Suchergebnisse

The rapid development of biomedicine demands a trustworthy, proactive regulatory regime that is able to manage progress with genuine regard for ethical, social and legal concerns. With its recent past of eugenics and euthanasia, Germany is particularly concerned with setting up a fair and transparent approach, able to respond quickly to scientific developments as well as societal concerns. This article reports on the development, implementation and evaluation of a citizen scenario workshop as a tool of participatory prognostics, integrating elements from participatory technology assessment and forecasting. In 7 days of highly structured work and expert support, 24 German participants developed four scenarios on "The Relationship of Biomedicine and the Economy in the Year 2014." Results and evaluation both show that the process (1) leads to scenarios that provide a useful perspective beyond expert opinion; (2) enriches the public and political discourse; and (3) offers a social learning opportunity appreciated by nonprofessionals and experts alike. We are confident in recommending this technique as a useful addition to existing foresight and horizon scanning activities. ; Peer Reviewed

The use of system data to make predictions about the future system state commonly known as prognostics is a rapidly developing field. Prognostics seeks to build on current diagnostic equipment capabilities for its predictive capability. Many military systems including the Joint Strike Fighter (JSF) are planning to include on-board prognostics systems to enhance system supportability and affordability. Current research efforts supporting these developments tend to focus on developing a prognostic tool for one specific system component. This dissertation research presents a comprehensive literature review of these developing research efforts. It also develops presents a mathematical model for the optimum allocation of prognostics sensors and their associated classifiers on a given system and all of its components. The model assumptions about system criticality are consistent with current industrial philosophies. This research also develops methodologies for combine sensor classifiers to allow for the selection of the best sensor ensemble.

This article describes the basic terms used in prognostics (future studies, forecasting the future, prognosis, prediction, expert) and introduces selected prognostic methods and techniques which could be applied in security area (brainstorming, focus group, trend extrapolation, Delphi method, scenarios etc.). The basic classifications and prognosis typologies are mentioned as well. Particular phases of prognosis making are described too. For the Czech security community the prognosis making has an important potential whose outcomes (of particular surveys) should be manifest in following years. This text has only tried to outline selected methods and techniques for their potential application.

Uncertainty is a basic condition of medicine - in research, in the clinic and in the intersection between the two domains. Prognostics is in its very nature an attempt to address and manage the uncertainty of the future. New technologies are continuously developed in order to make prognostics more precise and hence make the future more predictable. However, such technologies may not always serve to decrease uncertainty, but rather enhance or introduce new uncertainties, and thereby open other futures than those imagined. Our study follows an interdisciplinary Danish research group currently seeking to advance methods of neuroimaging in prognostics for unresponsive patients with uncertain consciousness due to anoxic brain injury after an out-of-hospital cardiac arrest. Applying an interdisciplinary approach combining ethnographic methods and philosophical analysis we trace the experience of uncertainty in this context of tool development at the intersection of scientific and clinical reasoning around disorders of consciousness. Specifically, we employ and develop the three-dimensional framework of uncertainty developed by Han, Klein and Arora (2011). We identify salient uncertainties from the perspective of the researchers and show how these lead to different uncertainties experienced by the clinicians. Additionally, we show that while ambiguity may be the source of different kinds of uncertainty, the context determines the nature of the source. Our investigation has a descriptive and theoretical focus, however, uncovering these details may serve as a basis for normative discussions of strategies for uncertainty management, as well as evidence evaluation in research and the clinic in the future.

The present paper briefly recalls theoretical preconditions for forming a new approach – linguistic political prognostics. The authors review theories and methods used for strengthening a future focus in political discourses and work out two main tools – a model of future and a scenario of future

The advanced use of the Information and Communication Technologies is evolving the way that systems are managed and maintained. A great number of techniques and methods have emerged in the light of these advances allowing to have an accurate and knowledge about the systems' condition evolution and remaining useful life. The advances are recognized as outcomes of an innovative discipline, nowadays discussed under the term of Prognostics and Health Management (PHM). In order to analyze how maintenance will change by using PHM, a conceptual model is proposed built upon three views. The model highlights: (i) how PHM may impact the definition of maintenance policies; (ii) how PHM fits within the Condition Based Maintenance (CBM) and (iii) how PHM can be integrated into Reliability Centered Maintenance (RCM) programs. The conceptual model is the research finding of this review note and helps to discuss the role of PHM in advanced maintenance systems. ; EU Framework Programme Horizon 2020, 645733 - Sustain-Owner - H2020-MSCA-RISE-2014

AbstractThis paper presents a new and flexible prognostics framework based on a higher‐order hidden semi‐Markov model (HOHSMM) for systems or components with unobservable health states and complex transition dynamics. The HOHSMM extends the basic hidden Markov model (HMM) by allowing the hidden state to depend on its more distant history and assuming generally distributed state duration. An effective Gibbs sampling algorithm is designed for statistical inference of the HOHSMM. We conduct a simulation study to evaluate the performance of the proposed HOHSMM sampler and examine the impacts of the distant‐history dependency. We design a decoding algorithm to estimate the hidden health states using the learned model. Remaining useful life is predicted using a simulation approach given the decoded hidden states. The practical utility of the proposed prognostics framework is demonstrated by a case study on National Aeronautics and Space Administration (NASA) turbofan engines. We further compare the RUL prediction performance between the proposed HOHSMM and a benchmark mixture of Gaussians HMM prognostics method. The results show that the HOHSMM‐based prognostics framework provides good hidden health‐state assessment and RUL estimation for complex systems.