Antibiotic use for community-acquired pneumonia in neonates and children: WHO evidence review.

Background Pneumonia is the most common cause of death in children worldwide, accounting for 15% of all deaths of children under 5 years of age. This review summarises the evidence for the empirical antibiotic treatment of community-acquired pneumonia in neonates and children and puts emphasis on publications since the release of the previous WHO Evidence Summary report published in 2014. Methods A systematic search for systematic reviews and meta-analyses of antibiotic therapy for community-acquired pneumonia was conducted between 1 January 2013 and 10 November 2016. Results The optimal dosing recommendation for amoxicillin remains unclear with limited pharmacological and clinical evidence. There is limited evidence from surveillance to indicate whether amoxicillin or broader spectrum antibiotics (e.g. third-generation cephalosporins) are being used most commonly for paediatric CAP in different WHO regions. Data are lacking on clinical efficacy in the context of pneumococcal, staphylococcal and mycoplasma disease and the relative contributions of varying first-line and step-down options to the selection of such resistance. Conclusion Further pragmatic trials are required to optimise management of hospitalised children with severe and very severe pneumonia

Attitudes toward hydraulic fracturing: The opposing forces of political conservatism and basic knowledge about fracking

publisher: Elsevier articletitle: Attitudes toward hydraulic fracturing: The opposing forces of political conservatism and basic knowledge about fracking journaltitle: Global Environmental Change articlelink: http://dx.doi.org/10.1016/j.gloenvcha.2016.03.004 content_type: article copyright: © 2016 Elsevier Ltd. All rights reserved

An epistemic community comes and goes? Local and national expressions of heart health promotion in Canada

<p>Abstract</p> <p>Background</p> <p>The objective of this study is to examine the existence and shape of epistemic communities for (heart) health promotion at the international, national, provincial and regional levels in Canada. Epistemic community may be defined as a network of experts with an authoritative claim to policy relevant knowledge in their area of expertise.</p> <p>Methods</p> <p>An interpretive policy analysis was employed using 60 documents (48 provincial, 8 national and 4 international) and 66 interviews (from 5 Canadian provinces). These data were entered into NUD*IST, a qualitative software analysis package, to assist in the development of codes and themes. These codes form the basis of the results.</p> <p>Results</p> <p>A scientific and policy epistemic community was identified at the international and Canadian federal levels. Provincially and regionally, the community is present as an idea but its implementation varies between jurisdictions.</p> <p>Conclusion</p> <p>The importance of economic, political and cultural factors shapes the presence and shape of the epistemic community in different jurisdictions. The community waxes and wanes but appears robust.</p

CONDITIONAL VALUE AT RISK CONDITIONAL VALUE AT RISK AS A CRITERION FOR OPTIMAL PORTFOLIO SELECTION MASTER OF SCIENCE (2005) (Mathematics) TITLE: Conditional Value at Risk as a Criterion for Optimal Portfolio Selection

ABSTRACT The focus of my master&apos;s project research involves Conditional Value at Risk (or Expected Shortfall) as a risk measure for optimal portfolio selection. The project is organized as follows. In the first chapter, we introduce and discuss the quantile based risk measures, Value at Risk (VaR) and Conditional Value at Risk (CVaR), with respect to axiomatic characterization of coherent risk measures. As an alternative to VaR, CVaR has been attracting attention since it is a coherent measure. The properties and advantages of CVaR are analyzed. The second chapter deals with mean-risk models of portfolio optimization. The common idea in all asset allocation models is the minimization of some measure of risk while simultaneously maximizing portfolio expected return. Portfolio optimization in a mean-CVaR framework has been actively discussed recently. CVaR is a numerically tractable measure, allowing optimal portfolios to be computed by means of convex programming. Most importantly for applications, however, a mean-CVaR model can be used with scenario simulation of loss distributions. We investigate the convergence of the Monte-Carlo based CVaR optimal portfolio algorithm when an analytical solution of the optimization problem can be obtained. The last chapter considers the benchmark (or relative) portfolio selection problem in terms of a multiobjective problem. Tracking error optimization in a mean-multirisk framework allows implementation of an interactive decision making and taking into account of the investor&apos;s preferences. iii ACKNOWLEDGEMENTS

An FPTAS for Total Weighted Earliness Tardiness Problem with Constant Number of Distinct Due Dates and Polynomially Related Weights AN FPTAS FOR TOTAL WEIGHTED EARLINESS TARDINESS PROBLEM WITH CONSTANT NUMBER OF DISTINCT DUE DATES AND POLYNOMIALLY RELATED

Abstract We are given a sequence of jobs on a single machine, and each job has a weight, processing time and a due date. A job is early when it finishes before or on its due date and its earliness is the amount of time between its completion time and its due date. A job is tardy when it finishes after its due date and its tardiness is the amount of time between its due date and its completion time. The TWET problem is to find a schedule which minimizes the total weighted earliness and tardiness. We are focusing on the TWET problem with a constant number of distinct due dates and polynomially related weights. This problem has been proven to be NP-hard. In this thesis, we present a dynamic programming algorithm for our TWET problem first and then convert it into an FPTAS by adopting a rounding scheme. There are several important points in our algorithm: we observe the importance of the straddlers and guess them at the beginning through exhaustive enumeration, and insert them back at the very end by solving a linear problem; we know a series of structural properties of the optimal schedule to shrink the state space of the DP; we increase each due date to get a new problem and adopt a rounding scheme of the DP for the new problem to avoid preemption