Managing the Iatrogenic Risks of Risk Management

Analogizing to concerns that led the practice of medicine to shift from a specialist to a team-based approach, Dr. Wiener suggests that public and environmental health objectives would be better served if, e.g., regulatory jurisdiction were less atomized

The Puzzle of Environmental Politics

In this report we study estimation of time-delays in linear dynamical systems with additive noise. Estimating time-delays is a common engineering problem, e.g. in automatic control, system identification and signal processing. The purpose with this work is to test and evaluate a certain class of methods for time-delay estimation, especially with automatic control applications in mind. Particularly interesting it is to determine the best method. Is one method best in all situations or should different methods be used for different situations? The tested class of methods consists essentially of thresholding the cross correlation between the output and input signals. This is a very common method for time-delay estimation. The methods are tested and evaluated experimentally with the aid of simulations and plots of RMS error, bias and confidence intervals. The results are: The methods often miss to detect because the threshold is too high. The threshold has nevertheless been selected to give the best result. All methods over-estimate the time-delay. Nearly the whole RMS error is due to the bias. None of the tested methods is always best. Which method is best depends on the system and what is done when missing detections. Some form of averaging of the cross correlation, e.g. integration to step response or CUSUM, is advantageous. Fast systems are easiest. White noise input signal is easiest and steps is hardest. The RMS-errors are high in average (approximately greater than 6 sampling intervals). The error is lower for fast system or for high SNR

Impact Assessment: Diffusion and Integration

Introduction: As societies and their governments seek tools to help foresee and evaluate the future impacts of their current choices, successful policy foresight can benefit from learning from hindsight – from retrospective studies of the accuracy and impact of RIA and EIA (and other IA systems) on past decisions, both to revise current policies and also to improve the accuracy of IA systems in the future. This chapter discusses the ongoing diffusion of IA, and the pros and cons of combining the array of existing IA systems into a new and better system of Integrated Impact Assessment (IIA) (both prospective and retrospective) encompassing the full portfolio of important impacts

Developing an advanced module for back-contact solar cells

This paper proposes a novel concept for integrating ultrathin solar cells into modules. It is conceived as a method for fabricating solar panels starting from back-contact crystalline silicon solar cells. However, compared to the current state of the art in module manufacturing for back-contact solar cells, this novel concept aims at improvements in performance, reliability, and cost through the use of an alternative encapsulant, namely silicones as opposed to ethylene vinyl acetate, an alternative deposition technology, being wet coating as opposed to dry lamination; and alternative module-level metallization techniques, as opposed to cell-level tabbing-stringing or conductive foil interconnects. The process flow is proposed, and the materials and fabrication technologies are discussed. As the durability of the module, translated into the module's lifetime, is very important in the targeted application, namely solar cell modules, modeling and reliability testing results and considerations are presented to illustrate how the experimental development process may be guided by experience and theoretical derivations. Finally, feasibility is demonstrated in some first proofs of the concept, and an outlook is given pointing out the direction for further research

Regulatory Improvement Legislation: Risk Assessment, Cost-Benefit Analysis, and Judicial Review

As the number, cost, and complexity of federal regulations have grown over the past twenty years, there has been growing interest in the use of analytic tools such as risk assessment and cost-benefit analysis to improve the regulatory process. The application of these tools to public health, safety, and environmental problems has become commonplace in the peer-reviewed scientific and medical literatures. Recent studies prepared by Resources for the Future, the American Enterprise Institute, the Brookings Institution, and the Harvard Center for Risk Analysis have demonstrated how formal analyses can and often do help government agencies achieve more protection against hazards at less cost than would otherwise occur. Although analytic tools hold great promise, their use by federal agencies is neither consistent nor rigorous. The 103rd, 104th, 105th and 106th Congresses demonstrated sustained interest in the passage of comprehensive legislation governing the employment of these tools in the federal regulatory process. While legislative proposals on this issue have attracted significant bipartisan interest, and recent amendments to particular enabling statutes have incorporated some of these analytical requirements, no comprehensive legislation has been enacted into law since passage of the Administrative Procedure Act in 1946. The inability to pass such legislation has been attributed to a variety of factors, but a common substantive concern has been uncertainty and controversy about how such legislation should address judicial review issues. For example, the judicial review portion of The Regulatory Improvement Act (S. 981), the 105th Congress\u27s major legislative initiative, was criticized simultaneously as meaningless (for allegedly offering too few opportunities for petitioners to challenge poorly reasoned agency rules) and dangerous (as supposedly enabling petitioners to paralyze even well-reasoned agency rules). Thus, a significant obstacle to regulatory improvement legislation appears to be the conflicting opinions among legal scholars and practitioners about how judicial review issues should be addressed in such legislation. The Clinton Administration and the authors of S. 981 believe they have crafted a workable compromise, one that accommodates the need to bring more rigor and transparency to an agency\u27s decisional processes without imposing excessive judicial review. Nevertheless, it is clear that their agreement on this subject, if included in future legislative deliberations, will be scrutinized and contested. Recognizing the importance of the judicial review issue to this and, indeed, any effort to improve the regulatory process, the Center for Risk Analysis at the Harvard School of Public Health convened an invitational Workshop of accomplished legal practitioners and scholars to discuss how judicial review should be handled in legislation of this kind. The full-day Workshop was conducted in Washington, D.C. on December 17, 1998. Its purpose was to discuss principles, experiences, and insights that might inform future public debate about how judicial review should be addressed in legislative proposals that entail use of risk assessment and/or cost-benefit analysis in agency decision-making (whether the proposals are comprehensive or agency-specific). In order to provide the Workshop a practical focus, participants analyzed the provisions of S. 981 (as modified at the request of the Clinton Administration). An exchange of letters between S. 981\u27s chief sponsors and the Clinton Administration defining the terms of the agreement was examined as well. This Report highlights the themes of the Workshop discussion and offers some specific commentary on how proposed legislation (including but not limited to S. 981) could be improved in future legislative deliberations

