WorldWideScience

Sample records for greatly reduce uncertainties

  1. Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach

    Science.gov (United States)

    Aguilo, Miguel A.; Warner, James E.

    2017-01-01

    This work presents a stochastic reduced order modeling strategy for the quantification and propagation of uncertainties in topology optimization. Uncertainty aware optimization problems can be computationally complex due to the substantial number of model evaluations that are necessary to accurately quantify and propagate uncertainties. This computational complexity is greatly magnified if a high-fidelity, physics-based numerical model is used for the topology optimization calculations. Stochastic reduced order model (SROM) methods are applied here to effectively 1) alleviate the prohibitive computational cost associated with an uncertainty aware topology optimization problem; and 2) quantify and propagate the inherent uncertainties due to design imperfections. A generic SROM framework that transforms the uncertainty aware, stochastic topology optimization problem into a deterministic optimization problem that relies only on independent calls to a deterministic numerical model is presented. This approach facilitates the use of existing optimization and modeling tools to accurately solve the uncertainty aware topology optimization problems in a fraction of the computational demand required by Monte Carlo methods. Finally, an example in structural topology optimization is presented to demonstrate the effectiveness of the proposed uncertainty aware structural topology optimization approach.

  2. Neural Mechanisms of Updating under Reducible and Irreducible Uncertainty.

    Science.gov (United States)

    Kobayashi, Kenji; Hsu, Ming

    2017-07-19

    Adaptive decision making depends on an agent's ability to use environmental signals to reduce uncertainty. However, because of multiple types of uncertainty, agents must take into account not only the extent to which signals violate prior expectations but also whether uncertainty can be reduced in the first place. Here we studied how human brains of both sexes respond to signals under conditions of reducible and irreducible uncertainty. We show behaviorally that subjects' value updating was sensitive to the reducibility of uncertainty, and could be quantitatively characterized by a Bayesian model where agents ignore expectancy violations that do not update beliefs or values. Using fMRI, we found that neural processes underlying belief and value updating were separable from responses to expectancy violation, and that reducibility of uncertainty in value modulated connections from belief-updating regions to value-updating regions. Together, these results provide insights into how agents use knowledge about uncertainty to make better decisions while ignoring mere expectancy violation. SIGNIFICANCE STATEMENT To make good decisions, a person must observe the environment carefully, and use these observations to reduce uncertainty about consequences of actions. Importantly, uncertainty should not be reduced purely based on how surprising the observations are, particularly because in some cases uncertainty is not reducible. Here we show that the human brain indeed reduces uncertainty adaptively by taking into account the nature of uncertainty and ignoring mere surprise. Behaviorally, we show that human subjects reduce uncertainty in a quasioptimal Bayesian manner. Using fMRI, we characterize brain regions that may be involved in uncertainty reduction, as well as the network they constitute, and dissociate them from brain regions that respond to mere surprise. Copyright © 2017 the authors 0270-6474/17/376972-11$15.00/0.

  3. Decision-making under great uncertainty

    International Nuclear Information System (INIS)

    Hansson, S.O.

    1992-01-01

    Five types of decision-uncertainty are distinguished: uncertainty of consequences, of values, of demarcation, of reliance, and of co-ordination. Strategies are proposed for each type of uncertainty. The general conclusion is that it is meaningful for decision theory to treat cases with greater uncertainty than the textbook case of 'decision-making under uncertainty'. (au)

  4. An audit of the global carbon budget: identifying and reducing sources of uncertainty

    Science.gov (United States)

    Ballantyne, A. P.; Tans, P. P.; Marland, G.; Stocker, B. D.

    2012-12-01

    Uncertainties in our carbon accounting practices may limit our ability to objectively verify emission reductions on regional scales. Furthermore uncertainties in the global C budget must be reduced to benchmark Earth System Models that incorporate carbon-climate interactions. Here we present an audit of the global C budget where we try to identify sources of uncertainty for major terms in the global C budget. The atmospheric growth rate of CO2 has increased significantly over the last 50 years, while the uncertainty in calculating the global atmospheric growth rate has been reduced from 0.4 ppm/yr to 0.2 ppm/yr (95% confidence). Although we have greatly reduced global CO2 growth rate uncertainties, there remain regions, such as the Southern Hemisphere, Tropics and Arctic, where changes in regional sources/sinks will remain difficult to detect without additional observations. Increases in fossil fuel (FF) emissions are the primary factor driving the increase in global CO2 growth rate; however, our confidence in FF emission estimates has actually gone down. Based on a comparison of multiple estimates, FF emissions have increased from 2.45 ± 0.12 PgC/yr in 1959 to 9.40 ± 0.66 PgC/yr in 2010. Major sources of increasing FF emission uncertainty are increased emissions from emerging economies, such as China and India, as well as subtle differences in accounting practices. Lastly, we evaluate emission estimates from Land Use Change (LUC). Although relative errors in emission estimates from LUC are quite high (2 sigma ~ 50%), LUC emissions have remained fairly constant in recent decades. We evaluate the three commonly used approaches to estimating LUC emissions- Bookkeeping, Satellite Imagery, and Model Simulations- to identify their main sources of error and their ability to detect net emissions from LUC.; Uncertainties in Fossil Fuel Emissions over the last 50 years.

  5. Reducing the top quark mass uncertainty with jet grooming

    Science.gov (United States)

    Andreassen, Anders; Schwartz, Matthew D.

    2017-10-01

    The measurement of the top quark mass has large systematic uncertainties coming from the Monte Carlo simulations that are used to match theory and experiment. We explore how much that uncertainty can be reduced by using jet grooming procedures. Using the ATLAS A14 tunes of pythia, we estimate the uncertainty from the choice of tuning parameters in what is meant by the Monte Carlo mass to be around 530 MeV without any corrections. This uncertainty can be reduced by 60% to 200 MeV by calibrating to the W mass and by 70% to 140 MeV by additionally applying soft-drop jet grooming (or to 170 MeV using trimming). At e + e - colliders, the associated uncertainty is around 110 MeV, reducing to 50 MeV after calibrating to the W mass. By analyzing the tuning parameters, we conclude that the importance of jet grooming after calibrating to the W -mass is to reduce sensitivity to the underlying event.

  6. Quantifying uncertainties of seismic Bayesian inversion of Northern Great Plains

    Science.gov (United States)

    Gao, C.; Lekic, V.

    2017-12-01

    Elastic waves excited by earthquakes are the fundamental observations of the seismological studies. Seismologists measure information such as travel time, amplitude, and polarization to infer the properties of earthquake source, seismic wave propagation, and subsurface structure. Across numerous applications, seismic imaging has been able to take advantage of complimentary seismic observables to constrain profiles and lateral variations of Earth's elastic properties. Moreover, seismic imaging plays a unique role in multidisciplinary studies of geoscience by providing direct constraints on the unreachable interior of the Earth. Accurate quantification of uncertainties of inferences made from seismic observations is of paramount importance for interpreting seismic images and testing geological hypotheses. However, such quantification remains challenging and subjective due to the non-linearity and non-uniqueness of geophysical inverse problem. In this project, we apply a reverse jump Markov chain Monte Carlo (rjMcMC) algorithm for a transdimensional Bayesian inversion of continental lithosphere structure. Such inversion allows us to quantify the uncertainties of inversion results by inverting for an ensemble solution. It also yields an adaptive parameterization that enables simultaneous inversion of different elastic properties without imposing strong prior information on the relationship between them. We present retrieved profiles of shear velocity (Vs) and radial anisotropy in Northern Great Plains using measurements from USArray stations. We use both seismic surface wave dispersion and receiver function data due to their complementary constraints of lithosphere structure. Furthermore, we analyze the uncertainties of both individual and joint inversion of those two data types to quantify the benefit of doing joint inversion. As an application, we infer the variation of Moho depths and crustal layering across the northern Great Plains.

  7. Quantifying and Reducing Curve-Fitting Uncertainty in Isc

    Energy Technology Data Exchange (ETDEWEB)

    Campanelli, Mark; Duck, Benjamin; Emery, Keith

    2015-06-14

    Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.

  8. The fertility response to the Great Recession in Europe and the United States: Structural economic conditions and perceived economic uncertainty

    Directory of Open Access Journals (Sweden)

    Chiara Ludovica Comolli

    2017-05-01

    Full Text Available Background: This study further develops Goldstein et al.'s (2013 analysis of the fertility response to the Great Recession in western economies. Objective: The purpose of this paper is to shed light on the fertility reaction to different indicators of the crisis. Beyond the structural labor market conditions, I investigate the dependence of fertility rates on economic policy uncertainty, government financial risk, and consumer confidence. Methods: Following Goldstein et al. (2013, I use log-log models to assess the elasticity of age-, parity-, and education-specific fertility rates to an array of indicators. Besides the inclusion of a wider set of explanatory variables, I include more recent data (2000−2013 and I enlarge the sample to 31 European countries plus the United States. Results: Fertility response to unemployment in some age- and parity-specific groups has been, in more recent years, larger than estimated by Goldstein et al. (2013. Female unemployment has also been significantly reducing fertility rates. Among uncertainty measures, the drop in consumer confidence is strongly related to fertility decline and in Southern European countries the fertility response to sovereign debt risk is comparable to that of unemployment. Economic policy uncertainty is negatively related to TFR even when controlling for unemployment. Conclusions: Theoretical and empirical investigation is needed to develop more tailored measures of economic and financial insecurity and their impact on birth rates. Contribution: The study shows the nonnegligible influence of economic and financial uncertainty on birth rates during the Great Recession in Western economies, over and above that of structural labor market conditions.

  9. Using prediction uncertainty analysis to design hydrologic monitoring networks: Example applications from the Great Lakes water availability pilot project

    Science.gov (United States)

    Fienen, Michael N.; Doherty, John E.; Hunt, Randall J.; Reeves, Howard W.

    2010-01-01

    The importance of monitoring networks for resource-management decisions is becoming more recognized, in both theory and application. Quantitative computer models provide a science-based framework to evaluate the efficacy and efficiency of existing and possible future monitoring networks. In the study described herein, two suites of tools were used to evaluate the worth of new data for specific predictions, which in turn can support efficient use of resources needed to construct a monitoring network. The approach evaluates the uncertainty of a model prediction and, by using linear propagation of uncertainty, estimates how much uncertainty could be reduced if the model were calibrated with addition information (increased a priori knowledge of parameter values or new observations). The theoretical underpinnings of the two suites of tools addressing this technique are compared, and their application to a hypothetical model based on a local model inset into the Great Lakes Water Availability Pilot model are described. Results show that meaningful guidance for monitoring network design can be obtained by using the methods explored. The validity of this guidance depends substantially on the parameterization as well; hence, parameterization must be considered not only when designing the parameter-estimation paradigm but also-importantly-when designing the prediction-uncertainty paradigm.

  10. Quantifying and Reducing Curve-Fitting Uncertainty in Isc: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Campanelli, Mark; Duck, Benjamin; Emery, Keith

    2015-09-28

    Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.

  11. Uncertainty, joint uncertainty, and the quantum uncertainty principle

    International Nuclear Information System (INIS)

    Narasimhachar, Varun; Poostindouz, Alireza; Gour, Gilad

    2016-01-01

    Historically, the element of uncertainty in quantum mechanics has been expressed through mathematical identities called uncertainty relations, a great many of which continue to be discovered. These relations use diverse measures to quantify uncertainty (and joint uncertainty). In this paper we use operational information-theoretic principles to identify the common essence of all such measures, thereby defining measure-independent notions of uncertainty and joint uncertainty. We find that most existing entropic uncertainty relations use measures of joint uncertainty that yield themselves to a small class of operational interpretations. Our notion relaxes this restriction, revealing previously unexplored joint uncertainty measures. To illustrate the utility of our formalism, we derive an uncertainty relation based on one such new measure. We also use our formalism to gain insight into the conditions under which measure-independent uncertainty relations can be found. (paper)

  12. Understanding and reducing statistical uncertainties in nebular abundance determinations

    Science.gov (United States)

    Wesson, R.; Stock, D. J.; Scicluna, P.

    2012-06-01

    Whenever observations are compared to theories, an estimate of the uncertainties associated with the observations is vital if the comparison is to be meaningful. However, many or even most determinations of temperatures, densities and abundances in photoionized nebulae do not quote the associated uncertainty. Those that do typically propagate the uncertainties using analytical techniques which rely on assumptions that generally do not hold. Motivated by this issue, we have developed Nebular Empirical Analysis Tool (NEAT), a new code for calculating chemical abundances in photoionized nebulae. The code carries out a standard analysis of lists of emission lines using long-established techniques to estimate the amount of interstellar extinction, calculate representative temperatures and densities, compute ionic abundances from both collisionally excited lines and recombination lines, and finally to estimate total elemental abundances using an ionization correction scheme. NEATuses a Monte Carlo technique to robustly propagate uncertainties from line flux measurements through to the derived abundances. We show that, for typical observational data, this approach is superior to analytic estimates of uncertainties. NEAT also accounts for the effect of upward biasing on measurements of lines with low signal-to-noise ratio, allowing us to accurately quantify the effect of this bias on abundance determinations. We find not only that the effect can result in significant overestimates of heavy element abundances derived from weak lines, but also that taking it into account reduces the uncertainty of these abundance determinations. Finally, we investigate the effect of possible uncertainties in R, the ratio of selective-to-total extinction, on abundance determinations. We find that the uncertainty due to this parameter is negligible compared to the statistical uncertainties due to typical line flux measurement uncertainties.

  13. Uncertainty analysis of the FRAP code

    International Nuclear Information System (INIS)

    Peck, S.O.

    1978-01-01

    A user oriented, automated uncertainty analysis capability has been built into the Fuel Rod Analysis Program (FRAP) code and has been applied to a pressurized water reactor (PWR) fuel rod undergoing a loss-of-coolant accident (LOCA). The method of uncertainty analysis is the response surface method. The automated version significantly reduced the time required to complete the analysis and, at the same time, greatly increased the problem scope. Results of the analysis showed a significant difference in the total and relative contributions to the uncertainty of the response parameters between steady state and transient conditions

  14. Combining observations and models to reduce uncertainty in the cloud response to global warming

    Science.gov (United States)

    Norris, J. R.; Myers, T.; Chellappan, S.

    2017-12-01

    Currently there is large uncertainty on how subtropical low-level clouds will respond to global warming and whether they will act as a positive feedback or negative feedback. Global climate models substantially agree on what changes in atmospheric structure and circulation will occur with global warming but greatly disagree over how clouds will respond to these changes in structure and circulation. An examination of models with the most realistic simulations of low-level cloudiness indicates that the model cloud response to atmospheric changes associated with global warming is quantitatively similar to the model cloud response to atmospheric changes at interannual time scales. For these models, the cloud response to global warming predicted by multilinear regression using coefficients derived from interannual time scales is quantitatively similar to the cloud response to global warming directly simulated by the model. Since there is a large spread among cloud response coefficients even among models with the most realistic cloud simulations, substitution of coefficients derived from satellite observations reduces the uncertainty range of the low-level cloud feedback. Increased sea surface temperature associated with global warming acts to reduce low-level cloudiness, which is partially offset by increased lower tropospheric stratification that acts to enhance low-level cloudiness. Changes in free-tropospheric relative humidity, subsidence, and horizontal advection have only a small impact on low-level cloud. The net reduction in subtropical low-level cloudiness increases absorption of solar radiation by the climate system, thus resulting in a weak positive feedback.

  15. Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Yannan; Hou, Zhangshuan; Meng, Da; Samaan, Nader A.; Makarov, Yuri V.; Huang, Zhenyu

    2016-07-17

    In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.

  16. Estabilishing requirements for the next generation of pressurized water reactors--reducing the uncertainty

    International Nuclear Information System (INIS)

    Chernock, W.P.; Corcoran, W.R.; Rasin, W.H.; Stahlkopf, K.E.

    1987-01-01

    The Electric Power Research Institute is managing a major effort to establish requirements for the next generation of U.S. light water reactors. This effort is the vital first step in preserving the viability of the nuclear option to contribute to meet U.S. national electric power capacity needs in the next century. Combustion Engineering, Inc. and Duke Power Company formed a team to participate in the EPRI program which is guided by a Utility Steering committee consisting of experienced utility technical executives. A major thrust of the program is to reduce the uncertainties which would be faced by the utility executives in choosing the nuclear option. The uncertainties to be reduced include those related to safety, economic, operational, and regulatory aspects of advanced light water reactors. This paper overviews the Requirements Document program as it relates to the U.S. Advanced Light Water Reactor (ALWR) effort in reducing these uncertainties and reports the status of efforts to establish requirements for the next generation of pressurized water reactors. It concentrates on progress made in reducing the uncertainties which would deter selection of the nuclear option for contributing to U.S. national electric power capacity needs in the next century and updates previous reports in the same area. (author)

  17. Quantifying data worth toward reducing predictive uncertainty

    Science.gov (United States)

    Dausman, A.M.; Doherty, J.; Langevin, C.D.; Sukop, M.C.

    2010-01-01

    The present study demonstrates a methodology for optimization of environmental data acquisition. Based on the premise that the worth of data increases in proportion to its ability to reduce the uncertainty of key model predictions, the methodology can be used to compare the worth of different data types, gathered at different locations within study areas of arbitrary complexity. The method is applied to a hypothetical nonlinear, variable density numerical model of salt and heat transport. The relative utilities of temperature and concentration measurements at different locations within the model domain are assessed in terms of their ability to reduce the uncertainty associated with predictions of movement of the salt water interface in response to a decrease in fresh water recharge. In order to test the sensitivity of the method to nonlinear model behavior, analyses were repeated for multiple realizations of system properties. Rankings of observation worth were similar for all realizations, indicating robust performance of the methodology when employed in conjunction with a highly nonlinear model. The analysis showed that while concentration and temperature measurements can both aid in the prediction of interface movement, concentration measurements, especially when taken in proximity to the interface at locations where the interface is expected to move, are of greater worth than temperature measurements. Nevertheless, it was also demonstrated that pairs of temperature measurements, taken in strategic locations with respect to the interface, can also lead to more precise predictions of interface movement. Journal compilation ?? 2010 National Ground Water Association.

  18. Calculating salt loads to Great Salt Lake and the associated uncertainties for water year 2013; updating a 48 year old standard

    Science.gov (United States)

    Shope, Christopher L.; Angeroth, Cory E.

    2015-01-01

    Effective management of surface waters requires a robust understanding of spatiotemporal constituent loadings from upstream sources and the uncertainty associated with these estimates. We compared the total dissolved solids loading into the Great Salt Lake (GSL) for water year 2013 with estimates of previously sampled periods in the early 1960s.We also provide updated results on GSL loading, quantitatively bounded by sampling uncertainties, which are useful for current and future management efforts. Our statistical loading results were more accurate than those from simple regression models. Our results indicate that TDS loading to the GSL in water year 2013 was 14.6 million metric tons with uncertainty ranging from 2.8 to 46.3 million metric tons, which varies greatly from previous regression estimates for water year 1964 of 2.7 million metric tons. Results also indicate that locations with increased sampling frequency are correlated with decreasing confidence intervals. Because time is incorporated into the LOADEST models, discrepancies are largely expected to be a function of temporally lagged salt storage delivery to the GSL associated with terrestrial and in-stream processes. By incorporating temporally variable estimates and statistically derived uncertainty of these estimates,we have provided quantifiable variability in the annual estimates of dissolved solids loading into the GSL. Further, our results support the need for increased monitoring of dissolved solids loading into saline lakes like the GSL by demonstrating the uncertainty associated with different levels of sampling frequency.

  19. Essential information: Uncertainty and optimal control of Ebola outbreaks.

    Science.gov (United States)

    Li, Shou-Li; Bjørnstad, Ottar N; Ferrari, Matthew J; Mummah, Riley; Runge, Michael C; Fonnesbeck, Christopher J; Tildesley, Michael J; Probert, William J M; Shea, Katriona

    2017-05-30

    Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.

  20. Essential information: Uncertainty and optimal control of Ebola outbreaks

    Science.gov (United States)

    Li, Shou-Li; Bjornstad, Ottar; Ferrari, Matthew J.; Mummah, Riley; Runge, Michael C.; Fonnesbeck, Christopher J.; Tildesley, Michael J.; Probert, William J. M.; Shea, Katriona

    2017-01-01

    Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.

  1. Reducing uncertainty in geostatistical description with well testing pressure data

    Energy Technology Data Exchange (ETDEWEB)

    Reynolds, A.C.; He, Nanqun [Univ. of Tulsa, OK (United States); Oliver, D.S. [Chevron Petroleum Technology Company, La Habra, CA (United States)

    1997-08-01

    Geostatistics has proven to be an effective tool for generating realizations of reservoir properties conditioned to static data, e.g., core and log data and geologic knowledge. Due to the lack of closely spaced data in the lateral directions, there will be significant variability in reservoir descriptions generated by geostatistical simulation, i.e., significant uncertainty in the reservoir descriptions. In past work, we have presented procedures based on inverse problem theory for generating reservoir descriptions (rock property fields) conditioned to pressure data and geostatistical information represented as prior means for log-permeability and porosity and variograms. Although we have shown that the incorporation of pressure data reduces the uncertainty below the level contained in the geostatistical model based only on static information (the prior model), our previous results assumed did not explicitly account for uncertainties in the prior means and the parameters defining the variogram model. In this work, we investigate how pressure data can help detect errors in the prior means. If errors in the prior means are large and are not taken into account, realizations conditioned to pressure data represent incorrect samples of the a posteriori probability density function for the rock property fields, whereas, if the uncertainty in the prior mean is incorporated properly into the model, one obtains realistic realizations of the rock property fields.

  2. Using performance indicators to reduce cost uncertainty of China's CO2 mitigation goals

    International Nuclear Information System (INIS)

    Xu, Yuan

    2013-01-01

    Goals on absolute emissions and intensity play key roles in CO 2 mitigation. However, like cap-and-trade policies with price uncertainty, they suffer from significant uncertainty in abatement costs. This article examines whether an indicator could be established to complement CO 2 mitigation goals and help reduce cost uncertainty with a particular focus on China. Performance indicators on CO 2 emissions per unit of energy consumption could satisfy three criteria: compared with the mitigation goals, (i) they are more closely associated with active mitigation efforts and (ii) their baselines have more stable projections from historical trajectories. (iii) Their abatement costs are generally higher than other mitigation methods, particularly energy efficiency and conservation. Performance indicators could be used in the following way: if a CO 2 goal on absolute emissions or intensity is attained, the performance indicator should still reach a lower threshold as a cost floor. If the goal cannot be attained, an upper performance threshold should be achieved as a cost ceiling. The narrower cost uncertainty may encourage wider and greater mitigation efforts. - Highlights: ► CO 2 emissions per unit of energy consumption could act as performance indicators. ► Performance indicators are more closely related to active mitigation activities. ► Performance indicators have more stable historical trajectories. ► Abatement costs are higher for performance indicators than for other activities. ► Performance thresholds could reduce the cost uncertainty of CO 2 mitigation goals.

  3. Great Tits (Parus major) reduce caterpillar damage in commercial apple orchards

    NARCIS (Netherlands)

    Mols, C.M.M.; Visser, M.E.

    2007-01-01

    Alternative ways to control caterpillar pests and reduce the use of pesticides in apple orchards are in the interest of the environment, farmers and the public. Great tits have already been shown to reduce damage under high caterpillar density when breeding in nest boxes in an experimental apple

  4. Attribute amnesia is greatly reduced with novel stimuli

    Directory of Open Access Journals (Sweden)

    Weijia Chen

    2017-11-01

    Full Text Available Attribute amnesia is the counterintuitive phenomenon where observers are unable to report a salient aspect of a stimulus (e.g., its colour or its identity immediately after the stimulus was presented, despite both attending to and processing the stimulus. Almost all previous attribute amnesia studies used highly familiar stimuli. Our study investigated whether attribute amnesia would also occur for unfamiliar stimuli. We conducted four experiments using stimuli that were highly familiar (colours or repeated animal images or that were unfamiliar to the observers (unique animal images. Our results revealed that attribute amnesia was present for both sets of familiar stimuli, colour (p < .001 and repeated animals (p = .001; but was greatly attenuated, and possibly eliminated, when the stimuli were unique animals (p = .02. Our data shows that attribute amnesia is greatly reduced for novel stimuli.

  5. Reducing Uncertainty: Implementation of Heisenberg Principle to Measure Company Performance

    Directory of Open Access Journals (Sweden)

    Anna Svirina

    2015-08-01

    Full Text Available The paper addresses the problem of uncertainty reduction in estimation of future company performance, which is a result of wide range of enterprise's intangible assets probable efficiency. To reduce this problem, the paper suggests to use quantum economy principles, i.e. implementation of Heisenberg principle to measure efficiency and potential of intangible assets of the company. It is proposed that for intangibles it is not possible to estimate both potential and efficiency at a certain time point. To provide a proof for these thesis, the data on resources potential and efficiency from mid-Russian companies was evaluated within deterministic approach, which did not allow to evaluate probability of achieving certain resource efficiency, and quantum approach, which allowed to estimate the central point around which the probable efficiency of resources in concentrated. Visualization of these approaches was performed by means of LabView software. It was proven that for tangible assets performance estimation a deterministic approach should be used; while for intangible assets the quantum approach allows better quality of future performance prediction. On the basis of these findings we proposed the holistic approach towards estimation of company resource efficiency in order to reduce uncertainty in modeling company performance.

  6. Can agent based models effectively reduce fisheries management implementation uncertainty?

    Science.gov (United States)

    Drexler, M.

    2016-02-01

    Uncertainty is an inherent feature of fisheries management. Implementation uncertainty remains a challenge to quantify often due to unintended responses of users to management interventions. This problem will continue to plague both single species and ecosystem based fisheries management advice unless the mechanisms driving these behaviors are properly understood. Equilibrium models, where each actor in the system is treated as uniform and predictable, are not well suited to forecast the unintended behaviors of individual fishers. Alternatively, agent based models (AMBs) can simulate the behaviors of each individual actor driven by differing incentives and constraints. This study evaluated the feasibility of using AMBs to capture macro scale behaviors of the US West Coast Groundfish fleet. Agent behavior was specified at the vessel level. Agents made daily fishing decisions using knowledge of their own cost structure, catch history, and the histories of catch and quota markets. By adding only a relatively small number of incentives, the model was able to reproduce highly realistic macro patterns of expected outcomes in response to management policies (catch restrictions, MPAs, ITQs) while preserving vessel heterogeneity. These simulations indicate that agent based modeling approaches hold much promise for simulating fisher behaviors and reducing implementation uncertainty. Additional processes affecting behavior, informed by surveys, are continually being added to the fisher behavior model. Further coupling of the fisher behavior model to a spatial ecosystem model will provide a fully integrated social, ecological, and economic model capable of performing management strategy evaluations to properly consider implementation uncertainty in fisheries management.

  7. Revealing, Reducing, and Representing Uncertainties in New Hydrologic Projections for Climate-changed Futures

    Science.gov (United States)

    Arnold, Jeffrey; Clark, Martyn; Gutmann, Ethan; Wood, Andy; Nijssen, Bart; Rasmussen, Roy

    2016-04-01

    The United States Army Corps of Engineers (USACE) has had primary responsibility for multi-purpose water resource operations on most of the major river systems in the U.S. for more than 200 years. In that time, the USACE projects and programs making up those operations have proved mostly robust against the range of natural climate variability encountered over their operating life spans. However, in some watersheds and for some variables, climate change now is known to be shifting the hydroclimatic baseline around which that natural variability occurs and changing the range of that variability as well. This makes historical stationarity an inappropriate basis for assessing continued project operations under climate-changed futures. That means new hydroclimatic projections are required at multiple scales to inform decisions about specific threats and impacts, and for possible adaptation responses to limit water-resource vulnerabilities and enhance operational resilience. However, projections of possible future hydroclimatologies have myriad complex uncertainties that require explicit guidance for interpreting and using them to inform those decisions about climate vulnerabilities and resilience. Moreover, many of these uncertainties overlap and interact. Recent work, for example, has shown the importance of assessing the uncertainties from multiple sources including: global model structure [Meehl et al., 2005; Knutti and Sedlacek, 2013]; internal climate variability [Deser et al., 2012; Kay et al., 2014]; climate downscaling methods [Gutmann et al., 2012; Mearns et al., 2013]; and hydrologic models [Addor et al., 2014; Vano et al., 2014; Mendoza et al., 2015]. Revealing, reducing, and representing these uncertainties is essential for defining the plausible quantitative climate change narratives required to inform water-resource decision-making. And to be useful, such quantitative narratives, or storylines, of climate change threats and hydrologic impacts must sample

  8. Subpixel edge localization with reduced uncertainty by violating the Nyquist criterion

    Science.gov (United States)

    Heidingsfelder, Philipp; Gao, Jun; Wang, Kun; Ott, Peter

    2014-12-01

    In this contribution, the extent to which the Nyquist criterion can be violated in optical imaging systems with a digital sensor, e.g., a digital microscope, is investigated. In detail, we analyze the subpixel uncertainty of the detected position of a step edge, the edge of a stripe with a varying width, and that of a periodic rectangular pattern for varying pixel pitches of the sensor, thus also in aliased conditions. The analysis includes the investigation of different algorithms of edge localization based on direct fitting or based on the derivative of the edge profile, such as the common centroid method. In addition to the systematic error of these algorithms, the influence of the photon noise (PN) is included in the investigation. A simplified closed form solution for the uncertainty of the edge position caused by the PN is derived. The presented results show that, in the vast majority of cases, the pixel pitch can exceed the Nyquist sampling distance by about 50% without an increase of the uncertainty of edge localization. This allows one to increase the field-of-view without increasing the resolution of the sensor and to decrease the size of the setup by reducing the magnification. Experimental results confirm the simulation results.

  9. A fractional factorial probabilistic collocation method for uncertainty propagation of hydrologic model parameters in a reduced dimensional space

    Science.gov (United States)

    Wang, S.; Huang, G. H.; Huang, W.; Fan, Y. R.; Li, Z.

    2015-10-01

    In this study, a fractional factorial probabilistic collocation method is proposed to reveal statistical significance of hydrologic model parameters and their multi-level interactions affecting model outputs, facilitating uncertainty propagation in a reduced dimensional space. The proposed methodology is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability, as well as its capability of revealing complex and dynamic parameter interactions. A set of reduced polynomial chaos expansions (PCEs) only with statistically significant terms can be obtained based on the results of factorial analysis of variance (ANOVA), achieving a reduction of uncertainty in hydrologic predictions. The predictive performance of reduced PCEs is verified by comparing against standard PCEs and the Monte Carlo with Latin hypercube sampling (MC-LHS) method in terms of reliability, sharpness, and Nash-Sutcliffe efficiency (NSE). Results reveal that the reduced PCEs are able to capture hydrologic behaviors of the Xiangxi River watershed, and they are efficient functional representations for propagating uncertainties in hydrologic predictions.

  10. Reducing Dose Uncertainty for Spot-Scanning Proton Beam Therapy of Moving Tumors by Optimizing the Spot Delivery Sequence

    International Nuclear Information System (INIS)

    Li, Heng; Zhu, X. Ronald; Zhang, Xiaodong

    2015-01-01

    Purpose: To develop and validate a novel delivery strategy for reducing the respiratory motion–induced dose uncertainty of spot-scanning proton therapy. Methods and Materials: The spot delivery sequence was optimized to reduce dose uncertainty. The effectiveness of the delivery sequence optimization was evaluated using measurements and patient simulation. One hundred ninety-one 2-dimensional measurements using different delivery sequences of a single-layer uniform pattern were obtained with a detector array on a 1-dimensional moving platform. Intensity modulated proton therapy plans were generated for 10 lung cancer patients, and dose uncertainties for different delivery sequences were evaluated by simulation. Results: Without delivery sequence optimization, the maximum absolute dose error can be up to 97.2% in a single measurement, whereas the optimized delivery sequence results in a maximum absolute dose error of ≤11.8%. In patient simulation, the optimized delivery sequence reduces the mean of fractional maximum absolute dose error compared with the regular delivery sequence by 3.3% to 10.6% (32.5-68.0% relative reduction) for different patients. Conclusions: Optimizing the delivery sequence can reduce dose uncertainty due to respiratory motion in spot-scanning proton therapy, assuming the 4-dimensional CT is a true representation of the patients' breathing patterns.

  11. Great tits (Parus major reduce caterpillar damage in commercial apple orchards.

    Directory of Open Access Journals (Sweden)

    Christel M M Mols

    Full Text Available Alternative ways to control caterpillar pests and reduce the use of pesticides in apple orchards are in the interest of the environment, farmers and the public. Great tits have already been shown to reduce damage under high caterpillar density when breeding in nest boxes in an experimental apple orchard. We tested whether this reduction also occurs under practical conditions of Integrated Pest Management (IPM, as well as Organic Farming (OF, by setting up an area with nest boxes while leaving a comparable area as a control within 12 commercial orchards. We showed that in IPM orchards, but not in OF orchards, in the areas with breeding great tits, apples had 50% of the caterpillar damage of the control areas. Offering nest boxes to attract insectivorous passerines in orchards can thus lead to more limited pesticide use, thereby adding to the natural biological diversity in an agricultural landscape, while also being economically profitable to the fruit growers.

  12. Great tits (Parus major) reduce caterpillar damage in commercial apple orchards.

    Science.gov (United States)

    Mols, Christel M M; Visser, Marcel E

    2007-02-07

    Alternative ways to control caterpillar pests and reduce the use of pesticides in apple orchards are in the interest of the environment, farmers and the public. Great tits have already been shown to reduce damage under high caterpillar density when breeding in nest boxes in an experimental apple orchard. We tested whether this reduction also occurs under practical conditions of Integrated Pest Management (IPM), as well as Organic Farming (OF), by setting up an area with nest boxes while leaving a comparable area as a control within 12 commercial orchards. We showed that in IPM orchards, but not in OF orchards, in the areas with breeding great tits, apples had 50% of the caterpillar damage of the control areas. Offering nest boxes to attract insectivorous passerines in orchards can thus lead to more limited pesticide use, thereby adding to the natural biological diversity in an agricultural landscape, while also being economically profitable to the fruit growers.

  13. How incorporating more data reduces uncertainty in recovery predictions

    Energy Technology Data Exchange (ETDEWEB)

    Campozana, F.P.; Lake, L.W.; Sepehrnoori, K. [Univ. of Texas, Austin, TX (United States)

    1997-08-01

    From the discovery to the abandonment of a petroleum reservoir, there are many decisions that involve economic risks because of uncertainty in the production forecast. This uncertainty may be quantified by performing stochastic reservoir modeling (SRM); however, it is not practical to apply SRM every time the model is updated to account for new data. This paper suggests a novel procedure to estimate reservoir uncertainty (and its reduction) as a function of the amount and type of data used in the reservoir modeling. Two types of data are analyzed: conditioning data and well-test data. However, the same procedure can be applied to any other data type. Three performance parameters are suggested to quantify uncertainty. SRM is performed for the following typical stages: discovery, primary production, secondary production, and infill drilling. From those results, a set of curves is generated that can be used to estimate (1) the uncertainty for any other situation and (2) the uncertainty reduction caused by the introduction of new wells (with and without well-test data) into the description.

  14. Reducing structural uncertainty in conceptual hydrological modeling in the semi-arid Andes

    Science.gov (United States)

    Hublart, P.; Ruelland, D.; Dezetter, A.; Jourde, H.

    2014-10-01

    The use of lumped, conceptual models in hydrological impact studies requires placing more emphasis on the uncertainty arising from deficiencies and/or ambiguities in the model structure. This study provides an opportunity to combine a multiple-hypothesis framework with a multi-criteria assessment scheme to reduce structural uncertainty in the conceptual modeling of a meso-scale Andean catchment (1515 km2) over a 30 year period (1982-2011). The modeling process was decomposed into six model-building decisions related to the following aspects of the system behavior: snow accumulation and melt, runoff generation, redistribution and delay of water fluxes, and natural storage effects. Each of these decisions was provided with a set of alternative modeling options, resulting in a total of 72 competing model structures. These structures were calibrated using the concept of Pareto optimality with three criteria pertaining to streamflow simulations and one to the seasonal dynamics of snow processes. The results were analyzed in the four-dimensional space of performance measures using a fuzzy c-means clustering technique and a differential split sample test, leading to identify 14 equally acceptable model hypotheses. A filtering approach was then applied to these best-performing structures in order to minimize the overall uncertainty envelope while maximizing the number of enclosed observations. This led to retain 8 model hypotheses as a representation of the minimum structural uncertainty that could be obtained with this modeling framework. Future work to better consider model predictive uncertainty should include a proper assessment of parameter equifinality and data errors, as well as the testing of new or refined hypotheses to allow for the use of additional auxiliary observations.

  15. Reducing structural uncertainty in conceptual hydrological modelling in the semi-arid Andes

    Science.gov (United States)

    Hublart, P.; Ruelland, D.; Dezetter, A.; Jourde, H.

    2015-05-01

    The use of lumped, conceptual models in hydrological impact studies requires placing more emphasis on the uncertainty arising from deficiencies and/or ambiguities in the model structure. This study provides an opportunity to combine a multiple-hypothesis framework with a multi-criteria assessment scheme to reduce structural uncertainty in the conceptual modelling of a mesoscale Andean catchment (1515 km2) over a 30-year period (1982-2011). The modelling process was decomposed into six model-building decisions related to the following aspects of the system behaviour: snow accumulation and melt, runoff generation, redistribution and delay of water fluxes, and natural storage effects. Each of these decisions was provided with a set of alternative modelling options, resulting in a total of 72 competing model structures. These structures were calibrated using the concept of Pareto optimality with three criteria pertaining to streamflow simulations and one to the seasonal dynamics of snow processes. The results were analyzed in the four-dimensional (4-D) space of performance measures using a fuzzy c-means clustering technique and a differential split sample test, leading to identify 14 equally acceptable model hypotheses. A filtering approach was then applied to these best-performing structures in order to minimize the overall uncertainty envelope while maximizing the number of enclosed observations. This led to retain eight model hypotheses as a representation of the minimum structural uncertainty that could be obtained with this modelling framework. Future work to better consider model predictive uncertainty should include a proper assessment of parameter equifinality and data errors, as well as the testing of new or refined hypotheses to allow for the use of additional auxiliary observations.

  16. How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications

    Science.gov (United States)

    McMillan, Hilary; Seibert, Jan; Petersen-Overleir, Asgeir; Lang, Michel; White, Paul; Snelder, Ton; Rutherford, Kit; Krueger, Tobias; Mason, Robert; Kiang, Julie

    2017-07-01

    Streamflow data are used for important environmental and economic decisions, such as specifying and regulating minimum flows, managing water supplies, and planning for flood hazards. Despite significant uncertainty in most flow data, the flow series for these applications are often communicated and used without uncertainty information. In this commentary, we argue that proper analysis of uncertainty in river flow data can reduce costs and promote robust conclusions in water management applications. We substantiate our argument by providing case studies from Norway and New Zealand where streamflow uncertainty analysis has uncovered economic costs in the hydropower industry, improved public acceptance of a controversial water management policy, and tested the accuracy of water quality trends. We discuss the need for practical uncertainty assessment tools that generate multiple flow series realizations rather than simple error bounds. Although examples of such tools are in development, considerable barriers for uncertainty analysis and communication still exist for practitioners, and future research must aim to provide easier access and usability of uncertainty estimates. We conclude that flow uncertainty analysis is critical for good water management decisions.

  17. Observation of quantum-memory-assisted entropic uncertainty relation under open systems, and its steering

    Science.gov (United States)

    Chen, Peng-Fei; Sun, Wen-Yang; Ming, Fei; Huang, Ai-Jun; Wang, Dong; Ye, Liu

    2018-01-01

    Quantum objects are susceptible to noise from their surrounding environments, interaction with which inevitably gives rise to quantum decoherence or dissipation effects. In this work, we examine how different types of local noise under an open system affect entropic uncertainty relations for two incompatible measurements. Explicitly, we observe the dynamics of the entropic uncertainty in the presence of quantum memory under two canonical categories of noisy environments: unital (phase flip) and nonunital (amplitude damping). Our study shows that the measurement uncertainty exhibits a non-monotonic dynamical behavior—that is, the amount of the uncertainty will first inflate, and subsequently decrease, with the growth of decoherence strengths in the two channels. In contrast, the uncertainty decreases monotonically with the growth of the purity of the initial state shared in prior. In order to reduce the measurement uncertainty in noisy environments, we put forward a remarkably effective strategy to steer the magnitude of uncertainty by means of a local non-unitary operation (i.e. weak measurement) on the qubit of interest. It turns out that this non-unitary operation can greatly reduce the entropic uncertainty, upon tuning the operation strength. Our investigations might thereby offer an insight into the dynamics and steering of entropic uncertainty in open systems.

  18. A new approach to reduce uncertainties in space radiation cancer risk predictions.

    Directory of Open Access Journals (Sweden)

    Francis A Cucinotta

    Full Text Available The prediction of space radiation induced cancer risk carries large uncertainties with two of the largest uncertainties being radiation quality and dose-rate effects. In risk models the ratio of the quality factor (QF to the dose and dose-rate reduction effectiveness factor (DDREF parameter is used to scale organ doses for cosmic ray proton and high charge and energy (HZE particles to a hazard rate for γ-rays derived from human epidemiology data. In previous work, particle track structure concepts were used to formulate a space radiation QF function that is dependent on particle charge number Z, and kinetic energy per atomic mass unit, E. QF uncertainties where represented by subjective probability distribution functions (PDF for the three QF parameters that described its maximum value and shape parameters for Z and E dependences. Here I report on an analysis of a maximum QF parameter and its uncertainty using mouse tumor induction data. Because experimental data for risks at low doses of γ-rays are highly uncertain which impacts estimates of maximum values of relative biological effectiveness (RBEmax, I developed an alternate QF model, denoted QFγAcute where QFs are defined relative to higher acute γ-ray doses (0.5 to 3 Gy. The alternate model reduces the dependence of risk projections on the DDREF, however a DDREF is still needed for risk estimates for high-energy protons and other primary or secondary sparsely ionizing space radiation components. Risk projections (upper confidence levels (CL for space missions show a reduction of about 40% (CL∼50% using the QFγAcute model compared the QFs based on RBEmax and about 25% (CL∼35% compared to previous estimates. In addition, I discuss how a possible qualitative difference leading to increased tumor lethality for HZE particles compared to low LET radiation and background tumors remains a large uncertainty in risk estimates.

  19. Applying the conservativeness principle to REDD to deal with the uncertainties of the estimates

    International Nuclear Information System (INIS)

    Grassi, Giacomo; Monni, Suvi; Achard, Frederic; Mollicone, Danilo; Federici, Sandro

    2008-01-01

    A common paradigm when the reduction of emissions from deforestations is estimated for the purpose of promoting it as a mitigation option in the context of the United Nations Framework Convention on Climate Change (UNFCCC) is that high uncertainties in input data-i.e., area change and C stock change/area-may seriously undermine the credibility of the estimates and therefore of reduced deforestation as a mitigation option. In this paper, we show how a series of concepts and methodological tools-already existing in UNFCCC decisions and IPCC guidance documents-may greatly help to deal with the uncertainties of the estimates of reduced emissions from deforestation

  20. Applying the conservativeness principle to REDD to deal with the uncertainties of the estimates

    Energy Technology Data Exchange (ETDEWEB)

    Grassi, Giacomo; Monni, Suvi; Achard, Frederic [Institute for Environment and Sustainability, Joint Research Centre of the European Commission, I-21020 Ispra (Italy); Mollicone, Danilo [Department of Geography, University of Alcala de Henares, Madrid (Spain); Federici, Sandro

    2008-07-15

    A common paradigm when the reduction of emissions from deforestations is estimated for the purpose of promoting it as a mitigation option in the context of the United Nations Framework Convention on Climate Change (UNFCCC) is that high uncertainties in input data-i.e., area change and C stock change/area-may seriously undermine the credibility of the estimates and therefore of reduced deforestation as a mitigation option. In this paper, we show how a series of concepts and methodological tools-already existing in UNFCCC decisions and IPCC guidance documents-may greatly help to deal with the uncertainties of the estimates of reduced emissions from deforestation.

  1. Optimal portfolio design to reduce climate-related conservation uncertainty in the Prairie Pothole Region.

    Science.gov (United States)

    Ando, Amy W; Mallory, Mindy L

    2012-04-24

    Climate change is likely to alter the spatial distributions of species and habitat types but the nature of such change is uncertain. Thus, climate change makes it difficult to implement standard conservation planning paradigms. Previous work has suggested some approaches to cope with such uncertainty but has not harnessed all of the benefits of risk diversification. We adapt Modern Portfolio Theory (MPT) to optimal spatial targeting of conservation activity, using wetland habitat conservation in the Prairie Pothole Region (PPR) as an example. This approach finds the allocations of conservation activity among subregions of the planning area that maximize the expected conservation returns for a given level of uncertainty or minimize uncertainty for a given expected level of returns. We find that using MPT instead of simple diversification in the PPR can achieve a value of the conservation objective per dollar spent that is 15% higher for the same level of risk. MPT-based portfolios can also have 21% less uncertainty over benefits or 6% greater expected benefits than the current portfolio of PPR conservation. Total benefits from conservation investment are higher if returns are defined in terms of benefit-cost ratios rather than benefits alone. MPT-guided diversification can work to reduce the climate-change-induced uncertainty of future ecosystem-service benefits from many land policy and investment initiatives, especially when outcomes are negatively correlated between subregions of a planning area.

  2. Reducing uncertainties in volumetric image based deformable organ registration

    International Nuclear Information System (INIS)

    Liang, J.; Yan, D.

    2003-01-01

    Applying volumetric image feedback in radiotherapy requires image based deformable organ registration. The foundation of this registration is the ability of tracking subvolume displacement in organs of interest. Subvolume displacement can be calculated by applying biomechanics model and the finite element method to human organs manifested on the multiple volumetric images. The calculation accuracy, however, is highly dependent on the determination of the corresponding organ boundary points. Lacking sufficient information for such determination, uncertainties are inevitable--thus diminishing the registration accuracy. In this paper, a method of consuming energy minimization was developed to reduce these uncertainties. Starting from an initial selection of organ boundary point correspondence on volumetric image sets, the subvolume displacement and stress distribution of the whole organ are calculated and the consumed energy due to the subvolume displacements is computed accordingly. The corresponding positions of the initially selected boundary points are then iteratively optimized to minimize the consuming energy under geometry and stress constraints. In this study, a rectal wall delineated from patient CT image was artificially deformed using a computer simulation and utilized to test the optimization. Subvolume displacements calculated based on the optimized boundary point correspondence were compared to the true displacements, and the calculation accuracy was thereby evaluated. Results demonstrate that a significant improvement on the accuracy of the deformable organ registration can be achieved by applying the consuming energy minimization in the organ deformation calculation

  3. Neutronic characteristics simulation of LMFBR of great size

    International Nuclear Information System (INIS)

    Kim, Y.C.

    1987-09-01

    The CONRAD experimental program to be executed on the critical mockup MASURCA in Cadarache and use all the european plutonium stock. The objectives of this program are to reduce the uncertainties on important project parameters such as the reactivity value of control rods, the flux distribution to valid calcul methods and data to use for new LMFBR conception (heterogeneous axial core by example) and to resolve the neutronic control problems for a LMFBR of great size. The present study has permitted to define this program and its physical characteristics [fr

  4. Reducing the uncertainty in robotic machining by modal analysis

    Science.gov (United States)

    Alberdi, Iñigo; Pelegay, Jose Angel; Arrazola, Pedro Jose; Ørskov, Klaus Bonde

    2017-10-01

    The use of industrial robots for machining could lead to high cost and energy savings for the manufacturing industry. Machining robots offer several advantages respect to CNC machines such as flexibility, wide working space, adaptability and relatively low cost. However, there are some drawbacks that are preventing a widespread adoption of robotic solutions namely lower stiffness, vibration/chatter problems and lower accuracy and repeatability. Normally due to these issues conservative cutting parameters are chosen, resulting in a low material removal rate (MRR). In this article, an example of a modal analysis of a robot is presented. For that purpose the Tap-testing technology is introduced, which aims at maximizing productivity, reducing the uncertainty in the selection of cutting parameters and offering a stable process free from chatter vibrations.

  5. Using FOSM-Based Data Worth Analyses to Design Geophysical Surveys to Reduce Uncertainty in a Regional Groundwater Model Update

    Science.gov (United States)

    Smith, B. D.; White, J.; Kress, W. H.; Clark, B. R.; Barlow, J.

    2016-12-01

    Hydrogeophysical surveys have become an integral part of understanding hydrogeological frameworks used in groundwater models. Regional models cover a large area where water well data is, at best, scattered and irregular. Since budgets are finite, priorities must be assigned to select optimal areas for geophysical surveys. For airborne electromagnetic (AEM) geophysical surveys, optimization of mapping depth and line spacing needs to take in account the objectives of the groundwater models. The approach discussed here uses a first-order, second-moment (FOSM) uncertainty analyses which assumes an approximate linear relation between model parameters and observations. This assumption allows FOSM analyses to be applied to estimate the value of increased parameter knowledge to reduce forecast uncertainty. FOSM is used to facilitate optimization of yet-to-be-completed geophysical surveying to reduce model forecast uncertainty. The main objective of geophysical surveying is assumed to estimate values and spatial variation in hydrologic parameters (i.e. hydraulic conductivity) as well as map lower permeability layers that influence the spatial distribution of recharge flux. The proposed data worth analysis was applied to Mississippi Embayment Regional Aquifer Study (MERAS) which is being updated. The objective of MERAS is to assess the ground-water availability (status and trends) of the Mississippi embayment aquifer system. The study area covers portions of eight states including Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, and Tennessee. The active model grid covers approximately 70,000 square miles, and incorporates some 6,000 miles of major rivers and over 100,000 water wells. In the FOSM analysis, a dense network of pilot points was used to capture uncertainty in hydraulic conductivity and recharge. To simulate the effect of AEM flight lines, the prior uncertainty for hydraulic conductivity and recharge pilots along potential flight lines was

  6. Reducing uncertainty in dust monitoring to detect aeolian sediment transport responses to land cover change

    Science.gov (United States)

    Webb, N.; Chappell, A.; Van Zee, J.; Toledo, D.; Duniway, M.; Billings, B.; Tedela, N.

    2017-12-01

    Anthropogenic land use and land cover change (LULCC) influence global rates of wind erosion and dust emission, yet our understanding of the magnitude of the responses remains poor. Field measurements and monitoring provide essential data to resolve aeolian sediment transport patterns and assess the impacts of human land use and management intensity. Data collected in the field are also required for dust model calibration and testing, as models have become the primary tool for assessing LULCC-dust cycle interactions. However, there is considerable uncertainty in estimates of dust emission due to the spatial variability of sediment transport. Field sampling designs are currently rudimentary and considerable opportunities are available to reduce the uncertainty. Establishing the minimum detectable change is critical for measuring spatial and temporal patterns of sediment transport, detecting potential impacts of LULCC and land management, and for quantifying the uncertainty of dust model estimates. Here, we evaluate the effectiveness of common sampling designs (e.g., simple random sampling, systematic sampling) used to measure and monitor aeolian sediment transport rates. Using data from the US National Wind Erosion Research Network across diverse rangeland and cropland cover types, we demonstrate how only large changes in sediment mass flux (of the order 200% to 800%) can be detected when small sample sizes are used, crude sampling designs are implemented, or when the spatial variation is large. We then show how statistical rigour and the straightforward application of a sampling design can reduce the uncertainty and detect change in sediment transport over time and between land use and land cover types.

  7. Crossing Science-Policy-Societal Boundaries to Reduce Scientific and Institutional Uncertainty in Small-Scale Fisheries

    Science.gov (United States)

    Sutton, Abigail M.; Rudd, Murray A.

    2016-10-01

    The governance of small-scale fisheries (SSF) is challenging due to the uncertainty, complexity, and interconnectedness of social, political, ecological, and economical processes. Conventional SSF management has focused on a centralized and top-down approach. A major criticism of conventional management is the over-reliance on `expert science' to guide decision-making and poor consideration of fishers' contextually rich knowledge. That is thought to exacerbate the already low governance potential of SSF. Integrating scientific knowledge with fishers' knowledge is increasingly popular and is often assumed to help reduce levels of biophysical and institutional uncertainties. Many projects aimed at encouraging knowledge integration have, however, been unsuccessful. Our objective in this research was to assess factors that influence knowledge integration and the uptake of integrated knowledge into policy-making. We report results from 54 semi-structured interviews with SSF researchers and practitioners from around the globe. Our analysis is framed in terms of scientific credibility, societal legitimacy, and policy saliency, and we discuss cases that have been partially or fully successful in reducing uncertainty via push-and-pull-oriented boundary crossing initiatives. Our findings suggest that two important factors affect the science-policy-societal boundary: a lack of consensus among stakeholders about what constitutes credible knowledge and institutional uncertainty resulting from shifting policies and leadership change. A lack of training for scientific leaders and an apparent `shelf-life' for community organizations highlight the importance of ongoing institutional support for knowledge integration projects. Institutional support may be enhanced through such investments, such as capacity building and specialized platforms for knowledge integration.

  8. Crossing Science-Policy-Societal Boundaries to Reduce Scientific and Institutional Uncertainty in Small-Scale Fisheries.

    Science.gov (United States)

    Sutton, Abigail M; Rudd, Murray A

    2016-10-01

    The governance of small-scale fisheries (SSF) is challenging due to the uncertainty, complexity, and interconnectedness of social, political, ecological, and economical processes. Conventional SSF management has focused on a centralized and top-down approach. A major criticism of conventional management is the over-reliance on 'expert science' to guide decision-making and poor consideration of fishers' contextually rich knowledge. That is thought to exacerbate the already low governance potential of SSF. Integrating scientific knowledge with fishers' knowledge is increasingly popular and is often assumed to help reduce levels of biophysical and institutional uncertainties. Many projects aimed at encouraging knowledge integration have, however, been unsuccessful. Our objective in this research was to assess factors that influence knowledge integration and the uptake of integrated knowledge into policy-making. We report results from 54 semi-structured interviews with SSF researchers and practitioners from around the globe. Our analysis is framed in terms of scientific credibility, societal legitimacy, and policy saliency, and we discuss cases that have been partially or fully successful in reducing uncertainty via push-and-pull-oriented boundary crossing initiatives. Our findings suggest that two important factors affect the science-policy-societal boundary: a lack of consensus among stakeholders about what constitutes credible knowledge and institutional uncertainty resulting from shifting policies and leadership change. A lack of training for scientific leaders and an apparent 'shelf-life' for community organizations highlight the importance of ongoing institutional support for knowledge integration projects. Institutional support may be enhanced through such investments, such as capacity building and specialized platforms for knowledge integration.

  9. A Best-Estimate Reactor Core Monitor Using State Feedback Strategies to Reduce Uncertainties

    International Nuclear Information System (INIS)

    Martin, Robert P.; Edwards, Robert M.

    2000-01-01

    The development and demonstration of a new algorithm to reduce modeling and state-estimation uncertainty in best-estimate simulation codes has been investigated. Demonstration is given by way of a prototype reactor core monitor. The architecture of this monitor integrates a control-theory-based, distributed-parameter estimation technique into a production-grade best-estimate simulation code. The Kalman Filter-Sequential Least-Squares (KFSLS) parameter estimation algorithm has been extended for application into the computational environment of the best-estimate simulation code RELAP5-3D. In control system terminology, this configuration can be thought of as a 'best-estimate' observer. The application to a distributed-parameter reactor system involves a unique modal model that approximates physical components, such as the reactor, by describing both states and parameters by an orthogonal expansion. The basic KFSLS parameter estimation is used to dynamically refine a spatially varying (distributed) parameter. The application of the distributed-parameter estimator is expected to complement a traditional nonlinear best-estimate simulation code by providing a mechanism for reducing both code input (modeling) and output (state-estimation) uncertainty in complex, distributed-parameter systems

  10. Optimal portfolio design to reduce climate-related conservation uncertainty in the Prairie Pothole Region

    Science.gov (United States)

    Ando, Amy W.; Mallory, Mindy L.

    2012-01-01

    Climate change is likely to alter the spatial distributions of species and habitat types but the nature of such change is uncertain. Thus, climate change makes it difficult to implement standard conservation planning paradigms. Previous work has suggested some approaches to cope with such uncertainty but has not harnessed all of the benefits of risk diversification. We adapt Modern Portfolio Theory (MPT) to optimal spatial targeting of conservation activity, using wetland habitat conservation in the Prairie Pothole Region (PPR) as an example. This approach finds the allocations of conservation activity among subregions of the planning area that maximize the expected conservation returns for a given level of uncertainty or minimize uncertainty for a given expected level of returns. We find that using MPT instead of simple diversification in the PPR can achieve a value of the conservation objective per dollar spent that is 15% higher for the same level of risk. MPT-based portfolios can also have 21% less uncertainty over benefits or 6% greater expected benefits than the current portfolio of PPR conservation. Total benefits from conservation investment are higher if returns are defined in terms of benefit–cost ratios rather than benefits alone. MPT-guided diversification can work to reduce the climate-change–induced uncertainty of future ecosystem-service benefits from many land policy and investment initiatives, especially when outcomes are negatively correlated between subregions of a planning area. PMID:22451914

  11. Uncertainty governance: an integrated framework for managing and communicating uncertainties

    International Nuclear Information System (INIS)

    Umeki, H.; Naito, M.; Takase, H.

    2004-01-01

    Treatment of uncertainty, or in other words, reasoning with imperfect information is widely recognised as being of great importance within performance assessment (PA) of the geological disposal mainly because of the time scale of interest and spatial heterogeneity that geological environment exhibits. A wide range of formal methods have been proposed for the optimal processing of incomplete information. Many of these methods rely on the use of numerical information, the frequency based concept of probability in particular, to handle the imperfections. However, taking quantitative information as a base for models that solve the problem of handling imperfect information merely creates another problem, i.e., how to provide the quantitative information. In many situations this second problem proves more resistant to solution, and in recent years several authors have looked at a particularly ingenious way in accordance with the rules of well-founded methods such as Bayesian probability theory, possibility theory, and the Dempster-Shafer theory of evidence. Those methods, while drawing inspiration from quantitative methods, do not require the kind of complete numerical information required by quantitative methods. Instead they provide information that, though less precise than that provided by quantitative techniques, is often, if not sufficient, the best that could be achieved. Rather than searching for the best method for handling all imperfect information, our strategy for uncertainty management, that is recognition and evaluation of uncertainties associated with PA followed by planning and implementation of measures to reduce them, is to use whichever method best fits the problem at hand. Such an eclectic position leads naturally to integration of the different formalisms. While uncertainty management based on the combination of semi-quantitative methods forms an important part of our framework for uncertainty governance, it only solves half of the problem

  12. Incorporating uncertainty analysis into life cycle estimates of greenhouse gas emissions from biomass production

    International Nuclear Information System (INIS)

    Johnson, David R.; Willis, Henry H.; Curtright, Aimee E.; Samaras, Constantine; Skone, Timothy

    2011-01-01

    Before further investments are made in utilizing biomass as a source of renewable energy, both policy makers and the energy industry need estimates of the net greenhouse gas (GHG) reductions expected from substituting biobased fuels for fossil fuels. Such GHG reductions depend greatly on how the biomass is cultivated, transported, processed, and converted into fuel or electricity. Any policy aiming to reduce GHGs with biomass-based energy must account for uncertainties in emissions at each stage of production, or else it risks yielding marginal reductions, if any, while potentially imposing great costs. This paper provides a framework for incorporating uncertainty analysis specifically into estimates of the life cycle GHG emissions from the production of biomass. We outline the sources of uncertainty, discuss the implications of uncertainty and variability on the limits of life cycle assessment (LCA) models, and provide a guide for practitioners to best practices in modeling these uncertainties. The suite of techniques described herein can be used to improve the understanding and the representation of the uncertainties associated with emissions estimates, thus enabling improved decision making with respect to the use of biomass for energy and fuel production. -- Highlights: → We describe key model, scenario and data uncertainties in LCAs of biobased fuels. → System boundaries and allocation choices should be consistent with study goals. → Scenarios should be designed around policy levers that can be controlled. → We describe a new way to analyze the importance of covariance between inputs.

  13. Use of Atmospheric Budget to Reduce Uncertainty in Estimated Water Availability over South Asia from Different Reanalyses

    Science.gov (United States)

    Sebastian, Dawn Emil; Pathak, Amey; Ghosh, Subimal

    2016-07-01

    Disagreements across different reanalyses over South Asia result into uncertainty in assessment of water availability, which is computed as the difference between Precipitation and Evapotranspiration (P-E). Here, we compute P-E directly from atmospheric budget with divergence of moisture flux for different reanalyses and find improved correlation with observed values of P-E, acquired from station and satellite data. We also find reduced closure terms for water cycle computed with atmospheric budget, analysed over South Asian landmass, when compared to that obtained with individual values of P and E. The P-E value derived with atmospheric budget is more consistent with energy budget, when we use top-of-atmosphere radiation for the same. For analysing water cycle, we use runoff from Global Land Data Assimilation System, and water storage from Gravity Recovery and Climate Experiment. We find improvements in agreements across different reanalyses, in terms of inter-annual cross correlation when atmospheric budget is used to estimate P-E and hence, emphasize to use the same for estimations of water availability in South Asia to reduce uncertainty. Our results on water availability with reduced uncertainty over highly populated monsoon driven South Asia will be useful for water management and agricultural decision making.

  14. Use of Atmospheric Budget to Reduce Uncertainty in Estimated Water Availability over South Asia from Different Reanalyses.

    Science.gov (United States)

    Sebastian, Dawn Emil; Pathak, Amey; Ghosh, Subimal

    2016-07-08

    Disagreements across different reanalyses over South Asia result into uncertainty in assessment of water availability, which is computed as the difference between Precipitation and Evapotranspiration (P-E). Here, we compute P-E directly from atmospheric budget with divergence of moisture flux for different reanalyses and find improved correlation with observed values of P-E, acquired from station and satellite data. We also find reduced closure terms for water cycle computed with atmospheric budget, analysed over South Asian landmass, when compared to that obtained with individual values of P and E. The P-E value derived with atmospheric budget is more consistent with energy budget, when we use top-of-atmosphere radiation for the same. For analysing water cycle, we use runoff from Global Land Data Assimilation System, and water storage from Gravity Recovery and Climate Experiment. We find improvements in agreements across different reanalyses, in terms of inter-annual cross correlation when atmospheric budget is used to estimate P-E and hence, emphasize to use the same for estimations of water availability in South Asia to reduce uncertainty. Our results on water availability with reduced uncertainty over highly populated monsoon driven South Asia will be useful for water management and agricultural decision making.

  15. Forest management under climatic and social uncertainty: trade-offs between reducing climate change impacts and fostering adaptive capacity.

