Thermodynamics of Al(1-x)Ga(x)N solid solution: Inclination for phase separation and ordering

Although Al(1-x)Ga(x)N semiconductors are used in lighting, displays and high-power amplifiers, there is no experimental thermodynamic information on nitride solid solutions. Thermodynamic data are useful for assessing the intrinsic stability of the solid solution with respect to phase separation and extrinsic stability in relation to other phases such as metallic contacts. The activity of GaN in Al(1-x)Ga(x)N solid solution is determined at 1100 K using a solid-state electrochemical cell: Ga + Al(1-x)Ga(x)N/Fe, Ca(3)N(2)//CaF(2)//Ca(3)N(2), N(2) (0.1 MPa), Fe. The solid-state cell is based on single crystal CaF(2) as the electrolyte and Ca(3)N(2) as the auxiliary electrode to convert the nitrogen chemical potential established by the equilibrium between Ga and Al(1-x)Ga(x)N solid solution into an equivalent fluorine potential. Excess Gibbs free energy of mixing of the solid solution is computed from the results. Results suggest an unusual mixing behavior: a mild tendency for ordering at three discrete compositions (x = 0.25, 0.5 and 0.75) superimposed on predominantly positive deviation from ideality. The lattice parameters exhibit slight deviation from Vegard's law, with the a-parameter showing positive and the c-parameter negative deviation. Although the solid solution is stable in the full range of compositions at growth temperatures, thermodynamic instability is indicated at temperatures below 410 K in the composition range 0.26 <= x <= 0.5. At 355 K, two biphasic regions appear, with terminal solid solutions stable only for 0 <= x <= 0.26 and 0.66 <= x <= 1. The range of terminal solid solubility reduces with decreasing temperature. (C) 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Incremental Cycle Detection, Topological Ordering, and Strong Component Maintenance

We present two online algorithms for maintaining a topological order of a directed n-vertex acyclic graph as arcs are added, and detecting a cycle when one is created. Our first algorithm handles m arc additions in O(m(3/2)) time. For sparse graphs (m/n = O(1)), this bound improves the best previous bound by a logarithmic factor, and is tight to within a constant factor among algorithms satisfying a natural locality property. Our second algorithm handles an arbitrary sequence of arc additions in O(n(5/2)) time. For sufficiently dense graphs, this bound improves the best previous bound by a polynomial factor. Our bound may be far from tight: we show that the algorithm can take Omega(n(2)2 root(2lgn)) time by relating its performance to a generalization of the k-levels problem of combinatorial geometry. A completely different algorithm running in Theta (n(2) log n) time was given recently by Bender, Fineman, and Gilbert. We extend both of our algorithms to the maintenance of strong components, without affecting the asymptotic time bounds.

Climate change induced risk in water quality control problems

A modeling framework is presented in this paper, integrating hydrologic scenarios projected from a General Circulation Model (GCM) with a water quality simulation model to quantify the future expected risk. Statistical downscaling with a Canonical Correlation Analysis (CCA) is carried out to develop the future scenarios of hydro-climate variables starting with simulations provided by a GCM. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold quality level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) presented in an earlier study is then used to develop adaptive policies to address the projected water quality risks. Application of the proposed methodology is demonstrated with the case study of Tunga-Bhadra river in India. The results showed that the projected changes in the hydro-climate variables tend to diminish DO levels, thus increasing the future risk levels of LWQ. (C) 2012 Elsevier B.V. All rights reserved.

Zero-Sum Risk-Sensitive Stochastic Differential Games

We study zero-sum risk-sensitive stochastic differential games on the infinite horizon with discounted and ergodic payoff criteria. Under certain assumptions, we establish the existence of values and saddle-point equilibria. We obtain our results by studying the corresponding Hamilton-Jacobi-Isaacs equations. Finally, we show that the value of the ergodic payoff criterion is a constant multiple of the maximal eigenvalue of the generators of the associated nonlinear semigroups.

A risk-estimation-based formulation for speech enhancement and its relation to Wiener filtering

The goal of speech enhancement algorithms is to provide an estimate of clean speech starting from noisy observations. The often-employed cost function is the mean square error (MSE). However, the MSE can never be computed in practice. Therefore, it becomes necessary to find practical alternatives to the MSE. In image denoising problems, the cost function (also referred to as risk) is often replaced by an unbiased estimator. Motivated by this approach, we reformulate the problem of speech enhancement from the perspective of risk minimization. Some recent contributions in risk estimation have employed Stein's unbiased risk estimator (SURE) together with a parametric denoising function, which is a linear expansion of threshold/bases (LET). We show that the first-order case of SURE-LET results in a Wiener-filter type solution if the denoising function is made frequency-dependent. We also provide enhancement results obtained with both techniques and characterize the improvement by means of local as well as global SNR calculations.

Optimal parameter selection for bilateral filters using Poisson unbiased risk estimate

Bilateral filters perform edge-preserving smoothing and are widely used for image denoising. The denoising performance is sensitive to the choice of the bilateral filter parameters. We propose an optimal parameter selection for bilateral filtering of images corrupted with Poisson noise. We employ the Poisson's Unbiased Risk Estimate (PURE), which is an unbiased estimate of the Mean Squared Error (MSE). It does not require a priori knowledge of the ground truth and is useful in practical scenarios where there is no access to the original image. Experimental results show that quality of denoising obtained with PURE-optimal bilateral filters is almost indistinguishable with that of the Oracle-MSE-optimal bilateral filters.

