Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

NASA Astrophysics Data System (ADS)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

Intolerance of uncertainty in opioid dependency - Relationship with trait anxiety and impulsivity.

PubMed

Garami, Julia; Haber, Paul; Myers, Catherine E; Allen, Michael T; Misiak, Blazej; Frydecka, Dorota; Moustafa, Ahmed A

2017-01-01

Intolerance of uncertainty (IU) is the tendency to interpret ambiguous situations as threatening and having negative consequences, resulting in feelings of distress and anxiety. IU has been linked to a number of anxiety disorders, and anxiety felt in the face of uncertainty may result in maladaptive behaviors such as impulsive decision making. Although there is strong evidence that anxiety and impulsivity are risk factors for addiction, there is a paucity of research examining the role of IU in this disorder. The rate of opioid addiction, in particular, has been rising steadily in recent years, which necessitates deeper understanding of risk factors in order to develop effective prevention and treatment methods. The current study tested for the first time whether opioid-dependent adults are less tolerant of uncertainty compared to a healthy comparison group. Opioid dependent patients undergoing methadone maintenance therapy (n = 114) and healthy comparisons (n = 69) completed the following scales: Intolerance of Uncertainty Scale, the Barrett Impulsivity Scale, and the State Trait Anxiety Inventory. Analysis revealed that these measures were positively correlated with each other and that opioid-dependent patients had significantly higher IU scores. Regression analysis revealed that anxiety mediated the relationship between IU and impulsivity. Hierarchical moderation regression found an interaction between addiction status and impulsivity on IU scores in that the relationship between these variables was only observed in the patient group. Findings suggest that IU is a feature of addiction but does not necessarily play a unique role. Further research is needed to explore the complex relationship between traits and how they may contribute to the development and maintenance of addiction.

Alleviating Search Uncertainty through Concept Associations: Automatic Indexing, Co-Occurrence Analysis, and Parallel Computing.

ERIC Educational Resources Information Center

Chen, Hsinchun; Martinez, Joanne; Kirchhoff, Amy; Ng, Tobun D.; Schatz, Bruce R.

1998-01-01

Grounded on object filtering, automatic indexing, and co-occurrence analysis, an experiment was performed using a parallel supercomputer to analyze over 400,000 abstracts in an INSPEC computer engineering collection. A user evaluation revealed that system-generated thesauri were better than the human-generated INSPEC subject thesaurus in concept…

Approaches to Evaluating Probability of Collision Uncertainty

NASA Technical Reports Server (NTRS)

Hejduk, Matthew D.; Johnson, Lauren C.

2016-01-01

While the two-dimensional probability of collision (Pc) calculation has served as the main input to conjunction analysis risk assessment for over a decade, it has done this mostly as a point estimate, with relatively little effort made to produce confidence intervals on the Pc value based on the uncertainties in the inputs. The present effort seeks to try to carry these uncertainties through the calculation in order to generate a probability density of Pc results rather than a single average value. Methods for assessing uncertainty in the primary and secondary objects' physical sizes and state estimate covariances, as well as a resampling approach to reveal the natural variability in the calculation, are presented; and an initial proposal for operationally-useful display and interpretation of these data for a particular conjunction is given.

Lognormal Approximations of Fault Tree Uncertainty Distributions.

PubMed

El-Shanawany, Ashraf Ben; Ardron, Keith H; Walker, Simon P

2018-01-26

Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed-form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling-based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed-form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks's method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models. © 2018 Society for Risk Analysis.

The nexus between geopolitical uncertainty and crude oil markets: An entropy-based wavelet analysis

NASA Astrophysics Data System (ADS)

Uddin, Gazi Salah; Bekiros, Stelios; Ahmed, Ali

2018-04-01

The global financial crisis and the subsequent geopolitical turbulence in energy markets have brought increased attention to the proper statistical modeling especially of the crude oil markets. In particular, we utilize a time-frequency decomposition approach based on wavelet analysis to explore the inherent dynamics and the casual interrelationships between various types of geopolitical, economic and financial uncertainty indices and oil markets. Via the introduction of a mixed discrete-continuous multiresolution analysis, we employ the entropic criterion for the selection of the optimal decomposition level of a MODWT as well as the continuous-time coherency and phase measures for the detection of business cycle (a)synchronization. Overall, a strong heterogeneity in the revealed interrelationships is detected over time and across scales.

An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping

PubMed Central

Feizizadeh, Bakhtiar; Blaschke, Thomas

2014-01-01

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results. PMID:27019609

Intolerance of uncertainty in opioid dependency – Relationship with trait anxiety and impulsivity

PubMed Central

Haber, Paul; Myers, Catherine E.; Allen, Michael T.; Misiak, Blazej; Frydecka, Dorota; Moustafa, Ahmed A.

2017-01-01

Intolerance of uncertainty (IU) is the tendency to interpret ambiguous situations as threatening and having negative consequences, resulting in feelings of distress and anxiety. IU has been linked to a number of anxiety disorders, and anxiety felt in the face of uncertainty may result in maladaptive behaviors such as impulsive decision making. Although there is strong evidence that anxiety and impulsivity are risk factors for addiction, there is a paucity of research examining the role of IU in this disorder. The rate of opioid addiction, in particular, has been rising steadily in recent years, which necessitates deeper understanding of risk factors in order to develop effective prevention and treatment methods. The current study tested for the first time whether opioid-dependent adults are less tolerant of uncertainty compared to a healthy comparison group. Opioid dependent patients undergoing methadone maintenance therapy (n = 114) and healthy comparisons (n = 69) completed the following scales: Intolerance of Uncertainty Scale, the Barrett Impulsivity Scale, and the State Trait Anxiety Inventory. Analysis revealed that these measures were positively correlated with each other and that opioid-dependent patients had significantly higher IU scores. Regression analysis revealed that anxiety mediated the relationship between IU and impulsivity. Hierarchical moderation regression found an interaction between addiction status and impulsivity on IU scores in that the relationship between these variables was only observed in the patient group. Findings suggest that IU is a feature of addiction but does not necessarily play a unique role. Further research is needed to explore the complex relationship between traits and how they may contribute to the development and maintenance of addiction. PMID:28759635

Persistent oscillations and backward bifurcation in a malaria model with varying human and mosquito populations: implications for control.

PubMed

Ngonghala, Calistus N; Teboh-Ewungkem, Miranda I; Ngwa, Gideon A

2015-06-01

We derive and study a deterministic compartmental model for malaria transmission with varying human and mosquito populations. Our model considers disease-related deaths, asymptomatic immune humans who are also infectious, as well as mosquito demography, reproduction and feeding habits. Analysis of the model reveals the existence of a backward bifurcation and persistent limit cycles whose period and size is determined by two threshold parameters: the vectorial basic reproduction number Rm, and the disease basic reproduction number R0, whose size can be reduced by reducing Rm. We conclude that malaria dynamics are indeed oscillatory when the methodology of explicitly incorporating the mosquito's demography, feeding and reproductive patterns is considered in modeling the mosquito population dynamics. A sensitivity analysis reveals important control parameters that can affect the magnitudes of Rm and R0, threshold quantities to be taken into consideration when designing control strategies. Both Rm and the intrinsic period of oscillation are shown to be highly sensitive to the mosquito's birth constant λm and the mosquito's feeding success probability pw. Control of λm can be achieved by spraying, eliminating breeding sites or moving them away from human habitats, while pw can be controlled via the use of mosquito repellant and insecticide-treated bed-nets. The disease threshold parameter R0 is shown to be highly sensitive to pw, and the intrinsic period of oscillation is also sensitive to the rate at which reproducing mosquitoes return to breeding sites. A global sensitivity and uncertainty analysis reveals that the ability of the mosquito to reproduce and uncertainties in the estimations of the rates at which exposed humans become infectious and infectious humans recover from malaria are critical in generating uncertainties in the disease classes.

Modelling of plasma-based dry reforming: how do uncertainties in the input data affect the calculation results?

NASA Astrophysics Data System (ADS)

Wang, Weizong; Berthelot, Antonin; Zhang, Quanzhi; Bogaerts, Annemie

2018-05-01

One of the main issues in plasma chemistry modeling is that the cross sections and rate coefficients are subject to uncertainties, which yields uncertainties in the modeling results and hence hinders the predictive capabilities. In this paper, we reveal the impact of these uncertainties on the model predictions of plasma-based dry reforming in a dielectric barrier discharge. For this purpose, we performed a detailed uncertainty analysis and sensitivity study. 2000 different combinations of rate coefficients, based on the uncertainty from a log-normal distribution, are used to predict the uncertainties in the model output. The uncertainties in the electron density and electron temperature are around 11% and 8% at the maximum of the power deposition for a 70% confidence level. Still, this can have a major effect on the electron impact rates and hence on the calculated conversions of CO2 and CH4, as well as on the selectivities of CO and H2. For the CO2 and CH4 conversion, we obtain uncertainties of 24% and 33%, respectively. For the CO and H2 selectivity, the corresponding uncertainties are 28% and 14%, respectively. We also identify which reactions contribute most to the uncertainty in the model predictions. In order to improve the accuracy and reliability of plasma chemistry models, we recommend using only verified rate coefficients, and we point out the need for dedicated verification experiments.

MODIS land cover uncertainty in regional climate simulations

NASA Astrophysics Data System (ADS)

Li, Xue; Messina, Joseph P.; Moore, Nathan J.; Fan, Peilei; Shortridge, Ashton M.

2017-12-01

MODIS land cover datasets are used extensively across the climate modeling community, but inherent uncertainties and associated propagating impacts are rarely discussed. This paper modeled uncertainties embedded within the annual MODIS Land Cover Type (MCD12Q1) products and propagated these uncertainties through the Regional Atmospheric Modeling System (RAMS). First, land cover uncertainties were modeled using pixel-based trajectory analyses from a time series of MCD12Q1 for Urumqi, China. Second, alternative land cover maps were produced based on these categorical uncertainties and passed into RAMS. Finally, simulations from RAMS were analyzed temporally and spatially to reveal impacts. Our study found that MCD12Q1 struggles to discriminate between grasslands and croplands or grasslands and barren in this study area. Such categorical uncertainties have significant impacts on regional climate model outputs. All climate variables examined demonstrated impact across the various regions, with latent heat flux affected most with a magnitude of 4.32 W/m2 in domain average. Impacted areas were spatially connected to locations of greater land cover uncertainty. Both biophysical characteristics and soil moisture settings in regard to land cover types contribute to the variations among simulations. These results indicate that formal land cover uncertainty analysis should be included in MCD12Q1-fed climate modeling as a routine procedure.

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

NASA Astrophysics Data System (ADS)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

When Advisors' True Intentions Are in Question. How Do Bank Customers Cope with Uncertainty in Financial Consultancies?

PubMed

Mackinger, Barbara; Jonas, Eva; Mühlberger, Christina

2017-01-01

When making financial decisions bank customers are confronted with two types of uncertainty: first, return on investments is uncertain and there is a risk of losing money. Second, customers cannot be certain about their financial advisor's true intentions. This might decrease customers' willingness to cooperate with advisors. However, the uncertainty management model and fairness heuristic theory predict that in uncertain situations customers are willing to cooperate with financial advisors when they perceive fairness. In the current study, we investigated how perceived fairness in the twofold uncertain situations increased people's intended future cooperation with an advisor. We asked customers of financial consultancies about their experienced uncertainty regarding both the investment decision and the advisor's intentions. Moreover, we asked them about their perceived fairness, as well as their intention to cooperate with the advisor in the future. A three-way moderation analysis showed that customers who faced high uncertainty regarding the investment decision and high uncertainty regarding the advisor's true intentions indicated the lowest intended cooperation with the advisor but high fairness increased their cooperation. Interestingly, when people were only uncertain about the advisor's intentions (but certain about the decision) they indicated less cooperation than when they were only uncertain about the decision (but certain about the advisor's intentions). A mediated moderation analysis revealed that this relationship was explained by customers' lower trust in their advisors.

Assessment of uncertainties of the models used in thermal-hydraulic computer codes

NASA Astrophysics Data System (ADS)

Gricay, A. S.; Migrov, Yu. A.

2015-09-01

The article deals with matters concerned with the problem of determining the statistical characteristics of variable parameters (the variation range and distribution law) in analyzing the uncertainty and sensitivity of calculation results to uncertainty in input data. A comparative analysis of modern approaches to uncertainty in input data is presented. The need to develop an alternative method for estimating the uncertainty of model parameters used in thermal-hydraulic computer codes, in particular, in the closing correlations of the loop thermal hydraulics block, is shown. Such a method shall feature the minimal degree of subjectivism and must be based on objective quantitative assessment criteria. The method includes three sequential stages: selecting experimental data satisfying the specified criteria, identifying the key closing correlation using a sensitivity analysis, and carrying out case calculations followed by statistical processing of the results. By using the method, one can estimate the uncertainty range of a variable parameter and establish its distribution law in the above-mentioned range provided that the experimental information is sufficiently representative. Practical application of the method is demonstrated taking as an example the problem of estimating the uncertainty of a parameter appearing in the model describing transition to post-burnout heat transfer that is used in the thermal-hydraulic computer code KORSAR. The performed study revealed the need to narrow the previously established uncertainty range of this parameter and to replace the uniform distribution law in the above-mentioned range by the Gaussian distribution law. The proposed method can be applied to different thermal-hydraulic computer codes. In some cases, application of the method can make it possible to achieve a smaller degree of conservatism in the expert estimates of uncertainties pertinent to the model parameters used in computer codes.

Uncertainty quantification of environmental performance metrics in heterogeneous aquifers with long-range correlations

NASA Astrophysics Data System (ADS)

Moslehi, Mahsa; de Barros, Felipe P. J.

2017-01-01

We investigate how the uncertainty stemming from disordered porous media that display long-range correlation in the hydraulic conductivity (K) field propagates to predictions of environmental performance metrics (EPMs). In this study, the EPMs are quantities that are of relevance to risk analysis and remediation, such as peak flux-averaged concentration, early and late arrival times among others. By using stochastic simulations, we quantify the uncertainty associated with the EPMs for a given disordered spatial structure of the K-field and identify the probability distribution function (PDF) model that best captures the statistics of the EPMs of interest. Results indicate that the probabilistic distribution of the EPMs considered in this study follows lognormal PDF. Finally, through the use of information theory, we reveal how the persistent/anti-persistent correlation structure of the K-field influences the EPMs and corresponding uncertainties.

Unquestioned answers or unanswered questions: beliefs about science guide responses to uncertainty in climate change risk communication.

PubMed

Rabinovich, Anna; Morton, Thomas A

2012-06-01

In two experimental studies we investigated the effect of beliefs about the nature and purpose of science (classical vs. Kuhnian models of science) on responses to uncertainty in scientific messages about climate change risk. The results revealed a significant interaction between both measured (Study 1) and manipulated (Study 2) beliefs about science and the level of communicated uncertainty on willingness to act in line with the message. Specifically, messages that communicated high uncertainty were more persuasive for participants who shared an understanding of science as debate than for those who believed that science is a search for absolute truth. In addition, participants who had a concept of science as debate were more motivated by higher (rather than lower) uncertainty in climate change messages. The results suggest that achieving alignment between the general public's beliefs about science and the style of the scientific messages is crucial for successful risk communication in science. Accordingly, rather than uncertainty always undermining the effectiveness of science communication, uncertainty can enhance message effects when it fits the audience's understanding of what science is. © 2012 Society for Risk Analysis.

International survey for good practices in forecasting uncertainty assessment and communication

NASA Astrophysics Data System (ADS)

Berthet, Lionel; Piotte, Olivier

2014-05-01

Achieving technically sound flood forecasts is a crucial objective for forecasters but remains of poor use if the users do not understand properly their significance and do not use it properly in decision making. One usual way to precise the forecasts limitations is to communicate some information about their uncertainty. Uncertainty assessment and communication to stakeholders are thus important issues for operational flood forecasting services (FFS) but remain open fields for research. French FFS wants to publish graphical streamflow and level forecasts along with uncertainty assessment in near future on its website (available to the greater public). In order to choose the technical options best adapted to its operational context, it carried out a survey among more than 15 fellow institutions. Most of these are providing forecasts and warnings to civil protection officers while some were mostly working for hydroelectricity suppliers. A questionnaire has been prepared in order to standardize the analysis of the practices of the surveyed institutions. The survey was conducted by gathering information from technical reports or from the scientific literature, as well as 'interviews' driven by phone, email discussions or meetings. The questionnaire helped in the exploration of practices in uncertainty assessment, evaluation and communication. Attention was paid to the particular context within which every insitution works, in the analysis drawn from raw results. Results show that most services interviewed assess their forecasts uncertainty. However, practices can differ significantly from a country to another. Popular techniques are ensemble approaches. They allow to take into account several uncertainty sources. Statistical past forecasts analysis (such as the quantile regressions) are also commonly used. Contrary to what was expected, only few services emphasize the role of the forecaster (subjective assessment). Similar contrasts can be observed in uncertainty communication practices. Some countries are quite advanced in uncertainty communication to the general public whereas most of them restrain this communication to pre-defined stakeholders who have previously been sensitized or trained. Differents forms of communication were met during the survey, from written comments to complex graphics. No form could claim a clear leadership. This survey revealed useful to identify some difficulties in the design of the next French forecast uncertainty assessment and communication schemes.

Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism

PubMed Central

Achcar, Fiona; Kerkhoven, Eduard J.; Bakker, Barbara M.; Barrett, Michael P.; Breitling, Rainer

2012-01-01

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies. PMID:22379410

Uncertainty analysis and global sensitivity analysis of techno-economic assessments for biodiesel production.

PubMed

Tang, Zhang-Chun; Zhenzhou, Lu; Zhiwen, Liu; Ningcong, Xiao

2015-01-01

There are various uncertain parameters in the techno-economic assessments (TEAs) of biodiesel production, including capital cost, interest rate, feedstock price, maintenance rate, biodiesel conversion efficiency, glycerol price and operating cost. However, fewer studies focus on the influence of these parameters on TEAs. This paper investigated the effects of these parameters on the life cycle cost (LCC) and the unit cost (UC) in the TEAs of biodiesel production. The results show that LCC and UC exhibit variations when involving uncertain parameters. Based on the uncertainty analysis, three global sensitivity analysis (GSA) methods are utilized to quantify the contribution of an individual uncertain parameter to LCC and UC. The GSA results reveal that the feedstock price and the interest rate produce considerable effects on the TEAs. These results can provide a useful guide for entrepreneurs when they plan plants. Copyright © 2014 Elsevier Ltd. All rights reserved.

Thermodynamic derivation of open circuit voltage in vanadium redox flow batteries

NASA Astrophysics Data System (ADS)

Pavelka, Michal; Wandschneider, Frank; Mazur, Petr

2015-10-01

Open circuit voltage of vanadium redox flow batteries is carefully calculated using equilibrium thermodynamics. This analysis reveals some terms in the Nernst relation which are usually omitted in literature. Due to the careful thermodynamic treatment, all uncertainties about the form of Nernst relation are removed except for uncertainties in activity coefficients of particular species. Moreover, it is shown (based again on equilibrium thermodynamics) that batteries with anion-exchange membranes follow different Nernst relation than batteries with cation-exchange membranes. The difference is calculated, and it is verified experimentally that the formula for anion-exchange membranes describes experiments with anion-exchange membranes better than the corresponding formula for cation-exchange membranes. In summary, careful thermodynamic calculation of open circuit voltage of vanadium redox flow batteries is presented, and the difference between voltage for anion-exchange and cation-exchange membranes is revealed.

Gendered uncertainty and variation in physicians' decisions for coronary heart disease: the double-edged sword of "atypical symptoms".

PubMed

Welch, Lisa C; Lutfey, Karen E; Gerstenberger, Eric; Grace, Matthew

2012-09-01

Nonmedical factors and diagnostic certainty contribute to variation in clinical decision making, but the process by which this occurs remains unclear. We examine how physicians' interpretations of patient sex-gender affect diagnostic certainty and, in turn, decision making for coronary heart disease. Data are from a factorial experiment of 256 physicians who viewed 1 of 16 video vignettes with different patient-actors presenting the same symptoms of coronary heart disease. Physician participants completed a structured interview and provided a narrative about their decision-making processes. Quantitative analysis showed that diagnostic uncertainty reduces the likelihood that physicians will order tests and medications appropriate for an urgent cardiac condition in particular. Qualitative analysis revealed that a subset of physicians applied knowledge that women have "atypical symptoms" as a generalization, which engendered uncertainty for some. Findings are discussed in relation to social-psychological processes that underlie clinical decision making and the social framing of medical knowledge.

Uncertainty Analysis for the Evaluation of a Passive Runway Arresting System

NASA Technical Reports Server (NTRS)

Deloach, Richard; Marlowe, Jill M.; Yager, Thomas J.

2009-01-01

This paper considers the stopping distance of an aircraft involved in a runway overrun incident when the runway has been provided with an extension comprised of a material engineered to induce high levels of rolling friction and drag. A formula for stopping distance is derived that is shown to be the product of a known formula for the case of friction without drag, and a dimensionless constant between 0 and 1 that quantifies the further reduction in stopping distance when drag is introduced. This additional quantity, identified as the Drag Reduction Factor, D, is shown to depend on the ratio of drag force to friction force experienced by the aircraft as it enters the overrun area. The specific functional form of D is shown to depend on how drag varies with speed. A detailed uncertainty analysis is presented which reveals how the uncertainty in estimates of stopping distance are influenced by experimental error in the force measurements that are acquired in a typical evaluation experiment conducted to assess candidate overrun materials.

Gendered Uncertainty and Variation in Physicians’ Decisions for Coronary Heart Disease: The Double-Edged Sword of “Atypical Symptoms”*

PubMed Central

Welch, Lisa C.; Lutfey, Karen E.; Gerstenberger, Eric; Grace, Matthew

2013-01-01

Nonmedical factors and diagnostic certainty contribute to variation in clinical decision making, but the process by which this occurs remains unclear. We examine how physicians’ interpretations of patient sex/gender affect diagnostic certainty and, in turn, decision making for coronary heart disease (CHD). Data are from a factorial experiment of 256 physicians who viewed one of 16 video vignettes with different patient-actors presenting the same CHD symptoms. Physician participants completed a structured interview and provided a narrative about their decision-making processes. Quantitative analysis showed that diagnostic uncertainty reduces the likelihood that physicians will order tests and medications appropriate for an urgent cardiac condition in particular. Qualitative analysis revealed that a subset of physicians applied knowledge that women have “atypical symptoms” as a generalization, which engendered uncertainty for some. Findings are discussed in relation to social-psychological processes that underlie clinical decision making and the social framing of medical knowledge. PMID:22933590

Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

NASA Astrophysics Data System (ADS)

Zahmatkesh, Zahra; Karamouz, Mohammad; Nazif, Sara

2015-09-01

Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis.

Evaluating the Uncertainty in Exchange Parameters Determined from Off-Resonance R1ρ Relaxation Dispersion for Systems in Fast Exchange

PubMed Central

Bothe, Jameson R.; Stein, Zachary W.; Al-Hashimi, Hashim M.

2014-01-01

Spin relaxation in the rotating frame (R1ρ) is a powerful NMR technique for characterizing fast microsecond timescale exchange processes directed toward short-lived excited states in biomolecules. At the limit of fast exchange, only kex = k1 + k−1 and Φıx = pGpE(Δω)2 can be determined from R1ρ data limiting the ability to characterize the structure and energetics of the excited state conformation. Here, we use simulations to examine the uncertainty with which exchange parameters can be determined for two state systems in intermediate-to-fast exchange using off-resonance R1ρ relaxation dispersion. R1ρ data computed by solving the Bloch-McConnell equations reveals small but significant asymmetry with respect to offset (R1ρ(ΔΩ) ≠ R1ρ(−ΔΩ)), which is a hallmark of slow-to-intermediate exchange, even under conditions of fast exchange for free precession chemical exchange line broadening (kex/Δω > 10). A grid search analysis combined with bootstrap and Monte-Carlo based statistical approaches for estimating uncertainty in exchange parameters reveals that both the sign and magnitude of Δω can be determined at a useful level of uncertainty for systems in fast exchange (kex/Δω < 10) but that this depends on the uncertainty in the R1ρ data and requires a thorough examination of the multidimensional variation of χ2 as a function of exchange parameters. Results from simulations are complemented by analysis of experimental R1ρ data measured in three nucleic acid systems with exchange processes occurring on the slow (kex/Δω = 0.2; pE = ~ 0.7%), fast (kex/Δω = ~10–16; pE = ~13%) and very fast (kex = 39,000 s−1) chemical shift timescales. PMID:24819426

Assessing uncertainties in land cover projections.

PubMed

Alexander, Peter; Prestele, Reinhard; Verburg, Peter H; Arneth, Almut; Baranzelli, Claudia; Batista E Silva, Filipe; Brown, Calum; Butler, Adam; Calvin, Katherine; Dendoncker, Nicolas; Doelman, Jonathan C; Dunford, Robert; Engström, Kerstin; Eitelberg, David; Fujimori, Shinichiro; Harrison, Paula A; Hasegawa, Tomoko; Havlik, Petr; Holzhauer, Sascha; Humpenöder, Florian; Jacobs-Crisioni, Chris; Jain, Atul K; Krisztin, Tamás; Kyle, Page; Lavalle, Carlo; Lenton, Tim; Liu, Jiayi; Meiyappan, Prasanth; Popp, Alexander; Powell, Tom; Sands, Ronald D; Schaldach, Rüdiger; Stehfest, Elke; Steinbuks, Jevgenijs; Tabeau, Andrzej; van Meijl, Hans; Wise, Marshall A; Rounsevell, Mark D A

2017-02-01

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover. © 2016 John Wiley & Sons Ltd.

Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study

NASA Astrophysics Data System (ADS)

Aleksankina, Ksenia; Heal, Mathew R.; Dore, Anthony J.; Van Oijen, Marcel; Reis, Stefan

2018-04-01

Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input-output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ±40 % variation range for the UK emissions of SO2, NOx, and NH3, from which regression coefficients for each input-output combination and each model grid ( > 10 000 across the UK) were calculated. Surface concentrations of SO2, NOx, and NH3 (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO3 and particulate SO42-, NO3-, and NH4+ to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO2, NOx, and NH3 (±4, ±10, and ±20 % respectively). The uncertainties in the surface concentrations of NH3 and NOx and the depositions of NHx and NOy were dominated by the uncertainties in emissions of NH3, and NOx respectively, whilst concentrations of SO2 and deposition of SOy were affected by the uncertainties in both SO2 and NH3 emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH4+, NO3-, SO42-, and HNO3) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.

Sensitivity and Uncertainty Analysis of Plutonium and Cesium Isotopes in Modeling of BR3 Reactor Spent Fuel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Conant, Andrew; Erickson, Anna; Robel, Martin

Nuclear forensics has a broad task to characterize recovered nuclear or radiological material and interpret the results of investigation. One approach to isotopic characterization of nuclear material obtained from a reactor is to chemically separate and perform isotopic measurements on the sample and verify the results with modeling of the sample history, for example, operation of a nuclear reactor. The major actinide plutonium and fission product cesium are commonly measured signatures of the fuel history in a reactor core. This study investigates the uncertainty of the plutonium and cesium isotope ratios of a fuel rod discharged from a research pressurizedmore » water reactor when the location of the sample is not known a priori. A sensitivity analysis showed overpredicted values for the 240Pu/ 239Pu ratio toward the axial center of the rod and revealed a lower probability of the rod of interest (ROI) being on the periphery of the assembly. The uncertainty analysis found the relative errors due to only the rod position and boron concentration to be 17% to 36% and 7% to 15% for the 240Pu/ 239Pu and 137Cs/ 135Cs ratios, respectively. Lastly, this study provides a method for uncertainty quantification of isotope concentrations due to the location of the ROI. Similar analyses can be performed to verify future chemical and isotopic analyses.« less

Sensitivity and Uncertainty Analysis of Plutonium and Cesium Isotopes in Modeling of BR3 Reactor Spent Fuel

DOE PAGES

Conant, Andrew; Erickson, Anna; Robel, Martin; ...

2017-02-03

Nuclear forensics has a broad task to characterize recovered nuclear or radiological material and interpret the results of investigation. One approach to isotopic characterization of nuclear material obtained from a reactor is to chemically separate and perform isotopic measurements on the sample and verify the results with modeling of the sample history, for example, operation of a nuclear reactor. The major actinide plutonium and fission product cesium are commonly measured signatures of the fuel history in a reactor core. This study investigates the uncertainty of the plutonium and cesium isotope ratios of a fuel rod discharged from a research pressurizedmore » water reactor when the location of the sample is not known a priori. A sensitivity analysis showed overpredicted values for the 240Pu/ 239Pu ratio toward the axial center of the rod and revealed a lower probability of the rod of interest (ROI) being on the periphery of the assembly. The uncertainty analysis found the relative errors due to only the rod position and boron concentration to be 17% to 36% and 7% to 15% for the 240Pu/ 239Pu and 137Cs/ 135Cs ratios, respectively. Lastly, this study provides a method for uncertainty quantification of isotope concentrations due to the location of the ROI. Similar analyses can be performed to verify future chemical and isotopic analyses.« less

HZETRN radiation transport validation using balloon-based experimental data

NASA Astrophysics Data System (ADS)

Warner, James E.; Norman, Ryan B.; Blattnig, Steve R.

2018-05-01

The deterministic radiation transport code HZETRN (High charge (Z) and Energy TRaNsport) was developed by NASA to study the effects of cosmic radiation on astronauts and instrumentation shielded by various materials. This work presents an analysis of computed differential flux from HZETRN compared with measurement data from three balloon-based experiments over a range of atmospheric depths, particle types, and energies. Model uncertainties were quantified using an interval-based validation metric that takes into account measurement uncertainty both in the flux and the energy at which it was measured. Average uncertainty metrics were computed for the entire dataset as well as subsets of the measurements (by experiment, particle type, energy, etc.) to reveal any specific trends of systematic over- or under-prediction by HZETRN. The distribution of individual model uncertainties was also investigated to study the range and dispersion of errors beyond just single scalar and interval metrics. The differential fluxes from HZETRN were generally well-correlated with balloon-based measurements; the median relative model difference across the entire dataset was determined to be 30%. The distribution of model uncertainties, however, revealed that the range of errors was relatively broad, with approximately 30% of the uncertainties exceeding ± 40%. The distribution also indicated that HZETRN systematically under-predicts the measurement dataset as a whole, with approximately 80% of the relative uncertainties having negative values. Instances of systematic bias for subsets of the data were also observed, including a significant underestimation of alpha particles and protons for energies below 2.5 GeV/u. Muons were found to be systematically over-predicted at atmospheric depths deeper than 50 g/cm2 but under-predicted for shallower depths. Furthermore, a systematic under-prediction of alpha particles and protons was observed below the geomagnetic cutoff, suggesting that improvements to the light ion production cross sections in HZETRN should be investigated.

Total costs of injury from accidents in the home and during education, sports and leisure activities: estimates for Norway with assessment of uncertainty.

PubMed

Veisten, Knut; Nossum, Ase; Akhtar, Juned

2009-07-01

Injury accidents occurring in the home, during educational, sports or leisure activities were estimated from samples of hospital data, combined with fatality data from vital statistics. Uncertainty of estimated figures was assessed in simulation-based analysis. Total economic costs to society from injuries and fatalities due to such accidents were estimated at approximately NOK 150 billion per year. The estimated costs reveal the scale of the public health problem and lead to arguments for the establishment of a proper injury register for the identification of preventive measures to reduce the costs to society.

Specifying design conservatism: Worst case versus probabilistic analysis

NASA Technical Reports Server (NTRS)

Miles, Ralph F., Jr.

1993-01-01

Design conservatism is the difference between specified and required performance, and is introduced when uncertainty is present. The classical approach of worst-case analysis for specifying design conservatism is presented, along with the modern approach of probabilistic analysis. The appropriate degree of design conservatism is a tradeoff between the required resources and the probability and consequences of a failure. A probabilistic analysis properly models this tradeoff, while a worst-case analysis reveals nothing about the probability of failure, and can significantly overstate the consequences of failure. Two aerospace examples will be presented that illustrate problems that can arise with a worst-case analysis.

Emulation and Sensitivity Analysis of the Community Multiscale Air Quality Model for a UK Ozone Pollution Episode.

PubMed

Beddows, Andrew V; Kitwiroon, Nutthida; Williams, Martin L; Beevers, Sean D

2017-06-06

Gaussian process emulation techniques have been used with the Community Multiscale Air Quality model, simulating the effects of input uncertainties on ozone and NO 2 output, to allow robust global sensitivity analysis (SA). A screening process ranked the effect of perturbations in 223 inputs, isolating the 30 most influential from emissions, boundary conditions (BCs), and reaction rates. Community Multiscale Air Quality (CMAQ) simulations of a July 2006 ozone pollution episode in the UK were made with input values for these variables plus ozone dry deposition velocity chosen according to a 576 point Latin hypercube design. Emulators trained on the output of these runs were used in variance-based SA of the model output to input uncertainties. Performing these analyses for every hour of a 21 day period spanning the episode and several days on either side allowed the results to be presented as a time series of sensitivity coefficients, showing how the influence of different input uncertainties changed during the episode. This is one of the most complex models to which these methods have been applied, and here, they reveal detailed spatiotemporal patterns of model sensitivities, with NO and isoprene emissions, NO 2 photolysis, ozone BCs, and deposition velocity being among the most influential input uncertainties.

A complete representation of uncertainties in layer-counted paleoclimatic archives

NASA Astrophysics Data System (ADS)

Boers, Niklas; Goswami, Bedartha; Ghil, Michael

2017-09-01

Accurate time series representation of paleoclimatic proxy records is challenging because such records involve dating errors in addition to proxy measurement errors. Rigorous attention is rarely given to age uncertainties in paleoclimatic research, although the latter can severely bias the results of proxy record analysis. Here, we introduce a Bayesian approach to represent layer-counted proxy records - such as ice cores, sediments, corals, or tree rings - as sequences of probability distributions on absolute, error-free time axes. The method accounts for both proxy measurement errors and uncertainties arising from layer-counting-based dating of the records. An application to oxygen isotope ratios from the North Greenland Ice Core Project (NGRIP) record reveals that the counting errors, although seemingly small, lead to substantial uncertainties in the final representation of the oxygen isotope ratios. In particular, for the older parts of the NGRIP record, our results show that the total uncertainty originating from dating errors has been seriously underestimated. Our method is next applied to deriving the overall uncertainties of the Suigetsu radiocarbon comparison curve, which was recently obtained from varved sediment cores at Lake Suigetsu, Japan. This curve provides the only terrestrial radiocarbon comparison for the time interval 12.5-52.8 kyr BP. The uncertainties derived here can be readily employed to obtain complete error estimates for arbitrary radiometrically dated proxy records of this recent part of the last glacial interval.

Managing Uncertainty in Water Infrastructure Design Using Info-gap Robustness

NASA Astrophysics Data System (ADS)

Irias, X.; Cicala, D.

2013-12-01

Info-gap theory, a tool for managing deep uncertainty, can be of tremendous value for design of water systems in areas of high seismic risk. Maintaining reliable water service in those areas is subject to significant uncertainties including uncertainty of seismic loading, unknown seismic performance of infrastructure, uncertain costs of innovative seismic-resistant construction, unknown costs to repair seismic damage, unknown societal impacts from downtime, and more. Practically every major earthquake that strikes a population center reveals additional knowledge gaps. In situations of such deep uncertainty, info-gap can offer advantages over traditional approaches, whether deterministic approaches that use empirical safety factors to address the uncertainties involved, or probabilistic methods that attempt to characterize various stochastic properties and target a compromise between cost and reliability. The reason is that in situations of deep uncertainty, it may not be clear what safety factor would be reasonable, or even if any safety factor is sufficient to address the uncertainties, and we may lack data to characterize the situation probabilistically. Info-gap is a tool that recognizes up front that our best projection of the future may be wrong. Thus, rather than seeking a solution that is optimal for that projection, info-gap seeks a solution that works reasonably well for all plausible conditions. In other words, info-gap seeks solutions that are robust in the face of uncertainty. Info-gap has been used successfully across a wide range of disciplines including climate change science, project management, and structural design. EBMUD is currently using info-gap to help it gain insight into possible solutions for providing reliable water service to an island community within its service area. The island, containing about 75,000 customers, is particularly vulnerable to water supply disruption from earthquakes, since it has negligible water storage and is entirely dependent on four potentially fragile water transmission mains for its day-to-day water supply. Using info-gap analysis, EBMUD is evaluating competing strategies for providing water supply to the island, for example submarine pipelines versus tunnels. The analysis considers not only the likely or 'average' results for each strategy, but also the worst-case performance of each strategy under varying levels of uncertainty. This analysis is improving the quality of the planning process, since it can identify strategies that ensure minimal disruption of water supply following a major earthquake, even if the earthquake and resulting damage fail to conform to our expectations. Results to date are presented, including a discussion of how info-gap analysis complements existing tools for comparing alternative strategies, and how info-gap improves our ability to quantify our tolerance for uncertainty.

Managing risk and expected financial return from selective expansion of operating room capacity: mean-variance analysis of a hospital's portfolio of surgeons.

PubMed

Dexter, Franklin; Ledolter, Johannes

2003-07-01

Surgeons using the same amount of operating room (OR) time differ in their achieved hospital contribution margins (revenue minus variable costs) by >1000%. Thus, to improve the financial return from perioperative facilities, OR strategic decisions should selectively focus additional OR capacity and capital purchasing on a few surgeons or subspecialties. These decisions use estimates of each surgeon's and/or subspecialty's contribution margin per OR hour. The estimates are subject to uncertainty (e.g., from outliers). We account for the uncertainties by using mean-variance portfolio analysis (i.e., quadratic programming). This method characterizes the problem of selectively expanding OR capacity based on the expected financial return and risk of different portfolios of surgeons. The assessment reveals whether the choices, of which surgeons have their OR capacity expanded, are sensitive to the uncertainties in the surgeons' contribution margins per OR hour. Thus, mean-variance analysis reduces the chance of making strategic decisions based on spurious information. We also assess the financial benefit of using mean-variance portfolio analysis when the planned expansion of OR capacity is well diversified over at least several surgeons or subspecialties. Our results show that, in such circumstances, there may be little benefit from further changing the portfolio to reduce its financial risk. Surgeon and subspecialty specific hospital financial data are uncertain, a fact that should be taken into account when making decisions about expanding operating room capacity. We show that mean-variance portfolio analysis can incorporate this uncertainty, thereby guiding operating room management decision-making and reducing the chance of a strategic decision being made based on spurious information.

Obsessions and worry beliefs in an inpatient OCD population.

PubMed

Calleo, Jessica S; Hart, John; Björgvinsson, Thröstur; Stanley, Melinda A

2010-12-01

Dysfunctional beliefs in obsessive-compulsive disorder (OCD) and worry are thought to contribute to vulnerability and maintenance of pathological anxiety. In this study, five belief domains concerning responsibility/threat estimation, perfectionism, intolerance of uncertainty, importance/control of thoughts and thought-action fusion were examined to see whether they differentially predicted worry and obsession severity in patients with severe OCD. Correlational analysis revealed that perfectionism and intolerance of uncertainty were associated with worry, whereas beliefs in the importance and control of thoughts and thought-action fusion were associated with obsession severity when obsession severity and worry, respectively, were controlled. In regression analyses, thought-action fusion and intolerance of uncertainty predicted OCD severity. The relation between dysfunctional beliefs and specific subtypes of OCD symptoms was also examined. Specific relationships were identified, including perfectionism with ordering, obsessions with control/importance of thoughts and checking and washing with threat estimation. Copyright © 2010 Elsevier Ltd. All rights reserved.

Validation and cultural adaptation of a German version of the Physicians' Reactions to Uncertainty scales

PubMed Central

Schneider, Antonius; Szecsenyi, Joachim; Barie, Stefan; Joest, Katharina; Rosemann, Thomas

2007-01-01

Background The aim of the study was to examine the validity of a translated and culturally adapted version of the Physicians' Reaction to Uncertainty scales (PRU) in primary care physicians. Methods In a structured process, the original questionnaire was translated, culturally adapted and assessed after administering it to 93 GPs. Test-retest reliability was tested by sending the questionnaire to the GPs again after two weeks. Results The principal factor analysis confirmed the postulated four-factor structure underlying the 15 items. In contrast to the original version, item 5 achieved a higher loading on the 'concern about bad outcomes' scale. Consequently, we rearranged the scales. Good item-scale correlations were obtained, with Pearson's correlation coefficient ranging from 0.56–0.84. As regards the item-discriminant validity between the scales 'anxiety due to uncertainty' and 'concern about bad outcomes', partially high correlations (Pearson's correlation coefficient 0.02–0.69; p < 0.001) were found, indicating an overlap between both constructs. The assessment of internal consistency revealed satisfactory values; Cronbach's alpha of the rearranged version was 0.86 or higher for all scales. Test-retest-reliability, assessed by means of the intraclass-correlation-coefficient (ICC), exceeded 0.84, except for the 'reluctance to disclose mistakes to physicians' scale (ICC = 0.66). In this scale, some substantial floor effects occurred, with 29.3% of answers showing the lowest possible value. Conclusion Dealing with uncertainty is an important issue in daily practice. The psychometric properties of the rearranged German version of the PRU are satisfying. The revealed floor effects do not limit the significance of the questionnaire. Thus, the German version of the PRU could contribute to the further evaluation of the impact of uncertainty in primary care physicians. PMID:17562018

Bio-physical vs. Economic Uncertainty in the Analysis of Climate Change Impacts on World Agriculture

NASA Astrophysics Data System (ADS)

Hertel, T. W.; Lobell, D. B.

2010-12-01

Accumulating evidence suggests that agricultural production could be greatly affected by climate change, but there remains little quantitative understanding of how these agricultural impacts would affect economic livelihoods in poor countries. The recent paper by Hertel, Burke and Lobell (GEC, 2010) considers three scenarios of agricultural impacts of climate change, corresponding to the fifth, fiftieth, and ninety fifth percentiles of projected yield distributions for the world’s crops in 2030. They evaluate the resulting changes in global commodity prices, national economic welfare, and the incidence of poverty in a set of 15 developing countries. Although the small price changes under the medium scenario are consistent with previous findings, their low productivity scenario reveals the potential for much larger food price changes than reported in recent studies which have hitherto focused on the most likely outcomes. The poverty impacts of price changes under the extremely adverse scenario are quite heterogeneous and very significant in some population strata. They conclude that it is critical to look beyond central case climate shocks and beyond a simple focus on yields and highly aggregated poverty impacts. In this paper, we conduct a more formal, systematic sensitivity analysis (SSA) with respect to uncertainty in the biophysical impacts of climate change on agriculture, by explicitly specifying joint distributions for global yield changes - this time focusing on 2050. This permits us to place confidence intervals on the resulting price impacts and poverty results which reflect the uncertainty inherited from the biophysical side of the analysis. We contrast this with the economic uncertainty inherited from the global general equilibrium model (GTAP), by undertaking SSA with respect to the behavioral parameters in that model. This permits us to assess which type of uncertainty is more important for regional price and poverty outcomes. Finally, we undertake a combined SSA, wherein climate change-induced productivity shocks are permitted to interact with the uncertain economic parameters. This permits us to examine potential interactions between the two sources of uncertainty.

First Monte Carlo analysis of fragmentation functions from single-inclusive e + e - annihilation

DOE PAGES

Sato, Nobuo; Ethier, J. J.; Melnitchouk, W.; ...

2016-12-02

Here, we perform the first iterative Monte Carlo (IMC) analysis of fragmentation functions constrained by all available data from single-inclusive $e^+ e^-$ annihilation into pions and kaons. The IMC method eliminates potential bias in traditional analyses based on single fits introduced by fixing parameters not well contrained by the data, and provides a statistically rigorous determination of uncertainties. Our analysis reveals specific features of fragmentation functions using the new IMC methodology and those obtained from previous analyses, especially for light quarks and for strange quark fragmentation to kaons.

The promise of complementarity: Using the methods of foresight for health workforce planning.

PubMed

Rees, Gareth H; Crampton, Peter; Gauld, Robin; MacDonell, Stephen

2018-05-01

Health workforce planning aims to meet a health system's needs with a sustainable and fit-for-purpose workforce, although its efficacy is reduced in conditions of uncertainty. This PhD breakthrough article offers foresight as a means of addressing this uncertainty and models its complementarity in the context of the health workforce planning problem. The article summarises the findings of a two-case multi-phase mixed method study that incorporates actor analysis, scenario development and policy Delphi. This reveals a few dominant actors of considerable influence who are in conflict over a few critical workforce issues. Using these to augment normative scenarios, developed from existing clinically developed model of care visions, a number of exploratory alternative descriptions of future workforce situations are produced for each case. Their analysis reveals that these scenarios are a reasonable facsimile of plausible futures, though some are favoured over others. Policy directions to support these favoured aspects can also be identified. This novel approach offers workforce planners and policy makers some guidance on the use of complimentary data, methods to overcome the limitations of conventional workforce forecasting and a framework for exploring the complexities and ambiguities of a health workforce's evolution.

A TIERED APPROACH TO PERFORMING UNCERTAINTY ANALYSIS IN CONDUCTING EXPOSURE ANALYSIS FOR CHEMICALS

EPA Science Inventory

The WHO/IPCS draft Guidance Document on Characterizing and Communicating Uncertainty in Exposure Assessment provides guidance on recommended strategies for conducting uncertainty analysis as part of human exposure analysis. Specifically, a tiered approach to uncertainty analysis ...

Gap Size Uncertainty Quantification in Advanced Gas Reactor TRISO Fuel Irradiation Experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pham, Binh T.; Einerson, Jeffrey J.; Hawkes, Grant L.

The Advanced Gas Reactor (AGR)-3/4 experiment is the combination of the third and fourth tests conducted within the tristructural isotropic fuel development and qualification research program. The AGR-3/4 test consists of twelve independent capsules containing a fuel stack in the center surrounded by three graphite cylinders and shrouded by a stainless steel shell. This capsule design enables temperature control of both the fuel and the graphite rings by varying the neon/helium gas mixture flowing through the four resulting gaps. Knowledge of fuel and graphite temperatures is crucial for establishing the functional relationship between fission product release and irradiation thermal conditions.more » These temperatures are predicted for each capsule using the commercial finite-element heat transfer code ABAQUS. Uncertainty quantification reveals that the gap size uncertainties are among the dominant factors contributing to predicted temperature uncertainty due to high input sensitivity and uncertainty. Gap size uncertainty originates from the fact that all gap sizes vary with time due to dimensional changes of the fuel compacts and three graphite rings caused by extended exposure to high temperatures and fast neutron irradiation. Gap sizes are estimated using as-fabricated dimensional measurements at the start of irradiation and post irradiation examination dimensional measurements at the end of irradiation. Uncertainties in these measurements provide a basis for quantifying gap size uncertainty. However, lack of gap size measurements during irradiation and lack of knowledge about the dimension change rates lead to gap size modeling assumptions, which could increase gap size uncertainty. In addition, the dimensional measurements are performed at room temperature, and must be corrected to account for thermal expansion of the materials at high irradiation temperatures. Uncertainty in the thermal expansion coefficients for the graphite materials used in the AGR-3/4 capsules also increases gap size uncertainty. This study focuses on analysis of modeling assumptions and uncertainty sources to evaluate their impacts on the gap size uncertainty.« less

Parent Perceptions of Illness Uncertainty and Child Depressive Symptoms in Juvenile Rheumatic Diseases: Examining Caregiver Demand and Parent Distress as Mediators.

PubMed

Chaney, John M; Gamwell, Kaitlyn L; Baraldi, Amanda N; Ramsey, Rachelle R; Cushing, Christopher C; Mullins, Alexandria J; Gillaspy, Stephen R; Jarvis, James N; Mullins, Larry L

2016-10-01

Examine caregiver demand and general parent distress as mediators in the parent illness uncertainty-child depressive symptom association in youth with juvenile rheumatic diseases. Children and adolescents completed the Child Depression Inventory; caregivers completed the Parent Perceptions of Uncertainty Scale, the Care for My Child with Rheumatic Disease Scale, and the Brief Symptom Inventory. The pediatric rheumatologist provided ratings of clinical disease status. Analyses revealed significant direct associations between illness uncertainty and caregiver demand, and between caregiver demand and both parent distress and child depressive symptoms. Results also revealed significant parent uncertainty → caregiver demand → parent distress and parent uncertainty → caregiver demand → child depressive symptom indirect paths. Results highlight the role of illness appraisals in adjustment to juvenile rheumatic diseases, and provide preliminary evidence that parent appraisals of illness uncertainty impact parent distress and child depressive symptoms indirectly through increased perceptions of caregiver demand. © The Author 2016. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

LCA to choose among alternative design solutions: the case study of a new Italian incineration line.

PubMed

Scipioni, A; Mazzi, A; Niero, M; Boatto, T

2009-09-01

At international level LCA is being increasingly used to objectively evaluate the performances of different Municipal Solid Waste (MSW) management solutions. One of the more important waste management options concerns MSW incineration. LCA is usually applied to existing incineration plants. In this study LCA methodology was applied to a new Italian incineration line, to facilitate the prediction, during the design phase, of its potential environmental impacts in terms of damage to human health, ecosystem quality and consumption of resources. The aim of the study was to analyse three different design alternatives: an incineration system with dry flue gas cleaning (without- and with-energy recovery) and one with wet flue gas cleaning. The last two technological solutions both incorporating facilities for energy recovery were compared. From the results of the study, the system with energy recovery and dry flue gas cleaning revealed lower environmental impacts in relation to the ecosystem quality. As LCA results are greatly affected by uncertainties of different types, the second part of the work provides for an uncertainty analysis aimed at detecting the extent output data from life cycle analysis are influenced by uncertainty of input data, and employs both qualitative (pedigree matrix) and quantitative methods (Monte Carlo analysis).

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

PubMed

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 reduce climate change impacts statistically insignificant (i.e., for approximately one third of the investigated management units of the AFF case study), fostering adaptive capacity is suggested as the preferred pathway for adaptation. We conclude that climate change adaptation needs to balance between anticipating expected future conditions and building the capacity to address unknowns and surprises. Copyright © 2012 Elsevier Ltd. All rights reserved.

Quantification of submarine groundwater discharge and its short-term dynamics by linking time-variant end-member mixing analysis and isotope mass balancing (222-Rn)

NASA Astrophysics Data System (ADS)

Petermann, Eric; Knöller, Kay; Stollberg, Reiner; Scholten, Jan; Rocha, Carlos; Weiß, Holger; Schubert, Michael

2017-04-01

Submarine groundwater discharge (SGD) plays a crucial role for the water quality of coastal waters due to associated fluxes of nutrients, organic compounds and/or heavy-metals. Thus, the quantification of SGD is essential for evaluating the vulnerability of coastal water bodies with regard to groundwater pollution as well as for understanding the matter cycles of the connected water bodies. Here, we present a scientific approach for quantifying discharge of fresh groundwater (GWf) and recirculated seawater (SWrec), including its short-term temporal dynamics, into the tide-affected Knysna estuary, South Africa. For a time-variant end-member mixing analysis we conducted time-series observations of radon (222Rn) and salinity within the estuary over two tidal cycles in combination with estimates of the related end-members for seawater, river water, GWf and SWrec. The mixing analysis was treated as constrained optimization problem for finding an end-member mixing ratio that simultaneously fits the observed data for radon and salinity best for every time-step. Uncertainty of each mixing ratio was quantified by Monte Carlo simulations of the optimization procedure considering uncertainty in end-member characterization. Results reveal the highest GWf and SWrec fraction in the estuary during peak low tide with averages of 0.8 % and 1.4 %, respectively. Further, we calculated a radon mass balance that revealed a daily radon flux of 4.8 * 108 Bq into the estuary equivalent to a GWf discharge of 29.000 m3/d (9.000-59.000 m3/d for 25th-75th percentile range) and a SWrec discharge of 80.000 m3/d (45.000-130.000 m3/d for 25th-75th percentile range). The uncertainty of SGD reflects the end-member uncertainty, i.e. the spatial heterogeneity of groundwater composition. The presented approach allows the calculation of mixing ratios of multiple uncertain end-members for time-series measurements of multiple parameters. Linking these results with a tracer mass balance allows conversion of end-member fractions to end-member fluxes.

Taxonomic and regional uncertainty in species-area relationships and the identification of richness hotspots

PubMed Central

Guilhaumon, François; Gimenez, Olivier; Gaston, Kevin J.; Mouillot, David

2008-01-01

Species-area relationships (SARs) are fundamental to the study of key and high-profile issues in conservation biology and are particularly widely used in establishing the broad patterns of biodiversity that underpin approaches to determining priority areas for biological conservation. Classically, the SAR has been argued in general to conform to a power-law relationship, and this form has been widely assumed in most applications in the field of conservation biology. Here, using nonlinear regressions within an information theoretical model selection framework, we included uncertainty regarding both model selection and parameter estimation in SAR modeling and conducted a global-scale analysis of the form of SARs for vascular plants and major vertebrate groups across 792 terrestrial ecoregions representing almost 97% of Earth's inhabited land. The results revealed a high level of uncertainty in model selection across biomes and taxa, and that the power-law model is clearly the most appropriate in only a minority of cases. Incorporating this uncertainty into a hotspots analysis using multimodel SARs led to the identification of a dramatically different set of global richness hotspots than when the power-law SAR was assumed. Our findings suggest that the results of analyses that assume a power-law model may be at severe odds with real ecological patterns, raising significant concerns for conservation priority-setting schemes and biogeographical studies. PMID:18832179

When, not if: the inescapability of an uncertain climate future.

PubMed

Ballard, Timothy; Lewandowsky, Stephan

2015-11-28

Climate change projections necessarily involve uncertainty. Analysis of the physics and mathematics of the climate system reveals that greater uncertainty about future temperature increases is nearly always associated with greater expected damages from climate change. In contrast to those normative constraints, uncertainty is frequently cited in public discourse as a reason to delay mitigative action. This failure to understand the actual implications of uncertainty may incur notable future costs. It is therefore important to communicate uncertainty in a way that improves people's understanding of climate change risks. We examined whether responses to projections were influenced by whether the projection emphasized uncertainty in the outcome or in its time of arrival. We presented participants with statements and graphs indicating projected increases in temperature, sea levels, ocean acidification and a decrease in arctic sea ice. In the uncertain-outcome condition, statements reported the upper and lower confidence bounds of the projected outcome at a fixed time point. In the uncertain time-of-arrival condition, statements reported the upper and lower confidence bounds of the projected time of arrival for a fixed outcome. Results suggested that people perceived the threat as more serious and were more likely to encourage mitigative action in the time-uncertain condition than in the outcome-uncertain condition. This finding has implications for effectively communicating the climate change risks to policy-makers and the general public. © 2015 The Author(s).

Uncertainty Budget Analysis for Dimensional Inspection Processes (U)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valdez, Lucas M.

2012-07-26

This paper is intended to provide guidance and describe how to prepare an uncertainty analysis of a dimensional inspection process through the utilization of an uncertainty budget analysis. The uncertainty analysis is stated in the same methodology as that of the ISO GUM standard for calibration and testing. There is a specific distinction between how Type A and Type B uncertainty analysis is used in a general and specific process. All theory and applications are utilized to represent both a generalized approach to estimating measurement uncertainty and how to report and present these estimations for dimensional measurements in a dimensionalmore » inspection process. The analysis of this uncertainty budget shows that a well-controlled dimensional inspection process produces a conservative process uncertainty, which can be attributed to the necessary assumptions in place for best possible results.« less

Revealing a quantum feature of dimensionless uncertainty in linear and quadratic potentials by changing potential intervals

NASA Astrophysics Data System (ADS)

Kheiri, R.

2016-09-01

As an undergraduate exercise, in an article (2012 Am. J. Phys. 80 780-14), quantum and classical uncertainties for dimensionless variables of position and momentum were evaluated in three potentials: infinite well, bouncing ball, and harmonic oscillator. While original quantum uncertainty products depend on {{\\hslash }} and the number of states (n), a dimensionless approach makes the comparison between quantum uncertainty and classical dispersion possible by excluding {{\\hslash }}. But the question is whether the uncertainty still remains dependent on quantum number n. In the above-mentioned article, there lies this contrast; on the one hand, the dimensionless quantum uncertainty of the potential box approaches classical dispersion only in the limit of large quantum numbers (n\\to ∞ )—consistent with the correspondence principle. On the other hand, similar evaluations for bouncing ball and harmonic oscillator potentials are equal to their classical counterparts independent of n. This equality may hide the quantum feature of low energy levels. In the current study, we change the potential intervals in order to make them symmetric for the linear potential and non-symmetric for the quadratic potential. As a result, it is shown in this paper that the dimensionless quantum uncertainty of these potentials in the new potential intervals is expressed in terms of quantum number n. In other words, the uncertainty requires the correspondence principle in order to approach the classical limit. Therefore, it can be concluded that the dimensionless analysis, as a useful pedagogical method, does not take away the quantum feature of the n-dependence of quantum uncertainty in general. Moreover, our numerical calculations include the higher powers of the position for the potentials.

Long-term stormwater quantity and quality analysis using continuous measurements in a French urban catchment.

PubMed

Sun, Siao; Barraud, Sylvie; Castebrunet, Hélène; Aubin, Jean-Baptiste; Marmonier, Pierre

2015-11-15

The assessment of urban stormwater quantity and quality is important for evaluating and controlling the impact of the stormwater to natural water and environment. This study mainly addresses long-term evolution of stormwater quantity and quality in a French urban catchment using continuous measured data from 2004 to 2011. Storm event-based data series are obtained (716 rainfall events and 521 runoff events are available) from measured continuous time series. The Mann-Kendall test is applied to these event-based data series for trend detection. A lack of trend is found in rainfall and an increasing trend in runoff is detected. As a result, an increasing trend is present in the runoff coefficient, likely due to growing imperviousness of the catchment caused by urbanization. The event mean concentration of the total suspended solid (TSS) in stormwater does not present a trend, whereas the event load of TSS has an increasing tendency, which is attributed to the increasing event runoff volume. Uncertainty analysis suggests that the major uncertainty in trend detection results lies in uncertainty due to available data. A lack of events due to missing data leads to dramatically increased uncertainty in trend detection results. In contrast, measurement uncertainty in time series data plays a trivial role. The intra-event distribution of TSS is studied based on both M(V) curves and pollutant concentrations of absolute runoff volumes. The trend detection test reveals no significant change in intra-event distributions of TSS in the studied catchment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Intolerance of Uncertainty, Fear of Anxiety, and Adolescent Worry

ERIC Educational Resources Information Center

Dugas, Michel J.; Laugesen, Nina; Bukowski, William M.

2012-01-01

A 5 year, ten wave longitudinal study of 338 adolescents assessed the association between two forms of cognitive vulnerability (intolerance of uncertainty and fear of anxiety) and worry. Multilevel mediational analyses revealed a bidirectional and reciprocal relation between intolerance of uncertainty and worry in which change in one variable…

Representation of analysis results involving aleatory and epistemic uncertainty.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jay Dean; Helton, Jon Craig; Oberkampf, William Louis

2008-08-01

Procedures are described for the representation of results in analyses that involve both aleatory uncertainty and epistemic uncertainty, with aleatory uncertainty deriving from an inherent randomness in the behavior of the system under study and epistemic uncertainty deriving from a lack of knowledge about the appropriate values to use for quantities that are assumed to have fixed but poorly known values in the context of a specific study. Aleatory uncertainty is usually represented with probability and leads to cumulative distribution functions (CDFs) or complementary cumulative distribution functions (CCDFs) for analysis results of interest. Several mathematical structures are available for themore » representation of epistemic uncertainty, including interval analysis, possibility theory, evidence theory and probability theory. In the presence of epistemic uncertainty, there is not a single CDF or CCDF for a given analysis result. Rather, there is a family of CDFs and a corresponding family of CCDFs that derive from epistemic uncertainty and have an uncertainty structure that derives from the particular uncertainty structure (i.e., interval analysis, possibility theory, evidence theory, probability theory) used to represent epistemic uncertainty. Graphical formats for the representation of epistemic uncertainty in families of CDFs and CCDFs are investigated and presented for the indicated characterizations of epistemic uncertainty.« less

Opening new institutional spaces for grappling with uncertainty: A constructivist perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duncan, Ronlyn, E-mail: Ronlyn.Duncan@lincoln.ac.nz

In the context of an increasing reliance on predictive computer simulation models to calculate potential project impacts, it has become common practice in impact assessment (IA) to call on proponents to disclose uncertainties in assumptions and conclusions assembled in support of a development project. Understandably, it is assumed that such disclosures lead to greater scrutiny and better policy decisions. This paper questions this assumption. Drawing on constructivist theories of knowledge and an analysis of the role of narratives in managing uncertainty, I argue that the disclosure of uncertainty can obscure as much as it reveals about the impacts of amore » development project. It is proposed that the opening up of institutional spaces that can facilitate the negotiation and deliberation of foundational assumptions and parameters that feed into predictive models could engender greater legitimacy and credibility for IA outcomes. - Highlights: Black-Right-Pointing-Pointer A reliance on supposedly objective disclosure is unreliable in the predictive model context in which IA is now embedded. Black-Right-Pointing-Pointer A reliance on disclosure runs the risk of reductionism and leaves unexamined the social-interactive aspects of uncertainty. Black-Right-Pointing-Pointer Opening new institutional spaces could facilitate deliberation on foundational predictive model assumptions.« less

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

PubMed Central

PONTIFEX, MATTHEW B.; GWIZDALA, KATHRYN L.; PARKS, ANDREW C.; BILLINGER, MARTIN; BRUNNER, CLEMENS

2017-01-01

Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink-related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college-aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back-projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back-projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies. PMID:28026876

Measurement uncertainty analysis techniques applied to PV performance measurements

NASA Astrophysics Data System (ADS)

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 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.

Some applications of uncertainty relations in quantum information

NASA Astrophysics Data System (ADS)

Majumdar, A. S.; Pramanik, T.

2016-08-01

We discuss some applications of various versions of uncertainty relations for both discrete and continuous variables in the context of quantum information theory. The Heisenberg uncertainty relation enables demonstration of the Einstein, Podolsky and Rosen (EPR) paradox. Entropic uncertainty relations (EURs) are used to reveal quantum steering for non-Gaussian continuous variable states. EURs for discrete variables are studied in the context of quantum memory where fine-graining yields the optimum lower bound of uncertainty. The fine-grained uncertainty relation is used to obtain connections between uncertainty and the nonlocality of retrieval games for bipartite and tripartite systems. The Robertson-Schrödinger (RS) uncertainty relation is applied for distinguishing pure and mixed states of discrete variables.

Quantifying Errors in TRMM-Based Multi-Sensor QPE Products Over Land in Preparation for GPM

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa D.; Tian, Yudong

2011-01-01

Determining uncertainties in satellite-based multi-sensor quantitative precipitation estimates over land of fundamental importance to both data producers and hydro climatological applications. ,Evaluating TRMM-era products also lays the groundwork and sets the direction for algorithm and applications development for future missions including GPM. QPE uncertainties result mostly from the interplay of systematic errors and random errors. In this work, we will synthesize our recent results quantifying the error characteristics of satellite-based precipitation estimates. Both systematic errors and total uncertainties have been analyzed for six different TRMM-era precipitation products (3B42, 3B42RT, CMORPH, PERSIANN, NRL and GSMap). For systematic errors, we devised an error decomposition scheme to separate errors in precipitation estimates into three independent components, hit biases, missed precipitation and false precipitation. This decomposition scheme reveals hydroclimatologically-relevant error features and provides a better link to the error sources than conventional analysis, because in the latter these error components tend to cancel one another when aggregated or averaged in space or time. For the random errors, we calculated the measurement spread from the ensemble of these six quasi-independent products, and thus produced a global map of measurement uncertainties. The map yields a global view of the error characteristics and their regional and seasonal variations, reveals many undocumented error features over areas with no validation data available, and provides better guidance to global assimilation of satellite-based precipitation data. Insights gained from these results and how they could help with GPM will be highlighted.

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks.

PubMed

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses.

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks

PubMed Central

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses. PMID:29163110

Mapping (dis)agreement in hydrologic projections

NASA Astrophysics Data System (ADS)

Melsen, Lieke A.; Addor, Nans; Mizukami, Naoki; Newman, Andrew J.; Torfs, Paul J. J. F.; Clark, Martyn P.; Uijlenhoet, Remko; Teuling, Adriaan J.

2018-03-01

Hydrologic projections are of vital socio-economic importance. However, they are also prone to uncertainty. In order to establish a meaningful range of storylines to support water managers in decision making, we need to reveal the relevant sources of uncertainty. Here, we systematically and extensively investigate uncertainty in hydrologic projections for 605 basins throughout the contiguous US. We show that in the majority of the basins, the sign of change in average annual runoff and discharge timing for the period 2070-2100 compared to 1985-2008 differs among combinations of climate models, hydrologic models, and parameters. Mapping the results revealed that different sources of uncertainty dominate in different regions. Hydrologic model induced uncertainty in the sign of change in mean runoff was related to snow processes and aridity, whereas uncertainty in both mean runoff and discharge timing induced by the climate models was related to disagreement among the models regarding the change in precipitation. Overall, disagreement on the sign of change was more widespread for the mean runoff than for the discharge timing. The results demonstrate the need to define a wide range of quantitative hydrologic storylines, including parameter, hydrologic model, and climate model forcing uncertainty, to support water resource planning.

A Bayesian Network Based Global Sensitivity Analysis Method for Identifying Dominant Processes in a Multi-physics Model

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2016-12-01

Sensitivity analysis has been an important tool in groundwater modeling to identify the influential parameters. Among various sensitivity analysis methods, the variance-based global sensitivity analysis has gained popularity for its model independence characteristic and capability of providing accurate sensitivity measurements. However, the conventional variance-based method only considers uncertainty contribution of single model parameters. In this research, we extended the variance-based method to consider more uncertainty sources and developed a new framework to allow flexible combinations of different uncertainty components. We decompose the uncertainty sources into a hierarchical three-layer structure: scenario, model and parametric. Furthermore, each layer of uncertainty source is capable of containing multiple components. An uncertainty and sensitivity analysis framework was then constructed following this three-layer structure using Bayesian network. Different uncertainty components are represented as uncertain nodes in this network. Through the framework, variance-based sensitivity analysis can be implemented with great flexibility of using different grouping strategies for uncertainty components. The variance-based sensitivity analysis thus is improved to be able to investigate the importance of an extended range of uncertainty sources: scenario, model, and other different combinations of uncertainty components which can represent certain key model system processes (e.g., groundwater recharge process, flow reactive transport process). For test and demonstration purposes, the developed methodology was implemented into a test case of real-world groundwater reactive transport modeling with various uncertainty sources. The results demonstrate that the new sensitivity analysis method is able to estimate accurate importance measurements for any uncertainty sources which were formed by different combinations of uncertainty components. The new methodology can provide useful information for environmental management and decision-makers to formulate policies and strategies.

ACCOUNTING FOR CALIBRATION UNCERTAINTIES IN X-RAY ANALYSIS: EFFECTIVE AREAS IN SPECTRAL FITTING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Hyunsook; Kashyap, Vinay L.; Drake, Jeremy J.

2011-04-20

While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here, we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can bemore » applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.« less

Impact of uncertainty on modeling and testing

NASA Technical Reports Server (NTRS)

Coleman, Hugh W.; Brown, Kendall K.

1995-01-01

A thorough understanding of the uncertainties associated with the modeling and testing of the Space Shuttle Main Engine (SSME) Engine will greatly aid decisions concerning hardware performance and future development efforts. This report will describe the determination of the uncertainties in the modeling and testing of the Space Shuttle Main Engine test program at the Technology Test Bed facility at Marshall Space Flight Center. Section 2 will present a summary of the uncertainty analysis methodology used and discuss the specific applications to the TTB SSME test program. Section 3 will discuss the application of the uncertainty analysis to the test program and the results obtained. Section 4 presents the results of the analysis of the SSME modeling effort from an uncertainty analysis point of view. The appendices at the end of the report contain a significant amount of information relative to the analysis, including discussions of venturi flowmeter data reduction and uncertainty propagation, bias uncertainty documentations, technical papers published, the computer code generated to determine the venturi uncertainties, and the venturi data and results used in the analysis.

Determination of Uncertainties for the New SSME Model

NASA Technical Reports Server (NTRS)

Coleman, Hugh W.; Hawk, Clark W.

1996-01-01

This report discusses the uncertainty analysis performed in support of a new test analysis and performance prediction model for the Space Shuttle Main Engine. The new model utilizes uncertainty estimates for experimental data and for the analytical model to obtain the most plausible operating condition for the engine system. This report discusses the development of the data sets and uncertainty estimates to be used in the development of the new model. It also presents the application of uncertainty analysis to analytical models and the uncertainty analysis for the conservation of mass and energy balance relations is presented. A new methodology for the assessment of the uncertainty associated with linear regressions is presented.

Estimating the uncertainty from sampling in pollution crime investigation: The importance of metrology in the forensic interpretation of environmental data.

PubMed

Barazzetti Barbieri, Cristina; de Souza Sarkis, Jorge Eduardo

2018-07-01

The forensic interpretation of environmental analytical data is usually challenging due to the high geospatial variability of these data. The measurements' uncertainty includes contributions from the sampling and from the sample handling and preparation processes. These contributions are often disregarded in analytical techniques results' quality assurance. A pollution crime investigation case was used to carry out a methodology able to address these uncertainties in two different environmental compartments, freshwater sediments and landfill leachate. The methodology used to estimate the uncertainty was the duplicate method (that replicates predefined steps of the measurement procedure in order to assess its precision) and the parameters used to investigate the pollution were metals (Cr, Cu, Ni, and Zn) in the leachate, the suspect source, and in the sediment, the possible sink. The metal analysis results were compared to statutory limits and it was demonstrated that Cr and Ni concentrations in sediment samples exceeded the threshold levels at all sites downstream the pollution sources, considering the expanded uncertainty U of the measurements and a probability of contamination >0.975, at most sites. Cu and Zn concentrations were above the statutory limits at two sites, but the classification was inconclusive considering the uncertainties of the measurements. Metal analyses in leachate revealed that Cr concentrations were above the statutory limits with a probability of contamination >0.975 in all leachate ponds while the Cu, Ni and Zn probability of contamination was below 0.025. The results demonstrated that the estimation of the sampling uncertainty, which was the dominant component of the combined uncertainty, is required for a comprehensive interpretation of the environmental analyses results, particularly in forensic cases. Copyright © 2018 Elsevier B.V. All rights reserved.

Nuclear Data Needs for the Neutronic Design of MYRRHA Fast Spectrum Research Reactor

NASA Astrophysics Data System (ADS)

Stankovskiy, A.; Malambu, E.; Van den Eynde, G.; Díez, C. J.

2014-04-01

A global sensitivity analysis of effective neutron multiplication factor to the change of nuclear data library has been performed. It revealed that the test version of JEFF-3.2 neutron-induced evaluated data library produces closer results to ENDF/B-VII.1 than JEFF-3.1.2 does. The analysis of contributions of individual evaluations into keff sensitivity resulted in the priority list of nuclides, uncertainties on cross sections and fission neutron multiplicities of which have to be improved by setting up dedicated differential and integral experiments.

Illness Uncertainty and Posttraumatic Stress in Young Adults With Congenital Heart Disease.

PubMed

Moreland, Patricia; Santacroce, Sheila Judge

2018-03-29

Young adults with congenital heart disease (CHD) are at risk for chronic illness uncertainty in 4 domains: ambiguity about the state of their illness; lack of information about the disease, its treatment, and comorbidities; complexity of the healthcare system and relationship with healthcare providers; and unpredictability of the illness course and outcome. Chronic uncertainty has been associated with posttraumatic stress symptoms (PTSS) and posttraumatic stress disorder (PTSD). The aims of this study were to explore how young adults with CHD experience uncertainty and to describe the relationship between PTSS and the appraisal and management process. An exploratory, mixed methods design was used. Data were collected in person and via Skype from 25 participants (19-35 years old), who were diagnosed with CHD during childhood and able to read and write English. In-depth interviews and the University of California at Los Angeles Posttraumatic Stress Disorder Reaction Index were used to collect data. Qualitative data were analyzed using the constant comparative method. The 4 domains of uncertainty were evident in the narratives. The PTSD mean (SD) score was 31.3 (7.7). Six participants met criteria for PTSD. Narrative analysis revealed a relationship between severity of PTSS and the appraisal and management of uncertainty. Participants with PTSD used management strategies that included avoidance, reexperiencing, and hyperarousal. Young adults with CHD may be at risk for the development of long-term psychological stress and PTSD in the setting of chronic uncertainty. Regular monitoring to identify PTSS/PTSD may be a means to promote treatment adherence and participation in healthcare.

Uncertainty in action-value estimation affects both action choice and learning rate of the choice behaviors of rats

PubMed Central

Funamizu, Akihiro; Ito, Makoto; Doya, Kenji; Kanzaki, Ryohei; Takahashi, Hirokazu

2012-01-01

The estimation of reward outcomes for action candidates is essential for decision making. In this study, we examined whether and how the uncertainty in reward outcome estimation affects the action choice and learning rate. We designed a choice task in which rats selected either the left-poking or right-poking hole and received a reward of a food pellet stochastically. The reward probabilities of the left and right holes were chosen from six settings (high, 100% vs. 66%; mid, 66% vs. 33%; low, 33% vs. 0% for the left vs. right holes, and the opposites) in every 20–549 trials. We used Bayesian Q-learning models to estimate the time course of the probability distribution of action values and tested if they better explain the behaviors of rats than standard Q-learning models that estimate only the mean of action values. Model comparison by cross-validation revealed that a Bayesian Q-learning model with an asymmetric update for reward and non-reward outcomes fit the choice time course of the rats best. In the action-choice equation of the Bayesian Q-learning model, the estimated coefficient for the variance of action value was positive, meaning that rats were uncertainty seeking. Further analysis of the Bayesian Q-learning model suggested that the uncertainty facilitated the effective learning rate. These results suggest that the rats consider uncertainty in action-value estimation and that they have an uncertainty-seeking action policy and uncertainty-dependent modulation of the effective learning rate. PMID:22487046

Uncertainty, culture and pathways to care in paediatric functional gastrointestinal disorders.

PubMed

Fortin, Sylvie; Gauthier, Annie; Gomez, Liliana; Faure, Christophe; Bibeau, Gilles; Rasquin, Andrée

2013-01-01

This paper examines how children and families of diverse ethnic backgrounds perceive, understand and treat symptoms related to functional gastrointestinal disorders (FGIDs). It is questioned how different ways of dealing with medical uncertainty (symptoms, diagnosis) may influence treatment pathways. Semi-structured interviews were conducted with 43 children of 38 family groups of immigrant and non-immigrant backgrounds. The analysis takes into account (a) the perceived symptoms; (b) the meaning attributed to them; and (c) the actions taken to relieve them. The social and cultural contexts that permeate these symptoms, meanings and actions were also examined. It is found that, in light of diagnostic and therapeutic uncertainty, non-immigrant families are more likely to consult health professionals. Immigrant families more readily rely upon home remedies, family support and, for some, religious beliefs to temper the uncertainty linked to abdominal pain. Furthermore, non-immigrant children lead a greater quest for legitimacy of their pain at home while most immigrant families place stomach aches in the range of normality. Intracultural variations nuance these findings, as well as family dynamics. It is concluded that different courses of action and family dynamics reveal that uncertainty is dealt with in multiple ways. Family support, the network, and trust in a child's expression of distress are key elements in order to tolerate uncertainty. Lastly, the medical encounter is described as a space permeated with relational uncertainty given the different registers of expression inherent within a cosmopolitan milieu. Narrative practices being an essential dynamic of this encounter, it is questioned whether families' voices are equally heard in these clinical spaces.

Probabilistic projections of 21st century climate change over Northern Eurasia

NASA Astrophysics Data System (ADS)

Monier, E.; Sokolov, A. P.; Schlosser, C. A.; Scott, J. R.; Gao, X.

2013-12-01

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an earth system model of intermediate complexity, with a two-dimensional zonal-mean atmosphere, to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three dimensional atmospheric model; and a statistical downscaling, where a pattern scaling algorithm uses climate-change patterns from 17 climate models. This framework allows for key sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections; climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate); natural variability; and structural uncertainty. Results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also nd that dierent initial conditions lead to dierences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider all sources of uncertainty when modeling climate impacts over Northern Eurasia.

Probabilistic projections of 21st century climate change over Northern Eurasia

NASA Astrophysics Data System (ADS)

Monier, Erwan; Sokolov, Andrei; Schlosser, Adam; Scott, Jeffery; Gao, Xiang

2013-12-01

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three-dimensional atmospheric model, and a statistical downscaling, where a pattern scaling algorithm uses climate change patterns from 17 climate models. This framework allows for four major sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections, climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate), natural variability, and structural uncertainty. The results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also find that different initial conditions lead to differences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider these sources of uncertainty when modeling climate impacts over Northern Eurasia.

Understanding the origins of uncertainty in landscape-scale variations of emissions of nitrous oxide

NASA Astrophysics Data System (ADS)

Milne, Alice; Haskard, Kathy; Webster, Colin; Truan, Imogen; Goulding, Keith

2014-05-01

Nitrous oxide is a potent greenhouse gas which is over 300 times more radiatively effective than carbon dioxide. In the UK, the agricultural sector is estimated to be responsible for over 80% of nitrous oxide emissions, with these emissions resulting from livestock and farmers adding nitrogen fertilizer to soils. For the purposes of reporting emissions to the IPCC, the estimates are calculated using simple models whereby readily-available national or international statistics are combined with IPCC default emission factors. The IPCC emission factor for direct emissions of nitrous oxide from soils has a very large uncertainty. This is primarily because the variability of nitrous oxide emissions in space is large and this results in uncertainty that may be regarded as sample noise. To both reduce uncertainty through improved modelling, and to communicate an understanding of this uncertainty, we must understand the origins of the variation. We analysed data on nitrous oxide emission rate and some other soil properties collected from a 7.5-km transect across contrasting land uses and parent materials in eastern England. We investigated the scale-dependence and spatial uniformity of the correlations between soil properties and emission rates from farm to landscape scale using wavelet analysis. The analysis revealed a complex pattern of scale-dependence. Emission rates were strongly correlated with a process-specific function of the water-filled pore space at the coarsest scale and nitrate at intermediate and coarsest scales. We also found significant correlations between pH and emission rates at the intermediate scales. The wavelet analysis showed that these correlations were not spatially uniform and that at certain scales changes in parent material coincided with significant changes in correlation. Our results indicate that, at the landscape scale, nitrate content and water-filled pore space are key soil properties for predicting nitrous oxide emissions and should therefore be incorporated into process models and emission factors for inventory calculations.

Analyzing the uncertainty of ensemble-based gridded observations in land surface simulations and drought assessment

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Moradkhani, Hamid

2017-12-01

Hydrologic modeling is one of the primary tools utilized for drought monitoring and drought early warning systems. Several sources of uncertainty in hydrologic modeling have been addressed in the literature. However, few studies have assessed the uncertainty of gridded observation datasets from a drought monitoring perspective. This study provides a hydrologic modeling oriented analysis of the gridded observation data uncertainties over the Pacific Northwest (PNW) and its implications on drought assessment. We utilized a recently developed 100-member ensemble-based observed forcing data to simulate hydrologic fluxes at 1/8° spatial resolution using Variable Infiltration Capacity (VIC) model, and compared the results with a deterministic observation. Meteorological and hydrological droughts are studied at multiple timescales over the basin, and seasonal long-term trends and variations of drought extent is investigated for each case. Results reveal large uncertainty of observed datasets at monthly timescale, with systematic differences for temperature records, mainly due to different lapse rates. The uncertainty eventuates in large disparities of drought characteristics. In general, an increasing trend is found for winter drought extent across the PNW. Furthermore, a ∼3% decrease per decade is detected for snow water equivalent (SWE) over the PNW, with the region being more susceptible to SWE variations of the northern Rockies than the western Cascades. The agricultural areas of southern Idaho demonstrate decreasing trend of natural soil moisture as a result of precipitation decline, which implies higher appeal for anthropogenic water storage and irrigation systems.

Fears, Uncertainties, and Hopes: Patient-Initiated Actions and Doctors’ Responses During Oncology Interviews*

PubMed Central

Beach, Wayne A.; Dozier, David M.

2015-01-01

New cancer patients frequently raise concerns about fears, uncertainties, and hopes during oncology interviews. This study sought to understand when and how patients raise their concerns, how doctors responded to these patient-initiated actions, and implications for communication satisfaction. A sub-sampling of video recorded and transcribed encounters was investigated involving 44 new patients and 14 oncologists. Patients completed pre-post self-report measures about fears, uncertainties, and hopes as well as post-evaluations of interview satisfaction. Conversation Analysis (CA) was employed to initially identify pairs of patient-initiated and doctor-responsive actions. A coding scheme was subsequently developed, and two independent coding teams, comprised of two coders each, reliably identified patient-initiated and doctor-responsive social actions. Interactional findings reveal that new cancer patients initiate actions much more frequently than previous research had identified, concerns are usually raised indirectly, and with minimal emotion. Doctors tend to respond to these concerns immediately, but with even less affect, and rarely partner with patients. From pre-post results it was determined that the higher patients’ reported fears, the higher their post-visit fears and lower their satisfaction. Patients with high uncertainty were highly proactive (e.g., asked more questions), yet reported even greater uncertainties following encounters. Hopeful patients also exited interviews with high hopes. Overall, new patients were very satisfied: Oncology interviews significantly decreased patients’ fears and uncertainties, while increasing hopes. Discussion raises key issues for improving communication and managing quality cancer care. PMID:26134261

Global Sensitivity Analysis for Identifying Important Parameters of Nitrogen Nitrification and Denitrification under Model and Scenario Uncertainties

NASA Astrophysics Data System (ADS)

Ye, M.; Chen, Z.; Shi, L.; Zhu, Y.; Yang, J.

2017-12-01

Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. While global sensitivity analysis is a vital tool for identifying the parameters important to nitrogen reactive transport, conventional global sensitivity analysis only considers parametric uncertainty. This may result in inaccurate selection of important parameters, because parameter importance may vary under different models and modeling scenarios. By using a recently developed variance-based global sensitivity analysis method, this paper identifies important parameters with simultaneous consideration of parametric uncertainty, model uncertainty, and scenario uncertainty. In a numerical example of nitrogen reactive transport modeling, a combination of three scenarios of soil temperature and two scenarios of soil moisture leads to a total of six scenarios. Four alternative models are used to evaluate reduction functions used for calculating actual rates of nitrification and denitrification. The model uncertainty is tangled with scenario uncertainty, as the reduction functions depend on soil temperature and moisture content. The results of sensitivity analysis show that parameter importance varies substantially between different models and modeling scenarios, which may lead to inaccurate selection of important parameters if model and scenario uncertainties are not considered. This problem is avoided by using the new method of sensitivity analysis in the context of model averaging and scenario averaging. The new method of sensitivity analysis can be applied to other problems of contaminant transport modeling when model uncertainty and/or scenario uncertainty are present.

Detailed Analysis of the Asteroid Pair (6070) Rheinland and (54827) 2001 NQ8

NASA Astrophysics Data System (ADS)

Vokrouhlický, David; Pravec, Petr; Ďurech, Josef; Hornoch, Kamil; Kušnirák, Peter; Galád, Adrián; Vraštil, Jan; Kučáková, Hana; Pollock, Joseph T.; Ortiz, Jose Luis; Morales, Nicolas; Gaftonyuk, Ninel M.; Pray, Donald P.; Krugly, Yurij N.; Inasaridze, Raguli Ya.; Ayvazian, Vova R.; Molotov, Igor E.; Colazo, Carlos A.

2017-06-01

The existence of asteroid pairs, two bodies on similar heliocentric orbits, reveals an ongoing process of rotational fission among asteroids. This newly found class of objects has not been studied in detail yet. Here we choose asteroids (6070) Rheinland and (54827) 2001 NQ8, the most suitable pair for an in-depth analysis. First, we use available optical photometry to determine their rotational state and convex shapes. Rotational pole of Rheinland is very near the south ecliptic pole with a latitude uncertainty of about 10°. There are two equivalent solutions for the pole of 2001 NQ8, either (72°, -49°) or (242°, -46°) (ecliptic longitude and latitude). In both cases, the longitude values have about 10° uncertainty and the latitude values have about 15° uncertainty (both 3σ uncertainties). The sidereal rotation period of 2001 NQ8 is 5.877186 ± 0.000002 hr. Second, we construct a precise numerical integrator to determine the past state vectors of the pair’s components, namely their heliocentric positions and velocities, and orientation of their spin vectors. Using this new tool, we investigate the origin of the (6070) Rheinland and (54827) 2001 NQ8 pair. We find a formal age solution of 16.34 ± 0.04 kyr. This includes effects of the most massive objects in the asteroid belt (Ceres, Pallas, and Vesta), but the unaccounted gravitational perturbations from other asteroids may imply that the realistic age uncertainty is slightly larger than its formal value. Analyzing results from our numerical simulation to 250 kya, we argue against a possibility that this pair would allow an older age. Initial spin vectors of the two asteroids, at the moment of their separation, were not collinear, but tilted by 38^\\circ +/- 12^\\circ .

The NASA Langley Multidisciplinary Uncertainty Quantification Challenge

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2014-01-01

This paper presents the formulation of an uncertainty quantification challenge problem consisting of five subproblems. These problems focus on key aspects of uncertainty characterization, sensitivity analysis, uncertainty propagation, extreme-case analysis, and robust design.

Development of an Uncertainty Quantification Predictive Chemical Reaction Model for Syngas Combustion

DOE PAGES

Slavinskaya, N. A.; Abbasi, M.; Starcke, J. H.; ...

2017-01-24

An automated data-centric infrastructure, Process Informatics Model (PrIMe), was applied to validation and optimization of a syngas combustion model. The Bound-to-Bound Data Collaboration (B2BDC) module of PrIMe was employed to discover the limits of parameter modifications based on uncertainty quantification (UQ) and consistency analysis of the model–data system and experimental data, including shock-tube ignition delay times and laminar flame speeds. Existing syngas reaction models are reviewed, and the selected kinetic data are described in detail. Empirical rules were developed and applied to evaluate the uncertainty bounds of the literature experimental data. Here, the initial H 2/CO reaction model, assembled frommore » 73 reactions and 17 species, was subjected to a B2BDC analysis. For this purpose, a dataset was constructed that included a total of 167 experimental targets and 55 active model parameters. Consistency analysis of the composed dataset revealed disagreement between models and data. Further analysis suggested that removing 45 experimental targets, 8 of which were self-inconsistent, would lead to a consistent dataset. This dataset was subjected to a correlation analysis, which highlights possible directions for parameter modification and model improvement. Additionally, several methods of parameter optimization were applied, some of them unique to the B2BDC framework. The optimized models demonstrated improved agreement with experiments compared to the initially assembled model, and their predictions for experiments not included in the initial dataset (i.e., a blind prediction) were investigated. The results demonstrate benefits of applying the B2BDC methodology for developing predictive kinetic models.« less

Development of an Uncertainty Quantification Predictive Chemical Reaction Model for Syngas Combustion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Slavinskaya, N. A.; Abbasi, M.; Starcke, J. H.

An automated data-centric infrastructure, Process Informatics Model (PrIMe), was applied to validation and optimization of a syngas combustion model. The Bound-to-Bound Data Collaboration (B2BDC) module of PrIMe was employed to discover the limits of parameter modifications based on uncertainty quantification (UQ) and consistency analysis of the model–data system and experimental data, including shock-tube ignition delay times and laminar flame speeds. Existing syngas reaction models are reviewed, and the selected kinetic data are described in detail. Empirical rules were developed and applied to evaluate the uncertainty bounds of the literature experimental data. Here, the initial H 2/CO reaction model, assembled frommore » 73 reactions and 17 species, was subjected to a B2BDC analysis. For this purpose, a dataset was constructed that included a total of 167 experimental targets and 55 active model parameters. Consistency analysis of the composed dataset revealed disagreement between models and data. Further analysis suggested that removing 45 experimental targets, 8 of which were self-inconsistent, would lead to a consistent dataset. This dataset was subjected to a correlation analysis, which highlights possible directions for parameter modification and model improvement. Additionally, several methods of parameter optimization were applied, some of them unique to the B2BDC framework. The optimized models demonstrated improved agreement with experiments compared to the initially assembled model, and their predictions for experiments not included in the initial dataset (i.e., a blind prediction) were investigated. The results demonstrate benefits of applying the B2BDC methodology for developing predictive kinetic models.« less

Cost Utility Analysis of Topical Steroids Compared With Dietary Elimination for Treatment of Eosinophilic Esophagitis.

PubMed

Cotton, Cary C; Erim, Daniel; Eluri, Swathi; Palmer, Sarah H; Green, Daniel J; Wolf, W Asher; Runge, Thomas M; Wheeler, Stephanie; Shaheen, Nicholas J; Dellon, Evan S

2017-06-01

Topical corticosteroids or dietary elimination are recommended as first-line therapies for eosinophilic esophagitis, but data to directly compare these therapies are scant. We performed a cost utility comparison of topical corticosteroids and the 6-food elimination diet (SFED) in treatment of eosinophilic esophagitis, from the payer perspective. We used a modified Markov model based on current clinical guidelines, in which transition between states depended on histologic response simulated at the individual cohort-member level. Simulation parameters were defined by systematic review and meta-analysis to determine the base-case estimates and bounds of uncertainty for sensitivity analysis. Meta-regression models included adjustment for differences in study and cohort characteristics. In the base-case scenario, topical fluticasone was about as effective as SFED but more expensive at a 5-year time horizon ($9261.58 vs $5719.72 per person). SFED was more effective and less expensive than topical fluticasone and topical budesonide in the base-case scenario. Probabilistic sensitivity analysis revealed little uncertainty in relative treatment effectiveness. There was somewhat greater uncertainty in the relative cost of treatments; most simulations found SFED to be less expensive. In a cost utility analysis comparing topical corticosteroids and SFED for first-line treatment of eosinophilic esophagitis, the therapies were similar in effectiveness. SFED was on average less expensive, and more cost effective in most simulations, than topical budesonide and topical fluticasone, from a payer perspective and not accounting for patient-level costs or quality of life. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

Detecting Trend and Seasonal Changes in Bathymetry Derived from HICO Imagery: A Case Study of Shark Bay, Western Australia

NASA Technical Reports Server (NTRS)

Garcia, Rodrigo A.; Fearns, Peter R. C. S.; Mckinna, Lachlan I. W.

2014-01-01

The Hyperspectral Imager for the Coastal Ocean (HICO) aboard the International Space Station has offered for the first time a dedicated space-borne hyperspectral sensor specifically designed for remote sensing of the coastal environment. However, several processing steps are required to convert calibrated top-of-atmosphere radiances to the desired geophysical parameter(s). These steps add various amounts of uncertainty that can cumulatively render the geophysical parameter imprecise and potentially unusable if the objective is to analyze trends and/or seasonal variability. This research presented here has focused on: (1) atmospheric correction of HICO imagery; (2) retrieval of bathymetry using an improved implementation of a shallow water inversion algorithm; (3) propagation of uncertainty due to environmental noise through the bathymetry retrieval process; (4) issues relating to consistent geo-location of HICO imagery necessary for time series analysis, and; (5) tide height corrections of the retrieved bathymetric dataset. The underlying question of whether a temporal change in depth is detectable above uncertainty is also addressed. To this end, nine HICO images spanning November 2011 to August 2012, over the Shark Bay World Heritage Area, Western Australia, were examined. The results presented indicate that precision of the bathymetric retrievals is dependent on the shallow water inversion algorithm used. Within this study, an average of 70% of pixels for the entire HICO-derived bathymetry dataset achieved a relative uncertainty of less than +/-20%. A per-pixel t-test analysis between derived bathymetry images at successive timestamps revealed observable changes in depth to as low as 0.4 m. However, the present geolocation accuracy of HICO is relatively poor and needs further improvements before extensive time series analysis can be performed.

Forward and backward uncertainty propagation: an oxidation ditch modelling example.

PubMed

Abusam, A; Keesman, K J; van Straten, G

2003-01-01

In the field of water technology, forward uncertainty propagation is frequently used, whereas backward uncertainty propagation is rarely used. In forward uncertainty analysis, one moves from a given (or assumed) parameter subspace towards the corresponding distribution of the output or objective function. However, in the backward uncertainty propagation, one moves in the reverse direction, from the distribution function towards the parameter subspace. Backward uncertainty propagation, which is a generalisation of parameter estimation error analysis, gives information essential for designing experimental or monitoring programmes, and for tighter bounding of parameter uncertainty intervals. The procedure of carrying out backward uncertainty propagation is illustrated in this technical note by working example for an oxidation ditch wastewater treatment plant. Results obtained have demonstrated that essential information can be achieved by carrying out backward uncertainty propagation analysis.

Pretest uncertainty analysis for chemical rocket engine tests

NASA Technical Reports Server (NTRS)

Davidian, Kenneth J.

1987-01-01

A parametric pretest uncertainty analysis has been performed for a chemical rocket engine test at a unique 1000:1 area ratio altitude test facility. Results from the parametric study provide the error limits required in order to maintain a maximum uncertainty of 1 percent on specific impulse. Equations used in the uncertainty analysis are presented.

The value of information for woodland management: Updating a state–transition model

USGS Publications Warehouse

Morris, William K.; Runge, Michael C.; Vesk, Peter A.

2017-01-01

Value of information (VOI) analyses reveal the expected benefit of reducing uncertainty to a decision maker. Most ecological VOI analyses have focused on population models rarely addressing more complex community models. We performed a VOI analysis for a complex state–transition model of Box-Ironbark Forest and Woodland management. With three management alternatives (limited harvest/firewood removal (HF), ecological thinning (ET), and no management), managing the system optimally (for 150 yr) with the original information would, on average, increase the amount of forest in a desirable state from 19% to 35% (a 16-percentage point increase). Resolving all uncertainty would, on average, increase the final percentage to 42% (a 19-percentage point increase). However, only resolving the uncertainty for a single parameter was worth almost two-thirds the value of resolving all uncertainty. We found the VOI to depend on the number of management options, increasing as the management flexibility increased. Our analyses show it is more cost-effective to monitor low-density regrowth forest than other states and more cost-effective to experiment with the no-management alternative than the other management alternatives. Importantly, the most cost-effective strategies did not include either the most desired forest states or the least understood management strategy, ET. This implies that managers cannot just rely on intuition to tell them where the most VOI will lie, as critical uncertainties in a complex system are sometimes cryptic.

Uncertainties in stormwater runoff data collection from a small urban catchment, Southeast China.

PubMed

Huang, Jinliang; Tu, Zhenshun; Du, Pengfei; Lin, Jie; Li, Qingsheng

2010-01-01

Monitoring data are often used to identify stormwater runoff characteristics and in stormwater runoff modelling without consideration of their inherent uncertainties. Integrated with discrete sample analysis and error propagation analysis, this study attempted to quantify the uncertainties of discrete chemical oxygen demand (COD), total suspended solids (TSS) concentration, stormwater flowrate, stormwater event volumes, COD event mean concentration (EMC), and COD event loads in terms of flow measurement, sample collection, storage and laboratory analysis. The results showed that the uncertainties due to sample collection, storage and laboratory analysis of COD from stormwater runoff are 13.99%, 19.48% and 12.28%. Meanwhile, flow measurement uncertainty was 12.82%, and the sample collection uncertainty of TSS from stormwater runoff was 31.63%. Based on the law of propagation of uncertainties, the uncertainties regarding event flow volume, COD EMC and COD event loads were quantified as 7.03%, 10.26% and 18.47%.

Trust Measurement using Multimodal Behavioral Analysis and Uncertainty Aware Trust Calibration

DTIC Science & Technology

2018-01-05

to estimate their performance based on their estimation on all prior trials. In the meanwhile via comparing the decisions of participants with the...it is easier compared with situations when more trials have been done. It should be noted that if a participant is good at memorizing the previous...them. The proposed study, being quantitative and explorative, are expected to reveal a number of findings that benefit interaction system design and

Climate Risk Assessment: Technical Guidance Manual for DoD Installations and Built Environment

DTIC Science & Technology

2016-09-06

climate change risks to DoD installations and the built environment. The approach, which we call “decision-scaling,” reveals the core sensitivity of...DoD installations to climate change . It is designed to illuminate the sensitivity of installations and their supporting infrastructure systems...including water and energy, to climate changes and other uncertainties without dependence on climate change projections. In this way the analysis and

Detailed Uncertainty Analysis of the ZEM-3 Measurement System

NASA Technical Reports Server (NTRS)

Mackey, Jon; Sehirlioglu, Alp; Dynys, Fred

2014-01-01

The measurement of Seebeck coefficient and electrical resistivity are critical to the investigation of all thermoelectric systems. Therefore, it stands that the measurement uncertainty must be well understood to report ZT values which are accurate and trustworthy. A detailed uncertainty analysis of the ZEM-3 measurement system has been performed. The uncertainty analysis calculates error in the electrical resistivity measurement as a result of sample geometry tolerance, probe geometry tolerance, statistical error, and multi-meter uncertainty. The uncertainty on Seebeck coefficient includes probe wire correction factors, statistical error, multi-meter uncertainty, and most importantly the cold-finger effect. The cold-finger effect plagues all potentiometric (four-probe) Seebeck measurement systems, as heat parasitically transfers through thermocouple probes. The effect leads to an asymmetric over-estimation of the Seebeck coefficient. A thermal finite element analysis allows for quantification of the phenomenon, and provides an estimate on the uncertainty of the Seebeck coefficient. The thermoelectric power factor has been found to have an uncertainty of +9-14 at high temperature and 9 near room temperature.

Probabilistic Assessment of National Wind Tunnel

NASA Technical Reports Server (NTRS)

Shah, A. R.; Shiao, M.; Chamis, C. C.

1996-01-01

A preliminary probabilistic structural assessment of the critical section of National Wind Tunnel (NWT) is performed using NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) computer code. Thereby, the capabilities of NESSUS code have been demonstrated to address reliability issues of the NWT. Uncertainties in the geometry, material properties, loads and stiffener location on the NWT are considered to perform the reliability assessment. Probabilistic stress, frequency, buckling, fatigue and proof load analyses are performed. These analyses cover the major global and some local design requirements. Based on the assumed uncertainties, the results reveal the assurance of minimum 0.999 reliability for the NWT. Preliminary life prediction analysis results show that the life of the NWT is governed by the fatigue of welds. Also, reliability based proof test assessment is performed.

Nuclear Effects in Quasi-Elastic and Delta Resonance Production at Low Momentum Transfer

NASA Astrophysics Data System (ADS)

Demgen, John Gibney

Analysis of data collected by the MINERvA experiment is done by showing the distribution of charged hadron energy for interactions that have low momentum transfer. This distribution reveals major discrepancies between the detector data and the standard MINERvA interaction model with only a simple global Fermi gas model. Adding additional model elements, the random phase approximation (RPA), meson exchange current (MEC), and a reduction of resonance delta production improve this discrepancy. Special attention is paid to resonance delta production systematic uncertainties, which do not make up these discrepancies even when added with resolution and biasing systematic uncertainties. Eye- scanning of events in this region also show a discrepancy, but we were insensitive to two-proton events, the predicted signature of the MEC process.

Watershed reliability, resilience and vulnerability analysis under uncertainty using water quality data.

PubMed

Hoque, Yamen M; Tripathi, Shivam; Hantush, Mohamed M; Govindaraju, Rao S

2012-10-30

A method for assessment of watershed health is developed by employing measures of reliability, resilience and vulnerability (R-R-V) using stream water quality data. Observed water quality data are usually sparse, so that a water quality time-series is often reconstructed using surrogate variables (streamflow). A Bayesian algorithm based on relevance vector machine (RVM) was employed to quantify the error in the reconstructed series, and a probabilistic assessment of watershed status was conducted based on established thresholds for various constituents. As an application example, observed water quality data for several constituents at different monitoring points within the Cedar Creek watershed in north-east Indiana (USA) were utilized. Considering uncertainty in the data for the period 2002-2007, the R-R-V analysis revealed that the Cedar Creek watershed tends to be in compliance with respect to selected pesticides, ammonia and total phosphorus. However, the watershed was found to be prone to violations of sediment standards. Ignoring uncertainty in the water quality time-series led to misleading results especially in the case of sediments. Results indicate that the methods presented in this study may be used for assessing the effects of different stressors over a watershed. The method shows promise as a management tool for assessing watershed health. Copyright © 2012 Elsevier Ltd. All rights reserved.

Evaluation of an analytical methodology using QuEChERS and GC-SQ/MS for the investigation of the level of pesticide residues in Brazilian melons.

PubMed

da Silva Sousa, Jonas; de Castro, Rubens Carius; de Albuquerque Andrade, Gilliane; Lima, Cleidiane Gomes; Lima, Lucélia Kátia; Milhome, Maria Aparecida Liberato; do Nascimento, Ronaldo Ferreira

2013-12-01

A multiresidue method based on the sample preparation by modified QuEChERS and detection by gas chromatography coupled to single quadruple mass spectrometers (GC-SQ/MS) was used for the analysis of 35 multiclass pesticides in melons (Cucumis melo inodorus) produced in Ceara-Brazil. The rates of recovery for pesticides studied were satisfactory (except for the etridiazole), ranging from 85% to 117% with a relative standard deviation (RSD) of less than 15%, at concentrations between 0.05 and 0.20 mg kg(-1). The limit of quantification (LOQ) for most compounds was below the MRLs established in Brazil. The combined relative uncertainty (Uc) and expanded uncertainty (Ue) was determined using repeatability, recovery and calibration curves data for each pesticide. Analysis of commercial melons samples revealed the presence of pesticides bifenthrin and imazalil at levels below the MRLs established by ANVISA, EU and USEPA. Copyright © 2013 Elsevier Ltd. All rights reserved.

The development of a green supply chain dual-objective facility by considering different levels of uncertainty

NASA Astrophysics Data System (ADS)

Khorasani, Sasan Torabzadeh; Almasifard, Maryam

2017-11-01

This paper presents a dual-objective facility programming model for a green supply chain network. The main objectives of the presented model are minimizing overall expenditure and negative environmental impacts of the supply chain. This study contributes to the existing literature by incorporating uncertainty in customer demand, suppliers, production, and casting capacity. An industrial case study is also analyzed to reveal the feasibility of the proposed model and its application. A fuzzy approach which is known as TH is used to solve the suggested dual-objective model. TH approach is integration of a max-min method (LH) and modified version of Werners' approach (MW). The outcome of this study reveals that the presented model can support green supply chain network in different levels of uncertainty. In presented model, cost and negative environmental impacts derived from the supply chain network will increase of higher levels of uncertainty.

Effects of relational uncertainty in heightening national identification and reactive approach motivation of Japanese.

PubMed

Terashima, Yuto; Takai, Jiro

2017-03-23

This study investigated whether relational uncertainty poses uncertainty threat, which causes compensatory behaviours among Japanese. We hypothesised that Japanese, as collectivists, would perceive relational uncertainty to pose uncertainty threat. In two experiments, we manipulated relational uncertainty, and confirmed that participants exhibited compensatory reactions to reduce aversive feelings due to it. In Study 1, we conducted direct comparison between relational uncertainty, independent self-uncertainty and control conditions. The results revealed that participants who were instructed to imagine events pertaining to relational uncertainty heightened national identification as compensation than did participants in the control condition, but independent self-uncertainty did not provoke such effects. In Study 2, we again manipulated relational uncertainty; however, we also manipulated participants' individualism-collectivism cultural orientation through priming, and the analyses yielded a significant interaction effect between these variables. Relational uncertainty evoked reactive approach motivation, a cause for compensatory behaviours, among participants primed with collectivism, but not for individualism. It was concluded that the effect of uncertainty on compensatory behaviour is influenced by cultural priming, and that relational uncertainty is important to Japanese. © 2017 International Union of Psychological Science.

The impact of (n, γ) reaction rate uncertainties of unstable isotopes near N = 50 on the i-process nucleosynthesis in He-shell flash white dwarfs

NASA Astrophysics Data System (ADS)

Denissenkov, Pavel; Perdikakis, Georgios; Herwig, Falk; Schatz, Hendrik; Ritter, Christian; Pignatari, Marco; Jones, Samuel; Nikas, Stylianos; Spyrou, Artemis

2018-05-01

The first-peak s-process elements Rb, Sr, Y and Zr in the post-AGB star Sakurai's object (V4334 Sagittarii) have been proposed to be the result of i-process nucleosynthesis in a post-AGB very-late thermal pulse event. We estimate the nuclear physics uncertainties in the i-process model predictions to determine whether the remaining discrepancies with observations are significant and point to potential issues with the underlying astrophysical model. We find that the dominant source in the nuclear physics uncertainties are predictions of neutron capture rates on unstable neutron rich nuclei, which can have uncertainties of more than a factor 20 in the band of the i-process. We use a Monte Carlo variation of 52 neutron capture rates and a 1D multi-zone post-processing model for the i-process in Sakurai's object to determine the cumulative effect of these uncertainties on the final elemental abundance predictions. We find that the nuclear physics uncertainties are large and comparable to observational errors. Within these uncertainties the model predictions are consistent with observations. A correlation analysis of the results of our MC simulations reveals that the strongest impact on the predicted abundances of Rb, Sr, Y and Zr is made by the uncertainties in the (n, γ) reaction rates of 85Br, 86Br, 87Kr, 88Kr, 89Kr, 89Rb, 89Sr, and 92Sr. This conclusion is supported by a series of multi-zone simulations in which we increased and decreased to their maximum and minimum limits one or two reaction rates per run. We also show that simple and fast one-zone simulations should not be used instead of more realistic multi-zone stellar simulations for nuclear sensitivity and uncertainty studies of convective–reactive processes. Our findings apply more generally to any i-process site with similar neutron exposure, such as rapidly accreting white dwarfs with near-solar metallicities.

Management Choices in an Uncertain Future: Navigating Snow, Precipitation, and Temperature Projections in the Pacific Northwest U.S. to Assess Water Management Alternatives

NASA Astrophysics Data System (ADS)

Luce, C.

2014-12-01

Climate and hydrology models are regularly applied to assess potential changes in water resources and to inform adaptation decisions. An increasingly common question is, "What if we are wrong?" While climate models show substantial agreement on metrics such as pressure, temperature, and wind, they are notoriously uncertain in projecting precipitation change. The response to that uncertainty varies depending on the water management context and the nature of the uncertainty. In the southwestern U.S., large storage reservoirs (relative to annual supply) and general expectations of decreasing precipitation have guided extensive discussion on water management towards uncertainties in annual-scale water balances, precipitation, and evapotranspiration. In contrast, smaller reservoirs and little expectation for change in annual precipitation have focused discussions of Pacific Northwest water management toward shifts in runoff seasonality. The relative certainty of temperature impacts on snowpacks compared to the substantial uncertainty in precipitation has yielded a consistent narrative on earlier snowmelt. This narrative has been reinforced by a perception of essentially the same behavior in the historical record. This perception has led to calls in the political arena for more reservoir storage to replace snowpack storage for water supplies. Recent findings on differences in trends in precipitation at high versus low elevations, however, has recalled the uncertainty in precipitation futures and generated questions about alternative water management strategies. An important question with respect to snowpacks is whether the precipitation changes matter in the context of such substantial projections for temperature change. Here we apply an empirical snowpack model to analyze spatial differences in the uncertainty of snowpack responses to temperature and precipitation forcing across the Pacific Northwest U.S. The analysis reveals a strong geographic gradient in uncertainty of snowpack response to future climate, from the coastal regions, where precipitation uncertainty is relatively inconsequential for snowpack changes, to interior mountains where minor uncertainties in precipitation are on par with expected changes relative to temperature.

Enhancing soil moisture monitoring via cosmic-ray neutron sensing in farmlands by combining field site tests with an uncertainty analysis

NASA Astrophysics Data System (ADS)

Oswald, S. E.; Scheiffele, L. M.; Baroni, G.; Ingwersen, J.; Schrön, M.

2017-12-01

One application of Cosmic-Ray Neutron Sensing (CRNS) is to investigate soil moisture on agricultural fields during the crop season. This fully employs the non-invasive character of CRNS without interference with agricultural practices of the farmland. The changing influence of vegetation on CRNS has to be dealt with as well as spatio-temporal influences, e.g. by irrigation or harvest. Previous work revealed that the CRNS signal on farmland shows complex and non-unique response because of the hydrogen pools in different depths and distances. This creates a challenge for soil moisture estimation and subsequent use for irrigation management or hydrological modelling. Thus, a special aim of our study was to assess the uncertainty of CRNS in cropped fields and to identify underlying causes of uncertainty. We have applied CRNS at two field sites during the growing season that were accompanied by intensive measurements of soil moisture, vegetation parameters, and irrigation events. Sources of uncertainty were identified from the experimental data. A Monte Carlo approach was used to propagate these uncertainties to CRNS soil moisture estimations. In addition, a sensitivity analysis was performed to identify the most important factors explaining this uncertainty. Results showed that CRNS soil moisture compares well to the soil moisture network when the point values were converted to weighted water content with all hydrogen pools included. However, when considered as a stand-alone method to retrieve volumetric soil moisture, the performance decreased. The support volume including its penetration depth showed also a considerable uncertainty, especially in relatively dry soil moisture conditions. Of seven factors analyzed, actual soil moisture profile, bulk density, incoming neutron correction and calibrated parameter N0 were found to play an important role. One possible improvement could be a simple correction factor based on independent data of soil moisture profiles to better account for the sensitivity of the CRNS signal to the upper soil layers. This is an important step to improve the method for validation of remote sensing products or agricultural water management and establish CRNS as an applied monitoring tool on farmland.

Measurement uncertainty associated with chromatic confocal profilometry for 3D surface texture characterization of natural human enamel.

PubMed

Mullan, F; Bartlett, D; Austin, R S

2017-06-01

To investigate the measurement performance of a chromatic confocal profilometer for quantification of surface texture of natural human enamel in vitro. Contributions to the measurement uncertainty from all potential sources of measurement error using a chromatic confocal profilometer and surface metrology software were quantified using a series of surface metrology calibration artifacts and pre-worn enamel samples. The 3D surface texture analysis protocol was optimized across 0.04mm 2 of natural and unpolished enamel undergoing dietary acid erosion (pH 3.2, titratable acidity 41.3mmolOH/L). Flatness deviations due to the x, y stage mechanical movement were the major contribution to the measurement uncertainty; with maximum Sz flatness errors of 0.49μm. Whereas measurement noise; non-linearity's in x, y, z and enamel sample dimensional instability contributed minimal errors. The measurement errors were propagated into an uncertainty budget following a Type B uncertainty evaluation in order to calculate the Standard Combined Uncertainty (u c ), which was ±0.28μm. Statistically significant increases in the median (IQR) roughness (Sa) of the polished samples occurred after 15 (+0.17 (0.13)μm), 30 (+0.12 (0.09)μm) and 45 (+0.18 (0.15)μm) min of erosion (P<0.001 vs. baseline). In contrast, natural unpolished enamel samples revealed a statistically significant decrease in Sa roughness of -0.14 (0.34) μm only after 45min erosion (P<0.05s vs. baseline). The main contribution to measurement uncertainty using chromatic confocal profilometry was from flatness deviations however by optimizing measurement protocols the profilometer successfully characterized surface texture changes in enamel from erosive wear in vitro. Copyright © 2017 The Academy of Dental Materials. All rights reserved.

Uncertainty in action-value estimation affects both action choice and learning rate of the choice behaviors of rats.

PubMed

Funamizu, Akihiro; Ito, Makoto; Doya, Kenji; Kanzaki, Ryohei; Takahashi, Hirokazu

2012-04-01

The estimation of reward outcomes for action candidates is essential for decision making. In this study, we examined whether and how the uncertainty in reward outcome estimation affects the action choice and learning rate. We designed a choice task in which rats selected either the left-poking or right-poking hole and received a reward of a food pellet stochastically. The reward probabilities of the left and right holes were chosen from six settings (high, 100% vs. 66%; mid, 66% vs. 33%; low, 33% vs. 0% for the left vs. right holes, and the opposites) in every 20-549 trials. We used Bayesian Q-learning models to estimate the time course of the probability distribution of action values and tested if they better explain the behaviors of rats than standard Q-learning models that estimate only the mean of action values. Model comparison by cross-validation revealed that a Bayesian Q-learning model with an asymmetric update for reward and non-reward outcomes fit the choice time course of the rats best. In the action-choice equation of the Bayesian Q-learning model, the estimated coefficient for the variance of action value was positive, meaning that rats were uncertainty seeking. Further analysis of the Bayesian Q-learning model suggested that the uncertainty facilitated the effective learning rate. These results suggest that the rats consider uncertainty in action-value estimation and that they have an uncertainty-seeking action policy and uncertainty-dependent modulation of the effective learning rate. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

An uncertainty analysis of the hydrogen source term for a station blackout accident in Sequoyah using MELCOR 1.8.5

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gauntt, Randall O.; Bixler, Nathan E.; Wagner, Kenneth Charles

2014-03-01

A methodology for using the MELCOR code with the Latin Hypercube Sampling method was developed to estimate uncertainty in various predicted quantities such as hydrogen generation or release of fission products under severe accident conditions. In this case, the emphasis was on estimating the range of hydrogen sources in station blackout conditions in the Sequoyah Ice Condenser plant, taking into account uncertainties in the modeled physics known to affect hydrogen generation. The method uses user-specified likelihood distributions for uncertain model parameters, which may include uncertainties of a stochastic nature, to produce a collection of code calculations, or realizations, characterizing themore » range of possible outcomes. Forty MELCOR code realizations of Sequoyah were conducted that included 10 uncertain parameters, producing a range of in-vessel hydrogen quantities. The range of total hydrogen produced was approximately 583kg 131kg. Sensitivity analyses revealed expected trends with respected to the parameters of greatest importance, however, considerable scatter in results when plotted against any of the uncertain parameters was observed, with no parameter manifesting dominant effects on hydrogen generation. It is concluded that, with respect to the physics parameters investigated, in order to further reduce predicted hydrogen uncertainty, it would be necessary to reduce all physics parameter uncertainties similarly, bearing in mind that some parameters are inherently uncertain within a range. It is suspected that some residual uncertainty associated with modeling complex, coupled and synergistic phenomena, is an inherent aspect of complex systems and cannot be reduced to point value estimates. The probabilistic analyses such as the one demonstrated in this work are important to properly characterize response of complex systems such as severe accident progression in nuclear power plants.« less

An improved method to represent DEM uncertainty in glacial lake outburst flood propagation using stochastic simulations

NASA Astrophysics Data System (ADS)

Watson, Cameron S.; Carrivick, Jonathan; Quincey, Duncan

2015-10-01

Modelling glacial lake outburst floods (GLOFs) or 'jökulhlaups', necessarily involves the propagation of large and often stochastic uncertainties throughout the source to impact process chain. Since flood routing is primarily a function of underlying topography, communication of digital elevation model (DEM) uncertainty should accompany such modelling efforts. Here, a new stochastic first-pass assessment technique was evaluated against an existing GIS-based model and an existing 1D hydrodynamic model, using three DEMs with different spatial resolution. The analysis revealed the effect of DEM uncertainty and model choice on several flood parameters and on the prediction of socio-economic impacts. Our new model, which we call MC-LCP (Monte Carlo Least Cost Path) and which is distributed in the supplementary information, demonstrated enhanced 'stability' when compared to the two existing methods, and this 'stability' was independent of DEM choice. The MC-LCP model outputs an uncertainty continuum within its extent, from which relative socio-economic risk can be evaluated. In a comparison of all DEM and model combinations, the Shuttle Radar Topography Mission (SRTM) DEM exhibited fewer artefacts compared to those with the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), and were comparable to those with a finer resolution Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument for Stereo Mapping (ALOS PRISM) derived DEM. Overall, we contend that the variability we find between flood routing model results suggests that consideration of DEM uncertainty and pre-processing methods is important when assessing flow routing and when evaluating potential socio-economic implications of a GLOF event. Incorporation of a stochastic variable provides an illustration of uncertainty that is important when modelling and communicating assessments of an inherently complex process.

A detailed description of the uncertainty analysis for high area ratio rocket nozzle tests at the NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Davidian, Kenneth J.; Dieck, Ronald H.; Chuang, Isaac

1987-01-01

A preliminary uncertainty analysis was performed for the High Area Ratio Rocket Nozzle test program which took place at the altitude test capsule of the Rocket Engine Test Facility at the NASA Lewis Research Center. Results from the study establish the uncertainty of measured and calculated parameters required for the calculation of rocket engine specific impulse. A generalized description of the uncertainty methodology used is provided. Specific equations and a detailed description of the analysis is presented. Verification of the uncertainty analysis model was performed by comparison with results from the experimental program's data reduction code. Final results include an uncertainty for specific impulse of 1.30 percent. The largest contributors to this uncertainty were calibration errors from the test capsule pressure and thrust measurement devices.

A detailed description of the uncertainty analysis for High Area Ratio Rocket Nozzle tests at the NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Davidian, Kenneth J.; Dieck, Ronald H.; Chuang, Isaac

1987-01-01

A preliminary uncertainty analysis has been performed for the High Area Ratio Rocket Nozzle test program which took place at the altitude test capsule of the Rocket Engine Test Facility at the NASA Lewis Research Center. Results from the study establish the uncertainty of measured and calculated parameters required for the calculation of rocket engine specific impulse. A generalized description of the uncertainty methodology used is provided. Specific equations and a detailed description of the analysis are presented. Verification of the uncertainty analysis model was performed by comparison with results from the experimental program's data reduction code. Final results include an uncertainty for specific impulse of 1.30 percent. The largest contributors to this uncertainty were calibration errors from the test capsule pressure and thrust measurement devices.

"Effects of Stress on Decisions Under Uncertainty: A Meta-Analysis": Correction to Starcke and Brand (2016).

PubMed

2016-09-01

Reports an error in "Effects of Stress on Decisions Under Uncertainty: A Meta-Analysis" by Katrin Starcke and Matthias Brand ( Psychological Bulletin , Advanced Online Publication, May 23, 2016, np). It should have been reported that the inverted u-shaped relationship between cortisol stress responses and decision-making performance was only observed in female, but not in male participants as suggested by the study by van den Bos, Harteveld, and Stoop (2009). Corrected versions of the affected sentences are provided. (The following abstract of the original article appeared in record 2016-25465-001.) The purpose of the present meta-analysis was to quantify the effects that stress has on decisions made under uncertainty. We hypothesized that stress increases reward seeking and risk taking through alterations of dopamine firing rates and reduces executive control by hindering optimal prefrontal cortex functioning. In certain decision situations, increased reward seeking and risk taking is dysfunctional, whereas in others, this is not the case. We also assumed that the type of stressor plays a role. In addition, moderating variables are analyzed, such as the hormonal stress response, the time between stress onset and decisions, and the participants' age and gender. We included studies in the meta-analysis that investigated decision making after a laboratory stress-induction versus a control condition (k = 32 datasets, N = 1829 participants). A random-effects model revealed that overall, stress conditions lead to decisions that can be described as more disadvantageous, more reward seeking, and more risk taking than nonstress conditions (d = .17). In those situations in which increased reward seeking and risk taking is disadvantageous, stress had significant effects (d = .26), whereas in other situations, no effects were observed (d = .01). Effects were observed under processive stressors (d = .19), but not under systemic ones (d = .09). Moderation analyses did not reveal any significant results. We concluded that stress deteriorates overall decision-making performance through the mechanisms proposed. The effects differ, depending on the decision situation and the type of stressor, but not on the characteristics of the individuals. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Uncertainty Analysis of NASA Glenn's 8- by 6-Foot Supersonic Wind Tunnel

NASA Technical Reports Server (NTRS)

Stephens, Julia E.; Hubbard, Erin P.; Walter, Joel A.; McElroy, Tyler

2016-01-01

An analysis was performed to determine the measurement uncertainty of the Mach Number of the 8- by 6-foot Supersonic Wind Tunnel at the NASA Glenn Research Center. This paper details the analysis process used, including methods for handling limited data and complicated data correlations. Due to the complexity of the equations used, a Monte Carlo Method was utilized for this uncertainty analysis. A summary of the findings are presented as pertains to understanding what the uncertainties are, how they impact various research tests in the facility, and methods of reducing the uncertainties in the future.

[A correlational study on uncertainty, mastery and appraisal of uncertainty in hospitalized children's mothers].

PubMed

Yoo, Kyung Hee

2007-06-01

This study was conducted to investigate the correlation among uncertainty, mastery and appraisal of uncertainty in hospitalized children's mothers. Self report questionnaires were used to measure the variables. Variables were uncertainty, mastery and appraisal of uncertainty. In data analysis, the SPSSWIN 12.0 program was utilized for descriptive statistics, Pearson's correlation coefficients, and regression analysis. Reliability of the instruments was cronbach's alpha=.84~.94. Mastery negatively correlated with uncertainty(r=-.444, p=.000) and danger appraisal of uncertainty(r=-.514, p=.000). In regression of danger appraisal of uncertainty, uncertainty and mastery were significant predictors explaining 39.9%. Mastery was a significant mediating factor between uncertainty and danger appraisal of uncertainty in hospitalized children's mothers. Therefore, nursing interventions which improve mastery must be developed for hospitalized children's mothers.

Uncertainty Analysis of the NASA Glenn 8x6 Supersonic Wind Tunnel

NASA Technical Reports Server (NTRS)

Stephens, Julia; Hubbard, Erin; Walter, Joel; McElroy, Tyler

2016-01-01

This paper presents methods and results of a detailed measurement uncertainty analysis that was performed for the 8- by 6-foot Supersonic Wind Tunnel located at the NASA Glenn Research Center. The statistical methods and engineering judgments used to estimate elemental uncertainties are described. The Monte Carlo method of propagating uncertainty was selected to determine the uncertainty of calculated variables of interest. A detailed description of the Monte Carlo method as applied for this analysis is provided. Detailed uncertainty results for the uncertainty in average free stream Mach number as well as other variables of interest are provided. All results are presented as random (variation in observed values about a true value), systematic (potential offset between observed and true value), and total (random and systematic combined) uncertainty. The largest sources contributing to uncertainty are determined and potential improvement opportunities for the facility are investigated.

Development of a special-purpose test surface guided by uncertainty analysis - Introduction of a new uncertainty analysis step

NASA Technical Reports Server (NTRS)

Wang, T.; Simon, T. W.

1988-01-01

Development of a recent experimental program to investigate the effects of streamwise curvature on boundary layer transition required making a bendable, heated and instrumented test wall, a rather nonconventional surface. The present paper describes this surface, the design choices made in its development and how uncertainty analysis was used, beginning early in the test program, to make such design choices. Published uncertainty analysis techniques were found to be of great value; but, it became clear that another step, one herein called the pre-test analysis, would aid the program development. Finally, it is shown how the uncertainty analysis was used to determine whether the test surface was qualified for service.

Uncertainty analysis of hydrological modeling in a tropical area using different algorithms

NASA Astrophysics Data System (ADS)

Rafiei Emam, Ammar; Kappas, Martin; Fassnacht, Steven; Linh, Nguyen Hoang Khanh

2018-01-01

Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and Rfactor, coefficient of determination (R 2), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor>0.83, R-factor <0.56 and R 2>0.91, NSE>0.89, and 0.18

Distinction between Externally vs. Internally Guided Decision-Making: Operational Differences, Meta-Analytical Comparisons and Their Theoretical Implications

PubMed Central

Nakao, Takashi; Ohira, Hideki; Northoff, Georg

2012-01-01

Most experimental studies of decision-making have specifically examined situations in which a single less-predictable correct answer exists (externally guided decision-making under uncertainty). Along with such externally guided decision-making, there are instances of decision-making in which no correct answer based on external circumstances is available for the subject (internally guided decision-making). Such decisions are usually made in the context of moral decision-making as well as in preference judgment, where the answer depends on the subject’s own, i.e., internal, preferences rather than on external, i.e., circumstantial, criteria. The neuronal and psychological mechanisms that allow guidance of decisions based on more internally oriented criteria in the absence of external ones remain unclear. This study was undertaken to compare decision-making of these two kinds empirically and theoretically. First, we reviewed studies of decision-making to clarify experimental–operational differences between externally guided and internally guided decision-making. Second, using multi-level kernel density analysis, a whole-brain-based quantitative meta-analysis of neuroimaging studies was performed. Our meta-analysis revealed that the neural network used predominantly for internally guided decision-making differs from that for externally guided decision-making under uncertainty. This result suggests that studying only externally guided decision-making under uncertainty is insufficient to account for decision-making processes in the brain. Finally, based on the review and results of the meta-analysis, we discuss the differences and relations between decision-making of these two types in terms of their operational, neuronal, and theoretical characteristics. PMID:22403525

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.

PubMed

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

Assessing and reporting uncertainties in dietary exposure analysis: Mapping of uncertainties in a tiered approach.

PubMed

Kettler, Susanne; Kennedy, Marc; McNamara, Cronan; Oberdörfer, Regina; O'Mahony, Cian; Schnabel, Jürgen; Smith, Benjamin; Sprong, Corinne; Faludi, Roland; Tennant, David

2015-08-01

Uncertainty analysis is an important component of dietary exposure assessments in order to understand correctly the strength and limits of its results. Often, standard screening procedures are applied in a first step which results in conservative estimates. If through those screening procedures a potential exceedance of health-based guidance values is indicated, within the tiered approach more refined models are applied. However, the sources and types of uncertainties in deterministic and probabilistic models can vary or differ. A key objective of this work has been the mapping of different sources and types of uncertainties to better understand how to best use uncertainty analysis to generate more realistic comprehension of dietary exposure. In dietary exposure assessments, uncertainties can be introduced by knowledge gaps about the exposure scenario, parameter and the model itself. With this mapping, general and model-independent uncertainties have been identified and described, as well as those which can be introduced and influenced by the specific model during the tiered approach. This analysis identifies that there are general uncertainties common to point estimates (screening or deterministic methods) and probabilistic exposure assessment methods. To provide further clarity, general sources of uncertainty affecting many dietary exposure assessments should be separated from model-specific uncertainties. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Uncertainties and understanding of experimental and theoretical results regarding reactions forming heavy and superheavy nuclei

NASA Astrophysics Data System (ADS)

Giardina, G.; Mandaglio, G.; Nasirov, A. K.; Anastasi, A.; Curciarello, F.; Fazio, G.

2018-02-01

Experimental and theoretical results of the PCN fusion probability of reactants in the entrance channel and the Wsur survival probability against fission at deexcitation of the compound nucleus formed in heavy-ion collisions are discussed. The theoretical results for a set of nuclear reactions leading to formation of compound nuclei (CNs) with the charge number Z = 102- 122 reveal a strong sensitivity of PCN to the characteristics of colliding nuclei in the entrance channel, dynamics of the reaction mechanism, and excitation energy of the system. We discuss the validity of assumptions and procedures for analysis of experimental data, and also the limits of validity of theoretical results obtained by the use of phenomenological models. The comparison of results obtained in many investigated reactions reveals serious limits of validity of the data analysis and calculation procedures.

AN OVERVIEW OF THE UNCERTAINTY ANALYSIS, SENSITIVITY ANALYSIS, AND PARAMETER ESTIMATION (UA/SA/PE) API AND HOW TO IMPLEMENT IT

EPA Science Inventory

The Application Programming Interface (API) for Uncertainty Analysis, Sensitivity Analysis, and
Parameter Estimation (UA/SA/PE API) (also known as Calibration, Optimization and Sensitivity and Uncertainty (CUSO)) was developed in a joint effort between several members of both ...

Durability reliability analysis for corroding concrete structures under uncertainty

NASA Astrophysics Data System (ADS)

Zhang, Hao

2018-02-01

This paper presents a durability reliability analysis of reinforced concrete structures subject to the action of marine chloride. The focus is to provide insight into the role of epistemic uncertainties on durability reliability. The corrosion model involves a number of variables whose probabilistic characteristics cannot be fully determined due to the limited availability of supporting data. All sources of uncertainty, both aleatory and epistemic, should be included in the reliability analysis. Two methods are available to formulate the epistemic uncertainty: the imprecise probability-based method and the purely probabilistic method in which the epistemic uncertainties are modeled as random variables. The paper illustrates how the epistemic uncertainties are modeled and propagated in the two methods, and shows how epistemic uncertainties govern the durability reliability.

Linked Sensitivity Analysis, Calibration, and Uncertainty Analysis Using a System Dynamics Model for Stroke Comparative Effectiveness Research.

PubMed

Tian, Yuan; Hassmiller Lich, Kristen; Osgood, Nathaniel D; Eom, Kirsten; Matchar, David B

2016-11-01

As health services researchers and decision makers tackle more difficult problems using simulation models, the number of parameters and the corresponding degree of uncertainty have increased. This often results in reduced confidence in such complex models to guide decision making. To demonstrate a systematic approach of linked sensitivity analysis, calibration, and uncertainty analysis to improve confidence in complex models. Four techniques were integrated and applied to a System Dynamics stroke model of US veterans, which was developed to inform systemwide intervention and research planning: Morris method (sensitivity analysis), multistart Powell hill-climbing algorithm and generalized likelihood uncertainty estimation (calibration), and Monte Carlo simulation (uncertainty analysis). Of 60 uncertain parameters, sensitivity analysis identified 29 needing calibration, 7 that did not need calibration but significantly influenced key stroke outcomes, and 24 not influential to calibration or stroke outcomes that were fixed at their best guess values. One thousand alternative well-calibrated baselines were obtained to reflect calibration uncertainty and brought into uncertainty analysis. The initial stroke incidence rate among veterans was identified as the most influential uncertain parameter, for which further data should be collected. That said, accounting for current uncertainty, the analysis of 15 distinct prevention and treatment interventions provided a robust conclusion that hypertension control for all veterans would yield the largest gain in quality-adjusted life years. For complex health care models, a mixed approach was applied to examine the uncertainty surrounding key stroke outcomes and the robustness of conclusions. We demonstrate that this rigorous approach can be practical and advocate for such analysis to promote understanding of the limits of certainty in applying models to current decisions and to guide future data collection. © The Author(s) 2016.

Uncertainty in monitoring E. coli concentrations in streams and stormwater runoff

NASA Astrophysics Data System (ADS)

Harmel, R. D.; Hathaway, J. M.; Wagner, K. L.; Wolfe, J. E.; Karthikeyan, R.; Francesconi, W.; McCarthy, D. T.

2016-03-01

Microbial contamination of surface waters, a substantial public health concern throughout the world, is typically identified by fecal indicator bacteria such as Escherichia coli. Thus, monitoring E. coli concentrations is critical to evaluate current conditions, determine restoration effectiveness, and inform model development and calibration. An often overlooked component of these monitoring and modeling activities is understanding the inherent random and systematic uncertainty present in measured data. In this research, a review and subsequent analysis was performed to identify, document, and analyze measurement uncertainty of E. coli data collected in stream flow and stormwater runoff as individual discrete samples or throughout a single runoff event. Data on the uncertainty contributed by sample collection, sample preservation/storage, and laboratory analysis in measured E. coli concentrations were compiled and analyzed, and differences in sampling method and data quality scenarios were compared. The analysis showed that: (1) manual integrated sampling produced the lowest random and systematic uncertainty in individual samples, but automated sampling typically produced the lowest uncertainty when sampling throughout runoff events; (2) sample collection procedures often contributed the highest amount of uncertainty, although laboratory analysis introduced substantial random uncertainty and preservation/storage introduced substantial systematic uncertainty under some scenarios; and (3) the uncertainty in measured E. coli concentrations was greater than that of sediment and nutrients, but the difference was not as great as may be assumed. This comprehensive analysis of uncertainty in E. coli concentrations measured in streamflow and runoff should provide valuable insight for designing E. coli monitoring projects, reducing uncertainty in quality assurance efforts, regulatory and policy decision making, and fate and transport modeling.

Quantitative identification of nitrate pollution sources and uncertainty analysis based on dual isotope approach in an agricultural watershed.

PubMed

Ji, Xiaoliang; Xie, Runting; Hao, Yun; Lu, Jun

2017-10-01

Quantitative identification of nitrate (NO 3 - -N) sources is critical to the control of nonpoint source nitrogen pollution in an agricultural watershed. Combined with water quality monitoring, we adopted the environmental isotope (δD-H 2 O, δ 18 O-H 2 O, δ 15 N-NO 3 - , and δ 18 O-NO 3 - ) analysis and the Markov Chain Monte Carlo (MCMC) mixing model to determine the proportions of riverine NO 3 - -N inputs from four potential NO 3 - -N sources, namely, atmospheric deposition (AD), chemical nitrogen fertilizer (NF), soil nitrogen (SN), and manure and sewage (M&S), in the ChangLe River watershed of eastern China. Results showed that NO 3 - -N was the main form of nitrogen in this watershed, accounting for approximately 74% of the total nitrogen concentration. A strong hydraulic interaction existed between the surface and groundwater for NO 3 - -N pollution. The variations of the isotopic composition in NO 3 - -N suggested that microbial nitrification was the dominant nitrogen transformation process in surface water, whereas significant denitrification was observed in groundwater. MCMC mixing model outputs revealed that M&S was the predominant contributor to riverine NO 3 - -N pollution (contributing 41.8% on average), followed by SN (34.0%), NF (21.9%), and AD (2.3%) sources. Finally, we constructed an uncertainty index, UI 90 , to quantitatively characterize the uncertainties inherent in NO 3 - -N source apportionment and discussed the reasons behind the uncertainties. Copyright © 2017 Elsevier Ltd. All rights reserved.

Treatment of uncertainties in atmospheric chemical systems: A combined modeling and experimental approach

NASA Astrophysics Data System (ADS)

Pun, Betty Kong-Ling

1998-12-01

Uncertainty is endemic in modeling. This thesis is a two- phase program to understand the uncertainties in urban air pollution model predictions and in field data used to validate them. Part I demonstrates how to improve atmospheric models by analyzing the uncertainties in these models and using the results to guide new experimentation endeavors. Part II presents an experiment designed to characterize atmospheric fluctuations, which have significant implications towards the model validation process. A systematic study was undertaken to investigate the effects of uncertainties in the SAPRC mechanism for gas- phase chemistry in polluted atmospheres. The uncertainties of more than 500 parameters were compiled, including reaction rate constants, product coefficients, organic composition, and initial conditions. Uncertainty propagation using the Deterministic Equivalent Modeling Method (DEMM) revealed that the uncertainties in ozone predictions can be up to 45% based on these parametric uncertainties. The key parameters found to dominate the uncertainties of the predictions include photolysis rates of NO2, O3, and formaldehyde; the rate constant for nitric acid formation; and initial amounts of NOx and VOC. Similar uncertainty analysis procedures applied to two other mechanisms used in regional air quality models led to the conclusion that in the presence of parametric uncertainties, the mechanisms cannot be discriminated. Research efforts should focus on reducing parametric uncertainties in photolysis rates, reaction rate constants, and source terms. A new tunable diode laser (TDL) infrared spectrometer was designed and constructed to measure multiple pollutants simultaneously in the same ambient air parcels. The sensitivities of the one hertz measurements were 2 ppb for ozone, 1 ppb for NO, and 0.5 ppb for NO2. Meteorological data were also collected for wind, temperature, and UV intensity. The field data showed clear correlations between ozone, NO, and NO2 in the one-second time scale. Fluctuations in pollutant concentrations were found to be extremely dependent on meteorological conditions. Deposition fluxes calculated using the Eddy Correlation technique were found to be small on concrete surfaces. These high time-resolution measurements were used to develop an understanding of the variability in atmospheric measurements, which would be useful in determining the acceptable discrepancy of model and observations. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

An integrated bi-level optimization model for air quality management of Beijing's energy system under uncertainty.

PubMed

Jin, S W; Li, Y P; Nie, S

2018-05-15

In this study, an interval chance-constrained bi-level programming (ICBP) method is developed for air quality management of municipal energy system under uncertainty. ICBP can deal with uncertainties presented as interval values and probability distributions as well as examine the risk of violating constraints. Besides, a leader-follower decision strategy is incorporated into the optimization process where two decision makers with different goals and preferences are involved. To solve the proposed model, a bi-level interactive algorithm based on satisfactory degree is introduced into the decision-making processes. Then, an ICBP based energy and environmental systems (ICBP-EES) model is formulated for Beijing, in which air quality index (AQI) is used for evaluating the integrated air quality of multiple pollutants. Result analysis can help different stakeholders adjust their tolerances to achieve the overall satisfaction of EES planning for the study city. Results reveal that natural gas is the main source for electricity-generation and heating that could lead to a potentially increment of imported energy for Beijing in future. Results also disclose that PM 10 is the major contributor to AQI. These findings can help decision makers to identify desired alternatives for EES planning and provide useful information for regional air quality management under uncertainty. Copyright © 2018 Elsevier B.V. All rights reserved.

Parent illness appraisals, parent adjustment, and parent-reported child quality of life in pediatric cancer.

PubMed

Mullins, Larry L; Cushing, Christopher C; Suorsa, Kristina I; Tackett, Alayna P; Molzon, Elizabeth S; Mayes, Sunnye; McNall-Knapp, Rene; Mullins, Alexandria J; Gamwell, Kaitlyn L; Chaney, John M

2016-08-01

Psychosocial distress is a salient construct experienced by families of children with newly diagnosed cancer, but little is known about parental appraisal of the child's illness and the subsequent impact this may have on child and parent functioning. The goal of the present study was to examine the interrelationships among multiple parent illness appraisals, parent adjustment outcomes, and parent-reported child quality of life in parents of children diagnosed with cancer. Parents completed measures of illness appraisal (illness uncertainty and attitude toward illness), parent adjustment (general distress, posttraumatic stress, parenting stress), and child quality of life (general and cancer-related). Path analysis revealed direct effects for parent illness uncertainty and illness attitudes on all 3 measures of parent adjustment. Illness uncertainty, but not illness attitudes, demonstrated a direct effect on parent-reported child general quality of life; parenting stress had direct effects on general and cancer-related quality of life. Exploratory analyses indicated that parent illness uncertainty and illness attitudes conferred indirect effects on parent-reported general and cancer-related quality of life through parenting stress. Negative parent illness appraisals appear to have adverse impacts on parents' psychosocial functioning and have implications for the well-being of their child with cancer.

Therapist in distress: team-supervision of social workers and family therapists who work and live under political uncertainty.

PubMed

Shamai, M

1998-01-01

This article describes and analyzes a 2-year supervision process with social workers and family therapists who live and work under conditions of uncertainty on the West Bank. The systemic orientation used in this specific approach to supervision emphasizes the double role of the therapist: one as part of the therapeutic system, and the second as a member of the same community that is living in political uncertainty. The analysis revealed that a long-term supervision process, in which the supervisor encouraged a containing context, was meaningful to the group. As a result of this secure atmosphere, the group was ready to talk about painful issues like loss as the result of war and terrorist attacks, loss as a result of immigration, and loss of ideals. Furthermore, the members of the group were ready to confront the possibility of relocation and their role in such a situation. The techniques used in the process, such as narrative and metaphors, were implemented by the members in their daily professional interventions. The flexibility between working on regular professional issues and issues related to stress and uncertainty seemed useful to the supervision, as well as the political dialogue that was created between the supervisor and the group.

Hydrological simulation and uncertainty analysis using the improved TOPMODEL in the arid Manas River basin, China.

PubMed

Xue, Lianqing; Yang, Fan; Yang, Changbing; Wei, Guanghui; Li, Wenqian; He, Xinlin

2018-01-11

Understanding the mechanism of complicated hydrological processes is important for sustainable management of water resources in an arid area. This paper carried out the simulations of water movement for the Manas River Basin (MRB) using the improved semi-distributed Topographic hydrologic model (TOPMODEL) with a snowmelt model and topographic index algorithm. A new algorithm is proposed to calculate the curve of topographic index using internal tangent circle on a conical surface. Based on the traditional model, the improved indicator of temperature considered solar radiation is used to calculate the amount of snowmelt. The uncertainty of parameters for the TOPMODEL model was analyzed using the generalized likelihood uncertainty estimation (GLUE) method. The proposed model shows that the distribution of the topographic index is concentrated in high mountains, and the accuracy of runoff simulation has certain enhancement by considering radiation. Our results revealed that the performance of the improved TOPMODEL is acceptable and comparable to runoff simulation in the MRB. The uncertainty of the simulations resulted from the parameters and structures of model, climatic and anthropogenic factors. This study is expected to serve as a valuable complement for widely application of TOPMODEL and identify the mechanism of hydrological processes in arid area.

Effects of nonspatial selective and divided visual attention on fMRI BOLD responses.

PubMed

Weerda, Riklef; Vallines, Ignacio; Thomas, James P; Rutschmann, Roland M; Greenlee, Mark W

2006-09-01

Using an uncertainty paradigm and functional magnetic resonance imaging (fMRI) we studied the effect of nonspatial selective and divided visual attention on the activity of specific areas of human extrastriate visual cortex. The stimuli were single ovals that differed from an implicit standard oval in either colour or width. The subjects' task was to classify the current stimulus as one of two possible alternatives per stimulus dimension. Three different experimental conditions were conducted: "colour-certainty", "shape-certainty" and "uncertainty". In all experimental conditions, the stimulus differed in only one stimulus dimension per trial. In the two certainty conditions, the subjects knew in advance which dimension this would be. During the uncertainty condition they had no such previous knowledge and had to monitor both dimensions simultaneously. Statistical analysis of the fMRI data (with SPM2) revealed a modest effect of the attended stimulus dimension on the neural activity in colour sensitive area V4 (more activity during attention to colour) and in shape sensitive area LOC (more activity during attention to shape). Furthermore, cortical areas known to be related to attention and working memory processes (e.g., lateral prefrontal and posterior parietal cortex) exhibit higher activity during the condition of divided attention ("uncertainty") than during that of selective attention ("certainty").

Uncertainties related to the representation of momentum transport in shallow convection

NASA Astrophysics Data System (ADS)

Schlemmer, Linda; Bechtold, Peter; Sandu, Irina; Ahlgrimm, Maike

2017-04-01

The vertical transport of horizontal momentum by convection has an important impact on the general circulation of the atmosphere as well as on the life cycle and track of cyclones. So far convective momentum transport (CMT) has mostly been studied for deep convection, whereas little is known about its characteristics and importance in shallow convection. In this study CMT by shallow convection is investigated by analyzing both data from large-eddy simulations (LES) and simulations performed with the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). In addition, the central terms underlying the bulk mass-flux parametrization of CMT are evaluated offline. Further, the uncertainties related to the representation of CMT are explored by running the stochastically perturbed parametrizations (SPP) approach of the IFS. The analyzed cases exhibit shallow convective clouds developing within considerable low-level wind shear. Analysis of the momentum fluxes in the LES data reveals significant momentum transport by the convection in both cases, which is directed down-gradient despite substantial organization of the cloud field. A detailed inspection of the convection parametrization reveals a very good representation of the entrainment and detrainment rates and an appropriate representation of the convective mass and momentum fluxes. To determine the correct values of mass-flux and in-cloud momentum at the cloud base in the parametrization yet remains challenging. The spread in convection-related quantities generated by the SPP is reasonable and addresses many of the identified uncertainties.

Seven years of aerosol scattering hygroscopic growth measurements from SGP: Factors influencing water uptake: Aerosol Scattering Hygroscopic Growth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jefferson, A.; Hageman, D.; Morrow, H.

Long-term measurements of changes in the aerosol scattering coefficient hygroscopic growth at the U.S. Department of Energy Southern Great Plains site provide information on the seasonal as well as size and chemical dependence of aerosol hygroscopic growth. Annual average sub 10 um fRH values (the ratio of aerosol scattering at 85%/40% RH) were 1.75 and 1.87 for the gamma and kappa fit algorithms, respectively. The study found higher growth rates in the winter and spring seasons that correlated with high aerosol nitrate mass fraction. FRH, exhibited strong, but differing correlations with the scattering Ångström exponent and backscatter fraction, two opticalmore » size-dependent parameters. The aerosol organic fraction had a strong influence, with fRH decreasing with increases in the organic mass fraction and absorption Ångström exponent and increasing with the aerosol single scatter albedo. Uncertainty analysis if the fit algorithms revealed high uncertainty at low scattering coefficients and slight increases in uncertainty at high RH and fit parameters values.« less

Numerical modelling of glacial lake outburst floods using physically based dam-breach models

NASA Astrophysics Data System (ADS)

Westoby, M. J.; Brasington, J.; Glasser, N. F.; Hambrey, M. J.; Reynolds, J. M.; Hassan, M. A. A. M.; Lowe, A.

2015-03-01

The instability of moraine-dammed proglacial lakes creates the potential for catastrophic glacial lake outburst floods (GLOFs) in high-mountain regions. In this research, we use a unique combination of numerical dam-breach and two-dimensional hydrodynamic modelling, employed within a generalised likelihood uncertainty estimation (GLUE) framework, to quantify predictive uncertainty in model outputs associated with a reconstruction of the Dig Tsho failure in Nepal. Monte Carlo analysis was used to sample the model parameter space, and morphological descriptors of the moraine breach were used to evaluate model performance. Multiple breach scenarios were produced by differing parameter ensembles associated with a range of breach initiation mechanisms, including overtopping waves and mechanical failure of the dam face. The material roughness coefficient was found to exert a dominant influence over model performance. The downstream routing of scenario-specific breach hydrographs revealed significant differences in the timing and extent of inundation. A GLUE-based methodology for constructing probabilistic maps of inundation extent, flow depth, and hazard is presented and provides a useful tool for communicating uncertainty in GLOF hazard assessment.

An uncertainty analysis of wildfire modeling [Chapter 13

Treesearch

Karin Riley; Matthew Thompson

2017-01-01

Before fire models can be understood, evaluated, and effectively applied to support decision making, model-based uncertainties must be analyzed. In this chapter, we identify and classify sources of uncertainty using an established analytical framework, and summarize results graphically in an uncertainty matrix. Our analysis facilitates characterization of the...

Analytic uncertainty and sensitivity analysis of models with input correlations

NASA Astrophysics Data System (ADS)

Zhu, Yueying; Wang, Qiuping A.; Li, Wei; Cai, Xu

2018-03-01

Probabilistic uncertainty analysis is a common means of evaluating mathematical models. In mathematical modeling, the uncertainty in input variables is specified through distribution laws. Its contribution to the uncertainty in model response is usually analyzed by assuming that input variables are independent of each other. However, correlated parameters are often happened in practical applications. In the present paper, an analytic method is built for the uncertainty and sensitivity analysis of models in the presence of input correlations. With the method, it is straightforward to identify the importance of the independence and correlations of input variables in determining the model response. This allows one to decide whether or not the input correlations should be considered in practice. Numerical examples suggest the effectiveness and validation of our analytic method in the analysis of general models. A practical application of the method is also proposed to the uncertainty and sensitivity analysis of a deterministic HIV model.

Seniors' uncertainty management of direct-to-consumer prescription drug advertising usefulness.

PubMed

DeLorme, Denise E; Huh, Jisu

2009-09-01

This study provides insight into seniors' perceptions of and responses to direct-to-consumer prescription drug advertising (DTCA) usefulness, examines support for DTCA regulation as a type of uncertainty management, and extends and gives empirical voice to previous survey results through methodological triangulation. In-depth interview findings revealed that, for most informants, DTCA usefulness was uncertain and this uncertainty stemmed from 4 sources. The majority had negative responses to DTCA uncertainty and relied on 2 uncertainty-management strategies: information seeking from physicians, and inferences of and support for some government regulation of DTCA. Overall, the findings demonstrate the viability of uncertainty management theory (Brashers, 2001, 2007) for mass-mediated health communication, specifically DTCA. The article concludes with practical implications and research recommendations.

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

NASA Astrophysics Data System (ADS)

Bolster, Carl H.; Vadas, Peter A.; Boykin, Debbie

2016-08-01

Phosphorous (P) fate and transport models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. Because all models are simplifications of complex systems, there will exist an inherent amount of uncertainty associated with their predictions. It is therefore important that efforts be directed at identifying, quantifying, and communicating the different sources of model uncertainties. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model. Our analysis included calculating parameter uncertainties and confidence and prediction intervals for five internal regression equations in APLE. We also estimated uncertainties of the model input variables based on values reported in the literature. We then predicted P loss for a suite of fields under different management and climatic conditions while accounting for uncertainties in the model parameters and inputs and compared the relative contributions of these two sources of uncertainty to the overall uncertainty associated with predictions of P loss. Both the overall magnitude of the prediction uncertainties and the relative contributions of the two sources of uncertainty varied depending on management practices and field characteristics. This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway. Inspection of the uncertainties in the five regression equations brought attention to a previously unrecognized limitation with the equation used to partition surface-applied fertilizer P between leaching and runoff losses. As a result, an alternate equation was identified that provided similar predictions with much less uncertainty. Our results demonstrate how a thorough uncertainty and model residual analysis can be used to identify limitations with a model. Such insight can then be used to guide future data collection and model development and evaluation efforts.

A structured analysis of uncertainty surrounding modeled impacts of groundwater-extraction rules

NASA Astrophysics Data System (ADS)

Guillaume, Joseph H. A.; Qureshi, M. Ejaz; Jakeman, Anthony J.

2012-08-01

Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models constructed to represent them. If not addressed, this uncertainty may be used to challenge the legitimacy of the models and decisions made using them. In this context, a preliminary uncertainty analysis was conducted of a dynamic coupled economic-groundwater model aimed at assessing groundwater extraction rules. The analysis demonstrates how a variety of uncertainties in such a model can be addressed. A number of methods are used including propagation of scenarios and bounds on parameters, multiple models, block bootstrap time-series sampling and robust linear regression for model calibration. These methods are described within the context of a theoretical uncertainty management framework, using a set of fundamental uncertainty management tasks and an uncertainty typology.

Uncertainty analysis for low-level radioactive waste disposal performance assessment at Oak Ridge National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, D.W.; Yambert, M.W.; Kocher, D.C.

1994-12-31

A performance assessment of the operating Solid Waste Storage Area 6 (SWSA 6) facility for the disposal of low-level radioactive waste at the Oak Ridge National Laboratory has been prepared to provide the technical basis for demonstrating compliance with the performance objectives of DOE Order 5820.2A, Chapter 111.2 An analysis of the uncertainty incorporated into the assessment was performed which addressed the quantitative uncertainty in the data used by the models, the subjective uncertainty associated with the models used for assessing performance of the disposal facility and site, and the uncertainty in the models used for estimating dose and humanmore » exposure. The results of the uncertainty analysis were used to interpret results and to formulate conclusions about the performance assessment. This paper discusses the approach taken in analyzing the uncertainty in the performance assessment and the role of uncertainty in performance assessment.« less

Numerical Uncertainty Quantification for Radiation Analysis Tools

NASA Technical Reports Server (NTRS)

Anderson, Brooke; Blattnig, Steve; Clowdsley, Martha

2007-01-01

Recently a new emphasis has been placed on engineering applications of space radiation analyses and thus a systematic effort of Verification, Validation and Uncertainty Quantification (VV&UQ) of the tools commonly used for radiation analysis for vehicle design and mission planning has begun. There are two sources of uncertainty in geometric discretization addressed in this paper that need to be quantified in order to understand the total uncertainty in estimating space radiation exposures. One source of uncertainty is in ray tracing, as the number of rays increase the associated uncertainty decreases, but the computational expense increases. Thus, a cost benefit analysis optimizing computational time versus uncertainty is needed and is addressed in this paper. The second source of uncertainty results from the interpolation over the dose vs. depth curves that is needed to determine the radiation exposure. The question, then, is what is the number of thicknesses that is needed to get an accurate result. So convergence testing is performed to quantify the uncertainty associated with interpolating over different shield thickness spatial grids.

Target Uncertainty Mediates Sensorimotor Error Correction

PubMed Central

Vijayakumar, Sethu; Wolpert, Daniel M.

2017-01-01

Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks that explain how humans solve these problems are Bayesian estimation and stochastic optimal feedback control. Here we examine the interaction between estimation and control by asking if uncertainty in estimates affects how subjects correct for errors that may arise during the movement. Unbeknownst to participants, we randomly shifted the visual feedback of their finger position as they reached to indicate the center of mass of an object. Even though participants were given ample time to compensate for this perturbation, they only fully corrected for the induced error on trials with low uncertainty about center of mass, with correction only partial in trials involving more uncertainty. The analysis of subjects’ scores revealed that participants corrected for errors just enough to avoid significant decrease in their overall scores, in agreement with the minimal intervention principle of optimal feedback control. We explain this behavior with a term in the loss function that accounts for the additional effort of adjusting one’s response. By suggesting that subjects’ decision uncertainty, as reflected in their posterior distribution, is a major factor in determining how their sensorimotor system responds to error, our findings support theoretical models in which the decision making and control processes are fully integrated. PMID:28129323

Target Uncertainty Mediates Sensorimotor Error Correction.

PubMed

Acerbi, Luigi; Vijayakumar, Sethu; Wolpert, Daniel M

2017-01-01

Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks that explain how humans solve these problems are Bayesian estimation and stochastic optimal feedback control. Here we examine the interaction between estimation and control by asking if uncertainty in estimates affects how subjects correct for errors that may arise during the movement. Unbeknownst to participants, we randomly shifted the visual feedback of their finger position as they reached to indicate the center of mass of an object. Even though participants were given ample time to compensate for this perturbation, they only fully corrected for the induced error on trials with low uncertainty about center of mass, with correction only partial in trials involving more uncertainty. The analysis of subjects' scores revealed that participants corrected for errors just enough to avoid significant decrease in their overall scores, in agreement with the minimal intervention principle of optimal feedback control. We explain this behavior with a term in the loss function that accounts for the additional effort of adjusting one's response. By suggesting that subjects' decision uncertainty, as reflected in their posterior distribution, is a major factor in determining how their sensorimotor system responds to error, our findings support theoretical models in which the decision making and control processes are fully integrated.

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

NASA Astrophysics Data System (ADS)

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.

Sensitivity analysis in economic evaluation: an audit of NICE current practice and a review of its use and value in decision-making.

PubMed

Andronis, L; Barton, P; Bryan, S

2009-06-01

To determine how we define good practice in sensitivity analysis in general and probabilistic sensitivity analysis (PSA) in particular, and to what extent it has been adhered to in the independent economic evaluations undertaken for the National Institute for Health and Clinical Excellence (NICE) over recent years; to establish what policy impact sensitivity analysis has in the context of NICE, and policy-makers' views on sensitivity analysis and uncertainty, and what use is made of sensitivity analysis in policy decision-making. Three major electronic databases, MEDLINE, EMBASE and the NHS Economic Evaluation Database, were searched from inception to February 2008. The meaning of 'good practice' in the broad area of sensitivity analysis was explored through a review of the literature. An audit was undertaken of the 15 most recent NICE multiple technology appraisal judgements and their related reports to assess how sensitivity analysis has been undertaken by independent academic teams for NICE. A review of the policy and guidance documents issued by NICE aimed to assess the policy impact of the sensitivity analysis and the PSA in particular. Qualitative interview data from NICE Technology Appraisal Committee members, collected as part of an earlier study, were also analysed to assess the value attached to the sensitivity analysis components of the economic analyses conducted for NICE. All forms of sensitivity analysis, notably both deterministic and probabilistic approaches, have their supporters and their detractors. Practice in relation to univariate sensitivity analysis is highly variable, with considerable lack of clarity in relation to the methods used and the basis of the ranges employed. In relation to PSA, there is a high level of variability in the form of distribution used for similar parameters, and the justification for such choices is rarely given. Virtually all analyses failed to consider correlations within the PSA, and this is an area of concern. Uncertainty is considered explicitly in the process of arriving at a decision by the NICE Technology Appraisal Committee, and a correlation between high levels of uncertainty and negative decisions was indicated. The findings suggest considerable value in deterministic sensitivity analysis. Such analyses serve to highlight which model parameters are critical to driving a decision. Strong support was expressed for PSA, principally because it provides an indication of the parameter uncertainty around the incremental cost-effectiveness ratio. The review and the policy impact assessment focused exclusively on documentary evidence, excluding other sources that might have revealed further insights on this issue. In seeking to address parameter uncertainty, both deterministic and probabilistic sensitivity analyses should be used. It is evident that some cost-effectiveness work, especially around the sensitivity analysis components, represents a challenge in making it accessible to those making decisions. This speaks to the training agenda for those sitting on such decision-making bodies, and to the importance of clear presentation of analyses by the academic community.

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

NASA Astrophysics Data System (ADS)

Han, Feng; Zheng, Yi

2018-06-01

Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.

Quantitative Analysis of Uncertainty in Medical Reporting: Creating a Standardized and Objective Methodology.

PubMed

Reiner, Bruce I

2018-04-01

Uncertainty in text-based medical reports has long been recognized as problematic, frequently resulting in misunderstanding and miscommunication. One strategy for addressing the negative clinical ramifications of report uncertainty would be the creation of a standardized methodology for characterizing and quantifying uncertainty language, which could provide both the report author and reader with context related to the perceived level of diagnostic confidence and accuracy. A number of computerized strategies could be employed in the creation of this analysis including string search, natural language processing and understanding, histogram analysis, topic modeling, and machine learning. The derived uncertainty data offers the potential to objectively analyze report uncertainty in real time and correlate with outcomes analysis for the purpose of context and user-specific decision support at the point of care, where intervention would have the greatest clinical impact.

Explaining the justice-performance relationship: trust as exchange deepener or trust as uncertainty reducer?

PubMed

Colquitt, Jason A; Lepine, Jeffery A; Piccolo, Ronald F; Zapata, Cindy P; Rich, Bruce L

2012-01-01

Past research has revealed significant relationships between organizational justice dimensions and job performance, and trust is thought to be one mediator of those relationships. However, trust has been positioned in justice theorizing in 2 different ways, either as an indicator of the depth of an exchange relationship or as a variable that reflects levels of work-related uncertainty. Moreover, trust scholars distinguish between multiple forms of trust, including affect- and cognition-based trust, and it remains unclear which form is most relevant to justice effects. To explore these issues, we built and tested a more comprehensive model of trust mediation in which procedural, interpersonal, and distributive justice predicted affect- and cognition-based trust, with those trust forms predicting both exchange- and uncertainty-based mechanisms. The results of a field study in a hospital system revealed that the trust variables did indeed mediate the relationships between the organizational justice dimensions and job performance, with affect-based trust driving exchange-based mediation and cognition-based trust driving uncertainty-based mediation.

Higgs CAT

NASA Astrophysics Data System (ADS)

Passarino, Giampiero

2014-05-01

Higgs Computed Axial Tomography, an excerpt. The Higgs boson lineshape ( and the devil hath power to assume a pleasing shape, Hamlet, Act II, scene 2) is analyzed for the process, with special emphasis on the off-shell tail which shows up for large values of the Higgs virtuality. The effect of including background and interference is also discussed. The main focus of this work is on residual theoretical uncertainties, discussing how much-improved constraint on the Higgs intrinsic width can be revealed by an improved approach to analysis.

High Resolution Soil Water from Regional Databases and Satellite Images

NASA Technical Reports Server (NTRS)

Morris, Robin D.; Smelyanskly, Vadim N.; Coughlin, Joseph; Dungan, Jennifer; Clancy, Daniel (Technical Monitor)

2002-01-01

This viewgraph presentation provides information on the ways in which plant growth can be inferred from satellite data and can then be used to infer soil water. There are several steps in this process, the first of which is the acquisition of data from satellite observations and relevant information databases such as the State Soil Geographic Database (STATSGO). Then probabilistic analysis and inversion with the Bayes' theorem reveals sources of uncertainty. The Markov chain Monte Carlo method is also used.

Uncertainty

USGS Publications Warehouse

Hunt, Randall J.

2012-01-01

Management decisions will often be directly informed by model predictions. However, we now know there can be no expectation of a single ‘true’ model; thus, model results are uncertain. Understandable reporting of underlying uncertainty provides necessary context to decision-makers, as model results are used for management decisions. This, in turn, forms a mechanism by which groundwater models inform a risk-management framework because uncertainty around a prediction provides the basis for estimating the probability or likelihood of some event occurring. Given that the consequences of management decisions vary, it follows that the extent of and resources devoted to an uncertainty analysis may depend on the consequences. For events with low impact, a qualitative, limited uncertainty analysis may be sufficient for informing a decision. For events with a high impact, on the other hand, the risks might be better assessed and associated decisions made using a more robust and comprehensive uncertainty analysis. The purpose of this chapter is to provide guidance on uncertainty analysis through discussion of concepts and approaches, which can vary from heuristic (i.e. the modeller’s assessment of prediction uncertainty based on trial and error and experience) to a comprehensive, sophisticated, statistics-based uncertainty analysis. Most of the material presented here is taken from Doherty et al. (2010) if not otherwise cited. Although the treatment here is necessarily brief, the reader can find citations for the source material and additional references within this chapter.

Joint analysis of epistemic and aleatory uncertainty in stability analysis for geo-hazard assessments

NASA Astrophysics Data System (ADS)

Rohmer, Jeremy; Verdel, Thierry

2017-04-01

Uncertainty analysis is an unavoidable task of stability analysis of any geotechnical systems. Such analysis usually relies on the safety factor SF (if SF is below some specified threshold), the failure is possible). The objective of the stability analysis is then to estimate the failure probability P for SF to be below the specified threshold. When dealing with uncertainties, two facets should be considered as outlined by several authors in the domain of geotechnics, namely "aleatoric uncertainty" (also named "randomness" or "intrinsic variability") and "epistemic uncertainty" (i.e. when facing "vague, incomplete or imprecise information" such as limited databases and observations or "imperfect" modelling). The benefits of separating both facets of uncertainty can be seen from a risk management perspective because: - Aleatoric uncertainty, being a property of the system under study, cannot be reduced. However, practical actions can be taken to circumvent the potentially dangerous effects of such variability; - Epistemic uncertainty, being due to the incomplete/imprecise nature of available information, can be reduced by e.g., increasing the number of tests (lab or in site survey), improving the measurement methods or evaluating calculation procedure with model tests, confronting more information sources (expert opinions, data from literature, etc.). Uncertainty treatment in stability analysis usually restricts to the probabilistic framework to represent both facets of uncertainty. Yet, in the domain of geo-hazard assessments (like landslides, mine pillar collapse, rockfalls, etc.), the validity of this approach can be debatable. In the present communication, we propose to review the major criticisms available in the literature against the systematic use of probability in situations of high degree of uncertainty. On this basis, the feasibility of using a more flexible uncertainty representation tool is then investigated, namely Possibility distributions (e.g., Baudrit et al., 2007) for geo-hazard assessments. A graphical tool is then developed to explore: 1. the contribution of both types of uncertainty, aleatoric and epistemic; 2. the regions of the imprecise or random parameters which contribute the most to the imprecision on the failure probability P. The method is applied on two case studies (a mine pillar and a steep slope stability analysis, Rohmer and Verdel, 2014) to investigate the necessity for extra data acquisition on parameters whose imprecision can hardly be modelled by probabilities due to the scarcity of the available information (respectively the extraction ratio and the cliff geometry). References Baudrit, C., Couso, I., & Dubois, D. (2007). Joint propagation of probability and possibility in risk analysis: Towards a formal framework. International Journal of Approximate Reasoning, 45(1), 82-105. Rohmer, J., & Verdel, T. (2014). Joint exploration of regional importance of possibilistic and probabilistic uncertainty in stability analysis. Computers and Geotechnics, 61, 308-315.

The Future is Now: Reducing Psychological Distance to Increase Public Engagement with Climate Change.

PubMed

Jones, Charlotte; Hine, Donald W; Marks, Anthony D G

2017-02-01

Many people perceive climate change as psychologically distant-a set of uncertain events that might occur far in the future, impacting distant places and affecting people dissimilar to themselves. In this study, we employed construal level theory to investigate whether a climate change communication intervention could increase public engagement by reducing the psychological distance of climate change. Australian residents (N = 333) were randomly assigned to one of two treatment conditions: one framed to increase psychological distance to climate change (distal frame), and the other framed to reduce psychological distance (proximal frame). Participants then completed measures of psychological distance of climate change impacts, climate change concern, and intentions to engage in mitigation behavior. Principal components analysis indicated that psychological distance to climate change was best conceptualized as a multidimensional construct consisting of four components: geographic, temporal, social, and uncertainty. Path analysis revealed the effect of the treatment frame on climate change concern and intentions was fully mediated by psychological distance dimensions related to uncertainty and social distance. Our results suggest that climate communications framed to reduce psychological distance represent a promising strategy for increasing public engagement with climate change. © 2016 Society for Risk Analysis.

Comprehensive Approach to Verification and Validation of CFD Simulations Applied to Backward Facing Step-Application of CFD Uncertainty Analysis

NASA Technical Reports Server (NTRS)

Groves, Curtis E.; LLie, Marcel; Shallhorn, Paul A.

2012-01-01

There are inherent uncertainties and errors associated with using Computational Fluid Dynamics (CFD) to predict the flow field and there is no standard method for evaluating uncertainty in the CFD community. This paper describes an approach to -validate the . uncertainty in using CFD. The method will use the state of the art uncertainty analysis applying different turbulence niodels and draw conclusions on which models provide the least uncertainty and which models most accurately predict the flow of a backward facing step.

Facility Measurement Uncertainty Analysis at NASA GRC

NASA Technical Reports Server (NTRS)

Stephens, Julia; Hubbard, Erin

2016-01-01

This presentation provides and overview of the measurement uncertainty analysis currently being implemented in various facilities at NASA GRC. This presentation includes examples pertinent to the turbine engine community (mass flow and fan efficiency calculation uncertainties.

Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment

NASA Technical Reports Server (NTRS)

Britton, Paul T.; Al Hassan, Mohammad; Ring, Robert W.

2017-01-01

"Uncertainty analysis itself is uncertain, therefore, you cannot evaluate it exactly," Source Uncertain Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This paper will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

Lognormal Uncertainty Estimation for Failure Rates

NASA Technical Reports Server (NTRS)

Britton, Paul T.; Al Hassan, Mohammad; Ring, Robert W.

2017-01-01

"Uncertainty analysis itself is uncertain, therefore, you cannot evaluate it exactly," Source Uncertain. Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This presentation will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

Confronting uncertainty in wildlife management: performance of grizzly bear management.

PubMed

Artelle, Kyle A; Anderson, Sean C; Cooper, Andrew B; Paquet, Paul C; Reynolds, John D; Darimont, Chris T

2013-01-01

Scientific management of wildlife requires confronting the complexities of natural and social systems. Uncertainty poses a central problem. Whereas the importance of considering uncertainty has been widely discussed, studies of the effects of unaddressed uncertainty on real management systems have been rare. We examined the effects of outcome uncertainty and components of biological uncertainty on hunt management performance, illustrated with grizzly bears (Ursus arctos horribilis) in British Columbia, Canada. We found that both forms of uncertainty can have serious impacts on management performance. Outcome uncertainty alone--discrepancy between expected and realized mortality levels--led to excess mortality in 19% of cases (population-years) examined. Accounting for uncertainty around estimated biological parameters (i.e., biological uncertainty) revealed that excess mortality might have occurred in up to 70% of cases. We offer a general method for identifying targets for exploited species that incorporates uncertainty and maintains the probability of exceeding mortality limits below specified thresholds. Setting targets in our focal system using this method at thresholds of 25% and 5% probability of overmortality would require average target mortality reductions of 47% and 81%, respectively. Application of our transparent and generalizable framework to this or other systems could improve management performance in the presence of uncertainty.

Coupled semivariogram uncertainty of hydrogeological and geophysical data on capture zone uncertainty analysis

USGS Publications Warehouse

Rahman, A.; Tsai, F.T.-C.; White, C.D.; Willson, C.S.

2008-01-01

This study investigates capture zone uncertainty that relates to the coupled semivariogram uncertainty of hydrogeological and geophysical data. Semivariogram uncertainty is represented by the uncertainty in structural parameters (range, sill, and nugget). We used the beta distribution function to derive the prior distributions of structural parameters. The probability distributions of structural parameters were further updated through the Bayesian approach with the Gaussian likelihood functions. Cokriging of noncollocated pumping test data and electrical resistivity data was conducted to better estimate hydraulic conductivity through autosemivariograms and pseudo-cross-semivariogram. Sensitivities of capture zone variability with respect to the spatial variability of hydraulic conductivity, porosity and aquifer thickness were analyzed using ANOVA. The proposed methodology was applied to the analysis of capture zone uncertainty at the Chicot aquifer in Southwestern Louisiana, where a regional groundwater flow model was developed. MODFLOW-MODPATH was adopted to delineate the capture zone. The ANOVA results showed that both capture zone area and compactness were sensitive to hydraulic conductivity variation. We concluded that the capture zone uncertainty due to the semivariogram uncertainty is much higher than that due to the kriging uncertainty for given semivariograms. In other words, the sole use of conditional variances of kriging may greatly underestimate the flow response uncertainty. Semivariogram uncertainty should also be taken into account in the uncertainty analysis. ?? 2008 ASCE.

Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis

DOE PAGES

Alderman, Phillip D.; Stanfill, Bryan

2016-10-06

Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncertainties due to model structural differences per se and those due to parameter value uncertainties. Additionally, despite increasing use of Bayesian parameter estimation approaches with field-scale crop models, inadequate attention has been given to the full posterior distributions for estimated parameters. The objectives of this study were to quantify the impact of parameter value uncertainty on prediction uncertainty for modeling spring wheat phenology using Bayesian analysis and to assess the relativemore » contributions of model-structure-driven and parameter-value-driven uncertainty to overall prediction uncertainty. This study used a random walk Metropolis algorithm to estimate parameters for 30 spring wheat genotypes using nine phenology models based on multi-location trial data for days to heading and days to maturity. Across all cases, parameter-driven uncertainty accounted for between 19 and 52% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 12 and 64%. Here, this study demonstrated the importance of quantifying both model-structure- and parameter-value-driven uncertainty when assessing overall prediction uncertainty in modeling spring wheat phenology. More generally, Bayesian parameter estimation provided a useful framework for quantifying and analyzing sources of prediction uncertainty.« less

Believable Statements of Uncertainty and Believable Science

PubMed Central

Lindstrom, Richard M.

2017-01-01

Nearly fifty years ago, two landmark papers appeared that should have cured the problem of ambiguous uncertainty statements in published data. Eisenhart’s paper in Science called for statistically meaningful numbers, and Currie’s Analytical Chemistry paper revealed the wide range in common definitions of detection limit. Confusion and worse can result when uncertainties are misinterpreted or ignored. The recent stories of cold fusion, variable radioactive decay, and piezonuclear reactions provide cautionary examples in which prior probability has been neglected. We show examples from our laboratory and others to illustrate the fact that uncertainty depends on both statistical and scientific judgment. PMID:28584391

A unified approach for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties

NASA Astrophysics Data System (ADS)

Lü, Hui; Shangguan, Wen-Bin; Yu, Dejie

2017-09-01

Automotive brake systems are always subjected to various types of uncertainties and two types of random-fuzzy uncertainties may exist in the brakes. In this paper, a unified approach is proposed for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties. In the proposed approach, two uncertainty analysis models with mixed variables are introduced to model the random-fuzzy uncertainties. The first one is the random and fuzzy model, in which random variables and fuzzy variables exist simultaneously and independently. The second one is the fuzzy random model, in which uncertain parameters are all treated as random variables while their distribution parameters are expressed as fuzzy numbers. Firstly, the fuzziness is discretized by using α-cut technique and the two uncertainty analysis models are simplified into random-interval models. Afterwards, by temporarily neglecting interval uncertainties, the random-interval models are degraded into random models, in which the expectations, variances, reliability indexes and reliability probabilities of system stability functions are calculated. And then, by reconsidering the interval uncertainties, the bounds of the expectations, variances, reliability indexes and reliability probabilities are computed based on Taylor series expansion. Finally, by recomposing the analysis results at each α-cut level, the fuzzy reliability indexes and probabilities can be obtained, by which the brake squeal instability can be evaluated. The proposed approach gives a general framework to deal with both types of random-fuzzy uncertainties that may exist in the brakes and its effectiveness is demonstrated by numerical examples. It will be a valuable supplement to the systematic study of brake squeal considering uncertainty.

Drought Persistence in Models and Observations

NASA Astrophysics Data System (ADS)

Moon, Heewon; Gudmundsson, Lukas; Seneviratne, Sonia

2017-04-01

Many regions of the world have experienced drought events that persisted several years and caused substantial economic and ecological impacts in the 20th century. However, it remains unclear whether there are significant trends in the frequency or severity of these prolonged drought events. In particular, an important issue is linked to systematic biases in the representation of persistent drought events in climate models, which impedes analysis related to the detection and attribution of drought trends. This study assesses drought persistence errors in global climate model (GCM) simulations from the 5th phase of Coupled Model Intercomparison Project (CMIP5), in the period of 1901-2010. The model simulations are compared with five gridded observational data products. The analysis focuses on two aspects: the identification of systematic biases in the models and the partitioning of the spread of drought-persistence-error into four possible sources of uncertainty: model uncertainty, observation uncertainty, internal climate variability and the estimation error of drought persistence. We use monthly and yearly dry-to-dry transition probabilities as estimates for drought persistence with drought conditions defined as negative precipitation anomalies. For both time scales we find that most model simulations consistently underestimated drought persistence except in a few regions such as India and Eastern South America. Partitioning the spread of the drought-persistence-error shows that at the monthly time scale model uncertainty and observation uncertainty are dominant, while the contribution from internal variability does play a minor role in most cases. At the yearly scale, the spread of the drought-persistence-error is dominated by the estimation error, indicating that the partitioning is not statistically significant, due to a limited number of considered time steps. These findings reveal systematic errors in the representation of drought persistence in current climate models and highlight the main contributors of uncertainty of drought-persistence-error. Future analyses will focus on investigating the temporal propagation of drought persistence to better understand the causes for the identified errors in the representation of drought persistence in state-of-the-art climate models.

Collaborative decision-analytic framework to maximize resilience of tidal marshes to climate change

USGS Publications Warehouse

Thorne, Karen M.; Mattsson, Brady J.; Takekawa, John Y.; Cummings, Jonathan; Crouse, Debby; Block, Giselle; Bloom, Valary; Gerhart, Matt; Goldbeck, Steve; Huning, Beth; Sloop, Christina; Stewart, Mendel; Taylor, Karen; Valoppi, Laura

2015-01-01

Decision makers that are responsible for stewardship of natural resources face many challenges, which are complicated by uncertainty about impacts from climate change, expanding human development, and intensifying land uses. A systematic process for evaluating the social and ecological risks, trade-offs, and cobenefits associated with future changes is critical to maximize resilience and conserve ecosystem services. This is particularly true in coastal areas where human populations and landscape conversion are increasing, and where intensifying storms and sea-level rise pose unprecedented threats to coastal ecosystems. We applied collaborative decision analysis with a diverse team of stakeholders who preserve, manage, or restore tidal marshes across the San Francisco Bay estuary, California, USA, as a case study. Specifically, we followed a structured decision-making approach, and we using expert judgment developed alternative management strategies to increase the capacity and adaptability to manage tidal marsh resilience while considering uncertainties through 2050. Because sea-level rise projections are relatively confident to 2050, we focused on uncertainties regarding intensity and frequency of storms and funding. Elicitation methods allowed us to make predictions in the absence of fully compatible models and to assess short- and long-term trade-offs. Specifically we addressed two questions. (1) Can collaborative decision analysis lead to consensus among a diverse set of decision makers responsible for environmental stewardship and faced with uncertainties about climate change, funding, and stakeholder values? (2) What is an optimal strategy for the conservation of tidal marshes, and what strategy is robust to the aforementioned uncertainties? We found that when taking this approach, consensus was reached among the stakeholders about the best management strategies to maintain tidal marsh integrity. A Bayesian decision network revealed that a strategy considering sea-level rise and storms explicitly in wetland restoration planning and designs was optimal, and it was robust to uncertainties about management effectiveness and budgets. We found that strategies that avoided explicitly accounting for future climate change had the lowest expected performance based on input from the team. Our decision-analytic framework is sufficiently general to offer an adaptable template, which can be modified for use in other areas that include a diverse and engaged stakeholder group.

Uncertainty quantification in flux balance analysis of spatially lumped and distributed models of neuron-astrocyte metabolism.

PubMed

Calvetti, Daniela; Cheng, Yougan; Somersalo, Erkki

2016-12-01

Identifying feasible steady state solutions of a brain energy metabolism model is an inverse problem that allows infinitely many solutions. The characterization of the non-uniqueness, or the uncertainty quantification of the flux balance analysis, is tantamount to identifying the degrees of freedom of the solution. The degrees of freedom of multi-compartment mathematical models for energy metabolism of a neuron-astrocyte complex may offer a key to understand the different ways in which the energetic needs of the brain are met. In this paper we study the uncertainty in the solution, using techniques of linear algebra to identify the degrees of freedom in a lumped model, and Markov chain Monte Carlo methods in its extension to a spatially distributed case. The interpretation of the degrees of freedom in metabolic terms, more specifically, glucose and oxygen partitioning, is then leveraged to derive constraints on the free parameters to guarantee that the model is energetically feasible. We demonstrate how the model can be used to estimate the stoichiometric energy needs of the cells as well as the household energy based on the measured oxidative cerebral metabolic rate of glucose and glutamate cycling. Moreover, our analysis shows that in the lumped model the net direction of lactate dehydrogenase (LDH) in the cells can be deduced from the glucose partitioning between the compartments. The extension of the lumped model to a spatially distributed multi-compartment setting that includes diffusion fluxes from capillary to tissue increases the number of degrees of freedom, requiring the use of statistical sampling techniques. The analysis of the distributed model reveals that some of the conclusions valid for the spatially lumped model, e.g., concerning the LDH activity and glucose partitioning, may no longer hold.

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

NASA Astrophysics Data System (ADS)

Raleigh, M. S.; Lundquist, J. D.; Clark, M. P.

2015-07-01

Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

Doppler Global Velocimeter Development for the Large Wind Tunnels at Ames Research Center

NASA Technical Reports Server (NTRS)

Reinath, Michael S.

1997-01-01

Development of an optical, laser-based flow-field measurement technique for large wind tunnels is described. The technique uses laser sheet illumination and charged coupled device detectors to rapidly measure flow-field velocity distributions over large planar regions of the flow. Sample measurements are presented that illustrate the capability of the technique. An analysis of measurement uncertainty, which focuses on the random component of uncertainty, shows that precision uncertainty is not dependent on the measured velocity magnitude. For a single-image measurement, the analysis predicts a precision uncertainty of +/-5 m/s. When multiple images are averaged, this uncertainty is shown to decrease. For an average of 100 images, for example, the analysis shows that a precision uncertainty of +/-0.5 m/s can be expected. Sample applications show that vectors aligned with an orthogonal coordinate system are difficult to measure directly. An algebraic transformation is presented which converts measured vectors to the desired orthogonal components. Uncertainty propagation is then used to show how the uncertainty propagates from the direct measurements to the orthogonal components. For a typical forward-scatter viewing geometry, the propagation analysis predicts precision uncertainties of +/-4, +/-7, and +/-6 m/s, respectively, for the U, V, and W components at 68% confidence.

Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty

DOE PAGES

Opgenoord, Max M. J.; Allaire, Douglas L.; Willcox, Karen E.

2016-09-12

Sensitivity analysis plays a critical role in quantifying uncertainty in the design of engineering systems. A variance-based global sensitivity analysis is often used to rank the importance of input factors, based on their contribution to the variance of the output quantity of interest. However, this analysis assumes that all input variability can be reduced to zero, which is typically not the case in a design setting. Distributional sensitivity analysis (DSA) instead treats the uncertainty reduction in the inputs as a random variable, and defines a variance-based sensitivity index function that characterizes the relative contribution to the output variance as amore » function of the amount of uncertainty reduction. This paper develops a computationally efficient implementation for the DSA formulation and extends it to include distributions commonly used in engineering design under uncertainty. Application of the DSA method to the conceptual design of a commercial jetliner demonstrates how the sensitivity analysis provides valuable information to designers and decision-makers on where and how to target uncertainty reduction efforts.« less

Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Opgenoord, Max M. J.; Allaire, Douglas L.; Willcox, Karen E.

Sensitivity analysis plays a critical role in quantifying uncertainty in the design of engineering systems. A variance-based global sensitivity analysis is often used to rank the importance of input factors, based on their contribution to the variance of the output quantity of interest. However, this analysis assumes that all input variability can be reduced to zero, which is typically not the case in a design setting. Distributional sensitivity analysis (DSA) instead treats the uncertainty reduction in the inputs as a random variable, and defines a variance-based sensitivity index function that characterizes the relative contribution to the output variance as amore » function of the amount of uncertainty reduction. This paper develops a computationally efficient implementation for the DSA formulation and extends it to include distributions commonly used in engineering design under uncertainty. Application of the DSA method to the conceptual design of a commercial jetliner demonstrates how the sensitivity analysis provides valuable information to designers and decision-makers on where and how to target uncertainty reduction efforts.« less

Proton therapy versus intensity modulated x-ray therapy in the treatment of prostate cancer: Estimating secondary cancer risks

NASA Astrophysics Data System (ADS)

Fontenot, Jonas David

External beam radiation therapy is used to treat nearly half of the more than 200,000 new cases of prostate cancer diagnosed in the United States each year. During a radiation therapy treatment, healthy tissues in the path of the therapeutic beam are exposed to high doses. In addition, the whole body is exposed to a low-dose bath of unwanted scatter radiation from the pelvis and leakage radiation from the treatment unit. As a result, survivors of radiation therapy for prostate cancer face an elevated risk of developing a radiogenic second cancer. Recently, proton therapy has been shown to reduce the dose delivered by the therapeutic beam to normal tissues during treatment compared to intensity modulated x-ray therapy (IMXT, the current standard of care). However, the magnitude of stray radiation doses from proton therapy, and their impact on this incidence of radiogenic second cancers, was not known. The risk of a radiogenic second cancer following proton therapy for prostate cancer relative to IMXT was determined for 3 patients of large, median, and small anatomical stature. Doses delivered to healthy tissues from the therapeutic beam were obtained from treatment planning system calculations. Stray doses from IMXT were taken from the literature, while stray doses from proton therapy were simulated using a Monte Carlo model of a passive scattering treatment unit and an anthropomorphic phantom. Baseline risk models were taken from the Biological Effects of Ionizing Radiation VII report. A sensitivity analysis was conducted to characterize the uncertainty of risk calculations to uncertainties in the risk model, the relative biological effectiveness (RBE) of neutrons for carcinogenesis, and inter-patient anatomical variations. The risk projections revealed that proton therapy carries a lower risk for radiogenic second cancer incidence following prostate irradiation compared to IMXT. The sensitivity analysis revealed that the results of the risk analysis depended only weakly on uncertainties in the risk model and inter-patient variations. Second cancer risks were sensitive to changes in the RBE of neutrons. However, the findings of the study were qualitatively consistent for all patient sizes and risk models considered, and for all neutron RBE values less than 100.

A Two-Step Approach to Uncertainty Quantification of Core Simulators

DOE PAGES

Yankov, Artem; Collins, Benjamin; Klein, Markus; ...

2012-01-01

For the multiple sources of error introduced into the standard computational regime for simulating reactor cores, rigorous uncertainty analysis methods are available primarily to quantify the effects of cross section uncertainties. Two methods for propagating cross section uncertainties through core simulators are the XSUSA statistical approach and the “two-step” method. The XSUSA approach, which is based on the SUSA code package, is fundamentally a stochastic sampling method. Alternatively, the two-step method utilizes generalized perturbation theory in the first step and stochastic sampling in the second step. The consistency of these two methods in quantifying uncertainties in the multiplication factor andmore » in the core power distribution was examined in the framework of phase I-3 of the OECD Uncertainty Analysis in Modeling benchmark. With the Three Mile Island Unit 1 core as a base model for analysis, the XSUSA and two-step methods were applied with certain limitations, and the results were compared to those produced by other stochastic sampling-based codes. Based on the uncertainty analysis results, conclusions were drawn as to the method that is currently more viable for computing uncertainties in burnup and transient calculations.« less

Parameter uncertainty and nonstationarity in regional extreme rainfall frequency analysis in Qu River Basin, East China

NASA Astrophysics Data System (ADS)

Zhu, Q.; Xu, Y. P.; Gu, H.

2014-12-01

Traditionally, regional frequency analysis methods were developed for stationary environmental conditions. Nevertheless, recent studies have identified significant changes in hydrological records, leading to the 'death' of stationarity. Besides, uncertainty in hydrological frequency analysis is persistent. This study aims to investigate the impact of one of the most important uncertainty sources, parameter uncertainty, together with nonstationarity, on design rainfall depth in Qu River Basin, East China. A spatial bootstrap is first proposed to analyze the uncertainty of design rainfall depth estimated by regional frequency analysis based on L-moments and estimated on at-site scale. Meanwhile, a method combining the generalized additive models with 30-year moving window is employed to analyze non-stationarity existed in the extreme rainfall regime. The results show that the uncertainties of design rainfall depth with 100-year return period under stationary conditions estimated by regional spatial bootstrap can reach 15.07% and 12.22% with GEV and PE3 respectively. On at-site scale, the uncertainties can reach 17.18% and 15.44% with GEV and PE3 respectively. In non-stationary conditions, the uncertainties of maximum rainfall depth (corresponding to design rainfall depth) with 0.01 annual exceedance probability (corresponding to 100-year return period) are 23.09% and 13.83% with GEV and PE3 respectively. Comparing the 90% confidence interval, the uncertainty of design rainfall depth resulted from parameter uncertainty is less than that from non-stationarity frequency analysis with GEV, however, slightly larger with PE3. This study indicates that the spatial bootstrap can be successfully applied to analyze the uncertainty of design rainfall depth on both regional and at-site scales. And the non-stationary analysis shows that the differences between non-stationary quantiles and their stationary equivalents are important for decision makes of water resources management and risk management.

Effect of Increased Intensity of Physiotherapy on Patient Outcomes After Stroke: An Economic Literature Review and Cost-Effectiveness Analysis

PubMed Central

Chan, B

2015-01-01

Background Functional improvements have been seen in stroke patients who have received an increased intensity of physiotherapy. This requires additional costs in the form of increased physiotherapist time. Objectives The objective of this economic analysis is to determine the cost-effectiveness of increasing the intensity of physiotherapy (duration and/or frequency) during inpatient rehabilitation after stroke, from the perspective of the Ontario Ministry of Health and Long-term Care. Data Sources The inputs for our economic evaluation were extracted from articles published in peer-reviewed journals and from reports from government sources or the Canadian Stroke Network. Where published data were not available, we sought expert opinion and used inputs based on the experts' estimates. Review Methods The primary outcome we considered was cost per quality-adjusted life-year (QALY). We also evaluated functional strength training because of its similarities to physiotherapy. We used a 2-state Markov model to evaluate the cost-effectiveness of functional strength training and increased physiotherapy intensity for stroke inpatient rehabilitation. The model had a lifetime timeframe with a 5% annual discount rate. We then used sensitivity analyses to evaluate uncertainty in the model inputs. Results We found that functional strength training and higher-intensity physiotherapy resulted in lower costs and improved outcomes over a lifetime. However, our sensitivity analyses revealed high levels of uncertainty in the model inputs, and therefore in the results. Limitations There is a high level of uncertainty in this analysis due to the uncertainty in model inputs, with some of the major inputs based on expert panel consensus or expert opinion. In addition, the utility outcomes were based on a clinical study conducted in the United Kingdom (i.e., 1 study only, and not in an Ontario or Canadian setting). Conclusions Functional strength training and higher-intensity physiotherapy may result in lower costs and improved health outcomes. However, these results should be interpreted with caution. PMID:26366241

Addressing global uncertainty and sensitivity in first-principles based microkinetic models by an adaptive sparse grid approach

NASA Astrophysics Data System (ADS)

Döpking, Sandra; Plaisance, Craig P.; Strobusch, Daniel; Reuter, Karsten; Scheurer, Christoph; Matera, Sebastian

2018-01-01

In the last decade, first-principles-based microkinetic modeling has been developed into an important tool for a mechanistic understanding of heterogeneous catalysis. A commonly known, but hitherto barely analyzed issue in this kind of modeling is the presence of sizable errors from the use of approximate Density Functional Theory (DFT). We here address the propagation of these errors to the catalytic turnover frequency (TOF) by global sensitivity and uncertainty analysis. Both analyses require the numerical quadrature of high-dimensional integrals. To achieve this efficiently, we utilize and extend an adaptive sparse grid approach and exploit the confinement of the strongly non-linear behavior of the TOF to local regions of the parameter space. We demonstrate the methodology on a model of the oxygen evolution reaction at the Co3O4 (110)-A surface, using a maximum entropy error model that imposes nothing but reasonable bounds on the errors. For this setting, the DFT errors lead to an absolute uncertainty of several orders of magnitude in the TOF. We nevertheless find that it is still possible to draw conclusions from such uncertain models about the atomistic aspects controlling the reactivity. A comparison with derivative-based local sensitivity analysis instead reveals that this more established approach provides incomplete information. Since the adaptive sparse grids allow for the evaluation of the integrals with only a modest number of function evaluations, this approach opens the way for a global sensitivity analysis of more complex models, for instance, models based on kinetic Monte Carlo simulations.

CASMO5/TSUNAMI-3D spent nuclear fuel reactivity uncertainty analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferrer, R.; Rhodes, J.; Smith, K.

2012-07-01

The CASMO5 lattice physics code is used in conjunction with the TSUNAMI-3D sequence in ORNL's SCALE 6 code system to estimate the uncertainties in hot-to-cold reactivity changes due to cross-section uncertainty for PWR assemblies at various burnup points. The goal of the analysis is to establish the multiplication factor uncertainty similarity between various fuel assemblies at different conditions in a quantifiable manner and to obtain a bound on the hot-to-cold reactivity uncertainty over the various assembly types and burnup attributed to fundamental cross-section data uncertainty. (authors)

Calibration of a stack of NaI scintillators at the Berkeley Bevalac

NASA Technical Reports Server (NTRS)

Schindler, S. M.; Buffington, A.; Lau, K.; Rasmussen, I. L.

1983-01-01

An analysis of the carbon and argon data reveals that essentially all of the charge-changing fragmentation reactions within the stack can be identified and removed by imposing the simple criteria relating the observed energy deposition profiles to the expected Bragg curve depositions. It is noted that these criteria are even capable of identifying approximately one-third of the expected neutron-stripping interactions, which in these cases have anomalous deposition profiles. The contribution of mass error from uncertainty in delta E has an upper limit of 0.25 percent for Mn; this produces an associated mass error for the experiment of about 0.14 amu. It is believed that this uncertainty will change little with changing gamma. Residual errors in the mapping produce even smaller mass errors for lighter isotopes, whereas photoelectron fluctuations and delta-ray effects are approximately the same independent of the charge and energy deposition.

Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS.

PubMed

Cui, Bingbo; Chen, Xiyuan; Xu, Yuan; Huang, Haoqian; Liu, Xiao

2017-01-01

In order to improve the accuracy and robustness of GNSS/INS navigation system, an improved iterated cubature Kalman filter (IICKF) is proposed by considering the state-dependent noise and system uncertainty. First, a simplified framework of iterated Gaussian filter is derived by using damped Newton-Raphson algorithm and online noise estimator. Then the effect of state-dependent noise coming from iterated update is analyzed theoretically, and an augmented form of CKF algorithm is applied to improve the estimation accuracy. The performance of IICKF is verified by field test and numerical simulation, and results reveal that, compared with non-iterated filter, iterated filter is less sensitive to the system uncertainty, and IICKF improves the accuracy of yaw, roll and pitch by 48.9%, 73.1% and 83.3%, respectively, compared with traditional iterated KF. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stephens, T. S.; Gonder, Jeff; Chen, Yuche

This report details a study of the potential effects of connected and automated vehicle (CAV) technologies on vehicle miles traveled (VMT), vehicle fuel efficiency, and consumer costs. Related analyses focused on a range of light-duty CAV technologies in conventional powertrain vehicles -- from partial automation to full automation, with and without ridesharing -- compared to today's base-case scenario. Analysis results revealed widely disparate upper- and lower-bound estimates for fuel use and VMT, ranging from a tripling of fuel use to decreasing light-duty fuel use to below 40% of today's level. This wide range reflects uncertainties in the ways that CAVmore » technologies can influence vehicle efficiency and use through changes in vehicle designs, driving habits, and travel behavior. The report further identifies the most significant potential impacting factors, the largest areas of uncertainty, and where further research is particularly needed.« less

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis☆

PubMed Central

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-01-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster–Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty–sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights. PMID:25843987

Photovoltaic Calibrations at the National Renewable Energy Laboratory and Uncertainty Analysis Following the ISO 17025 Guidelines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Emery, Keith

The measurement of photovoltaic (PV) performance with respect to reference conditions requires measuring current versus voltage for a given tabular reference spectrum, junction temperature, and total irradiance. This report presents the procedures implemented by the PV Cell and Module Performance Characterization Group at the National Renewable Energy Laboratory (NREL) to achieve the lowest practical uncertainty. A rigorous uncertainty analysis of these procedures is presented, which follows the International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement. This uncertainty analysis is required for the team’s laboratory accreditation under ISO standard 17025, “General Requirements for the Competence ofmore » Testing and Calibration Laboratories.” The report also discusses additional areas where the uncertainty can be reduced.« less

The Interplay between Uncertainty Monitoring and Working Memory: Can Metacognition Become Automatic?

PubMed Central

Coutinho, Mariana V. C.; Redford, Joshua S.; Church, Barbara A.; Zakrzewski, Alexandria C.; Couchman, Justin J.; Smith, J. David

2016-01-01

The uncertainty response has grounded the study of metacognition in nonhuman animals. Recent research has explored the processes supporting uncertainty monitoring in monkeys. It revealed that uncertainty responding in contrast to perceptual responding depends on significant working memory resources. The aim of the present study was to expand this research by examining whether uncertainty monitoring is also working memory demanding in humans. To explore this issue, human participants were tested with or without a cognitive load on a psychophysical discrimination task including either an uncertainty response (allowing the decline of difficult trials) or a middle-perceptual response (labeling the same intermediate trial levels). The results demonstrated that cognitive load reduced uncertainty responding, but increased middle responding. However, this dissociation between uncertainty and middle responding was only observed when participants either lacked training or had very little training with the uncertainty response. If more training was provided, the effect of load was small. These results suggest that uncertainty responding is resource demanding, but with sufficient training, human participants can respond to uncertainty either by using minimal working memory resources or effectively sharing resources. These results are discussed in relation to the literature on animal and human metacognition. PMID:25971878

Assessment of Radiative Heating Uncertainty for Hyperbolic Earth Entry

NASA Technical Reports Server (NTRS)

Johnston, Christopher O.; Mazaheri, Alireza; Gnoffo, Peter A.; Kleb, W. L.; Sutton, Kenneth; Prabhu, Dinesh K.; Brandis, Aaron M.; Bose, Deepak

2011-01-01

This paper investigates the shock-layer radiative heating uncertainty for hyperbolic Earth entry, with the main focus being a Mars return. In Part I of this work, a baseline simulation approach involving the LAURA Navier-Stokes code with coupled ablation and radiation is presented, with the HARA radiation code being used for the radiation predictions. Flight cases representative of peak-heating Mars or asteroid return are de ned and the strong influence of coupled ablation and radiation on their aerothermodynamic environments are shown. Structural uncertainties inherent in the baseline simulations are identified, with turbulence modeling, precursor absorption, grid convergence, and radiation transport uncertainties combining for a +34% and ..24% structural uncertainty on the radiative heating. A parametric uncertainty analysis, which assumes interval uncertainties, is presented. This analysis accounts for uncertainties in the radiation models as well as heat of formation uncertainties in the flow field model. Discussions and references are provided to support the uncertainty range chosen for each parameter. A parametric uncertainty of +47.3% and -28.3% is computed for the stagnation-point radiative heating for the 15 km/s Mars-return case. A breakdown of the largest individual uncertainty contributors is presented, which includes C3 Swings cross-section, photoionization edge shift, and Opacity Project atomic lines. Combining the structural and parametric uncertainty components results in a total uncertainty of +81.3% and ..52.3% for the Mars-return case. In Part II, the computational technique and uncertainty analysis presented in Part I are applied to 1960s era shock-tube and constricted-arc experimental cases. It is shown that experiments contain shock layer temperatures and radiative ux values relevant to the Mars-return cases of present interest. Comparisons between the predictions and measurements, accounting for the uncertainty in both, are made for a range of experiments. A measure of comparison quality is de ned, which consists of the percent overlap of the predicted uncertainty bar with the corresponding measurement uncertainty bar. For nearly all cases, this percent overlap is greater than zero, and for most of the higher temperature cases (T >13,000 K) it is greater than 50%. These favorable comparisons provide evidence that the baseline computational technique and uncertainty analysis presented in Part I are adequate for Mars-return simulations. In Part III, the computational technique and uncertainty analysis presented in Part I are applied to EAST shock-tube cases. These experimental cases contain wavelength dependent intensity measurements in a wavelength range that covers 60% of the radiative intensity for the 11 km/s, 5 m radius flight case studied in Part I. Comparisons between the predictions and EAST measurements are made for a range of experiments. The uncertainty analysis presented in Part I is applied to each prediction, and comparisons are made using the metrics defined in Part II. The agreement between predictions and measurements is excellent for velocities greater than 10.5 km/s. Both the wavelength dependent and wavelength integrated intensities agree within 30% for nearly all cases considered. This agreement provides confidence in the computational technique and uncertainty analysis presented in Part I, and provides further evidence that this approach is adequate for Mars-return simulations. Part IV of this paper reviews existing experimental data that include the influence of massive ablation on radiative heating. It is concluded that this existing data is not sufficient for the present uncertainty analysis. Experiments to capture the influence of massive ablation on radiation are suggested as future work, along with further studies of the radiative precursor and improvements in the radiation properties of ablation products.

Robustness Analysis and Optimally Robust Control Design via Sum-of-Squares

NASA Technical Reports Server (NTRS)

Dorobantu, Andrei; Crespo, Luis G.; Seiler, Peter J.

2012-01-01

A control analysis and design framework is proposed for systems subject to parametric uncertainty. The underlying strategies are based on sum-of-squares (SOS) polynomial analysis and nonlinear optimization to design an optimally robust controller. The approach determines a maximum uncertainty range for which the closed-loop system satisfies a set of stability and performance requirements. These requirements, de ned as inequality constraints on several metrics, are restricted to polynomial functions of the uncertainty. To quantify robustness, SOS analysis is used to prove that the closed-loop system complies with the requirements for a given uncertainty range. The maximum uncertainty range, calculated by assessing a sequence of increasingly larger ranges, serves as a robustness metric for the closed-loop system. To optimize the control design, nonlinear optimization is used to enlarge the maximum uncertainty range by tuning the controller gains. Hence, the resulting controller is optimally robust to parametric uncertainty. This approach balances the robustness margins corresponding to each requirement in order to maximize the aggregate system robustness. The proposed framework is applied to a simple linear short-period aircraft model with uncertain aerodynamic coefficients.

Introducing uncertainty analysis of nucleation and crystal growth models in Process Analytical Technology (PAT) system design of crystallization processes.

PubMed

Samad, Noor Asma Fazli Abdul; Sin, Gürkan; Gernaey, Krist V; Gani, Rafiqul

2013-11-01

This paper presents the application of uncertainty and sensitivity analysis as part of a systematic model-based process monitoring and control (PAT) system design framework for crystallization processes. For the uncertainty analysis, the Monte Carlo procedure is used to propagate input uncertainty, while for sensitivity analysis, global methods including the standardized regression coefficients (SRC) and Morris screening are used to identify the most significant parameters. The potassium dihydrogen phosphate (KDP) crystallization process is used as a case study, both in open-loop and closed-loop operation. In the uncertainty analysis, the impact on the predicted output of uncertain parameters related to the nucleation and the crystal growth model has been investigated for both a one- and two-dimensional crystal size distribution (CSD). The open-loop results show that the input uncertainties lead to significant uncertainties on the CSD, with appearance of a secondary peak due to secondary nucleation for both cases. The sensitivity analysis indicated that the most important parameters affecting the CSDs are nucleation order and growth order constants. In the proposed PAT system design (closed-loop), the target CSD variability was successfully reduced compared to the open-loop case, also when considering uncertainty in nucleation and crystal growth model parameters. The latter forms a strong indication of the robustness of the proposed PAT system design in achieving the target CSD and encourages its transfer to full-scale implementation. Copyright © 2013 Elsevier B.V. All rights reserved.

Drought Persistence Errors in Global Climate Models

NASA Astrophysics Data System (ADS)

Moon, H.; Gudmundsson, L.; Seneviratne, S. I.

2018-04-01

The persistence of drought events largely determines the severity of socioeconomic and ecological impacts, but the capability of current global climate models (GCMs) to simulate such events is subject to large uncertainties. In this study, the representation of drought persistence in GCMs is assessed by comparing state-of-the-art GCM model simulations to observation-based data sets. For doing so, we consider dry-to-dry transition probabilities at monthly and annual scales as estimates for drought persistence, where a dry status is defined as negative precipitation anomaly. Though there is a substantial spread in the drought persistence bias, most of the simulations show systematic underestimation of drought persistence at global scale. Subsequently, we analyzed to which degree (i) inaccurate observations, (ii) differences among models, (iii) internal climate variability, and (iv) uncertainty of the employed statistical methods contribute to the spread in drought persistence errors using an analysis of variance approach. The results show that at monthly scale, model uncertainty and observational uncertainty dominate, while the contribution from internal variability is small in most cases. At annual scale, the spread of the drought persistence error is dominated by the statistical estimation error of drought persistence, indicating that the partitioning of the error is impaired by the limited number of considered time steps. These findings reveal systematic errors in the representation of drought persistence in current GCMs and suggest directions for further model improvement.

Reducing risk and increasing confidence of decision making at a lower cost: In-situ pXRF assessment of metal-contaminated sites.

PubMed

Rouillon, Marek; Taylor, Mark P; Dong, Chenyin

2017-10-01

This study evaluates the in-situ use of field portable X-ray Fluorescence (pXRF) for metal-contaminated site assessments, and assesses the advantages of increased sampling to reduce risk, and increase confidence of decision making at a lower cost. Five metal-contaminated sites were assessed using both in-situ pXRF and ex-situ inductively coupled plasma mass spectrometry (ICP-MS) analyses at various sampling resolutions. Twenty second in-situ pXRF measurements of Mn, Zn and Pb were corrected using a subset of parallel ICP-MS measurements taken at each site. Field and analytical duplicates revealed sampling as the major contributor (>95% variation) to measurement uncertainties. This study shows that increased sampling led to several benefits including more representative site characterisation, higher soil-metal mapping resolution, reduced uncertainty around the site mean, and reduced sampling uncertainty. Real time pXRF data enabled efficient, on-site decision making for further judgemental sampling, without the need to return to the site. Additionally, in-situ pXRF was more cost effective than the current approach of ex-situ sampling and ICP-MS analysis, even with higher sampling at each site. Lastly, a probabilistic site assessment approach was applied to demonstrate the advantages of integrating estimated measurement uncertainties into site reporting. Copyright © 2017 Elsevier Ltd. All rights reserved.

Perceptual uncertainty and line-call challenges in professional tennis

PubMed Central

Mather, George

2008-01-01

Fast-moving sports such as tennis require both players and match officials to make rapid accurate perceptual decisions about dynamic events in the visual world. Disagreements arise regularly, leading to disputes about decisions such as line calls. A number of factors must contribute to these disputes, including lapses in concentration, bias and gamesmanship. Fundamental uncertainty or variability in the sensory information supporting decisions must also play a role. Modern technological innovations now provide detailed and accurate physical information that can be compared against the decisions of players and officials. The present paper uses this psychophysical data to assess the significance of perceptual limitations as a contributor to real-world decisions in professional tennis. A detailed analysis is presented of a large body of data on line-call challenges in professional tennis tournaments over the last 2 years. Results reveal that the vast majority of challenges can be explained in a direct highly predictable manner by a simple model of uncertainty in perceptual information processing. Both players and line judges are remarkably accurate at judging ball bounce position, with a positional uncertainty of less than 40 mm. Line judges are more reliable than players. Judgements are more difficult for balls bouncing near base and service lines than those bouncing near side and centre lines. There is no evidence for significant errors in localization due to image motion. PMID:18426755

Perceptual uncertainty and line-call challenges in professional tennis.

PubMed

Mather, George

2008-07-22

Fast-moving sports such as tennis require both players and match officials to make rapid accurate perceptual decisions about dynamic events in the visual world. Disagreements arise regularly, leading to disputes about decisions such as line calls. A number of factors must contribute to these disputes, including lapses in concentration, bias and gamesmanship. Fundamental uncertainty or variability in the sensory information supporting decisions must also play a role. Modern technological innovations now provide detailed and accurate physical information that can be compared against the decisions of players and officials. The present paper uses this psychophysical data to assess the significance of perceptual limitations as a contributor to real-world decisions in professional tennis. A detailed analysis is presented of a large body of data on line-call challenges in professional tennis tournaments over the last 2 years. Results reveal that the vast majority of challenges can be explained in a direct highly predictable manner by a simple model of uncertainty in perceptual information processing. Both players and line judges are remarkably accurate at judging ball bounce position, with a positional uncertainty of less than 40mm. Line judges are more reliable than players. Judgements are more difficult for balls bouncing near base and service lines than those bouncing near side and centre lines. There is no evidence for significant errors in localization due to image motion.

Astrosat/LAXPC Reveals the High-energy Variability of GRS 1915+105 in the X Class

NASA Astrophysics Data System (ADS)

Yadav, J. S.; Misra, Ranjeev; Verdhan Chauhan, Jai; Agrawal, P. C.; Antia, H. M.; Pahari, Mayukh; Dedhia, Dhiraj; Katoch, Tilak; Madhwani, P.; Manchanda, R. K.; Paul, B.; Shah, Parag; Ishwara-Chandra, C. H.

2016-12-01

We present the first quick look analysis of data from nine AstroSat's Large Area X-ray Proportional Counter (LAXPC) observations of GRS 1915+105 during 2016 March when the source had the characteristics of being in the Radio-quiet χ class. We find that a simple empirical model of a disk blackbody emission, with Comptonization and a broad Gaussian Iron line can fit the time-averaged 3-80 keV spectrum with a systematic uncertainty of 1.5% and a background flux uncertainty of 4%. A simple dead time corrected Poisson noise level spectrum matches well with the observed high-frequency power spectra till 50 kHz and as expected the data show no significant high-frequency (\\gt 20 {Hz}) features. Energy dependent power spectra reveal a strong low-frequency (2-8 Hz) quasi-periodic oscillation and its harmonic along with broadband noise. The QPO frequency changes rapidly with flux (nearly 4 Hz in ˜5 hr). With increasing QPO frequency, an excess noise component appears significantly in the high-energy regime (\\gt 8 keV). At the QPO frequencies, the time-lag as a function of energy has a non-monotonic behavior such that the lags decrease with energy till about 15-20 keV and then increase for higher energies. These first-look results benchmark the performance of LAXPC at high energies and confirms that its data can be used for more sophisticated analysis such as flux or frequency-resolved spectro-timing studies.

Uncertainty in Operational Atmospheric Analyses and Re-Analyses

NASA Astrophysics Data System (ADS)

Langland, R.; Maue, R. N.

2016-12-01

This talk will describe uncertainty in atmospheric analyses of wind and temperature produced by operational forecast models and in re-analysis products. Because the "true" atmospheric state cannot be precisely quantified, there is necessarily error in every atmospheric analysis, and this error can be estimated by computing differences ( variance and bias) between analysis products produced at various centers (e.g., ECMWF, NCEP, U.S Navy, etc.) that use independent data assimilation procedures, somewhat different sets of atmospheric observations and forecast models with different resolutions, dynamical equations, and physical parameterizations. These estimates of analysis uncertainty provide a useful proxy to actual analysis error. For this study, we use a unique multi-year and multi-model data archive developed at NRL-Monterey. It will be shown that current uncertainty in atmospheric analyses is closely correlated with the geographic distribution of assimilated in-situ atmospheric observations, especially those provided by high-accuracy radiosonde and commercial aircraft observations. The lowest atmospheric analysis uncertainty is found over North America, Europe and Eastern Asia, which have the largest numbers of radiosonde and commercial aircraft observations. Analysis uncertainty is substantially larger (by factors of two to three times) in most of the Southern hemisphere, the North Pacific ocean, and under-developed nations of Africa and South America where there are few radiosonde or commercial aircraft data. It appears that in regions where atmospheric analyses depend primarily on satellite radiance observations, analysis uncertainty of both temperature and wind remains relatively high compared to values found over North America and Europe.

Framing of Uncertainty in Scientific Publications: Towards Recommendations for Decision Support

NASA Astrophysics Data System (ADS)

Guillaume, J. H. A.; Helgeson, C.; Elsawah, S.; Jakeman, A. J.; Kummu, M.

2016-12-01

Uncertainty is recognised as an essential issue in environmental decision making and decision support. As modellers, we notably use a variety of tools and techniques within an analysis, for example related to uncertainty quantification and model validation. We also address uncertainty by how we present results. For example, experienced modellers are careful to distinguish robust conclusions from those that need further work, and the precision of quantitative results is tailored to their accuracy. In doing so, the modeller frames how uncertainty should be interpreted by their audience. This is an area which extends beyond modelling to fields such as philosophy of science, semantics, discourse analysis, intercultural communication and rhetoric. We propose that framing of uncertainty deserves greater attention in the context of decision support, and that there are opportunities in this area for fundamental research, synthesis and knowledge transfer, development of teaching curricula, and significant advances in managing uncertainty in decision making. This presentation reports preliminary results of a study of framing practices. Specifically, we analyse the framing of uncertainty that is visible in the abstracts from a corpus of scientific articles. We do this through textual analysis of the content and structure of those abstracts. Each finding that appears in an abstract is classified according to the uncertainty framing approach used, using a classification scheme that was iteratively revised based on reflection and comparison amongst three coders. This analysis indicates how frequently the different framing approaches are used, and provides initial insights into relationships between frames, how the frames relate to interpretation of uncertainty, and how rhetorical devices are used by modellers to communicate uncertainty in their work. We propose initial hypotheses for how the resulting insights might influence decision support, and help advance decision making to better address uncertainty.

CALIBRATION, OPTIMIZATION, AND SENSITIVITY AND UNCERTAINTY ALGORITHMS APPLICATION PROGRAMMING INTERFACE (COSU-API)

EPA Science Inventory

The Application Programming Interface (API) for Uncertainty Analysis, Sensitivity Analysis, and Parameter Estimation (UA/SA/PE API) tool development, here fore referred to as the Calibration, Optimization, and Sensitivity and Uncertainty Algorithms API (COSU-API), was initially d...

AN IMPROVEMENT TO THE MOUSE COMPUTERIZED UNCERTAINTY ANALYSIS SYSTEM

EPA Science Inventory

The original MOUSE (Modular Oriented Uncertainty System) system was designed to deal with the problem of uncertainties in Environmental engineering calculations, such as a set of engineering cast or risk analysis equations. It was especially intended for use by individuals with l...

Uncertainty Footprint: Visualization of Nonuniform Behavior of Iterative Algorithms Applied to 4D Cell Tracking

PubMed Central

Wan, Y.; Hansen, C.

2018-01-01

Research on microscopy data from developing biological samples usually requires tracking individual cells over time. When cells are three-dimensionally and densely packed in a time-dependent scan of volumes, tracking results can become unreliable and uncertain. Not only are cell segmentation results often inaccurate to start with, but it also lacks a simple method to evaluate the tracking outcome. Previous cell tracking methods have been validated against benchmark data from real scans or artificial data, whose ground truth results are established by manual work or simulation. However, the wide variety of real-world data makes an exhaustive validation impossible. Established cell tracking tools often fail on new data, whose issues are also difficult to diagnose with only manual examinations. Therefore, data-independent tracking evaluation methods are desired for an explosion of microscopy data with increasing scale and resolution. In this paper, we propose the uncertainty footprint, an uncertainty quantification and visualization technique that examines nonuniformity at local convergence for an iterative evaluation process on a spatial domain supported by partially overlapping bases. We demonstrate that the patterns revealed by the uncertainty footprint indicate data processing quality in two algorithms from a typical cell tracking workflow – cell identification and association. A detailed analysis of the patterns further allows us to diagnose issues and design methods for improvements. A 4D cell tracking workflow equipped with the uncertainty footprint is capable of self diagnosis and correction for a higher accuracy than previous methods whose evaluation is limited by manual examinations. PMID:29456279

Uncertainty Analysis of Seebeck Coefficient and Electrical Resistivity Characterization

NASA Technical Reports Server (NTRS)

Mackey, Jon; Sehirlioglu, Alp; Dynys, Fred

2014-01-01

In order to provide a complete description of a materials thermoelectric power factor, in addition to the measured nominal value, an uncertainty interval is required. The uncertainty may contain sources of measurement error including systematic bias error and precision error of a statistical nature. The work focuses specifically on the popular ZEM-3 (Ulvac Technologies) measurement system, but the methods apply to any measurement system. The analysis accounts for sources of systematic error including sample preparation tolerance, measurement probe placement, thermocouple cold-finger effect, and measurement parameters; in addition to including uncertainty of a statistical nature. Complete uncertainty analysis of a measurement system allows for more reliable comparison of measurement data between laboratories.

Multi-objective calibration and uncertainty analysis of hydrologic models; A comparative study between formal and informal methods

NASA Astrophysics Data System (ADS)

Shafii, M.; Tolson, B.; Matott, L. S.

2012-04-01

Hydrologic modeling has benefited from significant developments over the past two decades. This has resulted in building of higher levels of complexity into hydrologic models, which eventually makes the model evaluation process (parameter estimation via calibration and uncertainty analysis) more challenging. In order to avoid unreasonable parameter estimates, many researchers have suggested implementation of multi-criteria calibration schemes. Furthermore, for predictive hydrologic models to be useful, proper consideration of uncertainty is essential. Consequently, recent research has emphasized comprehensive model assessment procedures in which multi-criteria parameter estimation is combined with statistically-based uncertainty analysis routines such as Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. Such a procedure relies on the use of formal likelihood functions based on statistical assumptions, and moreover, the Bayesian inference structured on MCMC samplers requires a considerably large number of simulations. Due to these issues, especially in complex non-linear hydrological models, a variety of alternative informal approaches have been proposed for uncertainty analysis in the multi-criteria context. This study aims at exploring a number of such informal uncertainty analysis techniques in multi-criteria calibration of hydrological models. The informal methods addressed in this study are (i) Pareto optimality which quantifies the parameter uncertainty using the Pareto solutions, (ii) DDS-AU which uses the weighted sum of objective functions to derive the prediction limits, and (iii) GLUE which describes the total uncertainty through identification of behavioral solutions. The main objective is to compare such methods with MCMC-based Bayesian inference with respect to factors such as computational burden, and predictive capacity, which are evaluated based on multiple comparative measures. The measures for comparison are calculated both for calibration and evaluation periods. The uncertainty analysis methodologies are applied to a simple 5-parameter rainfall-runoff model, called HYMOD.

Data processing and error analysis for the CE-1 Lunar microwave radiometer

NASA Astrophysics Data System (ADS)

Feng, Jian-Qing; Su, Yan; Liu, Jian-Jun; Zou, Yong-Liao; Li, Chun-Lai

2013-03-01

The microwave radiometer (MRM) onboard the Chang' E-1 (CE-1) lunar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature (TB) of the lunar surface, from which the thickness, temperature, dielectric constant and other related properties of the lunar regolith can be derived. The working mode of the CE-1 MRM, the ground calibration (including the official calibration coefficients), as well as the acquisition and processing of the raw data are introduced. Our data analysis shows that TB increases with increasing frequency, decreases towards the lunar poles and is significantly affected by solar illumination. Our analysis also reveals that the main uncertainty in TB comes from ground calibration.

Uncertainty analysis of diffuse-gray radiation enclosure problems: A hypersensitive case study

NASA Technical Reports Server (NTRS)

Taylor, Robert P.; Luck, Rogelio; Hodge, B. K.; Steele, W. Glenn

1993-01-01

An uncertainty analysis of diffuse-gray enclosure problems is presented. The genesis was a diffuse-gray enclosure problem which proved to be hypersensitive to the specification of view factors. This genesis is discussed in some detail. The uncertainty analysis is presented for the general diffuse-gray enclosure problem and applied to the hypersensitive case study. It was found that the hypersensitivity could be greatly reduced by enforcing both closure and reciprocity for the view factors. The effects of uncertainties in the surface emissivities and temperatures are also investigated.

Seven years of aerosol scattering hygroscopic growth measurements from SGP: Factors influencing water uptake: Aerosol Scattering Hygroscopic Growth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jefferson, A.; Hageman, D.; Morrow, H.

Long-term measurements of changes in the aerosol scattering coefficient hygroscopic growth at the U.S. Department of Energy Southern Great Plains site provide information on the seasonal as well as size and chemical dependence of aerosol water uptake. Annual average sub-10 μm fRH values (the ratio of aerosol scattering at 85%/40% relative humidity (RH)) were 1.78 and 1.99 for the gamma and kappa fit algorithms, respectively. Our study found higher growth rates in the winter and spring seasons that correlated with a high aerosol nitrate mass fraction. fRH exhibited strong, but differing, correlations with the scattering Ångström exponent and backscatter fraction,more » two optical size-dependent parameters. The aerosol organic mass fraction had a strong influence on fRH. Increases in the organic mass fraction and absorption Ångström exponent coincided with a decrease in fRH. Similarly, fRH declined with decreases in the aerosol single scatter albedo. The uncertainty analysis of the fit algorithms revealed high uncertainty at low scattering coefficients and increased uncertainty at high RH and fit parameters values.« less

Seven years of aerosol scattering hygroscopic growth measurements from SGP: Factors influencing water uptake: Aerosol Scattering Hygroscopic Growth

DOE PAGES

Jefferson, A.; Hageman, D.; Morrow, H.; ...

2017-09-11

Long-term measurements of changes in the aerosol scattering coefficient hygroscopic growth at the U.S. Department of Energy Southern Great Plains site provide information on the seasonal as well as size and chemical dependence of aerosol water uptake. Annual average sub-10 μm fRH values (the ratio of aerosol scattering at 85%/40% relative humidity (RH)) were 1.78 and 1.99 for the gamma and kappa fit algorithms, respectively. Our study found higher growth rates in the winter and spring seasons that correlated with a high aerosol nitrate mass fraction. fRH exhibited strong, but differing, correlations with the scattering Ångström exponent and backscatter fraction,more » two optical size-dependent parameters. The aerosol organic mass fraction had a strong influence on fRH. Increases in the organic mass fraction and absorption Ångström exponent coincided with a decrease in fRH. Similarly, fRH declined with decreases in the aerosol single scatter albedo. The uncertainty analysis of the fit algorithms revealed high uncertainty at low scattering coefficients and increased uncertainty at high RH and fit parameters values.« less

Analysis of determinations of the distance between the sun and the galactic center

NASA Astrophysics Data System (ADS)

Malkin, Z. M.

2013-02-01

The paper investigates the question of whether or not determinations of the distance between the Sun and the Galactic center R 0 are affected by the so-called "bandwagon effect", leading to selection effects in published data that tend to be close to expected values, as was suggested by some authors. It is difficult to estimate numerically a systematic uncertainty in R 0 due to the bandwagon effect; however, it is highly probable that, even if widely accepted values differ appreciably from the true value, the published results should eventually approach the true value despite the bandwagon effect. This should be manifest as a trend in the published R 0 data: if this trend is statistically significant, the presence of the bandwagon effect can be suspected in the data. Fifty two determinations of R 0 published over the last 20 years were analyzed. These data reveal no statistically significant trend, suggesting they are unlikely to involve any systematic uncertainty due to the bandwagon effect. At the same time, the published data show a gradual and statistically significant decrease in the uncertainties in the R 0 determinations with time.

Performance Assessment Uncertainty Analysis for Japan's HLW Program Feasibility Study (H12)

DOE Office of Scientific and Technical Information (OSTI.GOV)

BABA,T.; ISHIGURO,K.; ISHIHARA,Y.

1999-08-30

Most HLW programs in the world recognize that any estimate of long-term radiological performance must be couched in terms of the uncertainties derived from natural variation, changes through time and lack of knowledge about the essential processes. The Japan Nuclear Cycle Development Institute followed a relatively standard procedure to address two major categories of uncertainty. First, a FEatures, Events and Processes (FEPs) listing, screening and grouping activity was pursued in order to define the range of uncertainty in system processes as well as possible variations in engineering design. A reference and many alternative cases representing various groups of FEPs weremore » defined and individual numerical simulations performed for each to quantify the range of conceptual uncertainty. Second, parameter distributions were developed for the reference case to represent the uncertainty in the strength of these processes, the sequencing of activities and geometric variations. Both point estimates using high and low values for individual parameters as well as a probabilistic analysis were performed to estimate parameter uncertainty. A brief description of the conceptual model uncertainty analysis is presented. This paper focuses on presenting the details of the probabilistic parameter uncertainty assessment.« less

Methods for Estimating the Uncertainty in Emergy Table-Form Models

EPA Science Inventory

Emergy studies have suffered criticism due to the lack of uncertainty analysis and this shortcoming may have directly hindered the wider application and acceptance of this methodology. Recently, to fill this gap, the sources of uncertainty in emergy analysis were described and an...

Quantile regression reveals hidden bias and uncertainty in habitat models

Treesearch

Brian S. Cade; Barry R. Noon; Curtis H. Flather

2005-01-01

We simulated the effects of missing information on statistical distributions of animal response that covaried with measured predictors of habitat to evaluate the utility and performance of quantile regression for providing more useful intervals of uncertainty in habitat relationships. These procedures were evaulated for conditions in which heterogeneity and hidden bias...

Irreducible Uncertainty in Terrestrial Carbon Projections

NASA Astrophysics Data System (ADS)

Lovenduski, N. S.; Bonan, G. B.

2016-12-01

We quantify and isolate the sources of uncertainty in projections of carbon accumulation by the ocean and terrestrial biosphere over 2006-2100 using output from Earth System Models participating in the 5th Coupled Model Intercomparison Project. We consider three independent sources of uncertainty in our analysis of variance: (1) internal variability, driven by random, internal variations in the climate system, (2) emission scenario, driven by uncertainty in future radiative forcing, and (3) model structure, wherein different models produce different projections given the same emission scenario. Whereas uncertainty in projections of ocean carbon accumulation by 2100 is 100 Pg C and driven primarily by emission scenario, uncertainty in projections of terrestrial carbon accumulation by 2100 is 50% larger than that of the ocean, and driven primarily by model structure. This structural uncertainty is correlated with emission scenario: the variance associated with model structure is an order of magnitude larger under a business-as-usual scenario (RCP8.5) than a mitigation scenario (RCP2.6). In an effort to reduce this structural uncertainty, we apply various model weighting schemes to our analysis of variance in terrestrial carbon accumulation projections. The largest reductions in uncertainty are achieved when giving all the weight to a single model; here the uncertainty is of a similar magnitude to the ocean projections. Such an analysis suggests that this structural uncertainty is irreducible given current terrestrial model development efforts.

Uncertainty analysis in vulnerability estimations for elements at risk- a review of concepts and some examples on landslides

NASA Astrophysics Data System (ADS)

Ciurean, R. L.; Glade, T.

2012-04-01

Decision under uncertainty is a constant of everyday life and an important component of risk management and governance. Recently, experts have emphasized the importance of quantifying uncertainty in all phases of landslide risk analysis. Due to its multi-dimensional and dynamic nature, (physical) vulnerability is inherently complex and the "degree of loss" estimates imprecise and to some extent even subjective. Uncertainty analysis introduces quantitative modeling approaches that allow for a more explicitly objective output, improving the risk management process as well as enhancing communication between various stakeholders for better risk governance. This study presents a review of concepts for uncertainty analysis in vulnerability of elements at risk to landslides. Different semi-quantitative and quantitative methods are compared based on their feasibility in real-world situations, hazard dependency, process stage in vulnerability assessment (i.e. input data, model, output), and applicability within an integrated landslide hazard and risk framework. The resulted observations will help to identify current gaps and future needs in vulnerability assessment, including estimation of uncertainty propagation, transferability of the methods, development of visualization tools, but also address basic questions like what is uncertainty and how uncertainty can be quantified or treated in a reliable and reproducible way.

Uncertainty Analysis of Consequence Management (CM) Data Products.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hunt, Brian D.; Eckert-Gallup, Aubrey Celia; Cochran, Lainy Dromgoole

The goal of this project is to develop and execute methods for characterizing uncertainty in data products that are deve loped and distributed by the DOE Consequence Management (CM) Program. A global approach to this problem is necessary because multiple sources of error and uncertainty from across the CM skill sets contribute to the ultimate p roduction of CM data products. This report presents the methods used to develop a probabilistic framework to characterize this uncertainty and provides results for an uncertainty analysis for a study scenario analyzed using this framework.

Influences of system uncertainties on the numerical transfer path analysis of engine systems

NASA Astrophysics Data System (ADS)

Acri, A.; Nijman, E.; Acri, A.; Offner, G.

2017-10-01

Practical mechanical systems operate with some degree of uncertainty. In numerical models uncertainties can result from poorly known or variable parameters, from geometrical approximation, from discretization or numerical errors, from uncertain inputs or from rapidly changing forcing that can be best described in a stochastic framework. Recently, random matrix theory was introduced to take parameter uncertainties into account in numerical modeling problems. In particular in this paper, Wishart random matrix theory is applied on a multi-body dynamic system to generate random variations of the properties of system components. Multi-body dynamics is a powerful numerical tool largely implemented during the design of new engines. In this paper the influence of model parameter variability on the results obtained from the multi-body simulation of engine dynamics is investigated. The aim is to define a methodology to properly assess and rank system sources when dealing with uncertainties. Particular attention is paid to the influence of these uncertainties on the analysis and the assessment of the different engine vibration sources. Examples of the effects of different levels of uncertainties are illustrated by means of examples using a representative numerical powertrain model. A numerical transfer path analysis, based on system dynamic substructuring, is used to derive and assess the internal engine vibration sources. The results obtained from this analysis are used to derive correlations between parameter uncertainties and statistical distribution of results. The derived statistical information can be used to advance the knowledge of the multi-body analysis and the assessment of system sources when uncertainties in model parameters are considered.

Measurement uncertainty of liquid chromatographic analyses visualized by Ishikawa diagrams.

PubMed

Meyer, Veronika R

2003-09-01

Ishikawa, or cause-and-effect diagrams, help to visualize the parameters that influence a chromatographic analysis. Therefore, they facilitate the set up of the uncertainty budget of the analysis, which can then be expressed in mathematical form. If the uncertainty is calculated as the Gaussian sum of all uncertainty parameters, it is necessary to quantitate them all, a task that is usually not practical. The other possible approach is to use the intermediate precision as a base for the uncertainty calculation. In this case, it is at least necessary to consider the uncertainty of the purity of the reference material in addition to the precision data. The Ishikawa diagram is then very simple, and so is the uncertainty calculation. This advantage is given by the loss of information about the parameters that influence the measurement uncertainty.

A comparative appraisal of hydrological behavior of SRTM DEM at catchment level

NASA Astrophysics Data System (ADS)

Sharma, Arabinda; Tiwari, K. N.

2014-11-01

The Shuttle Radar Topography Mission (SRTM) data has emerged as a global elevation data in the past one decade because of its free availability, homogeneity and consistent accuracy compared to other global elevation dataset. The present study explores the consistency in hydrological behavior of the SRTM digital elevation model (DEM) with reference to easily available regional 20 m contour interpolated DEM (TOPO DEM). Analysis ranging from simple vertical accuracy assessment to hydrological simulation of the studied Maithon catchment, using empirical USLE model and semidistributed, physical SWAT model, were carried out. Moreover, terrain analysis involving hydrological indices was performed for comparative assessment of the SRTM DEM with respect to TOPO DEM. Results reveal that the vertical accuracy of SRTM DEM (±27.58 m) in the region is less than the specified standard (±16 m). Statistical analysis of hydrological indices such as topographic wetness index (TWI), stream power index (SPI), slope length factor (SLF) and geometry number (GN) shows a significant differences in hydrological properties of the two studied DEMs. Estimation of soil erosion potentials of the catchment and conservation priorities of microwatersheds of the catchment using SRTM DEM and TOPO DEM produce considerably different results. Prediction of soil erosion potential using SRTM DEM is far higher than that obtained using TOPO DEM. Similarly, conservation priorities determined using the two DEMs are found to be agreed for only 34% of microwatersheds of the catchment. ArcSWAT simulation reveals that runoff predictions are less sensitive to selection of the two DEMs as compared to sediment yield prediction. The results obtained in the present study are vital to hydrological analysis as it helps understanding the hydrological behavior of the DEM without being influenced by the model structural as well as parameter uncertainty. It also reemphasized that SRTM DEM can be a valuable dataset for hydrological analysis provided any error/uncertainty therein is being properly evaluated and characterized.

Analysis of uncertainties in the estimates of nitrous oxide and methane emissions in the UK's greenhouse gas inventory for agriculture

NASA Astrophysics Data System (ADS)

Milne, Alice E.; Glendining, Margaret J.; Bellamy, Pat; Misselbrook, Tom; Gilhespy, Sarah; Rivas Casado, Monica; Hulin, Adele; van Oijen, Marcel; Whitmore, Andrew P.

2014-01-01

The UK's greenhouse gas inventory for agriculture uses a model based on the IPCC Tier 1 and Tier 2 methods to estimate the emissions of methane and nitrous oxide from agriculture. The inventory calculations are disaggregated at country level (England, Wales, Scotland and Northern Ireland). Before now, no detailed assessment of the uncertainties in the estimates of emissions had been done. We used Monte Carlo simulation to do such an analysis. We collated information on the uncertainties of each of the model inputs. The uncertainties propagate through the model and result in uncertainties in the estimated emissions. Using a sensitivity analysis, we found that in England and Scotland the uncertainty in the emission factor for emissions from N inputs (EF1) affected uncertainty the most, but that in Wales and Northern Ireland, the emission factor for N leaching and runoff (EF5) had greater influence. We showed that if the uncertainty in any one of these emission factors is reduced by 50%, the uncertainty in emissions of nitrous oxide reduces by 10%. The uncertainty in the estimate for the emissions of methane emission factors for enteric fermentation in cows and sheep most affected the uncertainty in methane emissions. When inventories are disaggregated (as that for the UK is) correlation between separate instances of each emission factor will affect the uncertainty in emissions. As more countries move towards inventory models with disaggregation, it is important that the IPCC give firm guidance on this topic.

Uncertainties in internal gas counting

NASA Astrophysics Data System (ADS)

Unterweger, M.; Johansson, L.; Karam, L.; Rodrigues, M.; Yunoki, A.

2015-06-01

The uncertainties in internal gas counting will be broken down into counting uncertainties and gas handling uncertainties. Counting statistics, spectrum analysis, and electronic uncertainties will be discussed with respect to the actual counting of the activity. The effects of the gas handling and quantities of counting and sample gases on the uncertainty in the determination of the activity will be included when describing the uncertainties arising in the sample preparation.

Extracting additional risk managers information from a risk assessment of Listeria monocytogenes in deli meats.

PubMed

Pérez-Rodríguez, F; van Asselt, E D; Garcia-Gimeno, R M; Zurera, G; Zwietering, M H

2007-05-01

The risk assessment study of Listeria monocytogenes in ready-to-eat foods conducted by the U.S. Food and Drug Administration is an example of an extensive quantitative microbiological risk assessment that could be used by risk analysts and other scientists to obtain information and by managers and stakeholders to make decisions on food safety management. The present study was conducted to investigate how detailed sensitivity analysis can be used by assessors to extract more information on risk factors and how results can be communicated to managers and stakeholders in an understandable way. The extended sensitivity analysis revealed that the extremes at the right side of the dose distribution (at consumption, 9 to 11.5 log CFU per serving) were responsible for most of the cases of listeriosis simulated. For concentration at retail, values below the detection limit of 0.04 CFU/g and the often used limit for L. monocytogenes of 100 CFU/g (also at retail) were associated with a high number of annual cases of listeriosis (about 29 and 82%, respectively). This association can be explained by growth of L. monocytogenes at both average and extreme values of temperature and time, indicating that a wide distribution can lead to high risk levels. Another finding is the importance of the maximal population density (i.e., the maximum concentration of L. monocytogenes assumed at a certain temperature) for accurately estimating the risk of infection by opportunistic pathogens such as L. monocytogenes. According to the obtained results, mainly concentrations corresponding to the highest maximal population densities caused risk in the simulation. However, sensitivity analysis applied to the uncertainty parameters revealed that prevalence at retail was the most important source of uncertainty in the model.

10 CFR 436.24 - Uncertainty analyses.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle cost...

10 CFR 436.24 - Uncertainty analyses.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle cost...

10 CFR 436.24 - Uncertainty analyses.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle cost...

10 CFR 436.24 - Uncertainty analyses.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Procedures for Life Cycle Cost Analyses § 436.24 Uncertainty analyses. If particular items of cost data or... impact of uncertainty on the calculation of life cycle cost effectiveness or the assignment of rank order... and probabilistic analysis. If additional analysis casts substantial doubt on the life cycle cost...

MOUSE (MODULAR ORIENTED UNCERTAINTY SYSTEM): A COMPUTERIZED UNCERTAINTY ANALYSIS SYSTEM (FOR MICRO- COMPUTERS)

EPA Science Inventory

Environmental engineering calculations involving uncertainties; either in the model itself or in the data, are far beyond the capabilities of conventional analysis for any but the simplest of models. There exist a number of general-purpose computer simulation languages, using Mon...

Estimation Of TMDLs And Margin Of Safety Under Conditions Of Uncertainty

EPA Science Inventory

In TMDL development, an adequate margin of safety (MOS) is required in the calculation process to provide a cushion needed because of uncertainties in the data and analysis. Current practices, however, rarely factor analysis' uncertainty in TMDL development and the MOS is largel...

MODEL UNCERTAINTY ANALYSIS, FIELD DATA COLLECTION AND ANALYSIS OF CONTAMINATED VAPOR INTRUSION INTO BUILDINGS

EPA Science Inventory

To address uncertainty associated with the evaluation of vapor intrusion problems we are working on a three part strategy that includes: evaluation of uncertainty in model-based assessments; collection of field data and assessment of sites using EPA and state protocols.

Transfer of uncertainty of space-borne high resolution rainfall products at ungauged regions

NASA Astrophysics Data System (ADS)

Tang, Ling

Hydrologically relevant characteristics of high resolution (˜ 0.25 degree, 3 hourly) satellite rainfall uncertainty were derived as a function of season and location using a six year (2002-2007) archive of National Aeronautics and Space Administration (NASA)'s Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) precipitation data. The Next Generation Radar (NEXRAD) Stage IV rainfall data over the continental United States was used as ground validation (GV) data. A geostatistical mapping scheme was developed and tested for transfer (i.e., spatial interpolation) of uncertainty information from GV regions to the vast non-GV regions by leveraging the error characterization work carried out in the earlier step. The open question explored here was, "If 'error' is defined on the basis of independent ground validation (GV) data, how are error metrics estimated for a satellite rainfall data product without the need for much extensive GV data?" After a quantitative analysis of the spatial and temporal structure of the satellite rainfall uncertainty, a proof-of-concept geostatistical mapping scheme (based on the kriging method) was evaluated. The idea was to understand how realistic the idea of 'transfer' is for the GPM era. It was found that it was indeed technically possible to transfer error metrics from a gauged to an ungauged location for certain error metrics and that a regionalized error metric scheme for GPM may be possible. The uncertainty transfer scheme based on a commonly used kriging method (ordinary kriging) was then assessed further at various timescales (climatologic, seasonal, monthly and weekly), and as a function of the density of GV coverage. The results indicated that if a transfer scheme for estimating uncertainty metrics was finer than seasonal scale (ranging from 3-6 hourly to weekly-monthly), the effectiveness for uncertainty transfer worsened significantly. Next, a comprehensive assessment of different kriging methods for spatial transfer (interpolation) of error metrics was performed. Three kriging methods for spatial interpolation are compared, which are: ordinary kriging (OK), indicator kriging (IK) and disjunctive kriging (DK). Additional comparison with the simple inverse distance weighting (IDW) method was also performed to quantify the added benefit (if any) of using geostatistical methods. The overall performance ranking of the kriging methods was found to be as follows: OK=DK > IDW > IK. Lastly, various metrics of satellite rainfall uncertainty were identified for two large continental landmasses that share many similar Koppen climate zones, United States and Australia. The dependence of uncertainty as a function of gauge density was then investigated. The investigation revealed that only the first and second ordered moments of error are most amenable to a Koppen-type climate type classification in different continental landmasses.

Experimental validation of 2D uncertainty quantification for DIC.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reu, Phillip L.

Because digital image correlation (DIC) has become such an important and standard tool in the toolbox of experimental mechanicists, a complete uncertainty quantification of the method is needed. It should be remembered that each DIC setup and series of images will have a unique uncertainty based on the calibration quality and the image and speckle quality of the analyzed images. Any pretest work done with a calibrated DIC stereo-rig to quantify the errors using known shapes and translations, while useful, do not necessarily reveal the uncertainty of a later test. This is particularly true with high-speed applications where actual testmore » images are often less than ideal. Work has previously been completed on the mathematical underpinnings of DIC uncertainty quantification and is already published, this paper will present corresponding experimental work used to check the validity of the uncertainty equations.« less

Experimental validation of 2D uncertainty quantification for digital image correlation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reu, Phillip L.

Because digital image correlation (DIC) has become such an important and standard tool in the toolbox of experimental mechanicists, a complete uncertainty quantification of the method is needed. It should be remembered that each DIC setup and series of images will have a unique uncertainty based on the calibration quality and the image and speckle quality of the analyzed images. Any pretest work done with a calibrated DIC stereo-rig to quantify the errors using known shapes and translations, while useful, do not necessarily reveal the uncertainty of a later test. This is particularly true with high-speed applications where actual testmore » images are often less than ideal. Work has previously been completed on the mathematical underpinnings of DIC uncertainty quantification and is already published, this paper will present corresponding experimental work used to check the validity of the uncertainty equations.« less

Are needs to manage uncertainty and threat associated with political conservatism or ideological extremity?

PubMed

Jost, John T; Napier, Jaime L; Thorisdottir, Hulda; Gosling, Samuel D; Palfai, Tibor P; Ostafin, Brian

2007-07-01

Three studies are conducted to assess the uncertainty- threat model of political conservatism, which posits that psychological needs to manage uncertainty and threat are associated with political orientation. Results from structural equation models provide consistent support for the hypothesis that uncertainty avoidance (e.g., need for order, intolerance of ambiguity, and lack of openness to experience) and threat management (e.g., death anxiety, system threat, and perceptions of a dangerous world) each contributes independently to conservatism (vs. liberalism). No support is obtained for alternative models, which predict that uncertainty and threat management are associated with ideological extremism or extreme forms of conservatism only. Study 3 also reveals that resistance to change fully mediates the association between uncertainty avoidance and conservatism, whereas opposition to equality partially mediates the association between threat and conservatism. Implications for understanding the epistemic and existential bases of political orientation are discussed.

`spup' - An R Package for Analysis of Spatial Uncertainty Propagation and Application to Trace Gas Emission Simulations

NASA Astrophysics Data System (ADS)

Sawicka, K.; Breuer, L.; Houska, T.; Santabarbara Ruiz, I.; Heuvelink, G. B. M.

2016-12-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Advances in uncertainty propagation analysis and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability, including case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the `spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo techniques, as well as several uncertainty visualization functions. Here we will demonstrate that the 'spup' package is an effective and easy-to-use tool to be applied even in a very complex study case, and that it can be used in multi-disciplinary research and model-based decision support. As an example, we use the ecological LandscapeDNDC model to analyse propagation of uncertainties associated with spatial variability of the model driving forces such as rainfall, nitrogen deposition and fertilizer inputs. The uncertainty propagation is analysed for the prediction of emissions of N2O and CO2 for a German low mountainous, agriculturally developed catchment. The study tests the effect of spatial correlations on spatially aggregated model outputs, and could serve as an advice for developing best management practices and model improvement strategies.

Uncertainty analysis and robust trajectory linearization control of a flexible air-breathing hypersonic vehicle

NASA Astrophysics Data System (ADS)

Pu, Zhiqiang; Tan, Xiangmin; Fan, Guoliang; Yi, Jianqiang

2014-08-01

Flexible air-breathing hypersonic vehicles feature significant uncertainties which pose huge challenges to robust controller designs. In this paper, four major categories of uncertainties are analyzed, that is, uncertainties associated with flexible effects, aerodynamic parameter variations, external environmental disturbances, and control-oriented modeling errors. A uniform nonlinear uncertainty model is explored for the first three uncertainties which lumps all uncertainties together and consequently is beneficial for controller synthesis. The fourth uncertainty is additionally considered in stability analysis. Based on these analyses, the starting point of the control design is to decompose the vehicle dynamics into five functional subsystems. Then a robust trajectory linearization control (TLC) scheme consisting of five robust subsystem controllers is proposed. In each subsystem controller, TLC is combined with the extended state observer (ESO) technique for uncertainty compensation. The stability of the overall closed-loop system with the four aforementioned uncertainties and additional singular perturbations is analyzed. Particularly, the stability of nonlinear ESO is also discussed from a Liénard system perspective. At last, simulations demonstrate the great control performance and the uncertainty rejection ability of the robust scheme.

Uncertainty Analysis of Instrument Calibration and Application

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Experimental aerodynamic researchers require estimated precision and bias uncertainties of measured physical quantities, typically at 95 percent confidence levels. Uncertainties of final computed aerodynamic parameters are obtained by propagation of individual measurement uncertainties through the defining functional expressions. In this paper, rigorous mathematical techniques are extended to determine precision and bias uncertainties of any instrument-sensor system. Through this analysis, instrument uncertainties determined through calibration are now expressed as functions of the corresponding measurement for linear and nonlinear univariate and multivariate processes. Treatment of correlated measurement precision error is developed. During laboratory calibration, calibration standard uncertainties are assumed to be an order of magnitude less than those of the instrument being calibrated. Often calibration standards do not satisfy this assumption. This paper applies rigorous statistical methods for inclusion of calibration standard uncertainty and covariance due to the order of their application. The effects of mathematical modeling error on calibration bias uncertainty are quantified. The effects of experimental design on uncertainty are analyzed. The importance of replication is emphasized, techniques for estimation of both bias and precision uncertainties using replication are developed. Statistical tests for stationarity of calibration parameters over time are obtained.

Comparison of methods for accurate end-point detection of potentiometric titrations

NASA Astrophysics Data System (ADS)

Villela, R. L. A.; Borges, P. P.; Vyskočil, L.

2015-01-01

Detection of the end point in potentiometric titrations has wide application on experiments that demand very low measurement uncertainties mainly for certifying reference materials. Simulations of experimental coulometric titration data and consequential error analysis of the end-point values were conducted using a programming code. These simulations revealed that the Levenberg-Marquardt method is in general more accurate than the traditional second derivative technique used currently as end-point detection for potentiometric titrations. Performance of the methods will be compared and presented in this paper.

Validation of the Transient Structural Response of a Threaded Assembly: Phase I

DOE Office of Scientific and Technical Information (OSTI.GOV)

Doebling, Scott W.; Hemez, Francois M.; Robertson, Amy N.

2004-04-01

This report explores the application of model validation techniques in structural dynamics. The problem of interest is the propagation of an explosive-driven mechanical shock through a complex threaded joint. The study serves the purpose of assessing whether validating a large-size computational model is feasible, which unit experiments are required, and where the main sources of uncertainty reside. The results documented here are preliminary, and the analyses are exploratory in nature. The results obtained to date reveal several deficiencies of the analysis, to be rectified in future work.

Landing Gear Noise Prediction and Analysis for Tube-and-Wing and Hybrid-Wing-Body Aircraft

NASA Technical Reports Server (NTRS)

Guo, Yueping; Burley, Casey L.; Thomas, Russell H.

2016-01-01

Improvements and extensions to landing gear noise prediction methods are developed. New features include installation effects such as reflection from the aircraft, gear truck angle effect, local flow calculation at the landing gear locations, gear size effect, and directivity for various gear designs. These new features have not only significantly improved the accuracy and robustness of the prediction tools, but also have enabled applications to unconventional aircraft designs and installations. Systematic validations of the improved prediction capability are then presented, including parametric validations in functional trends as well as validations in absolute amplitudes, covering a wide variety of landing gear designs, sizes, and testing conditions. The new method is then applied to selected concept aircraft configurations in the portfolio of the NASA Environmentally Responsible Aviation Project envisioned for the timeframe of 2025. The landing gear noise levels are on the order of 2 to 4 dB higher than previously reported predictions due to increased fidelity in accounting for installation effects and gear design details. With the new method, it is now possible to reveal and assess the unique noise characteristics of landing gear systems for each type of aircraft. To address the inevitable uncertainties in predictions of landing gear noise models for future aircraft, an uncertainty analysis is given, using the method of Monte Carlo simulation. The standard deviation of the uncertainty in predicting the absolute level of landing gear noise is quantified and determined to be 1.4 EPNL dB.

Precision and Accuracy in PDV and VISAR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ambrose, W. P.

2017-08-22

This is a technical report discussing our current level of understanding of a wide and varying distribution of uncertainties in velocity results from Photonic Doppler Velocimetry in its application to gas gun experiments. Using propagation of errors methods with statistical averaging of photon number fluctuation in the detected photocurrent and subsequent addition of electronic recording noise, we learn that the velocity uncertainty in VISAR can be written in closed form. For PDV, the non-linear frequency transform and peak fitting methods employed make propagation of errors estimates notoriously more difficult to write down in closed form expect in the limit ofmore » constant velocity and low time resolution (large analysis-window width). An alternative method of error propagation in PDV is to use Monte Carlo methods with a simulation of the time domain signal based on results from the spectral domain. A key problem for Monte Carlo estimation for an experiment is a correct estimate of that portion of the time-domain noise associated with the peak-fitting region-of-interesting in the spectral domain. Using short-time Fourier transformation spectral analysis and working with the phase dependent real and imaginary parts allows removal of amplitude-noise cross terms that invariably show up when working with correlation-based methods or FFT power spectra. Estimation of the noise associated with a given spectral region of interest is then possible. At this level of progress, we learn that Monte Carlo trials with random recording noise and initial (uncontrolled) phase yields velocity uncertainties that are not as large as those observed. In a search for additional noise sources, a speckleinterference modulation contribution with off axis rays was investigated, and was found to add a velocity variation beyond that from the recording noise (due to random interference between off axis rays), but in our experiments the speckle modulation precision was not as important as the recording noise precision. But from these investigations we do appreciate that the velocity-uncertainty itself has a wide distribution of values that varies with signal-amplitude modulation (is not a single value). To provide a rough rule of thumb for the velocity uncertainty, we computed the average of the relative standard deviation distributions from 60 recorded traces (with distributions of uncertainties roughly between 0.1 % to 1 % in each trace) and found a mean of the distribution of uncertainties for our experiments is not better than 0.4 % at an analysis window width of 5 ns (although for brief intervals it can be as good as 0.1 %). Further imagination and testing may be needed to reveal other possible hydrodynamics-related sources of velocity error in PDV.« less

Consensus building for interlaboratory studies, key comparisons, and meta-analysis

NASA Astrophysics Data System (ADS)

Koepke, Amanda; Lafarge, Thomas; Possolo, Antonio; Toman, Blaza

2017-06-01

Interlaboratory studies in measurement science, including key comparisons, and meta-analyses in several fields, including medicine, serve to intercompare measurement results obtained independently, and typically produce a consensus value for the common measurand that blends the values measured by the participants. Since interlaboratory studies and meta-analyses reveal and quantify differences between measured values, regardless of the underlying causes for such differences, they also provide so-called ‘top-down’ evaluations of measurement uncertainty. Measured values are often substantially over-dispersed by comparison with their individual, stated uncertainties, thus suggesting the existence of yet unrecognized sources of uncertainty (dark uncertainty). We contrast two different approaches to take dark uncertainty into account both in the computation of consensus values and in the evaluation of the associated uncertainty, which have traditionally been preferred by different scientific communities. One inflates the stated uncertainties by a multiplicative factor. The other adds laboratory-specific ‘effects’ to the value of the measurand. After distinguishing what we call recipe-based and model-based approaches to data reductions in interlaboratory studies, we state six guiding principles that should inform such reductions. These principles favor model-based approaches that expose and facilitate the critical assessment of validating assumptions, and give preeminence to substantive criteria to determine which measurement results to include, and which to exclude, as opposed to purely statistical considerations, and also how to weigh them. Following an overview of maximum likelihood methods, three general purpose procedures for data reduction are described in detail, including explanations of how the consensus value and degrees of equivalence are computed, and the associated uncertainty evaluated: the DerSimonian-Laird procedure; a hierarchical Bayesian procedure; and the Linear Pool. These three procedures have been implemented and made widely accessible in a Web-based application (NIST Consensus Builder). We illustrate principles, statistical models, and data reduction procedures in four examples: (i) the measurement of the Newtonian constant of gravitation; (ii) the measurement of the half-lives of radioactive isotopes of caesium and strontium; (iii) the comparison of two alternative treatments for carotid artery stenosis; and (iv) a key comparison where the measurand was the calibration factor of a radio-frequency power sensor.

The time-varying correlation between policy uncertainty and stock returns: Evidence from China

NASA Astrophysics Data System (ADS)

Xiong, Xiong; Bian, Yuxiang; Shen, Dehua

2018-06-01

In this paper, we use a new policy uncertainty index to investigate the time-varying correlation between economic policy uncertainty (EPU) and Chinese stock market returns. The correlation is examined in the period from January 1995 to December 2016. We show that absolute changes in EPU have a significant impact on stock market returns. Specifically, empirical results based on the DCC-GARCH model reveal that the correlation between EPU and stock returns has large fluctuations, especially during a financial crisis; in addition, the impact of EPU on the Shanghai stock market is greater than on the Shenzhen stock market. Robustness results reveal that the impact of EPU on state-owned enterprises is larger than on non-state enterprises. All of these results highlight the important role of EPU in the Chinese stock market, and shed light on such issues for future research.

Effect of information, uncertainty and parameter variability on profits in a queue with various pricing strategies

NASA Astrophysics Data System (ADS)

Sun, Wei; Li, Shiyong

2014-08-01

This paper presents an unobservable single-server queueing system with three types of uncertainty, where the service rate, or waiting cost or service quality is random variable that may obtain n(n > 2) values. The information about the realised values of parameters is only known to the server. We are concerned about the server's behaviour: revealing or concealing the information to customers. The n-value assumption and the server's behaviour enable us to consider various pricing strategies. In this paper, we analyse the effect of information and uncertainty on profits and make comparisons between the profits under different pricing strategies. Moreover, as for parameter variability reflected by the number of each parameter's possible choices n, we observe the effect of variable n on all types of profits and find that revealing the parameter information can much more benefit the server with the increase of n.

Importance analysis for Hudson River PCB transport and fate model parameters using robust sensitivity studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, S.; Toll, J.; Cothern, K.

1995-12-31

The authors have performed robust sensitivity studies of the physico-chemical Hudson River PCB model PCHEPM to identify the parameters and process uncertainties contributing the most to uncertainty in predictions of water column and sediment PCB concentrations, over the time period 1977--1991 in one segment of the lower Hudson River. The term ``robust sensitivity studies`` refers to the use of several sensitivity analysis techniques to obtain a more accurate depiction of the relative importance of different sources of uncertainty. Local sensitivity analysis provided data on the sensitivity of PCB concentration estimates to small perturbations in nominal parameter values. Range sensitivity analysismore » provided information about the magnitude of prediction uncertainty associated with each input uncertainty. Rank correlation analysis indicated which parameters had the most dominant influence on model predictions. Factorial analysis identified important interactions among model parameters. Finally, term analysis looked at the aggregate influence of combinations of parameters representing physico-chemical processes. The authors scored the results of the local and range sensitivity and rank correlation analyses. The authors considered parameters that scored high on two of the three analyses to be important contributors to PCB concentration prediction uncertainty, and treated them probabilistically in simulations. They also treated probabilistically parameters identified in the factorial analysis as interacting with important parameters. The authors used the term analysis to better understand how uncertain parameters were influencing the PCB concentration predictions. The importance analysis allowed us to reduce the number of parameters to be modeled probabilistically from 16 to 5. This reduced the computational complexity of Monte Carlo simulations, and more importantly, provided a more lucid depiction of prediction uncertainty and its causes.« less

Computer Model Inversion and Uncertainty Quantification in the Geosciences

NASA Astrophysics Data System (ADS)

White, Jeremy T.

The subject of this dissertation is use of computer models as data analysis tools in several different geoscience settings, including integrated surface water/groundwater modeling, tephra fallout modeling, geophysical inversion, and hydrothermal groundwater modeling. The dissertation is organized into three chapters, which correspond to three individual publication manuscripts. In the first chapter, a linear framework is developed to identify and estimate the potential predictive consequences of using a simple computer model as a data analysis tool. The framework is applied to a complex integrated surface-water/groundwater numerical model with thousands of parameters. Several types of predictions are evaluated, including particle travel time and surface-water/groundwater exchange volume. The analysis suggests that model simplifications have the potential to corrupt many types of predictions. The implementation of the inversion, including how the objective function is formulated, what minimum of the objective function value is acceptable, and how expert knowledge is enforced on parameters, can greatly influence the manifestation of model simplification. Depending on the prediction, failure to specifically address each of these important issues during inversion is shown to degrade the reliability of some predictions. In some instances, inversion is shown to increase, rather than decrease, the uncertainty of a prediction, which defeats the purpose of using a model as a data analysis tool. In the second chapter, an efficient inversion and uncertainty quantification approach is applied to a computer model of volcanic tephra transport and deposition. The computer model simulates many physical processes related to tephra transport and fallout. The utility of the approach is demonstrated for two eruption events. In both cases, the importance of uncertainty quantification is highlighted by exposing the variability in the conditioning provided by the observations used for inversion. The worth of different types of tephra data to reduce parameter uncertainty is evaluated, as is the importance of different observation error models. The analyses reveal the importance using tephra granulometry data for inversion, which results in reduced uncertainty for most eruption parameters. In the third chapter, geophysical inversion is combined with hydrothermal modeling to evaluate the enthalpy of an undeveloped geothermal resource in a pull-apart basin located in southeastern Armenia. A high-dimensional gravity inversion is used to define the depth to the contact between the lower-density valley fill sediments and the higher-density surrounding host rock. The inverted basin depth distribution was used to define the hydrostratigraphy for the coupled groundwater-flow and heat-transport model that simulates the circulation of hydrothermal fluids in the system. Evaluation of several different geothermal system configurations indicates that the most likely system configuration is a low-enthalpy, liquid-dominated geothermal system.

A stochastic approach to uncertainty quantification in residual moveout analysis

NASA Astrophysics Data System (ADS)

Johng-Ay, T.; Landa, E.; Dossou-Gbété, S.; Bordes, L.

2015-06-01

Oil and gas exploration and production relies usually on the interpretation of a single seismic image, which is obtained from observed data. However, the statistical nature of seismic data and the various approximations and assumptions are sources of uncertainties which may corrupt the evaluation of parameters. The quantification of these uncertainties is a major issue which supposes to help in decisions that have important social and commercial implications. The residual moveout analysis, which is an important step in seismic data processing is usually performed by a deterministic approach. In this paper we discuss a Bayesian approach to the uncertainty analysis.

Comparative Study of the Three-Dimensional Thermodynamical Structure of the Inner Corona of Solar Minimum Carrington Rotations 1915 and 2081

NASA Astrophysics Data System (ADS)

Lloveras, Diego G.; Vásquez, Alberto M.; Nuevo, Federico A.; Frazin, Richard A.

2017-10-01

Using differential emission measure tomography (DEMT) based on time series of EUV images, we carry out a quantitative comparative analysis of the three-dimensional (3D) structure of the electron density and temperature of the inner corona (r<1.25 R_{⊙}) between two specific rotations selected from the last two solar minima, namely Carrington Rotations (CR)1915 and CR-2081. The analysis places error bars on the results because of the systematic uncertainty of the sources. While the results for CR-2081 are characterized by a remarkable north-south symmetry, the southern hemisphere for CR-1915 exhibits higher densities and temperatures than the northern hemisphere. The core region of the streamer belt in both rotations is found to be populated by structures whose temperature decreases with height (called "down loops" in our previous articles). They are characterized by plasma β≳1, and may be the result of the efficient dissipation of Alfvén waves at low coronal heights. The comparative analysis reveals that the low latitudes of the equatorial streamer belt of CR-1915 exhibit higher densities than for CR-2081. This cannot be explained by the systematic uncertainties. In addition, the southern hemisphere of the streamer belt of CR-1915 is characterized by higher temperatures and density scale heights than for CR-2081. On the other hand, the coronal hole region of CR-1915 shows lower temperatures than for CR-2081. The reported differences are in the range ≈ 10 - 25%, depending on the specific physical quantity and region that is compared, as fully detailed in the analysis. For other regions and/or physical quantities, the uncertainties do not allow assessing the thermodynamical differences between the two rotations. Future investigation will involve a DEMT analysis of other Carrington rotations selected from both epochs, and also a comparison of their tomographic reconstructions with magnetohydrodynamical simulations of the inner corona.

Large-Scale Transport Model Uncertainty and Sensitivity Analysis: Distributed Sources in Complex Hydrogeologic Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sig Drellack, Lance Prothro

2007-12-01

The Underground Test Area (UGTA) Project of the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office is in the process of assessing and developing regulatory decision options based on modeling predictions of contaminant transport from underground testing of nuclear weapons at the Nevada Test Site (NTS). The UGTA Project is attempting to develop an effective modeling strategy that addresses and quantifies multiple components of uncertainty including natural variability, parameter uncertainty, conceptual/model uncertainty, and decision uncertainty in translating model results into regulatory requirements. The modeling task presents multiple unique challenges to the hydrological sciences as a result ofmore » the complex fractured and faulted hydrostratigraphy, the distributed locations of sources, the suite of reactive and non-reactive radionuclides, and uncertainty in conceptual models. Characterization of the hydrogeologic system is difficult and expensive because of deep groundwater in the arid desert setting and the large spatial setting of the NTS. Therefore, conceptual model uncertainty is partially addressed through the development of multiple alternative conceptual models of the hydrostratigraphic framework and multiple alternative models of recharge and discharge. Uncertainty in boundary conditions is assessed through development of alternative groundwater fluxes through multiple simulations using the regional groundwater flow model. Calibration of alternative models to heads and measured or inferred fluxes has not proven to provide clear measures of model quality. Therefore, model screening by comparison to independently-derived natural geochemical mixing targets through cluster analysis has also been invoked to evaluate differences between alternative conceptual models. Advancing multiple alternative flow models, sensitivity of transport predictions to parameter uncertainty is assessed through Monte Carlo simulations. The simulations are challenged by the distributed sources in each of the Corrective Action Units, by complex mass transfer processes, and by the size and complexity of the field-scale flow models. An efficient methodology utilizing particle tracking results and convolution integrals provides in situ concentrations appropriate for Monte Carlo analysis. Uncertainty in source releases and transport parameters including effective porosity, fracture apertures and spacing, matrix diffusion coefficients, sorption coefficients, and colloid load and mobility are considered. With the distributions of input uncertainties and output plume volumes, global analysis methods including stepwise regression, contingency table analysis, and classification tree analysis are used to develop sensitivity rankings of parameter uncertainties for each model considered, thus assisting a variety of decisions.« less

Uncertainty Measurement for Trace Element Analysis of Uranium and Plutonium Samples by Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gallimore, David L.

2012-06-13

The measurement uncertainty estimatino associated with trace element analysis of impurities in U and Pu was evaluated using the Guide to the Expression of Uncertainty Measurement (GUM). I this evalution the uncertainty sources were identified and standard uncertainties for the components were categorized as either Type A or B. The combined standard uncertainty was calculated and a coverage factor k = 2 was applied to obtain the expanded uncertainty, U. The ICP-AES and ICP-MS methods used were deveoped for the multi-element analysis of U and Pu samples. A typical analytical run consists of standards, process blanks, samples, matrix spiked samples,more » post digestion spiked samples and independent calibration verification standards. The uncertainty estimation was performed on U and Pu samples that have been analyzed previously as part of the U and Pu Sample Exchange Programs. Control chart results and data from the U and Pu metal exchange programs were combined with the GUM into a concentration dependent estimate of the expanded uncertainty. Comparison of trace element uncertainties obtained using this model was compared to those obtained for trace element results as part of the Exchange programs. This process was completed for all trace elements that were determined to be above the detection limit for the U and Pu samples.« less

Assessing Uncertainties in Surface Water Security: A Probabilistic Multi-model Resampling approach

NASA Astrophysics Data System (ADS)

Rodrigues, D. B. B.

2015-12-01

Various uncertainties are involved in the representation of processes that characterize interactions between societal needs, ecosystem functioning, and hydrological conditions. Here, we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multi-model and resampling framework. We consider several uncertainty sources including those related to: i) observed streamflow data; ii) hydrological model structure; iii) residual analysis; iv) the definition of Environmental Flow Requirement method; v) the definition of critical conditions for water provision; and vi) the critical demand imposed by human activities. We estimate the overall uncertainty coming from the hydrological model by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km² agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multi-model framework and provided by each model uncertainty estimation approach. The method is general and can be easily extended forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision making process.

Hierarchical models for informing general biomass equations with felled tree data

Treesearch

Brian J. Clough; Matthew B. Russell; Christopher W. Woodall; Grant M. Domke; Philip J. Radtke

2015-01-01

We present a hierarchical framework that uses a large multispecies felled tree database to inform a set of general models for predicting tree foliage biomass, with accompanying uncertainty, within the FIA database. Results suggest significant prediction uncertainty for individual trees and reveal higher errors when predicting foliage biomass for larger trees and for...

Assessing uncertainties in surface water security: An empirical multimodel approach

NASA Astrophysics Data System (ADS)

Rodrigues, Dulce B. B.; Gupta, Hoshin V.; Mendiondo, Eduardo M.; Oliveira, Paulo Tarso S.

2015-11-01

Various uncertainties are involved in the representation of processes that characterize interactions among societal needs, ecosystem functioning, and hydrological conditions. Here we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multimodel and resampling framework. We consider several uncertainty sources including those related to (i) observed streamflow data; (ii) hydrological model structure; (iii) residual analysis; (iv) the method for defining Environmental Flow Requirement; (v) the definition of critical conditions for water provision; and (vi) the critical demand imposed by human activities. We estimate the overall hydrological model uncertainty by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km2 agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multimodel framework and the uncertainty estimates provided by each model uncertainty estimation approach. The range of values obtained for the water security indicators suggests that the models/methods are robust and performs well in a range of plausible situations. The method is general and can be easily extended, thereby forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision-making process.

Holistic uncertainty analysis in river basin modeling for climate vulnerability assessment

NASA Astrophysics Data System (ADS)

Taner, M. U.; Wi, S.; Brown, C.

2017-12-01

The challenges posed by uncertain future climate are a prominent concern for water resources managers. A number of frameworks exist for assessing the impacts of climate-related uncertainty, including internal climate variability and anthropogenic climate change, such as scenario-based approaches and vulnerability-based approaches. While in many cases climate uncertainty may be dominant, other factors such as future evolution of the river basin, hydrologic response and reservoir operations are potentially significant sources of uncertainty. While uncertainty associated with modeling hydrologic response has received attention, very little attention has focused on the range of uncertainty and possible effects of the water resources infrastructure and management. This work presents a holistic framework that allows analysis of climate, hydrologic and water management uncertainty in water resources systems analysis with the aid of a water system model designed to integrate component models for hydrology processes and water management activities. The uncertainties explored include those associated with climate variability and change, hydrologic model parameters, and water system operation rules. A Bayesian framework is used to quantify and model the uncertainties at each modeling steps in integrated fashion, including prior and the likelihood information about model parameters. The framework is demonstrated in a case study for the St. Croix Basin located at border of United States and Canada.

Probability and possibility-based representations of uncertainty in fault tree analysis.

PubMed

Flage, Roger; Baraldi, Piero; Zio, Enrico; Aven, Terje

2013-01-01

Expert knowledge is an important source of input to risk analysis. In practice, experts might be reluctant to characterize their knowledge and the related (epistemic) uncertainty using precise probabilities. The theory of possibility allows for imprecision in probability assignments. The associated possibilistic representation of epistemic uncertainty can be combined with, and transformed into, a probabilistic representation; in this article, we show this with reference to a simple fault tree analysis. We apply an integrated (hybrid) probabilistic-possibilistic computational framework for the joint propagation of the epistemic uncertainty on the values of the (limiting relative frequency) probabilities of the basic events of the fault tree, and we use possibility-probability (probability-possibility) transformations for propagating the epistemic uncertainty within purely probabilistic and possibilistic settings. The results of the different approaches (hybrid, probabilistic, and possibilistic) are compared with respect to the representation of uncertainty about the top event (limiting relative frequency) probability. Both the rationale underpinning the approaches and the computational efforts they require are critically examined. We conclude that the approaches relevant in a given setting depend on the purpose of the risk analysis, and that further research is required to make the possibilistic approaches operational in a risk analysis context. © 2012 Society for Risk Analysis.

Robustness analysis of non-ordinary Petri nets for flexible assembly systems

NASA Astrophysics Data System (ADS)

Hsieh, Fu-Shiung

2010-05-01

Non-ordinary controlled Petri nets (NCPNs) have the advantages to model flexible assembly systems in which multiple identical resources may be required to perform an operation. However, existing studies on NCPNs are still limited. For example, the robustness properties of NCPNs have not been studied. This motivates us to develop an analysis method for NCPNs. Robustness analysis concerns the ability for a system to maintain operation in the presence of uncertainties. It provides an alternative way to analyse a perturbed system without reanalysis. In our previous research, we have analysed the robustness properties of several subclasses of ordinary controlled Petri nets. To study the robustness properties of NCPNs, we augment NCPNs with an uncertainty model, which specifies an upper bound on the uncertainties for each reachable marking. The resulting PN models are called non-ordinary controlled Petri nets with uncertainties (NCPNU). Based on NCPNU, the problem is to characterise the maximal tolerable uncertainties for each reachable marking. The computational complexities to characterise maximal tolerable uncertainties for each reachable marking grow exponentially with the size of the nets. Instead of considering general NCPNU, we limit our scope to a subclass of PN models called non-ordinary controlled flexible assembly Petri net with uncertainties (NCFAPNU) for assembly systems and study its robustness. We will extend the robustness analysis to NCFAPNU. We identify two types of uncertainties under which the liveness of NCFAPNU can be maintained.

Uncertainty loops in travel-time tomography from nonlinear wave physics.

PubMed

Galetti, Erica; Curtis, Andrew; Meles, Giovanni Angelo; Baptie, Brian

2015-04-10

Estimating image uncertainty is fundamental to guiding the interpretation of geoscientific tomographic maps. We reveal novel uncertainty topologies (loops) which indicate that while the speeds of both low- and high-velocity anomalies may be well constrained, their locations tend to remain uncertain. The effect is widespread: loops dominate around a third of United Kingdom Love wave tomographic uncertainties, changing the nature of interpretation of the observed anomalies. Loops exist due to 2nd and higher order aspects of wave physics; hence, although such structures must exist in many tomographic studies in the physical sciences and medicine, they are unobservable using standard linearized methods. Higher order methods might fruitfully be adopted.

Lived Experiences of "Illness Uncertainty" of Iranian Cancer Patients: A Phenomenological Hermeneutic Study.

PubMed

Sajjadi, Moosa; Rassouli, Maryam; Abbaszadeh, Abbas; Brant, Jeannine; Majd, Hamid Alavi

2016-01-01

For cancer patients, uncertainty is a pervasive experience and a major psychological stressor that affects many aspects of their lives. Uncertainty is a multifaceted concept, and its understanding for patients depends on many factors, including factors associated with various sociocultural contexts. Unfortunately, little is known about the concept of uncertainty in Iranian society and culture. This study aimed to clarify the concept and explain lived experiences of illness uncertainty in Iranian cancer patients. In this hermeneutic phenomenological study, 8 cancer patients participated in semistructured in-depth interviews about their experiences of uncertainty in illness. Interviews continued until data saturation was reached. All interviews were recorded, transcribed, analyzed, and interpreted using 6 stages of the van Manen phenomenological approach. Seven main themes emerged from patients' experiences of illness uncertainty of cancer. Four themes contributed to uncertainty including "Complexity of Cancer," "Confusion About Cancer," "Contradictory Information," and "Unknown Future." Two themes facilitated coping with uncertainty including "Seeking Knowledge" and "Need for Spiritual Peace." One theme, "Knowledge Ambivalence," revealed the struggle between wanting to know and not wanting to know, especially if bad news was delivered. Uncertainty experience for cancer patients in different societies is largely similar. However, some experiences (eg, ambiguity in access to medical resources) seemed unique to Iranian patients. This study provided an outlook of cancer patients' experiences of illness uncertainty in Iran. Cancer patients' coping ability to deal with uncertainty can be improved.

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling: GEOSTATISTICAL SENSITIVITY ANALYSIS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, Heng; Chen, Xingyuan; Ye, Ming

Sensitivity analysis is an important tool for quantifying uncertainty in the outputs of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a hierarchical sensitivity analysis method that (1) constructs an uncertainty hierarchy by analyzing the input uncertainty sources, and (2) accounts for the spatial correlation among parameters at each level ofmore » the hierarchy using geostatistical tools. The contribution of uncertainty source at each hierarchy level is measured by sensitivity indices calculated using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport in model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally as driven by the dynamic interaction between groundwater and river water at the site. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed parameters.« less

A Bayesian-based multilevel factorial analysis method for analyzing parameter uncertainty of hydrological model

NASA Astrophysics Data System (ADS)

Liu, Y. R.; Li, Y. P.; Huang, G. H.; Zhang, J. L.; Fan, Y. R.

2017-10-01

In this study, a Bayesian-based multilevel factorial analysis (BMFA) method is developed to assess parameter uncertainties and their effects on hydrological model responses. In BMFA, Differential Evolution Adaptive Metropolis (DREAM) algorithm is employed to approximate the posterior distributions of model parameters with Bayesian inference; factorial analysis (FA) technique is used for measuring the specific variations of hydrological responses in terms of posterior distributions to investigate the individual and interactive effects of parameters on model outputs. BMFA is then applied to a case study of the Jinghe River watershed in the Loess Plateau of China to display its validity and applicability. The uncertainties of four sensitive parameters, including soil conservation service runoff curve number to moisture condition II (CN2), soil hydraulic conductivity (SOL_K), plant available water capacity (SOL_AWC), and soil depth (SOL_Z), are investigated. Results reveal that (i) CN2 has positive effect on peak flow, implying that the concentrated rainfall during rainy season can cause infiltration-excess surface flow, which is an considerable contributor to peak flow in this watershed; (ii) SOL_K has positive effect on average flow, implying that the widely distributed cambisols can lead to medium percolation capacity; (iii) the interaction between SOL_AWC and SOL_Z has noticeable effect on the peak flow and their effects are dependent upon each other, which discloses that soil depth can significant influence the processes of plant uptake of soil water in this watershed. Based on the above findings, the significant parameters and the relationship among uncertain parameters can be specified, such that hydrological model's capability for simulating/predicting water resources of the Jinghe River watershed can be improved.

An integrated optimization method for river water quality management and risk analysis in a rural system.

PubMed

Liu, J; Li, Y P; Huang, G H; Zeng, X T; Nie, S

2016-01-01

In this study, an interval-stochastic-based risk analysis (RSRA) method is developed for supporting river water quality management in a rural system under uncertainty (i.e., uncertainties exist in a number of system components as well as their interrelationships). The RSRA method is effective in risk management and policy analysis, particularly when the inputs (such as allowable pollutant discharge and pollutant discharge rate) are expressed as probability distributions and interval values. Moreover, decision-makers' attitudes towards system risk can be reflected using a restricted resource measure by controlling the variability of the recourse cost. The RSRA method is then applied to a real case of water quality management in the Heshui River Basin (a rural area of China), where chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and soil loss are selected as major indicators to identify the water pollution control strategies. Results reveal that uncertainties and risk attitudes have significant effects on both pollutant discharge and system benefit. A high risk measure level can lead to a reduced system benefit; however, this reduction also corresponds to raised system reliability. Results also disclose that (a) agriculture is the dominant contributor to soil loss, TN, and TP loads, and abatement actions should be mainly carried out for paddy and dry farms; (b) livestock husbandry is the main COD discharger, and abatement measures should be mainly conducted for poultry farm; (c) fishery accounts for a high percentage of TN, TP, and COD discharges but a has low percentage of overall net benefit, and it may be beneficial to cease fishery activities in the basin. The findings can facilitate the local authority in identifying desired pollution control strategies with the tradeoff between socioeconomic development and environmental sustainability.

Development, validation and different approaches for the measurement uncertainty of a multi-class veterinary drugs residues LC-MS method for feeds.

PubMed

Valese, Andressa Camargo; Molognoni, Luciano; de Souza, Naielly Coelho; de Sá Ploêncio, Leandro Antunes; Costa, Ana Carolina Oliveira; Barreto, Fabiano; Daguer, Heitor

2017-05-15

A sensitive method for the simultaneous residues analysis of 62 veterinary drugs in feeds by liquid chromatography-tandem mass spectrometry has been developed and validated in accordance to Commission Decision 657/2002/EC. Additionally, limits of detection (LOD), limits of quantitation (LOQ), matrix effects and measurement uncertainty were also assessed. Extraction was performed for all analytes and respective internal standards in a single step and chromatographic separation was achieved in only 12min. LOQ were set to 0.63-5.00μgkg -1 (amphenicols), 0.63-30.00μgkg -1 (avermectins), 0.63μgkg -1 (benzimidazoles), 0.25-200.00μgkg -1 (coccidiostats), 0.63-200.00μgkg -1 (lincosamides and macrolides), 0.25-5.00μgkg -1 (nitrofurans), 0.63-20.00μgkg -1 (fluoroquinolones and quinolones), 15.00μgkg -1 (quinoxaline), 0.63-7.50μgkg -1 (sulfonamides), 0.63-20.00μgkg -1 (tetracyclines), 0.25μgkg -1 (β-agonists), and 30.00μgkg -1 (β-lactams). The top-down approach was adequate for the calculation of measurement uncertainty for all analytes, except the banned substances, which should be rather assessed by the bottom-up approach. Routine analysis of different types of feeds was then carried out. An interesting profile of residues of veterinary drugs among samples was revealed, enlightening the need for stricter control in producing animals. Among the total of 27 feed samples, 20 analytes could be detected/quantified, ranging from trace levels to very high concentrations. A high throughput screening/confirmatory method for the residue analysis of several veterinary drugs in feeds was proposed as a helpful control tool. Copyright © 2017 Elsevier B.V. All rights reserved.

New analysis strategies for micro aspheric lens metrology

NASA Astrophysics Data System (ADS)

Gugsa, Solomon Abebe

Effective characterization of an aspheric micro lens is critical for understanding and improving processing in micro-optic manufacturing. Since most microlenses are plano-convex, where the convex geometry is a conic surface, current practice is often limited to obtaining an estimate of the lens conic constant, which average out the surface geometry that departs from an exact conic surface and any addition surface irregularities. We have developed a comprehensive approach of estimating the best fit conic and its uncertainty, and in addition propose an alternative analysis that focuses on surface errors rather than best-fit conic constant. We describe our new analysis strategy based on the two most dominant micro lens metrology methods in use today, namely, scanning white light interferometry (SWLI) and phase shifting interferometry (PSI). We estimate several parameters from the measurement. The major uncertainty contributors for SWLI are the estimates of base radius of curvature, the aperture of the lens, the sag of the lens, noise in the measurement, and the center of the lens. In the case of PSI the dominant uncertainty contributors are noise in the measurement, the radius of curvature, and the aperture. Our best-fit conic procedure uses least squares minimization to extract a best-fit conic value, which is then subjected to a Monte Carlo analysis to capture combined uncertainty. In our surface errors analysis procedure, we consider the surface errors as the difference between the measured geometry and the best-fit conic surface or as the difference between the measured geometry and the design specification for the lens. We focus on a Zernike polynomial description of the surface error, and again a Monte Carlo analysis is used to estimate a combined uncertainty, which in this case is an uncertainty for each Zernike coefficient. Our approach also allows us to investigate the effect of individual uncertainty parameters and measurement noise on both the best-fit conic constant analysis and the surface errors analysis, and compare the individual contributions to the overall uncertainty.

Estimating Uncertainty in N2O Emissions from US Cropland Soils

USDA-ARS?s Scientific Manuscript database

A Monte Carlo analysis was combined with an empirically-based approach to quantify uncertainties in soil N2O emissions from US croplands estimated with the DAYCENT simulation model. Only a subset of croplands was simulated in the Monte Carlo analysis which was used to infer uncertainties across the ...

Uncertainty Analysis of Inertial Model Attitude Sensor Calibration and Application with a Recommended New Calibration Method

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.

Uncertainty analysis as essential step in the establishment of the dynamic Design Space of primary drying during freeze-drying.

PubMed

Mortier, Séverine Thérèse F C; Van Bockstal, Pieter-Jan; Corver, Jos; Nopens, Ingmar; Gernaey, Krist V; De Beer, Thomas

2016-06-01

Large molecules, such as biopharmaceuticals, are considered the key driver of growth for the pharmaceutical industry. Freeze-drying is the preferred way to stabilise these products when needed. However, it is an expensive, inefficient, time- and energy-consuming process. During freeze-drying, there are only two main process variables to be set, i.e. the shelf temperature and the chamber pressure, however preferably in a dynamic way. This manuscript focuses on the essential use of uncertainty analysis for the determination and experimental verification of the dynamic primary drying Design Space for pharmaceutical freeze-drying. Traditionally, the chamber pressure and shelf temperature are kept constant during primary drying, leading to less optimal process conditions. In this paper it is demonstrated how a mechanistic model of the primary drying step gives the opportunity to determine the optimal dynamic values for both process variables during processing, resulting in a dynamic Design Space with a well-known risk of failure. This allows running the primary drying process step as time efficient as possible, hereby guaranteeing that the temperature at the sublimation front does not exceed the collapse temperature. The Design Space is the multidimensional combination and interaction of input variables and process parameters leading to the expected product specifications with a controlled (i.e., high) probability. Therefore, inclusion of parameter uncertainty is an essential part in the definition of the Design Space, although it is often neglected. To quantitatively assess the inherent uncertainty on the parameters of the mechanistic model, an uncertainty analysis was performed to establish the borders of the dynamic Design Space, i.e. a time-varying shelf temperature and chamber pressure, associated with a specific risk of failure. A risk of failure acceptance level of 0.01%, i.e. a 'zero-failure' situation, results in an increased primary drying process time compared to the deterministic dynamic Design Space; however, the risk of failure is under control. Experimental verification revealed that only a risk of failure acceptance level of 0.01% yielded a guaranteed zero-defect quality end-product. The computed process settings with a risk of failure acceptance level of 0.01% resulted in a decrease of more than half of the primary drying time in comparison with a regular, conservative cycle with fixed settings. Copyright © 2016. Published by Elsevier B.V.

Toward best practice framing of uncertainty in scientific publications: A review of Water Resources Research abstracts

NASA Astrophysics Data System (ADS)

Guillaume, Joseph H. A.; Helgeson, Casey; Elsawah, Sondoss; Jakeman, Anthony J.; Kummu, Matti

2017-08-01

Uncertainty is recognized as a key issue in water resources research, among other sciences. Discussions of uncertainty typically focus on tools and techniques applied within an analysis, e.g., uncertainty quantification and model validation. But uncertainty is also addressed outside the analysis, in writing scientific publications. The language that authors use conveys their perspective of the role of uncertainty when interpreting a claim—what we call here "framing" the uncertainty. This article promotes awareness of uncertainty framing in four ways. (1) It proposes a typology of eighteen uncertainty frames, addressing five questions about uncertainty. (2) It describes the context in which uncertainty framing occurs. This is an interdisciplinary topic, involving philosophy of science, science studies, linguistics, rhetoric, and argumentation. (3) We analyze the use of uncertainty frames in a sample of 177 abstracts from the Water Resources Research journal in 2015. This helped develop and tentatively verify the typology, and provides a snapshot of current practice. (4) We make provocative recommendations to achieve a more influential, dynamic science. Current practice in uncertainty framing might be described as carefully considered incremental science. In addition to uncertainty quantification and degree of belief (present in ˜5% of abstracts), uncertainty is addressed by a combination of limiting scope, deferring to further work (˜25%) and indicating evidence is sufficient (˜40%)—or uncertainty is completely ignored (˜8%). There is a need for public debate within our discipline to decide in what context different uncertainty frames are appropriate. Uncertainty framing cannot remain a hidden practice evaluated only by lone reviewers.

Constrained sampling experiments reveal principles of detection in natural scenes.

PubMed

Sebastian, Stephen; Abrams, Jared; Geisler, Wilson S

2017-07-11

A fundamental everyday visual task is to detect target objects within a background scene. Using relatively simple stimuli, vision science has identified several major factors that affect detection thresholds, including the luminance of the background, the contrast of the background, the spatial similarity of the background to the target, and uncertainty due to random variations in the properties of the background and in the amplitude of the target. Here we use an experimental approach based on constrained sampling from multidimensional histograms of natural stimuli, together with a theoretical analysis based on signal detection theory, to discover how these factors affect detection in natural scenes. We sorted a large collection of natural image backgrounds into multidimensional histograms, where each bin corresponds to a particular luminance, contrast, and similarity. Detection thresholds were measured for a subset of bins spanning the space, where a natural background was randomly sampled from a bin on each trial. In low-uncertainty conditions, both the background bin and the amplitude of the target were fixed, and, in high-uncertainty conditions, they varied randomly on each trial. We found that thresholds increase approximately linearly along all three dimensions and that detection accuracy is unaffected by background bin and target amplitude uncertainty. The results are predicted from first principles by a normalized matched-template detector, where the dynamic normalizing gain factor follows directly from the statistical properties of the natural backgrounds. The results provide an explanation for classic laws of psychophysics and their underlying neural mechanisms.

Estimation of Uncertainties in the Global Distance Test (GDT_TS) for CASP Models.

PubMed

Li, Wenlin; Schaeffer, R Dustin; Otwinowski, Zbyszek; Grishin, Nick V

2016-01-01

The Critical Assessment of techniques for protein Structure Prediction (or CASP) is a community-wide blind test experiment to reveal the best accomplishments of structure modeling. Assessors have been using the Global Distance Test (GDT_TS) measure to quantify prediction performance since CASP3 in 1998. However, identifying significant score differences between close models is difficult because of the lack of uncertainty estimations for this measure. Here, we utilized the atomic fluctuations caused by structure flexibility to estimate the uncertainty of GDT_TS scores. Structures determined by nuclear magnetic resonance are deposited as ensembles of alternative conformers that reflect the structural flexibility, whereas standard X-ray refinement produces the static structure averaged over time and space for the dynamic ensembles. To recapitulate the structural heterogeneous ensemble in the crystal lattice, we performed time-averaged refinement for X-ray datasets to generate structural ensembles for our GDT_TS uncertainty analysis. Using those generated ensembles, our study demonstrates that the time-averaged refinements produced structure ensembles with better agreement with the experimental datasets than the averaged X-ray structures with B-factors. The uncertainty of the GDT_TS scores, quantified by their standard deviations (SDs), increases for scores lower than 50 and 70, with maximum SDs of 0.3 and 1.23 for X-ray and NMR structures, respectively. We also applied our procedure to the high accuracy version of GDT-based score and produced similar results with slightly higher SDs. To facilitate score comparisons by the community, we developed a user-friendly web server that produces structure ensembles for NMR and X-ray structures and is accessible at http://prodata.swmed.edu/SEnCS. Our work helps to identify the significance of GDT_TS score differences, as well as to provide structure ensembles for estimating SDs of any scores.

The Source Inversion Validation (SIV) Initiative: A Collaborative Study on Uncertainty Quantification in Earthquake Source Inversions

NASA Astrophysics Data System (ADS)

Mai, P. M.; Schorlemmer, D.; Page, M.

2012-04-01

Earthquake source inversions image the spatio-temporal rupture evolution on one or more fault planes using seismic and/or geodetic data. Such studies are critically important for earthquake seismology in general, and for advancing seismic hazard analysis in particular, as they reveal earthquake source complexity and help (i) to investigate earthquake mechanics; (ii) to develop spontaneous dynamic rupture models; (iii) to build models for generating rupture realizations for ground-motion simulations. In applications (i - iii), the underlying finite-fault source models are regarded as "data" (input information), but their uncertainties are essentially unknown. After all, source models are obtained from solving an inherently ill-posed inverse problem to which many a priori assumptions and uncertain observations are applied. The Source Inversion Validation (SIV) project is a collaborative effort to better understand the variability between rupture models for a single earthquake (as manifested in the finite-source rupture model database) and to develop robust uncertainty quantification for earthquake source inversions. The SIV project highlights the need to develop a long-standing and rigorous testing platform to examine the current state-of-the-art in earthquake source inversion, and to develop and test novel source inversion approaches. We will review the current status of the SIV project, and report the findings and conclusions of the recent workshops. We will briefly discuss several source-inversion methods, how they treat uncertainties in data, and assess the posterior model uncertainty. Case studies include initial forward-modeling tests on Green's function calculations, and inversion results for synthetic data from spontaneous dynamic crack-like strike-slip earthquake on steeply dipping fault, embedded in a layered crustal velocity-density structure.

Precipitation intensity-duration-frequency curves for central Belgium with an ensemble of EURO-CORDEX simulations, and associated uncertainties

NASA Astrophysics Data System (ADS)

Hosseinzadehtalaei, Parisa; Tabari, Hossein; Willems, Patrick

2018-02-01

An ensemble of 88 regional climate model (RCM) simulations at 0.11° and 0.44° spatial resolutions from the EURO-CORDEX project is analyzed for central Belgium to investigate the projected impact of climate change on precipitation intensity-duration-frequency (IDF) relationships and extreme precipitation quantiles typically used in water engineering designs. The rate of uncertainty arising from the choice of RCM, driving GCM, and radiative concentration pathway (RCP4.5 & RCP8.5) is quantified using a variance decomposition technique after reconstruction of missing data in GCM × RCM combinations. A comparative analysis between the historical simulations of the EURO-CORDEX 0.11° and 0.44° RCMs shows higher precipitation intensities by the finer resolution runs, leading to a larger overestimation of the observations-based IDFs by the 0.11° runs. The results reveal that making a temporal stationarity assumption for the climate system may lead to underestimation of precipitation quantiles up to 70% by the end of this century. This projected increase is generally larger for the 0.11° RCMs compared with the 0.44° RCMs. The relative changes in extreme precipitation do depend on return period and duration, indicating an amplification for larger return periods and for smaller durations. The variance decomposition approach generally identifies RCM as the most dominant component of uncertainty in changes of more extreme precipitation (return period of 10 years) for both 0.11° and 0.44° resolutions, followed by GCM and RCP scenario. The uncertainties associated with cross-contributions of RCMs, GCMs, and RCPs play a non-negligible role in the associated uncertainties of the changes.

Multi-Objective data analysis using Bayesian Inference for MagLIF experiments

NASA Astrophysics Data System (ADS)

Knapp, Patrick; Glinksy, Michael; Evans, Matthew; Gom, Matth; Han, Stephanie; Harding, Eric; Slutz, Steve; Hahn, Kelly; Harvey-Thompson, Adam; Geissel, Matthias; Ampleford, David; Jennings, Christopher; Schmit, Paul; Smith, Ian; Schwarz, Jens; Peterson, Kyle; Jones, Brent; Rochau, Gregory; Sinars, Daniel

2017-10-01

The MagLIF concept has recently demonstrated Gbar pressures and confinement of charged fusion products at stagnation. We present a new analysis methodology that allows for integration of multiple diagnostics including nuclear, x-ray imaging, and x-ray power to determine the temperature, pressure, liner areal density, and mix fraction. A simplified hot-spot model is used with a Bayesian inference network to determine the most probable model parameters that describe the observations while simultaneously revealing the principal uncertainties in the analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.

A Review On Accuracy and Uncertainty of Spatial Data and Analyses with special reference to Urban and Hydrological Modelling

NASA Astrophysics Data System (ADS)

Devendran, A. A.; Lakshmanan, G.

2014-11-01

Data quality for GIS processing and analysis is becoming an increased concern due to the accelerated application of GIS technology for problem solving and decision making roles. Uncertainty in the geographic representation of the real world arises as these representations are incomplete. Identification of the sources of these uncertainties and the ways in which they operate in GIS based representations become crucial in any spatial data representation and geospatial analysis applied to any field of application. This paper reviews the articles on the various components of spatial data quality and various uncertainties inherent in them and special focus is paid to two fields of application such as Urban Simulation and Hydrological Modelling. Urban growth is a complicated process involving the spatio-temporal changes of all socio-economic and physical components at different scales. Cellular Automata (CA) model is one of the simulation models, which randomly selects potential cells for urbanisation and the transition rules evaluate the properties of the cell and its neighbour. Uncertainty arising from CA modelling is assessed mainly using sensitivity analysis including Monte Carlo simulation method. Likewise, the importance of hydrological uncertainty analysis has been emphasized in recent years and there is an urgent need to incorporate uncertainty estimation into water resources assessment procedures. The Soil and Water Assessment Tool (SWAT) is a continuous time watershed model to evaluate various impacts of land use management and climate on hydrology and water quality. Hydrological model uncertainties using SWAT model are dealt primarily by Generalized Likelihood Uncertainty Estimation (GLUE) method.

Investigation into the limitations of straightness interferometers using a multisensor-based error separation method

NASA Astrophysics Data System (ADS)

Weichert, Christoph; Köchert, Paul; Schötka, Eugen; Flügge, Jens; Manske, Eberhard

2018-06-01

The uncertainty of a straightness interferometer is independent of the component used to introduce the divergence angle between the two probing beams, and is limited by three main error sources, which are linked to each other: their resolution, the influence of refractive index gradients and the topography of the straightness reflector. To identify the configuration with minimal uncertainties under laboratory conditions, a fully fibre-coupled heterodyne interferometer was successively equipped with three different wedge prisms, resulting in three different divergence angles (4°, 8° and 20°). To separate the error sources an independent reference with a smaller reproducibility is needed. Therefore, the straightness measurement capability of the Nanometer Comparator, based on a multisensor error separation method, was improved to provide measurements with a reproducibility of 0.2 nm. The comparison results revealed that the influence of the refractive index gradients of air did not increase with interspaces between the probing beams of more than 11.3 mm. Therefore, over a movement range of 220 mm, the lowest uncertainty was achieved with the largest divergence angle. The dominant uncertainty contribution arose from the mirror topography, which was additionally determined with a Fizeau interferometer. The measured topography agreed within  ±1.3 nm with the systematic deviations revealed in the straightness comparison, resulting in an uncertainty contribution of 2.6 nm for the straightness interferometer.

Improvement of constraint-based flux estimation during L-phenylalanine production with Escherichia coli using targeted knock-out mutants.

PubMed

Weiner, Michael; Tröndle, Julia; Albermann, Christoph; Sprenger, Georg A; Weuster-Botz, Dirk

2014-07-01

Fed-batch production of the aromatic amino acid L-phenylalanine was studied with recombinant Escherichia coli strains on a 15 L-scale using glycerol as carbon source. Flux Variability Analysis (FVA) was applied for intracellular flux estimation to obtain an insight into intracellular flux distribution during L-phenylalanine production. Variability analysis revealed great flux uncertainties in the central carbon metabolism, especially concerning malate consumption. Due to these results two recombinant strains were genetically engineered differing in the ability of malate degradation and anaplerotic reactions (E. coli FUS4.11 ΔmaeA pF81kan and E. coli FUS4.11 ΔmaeA ΔmaeB pF81kan). Applying these malic enzyme knock-out mutants in the standardized L-phenylalanine production process resulted in almost identical process performances (e.g., L-phenylalanine concentration, production rate and byproduct formation). This clearly highlighted great redundancies in central metabolism in E. coli. Uncertainties of intracellular flux estimations by constraint-based analyses during fed-batch production of L-phenylalanine were drastically reduced by application of the malic enzyme knock-out mutants. © 2014 Wiley Periodicals, Inc.

Uncertainty assessment of urban pluvial flood risk in a context of climate change adaptation decision making

NASA Astrophysics Data System (ADS)

Arnbjerg-Nielsen, Karsten; Zhou, Qianqian

2014-05-01

There has been a significant increase in climatic extremes in many regions. In Central and Northern Europe, this has led to more frequent and more severe floods. Along with improved flood modelling technologies this has enabled development of economic assessment of climate change adaptation to increasing urban flood risk. Assessment of adaptation strategies often requires a comprehensive risk-based economic analysis of current risk, drivers of change of risk over time, and measures to reduce the risk. However, such studies are often associated with large uncertainties. The uncertainties arise from basic assumptions in the economic analysis and the hydrological model, but also from the projection of future societies to local climate change impacts and suitable adaptation options. This presents a challenge to decision makers when trying to identify robust measures. We present an integrated uncertainty analysis, which can assess and quantify the overall uncertainty in relation to climate change adaptation to urban flash floods. The analysis is based on an uncertainty cascade that by means of Monte Carlo simulations of flood risk assessments incorporates climate change impacts as a key driver of risk changes over time. The overall uncertainty is then attributed to six bulk processes: climate change impact, urban rainfall-runoff processes, stage-depth functions, unit cost of repair, cost of adaptation measures, and discount rate. We apply the approach on an urban hydrological catchment in Odense, Denmark, and find that the uncertainty on the climate change impact appears to have the least influence on the net present value of the studied adaptation measures-. This does not imply that the climate change impact is not important, but that the uncertainties are not dominating when deciding on action or in-action. We then consider the uncertainty related to choosing between adaptation options given that a decision of action has been taken. In this case the major part of the uncertainty on the estimated net present values is identical for all adaptation options and will therefore not affect a comparison between adaptation measures. This makes the chose among the options easier. Furthermore, the explicit attribution of uncertainty also enables a reduction of the overall uncertainty by identifying the processes which contributes the most. This knowledge can then be used to further reduce the uncertainty related to decision making, as a substantial part of the remaining uncertainty is epistemic.

Expert nurses' clinical reasoning under uncertainty: representation, structure, and process.

PubMed Central

Fonteyn, M. E.; Grobe, S. J.

1992-01-01

How do expert nurses reason when planning care and making clinical decisions for a patient who is at risk, and whose outcome is uncertain? In this study, a case study involving a critically ill elderly woman whose condition deteriorated over time, was presented in segments to ten expert critical care nurses. Think aloud method was used to elicit knowledge from these experts to provide conceptual information about their knowledge and to reveal their reasoning processes and problem-solving strategies. The verbatim transcripts were then analyzed using a systematic three-step method that makes analysis easier and adds creditability to study findings by providing a means of retracing and explaining analysis results. Findings revealed information about how patient problems were represented during reasoning, the manner in which experts subjects structured their plan of care, and the reasoning processes and heuristics they used to formulate solutions for resolving the patient's problems and preventing deterioration in the patient's condition. PMID:1482907

Traceable Coulomb blockade thermometry

NASA Astrophysics Data System (ADS)

Hahtela, O.; Mykkänen, E.; Kemppinen, A.; Meschke, M.; Prunnila, M.; Gunnarsson, D.; Roschier, L.; Penttilä, J.; Pekola, J.

2017-02-01

We present a measurement and analysis scheme for determining traceable thermodynamic temperature at cryogenic temperatures using Coulomb blockade thermometry. The uncertainty of the electrical measurement is improved by utilizing two sampling digital voltmeters instead of the traditional lock-in technique. The remaining uncertainty is dominated by that of the numerical analysis of the measurement data. Two analysis methods are demonstrated: numerical fitting of the full conductance curve and measuring the height of the conductance dip. The complete uncertainty analysis shows that using either analysis method the relative combined standard uncertainty (k  =  1) in determining the thermodynamic temperature in the temperature range from 20 mK to 200 mK is below 0.5%. In this temperature range, both analysis methods produced temperature estimates that deviated from 0.39% to 0.67% from the reference temperatures provided by a superconducting reference point device calibrated against the Provisional Low Temperature Scale of 2000.

Development of a Prototype Model-Form Uncertainty Knowledge Base

NASA Technical Reports Server (NTRS)

Green, Lawrence L.

2016-01-01

Uncertainties are generally classified as either aleatory or epistemic. Aleatory uncertainties are those attributed to random variation, either naturally or through manufacturing processes. Epistemic uncertainties are generally attributed to a lack of knowledge. One type of epistemic uncertainty is called model-form uncertainty. The term model-form means that among the choices to be made during a design process within an analysis, there are different forms of the analysis process, which each give different results for the same configuration at the same flight conditions. Examples of model-form uncertainties include the grid density, grid type, and solver type used within a computational fluid dynamics code, or the choice of the number and type of model elements within a structures analysis. The objectives of this work are to identify and quantify a representative set of model-form uncertainties and to make this information available to designers through an interactive knowledge base (KB). The KB can then be used during probabilistic design sessions, so as to enable the possible reduction of uncertainties in the design process through resource investment. An extensive literature search has been conducted to identify and quantify typical model-form uncertainties present within aerospace design. An initial attempt has been made to assemble the results of this literature search into a searchable KB, usable in real time during probabilistic design sessions. A concept of operations and the basic structure of a model-form uncertainty KB are described. Key operations within the KB are illustrated. Current limitations in the KB, and possible workarounds are explained.

UNCERTAINTY ANALYSIS IN WATER QUALITY MODELING USING QUAL2E

EPA Science Inventory

A strategy for incorporating uncertainty analysis techniques (sensitivity analysis, first order error analysis, and Monte Carlo simulation) into the mathematical water quality model QUAL2E is described. The model, named QUAL2E-UNCAS, automatically selects the input variables or p...

Computational Fluid Dynamics Uncertainty Analysis Applied to Heat Transfer over a Flat Plate

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward; Ilie, Marcel; Schallhorn, Paul A.

2013-01-01

There have been few discussions on using Computational Fluid Dynamics (CFD) without experimental validation. Pairing experimental data, uncertainty analysis, and analytical predictions provides a comprehensive approach to verification and is the current state of the art. With pressed budgets, collecting experimental data is rare or non-existent. This paper investigates and proposes a method to perform CFD uncertainty analysis only from computational data. The method uses current CFD uncertainty techniques coupled with the Student-T distribution to predict the heat transfer coefficient over a at plate. The inputs to the CFD model are varied from a specified tolerance or bias error and the difference in the results are used to estimate the uncertainty. The variation in each input is ranked from least to greatest to determine the order of importance. The results are compared to heat transfer correlations and conclusions drawn about the feasibility of using CFD without experimental data. The results provide a tactic to analytically estimate the uncertainty in a CFD model when experimental data is unavailable

Funding the unfundable: mechanisms for managing uncertainty in decisions on the introduction of new and innovative technologies into healthcare systems.

PubMed

Stafinski, Tania; McCabe, Christopher J; Menon, Devidas

2010-01-01

As tensions between payers, responsible for ensuring prudent and principled use of scarce resources, and both providers and patients, who legitimately want access to technologies from which they could benefit, continue to mount, interest in approaches to managing the uncertainty surrounding the introduction of new health technologies has heightened. The purpose of this project was to compile an inventory of various types of 'access with evidence development' (AED) schemes, examining characteristics of the technologies to which they have been applied, the uncertainty they sought to address, the terms of arrangements of each scheme, and the policy outcomes. It also aimed to identify issues related to such schemes, including advantages and disadvantages from the perspectives of various stakeholder groups. A comprehensive search, review and appraisal of peer-reviewed and 'grey' literature were performed, followed by a facilitated workshop of academics and decision makers with expertise in AED schemes. Information was extracted and compiled in tabular form to identify patterns or trends. To enhance the validity of interpretations made, member checking was performed. Although the concept of AED is not new, evaluative data are sparse. Despite varying opinions on the 'right' answers to some of the questions raised, there appears to be consensus on a 'way forward'--development of methodological guidelines. All stakeholders seemed to share the view that AEDs offer the potential to facilitate patient access to promising new technologies and encourage innovation while ensuring effective use of scarce healthcare resources. There is no agreement on what constitutes 'sufficient evidence', and it depends on the specific uncertainty in question. There is agreement on the need for 'best practice' guidelines around the implementation and evaluation of AED schemes. This is the first attempt at a comprehensive analysis of methods that have been used to address uncertainty concerning a new drug or other technology. The analysis reveals that, although various approaches have been experimented with, many of them have not achieved the ostensible goal of the approach. This article outlines challenges related to AED schemes and issues that remain unresolved.

Probabilistic Radiological Performance Assessment Modeling and Uncertainty

NASA Astrophysics Data System (ADS)

Tauxe, J.

2004-12-01

A generic probabilistic radiological Performance Assessment (PA) model is presented. The model, built using the GoldSim systems simulation software platform, concerns contaminant transport and dose estimation in support of decision making with uncertainty. Both the U.S. Nuclear Regulatory Commission (NRC) and the U.S. Department of Energy (DOE) require assessments of potential future risk to human receptors of disposal of LLW. Commercially operated LLW disposal facilities are licensed by the NRC (or agreement states), and the DOE operates such facilities for disposal of DOE-generated LLW. The type of PA model presented is probabilistic in nature, and hence reflects the current state of knowledge about the site by using probability distributions to capture what is expected (central tendency or average) and the uncertainty (e.g., standard deviation) associated with input parameters, and propagating through the model to arrive at output distributions that reflect expected performance and the overall uncertainty in the system. Estimates of contaminant release rates, concentrations in environmental media, and resulting doses to human receptors well into the future are made by running the model in Monte Carlo fashion, with each realization representing a possible combination of input parameter values. Statistical summaries of the results can be compared to regulatory performance objectives, and decision makers are better informed of the inherently uncertain aspects of the model which supports their decision-making. While this information may make some regulators uncomfortable, they must realize that uncertainties which were hidden in a deterministic analysis are revealed in a probabilistic analysis, and the chance of making a correct decision is now known rather than hoped for. The model includes many typical features and processes that would be part of a PA, but is entirely fictitious. This does not represent any particular site and is meant to be a generic example. A practitioner could, however, start with this model as a GoldSim template and, by adding site specific features and parameter values (distributions), use this model as a starting point for a real model to be used in real decision making.

Uncertainty analysis of scintillometers methods in measuring sensible heat fluxes of forest ecosystem

NASA Astrophysics Data System (ADS)

Zheng, N.

2017-12-01

Sensible heat flux (H) is one of the driving factors of surface turbulent motion and energy exchange. Therefore, it is particularly important to measure sensible heat flux accurately at the regional scale. However, due to the heterogeneity of the underlying surface, hydrothermal regime, and different weather conditions, it is difficult to estimate the represented flux at the kilometer scale. The scintillometer have been developed into an effective and universal equipment for deriving heat flux at the regional-scale which based on the turbulence effect of light in the atmosphere since the 1980s. The parameter directly obtained by the scintillometer is the structure parameter of the refractive index of air based on the changes of light intensity fluctuation. Combine with parameters such as temperature structure parameter, zero-plane displacement, surface roughness, wind velocity, air temperature and the other meteorological data heat fluxes can be derived. These additional parameters increase the uncertainties of flux because the difference between the actual feature of turbulent motion and the applicable conditions of turbulence theory. Most previous studies often focused on the constant flux layers that are above the rough sub-layers and homogeneous flat surfaces underlying surfaces with suitable weather conditions. Therefore, the criteria and modified forms of key parameters are invariable. In this study, we conduct investment over the hilly area of northern China with different plants, such as cork oak, cedar-black and locust. On the basis of key research on the threshold and modified forms of saturation with different turbulence intensity, modified forms of Bowen ratio with different drying-and-wetting conditions, universal function for the temperature structure parameter under different atmospheric stability, the dominant sources of uncertainty will be determined. The above study is significant to reveal influence mechanism of uncertainty and explore influence degree of uncertainty with quantitative analysis. The study can provide theoretical basis and technical support for accurately measuring sensible heat fluxes of forest ecosystem with scintillometer method, and can also provide work foundation for further study on role of forest ecosystem in energy balance and climate change.

A methodology to estimate uncertainty for emission projections through sensitivity analysis.

PubMed

Lumbreras, Julio; de Andrés, Juan Manuel; Pérez, Javier; Borge, Rafael; de la Paz, David; Rodríguez, María Encarnación

2015-04-01

Air pollution abatement policies must be based on quantitative information on current and future emissions of pollutants. As emission projections uncertainties are inevitable and traditional statistical treatments of uncertainty are highly time/resources consuming, a simplified methodology for nonstatistical uncertainty estimation based on sensitivity analysis is presented in this work. The methodology was applied to the "with measures" scenario for Spain, concretely over the 12 highest emitting sectors regarding greenhouse gas and air pollutants emissions. Examples of methodology application for two important sectors (power plants, and agriculture and livestock) are shown and explained in depth. Uncertainty bands were obtained up to 2020 by modifying the driving factors of the 12 selected sectors and the methodology was tested against a recomputed emission trend in a low economic-growth perspective and official figures for 2010, showing a very good performance. A solid understanding and quantification of uncertainties related to atmospheric emission inventories and projections provide useful information for policy negotiations. However, as many of those uncertainties are irreducible, there is an interest on how they could be managed in order to derive robust policy conclusions. Taking this into account, a method developed to use sensitivity analysis as a source of information to derive nonstatistical uncertainty bands for emission projections is presented and applied to Spain. This method simplifies uncertainty assessment and allows other countries to take advantage of their sensitivity analyses.

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende

2014-01-01

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

Analysis of uncertainties in turbine metal temperature predictions

NASA Technical Reports Server (NTRS)

Stepka, F. S.

1980-01-01

An analysis was conducted to examine the extent to which various factors influence the accuracy of analytically predicting turbine blade metal temperatures and to determine the uncertainties in these predictions for several accuracies of the influence factors. The advanced turbofan engine gas conditions of 1700 K and 40 atmospheres were considered along with those of a highly instrumented high temperature turbine test rig and a low temperature turbine rig that simulated the engine conditions. The analysis showed that the uncertainty in analytically predicting local blade temperature was as much as 98 K, or 7.6 percent of the metal absolute temperature, with current knowledge of the influence factors. The expected reductions in uncertainties in the influence factors with additional knowledge and tests should reduce the uncertainty in predicting blade metal temperature to 28 K, or 2.1 percent of the metal absolute temperature.

Relating Data and Models to Characterize Parameter and Prediction Uncertainty

EPA Science Inventory

Applying PBPK models in risk analysis requires that we realistically assess the uncertainty of relevant model predictions in as quantitative a way as possible. The reality of human variability may add a confusing feature to the overall uncertainty assessment, as uncertainty and v...

Uncertainty in flood damage estimates and its potential effect on investment decisions

NASA Astrophysics Data System (ADS)

Wagenaar, Dennis; de Bruijn, Karin; Bouwer, Laurens; de Moel, Hans

2015-04-01

This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage models can lead to large uncertainties in flood damage estimates. This explanation is used to quantify this uncertainty with a Monte Carlo Analysis. This Monte Carlo analysis uses a damage function library with 272 functions from 7 different flood damage models. This results in uncertainties in the order of magnitude of a factor 2 to 5. This uncertainty is typically larger for small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

Uncertainty in flood damage estimates and its potential effect on investment decisions

NASA Astrophysics Data System (ADS)

Wagenaar, D. J.; de Bruijn, K. M.; Bouwer, L. M.; De Moel, H.

2015-01-01

This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage models can lead to large uncertainties in flood damage estimates. This explanation is used to quantify this uncertainty with a Monte Carlo Analysis. As input the Monte Carlo analysis uses a damage function library with 272 functions from 7 different flood damage models. This results in uncertainties in the order of magnitude of a factor 2 to 5. The resulting uncertainty is typically larger for small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

Overall uncertainty measurement for near infrared analysis of cryptotanshinone in tanshinone extract

NASA Astrophysics Data System (ADS)

Xue, Zhong; Xu, Bing; Shi, Xinyuan; Yang, Chan; Cui, Xianglong; Luo, Gan; Qiao, Yanjiang

2017-01-01

This study presented a new strategy of overall uncertainty measurement for near infrared (NIR) quantitative analysis of cryptotanshinone in tanshinone extract powders. The overall uncertainty of NIR analysis from validation data of precision, trueness and robustness study was fully investigated and discussed. Quality by design (QbD) elements, such as risk assessment and design of experiment (DOE) were utilized to organize the validation data. An "I × J × K" (series I, the number of repetitions J and level of concentrations K) full factorial design was used to calculate uncertainty from the precision and trueness data. And a 27-4 Plackett-Burmann matrix with four different influence factors resulted from the failure mode and effect analysis (FMEA) analysis was adapted for the robustness study. The overall uncertainty profile was introduced as a graphical decision making tool to evaluate the validity of NIR method over the predefined concentration range. In comparison with the T. Saffaj's method (Analyst, 2013, 138, 4677.) for overall uncertainty assessment, the proposed approach gave almost the same results, demonstrating that the proposed method was reasonable and valid. Moreover, the proposed method can help identify critical factors that influence the NIR prediction performance, which could be used for further optimization of the NIR analytical procedures in routine use.

Uncertainty and Sensitivity Analysis of Afterbody Radiative Heating Predictions for Earth Entry

NASA Technical Reports Server (NTRS)

West, Thomas K., IV; Johnston, Christopher O.; Hosder, Serhat

2016-01-01

The objective of this work was to perform sensitivity analysis and uncertainty quantification for afterbody radiative heating predictions of Stardust capsule during Earth entry at peak afterbody radiation conditions. The radiation environment in the afterbody region poses significant challenges for accurate uncertainty quantification and sensitivity analysis due to the complexity of the flow physics, computational cost, and large number of un-certain variables. In this study, first a sparse collocation non-intrusive polynomial chaos approach along with global non-linear sensitivity analysis was used to identify the most significant uncertain variables and reduce the dimensions of the stochastic problem. Then, a total order stochastic expansion was constructed over only the important parameters for an efficient and accurate estimate of the uncertainty in radiation. Based on previous work, 388 uncertain parameters were considered in the radiation model, which came from the thermodynamics, flow field chemistry, and radiation modeling. The sensitivity analysis showed that only four of these variables contributed significantly to afterbody radiation uncertainty, accounting for almost 95% of the uncertainty. These included the electronic- impact excitation rate for N between level 2 and level 5 and rates of three chemical reactions in uencing N, N(+), O, and O(+) number densities in the flow field.

Incorporating uncertainty regarding applicability of evidence from meta-analyses into clinical decision making.

PubMed

Kriston, Levente; Meister, Ramona

2014-03-01

Judging applicability (relevance) of meta-analytical findings to particular clinical decision-making situations remains challenging. We aimed to describe an evidence synthesis method that accounts for possible uncertainty regarding applicability of the evidence. We conceptualized uncertainty regarding applicability of the meta-analytical estimates to a decision-making situation as the result of uncertainty regarding applicability of the findings of the trials that were included in the meta-analysis. This trial-level applicability uncertainty can be directly assessed by the decision maker and allows for the definition of trial inclusion probabilities, which can be used to perform a probabilistic meta-analysis with unequal probability resampling of trials (adaptive meta-analysis). A case study with several fictitious decision-making scenarios was performed to demonstrate the method in practice. We present options to elicit trial inclusion probabilities and perform the calculations. The result of an adaptive meta-analysis is a frequency distribution of the estimated parameters from traditional meta-analysis that provides individually tailored information according to the specific needs and uncertainty of the decision maker. The proposed method offers a direct and formalized combination of research evidence with individual clinical expertise and may aid clinicians in specific decision-making situations. Copyright © 2014 Elsevier Inc. All rights reserved.

Uncertainty in BRCA1 cancer susceptibility testing.

PubMed

Baty, Bonnie J; Dudley, William N; Musters, Adrian; Kinney, Anita Y

2006-11-15

This study investigated uncertainty in individuals undergoing genetic counseling/testing for breast/ovarian cancer susceptibility. Sixty-three individuals from a single kindred with a known BRCA1 mutation rated uncertainty about 12 items on a five-point Likert scale before and 1 month after genetic counseling/testing. Factor analysis identified a five-item total uncertainty scale that was sensitive to changes before and after testing. The items in the scale were related to uncertainty about obtaining health care, positive changes after testing, and coping well with results. The majority of participants (76%) rated reducing uncertainty as an important reason for genetic testing. The importance of reducing uncertainty was stable across time and unrelated to anxiety or demographics. Yet, at baseline, total uncertainty was low and decreased after genetic counseling/testing (P = 0.004). Analysis of individual items showed that after genetic counseling/testing, there was less uncertainty about the participant detecting cancer early (P = 0.005) and coping well with their result (P < 0.001). Our findings support the importance to clients of genetic counseling/testing as a means of reducing uncertainty. Testing may help clients to reduce the uncertainty about items they can control, and it may be important to differentiate the sources of uncertainty that are more or less controllable. Genetic counselors can help clients by providing anticipatory guidance about the role of uncertainty in genetic testing. (c) 2006 Wiley-Liss, Inc.

Quantifying Uncertainty in Instantaneous Orbital Data Products of TRMM over Indian Subcontinent

NASA Astrophysics Data System (ADS)

Jayaluxmi, I.; Nagesh, D.

2013-12-01

In the last 20 years, microwave radiometers have taken satellite images of earth's weather proving to be a valuable tool for quantitative estimation of precipitation from space. However, along with the widespread acceptance of microwave based precipitation products, it has also been recognized that they contain large uncertainties. While most of the uncertainty evaluation studies focus on the accuracy of rainfall accumulated over time (e.g., season/year), evaluation of instantaneous rainfall intensities from satellite orbital data products are relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. Especially over land regions, where the highly varying land surface emissivity offer a myriad of complications hindering accurate rainfall estimation. The error components of orbital data products also tend to interact nonlinearly with hydrologic modeling uncertainty. Keeping these in mind, the present study fosters the development of uncertainty analysis using instantaneous satellite orbital data products (version 7 of 1B11, 2A25, 2A23) derived from the passive and active sensors onboard Tropical Rainfall Measuring Mission (TRMM) satellite, namely TRMM microwave imager (TMI) and Precipitation Radar (PR). The study utilizes 11 years of orbital data from 2002 to 2012 over the Indian subcontinent and examines the influence of various error sources on the convective and stratiform precipitation types. Analysis conducted over the land regions of India investigates three sources of uncertainty in detail. These include 1) Errors due to improper delineation of rainfall signature within microwave footprint (rain/no rain classification), 2) Uncertainty offered by the transfer function linking rainfall with TMI low frequency channels and 3) Sampling errors owing to the narrow swath and infrequent visits of TRMM sensors. Case study results obtained during the Indian summer monsoon months of June-September are presented using contingency table statistics, performance diagram, scatter plots and probability density functions. Our study demonstrates that theory of copula can be efficiently used to represent the highly non linear dependency structure of rainfall with respect to TMI low frequency channels of 19, 21, 37 GHz. This questions the exclusive usage of high frequency 85 GHz channel for TMI overland rainfall retrieval algorithms. Further, the PR sampling errors revealed using a statistical bootstrap technique was found to incur relative sampling errors <30% (for 2 degree grids) over India whose magnitudes were biased towards stratiform rainfall type and sampling technique employed. These findings clearly document that proper characterization of error structure offered by TMI and PR has wider implications for decision making prior to incorporating the resulting orbital products for basin scale hydrologic modeling.

Uncertainties in Forecasting Streamflow using Entropy Theory

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2017-12-01

Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

How does uncertainty shape patient experience in advanced illness? A secondary analysis of qualitative data.

PubMed

Etkind, Simon Noah; Bristowe, Katherine; Bailey, Katharine; Selman, Lucy Ellen; Murtagh, Fliss Em

2017-02-01

Uncertainty is common in advanced illness but is infrequently studied in this context. If poorly addressed, uncertainty can lead to adverse patient outcomes. We aimed to understand patient experiences of uncertainty in advanced illness and develop a typology of patients' responses and preferences to inform practice. Secondary analysis of qualitative interview transcripts. Studies were assessed for inclusion and interviews were sampled using maximum-variation sampling. Analysis used a thematic approach with 10% of coding cross-checked to enhance reliability. Qualitative interviews from six studies including patients with heart failure, chronic obstructive pulmonary disease, renal disease, cancer and liver failure. A total of 30 transcripts were analysed. Median age was 75 (range, 43-95), 12 patients were women. The impact of uncertainty was frequently discussed: the main related themes were engagement with illness, information needs, patient priorities and the period of time that patients mainly focused their attention on (temporal focus). A typology of patient responses to uncertainty was developed from these themes. Uncertainty influences patient experience in advanced illness through affecting patients' information needs, preferences and future priorities for care. Our typology aids understanding of how patients with advanced illness respond to uncertainty. Assessment of these three factors may be a useful starting point to guide clinical assessment and shared decision making.

The Uncertainties on the GIS Based Land Suitability Assessment for Urban and Rural Planning

NASA Astrophysics Data System (ADS)

Liu, H.; Zhan, Q.; Zhan, M.