    Science.gov (United States)

    Seidl, Rupert; Lexer, Manfred J

    2013-01-15

    The unabated continuation of anthropogenic greenhouse gas emissions and the lack of an international consensus on a stringent climate change mitigation policy underscore the importance of adaptation for coping with the all but inevitable changes in the climate system. Adaptation measures in forestry have particularly long lead times. A timely implementation is thus crucial for reducing the considerable climate vulnerability of forest ecosystems. However, since future environmental conditions as well as future societal demands on forests are inherently uncertain, a core requirement for adaptation is robustness to a wide variety of possible futures. Here we explicitly address the roles of climatic and social uncertainty in forest management, and tackle the question of robustness of adaptation measures in the context of multi-objective sustainable forest management (SFM). We used the Austrian Federal Forests (AFF) as a case study, and employed a comprehensive vulnerability assessment framework based on ecosystem modeling, multi-criteria decision analysis, and practitioner participation. We explicitly considered climate uncertainty by means of three climate change scenarios, and accounted for uncertainty in future social demands by means of three societal preference scenarios regarding SFM indicators. We found that the effects of climatic and social uncertainty on the projected performance of management were in the same order of magnitude, underlining the notion that climate change adaptation requires an integrated social-ecological perspective. Furthermore, our analysis of adaptation measures revealed considerable trade-offs between reducing adverse impacts of climate change and facilitating adaptive capacity. This finding implies that prioritization between these two general aims of adaptation is necessary in management planning, which we suggest can draw on uncertainty analysis: Where the variation induced by social-ecological uncertainty renders measures aiming to

  16. Intolerance of uncertainty mediates reduced reward anticipation in major depressive disorder.

    Science.gov (United States)

    Nelson, Brady D; Shankman, Stewart A; Proudfit, Greg H

    2014-04-01

    Reduced reward sensitivity has long been considered a fundamental deficit of major depressive disorder (MDD). One way this deficit has been measured is by an asymmetry in electroencephalogram (EEG) activity between left and right frontal brain regions. MDD has been associated with a reduced frontal EEG asymmetry (i.e., decreased left relative to right) while anticipating reward. However, the mechanism (or mediator) of this association is unclear. The present study examined whether intolerance of uncertainty (IU) mediated the association between depression and reduced reward anticipation. Data were obtained from a prior study reporting reduced frontal EEG asymmetry while anticipating reward in early-onset MDD. Participants included 156 individuals with early-onset MDD-only, panic disorder-only, both (comorbids), or controls. Frontal EEG asymmetry was recorded during an uncertain reward anticipation task. Participants completed a self-report measure of IU. All three psychopathology groups reported greater IU relative to controls. Across all participants, greater IU was associated with a reduced frontal EEG asymmetry. Furthermore, IU mediated the relationship between MDD and frontal EEG asymmetry and results remained significant after controlling for neuroticism, suggesting effects were not due to broad negative affectivity. MDD participants were limited to those with early-onset depression. Measures were collected cross-sectionally, precluding causal relationships. IU mediated the relationship between MDD and reduced reward anticipation, independent of neuroticism. Explanations are provided regarding how IU may contribute to reduced reward anticipation in depression. Overall, IU appears to be an important mechanism for the association between depression and reduced reward anticipation. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Religion in the face of uncertainty: an uncertainty-identity theory account of religiousness.

    Science.gov (United States)

    Hogg, Michael A; Adelman, Janice R; Blagg, Robert D

    2010-02-01

    The authors characterize religions as social groups and religiosity as the extent to which a person identifies with a religion, subscribes to its ideology or worldview, and conforms to its normative practices. They argue that religions have attributes that make them well suited to reduce feelings of self-uncertainty. According to uncertainty-identity theory, people are motivated to reduce feelings of uncertainty about or reflecting on self; and identification with groups, particularly highly entitative groups, is a very effective way to reduce uncertainty. All groups provide belief systems and normative prescriptions related to everyday life. However, religions also address the nature of existence, invoking sacred entities and associated rituals and ceremonies. They are entitative groups that provide a moral compass and rules for living that pervade a person's life, making them particularly attractive in times of uncertainty. The authors document data supporting their analysis and discuss conditions that transform religiosity into religious zealotry and extremism.

  18. Reducing prediction uncertainty of weather controlled systems

    NARCIS (Netherlands)

    Doeswijk, T.G.

    2007-01-01

    In closed agricultural systems the weather acts both as a disturbance and as a resource. By using weather forecasts in control strategies the effects of disturbances can be minimized whereas the resources can be utilized. In this situation weather forecast uncertainty and model based control are

  19. Proceedings of a workshop on dealing with uncertainties in the hydroelectric energy business. CD-ROM ed.

    International Nuclear Information System (INIS)

    2004-01-01

    This workshop was attended by experts in Canadian and international hydroelectric utilities to exchange information on current practices and opportunities for improvement or future cooperation. The discussions focused on reducing the uncertainties associated with hydroelectric power production. Although significant improvements have been made in the efficiency, reliability and safety of hydroelectric power production, the sector is still challenged by the uncertainty of water supply which depends greatly on weather conditions. Energy markets pose another challenge to power producers in terms of energy supply, energy demand and energy prices. The workshop focused on 3 themes: (1) weather and hydrologic uncertainty, (2) market uncertainty, and (3) decision making models using uncertainty principles surrounding water resource planning and operation. The workshop featured 22 presentations of which 11 have been indexed separately for inclusion in this database. refs., tabs., figs

  20. Impact of Climate Change on high and low flows across Great Britain: a temporal analysis and uncertainty assessment.

    Science.gov (United States)

    Beevers, Lindsay; Collet, Lila

    2017-04-01

    Over the past decade there have been significant challenges to water management posed by both floods and droughts. In the UK, since 2000 flooding has caused over £5Bn worth of damage, and direct costs from the recent drought (2011-12) are estimated to be between £70-165M, arising from impacts on public and industrial water supply. Projections of future climate change suggest an increase in temperature and precipitation trends which may exacerbate the frequency and severity of such hazards, but there is significant uncertainty associated with these projections. It thus becomes urgent to assess the possible impact of these changes on extreme flows and evaluate the uncertainties related to these projections, particularly changes in the seasonality of such hazards. This paper aims to assess the changes in seasonality of peak and low flows across Great Britain as a result of climate change. It is based on the Future Flow database; an 11-member ensemble of transient river flow projections from January 1951 to December 2098. We analyse the daily river flow over the baseline (1961-1990) and the 2080s (2069-2098) for 281 gauging stations. For each ensemble member, annual maxima (AMAX) and minima (AMIN) are extracted for both time periods for each gauging station. The month of the year the AMAX and AMIN occur respectively are recorded for each of the 30 years in the past and the future time periods. The uncertainty of the AMAX and AMIN occurrence temporally (monthly) is assessed across the 11 ensemble members, as well as the changes to this temporal signal between the baseline and the 2080s. Ultimately, this work gives a national picture (spatially) of high and low flows occurrence temporally and allows the assessment of possible changes in hydrological dynamics as a result of climate change in a statistical framework. Results will quantify the uncertainty related to the Climate Model parameters which are cascaded into the modelling chain. This study highlights the issues

  1. Accessing the uncertainties of seismic velocity and anisotropy structure of Northern Great Plains using a transdimensional Bayesian approach

    Science.gov (United States)

    Gao, C.; Lekic, V.

    2017-12-01

    Seismic imaging utilizing complementary seismic data provides unique insight on the formation, evolution and current structure of continental lithosphere. While numerous efforts have improved the resolution of seismic structure, the quantification of uncertainties remains challenging due to the non-linearity and the non-uniqueness of geophysical inverse problem. In this project, we use a reverse jump Markov chain Monte Carlo (rjMcMC) algorithm to incorporate seismic observables including Rayleigh and Love wave dispersion, Ps and Sp receiver function to invert for shear velocity (Vs), compressional velocity (Vp), density, and radial anisotropy of the lithospheric structure. The Bayesian nature and the transdimensionality of this approach allow the quantification of the model parameter uncertainties while keeping the models parsimonious. Both synthetic test and inversion of actual data for Ps and Sp receiver functions are performed. We quantify the information gained in different inversions by calculating the Kullback-Leibler divergence. Furthermore, we explore the ability of Rayleigh and Love wave dispersion data to constrain radial anisotropy. We show that when multiple types of model parameters (Vsv, Vsh, and Vp) are inverted simultaneously, the constraints on radial anisotropy are limited by relatively large data uncertainties and trade-off strongly with Vp. We then perform joint inversion of the surface wave dispersion (SWD) and Ps, Sp receiver functions, and show that the constraints on both isotropic Vs and radial anisotropy are significantly improved. To achieve faster convergence of the rjMcMC, we propose a progressive inclusion scheme, and invert SWD measurements and receiver functions from about 400 USArray stations in the Northern Great Plains. We start by only using SWD data due to its fast convergence rate. We then use the average of the ensemble as a starting model for the joint inversion, which is able to resolve distinct seismic signatures of

  2. Reducing, Maintaining, or Escalating Uncertainty? The Development and Validation of Four Uncertainty Preference Scales Related to Cancer Information Seeking and Avoidance.

    Science.gov (United States)

    Carcioppolo, Nick; Yang, Fan; Yang, Qinghua

    2016-09-01

    Uncertainty is a central characteristic of many aspects of cancer prevention, screening, diagnosis, and treatment. Brashers's (2001) uncertainty management theory details the multifaceted nature of uncertainty and describes situations in which uncertainty can both positively and negatively affect health outcomes. The current study extends theory on uncertainty management by developing four scale measures of uncertainty preferences in the context of cancer. Two national surveys were conducted to validate the scales and assess convergent and concurrent validity. Results support the factor structure of each measure and provide general support across multiple validity assessments. These scales can advance research on uncertainty and cancer communication by providing researchers with measures that address multiple aspects of uncertainty management.

  3. A hydrogen-oxidizing, Fe(III)-reducing microorganism from the Great Bay estuary, New Hampshire

    Science.gov (United States)

    Caccavo, F.; Blakemore, R.P.; Lovley, D.R.

    1992-01-01

    A dissimilatory Fe(III)- and Mn(IV)-reducing bacterium was isolated from bottom sediments of the Great Bay estuary, New Hampshire. The isolate was a facultatively anaerobic gram-negative rod which did not appear to fit into any previously described genus. It was temporarily designated strain BrY. BrY grew anaerobically in a defined medium with hydrogen or lactate as the electron donor and Fe(III) as the electron acceptor. BrY required citrate, fumarate, or malate as a carbon source for growth on H2 and Fe(III). With Fe(III) as the sole electron acceptor, BrY metabolized hydrogen to a minimum threshold at least 60-fold lower than the threshold reported for pure cultures of sulfate reducers. This finding supports the hypothesis that when Fe(III) is available, Fe(III) reducers can outcompete sulfate reducers for electron donors. Lactate was incompletely oxidized to acetate and carbon dioxide with Fe(III) as the electron acceptor. Lactate oxidation was also coupled to the reduction of Mn(IV), U(VI), fumarate, thiosulfate, or trimethylamine n-oxide under anaerobic conditions. BrY provides a model for how enzymatic metal reduction by respiratory metal-reducing microorganisms has the potential to contribute to the mobilization of iron and trace metals and to the immobilization of uranium in sediments of Great Bay Estuary.

  4. Benchmarking observational uncertainties for hydrology (Invited)

    Science.gov (United States)

    McMillan, H. K.; Krueger, T.; Freer, J. E.; Westerberg, I.

    2013-12-01

    There is a pressing need for authoritative and concise information on the expected error distributions and magnitudes in hydrological data, to understand its information content. Many studies have discussed how to incorporate uncertainty information into model calibration and implementation, and shown how model results can be biased if uncertainty is not appropriately characterised. However, it is not always possible (for example due to financial or time constraints) to make detailed studies of uncertainty for every research study. Instead, we propose that the hydrological community could benefit greatly from sharing information on likely uncertainty characteristics and the main factors that control the resulting magnitude. In this presentation, we review the current knowledge of uncertainty for a number of key hydrological variables: rainfall, flow and water quality (suspended solids, nitrogen, phosphorus). We collated information on the specifics of the data measurement (data type, temporal and spatial resolution), error characteristics measured (e.g. standard error, confidence bounds) and error magnitude. Our results were primarily split by data type. Rainfall uncertainty was controlled most strongly by spatial scale, flow uncertainty was controlled by flow state (low, high) and gauging method. Water quality presented a more complex picture with many component errors. For all variables, it was easy to find examples where relative error magnitude exceeded 40%. We discuss some of the recent developments in hydrology which increase the need for guidance on typical error magnitudes, in particular when doing comparative/regionalisation and multi-objective analysis. Increased sharing of data, comparisons between multiple catchments, and storage in national/international databases can mean that data-users are far removed from data collection, but require good uncertainty information to reduce bias in comparisons or catchment regionalisation studies. Recently it has

  5. Crop Model Improvement Reduces the Uncertainty of the Response to Temperature of Multi-Model Ensembles

    Science.gov (United States)

    Maiorano, Andrea; Martre, Pierre; Asseng, Senthold; Ewert, Frank; Mueller, Christoph; Roetter, Reimund P.; Ruane, Alex C.; Semenov, Mikhail A.; Wallach, Daniel; Wang, Enli

    2016-01-01

    To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT worldwide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures greater than 24 C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.

  6. Asphere cross testing: an exercise in uncertainty estimation

    Science.gov (United States)

    Murphy, Paul E.

    2017-10-01

    Aspheric surfaces can provide substantial improvements to optical designs, but they can also be difficult to manufacture cost-effectively. Asphere metrology contributes significantly to this difficulty, especially for high-precision aspheric surfaces. With the advent of computer-controlled fabrication machinery, optical surface quality is chiefly limited by the ability to measure it. Consequently, understanding the uncertainty of surface measurements is of great importance for determining what optical surface quality can be achieved. We measured sample aspheres using multiple techniques: profilometry, null interferometry, and subaperture stitching. We also obtained repeatability and reproducibility (R&R) measurement data by retesting the same aspheres under various conditions. We highlight some of the details associated with the different measurement techniques, especially efforts to reduce bias in the null tests via calibration. We compare and contrast the measurement results, and obtain an empirical view of the measurement uncertainty of the different techniques. We found fair agreement in overall surface form among the methods, but meaningful differences in reproducibility and mid-spatial frequency performance.

  7. MRS role in reducing technical uncertainties in geological disposal

    International Nuclear Information System (INIS)

    Ramspott, L.D.

    1990-06-01

    A high-level nuclear waste repository has inherent technical uncertainty due to its first-of-a-kind nature and the unprecedented time over which it must function. Three possible technical modifications to the currently planned US high-level nuclear waste system are reviewed in this paper. These modifications would be facilitated by inclusion of a monitored retrievable storage (MRS) in the system. The modifications are (1) an underground MRS at Yucca Mountain, (2) a phased repository, and (3) a ''cold'' repository. These modifications are intended to enhance scientific confidence that a repository system would function satisfactorily despite technical uncertainty. 12 refs

  8. Ecosystem Services Mapping Uncertainty Assessment: A Case Study in the Fitzroy Basin Mining Region

    Directory of Open Access Journals (Sweden)

    Zhenyu Wang

    2018-01-01

    Full Text Available Ecosystem services mapping is becoming increasingly popular through the use of various readily available mapping tools, however, uncertainties in assessment outputs are commonly ignored. Uncertainties from different sources have the potential to lower the accuracy of mapping outputs and reduce their reliability for decision-making. Using a case study in an Australian mining region, this paper assessed the impact of uncertainties on the modelling of the hydrological ecosystem service, water provision. Three types of uncertainty were modelled using multiple uncertainty scenarios: (1 spatial data sources; (2 modelling scales (temporal and spatial and (3 parameterization and model selection. We found that the mapping scales can induce significant changes to the spatial pattern of outputs and annual totals of water provision. In addition, differences in parameterization using differing sources from the literature also led to obvious differences in base flow. However, the impact of each uncertainty associated with differences in spatial data sources were not so great. The results of this study demonstrate the importance of uncertainty assessment and highlight that any conclusions drawn from ecosystem services mapping, such as the impacts of mining, are likely to also be a property of the uncertainty in ecosystem services mapping methods.

  9. Do regional methods really help reduce uncertainties in flood frequency analyses?

    Science.gov (United States)

    Cong Nguyen, Chi; Payrastre, Olivier; Gaume, Eric

    2013-04-01

    Flood frequency analyses are often based on continuous measured series at gauge sites. However, the length of the available data sets is usually too short to provide reliable estimates of extreme design floods. To reduce the estimation uncertainties, the analyzed data sets have to be extended either in time, making use of historical and paleoflood data, or in space, merging data sets considered as statistically homogeneous to build large regional data samples. Nevertheless, the advantage of the regional analyses, the important increase of the size of the studied data sets, may be counterbalanced by the possible heterogeneities of the merged sets. The application and comparison of four different flood frequency analysis methods to two regions affected by flash floods in the south of France (Ardèche and Var) illustrates how this balance between the number of records and possible heterogeneities plays in real-world applications. The four tested methods are: (1) a local statistical analysis based on the existing series of measured discharges, (2) a local analysis valuating the existing information on historical floods, (3) a standard regional flood frequency analysis based on existing measured series at gauged sites and (4) a modified regional analysis including estimated extreme peak discharges at ungauged sites. Monte Carlo simulations are conducted to simulate a large number of discharge series with characteristics similar to the observed ones (type of statistical distributions, number of sites and records) to evaluate to which extent the results obtained on these case studies can be generalized. These two case studies indicate that even small statistical heterogeneities, which are not detected by the standard homogeneity tests implemented in regional flood frequency studies, may drastically limit the usefulness of such approaches. On the other hand, these result show that the valuation of information on extreme events, either historical flood events at gauged

  10. Reduced dose uncertainty in MRI-based polymer gel dosimetry using parallel RF transmission with multiple RF sources

    International Nuclear Information System (INIS)

    Sang-Young Kim; Jung-Hoon Lee; Jin-Young Jung; Do-Wan Lee; Seu-Ran Lee; Bo-Young Choe; Hyeon-Man Baek; Korea University of Science and Technology, Daejeon; Dae-Hyun Kim; Jung-Whan Min; Ji-Yeon Park

    2014-01-01

    In this work, we present the feasibility of using a parallel RF transmit with multiple RF sources imaging method (MultiTransmit imaging) in polymer gel dosimetry. Image quality and B 1 field homogeneity was statistically better in the MultiTransmit imaging method than in conventional single source RF transmission imaging method. In particular, the standard uncertainty of R 2 was lower on the MultiTransmit images than on the conventional images. Furthermore, the MultiTransmit measurement showed improved dose resolution. Improved image quality and B 1 homogeneity results in reduced dose uncertainty, thereby suggesting the feasibility of MultiTransmit MR imaging in gel dosimetry. (author)

  11. One Strategy for Reducing Uncertainty in Climate Change Communications

    Science.gov (United States)

    Romm, J.

    2011-12-01

    Future impacts of climate change are invariably presented with a very wide range of impacts reflecting two different sets of uncertainties. The first concerns our uncertainty about precisely how much greenhouse gas emissions humanity will emit into the atmosphere. The second concerns our uncertainty about precisely what impact those emissions will have on the climate. By failing to distinguish between these two types of uncertainties, climate scientists have not clearly explained to the public and policymakers what the scientific literature suggests is likely to happen if we don't substantially alter our current emissions path. Indeed, much of climate communications has been built around describing the range of impacts from emissions paths that are increasingly implausible given political and technological constraints, such as a stabilization at 450 or 550 parts per million atmospheric of carbon dioxide. For the past decade, human emissions of greenhouse gases have trended near the worst-case scenarios of the Intergovernmental Panel on Climate Change, emissions paths that reach 800 ppm or even 1000 ppm. The current policies of the two biggest emitters, the United States and China, coupled with the ongoing failure of international negotiations to come to an agreement on restricting emissions, suggests that recent trends will continue for the foreseeable future. This in turn suggests that greater clarity in climate change communications could be achieved by more clearly explaining to the public what the scientific literature suggests the range of impacts are for our current high emissions path. This also suggests that more focus should be given in the scientific literature to better constraining the range of impacts from the high emissions scenarios.

  12. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

  13. A Bayesian Framework of Uncertainties Integration in 3D Geological Model

    Science.gov (United States)

    Liang, D.; Liu, X.

    2017-12-01

    3D geological model can describe complicated geological phenomena in an intuitive way while its application may be limited by uncertain factors. Great progress has been made over the years, lots of studies decompose the uncertainties of geological model to analyze separately, while ignored the comprehensive impacts of multi-source uncertainties. Great progress has been made over the years, while lots of studies ignored the comprehensive impacts of multi-source uncertainties when analyzed them item by item from each source. To evaluate the synthetical uncertainty, we choose probability distribution to quantify uncertainty, and propose a bayesian framework of uncertainties integration. With this framework, we integrated data errors, spatial randomness, and cognitive information into posterior distribution to evaluate synthetical uncertainty of geological model. Uncertainties propagate and cumulate in modeling process, the gradual integration of multi-source uncertainty is a kind of simulation of the uncertainty propagation. Bayesian inference accomplishes uncertainty updating in modeling process. Maximum entropy principle makes a good effect on estimating prior probability distribution, which ensures the prior probability distribution subjecting to constraints supplied by the given information with minimum prejudice. In the end, we obtained a posterior distribution to evaluate synthetical uncertainty of geological model. This posterior distribution represents the synthetical impact of all the uncertain factors on the spatial structure of geological model. The framework provides a solution to evaluate synthetical impact on geological model of multi-source uncertainties and a thought to study uncertainty propagation mechanism in geological modeling.

  14. Deterministic uncertainty analysis

    International Nuclear Information System (INIS)

    Worley, B.A.

    1987-01-01

    Uncertainties of computer results are of primary interest in applications such as high-level waste (HLW) repository performance assessment in which experimental validation is not possible or practical. This work presents an alternate deterministic approach for calculating uncertainties that has the potential to significantly reduce the number of computer runs required for conventional statistical analysis. 7 refs., 1 fig

  15. An Innovative Approach to Effective Climate Science Application through Stakeholder Participation in Great Plains Grasslands

    Science.gov (United States)

    Athearn, N.; Broska, J.

    2015-12-01

    For natural resource managers and other Great Plains stakeholders, climate uncertainties further confound decision-making on a highly altered landscape. Partner organizations comprising the Great Plains Landscape Conservation Cooperative (GPLCC) acknowledge climate change as a high-priority threat to grasslands and associated habitats, affecting water availability, species composition, and other factors. Despite its importance, incorporation of climate change impacts into planning is hindered by high uncertainty and lack of translation to a tangible outcome: effects on species and their habitats. In 2014, the GPLCC initiated a Landscape Conservation Design (LCD) process to ultimately improve the size and connectivity of grasslands - informing land managers of the landscape-scale impacts of local decisions about where to restore, enhance, protect, and develop lands. Defining this goal helped stakeholders envision a tangible product. High resolution land cover data recently completed for Texas and Oklahoma represent current grassland locations. By focusing climate change models to project changes in these land cover datasets, resulting land cover projections can be directly incorporated into LCD-based models to focus restoration where future climates will support grasslands. Broad organizational cooperation has been critical for this USGS-led project, which uses downscaled climate data and other support from the South Central Climate Science Center Consortium and builds on existing work including LCD efforts of the Playa Lakes Joint Venture and the Bureau of Land Management's Southern Great Plains Rapid Ecological Assessment. Ongoing stakeholder guidance through an advisory team ensures effective application of a product that will be both relevant to and understood by decision makers, for whom the primary role of research is to reduce uncertainties and clear the path for more efficient decision-making in the face of climatic uncertainty.

  16. State-independent uncertainty relations and entanglement detection

    Science.gov (United States)

    Qian, Chen; Li, Jun-Li; Qiao, Cong-Feng

    2018-04-01

    The uncertainty relation is one of the key ingredients of quantum theory. Despite the great efforts devoted to this subject, most of the variance-based uncertainty relations are state-dependent and suffering from the triviality problem of zero lower bounds. Here we develop a method to get uncertainty relations with state-independent lower bounds. The method works by exploring the eigenvalues of a Hermitian matrix composed by Bloch vectors of incompatible observables and is applicable for both pure and mixed states and for arbitrary number of N-dimensional observables. The uncertainty relation for the incompatible observables can be explained by geometric relations related to the parallel postulate and the inequalities in Horn's conjecture on Hermitian matrix sum. Practical entanglement criteria are also presented based on the derived uncertainty relations.

  17. Development of Property Models with Uncertainty Estimate for Process Design under Uncertainty

    DEFF Research Database (Denmark)

    Hukkerikar, Amol; Sarup, Bent; Abildskov, Jens

    more reliable predictions with a new and improved set of model parameters for GC (group contribution) based and CI (atom connectivity index) based models and to quantify the uncertainties in the estimated property values from a process design point-of-view. This includes: (i) parameter estimation using....... The comparison of model prediction uncertainties with reported range of measurement uncertainties is presented for the properties with related available data. The application of the developed methodology to quantify the effect of these uncertainties on the design of different unit operations (distillation column......, the developed methodology can be used to quantify the sensitivity of process design to uncertainties in property estimates; obtain rationally the risk/safety factors in process design; and identify additional experimentation needs in order to reduce most critical uncertainties....

  18. Understanding uncertainties in non-linear population trajectories: a Bayesian semi-parametric hierarchical approach to large-scale surveys of coral cover.

    Directory of Open Access Journals (Sweden)

    Julie Vercelloni

    Full Text Available Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making.

  19. Uncertainties in hydrogen combustion

    International Nuclear Information System (INIS)

    Stamps, D.W.; Wong, C.C.; Nelson, L.S.

    1988-01-01

    Three important areas of hydrogen combustion with uncertainties are identified: high-temperature combustion, flame acceleration and deflagration-to-detonation transition, and aerosol resuspension during hydrogen combustion. The uncertainties associated with high-temperature combustion may affect at least three different accident scenarios: the in-cavity oxidation of combustible gases produced by core-concrete interactions, the direct containment heating hydrogen problem, and the possibility of local detonations. How these uncertainties may affect the sequence of various accident scenarios is discussed and recommendations are made to reduce these uncertainties. 40 references

  20. Towards a different attitude to uncertainty

    Directory of Open Access Journals (Sweden)

    Guy Pe'er

    2014-10-01

    Full Text Available The ecological literature deals with uncertainty primarily from the perspective of how to reduce it to acceptable levels. However, the current rapid and ubiquitous environmental changes, as well as anticipated rates of change, pose novel conditions and complex dynamics due to which many sources of uncertainty are difficult or even impossible to reduce. These include both uncertainty in knowledge (epistemic uncertainty and societal responses to it. Under these conditions, an increasing number of studies ask how one can deal with uncertainty as it is. Here, we explore the question how to adopt an overall alternative attitude to uncertainty, which accepts or even embraces it. First, we show that seeking to reduce uncertainty may be counterproductive under some circumstances. It may yield overconfidence, ignoring early warning signs, policy- and societal stagnation, or irresponsible behaviour if personal certainty is offered by externalization of environmental costs. We then demonstrate that uncertainty can have positive impacts by driving improvements in knowledge, promoting cautious action, contributing to keeping societies flexible and adaptable, enhancing awareness, support and involvement of the public in nature conservation, and enhancing cooperation and communication. We discuss the risks of employing a certainty paradigm on uncertain knowledge, the potential benefits of adopting an alternative attitude to uncertainty, and the need to implement such an attitude across scales – from adaptive management at the local scale, to the evolving Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES at the global level.

  1. Data requirements of GREAT-ER: Modelling and validation using LAS in four UK catchments

    International Nuclear Information System (INIS)

    Price, Oliver R.; Munday, Dawn K.; Whelan, Mick J.; Holt, Martin S.; Fox, Katharine K.; Morris, Gerard; Young, Andrew R.

    2009-01-01

    Higher-tier environmental risk assessments on 'down-the-drain' chemicals in river networks can be conducted using models such as GREAT-ER (Geography-referenced Regional Exposure Assessment Tool for European Rivers). It is important these models are evaluated and their sensitivities to input variables understood. This study had two primary objectives: evaluate GREAT-ER model performance, comparing simulated modelled predictions for LAS (linear alkylbenzene sulphonate) with measured concentrations, for four rivers in the UK, and investigate model sensitivity to input variables. We demonstrate that the GREAT-ER model is very sensitive to variability in river discharges. However it is insensitive to the form of distributions used to describe chemical usage and removal rate in sewage treatment plants (STPs). It is concluded that more effort should be directed towards improving empirical estimates of effluent load and reducing uncertainty associated with usage and removal rates in STPs. Simulations could be improved by incorporating the effect of river depth on dissipation rates. - Validation of GREAT-ER.

  2. Data requirements of GREAT-ER: Modelling and validation using LAS in four UK catchments

    Energy Technology Data Exchange (ETDEWEB)

    Price, Oliver R., E-mail: oliver.price@unilever.co [Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ (United Kingdom); Munday, Dawn K. [Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ (United Kingdom); Whelan, Mick J. [Department of Natural Resources, School of Applied Sciences, Cranfield University, College Road, Cranfield, Bedfordshire MK43 0AL (United Kingdom); Holt, Martin S. [ECETOC, Ave van Nieuwenhuyse 4, Box 6, B-1160 Brussels (Belgium); Fox, Katharine K. [85 Park Road West, Birkenhead, Merseyside CH43 8SQ (United Kingdom); Morris, Gerard [Environment Agency, Phoenix House, Global Avenue, Leeds LS11 8PG (United Kingdom); Young, Andrew R. [Wallingford HydroSolutions Ltd, Maclean building, Crowmarsh Gifford, Wallingford, Oxon OX10 8BB (United Kingdom)

    2009-10-15

    Higher-tier environmental risk assessments on 'down-the-drain' chemicals in river networks can be conducted using models such as GREAT-ER (Geography-referenced Regional Exposure Assessment Tool for European Rivers). It is important these models are evaluated and their sensitivities to input variables understood. This study had two primary objectives: evaluate GREAT-ER model performance, comparing simulated modelled predictions for LAS (linear alkylbenzene sulphonate) with measured concentrations, for four rivers in the UK, and investigate model sensitivity to input variables. We demonstrate that the GREAT-ER model is very sensitive to variability in river discharges. However it is insensitive to the form of distributions used to describe chemical usage and removal rate in sewage treatment plants (STPs). It is concluded that more effort should be directed towards improving empirical estimates of effluent load and reducing uncertainty associated with usage and removal rates in STPs. Simulations could be improved by incorporating the effect of river depth on dissipation rates. - Validation of GREAT-ER.

  3. Multi-model analysis of terrestrial carbon cycles in Japan: reducing uncertainties in model outputs among different terrestrial biosphere models using flux observations

    Science.gov (United States)

    Ichii, K.; Suzuki, T.; Kato, T.; Ito, A.; Hajima, T.; Ueyama, M.; Sasai, T.; Hirata, R.; Saigusa, N.; Ohtani, Y.; Takagi, K.

    2009-08-01

    Terrestrial biosphere models show large uncertainties when simulating carbon and water cycles, and reducing these uncertainties is a priority for developing more accurate estimates of both terrestrial ecosystem statuses and future climate changes. To reduce uncertainties and improve the understanding of these carbon budgets, we investigated the ability of flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine-based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and an improved model (based on calibration using flux observations). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using flux observations (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs, and model calibration using flux observations significantly improved the model outputs. These results show that to reduce uncertainties among terrestrial biosphere models, we need to conduct careful validation and calibration with available flux observations. Flux observation data significantly improved terrestrial biosphere models, not only on a point scale but also on spatial scales.

  4. Uncertainty-embedded dynamic life cycle sustainability assessment framework: An ex-ante perspective on the impacts of alternative vehicle options

    International Nuclear Information System (INIS)

    Onat, Nuri Cihat; Kucukvar, Murat; Tatari, Omer

    2016-01-01

    Alternative vehicle technologies have a great potential to minimize the transportation-related environmental impacts, reduce the reliance of the U.S. on imported petroleum, and increase energy security. However, they introduce new uncertainties related to their environmental, economic, and social impacts and certain challenges for widespread adoption. In this study, a novel method, uncertainty-embedded dynamic life cycle sustainability assessment framework, is developed to address both methodological challenges and uncertainties in transportation sustainability research. The proposed approach provides a more comprehensive, system-based sustainability assessment framework by capturing the dynamic relations among the parameters within the U.S. transportation system as a whole with respect to its environmental, social, and economic impacts. Using multivariate uncertainty analysis, likelihood of the impact reduction potentials of different vehicle types, as well as the behavioral limits of the sustainability potentials of each vehicle type are analyzed. Seven sustainability impact categories are dynamically quantified for four different vehicle types (internal combustion, hybrid, plug-in hybrid, and battery electric vehicles) from 2015 to 2050. Although impacts of electric vehicles have the largest uncertainty, they are expected (90% confidence) to be the best alternative in long-term for reducing human health impacts and air pollution from transportation. While results based on deterministic (average) values indicate that electric vehicles have greater potential of reducing greenhouse gas emissions, plug-in hybrid vehicles have the largest potential according to the results with 90% confidence interval. - Highlights: • Uncertainty-embedded dynamic sustainability assessment framework, is developed. • Methodological challenges and uncertainties are addressed. • Seven impact categories are quantified for four different vehicle types.

  5. [Influence of Uncertainty and Uncertainty Appraisal on Self-management in Hemodialysis Patients].

    Science.gov (United States)

    Jang, Hyung Suk; Lee, Chang Suk; Yang, Young Hee

    2015-04-01

    This study was done to examine the relation of uncertainty, uncertainty appraisal, and self-management in patients undergoing hemodialysis, and to identify factors influencing self-management. A convenience sample of 92 patients receiving hemodialysis was selected. Data were collected using a structured questionnaire and medical records. The collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlations and multiple regression analysis with the SPSS/WIN 20.0 program. The participants showed a moderate level of uncertainty with the highest score being for ambiguity among the four uncertainty subdomains. Scores for uncertainty danger or opportunity appraisals were under the mid points. The participants were found to perform a high level of self-management such as diet control, management of arteriovenous fistula, exercise, medication, physical management, measurements of body weight and blood pressure, and social activity. The self-management of participants undergoing hemodialysis showed a significant relationship with uncertainty and uncertainty appraisal. The significant factors influencing self-management were uncertainty, uncertainty opportunity appraisal, hemodialysis duration, and having a spouse. These variables explained 32.8% of the variance in self-management. The results suggest that intervention programs to reduce the level of uncertainty and to increase the level of uncertainty opportunity appraisal among patients would improve the self-management of hemodialysis patients.

  6. Uncertainty Characterization of Reactor Vessel Fracture Toughness

    International Nuclear Information System (INIS)

    Li, Fei; Modarres, Mohammad

    2002-01-01

    To perform fracture mechanics analysis of reactor vessel, fracture toughness (K Ic ) at various temperatures would be necessary. In a best estimate approach, K Ic uncertainties resulting from both lack of sufficient knowledge and randomness in some of the variables of K Ic must be characterized. Although it may be argued that there is only one type of uncertainty, which is lack of perfect knowledge about the subject under study, as a matter of practice K Ic uncertainties can be divided into two types: aleatory and epistemic. Aleatory uncertainty is related to uncertainty that is very difficult to reduce, if not impossible; epistemic uncertainty, on the other hand, can be practically reduced. Distinction between aleatory and epistemic uncertainties facilitates decision-making under uncertainty and allows for proper propagation of uncertainties in the computation process. Typically, epistemic uncertainties representing, for example, parameters of a model are sampled (to generate a 'snapshot', single-value of the parameters), but the totality of aleatory uncertainties is carried through the calculation as available. In this paper a description of an approach to account for these two types of uncertainties associated with K Ic has been provided. (authors)

  7. Mapping carbon flux uncertainty and selecting optimal locations for future flux towers in the Great Plains

    Science.gov (United States)

    Gu, Yingxin; Howard, Daniel M.; Wylie, Bruce K.; Zhang, Li

    2012-01-01

    Flux tower networks (e. g., AmeriFlux, Agriflux) provide continuous observations of ecosystem exchanges of carbon (e. g., net ecosystem exchange), water vapor (e. g., evapotranspiration), and energy between terrestrial ecosystems and the atmosphere. The long-term time series of flux tower data are essential for studying and understanding terrestrial carbon cycles, ecosystem services, and climate changes. Currently, there are 13 flux towers located within the Great Plains (GP). The towers are sparsely distributed and do not adequately represent the varieties of vegetation cover types, climate conditions, and geophysical and biophysical conditions in the GP. This study assessed how well the available flux towers represent the environmental conditions or "ecological envelopes" across the GP and identified optimal locations for future flux towers in the GP. Regression-based remote sensing and weather-driven net ecosystem production (NEP) models derived from different extrapolation ranges (10 and 50%) were used to identify areas where ecological conditions were poorly represented by the flux tower sites and years previously used for mapping grassland fluxes. The optimal lands suitable for future flux towers within the GP were mapped. Results from this study provide information to optimize the usefulness of future flux towers in the GP and serve as a proxy for the uncertainty of the NEP map.

  8. Reusable launch vehicle model uncertainties impact analysis

    Science.gov (United States)

    Chen, Jiaye; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

    2018-03-01

    Reusable launch vehicle(RLV) has the typical characteristics of complex aerodynamic shape and propulsion system coupling, and the flight environment is highly complicated and intensely changeable. So its model has large uncertainty, which makes the nominal system quite different from the real system. Therefore, studying the influences caused by the uncertainties on the stability of the control system is of great significance for the controller design. In order to improve the performance of RLV, this paper proposes the approach of analyzing the influence of the model uncertainties. According to the typical RLV, the coupling dynamic and kinematics models are built. Then different factors that cause uncertainties during building the model are analyzed and summed up. After that, the model uncertainties are expressed according to the additive uncertainty model. Choosing the uncertainties matrix's maximum singular values as the boundary model, and selecting the uncertainties matrix's norm to show t how much the uncertainty factors influence is on the stability of the control system . The simulation results illustrate that the inertial factors have the largest influence on the stability of the system, and it is necessary and important to take the model uncertainties into consideration before the designing the controller of this kind of aircraft( like RLV, etc).

  9. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

    Science.gov (United States)

    Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

    2017-12-01

    Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

  10. Time-Varying Uncertainty in Shock and Vibration Applications Using the Impulse Response

    Directory of Open Access Journals (Sweden)

    J.B. Weathers

    2012-01-01

    Full Text Available Design of mechanical systems often necessitates the use of dynamic simulations to calculate the displacements (and their derivatives of the bodies in a system as a function of time in response to dynamic inputs. These types of simulations are especially prevalent in the shock and vibration community where simulations associated with models having complex inputs are routine. If the forcing functions as well as the parameters used in these simulations are subject to uncertainties, then these uncertainties will propagate through the models resulting in uncertainties in the outputs of interest. The uncertainty analysis procedure for these kinds of time-varying problems can be challenging, and in many instances, explicit data reduction equations (DRE's, i.e., analytical formulas, are not available because the outputs of interest are obtained from complex simulation software, e.g. FEA programs. Moreover, uncertainty propagation in systems modeled using nonlinear differential equations can prove to be difficult to analyze. However, if (1 the uncertainties propagate through the models in a linear manner, obeying the principle of superposition, then the complexity of the problem can be significantly simplified. If in addition, (2 the uncertainty in the model parameters do not change during the simulation and the manner in which the outputs of interest respond to small perturbations in the external input forces is not dependent on when the perturbations are applied, then the number of calculations required can be greatly reduced. Conditions (1 and (2 characterize a Linear Time Invariant (LTI uncertainty model. This paper seeks to explain one possible approach to obtain the uncertainty results based on these assumptions.

  11. Uncertainty Quantification with Applications to Engineering Problems

    DEFF Research Database (Denmark)

    Bigoni, Daniele

    in measurements, predictions and manufacturing, and we can say that any dynamical system used in engineering is subject to some of these uncertainties. The first part of this work presents an overview of the mathematical framework used in Uncertainty Quantification (UQ) analysis and introduces the spectral tensor...... and thus the UQ analysis of the associated systems will benefit greatly from the application of methods which require few function evaluations. We first consider the propagation of the uncertainty and the sensitivity analysis of the non-linear dynamics of railway vehicles with suspension components whose......-scale problems, where efficient methods are necessary with today’s computational resources. The outcome of this work was also the creation of several freely available Python modules for Uncertainty Quantification, which are listed and described in the appendix....

  12. Statistical analysis of the uncertainty related to flood hazard appraisal

    Science.gov (United States)

    Notaro, Vincenza; Freni, Gabriele

    2015-12-01

    The estimation of flood hazard frequency statistics for an urban catchment is of great interest in practice. It provides the evaluation of potential flood risk and related damage and supports decision making for flood risk management. Flood risk is usually defined as function of the probability, that a system deficiency can cause flooding (hazard), and the expected damage, due to the flooding magnitude (damage), taking into account both the exposure and the vulnerability of the goods at risk. The expected flood damage can be evaluated by an a priori estimation of potential damage caused by flooding or by interpolating real damage data. With regard to flood hazard appraisal several procedures propose to identify some hazard indicator (HI) such as flood depth or the combination of flood depth and velocity and to assess the flood hazard corresponding to the analyzed area comparing the HI variables with user-defined threshold values or curves (penalty curves or matrixes). However, flooding data are usually unavailable or piecemeal allowing for carrying out a reliable flood hazard analysis, therefore hazard analysis is often performed by means of mathematical simulations aimed at evaluating water levels and flow velocities over catchment surface. As results a great part of the uncertainties intrinsic to flood risk appraisal can be related to the hazard evaluation due to the uncertainty inherent to modeling results and to the subjectivity of the user defined hazard thresholds applied to link flood depth to a hazard level. In the present work, a statistical methodology was proposed for evaluating and reducing the uncertainties connected with hazard level estimation. The methodology has been applied to a real urban watershed as case study.

  13. ''Nature is unknowable''. The idea of uncertainty

    International Nuclear Information System (INIS)

    Crozon, M.

    2000-01-01

    This paper deals with one of the great idea of the twentieth century, the uncertainty principle of Heisenberg. With a philosophical approach the author explains this principle and presents its cultural impacts on mind. (A.L.B.)

  14. Reducing uncertainty based on model fitness: Application to a ...

    African Journals Online (AJOL)

    A weakness of global sensitivity and uncertainty analysis methodologies is the often subjective definition of prior parameter probability distributions, especially ... The reservoir representing the central part of the wetland, where flood waters separate into several independent distributaries, is a keystone area within the model.

  15. The Uncertainty Multiplier and Business Cycles

    OpenAIRE

    Saijo, Hikaru

    2013-01-01

    I study a business cycle model where agents learn about the state of the economy by accumulating capital. During recessions, agents invest less, and this generates noisier estimates of macroeconomic conditions and an increase in uncertainty. The endogenous increase in aggregate uncertainty further reduces economic activity, which in turn leads to more uncertainty, and so on. Thus, through changes in uncertainty, learning gives rise to a multiplier effect that amplifies business cycles. I use ...

  16. Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not.

    Science.gov (United States)

    MacDorman, Karl F; Chattopadhyay, Debaleena

    2016-01-01

    Human replicas may elicit unintended cold, eerie feelings in viewers, an effect known as the uncanny valley. Masahiro Mori, who proposed the effect in 1970, attributed it to inconsistencies in the replica's realism with some of its features perceived as human and others as nonhuman. This study aims to determine whether reducing realism consistency in visual features increases the uncanny valley effect. In three rounds of experiments, 548 participants categorized and rated humans, animals, and objects that varied from computer animated to real. Two sets of features were manipulated to reduce realism consistency. (For humans, the sets were eyes-eyelashes-mouth and skin-nose-eyebrows.) Reducing realism consistency caused humans and animals, but not objects, to appear eerier and colder. However, the predictions of a competing theory, proposed by Ernst Jentsch in 1906, were not supported: The most ambiguous representations-those eliciting the greatest category uncertainty-were neither the eeriest nor the coldest. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

  17. Assessing the Expected Value of Research Studies in Reducing Uncertainty and Improving Implementation Dynamics.

    Science.gov (United States)

    Grimm, Sabine E; Dixon, Simon; Stevens, John W

    2017-07-01

    With low implementation of cost-effective health technologies being a problem in many health systems, it is worth considering the potential effects of research on implementation at the time of health technology assessment. Meaningful and realistic implementation estimates must be of dynamic nature. To extend existing methods for assessing the value of research studies in terms of both reduction of uncertainty and improvement in implementation by considering diffusion based on expert beliefs with and without further research conditional on the strength of evidence. We use expected value of sample information and expected value of specific implementation measure concepts accounting for the effects of specific research studies on implementation and the reduction of uncertainty. Diffusion theory and elicitation of expert beliefs about the shape of diffusion curves inform implementation dynamics. We illustrate use of the resulting dynamic expected value of research in a preterm birth screening technology and results are compared with those from a static analysis. Allowing for diffusion based on expert beliefs had a significant impact on the expected value of research in the case study, suggesting that mistakes are made where static implementation levels are assumed. Incorporating the effects of research on implementation resulted in an increase in the expected value of research compared to the expected value of sample information alone. Assessing the expected value of research in reducing uncertainty and improving implementation dynamics has the potential to complement currently used analyses in health technology assessments, especially in recommendations for further research. The combination of expected value of research, diffusion theory, and elicitation described in this article is an important addition to the existing methods of health technology assessment.

  18. Greatly reduced emission of greenhouse gases from the wood-processing industry

    International Nuclear Information System (INIS)

    2004-01-01

    The strong support for biomass energy in the Norwegian wood-processing industry during the last 10-15 years has contributed greatly to a considerable reduction of the emission of greenhouse gases. The potential for further reductions is primarily linked with the use of oil and involves only a few works. Oil can be replaced by other fuels, and process-technical improvements can reduce the emissions. According to prognoses, emissions will go on decreasing until 2007, when the total emission of greenhouse gases from the wood-processing industry will be about 13 per cent less than in 1998. Carbon dioxide (CO 2 ) amounts to 90 per cent of the total emission, the remaining parts being methane (CH 4 ) from landfills and dumps, and small amounts of N 2 O

  19. Explaining Delusions: Reducing Uncertainty Through Basic and Computational Neuroscience.

    Science.gov (United States)

    Feeney, Erin J; Groman, Stephanie M; Taylor, Jane R; Corlett, Philip R

    2017-03-01

    Delusions, the fixed false beliefs characteristic of psychotic illness, have long defied understanding despite their response to pharmacological treatments (e.g., D2 receptor antagonists). However, it can be challenging to discern what makes beliefs delusional compared with other unusual or erroneous beliefs. We suggest mapping the putative biology to clinical phenomenology with a cognitive psychology of belief, culminating in a teleological approach to beliefs and brain function supported by animal and computational models. We argue that organisms strive to minimize uncertainty about their future states by forming and maintaining a set of beliefs (about the organism and the world) that are robust, but flexible. If uncertainty is generated endogenously, beliefs begin to depart from consensual reality and can manifest into delusions. Central to this scheme is the notion that formal associative learning theory can provide an explanation for the development and persistence of delusions. Beliefs, in animals and humans, may be associations between representations (e.g., of cause and effect) that are formed by minimizing uncertainty via new learning and attentional allocation. Animal research has equipped us with a deep mechanistic basis of these processes, which is now being applied to delusions. This work offers the exciting possibility of completing revolutions of translation, from the bedside to the bench and back again. The more we learn about animal beliefs, the more we may be able to apply to human beliefs and their aberrations, enabling a deeper mechanistic understanding. © The Author 2017. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  20. Uncertainty Analysis on the Health Risk Caused by a Radiological Terror

    International Nuclear Information System (INIS)

    Jeong, Hyojoon; Hwang, Wontae; Kim, Eunhan; Han, Moonhee

    2010-01-01

    Atmospheric dispersion modeling and uncertainty analysis were carried out support the decision making in case of radiological emergency events. The risk caused by inhalation is highly dependent on air concentrations for radionuclides, therefore air monitoring and dispersion modeling have to be performed carefully to reduce the uncertainty of the health risk assessment for the public. When an intentional release of radioactive materials occurs in an urban area, air quality for radioactive materials in the environment is of great importance to take action for countermeasures and environmental risk assessments. Atmospheric modeling is part of the decision making tasks and that it is particularly important for emergency managers as they often need to take actions quickly on very inadequate information. In this study, we assume an 137 Cs explosion of 50 TBq using RDDs in the metropolitan area of Soul, South Korea. California Puff Model is used to calculate an atmospheric dispersion and transport for 137 Cs, and environmental risk analysis is performed using the Monte Carlo method

  1. On treatment of uncertainty in system planning

    International Nuclear Information System (INIS)

    Flage, R.; Aven, T.

    2009-01-01

    In system planning and operation considerable efforts and resources are spent to reduce uncertainties, as a part of project management, uncertainty management and safety management. The basic idea seems to be that uncertainties are purely negative and should be reduced. In this paper we challenge this way of thinking, using a common industry practice as an example. In accordance with this industry practice, three uncertainty interval categories are used: ±40% intervals for the feasibility phase, ±30% intervals for the concept development phase and ±20% intervals for the engineering phase. The problem is that such a regime could easily lead to a conservative management regime encouraging the use of existing methods and tools, as new activities and novel solutions and arrangements necessarily mean increased uncertainties. In the paper we suggest an alternative approach based on uncertainty and risk descriptions, but having no predefined uncertainty reduction structures. The approach makes use of risk assessments and economic optimisation tools such as the expected net present value, but acknowledges the need for broad risk management processes which extend beyond the analyses. Different concerns need to be balanced, including economic aspects, uncertainties and risk, and practicability

  2. Incorporating parametric uncertainty into population viability analysis models

    Science.gov (United States)

    McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.

    2011-01-01

    Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.

  3. Bookending the Opportunity to Lower Wind’s LCOE by Reducing the Uncertainty Surrounding Annual Energy Production

    Energy Technology Data Exchange (ETDEWEB)

    Bolinger, Mark [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Analysis and Environmental Impacts Div.

    2017-06-01

    Reducing the performance risk surrounding a wind project can potentially lead to a lower weighted-average cost of capital (WACC), and hence a lower levelized cost of energy (LCOE), through an advantageous shift in capital structure, and possibly also a reduction in the cost of capital. Specifically, a reduction in performance risk will move the 1-year P99 annual energy production (AEP) estimate closer to the P50 AEP estimate, which in turn reduces the minimum debt service coverage ratio (DSCR) required by lenders, thereby allowing the project to be financed with a greater proportion of low-cost debt. In addition, a reduction in performance risk might also reduce the cost of one or more of the three sources of capital that are commonly used to finance wind projects: sponsor or cash equity, tax equity, and/or debt. Preliminary internal LBNL analysis of the maximum possible LCOE reduction attainable from reducing the performance risk of a wind project found a potentially significant opportunity for LCOE reduction of ~$10/MWh, by reducing the P50 DSCR to its theoretical minimum value of 1.0 (Bolinger 2015b, 2014) and by reducing the cost of sponsor equity and debt by one-third to one-half each (Bolinger 2015a, 2015b). However, with FY17 funding from the U.S. Department of Energy’s Atmosphere to Electrons (A2e) Performance Risk, Uncertainty, and Finance (PRUF) initiative, LBNL has been revisiting this “bookending” exercise in more depth, and now believes that its earlier preliminary assessment of the LCOE reduction opportunity was overstated. This reassessment is based on two new-found understandings: (1) Due to ever-present and largely irreducible inter-annual variability (IAV) in the wind resource, the minimum required DSCR cannot possibly fall to 1.0 (on a P50 basis), and (2) A reduction in AEP uncertainty will not necessarily lead to a reduction in the cost of capital, meaning that a shift in capital structure is perhaps the best that can be expected (perhaps

  4. Uncertainties associated with inertial-fusion ignition

    International Nuclear Information System (INIS)

    McCall, G.H.

    1981-01-01

    An estimate is made of a worst case driving energy which is derived from analytic and computer calculations. It will be shown that the uncertainty can be reduced by a factor of 10 to 100 if certain physical effects are understood. That is not to say that the energy requirement can necessarily be reduced below that of the worst case, but it is possible to reduce the uncertainty associated with ignition energy. With laser costs in the $0.5 to 1 billion per MJ range, it can be seen that such an exercise is worthwhile

  5. Reducing the uncertainty in the fidelity of seismic imaging results

    Science.gov (United States)

    Zhou, H. W.; Zou, Z.

    2017-12-01

    A key aspect in geoscientific inversion is quantifying the quality of the results. In seismic imaging, we must quantify the uncertainty of every imaging result based on field data, because data noise and methodology limitations may produce artifacts. Detection of artifacts is therefore an important aspect in uncertainty quantification in geoscientific inversion. Quantifying the uncertainty of seismic imaging solutions means assessing their fidelity, which defines the truthfulness of the imaged targets in terms of their resolution, position error and artifact. Key challenges to achieving the fidelity of seismic imaging include: (1) Difficulty to tell signal from artifact and noise; (2) Limitations in signal-to-noise ratio and seismic illumination; and (3) The multi-scale nature of the data space and model space. Most seismic imaging studies of the Earth's crust and mantle have employed inversion or modeling approaches. Though they are in opposite directions of mapping between the data space and model space, both inversion and modeling seek the best model to minimize the misfit in the data space, which unfortunately is not the output space. The fact that the selection and uncertainty of the output model are not judged in the output space has exacerbated the nonuniqueness problem for inversion and modeling. In contrast, the practice in exploration seismology has long established a two-fold approach of seismic imaging: Using velocity modeling building to establish the long-wavelength reference velocity models, and using seismic migration to map the short-wavelength reflectivity structures. Most interestingly, seismic migration maps the data into an output space called imaging space, where the output reflection images of the subsurface are formed based on an imaging condition. A good example is the reverse time migration, which seeks the reflectivity image as the best fit in the image space between the extrapolation of time-reversed waveform data and the prediction

  6. Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models

    Science.gov (United States)

    Qi, D.; Majda, A.

    2017-12-01

    A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with

  7. Uncertainty analysis in comparative NAA applied to geological and biological matrices

    International Nuclear Information System (INIS)

    Zahn, Guilherme S.; Ticianelli, Regina B.; Lange, Camila N.; Favaro, Deborah I.T.; Figueiredo, Ana M.G.

    2015-01-01

    Comparative nuclear activation analysis is a multielemental primary analytical technique that may be used in a rather broad spectrum of matrices with minimal-to-none sample preprocessing. Although the total activation of a chemical element in a sample depends on a rather large set of parameters, when the sample is irradiated together with a well-known comparator, most of these parameters are crossed out and the concentration of that element can be determined simply by using the activities and masses of the comparator and the sample, the concentration of this chemical element in the sample, the half-life of the formed radionuclide and the time between counting the sample and the comparator. This simplification greatly reduces not only the calculations required, but also the uncertainty associated with the measurement; nevertheless, a cautious analysis must be carried out in order to make sure all relevant uncertainties are properly treated, so that the final result can be as representative of the measurement as possible. In this work, this analysis was performed for geological matrices, where concentrations of the interest nuclides are rather high, but so is the density and average atomic number of the sample, as well as for a biological matrix, in order to allow for a comparison. The results show that the largest part of the uncertainty comes from the activity measurements and from the concentration of the comparator, and that while the influence of time-related terms in the final uncertainty can be safely neglected, the uncertainty in the masses may be relevant under specific circumstances. (author)

  8. Uncertainty analysis in comparative NAA applied to geological and biological matrices

    Energy Technology Data Exchange (ETDEWEB)

    Zahn, Guilherme S.; Ticianelli, Regina B.; Lange, Camila N.; Favaro, Deborah I.T.; Figueiredo, Ana M.G., E-mail: gzahn@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2015-07-01

    Comparative nuclear activation analysis is a multielemental primary analytical technique that may be used in a rather broad spectrum of matrices with minimal-to-none sample preprocessing. Although the total activation of a chemical element in a sample depends on a rather large set of parameters, when the sample is irradiated together with a well-known comparator, most of these parameters are crossed out and the concentration of that element can be determined simply by using the activities and masses of the comparator and the sample, the concentration of this chemical element in the sample, the half-life of the formed radionuclide and the time between counting the sample and the comparator. This simplification greatly reduces not only the calculations required, but also the uncertainty associated with the measurement; nevertheless, a cautious analysis must be carried out in order to make sure all relevant uncertainties are properly treated, so that the final result can be as representative of the measurement as possible. In this work, this analysis was performed for geological matrices, where concentrations of the interest nuclides are rather high, but so is the density and average atomic number of the sample, as well as for a biological matrix, in order to allow for a comparison. The results show that the largest part of the uncertainty comes from the activity measurements and from the concentration of the comparator, and that while the influence of time-related terms in the final uncertainty can be safely neglected, the uncertainty in the masses may be relevant under specific circumstances. (author)

  9. The National Ecosystem Services Classification System: A Framework for Identifying and Reducing Relevant Uncertainties

    Science.gov (United States)

    Rhodes, C. R.; Sinha, P.; Amanda, N.

    2013-12-01

    In recent years the gap between what scientists know and what policymakers should appreciate in environmental decision making has received more attention, as the costs of the disconnect have become more apparent to both groups. Particularly for water-related policies, the EPA's Office of Water has struggled with benefit estimates held low by the inability to quantify ecological and economic effects that theory, modeling, and anecdotal or isolated case evidence suggest may prove to be larger. Better coordination with ecologists and hydrologists is being explored as a solution. The ecosystem services (ES) concept now nearly two decades old links ecosystem functions and processes to the human value system. But there remains no clear mapping of which ecosystem goods and services affect which individual or economic values. The National Ecosystem Services Classification System (NESCS, 'nexus') project brings together ecologists, hydrologists, and social scientists to do this mapping for aquatic and other ecosystem service-generating systems. The objective is to greatly reduce the uncertainty in water-related policy making by mapping and ultimately quantifying the various functions and products of aquatic systems, as well as how changes to aquatic systems impact the human economy and individual levels of non-monetary appreciation for those functions and products. Primary challenges to fostering interaction between scientists, social scientists, and policymakers are lack of a common vocabulary, and the need for a cohesive comprehensive framework that organizes concepts across disciplines and accommodates scientific data from a range of sources. NESCS builds the vocabulary and the framework so both may inform a scalable transdisciplinary policy-making application. This talk presents for discussion the process and progress in developing both this vocabulary and a classifying framework capable of bridging the gap between a newer but existing ecosystem services classification

  10. Investment and uncertainty

    DEFF Research Database (Denmark)

    Greasley, David; Madsen, Jakob B.

    2006-01-01

    A severe collapse of fixed capital formation distinguished the onset of the Great Depression from other investment downturns between the world wars. Using a model estimated for the years 1890-2000, we show that the expected profitability of capital measured by Tobin's q, and the uncertainty...... surrounding expected profits indicated by share price volatility, were the chief influences on investment levels, and that heightened share price volatility played the dominant role in the crucial investment collapse in 1930. Investment did not simply follow the downward course of income at the onset...

  11. Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment

    Energy Technology Data Exchange (ETDEWEB)

    Khuwaileh, B.A., E-mail: bakhuwai@ncsu.edu; Abdel-Khalik, H.S.

    2015-01-15

    Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.

  12. Time-Resolved Particle Image Velocimetry Measurements with Wall Shear Stress and Uncertainty Quantification for the FDA Nozzle Model.

    Science.gov (United States)

    Raben, Jaime S; Hariharan, Prasanna; Robinson, Ronald; Malinauskas, Richard; Vlachos, Pavlos P

    2016-03-01

    We present advanced particle image velocimetry (PIV) processing, post-processing, and uncertainty estimation techniques to support the validation of computational fluid dynamics analyses of medical devices. This work is an extension of a previous FDA-sponsored multi-laboratory study, which used a medical device mimicking geometry referred to as the FDA benchmark nozzle model. Experimental measurements were performed using time-resolved PIV at five overlapping regions of the model for Reynolds numbers in the nozzle throat of 500, 2000, 5000, and 8000. Images included a twofold increase in spatial resolution in comparison to the previous study. Data was processed using ensemble correlation, dynamic range enhancement, and phase correlations to increase signal-to-noise ratios and measurement accuracy, and to resolve flow regions with large velocity ranges and gradients, which is typical of many blood-contacting medical devices. Parameters relevant to device safety, including shear stress at the wall and in bulk flow, were computed using radial basis functions. In addition, in-field spatially resolved pressure distributions, Reynolds stresses, and energy dissipation rates were computed from PIV measurements. Velocity measurement uncertainty was estimated directly from the PIV correlation plane, and uncertainty analysis for wall shear stress at each measurement location was performed using a Monte Carlo model. Local velocity uncertainty varied greatly and depended largely on local conditions such as particle seeding, velocity gradients, and particle displacements. Uncertainty in low velocity regions in the sudden expansion section of the nozzle was greatly reduced by over an order of magnitude when dynamic range enhancement was applied. Wall shear stress uncertainty was dominated by uncertainty contributions from velocity estimations, which were shown to account for 90-99% of the total uncertainty. This study provides advancements in the PIV processing methodologies over

  13. Use of screening techniques to reduce uncertainty in risk assessment at a former manufactured gas plant site

    International Nuclear Information System (INIS)

    Logan, C.M.; Walden, R.H.; Baker, S.R.; Pekar, Z.; LaKind, J.S.; MacFarlane, I.D.

    1995-01-01

    Preliminary analysis of risks from a former manufactured gas plant (MGP) site revealed six media associated with potential exposure pathways: soils, air, surface water, groundwater, estuarine sediments, and aquatic biota. Contaminants of concern (COCs) include polycyclic aromatic hydrocarbons, volatile organic hydrocarbons, metals, cyanide, and PCBs. Available chemical data, including site-specific measurements and existing data from other sources (e.g., agency monitoring programs, Chesapeake Bay Program), were evaluated for potential utility in risk assessment. Where sufficient data existed, risk calculations were performed using central tendency and reasonable maximum exposure estimates. Where site-specific data were not available, risks were estimated using conservatively high default assumptions for dose and/or exposure duration. Because of the large number of potential exposure pathways and COCs, a sensitivity analysis was conducted to determine which information most influences risk assessment outcome so that any additional data collection to reduce uncertainty can be cost-effectively targeted. The sensitivity analysis utilized two types of information: (1) the impact that uncertainty in risk input values has on output risk estimates, and (2) the potential improvement in key risk input values, and consequently output values, if better site-specific data were available. A decision matrix using both quantitative and qualitative information was developed to prioritize sampling strategies to minimize uncertainty in the final risk assessment

  14. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  15. Regulatory risk assessments: Is there a need to reduce uncertainty and enhance robustness?

    Science.gov (United States)

    Snodin, D J

    2015-12-01

    A critical evaluation of several recent regulatory risk assessments has been undertaken. These relate to propyl paraben (as a food additive, cosmetic ingredient or pharmaceutical excipient), cobalt (in terms of a safety-based limit for pharmaceuticals) and the cancer Threshold of Toxicological Concern as applied to food contaminants and pharmaceutical impurities. In all cases, a number of concerns can be raised regarding the reliability of the current assessments, some examples being absence of data audits, use of single-dose and/or non-good laboratory practice studies to determine safety metrics, use of a biased data set and questionable methodology and lack of consistency with precedents and regulatory guidance. Drawing on these findings, a set of recommendations is provided to reduce uncertainty and improve the quality and robustness of future regulatory risk assessments. © The Author(s) 2015.

