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Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/33340/1/0000737.pd

Atmospheric Heating and Wind Acceleration: Results for Cool Evolved Stars based on Proposed Processes

A chromosphere is a universal attribute of stars of spectral type later than ~F5. Evolved (K and M) giants and supergiants (including the zeta Aurigae binaries) show extended and highly turbulent chromospheres, which develop into slow massive winds. The associated continuous mass loss has a significant impact on stellar evolution, and thence on the chemical evolution of galaxies. Yet despite the fundamental importance of those winds in astrophysics, the question of their origin(s) remains unsolved. What sources heat a chromosphere? What is the role of the chromosphere in the formation of stellar winds? This chapter provides a review of the observational requirements and theoretical approaches for modeling chromospheric heating and the acceleration of winds in single cool, evolved stars and in eclipsing binary stars, including physical models that have recently been proposed. It describes the successes that have been achieved so far by invoking acoustic and MHD waves to provide a physical description of plasma heating and wind acceleration, and discusses the challenges that still remain.Comment: 46 pages, 9 figures, 1 table; modified and unedited manuscript; accepted version to appear in: Giants of Eclipse, eds. E. Griffin and T. Ake (Berlin: Springer

The Earth: Plasma Sources, Losses, and Transport Processes

This paper reviews the state of knowledge concerning the source of magnetospheric plasma at Earth. Source of plasma, its acceleration and transport throughout the system, its consequences on system dynamics, and its loss are all discussed. Both observational and modeling advances since the last time this subject was covered in detail (Hultqvist et al., Magnetospheric Plasma Sources and Losses, 1999) are addressed

Simulation analysis of the effect of herd immunity and age structure on infection of a cattle herd with bluetongue viruses in Queensland, Australia

A state-transition model based on Leslie matrix formulation was used to investigate the effects of herd immunity and age structure on the infection of a simulated cattle herd with bluetongue viruses under Australian climatic conditions. Increasing duration of immunity decreased the prevalence of infection. A duration of immunity of 33 months was consistent with prevalence estimates made from previous serological studies of bluetongue virus. Herd prevalence displayed slowly dampening cyclical variation over time (most pronounced when a short duration of immunity was simulated). Increasing calving and mortality risk rates in the simulated herd increased prevalence, whereas increasing age at first calving decreased prevalence. Manipulation of calving rates had the greatest effect on the predicted prevalence of infection in the herd. Simulation of a number of herd-management scenarios suggested that management systems in which cattle are bred early and where high calving rates are achieved are likely to contribute to high levels of infection with bluetongue viruses. Results confirm the importance of management factors in influencing the prevalence of infectious diseases in animal populations

Wie können Tierseuchenrisiken besser gehandhabt werden?

Livestock farming is imbued with a variety of disease-related risks. The threat of disease, and actions taken (or not taken) to mitigate it, can impact both the broader livestock sector and potentially society at large if the disease is what is termed zoonotic i.e. impacting both animal and human health alike. Understanding the nature of these risks is crucial to improve policymaking and requires multidisciplinary toolkits. In this article we identify various frameworks from economics and epidemiology to analyze the impact of animal disease risks in livestock environments. From this, we examine the policy responses implemented to better manage risk, and conclude by identifying the current gaps in the decision process related to risk and uncertainty, which is a reflection of imperfect knowledge. Recent advances in our modeling frameworks have helped streamline the complex decision space faced by policymakers and improved our understanding of the risks and consequences of mitigation strategies. At the same time, while there has been significant progress in integrating economic and epidemiological tools to analyze risk, more work is required to understand the feedbacks between disease and behaviour taken to control it