Noise tolerance under risk minimization

In this paper, we explore noise-tolerant learning of classifiers. We formulate the problem as follows. We assume that there is an unobservable training set that is noise free. The actual training set given to the learning algorithm is obtained from this ideal data set by corrupting the class label of each example. The probability that the class label of an example is corrupted is a function of the feature vector of the example. This would account for most kinds of noisy data one encounters in practice. We say that a learning method is noise tolerant if the classifiers learnt with noise-free data and with noisy data, both have the same classification accuracy on the noise-free data. In this paper, we analyze the noise-tolerance properties of risk minimization (under different loss functions). We show that risk minimization under 0-1 loss function has impressive noise-tolerance properties and that under squared error loss is tolerant only to uniform noise; risk minimization under other loss functions is not noise tolerant. We conclude this paper with some discussion on the implications of these theoretical results.

Fourteen gene GBM prognostic signature identifies association of immune response pathway and mesenchymal subtype with high risk group

Background: Recent research on glioblastoma (GBM) has focused on deducing gene signatures predicting prognosis. The present study evaluated the mRNA expression of selected genes and correlated with outcome to arrive at a prognostic gene signature. Methods: Patients with GBM (n = 123) were prospectively recruited, treated with a uniform protocol and followed up. Expression of 175 genes in GBM tissue was determined using qRT-PCR. A supervised principal component analysis followed by derivation of gene signature was performed. Independent validation of the signature was done using TCGA data. Gene Ontology and KEGG pathway analysis was carried out among patients from TCGA cohort. Results: A 14 gene signature was identified that predicted outcome in GBM. A weighted gene (WG) score was found to be an independent predictor of survival in multivariate analysis in the present cohort (HR = 2.507; B = 0.919; p < 0.001) and in TCGA cohort. Risk stratification by standardized WG score classified patients into low and high risk predicting survival both in our cohort (p = <0.001) and TCGA cohort (p = 0.001). Pathway analysis using the most differentially regulated genes (n = 76) between the low and high risk groups revealed association of activated inflammatory/immune response pathways and mesenchymal subtype in the high risk group. Conclusion: We have identified a 14 gene expression signature that can predict survival in GBM patients. A network analysis revealed activation of inflammatory response pathway specifically in high risk group. These findings may have implications in understanding of gliomagenesis, development of targeted therapies and selection of high risk cancer patients for alternate adjuvant therapies.

Supplier selection based on supply chain ecosystem, performance and risk criteria

A supply chain ecosystem consists of the elements of the supply chain and the entities that influence the goods, information and financial flows through the supply chain. These influences come through government regulations, human, financial and natural resources, logistics infrastructure and management, etc., and thus affect the supply chain performance. Similarly, all the ecosystem elements also contribute to the risk. The aim of this paper is to identify both performances-based and risk-based decision criteria, which are important and critical to the supply chain. A two step approach using fuzzy AHP and fuzzy technique for order of preference by similarity to ideal solution has been proposed for multi-criteria decision-making and illustrated using a numerical example. The first step does the selection without considering risks and then in the next step suppliers are ranked according to their risk profiles. Later, the two ranks are consolidated into one. In subsequent section, the method is also extended for multi-tier supplier selection. In short, we are presenting a method for the design of a resilient supply chain, in this paper.

Zero-sum risk-sensitive stochastic games on a countable state space

Infinite horizon discounted-cost and ergodic-cost risk-sensitive zero-sum stochastic games for controlled Markov chains with countably many states are analyzed. Upper and lower values for these games are established. The existence of value and saddle-point equilibria in the class of Markov strategies is proved for the discounted-cost game. The existence of value and saddle-point equilibria in the class of stationary strategies is proved under the uniform ergodicity condition for the ergodic-cost game. The value of the ergodic-cost game happens to be the product of the inverse of the risk-sensitivity factor and the logarithm of the common Perron-Frobenius eigenvalue of the associated controlled nonlinear kernels. (C) 2013 Elsevier B.V. All rights reserved.

Temperature dependent magnetic ordering and electrical transport behavior of nano zinc ferrite from 20 to 800 K

The nano ZnFe2O4 compound was prepared by eco-friendly hydrothermal method. The characterization of the sample for its structure, morphology and composition were done by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), dynamic light scattering, Fourier transform infrared spectroscopy, zeta surface profiler and UV-Visible spectroscopy studies. The PXRD measurement reveals that the compound shows spinel cubic phase belong Fd (3) over barm (227) space group. Morphology of the compound from SEM and surface profile shows nearly spherical agglomerated particles with well defined grains and grain boundaries. The material shows the semiconducting behavior with E-g of 2.3 eV at room temperature (RT). The variation in the magnetic ordering was observed for wide range of temperature. The compound behaves like a soft magnetic material with ferrimagnetic at various temperatures except at RT. Both magnetic and EPR studies supports the superparamagnetic behavior of the the sample. The DC conductivity, dielectric and AC conductivity behavior of the 1000 degrees C pellets sintered for 2 h shows good frequency dependent transport properties. The present study facilitate in selecting the suitable materials for the nanoelectronics and spintronic applications. (C) 2013 Elsevier B.V. All rights reserved.

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.

Beyond the ``Coffee Ring'': Re-entrant Ordering in an Evaporation-Driven Self-Assembly in a Colloidal Suspension on a Substrate

We study the phenomenon of evaporation-driven self-assembly of a colloid suspension of silica microspheres in the interior region and away from the rim of the droplet on a glass plate. In view of the importance of achieving a large-area, monolayer assembly, we first realize a suitable choice of experimental conditions, minimizing the influence of many other competing phenomena that usually complicate the understanding of fundamental concepts of such self-assembly processes in the interior region of a drying droplet. Under these simplifying conditions to bring out essential aspects, our experiments unveil an interesting competition between ordering and compaction in such drying systems in analogy to an impending glass transition. We establish a re-entrant behavior in the order disorder phase diagram as a function of the particle density, such that there is an optimal range of the particle density to realize the long-range ordering. The results are explained with the help of simulations and phenomenological theory.