  16. It's the parameters, stupid! Moving beyond multi-model and multi-physics approaches to characterize and reduce predictive uncertainty in process-based hydrological models

    Science.gov (United States)

    Clark, Martyn; Samaniego, Luis; Freer, Jim

    2014-05-01

    Multi-model and multi-physics approaches are a popular tool in environmental modelling, with many studies focusing on optimally combining output from multiple model simulations to reduce predictive errors and better characterize predictive uncertainty. However, a careful and systematic analysis of different hydrological models reveals that individual models are simply small permutations of a master modeling template, and inter-model differences are overwhelmed by uncertainty in the choice of the parameter values in the model equations. Furthermore, inter-model differences do not explicitly represent the uncertainty in modeling a given process, leading to many situations where different models provide the wrong results for the same reasons. In other cases, the available morphological data does not support the very fine spatial discretization of the landscape that typifies many modern applications of process-based models. To make the uncertainty characterization problem worse, the uncertain parameter values in process-based models are often fixed (hard-coded), and the models lack the agility necessary to represent the tremendous heterogeneity in natural systems. This presentation summarizes results from a systematic analysis of uncertainty in process-based hydrological models, where we explicitly analyze the myriad of subjective decisions made throughout both the model development and parameter estimation process. Results show that much of the uncertainty is aleatory in nature - given a "complete" representation of dominant hydrologic processes, uncertainty in process parameterizations can be represented using an ensemble of model parameters. Epistemic uncertainty associated with process interactions and scaling behavior is still important, and these uncertainties can be represented using an ensemble of different spatial configurations. Finally, uncertainty in forcing data can be represented using ensemble methods for spatial meteorological analysis. Our systematic

  17. On Commitments and Other Uncertainty Reduction Tools in Joint Action

    Directory of Open Access Journals (Sweden)

    Michael John

    2015-01-01

    Full Text Available In this paper, we evaluate the proposal that a central function of commitments within joint action is to reduce various kinds of uncertainty, and that this accounts for the prevalence of commitments in joint action. While this idea is prima facie attractive, we argue that it faces two serious problems. First, commitments can only reduce uncertainty if they are credible, and accounting for the credibility of commitments proves not to be straightforward. Second, there are many other ways in which uncertainty is commonly reduced within joint actions, which raises the possibility that commitments may be superfluous. Nevertheless, we argue that the existence of these alternative uncertainty reduction processes does not make commitments superfluous after all but, rather, helps to explain how commitments may contribute in various ways to uncertainty reduction.

  18. Forecasting the Number of Soil Samples Required to Reduce Remediation Cost Uncertainty

    OpenAIRE

    Demougeot-Renard, Hélène; de Fouquet, Chantal; Renard, Philippe

    2008-01-01

    Sampling scheme design is an important step in the management of polluted sites. It largely controls the accuracy of remediation cost estimates. In practice, however, sampling is seldom designed to comply with a given level of remediation cost uncertainty. In this paper, we present a new technique that allows one to estimate of the number of samples that should be taken at a given stage of investigation to reach a forecasted level of accuracy. The uncertainty is expressed both in terms of vol...

  19. Towards minimizing measurement uncertainty in total petroleum hydrocarbon determination by GC-FID

    Energy Technology Data Exchange (ETDEWEB)

    Saari, E.

    2009-07-01

    Despite tightened environmental legislation, spillages of petroleum products remain a serious problem worldwide. The environmental impacts of these spillages are always severe and reliable methods for the identification and quantitative determination of petroleum hydrocarbons in environmental samples are therefore needed. Great improvements in the definition and analysis of total petroleum hydrocarbons (TPH) were finally introduced by international organizations for standardization in 2004. This brought some coherence to the determination and, nowadays, most laboratories seem to employ ISO/DIS 16703:2004, ISO 9377-2:2000 and CEN prEN 14039:2004:E draft international standards for analysing TPH in soil. The implementation of these methods, however, usually fails because the reliability of petroleum hydrocarbon determination has proved to be poor.This thesis describes the assessment of measurement uncertainty for TPH determination in soil. Chemometric methods were used to both estimate the main uncertainty sources and identify the most significant factors affecting these uncertainty sources. The method used for the determinations was based on gas chromatography utilizing flame ionization detection (GC-FID).Chemometric methodology applied in estimating measurement uncertainty for TPH determination showed that the measurement uncertainty is in actual fact dominated by the analytical uncertainty. Within the specific concentration range studied, the analytical uncertainty accounted for as much as 68-80% of the measurement uncertainty. The robustness of the analytical method used for petroleum hydrocarbon determination was then studied in more detail. A two-level Plackett-Burman design and a D-optimal design were utilized to assess the main analytical uncertainty sources of the sample treatment and GC determination procedures. It was also found that the matrix-induced systematic error may also significantly reduce the reliability of petroleum hydrocarbon determination

  20. Great hammerhead sharks swim on their side to reduce transport costs.

    Science.gov (United States)

    Payne, Nicholas L; Iosilevskii, Gil; Barnett, Adam; Fischer, Chris; Graham, Rachel T; Gleiss, Adrian C; Watanabe, Yuuki Y

    2016-07-26

    Animals exhibit various physiological and behavioural strategies for minimizing travel costs. Fins of aquatic animals play key roles in efficient travel and, for sharks, the functions of dorsal and pectoral fins are considered well divided: the former assists propulsion and generates lateral hydrodynamic forces during turns and the latter generates vertical forces that offset sharks' negative buoyancy. Here we show that great hammerhead sharks drastically reconfigure the function of these structures, using an exaggerated dorsal fin to generate lift by swimming rolled on their side. Tagged wild sharks spend up to 90% of time swimming at roll angles between 50° and 75°, and hydrodynamic modelling shows that doing so reduces drag-and in turn, the cost of transport-by around 10% compared with traditional upright swimming. Employment of such a strongly selected feature for such a unique purpose raises interesting questions about evolutionary pathways to hydrodynamic adaptations, and our perception of form and function.

  1. Treatment of uncertainty in low-level waste performance assessment

    International Nuclear Information System (INIS)

    Kozak, M.W.; Olague, N.E.; Gallegos, D.P.; Rao, R.R.

    1991-01-01

    Uncertainties arise from a number of different sources in low-level waste performance assessment. In this paper the types of uncertainty are reviewed, and existing methods for quantifying and reducing each type of uncertainty are discussed. These approaches are examined in the context of the current low-level radioactive waste regulatory performance objectives, which are deterministic. The types of uncertainty discussed in this paper are model uncertainty, uncertainty about future conditions, and parameter uncertainty. The advantages and disadvantages of available methods for addressing uncertainty in low-level waste performance assessment are presented. 25 refs

  2. Visualizing Uncertainty of Point Phenomena by Redesigned Error Ellipses

    Science.gov (United States)

    Murphy, Christian E.

    2018-05-01

    Visualizing uncertainty remains one of the great challenges in modern cartography. There is no overarching strategy to display the nature of uncertainty, as an effective and efficient visualization depends, besides on the spatial data feature type, heavily on the type of uncertainty. This work presents a design strategy to visualize uncertainty con-nected to point features. The error ellipse, well-known from mathematical statistics, is adapted to display the uncer-tainty of point information originating from spatial generalization. Modified designs of the error ellipse show the po-tential of quantitative and qualitative symbolization and simultaneous point based uncertainty symbolization. The user can intuitively depict the centers of gravity, the major orientation of the point arrays as well as estimate the ex-tents and possible spatial distributions of multiple point phenomena. The error ellipse represents uncertainty in an intuitive way, particularly suitable for laymen. Furthermore it is shown how applicable an adapted design of the er-ror ellipse is to display the uncertainty of point features originating from incomplete data. The suitability of the error ellipse to display the uncertainty of point information is demonstrated within two showcases: (1) the analysis of formations of association football players, and (2) uncertain positioning of events on maps for the media.

  3. A programme of research to set priorities and reduce uncertainties for the prevention and treatment of skin disease

    OpenAIRE

    Thomas, K. S.; Batchelor, J. M.; Bath-Hextall, F.; Chalmers, J. R.; Clarke, T.; Crowe, S.; Delamere, F. M.; Eleftheriadou, V.; Evans, N.; Firkins, L.; Greenlaw, N.; Lansbury, L.; Lawton, S.; Layfield, C.; Leonardi-Bee, J.

    2016-01-01

    BACKGROUND: Skin diseases are very common and can have a large impact on the quality of life of patients and caregivers. This programme addressed four diseases: (1) eczema, (2) vitiligo, (3) squamous cell skin cancer (SCC) and (4) pyoderma gangrenosum (PG). OBJECTIVE: To set priorities and reduce uncertainties for the treatment and prevention of skin disease in our four chosen diseases. DESIGN: Mixed methods including eight systematic reviews, three prioritisation exercises, tw...

  4. High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments.

    Science.gov (United States)

    Tigges, Jan; Lakes, Tobia

    2017-10-04

    Urban forests reduce greenhouse gas emissions by storing and sequestering considerable amounts of carbon. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging for long-term prognoses. Therefore, this review aims to identify uncertainties in urban forest carbon offset assessment and discuss the extent to which such uncertainties can be reduced by recent progress in high resolution remote sensing. We do this by performing an extensive literature review and a case study combining remote sensing and life cycle assessment of urban forest carbon offset in Berlin, Germany. Recent progress in high resolution remote sensing and methods is adequate for delivering more precise details on the urban tree canopy, individual tree metrics, species, and age structures compared to conventional land use/cover class approaches. These area-wide consistent details can update life cycle inventories for more precise future prognoses. Additional improvements in classification accuracy can be achieved by a higher number of features derived from remote sensing data of increasing resolution, but first studies on this subject indicated that a smart selection of features already provides sufficient data that avoids redundancies and enables more efficient data processing. Our case study from Berlin could use remotely sensed individual tree species as consistent inventory of a life cycle assessment. However, a lack of growth, mortality and planting data forced us to make assumptions, therefore creating uncertainty in the long-term prognoses. Regarding temporal changes and reliable long-term estimates, more attention is required to detect changes of gradual growth, pruning and abrupt changes in tree planting and mortality. As such, precise long-term urban ecological monitoring using high resolution remote sensing should be intensified

  5. Section summary: Uncertainty and design considerations

    Science.gov (United States)

    Stephen Hagen

    2013-01-01

    Well planned sampling designs and robust approaches to estimating uncertainty are critical components of forest monitoring. The importance of uncertainty estimation increases as deforestation and degradation issues become more closely tied to financing incentives for reducing greenhouse gas emissions in the forest sector. Investors like to know risk and risk is tightly...

  6. Simulation codes and the impact of validation/uncertainty requirements

    International Nuclear Information System (INIS)

    Sills, H.E.

    1995-01-01

    Several of the OECD/CSNI members have adapted a proposed methodology for code validation and uncertainty assessment. Although the validation process adapted by members has a high degree of commonality, the uncertainty assessment processes selected are more variable, ranaing from subjective to formal. This paper describes the validation and uncertainty assessment process, the sources of uncertainty, methods of reducing uncertainty, and methods of assessing uncertainty.Examples are presented from the Ontario Hydro application of the validation methodology and uncertainty assessment to the system thermal hydraulics discipline and the TUF (1) system thermal hydraulics code. (author)

  7. The worth of data to reduce predictive uncertainty of an integrated catchment model by multi-constraint calibration

    Science.gov (United States)

    Koch, J.; Jensen, K. H.; Stisen, S.

    2017-12-01

    Hydrological models that integrate numerical process descriptions across compartments of the water cycle are typically required to undergo thorough model calibration in order to estimate suitable effective model parameters. In this study, we apply a spatially distributed hydrological model code which couples the saturated zone with the unsaturated zone and the energy portioning at the land surface. We conduct a comprehensive multi-constraint model calibration against nine independent observational datasets which reflect both the temporal and the spatial behavior of hydrological response of a 1000km2 large catchment in Denmark. The datasets are obtained from satellite remote sensing and in-situ measurements and cover five keystone hydrological variables: discharge, evapotranspiration, groundwater head, soil moisture and land surface temperature. Results indicate that a balanced optimization can be achieved where errors on objective functions for all nine observational datasets can be reduced simultaneously. The applied calibration framework was tailored with focus on improving the spatial pattern performance; however results suggest that the optimization is still more prone to improve the temporal dimension of model performance. This study features a post-calibration linear uncertainty analysis. This allows quantifying parameter identifiability which is the worth of a specific observational dataset to infer values to model parameters through calibration. Furthermore the ability of an observation to reduce predictive uncertainty is assessed as well. Such findings determine concrete implications on the design of model calibration frameworks and, in more general terms, the acquisition of data in hydrological observatories.

  8. [Dealing with diagnostic uncertainty in general practice].

    Science.gov (United States)

    Wübken, Magdalena; Oswald, Jana; Schneider, Antonius

    2013-01-01

    In general, the prevalence of diseases is low in primary care. Therefore, the positive predictive value of diagnostic tests is lower than in hospitals where patients are highly selected. In addition, the patients present with milder forms of disease; and many diseases might hide behind the initial symptom(s). These facts lead to diagnostic uncertainty which is somewhat inherent to general practice. This narrative review discusses different sources of and reasons for uncertainty and strategies to deal with it in the context of the current literature. Fear of uncertainty correlates with higher diagnostic activities. The attitude towards uncertainty correlates with the choice of medical speciality by vocational trainees or medical students. An intolerance of uncertainty, which still increases as medicine is making steady progress, might partly explain the growing shortage of general practitioners. The bio-psycho-social context appears to be important to diagnostic decision-making. The effect of intuition and heuristics are investigated by cognitive psychologists. It is still unclear whether these aspects are prone to bias or useful, which might depend on the context of medical decisions. Good communication is of great importance to share uncertainty with the patients in a transparent way and to alleviate shared decision-making. Dealing with uncertainty should be seen as an important core component of general practice and needs to be investigated in more detail to improve the respective medical decisions. Copyright © 2013. Published by Elsevier GmbH.

  9. Best Practices of Uncertainty Estimation for the National Solar Radiation Database (NSRDB 1998-2015): Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Habte, Aron M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sengupta, Manajit [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-12-19

    It is essential to apply a traceable and standard approach to determine the uncertainty of solar resource data. Solar resource data are used for all phases of solar energy conversion projects, from the conceptual phase to routine solar power plant operation, and to determine performance guarantees of solar energy conversion systems. These guarantees are based on the available solar resource derived from a measurement station or modeled data set such as the National Solar Radiation Database (NSRDB). Therefore, quantifying the uncertainty of these data sets provides confidence to financiers, developers, and site operators of solar energy conversion systems and ultimately reduces deployment costs. In this study, we implemented the Guide to the Expression of Uncertainty in Measurement (GUM) 1 to quantify the overall uncertainty of the NSRDB data. First, we start with quantifying measurement uncertainty, then we determine each uncertainty statistic of the NSRDB data, and we combine them using the root-sum-of-the-squares method. The statistics were derived by comparing the NSRDB data to the seven measurement stations from the National Oceanic and Atmospheric Administration's Surface Radiation Budget Network, National Renewable Energy Laboratory's Solar Radiation Research Laboratory, and the Atmospheric Radiation Measurement program's Southern Great Plains Central Facility, in Billings, Oklahoma. The evaluation was conducted for hourly values, daily totals, monthly mean daily totals, and annual mean monthly mean daily totals. Varying time averages assist to capture the temporal uncertainty of the specific modeled solar resource data required for each phase of a solar energy project; some phases require higher temporal resolution than others. Overall, by including the uncertainty of measurements of solar radiation made at ground stations, bias, and root mean square error, the NSRDB data demonstrated expanded uncertainty of 17 percent - 29 percent on hourly

  10. The Uncertainties of Risk Management

    DEFF Research Database (Denmark)

    Vinnari, Eija; Skærbæk, Peter

    2014-01-01

    for expanding risk management. More generally, such uncertainties relate to the professional identities and responsibilities of operational managers as defined by the framing devices. Originality/value – The paper offers three contributions to the extant literature: first, it shows how risk management itself......Purpose – The purpose of this paper is to analyse the implementation of risk management as a tool for internal audit activities, focusing on unexpected effects or uncertainties generated during its application. Design/methodology/approach – Public and confidential documents as well as semi......-structured interviews are analysed through the lens of actor-network theory to identify the effects of risk management devices in a Finnish municipality. Findings – The authors found that risk management, rather than reducing uncertainty, itself created unexpected uncertainties that would otherwise not have emerged...

  11. Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses

    NARCIS (Netherlands)

    Rijpkema, W.A.; Hendrix, E.M.T.; Rossi, R.; Vorst, van der J.G.A.J.

    2016-01-01

    To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality.

  12. A computer simulation platform for the estimation of measurement uncertainties in dimensional X-ray computed tomography

    DEFF Research Database (Denmark)

    Hiller, Jochen; Reindl, Leonard M

    2012-01-01

    into account the main error sources for the measurement. This method has the potential to deal with all kinds of systematic and random errors that influence a dimensional CT measurement. A case study demonstrates the practical application of the VCT simulator using numerically generated CT data and statistical......The knowledge of measurement uncertainty is of great importance in conformance testing in production. The tolerance limit for production must be reduced by the amounts of measurement uncertainty to ensure that the parts are in fact within the tolerance. Over the last 5 years, industrial X......-ray computed tomography (CT) has become an important technology for dimensional quality control. In this paper a computer simulation platform is presented which is able to investigate error sources in dimensional CT measurements. The typical workflow in industrial CT metrology is described and methods...

  13. Learning-induced uncertainty reduction in perceptual decisions is task-dependent

    Directory of Open Access Journals (Sweden)

    Feitong eYang

    2014-05-01

    Full Text Available Perceptual decision making in which decisions are reached primarily from extracting and evaluating sensory information requires close interactions between the sensory system and decision-related networks in the brain. Uncertainty pervades every aspect of this process and can be considered related to either the stimulus signal or decision criterion. Here, we investigated the learning-induced reduction of both the signal and criterion uncertainty in two perceptual decision tasks based on two Glass pattern stimulus sets. This was achieved by manipulating spiral angle and signal level of radial and concentric Glass patterns. The behavioral results showed that the participants trained with a task based on criterion comparison improved their categorization accuracy for both tasks, whereas the participants who were trained on a task based on signal detection improved their categorization accuracy only on their trained task. We fitted the behavioral data with a computational model that can dissociate the contribution of the signal and criterion uncertainties. The modeling results indicated that the participants trained on the criterion comparison task reduced both the criterion and signal uncertainty. By contrast, the participants who were trained on the signal detection task only reduced their signal uncertainty after training. Our results suggest that the signal uncertainty can be resolved by training participants to extract signals from noisy environments and to discriminate between clear signals, which are evidenced by reduced perception variance after both training procedures. Conversely, the criterion uncertainty can only be resolved by the training of fine discrimination. These findings demonstrate that uncertainty in perceptual decision-making can be reduced with training but that the reduction of different types of uncertainty is task-dependent.

  14. Evaluate prevailing climate change on Great Lakes water levels

    International Nuclear Information System (INIS)

    Islam, M.

    2009-01-01

    'Full text:'In this paper, results of a comprehensive water mass balance modeling for the Great Lakes against prevailing and different anticipated climate change scenarios would be presented. Modeling is done in evaluating the changes in the lake storages and then changes in the lake's water level considering present condition, uncertainty and variability of climate and hydrologic conditions in the future. Inflow-outflow and consequent changes in the five Great Lake's storages are simulated for the last 30 years and then projected to evaluate the changes in the lake storages for the next 50 years. From the predicted changes in the lake storage data, water level is calculated using mass to linear conversion equation. Modeling and analysis results are expected to be helpful in understanding the possible impacts of the climate change on the Great Lakes water environment and preparing strategic plan for the sustainable management of lake's water resources. From the recent past, it is observed that there is a depleting trend in the lakes water level and hence there is a potential threat to lake's water environment and uncertainty of the availability of quality and quantity of water for the future generations, especially against prevailing and anticipated climate changes. For this reason, it is an urgent issue of understanding and quantifying the potential impacts of climate change on the Great Lake's water levels and storages. (author)

  15. How risk and uncertainty is used in Supply Chain Management: a literature study

    DEFF Research Database (Denmark)

    Bøge Sørensen, Lars

    2004-01-01

    Keywords Supply Chain Management, Risk Management, Supply Chain Risk ManagementAbstract To comply with Supply Chain Management dogma companies have cut their inventoriesto a minimum, lead times have been shortened, new suppliers have been chosen and the customerportfolio has been reduced. All...... of these activities impose a great deal of risk on the firms,jeopardizing the survival of entire supply chains. In this article the author intends to investigateand document the use and meaning of Risk and Uncertainty within journals publishing material onSupply Chain Management and Logistics. Subsequently...... suggestions for further research areproposed - the integration of Risk Management into the discipline of Supply Chain Design....

  16. Can you put too much on your plate? Uncertainty exposure in servitized triads

    DEFF Research Database (Denmark)

    Kreye, Melanie E.

    2017-01-01

    -national servitized triad in a European-North African set-up which was collected through 29 semi-structured interviews and secondary data. Findings: The empirical study identified the existence of the three uncertainty types and directional knock-on effects between them. Specifically, environmental uncertainty...... relational governance reduced relational uncertainty. The knock-on effects were reduced through organisational and relational responses. Originality: This paper makes two contributions. First, a structured analysis of the uncertainty exposure in servitized triads is presented which shows the existence...... of three individual uncertainty types and the knock-on effects between them. Second, organisational responses to reduce the three uncertainty types individually and the knock-on effects between them are presented....

  17. Measurement uncertainty: Friend or foe?

    Science.gov (United States)

    Infusino, Ilenia; Panteghini, Mauro

    2018-02-02

    The definition and enforcement of a reference measurement system, based on the implementation of metrological traceability of patients' results to higher order reference methods and materials, together with a clinically acceptable level of measurement uncertainty, are fundamental requirements to produce accurate and equivalent laboratory results. The uncertainty associated with each step of the traceability chain should be governed to obtain a final combined uncertainty on clinical samples fulfilling the requested performance specifications. It is important that end-users (i.e., clinical laboratory) may know and verify how in vitro diagnostics (IVD) manufacturers have implemented the traceability of their calibrators and estimated the corresponding uncertainty. However, full information about traceability and combined uncertainty of calibrators is currently very difficult to obtain. Laboratory professionals should investigate the need to reduce the uncertainty of the higher order metrological references and/or to increase the precision of commercial measuring systems. Accordingly, the measurement uncertainty should not be considered a parameter to be calculated by clinical laboratories just to fulfil the accreditation standards, but it must become a key quality indicator to describe both the performance of an IVD measuring system and the laboratory itself. Copyright © 2018 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

  18. Reducing uncertainty in wind turbine blade health inspection with image processing techniques

    Science.gov (United States)

    Zhang, Huiyi

    Structural health inspection has been widely applied in the operation of wind farms to find early cracks in wind turbine blades (WTBs). Increased numbers of turbines and expanded rotor diameters are driving up the workloads and safety risks for site employees. Therefore, it is important to automate the inspection process as well as minimize the uncertainties involved in routine blade health inspection. In addition, crack documentation and trending is vital to assess rotor blade and turbine reliability in the 20 year designed life span. A new crack recognition and classification algorithm is described that can support automated structural health inspection of the surface of large composite WTBs. The first part of the study investigated the feasibility of digital image processing in WTB health inspection and defined the capability of numerically detecting cracks as small as hairline thickness. The second part of the study identified and analyzed the uncertainty of the digital image processing method. A self-learning algorithm was proposed to recognize and classify cracks without comparing a blade image to a library of crack images. The last part of the research quantified the uncertainty in the field conditions and the image processing methods.

  19. Range uncertainties in proton therapy and the role of Monte Carlo simulations

    International Nuclear Information System (INIS)

    Paganetti, Harald

    2012-01-01

    The main advantages of proton therapy are the reduced total energy deposited in the patient as compared to photon techniques and the finite range of the proton beam. The latter adds an additional degree of freedom to treatment planning. The range in tissue is associated with considerable uncertainties caused by imaging, patient setup, beam delivery and dose calculation. Reducing the uncertainties would allow a reduction of the treatment volume and thus allow a better utilization of the advantages of protons. This paper summarizes the role of Monte Carlo simulations when aiming at a reduction of range uncertainties in proton therapy. Differences in dose calculation when comparing Monte Carlo with analytical algorithms are analyzed as well as range uncertainties due to material constants and CT conversion. Range uncertainties due to biological effects and the role of Monte Carlo for in vivo range verification are discussed. Furthermore, the current range uncertainty recipes used at several proton therapy facilities are revisited. We conclude that a significant impact of Monte Carlo dose calculation can be expected in complex geometries where local range uncertainties due to multiple Coulomb scattering will reduce the accuracy of analytical algorithms. In these cases Monte Carlo techniques might reduce the range uncertainty by several mm. (topical review)

  20. Where do uncertainties reside within environmental risk assessments? Expert opinion on uncertainty distributions for pesticide risks to surface water organisms.

    Science.gov (United States)

    Skinner, Daniel J C; Rocks, Sophie A; Pollard, Simon J T

    2016-12-01

    A reliable characterisation of uncertainties can aid uncertainty identification during environmental risk assessments (ERAs). However, typologies can be implemented inconsistently, causing uncertainties to go unidentified. We present an approach based on nine structured elicitations, in which subject-matter experts, for pesticide risks to surface water organisms, validate and assess three dimensions of uncertainty: its level (the severity of uncertainty, ranging from determinism to ignorance); nature (whether the uncertainty is epistemic or aleatory); and location (the data source or area in which the uncertainty arises). Risk characterisation contains the highest median levels of uncertainty, associated with estimating, aggregating and evaluating the magnitude of risks. Regarding the locations in which uncertainty is manifest, data uncertainty is dominant in problem formulation, exposure assessment and effects assessment. The comprehensive description of uncertainty described will enable risk analysts to prioritise the required phases, groups of tasks, or individual tasks within a risk analysis according to the highest levels of uncertainty, the potential for uncertainty to be reduced or quantified, or the types of location-based uncertainty, thus aiding uncertainty prioritisation during environmental risk assessments. In turn, it is expected to inform investment in uncertainty reduction or targeted risk management action. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  1. DUALISM OF GEOSTRATEGIC PROSPECTS OF GREAT BRITAIN IN THE MODERN SYSTEM OF GLOBAL UNCERTAINTIES

    Directory of Open Access Journals (Sweden)

    Alexey N. Yeletsky

    2013-01-01

    Full Text Available Peculiarities of a modern position of Great Britain in the global economy are analysed. Its role as one of the local centres of influence in the European Union is emphasized. «Special relationship» between England and the United States in the context of formation of a new «Anglo-Saxon empire» is examined. Particular attention is paid to the key role of Great Britain in the alliance of English-speaking powers.

  2. Error Analysis of CM Data Products Sources of Uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Hunt, Brian D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eckert-Gallup, Aubrey Celia [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Cochran, Lainy Dromgoole [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kraus, Terrence D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Allen, Mark B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Beal, Bill [National Security Technologies, Joint Base Andrews, MD (United States); Okada, Colin [National Security Technologies, LLC. (NSTec), Las Vegas, NV (United States); Simpson, Mathew [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2017-02-01

    This goal of this project is to address the current inability to assess the overall error and uncertainty of data products developed and distributed by DOE’s Consequence Management (CM) Program. This is a widely recognized shortfall, the resolution of which would provide a great deal of value and defensibility to the analysis results, data products, and the decision making process that follows this work. A global approach to this problem is necessary because multiple sources of error and uncertainty contribute to the ultimate production of CM data products. Therefore, this project will require collaboration with subject matter experts across a wide range of FRMAC skill sets in order to quantify the types of uncertainty that each area of the CM process might contain and to understand how variations in these uncertainty sources contribute to the aggregated uncertainty present in CM data products. The ultimate goal of this project is to quantify the confidence level of CM products to ensure that appropriate public and worker protections decisions are supported by defensible analysis.

  3. Uncertainty Categorization, Modeling, and Management for Regional Water Supply Planning

    Science.gov (United States)

    Fletcher, S.; Strzepek, K. M.; AlSaati, A.; Alhassan, A.

    2016-12-01

    Many water planners face increased pressure on water supply systems from growing demands, variability in supply and a changing climate. Short-term variation in water availability and demand; long-term uncertainty in climate, groundwater storage, and sectoral competition for water; and varying stakeholder perspectives on the impacts of water shortages make it difficult to assess the necessity of expensive infrastructure investments. We categorize these uncertainties on two dimensions: whether they are the result of stochastic variation or epistemic uncertainty, and whether the uncertainties can be described probabilistically or are deep uncertainties whose likelihood is unknown. We develop a decision framework that combines simulation for probabilistic uncertainty, sensitivity analysis for deep uncertainty and Bayesian decision analysis for uncertainties that are reduced over time with additional information. We apply this framework to two contrasting case studies - drought preparedness in Melbourne, Australia and fossil groundwater depletion in Riyadh, Saudi Arabia - to assess the impacts of different types of uncertainty on infrastructure decisions. Melbourne's water supply system relies on surface water, which is impacted by natural variation in rainfall, and a market-based system for managing water rights. Our results show that small, flexible investment increases can mitigate shortage risk considerably at reduced cost. Riyadh, by contrast, relies primarily on desalination for municipal use and fossil groundwater for agriculture, and a centralized planner makes allocation decisions. Poor regional groundwater measurement makes it difficult to know when groundwater pumping will become uneconomical, resulting in epistemic uncertainty. However, collecting more data can reduce the uncertainty, suggesting the need for different uncertainty modeling and management strategies in Riyadh than in Melbourne. We will categorize the two systems and propose appropriate

  4. Uncertainty quantification of surface-water/groundwater exchange estimates in large wetland systems using Python

    Science.gov (United States)

    Hughes, J. D.; Metz, P. A.

    2014-12-01

    Most watershed studies include observation-based water budget analyses to develop first-order estimates of significant flow terms. Surface-water/groundwater (SWGW) exchange is typically assumed to be equal to the residual of the sum of inflows and outflows in a watershed. These estimates of SWGW exchange, however, are highly uncertain as a result of the propagation of uncertainty inherent in the calculation or processing of the other terms of the water budget, such as stage-area-volume relations, and uncertainties associated with land-cover based evapotranspiration (ET) rate estimates. Furthermore, the uncertainty of estimated SWGW exchanges can be magnified in large wetland systems that transition from dry to wet during wet periods. Although it is well understood that observation-based estimates of SWGW exchange are uncertain it is uncommon for the uncertainty of these estimates to be directly quantified. High-level programming languages like Python can greatly reduce the effort required to (1) quantify the uncertainty of estimated SWGW exchange in large wetland systems and (2) evaluate how different approaches for partitioning land-cover data in a watershed may affect the water-budget uncertainty. We have used Python with the Numpy, Scipy.stats, and pyDOE packages to implement an unconstrained Monte Carlo approach with Latin Hypercube sampling to quantify the uncertainty of monthly estimates of SWGW exchange in the Floral City watershed of the Tsala Apopka wetland system in west-central Florida, USA. Possible sources of uncertainty in the water budget analysis include rainfall, ET, canal discharge, and land/bathymetric surface elevations. Each of these input variables was assigned a probability distribution based on observation error or spanning the range of probable values. The Monte Carlo integration process exposes the uncertainties in land-cover based ET rate estimates as the dominant contributor to the uncertainty in SWGW exchange estimates. We will discuss

  5. Reducing Multisensor Satellite Monthly Mean Aerosol Optical Depth Uncertainty: 1. Objective Assessment of Current AERONET Locations

    Science.gov (United States)

    Li, Jing; Li, Xichen; Carlson, Barbara E.; Kahn, Ralph A.; Lacis, Andrew A.; Dubovik, Oleg; Nakajima, Teruyuki

    2016-01-01

    Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)- based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by approximately 27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty

  6. The role of uncertainty in climate change adaptation strategies — A Danish water management example

    DEFF Research Database (Denmark)

    Refsgaard, J.C.; Arnbjerg-Nielsen, Karsten; Drews, Martin

    2013-01-01

    We propose a generic framework to characterize climate change adaptation uncertainty according to three dimensions: level, source and nature. Our framework is different, and in this respect more comprehensive, than the present UN Intergovernmental Panel on Climate Change (IPCC) approach and could...... be used to address concerns that the IPCC approach is oversimplified. We have studied the role of uncertainty in climate change adaptation planning using examples from four Danish water related sectors. The dominating sources of uncertainty differ greatly among issues; most uncertainties on impacts...

  7. Visualizing Summary Statistics and Uncertainty

    KAUST Repository

    Potter, K.; Kniss, J.; Riesenfeld, R.; Johnson, C.R.

    2010-01-01

    The graphical depiction of uncertainty information is emerging as a problem of great importance. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence. The visual portrayal of this information is a challenging task. This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty. The canonical box plot is reexamined and a new hybrid summary plot is presented that incorporates a collection of descriptive statistics to highlight salient features of the data. Additionally, we present an extension of the summary plot to two dimensional distributions. Finally, a use-case of these new plots is presented, demonstrating their ability to present high-level overviews as well as detailed insight into the salient features of the underlying data distribution. © 2010 The Eurographics Association and Blackwell Publishing Ltd.

  8. Visualizing Summary Statistics and Uncertainty

    KAUST Repository

    Potter, K.

    2010-08-12

    The graphical depiction of uncertainty information is emerging as a problem of great importance. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence. The visual portrayal of this information is a challenging task. This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty. The canonical box plot is reexamined and a new hybrid summary plot is presented that incorporates a collection of descriptive statistics to highlight salient features of the data. Additionally, we present an extension of the summary plot to two dimensional distributions. Finally, a use-case of these new plots is presented, demonstrating their ability to present high-level overviews as well as detailed insight into the salient features of the underlying data distribution. © 2010 The Eurographics Association and Blackwell Publishing Ltd.

  9. Accounting for uncertainty in marine reserve design.

    Science.gov (United States)

    Halpern, Benjamin S; Regan, Helen M; Possingham, Hugh P; McCarthy, Michael A

    2006-01-01

    Ecosystems and the species and communities within them are highly complex systems that defy predictions with any degree of certainty. Managing and conserving these systems in the face of uncertainty remains a daunting challenge, particularly with respect to developing networks of marine reserves. Here we review several modelling frameworks that explicitly acknowledge and incorporate uncertainty, and then use these methods to evaluate reserve spacing rules given increasing levels of uncertainty about larval dispersal distances. Our approach finds similar spacing rules as have been proposed elsewhere - roughly 20-200 km - but highlights several advantages provided by uncertainty modelling over more traditional approaches to developing these estimates. In particular, we argue that uncertainty modelling can allow for (1) an evaluation of the risk associated with any decision based on the assumed uncertainty; (2) a method for quantifying the costs and benefits of reducing uncertainty; and (3) a useful tool for communicating to stakeholders the challenges in managing highly uncertain systems. We also argue that incorporating rather than avoiding uncertainty will increase the chances of successfully achieving conservation and management goals.

  10. Using internal discharge data in a distributed conceptual model to reduce uncertainty in streamflow simulations

    Science.gov (United States)

    Guerrero, J.; Halldin, S.; Xu, C.; Lundin, L.

    2011-12-01

    Distributed hydrological models are important tools in water management as they account for the spatial variability of the hydrological data, as well as being able to produce spatially distributed outputs. They can directly incorporate and assess potential changes in the characteristics of our basins. A recognized problem for models in general is equifinality, which is only exacerbated for distributed models who tend to have a large number of parameters. We need to deal with the fundamentally ill-posed nature of the problem that such models force us to face, i.e. a large number of parameters and very few variables that can be used to constrain them, often only the catchment discharge. There is a growing but yet limited literature showing how the internal states of a distributed model can be used to calibrate/validate its predictions. In this paper, a distributed version of WASMOD, a conceptual rainfall runoff model with only three parameters, combined with a routing algorithm based on the high-resolution HydroSHEDS data was used to simulate the discharge in the Paso La Ceiba basin in Honduras. The parameter space was explored using Monte-Carlo simulations and the region of space containing the parameter-sets that were considered behavioral according to two different criteria was delimited using the geometric concept of alpha-shapes. The discharge data from five internal sub-basins was used to aid in the calibration of the model and to answer the following questions: Can this information improve the simulations at the outlet of the catchment, or decrease their uncertainty? Also, after reducing the number of model parameters needing calibration through sensitivity analysis: Is it possible to relate them to basin characteristics? The analysis revealed that in most cases the internal discharge data can be used to reduce the uncertainty in the discharge at the outlet, albeit with little improvement in the overall simulation results.

  11. Crashworthiness uncertainty analysis of typical civil aircraft based on Box–Behnken method

    OpenAIRE

    Ren Yiru; Xiang Jinwu

    2014-01-01

    The crashworthiness is an important design factor of civil aircraft related with the safety of occupant during impact accident. It is a highly nonlinear transient dynamic problem and may be greatly influenced by the uncertainty factors. Crashworthiness uncertainty analysis is conducted to investigate the effects of initial conditions, structural dimensions and material properties. Simplified finite element model is built based on the geometrical model and basic physics phenomenon. Box–Behnken...

  12. Evaluation of Sources of Uncertainties in Solar Resource Measurement

    Energy Technology Data Exchange (ETDEWEB)

    Habte, Aron M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sengupta, Manajit [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-09-25

    This poster presents a high-level overview of sources of uncertainties in solar resource measurement, demonstrating the impact of various sources of uncertainties -- such as cosine response, thermal offset, spectral response, and others -- on the accuracy of data from several radiometers. The study provides insight on how to reduce the impact of some of the sources of uncertainties.

  13. Managing Measurement Uncertainty in Building Acoustics

    Directory of Open Access Journals (Sweden)

    Chiara Scrosati

    2015-12-01

    Full Text Available In general, uncertainties should preferably be determined following the principles laid down in ISO/IEC Guide 98-3, the Guide to the expression of uncertainty in measurement (GUM:1995. According to current knowledge, it seems impossible to formulate these models for the different quantities in building acoustics. Therefore, the concepts of repeatability and reproducibility are necessary to determine the uncertainty of building acoustics measurements. This study shows the uncertainty of field measurements of a lightweight wall, a heavyweight floor, a façade with a single glazing window and a façade with double glazing window that were analyzed by a Round Robin Test (RRT, conducted in a full-scale experimental building at ITC-CNR (Construction Technologies Institute of the National Research Council of Italy. The single number quantities and their uncertainties were evaluated in both narrow and enlarged range and it was shown that including or excluding the low frequencies leads to very significant differences, except in the case of the sound insulation of façades with single glazing window. The results obtained in these RRTs were compared with other results from literature, which confirm the increase of the uncertainty of single number quantities due to the low frequencies extension. Having stated the measurement uncertainty for a single measurement, in building acoustics, it is also very important to deal with sampling for the purposes of classification of buildings or building units. Therefore, this study also shows an application of the sampling included in the Italian Standard on the acoustic classification of building units on a serial type building consisting of 47 building units. It was found that the greatest variability is observed in the façade and it depends on both the great variability of window’s typologies and on workmanship. Finally, it is suggested how to manage the uncertainty in building acoustics, both for one single

  14. To be or not to be: How do we speak about uncertainty in public?

    Science.gov (United States)

    Todesco, Micol; Lolli, Barbara; Sheldrake, Tom; Odbert, Henry

    2016-04-01

    One of the challenges related to hazard communication concerns the public perception and understanding of scientific uncertainties, and of its implications in terms of hazard assessment and mitigation. Often science is perceived as an effective dispenser of resolving answers to the main issues posed by the complexities of life and nature. In this perspective, uncertainty is seen as a pernicious lack of knowledge that hinders our ability to face complex problems. From a scientific perspective, however, the definition of uncertainty is the only valuable tool we have to handle errors affecting our data and propagating through the increasingly complex models we develop to describe reality. Through uncertainty, scientists acknowledge the great variability that characterises natural systems and account for it in their assessment of possible scenarios. From this point of view, uncertainty is not ignorance, but it rather provides a great deal of information that is needed to inform decision making. To find effective ways to bridge the gap between these different meaning of uncertainty, we asked high-school students for assistance. With their help, we gathered definitions of the term 'uncertainty' interviewing different categories of peoples, including schoolmates and professors, neighbours, families and friends. These definitions will be compared with those provided by scientists, to find differences and similarity. To understand the role of uncertainty on judgment, a hands-on experiment is performed where students will have to estimate the exact time of explosion of party poppers subjected to a variable degree of pull. At the end of the project, the students will express their own understanding of uncertainty in a video, which will be made available for sharing. Materials collected during all the activities will contribute to our understanding of how uncertainty is portrayed and can be better expressed to improve our hazard communication.

  15. Reduced uncertainty of regional scale CLM predictions of net carbon fluxes and leaf area indices with estimated plant-specific parameters

    Science.gov (United States)

    Post, Hanna; Hendricks Franssen, Harrie-Jan; Han, Xujun; Baatz, Roland; Montzka, Carsten; Schmidt, Marius; Vereecken, Harry

    2016-04-01

    Reliable estimates of carbon fluxes and states at regional scales are required to reduce uncertainties in regional carbon balance estimates and to support decision making in environmental politics. In this work the Community Land Model version 4.5 (CLM4.5-BGC) was applied at a high spatial resolution (1 km2) for the Rur catchment in western Germany. In order to improve the model-data consistency of net ecosystem exchange (NEE) and leaf area index (LAI) for this study area, five plant functional type (PFT)-specific CLM4.5-BGC parameters were estimated with time series of half-hourly NEE data for one year in 2011/2012, using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, a Markov Chain Monte Carlo (MCMC) approach. The parameters were estimated separately for four different plant functional types (needleleaf evergreen temperate tree, broadleaf deciduous temperate tree, C3-grass and C3-crop) at four different sites. The four sites are located inside or close to the Rur catchment. We evaluated modeled NEE for one year in 2012/2013 with NEE measured at seven eddy covariance sites in the catchment, including the four parameter estimation sites. Modeled LAI was evaluated by means of LAI derived from remotely sensed RapidEye images of about 18 days in 2011/2012. Performance indices were based on a comparison between measurements and (i) a reference run with CLM default parameters, and (ii) a 60 instance CLM ensemble with parameters sampled from the DREAM posterior probability density functions (pdfs). The difference between the observed and simulated NEE sum reduced 23% if estimated parameters instead of default parameters were used as input. The mean absolute difference between modeled and measured LAI was reduced by 59% on average. Simulated LAI was not only improved in terms of the absolute value but in some cases also in terms of the timing (beginning of vegetation onset), which was directly related to a substantial improvement of the NEE estimates in

  16. The uncertainties in estimating measurement uncertainties

    International Nuclear Information System (INIS)

    Clark, J.P.; Shull, A.H.

    1994-01-01

    All measurements include some error. Whether measurements are used for accountability, environmental programs or process support, they are of little value unless accompanied by an estimate of the measurements uncertainty. This fact is often overlooked by the individuals who need measurements to make decisions. This paper will discuss the concepts of measurement, measurements errors (accuracy or bias and precision or random error), physical and error models, measurement control programs, examples of measurement uncertainty, and uncertainty as related to measurement quality. Measurements are comparisons of unknowns to knowns, estimates of some true value plus uncertainty; and are no better than the standards to which they are compared. Direct comparisons of unknowns that match the composition of known standards will normally have small uncertainties. In the real world, measurements usually involve indirect comparisons of significantly different materials (e.g., measuring a physical property of a chemical element in a sample having a matrix that is significantly different from calibration standards matrix). Consequently, there are many sources of error involved in measurement processes that can affect the quality of a measurement and its associated uncertainty. How the uncertainty estimates are determined and what they mean is as important as the measurement. The process of calculating the uncertainty of a measurement itself has uncertainties that must be handled correctly. Examples of chemistry laboratory measurement will be reviewed in this report and recommendations made for improving measurement uncertainties

  17. Potential effects of organizational uncertainty on safety

    Energy Technology Data Exchange (ETDEWEB)

    Durbin, N.E. [MPD Consulting Group, Kirkland, WA (United States); Lekberg, A. [Swedish Nuclear Power Inspectorate, Stockholm (Sweden); Melber, B.D. [Melber Consulting, Seattle WA (United States)

    2001-12-01

    When organizations face significant change - reorganization, mergers, acquisitions, down sizing, plant closures or decommissioning - both the organizations and the workers in those organizations experience significant uncertainty about the future. This uncertainty affects the organization and the people working in the organization - adversely affecting morale, reducing concentration on safe operations, and resulting in the loss of key staff. Hence, organizations, particularly those using high risk technologies, which are facing significant change need to consider and plan for the effects of organizational uncertainty on safety - as well as planning for other consequences of change - technical, economic, emotional, and productivity related. This paper reviews some of what is known about the effects of uncertainty on organizations and individuals, discusses the potential consequences of uncertainty on organizational and individual behavior, and presents some of the implications for safety professionals.

  18. Potential effects of organizational uncertainty on safety

    International Nuclear Information System (INIS)

    Durbin, N.E.; Lekberg, A.; Melber, B.D.

    2001-12-01

    When organizations face significant change - reorganization, mergers, acquisitions, down sizing, plant closures or decommissioning - both the organizations and the workers in those organizations experience significant uncertainty about the future. This uncertainty affects the organization and the people working in the organization - adversely affecting morale, reducing concentration on safe operations, and resulting in the loss of key staff. Hence, organizations, particularly those using high risk technologies, which are facing significant change need to consider and plan for the effects of organizational uncertainty on safety - as well as planning for other consequences of change - technical, economic, emotional, and productivity related. This paper reviews some of what is known about the effects of uncertainty on organizations and individuals, discusses the potential consequences of uncertainty on organizational and individual behavior, and presents some of the implications for safety professionals

  19. Use of Paired Simple and Complex Models to Reduce Predictive Bias and Quantify Uncertainty

    DEFF Research Database (Denmark)

    Doherty, John; Christensen, Steen

    2011-01-01

    -constrained uncertainty analysis. Unfortunately, however, many system and process details on which uncertainty may depend are, by design, omitted from simple models. This can lead to underestimation of the uncertainty associated with many predictions of management interest. The present paper proposes a methodology...... of these details born of the necessity for model outputs to replicate observations of historical system behavior. In contrast, the rapid run times and general numerical reliability of simple models often promulgates good calibration and ready implementation of sophisticated methods of calibration...... that attempts to overcome the problems associated with complex models on the one hand and simple models on the other hand, while allowing access to the benefits each of them offers. It provides a theoretical analysis of the simplification process from a subspace point of view, this yielding insights...

  20. Deterministic uncertainty analysis

    International Nuclear Information System (INIS)

    Worley, B.A.

    1987-12-01

    This paper presents a deterministic uncertainty analysis (DUA) method for calculating uncertainties that has the potential to significantly reduce the number of computer runs compared to conventional statistical analysis. The method is based upon the availability of derivative and sensitivity data such as that calculated using the well known direct or adjoint sensitivity analysis techniques. Formation of response surfaces using derivative data and the propagation of input probability distributions are discussed relative to their role in the DUA method. A sample problem that models the flow of water through a borehole is used as a basis to compare the cumulative distribution function of the flow rate as calculated by the standard statistical methods and the DUA method. Propogation of uncertainties by the DUA method is compared for ten cases in which the number of reference model runs was varied from one to ten. The DUA method gives a more accurate representation of the true cumulative distribution of the flow rate based upon as few as two model executions compared to fifty model executions using a statistical approach. 16 refs., 4 figs., 5 tabs

  1. Water supply infrastructure planning under multiple uncertainties: A differentiated approach

    Science.gov (United States)

    Fletcher, S.; Strzepek, K.

    2017-12-01

    Many water planners face increased pressure on water supply systems from increasing demands from population and economic growth in combination with uncertain water supply. Supply uncertainty arises from short-term climate variability and long-term climate change as well as uncertainty in groundwater availability. Social and economic uncertainties - such as sectoral competition for water, food and energy security, urbanization, and environmental protection - compound physical uncertainty. Further, the varying risk aversion of stakeholders and water managers makes it difficult to assess the necessity of expensive infrastructure investments to reduce risk. We categorize these uncertainties on two dimensions: whether they can be updated over time by collecting additional information, and whether the uncertainties can be described probabilistically or are "deep" uncertainties whose likelihood is unknown. Based on this, we apply a decision framework that combines simulation for probabilistic uncertainty, scenario analysis for deep uncertainty, and multi-stage decision analysis for uncertainties that are reduced over time with additional information. In light of these uncertainties and the investment costs of large infrastructure, we propose the assessment of staged, modular infrastructure and information updating as a hedge against risk. We apply this framework to cases in Melbourne, Australia and Riyadh, Saudi Arabia. Melbourne is a surface water system facing uncertain population growth and variable rainfall and runoff. A severe drought from 1997 to 2009 prompted investment in a 150 MCM/y reverse osmosis desalination plan with a capital cost of 3.5 billion. Our analysis shows that flexible design in which a smaller portion of capacity is developed initially with the option to add modular capacity in the future can mitigate uncertainty and reduce the expected lifetime costs by up to 1 billion. In Riyadh, urban water use relies on fossil groundwater aquifers and

  2. Characterizing Epistemic Uncertainty for Launch Vehicle Designs

    Science.gov (United States)

    Novack, Steven D.; Rogers, Jim; Hark, Frank; Al Hassan, Mohammad

    2016-01-01

    NASA Probabilistic Risk Assessment (PRA) has the task of estimating the aleatory (randomness) and epistemic (lack of knowledge) uncertainty of launch vehicle loss of mission and crew risk and communicating the results. Launch vehicles are complex engineered systems designed with sophisticated subsystems that are built to work together to accomplish mission success. Some of these systems or subsystems are in the form of heritage equipment, while some have never been previously launched. For these cases, characterizing the epistemic uncertainty is of foremost importance, and it is anticipated that the epistemic uncertainty of a modified launch vehicle design versus a design of well understood heritage equipment would be greater. For reasons that will be discussed, standard uncertainty propagation methods using Monte Carlo simulation produce counter intuitive results and significantly underestimate epistemic uncertainty for launch vehicle models. Furthermore, standard PRA methods such as Uncertainty-Importance analyses used to identify components that are significant contributors to uncertainty are rendered obsolete since sensitivity to uncertainty changes are not reflected in propagation of uncertainty using Monte Carlo methods.This paper provides a basis of the uncertainty underestimation for complex systems and especially, due to nuances of launch vehicle logic, for launch vehicles. It then suggests several alternative methods for estimating uncertainty and provides examples of estimation results. Lastly, the paper shows how to implement an Uncertainty-Importance analysis using one alternative approach, describes the results, and suggests ways to reduce epistemic uncertainty by focusing on additional data or testing of selected components.

  3. Evaluating sub-national building-energy efficiency policy options under uncertainty: Efficient sensitivity testing of alternative climate, technological, and socioeconomic futures in a regional integrated-assessment model

    International Nuclear Information System (INIS)

    Scott, Michael J.; Daly, Don S.; Zhou, Yuyu; Rice, Jennie S.; Patel, Pralit L.; McJeon, Haewon C.; Page Kyle, G.; Kim, Son H.; Eom, Jiyong

    2014-01-01

    Improving the energy efficiency of building stock, commercial equipment, and household appliances can have a major positive impact on energy use, carbon emissions, and building services. Sub-national regions such as the U.S. states wish to increase energy efficiency, reduce carbon emissions, or adapt to climate change. Evaluating sub-national policies to reduce energy use and emissions is difficult because of the large uncertainties in socioeconomic factors, technology performance and cost, and energy and climate policies. Climate change itself may undercut such policies. However, assessing all of the uncertainties of large-scale energy and climate models by performing thousands of model runs can be a significant modeling effort with its accompanying computational burden. By applying fractional–factorial methods to the GCAM-USA 50-state integrated-assessment model in the context of a particular policy question, this paper demonstrates how a decision-focused sensitivity analysis strategy can greatly reduce computational burden in the presence of uncertainty and reveal the important drivers for decisions and more detailed uncertainty analysis. - Highlights: • We evaluate building energy codes and standards for climate mitigation. • We use an integrated assessment model and fractional factorial methods. • Decision criteria are energy use, CO2 emitted, and building service cost. • We demonstrate sensitivity analysis for three states. • We identify key variables to propagate with Monte Carlo or surrogate models

  4. Reducing Reliability Uncertainties for Marine Renewable Energy

    Directory of Open Access Journals (Sweden)

    Sam D. Weller

    2015-11-01

    Full Text Available Technology Readiness Levels (TRLs are a widely used metric of technology maturity and risk for marine renewable energy (MRE devices. To-date, a large number of device concepts have been proposed which have reached the early validation stages of development (TRLs 1–3. Only a handful of mature designs have attained pre-commercial development status following prototype sea trials (TRLs 7–8. In order to navigate through the aptly named “valley of death” (TRLs 4–6 towards commercial realisation, it is necessary for new technologies to be de-risked in terms of component durability and reliability. In this paper the scope of the reliability assessment module of the DTOcean Design Tool is outlined including aspects of Tool integration, data provision and how prediction uncertainties are accounted for. In addition, two case studies are reported of mooring component fatigue testing providing insight into long-term component use and system design for MRE devices. The case studies are used to highlight how test data could be utilised to improve the prediction capabilities of statistical reliability assessment approaches, such as the bottom–up statistical method.

  5. Incorporating outcome uncertainty and prior outcome beliefs in stated preferences

    DEFF Research Database (Denmark)

    Lundhede, Thomas; Jacobsen, Jette Bredahl; Hanley, Nick

    2015-01-01

    Stated preference studies tell respondents that policies create environmental changes with varying levels of uncertainty. However, respondents may include their own a priori assessments of uncertainty when making choices among policy options. Using a choice experiment eliciting respondents......’ preferences for conservation policies under climate change, we find that higher outcome uncertainty reduces utility. When accounting for endogeneity, we find that prior beliefs play a significant role in this cost of uncertainty. Thus, merely stating “objective” levels of outcome uncertainty...

  6. Radiotherapy for breast cancer: respiratory and set-up uncertainties

    International Nuclear Information System (INIS)

    Saliou, M.G.; Giraud, P.; Simon, L.; Fournier-Bidoz, N.; Fourquet, A.; Dendale, R.; Rosenwald, J.C.; Cosset, J.M.

    2005-01-01

    Adjuvant Radiotherapy has been shown to significantly reduce locoregional recurrence but this advantage is associated with increased cardiovascular and pulmonary morbidities. All uncertainties inherent to conformal radiation therapy must be identified in order to increase the precision of treatment; misestimation of these uncertainties increases the potential risk of geometrical misses with, as a consequence, under-dosage of the tumor and/or overdosage of healthy tissues. Geometric uncertainties due to respiratory movements or set-up errors are well known. Two strategies have been proposed to limit their effect: quantification of these uncertainties, which are then taken into account in the final calculation of safety margins and/or reduction of respiratory and set-up uncertainties by an efficient immobilization or gating systems. Measured on portal films with two tangential fields. CLD (central lung distance), defined as the distance between the deep field edge and the interior chest wall at the central axis, seems to be the best predictor of set-up uncertainties. Using CLD, estimated mean set-up errors from the literature are 3.8 and 3.2 mm for the systematic and random errors respectively. These depend partly on the type of immobilization device and could be reduced by the use of portal imaging systems. Furthermore, breast is mobile during respiration with motion amplitude as high as 0.8 to 10 mm in the anteroposterior direction. Respiratory gating techniques, currently on evaluation, have the potential to reduce effect of these movements. Each radiotherapy department should perform its own assessments and determine the geometric uncertainties with respect of the equipment used and its particular treatment practices. This paper is a review of the main geometric uncertainties in breast treatment, due to respiration and set-up, and solutions proposed to limit their impact. (author)

  7. Derivation of RCM-driven potential evapotranspiration for hydrological climate change impact analysis in Great Britain: a comparison of methods and associated uncertainty in future projections

    Directory of Open Access Journals (Sweden)

    C. Prudhomme

    2013-04-01

    the range of uncertainty defined by the ensemble of 12 PET equations. The changes show a clear northwest–southeast gradient of PET increase with largest (smallest changes in the northwest in January (July and October respectively. However, the range in magnitude of PET changes due to the choice of PET method shown in this study for Great Britain suggests that PET uncertainty is a challenge facing the assessment of climate change impact on hydrology mostly ignored up to now.

  8. Assessing concentration uncertainty estimates from passive microwave sea ice products

    Science.gov (United States)

    Meier, W.; Brucker, L.; Miller, J. A.

    2017-12-01

    Sea ice concentration is an essential climate variable and passive microwave derived estimates of concentration are one of the longest satellite-derived climate records. However, until recently uncertainty estimates were not provided. Numerous validation studies provided insight into general error characteristics, but the studies have found that concentration error varied greatly depending on sea ice conditions. Thus, an uncertainty estimate from each observation is desired, particularly for initialization, assimilation, and validation of models. Here we investigate three sea ice products that include an uncertainty for each concentration estimate: the NASA Team 2 algorithm product, the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF) product, and the NOAA/NSIDC Climate Data Record (CDR) product. Each product estimates uncertainty with a completely different approach. The NASA Team 2 product derives uncertainty internally from the algorithm method itself. The OSI-SAF uses atmospheric reanalysis fields and a radiative transfer model. The CDR uses spatial variability from two algorithms. Each approach has merits and limitations. Here we evaluate the uncertainty estimates by comparing the passive microwave concentration products with fields derived from the NOAA VIIRS sensor. The results show that the relationship between the product uncertainty estimates and the concentration error (relative to VIIRS) is complex. This may be due to the sea ice conditions, the uncertainty methods, as well as the spatial and temporal variability of the passive microwave and VIIRS products.

  9. Uncertainty quantification in nanomechanical measurements using the atomic force microscope

    International Nuclear Information System (INIS)

    Wagner, Ryan; Raman, Arvind; Moon, Robert; Pratt, Jon; Shaw, Gordon

    2011-01-01

    Quantifying uncertainty in measured properties of nanomaterials is a prerequisite for the manufacture of reliable nanoengineered materials and products. Yet, rigorous uncertainty quantification (UQ) is rarely applied for material property measurements with the atomic force microscope (AFM), a widely used instrument that can measure properties at nanometer scale resolution of both inorganic and biological surfaces and nanomaterials. We present a framework to ascribe uncertainty to local nanomechanical properties of any nanoparticle or surface measured with the AFM by taking into account the main uncertainty sources inherent in such measurements. We demonstrate the framework by quantifying uncertainty in AFM-based measurements of the transverse elastic modulus of cellulose nanocrystals (CNCs), an abundant, plant-derived nanomaterial whose mechanical properties are comparable to Kevlar fibers. For a single, isolated CNC the transverse elastic modulus was found to have a mean of 8.1 GPa and a 95% confidence interval of 2.7–20 GPa. A key result is that multiple replicates of force–distance curves do not sample the important sources of uncertainty, which are systematic in nature. The dominant source of uncertainty is the nondimensional photodiode sensitivity calibration rather than the cantilever stiffness or Z-piezo calibrations. The results underscore the great need for, and open a path towards, quantifying and minimizing uncertainty in AFM-based material property measurements of nanoparticles, nanostructured surfaces, thin films, polymers and biomaterials.

  10. Climate change impacts on groundwater hydrology – where are the main uncertainties and can they be reduced?

    DEFF Research Database (Denmark)

    Refsgaard, Jens C.; Sonnenborg, Torben; Butts, Michael

    2016-01-01

    This paper assesses how various sources of uncertainty propagate through the uncertainty cascade from emission scenarios through climate models and hydrological models to impacts with particular focus on groundwater aspects for a number of coordinated studies in Denmark. We find results similar...... to surface water studies showing that climate model uncertainty dominates for projections of climate change impacts on streamflow and groundwater heads. However, we find uncertainties related to geological conceptualisation and hydrological model discretisation to be dominating for projections of well field...... climate-hydrology models....

  11. Impacts of Spatial Climatic Representation on Hydrological Model Calibration and Prediction Uncertainty: A Mountainous Catchment of Three Gorges Reservoir Region, China

    Directory of Open Access Journals (Sweden)

    Yan Li

    2016-02-01

    Full Text Available Sparse climatic observations represent a major challenge for hydrological modeling of mountain catchments with implications for decision-making in water resources management. Employing elevation bands in the Soil and Water Assessment Tool-Sequential Uncertainty Fitting (SWAT2012-SUFI2 model enabled representation of precipitation and temperature variation with altitude in the Daning river catchment (Three Gorges Reservoir Region, China where meteorological inputs are limited in spatial extent and are derived from observations from relatively low lying locations. Inclusion of elevation bands produced better model performance for 1987–1993 with the Nash–Sutcliffe efficiency (NSE increasing by at least 0.11 prior to calibration. During calibration prediction uncertainty was greatly reduced. With similar R-factors from the earlier calibration iterations, a further 11% of observations were included within the 95% prediction uncertainty (95PPU compared to the model without elevation bands. For behavioral simulations defined in SWAT calibration using a NSE threshold of 0.3, an additional 3.9% of observations were within the 95PPU while the uncertainty reduced by 7.6% in the model with elevation bands. The calibrated model with elevation bands reproduced observed river discharges with the performance in the calibration period changing to “very good” from “poor” without elevation bands. The output uncertainty of calibrated model with elevation bands was satisfactory, having 85% of flow observations included within the 95PPU. These results clearly demonstrate the requirement to account for orographic effects on precipitation and temperature in hydrological models of mountainous catchments.

  12. Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models

    Directory of Open Access Journals (Sweden)

    A. E. Sikorska

    2012-04-01

    Full Text Available Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.

  13. Fuzzy Uncertainty Evaluation for Fault Tree Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Ki Beom; Shim, Hyung Jin [Seoul National University, Seoul (Korea, Republic of); Jae, Moo Sung [Hanyang University, Seoul (Korea, Republic of)

    2015-05-15

    This traditional probabilistic approach can calculate relatively accurate results. However it requires a long time because of repetitive computation due to the MC method. In addition, when informative data for statistical analysis are not sufficient or some events are mainly caused by human error, the probabilistic approach may not be possible because uncertainties of these events are difficult to be expressed by probabilistic distributions. In order to reduce the computation time and quantify uncertainties of top events when basic events whose uncertainties are difficult to be expressed by probabilistic distributions exist, the fuzzy uncertainty propagation based on fuzzy set theory can be applied. In this paper, we develop a fuzzy uncertainty propagation code and apply the fault tree of the core damage accident after the large loss of coolant accident (LLOCA). The fuzzy uncertainty propagation code is implemented and tested for the fault tree of the radiation release accident. We apply this code to the fault tree of the core damage accident after the LLOCA in three cases and compare the results with those computed by the probabilistic uncertainty propagation using the MC method. The results obtained by the fuzzy uncertainty propagation can be calculated in relatively short time, covering the results obtained by the probabilistic uncertainty propagation.

  14. Evaluating a multispecies adaptive management framework: Must uncertainty impede effective decision-making?

    Science.gov (United States)

    Smith, David R.; McGowan, Conor P.; Daily, Jonathan P.; Nichols, James D.; Sweka, John A.; Lyons, James E.

    2013-01-01

    Application of adaptive management to complex natural resource systems requires careful evaluation to ensure that the process leads to improved decision-making. As part of that evaluation, adaptive policies can be compared with alternative nonadaptive management scenarios. Also, the value of reducing structural (ecological) uncertainty to achieving management objectives can be quantified.A multispecies adaptive management framework was recently adopted by the Atlantic States Marine Fisheries Commission for sustainable harvest of Delaware Bay horseshoe crabs Limulus polyphemus, while maintaining adequate stopover habitat for migrating red knots Calidris canutus rufa, the focal shorebird species. The predictive model set encompassed the structural uncertainty in the relationships between horseshoe crab spawning, red knot weight gain and red knot vital rates. Stochastic dynamic programming was used to generate a state-dependent strategy for harvest decisions given that uncertainty. In this paper, we employed a management strategy evaluation approach to evaluate the performance of this adaptive management framework. Active adaptive management was used by including model weights as state variables in the optimization and reducing structural uncertainty by model weight updating.We found that the value of information for reducing structural uncertainty is expected to be low, because the uncertainty does not appear to impede effective management. Harvest policy responded to abundance levels of both species regardless of uncertainty in the specific relationship that generated those abundances. Thus, the expected horseshoe crab harvest and red knot abundance were similar when the population generating model was uncertain or known, and harvest policy was robust to structural uncertainty as specified.Synthesis and applications. The combination of management strategy evaluation with state-dependent strategies from stochastic dynamic programming was an informative approach to

  15. Guidance for treatment of variability and uncertainty in ecological risk assessments of contaminated sites

    International Nuclear Information System (INIS)

    1998-06-01

    Uncertainty is a seemingly simple concept that has caused great confusion and conflict in the field of risk assessment. This report offers guidance for the analysis and presentation of variability and uncertainty in ecological risk assessments, an important issue in the remedial investigation and feasibility study processes. This report discusses concepts of probability in terms of variance and uncertainty, describes how these concepts differ in ecological risk assessment from human health risk assessment, and describes probabilistic aspects of specific ecological risk assessment techniques. The report ends with 17 points to consider in performing an uncertainty analysis for an ecological risk assessment of a contaminated site

  16. Projected uranium measurement uncertainties for the Gas Centrifuge Enrichment Plant

    International Nuclear Information System (INIS)

    Younkin, J.M.

