Preliminary design of two Space Shuttle fluid physics experiments

The mid-deck lockers of the STS and the requirements for operating an experiment in this region are described. The design of the surface tension induced convection and the free surface phenomenon experiments use a two locker volume with an experiment unique structure as a housing. A manual mode is developed for the Surface Tension Induced Convection experiment. The fluid is maintained in an accumulator pre-flight. To begin the experiment, a pressurized gas drives the fluid into the experiment container. The fluid is an inert silicone oil and the container material is selected to be comparable. A wound wire heater, located axisymmetrically above the fluid can deliver three wattages to a spot on the fluid surface. These wattages vary from 1-15 watts. Fluid flow is observed through the motion of particles in the fluid. A 5 mw He/Ne laser illuminates the container. Scattered light is recorded by a 35mm camera. The free surface phenomena experiment consists of a trapezoidal cell which is filled from the bottom. The fluid is photographed at high speed using a 35mm camera which incorporated the entire cell length in the field of view. The assembly can incorporate four cells in one flight. For each experiment, an electronics block diagram is provided. A control panel concept is given for the surface induced convection. Both experiments are within the mid-deck locker weight and c-g limits

Fire History of the Appalachian Region: A Review and Synthesis

The importance of fire in shaping Appalachian vegetation has become increasingly apparent over the last 25 years. This period has seen declines in oak (Quercus) and pine (Pinus) forests and other fire-dependent ecosystems, which in the near-exclusion of fire are being replaced by fire-sensitive mesophytic vegetation. These vegetation changes imply that Appalachian vegetation had developed under a history of burning before the fire-exclusion era, a possibility that has motivated investigations of Appalachian fire history using proxy evidence. Here we synthesize those investigations to obtain an up-to-date portrayal of Appalachian fire history. We organize the report by data type, beginning with studies of high-resolution data on recent fires to provide a context for interpreting the lower-resolution proxy data. Each proxy is addressed in a subsequent chapter, beginning with witness trees and continuing to fire-scarred trees, stand age structure, and soil and sediment charcoal. Taken together, these proxies portray frequent burning in the past. Fires had occurred at short intervals (a few years) for centuries before the fire-exclusion era. Indeed, burning has played an important ecological role for millennia. Fires were especially common and spatially extensive on landscapes with large expanses of oak and pine forest, notably in the Ridge and Valley province and the Blue Ridge Mountains. Burning favored oak and pine at the expense of mesophytic competitors, but fire exclusion has enabled mesophytic plants to expand from fire-sheltered sites onto dry slopes that formerly supported pyrogenic vegetation. These changes underscore the need to restore fire-dependent ecosystems

The spread of fire on a random multigraph

We study a model for the destruction of a random network by fire. Suppose that we are given a multigraph of minimum degree at least 2 having real-valued edge-lengths. We pick a uniform point from along the length and set it alight; the edges of the multigraph burn at speed 1. If the fire reaches a vertex of degree 2, the fire gets directly passed on to the neighbouring edge; a vertex of degree at least 3, however, passes the fire either to all of its neighbours or none, each with probability $1/2$. If the fire goes out before the whole network is burnt, we again set fire to a uniform point. We are interested in the number of fires which must be set in order to burn the whole network, and the number of points which are burnt from two different directions. We analyse these quantities for a random multigraph having $n$ vertices of degree 3 and $\alpha(n)$ vertices of degree 4, where $\alpha(n)/n \to 0$ as $n \to \infty$, with i.i.d. standard exponential edge-lengths. Depending on whether $\alpha(n) \gg \sqrt{n}$ or $\alpha(n)=O(\sqrt{n})$, we prove that as $n \to \infty$ these quantities converge jointly in distribution when suitably rescaled to either a pair of constants or to (complicated) functionals of Brownian motion. We use our analysis of this model to make progress towards a conjecture of Aronson, Frieze and Pittel concerning the number of vertices which remain unmatched when we use the Karp-Sipser algorithm to find a matching on the Erd\H{o}s-R\'enyi random graph.Comment: 42 page

