Quantifying the Hurricane Catastrophe Risk to Offshore Wind Power

The U.S. Department of Energy has estimated that over 50 GW of offshore wind power will be required for the United States to generate 20% of its electricity from wind. Developers are actively planning offshore wind farms along the U.S. Atlantic and Gulf coasts and several leases have been signed for offshore sites. These planned projects are in areas that are sometimes struck by hurricanes. We present a method to estimate the catastrophe risk to offshore wind power using simulated hurricanes. Using this method, we estimate the fraction of offshore wind power simultaneously offline and the cumulative damage in a region. In Texas, the most vulnerable region we studied, 10% of offshore wind power could be offline simultaneously because of hurricane damage with a 100‐year return period and 6% could be destroyed in any 10‐year period. We also estimate the risks to single wind farms in four representative locations; we find the risks are significant but lower than those estimated in previously published results. Much of the hurricane risk to offshore wind turbines can be mitigated by designing turbines for higher maximum wind speeds, ensuring that turbine nacelles can turn quickly to track the wind direction even when grid power is lost, and building in areas with lower risk.     

Assessing Engineering Resilience for Systems with Multiple Performance Measures

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other “ilities” directly in the value hierarchy, while the second method quantifies mission resilience and uses the “ilities” in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems‐of‐systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.     

Combining Occurrence and Toxicity Information to Identify Priorities for Drinking-Water Mixture Research

Characterizing all possible chemical mixtures in drinking water is a potentially overwhelming project, and the task of assessing each mixture's net toxicity even more daunting. We propose that analyzing occurrence information on mixtures in drinking water may help to narrow the priorities and inform the approaches taken by researchers in mixture toxicology. To illustrate the utility of environmental data for refining the mixtures problem, we use a recent compilation of national ground-water-quality data to examine proposed U.S. Environmental Protection Agency (EPA) and Agency for Toxic Substances and Disease Registry (ATSDR) models of noncancer mixture toxicity. We use data on the occurrence of binary and ternary mixtures of arsenic, cadmium, and manganese to parameterize an additive model and compute hazard index scores for each drinking-water source in the data set. We also use partially parameterized interaction models to perform a bounding analysis estimating the interaction potential of several binary and ternary mixtures for which the toxicological literature is limited. From these results, we estimate a relative value of additional toxicological information for each mixture. For example, we find that according to the U.S. EPA's interaction model, the levels of arsenic and cadmium found in U.S. drinking water are unlikely to have synergistic cardiovascular effects, but the same mixture's potential for synergistic neurological effects merits further study. Similar analysis could in future be used to prioritize toxicological studies based on their potential to reduce scientific and regulatory uncertainty. Environmental data may also provide a means to explore the implications of alternative risk models for the toxicity and interaction of complex mixtures.     

Disability and identity: the challenge of epilepsy

Through examining the case of people with epilepsy (which, as we demonstrate, has an ambiguous status in relation to both popular and academic conceptions of disability) we explore the fluid, negotiable and contingent nature of identity and, in particular, the identification as ‘disabled’. Disability, we argue, cannot be reduced to either biology or social oppression, or even primarily to biological or social factors: it is the outcome of a complex interaction between a multiplicity of factors – biological, environmental, social, psychological, cultural and political – which will interact and be experienced differently by different people, at different times and in different situations. Rather than conceiving of disability in ‘all or nothing’ terms and of differing explanations as competing and mutually exclusive, it would be more productive to see them as partial and potentially complementary contributions to the better understanding of a complex and multifarious reality.     

Are Poor Neighborhoods Resource Deprived? A Case Study of Childcare Centers in New York*

Objective. Many social scientists believe poor mothers are better off in middle‐class than in poor neighborhoods, partly because the latter are deprived of important institutional resources. We test whether poor neighborhoods are more likely to lack one critical institutional resource, the childcare center. Methods. We use geocoded data on all licensed centers in the City of New York, address matched to Census tracts. We estimate logit models of presence of center in tract, testing for the linear and nonlinear effects of tract poverty level after controlling for residential instability, joblessness, ethnic makeup, and other demographic factors. We complement the analysis with documentary, interview, and ethnographic data on centers in one poor and one nonpoor neighborhood in the city. Results. We find (1) that the probability of presence of a childcare center does not decrease as poverty level increases; (2) the relationship depends strongly on funding source, with privately funded centers being less likely and publicly funded ones more likely to be present in poor neighborhoods; and (3) at least two factors affect why poor neighborhoods are more likely to have certain centers, the local state and the (often neglected) nonprofit infrastructure. Conclusions. The findings suggest that poor mothers are not necessarily better off in middle‐class neighborhoods in this respect. The market assumptions underlying the initial hypothesis should be modified. More empirical research on the effect of the nonprofit sector on the prevalence of neighborhood institutions is needed.     

Evaluating the Effectiveness of Risk-Reduction Strategies for Consumer Chemical Products

Communication about risks offers a voluntary approach to reducing exposure to pollutants. Its adequacy depends on its impact on behavior. Estimating those impacts first requires characterizing current activities and their associated risk levels, and then predicting the effectiveness of risk-reduction strategies. Characterizing the risks from chemical consumer products requires knowledge of both the physical and the behavioral processes that influence exposures. This article presents an integrated approach that combines consumer interviews, users' beliefs and behaviors, and quantitative exposure modeling. This model was demonstrated in the context of consumer exposure to a methylene chloride-based paint stripper, showing how it could be used to evaluate current levels of risk and predict the effectiveness of proposed voluntary risk-reduction strategies.     

The limiting power of point optimal autocorrelation tests

This paper considers the point optimal tests for AR(1) errors in the linear regression model. It is shown that these tests have the same limiting power characteristics as the Durbin-Watson test. . The limiting power is zero or one when the regression has no intercept, but lies strictly between these values when an intercept is included.     

Monotone B-Spline Smoothing for a Generalized Linear Model Response

Various methods have been proposed for smoothing under the monotonicity constraint. We review the literature and implement an approach of monotone smoothing with B-splines for a generalized linear model response. The approach is expressed as a quadratic programming problem and is easily solved using the statistical software R. In a simulation study, we find that the approach performs better than other approaches with much faster computation time. The approach can also be used for smoothing under other shape constraints or mixed constraints. Supplementary materials of the appendices and R code to implement the developed approach is available online.     

Multiple roots of estimating functions

Estimating functions can have multiple roots. In such cases, the statistician must choose among the roots to estimate the parameter. Standard asymptotic theory shows that in a wide variety of cases, there exists a unique consistent root, and that this root will lie asymptotically close to other consistent (possibly inefficient) estimators for the parameter. For this reason, attention has largely focused on the problem of selecting this root and determining its approximate asymptotic distribution. In this paper, however, we concentrate on the exact distribution of the roots as a random set. In particular, we propose the use of higher-order root intensity functions as a tool for examining the properties of the roots and determining their most problematic features. The use of root intensity functions of first and second order is illustrated by application to the score function for the Cauchy location model.