    1979-02-01

    An analysis was made of the uncertainties associated with the measurements of the declared uranium streams in the Portsmouth Gas Centrifuge Enrichment Plant (GCEP). The total uncertainty for the GCEP is projected to be from 54 to 108 kg 235 U/year out of a measured total of 200,000 kg 235 U/year. The systematic component of uncertainty of the UF 6 streams is the largest and the dominant contributor to the total uncertainty. A possible scheme for reducing the total uncertainty is given

  17. Reducing uncertainty in nitrogen budgets for African livestock systems

    International Nuclear Information System (INIS)

    Rufino, M C; Brandt, P; Herrero, M; Butterbach-Bahl, K

    2014-01-01

    Livestock is poorly represented in N budgets for the African continent although some studies have examined livestock-related N flows at different levels. Livestock plays an important role in N cycling and therefore on N budgets including livestock-related flows. This study reviews the literature on N budgets for Africa to identify factors contributing to uncertainties. Livestock densities are usually modelled because of the lack of observational spatial data. Even though feed availability and quality varies across seasons, most studies use constant livestock excretion rates, and excreta are usually assumed to be uniformly distributed onto the land. Major uncertainties originate in the fraction of manure managed, and emission factors which may not reflect the situation of Africa. N budgets use coarse assumptions on production, availability, and use of crop residues as livestock feed. No flows between croplands–livestock and rangelands reflect the lack of data. Joint efforts are needed for spatial data collection of livestock data, crowdsourcing appears to be a promising option. The focus of the assessment of N budgets must go beyond croplands to include livestock and crop–livestock flows. We propose a nested systems definition of livestock systems to link local, regional level, and continental level and to increase the usefulness of point measurements of N losses. Scientists working at all levels should generate data to calibrate process-based models. Measurements in the field should not only concentrate on greenhouse gas emissions, but need to include crop and livestock production measurements, soil stock changes and other N loss pathways such as leaching, run-off and volatilization to assess management practices and trade-offs. Compared to the research done in other continents on N flows in livestock systems, there are few data for Africa, and therefore concerted effort will be needed to generate sufficient data for modelling. (paper)

  18. Comments on Uncertainty in Groundwater Governance in the Volcanic Canary Islands, Spain

    OpenAIRE

    Custodio, Emilio; Cabrera, María; Poncela, Roberto; Cruz-Fuentes, Tatiana; Naranjo, Gema; Miguel, Luis de

    2015-01-01

    The uncertainty associated with natural magnitudes and processes is conspicuous in water resources and groundwater evaluation. This uncertainty has an essential component and a part that can be reduced to some extent by increasing knowledge, improving monitoring coverage, continuous elaboration of data and accuracy and addressing the related economic and social aspects involved. Reducing uncertainty has a cost that may not be justified by the improvement that is obtainable, but that has to be...

  19. The uncertainty analysis of model results a practical guide

    CERN Document Server

    Hofer, Eduard

    2018-01-01

    This book is a practical guide to the uncertainty analysis of computer model applications. Used in many areas, such as engineering, ecology and economics, computer models are subject to various uncertainties at the level of model formulations, parameter values and input data. Naturally, it would be advantageous to know the combined effect of these uncertainties on the model results as well as whether the state of knowledge should be improved in order to reduce the uncertainty of the results most effectively. The book supports decision-makers, model developers and users in their argumentation for an uncertainty analysis and assists them in the interpretation of the analysis results.

  20. Decisions under uncertainty using Bayesian analysis

    Directory of Open Access Journals (Sweden)

    Stelian STANCU

    2006-01-01

    Full Text Available The present paper makes a short presentation of the Bayesian decions method, where extrainformation brings a great support to decision making process, but also attract new costs. In this situation, getting new information, generally experimentaly based, contributes to diminushing the uncertainty degree that influences decision making process. As a conclusion, in a large number of decision problems, there is the possibility that the decision makers will renew some decisions already taken because of the facilities offered by obtainig extrainformation.

  1. How Well Does Fracture Set Characterization Reduce Uncertainty in Capture Zone Size for Wells Situated in Sedimentary Bedrock Aquifers?

    Science.gov (United States)

    West, A. C.; Novakowski, K. S.

    2005-12-01

    Regional groundwater flow models are rife with uncertainty. The three-dimensional flux vector fields must generally be inferred using inverse modelling from sparse measurements of hydraulic head, from measurements of hydraulic parameters at a scale that is miniscule in comparison to that of the domain, and from none to a very few measurements of recharge or discharge rate. Despite the inherent uncertainty in these models they are routinely used to delineate steady-state or time-of-travel capture zones for the purpose of wellhead protection. The latter are defined as the volume of the aquifer within which released particles will arrive at the well within the specified time and their delineation requires the additional step of dividing the magnitudes of the flux vectors by the assumed porosity to arrive at the ``average linear groundwater velocity'' vector field. Since the porosity is usually assumed constant over the domain one could be forgiven for thinking that the uncertainty introduced at this step is minor in comparison to the flow model calibration step. We consider this question when the porosity in question is fracture porosity in flat-lying sedimentary bedrock. We also consider whether or not the diffusive uptake of solute into the rock matrix which lies between the source and the production well reduces or enhances the uncertainty. To evaluate the uncertainty an aquifer cross section is conceptualized as an array of horizontal, randomly-spaced, parallel-plate fractures of random aperture, with adjacent horizontal fractures connected by vertical fractures again of random spacing and aperture. The source is assumed to be a continuous concentration (i.e. a dirichlet boundary condition) representing a leaking tank or a DNAPL pool, and the receptor is a fully pentrating well located in the down-gradient direction. In this context the time-of-travel capture zone is defined as the separation distance required such that the source does not contaminate the well

  2. One Approach to the Fire PSA Uncertainty Analysis

    International Nuclear Information System (INIS)

    Simic, Z.; Mikulicic, V.; Vukovic, I.

    2002-01-01

    Experienced practical events and findings from the number of fire probabilistic safety assessment (PSA) studies show that fire has high relative importance for nuclear power plant safety. Fire PSA is a very challenging phenomenon and a number of issues are still in the area of research and development. This has a major impact on the conservatism of fire PSA findings. One way to reduce the level of conservatism is to conduct uncertainty analysis. At the top-level, uncertainty of the fire PSA can be separated in to three segments. The first segment is related to fire initiating events frequencies. The second uncertainty segment is connected to the uncertainty of fire damage. Finally, there is uncertainty related to the PSA model, which propagates this fire-initiated damage to the core damage or other analyzed risk. This paper discusses all three segments of uncertainty. Some recent experience with fire PSA study uncertainty analysis, usage of fire analysis code COMPBRN IIIe, and uncertainty evaluation importance to the final result is presented.(author)

  3. Network optimization including gas lift and network parameters under subsurface uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Schulze-Riegert, R.; Baffoe, J.; Pajonk, O. [SPT Group GmbH, Hamburg (Germany); Badalov, H.; Huseynov, S. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE; Trick, M. [SPT Group, Calgary, AB (Canada)

    2013-08-01

    Optimization of oil and gas field production systems poses a great challenge to field development due to complex and multiple interactions between various operational design parameters and subsurface uncertainties. Conventional analytical methods are capable of finding local optima based on single deterministic models. They are less applicable for efficiently generating alternative design scenarios in a multi-objective context. Practical implementations of robust optimization workflows integrate the evaluation of alternative design scenarios and multiple realizations of subsurface uncertainty descriptions. Production or economic performance indicators such as NPV (Net Present Value) are linked to a risk-weighted objective function definition to guide the optimization processes. This work focuses on an integrated workflow using a reservoir-network simulator coupled to an optimization framework. The work will investigate the impact of design parameters while considering the physics of the reservoir, wells, and surface facilities. Subsurface uncertainties are described by well parameters such as inflow performance. Experimental design methods are used to investigate parameter sensitivities and interactions. Optimization methods are used to find optimal design parameter combinations which improve key performance indicators of the production network system. The proposed workflow will be applied to a representative oil reservoir coupled to a network which is modelled by an integrated reservoir-network simulator. Gas-lift will be included as an explicit measure to improve production. An objective function will be formulated for the net present value of the integrated system including production revenue and facility costs. Facility and gas lift design parameters are tuned to maximize NPV. Well inflow performance uncertainties are introduced with an impact on gas lift performance. Resulting variances on NPV are identified as a risk measure for the optimized system design. A

  4. Impact of dose-distribution uncertainties on rectal ntcp modeling I: Uncertainty estimates

    International Nuclear Information System (INIS)

    Fenwick, John D.; Nahum, Alan E.

    2001-01-01

    A trial of nonescalated conformal versus conventional radiotherapy treatment of prostate cancer has been carried out at the Royal Marsden NHS Trust (RMH) and Institute of Cancer Research (ICR), demonstrating a significant reduction in the rate of rectal bleeding reported for patients treated using the conformal technique. The relationship between planned rectal dose-distributions and incidences of bleeding has been analyzed, showing that the rate of bleeding falls significantly as the extent of the rectal wall receiving a planned dose-level of more than 57 Gy is reduced. Dose-distributions delivered to the rectal wall over the course of radiotherapy treatment inevitably differ from planned distributions, due to sources of uncertainty such as patient setup error, rectal wall movement and variation in the absolute rectal wall surface area. In this paper estimates of the differences between planned and treated rectal dose-distribution parameters are obtained for the RMH/ICR nonescalated conformal technique, working from a distribution of setup errors observed during the RMH/ICR trial, movement data supplied by Lebesque and colleagues derived from repeat CT scans, and estimates of rectal circumference variations extracted from the literature. Setup errors and wall movement are found to cause only limited systematic differences between mean treated and planned rectal dose-distribution parameter values, but introduce considerable uncertainties into the treated values of some dose-distribution parameters: setup errors lead to 22% and 9% relative uncertainties in the highly dosed fraction of the rectal wall and the wall average dose, respectively, with wall movement leading to 21% and 9% relative uncertainties. Estimates obtained from the literature of the uncertainty in the absolute surface area of the distensible rectal wall are of the order of 13%-18%. In a subsequent paper the impact of these uncertainties on analyses of the relationship between incidences of bleeding

  5. Climate change decision-making: Model & parameter uncertainties explored

    Energy Technology Data Exchange (ETDEWEB)

    Dowlatabadi, H.; Kandlikar, M.; Linville, C.

    1995-12-31

    A critical aspect of climate change decision-making is uncertainties in current understanding of the socioeconomic, climatic and biogeochemical processes involved. Decision-making processes are much better informed if these uncertainties are characterized and their implications understood. Quantitative analysis of these uncertainties serve to inform decision makers about the likely outcome of policy initiatives, and help set priorities for research so that outcome ambiguities faced by the decision-makers are reduced. A family of integrated assessment models of climate change have been developed at Carnegie Mellon. These models are distinguished from other integrated assessment efforts in that they were designed from the outset to characterize and propagate parameter, model, value, and decision-rule uncertainties. The most recent of these models is ICAM 2.1. This model includes representation of the processes of demographics, economic activity, emissions, atmospheric chemistry, climate and sea level change and impacts from these changes and policies for emissions mitigation, and adaptation to change. The model has over 800 objects of which about one half are used to represent uncertainty. In this paper we show, that when considering parameter uncertainties, the relative contribution of climatic uncertainties are most important, followed by uncertainties in damage calculations, economic uncertainties and direct aerosol forcing uncertainties. When considering model structure uncertainties we find that the choice of policy is often dominated by model structure choice, rather than parameter uncertainties.

  6. Uncertainty in simulating wheat yields under climate change

    DEFF Research Database (Denmark)

    Asseng, A; Ewert, F; Rosenzweig, C

    2013-01-01

    of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models...... than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi...

  7. Optimization Under Uncertainty for Wake Steering Strategies

    Energy Technology Data Exchange (ETDEWEB)

    Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Annoni, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Fleming, Paul A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ning, Andrew [Brigham Young University

    2017-08-03

    Offsetting turbines' yaw orientations from incoming wind is a powerful tool that may be leveraged to reduce undesirable wake effects on downstream turbines. First, we examine a simple two-turbine case to gain intuition as to how inflow direction uncertainty affects the optimal solution. The turbines are modeled with unidirectional inflow such that one turbine directly wakes the other, using ten rotor diameter spacing. We perform optimization under uncertainty (OUU) via a parameter sweep of the front turbine. The OUU solution generally prefers less steering. We then do this optimization for a 60-turbine wind farm with unidirectional inflow, varying the degree of inflow uncertainty and approaching this OUU problem by nesting a polynomial chaos expansion uncertainty quantification routine within an outer optimization. We examined how different levels of uncertainty in the inflow direction effect the ratio of the expected values of deterministic and OUU solutions for steering strategies in the large wind farm, assuming the directional uncertainty used to reach said OUU solution (this ratio is defined as the value of the stochastic solution or VSS).

  8. Mass discharge estimation from contaminated sites: Multi-model solutions for assessment of conceptual uncertainty

    DEFF Research Database (Denmark)

    Thomsen, Nanna Isbak; Troldborg, Mads; McKnight, Ursula S.

    2012-01-01

    site. The different conceptual models consider different source characterizations and hydrogeological descriptions. The idea is to include a set of essentially different conceptual models where each model is believed to be realistic representation of the given site, based on the current level...... the appropriate management option. The uncertainty of mass discharge estimates depends greatly on the extent of the site characterization. A good approach for uncertainty estimation will be flexible with respect to the investigation level, and account for both parameter and conceptual model uncertainty. We...... propose a method for quantifying the uncertainty of dynamic mass discharge estimates from contaminant point sources on the local scale. The method considers both parameter and conceptual uncertainty through a multi-model approach. The multi-model approach evaluates multiple conceptual models for the same...

  9. Research of Uncertainty Reasoning in Pineapple Disease Identification System

    Science.gov (United States)

    Liu, Liqun; Fan, Haifeng

    In order to deal with the uncertainty of evidences mostly existing in pineapple disease identification system, a reasoning model based on evidence credibility factor was established. The uncertainty reasoning method is discussed,including: uncertain representation of knowledge, uncertain representation of rules, uncertain representation of multi-evidences and update of reasoning rules. The reasoning can fully reflect the uncertainty in disease identification and reduce the influence of subjective factors on the accuracy of the system.

  10. Uncertainty analysis of multiple canister repository model by large-scale calculation

    International Nuclear Information System (INIS)

    Tsujimoto, K.; Okuda, H.; Ahn, J.

    2007-01-01

    A prototype uncertainty analysis has been made by using the multiple-canister radionuclide transport code, VR, for performance assessment for the high-level radioactive waste repository. Fractures in the host rock determine main conduit of groundwater, and thus significantly affect the magnitude of radionuclide release rates from the repository. In this study, the probability distribution function (PDF) for the number of connected canisters in the same fracture cluster that bears water flow has been determined in a Monte-Carlo fashion by running the FFDF code with assumed PDFs for fracture geometry. The uncertainty for the release rate of 237 Np from a hypothetical repository containing 100 canisters has been quantitatively evaluated by using the VR code with PDFs for the number of connected canisters and the near field rock porosity. The calculation results show that the mass transport is greatly affected by (1) the magnitude of the radionuclide source determined by the number of connected canisters by the fracture cluster, and (2) the canister concentration effect in the same fracture network. The results also show the two conflicting tendencies that the more fractures in the repository model space, the greater average value but the smaller uncertainty of the peak fractional release rate is. To perform a vast amount of calculation, we have utilized the Earth Simulator and SR8000. The multi-level hybrid programming method is applied in the optimization to exploit high performance of the Earth Simulator. The Latin Hypercube Sampling has been utilized to reduce the number of samplings in Monte-Carlo calculation. (authors)

  11. Uncertainty assessing of measure result of tungsten in U3O8 by ICP-AES

    International Nuclear Information System (INIS)

    Du Guirong; Nie Jie; Tang Lilei

    2011-01-01

    According as the determining method and the assessing criterion,the uncertainty assessing of measure result of tungsten in U 3 O 8 by ICP-AES is researched. With the assessment of each component in detail, the result shows that u rel (sc)> u rel (c)> u rel (F)> u rel (m) by uncertainty contribution. Other uncertainty is random, calculated by repetition. u rel (sc) is contributed to uncertainty mainly. So the general uncertainty is reduced with strict operation to reduce u rel (sc). (authors)

  12. The dynamic correlation between policy uncertainty and stock market returns in China

    Science.gov (United States)

    Yang, Miao; Jiang, Zhi-Qiang

    2016-11-01

    The dynamic correlation is examined between government's policy uncertainty and Chinese stock market returns in the period from January 1995 to December 2014. We find that the stock market is significantly correlated to policy uncertainty based on the results of the Vector Auto Regression (VAR) and Structural Vector Auto Regression (SVAR) models. In contrast, the results of the Dynamic Conditional Correlation Generalized Multivariate Autoregressive Conditional Heteroscedasticity (DCC-MGARCH) model surprisingly show a low dynamic correlation coefficient between policy uncertainty and market returns, suggesting that the fluctuations of each variable are greatly influenced by their values in the preceding period. Our analysis highlights the understanding of the dynamical relationship between stock market and fiscal and monetary policy.

  13. Uncertainty Margin of Void Packet Determination for Ultrasonic Test in NPP

    International Nuclear Information System (INIS)

    Lee, Seungchan; Sung, Jejung; Lee, Jongchan; Kim, Jonguk

    2014-01-01

    In this study, the uncertainty of the void packet determination is estimated and the conservatism is reviewed by comparing with realistic uncertainty of Heckle's uncertainty. The methodology of ISO GUM is fully applied to calculate uncertainty, combined uncertainty and effective degree of freedom. Here some results are achieved as below: Combined uncertainty(UT) : 4.98%, Combined uncertainty(Heckle) : 1.44%, Degree of freedom: 5 ∼ 15, Effective degree of freedom(UT): 24.11, Effective degree of freedom(Heckle): 28.54, K value of t-distribution(UT): 2.042, K value of t-distribution(Heckle): 2.04, The uncertainty of this study using UT is enough in the case of achieving conservatism when the void packet determination of the safety related system is determined. As result of this study, UT uncertainty is more conservative than the Heckle's realistic uncertainty. From these results, it is shown that UT method has the great safety margin in determining the void packet. In comparing UT uncertainty with realistic uncertainty, this study (UT) has the conservatism of more than 3.4 times. UT method is good method to determine the void packet of ECCS pipe and to achieve the safety margin. In a safety related system, a void packet determination is issued by US NRC through the Generic Letter 2008-01. In case of the safety function, ECCS, CSS, and RHR systems are affected by the void packet. The related study has been being carried out by KHNP since 2012. In this study, the void packet determination using a ultra sonic test method has been carried out in some sites. This paper shows the uncertainty of the method using the ultra sonic test. The key parameters are introduced and estimated. Specially, the measurement conservatism for NPP is introduced to show the uncertainty margin

  14. Uncertainty Margin of Void Packet Determination for Ultrasonic Test in NPP

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Seungchan; Sung, Jejung [Korea Hydro Nuclear Power Electricity Co., Daejeon (Korea, Republic of); Lee, Jongchan; Kim, Jonguk [FNC Technology Co., LTD., Yongin (Korea, Republic of)

    2014-05-15

    In this study, the uncertainty of the void packet determination is estimated and the conservatism is reviewed by comparing with realistic uncertainty of Heckle's uncertainty. The methodology of ISO GUM is fully applied to calculate uncertainty, combined uncertainty and effective degree of freedom. Here some results are achieved as below: Combined uncertainty(UT) : 4.98%, Combined uncertainty(Heckle) : 1.44%, Degree of freedom: 5 ∼ 15, Effective degree of freedom(UT): 24.11, Effective degree of freedom(Heckle): 28.54, K value of t-distribution(UT): 2.042, K value of t-distribution(Heckle): 2.04, The uncertainty of this study using UT is enough in the case of achieving conservatism when the void packet determination of the safety related system is determined. As result of this study, UT uncertainty is more conservative than the Heckle's realistic uncertainty. From these results, it is shown that UT method has the great safety margin in determining the void packet. In comparing UT uncertainty with realistic uncertainty, this study (UT) has the conservatism of more than 3.4 times. UT method is good method to determine the void packet of ECCS pipe and to achieve the safety margin. In a safety related system, a void packet determination is issued by US NRC through the Generic Letter 2008-01. In case of the safety function, ECCS, CSS, and RHR systems are affected by the void packet. The related study has been being carried out by KHNP since 2012. In this study, the void packet determination using a ultra sonic test method has been carried out in some sites. This paper shows the uncertainty of the method using the ultra sonic test. The key parameters are introduced and estimated. Specially, the measurement conservatism for NPP is introduced to show the uncertainty margin.

  15. Exploring entropic uncertainty relation in the Heisenberg XX model with inhomogeneous magnetic field

    Science.gov (United States)

    Huang, Ai-Jun; Wang, Dong; Wang, Jia-Ming; Shi, Jia-Dong; Sun, Wen-Yang; Ye, Liu

    2017-08-01

    In this work, we investigate the quantum-memory-assisted entropic uncertainty relation in a two-qubit Heisenberg XX model with inhomogeneous magnetic field. It has been found that larger coupling strength J between the two spin-chain qubits can effectively reduce the entropic uncertainty. Besides, we observe the mechanics of how the inhomogeneous field influences the uncertainty, and find out that when the inhomogeneous field parameter b1. Intriguingly, the entropic uncertainty can shrink to zero when the coupling coefficients are relatively large, while the entropic uncertainty only reduces to 1 with the increase of the homogeneous magnetic field. Additionally, we observe the purity of the state and Bell non-locality and obtain that the entropic uncertainty is anticorrelated with both the purity and Bell non-locality of the evolution state.

  16. Comments on Uncertainty in Groundwater Governance in the Volcanic Canary Islands, Spain

    Directory of Open Access Journals (Sweden)

    Emilio Custodio

    2015-06-01

    Full Text Available The uncertainty associated with natural magnitudes and processes is conspicuous in water resources and groundwater evaluation. This uncertainty has an essential component and a part that can be reduced to some extent by increasing knowledge, improving monitoring coverage, continuous elaboration of data and accuracy and addressing the related economic and social aspects involved. Reducing uncertainty has a cost that may not be justified by the improvement that is obtainable, but that has to be known to make the right decisions. With this idea, this paper contributes general comments on the evaluation of groundwater resources in the semiarid Canary Islands and on some of the main sources of uncertainty, but a full treatment is not attempted, nor how to reduce it. Although the point of view is local, these comments may help to address similar situations on other islands where similar problems appear. A consequence of physical and hydrological uncertainty is that different hydrogeological and water resource studies and evaluations may yield different results. Understanding and coarsely evaluating uncertainty helps in reducing administrative instability, poor decisions that may harm groundwater property rights, the rise of complaints and the sub-optimal use of the scarce water resources available in semiarid areas. Transparency and honesty are needed, but especially a clear understanding of what numbers mean and the uncertainty around them, to act soundly and avoid conflicting and damaging rigid attitudes. However, the different situations could condition that what may be good in a place, may not always be the case in other places.

  17. Perseveration induces dissociative uncertainty in obsessive-compulsive disorder.

    Science.gov (United States)

    Giele, Catharina L; van den Hout, Marcel A; Engelhard, Iris M; Dek, Eliane C P; Toffolo, Marieke B J; Cath, Danielle C

    2016-09-01

    Obsessive compulsive (OC)-like perseveration paradoxically increases feelings of uncertainty. We studied whether the underlying mechanism between perseveration and uncertainty is a reduced accessibility of meaning ('semantic satiation'). OCD patients (n = 24) and matched non-clinical controls (n = 24) repeated words 2 (non-perseveration) or 20 times (perseveration). They decided whether this word was related to another target word. Speed of relatedness judgments and feelings of dissociative uncertainty were measured. The effects of real-life perseveration on dissociative uncertainty were tested in a smaller subsample of the OCD group (n = 9). Speed of relatedness judgments was not affected by perseveration. However, both groups reported more dissociative uncertainty after perseveration compared to non-perseveration, which was higher in OCD patients. Patients reported more dissociative uncertainty after 'clinical' perseveration compared to non-perseveration.. Both parts of this study are limited by some methodological issues and a small sample size. Although the mechanism behind 'perseveration → uncertainty' is still unclear, results suggest that the effects of perseveration are counterproductive. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Statistically based uncertainty assessments in nuclear risk analysis

    International Nuclear Information System (INIS)

    Spencer, F.W.; Diegert, K.V.; Easterling, R.G.

    1987-01-01

    Over the last decade, the problems of estimation and uncertainty assessment in probabilistics risk assessment (PRAs) have been addressed in a variety of NRC and industry-sponsored projects. These problems have received attention because of a recognition that major uncertainties in risk estimation exist, which can be reduced by collecting more and better data and other information, and because of a recognition that better methods for assessing these uncertainties are needed. In particular, a clear understanding of the nature and magnitude of various sources of uncertainty is needed to facilitate descision-making on possible plant changes and research options. Recent PRAs have employed methods of probability propagation, sometimes involving the use of Bayes Theorem, and intended to formalize the use of ''engineering judgment'' or ''expert opinion.'' All sources, or feelings, of uncertainty are expressed probabilistically, so that uncertainty analysis becomes simply a matter of probability propagation. Alternatives to forcing a probabilistic framework at all stages of a PRA are a major concern in this paper, however

  19. Uncertainty evaluation methods for waste package performance assessment

    International Nuclear Information System (INIS)

    Wu, Y.T.; Nair, P.K.; Journel, A.G.; Abramson, L.R.

    1991-01-01

    This report identifies and investigates methodologies to deal with uncertainties in assessing high-level nuclear waste package performance. Four uncertainty evaluation methods (probability-distribution approach, bounding approach, expert judgment, and sensitivity analysis) are suggested as the elements of a methodology that, without either diminishing or enhancing the input uncertainties, can evaluate performance uncertainty. Such a methodology can also help identify critical inputs as a guide to reducing uncertainty so as to provide reasonable assurance that the risk objectives are met. This report examines the current qualitative waste containment regulation and shows how, in conjunction with the identified uncertainty evaluation methodology, a framework for a quantitative probability-based rule can be developed that takes account of the uncertainties. Current US Nuclear Regulatory Commission (NRC) regulation requires that the waste packages provide ''substantially complete containment'' (SCC) during the containment period. The term ''SCC'' is ambiguous and subject to interpretation. This report, together with an accompanying report that describes the technical considerations that must be addressed to satisfy high-level waste containment requirements, provides a basis for a third report to develop recommendations for regulatory uncertainty reduction in the ''containment''requirement of 10 CFR Part 60. 25 refs., 3 figs., 2 tabs

  20. Paradoxical effects of compulsive perseveration : Sentence repetition causes semantic uncertainty

    NARCIS (Netherlands)

    Giele, Catharina L.; van den Hout, Marcel A.; Engelhard, Iris M.; Dek, Eliane C P

    2014-01-01

    Many patients with obsessive compulsive disorder (OCD) perform perseverative checking behavior to reduce uncertainty, but studies have shown that this ironically increases uncertainty. Some patients also tend to perseveratively repeat sentences. The aim of this study was to examine whether sentence

  1. Uncertainty analysis in Monte Carlo criticality computations

    International Nuclear Information System (INIS)

    Qi Ao

    2011-01-01

    Highlights: ► Two types of uncertainty methods for k eff Monte Carlo computations are examined. ► Sampling method has the least restrictions on perturbation but computing resources. ► Analytical method is limited to small perturbation on material properties. ► Practicality relies on efficiency, multiparameter applicability and data availability. - Abstract: Uncertainty analysis is imperative for nuclear criticality risk assessments when using Monte Carlo neutron transport methods to predict the effective neutron multiplication factor (k eff ) for fissionable material systems. For the validation of Monte Carlo codes for criticality computations against benchmark experiments, code accuracy and precision are measured by both the computational bias and uncertainty in the bias. The uncertainty in the bias accounts for known or quantified experimental, computational and model uncertainties. For the application of Monte Carlo codes for criticality analysis of fissionable material systems, an administrative margin of subcriticality must be imposed to provide additional assurance of subcriticality for any unknown or unquantified uncertainties. Because of a substantial impact of the administrative margin of subcriticality on economics and safety of nuclear fuel cycle operations, recently increasing interests in reducing the administrative margin of subcriticality make the uncertainty analysis in criticality safety computations more risk-significant. This paper provides an overview of two most popular k eff uncertainty analysis methods for Monte Carlo criticality computations: (1) sampling-based methods, and (2) analytical methods. Examples are given to demonstrate their usage in the k eff uncertainty analysis due to uncertainties in both neutronic and non-neutronic parameters of fissionable material systems.

  2. Quantifying and managing uncertainty in operational modal analysis

    Science.gov (United States)

    Au, Siu-Kui; Brownjohn, James M. W.; Mottershead, John E.

    2018-03-01

    Operational modal analysis aims at identifying the modal properties (natural frequency, damping, etc.) of a structure using only the (output) vibration response measured under ambient conditions. Highly economical and feasible, it is becoming a common practice in full-scale vibration testing. In the absence of (input) loading information, however, the modal properties have significantly higher uncertainty than their counterparts identified from free or forced vibration (known input) tests. Mastering the relationship between identification uncertainty and test configuration is of great interest to both scientists and engineers, e.g., for achievable precision limits and test planning/budgeting. Addressing this challenge beyond the current state-of-the-art that are mostly concerned with identification algorithms, this work obtains closed form analytical expressions for the identification uncertainty (variance) of modal parameters that fundamentally explains the effect of test configuration. Collectively referred as 'uncertainty laws', these expressions are asymptotically correct for well-separated modes, small damping and long data; and are applicable under non-asymptotic situations. They provide a scientific basis for planning and standardization of ambient vibration tests, where factors such as channel noise, sensor number and location can be quantitatively accounted for. The work is reported comprehensively with verification through synthetic and experimental data (laboratory and field), scientific implications and practical guidelines for planning ambient vibration tests.

  3. Uncertainty and global climate change research

    Energy Technology Data Exchange (ETDEWEB)

    Tonn, B.E. [Oak Ridge National Lab., TN (United States); Weiher, R. [National Oceanic and Atmospheric Administration, Boulder, CO (United States)

    1994-06-01

    The Workshop on Uncertainty and Global Climate Change Research March 22--23, 1994, in Knoxville, Tennessee. This report summarizes the results and recommendations of the workshop. The purpose of the workshop was to examine in-depth the concept of uncertainty. From an analytical point of view, uncertainty is a central feature of global climate science, economics and decision making. The magnitude and complexity of uncertainty surrounding global climate change has made it quite difficult to answer even the most simple and important of questions-whether potentially costly action is required now to ameliorate adverse consequences of global climate change or whether delay is warranted to gain better information to reduce uncertainties. A major conclusion of the workshop is that multidisciplinary integrated assessments using decision analytic techniques as a foundation is key to addressing global change policy concerns. First, uncertainty must be dealt with explicitly and rigorously since it is and will continue to be a key feature of analysis and recommendations on policy questions for years to come. Second, key policy questions and variables need to be explicitly identified, prioritized, and their uncertainty characterized to guide the entire scientific, modeling, and policy analysis process. Multidisciplinary integrated assessment techniques and value of information methodologies are best suited for this task. In terms of timeliness and relevance of developing and applying decision analytic techniques, the global change research and policy communities are moving rapidly toward integrated approaches to research design and policy analysis.

  4. A Peep into the Uncertainty-Complexity-Relevance Modeling Trilemma through Global Sensitivity and Uncertainty Analysis

    Science.gov (United States)

    Munoz-Carpena, R.; Muller, S. J.; Chu, M.; Kiker, G. A.; Perz, S. G.

    2014-12-01

    Model Model complexity resulting from the need to integrate environmental system components cannot be understated. In particular, additional emphasis is urgently needed on rational approaches to guide decision making through uncertainties surrounding the integrated system across decision-relevant scales. However, in spite of the difficulties that the consideration of modeling uncertainty represent for the decision process, it should not be avoided or the value and science behind the models will be undermined. These two issues; i.e., the need for coupled models that can answer the pertinent questions and the need for models that do so with sufficient certainty, are the key indicators of a model's relevance. Model relevance is inextricably linked with model complexity. Although model complexity has advanced greatly in recent years there has been little work to rigorously characterize the threshold of relevance in integrated and complex models. Formally assessing the relevance of the model in the face of increasing complexity would be valuable because there is growing unease among developers and users of complex models about the cumulative effects of various sources of uncertainty on model outputs. In particular, this issue has prompted doubt over whether the considerable effort going into further elaborating complex models will in fact yield the expected payback. New approaches have been proposed recently to evaluate the uncertainty-complexity-relevance modeling trilemma (Muller, Muñoz-Carpena and Kiker, 2011) by incorporating state-of-the-art global sensitivity and uncertainty analysis (GSA/UA) in every step of the model development so as to quantify not only the uncertainty introduced by the addition of new environmental components, but the effect that these new components have over existing components (interactions, non-linear responses). Outputs from the analysis can also be used to quantify system resilience (stability, alternative states, thresholds or tipping

  5. Reducing the sensitivity of IMPT treatment plans to setup errors and range uncertainties via probabilistic treatment planning

    International Nuclear Information System (INIS)

    Unkelbach, Jan; Bortfeld, Thomas; Martin, Benjamin C.; Soukup, Martin

    2009-01-01

    Treatment plans optimized for intensity modulated proton therapy (IMPT) may be very sensitive to setup errors and range uncertainties. If these errors are not accounted for during treatment planning, the dose distribution realized in the patient may by strongly degraded compared to the planned dose distribution. The authors implemented the probabilistic approach to incorporate uncertainties directly into the optimization of an intensity modulated treatment plan. Following this approach, the dose distribution depends on a set of random variables which parameterize the uncertainty, as does the objective function used to optimize the treatment plan. The authors optimize the expected value of the objective function. They investigate IMPT treatment planning regarding range uncertainties and setup errors. They demonstrate that incorporating these uncertainties into the optimization yields qualitatively different treatment plans compared to conventional plans which do not account for uncertainty. The sensitivity of an IMPT plan depends on the dose contributions of individual beam directions. Roughly speaking, steep dose gradients in beam direction make treatment plans sensitive to range errors. Steep lateral dose gradients make plans sensitive to setup errors. More robust treatment plans are obtained by redistributing dose among different beam directions. This can be achieved by the probabilistic approach. In contrast, the safety margin approach as widely applied in photon therapy fails in IMPT and is neither suitable for handling range variations nor setup errors.

  6. Uncertainty Assessment: What Good Does it Do? (Invited)

    Science.gov (United States)

    Oreskes, N.; Lewandowsky, S.

    2013-12-01

    The scientific community has devoted considerable time and energy to understanding, quantifying and articulating the uncertainties related to anthropogenic climate change. However, informed decision-making and good public policy arguably rely far more on a central core of understanding of matters that are scientifically well established than on detailed understanding and articulation of all relevant uncertainties. Advocates of vaccination, for example, stress its overall efficacy in preventing morbidity and mortality--not the uncertainties over how long the protective effects last. Advocates for colonoscopy for cancer screening stress its capacity to detect polyps before they become cancerous, with relatively little attention paid to the fact that many, if not most, polyps, would not become cancerous even if left unremoved. So why has the climate science community spent so much time focused on uncertainty? One reason, of course, is that articulation of uncertainty is a normal and appropriate part of scientific work. However, we argue that there is another reason that involves the pressure that the scientific community has experienced from individuals and groups promoting doubt about anthropogenic climate change. Specifically, doubt-mongering groups focus public attention on scientific uncertainty as a means to undermine scientific claims, equating uncertainty with untruth. Scientists inadvertently validate these arguments by agreeing that much of the science is uncertain, and thus seemingly implying that our knowledge is insecure. The problem goes further, as the scientific community attempts to articulate more clearly, and reduce, those uncertainties, thus, seemingly further agreeing that the knowledge base is insufficient to warrant public and governmental action. We refer to this effect as 'seepage,' as the effects of doubt-mongering seep into the scientific community and the scientific agenda, despite the fact that addressing these concerns does little to alter

  7. Sensitivity functions for uncertainty analysis: Sensitivity and uncertainty analysis of reactor performance parameters

    International Nuclear Information System (INIS)

    Greenspan, E.

    1982-01-01

    This chapter presents the mathematical basis for sensitivity functions, discusses their physical meaning and information they contain, and clarifies a number of issues concerning their application, including the definition of group sensitivities, the selection of sensitivity functions to be included in the analysis, and limitations of sensitivity theory. Examines the theoretical foundation; criticality reset sensitivities; group sensitivities and uncertainties; selection of sensitivities included in the analysis; and other uses and limitations of sensitivity functions. Gives the theoretical formulation of sensitivity functions pertaining to ''as-built'' designs for performance parameters of the form of ratios of linear flux functionals (such as reaction-rate ratios), linear adjoint functionals, bilinear functions (such as reactivity worth ratios), and for reactor reactivity. Offers a consistent procedure for reducing energy-dependent or fine-group sensitivities and uncertainties to broad group sensitivities and uncertainties. Provides illustrations of sensitivity functions as well as references to available compilations of such functions and of total sensitivities. Indicates limitations of sensitivity theory originating from the fact that this theory is based on a first-order perturbation theory

  8. Uncertainty in techno-economic estimates of cellulosic ethanol production due to experimental measurement uncertainty

    Directory of Open Access Journals (Sweden)

    Vicari Kristin J

    2012-04-01

    the TE model predictions. This analysis highlights the primary measurements that merit further development to reduce the uncertainty associated with their use in TE models. While we develop and apply this mathematical framework to a specific biorefinery scenario here, this analysis can be readily adapted to other types of biorefining processes and provides a general framework for propagating uncertainty due to analytical measurements through a TE model.

  9. Confronting the Uncertainty in Aerosol Forcing Using Comprehensive Observational Data

    Science.gov (United States)

    Johnson, J. S.; Regayre, L. A.; Yoshioka, M.; Pringle, K.; Sexton, D.; Lee, L.; Carslaw, K. S.

    2017-12-01

    The effect of aerosols on cloud droplet concentrations and radiative properties is the largest uncertainty in the overall radiative forcing of climate over the industrial period. In this study, we take advantage of a large perturbed parameter ensemble of simulations from the UK Met Office HadGEM-UKCA model (the aerosol component of the UK Earth System Model) to comprehensively sample uncertainty in aerosol forcing. Uncertain aerosol and atmospheric parameters cause substantial aerosol forcing uncertainty in climatically important regions. As the aerosol radiative forcing itself is unobservable, we investigate the potential for observations of aerosol and radiative properties to act as constraints on the large forcing uncertainty. We test how eight different theoretically perfect aerosol and radiation observations can constrain the forcing uncertainty over Europe. We find that the achievable constraint is weak unless many diverse observations are used simultaneously. This is due to the complex relationships between model output responses and the multiple interacting parameter uncertainties: compensating model errors mean there are many ways to produce the same model output (known as model equifinality) which impacts on the achievable constraint. However, using all eight observable quantities together we show that the aerosol forcing uncertainty can potentially be reduced by around 50%. This reduction occurs as we reduce a large sample of model variants (over 1 million) that cover the full parametric uncertainty to around 1% that are observationally plausible.Constraining the forcing uncertainty using real observations is a more complex undertaking, in which we must account for multiple further uncertainties including measurement uncertainties, structural model uncertainties and the model discrepancy from reality. Here, we make a first attempt to determine the true potential constraint on the forcing uncertainty from our model that is achievable using a comprehensive

  10. A taxonomy of endogenous and exogenous uncertainty in high-risk, high-impact contexts.

    Science.gov (United States)

    Alison, Laurence; Power, Nicola; van den Heuvel, Claudia; Waring, Sara

    2015-07-01

    By reference to a live hostage negotiation exercise, this study presents a taxonomy of uncertainty that can be usefully applied to assist in the categorization and application of findings from decision-making research conducted in naturalistic (specifically critical incident) settings. Uncertainty was measured via observational methods (during the exercise and by reference to video footage), decision logs, and postincident simulated recall interviews with trainee police officers. Transcripts were coded and analyzed thematically. Uncertainty was dichotomized as deriving from either endogenous sources (about the problem situation itself) or exogenous sources (about the operating system that is dealing with the incident). Overall, exogenous uncertainty (75%) was more prevalent than endogenous uncertainty (25%), specifically during discussions on plan formulation and execution. It was also qualitatively associated with poor role understanding and trust. Endogenous uncertainty was more prevalent during discussions on situation assessment and plan formulation. The taxonomy provides a useful way for organizational researchers to categorize uncertainty during the naturalistic observations of workplace interactions and decision making. It reduces the complexity associated with observational research to allow organizational psychologists to better tailor their recommendations for reducing uncertainty. Dealing with endogenous uncertainties would entail targeting decision making specific to the problem incident (e.g., introduce training or policy to reduce redundant fixation on rote-repetitive superordinate goals and focus on more short-term actionable goals during situation assessments). Dealing with exogenous uncertainties would entail improving decision making relating to management and team processes across critical incidents (e.g., training to clarify distributed roles in critical incident teams to aid plan formulation and execution). Organizational researchers interested

  11. Spatial GHG Inventory: Analysis of Uncertainty Sources. A Case Study for Ukraine

    International Nuclear Information System (INIS)

    Bun, R.; Gusti, M.; Kujii, L.; Tokar, O.; Tsybrivskyy, Y.; Bun, A.

    2007-01-01

    A geoinformation technology for creating spatially distributed greenhouse gas inventories based on a methodology provided by the Intergovernmental Panel on Climate Change and special software linking input data, inventory models, and a means for visualization are proposed. This technology opens up new possibilities for qualitative and quantitative spatially distributed presentations of inventory uncertainty at the regional level. Problems concerning uncertainty and verification of the distributed inventory are discussed. A Monte Carlo analysis of uncertainties in the energy sector at the regional level is performed, and a number of simulations concerning the effectiveness of uncertainty reduction in some regions are carried out. Uncertainties in activity data have a considerable influence on overall inventory uncertainty, for example, the inventory uncertainty in the energy sector declines from 3.2 to 2.0% when the uncertainty of energy-related statistical data on fuels combusted in the energy industries declines from 10 to 5%. Within the energy sector, the 'energy industries' subsector has the greatest impact on inventory uncertainty. The relative uncertainty in the energy sector inventory can be reduced from 2.19 to 1.47% if the uncertainty of specific statistical data on fuel consumption decreases from 10 to 5%. The 'energy industries' subsector has the greatest influence in the Donetsk oblast. Reducing the uncertainty of statistical data on electricity generation in just three regions - the Donetsk, Dnipropetrovsk, and Luhansk oblasts - from 7.5 to 4.0% results in a decline from 2.6 to 1.6% in the uncertainty in the national energy sector inventory

  12. Climate-carbon cycle feedbacks under stabilization: uncertainty and observational constraints

    International Nuclear Information System (INIS)

    Jones, Chris D.; Cox, Peter M.; Huntingford, Chris

    2006-01-01

    Avoiding 'dangerous climate change' by stabilization of atmospheric CO 2 concentrations at a desired level requires reducing the rate of anthropogenic carbon emissions so that they are balanced by uptake of carbon by the natural terrestrial and oceanic carbon cycles. Previous calculations of profiles of emissions which lead to stabilized CO 2 levels have assumed no impact of climate change on this natural carbon uptake. However, future climate change effects on the land carbon cycle are predicted to reduce its ability to act as a sink for anthropogenic carbon emissions and so quantification of this feedback is required to determine future permissible emissions. Here, we assess the impact of the climate-carbon cycle feedback and attempt to quantify its uncertainty due to both within-model parameter uncertainty and between-model structural uncertainty. We assess the use of observational constraints to reduce uncertainty in the future permissible emissions for climate stabilization and find that all realistic carbon cycle feedbacks consistent with the observational record give permissible emissions significantly less than previously assumed. However, the observational record proves to be insufficient to tightly constrain carbon cycle processes or future feedback strength with implications for climate-carbon cycle model evaluation

  13. Robust nonlinear control of nuclear reactors under model uncertainty

    International Nuclear Information System (INIS)

    Park, Moon Ghu

    1993-02-01

    A nonlinear model-based control method is developed for the robust control of a nuclear reactor. The nonlinear plant model is used to design a unique control law which covers a wide operating range. The robustness is a crucial factor for the fully automatic control of reactor power due to time-varying, uncertain parameters, and state estimation error, or unmodeled dynamics. A variable structure control (VSC) method is introduced which consists of an adaptive performance specification (fime control) after the tracking error reaches the narrow boundary-layer by a time-optimal control (coarse control). Variable structure control is a powerful method for nonlinear system controller design which has inherent robustness to parameter variations or external disturbances using the known uncertainty bounds, and it requires very low computational efforts. In spite of its desirable properties, conventional VSC presents several important drawbacks that limit its practical applicability. One of the most undesirable phenomena is chattering, which implies extremely high control activity and may excite high-frequency unmodeled dynamics. This problem is due to the neglected actuator time-delay or sampling effects. The problem was partially remedied by replacing chattering control by a smooth control inter-polation in a boundary layer neighnboring a time-varying sliding surface. But, for the nuclear reactor systems which has very fast dynamic response, the sampling effect may destroy the narrow boundary layer when a large uncertainty bound is used. Due to the very short neutron life time, large uncertainty bound leads to the high gain in feedback control. To resolve this problem, a derivative feedback is introduced that gives excellent performance by reducing the uncertainty bound. The stability of tracking error dynamics is guaranteed by the second method of Lyapunov using the two-level uncertainty bounds that are obtained from the knowledge of uncertainty bound and the estimated

  14. Mixtures of Gaussians for uncertainty description in bivariate latent heat flux proxies

    NARCIS (Netherlands)

    Wójcik, R.; Troch, P.A.A.; Stricker, J.N.M.; Torfs, P.J.J.F.

    2006-01-01

    This paper proposes a new probabilistic approach for describing uncertainty in the ensembles of latent heat flux proxies. The proxies are obtained from hourly Bowen ratio and satellite-derived measurements, respectively, at several locations in the southern Great Plains region in the United States.

  15. Dealing with rainfall forecast uncertainties in real-time flood control along the Demer river

    Directory of Open Access Journals (Sweden)

    Vermuyten Evert

    2016-01-01

    Full Text Available Real-time Model Predictive Control (MPC of hydraulic structures strongly reduces flood consequences under ideal circumstances. The performance of such flood control may, however, be significantly affected by uncertainties. This research quantifies the influence of rainfall forecast uncertainties and related uncertainties in the catchment rainfall-runoff discharges on the control performance for the Herk river case study in Belgium. To limit the model computational times, a fast conceptual model is applied. It is calibrated to a full hydrodynamic river model. A Reduced Genetic Algorithm is used as optimization method. Next to the analysis of the impact of the rainfall forecast uncertainties on the control performance, a Multiple Model Predictive Control (MMPC approach is tested to reduce this impact. Results show that the deterministic MPC-RGA outperforms the MMPC and that it is inherently robust against rainfall forecast uncertainties due to its receding horizon strategy.

  16. Maximizing the probability of satisfying the clinical goals in radiation therapy treatment planning under setup uncertainty

    International Nuclear Information System (INIS)

    Fredriksson, Albin; Hårdemark, Björn; Forsgren, Anders

    2015-01-01

    Purpose: This paper introduces a method that maximizes the probability of satisfying the clinical goals in intensity-modulated radiation therapy treatments subject to setup uncertainty. Methods: The authors perform robust optimization in which the clinical goals are constrained to be satisfied whenever the setup error falls within an uncertainty set. The shape of the uncertainty set is included as a variable in the optimization. The goal of the optimization is to modify the shape of the uncertainty set in order to maximize the probability that the setup error will fall within the modified set. Because the constraints enforce the clinical goals to be satisfied under all setup errors within the uncertainty set, this is equivalent to maximizing the probability of satisfying the clinical goals. This type of robust optimization is studied with respect to photon and proton therapy applied to a prostate case and compared to robust optimization using an a priori defined uncertainty set. Results: Slight reductions of the uncertainty sets resulted in plans that satisfied a larger number of clinical goals than optimization with respect to a priori defined uncertainty sets, both within the reduced uncertainty sets and within the a priori, nonreduced, uncertainty sets. For the prostate case, the plans taking reduced uncertainty sets into account satisfied 1.4 (photons) and 1.5 (protons) times as many clinical goals over the scenarios as the method taking a priori uncertainty sets into account. Conclusions: Reducing the uncertainty sets enabled the optimization to find better solutions with respect to the errors within the reduced as well as the nonreduced uncertainty sets and thereby achieve higher probability of satisfying the clinical goals. This shows that asking for a little less in the optimization sometimes leads to better overall plan quality

  17. Uncertainty in BMP evaluation and optimization for watershed management

    Science.gov (United States)

    Chaubey, I.; Cibin, R.; Sudheer, K.; Her, Y.

    2012-12-01

    Use of computer simulation models have increased substantially to make watershed management decisions and to develop strategies for water quality improvements. These models are often used to evaluate potential benefits of various best management practices (BMPs) for reducing losses of pollutants from sources areas into receiving waterbodies. Similarly, use of simulation models in optimizing selection and placement of best management practices under single (maximization of crop production or minimization of pollutant transport) and multiple objective functions has increased recently. One of the limitations of the currently available assessment and optimization approaches is that the BMP strategies are considered deterministic. Uncertainties in input data (e.g. precipitation, streamflow, sediment, nutrient and pesticide losses measured, land use) and model parameters may result in considerable uncertainty in watershed response under various BMP options. We have developed and evaluated options to include uncertainty in BMP evaluation and optimization for watershed management. We have also applied these methods to evaluate uncertainty in ecosystem services from mixed land use watersheds. In this presentation, we will discuss methods to to quantify uncertainties in BMP assessment and optimization solutions due to uncertainties in model inputs and parameters. We have used a watershed model (Soil and Water Assessment Tool or SWAT) to simulate the hydrology and water quality in mixed land use watershed located in Midwest USA. The SWAT model was also used to represent various BMPs in the watershed needed to improve water quality. SWAT model parameters, land use change parameters, and climate change parameters were considered uncertain. It was observed that model parameters, land use and climate changes resulted in considerable uncertainties in BMP performance in reducing P, N, and sediment loads. In addition, climate change scenarios also affected uncertainties in SWAT

  18. Strategies for Reduced-Order Models in Uncertainty Quantification of Complex Turbulent Dynamical Systems

    Science.gov (United States)

    Qi, Di

    Turbulent dynamical systems are ubiquitous in science and engineering. Uncertainty quantification (UQ) in turbulent dynamical systems is a grand challenge where the goal is to obtain statistical estimates for key physical quantities. In the development of a proper UQ scheme for systems characterized by both a high-dimensional phase space and a large number of instabilities, significant model errors compared with the true natural signal are always unavoidable due to both the imperfect understanding of the underlying physical processes and the limited computational resources available. One central issue in contemporary research is the development of a systematic methodology for reduced order models that can recover the crucial features both with model fidelity in statistical equilibrium and with model sensitivity in response to perturbations. In the first part, we discuss a general mathematical framework to construct statistically accurate reduced-order models that have skill in capturing the statistical variability in the principal directions of a general class of complex systems with quadratic nonlinearity. A systematic hierarchy of simple statistical closure schemes, which are built through new global statistical energy conservation principles combined with statistical equilibrium fidelity, are designed and tested for UQ of these problems. Second, the capacity of imperfect low-order stochastic approximations to model extreme events in a passive scalar field advected by turbulent flows is investigated. The effects in complicated flow systems are considered including strong nonlinear and non-Gaussian interactions, and much simpler and cheaper imperfect models with model error are constructed to capture the crucial statistical features in the stationary tracer field. Several mathematical ideas are introduced to improve the prediction skill of the imperfect reduced-order models. Most importantly, empirical information theory and statistical linear response theory are

  19. Modelling small groundwater systems - the role of targeted field investigations and observational data in reducing model uncertainty

    Science.gov (United States)

    Abesser, Corinna; Hughes, Andrew; Boon, David

    2017-04-01

    the fit between predicted and observed heads and reduction in overall model uncertainty. The impact of availability of observational data on model calibration was tested as part of this study, confirming that equifinality remains an issue despite improved system characterisation and suggesting that uncertainty relating to the distribution of hydraulic conductivity (K) within the dune system must be further reduced. This study illustrates that groundwater modelling is not linear but should be an iterative process, especially in systems where large geological uncertainties exist. It should be carried out in conjunction with field studies, i.e. not as a postscript, but as ongoing interaction. This interaction is required throughout the investigation process and is key to heuristic learning and improved system understanding. Given that the role of modelling is to raise questions as well as answer them, this study demonstrates that this applies even in small systems that are thought to be well understood. This research is funded by the UK Natural Environmental Research Council (NERC). The work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This licence does not conflict with the regulations of the Crown Copyright.

  20. Reducing the extinction risk of stochastic populations via nondemographic noise

    Science.gov (United States)

    Be'er, Shay; Assaf, Michael

    2018-02-01

    We consider nondemographic noise in the form of uncertainty in the reaction step size and reveal a dramatic effect this noise may have on the stability of self-regulating populations. Employing the reaction scheme m A →k A but allowing, e.g., the product number k to be a priori unknown and sampled from a given distribution, we show that such nondemographic noise can greatly reduce the population's extinction risk compared to the fixed k case. Our analysis is tested against numerical simulations, and by using empirical data of different species, we argue that certain distributions may be more evolutionary beneficial than others.

  1. Integrating uncertainties for climate change mitigation

    Science.gov (United States)

    Rogelj, Joeri; McCollum, David; Reisinger, Andy; Meinshausen, Malte; Riahi, Keywan

    2013-04-01

    geophysical, future energy demand, and mitigation technology uncertainties. This information provides central information for policy making, since it helps to understand the relationship between mitigation costs and their potential to reduce the risk of exceeding 2°C, or other temperature limits like 3°C or 1.5°C, under a wide range of scenarios.

  2. Uncertainty analysis of accident notification time and emergency medical service response time in work zone traffic accidents.

    Science.gov (United States)

    Meng, Qiang; Weng, Jinxian

    2013-01-01

    Taking into account the uncertainty caused by exogenous factors, the accident notification time (ANT) and emergency medical service (EMS) response time were modeled as 2 random variables following the lognormal distribution. Their mean values and standard deviations were respectively formulated as the functions of environmental variables including crash time, road type, weekend, holiday, light condition, weather, and work zone type. Work zone traffic accident data from the Fatality Analysis Report System between 2002 and 2009 were utilized to determine the distributions of the ANT and the EMS arrival time in the United States. A mixed logistic regression model, taking into account the uncertainty associated with the ANT and the EMS response time, was developed to estimate the risk of death. The results showed that the uncertainty of the ANT was primarily influenced by crash time and road type, whereas the uncertainty of EMS response time is greatly affected by road type, weather, and light conditions. In addition, work zone accidents occurring during a holiday and in poor light conditions were found to be statistically associated with a longer mean ANT and longer EMS response time. The results also show that shortening the ANT was a more effective approach in reducing the risk of death than the EMS response time in work zones. To shorten the ANT and the EMS response time, work zone activities are suggested to be undertaken during non-holidays, during the daytime, and in good weather and light conditions.

  3. Effects of utility demand-side management programs on uncertainty

    International Nuclear Information System (INIS)

    Hirst, E.

    1994-01-01

    Electric utilities face a variety of uncertainties that complicate their long-term resource planning. These uncertainties include future economic and load growths, fuel prices, environmental and economic regulations, performance of existing power plants, cost and availability of purchased power, and the costs and performance of new demand and supply resources. As utilities increasingly turn to demand-side management (DSM) programs to provide resources, it becomes more important to analyze the interactions between these programs and the uncertainties facing utilities. This paper uses a dynamic planning model to quantify the uncertainty effects of supply-only vs DSM + supply resource portfolios. The analysis considers four sets of uncertainties: economic growth, fuel prices, the costs to build new power plants, and the costs to operate DSM programs. The two types of portfolios are tested against these four sets of uncertainties for the period 1990 to 2010. Sensitivity, scenario, and worst-case analysis methods are used. The sensitivity analyses show that the DSM + supply resource portfolio is less sensitive to unanticipated changes in economic growth, fuel prices, and power-plant construction costs than is the supply-only portfolio. The supply-only resource mix is better only with respect to uncertainties about the costs of DSM programs. The base-case analysis shows that including DSM programs in the utility's resource portfolio reduces the net present value of revenue requirements (NPV-RR) by 490 million dollars. The scenario-analysis results show an additional 30 million dollars (6%) in benefits associated with reduction in these uncertainties. In the worst-case analysis, the DSM + supply portfolio again reduces the cost penalty associated with guessing wrong for both cases, when the utility plans for high needs and learns it has low needs and vice versa. 20 refs

  4. Critical loads - assessment of uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Barkman, A.

    1998-10-01

    uncertainties in CL and EX estimates were found to be efficiently mitigated by reducing data uncertainty in the critical limit of the chemical criteria - the BC/Al ratio. The distributed CL and EX assessment on local level in Sweden was found to be efficiently improved by enhancing the resolution of the underlying vegetation map 68 refs, 15 figs, 3 tabs

  5. Inflation and Inflation Uncertainty Revisited: Evidence from Egypt

    Directory of Open Access Journals (Sweden)

    Mesbah Fathy Sharaf

    2015-07-01

    Full Text Available The welfare costs of inflation and inflation uncertainty are well documented in the literature and empirical evidence on the link between the two is sparse in the case of Egypt. This paper investigates the causal relationship between inflation and inflation uncertainty in Egypt using monthly time series data during the period January 1974–April 2015. To endogenously control for any potential structural breaks in the inflation time series, Zivot and Andrews (2002 and Clemente–Montanes–Reyes (1998 unit root tests are used. The inflation–inflation uncertainty relation is modeled by the standard two-step approach as well as simultaneously using various versions of the GARCH-M model to control for any potential feedback effects. The analyses explicitly control for the effect of the Economic Reform and Structural Adjustment Program (ERSAP undertaken by the Egyptian government in the early 1990s, which affected inflation rate and its associated volatility. Results show a high degree of inflation–volatility persistence in the response to inflationary shocks. Granger-causality test along with symmetric and asymmetric GARCH-M models indicate a statistically significant bi-directional positive relationship between inflation and inflation uncertainty, supporting both the Friedman–Ball and the Cukierman–Meltzer hypotheses. The findings are robust to the various estimation methods and model specifications. The findings of this paper support the view of adopting inflation-targeting policy in Egypt, after fulfilling its preconditions, to reduce the welfare cost of inflation and its related uncertainties. Monetary authorities in Egypt should enhance the credibility of monetary policy and attempt to reduce inflation uncertainty, which will help lower inflation rates.

  6. Understanding uncertainty

    CERN Document Server

    Lindley, Dennis V

    2013-01-01

    Praise for the First Edition ""...a reference for everyone who is interested in knowing and handling uncertainty.""-Journal of Applied Statistics The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made.

  7. Uncertainty relations and reduced density matrices: Mapping many-body quantum mechanics onto four particles

    Science.gov (United States)

    Mazziotti, David A.; Erdahl, Robert M.

    2001-04-01

    For the description of ground-state correlation phenomena an accurate mapping of many-body quantum mechanics onto four particles is developed. The energy for a quantum system with no more than two-particle interactions may be expressed in terms of a two-particle reduced density matrix (2-RDM), but variational optimization of the 2-RDM requires that it corresponds to an N-particle wave function. We derive N-representability conditions on the 2-RDM that guarantee the validity of the uncertainty relations for all operators with two-particle interactions. One of these conditions is shown to be necessary and sufficient to make the RDM solutions of the dispersion condition equivalent to those from the contracted Schrödinger equation (CSE) [Mazziotti, Phys. Rev. A 57, 4219 (1998)]. In general, the CSE is a stronger N-representability condition than the dispersion condition because the CSE implies the dispersion condition as well as additional N-representability constraints from the Hellmann-Feynman theorem. Energy minimization subject to the representability constraints is performed for a boson model with 10, 30, and 75 particles. Even when traditional wave-function methods fail at large perturbations, the present method yields correlation energies within 2%.

  8. Development of Evaluation Code for MUF Uncertainty

    International Nuclear Information System (INIS)

    Won, Byung Hee; Han, Bo Young; Shin, Hee Sung; Ahn, Seong-Kyu; Park, Geun-Il; Park, Se Hwan

    2015-01-01

    Material Unaccounted For (MUF) is the material balance evaluated by measured nuclear material in a Material Balance Area (MBA). Assuming perfect measurements and no diversion from a facility, one can expect a zero MUF. However, non-zero MUF is always occurred because of measurement uncertainty even though the facility is under normal operation condition. Furthermore, there are many measurements using different equipment at various Key Measurement Points (KMPs), and the MUF uncertainty is affected by errors of those measurements. Evaluating MUF uncertainty is essentially required to develop safeguards system including nuclear measurement system in pyroprocessing, which is being developed for reducing radioactive waste from spent fuel in Korea Atomic Energy Research Institute (KAERI). The evaluation code for analyzing MUF uncertainty has been developed and it was verified using sample problem from the IAEA reference. MUF uncertainty can be simply and quickly calculated by using this evaluation code which is made based on graphical user interface for user friendly. It is also expected that the code will make the sensitivity analysis on the MUF uncertainty for the various safeguards systems easy and more systematic. It is suitable for users who want to evaluate the conventional safeguards system as well as to develop a new system for developing facilities

  9. Development of Evaluation Code for MUF Uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Won, Byung Hee; Han, Bo Young; Shin, Hee Sung; Ahn, Seong-Kyu; Park, Geun-Il; Park, Se Hwan [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2015-05-15

    Material Unaccounted For (MUF) is the material balance evaluated by measured nuclear material in a Material Balance Area (MBA). Assuming perfect measurements and no diversion from a facility, one can expect a zero MUF. However, non-zero MUF is always occurred because of measurement uncertainty even though the facility is under normal operation condition. Furthermore, there are many measurements using different equipment at various Key Measurement Points (KMPs), and the MUF uncertainty is affected by errors of those measurements. Evaluating MUF uncertainty is essentially required to develop safeguards system including nuclear measurement system in pyroprocessing, which is being developed for reducing radioactive waste from spent fuel in Korea Atomic Energy Research Institute (KAERI). The evaluation code for analyzing MUF uncertainty has been developed and it was verified using sample problem from the IAEA reference. MUF uncertainty can be simply and quickly calculated by using this evaluation code which is made based on graphical user interface for user friendly. It is also expected that the code will make the sensitivity analysis on the MUF uncertainty for the various safeguards systems easy and more systematic. It is suitable for users who want to evaluate the conventional safeguards system as well as to develop a new system for developing facilities.

