Synthesis, luminescence and EPR studies on CaSiO 3: Pb, Mn-nano phosphors synthesized by the solution combustion method

Nano-ceramic phosphor CaSiO 3 doped with Pb and Mn was synthesized by the low temperature solution combustion method. The materials were characterized by Powder X-Ray Diffraction (XRD), Thermo-gravimetric and Differential Thermal Analysis (TG-DTA), Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The Electron Paramagnetic Resonance (EPR) spectrum of the investigated sample exhibits a broad resonance signal centered at g=1.994. The number of spins participating in resonance (N) and its paramagnetic susceptibility (�) have been evaluated. Photoluminescence of doped CaSiO 3 was investigated when excited by UV radiation of 256 nm. The phosphor exhibits an emission peak at 353 nm in the UV range due to Pb 2+. Further, a broad emission peak in the visible range 550-625 nm can be attributed to 4T 1� 6A 1 transition of Mn 2+ ions. The investigation reveals that doping perovskite nano-ceramics with transition metal ions leads to excellent phosphor materials for potential applications. © 2012 Elsevier Ltd and Techna Group S.r.l.

Structural characterization, EPR and thermoluminescence properties of Cd-1 (-) xNixSiO3 nanocrystalline phosphors

Cd-1 - xNixSiO3 (x = 1-7 mol%) nanophosphors have been prepared for the first time by the combustion method using oxylyldihydrizide as a fuel. Powder X-ray diffraction results confirm the formation of monoclinic phase. Scanning electron micrographs show that Ni2+ influences the porosity of samples. The optical energy gap is widened with increase of Ni2+ ion dopant. The electron paramagnetic resonance spectrum of Ni2+ ions in CdSiO3 exhibits a symmetric absorption at g = 2.343 and the site symmetry around Ni2+ ions is predominantly octahedral. The number of spins participating in resonance (N) and the paramagnetic susceptibility (chi) has been evaluated. The thermoluminescence intensity is found to increase up to similar to 20 min ultra-violet exposure and thereafter, decrease with further increase of ultra-violet dose. The kinetic parameters such as activation energy (E), frequency factor (s)and order of kinetics was estimated using glow peak shape method and the results are discussed. (c) 2012 Elsevier Ltd. All rights reserved.

Coupled polynomial field approach for elimination of flexure and torsion locking phenomena in the Timoshenko and Euler-Bernoulli curved beam elements

The curvature related locking phenomena in the out-of-plane deformation of Timoshenko and Euler-Bernoulli curved beam elements are demonstrated and a novel approach is proposed to circumvent them. Both flexure and Torsion locking phenomena are noticed in Timoshenko beam and torsion locking phenomenon alone in Euler-Bernoulli beam. Two locking-free curved beam finite element models are developed using coupled polynomial displacement field interpolations to eliminate these locking effects. The coupled polynomial interpolation fields are derived independently for Timoshenko and Euler-Bernoulli beam elements using the governing equations. The presented of penalty terms in the couple displacement fields incorporates the flexure-torsion coupling and flexure-shear coupling effects in an approximate manner and produce no spurious constraints in the extreme geometric limits of flexure, torsion and shear stiffness. the proposed couple polynomial finite element models, as special cases, reduce to the conventional Timoshenko beam element and Euler-Bernoulli beam element, respectively. These models are shown to perform consistently over a wide range of flexure-to-shear (EI/GA) and flexure-to-torsion (EI/GJ) stiffness ratios and are inherently devoid of flexure, torsion and shear locking phenomena. The efficacy, accuracy and reliability of the proposed models to straight and curved beam applications are demonstrated through numerical examples. (C) 2012 Elsevier B.V. All rights reserved.

