Low-speed aerodynamic characteristics of a 0.08-scale YF-17 airplane model at high angles of attack and sideslip

Data were obtained with and without the nose boom and with several strake configurations; also, data were obtained for various control surface deflections. Analysis of the results revealed that selected strake configurations adequately provided low Reynolds number simulation of the high Reynolds number characteristics. The addition of the boom in general tended to reduce the Reynolds number effects

Self Assessment Model for Local Boards of Education to Evaluate Effectiveness

Statement of Purpose The purpose of this field study was to develop a model self-assessment process for local boards of education. Since 1977, this researcher has served as Assistant Regional Superintendent of Schools in Champaign and Ford Counties. From that experience the researcher determined that many local boards do not define and implement a formal self-evaluation process. Of the twenty-one school districts in Champaign and Ford Counties only three have planned and conducted a self-assessment program during the past four years. Since 1983, 57 per cent of the total school board membership of these districts has changed. In 1985, the Illinois General Assembly passed significant school reform legislation which placed additional expectations on local school districts and the boards of education which govern those districts. Thus, this study was timely and relevant to the need for local oards to evaluate their effectiveness in providing leadership and direction for their local school districts. Procedure Although this field study was directed for use by the school districts in Champaign and Ford Counties, the researcher believes it may be modified and adapted to serve other school districts. The study was divided into four chapters. Chapter I provides background information on the problems and concerns confronting local school boards. Chapter II provides a review of the research and literature concerning the role and responsibilities of local boards of education. Justification of the need for the development and use of a model to assess school board effectiveness is presented. Chapter III presents the self-assessment model for use by the boards of education in Champaign and Ford Counties. This chapter identifies the important components which need to be evaluated for effectiveness in the self-evaluation process. These components are the basis for the questionnaire which is to be completed by the members of the local board of education. This chapter provides a guide for using the model and presents a list of recommendations which can be implemented by the local board to improve effectiveness. Chapter IV provides the summary, findings and recommendations for the utilization of the self-assessment model by the local board of education

Modelling alternative strategies for delivering hepatitis B vaccine in prisons : the impact on the vaccination coverage of the injecting drug user population

Since 2001 hepatitis B vaccination has been offered to prisoners on reception into prisons in England and Wales. However, short campaigns of vaccinating the entire population of individual prisons have achieved high vaccination coverage for limited periods, suggesting that short campaigns may be a preferable way of vaccinating prisoners. A model is used that describes the flow of prisoners through prisons stratified by injecting status to compare a range of vaccination scenarios that describe vaccination on prison reception or via regular short campaigns. Model results suggest that vaccinating on prison reception can capture a greater proportion of the injecting drug user (IDU) population than the comparable campaign scenarios (63% vs. 55 . 6% respectively). Vaccination on prison reception is also more efficient at capturing IDUs for vaccination than vaccination via a campaign, although vaccination via campaigns may have a role with some infections for overall control

Climate change: Why the conspiracy theories are dangerous

Uncertainty surrounds the public understanding of climate change and provides fertile ground for conspiracy theories. Typically, such conspiracy theories assert that climate scientists and politicians are distorting or hijacking the science to suit their own purposes. Climate change conspiracy theories resemble other conspiracy theories in some respects, but in others they appear to be quite different. For example, climate change conspiracy theories appear to be motivated by the desire to deny or minimize an unwelcome and threatening conclusion. They also appear to be more contentious than other types of conspiracy theories. Perhaps to an unparalleled extent, people on both sides of the issue champion climate change conspiracy theories. Finally, more than other conspiracy theories, those concerning climate change appear to be more politically loaded, dividing opinion across the left-right continuum. Some empirical evidence suggests that climate change conspiracy theories may be harmful, steering people away from environmentally friendly initiatives. They therefore present a significant challenge for governments and environmental organizations that are attempting to convince people to take action against global warming

Geometry of Policy Improvement

We investigate the geometry of optimal memoryless time independent decision making in relation to the amount of information that the acting agent has about the state of the system. We show that the expected long term reward, discounted or per time step, is maximized by policies that randomize among at most $k$ actions whenever at most $k$ world states are consistent with the agent's observation. Moreover, we show that the expected reward per time step can be studied in terms of the expected discounted reward. Our main tool is a geometric version of the policy improvement lemma, which identifies a polyhedral cone of policy changes in which the state value function increases for all states.Comment: 8 page

Regret Bounds for Reinforcement Learning with Policy Advice

In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinforcement learning task at hand. We prove that RLPA has a sub-linear regret of \tilde O(\sqrt{T}) relative to the best input policy, and that both this regret and its computational complexity are independent of the size of the state and action space. Our empirical simulations support our theoretical analysis. This suggests RLPA may offer significant advantages in large domains where some prior good policies are provided