  10. Uncertainty Einstein, Heisenberg, Bohr, and the struggle for the soul of science

    CERN Document Server

    Lindley, David

    2007-01-01

    The uncertainty in this delightful book refers to Heisenberg's Uncertainty Principle, an idea first postulated in 1927 by physicist Werner Heisenberg in his attempt to make sense out of the developing field of quantum mechanics. As Lindley so well explains it, the concept of uncertainty shook the philosophical underpinnings of science. It was Heisenberg's work that, to a great extent, kept Einstein from accepting quantum mechanics as a full explanation for physical reality. Similarly, it was the Uncertainty Principle that demonstrated the limits of scientific investigation: if Heisenberg is correct there are some aspects of the physical universe that are to remain beyond the reach of scientists. As he has done expertly in books like Boltzmann's Atom, Lindley brings to life a critical period in the history of science, explaining complex issues to the general reader, presenting the major players in an engaging fashion, delving into the process of scientific discovery and discussing the interaction between scien...

  11. Statistical uncertainty of response characteristic of building-appendage system for spectrum-compatible artificial earthquake motion

    International Nuclear Information System (INIS)

    Kurosaki, A.; Kozeki, M.

    1981-01-01

    Spectrum-compatible artificial time histories of ground motions are frequently used for the seismic design of nuclear power plant structures and components. However, statistical uncertainty of the responses of building structures and mechanical components mounted on the building (building-appendage systems) are anticipated, since an artificial time history is no more than one sample from a population of such time histories that match a specified design response spectrum. This uncertainty may spoil the reliability of the seismic design and therefore the extent of the uncertainty of the response characteristic is a matter of great concern. In this paper, above-mentioned uncertainty of the dynamic response characteristics of the building-appendage system to the spectrum-compatible artificial earthquake is investigated. (orig./RW)

  12. Energy price uncertainty, energy intensity and firm investment

    International Nuclear Information System (INIS)

    Yoon, Kyung Hwan; Ratti, Ronald A.

    2011-01-01

    This paper examines the effect of energy price uncertainty on firm-level investment. An error correction model of capital stock adjustment is estimated with data on U.S. manufacturing firms. Higher energy price uncertainty is found to make firms more cautious by reducing the responsiveness of investment to sales growth. The result is robust to consideration of energy intensity by industry. The effect is greater for high growth firms. It must be emphasized that the direct effect of uncertainty is not estimated. Conditional variance of energy price is obtained from a GARCH model. Findings suggest that stability in energy prices would be conducive to greater stability in firm-level investment. (author)

  13. Sensitivity of ocean acidification and oxygen to the uncertainty in climate change

    International Nuclear Information System (INIS)

    Cao, Long; Wang, Shuangjing; Zheng, Meidi; Zhang, Han

    2014-01-01

    Due to increasing atmospheric CO 2 concentrations and associated climate change, the global ocean is undergoing substantial physical and biogeochemical changes. Among these, changes in ocean oxygen and carbonate chemistry have great implication for marine biota. There is considerable uncertainty in the projections of future climate change, and it is unclear how the uncertainty in climate change would also affect the projection of oxygen and carbonate chemistry. To investigate this issue, we use an Earth system model of intermediate complexity to perform a set of simulations, including that which involves no radiative effect of atmospheric CO 2 and those which involve CO 2 -induced climate change with climate sensitivity varying from 0.5 °C to 4.5 °C. Atmospheric CO 2 concentration is prescribed to follow RCP 8.5 pathway and its extensions. Climate change affects carbonate chemistry and oxygen mainly through its impact on ocean temperature, ocean ventilation, and concentration of dissolved inorganic carbon and alkalinity. It is found that climate change mitigates the decrease of carbonate ions at the ocean surface but has negligible effect on surface ocean pH. Averaged over the whole ocean, climate change acts to decrease oxygen concentration but mitigates the CO 2 -induced reduction of carbonate ion and pH. In our simulations, by year 2500, every degree increase of climate sensitivity warms the ocean by 0.8 °C and reduces ocean-mean dissolved oxygen concentration by 5.0%. Meanwhile, every degree increase of climate sensitivity buffers CO 2 -induced reduction in ocean-mean carbonate ion concentration and pH by 3.4% and 0.02 units, respectively. Our study demonstrates different sensitivities of ocean temperature, carbonate chemistry, and oxygen, in terms of both the sign and magnitude to the amount of climate change, which have great implications for understanding the response of ocean biota to climate change. (letters)

  14. Characterization of XR-RV3 GafChromic{sup ®} films in standard laboratory and in clinical conditions and means to evaluate uncertainties and reduce errors

    Energy Technology Data Exchange (ETDEWEB)

    Farah, J., E-mail: jad.farah@irsn.fr; Clairand, I.; Huet, C. [External Dosimetry Department, Institut de Radioprotection et de Sûreté Nucléaire (IRSN), BP-17, 92260 Fontenay-aux-Roses (France); Trianni, A. [Medical Physics Department, Udine University Hospital S. Maria della Misericordia (AOUD), p.le S. Maria della Misericordia, 15, 33100 Udine (Italy); Ciraj-Bjelac, O. [Vinca Institute of Nuclear Sciences (VINCA), P.O. Box 522, 11001 Belgrade (Serbia); De Angelis, C. [Department of Technology and Health, Istituto Superiore di Sanità (ISS), Viale Regina Elena 299, 00161 Rome (Italy); Delle Canne, S. [Fatebenefratelli San Giovanni Calibita Hospital (FBF), UOC Medical Physics - Isola Tiberina, 00186 Rome (Italy); Hadid, L.; Waryn, M. J. [Radiology Department, Hôpital Jean Verdier (HJV), Avenue du 14 Juillet, 93140 Bondy Cedex (France); Jarvinen, H.; Siiskonen, T. [Radiation and Nuclear Safety Authority (STUK), P.O. Box 14, 00881 Helsinki (Finland); Negri, A. [Veneto Institute of Oncology (IOV), Via Gattamelata 64, 35124 Padova (Italy); Novák, L. [National Radiation Protection Institute (NRPI), Bartoškova 28, 140 00 Prague 4 (Czech Republic); Pinto, M. [Istituto Nazionale di Metrologia delle Radiazioni Ionizzanti (ENEA-INMRI), C.R. Casaccia, Via Anguillarese 301, I-00123 Santa Maria di Galeria (RM) (Italy); Knežević, Ž. [Ruđer Bošković Institute (RBI), Bijenička c. 54, 10000 Zagreb (Croatia)

    2015-07-15

    Purpose: To investigate the optimal use of XR-RV3 GafChromic{sup ®} films to assess patient skin dose in interventional radiology while addressing the means to reduce uncertainties in dose assessment. Methods: XR-Type R GafChromic films have been shown to represent the most efficient and suitable solution to determine patient skin dose in interventional procedures. As film dosimetry can be associated with high uncertainty, this paper presents the EURADOS WG 12 initiative to carry out a comprehensive study of film characteristics with a multisite approach. The considered sources of uncertainties include scanner, film, and fitting-related errors. The work focused on studying film behavior with clinical high-dose-rate pulsed beams (previously unavailable in the literature) together with reference standard laboratory beams. Results: First, the performance analysis of six different scanner models has shown that scan uniformity perpendicular to the lamp motion axis and that long term stability are the main sources of scanner-related uncertainties. These could induce errors of up to 7% on the film readings unless regularly checked and corrected. Typically, scan uniformity correction matrices and reading normalization to the scanner-specific and daily background reading should be done. In addition, the analysis on multiple film batches has shown that XR-RV3 films have generally good uniformity within one batch (<1.5%), require 24 h to stabilize after the irradiation and their response is roughly independent of dose rate (<5%). However, XR-RV3 films showed large variations (up to 15%) with radiation quality both in standard laboratory and in clinical conditions. As such, and prior to conducting patient skin dose measurements, it is mandatory to choose the appropriate calibration beam quality depending on the characteristics of the x-ray systems that will be used clinically. In addition, yellow side film irradiations should be preferentially used since they showed a lower

  15. Uncertainty Analysis of the Temperature–Resistance Relationship of Temperature Sensing Fabric

    Directory of Open Access Journals (Sweden)

    Muhammad Dawood Husain

    2016-11-01

    Full Text Available This paper reports the uncertainty analysis of the temperature–resistance (TR data of the newly developed temperature sensing fabric (TSF, which is a double-layer knitted structure fabricated on an electronic flat-bed knitting machine, made of polyester as a basal yarn, and embedded with fine metallic wire as sensing element. The measurement principle of the TSF is identical to temperature resistance detector (RTD; that is, change in resistance due to change in temperature. The regression uncertainty (uncertainty within repeats and repeatability uncertainty (uncertainty among repeats were estimated by analysing more than 300 TR experimental repeats of 50 TSF samples. The experiments were performed under dynamic heating and cooling environments on a purpose-built test rig within the temperature range of 20–50 °C. The continuous experimental data was recorded through LabVIEW-based graphical user interface. The result showed that temperature and resistance values were not only repeatable but reproducible, with only minor variations. The regression uncertainty was found to be less than ±0.3 °C; the TSF sample made of Ni and W wires showed regression uncertainty of <±0.13 °C in comparison to Cu-based TSF samples (>±0.18 °C. The cooling TR data showed considerably reduced values (±0.07 °C of uncertainty in comparison with the heating TR data (±0.24 °C. The repeatability uncertainty was found to be less than ±0.5 °C. By increasing the number of samples and repeats, the uncertainties may be reduced further. The TSF could be used for continuous measurement of the temperature profile on the surface of the human body.

  16. Policy Uncertainty and the US Ethanol Industry

    Directory of Open Access Journals (Sweden)

    Jason P. H. Jones

    2017-11-01

    Full Text Available The Renewable Fuel Standard (RFS2, as implemented, has introduced uncertainty into US ethanol producers and the supporting commodity market. First, the fixed mandate for what is mainly cornstarch-based ethanol has increased feedstock price volatility and exerts a general effect across the agricultural sector. Second, the large discrepancy between the original Energy Independence and Security Act (EISA intentions and the actual RFS2 implementation for some fuel classes has increased the investment uncertainty facing investors in biofuel production, distribution, and consumption. Here we discuss and analyze the sources of uncertainty and evaluate the effect of potential RFS2 adjustments as they influence these uncertainties. This includes the use of a flexible, production dependent mandate on corn starch ethanol. We find that a flexible mandate on cornstarch ethanol relaxed during drought could significantly reduce commodity price spikes and alleviate the decline of livestock production in cases of feedstock production shortfalls, but it would increase the risk for ethanol investors.

  17. Reducing uncertainty in load forecasts and using real options for improving capacity dispatch management through the utilization of weather and hydrologic forecasts

    International Nuclear Information System (INIS)

    Davis, T.

    2004-01-01

    The effect of weather on electricity markets was discussed with particular focus on reducing weather uncertainty by improving short term weather forecasts. The implications of weather for hydroelectric power dispatch and use were also discussed. Although some errors in weather forecasting can result in economic benefits, most errors are associated with more costs than benefits. This presentation described how a real options analysis can make weather a favorable option. Four case studies were presented for exploratory data analysis of regional weather phenomena. These included: (1) the 2001 California electricity crisis, (2) the delta breeze effects on the California ISO, (3) the summer 2002 weather forecast error for ISO New England, and (4) the hydro plant asset valuation using weather uncertainty. It was concluded that there is a need for more economic methodological studies on the effect of weather on energy markets and costs. It was suggested that the real options theory should be applied to weather planning and utility applications. tabs., figs

  18. Reduction and Uncertainty Analysis of Chemical Mechanisms Based on Local and Global Sensitivities

    Science.gov (United States)

    Esposito, Gaetano

    Numerical simulations of critical reacting flow phenomena in hypersonic propulsion devices require accurate representation of finite-rate chemical kinetics. The chemical kinetic models available for hydrocarbon fuel combustion are rather large, involving hundreds of species and thousands of reactions. As a consequence, they cannot be used in multi-dimensional computational fluid dynamic calculations in the foreseeable future due to the prohibitive computational cost. In addition to the computational difficulties, it is also known that some fundamental chemical kinetic parameters of detailed models have significant level of uncertainty due to limited experimental data available and to poor understanding of interactions among kinetic parameters. In the present investigation, local and global sensitivity analysis techniques are employed to develop a systematic approach of reducing and analyzing detailed chemical kinetic models. Unlike previous studies in which skeletal model reduction was based on the separate analysis of simple cases, in this work a novel strategy based on Principal Component Analysis of local sensitivity values is presented. This new approach is capable of simultaneously taking into account all the relevant canonical combustion configurations over different composition, temperature and pressure conditions. Moreover, the procedure developed in this work represents the first documented inclusion of non-premixed extinction phenomena, which is of great relevance in hypersonic combustors, in an automated reduction algorithm. The application of the skeletal reduction to a detailed kinetic model consisting of 111 species in 784 reactions is demonstrated. The resulting reduced skeletal model of 37--38 species showed that the global ignition/propagation/extinction phenomena of ethylene-air mixtures can be predicted within an accuracy of 2% of the full detailed model. The problems of both understanding non-linear interactions between kinetic parameters and

  19. Management of internal communication in times of uncertainty

    International Nuclear Information System (INIS)

    Fernandez de la Gala, F.

    2014-01-01

    Garona is having a strong media coverage since 2009. The continuity process is under great controversy that has generated increased uncertainty for workers and their families, affecting motivation. Although internal communication has sought to manage its effects on the structure of the company, the rate of spread of alien information has made this complex mission. The regulatory body has been interested in its potential impact on safety culture, making a significant difference compared to other industrial sectors. (Author)

  20. Interactive uncertainty reduction strategies and verbal affection in computer-mediated communication

    NARCIS (Netherlands)

    Antheunis, M.L.; Schouten, A.P.; Valkenburg, P.M.; Peter, J.

    2012-01-01

    The goal of this study was to investigate the language-based strategies that computer-mediated communication (CMC) users employ to reduce uncertainty in the absence of nonverbal cues. Specifically, this study investigated the prevalence of three interactive uncertainty reduction strategies (i.e.,

  1. Effect of uncertainty parameters on graphene sheets Young's modulus prediction

    International Nuclear Information System (INIS)

    Sahlaoui, Habib; Sidhom Habib; Guedri, Mohamed

    2013-01-01

    Software based on molecular structural mechanics approach (MSMA) and using finite element method (FEM) has been developed to predict the Young's modulus of graphene sheets. Obtained results have been compared to results available in the literature and good agreement has been shown when the same values of uncertainty parameters are used. A sensibility of the models to their uncertainty parameters has been investigated using a stochastic finite element method (SFEM). The different values of the used uncertainty parameters, such as molecular mechanics force field constants k_r and k_θ, thickness (t) of a graphene sheet and length ( L_B) of a carbon carbon bonds, have been collected from the literature. Strong sensibilities of 91% to the thickness and of 21% to the stretching force (k_r) have been shown. The results justify the great difference between Young's modulus predicted values of the graphene sheets and their large disagreement with experimental results.

  2. Alpine grassland soil organic carbon stock and its uncertainty in the three rivers source region of the Tibetan Plateau.

    Directory of Open Access Journals (Sweden)

    Xiaofeng Chang

    Full Text Available Alpine grassland of the Tibetan Plateau is an important component of global soil organic carbon (SOC stocks, but insufficient field observations and large spatial heterogeneity leads to great uncertainty in their estimation. In the Three Rivers Source Region (TRSR, alpine grasslands account for more than 75% of the total area. However, the regional carbon (C stock estimate and their uncertainty have seldom been tested. Here we quantified the regional SOC stock and its uncertainty using 298 soil profiles surveyed from 35 sites across the TRSR during 2006-2008. We showed that the upper soil (0-30 cm depth in alpine grasslands of the TRSR stores 2.03 Pg C, with a 95% confidence interval ranging from 1.25 to 2.81 Pg C. Alpine meadow soils comprised 73% (i.e. 1.48 Pg C of the regional SOC estimate, but had the greatest uncertainty at 51%. The statistical power to detect a deviation of 10% uncertainty in grassland C stock was less than 0.50. The required sample size to detect this deviation at a power of 90% was about 6-7 times more than the number of sample sites surveyed. Comparison of our observed SOC density with the corresponding values from the dataset of Yang et al. indicates that these two datasets are comparable. The combined dataset did not reduce the uncertainty in the estimate of the regional grassland soil C stock. This result could be mainly explained by the underrepresentation of sampling sites in large areas with poor accessibility. Further research to improve the regional SOC stock estimate should optimize sampling strategy by considering the number of samples and their spatial distribution.

  3. An uncertainty inventory demonstration - a primary step in uncertainty quantification

    Energy Technology Data Exchange (ETDEWEB)

    Langenbrunner, James R. [Los Alamos National Laboratory; Booker, Jane M [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Salazar, Issac F [Los Alamos National Laboratory; Ross, Timothy J [UNM

    2009-01-01

    Tools, methods, and theories for assessing and quantifying uncertainties vary by application. Uncertainty quantification tasks have unique desiderata and circumstances. To realistically assess uncertainty requires the engineer/scientist to specify mathematical models, the physical phenomena of interest, and the theory or framework for assessments. For example, Probabilistic Risk Assessment (PRA) specifically identifies uncertainties using probability theory, and therefore, PRA's lack formal procedures for quantifying uncertainties that are not probabilistic. The Phenomena Identification and Ranking Technique (PIRT) proceeds by ranking phenomena using scoring criteria that results in linguistic descriptors, such as importance ranked with words, 'High/Medium/Low.' The use of words allows PIRT to be flexible, but the analysis may then be difficult to combine with other uncertainty theories. We propose that a necessary step for the development of a procedure or protocol for uncertainty quantification (UQ) is the application of an Uncertainty Inventory. An Uncertainty Inventory should be considered and performed in the earliest stages of UQ.

  4. ECG movement artefacts can be greatly reduced with the aid of a movement absorbing device

    DEFF Research Database (Denmark)

    Harrison, Adrian Paul; Wandall, Kirsten; Thorball, Jørgen

    2007-01-01

    Accurate ECG signal analysis can be confounded by electric lead, and/or electrode movements varying in origin from, for example, hiccups, tremor or patient restlessness. ECG signals recorded using either a conventional electrode holder or with the aid of an electrode holder capable of absorbing...... movement artefacts, were measured on a healthy human subject. Results show a greatly improved stability of the ECG signal recorded using an electrode holder capable of absorbing movement artefacts during periods of lead disturbance, and highlight the movement artefacts that develop when the recording lead...... of a conventional ECG electrode holder is tugged or pulled during theperiod of monitoring. It is concluded that the new design of ECG electrode holder will not only enable clearer signal recordings for clinical assessment, but will reduce the ECG artefacts associated with the transportation of patients, and may...

  5. Uncertainty Regarding Waste Handling in Everyday Life

    Directory of Open Access Journals (Sweden)

    Susanne Ewert

    2010-09-01

    Full Text Available According to our study, based on interviews with households in a residential area in Sweden, uncertainty is a cultural barrier to improved recycling. Four causes of uncertainty are identified. Firstly, professional categories not matching cultural categories—people easily discriminate between certain categories (e.g., materials such as plastic and paper but not between others (e.g., packaging and “non-packaging”. Thus a frequent cause of uncertainty is that the basic categories of the waste recycling system do not coincide with the basic categories used in everyday life. Challenged habits—source separation in everyday life is habitual, but when a habit is challenged, by a particular element or feature of the waste system, uncertainty can arise. Lacking fractions—some kinds of items cannot be left for recycling and this makes waste collection incomplete from the user’s point of view and in turn lowers the credibility of the system. Missing or contradictory rules of thumb—the above causes seem to be particularly relevant if no motivating principle or rule of thumb (within the context of use is successfully conveyed to the user. This paper discusses how reducing uncertainty can improve recycling.

  6. Offshore wind farms for hydrogen production subject to uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    Kassem, Nabil [Royal Inst. of Tech., Stockholm (Sweden). Dept. of Energy Processes

    2002-07-01

    Wind power is a source of clean, nonpolluting electricity, which is fully competitive, if installed at favorable wind sites, with fossil fuel and nuclear power generation. Major technical growth has been in Europe, where government policies and high conventional energy costs favor the use of wind power. As part of its strategy, the EU-Commission has launched a target to increase the installed capacity of Wind power from 7 GWe, in 1998 to 40 GWe by year 2012. Wind power is an intermittent electricity generator, thus it does not provide electric power on an 'as needed' basis. Off-peak power generated from offshore wind farms can be utilized for hydrogen production using water electrolysis. Like electricity, hydrogen is a second energy carrier, which will pave the way for future sustainable energy systems. It is environmentally friendly, versatile, with great potentials in stationary and mobile power applications. Water electrolysis is a well-established technology, which depends on the availability of cheap electrical power. Offshore wind farms have longer lifetime due to lower mechanical fatigue loads, yet to be economic, they have to be of sizes greater than 150 MW using large turbines (> 1.5 MW). The major challenge in wind energy assessment is how accurately the wind speed and hence the error in wind energy can be predicted. Therefore, wind power is subject to a great deal of uncertainties, which should be accounted for in order to provide meaningful and reliable estimates of performance and economic figures-of-merit. Failure to account for uncertainties would result in deterministic estimates that tend to overstate performance and underestimate costs. This study uses methods of risk analysis to evaluate the simultaneous effect of multiple input uncertainties, and provide Life Cycle Assessment (LCA) of the-economic viability of offshore wind systems for hydrogen production subject to technical and economical uncertainties (Published in summary form only)

  7. Recognizing and responding to uncertainty: a grounded theory of nurses' uncertainty.

    Science.gov (United States)

    Cranley, Lisa A; Doran, Diane M; Tourangeau, Ann E; Kushniruk, Andre; Nagle, Lynn

    2012-08-01

    There has been little research to date exploring nurses' uncertainty in their practice. Understanding nurses' uncertainty is important because it has potential implications for how care is delivered. The purpose of this study is to develop a substantive theory to explain how staff nurses experience and respond to uncertainty in their practice. Between 2006 and 2008, a grounded theory study was conducted that included in-depth semi-structured interviews. Fourteen staff nurses working in adult medical-surgical intensive care units at two teaching hospitals in Ontario, Canada, participated in the study. The theory recognizing and responding to uncertainty characterizes the processes through which nurses' uncertainty manifested and how it was managed. Recognizing uncertainty involved the processes of assessing, reflecting, questioning, and/or being unable to predict aspects of the patient situation. Nurses' responses to uncertainty highlighted the cognitive-affective strategies used to manage uncertainty. Study findings highlight the importance of acknowledging uncertainty and having collegial support to manage uncertainty. The theory adds to our understanding the processes involved in recognizing uncertainty, strategies and outcomes of managing uncertainty, and influencing factors. Tailored nursing education programs should be developed to assist nurses in developing skills in articulating and managing their uncertainty. Further research is needed to extend, test and refine the theory of recognizing and responding to uncertainty to develop strategies for managing uncertainty. This theory advances the nursing perspective of uncertainty in clinical practice. The theory is relevant to nurses who are faced with uncertainty and complex clinical decisions, to managers who support nurses in their clinical decision-making, and to researchers who investigate ways to improve decision-making and care delivery. ©2012 Sigma Theta Tau International.

  8. Model parameter uncertainty analysis for annual field-scale P loss model

    Science.gov (United States)

    Phosphorous (P) loss models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. All P loss models, however, have an inherent amount of uncertainty associated with them. In this study, we conducted an uncertainty analysis with ...

  9. Uncertainty in Simulating Wheat Yields Under Climate Change

    Science.gov (United States)

    Asseng, S.; Ewert, F.; Rosenzweig, Cynthia; Jones, J. W.; Hatfield, J. W.; Ruane, A. C.; Boote, K. J.; Thornburn, P. J.; Rotter, R. P.; Cammarano, D.; hide

    2013-01-01

    Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1,3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.

  10. Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model

    DEFF Research Database (Denmark)

    Zhang, Donghua; Madsen, Henrik; Ridler, Marc E.

    2015-01-01

    The ensemble Kalman filter (EnKF) is a popular data assimilation (DA) technique that has been extensively used in environmental sciences for combining complementary information from model predictions and observations. One of the major challenges in EnKF applications is the description of model un...... with respect to performance and sensitivity. Results show that inappropriate definition of model uncertainty can greatly degrade the assimilation performance, and an appropriate combination of different model uncertainty sources is advised....

  11. Breaking through the uncertainty ceiling in LA-ICP-MS U-Pb geochronology

    Science.gov (United States)

    Horstwood, M.

    2016-12-01

    Sources of systematic uncertainty associated with session-to-session bias are the dominant contributor to the 2% (2s) uncertainty ceiling that currently limits the accuracy of LA-ICP-MS U-Pb geochronology. Sources include differential downhole fractionation (LIEF), `matrix effects' and ablation volume differences, which result in irreproducibility of the same reference material across sessions. Current mitigation methods include correcting for LIEF mathematically, using matrix-matched reference materials, annealing material to reduce or eliminate radiation damage effects and tuning for robust plasma conditions. Reducing the depth and volume of ablation can also mitigate these problems and should contribute to the reduction of the uncertainty ceiling. Reducing analysed volume leads to increased detection efficiency, reduced matrix-effects, eliminates LIEF, obviates ablation rate differences and reduces the likelihood of intercepting complex growth zones with depth, thereby apparently improving material homogeneity. High detection efficiencies (% level) and low sampling volumes (20um box, 1-2um deep) can now be achieved using MC-ICP-MS such that low volume ablations should be considered part of the toolbox of methods targeted at improving the reproducibility of LA-ICP-MS U-Pb geochronology. In combination with other strategies these improvements should be feasible on any ICP platform. However, reducing the volume of analysis reduces detected counts and requires a change of analytical approach in order to mitigate this. Appropriate strategies may include the use of high efficiency cell and torch technologies and the optimisation of acquisition protocols and data handling techniques such as condensing signal peaks, using log ratios and total signal integration. The tools required to break the 2% (2s) uncertainty ceiling in LA-ICP-MS U-Pb geochronology are likely now known but require a coherent strategy and change of approach to combine their implementation and realise

  12. Exploring Heterogeneous Multicore Architectures for Advanced Embedded Uncertainty Quantification.

    Energy Technology Data Exchange (ETDEWEB)

    Phipps, Eric T.; Edwards, Harold C.; Hu, Jonathan J.

    2014-09-01

    We explore rearrangements of classical uncertainty quantification methods with the aim of achieving higher aggregate performance for uncertainty quantification calculations on emerging multicore and manycore architectures. We show a rearrangement of the stochastic Galerkin method leads to improved performance and scalability on several computational architectures whereby un- certainty information is propagated at the lowest levels of the simulation code improving memory access patterns, exposing new dimensions of fine grained parallelism, and reducing communica- tion. We also develop a general framework for implementing such rearrangements for a diverse set of uncertainty quantification algorithms as well as computational simulation codes to which they are applied.

  13. Climate policy uncertainty and investment risk

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2007-06-21

    Our climate is changing. This is certain. Less certain, however, is the timing and magnitude of climate change, and the cost of transition to a low-carbon world. Therefore, many policies and programmes are still at a formative stage, and policy uncertainty is very high. This book identifies how climate change policy uncertainty may affect investment behaviour in the power sector. For power companies, where capital stock is intensive and long-lived, those risks rank among the biggest and can create an incentive to delay investment. Our analysis results show that the risk premiums of climate change uncertainty can add 40% of construction costs of the plant for power investors, and 10% of price surcharges for the electricity end-users. This publication tells what can be done in policy design to reduce these costs. Incorporating the results of quantitative analysis, this publication also shows the sensitivity of different power sector investment decisions to different risks. It compares the effects of climate policy uncertainty with energy market uncertainty, showing the relative importance of these sources of risk for different technologies in different market types. Drawing on extensive consultation with power companies and financial investors, it also assesses the implications for policy makers, allowing the key messages to be transferred into policy designs. This book is a useful tool for governments to improve climate policy mechanisms and create more certainty for power investors.

  14. Measurement uncertainty of ester number, acid number and patchouli alcohol of patchouli oil produced in Yogyakarta

    Science.gov (United States)

    Istiningrum, Reni Banowati; Saepuloh, Azis; Jannah, Wirdatul; Aji, Didit Waskito

    2017-03-01

    Yogyakarta is one of patchouli oil distillation center in Indonesia. The quality of patchouli oil greatly affect its market price. Therefore, testing quality of patchouli oil parameters is an important concern, one through determination of the measurement uncertainty. This study will determine the measurement uncertainty of ester number, acid number and content of patchouli alcohol through a bottom up approach. Source contributor to measurement uncertainty of ester number is a mass of the sample, a blank and sample titration volume, the molar mass of KOH, HCl normality, and replication. While the source contributor of the measurement uncertainty of acid number is the mass of the sample, the sample titration volume, the relative mass and normality of KOH, and repetition. Determination of patchouli alcohol by Gas Chromatography considers the sources of measurement uncertainty only from repeatability because reference materials are not available.

  15. Analogy as a strategy for supporting complex problem solving under uncertainty.

    Science.gov (United States)

    Chan, Joel; Paletz, Susannah B F; Schunn, Christian D

    2012-11-01

    Complex problem solving in naturalistic environments is fraught with uncertainty, which has significant impacts on problem-solving behavior. Thus, theories of human problem solving should include accounts of the cognitive strategies people bring to bear to deal with uncertainty during problem solving. In this article, we present evidence that analogy is one such strategy. Using statistical analyses of the temporal dynamics between analogy and expressed uncertainty in the naturalistic problem-solving conversations among scientists on the Mars Rover Mission, we show that spikes in expressed uncertainty reliably predict analogy use (Study 1) and that expressed uncertainty reduces to baseline levels following analogy use (Study 2). In addition, in Study 3, we show with qualitative analyses that this relationship between uncertainty and analogy is not due to miscommunication-related uncertainty but, rather, is primarily concentrated on substantive problem-solving issues. Finally, we discuss a hypothesis about how analogy might serve as an uncertainty reduction strategy in naturalistic complex problem solving.

  16. Reducing uncertainty in sustainable interpersonal service relationships: the role of aesthetics.

    Science.gov (United States)

    Xenakis, Ioannis

    2018-05-01

    Sustainable interpersonal service relationships (SISRs) are the outcome of a design process that supports situated meaningful interactions between those being served and those in service. Service design is not just directed to simply satisfy the ability to perceive the psychological state of others, but more importantly, it should aim at preserving these relationships in relation to the contextual requirements that they functionally need, in order to be or remain sustainable. However, SISRs are uncertain since they have many possibilities to be in error in the sense that the constructed, situated meanings may finally be proven unsuccessful for the anticipations and the goals of those people engaged in a SISR. The endeavor of this paper is to show that aesthetic behavior plays a crucial role in the reduction of the uncertainty that characterizes such relationships. Aesthetic behavior, as an organized network of affective and cognitive processes, has an anticipatory evaluative function with a strong influence on perception by providing significance and value for those aspects in SISRs that exhibit many possibilities to serve goals that correspond to sustainable challenges. Thus, aesthetic behavior plays an important role in the construction of meanings that are related to both empathic and contextual aspects that constitute the entire situation in which a SISR takes place. Aesthetic behavior has a strong influence in meaning-making, motivating the selection of actions that contribute to our initial goal of interacting with uncertainty, to make the world a bit less puzzling and, thus, to improve our lives, or in other words, to design.

  17. Uncertainty Analyses and Strategy

    International Nuclear Information System (INIS)

    Kevin Coppersmith

    2001-01-01

    The DOE identified a variety of uncertainties, arising from different sources, during its assessment of the performance of a potential geologic repository at the Yucca Mountain site. In general, the number and detail of process models developed for the Yucca Mountain site, and the complex coupling among those models, make the direct incorporation of all uncertainties difficult. The DOE has addressed these issues in a number of ways using an approach to uncertainties that is focused on producing a defensible evaluation of the performance of a potential repository. The treatment of uncertainties oriented toward defensible assessments has led to analyses and models with so-called ''conservative'' assumptions and parameter bounds, where conservative implies lower performance than might be demonstrated with a more realistic representation. The varying maturity of the analyses and models, and uneven level of data availability, result in total system level analyses with a mix of realistic and conservative estimates (for both probabilistic representations and single values). That is, some inputs have realistically represented uncertainties, and others are conservatively estimated or bounded. However, this approach is consistent with the ''reasonable assurance'' approach to compliance demonstration, which was called for in the U.S. Nuclear Regulatory Commission's (NRC) proposed 10 CFR Part 63 regulation (64 FR 8640 [DIRS 101680]). A risk analysis that includes conservatism in the inputs will result in conservative risk estimates. Therefore, the approach taken for the Total System Performance Assessment for the Site Recommendation (TSPA-SR) provides a reasonable representation of processes and conservatism for purposes of site recommendation. However, mixing unknown degrees of conservatism in models and parameter representations reduces the transparency of the analysis and makes the development of coherent and consistent probability statements about projected repository

  18. Analysis and Reduction of Complex Networks Under Uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Knio, Omar M

    2014-04-09

    This is a collaborative proposal that aims at developing new methods for the analysis and reduction of complex multiscale networks under uncertainty. The approach is based on combining methods of computational singular perturbation (CSP) and probabilistic uncertainty quantification. In deterministic settings, CSP yields asymptotic approximations of reduced-dimensionality “slow manifolds” on which a multiscale dynamical system evolves. Introducing uncertainty raises fundamentally new issues, particularly concerning its impact on the topology of slow manifolds, and means to represent and quantify associated variability. To address these challenges, this project uses polynomial chaos (PC) methods to reformulate uncertain network models, and to analyze them using CSP in probabilistic terms. Specific objectives include (1) developing effective algorithms that can be used to illuminate fundamental and unexplored connections among model reduction, multiscale behavior, and uncertainty, and (2) demonstrating the performance of these algorithms through applications to model problems.

  19. Bayesian Chance-Constrained Hydraulic Barrier Design under Geological Structure Uncertainty.

    Science.gov (United States)

    Chitsazan, Nima; Pham, Hai V; Tsai, Frank T-C

    2015-01-01

    The groundwater community has widely recognized geological structure uncertainty as a major source of model structure uncertainty. Previous studies in aquifer remediation design, however, rarely discuss the impact of geological structure uncertainty. This study combines chance-constrained (CC) programming with Bayesian model averaging (BMA) as a BMA-CC framework to assess the impact of geological structure uncertainty in remediation design. To pursue this goal, the BMA-CC method is compared with traditional CC programming that only considers model parameter uncertainty. The BMA-CC method is employed to design a hydraulic barrier to protect public supply wells of the Government St. pump station from salt water intrusion in the "1500-foot" sand and the "1700-foot" sand of the Baton Rouge area, southeastern Louisiana. To address geological structure uncertainty, three groundwater models based on three different hydrostratigraphic architectures are developed. The results show that using traditional CC programming overestimates design reliability. The results also show that at least five additional connector wells are needed to achieve more than 90% design reliability level. The total amount of injected water from the connector wells is higher than the total pumpage of the protected public supply wells. While reducing the injection rate can be achieved by reducing the reliability level, the study finds that the hydraulic barrier design to protect the Government St. pump station may not be economically attractive. © 2014, National Ground Water Association.

  20. Quantification of margins and uncertainties: Alternative representations of epistemic uncertainty

    International Nuclear Information System (INIS)

    Helton, Jon C.; Johnson, Jay D.

    2011-01-01

    In 2001, the National Nuclear Security Administration of the U.S. Department of Energy in conjunction with the national security laboratories (i.e., Los Alamos National Laboratory, Lawrence Livermore National Laboratory and Sandia National Laboratories) initiated development of a process designated Quantification of Margins and Uncertainties (QMU) for the use of risk assessment methodologies in the certification of the reliability and safety of the nation's nuclear weapons stockpile. A previous presentation, 'Quantification of Margins and Uncertainties: Conceptual and Computational Basis,' describes the basic ideas that underlie QMU and illustrates these ideas with two notional examples that employ probability for the representation of aleatory and epistemic uncertainty. The current presentation introduces and illustrates the use of interval analysis, possibility theory and evidence theory as alternatives to the use of probability theory for the representation of epistemic uncertainty in QMU-type analyses. The following topics are considered: the mathematical structure of alternative representations of uncertainty, alternative representations of epistemic uncertainty in QMU analyses involving only epistemic uncertainty, and alternative representations of epistemic uncertainty in QMU analyses involving a separation of aleatory and epistemic uncertainty. Analyses involving interval analysis, possibility theory and evidence theory are illustrated with the same two notional examples used in the presentation indicated above to illustrate the use of probability to represent aleatory and epistemic uncertainty in QMU analyses.

  1. Uncertainty analysis guide

    International Nuclear Information System (INIS)

    Andres, T.H.

    2002-05-01

    This guide applies to the estimation of uncertainty in quantities calculated by scientific, analysis and design computer programs that fall within the scope of AECL's software quality assurance (SQA) manual. The guide weaves together rational approaches from the SQA manual and three other diverse sources: (a) the CSAU (Code Scaling, Applicability, and Uncertainty) evaluation methodology; (b) the ISO Guide,for the Expression of Uncertainty in Measurement; and (c) the SVA (Systems Variability Analysis) method of risk analysis. This report describes the manner by which random and systematic uncertainties in calculated quantities can be estimated and expressed. Random uncertainty in model output can be attributed to uncertainties of inputs. The propagation of these uncertainties through a computer model can be represented in a variety of ways, including exact calculations, series approximations and Monte Carlo methods. Systematic uncertainties emerge from the development of the computer model itself, through simplifications and conservatisms, for example. These must be estimated and combined with random uncertainties to determine the combined uncertainty in a model output. This report also addresses the method by which uncertainties should be employed in code validation, in order to determine whether experiments and simulations agree, and whether or not a code satisfies the required tolerance for its application. (author)

  2. Uncertainty analysis guide

    Energy Technology Data Exchange (ETDEWEB)

    Andres, T.H

    2002-05-01

    This guide applies to the estimation of uncertainty in quantities calculated by scientific, analysis and design computer programs that fall within the scope of AECL's software quality assurance (SQA) manual. The guide weaves together rational approaches from the SQA manual and three other diverse sources: (a) the CSAU (Code Scaling, Applicability, and Uncertainty) evaluation methodology; (b) the ISO Guide,for the Expression of Uncertainty in Measurement; and (c) the SVA (Systems Variability Analysis) method of risk analysis. This report describes the manner by which random and systematic uncertainties in calculated quantities can be estimated and expressed. Random uncertainty in model output can be attributed to uncertainties of inputs. The propagation of these uncertainties through a computer model can be represented in a variety of ways, including exact calculations, series approximations and Monte Carlo methods. Systematic uncertainties emerge from the development of the computer model itself, through simplifications and conservatisms, for example. These must be estimated and combined with random uncertainties to determine the combined uncertainty in a model output. This report also addresses the method by which uncertainties should be employed in code validation, in order to determine whether experiments and simulations agree, and whether or not a code satisfies the required tolerance for its application. (author)

  3. SunShot solar power reduces costs and uncertainty in future low-carbon electricity systems.

    Science.gov (United States)

    Mileva, Ana; Nelson, James H; Johnston, Josiah; Kammen, Daniel M

    2013-08-20

    The United States Department of Energy's SunShot Initiative has set cost-reduction targets of $1/watt for central-station solar technologies. We use SWITCH, a high-resolution electricity system planning model, to study the implications of achieving these targets for technology deployment and electricity costs in western North America, focusing on scenarios limiting carbon emissions to 80% below 1990 levels by 2050. We find that achieving the SunShot target for solar photovoltaics would allow this technology to provide more than a third of electric power in the region, displacing natural gas in the medium term and reducing the need for nuclear and carbon capture and sequestration (CCS) technologies, which face technological and cost uncertainties, by 2050. We demonstrate that a diverse portfolio of technological options can help integrate high levels of solar generation successfully and cost-effectively. The deployment of GW-scale storage plays a central role in facilitating solar deployment and the availability of flexible loads could increase the solar penetration level further. In the scenarios investigated, achieving the SunShot target can substantially mitigate the cost of implementing a carbon cap, decreasing power costs by up to 14% and saving up to $20 billion ($2010) annually by 2050 relative to scenarios with Reference solar costs.

  4. Determination of a PWR key neutron parameters uncertainties and conformity studies applications

    International Nuclear Information System (INIS)

    Bernard, D.

    2002-01-01

    The aim of this thesis was to evaluate uncertainties of key neutron parameters of slab reactors. Uncertainties sources have many origins, technologic origin for parameters of fabrication and physical origin for nuclear data. First, each contribution of uncertainties is calculated and finally, a factor of uncertainties is associated to key slab parameter like reactivity, isotherm reactivity coefficient, control rod efficiency, power form factor before irradiation and lifetime. This factors of uncertainties were computed by Generalized Perturbations Theory in case of step 0 and by directs calculations in case of irradiation problems. One of neutronic conformity applications was about fabrication and nuclear data targets precision adjustments. Statistic (uncertainties) and deterministic (deviations) approaches were studied. Then neutronics key slab parameters uncertainties were reduced and so nuclear performances were optimised. (author)

  5. Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

    International Nuclear Information System (INIS)

    Sanchez, Ana; Carlos, Sofia; Martorell, Sebastian; Villanueva, Jose F.

    2009-01-01

    Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral

  6. Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

    Energy Technology Data Exchange (ETDEWEB)

    Sanchez, Ana [Department of Statistics and Operational Research, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain); Carlos, Sofia [Department of Chemical and Nuclear Engineering, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain); Martorell, Sebastian [Department of Chemical and Nuclear Engineering, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain)], E-mail: smartore@iqn.upv.es; Villanueva, Jose F. [Department of Chemical and Nuclear Engineering, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain)

    2009-01-15

    Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral.

  7. A framework to quantify uncertainties of seafloor backscatter from swath mapping echosounders

    Science.gov (United States)

    Malik, Mashkoor; Lurton, Xavier; Mayer, Larry

    2018-06-01

    Multibeam echosounders (MBES) have become a widely used acoustic remote sensing tool to map and study the seafloor, providing co-located bathymetry and seafloor backscatter. Although the uncertainty associated with MBES-derived bathymetric data has been studied extensively, the question of backscatter uncertainty has been addressed only minimally and hinders the quantitative use of MBES seafloor backscatter. This paper explores approaches to identifying uncertainty sources associated with MBES-derived backscatter measurements. The major sources of uncertainty are catalogued and the magnitudes of their relative contributions to the backscatter uncertainty budget are evaluated. These major uncertainty sources include seafloor insonified area (1-3 dB), absorption coefficient (up to > 6 dB), random fluctuations in echo level (5.5 dB for a Rayleigh distribution), and sonar calibration (device dependent). The magnitudes of these uncertainty sources vary based on how these effects are compensated for during data acquisition and processing. Various cases (no compensation, partial compensation and full compensation) for seafloor insonified area, transmission losses and random fluctuations were modeled to estimate their uncertainties in different scenarios. Uncertainty related to the seafloor insonified area can be reduced significantly by accounting for seafloor slope during backscatter processing while transmission losses can be constrained by collecting full water column absorption coefficient profiles (temperature and salinity profiles). To reduce random fluctuations to below 1 dB, at least 20 samples are recommended to be used while computing mean values. The estimation of uncertainty in backscatter measurements is constrained by the fact that not all instrumental components are characterized and documented sufficiently for commercially available MBES. Further involvement from manufacturers in providing this essential information is critically required.

  8. Position-momentum uncertainty relations in the presence of quantum memory

    Energy Technology Data Exchange (ETDEWEB)

    Furrer, Fabian, E-mail: furrer@eve.phys.s.u-tokyo.ac.jp [Department of Physics, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 (Japan); Berta, Mario [Institute for Quantum Information and Matter, Caltech, Pasadena, California 91125 (United States); Institute for Theoretical Physics, ETH Zurich, Wolfgang-Pauli-Str. 27, 8093 Zürich (Switzerland); Tomamichel, Marco [School of Physics, The University of Sydney, Sydney 2006 (Australia); Centre for Quantum Technologies, National University of Singapore, Singapore 117543 (Singapore); Scholz, Volkher B. [Institute for Theoretical Physics, ETH Zurich, Wolfgang-Pauli-Str. 27, 8093 Zürich (Switzerland); Christandl, Matthias [Institute for Theoretical Physics, ETH Zurich, Wolfgang-Pauli-Str. 27, 8093 Zürich (Switzerland); Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen (Denmark)

    2014-12-15

    A prominent formulation of the uncertainty principle identifies the fundamental quantum feature that no particle may be prepared with certain outcomes for both position and momentum measurements. Often the statistical uncertainties are thereby measured in terms of entropies providing a clear operational interpretation in information theory and cryptography. Recently, entropic uncertainty relations have been used to show that the uncertainty can be reduced in the presence of entanglement and to prove security of quantum cryptographic tasks. However, much of this recent progress has been focused on observables with only a finite number of outcomes not including Heisenberg’s original setting of position and momentum observables. Here, we show entropic uncertainty relations for general observables with discrete but infinite or continuous spectrum that take into account the power of an entangled observer. As an illustration, we evaluate the uncertainty relations for position and momentum measurements, which is operationally significant in that it implies security of a quantum key distribution scheme based on homodyne detection of squeezed Gaussian states.

  9. Mass discharge estimation from contaminated sites: Multi-model solutions for assessment of conceptual uncertainty

    Science.gov (United States)

    Thomsen, N. I.; Troldborg, M.; McKnight, U. S.; Binning, P. J.; Bjerg, P. L.

    2012-04-01

    Mass discharge estimates are increasingly being used in the management of contaminated sites. Such estimates have proven useful for supporting decisions related to the prioritization of contaminated sites in a groundwater catchment. Potential management options can be categorised as follows: (1) leave as is, (2) clean up, or (3) further investigation needed. However, mass discharge estimates are often very uncertain, which may hamper the management decisions. If option 1 is incorrectly chosen soil and water quality will decrease, threatening or destroying drinking water resources. The risk of choosing option 2 is to spend money on remediating a site that does not pose a problem. Choosing option 3 will often be safest, but may not be the optimal economic solution. Quantification of the uncertainty in mass discharge estimates can therefore greatly improve the foundation for selecting the appropriate management option. The uncertainty of mass discharge estimates depends greatly on the extent of the site characterization. A good approach for uncertainty estimation will be flexible with respect to the investigation level, and account for both parameter and conceptual model uncertainty. We propose a method for quantifying the uncertainty of dynamic mass discharge estimates from contaminant point sources on the local scale. The method considers both parameter and conceptual uncertainty through a multi-model approach. The multi-model approach evaluates multiple conceptual models for the same site. The different conceptual models consider different source characterizations and hydrogeological descriptions. The idea is to include a set of essentially different conceptual models where each model is believed to be realistic representation of the given site, based on the current level of information. Parameter uncertainty is quantified using Monte Carlo simulations. For each conceptual model we calculate a transient mass discharge estimate with uncertainty bounds resulting from

  10. Uncertainty of climate change impacts and consequences on the prediction of future hydrological trends

    International Nuclear Information System (INIS)

    Minville, M.; Brissette, F.; Leconte, R.

    2008-01-01

    In the future, water is very likely to be the resource that will be most severely affected by climate change. It has been shown that small perturbations in precipitation frequency and/or quantity can result in significant impacts on the mean annual discharge. Moreover, modest changes in natural inflows result in larger changes in reservoir storage. There is however great uncertainty linked to changes in both the magnitude and direction of future hydrological trends. This presentation discusses the various sources of this uncertainty and their potential impact on the prediction of future hydrological trends. A companion paper will look at adaptation potential, taking into account some of the sources of uncertainty discussed in this presentation. Uncertainty is separated into two main components: climatic uncertainty and 'model and methods' uncertainty. Climatic uncertainty is linked to uncertainty in future greenhouse gas emission scenarios (GHGES) and to general circulation models (GCMs), whose representation of topography and climate processes is imperfect, in large part due to computational limitations. The uncertainty linked to natural variability (which may or may not increase) is also part of the climatic uncertainty. 'Model and methods' uncertainty regroups the uncertainty linked to the different approaches and models needed to transform climate data so that they can be used by hydrological models (such as downscaling methods) and the uncertainty of the models themselves and of their use in a changed climate. The impacts of the various sources of uncertainty on the hydrology of a watershed are demonstrated on the Peribonka River basin (Quebec, Canada). The results indicate that all sources of uncertainty can be important and outline the importance of taking these sources into account for any impact and adaptation studies. Recommendations are outlined for such studies. (author)

  11. A new uncertainty reduction method for PWR cores with erbia bearing fuel

    International Nuclear Information System (INIS)

    Takeda, Toshikazu; Sano, Tadafumi; Kitada, Takanori; Kuroishi, Takeshi; Yamasaki, Masatoshi; Unesaki, Hironobu

    2008-01-01

    The concept of a PWR with erbia bearing high burnup fuel has been proposed. The erbia is added to all fuel with over 5% 235 U enrichment to retain the neutronics characteristics to that within 5% 235 U enrichment. There is a problem of the prediction accuracy of the neutronics characteristics with erbia bearing fuel because of the short of experimental data of erbia bearing fuel. The purpose of the present work is to reduce the uncertainty. A new method has been proposed by combining the bias factor method and the cross section adjustment method. For the PWR core, the uncertainty reduction, which shows the rate of reduction of uncertainty, of the k eff is 0.865 by the present method and 0.801 by the conventional bias factor method. Thus the prediction uncertainties are reduced by the present method compared to the bias factor method. (authors)

  12. Optimization Under Uncertainty for Wake Steering Strategies: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Annoni, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Fleming, Paul A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ning, Andrew [Brigham Young University

    2017-05-01

    Wind turbines in a wind power plant experience significant power losses because of aerodynamic interactions between turbines. One control strategy to reduce these losses is known as 'wake steering,' in which upstream turbines are yawed to direct wakes away from downstream turbines. Previous wake steering research has assumed perfect information, however, there can be significant uncertainty in many aspects of the problem, including wind inflow and various turbine measurements. Uncertainty has significant implications for performance of wake steering strategies. Consequently, the authors formulate and solve an optimization under uncertainty (OUU) problem for finding optimal wake steering strategies in the presence of yaw angle uncertainty. The OUU wake steering strategy is demonstrated on a two-turbine test case and on the utility-scale, offshore Princess Amalia Wind Farm. When we accounted for yaw angle uncertainty in the Princess Amalia Wind Farm case, inflow-direction-specific OUU solutions produced between 0% and 1.4% more power than the deterministically optimized steering strategies, resulting in an overall annual average improvement of 0.2%. More importantly, the deterministic optimization is expected to perform worse and with more downside risk than the OUU result when realistic uncertainty is taken into account. Additionally, the OUU solution produces fewer extreme yaw situations than the deterministic solution.

  13. A Statistical Modeling Framework for Characterising Uncertainty in Large Datasets: Application to Ocean Colour

    Directory of Open Access Journals (Sweden)

    Peter E. Land

    2018-05-01

    Full Text Available Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data.

  14. Establishing and maintaining a measurement uncertainty programme at the RPII dosimetry and calibration service

    International Nuclear Information System (INIS)

    Spain, D.; Currivan, L.; Fitzgerald, H.; Pollard, D.

    2005-01-01

    Full text: At the Dosimetry and Calibration Service of the Radiological Protection Institute of Ireland (RPII) approximately 70,000 thermoluminescent dosemeters (TLDs) are issued each year to monitor occupationally exposed workers in Ireland. In addition the service offers a calibration service for radiation survey meters, contamination monitors and electronic personal dosemeters. In order to meet the requirements of ISO/IEC 17025, it is necessary to quantify the uncertainty of measurement using well defined concepts and to maintain an up to date estimate. In this work it is shown how the measurement uncertainty in the Dosimetry and Calibration Service has been estimated. When estimating the uncertainty of measurement, all uncertainty components which are of importance in the given situation are taken into account. The combined uncertainty of the system is determined by considering a number of systematic and random errors. The analysis will include assumptions made and these have been documented and justified. Components of uncertainty were determined in accordance with such documents as IEC 61066, Guide to Expression of Uncertainty in Measurement, and the National Physical Laboratory Measurement Good Practice Guide No. 11, as appropriate. Results of intercomparisons are also presented, which adds confidence to the uncertainty estimate. Although a great deal of work is involved is estimating uncertainty in both laboratories it is felt that a reasonable estimate of measurement uncertainty has been achieved given the available information. Furthermore, in keeping with the laboratory's commitment to continuous improvement, it is necessary to evaluate periodically the measurement uncertainties associated with the relevant procedures and a programme for the future is outlined. (author)

  15. Resolving structural uncertainty in natural resources management using POMDP approaches

    Science.gov (United States)

    Williams, B.K.

    2011-01-01

    In recent years there has been a growing focus on the uncertainties of natural resources management, and the importance of accounting for uncertainty in assessing management effectiveness. This paper focuses on uncertainty in resource management in terms of discrete-state Markov decision processes (MDP) under structural uncertainty and partial observability. It describes the treatment of structural uncertainty with approaches developed for partially observable resource systems. In particular, I show how value iteration for partially observable MDPs (POMDP) can be extended to structurally uncertain MDPs. A key difference between these process classes is that structurally uncertain MDPs require the tracking of system state as well as a probability structure for the structure uncertainty, whereas with POMDPs require only a probability structure for the observation uncertainty. The added complexity of the optimization problem under structural uncertainty is compensated by reduced dimensionality in the search for optimal strategy. A solution algorithm for structurally uncertain processes is outlined for a simple example in conservation biology. By building on the conceptual framework developed for POMDPs, natural resource analysts and decision makers who confront structural uncertainties in natural resources can take advantage of the rapid growth in POMDP methods and approaches, and thereby produce better conservation strategies over a larger class of resource problems. ?? 2011.

  16. Effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model output

    Science.gov (United States)

    Jacquin, A. P.

    2012-04-01

    This study analyses the effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model's discharge estimates. Prediction uncertainty bounds are derived using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation (at a single station within the catchment) and a precipitation factor FPi. Thus, these factors provide a simplified representation of the spatial variation of precipitation, specifically the shape of the functional relationship between precipitation and height. In the absence of information about appropriate values of the precipitation factors FPi, these are estimated through standard calibration procedures. The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. Monte Carlo samples of the model output are obtained by randomly varying the model parameters within their feasible ranges. In the first experiment, the precipitation factors FPi are considered unknown and thus included in the sampling process. The total number of unknown parameters in this case is 16. In the second experiment, precipitation factors FPi are estimated a priori, by means of a long term water balance between observed discharge at the catchment outlet, evapotranspiration estimates and observed precipitation. In this case, the number of unknown parameters reduces to 11. The feasible ranges assigned to the precipitation factors in the first experiment are slightly wider than the range of fixed precipitation factors used in the second experiment. The mean squared error of the Box-Cox transformed discharge during the calibration period is used for the evaluation of the

  17. Uncertainty and stress: Why it causes diseases and how it is mastered by the brain.

    Science.gov (United States)

    Peters, Achim; McEwen, Bruce S; Friston, Karl

    2017-09-01

    The term 'stress' - coined in 1936 - has many definitions, but until now has lacked a theoretical foundation. Here we present an information-theoretic approach - based on the 'free energy principle' - defining the essence of stress; namely, uncertainty. We address three questions: What is uncertainty? What does it do to us? What are our resources to master it? Mathematically speaking, uncertainty is entropy or 'expected surprise'. The 'free energy principle' rests upon the fact that self-organizing biological agents resist a tendency to disorder and must therefore minimize the entropy of their sensory states. Applied to our everyday life, this means that we feel uncertain, when we anticipate that outcomes will turn out to be something other than expected - and that we are unable to avoid surprise. As all cognitive systems strive to reduce their uncertainty about future outcomes, they face a critical constraint: Reducing uncertainty requires cerebral energy. The characteristic of the vertebrate brain to prioritize its own high energy is captured by the notion of the 'selfish brain'. Accordingly, in times of uncertainty, the selfish brain demands extra energy from the body. If, despite all this, the brain cannot reduce uncertainty, a persistent cerebral energy crisis may develop, burdening the individual by 'allostatic load' that contributes to systemic and brain malfunction (impaired memory, atherogenesis, diabetes and subsequent cardio- and cerebrovascular events). Based on the basic tenet that stress originates from uncertainty, we discuss the strategies our brain uses to avoid surprise and thereby resolve uncertainty. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  18. Evidence-based quantification of uncertainties induced via simulation-based modeling

    International Nuclear Information System (INIS)

    Riley, Matthew E.

    2015-01-01

    The quantification of uncertainties in simulation-based modeling traditionally focuses upon quantifying uncertainties in the parameters input into the model, referred to as parametric uncertainties. Often neglected in such an approach are the uncertainties induced by the modeling process itself. This deficiency is often due to a lack of information regarding the problem or the models considered, which could theoretically be reduced through the introduction of additional data. Because of the nature of this epistemic uncertainty, traditional probabilistic frameworks utilized for the quantification of uncertainties are not necessarily applicable to quantify the uncertainties induced in the modeling process itself. This work develops and utilizes a methodology – incorporating aspects of Dempster–Shafer Theory and Bayesian model averaging – to quantify uncertainties of all forms for simulation-based modeling problems. The approach expands upon classical parametric uncertainty approaches, allowing for the quantification of modeling-induced uncertainties as well, ultimately providing bounds on classical probability without the loss of epistemic generality. The approach is demonstrated on two different simulation-based modeling problems: the computation of the natural frequency of a simple two degree of freedom non-linear spring mass system and the calculation of the flutter velocity coefficient for the AGARD 445.6 wing given a subset of commercially available modeling choices. - Highlights: • Modeling-induced uncertainties are often mishandled or ignored in the literature. • Modeling-induced uncertainties are epistemic in nature. • Probabilistic representations of modeling-induced uncertainties are restrictive. • Evidence theory and Bayesian model averaging are integrated. • Developed approach is applicable for simulation-based modeling problems

  19. Model parameter uncertainty analysis for an annual field-scale phosphorus loss model

    Science.gov (United States)

    Phosphorous (P) loss models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. All P loss models, however, have an inherent amount of uncertainty associated with them. In this study, we conducted an uncertainty analysis with ...

  20. Changes of heart: the switch-value method for assessing value uncertainty.

    Science.gov (United States)

    John, Leslie K; Fischhoff, Baruch

    2010-01-01

    Medical choices often evoke great value uncertainty, as patients face difficult, unfamiliar tradeoffs. Those seeking to aid such choices must be able to assess patients' ability to reduce that uncertainty, to reach stable, informed choices. The authors demonstrate a new method for evaluating how well people have articulated their preferences for difficult health decisions. The method uses 2 evaluative criteria. One is internal consistency, across formally equivalent ways of posing a choice. The 2nd is compliance with principles of prospect theory, indicating sufficient task mastery to respond in predictable ways. Subjects considered a hypothetical choice between noncurative surgery and palliative care, posed by a brain tumor. The choice options were characterized on 6 outcomes (e.g., pain, life expectancy, treatment risk), using a drug facts box display. After making an initial choice, subjects indicated their willingness to switch, given plausible changes in the outcomes. These changes involved either gains (improvements) in the unchosen option or losses (worsening) in the chosen one. A 2 x 2 mixed design manipulated focal change (gains v. losses) within subjects and change order between subjects. In this demonstration, subjects' preferences were generally consistent 1) with one another: with similar percentages willing to switch for gains and losses, and 2) with prospect theory, requiring larger gains than losses, to make those switches. Informed consent requires understanding decisions well enough to articulate coherent references. The authors' method allows assessing individuals' success in doing so.

  1. Implications of nuclear data uncertainties to reactor design

    International Nuclear Information System (INIS)

    Greebler, P.; Hutchins, B.A.; Cowan, C.L.

    1970-01-01

    Uncertainties in nuclear data require significant allowances to be made in the design and the operating conditions of reactor cores and of shielded-reactor-plant and fuel-processing systems. These allowances result in direct cost increases due to overdesign of components and equipment and reduced core and fuel operating performance. Compromising the allowances for data uncertainties has indirect cost implications due to increased risks of failure to meet plant and fuel performance objectives, with warrantees involved in some cases, and to satisfy licensed safety requirements. Fast breeders are the most sensitive power reactors to the uncertainties in nuclear data over the neutron energy range of interest for fission reactors, and this paper focuses on the implications of the data uncertainties to design and operation of fast breeder reactors and fuel-processing systems. The current status of uncertainty in predicted physics parameters due to data uncertainties is reviewed and compared with the situation in 1966 and that projected for within the next two years due to anticipated data improvements. Implications of the uncertainties in the predicted physics parameters to design and operation are discussed for both a near-term prototype or demonstration breeder plant (∼300 MW(e)) and a longer-term large (∼1000 MW(e)) plant. Significant improvements in the nuclear data have been made during the past three years, the most important of these to fast power reactors being the 239 Pu alpha below 15 keV. The most important remaining specific data uncertainties are illustrated by their individual contributions to the computational uncertainty of selected physics parameters, and recommended priorities and accuracy requirements for improved data are presented

  2. Tailored complex degree of mutual coherence for plane-of-interest interferometry with reduced measurement uncertainty

    Science.gov (United States)

    Fütterer, G.

    2017-10-01

    A problem of interferometers is the elimination of parasitic reflections. Parasitic reflections and modulated intensity signals, which are not related to the reference surface (REF) or the surface under test (SUT) in a direct way, can increase the measurement uncertainty significantly. In some situations standard methods might be used in order to eliminate reflections from the backside of the optical element under test. For instance, match the test object to an absorber, while taking the complex refractive index into account, can cancel out back reflections completely. This causes additional setup time and chemical contamination. In some situations an angular offset might be combined with an aperture stop. This reduces spatial resolution and it does not work if the disturbing wave field propagates in the same direction as the wave field, which propagates from the SUT. However, a stack of surfaces is a problem. An increased spectral bandwidth might be used in order to obtain a separation of the plane-of-interest from other planes. Depending on the interferometer used, this might require an optical path difference of zero or it might cause a reduction of the visibility to V embodiment of a modified interferometer, will be discussed.

  3. Ambiguity and uncertainty tolerance, need for cognition, and their association with stress. A study among Italian practicing physicians.

    Science.gov (United States)

    Iannello, Paola; Mottini, Anna; Tirelli, Simone; Riva, Silvia; Antonietti, Alessandro

    2017-01-01

    Medical practice is inherently ambiguous and uncertain. The physicians' ability to tolerate ambiguity and uncertainty has been proved to have a great impact on clinical practice. The primary aim of the present study was to test the hypothesis that higher degree of physicians' ambiguity and uncertainty intolerance and higher need for cognitive closure will predict higher work stress. Two hundred and twelve physicians (mean age = 42.94 years; SD = 10.72) from different medical specialties with different levels of expertise were administered a set of questionnaires measuring perceived levels of work-related stress, individual ability to tolerate ambiguity, stress deriving from uncertainty, and personal need for cognitive closure. A linear regression analysis was performed to examine which variables predict the perceived level of stress. The regression model was statistically significant [R 2  = .32; F(10,206) = 8.78, p ≤ .001], thus showing that, after controlling for gender and medical specialty, ambiguity and uncertainty tolerance, decisiveness (a dimension included in need for closure), and the years of practice were significant predictors of perceived work-related stress. Findings from the present study have some implications for medical education. Given the great impact that the individual ability to tolerate ambiguity and uncertainty has on the physicians' level of perceived work-related stress, it would be worth paying particular attention to such a skill in medical education settings. It would be crucial to introduce or to empower educational tools and strategies that could increase medical students' ability to tolerate ambiguity and uncertainty. JSQ: Job stress questionnaire; NFCS: Need for cognitive closure scale; PRU: Physicians' reactions to uncertainty; TFA: Tolerance for ambiguity.