The origins of the evolutionary signal used to predict protein-protein interactions

Background: The correlation of genetic distances between pairs of protein sequence alignments has been used to infer protein-protein interactions. It has been suggested that these correlations are based on the signal of co-evolution between interacting proteins. However, although mutations in different proteins associated with maintaining an interaction clearly occur (particularly in binding interfaces and neighbourhoods), many other factors contribute to correlated rates of sequence evolution. Proteins in the same genome are usually linked by shared evolutionary history and so it would be expected that there would be topological similarities in their phylogenetic trees, whether they are interacting or not. For this reason the underlying species tree is often corrected for. Moreover processes such as expression level, are known to effect evolutionary rates. However, it has been argued that the correlated rates of evolution used to predict protein interaction explicitly includes shared evolutionary history; here we test this hypothesis. Results: In order to identify the evolutionary mechanisms giving rise to the correlations between interaction proteins, we use phylogenetic methods to distinguish similarities in tree topologies from similarities in genetic distances. We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae. We find that the signal of correlated evolution between interacting proteins is predominantly a result of shared evolutionary rates, rather than similarities in tree topology, independent of evolutionary divergence. Conclusions: Since interacting proteins do not have tree topologies that are more similar than the control group of non-interacting proteins, it is likely that coevolution does not contribute much to, if any, of the observed correlations.

Material uncertainty effect on vibration control of smart composite plate using polynomial chaos expansion

Polynomial Chaos Expansion with Latin Hypercube sampling is used to study the effect of material uncertainty on vibration control of a smart composite plate with piezoelectric sensors/actuators. Composite material properties and piezoelectric coefficients are considered as independent and normally distributed random variables. Numerical results show substantial variation in structural dynamic response due to material uncertainty of active vibration control system. This change in response due to material uncertainty can be compensated by actively tuning the feedback control system. Numerical results also show variation in dispersion of dynamic characteristics and control parameters with respect to ply angle and stacking sequence.

Structural, EPR, optical and magnetic properties of alpha-Fe2O3 nanoparticles

alpha-Fe2O3 nanoparticles were synthesized by a low temperature solution combustion method. The structural, magnetic and luminescence properties were studied. Powder X-ray diffraction (PXRD) pattern of alpha-Fe2O3 exhibits pure rhombohedral structure. SEM micrographs reveal the dumbbell shaped particles. The EPR spectrum shows an intense resonance signal at g approximate to 5.61 corresponding to isolated Fe3+ ions situated in axially distorted sites, whereas the g approximate to 2.30 is due to Fe3+ ions coupled by exchange interaction. Raman studies show A(1g) (225 cm(-1)) and E-g (293 and 409 cm(-1)) phonon modes. The absorption at 300 nm results from the ligand to metal charge transfer transitions whereas the 540 nm peak is mainly due to the (6)A(1) + (6)A(1) —> T-4(1)(4G) + T-4(1)(4G) excitation of an Fe3+-Fe3+ pair. A prominent TL glow peak was observed at 140 C at heating rate of 5 degrees C s(-1). The trapping parameters namely activation energy (E), frequency factor (s) and order of kinetics (b) were evaluated and discussed. (C) 2012 Elsevier B.V. All rights reserved.

Synthesis, characterization, EPR, photo and thermoluminescence properties of YAlO3:Ni2+ nanophosphors

YAlO3:Ni2+ (0.1 mol%) doped nanophosphor was synthesised by a low temperature solution combustion method. Powder X-ray diffraction (PXRD) confirms the orthorhombic phase of yttrium aluminate (YAlO3) along with traces of Y3Al5O12. Scanning Electron microscopy (SEM) shows that the powder particles appears to be spherical in shape with large agglomeration. The average crystallite sizes appeared to be in the range 45-90 nm and the same was confirmed by transmission electron microscopy (TEM) and Williamson-Hall (W-H) plots. Electron Paramagnetic Resonance (EPR) and photoluminescence (PL) studies reveal that Ni2+ ions are in octahedral coordination. Thermoluminescence (TL) glow curve consists of two peaks with the main peak at similar to 224 degrees C and a shouldered peak at 285 degrees C was recorded in the range 0.2-15 kGy gamma-irradiated samples. The TL intensity was found to be increasing linearly for 224 degrees C and 285 degrees C peaks up to 1 kGy and thereafter it shows sub-linear (up to 8 kGy) and saturation behavior. The trap parameters namely activation energy (E), order of kinetics (b), frequency factor (s) at different gamma-doses were determined using Chens glow peak shape and Luschiks methods then the results are discussed in detail. Simple glow peak structure, the 224 degrees C peak in YAlO3:Ni2+ nanophosphor can be used in personal dosimetry. (C) 2012 Elsevier B.V. All rights reserved.

