO0.1 yields more carbon product. The structure of catalyst and CNTs are studied by using SEM, XRD, and Raman spectroscopy. SEM analysis shows the formation of longer CNTs with average diameter of 40-50 nm.

Design Optimization of Thin-Film Transistors Based on a Metal-Substrate-Semiconductor Architecture for High DC Voltage Sensing

We discuss the potential application of high dc voltage sensing using thin-film transistors (TFTs) on flexible substrates. High voltage sensing has potential applications for power transmission instrumentation. For this, we consider a gate metal-substrate-semiconductor architecture for TFTs. In this architecture, the flexible substrate not only provides mechanical support but also plays the role of the gate dielectric of the TFT. Hence, the thickness of the substrate needs to be optimized for maximizing transconductance, minimizing mechanical stress, and minimizing gate leakage currents. We discuss this optimization, and develop n-type and p-type organic TFTs using polyvinyldene fluoride as the substrate-gate insulator. Circuits are also realized to achieve level shifting, amplification, and high drain voltage operation.

SMO: An Integrated Approach to Intra-array and Inter-array Storage Optimization

The polyhedral model provides an expressive intermediate representation that is convenient for the analysis and subsequent transformation of affine loop nests. Several heuristics exist for achieving complex program transformations in this model. However, there is also considerable scope to utilize this model to tackle the problem of automatic memory footprint optimization. In this paper, we present a new automatic storage optimization technique which can be used to achieve both intra-array as well as inter-array storage reuse with a pre-determined schedule for the computation. Our approach works by finding statement-wise storage partitioning hyper planes that partition a unified global array space so that values with overlapping live ranges are not mapped to the same partition. Our heuristic is driven by a fourfold objective function which not only minimizes the dimensionality and storage requirements of arrays required for each high-level statement, but also maximizes inter statement storage reuse. The storage mappings obtained using our heuristic can be asymptotically better than those obtained by any existing technique. We implement our technique and demonstrate its practical impact by evaluating its effectiveness on several benchmarks chosen from the domains of image processing, stencil computations, and high-performance computing.