  4. Analysis on Calibration and Uncertainty for TD-LTE Radio Test System

    Directory of Open Access Journals (Sweden)

    Zhang Weipeng

    2014-06-01

    Full Text Available TD-LTE base station radio test system measures radio signal with a required accuracy, so calibration need to be done for transmission path between base station and measurement instruments before test. Considering Transmitter OFF Power measurement within OFF period, modulated signal generator and spectrum analyzer inside test system is used for calibration, to get accurate transmission parameters of the paths, and to reduce test cost without more instruments. The paper describes the uncertainty of test system, analyzes uncertainty contribution of interface mismatch, calculates uncertainty for Transmitter OFF Power measurement, uncertainty is 1.193 dB, within the requirement of 3GPP specification.

  5. Unrealized Global Temperature Increase: Implications of Current Uncertainties

    Science.gov (United States)

    Schwartz, Stephen E.

    2018-04-01

    Unrealized increase in global mean surface air temperature (GMST) may result from the climate system not being in steady state with forcings and/or from cessation of negative aerosol forcing that would result from decreases in emissions. An observation-constrained method is applied to infer the dependence of Earth's climate sensitivity on forcing by anthropogenic aerosols within the uncertainty on that forcing given by the Fifth (2013) Assessment Report of the Intergovernmental Panel on Climate Change. Within these uncertainty ranges the increase in GMST due to temperature lag for future forcings held constant is slight (0.09-0.19 K over 20 years; 0.12-0.26 K over 100 years). However, the incremental increase in GMST that would result from a hypothetical abrupt cessation of sources of aerosols could be quite large but is highly uncertain, 0.1-1.3 K over 20 years. Decrease in CO2 abundance and forcing following abrupt cessation of emissions would offset these increases in GMST over 100 years by as little as 0.09 K to as much as 0.8 K. The uncertainties quantified here greatly limit confidence in projections of change in GMST that would result from any strategy for future reduction of emissions.

  6. Parameter Uncertainty on AGCM-simulated Tropical Cyclones

    Science.gov (United States)

    He, F.

    2015-12-01

    This work studies the parameter uncertainty on tropical cyclone (TC) simulations in Atmospheric General Circulation Models (AGCMs) using the Reed-Jablonowski TC test case, which is illustrated in Community Atmosphere Model (CAM). It examines the impact from 24 parameters across the physical parameterization schemes that represent the convection, turbulence, precipitation and cloud processes in AGCMs. The one-at-a-time (OAT) sensitivity analysis method first quantifies their relative importance on TC simulations and identifies the key parameters to the six different TC characteristics: intensity, precipitation, longwave cloud radiative forcing (LWCF), shortwave cloud radiative forcing (SWCF), cloud liquid water path (LWP) and ice water path (IWP). Then, 8 physical parameters are chosen and perturbed using the Latin-Hypercube Sampling (LHS) method. The comparison between OAT ensemble run and LHS ensemble run shows that the simulated TC intensity is mainly affected by the parcel fractional mass entrainment rate in Zhang-McFarlane (ZM) deep convection scheme. The nonlinear interactive effect among different physical parameters is negligible on simulated TC intensity. In contrast, this nonlinear interactive effect plays a significant role in other simulated tropical cyclone characteristics (precipitation, LWCF, SWCF, LWP and IWP) and greatly enlarge their simulated uncertainties. The statistical emulator Extended Multivariate Adaptive Regression Splines (EMARS) is applied to characterize the response functions for nonlinear effect. Last, we find that the intensity uncertainty caused by physical parameters is in a degree comparable to uncertainty caused by model structure (e.g. grid) and initial conditions (e.g. sea surface temperature, atmospheric moisture). These findings suggest the importance of using the perturbed physics ensemble (PPE) method to revisit tropical cyclone prediction under climate change scenario.

  7. The uncertainty of reference standards--a guide to understanding factors impacting uncertainty, uncertainty calculations, and vendor certifications.

    Science.gov (United States)

    Gates, Kevin; Chang, Ning; Dilek, Isil; Jian, Huahua; Pogue, Sherri; Sreenivasan, Uma

    2009-10-01

    Certified solution standards are widely used in forensic toxicological, clinical/diagnostic, and environmental testing. Typically, these standards are purchased as ampouled solutions with a certified concentration. Vendors present concentration and uncertainty differently on their Certificates of Analysis. Understanding the factors that impact uncertainty and which factors have been considered in the vendor's assignment of uncertainty are critical to understanding the accuracy of the standard and the impact on testing results. Understanding these variables is also important for laboratories seeking to comply with ISO/IEC 17025 requirements and for those preparing reference solutions from neat materials at the bench. The impact of uncertainty associated with the neat material purity (including residual water, residual solvent, and inorganic content), mass measurement (weighing techniques), and solvent addition (solution density) on the overall uncertainty of the certified concentration is described along with uncertainty calculations.

  8. Heisenberg's principle of uncertainty and the uncertainty relations

    International Nuclear Information System (INIS)

    Redei, Miklos

    1987-01-01

    The usual verbal form of the Heisenberg uncertainty principle and the usual mathematical formulation (the so-called uncertainty theorem) are not equivalent. The meaning of the concept 'uncertainty' is not unambiguous and different interpretations are used in the literature. Recently a renewed interest has appeared to reinterpret and reformulate the precise meaning of Heisenberg's principle and to find adequate mathematical form. The suggested new theorems are surveyed and critically analyzed. (D.Gy.) 20 refs

  9. Measurement Uncertainty

    Science.gov (United States)

    Koch, Michael

    Measurement uncertainty is one of the key issues in quality assurance. It became increasingly important for analytical chemistry laboratories with the accreditation to ISO/IEC 17025. The uncertainty of a measurement is the most important criterion for the decision whether a measurement result is fit for purpose. It also delivers help for the decision whether a specification limit is exceeded or not. Estimation of measurement uncertainty often is not trivial. Several strategies have been developed for this purpose that will shortly be described in this chapter. In addition the different possibilities to take into account the uncertainty in compliance assessment are explained.

  10. Effect of Baseflow Separation on Uncertainty of Hydrological Modeling in the Xinanjiang Model

    Directory of Open Access Journals (Sweden)

    Kairong Lin

    2014-01-01

    Full Text Available Based on the idea of inputting more available useful information for evaluation to gain less uncertainty, this study focuses on how well the uncertainty can be reduced by considering the baseflow estimation information obtained from the smoothed minima method (SMM. The Xinanjiang model and the generalized likelihood uncertainty estimation (GLUE method with the shuffled complex evolution Metropolis (SCEM-UA sampling algorithm were used for hydrological modeling and uncertainty analysis, respectively. The Jiangkou basin, located in the upper of the Hanjiang River, was selected as case study. It was found that the number and standard deviation of behavioral parameter sets both decreased when the threshold value for the baseflow efficiency index increased, and the high Nash-Sutcliffe efficiency coefficients correspond well with the high baseflow efficiency coefficients. The results also showed that uncertainty interval width decreased significantly, while containing ratio did not decrease by much and the simulated runoff with the behavioral parameter sets can fit better to the observed runoff, when threshold for the baseflow efficiency index was taken into consideration. These implied that using the baseflow estimation information can reduce the uncertainty in hydrological modeling to some degree and gain more reasonable prediction bounds.

  11. Uncertainty analysis of time-dependent nonlinear systems: theory and application to transient thermal hydraulics

    International Nuclear Information System (INIS)

    Barhen, J.; Bjerke, M.A.; Cacuci, D.G.; Mullins, C.B.; Wagschal, G.G.

    1982-01-01

    An advanced methodology for performing systematic uncertainty analysis of time-dependent nonlinear systems is presented. This methodology includes a capability for reducing uncertainties in system parameters and responses by using Bayesian inference techniques to consistently combine prior knowledge with additional experimental information. The determination of best estimates for the system parameters, for the responses, and for their respective covariances is treated as a time-dependent constrained minimization problem. Three alternative formalisms for solving this problem are developed. The two ''off-line'' formalisms, with and without ''foresight'' characteristics, require the generation of a complete sensitivity data base prior to performing the uncertainty analysis. The ''online'' formalism, in which uncertainty analysis is performed interactively with the system analysis code, is best suited for treatment of large-scale highly nonlinear time-dependent problems. This methodology is applied to the uncertainty analysis of a transient upflow of a high pressure water heat transfer experiment. For comparison, an uncertainty analysis using sensitivities computed by standard response surface techniques is also performed. The results of the analysis indicate the following. Major reduction of the discrepancies in the calculation/experiment ratios is achieved by using the new methodology. Incorporation of in-bundle measurements in the uncertainty analysis significantly reduces system uncertainties. Accuracy of sensitivities generated by response-surface techniques should be carefully assessed prior to using them as a basis for uncertainty analyses of transient reactor safety problems

  12. Production scheduling of a lignite mine under quality and reserves uncertainty

    International Nuclear Information System (INIS)

    Galetakis, Michael; Roumpos, Christos; Alevizos, George; Vamvuka, Despina

    2011-01-01

    The effect of uncertainty sources to the stochastic optimization of the combined project of a new surface lignite mine exploitation and power plant operation for electricity generation is investigated. Major sources of uncertainty that were considered are the reserves and the quality of the lignite. Since probability distribution functions for these uncertainties were estimated during the detailed exploration phase of the deposit, the overall goal is then to determine the optimal capacity of the power plant and consequently the optimal production rate of the mine over the time. The optimization objective that was selected is the maximization of the net present value of the project. Emphasis is placed on the sensitivity analysis for the investigation of the effect of quality and reserves uncertainty on project optimization, on the mathematical formulation of risk attitude strategy and on increasing the efficiency of the optimization process by creating a limited set of feasible solutions applying empirical rules. The developed methodology was applied for the determination of the optimal annual production rate of a new surface lignite mine in the area of Ptolemais–Amynteon in Northern Greece. - Highlights: ► Quality and reserves uncertainty affects considerably the production scheduling. ► Stochastic optimization is greatly accelerated by incorporating Taylor's rule. ► Decisions can be made considering different risk level attitudes.

  13. Estimation of uncertainty in pKa values determined by potentiometric titration.

    Science.gov (United States)

    Koort, Eve; Herodes, Koit; Pihl, Viljar; Leito, Ivo

    2004-06-01

    A procedure is presented for estimation of uncertainty in measurement of the pK(a) of a weak acid by potentiometric titration. The procedure is based on the ISO GUM. The core of the procedure is a mathematical model that involves 40 input parameters. A novel approach is used for taking into account the purity of the acid, the impurities are not treated as inert compounds only, their possible acidic dissociation is also taken into account. Application to an example of practical pK(a) determination is presented. Altogether 67 different sources of uncertainty are identified and quantified within the example. The relative importance of different uncertainty sources is discussed. The most important source of uncertainty (with the experimental set-up of the example) is the uncertainty of pH measurement followed by the accuracy of the burette and the uncertainty of weighing. The procedure gives uncertainty separately for each point of the titration curve. The uncertainty depends on the amount of titrant added, being lowest in the central part of the titration curve. The possibilities of reducing the uncertainty and interpreting the drift of the pK(a) values obtained from the same curve are discussed.

  14. Uncertainty modeling of CCS investment strategy in China's power sector

    International Nuclear Information System (INIS)

    Zhou, Wenji; Zhu, Bing; Fuss, Sabine; Szolgayova, Jana; Obersteiner, Michael; Fei, Weiyang

    2010-01-01

    The increasing pressure resulting from the need for CO 2 mitigation is in conflict with the predominance of coal in China's energy structure. A possible solution to this tension between climate change and fossil fuel consumption fact could be the introduction of the carbon capture and storage (CCS) technology. However, high cost and other problems give rise to great uncertainty in R and D and popularization of carbon capture technology. This paper presents a real options model incorporating policy uncertainty described by carbon price scenarios (including stochasticity), allowing for possible technological change. This model is further used to determine the best strategy for investing in CCS technology in an uncertain environment in China and the effect of climate policy on the decision-making process of investment into carbon-saving technologies.

  15. Sensitivity of modeled ozone concentrations to uncertainties in biogenic emissions

    International Nuclear Information System (INIS)

    Roselle, S.J.

    1992-06-01

    The study examines the sensitivity of regional ozone (O3) modeling to uncertainties in biogenic emissions estimates. The United States Environmental Protection Agency's (EPA) Regional Oxidant Model (ROM) was used to simulate the photochemistry of the northeastern United States for the period July 2-17, 1988. An operational model evaluation showed that ROM had a tendency to underpredict O3 when observed concentrations were above 70-80 ppb and to overpredict O3 when observed values were below this level. On average, the model underpredicted daily maximum O3 by 14 ppb. Spatial patterns of O3, however, were reproduced favorably by the model. Several simulations were performed to analyze the effects of uncertainties in biogenic emissions on predicted O3 and to study the effectiveness of two strategies of controlling anthropogenic emissions for reducing high O3 concentrations. Biogenic hydrocarbon emissions were adjusted by a factor of 3 to account for the existing range of uncertainty in these emissions. The impact of biogenic emission uncertainties on O3 predictions depended upon the availability of NOx. In some extremely NOx-limited areas, increasing the amount of biogenic emissions decreased O3 concentrations. Two control strategies were compared in the simulations: (1) reduced anthropogenic hydrocarbon emissions, and (2) reduced anthropogenic hydrocarbon and NOx emissions. The simulations showed that hydrocarbon emission controls were more beneficial to the New York City area, but that combined NOx and hydrocarbon controls were more beneficial to other areas of the Northeast. Hydrocarbon controls were more effective as biogenic hydrocarbon emissions were reduced, whereas combined NOx and hydrocarbon controls were more effective as biogenic hydrocarbon emissions were increased

  16. Reducing uncertainty of Monte Carlo estimated fatigue damage in offshore wind turbines using FORM

    DEFF Research Database (Denmark)

    H. Horn, Jan-Tore; Jensen, Jørgen Juncher

    2016-01-01

    Uncertainties related to fatigue damage estimation of non-linear systems are highly dependent on the tail behaviour and extreme values of the stress range distribution. By using a combination of the First Order Reliability Method (FORM) and Monte Carlo simulations (MCS), the accuracy of the fatigue...

  17. A new evaluation of the uncertainty associated with CDIAC estimates of fossil fuel carbon dioxide emission

    Directory of Open Access Journals (Sweden)

    Robert J. Andres

    2014-07-01

    Full Text Available Three uncertainty assessments associated with the global total of carbon dioxide emitted from fossil fuel use and cement production are presented. Each assessment has its own strengths and weaknesses and none give a full uncertainty assessment of the emission estimates. This approach grew out of the lack of independent measurements at the spatial and temporal scales of interest. Issues of dependent and independent data are considered as well as the temporal and spatial relationships of the data. The result is a multifaceted examination of the uncertainty associated with fossil fuel carbon dioxide emission estimates. The three assessments collectively give a range that spans from 1.0 to 13% (2 σ. Greatly simplifying the assessments give a global fossil fuel carbon dioxide uncertainty value of 8.4% (2 σ. In the largest context presented, the determination of fossil fuel emission uncertainty is important for a better understanding of the global carbon cycle and its implications for the physical, economic and political world.

  18. Synchronous Sounds Enhance Visual Sensitivity without Reducing Target Uncertainty

    Directory of Open Access Journals (Sweden)

    Yi-Chuan Chen

    2011-10-01

    Full Text Available We examined the crossmodal effect of the presentation of a simultaneous sound on visual detection and discrimination sensitivity using the equivalent noise paradigm (Dosher & Lu, 1998. In each trial, a tilted Gabor patch was presented in either the first or second of two intervals consisting of dynamic 2D white noise with one of seven possible contrast levels. The results revealed that the sensitivity of participants' visual detection and discrimination performance were both enhanced by the presentation of a simultaneous sound, though only close to the noise level at which participants' target contrast thresholds started to increase with the increasing noise contrast. A further analysis of the psychometric function at this noise level revealed that the increase in sensitivity could not be explained by the reduction of participants' uncertainty regarding the onset time of the visual target. We suggest that this crossmodal facilitatory effect may be accounted for by perceptual enhancement elicited by a simultaneously-presented sound, and that the crossmodal facilitation was easier to observe when the visual system encountered a level of noise that happened to be close to the level of internal noise embedded within the system.

  19. Accounting for Epistemic Uncertainty in Mission Supportability Assessment: A Necessary Step in Understanding Risk and Logistics Requirements

    Science.gov (United States)

    Owens, Andrew; De Weck, Olivier L.; Stromgren, Chel; Goodliff, Kandyce; Cirillo, William

    2017-01-01

    Future crewed missions to Mars present a maintenance logistics challenge that is unprecedented in human spaceflight. Mission endurance – defined as the time between resupply opportunities – will be significantly longer than previous missions, and therefore logistics planning horizons are longer and the impact of uncertainty is magnified. Maintenance logistics forecasting typically assumes that component failure rates are deterministically known and uses them to represent aleatory uncertainty, or uncertainty that is inherent to the process being examined. However, failure rates cannot be directly measured; rather, they are estimated based on similarity to other components or statistical analysis of observed failures. As a result, epistemic uncertainty – that is, uncertainty in knowledge of the process – exists in failure rate estimates that must be accounted for. Analyses that neglect epistemic uncertainty tend to significantly underestimate risk. Epistemic uncertainty can be reduced via operational experience; for example, the International Space Station (ISS) failure rate estimates are refined using a Bayesian update process. However, design changes may re-introduce epistemic uncertainty. Thus, there is a tradeoff between changing a design to reduce failure rates and operating a fixed design to reduce uncertainty. This paper examines the impact of epistemic uncertainty on maintenance logistics requirements for future Mars missions, using data from the ISS Environmental Control and Life Support System (ECLS) as a baseline for a case study. Sensitivity analyses are performed to investigate the impact of variations in failure rate estimates and epistemic uncertainty on spares mass. The results of these analyses and their implications for future system design and mission planning are discussed.

  20. The Bertlmann-Martin Inequalities and the Uncertainty Principle

    International Nuclear Information System (INIS)

    Ighezou, F.Z.; Kerris, A.T.; Lombard, R.J.

    2008-01-01

    A lower bound to (r) 1s is established from the Thomas-Reiche-Kuhn sum rule applied to the reduced equation for the s-states. It is linked to the average value of (r 2 ) 1s We discuss, on few examples, how the use of approximate value for (r 2 ) 1s , derived from the generalized Bertlmann and Martin inequalities, preserves the lower bound character of (r) 1s . Finally, by using the uncertainty principle and the uncertainty in the radial position, we derive a low bound to the ground state kinetic energy

  1. Insights into water managers' perception and handling of uncertainties - a study of the role of uncertainty in practitioners' planning and decision-making

    Science.gov (United States)

    Höllermann, Britta; Evers, Mariele

    2017-04-01

    Planning and decision-making under uncertainty is common in water management due to climate variability, simplified models, societal developments, planning restrictions just to name a few. Dealing with uncertainty can be approached from two sites, hereby affecting the process and form of communication: Either improve the knowledge base by reducing uncertainties or apply risk-based approaches to acknowledge uncertainties throughout the management process. Current understanding is that science more strongly focusses on the former approach, while policy and practice are more actively applying a risk-based approach to handle incomplete and/or ambiguous information. The focus of this study is on how water managers perceive and handle uncertainties at the knowledge/decision interface in their daily planning and decision-making routines. How they evaluate the role of uncertainties for their decisions and how they integrate this information into the decision-making process. Expert interviews and questionnaires among practitioners and scientists provided an insight into their perspectives on uncertainty handling allowing a comparison of diverse strategies between science and practice as well as between different types of practitioners. Our results confirmed the practitioners' bottom up approach from potential measures upwards instead of impact assessment downwards common in science-based approaches. This science-practice gap may hinder effective uncertainty integration and acknowledgement in final decisions. Additionally, the implementation of an adaptive and flexible management approach acknowledging uncertainties is often stalled by rigid regulations favouring a predict-and-control attitude. However, the study showed that practitioners' level of uncertainty recognition varies with respect to his or her affiliation to type of employer and business unit, hence, affecting the degree of the science-practice-gap with respect to uncertainty recognition. The level of working

  2. Kofi Annan, Syria and the Uses of Uncertainty in Mediation

    Directory of Open Access Journals (Sweden)

    Richard Gowan

    2013-03-01

    Full Text Available One year after Kofi Annan presented his six-point plan for ending the Syrian civil war, it can only be called a failure. But it is necessary to recall the situation facing the UN-Arab League envoy and his team in early 2012. The Syrian conflict had created serious tensions between the major powers. A Western military intervention appeared unlikely but could not be ruled out with absolute certainty. This commentary contends that Annan’s initial priority was to reduce the level of uncertainty inside and outside Syria, thereby creating a framework for political talks.  However, in lowering the level of uncertainty, Annan reduced his own leverage as the Syrian government correctly concluded that it would not be punished for failing to cooperate in good faith.  The commentary concludes that there are occasions where it is advisable for international mediators to maintain and exploit a degree of uncertainty about how a conflict may develop.

  3. Uncertainty vs. learning in climate policy: Some classical results and new directions

    Energy Technology Data Exchange (ETDEWEB)

    Lange, A. [Univ. of Maryland (United States); Treich, N. [Univ. of Toulouse (France)

    2007-07-01

    Climate policy decisions today have to be made under substantial uncertainty: the impact of accumulating greenhouse gases in the atmosphere is not perfectly known, the future economic and social consequences of climate change, in particular the valuation of possible damages, are uncertain. However, learning will change the basis of making future decisions on abatement policies. These important issues of uncertainty and learning are often presented in a colloquial sense. Two opposing effects are typically put forward: First, uncertainty about future climate damage, which is often associated with the possibility of a catastrophic scenario is said to give a premium to slow down global warming and therefore to increase abatement efforts today. Second learning opportunities will reduce scientific undertainty about climate damage over time. This is often used as an argument to postpone abatement efforts until new information is received. The effects of uncertainty and learning on the optimal design of current climate policy are still much debated both in the academic and the political arena. In this paper, the authors study and contrast the effect of uncertainty and learning in a two-decision model that encompasses most existing microeconomics models of climate change. They first consider the common expected utility framework: While uncertainty has generally no or a negative effect on welfare, learning has always a positive, and thus opposite, effect. The effects of both uncertainty and learning on decisions are less clear. Neither uncertainty nor learning can be used as an argument to increase or reduce emissions today, independently on the degree of risk aversion of the decision-marker and on the nature of irreversibility constraints. The authors then deviate from the expected utility framework and consider a model with ambiguity aversion. The model accounts well for situations of imprecise or multiple probability distributions, as present in the context of climate

  4. Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.

    Directory of Open Access Journals (Sweden)

    Elise Payzan-LeNestour

    Full Text Available Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating.

  5. The Great Recession and risk for child abuse and neglect.

    Science.gov (United States)

    Schneider, William; Waldfogel, Jane; Brooks-Gunn, Jeanne

    2017-01-01

    This paper examines the association between the Great Recession and four measures of the risk for maternal child abuse and neglect: (1) maternal physical aggression; (2) maternal psychological aggression; (3) physical neglect by mothers; and (4) supervisory/exposure neglect by mothers. It draws on rich longitudinal data from the Fragile Families and Child Wellbeing Study, a longitudinal birth cohort study of families in 20 U.S. cities (N = 3,177; 50% African American, 25% Hispanic; 22% non-Hispanic white; 3% other). The study collected information for the 9-year follow-up survey before, during, and after the Great Recession (2007-2010). Interview dates were linked to two macroeconomic measures of the Great Recession: the national Consumer Sentiment Index and the local unemployment rate. Also included are a wide range of socio-demographic controls, as well as city fixed effects and controls for prior parenting. Results indicate that the Great Recession was associated with increased risk of child abuse but decreased risk of child neglect. Households with social fathers present may have been particularly adversely affected. Results also indicate that economic uncertainty during the Great Recession, as measured by the Consumer Sentiment Index and the unemployment rate, had direct effects on the risk of abuse or neglect, which were not mediated by individual-level measures of economic hardship or poor mental health.

  6. Assessing flood forecast uncertainty with fuzzy arithmetic

    Directory of Open Access Journals (Sweden)

    de Bruyn Bertrand

    2016-01-01

    Full Text Available Providing forecasts for flow rates and water levels during floods have to be associated with uncertainty estimates. The forecast sources of uncertainty are plural. For hydrological forecasts (rainfall-runoff performed using a deterministic hydrological model with basic physics, two main sources can be identified. The first obvious source is the forcing data: rainfall forecast data are supplied in real time by meteorological forecasting services to the Flood Forecasting Service within a range between a lowest and a highest predicted discharge. These two values define an uncertainty interval for the rainfall variable provided on a given watershed. The second source of uncertainty is related to the complexity of the modeled system (the catchment impacted by the hydro-meteorological phenomenon, the number of variables that may describe the problem and their spatial and time variability. The model simplifies the system by reducing the number of variables to a few parameters. Thus it contains an intrinsic uncertainty. This model uncertainty is assessed by comparing simulated and observed rates for a large number of hydro-meteorological events. We propose a method based on fuzzy arithmetic to estimate the possible range of flow rates (and levels of water making a forecast based on possible rainfalls provided by forcing and uncertainty model. The model uncertainty is here expressed as a range of possible values. Both rainfall and model uncertainties are combined with fuzzy arithmetic. This method allows to evaluate the prediction uncertainty range. The Flood Forecasting Service of Oise and Aisne rivers, in particular, monitors the upstream watershed of the Oise at Hirson. This watershed’s area is 310 km2. Its response time is about 10 hours. Several hydrological models are calibrated for flood forecasting in this watershed and use the rainfall forecast. This method presents the advantage to be easily implemented. Moreover, it permits to be carried out

  7. Critical mid-term uncertainties in long-term decarbonisation pathways

    International Nuclear Information System (INIS)

    Usher, Will; Strachan, Neil

    2012-01-01

    Over the next decade, large energy investments are required in the UK to meet growing energy service demands and legally binding emission targets under a pioneering policy agenda. These are necessary despite deep mid-term (2025–2030) uncertainties over which national policy makers have little control. We investigate the effect of two critical mid-term uncertainties on optimal near-term investment decisions using a two-stage stochastic energy system model. The results show that where future fossil fuel prices are uncertain: (i) the near term hedging strategy to 2030 differs from any one deterministic fuel price scenario and is structurally dissimilar to a simple ‘average’ of the deterministic scenarios, and (ii) multiple recourse strategies from 2030 are perturbed by path dependencies caused by hedging investments. Evaluating the uncertainty under a decarbonisation agenda shows that fossil fuel price uncertainty is very expensive at around £20 billion. The addition of novel mitigation options reduces the value of fossil fuel price uncertainty to £11 billion. Uncertain biomass import availability shows a much lower value of uncertainty at £300 million. This paper reveals the complex relationship between the flexibility of the energy system and mitigating the costs of uncertainty due to the path-dependencies caused by the long-life times of both infrastructures and generation technologies. - Highlights: ► Critical mid-term uncertainties affect near-term investments in UK energy system. ► Deterministic scenarios give conflicting near-term actions. ► Stochastic scenarios give one near-term hedging strategy. ► Technologies exhibit path dependency or flexibility. ► Fossil fuel price uncertainty is very expensive, biomass availability uncertainty is not.

  8. Measurement uncertainty analysis techniques applied to PV performance measurements

    International Nuclear Information System (INIS)

    Wells, C.

    1992-10-01

    The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis? It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment's final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis? A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results

  9. Measurement uncertainty analysis techniques applied to PV performance measurements

    Energy Technology Data Exchange (ETDEWEB)

    Wells, C.

    1992-10-01

    The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment's final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.

  10. Measurement uncertainty analysis techniques applied to PV performance measurements

    Energy Technology Data Exchange (ETDEWEB)

    Wells, C

    1992-10-01

    The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis? It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment`s final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis? A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.

  11. Uncertainties in radioecological assessment models-Their nature and approaches to reduce them

    International Nuclear Information System (INIS)

    Kirchner, G.; Steiner, M.

    2008-01-01

    Radioecological assessment models are necessary tools for estimating the radiation exposure of humans and non-human biota. This paper focuses on factors affecting their predictive accuracy, discusses the origin and nature of the different contributions to uncertainty and variability and presents approaches to separate and quantify them. The key role of the conceptual model, notably in relation to its structure and complexity, as well as the influence of the number and type of input parameters, are highlighted. Guidelines are provided to improve the degree of reliability of radioecological models

  12. Renewables, nuclear, or fossil fuels? Scenarios for Great Britain’s power system considering costs, emissions and energy security

    International Nuclear Information System (INIS)

    Pfenninger, Stefan; Keirstead, James

    2015-01-01

    Highlights: • We compare a large number of cost-optimal future power systems for Great Britain. • Scenarios are assessed on cost, emissions reductions, and energy security. • Up to 60% of variable renewable capacity is possible with little cost increase. • Higher shares require storage, imports or dispatchable renewables such as tidal range. - Abstract: Mitigating climate change is driving the need to decarbonize the electricity sector, for which various possible technological options exist, alongside uncertainty over which options are preferable in terms of cost, emissions reductions, and energy security. To reduce this uncertainty, we here quantify two questions for the power system of Great Britain (England, Wales and Scotland): First, when compared within the same high-resolution modeling framework, how much do different combinations of technologies differ in these three respects? Second, how strongly does the cost and availability of grid-scale storage affect overall system cost, and would it favor some technology combinations above others? We compare three main possible generation technologies: (1) renewables, (2) nuclear, and (3) fossil fuels (with/without carbon capture and storage). Our results show that across a wide range of these combinations, the overall costs remain similar, implying that different configurations are equally feasible both technically and economically. However, the most economically favorable scenarios are not necessarily favorable in terms of emissions or energy security. The availability of grid-scale storage in scenarios with little dispatchable generation can reduce overall levelized electricity cost by up to 50%, depending on storage capacity costs. The UK can rely on its domestic wind and solar PV generation at lower renewable shares, with levelized costs only rising more than 10% above the mean of 0.084 GBP/kWh for shares of 50% and below at a 70% share, which is 35% higher. However, for more than an 80% renewable

  13. Causal uncertainty, claimed and behavioural self-handicapping.

    Science.gov (United States)

    Thompson, Ted; Hepburn, Jonathan

    2003-06-01

    Causal uncertainty beliefs involve doubts about the causes of events, and arise as a consequence of non-contingent evaluative feedback: feedback that leaves the individual uncertain about the causes of his or her achievement outcomes. Individuals high in causal uncertainty are frequently unable to confidently attribute their achievement outcomes, experience anxiety in achievement situations and as a consequence are likely to engage in self-handicapping behaviour. Accordingly, we sought to establish links between trait causal uncertainty, claimed and behavioural self-handicapping. Participants were N=72 undergraduate students divided equally between high and low causally uncertain groups. We used a 2 (causal uncertainty status: high, low) x 3 (performance feedback condition: success, non-contingent success, non-contingent failure) between-subjects factorial design to examine the effects of causal uncertainty on achievement behaviour. Following performance feedback, participants completed 20 single-solution anagrams and 12 remote associate tasks serving as performance measures, and 16 unicursal tasks to assess practice effort. Participants also completed measures of claimed handicaps, state anxiety and attributions. Relative to low causally uncertain participants, high causally uncertain participants claimed more handicaps prior to performance on the anagrams and remote associates, reported higher anxiety, attributed their failure to internal, stable factors, and reduced practice effort on the unicursal tasks, evident in fewer unicursal tasks solved. These findings confirm links between trait causal uncertainty and claimed and behavioural self-handicapping, highlighting the need for educators to facilitate means by which students can achieve surety in the manner in which they attribute the causes of their achievement outcomes.

  14. Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

    International Nuclear Information System (INIS)

    Xiao, H.; Wu, J.-L.; Wang, J.-X.; Sun, R.; Roy, C.J.

    2016-01-01

    Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has

  15. Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

    Energy Technology Data Exchange (ETDEWEB)

    Xiao, H., E-mail: hengxiao@vt.edu; Wu, J.-L.; Wang, J.-X.; Sun, R.; Roy, C.J.

    2016-11-01

    Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach

  16. Reducing uncertainty in Climate Response Time Scale by Bayesian Analysis of the 8.2 ka event

    Science.gov (United States)

    Lorenz, A.; Held, H.; Bauer, E.; Schneider von Deimling, T.

    2009-04-01

    We analyze the possibility of uncertainty reduction in Climate Response Time Scale by utilizing Greenland ice-core data that contain the 8.2 ka event within a Bayesian model-data intercomparison with the Earth system model of intermediate complexity, CLIMBER-2.3. Within a stochastic version of the model it has been possible to mimic the 8.2 ka event within a plausible experimental setting and with relatively good accuracy considering the timing of the event in comparison to other modeling exercises [1]. The simulation of the centennial cold event is effectively determined by the oceanic cooling rate which depends largely on the ocean diffusivity described by diffusion coefficients of relatively wide uncertainty ranges. The idea now is to discriminate between the different values of diffusivities according to their likelihood to rightly represent the duration of the 8.2 ka event and thus to exploit the paleo data to constrain uncertainty in model parameters in analogue to [2]. Implementing this inverse Bayesian Analysis with this model the technical difficulty arises to establish the related likelihood numerically in addition to the uncertain model parameters: While mainstream uncertainty analyses can assume a quasi-Gaussian shape of likelihood, with weather fluctuating around a long term mean, the 8.2 ka event as a highly nonlinear effect precludes such an a priori assumption. As a result of this study [3] the Bayesian Analysis showed a reduction of uncertainty in vertical ocean diffusivity parameters of factor 2 compared to prior knowledge. This learning effect on the model parameters is propagated to other model outputs of interest; e.g. the inverse ocean heat capacity, which is important for the dominant time scale of climate response to anthropogenic forcing which, in combination with climate sensitivity, strongly influences the climate systems reaction for the near- and medium-term future. 1 References [1] E. Bauer, A. Ganopolski, M. Montoya: Simulation of the

  17. Methodology for optimization of process integration schemes in a biorefinery under uncertainty

    International Nuclear Information System (INIS)

    Marta Abreu de las Villas (Cuba))" data-affiliation=" (Departamento de Ingeniería Química. Facultad de Química y Farmacia. Universidad Central Marta Abreu de las Villas (Cuba))" >González-Cortés, Meilyn; Marta Abreu de las Villas (Cuba))" data-affiliation=" (Departamento de Ingeniería Química. Facultad de Química y Farmacia. Universidad Central Marta Abreu de las Villas (Cuba))" >Martínez-Martínez, Yenisleidys; Marta Abreu de las Villas (Cuba))" data-affiliation=" (Departamento de Ingeniería Química. Facultad de Química y Farmacia. Universidad Central Marta Abreu de las Villas (Cuba))" >Albernas-Carvajal, Yailet; Marta Abreu de las Villas (Cuba))" data-affiliation=" (Departamento de Ingeniería Química. Facultad de Química y Farmacia. Universidad Central Marta Abreu de las Villas (Cuba))" >Pedraza-Garciga, Julio; Marta Abreu de las Villas (Cuba))" data-affiliation=" (Departamento de Ingeniería Química. Facultad de Química y Farmacia. Universidad Central Marta Abreu de las Villas (Cuba))" >Morales-Zamora, Marlen

    2017-01-01

    The uncertainty has a great impact in the investment decisions, operability of the plants and in the feasibility of integration opportunities in the chemical processes. This paper, presents the steps to consider the optimization of process investment in the processes integration under conditions of uncertainty. It is shown the potentialities of the biomass cane of sugar for the integration with several plants in a biorefinery scheme for the obtaining chemical products, thermal and electric energy. Among the factories with potentialities for this integration are the pulp and paper and sugar factories and other derivative processes. Theses factories have common resources and also have a variety of products that can be exchange between them so certain products generated in a one of them can be raw matter in another plant. The methodology developed guide to obtaining of feasible investment projects under uncertainty. As objective function was considered the maximization of net profitable value in different scenarios that are generated from the integration scheme. (author)

  18. Costs of travel time uncertainty and benefits of travel time information: Conceptual model and numerical examples

    NARCIS (Netherlands)

    Ettema, D.F.; Timmermans, H.J.P.

    2006-01-01

    A negative effect of congestion that tends to be overlooked is travel time uncertainty. Travel time uncertainty causes scheduling costs due to early or late arrival. The negative effects of travel time uncertainty can be reduced by providing travellers with travel time information, which improves

  19. Key uncertainties in climate change policy: Results from ICAM-2

    Energy Technology Data Exchange (ETDEWEB)

    Dowlatabadi, H.; Kandlikar, M.

    1995-12-31

    A critical aspect of climate change decision-making is uncertainties in current understanding of the socioeconomic, climatic and biogeochemical processes involved. Decision-making processes are much better informed if these uncertainties are characterized and their implications understood. Quantitative analysis of these uncertainties serve to: inform decision makers about the likely outcome of policy initiatives; and help set priorities for research so that outcome ambiguities faced by the decision-makers are reduced. A family of integrated assessment models of climate change have been developed at Carnegie Mellon. These models are distinguished from other integrated assessment efforts in that they were designed from the outset to characterize and propagate parameter, model, value, and decision-rule uncertainties. The most recent of these models is ICAM 2.0. This model includes demographics, economic activities, emissions, atmospheric chemistry, climate change, sea level rise and other impact modules and the numerous associated feedbacks. The model has over 700 objects of which over 1/3 are uncertain. These have been grouped into seven different classes of uncertain items. The impact of uncertainties in each of these items can be considered individually or in combinations with the others. In this paper we demonstrate the relative contribution of various sources of uncertainty to different outcomes in the model. The analysis shows that climatic uncertainties are most important, followed by uncertainties in damage calculations, economic uncertainties and direct aerosol forcing uncertainties. Extreme uncertainties in indirect aerosol forcing and behavioral response to climate change (adaptation) were characterized by using bounding analyses; the results suggest that these extreme uncertainties can dominate the choice of policy outcomes.

  20. Artificial intelligence metamodel comparison and application to wind turbine airfoil uncertainty analysis

    Directory of Open Access Journals (Sweden)

    Yaping Ju

    2016-05-01

    Full Text Available The Monte Carlo simulation method for turbomachinery uncertainty analysis often requires performing a huge number of simulations, the computational cost of which can be greatly alleviated with the help of metamodeling techniques. An intensive comparative study was performed on the approximation performance of three prospective artificial intelligence metamodels, that is, artificial neural network, radial basis function, and support vector regression. The genetic algorithm was used to optimize the predetermined parameters of each metamodel for the sake of a fair comparison. Through testing on 10 nonlinear functions with different problem scales and sample sizes, the genetic algorithm–support vector regression metamodel was found more accurate and robust than the other two counterparts. Accordingly, the genetic algorithm–support vector regression metamodel was selected and combined with the Monte Carlo simulation method for the uncertainty analysis of a wind turbine airfoil under two types of surface roughness uncertainties. The results show that the genetic algorithm–support vector regression metamodel can capture well the uncertainty propagation from the surface roughness to the airfoil aerodynamic performance. This work is useful to the application of metamodeling techniques in the robust design optimization of turbomachinery.

  1. Quantum-memory-assisted entropic uncertainty in spin models with Dzyaloshinskii-Moriya interaction

    Science.gov (United States)

    Huang, Zhiming

    2018-02-01

    In this article, we investigate the dynamics and correlations of quantum-memory-assisted entropic uncertainty, the tightness of the uncertainty, entanglement, quantum correlation and mixedness for various spin chain models with Dzyaloshinskii-Moriya (DM) interaction, including the XXZ model with DM interaction, the XY model with DM interaction and the Ising model with DM interaction. We find that the uncertainty grows to a stable value with growing temperature but reduces as the coupling coefficient, anisotropy parameter and DM values increase. It is found that the entropic uncertainty is closely correlated with the mixedness of the system. The increasing quantum correlation can result in a decrease in the uncertainty, and the robustness of quantum correlation is better than entanglement since entanglement means sudden birth and death. The tightness of the uncertainty drops to zero, apart from slight volatility as various parameters increase. Furthermore, we propose an effective approach to steering the uncertainty by weak measurement reversal.

  2. The Great London Smog of 1952.

    Science.gov (United States)

    Polivka, Barbara J

    2018-04-01

    : The Great London Smog of December 1952 lasted five days and killed up to 12,000 people. The smog developed primarily because of extensive burning of high-sulfur coal. The health effects were both immediate and long lasting, with a recent study revealing an increased likelihood of childhood asthma development in those exposed to the Great Smog while in utero or during their first year of life. Subsequent pollution legislation-including the U.S. Clean Air Act and its amendments-have demonstrably reduced air pollution and positively impacted health outcomes. With poor air quality events like the Great Smog continuing to occur today, nurses need to be aware of the impact such environmental disasters can have on human health.

  3. The role of uncertainty in climate change adaptation strategies — A Danish water management example

    DEFF Research Database (Denmark)

    Refsgaard, J.C.; Arnbjerg-Nielsen, Karsten; Drews, Martin

    2013-01-01

    We propose a generic framework to characterize climate change adaptation uncertainty according to three dimensions: level, source and nature. Our framework is different, and in this respect more comprehensive, than the present UN Intergovernmental Panel on Climate Change (IPCC) approach and could...... are epistemic (reducible) by nature but uncertainties on adaptation measures are complex, with ambiguity often being added to impact uncertainties. Strategies to deal with uncertainty in climate change adaptation should reflect the nature of the uncertainty sources and how they interact with risk level...

  4. Instrument uncertainty predictions

    International Nuclear Information System (INIS)

    Coutts, D.A.

    1991-07-01

    The accuracy of measurements and correlations should normally be provided for most experimental activities. The uncertainty is a measure of the accuracy of a stated value or equation. The uncertainty term reflects a combination of instrument errors, modeling limitations, and phenomena understanding deficiencies. This report provides several methodologies to estimate an instrument's uncertainty when used in experimental work. Methods are shown to predict both the pretest and post-test uncertainty

  5. A new optimization framework using genetic algorithm and artificial neural network to reduce uncertainties in petroleum reservoir models

    Science.gov (United States)

    Maschio, Célio; José Schiozer, Denis

    2015-01-01

    In this article, a new optimization framework to reduce uncertainties in petroleum reservoir attributes using artificial intelligence techniques (neural network and genetic algorithm) is proposed. Instead of using the deterministic values of the reservoir properties, as in a conventional process, the parameters of the probability density function of each uncertain attribute are set as design variables in an optimization process using a genetic algorithm. The objective function (OF) is based on the misfit of a set of models, sampled from the probability density function, and a symmetry factor (which represents the distribution of curves around the history) is used as weight in the OF. Artificial neural networks are trained to represent the production curves of each well and the proxy models generated are used to evaluate the OF in the optimization process. The proposed method was applied to a reservoir with 16 uncertain attributes and promising results were obtained.

  6. Isotopic techniques in radioactive waste disposal site evaluation: a method for reducing uncertainties I. T, T/3He, 4He, 14C, 36Cl

    International Nuclear Information System (INIS)

    Muller, A.B.

    1981-01-01

    This paper introduces five of the isotopic techniques which can help reduce uncertainties associated with the assessment of radioactive waste disposal sites. The basic principles and practical considerations of these best known techniques have been presented, showing how much additional site specific information can be acquired at little cost or consequence to containment efficiency. These methods, and the more experimental methods appearing in the figure but not discussed here, should be considered in any detailed site characterization, data collection and analysis

  7. Effect of uncertainty components such as recalibration on the performance of quality control charts

    DEFF Research Database (Denmark)

    Winkel, P; Zhang, Nevin

    2005-01-01

    Uncertainty components (recalibration, new reagent lots, etc.) may be the source of random changes in the level of quality control (QC) values, thus causing false alarms. We propose a method for reducing false alarms.......Uncertainty components (recalibration, new reagent lots, etc.) may be the source of random changes in the level of quality control (QC) values, thus causing false alarms. We propose a method for reducing false alarms....

  8. Uncertainty in hydrological signatures

    Science.gov (United States)

    McMillan, Hilary; Westerberg, Ida

    2015-04-01

    Information that summarises the hydrological behaviour or flow regime of a catchment is essential for comparing responses of different catchments to understand catchment organisation and similarity, and for many other modelling and water-management applications. Such information types derived as an index value from observed data are known as hydrological signatures, and can include descriptors of high flows (e.g. mean annual flood), low flows (e.g. mean annual low flow, recession shape), the flow variability, flow duration curve, and runoff ratio. Because the hydrological signatures are calculated from observed data such as rainfall and flow records, they are affected by uncertainty in those data. Subjective choices in the method used to calculate the signatures create a further source of uncertainty. Uncertainties in the signatures may affect our ability to compare different locations, to detect changes, or to compare future water resource management scenarios. The aim of this study was to contribute to the hydrological community's awareness and knowledge of data uncertainty in hydrological signatures, including typical sources, magnitude and methods for its assessment. We proposed a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrated it for a variety of commonly used signatures. The study was made for two data rich catchments, the 50 km2 Mahurangi catchment in New Zealand and the 135 km2 Brue catchment in the UK. For rainfall data the uncertainty sources included point measurement uncertainty, the number of gauges used in calculation of the catchment spatial average, and uncertainties relating to lack of quality control. For flow data the uncertainty sources included uncertainties in stage/discharge measurement and in the approximation of the true stage-discharge relation by a rating curve. The resulting uncertainties were compared across the different signatures and catchments, to quantify uncertainty

  9. Pacific salmonines in the Great Lakes Basin

    Science.gov (United States)

    Claramunt, Randall M.; Madenjian, Charles P.; Clapp, David; Taylor, William W.; Lynch, Abigail J.; Léonard, Nancy J.

    2012-01-01

    Pacific salmon (genus Oncorhynchus) are a valuable resource, both within their native range in the North Pacific rim and in the Great Lakes basin. Understanding their value from a biological and economic perspective in the Great Lakes, however, requires an understanding of changes in the ecosystem and of management actions that have been taken to promote system stability, integrity, and sustainable fisheries. Pacific salmonine introductions to the Great Lakes are comprised mainly of Chinook salmon, coho salmon, and steelhead and have accounted for 421, 177, and 247 million fish, respectively, stocked during 1966-2007. Stocking of Pacific salmonines has been effective in substantially reducing exotic prey fish abundances in several of the Great Lakes (e.g., lakes Michigan, Huron, and Ontario). The goal of our evaluation was to highlight differences in management strategies and perspectives across the basin, and to evaluate policies for Pacific salmonine management in the Great Lakes. Currently, a potential conflict exists between Pacific salmonine management and native fish rehabilitation goals because of the desire to sustain recreational fisheries and to develop self-sustaining populations of stocked Pacific salmonines in the Great Lakes. We provide evidence that suggests Pacific salmonines have not only become naturalized to the food webs of the Great Lakes, but that their populations (specifically Chinook salmon) may be fluctuating in concert with specific prey (i.e., alewives) whose populations are changing relative to environmental conditions and ecosystem disturbances. Remaining questions, however, are whether or not “natural” fluctuations in predator and prey provide enough “stability” in the Great Lakes food webs, and even more importantly, would a choice by managers to attempt to reduce the severity of predator-prey oscillations be antagonistic to native fish restoration efforts. We argue that, on each of the Great Lakes, managers are pursuing

  10. Uncertainty analysis in calculations of a road accident consequences

    International Nuclear Information System (INIS)

    Bonnefous, S.; Brenot, J.; Hubert, P.

    1995-01-01

    This paper develops a concrete situation witch is the search for an evacuation distance in case of a road accident implying a chlorine tank. The methodological aspect is how implementing uncertainty analysis in deterministic models with random parameters. The study demonstrates a great dispersion in the results. It allows to establish satisfactory decision rules and a hierarchy on parameters witch is useful to define priorities in the search for information and to improve the treatment of these parameters. (authors). 8 refs., 1 fig., 2 tabs

  11. Status of uncertainty assessment in k0-NAA measurement. Anything still missing?

    International Nuclear Information System (INIS)

    Borut Smodis; Tinkara Bucar

    2014-01-01

    Several approaches to quantifying measurement uncertainty in k 0 -based neutron activation analysis (k 0 -NAA) are reviewed, comprising the original approach, the spreadsheet approach, the dedicated computer program involving analytical calculations and the two k 0 -NAA programs available on the market. Two imperfectness in the dedicated programs are identified, their impact assessed and possible improvements presented for a concrete experimental situation. The status of uncertainty assessment in k 0 -NAA is discussed and steps for improvement are recommended. It is concluded that the present magnitude of measurement uncertainty should further be improved by making additional efforts in reducing uncertainties of the relevant nuclear constants used. (author)

  12. Performance of uncertainty quantification methodologies and linear solvers in cardiovascular simulations

    Science.gov (United States)

    Seo, Jongmin; Schiavazzi, Daniele; Marsden, Alison

    2017-11-01

    Cardiovascular simulations are increasingly used in clinical decision making, surgical planning, and disease diagnostics. Patient-specific modeling and simulation typically proceeds through a pipeline from anatomic model construction using medical image data to blood flow simulation and analysis. To provide confidence intervals on simulation predictions, we use an uncertainty quantification (UQ) framework to analyze the effects of numerous uncertainties that stem from clinical data acquisition, modeling, material properties, and boundary condition selection. However, UQ poses a computational challenge requiring multiple evaluations of the Navier-Stokes equations in complex 3-D models. To achieve efficiency in UQ problems with many function evaluations, we implement and compare a range of iterative linear solver and preconditioning techniques in our flow solver. We then discuss applications to patient-specific cardiovascular simulation and how the problem/boundary condition formulation in the solver affects the selection of the most efficient linear solver. Finally, we discuss performance improvements in the context of uncertainty propagation. Support from National Institute of Health (R01 EB018302) is greatly appreciated.

  13. Optimum design of forging process parameters and preform shape under uncertainties

    International Nuclear Information System (INIS)

    Repalle, Jalaja; Grandhi, Ramana V.

    2004-01-01

    Forging is a highly complex non-linear process that is vulnerable to various uncertainties, such as variations in billet geometry, die temperature, material properties, workpiece and forging equipment positional errors and process parameters. A combination of these uncertainties could induce heavy manufacturing losses through premature die failure, final part geometric distortion and production risk. Identifying the sources of uncertainties, quantifying and controlling them will reduce risk in the manufacturing environment, which will minimize the overall cost of production. In this paper, various uncertainties that affect forging tool life and preform design are identified, and their cumulative effect on the forging process is evaluated. Since the forging process simulation is computationally intensive, the response surface approach is used to reduce time by establishing a relationship between the system performance and the critical process design parameters. Variability in system performance due to randomness in the parameters is computed by applying Monte Carlo Simulations (MCS) on generated Response Surface Models (RSM). Finally, a Robust Methodology is developed to optimize forging process parameters and preform shape. The developed method is demonstrated by applying it to an axisymmetric H-cross section disk forging to improve the product quality and robustness

  14. Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty

    International Nuclear Information System (INIS)

    Dal-Mas, Matteo; Giarola, Sara; Zamboni, Andrea; Bezzo, Fabrizio

    2011-01-01

    Fossil fuel depletion and the increase of greenhouse gases emissions has been pushing the search for alternative fuels for automotive transport. The European Union has identified biofuel technology as one option for reducing its dependence on imported energy. Ethanol is a promising biofuel, but great uncertainty on the business profitability has recently determined a slowdown in the industry expansion. In particular, geographical plant location, biomass price fluctuation and fuel demand variability severely constrain the economic viability of new ethanol facilities. In this work a dynamic, spatially explicit and multi-echelon Mixed Integer Linear Program (MILP) modeling framework is presented to help decision-makers and potential investors assessing economic performances and risk on investment of the entire biomass-based ethanol supply chain. A case study concerning the corn-to-ethanol production supply chain in Northern Italy is used to demonstrate the effectiveness of the proposed modeling approach. The mathematical pattern addresses the issue of optimizing the ethanol supply network over a ten years' time period under uncertainty on biomass production cost and product selling price. The model allows optimizing economic performances and minimize financial risk on investment by identifying the best network topology in terms of biomass cultivation site locations, ethanol production plant capacities, location and transport logistics. -- Highlights: →A dynamic spatially explicit Mixed Integer Linear Program (MILP) of the entire corn-based ethanol supply chain is proposed. →Uncertainty on corn price and ethanol selling price is taken into account. →The model allows assessing and optimizing the supply chain economic performance and risk on investment. →A case study concerning the corn-to-ethanol production in Northern Italy demonstrates the effectiveness of the approach.

  15. Honesty-humility under threat: Self-uncertainty destroys trust among the nice guys.

    Science.gov (United States)

    Pfattheicher, Stefan; Böhm, Robert

    2018-01-01

    Recent research on humans' prosociality has highlighted the crucial role of Honesty-Humility, a basic trait in the HEXACO personality model. There is overwhelming evidence that Honesty-Humility predicts prosocial behavior across a vast variety of situations. In the present contribution, we cloud this rosy picture, examining a condition under which individuals high in Honesty-Humility reduce prosocial behavior. Specifically, we propose that under self-uncertainty, it is particularly those individuals high in Honesty-Humility who reduce trust in unknown others and become less prosocial. In 5 studies, we assessed Honesty-Humility, manipulated self-uncertainty, and measured interpersonal trust or trust in social institutions using behavioral or questionnaire measures. In Study 1, individuals high (vs. low) in Honesty-Humility showed higher levels of trust. This relation was mediated by their positive social expectations about the trustworthiness of others. Inducing self-uncertainty decreased trust, particularly in individuals high in Honesty-Humility (Studies 2-5). Making use of measuring the mediator (Studies 2 and 3) and applying a causal chain design (Studies 4a and 4b), it is shown that individuals high in Honesty-Humility reduced trust because self-uncertainty decreased positive social expectations about others. We end with an applied perspective, showing that Honesty-Humility is predictive of trust in social institutions (e.g., trust in the police; Study 5a), and that self-uncertainty undermined trust in the police especially for individuals high in Honesty-Humility (Study 5b). By these means, the present research shows that individuals high in Honesty-Humility are not unconditionally prosocial. Further implications for Honesty-Humility as well as for research on self-uncertainty and trust are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  16. Uncertainty and measurement

    International Nuclear Information System (INIS)

    Landsberg, P.T.

    1990-01-01

    This paper explores how the quantum mechanics uncertainty relation can be considered to result from measurements. A distinction is drawn between the uncertainties obtained by scrutinising experiments and the standard deviation type of uncertainty definition used in quantum formalism. (UK)

  17. UNCERTAINTY IN THE PROCESS INTEGRATION FOR THE BIOREFINERIES DEVELOPMENT

    Directory of Open Access Journals (Sweden)

    Meilyn González Cortés

    2015-07-01

    Full Text Available This paper presents how the design approaches with high level of flexibility can reduce the additional costs of the strategies that apply overdesign factors to consider parameters with uncertainty that impact on the economic feasibility of a project. The elements with associate uncertainties and that are important in the configurations of the process integration under a biorefinery scheme are: raw material, raw material technologies of conversion, and variety of products that can be obtained. From the analysis it is obtained that in the raw materials and products with potentialities in a biorefinery scheme, there are external uncertainties such as availability, demands and prices in the market. Those external uncertainties can determine their impact on the biorefinery and also in the product prices we can find minimum and maximum limits that can be identified in intervals which should be considered for the project economic evaluation and the sensibility analysis due to varied conditions.

  18. Experiences of Uncertainty in Men With an Elevated PSA.

    Science.gov (United States)

    Biddle, Caitlin; Brasel, Alicia; Underwood, Willie; Orom, Heather

    2015-05-15

    A significant proportion of men, ages 50 to 70 years, have, and continue to receive prostate specific antigen (PSA) tests to screen for prostate cancer (PCa). Approximately 70% of men with an elevated PSA level will not subsequently be diagnosed with PCa. Semistructured interviews were conducted with 13 men with an elevated PSA level who had not been diagnosed with PCa. Uncertainty was prominent in men's reactions to the PSA results, stemming from unanswered questions about the PSA test, PCa risk, and confusion about their management plan. Uncertainty was exacerbated or reduced depending on whether health care providers communicated in lay and empathetic ways, and provided opportunities for question asking. To manage uncertainty, men engaged in information and health care seeking, self-monitoring, and defensive cognition. Results inform strategies for meeting informational needs of men with an elevated PSA and confirm the primary importance of physician communication behavior for open information exchange and uncertainty reduction. © The Author(s) 2015.

  19. The role of sensitivity analysis in assessing uncertainty

    International Nuclear Information System (INIS)

    Crick, M.J.; Hill, M.D.

    1987-01-01

    Outside the specialist world of those carrying out performance assessments considerable confusion has arisen about the meanings of sensitivity analysis and uncertainty analysis. In this paper we attempt to reduce this confusion. We then go on to review approaches to sensitivity analysis within the context of assessing uncertainty, and to outline the types of test available to identify sensitive parameters, together with their advantages and disadvantages. The views expressed in this paper are those of the authors; they have not been formally endorsed by the National Radiological Protection Board and should not be interpreted as Board advice

  20. Technical note: Design flood under hydrological uncertainty

    Science.gov (United States)

    Botto, Anna; Ganora, Daniele; Claps, Pierluigi; Laio, Francesco

    2017-07-01

    Planning and verification of hydraulic infrastructures require a design estimate of hydrologic variables, usually provided by frequency analysis, and neglecting hydrologic uncertainty. However, when hydrologic uncertainty is accounted for, the design flood value for a specific return period is no longer a unique value, but is represented by a distribution of values. As a consequence, the design flood is no longer univocally defined, making the design process undetermined. The Uncertainty Compliant Design Flood Estimation (UNCODE) procedure is a novel approach that, starting from a range of possible design flood estimates obtained in uncertain conditions, converges to a single design value. This is obtained through a cost-benefit criterion with additional constraints that is numerically solved in a simulation framework. This paper contributes to promoting a practical use of the UNCODE procedure without resorting to numerical computation. A modified procedure is proposed by using a correction coefficient that modifies the standard (i.e., uncertainty-free) design value on the basis of sample length and return period only. The procedure is robust and parsimonious, as it does not require additional parameters with respect to the traditional uncertainty-free analysis. Simple equations to compute the correction term are provided for a number of probability distributions commonly used to represent the flood frequency curve. The UNCODE procedure, when coupled with this simple correction factor, provides a robust way to manage the hydrologic uncertainty and to go beyond the use of traditional safety factors. With all the other parameters being equal, an increase in the sample length reduces the correction factor, and thus the construction costs, while still keeping the same safety level.

  1. Analysis of uncertainty propagation in nuclear fuel cycle scenarios

    International Nuclear Information System (INIS)

    Krivtchik, Guillaume

    2014-01-01

    Nuclear scenario studies model nuclear fleet over a given period. They enable the comparison of different options for the reactor fleet evolution, and the management of the future fuel cycle materials, from mining to disposal, based on criteria such as installed capacity per reactor technology, mass inventories and flows, in the fuel cycle and in the waste. Uncertainties associated with nuclear data and scenario parameters (fuel, reactors and facilities characteristics) propagate along the isotopic chains in depletion calculations, and through out the scenario history, which reduces the precision of the results. The aim of this work is to develop, implement and use a stochastic uncertainty propagation methodology adapted to scenario studies. The method chosen is based on development of depletion computation surrogate models, which reduce the scenario studies computation time, and whose parameters include perturbations of the depletion model; and fabrication of equivalence model which take into account cross-sections perturbations for computation of fresh fuel enrichment. Then the uncertainty propagation methodology is applied to different scenarios of interest, considering different options of evolution for the French PWR fleet with SFR deployment. (author) [fr

  2. Concept of uncertainty in relation to the foresight research

    Directory of Open Access Journals (Sweden)

    Magruk Andrzej

    2017-03-01

    Full Text Available Uncertainty is one of the most important features of many areas of social and economic life, especially in the forward-looking context. On the one hand, the degree of uncertainty is associated with the objective essence of randomness of the phenomenon, and on the other, with the subjective perspective of a man. Future-oriented perception of human activities is laden with an incomplete specificity of the analysed phenomena, their volatility, and lack of continuity. A man is unable to determine, with complete certainty, the further course of these phenomena. According to the author of this article, in order to significantly reduce the uncertainty while making strategic decisions in a complex environment, we should focus our actions on the future through systemic research of foresight. This article attempts to answer the following research questions: 1 What is the relationship between foresight studies in the system perspective to studies of the uncertainty? 2 What classes of foresight methods enable the research of uncertainty in the process of system inquiry of the future? This study conducted deductive reasoning based on the results of the analysis methods and criticism of literature.

  3. Information Theory for Correlation Analysis and Estimation of Uncertainty Reduction in Maps and Models

    Directory of Open Access Journals (Sweden)

    J. Florian Wellmann

    2013-04-01

    Full Text Available The quantification and analysis of uncertainties is important in all cases where maps and models of uncertain properties are the basis for further decisions. Once these uncertainties are identified, the logical next step is to determine how they can be reduced. Information theory provides a framework for the analysis of spatial uncertainties when different subregions are considered as random variables. In the work presented here, joint entropy, conditional entropy, and mutual information are applied for a detailed analysis of spatial uncertainty correlations. The aim is to determine (i which areas in a spatial analysis share information, and (ii where, and by how much, additional information would reduce uncertainties. As an illustration, a typical geological example is evaluated: the case of a subsurface layer with uncertain depth, shape and thickness. Mutual information and multivariate conditional entropies are determined based on multiple simulated model realisations. Even for this simple case, the measures not only provide a clear picture of uncertainties and their correlations but also give detailed insights into the potential reduction of uncertainties at each position, given additional information at a different location. The methods are directly applicable to other types of spatial uncertainty evaluations, especially where multiple realisations of a model simulation are analysed. In summary, the application of information theoretic measures opens up the path to a better understanding of spatial uncertainties, and their relationship to information and prior knowledge, for cases where uncertain property distributions are spatially analysed and visualised in maps and models.

  4. Quantification of the impact of precipitation spatial distribution uncertainty on predictive uncertainty of a snowmelt runoff model

    Science.gov (United States)

    Jacquin, A. P.