MODELING ECONOMIC AND ECOLOGICAL BENEFITS OF POST-FIRE REVEGETATION IN THE GREAT BASIN

This study employs a Markov chain model of vegetation dynamics to examine the economic and ecological benefits of post-fire revegetation in the Great Basin sagebrush steppe. The analysis is important because synergies between wildland fire and invasive weeds in this ecosystem are likely to result in the loss of native biodiversity, less predictable forage availability for livestock and wildlife, reduced watershed stability and water quality, and increased costs and risk associated with firefighting. The analysis is based on a parameterized state-and-transition model of vegetation change for Wyoming big sagebrush community in the Great Basin sagebrush steppe. This conceptual model was formulated into a quantitative, predictive model by implementing it as a Markov chain process that links vegetation change, management, and costs. Simulation results were used to develop cost curves for achieving ecological goals and to evaluate uncertainty in future vegetation conditions. The Markov chain model shows that post-fire revegetation using either a native seed mix or crested wheatgrass was more effective than no revegetation for achieving ecosystem objectives. Further, post-fire revegetation with either seed mix cost less than no revegetation because of resulting reductions in fire suppression costs. Consequently, post-fire revegetation makes both ecological and economic sense, and the choice of seed mix should depend on the prioritization of management objectives. Identifying the economic and ecological tradeoffs of different management strategies should enable improved management of the sagebrush-steppe, and Markov processes provide a straight-forward method for identifying these trade-offs.Resource /Energy Economics and Policy,

A Methodology to Develop a Decision Model Using a Large Categorical Database with Application to Identifying Critical Variables during a Transport-Related Hazardous Materials Release

An important problem in the use of large categorical databases is extracting information to make decisions, including identification of critical variables. Due to the complexity of a dataset containing many records, variables, and categories, a methodology for simplification and measurement of associations is needed to build the decision model. To this end, the proposed methodology uses existing methods for categorical exploratory analysis. Specifically, latent class analysis and loglinear modeling, which together constitute a three-step, non-simultaneous approach, were used to simplify the variables and measure their associations, respectively. This methodology has not been used to extract data-driven decision models from large categorical databases. A case in point is a large categorical database at the DoT for hazardous materials releases during transportation. This dataset is important due to the risk from an unintentional release. However, due to the lack of a data-congruent decision model of a hazmat release, current decision making, including critical variable identification, is limited at the Office of Hazardous Materials within the DoT. This gap in modeling of a release is paralleled by a similar gap in the hazmat transportation literature. The literature has an operations research and quantitative risk assessment focus, in which the models consist of simple risk equations or more complex, theoretical equations. Thus, based on critical opportunities at the DoT and gaps in the literature, the proposed methodology was demonstrated using the hazmat release database. The methodology can be applied to other categorical databases for extracting decision models, such as those at the National Center for Health Statistics. A key goal of the decision model, a Bayesian network, was identification of the most influential variables relative to two consequences or measures of risk in a hazmat release, dollar loss and release quantity. The most influential variables for dollar loss were found to be variables related to container failure, specifically the causing object and item-area of failure on the container. Similarly, for release quantity, the container failure variables were also most influential, specifically the contributing action and failure mode. In addition, potential changes in these variables for reducing consequences were identified

Towards a Framework for Visual Intelligence in Service Robotics:Epistemic Requirements and Gap Analysis

A key capability required by service robots operating in real-world, dynamic environments is that of Visual Intelligence, i.e., the ability to use their vision system, reasoning components and background knowledge to make sense of their environment. In this paper, we analyse the epistemic requirements for Visual Intelligence, both in a top-down fashion, using existing frameworks for human-like Visual Intelligence in the literature, and from the bottom up, based on the errors emerging from object recognition trials in a real-world robotic scenario. Finally, we use these requirements to evaluate current Knowledge Basesfor Service Robotics and to identify gaps in the support they provide for Visual Intelligence.These gaps provide the basis of a research agenda for developing more effective knowledge representations for Visual Intelligence