A POSSIBLE EVOLUTIONARY SCENARIO OF HIGHLY MAGNETIZED SUPER-CHANDRASEKHAR WHITE DWARFS: PROGENITORS OF PECULIAR TYPE Ia SUPERNOVAE

Several recently discovered peculiar Type Ia supernovae seem to demand an altogether new formation theory that might help explain the puzzling dissimilarities between them and the standard Type Ia supernovae. The most striking aspect of the observational analysis is the necessity of invoking super-Chandrasekhar white dwarfs having masses similar to 2.1-2.8 M-circle dot, M-circle dot being the mass of Sun, as their most probable progenitors. Strongly magnetized white dwarfs having super-Chandrasekhar masses have already been established as potential candidates for the progenitors of peculiar Type Ia supernovae. Owing to the Landau quantization of the underlying electron degenerate gas, theoretical results yielded the observationally inferred mass range. Here, we sketch a possible evolutionary scenario by which super-Chandrasekhar white dwarfs could be formed by accretion on to a commonly observed magnetized white dwarf, invoking the phenomenon of flux freezing. This opens multiple possible evolution scenarios ending in supernova explosions of super-Chandrasekhar white dwarfs having masses within the range stated above. We point out that our proposal has observational support, such as the recent discovery of a large number of magnetized white dwarfs by the Sloan Digital Sky Survey.

Synthesis, characterization, EPR and thermoluminescence properties of CaTiO3 nanophosphor

Calcium titanate (CaTiO3) nanophosphors were synthesized by three different routes namely solution combustion (SC), modified solid-state reaction (MSS) and solid-state (SS) methods. Rietveld refinement studies revealed the presence of an orthorhombic structure with traces of CaCO3. The crystallite sizes were found to be in the 43-45 nm range. TEM studies also confirm the nano size with well crystalline nature. EPR spectrum for SS method exhibits a broad resonance signal at g = 2.027 is attributed to TiO6](9-) center, whereas in MSS sample the resonance signals are attributed to surface electron and hole trapping sites. The TL behavior has been investigated for the first time using gamma-irradiation. TL glow peak at 169 degrees C were recorded in CaTiO3 prepared by SC, MSS and SS methods. The trapping parameters such as activation energy (E) and order of kinetics (b) were estimated using peak shape method and results are discussed in detail. (C) 2013 Elsevier Ltd. All rights reserved.

Polynomial time and parameterized approximation algorithms for boxicity

The boxicity (cubicity) of a graph G, denoted by box(G) (respectively cub(G)), is the minimum integer k such that G can be represented as the intersection graph of axis parallel boxes (cubes) in ℝ k . The problem of computing boxicity (cubicity) is known to be inapproximable in polynomial time even for graph classes like bipartite, co-bipartite and split graphs, within an O(n 0.5 − ε ) factor for any ε > 0, unless NP = ZPP. We prove that if a graph G on n vertices has a clique on n − k vertices, then box(G) can be computed in time n22O(k2logk) . Using this fact, various FPT approximation algorithms for boxicity are derived. The parameter used is the vertex (or edge) edit distance of the input graph from certain graph families of bounded boxicity - like interval graphs and planar graphs. Using the same fact, we also derive an O(nloglogn√logn√) factor approximation algorithm for computing boxicity, which, to our knowledge, is the first o(n) factor approximation algorithm for the problem. We also present an FPT approximation algorithm for computing the cubicity of graphs, with vertex cover number as the parameter.

