Dynamic Routing in Translucent WDM Optical Networks: The Intradomain Case

Translucent wavelength-division multiplexing optical networks use sparse placement of regenerators to overcome physical impairments and wavelength contention introduced by fully transparent networks, and achieve a performance close to fully opaque networks at a much less cost. In previous studies, we addressed the placement of regenerators based on static schemes, allowing for only a limited number of regenerators at fixed locations. This paper furthers those studies by proposing a dynamic resource allocation and dynamic routing scheme to operate translucent networks. This scheme is realized through dynamically sharing regeneration resources, including transmitters, receivers, and electronic interfaces, between regeneration and access functions under a multidomain hierarchical translucent network model. An intradomain routing algorithm, which takes into consideration optical-layer constraints as well as dynamic allocation of regeneration resources, is developed to address the problem of translucent dynamic routing in a single routing domain. Network performance in terms of blocking probability, resource utilization, and running times under different resource allocation and routing schemes is measured through simulation experiments.

Hidden Effects of Chronic Tuberculosis in African Buffalo

Infectious diseases can bring about population declines and local host extinctions, contributing significantly to the global biodiversity crisis. Nonetheless, studies measuring population-level effects of pathogens in wild host populations are rare, and taxonomically biased toward avian hosts and macroparasitic infections. We investigated the effects of bovine tuberculosis (bTB), caused by the bacterial pathogen Mycobacterium bovis, on African buffalo (Syncerus caffer) at Hluhluwe-iMfolozi Park, South Africa. We tested 1180 buffalo for bTB infection between May 2000 and November 2001. Most infections were mild, confirming the chronic nature of the disease in buffalo. However, our data indicate that bTB affects both adult survival and fecundity. Using an age-structured population model, we demonstrate that the pathogen can reduce population growth rate drastically; yet its effects appear difficult to detect at the population level: bTB causes no conspicuous mass mortalities or fast population declines, nor does it alter host-population age structure significantly. Our models suggest that this syndrome—low detectability coupled with severe impacts on population growth rate and, therefore, resilience—may be characteristic of chronic diseases in large mammals.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.