    2012-04-01

    This study is intended to quantify the impact of uncertainty about precipitation spatial distribution on predictive uncertainty of a snowmelt runoff model. This problem is especially relevant in mountain catchments with a sparse precipitation observation network and relative short precipitation records. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment's glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation at a station and a precipitation factor FPi. If other precipitation data are not available, these precipitation factors must be adjusted during the calibration process and are thus seen as parameters of the model. In the case of the fifth zone, glaciers are seen as an inexhaustible source of water that melts when the snow cover is depleted.The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. The model's predictive uncertainty is measured in terms of the output variance of the mean squared error of the Box-Cox transformed discharge, the relative volumetric error, and the weighted average of snow water equivalent in the elevation zones at the end of the simulation period. Sobol's variance decomposition (SVD) method is used for assessing the impact of precipitation spatial distribution, represented by the precipitation factors FPi, on the models' predictive uncertainty. In the SVD method, the first order effect of a parameter (or group of parameters) indicates the fraction of predictive uncertainty that could be reduced if the true value of this parameter (or group) was known. Similarly, the total effect of a parameter (or group) measures the fraction of predictive uncertainty that would remain if the true value of this parameter (or group) was unknown, but all the remaining model parameters could be fixed

  5. Chemical kinetic model uncertainty minimization through laminar flame speed measurements

    Science.gov (United States)

    Park, Okjoo; Veloo, Peter S.; Sheen, David A.; Tao, Yujie; Egolfopoulos, Fokion N.; Wang, Hai

    2016-01-01

    Laminar flame speed measurements were carried for mixture of air with eight C3-4 hydrocarbons (propene, propane, 1,3-butadiene, 1-butene, 2-butene, iso-butene, n-butane, and iso-butane) at the room temperature and ambient pressure. Along with C1-2 hydrocarbon data reported in a recent study, the entire dataset was used to demonstrate how laminar flame speed data can be utilized to explore and minimize the uncertainties in a reaction model for foundation fuels. The USC Mech II kinetic model was chosen as a case study. The method of uncertainty minimization using polynomial chaos expansions (MUM-PCE) (D.A. Sheen and H. Wang, Combust. Flame 2011, 158, 2358–2374) was employed to constrain the model uncertainty for laminar flame speed predictions. Results demonstrate that a reaction model constrained only by the laminar flame speed values of methane/air flames notably reduces the uncertainty in the predictions of the laminar flame speeds of C3 and C4 alkanes, because the key chemical pathways of all of these flames are similar to each other. The uncertainty in model predictions for flames of unsaturated C3-4 hydrocarbons remain significant without considering fuel specific laminar flames speeds in the constraining target data set, because the secondary rate controlling reaction steps are different from those in the saturated alkanes. It is shown that the constraints provided by the laminar flame speeds of the foundation fuels could reduce notably the uncertainties in the predictions of laminar flame speeds of C4 alcohol/air mixtures. Furthermore, it is demonstrated that an accurate prediction of the laminar flame speed of a particular C4 alcohol/air mixture is better achieved through measurements for key molecular intermediates formed during the pyrolysis and oxidation of the parent fuel. PMID:27890938

  6. Real Options Effect of Uncertainty and Labor Demand Shocks on the Housing Market

    OpenAIRE

    Lee, Gabriel; Nguyen Thanh, Binh; Strobel, Johannes

    2016-01-01

    This paper shows that uncertainty affects the housing market in two significant ways. First, uncertainty shocks adversely affect housing prices but not the quantities that are traded. Controlling for a broad set of variables in fixed-effects regressions, we find that uncertainty shocks reduce housing prices and median sales prices in the amount of 1.4% and 1.8%, respectively, but the effect is not statistically significant for the percentage changes of all homes sold. Second, when...

  7. Collaborative framework for PIV uncertainty quantification: the experimental database

    International Nuclear Information System (INIS)

    Neal, Douglas R; Sciacchitano, Andrea; Scarano, Fulvio; Smith, Barton L

    2015-01-01

    The uncertainty quantification of particle image velocimetry (PIV) measurements has recently become a topic of great interest as shown by the recent appearance of several different methods within the past few years. These approaches have different working principles, merits and limitations, which have been speculated upon in subsequent studies. This paper reports a unique experiment that has been performed specifically to test the efficacy of PIV uncertainty methods. The case of a rectangular jet, as previously studied by Timmins et al (2012) and Wilson and Smith (2013b), is used. The novel aspect of the experiment is simultaneous velocity measurements using two different time-resolved PIV systems and a hot-wire anemometry (HWA) system. The first PIV system, called the PIV measurement system (‘PIV-MS’), is intended for nominal measurements of which the uncertainty is to be evaluated. It is based on a single camera and features a dynamic velocity range (DVR) representative of typical PIV experiments. The second PIV system, called the ‘PIV-HDR’ (high dynamic range) system, features a significantly higher DVR obtained with a higher digital imaging resolution. The hot-wire is placed in close proximity to the PIV measurement domain. The three measurement systems were carefully set to simultaneously measure the flow velocity at the same time and location. The comparison between the PIV-HDR system and the HWA provides an estimate of the measurement precision of the reference velocity for evaluation of the instantaneous error in the measurement system. The discrepancy between the PIV-MS and the reference data provides the measurement error, which is later used to assess the different uncertainty quantification methods proposed in the literature. A detailed comparison of the uncertainty estimation methods based on the present datasets is presented in a second paper from Sciacchitano et al (2015). Furthermore, this database offers the potential to be used for

  8. Uncertainty as Knowledge: Constraints on Policy Choices Provided by Analysis of Uncertainty

    Science.gov (United States)

    Lewandowsky, S.; Risbey, J.; Smithson, M.; Newell, B. R.

    2012-12-01

    Uncertainty forms an integral part of climate science, and it is often cited in connection with arguments against mitigative action. We argue that an analysis of uncertainty must consider existing knowledge as well as uncertainty, and the two must be evaluated with respect to the outcomes and risks associated with possible policy options. Although risk judgments are inherently subjective, an analysis of the role of uncertainty within the climate system yields two constraints that are robust to a broad range of assumptions. Those constraints are that (a) greater uncertainty about the climate system is necessarily associated with greater expected damages from warming, and (b) greater uncertainty translates into a greater risk of the failure of mitigation efforts. These ordinal constraints are unaffected by subjective or cultural risk-perception factors, they are independent of the discount rate, and they are independent of the magnitude of the estimate for climate sensitivity. The constraints mean that any appeal to uncertainty must imply a stronger, rather than weaker, need to cut greenhouse gas emissions than in the absence of uncertainty.

  9. Evaluation method for uncertainty of effective delayed neutron fraction βeff

    International Nuclear Information System (INIS)

    Zukeran, Atsushi

    1999-01-01

    Uncertainty of effective delayed neutron fraction β eff is evaluated in terms of three quantities; uncertainties of the basic delayed neutron constants, energy dependence of delayed neutron yield ν d m , and the uncertainties of the fission cross sections of fuel elements. The uncertainty of β eff due to the delayed neutron yield is expressed by a linearized formula assuming that the delayed neutron yield does not depend on the incident energy, and the energy dependence is supplemented by using the detailed energy dependence proposed by D'Angelo and Filip. The third quantity, uncertainties of fission cross section, is evaluated on the basis of the generalized perturbation theory in relation to reaction rate rations such as central spectral indexes or average reaction rate ratios. Resultant uncertainty of β eff is about 4 to 5%s, in which primary factor is the delayed neutron yield, and the secondary one is the fission cross section uncertainty, especially for 238 U. The energy dependence of ν d m systematically reduces the magnitude of β eff about 1.4% to 1.7%, depending on the model of the energy vs. ν d m correlation curve. (author)

  10. Evaluation of long-term RD and D programs in the presence of market uncertainties

    International Nuclear Information System (INIS)

    Hazelrigg, G.A. Jr.

    1982-01-01

    Long-term research, development, and demonstration (RD and D) programs such as fusion research can span several decades, progressing through a number of discrete RD and D phases. Pursuit of a technology such as fusion does not mean commitment to the entire RD and D program, but only to the next phase of RD and D. The evaluation of a long-term RD and D program must account for the decision process to continue, modify, or discontinue the program upon completion of each RD and D phase, the technological uncertainties inherent in a long-term RD and D program, and the uncertainty inherent in the future marketplace for the technology if and when it becomes available. Presented here is a methodology that does this. An application of the methodology to fusion research is included. The example application shows that the perceived economic value of fusion research is strongly dependent on market uncertainty, with increasing market uncertainty yielding greatly increased perceived value to the research effort. 7 references, 8 figures, 2 tables

  11. Self-Uncertainty and the Influence of Alternative Goals on Self-Regulation.

    Science.gov (United States)

    Light, Alysson E; Rios, Kimberly; DeMarree, Kenneth G

    2018-01-01

    The current research examines factors that facilitate or undermine goal pursuit. Past research indicates that attempts to reduce self-uncertainty can result in increased goal motivation. We explore a critical boundary condition of this effect-the presence of alternative goals. Though self-regulatory processes usually keep interest in alternative goals in check, uncertainty reduction may undermine these self-regulatory efforts by (a) reducing conflict monitoring and (b) increasing valuation of alternative goals. As such, reminders of alternative goals will draw effort away from focal goals for self-uncertain (but not self-certain) participants. Across four studies and eight supplemental studies, using different focal goals (e.g., academic achievement, healthy eating) and alternative goals (e.g., social/emotional goals, attractiveness, indulgence), we found that alternative goal salience does not negatively influence goal-directed behavior among participants primed with self-certainty, but that reminders of alternative goals undermine goal pursuit among participants primed with self-uncertainty.

  12. Trapped between two tails: trading off scientific uncertainties via climate targets

    International Nuclear Information System (INIS)

    Lemoine, Derek; McJeon, Haewon C

    2013-01-01

    Climate change policies must trade off uncertainties about future warming, about the social and ecological impacts of warming, and about the cost of reducing greenhouse gas emissions. We show that laxer carbon targets produce broader distributions for climate damages, skewed towards severe outcomes. However, if potential low-carbon technologies fill overlapping niches, then more stringent carbon targets produce broader distributions for the cost of reducing emissions, skewed towards high-cost outcomes. We use the technology-rich GCAM integrated assessment model to assess the robustness of 450 and 500 ppm carbon targets to each uncertain factor. The 500 ppm target provides net benefits across a broad range of futures. The 450 ppm target provides net benefits only when impacts are greater than conventionally assumed, when multiple technological breakthroughs lower the cost of abatement, or when evaluated with a low discount rate. Policy evaluations are more sensitive to uncertainty about abatement technology and impacts than to uncertainty about warming. (letter)

  13. Trapped between two tails: trading off scientific uncertainties via climate targets

    Science.gov (United States)

    Lemoine, Derek; McJeon, Haewon C.

    2013-09-01

    Climate change policies must trade off uncertainties about future warming, about the social and ecological impacts of warming, and about the cost of reducing greenhouse gas emissions. We show that laxer carbon targets produce broader distributions for climate damages, skewed towards severe outcomes. However, if potential low-carbon technologies fill overlapping niches, then more stringent carbon targets produce broader distributions for the cost of reducing emissions, skewed towards high-cost outcomes. We use the technology-rich GCAM integrated assessment model to assess the robustness of 450 and 500 ppm carbon targets to each uncertain factor. The 500 ppm target provides net benefits across a broad range of futures. The 450 ppm target provides net benefits only when impacts are greater than conventionally assumed, when multiple technological breakthroughs lower the cost of abatement, or when evaluated with a low discount rate. Policy evaluations are more sensitive to uncertainty about abatement technology and impacts than to uncertainty about warming.

  14. ["Great jobs"-also in psychiatry?].

    Science.gov (United States)

    Spiessl, H; Hübner-Liebermann, B

    2003-09-01

    Against the background of a beginning shortage of psychiatrists, results from interviews with 112 employees of an automotive company with the topic "Great Job" are presented to discuss their relevance to psychiatry. The interviews were analysed by means of a qualitative content analysis. Most employees assigned importance to great pay, constructive collaboration with colleagues, and work appealing to personal interests. Further statements particularly relevant to psychiatry were: successful career, flexible working hours, manageable job, work-life balance, well-founded training, no bureaucracy within the company, and personal status in society. The well-known economic restrictions in health care and the still negative attitude towards psychiatry currently reduce the attraction of psychiatry as a profession. From the viewpoint of personnel management, the attractors of a great job revealed in this study are proposed as important clues for the recruitment of medical students for psychiatry and the development of psychiatric staff.

  15. A sequential factorial analysis approach to characterize the effects of uncertainties for supporting air quality management

    Science.gov (United States)

    Wang, S.; Huang, G. H.; Veawab, A.

    2013-03-01

    This study proposes a sequential factorial analysis (SFA) approach for supporting regional air quality management under uncertainty. SFA is capable not only of examining the interactive effects of input parameters, but also of analyzing the effects of constraints. When there are too many factors involved in practical applications, SFA has the advantage of conducting a sequence of factorial analyses for characterizing the effects of factors in a systematic manner. The factor-screening strategy employed in SFA is effective in greatly reducing the computational effort. The proposed SFA approach is applied to a regional air quality management problem for demonstrating its applicability. The results indicate that the effects of factors are evaluated quantitatively, which can help decision makers identify the key factors that have significant influence on system performance and explore the valuable information that may be veiled beneath their interrelationships.

  16. Evaluation of uncertainties in MUF for a LWR fuel fabrication plant. Pt.2 - Pt.4

    International Nuclear Information System (INIS)

    Mennerdahl, D.

    1984-09-01

    MUF (Material Unaccounted For) is a parameter defined as the estimated loss of materials during a certain period of time. A suitable method for uncertainty and bias estimations has been developed. The method was specifically adjusted for a facility like the ASEA-ATOM fuel fabrication plant. Operations that are expected to contribute to the uncertainties have been compiled. Information that is required for the application of the developed method is described. Proposals for simplification of the required information without losing the accuracy are suggested. ASEA-ATOM had earlier determined uncertainty data for the scales that are used for nuclear materials. The statistical uncertainties included random errors, short-term and long-term systematic errors. Information for the determination of biases was also determined (constants and formulas). The method proposed by ASEA-ATOM for the determination of uncertainties due to the scales is compatible with the method proposed in this report. For other operations than weighing, the information from ASEA-ATOM is limited. Such operations are completely dominating the total uncertainty in MUF. Examples of calculations of uncertainties and bias are given for uranium oxide powders in large containers. Examples emphasize the differences between various statistical errors (random and systematic errors) and biases (known errors). The importance of correlations between different items in the inventories is explained. A specific correlation of great importance is the use of nominal factors (uranium concentration). A portable personal computer can be used to determine uncertainties in MUF. (author)

  17. Uncertainty in social dilemmas

    OpenAIRE

    Kwaadsteniet, Erik Willem de

    2007-01-01

    This dissertation focuses on social dilemmas, and more specifically, on environmental uncertainty in these dilemmas. Real-life social dilemma situations are often characterized by uncertainty. For example, fishermen mostly do not know the exact size of the fish population (i.e., resource size uncertainty). Several researchers have therefore asked themselves the question as to how such uncertainty influences people’s choice behavior. These researchers have repeatedly concluded that uncertainty...

  18. Uncertainty theory

    CERN Document Server

    Liu, Baoding

    2015-01-01

    When no samples are available to estimate a probability distribution, we have to invite some domain experts to evaluate the belief degree that each event will happen. Perhaps some people think that the belief degree should be modeled by subjective probability or fuzzy set theory. However, it is usually inappropriate because both of them may lead to counterintuitive results in this case. In order to rationally deal with belief degrees, uncertainty theory was founded in 2007 and subsequently studied by many researchers. Nowadays, uncertainty theory has become a branch of axiomatic mathematics for modeling belief degrees. This is an introductory textbook on uncertainty theory, uncertain programming, uncertain statistics, uncertain risk analysis, uncertain reliability analysis, uncertain set, uncertain logic, uncertain inference, uncertain process, uncertain calculus, and uncertain differential equation. This textbook also shows applications of uncertainty theory to scheduling, logistics, networks, data mining, c...

  19. Assessment of uncertainties associated with characterization of geological environment in the Tono area. Japanese fiscal year, 2006 (Contract research)

    International Nuclear Information System (INIS)

    Toida, Masaru; Suyama, Yasuhiro; Seno, Shoji; Atsumi, Hiroyuki; Ogata, Nobuhisa

    2008-03-01

    'Geoscientific research' performed at the Tono Geoscience Center is developing site investigation, characterization and assessment techniques for understanding of geological environment. Their important themes are to establish a methodology for analyzing uncertainties in heterogeneous geological environment, and to develop investigation techniques for reducing the uncertainties efficiently. This study proposes a new approach where all the possible options in the models and data-sets that cannot be excluded in the light of the evidence available, are identified. This approach enables uncertainties associated with the understanding at a given stage of the site characterization to be made explicitly using an uncertainty analysis technique based on Fuzzy geostatistics. This, in turn, supports the design of the following investigation stage to reduce the uncertainties efficiently. In the study, current knowledge had been compiled, and the technique had been advanced through geological modeling and groundwater analyses in the Tono area. This report systematized the uncertainty analysis methodology associated with the characterization of the geological environment, and organized the procedure of the methodology with the application examples in the study. This report also dealt with investigation techniques for reducing the uncertainties efficiently, and underground facility design options for handling geological uncertainties based on the characterization of the geological environment. (author)

  20. A conservation paradox in the Great Basin—Altering sagebrush landscapes with fuel breaks to reduce habitat loss from wildfire

    Science.gov (United States)

    Shinneman, Douglas J.; Aldridge, Cameron L.; Coates, Peter S.; Germino, Matthew J.; Pilliod, David S.; Vaillant, Nicole M.

    2018-03-15

    Interactions between fire and nonnative, annual plant species (that is, “the grass/fire cycle”) represent one of the greatest threats to sagebrush (Artemisia spp.) ecosystems and associated wildlife, including the greater sage-grouse (Centrocercus urophasianus). In 2015, U.S. Department of the Interior called for a “science-based strategy to reduce the threat of large-scale rangeland fire to habitat for the greater sage-grouse and the sagebrush-steppe ecosystem.” An associated guidance document, the “Integrated Rangeland Fire Management Strategy Actionable Science Plan,” identified fuel breaks as high priority areas for scientific research. Fuel breaks are intended to reduce fire size and frequency, and potentially they can compartmentalize wildfire spatial distribution in a landscape. Fuel breaks are designed to reduce flame length, fireline intensity, and rates of fire spread in order to enhance firefighter access, improve response times, and provide safe and strategic anchor points for wildland fire-fighting activities. To accomplish these objectives, fuel breaks disrupt fuel continuity, reduce fuel accumulation, and (or) increase plants with high moisture content through the removal or modification of vegetation in strategically placed strips or blocks of land.Fuel breaks are being newly constructed, enhanced, or proposed across large areas of the Great Basin to reduce wildfire risk and to protect remaining sagebrush ecosystems (including greater sage-grouse habitat). These projects are likely to result in thousands of linear miles of fuel breaks that will have direct ecological effects across hundreds of thousands of acres through habitat loss and conversion. These projects may also affect millions of acres indirectly because of edge effects and habitat fragmentation created by networks of fuel breaks. Hence, land managers are often faced with a potentially paradoxical situation: the need to substantially alter sagebrush habitats with fuel breaks

  1. Statistical Uncertainty Quantification of Physical Models during Reflood of LBLOCA

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Deog Yeon; Seul, Kwang Won; Woo, Sweng Woong [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)

    2015-05-15

    The use of the best-estimate (BE) computer codes in safety analysis for loss-of-coolant accident (LOCA) is the major trend in many countries to reduce the significant conservatism. A key feature of this BE evaluation requires the licensee to quantify the uncertainty of the calculations. So, it is very important how to determine the uncertainty distribution before conducting the uncertainty evaluation. Uncertainty includes those of physical model and correlation, plant operational parameters, and so forth. The quantification process is often performed mainly by subjective expert judgment or obtained from reference documents of computer code. In this respect, more mathematical methods are needed to reasonably determine the uncertainty ranges. The first uncertainty quantification are performed with the various increments for two influential uncertainty parameters to get the calculated responses and their derivatives. The different data set with two influential uncertainty parameters for FEBA tests, are chosen applying more strict criteria for selecting responses and their derivatives, which may be considered as the user’s effect in the CIRCÉ applications. Finally, three influential uncertainty parameters are considered to study the effect on the number of uncertainty parameters due to the limitation of CIRCÉ method. With the determined uncertainty ranges, uncertainty evaluations for FEBA tests are performed to check whether the experimental responses such as the cladding temperature or pressure drop are inside the limits of calculated uncertainty bounds. A confirmation step will be performed to evaluate the quality of the information in the case of the different reflooding PERICLES experiments. The uncertainty ranges of physical model in MARS-KS thermal-hydraulic code during the reflooding were quantified by CIRCÉ method using FEBA experiment tests, instead of expert judgment. Also, through the uncertainty evaluation for FEBA and PERICLES tests, it was confirmed

  2. Blockchain to Rule the Waves - Nascent Design Principles for Reducing Risk and Uncertainty in Decentralized Environments

    DEFF Research Database (Denmark)

    Nærland, Kristoffer; Müller-Bloch, Christoph; Beck, Roman

    2017-01-01

    Many decentralized, inter-organizational environments such as supply chains are characterized by high transactional uncertainty and risk. At the same time, blockchain technology promises to mitigate these issues by introducing certainty into economic transactions. This paper discusses the findings...... of a Design Science Research project involving the construction and evaluation of an information technology artifact in collaboration with Maersk, a leading international shipping company, where central documents in shipping, such as the Bill of Lading, are turned into a smart contract on blockchain. Based...... on our insights from the project, we provide first evidence for preliminary design principles for applications that aim to mitigate the transactional risk and uncertainty in decentralized environments using blockchain. Both the artifact and the first evidence for emerging design principles are novel...

  3. Uncertainty estimation of a complex water quality model: The influence of Box-Cox transformation on Bayesian approaches and comparison with a non-Bayesian method

    Science.gov (United States)

    Freni, Gabriele; Mannina, Giorgio

    In urban drainage modelling, uncertainty analysis is of undoubted necessity. However, uncertainty analysis in urban water-quality modelling is still in its infancy and only few studies have been carried out. Therefore, several methodological aspects still need to be experienced and clarified especially regarding water quality modelling. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Nevertheless, the Bayesian approach relies on some restrictive hypotheses that are not present in less formal methods like the Generalised Likelihood Uncertainty Estimation (GLUE). One crucial point in the application of Bayesian method is the formulation of a likelihood function that is conditioned by the hypotheses made regarding model residuals. Statistical transformations, such as the use of Box-Cox equation, are generally used to ensure the homoscedasticity of residuals. However, this practice may affect the reliability of the analysis leading to a wrong uncertainty estimation. The present paper aims to explore the influence of the Box-Cox equation for environmental water quality models. To this end, five cases were considered one of which was the “real” residuals distributions (i.e. drawn from available data). The analysis was applied to the Nocella experimental catchment (Italy) which is an agricultural and semi-urbanised basin where two sewer systems, two wastewater treatment plants and a river reach were monitored during both dry and wet weather periods. The results show that the uncertainty estimation is greatly affected by residual transformation and a wrong assumption may also affect the evaluation of model uncertainty. The use of less formal methods always provide an overestimation of modelling uncertainty with respect to Bayesian method but such effect is reduced if a wrong assumption is made regarding the

  4. Application of status uncertainty analysis methods for AP1000 LBLOCA calculation

    International Nuclear Information System (INIS)

    Zhang Shunxiang; Liang Guoxing

    2012-01-01

    Parameter uncertainty analysis is developed by using the reasonable method to establish the response relations between input parameter uncertainties and output uncertainties. The application of the parameter uncertainty analysis makes the simulation of plant state more accuracy and improves the plant economy with reasonable security assurance. The AP1000 LBLOCA was analyzed in this paper and the results indicate that the random sampling statistical analysis method, sensitivity analysis numerical method and traditional error propagation analysis method can provide quite large peak cladding temperature (PCT) safety margin, which is much helpful for choosing suitable uncertainty analysis method to improve the plant economy. Additionally, the random sampling statistical analysis method applying mathematical statistics theory makes the largest safety margin due to the reducing of the conservation. Comparing with the traditional conservative bounding parameter analysis method, the random sampling method can provide the PCT margin of 100 K, while the other two methods can only provide 50-60 K. (authors)

  5. Moving Beyond 2% Uncertainty: A New Framework for Quantifying Lidar Uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Newman, Jennifer F.; Clifton, Andrew

    2017-03-08

    Remote sensing of wind using lidar is revolutionizing wind energy. However, current generations of wind lidar are ascribed a climatic value of uncertainty, which is based on a poor description of lidar sensitivity to external conditions. In this presentation, we show how it is important to consider the complete lidar measurement process to define the measurement uncertainty, which in turn offers the ability to define a much more granular and dynamic measurement uncertainty. This approach is a progression from the 'white box' lidar uncertainty method.

  6. Factoring uncertainty into restoration modeling of in-situ leach uranium mines

    Science.gov (United States)

    Johnson, Raymond H.; Friedel, Michael J.

    2009-01-01

    Postmining restoration is one of the greatest concerns for uranium in-situ leach (ISL) mining operations. The ISL-affected aquifer needs to be returned to conditions specified in the mining permit (either premining or other specified conditions). When uranium ISL operations are completed, postmining restoration is usually achieved by injecting reducing agents into the mined zone. The objective of this process is to restore the aquifer to premining conditions by reducing the solubility of uranium and other metals in the ground water. Reactive transport modeling is a potentially useful method for simulating the effectiveness of proposed restoration techniques. While reactive transport models can be useful, they are a simplification of reality that introduces uncertainty through the model conceptualization, parameterization, and calibration processes. For this reason, quantifying the uncertainty in simulated temporal and spatial hydrogeochemistry is important for postremedial risk evaluation of metal concentrations and mobility. Quantifying the range of uncertainty in key predictions (such as uranium concentrations at a specific location) can be achieved using forward Monte Carlo or other inverse modeling techniques (trial-and-error parameter sensitivity, calibration constrained Monte Carlo). These techniques provide simulated values of metal concentrations at specified locations that can be presented as nonlinear uncertainty limits or probability density functions. Decisionmakers can use these results to better evaluate environmental risk as future metal concentrations with a limited range of possibilities, based on a scientific evaluation of uncertainty.

  7. Using Uncertainty Analysis to Guide the Development of Accelerated Stress Tests (Presentation)

    Energy Technology Data Exchange (ETDEWEB)

    Kempe, M.

    2014-03-01

    Extrapolation of accelerated testing to the long-term results expected in the field has uncertainty associated with the acceleration factors and the range of possible stresses in the field. When multiple stresses (such as temperature and humidity) can be used to increase the acceleration, the uncertainty may be reduced according to which stress factors are used to accelerate the degradation.

  8. Characterization of geometrical random uncertainty distribution for a group of patients in radiotherapy

    International Nuclear Information System (INIS)

    Munoz Montplet, C.; Jurado Bruggeman, D.

    2010-01-01

    Geometrical random uncertainty in radiotherapy is usually characterized by a unique value in each group of patients. We propose a novel approach based on a statistically accurate characterization of the uncertainty distribution, thus reducing the risk of obtaining potentially unsafe results in CTV-PTV margins or in the selection of correction protocols.

  9. Evaluation and Quantification of Uncertainty in the Modeling of Contaminant Transport and Exposure Assessment at a Radioactive Waste Disposal Site

    Science.gov (United States)

    Tauxe, J.; Black, P.; Carilli, J.; Catlett, K.; Crowe, B.; Hooten, M.; Rawlinson, S.; Schuh, A.; Stockton, T.; Yucel, V.

    2002-12-01

    scenarios is of great utility in cost-benefit analysis. In addition to providing decision makers with realistically uncertain modeling results, probabilistic assessment is also useful in understanding nonlinear model behavior and in guiding research efforts aimed at reducing the uncertainty in key components of the model. A sensitivity analysis of the modeling results identifies which model parameters are most significant (and over which ranges) in determining estimated doses, for example, thus providing justification for the allocation of limited research funding to reduce uncertainty in parameters that are both poorly constrained and significant to the model behavior.

  10. The influence of perceived uncertainty on entrepreneurial action in emerging renewable energy technology; biomass gasification projects in the Netherlands

    International Nuclear Information System (INIS)

    Meijer, Ineke S.M.; Hekkert, Marko P.; Koppenjan, Joop F.M.

    2007-01-01

    Emerging renewable energy technologies cannot break through without the involvement of entrepreneurs who dare to take action amidst uncertainty. The uncertainties that the entrepreneurs involved perceive will greatly affect their innovation decisions and can prevent them from engaging in innovation projects aimed at developing and implementing emerging renewable energy technologies. This article analyzes how perceived uncertainties and motivation influence an entrepreneur's decision to act, using empirical data on biomass gasification projects in the Netherlands. Our empirical results show that technological, political and resource uncertainty are the most dominant sources of perceived uncertainty influencing entrepreneurial decision-making. By performing a dynamic analysis, we furthermore demonstrate that perceived uncertainties and motivation are not stable, but evolve over time. We identify critical factors in the project's internal and external environment which influence these changes in perceived uncertainties and motivation, and describe how various interactions between the different variables in the conceptual model (internal and external factors, perceived uncertainty, motivation and previous actions of the entrepreneurs) positively or negatively influence the decision of entrepreneurs to continue entrepreneurial action. We discuss how policymakers can use these insights for stimulating the development and diffusion of emerging renewable energy technologies

  11. Socializing Identity Through Practice: A Mixed Methods Approach to Family Medicine Resident Perspectives on Uncertainty.

    Science.gov (United States)

    Ledford, Christy J W; Cafferty, Lauren A; Seehusen, Dean A

    2015-01-01

    Uncertainty is a central theme in the practice of medicine and particularly primary care. This study explored how family medicine resident physicians react to uncertainty in their practice. This study incorporated a two-phase mixed methods approach, including semi-structured personal interviews (n=21) and longitudinal self-report surveys (n=21) with family medicine residents. Qualitative analysis showed that though residents described uncertainty as an implicit part of their identity, they still developed tactics to minimize or manage uncertainty in their practice. Residents described increasing comfort with uncertainty the longer they practiced and anticipated that growth continuing throughout their careers. Quantitative surveys showed that reactions to uncertainty were more positive over time; however, the difference was not statistically significant. Qualitative and quantitative results show that as family medicine residents practice medicine their perception of uncertainty changes. To reduce uncertainty, residents use relational information-seeking strategies. From a broader view of practice, residents describe uncertainty neutrally, asserting that uncertainty is simply part of the practice of family medicine.

  12. Integration of inaccurate data into model building and uncertainty assessment

    Energy Technology Data Exchange (ETDEWEB)

    Coleou, Thierry

    1998-12-31

    Model building can be seen as integrating numerous measurements and mapping through data points considered as exact. As the exact data set is usually sparse, using additional non-exact data improves the modelling and reduces the uncertainties. Several examples of non-exact data are discussed and a methodology to honor them in a single pass, along with the exact data is presented. This automatic procedure is valid for both ``base case`` model building and stochastic simulations for uncertainty analysis. 5 refs., 3 figs.

  13. An algorithm to improve sampling efficiency for uncertainty propagation using sampling based method

    International Nuclear Information System (INIS)

    Campolina, Daniel; Lima, Paulo Rubens I.; Pereira, Claubia; Veloso, Maria Auxiliadora F.

    2015-01-01

    Sample size and computational uncertainty were varied in order to investigate sample efficiency and convergence of the sampling based method for uncertainty propagation. Transport code MCNPX was used to simulate a LWR model and allow the mapping, from uncertain inputs of the benchmark experiment, to uncertain outputs. Random sampling efficiency was improved through the use of an algorithm for selecting distributions. Mean range, standard deviation range and skewness were verified in order to obtain a better representation of uncertainty figures. Standard deviation of 5 pcm in the propagated uncertainties for 10 n-samples replicates was adopted as convergence criterion to the method. Estimation of 75 pcm uncertainty on reactor k eff was accomplished by using sample of size 93 and computational uncertainty of 28 pcm to propagate 1σ uncertainty of burnable poison radius. For a fixed computational time, in order to reduce the variance of the uncertainty propagated, it was found, for the example under investigation, it is preferable double the sample size than double the amount of particles followed by Monte Carlo process in MCNPX code. (author)

  14. Reducing the uncertainty of the primary damage production in Fe

    International Nuclear Information System (INIS)

    Bjorkas, C.; Nordlund, K.

    2007-01-01

    Full text of publication follows: One of the key questions for understanding neutron irradiation damage buildup in fission and fusion reactor steels is knowing the primary damage state produced by neutron-induced atomic recoils in Fe. Supporting this is our recent study revealing that the initial damage in Fe 0.9 Cr 0.1 is essentially the same as in pure Fe [1]. In spite of decades of study, the question of what the amount and distribution of defects in Fe is, has remained highly unclear. Different computer simulations modules have given a good qualitative understanding of the cascade development [1,2]. However, quantitative differences of more than a factor of three have remained in the predicted clustered defect production numbers [2]. The disagreements between the potentials pose problems for finding a reliable predictive model for the behavior of Fe under irradiation. In this study we analyze the initial damage as predicted by three recent interatomic potentials for Fe. These are well suited for a comparison because they have very different physical motivations and functional forms, but are comparable in overall quality and in particular reproduce the energetics of interstitials in different configurations well. The potentials are those by Ackland and Mendelev et al. (AMS) [3], the 'magnetic' potential by Dudarev and Derlet (DD) [4] and the Tersoff-like analytical potential by Mueller, Erhart and Albe (MEA) [5]. The DD and MEA potentials were modified by us to describe high-energy repulsive interactions well. All potentials were then used in recoil collision cascade simulations carried out and analyzed in exactly the same manner for all potentials. Analysis of the resulting damage showed a much smaller uncertainty regarding the damage production than that of previous potentials. The total defect production numbers essentially agree within the statistical uncertainty for the three potentials. Some differences remains regarding the defect clustered fractions, but

  15. Icarus's discovery: Acting on global climate change in the face of uncertainty

    International Nuclear Information System (INIS)

    Brooks, D.G.; Maracas, K.B.; Hayslip, R.M.

    1994-01-01

    The mythological character Icarus had the misfortune of learning the consequences of his decision to fly too near the sun at the same time he employed his decision. Although Daedalus tried to reduce the uncertainties of his son's decision by warning Icarus of the possible outcome, Icarus had no empirical knowledge of what would actually happen until his waxen wings melted and he fell to the sea. Like Icarus, man has no empirical knowledge or conclusive evidence today of the possible effects of global climate change. And though the consequences of policy decisions toward global climate change may not be as catastrophic as falling into the sea, the social and economic impacts of those decisions will be substantial. There are broad uncertainties related to the scientific and ecological aspects of global climate change. But clearly the ''politics'' of global climate change issues are moving at a faster rate than the science. There is a public outcry for action now, in the face of uncertainty. This paper profiles a case study of a southwestern utility's use of multi-attribute preference theory to reduce uncertainties and analyze its options for addressing global climate change issues

  16. Reprint of: Production scheduling of a lignite mine under quality and reserves uncertainty

    International Nuclear Information System (INIS)

    Galetakis, Michael; Roumpos, Christos; Alevizos, George; Vamvuka, Despina

    2012-01-01

    The effect of uncertainty sources to the stochastic optimization of the combined project of a new surface lignite mine exploitation and power plant operation for electricity generation is investigated. Major sources of uncertainty that were considered are the reserves and the quality of the lignite. Since probability distribution functions for these uncertainties were estimated during the detailed exploration phase of the deposit, the overall goal is then to determine the optimal capacity of the power plant and consequently the optimal production rate of the mine over the time. The optimization objective that was selected is the maximization of the net present value of the project. Emphasis is placed on the sensitivity analysis for the investigation of the effect of quality and reserves uncertainty on project optimization, on the mathematical formulation of risk attitude strategy and on increasing the efficiency of the optimization process by creating a limited set of feasible solutions applying empirical rules. The developed methodology was applied for the determination of the optimal annual production rate of a new surface lignite mine in the area of Ptolemais–Amynteon in Northern Greece. - Highlights: ► Quality and reserves uncertainty affects considerably the production scheduling. ► Stochastic optimization is greatly accelerated by incorporating Taylor's rule. ► Decisions can be made considering different risk level attitudes.

  17. Model-specification uncertainty in future forest pest outbreak.

    Science.gov (United States)

    Boulanger, Yan; Gray, David R; Cooke, Barry J; De Grandpré, Louis

    2016-04-01

    Climate change will modify forest pest outbreak characteristics, although there are disagreements regarding the specifics of these changes. A large part of this variability may be attributed to model specifications. As a case study, we developed a consensus model predicting spruce budworm (SBW, Choristoneura fumiferana [Clem.]) outbreak duration using two different predictor data sets and six different correlative methods. The model was used to project outbreak duration and the uncertainty associated with using different data sets and correlative methods (=model-specification uncertainty) for 2011-2040, 2041-2070 and 2071-2100, according to three forcing scenarios (RCP 2.6, RCP 4.5 and RCP 8.5). The consensus model showed very high explanatory power and low bias. The model projected a more important northward shift and decrease in outbreak duration under the RCP 8.5 scenario. However, variation in single-model projections increases with time, making future projections highly uncertain. Notably, the magnitude of the shifts in northward expansion, overall outbreak duration and the patterns of outbreaks duration at the southern edge were highly variable according to the predictor data set and correlative method used. We also demonstrated that variation in forcing scenarios contributed only slightly to the uncertainty of model projections compared with the two sources of model-specification uncertainty. Our approach helped to quantify model-specification uncertainty in future forest pest outbreak characteristics. It may contribute to sounder decision-making by acknowledging the limits of the projections and help to identify areas where model-specification uncertainty is high. As such, we further stress that this uncertainty should be strongly considered when making forest management plans, notably by adopting adaptive management strategies so as to reduce future risks. © 2015 Her Majesty the Queen in Right of Canada Global Change Biology © 2015 Published by John

  18. Diversity Dynamics in Nymphalidae Butterflies: Effect of Phylogenetic Uncertainty on Diversification Rate Shift Estimates

    Science.gov (United States)

    Peña, Carlos; Espeland, Marianne

    2015-01-01

    The species rich butterfly family Nymphalidae has been used to study evolutionary interactions between plants and insects. Theories of insect-hostplant dynamics predict accelerated diversification due to key innovations. In evolutionary biology, analysis of maximum credibility trees in the software MEDUSA (modelling evolutionary diversity using stepwise AIC) is a popular method for estimation of shifts in diversification rates. We investigated whether phylogenetic uncertainty can produce different results by extending the method across a random sample of trees from the posterior distribution of a Bayesian run. Using the MultiMEDUSA approach, we found that phylogenetic uncertainty greatly affects diversification rate estimates. Different trees produced diversification rates ranging from high values to almost zero for the same clade, and both significant rate increase and decrease in some clades. Only four out of 18 significant shifts found on the maximum clade credibility tree were consistent across most of the sampled trees. Among these, we found accelerated diversification for Ithomiini butterflies. We used the binary speciation and extinction model (BiSSE) and found that a hostplant shift to Solanaceae is correlated with increased net diversification rates in Ithomiini, congruent with the diffuse cospeciation hypothesis. Our results show that taking phylogenetic uncertainty into account when estimating net diversification rate shifts is of great importance, as very different results can be obtained when using the maximum clade credibility tree and other trees from the posterior distribution. PMID:25830910

  19. Diversity dynamics in Nymphalidae butterflies: effect of phylogenetic uncertainty on diversification rate shift estimates.

    Directory of Open Access Journals (Sweden)

    Carlos Peña

    Full Text Available The species rich butterfly family Nymphalidae has been used to study evolutionary interactions between plants and insects. Theories of insect-hostplant dynamics predict accelerated diversification due to key innovations. In evolutionary biology, analysis of maximum credibility trees in the software MEDUSA (modelling evolutionary diversity using stepwise AIC is a popular method for estimation of shifts in diversification rates. We investigated whether phylogenetic uncertainty can produce different results by extending the method across a random sample of trees from the posterior distribution of a Bayesian run. Using the MultiMEDUSA approach, we found that phylogenetic uncertainty greatly affects diversification rate estimates. Different trees produced diversification rates ranging from high values to almost zero for the same clade, and both significant rate increase and decrease in some clades. Only four out of 18 significant shifts found on the maximum clade credibility tree were consistent across most of the sampled trees. Among these, we found accelerated diversification for Ithomiini butterflies. We used the binary speciation and extinction model (BiSSE and found that a hostplant shift to Solanaceae is correlated with increased net diversification rates in Ithomiini, congruent with the diffuse cospeciation hypothesis. Our results show that taking phylogenetic uncertainty into account when estimating net diversification rate shifts is of great importance, as very different results can be obtained when using the maximum clade credibility tree and other trees from the posterior distribution.

  20. Has the great recession and its aftermath reduced traffic fatalities?

    Science.gov (United States)

    Noland, Robert B; Zhou, Yuhan

    2017-01-01

    An analysis of state-level data from 1984 to 2014 provides evidence on the relationship between economic recessions and US traffic fatalities. While there are large reductions associated with decreases in household median income, other policy variables tend to have additional and in some cases, larger effects. An increase in the inequality of the income distribution, measured by the Gini index, has reduced traffic fatalities. Graduated licensing policies, cell phone laws, and motorcycle helmet requirements are all associated with reductions in fatalities. Other factors include a proxy for medical technology, and access to emergency medical services (based on the percent of vehicle miles traveled in rural areas); reductions in the latter accounted for a substantial reduction in fatalities and is likely another indicator of reduced economic activity. Changes in the road network, mainly increases in the percent of collector roads has increased fatalities. Population growth is associated with increased traffic fatalities and changes in age cohorts has a small negative effect. Overall, results suggest that there has been a beneficial impact on traffic fatalities from reduced economic activity, but various policies adopted by the states have also reduced traffic fatalities. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

    Science.gov (United States)

    Suzuki, Kazuyoshi; Zupanski, Milija

    2018-01-01

    In this study, we investigate the uncertainties associated with land surface processes in an ensemble predication context. Specifically, we compare the uncertainties produced by a coupled atmosphere-land modeling system with two different land surface models, the Noah- MP land surface model (LSM) and the Noah LSM, by using the Maximum Likelihood Ensemble Filter (MLEF) data assimilation system as a platform for ensemble prediction. We carried out 24-hour prediction simulations in Siberia with 32 ensemble members beginning at 00:00 UTC on 5 March 2013. We then compared the model prediction uncertainty of snow depth and solid precipitation with observation-based research products and evaluated the standard deviation of the ensemble spread. The prediction skill and ensemble spread exhibited high positive correlation for both LSMs, indicating a realistic uncertainty estimation. The inclusion of a multiple snowlayer model in the Noah-MP LSM was beneficial for reducing the uncertainties of snow depth and snow depth change compared to the Noah LSM, but the uncertainty in daily solid precipitation showed minimal difference between the two LSMs. The impact of LSM choice in reducing temperature uncertainty was limited to surface layers of the atmosphere. In summary, we found that the more sophisticated Noah-MP LSM reduces uncertainties associated with land surface processes compared to the Noah LSM. Thus, using prediction models with improved skill implies improved predictability and greater certainty of prediction.

  2. Venice: Fifty years after the great flood of November 4, 1966

    Science.gov (United States)

    Rizzoli, P. M.

    2017-12-01

    Fifty years ago Venice and its lagoon suffered the most devastating flood in their millennial history. The causes of the increasingly recurring floods will be examined, namely the man-induced subsidence in the period 1925-1970 and the storm surges of the Adriatic sea. The engineering solution designed for their protection , named the MOSE system, will be discussed in detail. The MOSE was started in 2003 and is near completion. It consists of four barriers , invisible in normal conditions, which will close the inlets to the lagoon under the prediction of a forthcoming flood. Finally, the perspective of the MOSE capability of protecting the city under scenarios of future global sea level rise will be assessed. This assessment must critically take into account that Venice and its lagoon are confined in the northernmost corner of the semi-enclosed, marginal Mediterranean sea for which the uncertainties of future sea level rise greatly exceed the uncertainties of the global averages.

  3. Results from the Application of Uncertainty Methods in the CSNI Uncertainty Methods Study (UMS)

    International Nuclear Information System (INIS)

    Glaeser, H.

    2008-01-01

    Within licensing procedures there is the incentive to replace the conservative requirements for code application by a - best estimate - concept supplemented by an uncertainty analysis to account for predictive uncertainties of code results. Methods have been developed to quantify these uncertainties. The Uncertainty Methods Study (UMS) Group, following a mandate from CSNI, has compared five methods for calculating the uncertainty in the predictions of advanced -best estimate- thermal-hydraulic codes. Most of the methods identify and combine input uncertainties. The major differences between the predictions of the methods came from the choice of uncertain parameters and the quantification of the input uncertainties, i.e. the wideness of the uncertainty ranges. Therefore, suitable experimental and analytical information has to be selected to specify these uncertainty ranges or distributions. After the closure of the Uncertainty Method Study (UMS) and after the report was issued comparison calculations of experiment LSTF-SB-CL-18 were performed by University of Pisa using different versions of the RELAP 5 code. It turned out that the version used by two of the participants calculated a 170 K higher peak clad temperature compared with other versions using the same input deck. This may contribute to the differences of the upper limit of the uncertainty ranges.

  4. Information Seeking in Uncertainty Management Theory: Exposure to Information About Medical Uncertainty and Information-Processing Orientation as Predictors of Uncertainty Management Success.

    Science.gov (United States)

    Rains, Stephen A; Tukachinsky, Riva

    2015-01-01

    Uncertainty management theory outlines the processes through which individuals cope with health-related uncertainty. Information seeking has been frequently documented as an important uncertainty management strategy. The reported study investigates exposure to specific types of medical information during a search, and one's information-processing orientation as predictors of successful uncertainty management (i.e., a reduction in the discrepancy between the level of uncertainty one feels and the level one desires). A lab study was conducted in which participants were primed to feel more or less certain about skin cancer and then were allowed to search the World Wide Web for skin cancer information. Participants' search behavior was recorded and content analyzed. The results indicate that exposure to two health communication constructs that pervade medical forms of uncertainty (i.e., severity and susceptibility) and information-processing orientation predicted uncertainty management success.

  5. Conditional uncertainty principle

    Science.gov (United States)

    Gour, Gilad; Grudka, Andrzej; Horodecki, Michał; Kłobus, Waldemar; Łodyga, Justyna; Narasimhachar, Varun

    2018-04-01

    We develop a general operational framework that formalizes the concept of conditional uncertainty in a measure-independent fashion. Our formalism is built upon a mathematical relation which we call conditional majorization. We define conditional majorization and, for the case of classical memory, we provide its thorough characterization in terms of monotones, i.e., functions that preserve the partial order under conditional majorization. We demonstrate the application of this framework by deriving two types of memory-assisted uncertainty relations, (1) a monotone-based conditional uncertainty relation and (2) a universal measure-independent conditional uncertainty relation, both of which set a lower bound on the minimal uncertainty that Bob has about Alice's pair of incompatible measurements, conditioned on arbitrary measurement that Bob makes on his own system. We next compare the obtained relations with their existing entropic counterparts and find that they are at least independent.

  6. Uncertainty Evaluation of the SFR Subchannel Thermal-Hydraulic Modeling Using a Hot Channel Factors Analysis

    International Nuclear Information System (INIS)

    Choi, Sun Rock; Cho, Chung Ho; Kim, Sang Ji

    2011-01-01

    In an SFR core analysis, a hot channel factors (HCF) method is most commonly used to evaluate uncertainty. It was employed to the early design such as the CRBRP and IFR. In other ways, the improved thermal design procedure (ITDP) is able to calculate the overall uncertainty based on the Root Sum Square technique and sensitivity analyses of each design parameters. The Monte Carlo method (MCM) is also employed to estimate the uncertainties. In this method, all the input uncertainties are randomly sampled according to their probability density functions and the resulting distribution for the output quantity is analyzed. Since an uncertainty analysis is basically calculated from the temperature distribution in a subassembly, the core thermal-hydraulic modeling greatly affects the resulting uncertainty. At KAERI, the SLTHEN and MATRA-LMR codes have been utilized to analyze the SFR core thermal-hydraulics. The SLTHEN (steady-state LMR core thermal hydraulics analysis code based on the ENERGY model) code is a modified version of the SUPERENERGY2 code, which conducts a multi-assembly, steady state calculation based on a simplified ENERGY model. The detailed subchannel analysis code MATRA-LMR (Multichannel Analyzer for Steady-State and Transients in Rod Arrays for Liquid Metal Reactors), an LMR version of MATRA, was also developed specifically for the SFR core thermal-hydraulic analysis. This paper describes comparative studies for core thermal-hydraulic models. The subchannel analysis and a hot channel factors based uncertainty evaluation system is established to estimate the core thermofluidic uncertainties using the MATRA-LMR code and the results are compared to those of the SLTHEN code

  7. Uncertainty studies and risk assessment for CO2 storage in geological formations

    International Nuclear Information System (INIS)

    Walter, Lena Sophie

    2013-01-01

    Carbon capture and storage (CCS) in deep geological formations is one possible option to mitigate the greenhouse gas effect by reducing CO 2 emissions into the atmosphere. The assessment of the risks related to CO 2 storage is an important task. Events such as CO 2 leakage and brine displacement could result in hazards for human health and the environment. In this thesis, a systematic and comprehensive risk assessment concept is presented to investigate various levels of uncertainties and to assess risks using numerical simulations. Depending on the risk and the processes, which should be assessed, very complex models, large model domains, large time scales, and many simulations runs for estimating probabilities are required. To reduce the resulting high computational costs, a model reduction technique (the arbitrary polynomial chaos expansion) and a method for model coupling in space are applied. The different levels of uncertainties are: statistical uncertainty in parameter distributions, scenario uncertainty, e.g. different geological features, and recognized ignorance due to assumptions in the conceptual model set-up. Recognized ignorance and scenario uncertainty are investigated by simulating well defined model set-ups and scenarios. According to damage values, which are defined as a model output, the set-ups and scenarios can be compared and ranked. For statistical uncertainty probabilities can be determined by running Monte Carlo simulations with the reduced model. The results are presented in various ways: e.g., mean damage, probability density function, cumulative distribution function, or an overall risk value by multiplying the damage with the probability. If the model output (damage) cannot be compared to provided criteria (e.g. water quality criteria), analytical approximations are presented to translate the damage into comparable values. The overall concept is applied for the risks related to brine displacement and infiltration into drinking water

  8. Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study

    International Nuclear Information System (INIS)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2016-01-01

    The uncertainties of renewable energy have brought great challenges to power system commitment, dispatches and reserve requirement. This paper presents a comparative study on integration of renewable generation uncertainties into SCUC (stochastic security-constrained unit commitment) considering reserve and risk. Renewable forecast uncertainties are captured by a list of PIs (prediction intervals). A new scenario generation method is proposed to generate scenarios from these PIs. Different system uncertainties are considered as scenarios in the stochastic SCUC problem formulation. Two comparative simulations with single (E1: wind only) and multiple sources of uncertainty (E2: load, wind, solar and generation outages) are investigated. Five deterministic and four stochastic case studies are performed. Different generation costs, reserve strategies and associated risks are compared under various scenarios. Demonstrated results indicate the overall costs of E2 is lower than E1 due to penetration of solar power and the associated risk in deterministic cases of E2 is higher than E1. It implies the superimposed effect of uncertainties during uncertainty integration. The results also demonstrate that power systems run a higher level of risk during peak load hours, and that stochastic models are more robust than deterministic ones. - Highlights: • An extensive comparative study for renewable integration is presented. • A novel scenario generation method is proposed. • Wind and solar uncertainties are represented by a list of prediction intervals. • Unit commitment and dispatch costs are discussed considering reserve and risk.

  9. The Great Recession was not so Great

    NARCIS (Netherlands)

    van Ours, J.C.

    2015-01-01

    The Great Recession is characterized by a GDP-decline that was unprecedented in the past decades. This paper discusses the implications of the Great Recession analyzing labor market data from 20 OECD countries. Comparing the Great Recession with the 1980s recession it is concluded that there is a

  10. Uncertainty quantification in lattice QCD calculations for nuclear physics

    Energy Technology Data Exchange (ETDEWEB)

    Beane, Silas R. [Univ. of Washington, Seattle, WA (United States); Detmold, William [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Orginos, Kostas [College of William and Mary, Williamsburg, VA (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Savage, Martin J. [Institute for Nuclear Theory, Seattle, WA (United States)

    2015-02-05

    The numerical technique of Lattice QCD holds the promise of connecting the nuclear forces, nuclei, the spectrum and structure of hadrons, and the properties of matter under extreme conditions with the underlying theory of the strong interactions, quantum chromodynamics. A distinguishing, and thus far unique, feature of this formulation is that all of the associated uncertainties, both statistical and systematic can, in principle, be systematically reduced to any desired precision with sufficient computational and human resources. As a result, we review the sources of uncertainty inherent in Lattice QCD calculations for nuclear physics, and discuss how each is quantified in current efforts.

  11. Mapping of uncertainty relations between continuous and discrete time.

    Science.gov (United States)

    Chiuchiù, Davide; Pigolotti, Simone

    2018-03-01

    Lower bounds on fluctuations of thermodynamic currents depend on the nature of time, discrete or continuous. To understand the physical reason, we compare current fluctuations in discrete-time Markov chains and continuous-time master equations. We prove that current fluctuations in the master equations are always more likely, due to random timings of transitions. This comparison leads to a mapping of the moments of a current between discrete and continuous time. We exploit this mapping to obtain uncertainty bounds. Our results reduce the quests for uncertainty bounds in discrete and continuous time to a single problem.

  12. Uncertainty and Cognitive Control

    Directory of Open Access Journals (Sweden)

    Faisal eMushtaq

    2011-10-01

    Full Text Available A growing trend of neuroimaging, behavioural and computational research has investigated the topic of outcome uncertainty in decision-making. Although evidence to date indicates that humans are very effective in learning to adapt to uncertain situations, the nature of the specific cognitive processes involved in the adaptation to uncertainty are still a matter of debate. In this article, we reviewed evidence suggesting that cognitive control processes are at the heart of uncertainty in decision-making contexts. Available evidence suggests that: (1 There is a strong conceptual overlap between the constructs of uncertainty and cognitive control; (2 There is a remarkable overlap between the neural networks associated with uncertainty and the brain networks subserving cognitive control; (3 The perception and estimation of uncertainty might play a key role in monitoring processes and the evaluation of the need for control; (4 Potential interactions between uncertainty and cognitive control might play a significant role in several affective disorders.

  13. TH-C-BRD-05: Reducing Proton Beam Range Uncertainty with Patient-Specific CT HU to RSP Calibrations Based On Single-Detector Proton Radiography

    Energy Technology Data Exchange (ETDEWEB)

    Doolan, P [University College London, London (United Kingdom); Massachusetts General Hospital, Boston, MA (United States); Sharp, G; Testa, M; Lu, H-M [Massachusetts General Hospital, Boston, MA (United States); Bentefour, E [Ion Beam Applications (IBA), Louvain la Neuve (Belgium); Royle, G [University College London, London (United Kingdom)

    2014-06-15

    Purpose: Beam range uncertainty in proton treatment comes primarily from converting the patient's X-ray CT (xCT) dataset to relative stopping power (RSP). Current practices use a single curve for this conversion, produced by a stoichiometric calibration based on tissue composition data for average, healthy, adult humans, but not for the individual in question. Proton radiographs produce water-equivalent path length (WEPL) maps, dependent on the RSP of tissues within the specific patient. This work investigates the use of such WEPL maps to optimize patient-specific calibration curves for reducing beam range uncertainty. Methods: The optimization procedure works on the principle of minimizing the difference between the known WEPL map, obtained from a proton radiograph, and a digitally-reconstructed WEPL map (DRWM) through an RSP dataset, by altering the calibration curve that is used to convert the xCT into an RSP dataset. DRWMs were produced with Plastimatch, an in-house developed software, and an optimization procedure was implemented in Matlab. Tests were made on a range of systems including simulated datasets with computed WEPL maps and phantoms (anthropomorphic and real biological tissue) with WEPL maps measured by single detector proton radiography. Results: For the simulated datasets, the optimizer showed excellent results. It was able to either completely eradicate or significantly reduce the root-mean-square-error (RMSE) in the WEPL for the homogeneous phantoms (to zero for individual materials or from 1.5% to 0.2% for the simultaneous optimization of multiple materials). For the heterogeneous phantom the RMSE was reduced from 1.9% to 0.3%. Conclusion: An optimization procedure has been designed to produce patient-specific calibration curves. Test results on a range of systems with different complexities and sizes have been promising for accurate beam range control in patients. This project was funded equally by the Engineering and Physical Sciences

  14. Exploring the implication of climate process uncertainties within the Earth System Framework

    Science.gov (United States)

    Booth, B.; Lambert, F. H.; McNeal, D.; Harris, G.; Sexton, D.; Boulton, C.; Murphy, J.

    2011-12-01

    Uncertainties in the magnitude of future climate change have been a focus of a great deal of research. Much of the work with General Circulation Models has focused on the atmospheric response to changes in atmospheric composition, while other processes remain outside these frameworks. Here we introduce an ensemble of new simulations, based on an Earth System configuration of HadCM3C, designed to explored uncertainties in both physical (atmospheric, oceanic and aerosol physics) and carbon cycle processes, using perturbed parameter approaches previously used to explore atmospheric uncertainty. Framed in the context of the climate response to future changes in emissions, the resultant future projections represent significantly broader uncertainty than existing concentration driven GCM assessments. The systematic nature of the ensemble design enables interactions between components to be explored. For example, we show how metrics of physical processes (such as climate sensitivity) are also influenced carbon cycle parameters. The suggestion from this work is that carbon cycle processes represent a comparable contribution to uncertainty in future climate projections as contributions from atmospheric feedbacks more conventionally explored. The broad range of climate responses explored within these ensembles, rather than representing a reason for inaction, provide information on lower likelihood but high impact changes. For example while the majority of these simulations suggest that future Amazon forest extent is resilient to the projected climate changes, a small number simulate dramatic forest dieback. This ensemble represents a framework to examine these risks, breaking them down into physical processes (such as ocean temperature drivers of rainfall change) and vegetation processes (where uncertainties point towards requirements for new observational constraints).

  15. Decoherence effect on quantum-memory-assisted entropic uncertainty relations

    Science.gov (United States)

    Ming, Fei; Wang, Dong; Huang, Ai-Jun; Sun, Wen-Yang; Ye, Liu

    2018-01-01

    Uncertainty principle significantly provides a bound to predict precision of measurement with regard to any two incompatible observables, and thereby plays a nontrivial role in quantum precision measurement. In this work, we observe the dynamical features of the quantum-memory-assisted entropic uncertainty relations (EUR) for a pair of incompatible measurements in an open system characterized by local generalized amplitude damping (GAD) noises. Herein, we derive the dynamical evolution of the entropic uncertainty with respect to the measurement affecting by the canonical GAD noises when particle A is initially entangled with quantum memory B. Specifically, we examine the dynamics of EUR in the frame of three realistic scenarios: one case is that particle A is affected by environmental noise (GAD) while particle B as quantum memory is free from any noises, another case is that particle B is affected by the external noise while particle A is not, and the last case is that both of the particles suffer from the noises. By analytical methods, it turns out that the uncertainty is not full dependent of quantum correlation evolution of the composite system consisting of A and B, but the minimal conditional entropy of the measured subsystem. Furthermore, we present a possible physical interpretation for the behavior of the uncertainty evolution by means of the mixedness of the observed system; we argue that the uncertainty might be dramatically correlated with the systematic mixedness. Furthermore, we put forward a simple and effective strategy to reduce the measuring uncertainty of interest upon quantum partially collapsed measurement. Therefore, our explorations might offer an insight into the dynamics of the entropic uncertainty relation in a realistic system, and be of importance to quantum precision measurement during quantum information processing.

  16. Cellular and molecular research to reduce uncertainties in estimates of health effects from low-level radiation

    Energy Technology Data Exchange (ETDEWEB)

    Elkind, M.M.; Bedford, J.; Benjamin, S.A.; Waldren, C.A. (Colorado State Univ., Fort Collins, CO (USA)); Gotchy, R.L. (Science Applications International Corp., McLean, VA (USA))

    1990-10-01

    A study was undertaken by five radiation scientists to examine the feasibility of reducing the uncertainties in the estimation of risk due to protracted low doses of ionizing radiation. In addressing the question of feasibility, a review was made by the study group: of the cellular, molecular, and mammalian radiation data that are available; of the way in which altered oncogene properties could be involved in the loss of growth control that culminates in tumorigenesis; and of the progress that had been made in the genetic characterizations of several human and animal neoplasms. On the basis of this analysis, the study group concluded that, at the present time, it is feasible to mount a program of radiation research directed at the mechanism(s) of radiation-induced cancer with special reference to risk of neoplasia due to protracted, low doses of sparsely ionizing radiation. To implement a program of research, a review was made of the methods, techniques, and instruments that would be needed. This review was followed by a survey of the laboratories and institutions where scientific personnel and facilities are known to be available. A research agenda of the principal and broad objectives of the program is also discussed. 489 refs., 21 figs., 14 tabs.

  17. Cellular and molecular research to reduce uncertainties in estimates of health effects from low-level radiation

    International Nuclear Information System (INIS)

    Elkind, M.M.; Bedford, J.; Benjamin, S.A.; Waldren, C.A.; Gotchy, R.L.

    1990-10-01

    A study was undertaken by five radiation scientists to examine the feasibility of reducing the uncertainties in the estimation of risk due to protracted low doses of ionizing radiation. In addressing the question of feasibility, a review was made by the study group: of the cellular, molecular, and mammalian radiation data that are available; of the way in which altered oncogene properties could be involved in the loss of growth control that culminates in tumorigenesis; and of the progress that had been made in the genetic characterizations of several human and animal neoplasms. On the basis of this analysis, the study group concluded that, at the present time, it is feasible to mount a program of radiation research directed at the mechanism(s) of radiation-induced cancer with special reference to risk of neoplasia due to protracted, low doses of sparsely ionizing radiation. To implement a program of research, a review was made of the methods, techniques, and instruments that would be needed. This review was followed by a survey of the laboratories and institutions where scientific personnel and facilities are known to be available. A research agenda of the principal and broad objectives of the program is also discussed. 489 refs., 21 figs., 14 tabs

  18. Aleatoric and epistemic uncertainties in sampling based nuclear data uncertainty and sensitivity analyses

    International Nuclear Information System (INIS)

    Zwermann, W.; Krzykacz-Hausmann, B.; Gallner, L.; Klein, M.; Pautz, A.; Velkov, K.

    2012-01-01

    Sampling based uncertainty and sensitivity analyses due to epistemic input uncertainties, i.e. to an incomplete knowledge of uncertain input parameters, can be performed with arbitrary application programs to solve the physical problem under consideration. For the description of steady-state particle transport, direct simulations of the microscopic processes with Monte Carlo codes are often used. This introduces an additional source of uncertainty, the aleatoric sampling uncertainty, which is due to the randomness of the simulation process performed by sampling, and which adds to the total combined output sampling uncertainty. So far, this aleatoric part of uncertainty is minimized by running a sufficiently large number of Monte Carlo histories for each sample calculation, thus making its impact negligible as compared to the impact from sampling the epistemic uncertainties. Obviously, this process may cause high computational costs. The present paper shows that in many applications reliable epistemic uncertainty results can also be obtained with substantially lower computational effort by performing and analyzing two appropriately generated series of samples with much smaller number of Monte Carlo histories each. The method is applied along with the nuclear data uncertainty and sensitivity code package XSUSA in combination with the Monte Carlo transport code KENO-Va to various critical assemblies and a full scale reactor calculation. It is shown that the proposed method yields output uncertainties and sensitivities equivalent to the traditional approach, with a high reduction of computing time by factors of the magnitude of 100. (authors)

  19. Uncertainty, probability and information-gaps

    International Nuclear Information System (INIS)

    Ben-Haim, Yakov

    2004-01-01

    This paper discusses two main ideas. First, we focus on info-gap uncertainty, as distinct from probability. Info-gap theory is especially suited for modelling and managing uncertainty in system models: we invest all our knowledge in formulating the best possible model; this leaves the modeller with very faulty and fragmentary information about the variation of reality around that optimal model. Second, we examine the interdependence between uncertainty modelling and decision-making. Good uncertainty modelling requires contact with the end-use, namely, with the decision-making application of the uncertainty model. The most important avenue of uncertainty-propagation is from initial data- and model-uncertainties into uncertainty in the decision-domain. Two questions arise. Is the decision robust to the initial uncertainties? Is the decision prone to opportune windfall success? We apply info-gap robustness and opportunity functions to the analysis of representation and propagation of uncertainty in several of the Sandia Challenge Problems

  20. Fundamental uncertainty limit of optical flow velocimetry according to Heisenberg's uncertainty principle.

    Science.gov (United States)

    Fischer, Andreas

    2016-11-01

    Optical flow velocity measurements are important for understanding the complex behavior of flows. Although a huge variety of methods exist, they are either based on a Doppler or a time-of-flight measurement principle. Doppler velocimetry evaluates the velocity-dependent frequency shift of light scattered at a moving particle, whereas time-of-flight velocimetry evaluates the traveled distance of a scattering particle per time interval. Regarding the aim of achieving a minimal measurement uncertainty, it is unclear if one principle allows to achieve lower uncertainties or if both principles can achieve equal uncertainties. For this reason, the natural, fundamental uncertainty limit according to Heisenberg's uncertainty principle is derived for Doppler and time-of-flight measurement principles, respectively. The obtained limits of the velocity uncertainty are qualitatively identical showing, e.g., a direct proportionality for the absolute value of the velocity to the power of 32 and an indirect proportionality to the square root of the scattered light power. Hence, both measurement principles have identical potentials regarding the fundamental uncertainty limit due to the quantum mechanical behavior of photons. This fundamental limit can be attained (at least asymptotically) in reality either with Doppler or time-of-flight methods, because the respective Cramér-Rao bounds for dominating photon shot noise, which is modeled as white Poissonian noise, are identical with the conclusions from Heisenberg's uncertainty principle.

