Numerical and physical simulation of rapid microstructural evolution of gas atomised Ni superalloy powders

The rapid microstructural evolution of gas atomised Ni superalloy powder compacts over timescales of a few seconds was studied using a Gleeble 3500 thermomechanical simulator, finite element based numerical model and electron microscopy. The study found that the microstructural changes were governed by the characteristic temperatures of the alloy. At a temperature below the γ' solvus, the powders maintained dendritic structures. Above the γ' solvus temperature but in the solid-state, rapid grain spheroidisation and coarsening occurred, although the fine-scale microstructures were largely retained. Once the incipient melting temperature of the alloy was exceeded, microstructural change was rapid, and when the temperature was increased into the solid + liquid state, the powder compact partially melted and then re-solidified with no trace of the original structures, despite the fast timescales. The study reveals the relationship between short, severe thermal excursions and microstructural evolution in powder processed components, and gives guidance on the upper limit of temperature and time for powder-based processes if desirable fine-scale features of powders are to be preserved

Sound-Induced Flash Illusion is Resistant to Feedback Training

A single flash accompanied by two auditory beeps tends to be perceived as two flashes (Shams et al. Nature 408:788, 2000, Cogn Brain Res 14:147–152, 2002). This phenomenon is known as ‘sound-induced flash illusion.’ Previous neuroimaging studies have shown that this illusion is correlated with modulation of activity in early visual cortical areas (Arden et al. Vision Res 43(23):2469–2478, 2003; Bhattacharya et al. NeuroReport 13:1727–1730, 2002; Shams et al. NeuroReport 12(17):3849–3852, 2001, Neurosci Lett 378(2):76–81, 2005; Watkins et al. Neuroimage 31:1247–1256, 2006, Neuroimage 37:572–578, 2007; Mishra et al. J Neurosci 27(15):4120–4131, 2007). We examined how robust the illusion is by testing whether the frequency of the illusion can be reduced by providing feedback. We found that the sound-induced flash illusion was resistant to feedback training, except when the amount of monetary reward was made dependent on accuracy in performance. However, even in the latter case the participants reported that they still perceived illusory two flashes even though they correctly reported single flash. Moreover, the feedback training effect seemed to disappear once the participants were no longer provided with feedback suggesting a short-lived refinement of discrimination between illusory and physical double flashes rather than vanishing of the illusory percept. These findings indicate that the effect of sound on the perceptual representation of visual stimuli is strong and robust to feedback training, and provide further evidence against decision factors accounting for the sound-induced flash illusion

Effect of nickel on the microstructure and mechanical property of die-cast Al–Mg–Si–Mn alloy

The effect of nickel on the microstructure and mechanical properties of a die-cast Al–Mg–Si–Mn alloy has been investigated. The results show that the presence of Ni in the alloy promotes the formation of Ni-rich intermetallics. These occur consistently during solidification in the die-cast Al–Mg–Si–Mn alloy across different levels of Ni content. The Ni-rich intermetallics exhibit dendritic morphology during the primary solidification and lamellar morphology during the eutectic solidification stage. Ni was found to be always associated with iron forming AlFeMnSiNi intermetallics, and no Al3Ni intermetallic was observed when Ni concentrations were up to 2.06 wt% in the alloy. Although with different morphologies, the Ni-rich intermetallics were identified as the same AlFeMnSiNi phase bearing a typical composition of Al[100–140](Fe,Mn)[2–7]SiNi[4–9]. With increasing Ni content, the spacing of the α-Al–Mg2Si eutectic phase was enlarged in the Al–Mg–Si–Mn alloy. The addition of Ni to the alloy resulted in a slight increase in the yield strength, but a significant decrease in the elongation. The ultimate tensile strength (UTS) increased slightly from 300 to 320 MPa when a small amount (e.g. 0.16 wt%) of Ni was added to the alloy, but further increase of the Ni content resulted in a decrease of the UTS.The Engineering and Physical Sciences Research Council (EPSRC), Technology Strategy Board (TSB) and Jaguar Land Rover (JLR) in the United Kingdom

Azimuthal distributions of radial momentum and velocity in relativistic heavy ion collisions

Azimuthal distributions of radial (transverse) momentum, mean radial momentum, and mean radial velocity of final state particles are suggested for relativistic heavy ion collisions. Using transport model AMPT with string melting, these distributions for Au + Au collisions at 200 GeV are presented and studied. It is demonstrated that the distribution of total radial momentum is more sensitive to the anisotropic expansion, as the anisotropies of final state particles and their associated transverse momentums are both counted in the measure. The mean radial velocity distribution is compared with the radial {\deg}ow velocity. The thermal motion contributes an isotropic constant to mean radial velocity

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/