Relationship between cooling rate and microsegregation in bottom-chilled directionally solidified ductile irons

This study explores the relationship between cooling rate and microsegregation of directionally solidified ductile iron. The unidirectional heat transfer system used in this research is made up of a copper mold kept chilled by circulating water and embedded in the bottom of Furan sand mold. Thermocouples are connected to the computer measuring system to record the cooling curves of the castings at a distance of 0, 30, 60 and 90 mm from the chilled copper mold surface. Alloys including Mn, Cr, Cu, Ni and Ti were added to the specimens. Electron microprobe analysis (EPMA) was employed to examine distribution of elements between the dendrite arms and nodular graphite. Results show that unidirectional heat transfer affects directly the solidification mode and microstructure of the casting. The cooling curves reveal that local solidification time increases with increasing distance from the chilled copper mold surface. Different solidification rates with corresponding microstructure and element segregation were observed in the same unidirectionally solidified casting. Local solidification time was closely related to element segregation. The effective segregation coefficient (Keff) calculated using the Scheil equation was found to vary, according to the stage of solidification. The actual segregation characteristics of complex alloys generally follow the Scheil equation

Search for TeV Scale Physics in Heavy Flavour Decays

The subject of heavy flavour decays as probes for physics beyond the TeV scale is covered from the experimental perspective. Emphasis is placed on the more traditional Beyond the Standard Model topics that have potential for impact in the short term, with the physics explained. We do unabashedly promote our own phemonenology work.Comment: 10 pages, 9 figures (now fixed); Submitted for the SUSY07 proceeding

Ionization degree of the electron-hole plasma in semiconductor quantum wells

The degree of ionization of a nondegenerate two-dimensional electron-hole plasma is calculated using the modified law of mass action, which takes into account all bound and unbound states in a screened Coulomb potential. Application of the variable phase method to this potential allows us to treat scattering and bound states on the same footing. Inclusion of the scattering states leads to a strong deviation from the standard law of mass action. A qualitative difference between mid- and wide-gap semiconductors is demonstrated. For wide-gap semiconductors at room temperature, when the bare exciton binding energy is of the order of T, the equilibrium consists of an almost equal mixture of correlated electron-hole pairs and uncorrelated free carriers.Comment: 22 pages, 6 figure

Identifying topological edge states in 2D optical lattices using light scattering

We recently proposed in a Letter [Physical Review Letters 108 255303] a novel scheme to detect topological edge states in an optical lattice, based on a generalization of Bragg spectroscopy. The scope of the present article is to provide a more detailed and pedagogical description of the system - the Hofstadter optical lattice - and probing method. We first show the existence of topological edge states, in an ultra-cold gas trapped in a 2D optical lattice and subjected to a synthetic magnetic field. The remarkable robustness of the edge states is verified for a variety of external confining potentials. Then, we describe a specific laser probe, made from two lasers in Laguerre-Gaussian modes, which captures unambiguous signatures of these edge states. In particular, the resulting Bragg spectra provide the dispersion relation of the edge states, establishing their chiral nature. In order to make the Bragg signal experimentally detectable, we introduce a "shelving method", which simultaneously transfers angular momentum and changes the internal atomic state. This scheme allows to directly visualize the selected edge states on a dark background, offering an instructive view on topological insulating phases, not accessible in solid-state experiments.Comment: 17 pages, 10 figures. Revised and extended version, to appear in EJP Special Topic for the special issue on "Novel Quantum Phases and Mesoscopic Physics in Quantum Gases". Extended version of arXiv:1203.124

Bayesian Semi-parametric Expected Shortfall Forecasting in Financial Markets

Bayesian semi-parametric estimation has proven effective for quantile estimation in general and specifically in financial Value at Risk forecasting. Expected short-fall is a competing tail risk measure, involving a conditional expectation beyond a quantile, that has recently been semi-parametrically estimated via asymmetric least squares and so-called expectiles. An asymmetric Gaussian density is proposed allowing a likelihood to be developed that leads to Bayesian semi-parametric estimation and forecasts of expectiles and expected shortfall. Further, the conditional autoregressive expectile class of model is generalised to two fully nonlinear families. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation in these families. The proposed models are clearly favoured in an empirical study forecasting eleven financial return series: clear evidence of more accurate expected shortfall forecasting, compared to a range of competing methods is found. Further, the most favoured models are those estimated by Bayesian methods

Bayesian Assessment of Dynamic Quantile Forecasts

Methods for Bayesian testing and assessment of dynamic quantile forecasts are proposed. Specifically, Bayes factor analogues of popular frequentist tests for independence of violations from, and for correct coverage of a time series of, quantile forecasts are developed. To evaluate the relevant marginal likelihoods involved, analytic integration methods are utilised when possible, otherwise multivariate adaptive quadrature methods are employed to estimate the required quantities. The usual Bayesian interval estimate for a proportion is also examined in this context. The size and power properties of the proposed methods are examined via a simulation study, illustrating favourable comparisons both overall and with their frequentist counterparts. An empirical study employs the proposed methods, in comparison with standard tests, to assess the adequacy of a range of forecasting models for Value at Risk (VaR) in several financial market data series

Chorea as a First Manifestation in Young Patients with Systemic Lupus Erythematosus Who Was Initially Diagnosed With Rheumatic Fever

Chorea is a rare manifestation of systemic lupus erythematosus (SLE). We report on a young patient with chorea who was diagnosed initially with rheumatic fever. Follow up and further evaluation confirmed the diagnosis of SLE and anti-phospholipid syndrome. Of special interest were the negative antiphospholipid (aPL) antibodies and the initial diagnosis of rheumatic fever which is still not uncommon problem in our region. The rarity of such presentation with joint and non specific increase of antistreptolysin O (ASO) titer might be the factors that led to an incorrect diagnosis. Early diagnosis and treatment of SLE and anti-phospholipid syndrome are very crucial and should be considered with such presentation

Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis

Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student-t, skewed-t and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia-Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models out-performed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre-crisis; while at the 1% level during and post-crisis, for a 1 day horizon, models with skewed-t errors ranked best, while IGARCH models were favoured at the 5% level; (iii) all models forecasted VaR less accurately and anti-conservatively post-crisi

Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis

Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student-t, skewed-t and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia-Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models out-performed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre-crisis; while at the 1% level during and post-crisis, for a 1 day horizon, models with skewed-t errors ranked best, while IGARCH models were favoured at the 5% level; (iii) all models forecasted VaR less accurately and anti-conservatively post-crisi

Search for direct production of charginos and neutralinos in events with three leptons and missing transverse momentum in √s = 7 TeV pp collisions with the ATLAS detector

A search for the direct production of charginos and neutralinos in final states with three electrons or muons and missing transverse momentum is presented. The analysis is based on 4.7 fb−1 of proton–proton collision data delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in three signal regions that are either depleted or enriched in Z-boson decays. Upper limits at 95% confidence level are set in R-parity conserving phenomenological minimal supersymmetric models and in simplified models, significantly extending previous results