Local environmental policy and local government restructuring in Britain: The tensions between compulsory competitive tendering and local agenda 21

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University

Turbofan noise generation. Volume 1: Analysis

Computer programs were developed which calculate the in-duct acoustic modes excited by a fan/stator stae operating at subsonic tip speed. Three noise source mechanisms are included: (1) sound generated by the rotor blades interacting with turbulence ingested into, or generated within, the inlet duct; (2) sound generated by the stator vanes interacting with the turbulent wakes of the rotors blades; and (3) sound generated by the stator vanes interacting with the mean velocity deficit wakes of the rotor blades. The fan/stator stage is modeled as an ensemble of blades and vanes of zero camber and thickness enclosed within an infinite hard-walled annular duct. Turbulence drawn into or generated within the inlet duct is modeled as nonhomogeneous and anisotropic random fluid motion, superimposed upon a uniform axial mean flow, and convected with that flow. Equations for the duct mode amplitudes, or expected values of the amplitudes, are derived

Turbofan noise generation. Volume 2: Computer programs

The use of a package of computer programs developed to calculate the in duct acoustic mods excited by a fan/stator stage operating at subsonic tip speed is described. The following three noise source mechanisms are included: (1) sound generated by the rotor blades interacting with turbulence ingested into, or generated within, the inlet duct; (2) sound generated by the stator vanes interacting with the turbulent wakes of the rotor blades; and (3) sound generated by the stator vanes interacting with the velocity deficits in the mean wakes of the rotor blades. The computations for three different noise mechanisms are coded as three separate computer program packages. The computer codes are described by means of block diagrams, tables of data and variables, and example program executions; FORTRAN listings are included

Accuracy of medical billing data against the electronic health record in the measurement of colorectal cancer screening rates.

ObjectiveMedical billing data are an attractive source of secondary analysis because of their ease of use and potential to answer population-health questions with statistical power. Although these datasets have known susceptibilities to biases, the degree to which they can distort the assessment of quality measures such as colorectal cancer screening rates are not widely appreciated, nor are their causes and possible solutions.MethodsUsing a billing code database derived from our institution's electronic health records, we estimated the colorectal cancer screening rate of average-risk patients aged 50-74 years seen in primary care or gastroenterology clinic in 2016-2017. 200 records (150 unscreened, 50 screened) were sampled to quantify the accuracy against manual review.ResultsOut of 4611 patients, an analysis of billing data suggested a 61% screening rate, an estimate that matches the estimate by the Centers for Disease Control. Manual review revealed a positive predictive value of 96% (86%-100%), negative predictive value of 21% (15%-29%) and a corrected screening rate of 85% (81%-90%). Most false negatives occurred due to examinations performed outside the scope of the database-both within and outside of our institution-but 21% of false negatives fell within the database's scope. False positives occurred due to incomplete examinations and inadequate bowel preparation. Reasons for screening failure include ordered but incomplete examinations (48%), lack of or incorrect documentation by primary care (29%) including incorrect screening intervals (13%) and patients declining screening (13%).ConclusionsBilling databases are prone to substantial bias that may go undetected even in the presence of confirmatory external estimates. Caution is recommended when performing population-level inference from these data. We propose several solutions to improve the use of these data for the assessment of healthcare quality

Simulation of flood flow in a river system using artificial neural networks

International audienceArtificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets

Endocytosis Occurs Independently Of Annexin-Vi In Human A431 Cells

Annexin VI is one of a family of calcium-dependent phospholipid-binding proteins. Although the function of this protein is not known, various physiological roles have been proposed, including a role in the budding of clathrin-coated pits (Lin et al., 1992. Cell. 70:283-291.). In this study we have investigated a possible endocytotic role for annexin VI in intact cells, using the human squamous carcinoma cell line A431, and report that these cells do not express endogenous annexin VI, as judged by Western and Northern blotting and PCR/Southern blotting. To examine whether endocytosis might in some way be either facilitated or inhibited by the presence of annexin VI, a series of A431 clones were isolated in which annexin VI expression was achieved by stable transfection. These cells expressed annexin VI at similar levels to other human cell types. Using assays for endocytosis and recycling of the transferrin receptor, we report that each of these cellular processes occurs with identical kinetics in both transfected and wild-type A431 cells. In addition, purified annexin VI failed to support the scission of coated pits in permeabilized A431 cells. We conclude that annexin VI is not an essential component of the endocytic pathway, and that in A431 cells, annexin VI fails to exert any influence on internalization and recycling of the transferrin receptor

Accurate Structural Correlations from Maximum Likelihood Superpositions

The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology