Lower atmospheric temperature profile measurements using a Raman lidar

A Raman lidar system was used to measure the temperature profile of the upper troposphere and lower stratosphere. The system consists of a tripled Nd-YAG laser and a 1.5 meter diameter telescope. Two photomultipliers are used at the output of the telescope to allow for measurements at both the laser wavelength and at the Raman shifted wavelength due to atmospheric nitrogen. The signal from the photomultipliers is recorded as photon counts in 1 microsec bins. The results of a number of laser shots are summed together to provide atmospheric returns which have acceptable signal to noise characteristics. Measurements of the Raman nitrogen return were acquired up to an altitude in excess of 20 km. Temperature profiles were retrieved from the attenuation corrected Raman nitrogen return assuming the atmosphere to be in hydrostatic equilibrium and using the ideal gas law. Retrieved temperature profiles are shown compared with independent temperature measurements

Computational modelling of some problems of elasticity and viscoelasticity with applications to thermoforming process

Copyright @ 2012 Northwestern Polytechnical University and ISCIThe reliability of computational models of physical processes has received much attention and involves issues such as the validity of the mathematical models being used, the error in any data that the models need, and the accuracy of the numerical schemes being used. These issues are considered in the context of elastic, viscoelastic and hyperelastic deformation, when finite element approximations are applied. Goal oriented techniques using specific quantities of interest (QoI) are described for estimating discretisation and modelling errors in the hyperelastic case. The computational modelling of the rapid large inflation of hyperelastic circular sheets modelled as axisymmetric membranes is then treated, with the aim of estimating engineering QoI and their errors. Fine (involving inertia terms) and coarse (quasi-static) models of the inflation are considered. The techniques are applied to thermoforming processes where sheets are inflated into moulds to form thin-walled structures

Material parameter estimation and hypothesis testing on a 1D viscoelastic stenosis model: Methodology

This is the post-print version of the final published paper that is available from the link below. Copyright @ 2013 Walter de Gruyter GmbH.Non-invasive detection, localization and characterization of an arterial stenosis (a blockage or partial blockage in the artery) continues to be an important problem in medicine. Partial blockage stenoses are known to generate disturbances in blood flow which generate shear waves in the chest cavity. We examine a one-dimensional viscoelastic model that incorporates Kelvin–Voigt damping and internal variables, and develop a proof-of-concept methodology using simulated data. We first develop an estimation procedure for the material parameters. We use this procedure to determine confidence intervals for the estimated parameters, which indicates the efficacy of finding parameter estimates in practice. Confidence intervals are computed using asymptotic error theory as well as bootstrapping. We then develop a model comparison test to be used in determining if a particular data set came from a low input amplitude or a high input amplitude; this we anticipate will aid in determining when stenosis is present. These two thrusts together will serve as the methodological basis for our continuing analysis using experimental data currently being collected.National Institute of Allergy and Infectious Diseases, Air Force Office of Scientific Research, Department of Education, and Engineering and Physical Sciences Research Council

High-order space-time finite element schemes for acoustic and viscodynamic wave equations with temporal decoupling

Copyright @ 2014 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.We revisit a method originally introduced by Werder et al. (in Comput. Methods Appl. Mech. Engrg., 190:6685–6708, 2001) for temporally discontinuous Galerkin FEMs applied to a parabolic partial differential equation. In that approach, block systems arise because of the coupling of the spatial systems through inner products of the temporal basis functions. If the spatial finite element space is of dimension D and polynomials of degree r are used in time, the block system has dimension (r + 1)D and is usually regarded as being too large when r > 1. Werder et al. found that the space-time coupling matrices are diagonalizable over inline image for r ⩽100, and this means that the time-coupled computations within a time step can actually be decoupled. By using either continuous Galerkin or spectral element methods in space, we apply this DG-in-time methodology, for the first time, to second-order wave equations including elastodynamics with and without Kelvin–Voigt and Maxwell–Zener viscoelasticity. An example set of numerical results is given to demonstrate the favourable effect on error and computational work of the moderately high-order (up to degree 7) temporal and spatio-temporal approximations, and we also touch on an application of this method to an ambitious problem related to the diagnosis of coronary artery disease

Mapping lung squamous cell carcinoma pathogenesis through in vitro and in vivo models

Lung cancer is the main cause of cancer death worldwide, with lung squamous cell carcinoma (LUSC) being the second most frequent subtype. Preclinical LUSC models recapitulating human disease pathogenesis are key for the development of early intervention approaches and improved therapies. Here, we review advances and challenges in the generation of LUSC models, from 2D and 3D cultures, to murine models. We discuss how molecular profiling of premalignant lesions and invasive LUSC has contributed to the refinement of in vitro and in vivo models, and in turn, how these systems have increased our understanding of LUSC biology and therapeutic vulnerabilities

Victim’s profile analysis reveals homicide affinity for minorities and the youth

BACKGROUND: In this study we have examined the risk of death by homicide in Miami-Dade County and Broward County (BC); and examined the association between socioeconomic status and homicide while describing victim's typical characteristics such as age, gender, race/ethnicity, and type of injury. METHODS: Data was collected from the County's Medical Examiner's Offices, Census Bureau, and Federal Bureau of Investigation between 2004 and 2007. RESULTS: There has been an increase in the risk of dying by homicide in the studied area; the homicide rate for the selected period was two times higher than the national average. Although Black Non-Hispanics count for 19% of the population of Miami-Dade County and 23% in Broward County, 56% and 53% of homicide victims are among this ethnic group in Miami-Dade County and Broward County respectively. Hispanics were more at risk of being a victim of homicide in 2007 than they were in 2004. CONCLUSIONS: A substantial proportion of the homicide victims were 22 years of age or younger. . In fact, the homicide victims' average age has been steadily decreasing in recent years. The drastic increase in the risk of death by gunshot among young Black non-Hispanic and Hispanic residents warrants cause for concern

Performance modeling of ultraviolet Raman lidar systems for daytime profiling of atmospheric water vapor

We describe preliminary results from a comprehensive computer model developed to guide optimization of a Raman lidar system for measuring daytime profiles of atmospheric water vapor, emphasizing an ultraviolet, solar-blind approach

Bayesian inference for group-level cortical surface image-on-scalar-regression with Gaussian process priors

In regression-based analyses of group-level neuroimage data researchers typically fit a series of marginal general linear models to image outcomes at each spatially-referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses, however the number of locations in a typical neuroimage can preclude standard computation with explicitly spatial models. Here we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple nonstationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through Vecchia approximation of our prior which, critically, can be constructed for a wide class of spatial correlation functions and results in prior models that retain full spatial rank. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses. Finally we illustrate our method in an analysis of cortical surface fMRI task contrast data from a large cohort of children enrolled in the Adolescent Brain Cognitive Development study