Ready or Not? Protecting the Public's Health in the Age of Bioterrorism, 2004

Examines ten key indicators to evaluate state preparedness to respond to bioterrorist attacks and other public health emergencies. Evaluates the federal government's role and performance, and offers recommendations for improving readiness

A review of operations research methods applicable to wildfire management

Across the globe, wildfire-related destruction appears to be worsening despite increased fire suppression expenditure. At the same time, wildfire management is becoming increasingly complicated owing to factors such as an expanding wildland-urban interface, interagency resource sharing and the recognition of the beneficial effects of fire on ecosystems. Operations research is the use of analytical techniques such as mathematical modelling to analyse interactions between people, resources and the environment to aid decision-making in complex systems. Fire managers operate in a highly challenging decision environment characterised by complexity, multiple conflicting objectives and uncertainty. We assert that some of these difficulties can be resolved with the use of operations research methods. We present a range of operations research methods and discuss their applicability to wildfire management with illustrative examples drawn from the wildfire and disaster operations research literature

F as in Fat: How Obesity Policies Are Failing in America, 2004

Examines national and state obesity rates and government policies. Focuses on setting a baseline of current policies and programs, and offers a comprehensive look at their range and quality

F as in Fat: How Obesity Policies Are Failing in America, 2005

Examines national and state obesity rates and government policies. Challenges the research community to focus on major research questions to inform policy decisions, and policymakers to pursue actions to combat the obesity crisis

Identifying Market Preferences for High Selenium Beef

Selenium is an element found in relatively high concentrations in crops and livestock raised on high-selenium soils located in North and South Dakota. Evidence suggests that a high-selenium diet such as would be obtained from consuming these products can reduce the risk of certain cancers. The region's livestock and grain producers are exploring potential high-selenium product marketing opportunities. A choice experiment was conducted to identify preferred attributes for a high-selenium beef product and the characteristics of potential market segments. In a national survey, participants chose between different levels of health claim approval and research, prices, and selenium origin. A multinomial logit regression model was estimated. Labeling reflecting scientific support linking selenium and reduced cancer risk, and natural-source selenium was ineffective. Marketing opportunities identified are consistent with existing functional food market segments and include consumers with higher income and education, 45 to 55 years of age, and with children.Choice Experiment, FDA approval, Functional Foods, Health Claim, Labeling, Selenium, Consumer/Household Economics, Livestock Production/Industries,

A study of the effects of environmental and ablator performance uncertainties on heat shielding requirements for hyperbolic entry vehicles. Volume 2 - Summary of calculations

Calculated ablative quantities for nylon phenolic heat shielding materials of hyperbolic reentry vehicle

Fuel treatment planning: Fragmenting high fuel load areas while maintaining availability and connectivity of faunal habitat

Reducing the fuel load in fire-prone landscapes is aimed at mitigating the risk of catastrophic wildfires but there are ecological consequences. Maintaining habitat for fauna of both sufficient extent and connectivity while fragmenting areas of high fuel loads presents land managers with seemingly contrasting objectives. Faced with this dichotomy, we propose a Mixed Integer Programming (MIP) model that can optimally schedule fuel treatments to reduce fuel hazards by fragmenting high fuel load regions while considering critical ecological requirements over time and space. The model takes into account both the frequency of fire that vegetation can tolerate and the frequency of fire necessary for fire-dependent species. Our approach also ensures that suitable alternate habitat is available and accessible to fauna affected by a treated area. More importantly, to conserve fauna the model sets a minimum acceptable target for the connectivity of habitat at any time. These factors are all included in the formulation of a model that yields a multi-period spatially-explicit schedule for treatment planning. Our approach is then demonstrated in a series of computational experiments with hypothetical landscapes, a single vegetation type and a group of faunal species with the same habitat requirements. Our experiments show that it is possible to fragment areas of high fuel loads while ensuring sufficient connectivity of habitat over both space and time. Furthermore, it is demonstrated that the habitat connectivity constraint is more effective than neighbourhood habitat constraints. This is critical for the conservation of fauna and of special concern for vulnerable or endangered species

A spatial decomposition based math-heuristic approach to the asset protection problem

This paper addresses the highly critical task of planning asset protection activities during uncontrollable wildfires known in the literature as the Asset Protection Problem (APP). In the APP each asset requires a protective service to be performed by a set of emergency response vehicles within a specific time period defined by the spread of fire. We propose a new spatial decomposition based math-heuristic approach for the solution of large-scale APPs. The heuristic exploits the property that time windows are geographically correlated as fire spreads across a landscape. Thus an appropriate division of the landscape allows the problem to be decomposed into smaller more tractable sub-problems. The main challenge then is to minimise the difference between the final locations of vehicles from one division to the optimal starting locations of the next division. The performance of the proposed approach is tested on a set of benchmark instances from the literature and compared to the most recent Adaptive Large Neighborhood Search (ALNS) algorithm developed for the APP. The results show that our proposed solution approach outperforms the ALNS algorithm on all instances with comparable computation time. We also see a trend with the margin of out-performance becoming more significant as the problems become larger