Updating predictive accident models of modern rural single carriageway A-roads

Reliable predictive accident models (PAMs) are essential to design and maintain safe road networks and yet the models most commonly used in the UK were derived using data collected 20 to 30 years ago. Given that the national personal injury accident total fell by some 30% in the last 25 years, while road traffic increased by over 60%, significant errors in scheme appraisal and evaluation based on the models currently in use seem inevitable. In this paper the temporal transferability of PAMs for modern rural single carriageway A-roads is investigated and their predictive performance is evaluated against a recent data set. Despite the age of these models, the PAMs for predicting the total accidents provide a remarkably good fit to recent data and these are more accurate than models where accidents are disaggregated by type. The performance of the models can be improved by calibrating them against recent data

KMOS LENsing Survey (KLENS) : morpho-kinematic analysis of star-forming galaxies at z∼2z \sim 2

We present results from the KMOS lensing survey-KLENS which is exploiting gravitational lensing to study the kinematics of 24 star forming galaxies at $1.4<z<3.5$ with a median mass of $\rm log(M_\star/M_\odot)=9.6$ and median star formation rate (SFR) of $\rm 7.5\,M_\odot\,yr^{-1}$. We find that 25% of these low-mass/low-SFR galaxies are rotation dominated, while the majority of our sample shows no velocity gradient. When combining our data with other surveys, we find that the fraction of rotation dominated galaxies increases with the stellar mass, and decreases for galaxies with a positive offset from the main sequence. We also investigate the evolution of the intrinsic velocity dispersion, $\sigma_0$, as a function of the redshift, $z$, and stellar mass, $\rm M_\star$, assuming galaxies in quasi-equilibrium (Toomre Q parameter equal to 1). From the $z-\sigma_0$ relation, we find that the redshift evolution of the velocity dispersion is mostly expected for massive galaxies ($\rm log(M_\star/M_\odot)>10$). We derive a $\rm M_\star-\sigma_0$ relation, using the Tully-Fisher relation, which highlights that a different evolution of the velocity dispersion is expected depending on the stellar mass, with lower velocity dispersions for lower masses, and an increase for higher masses, stronger at higher redshift. The observed velocity dispersions from this work and from comparison samples spanning $0<z<3.5$ appear to follow this relation, except at higher redshift ($z>2$), where we observe higher velocity dispersions for low masses ($\rm log(M_\star/M_\odot)\sim 9.6$) and lower velocity dispersions for high masses ($\rm log(M_\star/M_\odot)\sim 10.9$) than expected. This discrepancy could, for instance, suggest that galaxies at high-$z$ do not satisfy the stability criterion, or that the adopted parametrisation of the specific star formation rate and molecular properties fail at high redshift.Comment: Accepted for publication in A&A, 21 pages, 10 figure

Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are probabilistic and non-parametric and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. The focus of this paper is scalable approximate Bayesian learning of these networks. The paper develops a novel and efficient extension of probabilistic backpropagation, a state-of-the-art method for training Bayesian neural networks, that can be used to train DGPs. The new method leverages a recently proposed method for scaling Expectation Propagation, called stochastic Expectation Propagation. The method is able to automatically discover useful input warping, expansion or compression, and it is therefore is a flexible form of Bayesian kernel design. We demonstrate the success of the new method for supervised learning on several real-world datasets, showing that it typically outperforms GP regression and is never much worse

Assessment of Computational Fluid Dynamics (CFD) Models for Shock Boundary-Layer Interaction

A workshop on the computational fluid dynamics (CFD) prediction of shock boundary-layer interactions (SBLIs) was held at the 48th AIAA Aerospace Sciences Meeting. As part of the workshop numerous CFD analysts submitted solutions to four experimentally measured SBLIs. This paper describes the assessment of the CFD predictions. The assessment includes an uncertainty analysis of the experimental data, the definition of an error metric and the application of that metric to the CFD solutions. The CFD solutions provided very similar levels of error and in general it was difficult to discern clear trends in the data. For the Reynolds Averaged Navier-Stokes methods the choice of turbulence model appeared to be the largest factor in solution accuracy. Large-eddy simulation methods produced error levels similar to RANS methods but provided superior predictions of normal stresses

