Effect of alumina-formers addition on the isothermal oxidation of TI-AL based intermetallics

The main objective of this study is to investigate the effect of adding the alumina former elements on the isothermal oxidation behavior of Ti-Al based intermetallics. High temperature oxidation test was carried out on Ti-Al based intermetallics namely the Ti-48Al-0.5Ag, Ti-48Al-2Cr-1.5Ag and Ti-48Al-2Cr- 1.5Ag-0.5W oxidized isothermally at 900°C. The kinetic rates of oxidation for the intermetallics were near to parabolic and the addition of Chromium (Cr) increased the kinetic rate of oxidation. Examination on the surfaces of oxide scales by using the Field Emission Scanning Electron Microscopy (FESEM), Atomic Force Microscopy (AFM) and the X-ray Diffraction (XRD), revealed that the phases formed on the scale surfaces were dependent on the composition of the base alloy and the kinetic rates of oxidation. Analysis of the scale cross section found that the adherence of the scale to the base alloy improved by the addition of the alumina former elements. Based on the Energy Dispersive X-ray (EDX) spot and line scan analysis performed on the cross sectional of the scale, all the intermetallics showed an Al-depleted zone and the formation of aluminum oxide in the scale even at the early stage of the scale development. This indicated that the outward diffusion of aluminum to form Al2O3 is promoted by the addition of alumina former elements. Microhardness-indentation results revealed that the hardness values were different across the cross section of the scale. The hardness was the highest in the scale due to the presence of high TiO2 content

Economic Growth Nonlinearities

Nonlinearities in growth have important implications for cross-country income inequality. In particular, they imply that countries may spend long periods of time in a low-growth poverty trap. However, finding evidence of such nonlinearities in the data and accounting for their emergence pose unique challenges to researchers.

Statistical Properties of Convex Clustering

In this manuscript, we study the statistical properties of convex clustering. We establish that convex clustering is closely related to single linkage hierarchical clustering and $k$-means clustering. In addition, we derive the range of tuning parameter for convex clustering that yields a non-trivial solution. We also provide an unbiased estimate of the degrees of freedom, and provide a finite sample bound for the prediction error for convex clustering. We compare convex clustering to some traditional clustering methods in simulation studies.Comment: 20 pages, 5 figure

The Epsilon-Expansion from Conformal Field Theory

Conformal multiplets of $\phi$ and $\phi^3$ recombine at the Wilson-Fisher fixed point, as a consequence of the equations of motion. Using this fact and other constraints from conformal symmetry, we reproduce the lowest nontrivial order results for the anomalous dimensions of operators, without any input from perturbation theory.Comment: 24 pages; v2 - references added, minor changes; v3 - refs and comments added, misprints corrected, version to appear in J.Phys.

Selection Bias Correction and Effect Size Estimation under Dependence

We consider large-scale studies in which it is of interest to test a very large number of hypotheses, and then to estimate the effect sizes corresponding to the rejected hypotheses. For instance, this setting arises in the analysis of gene expression or DNA sequencing data. However, naive estimates of the effect sizes suffer from selection bias, i.e., some of the largest naive estimates are large due to chance alone. Many authors have proposed methods to reduce the effects of selection bias under the assumption that the naive estimates of the effect sizes are independent. Unfortunately, when the effect size estimates are dependent, these existing techniques can have very poor performance, and in practice there will often be dependence. We propose an estimator that adjusts for selection bias under a recently-proposed frequentist framework, without the independence assumption. We study some properties of the proposed estimator, and illustrate that it outperforms past proposals in a simulation study and on two gene expression data sets.Comment: 21 pages, 2 figure

Understanding Preferences For Income Redestribution

Recent research suggests that income redistribution preferences vary across identity groups. We employ a new pattern recognition technology, tree regression analysis, to uncover what these groups are. Using data from the General Social Survey, we present a new stylized fact that preferences for governmental provision of income redistribution vary systematically with race, gender, and class background. We explore the extent to which existing theories of income redistribution can explain our results, but conclude that current approaches do not fully explain the findings.

Understanding Divergent Views on Redistribution Policy in the United States

Particular demographic groups are often associated with distinct points of view across various dimensions of redistribution policy. In this paper, we investigate which demographic groups account for heterogeneity in views on welfare policy and views on appropriate levels of overall redistribution. Using data from the General Social Survey and classification tools, we find evidence that classifications of the population by race, socioeconomic status, and age have some predictive power. However, much heterogeneity in views on redistribution policy persists even within these demographic groupings and remains unexplained. Our results suggest that identity-based explanations for variations in these views have to be interpreted with caution.Data mining, classification and regression trees, random forests, redistribution preferences, welfare, identity