Modeling the detection rates of fires in nuclear plants: development and application of a methodology for treating imprecise evidence

A model is developed for the detection time of fires in nuclear power plants, which differentiates between competing modes of detection and between different initial fire severities. Our state-of-knowledge uncertainties in the values of the model parameters are assessed from industry experience using Bayesian methods. Because the available data are sparse, we propose means to interpret imprecise forms of evidence to the develop quantitative information, which can be used in a statistical analysis; the intent is to maximize our use of all available information. Sensitivity analyses are performed to indicate the importance of structural and distributional assumptions made in the study. The methods used to treat imprecise evidence can be applied to a wide variety of problems. The specific equations developed in this analysis are useful in general situations, where the random quantity of interest is the minimum of a set of random variables (e.g., in "competing risks" models). The computational results indicate that the competing modes formulation can lead to distributions different from those obtained via analytically simpler models, which treat each mode independently of the others.     

The Role of Risk and Future Land Use in Cleanup Decisions at the Department of Energy

As a result of the legacy of the Cold War, the Departments of Energy and Defense are involved in massive cleanup and remediation projects. While health risk to humans and ecological receptors is perceived to be the basis for remediation, this assumption is rarely examined. In this article, we examine the role of risk and future land-use designations in cleanup decisions, using the Department of Energy's self-assessment of 36 sites. We then discuss the risk-related tools that might be required to address the cleanup challenge. Much of the current cleanup program is driven by compliance with federal and state statutes and regulations, presumably to protect human health and the environment. Compliance, however, is not synonymous with cleanup. Although some of these laws and regulations take risk into account, the lack of site-specific data on exposures and risk scenarios, and the lack of attention to future land use or end states, has often resulted in disconnects between risk and cleanup goals, risk and final end states, and cleanup levels and end state or subsequent land use. Partly, these disconnects result from the need for a range of technical, economic, sociological, and public policy tools to address the issues. A better transfer of information among and within Department of Energy facilities, operations offices, and DOE headquarters is required. Further, linking cleanup decisions and goals with the final end state involves a number of risk tradeoffs, including (1) ecological versus human health, (2) worker versus public health, (3) among competing contaminated areas, (4) among temporal patterns of cleanup, (5) among different ecological receptors (plants vs. animals, one animal vs. another), and (6) among the sites across the DOE complex. For the nation, balancing among risks is essential within sites and among Department of Energy sites, as well as among other remediation sites (such as those of Department of Defense and Superfund sites).     

转型经济背景下的组织复杂性动因研究:环境不确定性和战略导向的作用

基于468家中国企业的实证研究,考察了不同环境及战略导向对组织复杂性的驱动作用。在分析相关文献形成研究假设的基础上运用实证的方法进行检验。研究结论验证了当前转型经济背景下不确定环境特征和战略导向对企业组织复杂性的不同驱动作用,对企业根据所处环境及其战略导向认识并有的放矢的应对不同组织复杂性有重要意义。