Investigation of the aerodynamic characteristics of a lifting body in ground proximity

The use of cambered hull shapes in the next generation of lighter-than-air vehicles to enhance aerodynamic performance, together with optimized take-off manoeuvre profiles, will require a more detailed understanding of ground proximity effects for such aircraft. A series of sub-scale wind tunnel tests at Re = 1.4 x 106 on a 6:1 prolate spheroid are used to identify potential changes in aerodynamic lift, drag and pitching moment coefficients that are likely to be experienced on the vehicle hull in isolation when in close ground proximity. The experimental data is supported by a preliminary assessment of surface pressure changes using a high order panel method (PANAIR) and RANS CFD simulations to assess the flow structure. The effect of ground proximity, most evident when non-dimensional ground clearance (h/c) < 0.3, is to reduce lift coefficient, increase drag coefficient and increase the body pitching moment coefficient

Portfolio Diversification, Leverage, and Financial Contagion

This paper studies the extent to which basic principles of portfolio diversification explain "contagious selling" of financial assets when there are purely local shocks (e.g., a financial crisis in one country). The paper demonstrates that elementary portfolio theory offers key insights into "contagion." Most important, portfolio diversification and leverage are sufficient to explain why an investor will find it optimal to significantly reduce all risky asset positions when an adverse shock impacts just one asset. This result does not depend on margin calls: it applies to portfolios and institutions that rely on borrowed funds. The paper also shows that Value-at-Risk portfolio management rules do not have significantly different consequences for portfolio rebalancing than a variety of other rules. Copyright 2000, International Monetary Fund

Determinants of hospital death in haematological cancers: findings from a qualitative study

© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. Objectives: Current UK health policy promotes enabling people to die in a place they choose, which for most is home. Despite this, patients with haematological malignancies (leukaemias, lymphomas and myeloma) are more likely to die in hospital than those with other cancers, and this is often considered a reflection of poor quality end-of-life care. This study aimed to explore the experiences of clinicians and relatives to determine why hospital deaths predominate in these diseases.Methods: The study was set within the Haematological Malignancy Research Network (HMRN-www.hmrn.org), an ongoing population-based cohort that provides infrastructure for evidence-based research. Qualitative interviews were conducted with clinical staff in haematology, palliative care and general practice (n=45) and relatives of deceased HMRN patients (n=10). Data were analysed for thematic content and coding and classification was inductive. Interpretation involved seeking meaning, salience and connections within the data. Results: Five themes were identified relating to: the characteristics and trajectory of haematological cancers, a mismatch between the expectations and reality of home death, preference for hospital death, barriers to home/hospice death and suggested changes to practice to support non-hospital death, when preferred. Conclusions: Hospital deaths were largely determined by the characteristics of haematological malignancies, which included uncertain trajectories, indistinct transitions and difficulties predicting prognosis and identifying if or when to withdraw treatment. Advance planning (where possible) and better communication between primary and secondary care may facilitate non-hospital death

Leadership Characteristics and Team Outcomes in the Development of a Marketing Web Page

Team structures are changing under the pressures of e-commerce and globalization. Today teams have to manage the challenges of working across functional boundaries, such as marketing and web development. In such an environment, research in the disciplines of management and psychology have found that shared leadership among team members may be superior to traditional leadership by a single team member, but this notion has not been tested in the contexts of marketing and web development. This paper presents the results of an empirical study showing that teams with shared leadership exhibited better performance and greater member satisfaction than teams with clear leaders. In addition, no relationship was found between the education, experience, Internet self-efficacy, or personal style of leaders and team outcomes of structure, performance, or satisfaction. This research suggests that the superiority of shared leadership found in the areas of management and psychology is applicable to teams in technical areas, such as web development and marketing. The reported study confirms previous research and applies it in an under-research context, marketing web page development

Statistical Computations with AstroGrid and the Grid

We outline our first steps towards marrying two new and emerging technologies; the Virtual Observatory (e.g, AstroGrid) and the computational grid. We discuss the construction of VOTechBroker, which is a modular software tool designed to abstract the tasks of submission and management of a large number of computational jobs to a distributed computer system. The broker will also interact with the AstroGrid workflow and MySpace environments. We present our planned usage of the VOTechBroker in computing a huge number of n-point correlation functions from the SDSS, as well as fitting over a million CMBfast models to the WMAP data.Comment: Invited talk to appear in "Proceedings of PHYSTAT05: Statistical Problems in Particle Physics, Astrophysics and Cosmology

Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge

Primary evolving networks and the comparative analysis of robust and fragile structures

In this paper we consider the structure of dynamically evolving networks modelling information and activity moving across a large set of vertices. We adopt the communicability concept that generalizes that of centrality which is defined for static networks. We define the primary network structure within the whole as comprising of the most influential vertices (both as senders and receivers of dynamically sequenced activity). We present a methodology based on successive vertex knockouts, up to a very small fraction of the whole primary network,that can characterize the nature of the primary network as being either relatively robust and lattice-like (with redundancies built in) or relatively fragile and tree-like (with sensitivities and few redundancies). We apply these ideas to the analysis of evolving networks derived from fMRI scans of resting human brains. We show that the estimation of performance parameters via the structure tests of the corresponding primary networks is subject to less variability than that observed across a very large population of such scans. Hence the differences within the population are significant

Type-1 error inflation in the traditional by-participant analysis to metamemory accuracy: a generalized mixed-effects model perspective

In order to examine metacognitive accuracy (i.e., the relationship between metacognitive judgment and memory performance), researchers often rely on by-participant analysis, where metacognitive accuracy (e.g., resolution, as measured by the gamma coefficient or signal detection measures) is computed for each participant and the computed values are entered into group-level statistical tests such as the t-test. In the current work, we argue that the by-participant analysis, regardless of the accuracy measurements used, would produce a substantial inflation of Type-1 error rates, when a random item effect is present. A mixed-effects model is proposed as a way to effectively address the issue, and our simulation studies examining Type-1 error rates indeed showed superior performance of mixed-effects model analysis as compared to the conventional by-participant analysis. We also present real data applications to illustrate further strengths of mixed-effects model analysis. Our findings imply that caution is needed when using the by-participant analysis, and recommend the mixed-effects model analysis