Oxidation and emittance of superalloys in heat shield applications

Recently developed superalloys that form alumina coatings have a high potential for heat shield applications for advanced aerospace vehicles at temperatures above 1095C. Both INCOLOY alloy MA 956 (of the Inco Alloys International, Inc.), an iron-base oxide-dispersion-strengthened alloy, and CABOT alloy No. 214 (of the Cabot Corporation), an alumina-forming nickel-chromium alloy, have good oxidation resistance and good elevated temperature strength. The oxidation resistance of both alloys has been attributed to the formation of a thin alumina layer (alpha-Al2O3) at the surface. Emittance and oxidation data were obtained for simulated Space Shuttle reentry conditions using a hypersonic arc-heated wind tunnel. The surface oxides and substrate alloys were characterized using X-ray diffraction and scanning and transmission electron microscopy with an energy-dispersive X-ray analysis unit. The mass loss and emittance characteristics of the two alloys are discussed

Macroeconomic effects on emerging-markets sovereign credit spreads

This paper investigates the explanatory and forecasting power of macroeconomic fundamentals on emerging market sovereign credit spreads. We pay special attention to a new set of macroeconomic factors related to market values that reflect investor expectations concerning future economic performance. The model we propose captures a significant part of the empirical variation in spreads. Importantly, it also includes a powerful forecasting component that extends up to 12 months outside the sample period. The forward-looking variables that we construct are significant and complement and enhance the explanatory content of the conventional variables found in the extant literature

Exploiting stochastic dominance to generate abnormal stock returns

We construct zero cost portfolios based on second and third degree stochastic dominance and show that they produce systematic, statistically significant, abnormal returns. These returns are robust with respect to the single index CAPM, the Fama-French 3-factor model, the Carhart 4-factor model and the liquidity 5-factor model. They are also robust with respect to momentum portfolios, transactions costs, varying time periods and when broken down by a range of risk factors, such as firm size, leverage, age, return volatility, cash flow volatility and trading volume

A Global Model of β−\beta^--Decay Half-Lives Using Neural Networks

Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of $\beta^-$-decay halflives of the class of nuclei that decay 100% by $\beta^-$ mode in their ground states. A fully-connected multilayered feed forward network has been trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and cross-validation. The halflife estimates generated by the model are discussed and compared with the available experimental data, with previous results obtained with neural networks, and with estimates coming from traditional global nuclear models. Predictions of the new neural-network model are given for nuclei far from stability, with particular attention to those involved in r-process nucleosynthesis. This study demonstrates that in the framework of the $\beta^-$-decay problem considered here, global models based on ANNs can at least match the predictive performance of the best conventional global models rooted in nuclear theory. Accordingly, such statistical models can provide a valuable tool for further mapping of the nuclidic chart.Comment: Proceedings of the 16th Panhellenic Symposium of the Hellenic Nuclear Physics Societ

Performance statistics of the FORTRAN 4 /H/ library for the IBM system/360

Test procedures and results for accuracy and timing tests of the basic IBM 360/50 FORTRAN 4 /H/ subroutine library are reported. The testing was undertaken to verify performance capability and as a prelude to providing some replacement routines of improved performance

Nuclear mass systematics by complementing the Finite Range Droplet Model with neural networks

A neural-network model is developed to reproduce the differences between experimental nuclear mass-excess values and the theoretical values given by the Finite Range Droplet Model. The results point to the existence of subtle regularities of nuclear structure not yet contained in the best microscopic/phenomenological models of atomic masses. Combining the FRDM and the neural-network model, we create a hybrid model with improved predictive performance on nuclear-mass systematics and related quantities.Comment: Proceedings for the 15th Hellenic Symposium on Nuclear Physic

Colliders and Brane Vector Phenomenology

Brane world oscillations manifest themselves as massive vector gauge fields. Their coupling to the Standard Model is deduced using the method of nonlinear realizations of the spontaneously broken higher dimensional space-time symmetries. Brane vectors are stable and weakly interacting, and therefore escape particle detectors unnoticed. LEP and Tevatron data on the production of a single photon in conjunction with missing energy are used to delineate experimentally excluded regions of brane vector parameter space. The additional region of parameter space accessible to the LHC as well as a future lepton linear collider is also determined by means of this process.Comment: 12 pages, 13 figure