Regulatory Improvement Legislation: Risk Assessment, Cost-Benefit Analysis, and Judicial Review

As the number, cost, and complexity of federal regulations have grown over the past twenty years, there has been growing interest in the use of analytic tools such as risk assessment and cost-benefit analysis to improve the regulatory process. The application of these tools to public health, safety, and environmental problems has become commonplace in the peer-reviewed scientific and medical literatures. Recent studies prepared by Resources for the Future, the American Enterprise Institute, the Brookings Institution, and the Harvard Center for Risk Analysis have demonstrated how formal analyses can and often do help government agencies achieve more protection against hazards at less cost than would otherwise occur. Although analytic tools hold great promise, their use by federal agencies is neither consistent nor rigorous. The 103rd, 104th, 105th and 106th Congresses demonstrated sustained interest in the passage of comprehensive legislation governing the employment of these tools in the federal regulatory process. While legislative proposals on this issue have attracted significant bipartisan interest, and recent amendments to particular enabling statutes have incorporated some of these analytical requirements, no comprehensive legislation has been enacted into law since passage of the Administrative Procedure Act in 1946. The inability to pass such legislation has been attributed to a variety of factors, but a common substantive concern has been uncertainty and controversy about how such legislation should address judicial review issues. For example, the judicial review portion of The Regulatory Improvement Act (S. 981), the 105th Congress\u27s major legislative initiative, was criticized simultaneously as meaningless (for allegedly offering too few opportunities for petitioners to challenge poorly reasoned agency rules) and dangerous (as supposedly enabling petitioners to paralyze even well-reasoned agency rules). Thus, a significant obstacle to regulatory improvement legislation appears to be the conflicting opinions among legal scholars and practitioners about how judicial review issues should be addressed in such legislation. The Clinton Administration and the authors of S. 981 believe they have crafted a workable compromise, one that accommodates the need to bring more rigor and transparency to an agency\u27s decisional processes without imposing excessive judicial review. Nevertheless, it is clear that their agreement on this subject, if included in future legislative deliberations, will be scrutinized and contested. Recognizing the importance of the judicial review issue to this and, indeed, any effort to improve the regulatory process, the Center for Risk Analysis at the Harvard School of Public Health convened an invitational Workshop of accomplished legal practitioners and scholars to discuss how judicial review should be handled in legislation of this kind. The full-day Workshop was conducted in Washington, D.C. on December 17, 1998. Its purpose was to discuss principles, experiences, and insights that might inform future public debate about how judicial review should be addressed in legislative proposals that entail use of risk assessment and/or cost-benefit analysis in agency decision-making (whether the proposals are comprehensive or agency-specific). In order to provide the Workshop a practical focus, participants analyzed the provisions of S. 981 (as modified at the request of the Clinton Administration). An exchange of letters between S. 981\u27s chief sponsors and the Clinton Administration defining the terms of the agreement was examined as well. This Report highlights the themes of the Workshop discussion and offers some specific commentary on how proposed legislation (including but not limited to S. 981) could be improved in future legislative deliberations

Single-cell approaches for the characterization of microbial population dynamics in bioprocesses

Some evidences show that a clonal population of microbial cells exhibits variation at their physiological level. For this reason, several methods that allow cellular characterization with a single-cell resolution have been strongly developed this last decade. For instance, flow cytometry is a reliable analytic method to study the complex distribution of physiologies occurring among microbial populations. This so called “phenotypic heterogeneity” has been extensively discussed in the scientific literature and remains a hot topic for biotechnology development suggesting that clonal cells have not the same ability to synthesize a product of interest during bioprocesses course. However, phenotypic heterogeneity patterns are not commonly interpreted in term of biological performances. Therefore, the current main challenges in single-cell techniques lies in an accurate understanding of the sources of biological inefficiency through a relevant interpretation of the phenotypic heterogeneity occurring in microbial populations. For this purpose, this work investigates the potentialities of both genetically encoded and exogenous biosensors to support the implementations of innovative optimization strategies considering biological traits of cell factories. Moreover, correlation between cell population heterogeneity and bioreactor heterogeneity has also been addressed by studying the response of biosensors under intensive culture conditions that occurs in industrial bioreactors. Thus, thanks to a deep analysis of biosensor signals, this work point out the added value brought by the single-cell concepts and make possible a better understanding of microbial physiology in bioprocesses conditions. Additionally, in parallel with an extended experimental strategy, this work proposed an original formalism in order to valorize the different component of single-cell technology and to facilitate its transfer towards industrial applications. Finally, beside challenges in link with biosensors signals interpretation, flow cytometry analysis leads to the high-throughput characterization of cell suspension and then, provides thousands of data. This high information diversity compels to cope with strong data management challenges. Actually, the question is: “How structure and treat single-cell data to improve their interpretations accuracy? In this frame, this study demonstrates the potential of single-cell distribution statistical treatment to rationally discriminate cellular samples which present different biological traits. In that way, we shown experimentally that the higher performant biological system can also be the more heterogeneous which is in opposite to the paradigm stating that only homogenous population are attractive for bioprocess applications. In a nutshell, this work set up the basis to study the relation between phenotypic heterogeneity and biological performance through the discussion of serval fundamental and applied concepts. That supports the proposal of rational optimization strategies while considering biological inputs and ensuring the valorization of single-cell concept as a response to current major industrial challenges