  1. A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system

    International Nuclear Information System (INIS)

    Kim, Kihyung; Spakovsky, Michael R. von; Wang, M.; Nelson, Douglas J.

    2011-01-01

    During system development, large-scale, complex energy systems require multi-disciplinary efforts to achieve system quality, cost, and performance goals. As systems become larger and more complex, the number of possible system configurations and technologies, which meet the designer's objectives optimally, increases greatly. In addition, both transient and environmental effects may need to be taken into account. Thus, the difficulty of developing the system via the formulation of a single optimization problem in which the optimal synthesis/design and operation/control of the system are achieved simultaneously is great and rather problematic. This difficulty is further heightened with the introduction of uncertainty analysis, which transforms the problem from a purely deterministic one into a probabilistic one. Uncertainties, system complexity and nonlinearity, and large numbers of decision variables quickly render the single optimization problem unsolvable by conventional, single-level, optimization strategies. To address these difficulties, the strategy adopted here combines a dynamic physical decomposition technique for large-scale optimization with a response sensitivity analysis method for quantifying system response uncertainties to given uncertainty sources. The feasibility of such a hybrid approach is established by applying it to the synthesis/design and operation/control of a 5 kW proton exchange membrane (PEM) fuel cell system.

  2. Experimental Test of Entropic Noise-Disturbance Uncertainty Relations for Spin-1/2 Measurements.

    Science.gov (United States)

    Sulyok, Georg; Sponar, Stephan; Demirel, Bülent; Buscemi, Francesco; Hall, Michael J W; Ozawa, Masanao; Hasegawa, Yuji

    2015-07-17

    Information-theoretic definitions for noise and disturbance in quantum measurements were given in [Phys. Rev. Lett. 112, 050401 (2014)] and a state-independent noise-disturbance uncertainty relation was obtained. Here, we derive a tight noise-disturbance uncertainty relation for complementary qubit observables and carry out an experimental test. Successive projective measurements on the neutron's spin-1/2 system, together with a correction procedure which reduces the disturbance, are performed. Our experimental results saturate the tight noise-disturbance uncertainty relation for qubits when an optimal correction procedure is applied.

  3. Evaluation of uncertainties in irradiated hardware characterization: Final report, September 30, 1986-March 31, 1987

    International Nuclear Information System (INIS)

    Bedore, N.; Levin, A.; Tuite, P.

    1987-10-01

    Waste Management Group, Inc. has evaluated the techniques used by industry to characterize and classify irradiated hardware components for disposal. This report describes the current practices used to characterize the radionuclide content of hardware components, identifies the uncertainties associated with the techniques and practices considered, and recommends areas for improvement which could reduce uncertainty. Industry uses two different characterization methods. The first uses a combination of gamma scanning, direct sampling, underwater radiation profiling and radiochemical analysis to determine radionuclide content, while the second uses a form of activation analysis in conjunction with underwater radiation profiling. Both methods employ the determination of Cobalt 60 content, and the determination of scaling factors for hard-to-detect Part 61 radionuclides. The accurate determination of Cobalt-60 is critical since the Part 61 activation product radionuclides which affect Part 61 classification are scaled from Cobalt-60. Current uncertainties in Cobalt-60 determination can be reduced by improving underwater radiation profiling equipment and techniques. The calculational techniques used for activation analysis can also be refined to reduce the uncertainties with Cobalt-60 determination. 33 refs., 11 figs., 10 tabs

  4. Risk Assessment and Decision-Making under Uncertainty in Tunnel and Underground Engineering

    Directory of Open Access Journals (Sweden)

    Yuanpu Xia

    2017-10-01

    Full Text Available The impact of uncertainty on risk assessment and decision-making is increasingly being prioritized, especially for large geotechnical projects such as tunnels, where uncertainty is often the main source of risk. Epistemic uncertainty, which can be reduced, is the focus of attention. In this study, the existing entropy-risk decision model is first discussed and analyzed, and its deficiencies are improved upon and overcome. Then, this study addresses the fact that existing studies only consider parameter uncertainty and ignore the influence of the model uncertainty. Here, focus is on the issue of model uncertainty and differences in risk consciousness with different decision-makers. The utility theory is introduced in the model. Finally, a risk decision model is proposed based on the sensitivity analysis and the tolerance cost, which can improve decision-making efficiency. This research can provide guidance or reference for the evaluation and decision-making of complex systems engineering problems, and indicate a direction for further research of risk assessment and decision-making issues.

  5. Sensitivity of Earthquake Loss Estimates to Source Modeling Assumptions and Uncertainty

    Science.gov (United States)

    Reasenberg, Paul A.; Shostak, Nan; Terwilliger, Sharon

    2006-01-01

    Introduction: This report explores how uncertainty in an earthquake source model may affect estimates of earthquake economic loss. Specifically, it focuses on the earthquake source model for the San Francisco Bay region (SFBR) created by the Working Group on California Earthquake Probabilities. The loss calculations are made using HAZUS-MH, a publicly available computer program developed by the Federal Emergency Management Agency (FEMA) for calculating future losses from earthquakes, floods and hurricanes within the United States. The database built into HAZUS-MH includes a detailed building inventory, population data, data on transportation corridors, bridges, utility lifelines, etc. Earthquake hazard in the loss calculations is based upon expected (median value) ground motion maps called ShakeMaps calculated for the scenario earthquake sources defined in WGCEP. The study considers the effect of relaxing certain assumptions in the WG02 model, and explores the effect of hypothetical reductions in epistemic uncertainty in parts of the model. For example, it addresses questions such as what would happen to the calculated loss distribution if the uncertainty in slip rate in the WG02 model were reduced (say, by obtaining additional geologic data)? What would happen if the geometry or amount of aseismic slip (creep) on the region's faults were better known? And what would be the effect on the calculated loss distribution if the time-dependent earthquake probability were better constrained, either by eliminating certain probability models or by better constraining the inherent randomness in earthquake recurrence? The study does not consider the effect of reducing uncertainty in the hazard introduced through models of attenuation and local site characteristics, although these may have a comparable or greater effect than does source-related uncertainty. Nor does it consider sources of uncertainty in the building inventory, building fragility curves, and other assumptions

  6. SCALE-6 Sensitivity/Uncertainty Methods and Covariance Data

    International Nuclear Information System (INIS)

    Williams, Mark L.; Rearden, Bradley T.

    2008-01-01

    Computational methods and data used for sensitivity and uncertainty analysis within the SCALE nuclear analysis code system are presented. The methodology used to calculate sensitivity coefficients and similarity coefficients and to perform nuclear data adjustment is discussed. A description is provided of the SCALE-6 covariance library based on ENDF/B-VII and other nuclear data evaluations, supplemented by 'low-fidelity' approximate covariances. SCALE (Standardized Computer Analyses for Licensing Evaluation) is a modular code system developed by Oak Ridge National Laboratory (ORNL) to perform calculations for criticality safety, reactor physics, and radiation shielding applications. SCALE calculations typically use sequences that execute a predefined series of executable modules to compute particle fluxes and responses like the critical multiplication factor. SCALE also includes modules for sensitivity and uncertainty (S/U) analysis of calculated responses. The S/U codes in SCALE are collectively referred to as TSUNAMI (Tools for Sensitivity and UNcertainty Analysis Methodology Implementation). SCALE-6-scheduled for release in 2008-contains significant new capabilities, including important enhancements in S/U methods and data. The main functions of TSUNAMI are to (a) compute nuclear data sensitivity coefficients and response uncertainties, (b) establish similarity between benchmark experiments and design applications, and (c) reduce uncertainty in calculated responses by consolidating integral benchmark experiments. TSUNAMI includes easy-to-use graphical user interfaces for defining problem input and viewing three-dimensional (3D) geometries, as well as an integrated plotting package.

  7. Instrumentation-related uncertainty of reflectance and transmittance measurements with a two-channel spectrophotometer.

    Science.gov (United States)

    Peest, Christian; Schinke, Carsten; Brendel, Rolf; Schmidt, Jan; Bothe, Karsten

    2017-01-01

    Spectrophotometers are operated in numerous fields of science and industry for a variety of applications. In order to provide confidence for the measured data, analyzing the associated uncertainty is valuable. However, the uncertainty of the measurement results is often unknown or reduced to sample-related contributions. In this paper, we describe our approach for the systematic determination of the measurement uncertainty of the commercially available two-channel spectrophotometer Agilent Cary 5000 in accordance with the Guide to the expression of uncertainty in measurements. We focus on the instrumentation-related uncertainty contributions rather than the specific application and thus outline a general procedure which can be adapted for other instruments. Moreover, we discover a systematic signal deviation due to the inertia of the measurement amplifier and develop and apply a correction procedure. Thereby we increase the usable dynamic range of the instrument by more than one order of magnitude. We present methods for the quantification of the uncertainty contributions and combine them into an uncertainty budget for the device.

  8. A review of the uncertainties in the assessment of radiological consequences of spent nuclear fuel disposal

    International Nuclear Information System (INIS)

    Wiborgh, M.; Elert, M.; Hoeglund, L.O.; Jones, C.; Grundfelt, B.; Skagius, K.; Bengtsson, A.

    1992-06-01

    Radioactive waste disposal systems for spent nuclear fuel are designed to isolate the radioactive waste from the human environment for long period of time. The isolation is provided by a combination of engineered and natural barriers. Safety assessments are performed to describe and quantify the performance of the individual barriers and the disposal system over long-term periods. These assessments will always be associated with uncertainties. Uncertainties can originate from the variability of natural systems and will also be introduced in the predictive modelling performed to quantitatively evaluate the behaviour of the disposal system as a consequence of the incomplete knowledge about the governing processes. Uncertainties in safety assessments can partly be reduced by additional measurements and research. The aim of this study has been to identify uncertainties in assessments of radiological consequences from the disposal of spent nuclear fuel based on the Swedish KBS-3 concept. The identified uncertainties have been classified with respect to their origin, i.e. in conceptual, modelling and data uncertainties. The possibilities to reduce the uncertainties are also commented upon. In assessments it is important to decrease uncertainties which are of major importance for the performance of the disposal system. These could to some extent be identified by uncertainty analysis. However, conceptual uncertainties and some type of model uncertainties are difficult to evaluate. To be able to decrease uncertainties in conceptual models, it is essential that the processes describing and influencing the radionuclide transport in the engineered and natural barriers are sufficiently understood. In this study a qualitative approach has been used. The importance of different barriers and processes are indicated by their influence on the release of some representative radionuclides. (122 refs.) (au)

  9. Uncertainty measurement in the homogenization and sample reduction in the physical classification of rice and beans

    Directory of Open Access Journals (Sweden)

    Dieisson Pivoto

    2016-04-01

    Full Text Available ABSTRACT: The study aimed to i quantify the measurement uncertainty in the physical tests of rice and beans for a hypothetical defect, ii verify whether homogenization and sample reduction in the physical classification tests of rice and beans is effective to reduce the measurement uncertainty of the process and iii determine whether the increase in size of beans sample increases accuracy and reduces measurement uncertainty in a significant way. Hypothetical defects in rice and beans with different damage levels were simulated according to the testing methodology determined by the Normative Ruling of each product. The homogenization and sample reduction in the physical classification of rice and beans are not effective, transferring to the final test result a high measurement uncertainty. The sample size indicated by the Normative Ruling did not allow an appropriate homogenization and should be increased.

  10. A Variation on Uncertainty Principle and Logarithmic Uncertainty Principle for Continuous Quaternion Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    Mawardi Bahri

    2017-01-01

    Full Text Available The continuous quaternion wavelet transform (CQWT is a generalization of the classical continuous wavelet transform within the context of quaternion algebra. First of all, we show that the directional quaternion Fourier transform (QFT uncertainty principle can be obtained using the component-wise QFT uncertainty principle. Based on this method, the directional QFT uncertainty principle using representation of polar coordinate form is easily derived. We derive a variation on uncertainty principle related to the QFT. We state that the CQWT of a quaternion function can be written in terms of the QFT and obtain a variation on uncertainty principle related to the CQWT. Finally, we apply the extended uncertainty principles and properties of the CQWT to establish logarithmic uncertainty principles related to generalized transform.

  11. Siting uncertainties and challenges in Appalachia

    International Nuclear Information System (INIS)

    Vincenti, J.R.

    1992-01-01

    The purpose of this paper is to discuss the uncertainties and challenges facing users of radioactive isotopes and the generators of low-level radioactive waste (LLRW) in the United States. This paper focuses specially on those user/generators in Delaware, Maryland, Pennsylvania, and West Virginia, which make up the Appalachian States Compact. These uncertainties are based on legal and political actions that have thwarted siting and licensing of LLRW throughout the United States. The challenges facing users of radioactive isotopes are numerous. They stem from the need to reduce or minimize waste volume and to treat or eliminate the generation of waste, especially mixed waste. The basic problem, after the attention to waste management, is that some users are still left with a waste that must be disposed of in a regional or national site for long-term storage and monitoring. This problem will not go away

  12. On the relationship between aerosol model uncertainty and radiative forcing uncertainty.

    Science.gov (United States)

    Lee, Lindsay A; Reddington, Carly L; Carslaw, Kenneth S

    2016-05-24

    The largest uncertainty in the historical radiative forcing of climate is caused by the interaction of aerosols with clouds. Historical forcing is not a directly measurable quantity, so reliable assessments depend on the development of global models of aerosols and clouds that are well constrained by observations. However, there has been no systematic assessment of how reduction in the uncertainty of global aerosol models will feed through to the uncertainty in the predicted forcing. We use a global model perturbed parameter ensemble to show that tight observational constraint of aerosol concentrations in the model has a relatively small effect on the aerosol-related uncertainty in the calculated forcing between preindustrial and present-day periods. One factor is the low sensitivity of present-day aerosol to natural emissions that determine the preindustrial aerosol state. However, the major cause of the weak constraint is that the full uncertainty space of the model generates a large number of model variants that are equally acceptable compared to present-day aerosol observations. The narrow range of aerosol concentrations in the observationally constrained model gives the impression of low aerosol model uncertainty. However, these multiple "equifinal" models predict a wide range of forcings. To make progress, we need to develop a much deeper understanding of model uncertainty and ways to use observations to constrain it. Equifinality in the aerosol model means that tuning of a small number of model processes to achieve model-observation agreement could give a misleading impression of model robustness.

  13. Heterogeneous rupture in the great Cascadia earthquake of 1700 inferred from coastal subsidence estimates

    Science.gov (United States)

    Wang, Pei-Ling; Engelhart, Simon E.; Wang, Kelin; Hawkes, Andrea D.; Horton, Benjamin P.; Nelson, Alan R.; Witter, Robert C.

    2013-01-01

    Past earthquake rupture models used to explain paleoseismic estimates of coastal subsidence during the great A.D. 1700 Cascadia earthquake have assumed a uniform slip distribution along the megathrust. Here we infer heterogeneous slip for the Cascadia margin in A.D. 1700 that is analogous to slip distributions during instrumentally recorded great subduction earthquakes worldwide. The assumption of uniform distribution in previous rupture models was due partly to the large uncertainties of then available paleoseismic data used to constrain the models. In this work, we use more precise estimates of subsidence in 1700 from detailed tidal microfossil studies. We develop a 3-D elastic dislocation model that allows the slip to vary both along strike and in the dip direction. Despite uncertainties in the updip and downdip slip extensions, the more precise subsidence estimates are best explained by a model with along-strike slip heterogeneity, with multiple patches of high-moment release separated by areas of low-moment release. For example, in A.D. 1700, there was very little slip near Alsea Bay, Oregon (~44.4°N), an area that coincides with a segment boundary previously suggested on the basis of gravity anomalies. A probable subducting seamount in this area may be responsible for impeding rupture during great earthquakes. Our results highlight the need for more precise, high-quality estimates of subsidence or uplift during prehistoric earthquakes from the coasts of southern British Columbia, northern Washington (north of 47°N), southernmost Oregon, and northern California (south of 43°N), where slip distributions of prehistoric earthquakes are poorly constrained.

  14. Uncertainty and sensitivity analyses of ballast life-cycle cost and payback period

    OpenAIRE

    Mcmahon, James E.

    2000-01-01

    The paper introduces an innovative methology for evaluating the relative significance of energy-efficient technologies applied to fluorescent lamp ballasts. The method involves replacing the point estimates of life cycle cost of the ballasts with uncertainty distributions reflecting the whole spectrum of possible costs, and the assessed probability associated with each value. The results of uncertainty and sensitivity analyses will help analysts reduce effort in data collection and carry on a...

  15. Uncertainty, financial development and economic growth : an empirical analysis

    NARCIS (Netherlands)

    Lensink, Robert

    1999-01-01

    This paper examines whether financial sector development may partly undo growth-reducing effects of policy uncertainty. By performing a cross-country growth regression for the 1970-1995 period I find evidence that countries with a more developed financial sector are better able to nullify the

  16. Health technology assessment and primary data collection for reducing uncertainty in decision making.

    Science.gov (United States)

    Goeree, Ron; Levin, Les; Chandra, Kiran; Bowen, James M; Blackhouse, Gord; Tarride, Jean-Eric; Burke, Natasha; Bischof, Matthias; Xie, Feng; O'Reilly, Daria

    2009-05-01

    Health care expenditures continue to escalate, and pressures for increased spending will continue. Health care decision makers from publicly financed systems, private insurance companies, or even from individual health care institutions, will continue to be faced with making difficult purchasing, access, and reimbursement decisions. As a result, decision makers are increasingly turning to evidence-based platforms to help control costs and make the most efficient use of existing resources. Most tools used to assist with evidence-based decision making focus on clinical outcomes. Health technology assessment (HTA) is increasing in popularity because it also considers other factors important for decision making, such as cost, social and ethical values, legal issues, and factors such as the feasibility of implementation. In some jurisdictions, HTAs have also been supplemented with primary data collection to help address uncertainty that may still exist after conducting a traditional HTA. The HTA process adopted in Ontario, Canada, is unique in that assessments are also made to determine what primary data research should be conducted and what should be collected in these studies. In this article, concerns with the traditional HTA process are discussed, followed by a description of the HTA process that has been established in Ontario, with a particular focus on the data collection program followed by the Programs for Assessment of Technology in Health Research Institute. An illustrative example is used to show how the Ontario HTA process works and the role value of information analyses plays in addressing decision uncertainty, determining research feasibility, and determining study data collection needs.

  17. Risk Management and Uncertainty in Infrastructure Projects

    DEFF Research Database (Denmark)

    Harty, Chris; Neerup Themsen, Tim; Tryggestad, Kjell

    2014-01-01

    The assumption that large complex projects should be managed in order to reduce uncertainty and increase predictability is not new. What is relatively new, however, is that uncertainty reduction can and should be obtained through formal risk management approaches. We question both assumptions...... by addressing a more fundamental question about the role of knowledge in current risk management practices. Inquiries into the predominant approaches to risk management in large infrastructure and construction projects reveal their assumptions about knowledge and we discuss the ramifications these have...... for project and construction management. Our argument and claim is that predominant risk management approaches tends to reinforce conventional ideas of project control whilst undermining other notions of value and relevance of built assets and project management process. These approaches fail to consider...

  18. Probability and uncertainty in nuclear safety decisions

    International Nuclear Information System (INIS)

    Pate-Cornell, M.E.

    1986-01-01

    In this paper, we examine some problems posed by the use of probabilities in Nuclear Safety decisions. We discuss some of the theoretical difficulties due to the collective nature of regulatory decisions, and, in particular, the calibration and the aggregation of risk information (e.g., experts opinions). We argue that, if one chooses numerical safety goals as a regulatory basis, one can reduce the constraints to an individual safety goal and a cost-benefit criterion. We show the relevance of risk uncertainties in this kind of regulatory framework. We conclude that, whereas expected values of future failure frequencies are adequate to show compliance with economic constraints, the use of a fractile (e.g., 95%) to be specified by the regulatory agency is justified to treat hazard uncertainties for the individual safety goal. (orig.)

  19. DS02 uncertainty analysis

    International Nuclear Information System (INIS)

    Kaul, Dean C.; Egbert, Stephen D.; Woolson, William A.

    2005-01-01

    In order to avoid the pitfalls that so discredited DS86 and its uncertainty estimates, and to provide DS02 uncertainties that are both defensible and credible, this report not only presents the ensemble uncertainties assembled from uncertainties in individual computational elements and radiation dose components but also describes how these relate to comparisons between observed and computed quantities at critical intervals in the computational process. These comparisons include those between observed and calculated radiation free-field components, where observations include thermal- and fast-neutron activation and gamma-ray thermoluminescence, which are relevant to the estimated systematic uncertainty for DS02. The comparisons also include those between calculated and observed survivor shielding, where the observations consist of biodosimetric measurements for individual survivors, which are relevant to the estimated random uncertainty for DS02. (J.P.N.)

  20. Uncertainty and risk management after the Great Moderation: the role of risk (mis)management by financial institutions

    NARCIS (Netherlands)

    Blommestein, H.J.; Hoogduin, L.H.; Peeters, J.J.W.

    2009-01-01

    Since the early eighties volatility of GDP and inflation has been declining steadily in many countries. Financial innovation has been identified as one of the key factors driving this „Great Moderation‟. Financial innovation was considered to have improved significantly the allocation and sharing of

  1. Uncertainty about social interactions leads to the evolution of social heuristics.

    Science.gov (United States)

    van den Berg, Pieter; Wenseleers, Tom

    2018-05-31

    Individuals face many types of social interactions throughout their lives, but they often cannot perfectly assess what the consequences of their actions will be. Although it is known that unpredictable environments can profoundly affect the evolutionary process, it remains unclear how uncertainty about the nature of social interactions shapes the evolution of social behaviour. Here, we present an evolutionary simulation model, showing that even intermediate uncertainty leads to the evolution of simple cooperation strategies that disregard information about the social interaction ('social heuristics'). Moreover, our results show that the evolution of social heuristics can greatly affect cooperation levels, nearly doubling cooperation rates in our simulations. These results provide new insight into why social behaviour, including cooperation in humans, is often observed to be seemingly suboptimal. More generally, our results show that social behaviour that seems maladaptive when considered in isolation may actually be well-adapted to a heterogeneous and uncertain world.

  2. Application of a Novel Dose-Uncertainty Model for Dose-Uncertainty Analysis in Prostate Intensity-Modulated Radiotherapy

    International Nuclear Information System (INIS)

    Jin Hosang; Palta, Jatinder R.; Kim, You-Hyun; Kim, Siyong

    2010-01-01

    Purpose: To analyze dose uncertainty using a previously published dose-uncertainty model, and to assess potential dosimetric risks existing in prostate intensity-modulated radiotherapy (IMRT). Methods and Materials: The dose-uncertainty model provides a three-dimensional (3D) dose-uncertainty distribution in a given confidence level. For 8 retrospectively selected patients, dose-uncertainty maps were constructed using the dose-uncertainty model at the 95% CL. In addition to uncertainties inherent to the radiation treatment planning system, four scenarios of spatial errors were considered: machine only (S1), S1 + intrafraction, S1 + interfraction, and S1 + both intrafraction and interfraction errors. To evaluate the potential risks of the IMRT plans, three dose-uncertainty-based plan evaluation tools were introduced: confidence-weighted dose-volume histogram, confidence-weighted dose distribution, and dose-uncertainty-volume histogram. Results: Dose uncertainty caused by interfraction setup error was more significant than that of intrafraction motion error. The maximum dose uncertainty (95% confidence) of the clinical target volume (CTV) was smaller than 5% of the prescribed dose in all but two cases (13.9% and 10.2%). The dose uncertainty for 95% of the CTV volume ranged from 1.3% to 2.9% of the prescribed dose. Conclusions: The dose uncertainty in prostate IMRT could be evaluated using the dose-uncertainty model. Prostate IMRT plans satisfying the same plan objectives could generate a significantly different dose uncertainty because a complex interplay of many uncertainty sources. The uncertainty-based plan evaluation contributes to generating reliable and error-resistant treatment plans.

  3. The uncertainty of crop yield projections is reduced by improved temperature response functions

    DEFF Research Database (Denmark)

    Wang, Enli; Martre, Pierre; Zhao, Zhigan

    2017-01-01

    , we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature......Quality) and analysing their results against the HSC data and an additional global dataset from the International Heat Stress Genotpye Experiment (IHSGE)8 carried out by the International Maize and Wheat Improvement Center (CIMMYT). More importantly, we derive, based on newest knowledge and data, a set of new...

  4. Antiprotons from dark matter annihilation in the Galaxy. Astrophysical uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    Evoli, Carmelo [Chinese Academy of Sciences, Beijing (China). National Astronomical Observatories; Cholis, Ilias; Ullio, Piero [SISSA, Sezione di Trieste (Italy); INFN, Sezione di Trieste (Italy); Grasso, Dario [INFN, Sezione di Pisa (Italy); Maccione, Luca [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)

    2011-08-15

    The latest years have seen steady progresses in WIMP dark matter (DM) searches, with hints of possible signals suggested by both direct and indirect detection experiments. Antiprotons can play a key role validating those interpretations since they are copiously produced by WIMP annihilations in the Galactic halo, and the secondary antiproton background produced by Cosmic Ray (CR) interactions is predicted with fair accuracy and matches the observed spectrum very well. Using the publicly available numerical DRAGON code, we reconsider antiprotons as a tool to constrain DM models discussing its power and limitations. We provide updated constraints on a wide class of annihilating DM models by comparing our predictions against the most up-to-date anti p measurements, taking also into account the latest spectral information on the p, He and other CR nuclei fluxes. Doing that, we probe carefully the uncertainties associated to both secondary and DM originated antiprotons, by using a variety of distinctively different assumptions for the propagation of CRs and for the DM distribution in the Galaxy. We find that the impact of the astrophysical uncertainties on constraining the DM properties can be much stronger, up to a factor of {proportional_to}50, than the one due to uncertainties on the DM distribution ({proportional_to}2-6). Remarkably, even reducing the uncertainties on the propagation parameters derived by local observables, non-local effects can still change DM model constraints even by 50%. Nevertheless, current anti p data place tight constraints on DM models, excluding some of those suggested in connection with indirect and direct searches. Finally we discuss the power of upcoming CR spectral data from the AMS-02 observatory to drastically reduce the uncertainties discussed in this paper and estimate the expected sensitivity of this instrument to some sets of DM models. (orig.)

  5. Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties

    Science.gov (United States)

    Exbrayat, Jean-François; Bloom, A. Anthony; Falloon, Pete; Ito, Akihiko; Smallman, T. Luke; Williams, Mathew

    2018-02-01

    Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in Earth system modelling. Here, we use three global observationally orientated estimates of current net primary productivity (NPP) to perform a reliability ensemble averaging (REA) method using 30 global simulations of the 21st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) business as usual emissions scenario. We find that the three REA methods support an increase in global NPP by the end of the 21st century (2095-2099) compared to 2001-2005, which is 2-3 % stronger than the ensemble ISIMIP mean value of 24.2 Pg C y-1. Using REA also leads to a 45-68 % reduction in the global uncertainty of 21st century NPP projection, which strengthens confidence in the resilience of the CO2 fertilization effect to climate change. This reduction in uncertainty is especially clear for boreal ecosystems although it may be an artefact due to the lack of representation of nutrient limitations on NPP in most models. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions.

  6. Ruminations On NDA Measurement Uncertainty Compared TO DA Uncertainty

    International Nuclear Information System (INIS)

    Salaymeh, S.; Ashley, W.; Jeffcoat, R.

    2010-01-01

    It is difficult to overestimate the importance that physical measurements performed with nondestructive assay instruments play throughout the nuclear fuel cycle. They underpin decision making in many areas and support: criticality safety, radiation protection, process control, safeguards, facility compliance, and waste measurements. No physical measurement is complete or indeed meaningful, without a defensible and appropriate accompanying statement of uncertainties and how they combine to define the confidence in the results. The uncertainty budget should also be broken down in sufficient detail suitable for subsequent uses to which the nondestructive assay (NDA) results will be applied. Creating an uncertainty budget and estimating the total measurement uncertainty can often be an involved process, especially for non routine situations. This is because data interpretation often involves complex algorithms and logic combined in a highly intertwined way. The methods often call on a multitude of input data subject to human oversight. These characteristics can be confusing and pose a barrier to developing and understanding between experts and data consumers. ASTM subcommittee C26-10 recognized this problem in the context of how to summarize and express precision and bias performance across the range of standards and guides it maintains. In order to create a unified approach consistent with modern practice and embracing the continuous improvement philosophy a consensus arose to prepare a procedure covering the estimation and reporting of uncertainties in non destructive assay of nuclear materials. This paper outlines the needs analysis, objectives and on-going development efforts. In addition to emphasizing some of the unique challenges and opportunities facing the NDA community we hope this article will encourage dialog and sharing of best practice and furthermore motivate developers to revisit the treatment of measurement uncertainty.

  7. RUMINATIONS ON NDA MEASUREMENT UNCERTAINTY COMPARED TO DA UNCERTAINTY

    Energy Technology Data Exchange (ETDEWEB)

    Salaymeh, S.; Ashley, W.; Jeffcoat, R.

    2010-06-17

    It is difficult to overestimate the importance that physical measurements performed with nondestructive assay instruments play throughout the nuclear fuel cycle. They underpin decision making in many areas and support: criticality safety, radiation protection, process control, safeguards, facility compliance, and waste measurements. No physical measurement is complete or indeed meaningful, without a defensible and appropriate accompanying statement of uncertainties and how they combine to define the confidence in the results. The uncertainty budget should also be broken down in sufficient detail suitable for subsequent uses to which the nondestructive assay (NDA) results will be applied. Creating an uncertainty budget and estimating the total measurement uncertainty can often be an involved process, especially for non routine situations. This is because data interpretation often involves complex algorithms and logic combined in a highly intertwined way. The methods often call on a multitude of input data subject to human oversight. These characteristics can be confusing and pose a barrier to developing and understanding between experts and data consumers. ASTM subcommittee C26-10 recognized this problem in the context of how to summarize and express precision and bias performance across the range of standards and guides it maintains. In order to create a unified approach consistent with modern practice and embracing the continuous improvement philosophy a consensus arose to prepare a procedure covering the estimation and reporting of uncertainties in non destructive assay of nuclear materials. This paper outlines the needs analysis, objectives and on-going development efforts. In addition to emphasizing some of the unique challenges and opportunities facing the NDA community we hope this article will encourage dialog and sharing of best practice and furthermore motivate developers to revisit the treatment of measurement uncertainty.

  8. Aeroelastic Uncertainty Quantification Studies Using the S4T Wind Tunnel Model

    Science.gov (United States)

    Nikbay, Melike; Heeg, Jennifer

    2017-01-01

    This paper originates from the joint efforts of an aeroelastic study team in the Applied Vehicle Technology Panel from NATO Science and Technology Organization, with the Task Group number AVT-191, titled "Application of Sensitivity Analysis and Uncertainty Quantification to Military Vehicle Design." We present aeroelastic uncertainty quantification studies using the SemiSpan Supersonic Transport wind tunnel model at the NASA Langley Research Center. The aeroelastic study team decided treat both structural and aerodynamic input parameters as uncertain and represent them as samples drawn from statistical distributions, propagating them through aeroelastic analysis frameworks. Uncertainty quantification processes require many function evaluations to asses the impact of variations in numerous parameters on the vehicle characteristics, rapidly increasing the computational time requirement relative to that required to assess a system deterministically. The increased computational time is particularly prohibitive if high-fidelity analyses are employed. As a remedy, the Istanbul Technical University team employed an Euler solver in an aeroelastic analysis framework, and implemented reduced order modeling with Polynomial Chaos Expansion and Proper Orthogonal Decomposition to perform the uncertainty propagation. The NASA team chose to reduce the prohibitive computational time by employing linear solution processes. The NASA team also focused on determining input sample distributions.

  9. Uncertainty Reduction for Stochastic Processes on Complex Networks

    Science.gov (United States)

    Radicchi, Filippo; Castellano, Claudio

    2018-05-01

    Many real-world systems are characterized by stochastic dynamical rules where a complex network of interactions among individual elements probabilistically determines their state. Even with full knowledge of the network structure and of the stochastic rules, the ability to predict system configurations is generally characterized by a large uncertainty. Selecting a fraction of the nodes and observing their state may help to reduce the uncertainty about the unobserved nodes. However, choosing these points of observation in an optimal way is a highly nontrivial task, depending on the nature of the stochastic process and on the structure of the underlying interaction pattern. In this paper, we introduce a computationally efficient algorithm to determine quasioptimal solutions to the problem. The method leverages network sparsity to reduce computational complexity from exponential to almost quadratic, thus allowing the straightforward application of the method to mid-to-large-size systems. Although the method is exact only for equilibrium stochastic processes defined on trees, it turns out to be effective also for out-of-equilibrium processes on sparse loopy networks.

  10. Use of Sobol's quasirandom sequence generator for integration of modified uncertainty importance measure

    International Nuclear Information System (INIS)

    Homma, Toshimitsu; Saltelli, A.

    1995-01-01

    Sensitivity analysis of model output is relevant to a number of practices, including verification of models and computer code quality assurance. It deals with the identification of influential model parameters, especially in complex models implemented in computer programs with many uncertain input variables. In a recent article a new method for sensitivity analysis, named HIM * based on a rank transformation of the uncertainty importance measure suggested by Hora and Iman was proved very powerful for performing automated sensitivity analysis of model output, even in presence of model non-monotonicity. The same was not true of other widely used non-parametric techniques such as standardized rank regression coefficients. A drawback of the HIM * method was the large dimension of the stochastic sample needed for its estimation, which made HIM * impracticable for systems with large number of uncertain parameters. In the present note a more effective sampling algorithm, based on Sobol's quasirandom generator is coupled with HIM * , thereby greatly reducing the sample size needed for an effective identification of influential variables. The performances of the new technique are investigated for two different benchmarks. (author)

  11. Influence of resonance parameters' correlations on the resonance integral uncertainty; 55Mn case

    International Nuclear Information System (INIS)

    Zerovnik, Gasper; Trkov, Andrej; Capote, Roberto; Rochman, Dimitri

    2011-01-01

    For nuclides with a large number of resonances the covariance matrix of resonance parameters can become very large and expensive to process in terms of the computation time. By converting covariance matrix of resonance parameters into covariance matrices of background cross-section in a more or less coarse group structure a considerable amount of computer time and memory can be saved. The question is how important is the information that is discarded in the process. First, the uncertainty of the 55 Mn resonance integral was estimated in narrow resonance approximation for different levels of self-shielding using Bondarenko method by random sampling of resonance parameters according to their covariance matrices from two different 55 Mn evaluations: one from Nuclear Research and Consultancy Group NRG (with large uncertainties but no correlations between resonances), the other from Oak Ridge National Laboratory (with smaller uncertainties but full covariance matrix). We have found out that if all (or at least significant part of the) resonance parameters are correlated, the resonance integral uncertainty greatly depends on the level of self-shielding. Second, it was shown that the commonly used 640-group SAND-II representation cannot describe the increase of the resonance integral uncertainty. A much finer energy mesh for the background covariance matrix would have to be used to take the resonance structure into account explicitly, but then the objective of a more compact data representation is lost.

  12. Embracing uncertainty in applied ecology.

    Science.gov (United States)

    Milner-Gulland, E J; Shea, K

    2017-12-01

    Applied ecologists often face uncertainty that hinders effective decision-making.Common traps that may catch the unwary are: ignoring uncertainty, acknowledging uncertainty but ploughing on, focussing on trivial uncertainties, believing your models, and unclear objectives.We integrate research insights and examples from a wide range of applied ecological fields to illustrate advances that are generally underused, but could facilitate ecologists' ability to plan and execute research to support management.Recommended approaches to avoid uncertainty traps are: embracing models, using decision theory, using models more effectively, thinking experimentally, and being realistic about uncertainty. Synthesis and applications . Applied ecologists can become more effective at informing management by using approaches that explicitly take account of uncertainty.

  13. Decision-Making under Criteria Uncertainty

    Science.gov (United States)

    Kureychik, V. M.; Safronenkova, I. B.

    2018-05-01

    Uncertainty is an essential part of a decision-making procedure. The paper deals with the problem of decision-making under criteria uncertainty. In this context, decision-making under uncertainty, types and conditions of uncertainty were examined. The decision-making problem under uncertainty was formalized. A modification of the mathematical decision support method under uncertainty via ontologies was proposed. A critical distinction of the developed method is ontology usage as its base elements. The goal of this work is a development of a decision-making method under criteria uncertainty with the use of ontologies in the area of multilayer board designing. This method is oriented to improvement of technical-economic values of the examined domain.

  14. Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion

    CERN Document Server

    Servin, Christian

    2015-01-01

    On various examples ranging from geosciences to environmental sciences, this book explains how to generate an adequate description of uncertainty, how to justify semiheuristic algorithms for processing uncertainty, and how to make these algorithms more computationally efficient. It explains in what sense the existing approach to uncertainty as a combination of random and systematic components is only an approximation, presents a more adequate three-component model with an additional periodic error component, and explains how uncertainty propagation techniques can be extended to this model. The book provides a justification for a practically efficient heuristic technique (based on fuzzy decision-making). It explains how the computational complexity of uncertainty processing can be reduced. The book also shows how to take into account that in real life, the information about uncertainty is often only partially known, and, on several practical examples, explains how to extract the missing information about uncer...

  15. Reducing uncertainty for estimating forest carbon stocks and dynamics using integrated remote sensing, forest inventory and process-based modeling

    Science.gov (United States)

    Poulter, B.; Ciais, P.; Joetzjer, E.; Maignan, F.; Luyssaert, S.; Barichivich, J.

    2015-12-01

    Accurately estimating forest biomass and forest carbon dynamics requires new integrated remote sensing, forest inventory, and carbon cycle modeling approaches. Presently, there is an increasing and urgent need to reduce forest biomass uncertainty in order to meet the requirements of carbon mitigation treaties, such as Reducing Emissions from Deforestation and forest Degradation (REDD+). Here we describe a new parameterization and assimilation methodology used to estimate tropical forest biomass using the ORCHIDEE-CAN dynamic global vegetation model. ORCHIDEE-CAN simulates carbon uptake and allocation to individual trees using a mechanistic representation of photosynthesis, respiration and other first-order processes. The model is first parameterized using forest inventory data to constrain background mortality rates, i.e., self-thinning, and productivity. Satellite remote sensing data for forest structure, i.e., canopy height, is used to constrain simulated forest stand conditions using a look-up table approach to match canopy height distributions. The resulting forest biomass estimates are provided for spatial grids that match REDD+ project boundaries and aim to provide carbon estimates for the criteria described in the IPCC Good Practice Guidelines Tier 3 category. With the increasing availability of forest structure variables derived from high-resolution LIDAR, RADAR, and optical imagery, new methodologies and applications with process-based carbon cycle models are becoming more readily available to inform land management.

  16. Introducing nonpoint source transferable quotas in nitrogen trading: The effects of transaction costs and uncertainty.

    Science.gov (United States)

    Zhou, Xiuru; Ye, Weili; Zhang, Bing

    2016-03-01

    Transaction costs and uncertainty are considered to be significant obstacles in the emissions trading market, especially for including nonpoint source in water quality trading. This study develops a nonlinear programming model to simulate how uncertainty and transaction costs affect the performance of point/nonpoint source (PS/NPS) water quality trading in the Lake Tai watershed, China. The results demonstrate that PS/NPS water quality trading is a highly cost-effective instrument for emissions abatement in the Lake Tai watershed, which can save 89.33% on pollution abatement costs compared to trading only between nonpoint sources. However, uncertainty can significantly reduce the cost-effectiveness by reducing trading volume. In addition, transaction costs from bargaining and decision making raise total pollution abatement costs directly and cause the offset system to deviate from the optimal state. While proper investment in monitoring and measuring of nonpoint emissions can decrease uncertainty and save on the total abatement costs. Finally, we show that the dispersed ownership of China's farmland will bring high uncertainty and transaction costs into the PS/NPS offset system, even if the pollution abatement cost is lower than for point sources. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Physical Uncertainty Bounds (PUB)

    Energy Technology Data Exchange (ETDEWEB)

    Vaughan, Diane Elizabeth [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Preston, Dean L. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-03-19

    This paper introduces and motivates the need for a new methodology for determining upper bounds on the uncertainties in simulations of engineered systems due to limited fidelity in the composite continuum-level physics models needed to simulate the systems. We show that traditional uncertainty quantification methods provide, at best, a lower bound on this uncertainty. We propose to obtain bounds on the simulation uncertainties by first determining bounds on the physical quantities or processes relevant to system performance. By bounding these physics processes, as opposed to carrying out statistical analyses of the parameter sets of specific physics models or simply switching out the available physics models, one can obtain upper bounds on the uncertainties in simulated quantities of interest.

  18. Enhancing uncertainty tolerance in the modelling creep of ligaments

    International Nuclear Information System (INIS)

    Taha, M M Reda; Lucero, J

    2006-01-01

    The difficulty in performing biomechanical tests and the scarcity of biomechanical experimental databases necessitate extending the current knowledge base to allow efficient modelling using limited data sets. This study suggests a framework to reduce uncertainties in biomechanical systems using limited data sets. The study also shows how sparse data and epistemic input can be exploited using fuzzy logic to represent biomechanical relations. An example application to model collagen fibre recruitment in the medial collateral ligaments during time-dependent deformation under cyclic loading (creep) is presented. The study suggests a quality metric that can be employed to observe and enhance uncertainty tolerance in the modelling process

  19. Multi data reservior history matching and uncertainty quantification framework

    KAUST Repository

    Katterbauer, Klemens

    2015-11-26

    A multi-data reservoir history matching and uncertainty quantification framework is provided. The framework can utilize multiple data sets such as production, seismic, electromagnetic, gravimetric and surface deformation data for improving the history matching process. The framework can consist of a geological model that is interfaced with a reservoir simulator. The reservoir simulator can interface with seismic, electromagnetic, gravimetric and surface deformation modules to predict the corresponding observations. The observations can then be incorporated into a recursive filter that subsequently updates the model state and parameters distributions, providing a general framework to quantify and eventually reduce with the data, uncertainty in the estimated reservoir state and parameters.

  20. Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-Bounded Flows

    Science.gov (United States)

    Schaefer, John; West, Thomas; Hosder, Serhat; Rumsey, Christopher; Carlson, Jan-Renee; Kleb, William

    2015-01-01

    The goal of this work was to quantify the uncertainty and sensitivity of commonly used turbulence models in Reynolds-Averaged Navier-Stokes codes due to uncertainty in the values of closure coefficients for transonic, wall-bounded flows and to rank the contribution of each coefficient to uncertainty in various output flow quantities of interest. Specifically, uncertainty quantification of turbulence model closure coefficients was performed for transonic flow over an axisymmetric bump at zero degrees angle of attack and the RAE 2822 transonic airfoil at a lift coefficient of 0.744. Three turbulence models were considered: the Spalart-Allmaras Model, Wilcox (2006) k-w Model, and the Menter Shear-Stress Trans- port Model. The FUN3D code developed by NASA Langley Research Center was used as the flow solver. The uncertainty quantification analysis employed stochastic expansions based on non-intrusive polynomial chaos as an efficient means of uncertainty propagation. Several integrated and point-quantities are considered as uncertain outputs for both CFD problems. All closure coefficients were treated as epistemic uncertain variables represented with intervals. Sobol indices were used to rank the relative contributions of each closure coefficient to the total uncertainty in the output quantities of interest. This study identified a number of closure coefficients for each turbulence model for which more information will reduce the amount of uncertainty in the output significantly for transonic, wall-bounded flows.

  1. Uncertainty Propagation in OMFIT

    Science.gov (United States)

    Smith, Sterling; Meneghini, Orso; Sung, Choongki

    2017-10-01

    A rigorous comparison of power balance fluxes and turbulent model fluxes requires the propagation of uncertainties in the kinetic profiles and their derivatives. Making extensive use of the python uncertainties package, the OMFIT framework has been used to propagate covariant uncertainties to provide an uncertainty in the power balance calculation from the ONETWO code, as well as through the turbulent fluxes calculated by the TGLF code. The covariant uncertainties arise from fitting 1D (constant on flux surface) density and temperature profiles and associated random errors with parameterized functions such as a modified tanh. The power balance and model fluxes can then be compared with quantification of the uncertainties. No effort is made at propagating systematic errors. A case study will be shown for the effects of resonant magnetic perturbations on the kinetic profiles and fluxes at the top of the pedestal. A separate attempt at modeling the random errors with Monte Carlo sampling will be compared to the method of propagating the fitting function parameter covariant uncertainties. Work supported by US DOE under DE-FC02-04ER54698, DE-FG2-95ER-54309, DE-SC 0012656.

  2. Uncertainty: a discriminator for above and below boiling repository design decisions

    International Nuclear Information System (INIS)

    Wilder, D G; Lin, W; Buscheck, T A; Wolery, T J; Francis, N D

    2000-01-01

    The US nuclear waste disposal program is evaluating the Yucca Mountain (YM) site for possible disposal of nuclear waste. Radioactive decay of the waste, particularly spent fuel, generates sufficient heat to significantly raise repository temperatures. Environmental conditions in the repository system evolve in response to this heat. The amount of temperature increase, and thus environmental changes, depends on repository design and operations. Because the evolving environment cannot be directly measured until after waste is emplaced, licensing decisions must be based upon model and analytical projections of the environmental conditions. These analyses have inherent uncertainties. There is concern that elevated temperatures increase uncertainty, because most chemical reaction rates increase with temperature and boiling introduces additional complexity of vapor phase reactions and transport. This concern was expressed by the NWTRB, particularly for above boiling temperatures. They state that ''the cooler the repository, the lower the uncertainty about heat-driven water migration and the better the performance of waste package materials. Above this temperature, technical uncertainties tend to be significantly higher than those associated with below-boiling conditions.'' (Cohon 1999). However, not all uncertainties are reduced by lower temperatures, indeed some may even be increased. This paper addresses impacts of temperatures on uncertainties

  3. Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States

    Science.gov (United States)

    Nolan, Bernard T.; Malone, Robert W.; Doherty, John E.; Barbash, Jack E.; Ma, Liwang; Shaner, Dale L.

    2015-01-01

    BACKGROUND Complex environmental models are frequently extrapolated to overcome data limitations in space and time, but quantifying data worth to such models is rarely attempted. The authors determined which field observations most informed the parameters of agricultural system models applied to field sites in Nebraska (NE) and Maryland (MD), and identified parameters and observations that most influenced prediction uncertainty. RESULTS The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty.   

  4. Stereo-particle image velocimetry uncertainty quantification

    International Nuclear Information System (INIS)

    Bhattacharya, Sayantan; Vlachos, Pavlos P; Charonko, John J

    2017-01-01

    Particle image velocimetry (PIV) measurements are subject to multiple elemental error sources and thus estimating overall measurement uncertainty is challenging. Recent advances have led to a posteriori uncertainty estimation methods for planar two-component PIV. However, no complete methodology exists for uncertainty quantification in stereo PIV. In the current work, a comprehensive framework is presented to quantify the uncertainty stemming from stereo registration error and combine it with the underlying planar velocity uncertainties. The disparity in particle locations of the dewarped images is used to estimate the positional uncertainty of the world coordinate system, which is then propagated to the uncertainty in the calibration mapping function coefficients. Next, the calibration uncertainty is combined with the planar uncertainty fields of the individual cameras through an uncertainty propagation equation and uncertainty estimates are obtained for all three velocity components. The methodology was tested with synthetic stereo PIV data for different light sheet thicknesses, with and without registration error, and also validated with an experimental vortex ring case from 2014 PIV challenge. Thorough sensitivity analysis was performed to assess the relative impact of the various parameters to the overall uncertainty. The results suggest that in absence of any disparity, the stereo PIV uncertainty prediction method is more sensitive to the planar uncertainty estimates than to the angle uncertainty, although the latter is not negligible for non-zero disparity. Overall the presented uncertainty quantification framework showed excellent agreement between the error and uncertainty RMS values for both the synthetic and the experimental data and demonstrated reliable uncertainty prediction coverage. This stereo PIV uncertainty quantification framework provides the first comprehensive treatment on the subject and potentially lays foundations applicable to volumetric

  5. Intimate Partner Violence in the Great Recession.

    Science.gov (United States)

    Schneider, Daniel; Harknett, Kristen; McLanahan, Sara

    2016-04-01

    In the United States, the Great Recession was marked by severe negative shocks to labor market conditions. In this study, we combine longitudinal data from the Fragile Families and Child Wellbeing Study with U.S. Bureau of Labor Statistics data on local area unemployment rates to examine the relationship between adverse labor market conditions and mothers' experiences of abusive behavior between 2001 and 2010. Unemployment and economic hardship at the household level were positively related to abusive behavior. Further, rapid increases in the unemployment rate increased men's controlling behavior toward romantic partners even after we adjust for unemployment and economic distress at the household level. We interpret these findings as demonstrating that the uncertainty and anticipatory anxiety that go along with sudden macroeconomic downturns have negative effects on relationship quality, above and beyond the effects of job loss and material hardship.

  6. Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin

    Science.gov (United States)

    Ahmadalipour, Ali; Moradkhani, Hamid; Rana, Arun

    2018-01-01

    Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970-2000 as well as future period of 2010-2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation.

  7. Methodologies of Uncertainty Propagation Calculation

    International Nuclear Information System (INIS)

    Chojnacki, Eric

    2002-01-01

    After recalling the theoretical principle and the practical difficulties of the methodologies of uncertainty propagation calculation, the author discussed how to propagate input uncertainties. He said there were two kinds of input uncertainty: - variability: uncertainty due to heterogeneity, - lack of knowledge: uncertainty due to ignorance. It was therefore necessary to use two different propagation methods. He demonstrated this in a simple example which he generalised, treating the variability uncertainty by the probability theory and the lack of knowledge uncertainty by the fuzzy theory. He cautioned, however, against the systematic use of probability theory which may lead to unjustifiable and illegitimate precise answers. Mr Chojnacki's conclusions were that the importance of distinguishing variability and lack of knowledge increased as the problem was getting more and more complex in terms of number of parameters or time steps, and that it was necessary to develop uncertainty propagation methodologies combining probability theory and fuzzy theory

  8. Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis

    Science.gov (United States)

    Doherty, John E.; Hunt, Randall J.; Tonkin, Matthew J.

    2010-01-01

    Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty in those parameters. In fact, in many cases, enforcement of calibration constraints simply reduces the uncertainties associated with a number of broad-scale combinations of model parameters that collectively describe spatially averaged system properties. The uncertainties associated with other combinations of parameters, especially those that pertain to small-scale parameter heterogeneity, may not be reduced through the calibration process. To the extent that a prediction depends on system-property detail, its postcalibration variability may be reduced very little, if at all, by applying calibration constraints; knowledge constraints remain the only limits on the variability of predictions that depend on such detail. Regrettably, in many common modeling applications, these constraints are weak. Though the PEST software suite was initially developed as a tool for model calibration, recent developments have focused on the evaluation of model-parameter and predictive uncertainty. As a complement to functionality that it provides for highly parameterized inversion (calibration) by means of formal mathematical regularization techniques, the PEST suite provides utilities for linear and nonlinear error-variance and uncertainty analysis in

  9. Stochastic dynamic analysis of marine risers considering Gaussian system uncertainties

    Science.gov (United States)

    Ni, Pinghe; Li, Jun; Hao, Hong; Xia, Yong

    2018-03-01

    This paper performs the stochastic dynamic response analysis of marine risers with material uncertainties, i.e. in the mass density and elastic modulus, by using Stochastic Finite Element Method (SFEM) and model reduction technique. These uncertainties are assumed having Gaussian distributions. The random mass density and elastic modulus are represented by using the Karhunen-Loève (KL) expansion. The Polynomial Chaos (PC) expansion is adopted to represent the vibration response because the covariance of the output is unknown. Model reduction based on the Iterated Improved Reduced System (IIRS) technique is applied to eliminate the PC coefficients of the slave degrees of freedom to reduce the dimension of the stochastic system. Monte Carlo Simulation (MCS) is conducted to obtain the reference response statistics. Two numerical examples are studied in this paper. The response statistics from the proposed approach are compared with those from MCS. It is noted that the computational time is significantly reduced while the accuracy is kept. The results demonstrate the efficiency of the proposed approach for stochastic dynamic response analysis of marine risers.

  10. Impact of petrophysical uncertainty on Bayesian hydrogeophysical inversion and model selection

    Science.gov (United States)

    Brunetti, Carlotta; Linde, Niklas

    2018-01-01

    Quantitative hydrogeophysical studies rely heavily on petrophysical relationships that link geophysical properties to hydrogeological properties and state variables. Coupled inversion studies are frequently based on the questionable assumption that these relationships are perfect (i.e., no scatter). Using synthetic examples and crosshole ground-penetrating radar (GPR) data from the South Oyster Bacterial Transport Site in Virginia, USA, we investigate the impact of spatially-correlated petrophysical uncertainty on inferred posterior porosity and hydraulic conductivity distributions and on Bayes factors used in Bayesian model selection. Our study shows that accounting for petrophysical uncertainty in the inversion (I) decreases bias of the inferred variance of hydrogeological subsurface properties, (II) provides more realistic uncertainty assessment and (III) reduces the overconfidence in the ability of geophysical data to falsify conceptual hydrogeological models.

  11. Embracing uncertainty in climate change policy

    Science.gov (United States)

    Otto, Friederike E. L.; Frame, David J.; Otto, Alexander; Allen, Myles R.

    2015-10-01

    The 'pledge and review' approach to reducing greenhouse-gas emissions presents an opportunity to link mitigation goals explicitly to the evolving climate response. This seems desirable because the progression from the Intergovernmental Panel on Climate Change's fourth to fifth assessment reports has seen little reduction in uncertainty. A common reaction to persistent uncertainties is to advocate mitigation policies that are robust even under worst-case scenarios, thereby focusing attention on upper extremes of both the climate response and the costs of impacts and mitigation, all of which are highly contestable. Here we ask whether those contributing to the formation of climate policies can learn from 'adaptive management' techniques. Recognizing that long-lived greenhouse gas emissions have to be net zero by the time temperatures reach a target stabilization level, such as 2 °C above pre-industrial levels, and anchoring commitments to an agreed index of attributable anthropogenic warming would provide a transparent approach to meeting such a temperature goal without prior consensus on the climate response.

  12. Uncertainty studies and risk assessment for CO{sub 2} storage in geological formations

    Energy Technology Data Exchange (ETDEWEB)

    Walter, Lena Sophie

    2013-07-01

    Carbon capture and storage (CCS) in deep geological formations is one possible option to mitigate the greenhouse gas effect by reducing CO{sub 2} emissions into the atmosphere. The assessment of the risks related to CO{sub 2} storage is an important task. Events such as CO{sub 2} leakage and brine displacement could result in hazards for human health and the environment. In this thesis, a systematic and comprehensive risk assessment concept is presented to investigate various levels of uncertainties and to assess risks using numerical simulations. Depending on the risk and the processes, which should be assessed, very complex models, large model domains, large time scales, and many simulations runs for estimating probabilities are required. To reduce the resulting high computational costs, a model reduction technique (the arbitrary polynomial chaos expansion) and a method for model coupling in space are applied. The different levels of uncertainties are: statistical uncertainty in parameter distributions, scenario uncertainty, e.g. different geological features, and recognized ignorance due to assumptions in the conceptual model set-up. Recognized ignorance and scenario uncertainty are investigated by simulating well defined model set-ups and scenarios. According to damage values, which are defined as a model output, the set-ups and scenarios can be compared and ranked. For statistical uncertainty probabilities can be determined by running Monte Carlo simulations with the reduced model. The results are presented in various ways: e.g., mean damage, probability density function, cumulative distribution function, or an overall risk value by multiplying the damage with the probability. If the model output (damage) cannot be compared to provided criteria (e.g. water quality criteria), analytical approximations are presented to translate the damage into comparable values. The overall concept is applied for the risks related to brine displacement and infiltration into

  13. Validation of methodology and uncertainty assessment of antimony determination in environmental materials using Neutron Activation Analysis

    International Nuclear Information System (INIS)

    Matsubara, Tassiane C.M.; Saiki, Mitiko; Zahn, Guilherme S.; Moreira, Edson G.

    2013-01-01

    Antimony is an element found in low concentrations in the environment. However, its determination has attracted great interest because of the knowledge of its toxicity and increasing application. Neutron activation analysis (NAA) is a suitable method for the determination of several elements in different types, but in case of Sb, the analysis presents some difficulties due to spectral interferences. The objective of this research was to validate the method of NAA and uncertainty assessment for Sb determination in environmental samples. The experimental procedure consisted of irradiating twelve certified reference samples of different kind of matrices. The samples were irradiated in the nuclear research reactor IEA R1 IPEN/CNEN/SP followed by measurement of induced radioactivity, using a hyperpure germanium detector coupled to a gamma ray spectrometry. The radioisotopes 122 Sb and 124 Sb were measured and the Sb concentrations with their respective uncertainties were obtained by the comparative method. Relative errors and values of Z scores were calculated to evaluate the accuracy of the results for Sb determination in certified reference materials. The evaluation of the components that contribute to uncertainty measurement of the Sb concentration, showed that the major uncertainty contribution is due to statistical counting. The results also indicated that the uncertainty value of the combined standard uncertainty depends on the radioisotope measured and the decay time used for counting. (author)

  14. The state of the art of the impact of sampling uncertainty on measurement uncertainty

    Science.gov (United States)

    Leite, V. J.; Oliveira, E. C.

    2018-03-01

    The measurement uncertainty is a parameter that marks the reliability and can be divided into two large groups: sampling and analytical variations. Analytical uncertainty is a controlled process, performed in the laboratory. The same does not occur with the sampling uncertainty, which, because it faces several obstacles and there is no clarity on how to perform the procedures, has been neglected, although it is admittedly indispensable to the measurement process. This paper aims at describing the state of the art of sampling uncertainty and at assessing its relevance to measurement uncertainty.

  15. Utility of population models to reduce uncertainty and increase value relevance in ecological risk assessments of pesticides: an example based on acute mortality data for daphnids.

    Science.gov (United States)

    Hanson, Niklas; Stark, John D

    2012-04-01

    Traditionally, ecological risk assessments (ERA) of pesticides have been based on risk ratios, where the predicted concentration of the chemical is compared to the concentration that causes biological effects. The concentration that causes biological effect is mostly determined from laboratory experiments using endpoints on the level of the individual (e.g., mortality and reproduction). However, the protection goals are mostly defined at the population level. To deal with the uncertainty in the necessary extrapolations, safety factors are used. Major disadvantages with this simplified approach is that it is difficult to relate a risk ratio to the environmental protection goals, and that the use of fixed safety factors can result in over- as well as underprotective assessments. To reduce uncertainty and increase value relevance in ERA, it has been argued that population models should be used more frequently. In the present study, we have used matrix population models for 3 daphnid species (Ceriodaphnia dubia, Daphnia magna, and D. pulex) to reduce uncertainty and increase value relevance in the ERA of a pesticide (spinosad). The survival rates in the models were reduced in accordance with data from traditional acute mortality tests. As no data on reproductive effects were available, the conservative assumption that no reproduction occurred during the exposure period was made. The models were used to calculate the minimum population size and the time to recovery. These endpoints can be related to the European Union (EU) protection goals for aquatic ecosystems in the vicinity of agricultural fields, which state that reversible population level effects are acceptable if there is recovery within an acceptable (undefined) time frame. The results of the population models were compared to the acceptable (according to EU documents) toxicity exposure ratio (TER) that was based on the same data. At the acceptable TER, which was based on the most sensitive species (C. dubia

  16. Enhanced rice production but greatly reduced carbon emission following biochar amendment in a metal-polluted rice paddy.

    Science.gov (United States)

    Zhang, Afeng; Bian, Rongjun; Li, Lianqing; Wang, Xudong; Zhao, Ying; Hussain, Qaiser; Pan, Genxing

    2015-12-01

    Soil amendment of biochar (BSA) had been shown effective for mitigating greenhouse gas (GHG) emission and alleviating metal stress to plants and microbes in soil. It has not yet been addressed if biochar exerts synergy effects on crop production, GHG emission, and microbial activity in metal-polluted soils. In a field experiment, biochar was amended at sequential rates at 0, 10, 20, and 40 t ha(-1), respectively, in a cadmium- and lead-contaminated rice paddy from the Tai lake Plain, China, before rice cropping in 2010. Fluxes of soil carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) were monitored using a static chamber method during the whole rice growing season (WRGS) of 2011. BSA significantly reduced soil CaCl2 extractable pool of Cd, and DTPA extractable pool of Cd and Pb. As compared to control, soil CO2 emission under BSA was observed to have no change at 10 t ha(-1) but decreased by 16-24% at 20 and 40 t ha(-1). In a similar trend, BSA at 20 and 40 t ha(-1) increased rice yield by 25-26% and thus enhanced ecosystem CO2 sequestration by 47-55% over the control. Seasonal total N2O emission was reduced by 7.1, 30.7, and 48.6% under BSA at 10, 20, and 40 t ha(-1), respectively. Overall, a net reduction in greenhouse gas balance (NGHGB) by 53.9-62.8% and in greenhouse gas intensity (GHGI) by 14.3-28.6% was observed following BSA at 20 and 40 t ha(-1). The present study suggested a great potential of biochar to enhancing grain yield while reducing carbon emission in metal-polluted rice paddies.

  17. Marginal greenhouse gas emissions displacement of wind power in Great Britain

    International Nuclear Information System (INIS)

    Thomson, R. Camilla; Harrison, Gareth P.; Chick, John P.

    2017-01-01

    There is considerable uncertainty over the effect of wind power on the operation of power systems, and the consequent greenhouse gas (GHG) emissions displacement; this is used to project emissions reductions that inform energy policy. Currently, it is approximated as the average emissions of the whole system, despite an acknowledgement that wind will actually displace only the generators operating on the margin. This article presents a methodology to isolate the marginal emissions displacement of wind power from historical empirical data, taking into account the impact on the operating efficiency of coal and CCGT plants. For Great Britain over 2009–2014, it was found that marginal emissions displacement has genera