Jet Engine Exhaust Nozzle Flow Effector

A jet engine exhaust nozzle flow effector is a chevron formed with a radius of curvature with surfaces of the flow effector being defined and opposing one another. At least one shape memory alloy (SMA) member is embedded in the chevron closer to one of the chevron's opposing surfaces and substantially spanning from at least a portion of the chevron's root to the chevron's tip

Enabling quantitative data analysis through e-infrastructures

This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can benefit from e-infrastructure support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences

The X-ray Reflectors in the Nucleus of the Seyfert Galaxy NGC 1068

(abridged) Based on observations of the Seyfert nucleus in NGC1068 with ASCA, RXTE and BeppoSAX, we report the discovery of a flare (increase in flux by a factor of ~1.6) in the 6.7 keV Fe K line component between observations obtained 4 months apart, with no significant change in the other (6.21, 6.4, and 6.97 keV) Fe K_alpha line components. During this time, the continuum flux decreased by ~20%. The RXTE spectrum requires an Fe K absorption edge near 8.6 keV (Fe XXIII - XXV). The spectral data indicate that the 2-10 keV continuum emission is dominated (~2/3 of the luminosity) by reflection from a previously unidentified region of warm, ionized gas located <~ 0.2 pc from the AGN. The remaining ~1/3 of the observed X-ray emission is reflected from optically thick, neutral gas. The inferred properties of the warm reflector (WR) are: size (diameter) ~ 10^{5.5} /cm3, ionization parameter xi approx 10^{3.5} erg cm/s, and covering fraction 0.003 (L_0/10^{43.5} erg/s)^{-1} < (Omega/4 pi) < 0.024 (L_0/10^{43.5})^{-1}, where L_0 is the intrinsic 2-10 keV X-ray luminosity of the AGN. We suggest that the WR gas is the source of the (variable) 6.7 keV Fe line emission, and the 6.97 keV Fe line emission. The 6.7 keV line flare is assumed to be due to an increase in the emissivity of the WR gas from a decrease (by 20-30%) in L_0. The properties of the WR are most consistent with an intrinsically X-ray weak AGN with L_0 approx 10^{43.0} erg/s. The optical and UV emission that scatters from the WR into our line of sight is required to suffer strong extinction, which can be reconciled if the line-of-sight skims the outer surface of the torus. Thermal bremsstrahlung radio emission from the WR may be detectable in VLBA radio maps of the NGC 1068 nucleus.Comment: 39 pages (9 postscript figures) AASTEX, ApJ, accepte

CBR Anisotropy from Primordial Gravitational Waves in Two-Component Inflationary Cosmology

We examine stochastic temperature fluctuations of the cosmic background radiation (CBR) arising via the Sachs-Wolfe effect from gravitational wave perturbations produced in the early universe. We consider spatially flat, perturbed FRW models that begin with an inflationary phase, followed by a mixed phase containing both radiation and dust. The scale factor during the mixed phase takes the form $a(\eta)=c_1\eta^2+c_2\eta+c_3$, where $c_i$ are constants. During the mixed phase the universe smoothly transforms from being radiation to dust dominated. We find analytic expressions for the graviton mode function during the mixed phase in terms of spheroidal wave functions. This mode function is used to find an analytic expression for the multipole moments $\langle a_l^2\rangle$ of the two-point angular correlation function $C(\gamma)$ for the CBR anisotropy. The analytic expression for the multipole moments is written in terms of two integrals, which are evaluated numerically. The results are compared to multipoles calculated for models that are {\it completely} dust dominated at last-scattering. We find that the multipoles $\langle a_l^2\rangle$ of the CBR temperature perturbations for $l>10$ are significantly larger for a universe that contains both radiation and dust at last-scattering. We compare our results with recent, similar numerical work and find good agreement. The spheroidal wave functions may have applications to other problems of cosmological interest.Comment: 28 pgs + 6 postscript figures, RevTe