Risk Analysis for U.S. Offshore Wind Farms: The Need for an Integrated Approach

Wind power is becoming an increasingly important part of the global energy portfolio, and there is growing interest in developing offshore wind farms in the United States to better utilize this resource. Wind farms have certain environmental benefits, notably near‐zero emissions of greenhouse gases, particulates, and other contaminants of concern. However, there are significant challenges ahead in achieving large‐scale integration of wind power in the United States, particularly offshore wind. Environmental impacts from wind farms are a concern, and these are subject to a number of on‐going studies focused on risks to the environment. However, once a wind farm is built, the farm itself will face a number of risks from a variety of hazards, and managing these risks is critical to the ultimate achievement of long‐term reductions in pollutant emissions from clean energy sources such as wind. No integrated framework currently exists for assessing risks to offshore wind farms in the United States, which poses a challenge for wind farm risk management. In this “Perspective”, we provide an overview of the risks faced by an offshore wind farm, argue that an integrated framework is needed, and give a preliminary starting point for such a framework to illustrate what it might look like. This is not a final framework; substantial work remains. Our intention here is to highlight the research need in this area in the hope of spurring additional research about the risks to wind farms to complement the substantial amount of on‐going research on the risks from wind farms.     

Extension of the Application of Conway‐Maxwell‐Poisson Models: Analyzing Traffic Crash Data Exhibiting Underdispersion

The objective of this article is to evaluate the performance of the COM‐Poisson GLM for analyzing crash data exhibiting underdispersion (when conditional on the mean). The COM‐Poisson distribution, originally developed in 1962, has recently been reintroduced by statisticians for analyzing count data subjected to either over‐ or underdispersion. Over the last year, the COM‐Poisson GLM has been evaluated in the context of crash data analysis and it has been shown that the model performs as well as the Poisson‐gamma model for crash data exhibiting overdispersion. To accomplish the objective of this study, several COM‐Poisson models were estimated using crash data collected at 162 railway‐highway crossings in South Korea between 1998 and 2002. This data set has been shown to exhibit underdispersion when models linking crash data to various explanatory variables are estimated. The modeling results were compared to those produced from the Poisson and gamma probability models documented in a previous published study. The results of this research show that the COM‐Poisson GLM can handle crash data when the modeling output shows signs of underdispersion. Finally, they also show that the model proposed in this study provides better statistical performance than the gamma probability and the traditional Poisson models, at least for this data set.     

A Model for Setting Performance Objectives for Salmonella in the Broiler Supply Chain

A stochastic model for setting performance objectives for Salmonella in the broiler supply chain was developed. The goal of this study was to develop a model by which performance objectives for Salmonella prevalence at various points in the production chain can be determined, based on a preset final performance objective at the end of the processing line. The transmission of Salmonella through the broiler production chain was modeled. The prevalence at flock level was calculated from the measured prevalence at sample level. The transmission model is based on data on the occurrence of Salmonella collected in the Dutch broiler production chain during several years. The developed model can be used by policymakers and industry to determine economically and politically acceptable performance objectives for various points of the production chain and to draw conclusions about which interventions are most appropriate.     

Race, Class, and Gender Differences in High School Seniors' Values: Applying Intersection Theory in Empirical Analysis

Objective . This article demonstrates how intersection theory can be applied in empirical studies by testing whether an individual's race, class, and gender have interactive effects. Methods . Data on high school students' ratings of the importance of equality, money, career, and family from the Monitoring the Future study are used. A stepwise regression model is employed to determine first whether additive effects exist in the data, and then whether interactions exist among race, class, and gender characteristics. Results . Previous findings of additive effects were generally confirmed in the first stage of the regressions, with some exceptions. For the money and family values, significant interactions exist between some pairs of characteristics, but three-way interactions were not significant. For equality and career values, no significant interactions were found. Conclusions . Although there are times when additive models are sufficient to describe differences among groups, the omission of interaction terms can sometimes lead to misleading conclusions about the effects of race, class, and gender. Using intersection theory in empirical models will guard against this oversight and may lead to a broad theory of when significant race, class, and gender interactions should be expected.     

On the Limitations of Redundancies in the Improvement of System Reliability

Some program managers share a common belief that adding a redundant component to a system reduces the probability of failure by half. This is true only if the failures of the redundant components are independent events, which is rarely the case. For example, the redundant components may be subjected to the same external loads. There is, however, in general a decrease in the failure probability of the system. Nonetheless, the redundant element comes at a cost, even if it is less than that of developing the first one when both are based on the same design. Identical parts save the most in terms of design costs, but are subjected to common failure modes from possible design errors that limit the effectiveness of the redundancy. In the development of critical systems, managers thus need to decide if the costs of a parallel system are justified by the increase in the system's reliability. NASA, for example, has used redundant spacecraft to increase the chances of mission success, which worked well in the cases of the Viking and Voyager missions. These two successes, however, do not guarantee future ones. We present here a risk analysis framework accounting for dependencies to support the decision to launch at the same time a twin mission of identical spacecraft, given incremental costs and risk-reduction benefits of the second one. We illustrate this analytical approach with the case of the Mars Exploration Rovers launched by NASA in 2003, for which we had performed this assessment in 2001.     

Genetic Studies of Pathological Gambling: A Review of Methodology and Analyses of Data from the Vietnam Era Twin Registry

Twin studies represent an important and powerful approach to estimating the relative contributions of environmental and genetic factors to the expression of psychiatric illnesses. In this article, we first explain the rationale for using large populations of monozygotic and dizygotic twin pairs to estimate genetic and environmental contributions to a disorder and the statistical modeling associated with this approach. We then review results from studies of the Vietnam Era Twin Registry that use this methodology to examine the etiology of pathological gambling (PG) behaviors. The results provide strong evidence for genetic contributions to the development of PG in men and set the foundation for future studies aimed at identifying the manner in which specific genes and environmental factors individually and in conjunction contribute to PG.     

Impact of competitive conditions on supplier evaluation: a construction supply chain case study

This research proposes a multi-criteria decision-making (MCDM) framework and demonstrates the impact of competitive conditions on supplier evaluation process for construction supply chains. The paper focuses on the supply chain of a large-scale housing project in order to illustrate the role of competitive capability and suppliers’ profile and its influence on supplier evaluation based on prevailing supply/market conditions. Various scenarios are investigated to demonstrate the impact of competition on supplier evaluation. The contribution of the study lies in highlighting the impact of supply/market conditions on MCDM decisions causing supplier evaluation ‘imbalance’ and MCDM usage. It is expected that the study will be useful for project management, construction, supply chain management, sourcing professionals. The findings of the study are generalisable to projects-based situations such as petroleum refinery and ship building where bill of materials typically consists of thousands of items and a large number of suppliers are involved.