Probabilistic least squares approach to ordinal regression

This paper proposes a novel approach to solve the ordinal regression problem using Gaussian processes. The proposed approach, probabilistic least squares ordinal regression (PLSOR), obtains the probability distribution over ordinal labels using a particular likelihood function. It performs model selection (hyperparameter optimization) using the leave-one-out cross-validation (LOO-CV) technique. PLSOR has conceptual simplicity and ease of implementation of least squares approach. Unlike the existing Gaussian process ordinal regression (GPOR) approaches, PLSOR does not use any approximation techniques for inference. We compare the proposed approach with the state-of-the-art GPOR approaches on some synthetic and benchmark data sets. Experimental results show the competitiveness of the proposed approach.

Validation based sparse Gaussian processes for ordinal regression

This paper proposes a sparse modeling approach to solve ordinal regression problems using Gaussian processes (GP). Designing a sparse GP model is important from training time and inference time viewpoints. We first propose a variant of the Gaussian process ordinal regression (GPOR) approach, leave-one-out GPOR (LOO-GPOR). It performs model selection using the leave-one-out cross-validation (LOO-CV) technique. We then provide an approach to design a sparse model for GPOR. The sparse GPOR model reduces computational time and storage requirements. Further, it provides faster inference. We compare the proposed approaches with the state-of-the-art GPOR approach on some benchmark data sets. Experimental results show that the proposed approaches are competitive.

Sustaining cooperation on networks: an analytical study based on evolutionary game theory

We analytically study the role played by the network topology in sustaining cooperation in a society of myopic agents in an evolutionary setting. In our model, each agent plays the Prisoner's Dilemma (PD) game with its neighbors, as specified by a network. Cooperation is the incumbent strategy, whereas defectors are the mutants. Starting with a population of cooperators, some agents are switched to defection. The agents then play the PD game with their neighbors and compute their fitness. After this, an evolutionary rule, or imitation dynamic is used to update the agent strategy. A defector switches back to cooperation if it has a cooperator neighbor with higher fitness. The network is said to sustain cooperation if almost all defectors switch to cooperation. Earlier work on the sustenance of cooperation has largely consisted of simulation studies, and we seek to complement this body of work by providing analytical insight for the same. We find that in order to sustain cooperation, a network should satisfy some properties such as small average diameter, densification, and irregularity. Real-world networks have been empirically shown to exhibit these properties, and are thus candidates for the sustenance of cooperation. We also analyze some specific graphs to determine whether or not they sustain cooperation. In particular, we find that scale-free graphs belonging to a certain family sustain cooperation, whereas Erdos-Renyi random graphs do not. To the best of our knowledge, ours is the first analytical attempt to determine which networks sustain cooperation in a population of myopic agents in an evolutionary setting.

Large-MIMO Receiver based on Linear Regression of MMSE Residual

Multiple input multiple output (MIMO) systems with large number of antennas have been gaining wide attention as they enable very high throughputs. A major impediment is the complexity at the receiver needed to detect the transmitted data. To this end we propose a new receiver, called LRR (Linear Regression of MMSE Residual), which improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel), to find the linear regression parameters. The proposed receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs quite well: at a bit error rate (BER) of 10(-3), the SNR gain over MMSE receiver is about 7 dB for a 16 x 16 system; for a 64 x 64 system the gain is about 8.5 dB. For large coherence time, the complexity order of the LRR receiver is the same as that of the MMSE receiver, and in simulations we find that it needs about 4 times as many floating point operations. We also show that further gain of about 4 dB is obtained by local search around the estimate given by the LRR receiver.

Spatially Adaptive Kernel Regression Using Risk Estimation

An important question in kernel regression is one of estimating the order and bandwidth parameters from available noisy data. We propose to solve the problem within a risk estimation framework. Considering an independent and identically distributed (i.i.d.) Gaussian observations model, we use Stein's unbiased risk estimator (SURE) to estimate a weighted mean-square error (MSE) risk, and optimize it with respect to the order and bandwidth parameters. The two parameters are thus spatially adapted in such a manner that noise smoothing and fine structure preservation are simultaneously achieved. On the application side, we consider the problem of image restoration from uniform/non-uniform data, and show that the SURE approach to spatially adaptive kernel regression results in better quality estimation compared with its spatially non-adaptive counterparts. The denoising results obtained are comparable to those obtained using other state-of-the-art techniques, and in some scenarios, superior.