2017-09-01

The majority of the research on the uncertainties of spatial data and spatial analysis focuses on some specific data feature or analysis tool. Few have accomplished the uncertainties of the whole process of an application like planning, making the research of uncertainties detached from practical applications. The paper discusses the uncertainties of the geographical information systems (GIS) based land suitability assessment in planning on the basis of literature review. The uncertainties considered range from index system establishment to the classification of the final result. Methods to reduce the uncertainties arise from the discretization of continuous raster data and the index weight determination are summarized. The paper analyzes the merits and demerits of the "Nature Breaks" method which is broadly used by planners. It also explores the other factors which impact the accuracy of the final classification like the selection of class numbers, intervals and the autocorrelation of the spatial data. In the conclusion part, the paper indicates that the adoption of machine learning methods should be modified to integrate the complexity of land suitability assessment. The work contributes to the application of spatial data and spatial analysis uncertainty research on land suitability assessment, and promotes the scientific level of the later planning and decision-making.

Comparison of the uncertainties of several European low-dose calibration facilities

NASA Astrophysics Data System (ADS)

Dombrowski, H.; Cornejo Díaz, N. A.; Toni, M. P.; Mihelic, M.; Röttger, A.

2018-04-01

The typical uncertainty of a low-dose rate calibration of a detector, which is calibrated in a dedicated secondary national calibration laboratory, is investigated, including measurements in the photon field of metrology institutes. Calibrations at low ambient dose equivalent rates (at the level of the natural ambient radiation) are needed when environmental radiation monitors are to be characterised. The uncertainties of calibration measurements in conventional irradiation facilities above ground are compared with those obtained in a low-dose rate irradiation facility located deep underground. Four laboratories quantitatively evaluated the uncertainties of their calibration facilities, in particular for calibrations at low dose rates (250 nSv/h and 1 μSv/h). For the first time, typical uncertainties of European calibration facilities are documented in a comparison and the main sources of uncertainty are revealed. All sources of uncertainties are analysed, including the irradiation geometry, scattering, deviations of real spectra from standardised spectra, etc. As a fundamental metrological consequence, no instrument calibrated in such a facility can have a lower total uncertainty in subsequent measurements. For the first time, the need to perform calibrations at very low dose rates (< 100 nSv/h) deep underground is underpinned on the basis of quantitative data.

Variability And Uncertainty Analysis Of Contaminant Transport Model Using Fuzzy Latin Hypercube Sampling Technique

NASA Astrophysics Data System (ADS)

Kumar, V.; Nayagum, D.; Thornton, S.; Banwart, S.; Schuhmacher2, M.; Lerner, D.

2006-12-01

Characterization of uncertainty associated with groundwater quality models is often of critical importance, as for example in cases where environmental models are employed in risk assessment. Insufficient data, inherent variability and estimation errors of environmental model parameters introduce uncertainty into model predictions. However, uncertainty analysis using conventional methods such as standard Monte Carlo sampling (MCS) may not be efficient, or even suitable, for complex, computationally demanding models and involving different nature of parametric variability and uncertainty. General MCS or variant of MCS such as Latin Hypercube Sampling (LHS) assumes variability and uncertainty as a single random entity and the generated samples are treated as crisp assuming vagueness as randomness. Also when the models are used as purely predictive tools, uncertainty and variability lead to the need for assessment of the plausible range of model outputs. An improved systematic variability and uncertainty analysis can provide insight into the level of confidence in model estimates, and can aid in assessing how various possible model estimates should be weighed. The present study aims to introduce, Fuzzy Latin Hypercube Sampling (FLHS), a hybrid approach of incorporating cognitive and noncognitive uncertainties. The noncognitive uncertainty such as physical randomness, statistical uncertainty due to limited information, etc can be described by its own probability density function (PDF); whereas the cognitive uncertainty such estimation error etc can be described by the membership function for its fuzziness and confidence interval by ?-cuts. An important property of this theory is its ability to merge inexact generated data of LHS approach to increase the quality of information. The FLHS technique ensures that the entire range of each variable is sampled with proper incorporation of uncertainty and variability. A fuzzified statistical summary of the model results will produce indices of sensitivity and uncertainty that relate the effects of heterogeneity and uncertainty of input variables to model predictions. The feasibility of the method is validated to assess uncertainty propagation of parameter values for estimation of the contamination level of a drinking water supply well due to transport of dissolved phenolics from a contaminated site in the UK.

The Iquique earthquake sequence of April 2014: Bayesian modeling accounting for prediction uncertainty

USGS Publications Warehouse

Duputel, Zacharie; Jiang, Junle; Jolivet, Romain; Simons, Mark; Rivera, Luis; Ampuero, Jean-Paul; Riel, Bryan; Owen, Susan E; Moore, Angelyn W; Samsonov, Sergey V; Ortega Culaciati, Francisco; Minson, Sarah E.

2016-01-01

The subduction zone in northern Chile is a well-identified seismic gap that last ruptured in 1877. On 1 April 2014, this region was struck by a large earthquake following a two week long series of foreshocks. This study combines a wide range of observations, including geodetic, tsunami, and seismic data, to produce a reliable kinematic slip model of the Mw=8.1 main shock and a static slip model of the Mw=7.7 aftershock. We use a novel Bayesian modeling approach that accounts for uncertainty in the Green's functions, both static and dynamic, while avoiding nonphysical regularization. The results reveal a sharp slip zone, more compact than previously thought, located downdip of the foreshock sequence and updip of high-frequency sources inferred by back-projection analysis. Both the main shock and the Mw=7.7 aftershock did not rupture to the trench and left most of the seismic gap unbroken, leaving the possibility of a future large earthquake in the region.

DRAINMOD-GIS: a lumped parameter watershed scale drainage and water quality model

Treesearch

G.P. Fernandez; G.M. Chescheir; R.W. Skaggs; D.M. Amatya

2006-01-01

A watershed scale lumped parameter hydrology and water quality model that includes an uncertainty analysis component was developed and tested on a lower coastal plain watershed in North Carolina. Uncertainty analysis was used to determine the impacts of uncertainty in field and network parameters of the model on the predicted outflows and nitrate-nitrogen loads at the...

Uncertainty Analysis of Sonic Boom Levels Measured in a Simulator at NASA Langley

NASA Technical Reports Server (NTRS)

Rathsam, Jonathan; Ely, Jeffry W.

2012-01-01

A sonic boom simulator has been constructed at NASA Langley Research Center for testing the human response to sonic booms heard indoors. Like all measured quantities, sonic boom levels in the simulator are subject to systematic and random errors. To quantify these errors, and their net influence on the measurement result, a formal uncertainty analysis is conducted. Knowledge of the measurement uncertainty, or range of values attributable to the quantity being measured, enables reliable comparisons among measurements at different locations in the simulator as well as comparisons with field data or laboratory data from other simulators. The analysis reported here accounts for acoustic excitation from two sets of loudspeakers: one loudspeaker set at the facility exterior that reproduces the exterior sonic boom waveform and a second set of interior loudspeakers for reproducing indoor rattle sounds. The analysis also addresses the effect of pressure fluctuations generated when exterior doors of the building housing the simulator are opened. An uncertainty budget is assembled to document each uncertainty component, its sensitivity coefficient, and the combined standard uncertainty. The latter quantity will be reported alongside measurement results in future research reports to indicate data reliability.

Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

Monte Carlo uncertainty analysis of dose estimates in radiochromic film dosimetry with single-channel and multichannel algorithms.

PubMed

Vera-Sánchez, Juan Antonio; Ruiz-Morales, Carmen; González-López, Antonio

2018-03-01

To provide a multi-stage model to calculate uncertainty in radiochromic film dosimetry with Monte-Carlo techniques. This new approach is applied to single-channel and multichannel algorithms. Two lots of Gafchromic EBT3 are exposed in two different Varian linacs. They are read with an EPSON V800 flatbed scanner. The Monte-Carlo techniques in uncertainty analysis provide a numerical representation of the probability density functions of the output magnitudes. From this numerical representation, traditional parameters of uncertainty analysis as the standard deviations and bias are calculated. Moreover, these numerical representations are used to investigate the shape of the probability density functions of the output magnitudes. Also, another calibration film is read in four EPSON scanners (two V800 and two 10000XL) and the uncertainty analysis is carried out with the four images. The dose estimates of single-channel and multichannel algorithms show a Gaussian behavior and low bias. The multichannel algorithms lead to less uncertainty in the final dose estimates when the EPSON V800 is employed as reading device. In the case of the EPSON 10000XL, the single-channel algorithms provide less uncertainty in the dose estimates for doses higher than four Gy. A multi-stage model has been presented. With the aid of this model and the use of the Monte-Carlo techniques, the uncertainty of dose estimates for single-channel and multichannel algorithms are estimated. The application of the model together with Monte-Carlo techniques leads to a complete characterization of the uncertainties in radiochromic film dosimetry. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

How much swamp are we talking here?: Propagating uncertainty about the area of coastal wetlands into the U.S. greenhouse gas inventory

NASA Astrophysics Data System (ADS)

Holmquist, J. R.; Crooks, S.; Windham-Myers, L.; Megonigal, P.; Weller, D.; Lu, M.; Bernal, B.; Byrd, K. B.; Morris, J. T.; Troxler, T.; McCombs, J.; Herold, N.

2017-12-01

Stable coastal wetlands can store substantial amounts of carbon (C) that can be released when they are degraded or eroded. The EPA recently incorporated coastal wetland net-storage and emissions within the Agricultural Forested and Other Land Uses category of the U.S. National Greenhouse Gas Inventory (NGGI). This was a seminal analysis, but its quantification of uncertainty needs improvement. We provide a value-added analysis by estimating that uncertainty, focusing initially on the most basic assumption, the area of coastal wetlands. We considered three sources: uncertainty in the areas of vegetation and salinity subclasses, uncertainty in the areas of changing or stable wetlands, and uncertainty in the inland extent of coastal wetlands. The areas of vegetation and salinity subtypes, as well as stable or changing, were estimated from 2006 and 2010 maps derived from Landsat imagery by the Coastal Change Analysis Program (C-CAP). We generated unbiased area estimates and confidence intervals for C-CAP, taking into account mapped area, proportional areas of commission and omission errors, as well as the number of observations. We defined the inland extent of wetlands as all land below the current elevation of twice monthly highest tides. We generated probabilistic inundation maps integrating wetland-specific bias and random error in light-detection and ranging elevation maps, with the spatially explicit random error in tidal surfaces generated from tide gauges. This initial uncertainty analysis will be extended to calculate total propagated uncertainty in the NGGI by including the uncertainties in the amount of C lost from eroded and degraded wetlands, stored annually in stable wetlands, and emitted in the form of methane by tidal freshwater wetlands.

Awe, uncertainty, and agency detection.

PubMed

Valdesolo, Piercarlo; Graham, Jesse

2014-01-01

Across five studies, we found that awe increases both supernatural belief (Studies 1, 2, and 5) and intentional-pattern perception (Studies 3 and 4)-two phenomena that have been linked to agency detection, or the tendency to interpret events as the consequence of intentional and purpose-driven agents. Effects were both directly and conceptually replicated, and mediational analyses revealed that these effects were driven by the influence of awe on tolerance for uncertainty. Experiences of awe decreased tolerance for uncertainty, which, in turn, increased the tendency to believe in nonhuman agents and to perceive human agency in random events.

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

The potential for meta-analysis to support decision analysis in ecology.

PubMed

Mengersen, Kerrie; MacNeil, M Aaron; Caley, M Julian

2015-06-01

Meta-analysis and decision analysis are underpinned by well-developed methods that are commonly applied to a variety of problems and disciplines. While these two fields have been closely linked in some disciplines such as medicine, comparatively little attention has been paid to the potential benefits of linking them in ecology, despite reasonable expectations that benefits would be derived from doing so. Meta-analysis combines information from multiple studies to provide more accurate parameter estimates and to reduce the uncertainty surrounding them. Decision analysis involves selecting among alternative choices using statistical information that helps to shed light on the uncertainties involved. By linking meta-analysis to decision analysis, improved decisions can be made, with quantification of the costs and benefits of alternate decisions supported by a greater density of information. Here, we briefly review concepts of both meta-analysis and decision analysis, illustrating the natural linkage between them and the benefits from explicitly linking one to the other. We discuss some examples in which this linkage has been exploited in the medical arena and how improvements in precision and reduction of structural uncertainty inherent in a meta-analysis can provide substantive improvements to decision analysis outcomes by reducing uncertainty in expected loss and maximising information from across studies. We then argue that these significant benefits could be translated to ecology, in particular to the problem of making optimal ecological decisions in the face of uncertainty. Copyright © 2013 John Wiley & Sons, Ltd.

A multi-model assessment of terrestrial biosphere model data needs

NASA Astrophysics Data System (ADS)

Gardella, A.; Cowdery, E.; De Kauwe, M. G.; Desai, A. R.; Duveneck, M.; Fer, I.; Fisher, R.; Knox, R. G.; Kooper, R.; LeBauer, D.; McCabe, T.; Minunno, F.; Raiho, A.; Serbin, S.; Shiklomanov, A. N.; Thomas, A.; Walker, A.; Dietze, M.

2017-12-01

Terrestrial biosphere models provide us with the means to simulate the impacts of climate change and their uncertainties. Going beyond direct observation and experimentation, models synthesize our current understanding of ecosystem processes and can give us insight on data needed to constrain model parameters. In previous work, we leveraged the Predictive Ecosystem Analyzer (PEcAn) to assess the contribution of different parameters to the uncertainty of the Ecosystem Demography model v2 (ED) model outputs across various North American biomes (Dietze et al., JGR-G, 2014). While this analysis identified key research priorities, the extent to which these priorities were model- and/or biome-specific was unclear. Furthermore, because the analysis only studied one model, we were unable to comment on the effect of variability in model structure to overall predictive uncertainty. Here, we expand this analysis to all biomes globally and a wide sample of models that vary in complexity: BioCro, CABLE, CLM, DALEC, ED2, FATES, G'DAY, JULES, LANDIS, LINKAGES, LPJ-GUESS, MAESPA, PRELES, SDGVM, SIPNET, and TEM. Prior to performing uncertainty analyses, model parameter uncertainties were assessed by assimilating all available trait data from the combination of the BETYdb and TRY trait databases, using an updated multivariate version of PEcAn's Hierarchical Bayesian meta-analysis. Next, sensitivity analyses were performed for all models across a range of sites globally to assess sensitivities for a range of different outputs (GPP, ET, SH, Ra, NPP, Rh, NEE, LAI) at multiple time scales from the sub-annual to the decadal. Finally, parameter uncertainties and model sensitivities were combined to evaluate the fractional contribution of each parameter to the predictive uncertainty for a specific variable at a specific site and timescale. Facilitated by PEcAn's automated workflows, this analysis represents the broadest assessment of the sensitivities and uncertainties in terrestrial models to date, and provides a comprehensive roadmap for constraining model uncertainties through model development and data collection.

Numerical Uncertainty Analysis for Computational Fluid Dynamics using Student T Distribution -- Application of CFD Uncertainty Analysis Compared to Exact Analytical Solution

NASA Technical Reports Server (NTRS)

Groves, Curtis E.; Ilie, marcel; Shallhorn, Paul A.

2014-01-01

Computational Fluid Dynamics (CFD) is the standard numerical tool used by Fluid Dynamists to estimate solutions to many problems in academia, government, and industry. CFD is known to have errors and uncertainties and there is no universally adopted method to estimate such quantities. This paper describes an approach to estimate CFD uncertainties strictly numerically using inputs and the Student-T distribution. The approach is compared to an exact analytical solution of fully developed, laminar flow between infinite, stationary plates. It is shown that treating all CFD input parameters as oscillatory uncertainty terms coupled with the Student-T distribution can encompass the exact solution.

Removal of Asperger's syndrome from the DSM V: community response to uncertainty.

PubMed

Parsloe, Sarah M; Babrow, Austin S

2016-01-01

The May 2013 release of the new version of the Diagnostic and Statistical Manual of Mental Disorders (DSM V) subsumed Asperger's syndrome under the wider diagnostic label of autism spectrum disorder (ASD). The revision has created much uncertainty in the community affected by this condition. This study uses problematic integration theory and thematic analysis to investigate how participants in Wrong Planet, a large online community associated with autism and Asperger's syndrome, have constructed these uncertainties. The analysis illuminates uncertainties concerning both the likelihood of diagnosis and value of diagnosis, and it details specific issues within these two general areas of uncertainty. The article concludes with both conceptual and practical implications.

Proton and neutron electromagnetic form factors and uncertainties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ye, Zhihong; Arrington, John; Hill, Richard J.

We determine the nucleon electromagnetic form factors and their uncertainties from world electron scattering data. The analysis incorporates two-photon exchange corrections, constraints on the low-Q 2 and high-Q 2 behavior, and additional uncertainties to account for tensions between different data sets and uncertainties in radiative corrections.

Proton and neutron electromagnetic form factors and uncertainties

DOE PAGES

Ye, Zhihong; Arrington, John; Hill, Richard J.; ...

2017-12-06

We determine the nucleon electromagnetic form factors and their uncertainties from world electron scattering data. The analysis incorporates two-photon exchange corrections, constraints on the low-Q 2 and high-Q 2 behavior, and additional uncertainties to account for tensions between different data sets and uncertainties in radiative corrections.

Quantifying uncertainty in forest nutrient budgets

Treesearch

Ruth D. Yanai; Carrie R. Levine; Mark B. Green; John L. Campbell

2012-01-01

Nutrient budgets for forested ecosystems have rarely included error analysis, in spite of the importance of uncertainty to interpretation and extrapolation of the results. Uncertainty derives from natural spatial and temporal variation and also from knowledge uncertainty in measurement and models. For example, when estimating forest biomass, researchers commonly report...

Extending water vapor trend observations over Boulder into the tropopause region: Trend uncertainties and resulting radiative forcing.

PubMed

Kunz, A; Müller, R; Homonnai, V; Jánosi, I M; Hurst, D; Rap, A; Forster, P M; Rohrer, F; Spelten, N; Riese, M

2013-10-16

Thirty years of balloon-borne measurements over Boulder (40°N, 105°W) are used to investigate the water vapor trend in the tropopause region. This analysis extends previously published trends, usually focusing on altitudes greater than 16 km, to lower altitudes. Two new concepts are applied: (1) Trends are presented in a thermal tropopause (TP) relative coordinate system from -2 km below to 10 km above the TP, and (2) sonde profiles are selected according to TP height. Tropical (TP z > 14 km), extratropical (TP z < 12 km), and transitional air mass types (12 km < TP z < 14 km) reveal three different water vapor reservoirs. The analysis based on these concepts reduces the dynamically induced water vapor variability at the TP and principally favors refined water vapor trend studies in the upper troposphere and lower stratosphere. Nonetheless, this study shows how uncertain trends are at altitudes -2 to +4 km around the TP. This uncertainty in turn has an influence on the uncertainty and interpretation of water vapor radiative effects at the TP, which are locally estimated for the 30 year period to be of uncertain sign. The much discussed decrease in water vapor at the beginning of 2001 is not detectable between -2 and 2 km around the TP. On lower stratospheric isentropes, the water vapor change at the beginning of 2001 is more intense for extratropical than for tropical air mass types. This suggests a possible link with changing dynamics above the jet stream such as changes in the shallow branch of the Brewer-Dobson circulation.

On-orbit servicing system assessment and optimization methods based on lifecycle simulation under mixed aleatory and epistemic uncertainties

NASA Astrophysics Data System (ADS)

Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; van Tooren, Michel

2013-06-01

To assess the on-orbit servicing (OOS) paradigm and optimize its utilities by taking advantage of its inherent flexibility and responsiveness, the OOS system assessment and optimization methods based on lifecycle simulation under uncertainties are studied. The uncertainty sources considered in this paper include both the aleatory (random launch/OOS operation failure and on-orbit component failure) and the epistemic (the unknown trend of the end-used market price) types. Firstly, the lifecycle simulation under uncertainties is discussed. The chronological flowchart is presented. The cost and benefit models are established, and the uncertainties thereof are modeled. The dynamic programming method to make optimal decision in face of the uncertain events is introduced. Secondly, the method to analyze the propagation effects of the uncertainties on the OOS utilities is studied. With combined probability and evidence theory, a Monte Carlo lifecycle Simulation based Unified Uncertainty Analysis (MCS-UUA) approach is proposed, based on which the OOS utility assessment tool under mixed uncertainties is developed. Thirdly, to further optimize the OOS system under mixed uncertainties, the reliability-based optimization (RBO) method is studied. To alleviate the computational burden of the traditional RBO method which involves nested optimum search and uncertainty analysis, the framework of Sequential Optimization and Mixed Uncertainty Analysis (SOMUA) is employed to integrate MCS-UUA, and the RBO algorithm SOMUA-MCS is developed. Fourthly, a case study on the OOS system for a hypothetical GEO commercial communication satellite is investigated with the proposed assessment tool. Furthermore, the OOS system is optimized with SOMUA-MCS. Lastly, some conclusions are given and future research prospects are highlighted.

Techniques for analyses of trends in GRUAN data

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Kremser, S.

2015-04-01

The Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) provides reference quality RS92 radiosonde measurements of temperature, pressure and humidity. A key attribute of reference quality measurements, and hence GRUAN data, is that each datum has a well characterized and traceable estimate of the measurement uncertainty. The long-term homogeneity of the measurement records, and their well characterized uncertainties, make these data suitable for reliably detecting changes in global and regional climate on decadal time scales. Considerable effort is invested in GRUAN operations to (i) describe and analyse all sources of measurement uncertainty to the extent possible, (ii) quantify and synthesize the contribution of each source of uncertainty to the total measurement uncertainty, and (iii) verify that the evaluated net uncertainty is within the required target uncertainty. However, if the climate science community is not sufficiently well informed on how to capitalize on this added value, the significant investment in estimating meaningful measurement uncertainties is largely wasted. This paper presents and discusses the techniques that will need to be employed to reliably quantify long-term trends in GRUAN data records. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper discusses the construction of linear least squares regression models for trend analysis, boot-strapping approaches to determine uncertainties in trends, dealing with the combined effects of autocorrelation in the data and measurement uncertainties in calculating the uncertainty on trends, best practice for determining seasonality in trends, how to deal with co-linear basis functions, and interpreting derived trends. Synthetic data sets are used to demonstrate these concepts which are then applied to a first analysis of temperature trends in RS92 radiosonde upper air soundings at the GRUAN site at Lindenberg, Germany (52.21° N, 14.12° E).

Techniques for analyses of trends in GRUAN data

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Kremser, S.

2014-12-01

The Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) provides reference quality RS92 radiosonde measurements of temperature, pressure and humidity. A key attribute of reference quality measurements, and hence GRUAN data, is that each datum has a well characterised and traceable estimate of the measurement uncertainty. The long-term homogeneity of the measurement records, and their well characterised uncertainties, make these data suitable for reliably detecting changes in global and regional climate on decadal time scales. Considerable effort is invested in GRUAN operations to (i) describe and analyse all sources of measurement uncertainty to the extent possible, (ii) quantify and synthesize the contribution of each source of uncertainty to the total measurement uncertainty, and (iii) verify that the evaluated net uncertainty is within the required target uncertainty. However, if the climate science community is not sufficiently well informed on how to capitalize on this added value, the significant investment in estimating meaningful measurement uncertainties is largely wasted. This paper presents and discusses the techniques that will need to be employed to reliably quantify long-term trends in GRUAN data records. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper discusses the construction of linear least squares regression models for trend analysis, boot-strapping approaches to determine uncertainties in trends, dealing with the combined effects of autocorrelation in the data and measurement uncertainties in calculating the uncertainty on trends, best practice for determining seasonality in trends, how to deal with co-linear basis functions, and interpreting derived trends. Synthetic data sets are used to demonstrate these concepts which are then applied to a first analysis of temperature trends in RS92 radiosonde upper air soundings at the GRUAN site at Lindenberg, Germany (52.21° N, 14.12° E).

Sensitivity Analysis of Expected Wind Extremes over the Northwestern Sahara and High Atlas Region.

NASA Astrophysics Data System (ADS)

Garcia-Bustamante, E.; González-Rouco, F. J.; Navarro, J.

2017-12-01

A robust statistical framework in the scientific literature allows for the estimation of probabilities of occurrence of severe wind speeds and wind gusts, but does not prevent however from large uncertainties associated with the particular numerical estimates. An analysis of such uncertainties is thus required. A large portion of this uncertainty arises from the fact that historical observations are inherently shorter that the timescales of interest for the analysis of return periods. Additional uncertainties stem from the different choices of probability distributions and other aspects related to methodological issues or physical processes involved. The present study is focused on historical observations over the Ouarzazate Valley (Morocco) and in a high-resolution regional simulation of the wind in the area of interest. The aim is to provide extreme wind speed and wind gust return values and confidence ranges based on a systematic sampling of the uncertainty space for return periods up to 120 years.

Fukushima Daiichi unit 1 uncertainty analysis--Preliminary selection of uncertain parameters and analysis methodology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cardoni, Jeffrey N.; Kalinich, Donald A.

2014-02-01

Sandia National Laboratories (SNL) plans to conduct uncertainty analyses (UA) on the Fukushima Daiichi unit (1F1) plant with the MELCOR code. The model to be used was developed for a previous accident reconstruction investigation jointly sponsored by the US Department of Energy (DOE) and Nuclear Regulatory Commission (NRC). However, that study only examined a handful of various model inputs and boundary conditions, and the predictions yielded only fair agreement with plant data and current release estimates. The goal of this uncertainty study is to perform a focused evaluation of uncertainty in core melt progression behavior and its effect on keymore » figures-of-merit (e.g., hydrogen production, vessel lower head failure, etc.). In preparation for the SNL Fukushima UA work, a scoping study has been completed to identify important core melt progression parameters for the uncertainty analysis. The study also lays out a preliminary UA methodology.« less

Minimum entropy density method for the time series analysis

NASA Astrophysics Data System (ADS)

Lee, Jeong Won; Park, Joongwoo Brian; Jo, Hang-Hyun; Yang, Jae-Suk; Moon, Hie-Tae

2009-01-01

The entropy density is an intuitive and powerful concept to study the complicated nonlinear processes derived from physical systems. We develop the minimum entropy density method (MEDM) to detect the structure scale of a given time series, which is defined as the scale in which the uncertainty is minimized, hence the pattern is revealed most. The MEDM is applied to the financial time series of Standard and Poor’s 500 index from February 1983 to April 2006. Then the temporal behavior of structure scale is obtained and analyzed in relation to the information delivery time and efficient market hypothesis.

RECONSTRUCTING EXPOSURE SCENARIOS USING DOSE BIOMARKERS - AN APPLICATION OF BAYESIAN UNCERTAINTY ANALYSIS

EPA Science Inventory

We use Bayesian uncertainty analysis to explore how to estimate pollutant exposures from biomarker concentrations. The growing number of national databases with exposure data makes such an analysis possible. They contain datasets of pharmacokinetic biomarkers for many polluta...

APPLICATION OF BAYESIAN MONTE CARLO ANALYSIS TO A LAGRANGIAN PHOTOCHEMICAL AIR QUALITY MODEL. (R824792)

EPA Science Inventory

Uncertainties in ozone concentrations predicted with a Lagrangian photochemical air quality model have been estimated using Bayesian Monte Carlo (BMC) analysis. Bayesian Monte Carlo analysis provides a means of combining subjective "prior" uncertainty estimates developed ...

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

NASA Astrophysics Data System (ADS)

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 residuals distribution. If residuals are not normally distributed, the uncertainty is over-estimated if Box-Cox transformation is not applied or non-calibrated parameter is used.

Final Technical Report: Advanced Measurement and Analysis of PV Derate Factors.

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, Bruce Hardison; Burton, Patrick D.; Hansen, Clifford

2015-12-01

The Advanced Measurement and Analysis of PV Derate Factors project focuses on improving the accuracy and reducing the uncertainty of PV performance model predictions by addressing a common element of all PV performance models referred to as “derates”. Widespread use of “rules of thumb”, combined with significant uncertainty regarding appropriate values for these factors contribute to uncertainty in projected energy production.

Uncertainty analysis on simple mass balance model to calculate critical loads for soil acidity

Treesearch

Harbin Li; Steven G. McNulty

2007-01-01

Simple mass balance equations (SMBE) of critical acid loads (CAL) in forest soil were developed to assess potential risks of air pollutants to ecosystems. However, to apply SMBE reliably at large scales, SMBE must be tested for adequacy and uncertainty. Our goal was to provide a detailed analysis of uncertainty in SMBE so that sound strategies for scaling up CAL...

Monte Carlo analysis of uncertainty propagation in a stratospheric model. 2: Uncertainties due to reaction rates

NASA Technical Reports Server (NTRS)

Stolarski, R. S.; Butler, D. M.; Rundel, R. D.

1977-01-01

A concise stratospheric model was used in a Monte-Carlo analysis of the propagation of reaction rate uncertainties through the calculation of an ozone perturbation due to the addition of chlorine. Two thousand Monte-Carlo cases were run with 55 reaction rates being varied. Excellent convergence was obtained in the output distributions because the model is sensitive to the uncertainties in only about 10 reactions. For a 1 ppby chlorine perturbation added to a 1.5 ppby chlorine background, the resultant 1 sigma uncertainty on the ozone perturbation is a factor of 1.69 on the high side and 1.80 on the low side. The corresponding 2 sigma factors are 2.86 and 3.23. Results are also given for the uncertainties, due to reaction rates, in the ambient concentrations of stratospheric species.

Uncertainty Analysis and Parameter Estimation For Nearshore Hydrodynamic Models

NASA Astrophysics Data System (ADS)

Ardani, S.; Kaihatu, J. M.

2012-12-01

Numerical models represent deterministic approaches used for the relevant physical processes in the nearshore. Complexity of the physics of the model and uncertainty involved in the model inputs compel us to apply a stochastic approach to analyze the robustness of the model. The Bayesian inverse problem is one powerful way to estimate the important input model parameters (determined by apriori sensitivity analysis) and can be used for uncertainty analysis of the outputs. Bayesian techniques can be used to find the range of most probable parameters based on the probability of the observed data and the residual errors. In this study, the effect of input data involving lateral (Neumann) boundary conditions, bathymetry and off-shore wave conditions on nearshore numerical models are considered. Monte Carlo simulation is applied to a deterministic numerical model (the Delft3D modeling suite for coupled waves and flow) for the resulting uncertainty analysis of the outputs (wave height, flow velocity, mean sea level and etc.). Uncertainty analysis of outputs is performed by random sampling from the input probability distribution functions and running the model as required until convergence to the consistent results is achieved. The case study used in this analysis is the Duck94 experiment, which was conducted at the U.S. Army Field Research Facility at Duck, North Carolina, USA in the fall of 1994. The joint probability of model parameters relevant for the Duck94 experiments will be found using the Bayesian approach. We will further show that, by using Bayesian techniques to estimate the optimized model parameters as inputs and applying them for uncertainty analysis, we can obtain more consistent results than using the prior information for input data which means that the variation of the uncertain parameter will be decreased and the probability of the observed data will improve as well. Keywords: Monte Carlo Simulation, Delft3D, uncertainty analysis, Bayesian techniques, MCMC

IAEA Coordinated Research Project on HTGR Reactor Physics, Thermal-hydraulics and Depletion Uncertainty Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Strydom, Gerhard; Bostelmann, F.

The continued development of High Temperature Gas Cooled Reactors (HTGRs) requires verification of HTGR design and safety features with reliable high fidelity physics models and robust, efficient, and accurate codes. The predictive capability of coupled neutronics/thermal-hydraulics and depletion simulations for reactor design and safety analysis can be assessed with sensitivity analysis (SA) and uncertainty analysis (UA) methods. Uncertainty originates from errors in physical data, manufacturing uncertainties, modelling and computational algorithms. (The interested reader is referred to the large body of published SA and UA literature for a more complete overview of the various types of uncertainties, methodologies and results obtained).more » SA is helpful for ranking the various sources of uncertainty and error in the results of core analyses. SA and UA are required to address cost, safety, and licensing needs and should be applied to all aspects of reactor multi-physics simulation. SA and UA can guide experimental, modelling, and algorithm research and development. Current SA and UA rely either on derivative-based methods such as stochastic sampling methods or on generalized perturbation theory to obtain sensitivity coefficients. Neither approach addresses all needs. In order to benefit from recent advances in modelling and simulation and the availability of new covariance data (nuclear data uncertainties) extensive sensitivity and uncertainty studies are needed for quantification of the impact of different sources of uncertainties on the design and safety parameters of HTGRs. Only a parallel effort in advanced simulation and in nuclear data improvement will be able to provide designers with more robust and well validated calculation tools to meet design target accuracies. In February 2009, the Technical Working Group on Gas-Cooled Reactors (TWG-GCR) of the International Atomic Energy Agency (IAEA) recommended that the proposed Coordinated Research Program (CRP) on the HTGR Uncertainty Analysis in Modelling (UAM) be implemented. This CRP is a continuation of the previous IAEA and Organization for Economic Co-operation and Development (OECD)/Nuclear Energy Agency (NEA) international activities on Verification and Validation (V&V) of available analytical capabilities for HTGR simulation for design and safety evaluations. Within the framework of these activities different numerical and experimental benchmark problems were performed and insight was gained about specific physics phenomena and the adequacy of analysis methods.« less

Sensitivity of wildlife habitat models to uncertainties in GIS data

NASA Technical Reports Server (NTRS)

Stoms, David M.; Davis, Frank W.; Cogan, Christopher B.

1992-01-01

Decision makers need to know the reliability of output products from GIS analysis. For many GIS applications, it is not possible to compare these products to an independent measure of 'truth'. Sensitivity analysis offers an alternative means of estimating reliability. In this paper, we present a CIS-based statistical procedure for estimating the sensitivity of wildlife habitat models to uncertainties in input data and model assumptions. The approach is demonstrated in an analysis of habitat associations derived from a GIS database for the endangered California condor. Alternative data sets were generated to compare results over a reasonable range of assumptions about several sources of uncertainty. Sensitivity analysis indicated that condor habitat associations are relatively robust, and the results have increased our confidence in our initial findings. Uncertainties and methods described in the paper have general relevance for many GIS applications.

3.8 Proposed approach to uncertainty quantification and sensitivity analysis in the next PA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Flach, Greg; Wohlwend, Jen

2017-10-02

This memorandum builds upon Section 3.8 of SRNL (2016) and Flach (2017) by defining key error analysis, uncertainty quantification, and sensitivity analysis concepts and terms, in preparation for the next E-Area Performance Assessment (WSRC 2008) revision.

Parameter sensitivity analysis of a 1-D cold region lake model for land-surface schemes

NASA Astrophysics Data System (ADS)

Guerrero, José-Luis; Pernica, Patricia; Wheater, Howard; Mackay, Murray; Spence, Chris

2017-12-01

Lakes might be sentinels of climate change, but the uncertainty in their main feedback to the atmosphere - heat-exchange fluxes - is often not considered within climate models. Additionally, these fluxes are seldom measured, hindering critical evaluation of model output. Analysis of the Canadian Small Lake Model (CSLM), a one-dimensional integral lake model, was performed to assess its ability to reproduce diurnal and seasonal variations in heat fluxes and the sensitivity of simulated fluxes to changes in model parameters, i.e., turbulent transport parameters and the light extinction coefficient (Kd). A C++ open-source software package, Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), was used to perform sensitivity analysis (SA) and identify the parameters that dominate model behavior. The generalized likelihood uncertainty estimation (GLUE) was applied to quantify the fluxes' uncertainty, comparing daily-averaged eddy-covariance observations to the output of CSLM. Seven qualitative and two quantitative SA methods were tested, and the posterior likelihoods of the modeled parameters, obtained from the GLUE analysis, were used to determine the dominant parameters and the uncertainty in the modeled fluxes. Despite the ubiquity of the equifinality issue - different parameter-value combinations yielding equivalent results - the answer to the question was unequivocal: Kd, a measure of how much light penetrates the lake, dominates sensible and latent heat fluxes, and the uncertainty in their estimates is strongly related to the accuracy with which Kd is determined. This is important since accurate and continuous measurements of Kd could reduce modeling uncertainty.

Multivariate Probabilistic Analysis of an Hydrological Model

NASA Astrophysics Data System (ADS)

Franceschini, Samuela; Marani, Marco

2010-05-01

Model predictions derived based on rainfall measurements and hydrological model results are often limited by the systematic error of measuring instruments, by the intrinsic variability of the natural processes and by the uncertainty of the mathematical representation. We propose a means to identify such sources of uncertainty and to quantify their effects based on point-estimate approaches, as a valid alternative to cumbersome Montecarlo methods. We present uncertainty analyses on the hydrologic response to selected meteorological events, in the mountain streamflow-generating portion of the Brenta basin at Bassano del Grappa, Italy. The Brenta river catchment has a relatively uniform morphology and quite a heterogeneous rainfall-pattern. In the present work, we evaluate two sources of uncertainty: data uncertainty (the uncertainty due to data handling and analysis) and model uncertainty (the uncertainty related to the formulation of the model). We thus evaluate the effects of the measurement error of tipping-bucket rain gauges, the uncertainty in estimating spatially-distributed rainfall through block kriging, and the uncertainty associated with estimated model parameters. To this end, we coupled a deterministic model based on the geomorphological theory of the hydrologic response to probabilistic methods. In particular we compare the results of Monte Carlo Simulations (MCS) to the results obtained, in the same conditions, using Li's Point Estimate Method (LiM). The LiM is a probabilistic technique that approximates the continuous probability distribution function of the considered stochastic variables by means of discrete points and associated weights. This allows to satisfactorily reproduce results with only few evaluations of the model function. The comparison between the LiM and MCS results highlights the pros and cons of using an approximating method. LiM is less computationally demanding than MCS, but has limited applicability especially when the model response is highly nonlinear. Higher-order approximations can provide more accurate estimations, but reduce the numerical advantage of the LiM. The results of the uncertainty analysis identify the main sources of uncertainty in the computation of river discharge. In this particular case the spatial variability of rainfall and the model parameters uncertainty are shown to have the greatest impact on discharge evaluation. This, in turn, highlights the need to support any estimated hydrological response with probability information and risk analysis results in order to provide a robust, systematic framework for decision making.

Informative Bayesian Type A uncertainty evaluation, especially applicable to a small number of observations

NASA Astrophysics Data System (ADS)

Cox, M.; Shirono, K.

2017-10-01

A criticism levelled at the Guide to the Expression of Uncertainty in Measurement (GUM) is that it is based on a mixture of frequentist and Bayesian thinking. In particular, the GUM’s Type A (statistical) uncertainty evaluations are frequentist, whereas the Type B evaluations, using state-of-knowledge distributions, are Bayesian. In contrast, making the GUM fully Bayesian implies, among other things, that a conventional objective Bayesian approach to Type A uncertainty evaluation for a number n of observations leads to the impractical consequence that n must be at least equal to 4, thus presenting a difficulty for many metrologists. This paper presents a Bayesian analysis of Type A uncertainty evaluation that applies for all n ≥slant 2 , as in the frequentist analysis in the current GUM. The analysis is based on assuming that the observations are drawn from a normal distribution (as in the conventional objective Bayesian analysis), but uses an informative prior based on lower and upper bounds for the standard deviation of the sampling distribution for the quantity under consideration. The main outcome of the analysis is a closed-form mathematical expression for the factor by which the standard deviation of the mean observation should be multiplied to calculate the required standard uncertainty. Metrological examples are used to illustrate the approach, which is straightforward to apply using a formula or look-up table.

Active subspace uncertainty quantification for a polydomain ferroelectric phase-field model

NASA Astrophysics Data System (ADS)

Leon, Lider S.; Smith, Ralph C.; Miles, Paul; Oates, William S.

2018-03-01

Quantum-informed ferroelectric phase field models capable of predicting material behavior, are necessary for facilitating the development and production of many adaptive structures and intelligent systems. Uncertainty is present in these models, given the quantum scale at which calculations take place. A necessary analysis is to determine how the uncertainty in the response can be attributed to the uncertainty in the model inputs or parameters. A second analysis is to identify active subspaces within the original parameter space, which quantify directions in which the model response varies most dominantly, thus reducing sampling effort and computational cost. In this investigation, we identify an active subspace for a poly-domain ferroelectric phase-field model. Using the active variables as our independent variables, we then construct a surrogate model and perform Bayesian inference. Once we quantify the uncertainties in the active variables, we obtain uncertainties for the original parameters via an inverse mapping. The analysis provides insight into how active subspace methodologies can be used to reduce computational power needed to perform Bayesian inference on model parameters informed by experimental or simulated data.

Error Analysis of CM Data Products Sources of Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hunt, Brian D.; Eckert-Gallup, Aubrey Celia; Cochran, Lainy Dromgoole

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 amore » 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.« less

The Value of Heterogeneity for Cost-Effectiveness Subgroup Analysis

PubMed Central

Manca, Andrea; Claxton, Karl; Sculpher, Mark J.

2014-01-01

This article develops a general framework to guide the use of subgroup cost-effectiveness analysis for decision making in a collectively funded health system. In doing so, it addresses 2 key policy questions, namely, the identification and selection of subgroups, while distinguishing 2 sources of potential value associated with heterogeneity. These are 1) the value of revealing the factors associated with heterogeneity in costs and outcomes using existing evidence (static value) and 2) the value of acquiring further subgroup-related evidence to resolve the uncertainty given the current understanding of heterogeneity (dynamic value). Consideration of these 2 sources of value can guide subgroup-specific treatment decisions and inform whether further research should be conducted to resolve uncertainty to explain variability in costs and outcomes. We apply the proposed methods to a cost-effectiveness analysis for the management of patients with acute coronary syndrome. This study presents the expected net benefits under current and perfect information when subgroups are defined based on the use and combination of 6 binary covariates. The results of the case study confirm the theoretical expectations. As more subgroups are considered, the marginal net benefit gains obtained under the current information show diminishing marginal returns, and the expected value of perfect information shows a decreasing trend. We present a suggested algorithm that synthesizes the results to guide policy. PMID:24944196

The value of heterogeneity for cost-effectiveness subgroup analysis: conceptual framework and application.

PubMed

Espinoza, Manuel A; Manca, Andrea; Claxton, Karl; Sculpher, Mark J

2014-11-01

This article develops a general framework to guide the use of subgroup cost-effectiveness analysis for decision making in a collectively funded health system. In doing so, it addresses 2 key policy questions, namely, the identification and selection of subgroups, while distinguishing 2 sources of potential value associated with heterogeneity. These are 1) the value of revealing the factors associated with heterogeneity in costs and outcomes using existing evidence (static value) and 2) the value of acquiring further subgroup-related evidence to resolve the uncertainty given the current understanding of heterogeneity (dynamic value). Consideration of these 2 sources of value can guide subgroup-specific treatment decisions and inform whether further research should be conducted to resolve uncertainty to explain variability in costs and outcomes. We apply the proposed methods to a cost-effectiveness analysis for the management of patients with acute coronary syndrome. This study presents the expected net benefits under current and perfect information when subgroups are defined based on the use and combination of 6 binary covariates. The results of the case study confirm the theoretical expectations. As more subgroups are considered, the marginal net benefit gains obtained under the current information show diminishing marginal returns, and the expected value of perfect information shows a decreasing trend. We present a suggested algorithm that synthesizes the results to guide policy. © The Author(s) 2014.

Trend analysis of the aerosol optical depth from fusion of MISR and MODIS retrievals over China

NASA Astrophysics Data System (ADS)

Guo, Jing; Gu, Xingfa; Yu, Tao; Cheng, Tianhai; Chen, Hao

2014-03-01

Atmospheric aerosol plays an important role in the climate change though direct and indirect processes. In order to evaluate the effects of aerosols on climate, it is necessary to have a research on their spatial and temporal distributions. Satellite aerosol remote sensing is a developing technology that may provide good temporal sampling and superior spatial coverage to study aerosols. The Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) have provided aerosol observations since 2000, with large coverage and high accuracy. However, due to the complex surface, cloud contamination, and aerosol models used in the retrieving process, the uncertainties still exist in current satellite aerosol products. There are several observed differences in comparing the MISR and MODIS AOD data with the AERONET AOD. Combing multiple sensors could reduce uncertainties and improve observational accuracy. The validation results reveal that a better agreement between fusion AOD and AERONET AOD. The results confirm that the fusion AOD values are more accurate than single sensor. We have researched the trend analysis of the aerosol properties over China based on nine-year (2002-2010) fusion data. Compared with trend analysis in Jingjintang and Yangtze River Delta, the accuracy has increased by 5% and 3%, respectively. It is obvious that the increasing trend of the AOD occurred in Yangtze River Delta, where human activities may be the main source of the increasing AOD.

Identifying influences on model uncertainty: an application using a forest carbon budget model

Treesearch

James E. Smith; Linda S. Heath

2001-01-01

Uncertainty is an important consideration for both developers and users of environmental simulation models. Establishing quantitative estimates of uncertainty for deterministic models can be difficult when the underlying bases for such information are scarce. We demonstrate an application of probabilistic uncertainty analysis that provides for refinements in...

The Impact of Uncertainty and Irreversibility on Investments in Online Learning

ERIC Educational Resources Information Center

Oslington, Paul

2004-01-01

Uncertainty and irreversibility are central to online learning projects, but have been neglected in the existing educational cost-benefit analysis literature. This paper builds some simple illustrative models of the impact of irreversibility and uncertainty, and shows how different types of cost and demand uncertainty can have substantial impacts…

A Global Fitting Approach For Doppler Broadening Thermometry

NASA Astrophysics Data System (ADS)

Amodio, Pasquale; Moretti, Luigi; De Vizia, Maria Domenica; Gianfrani, Livio

2014-06-01

Very recently, a spectroscopic determination of the Boltzmann constant, kB, has been performed at the Second University of Naples by means of a rather sophisticated implementation of Doppler Broadening Thermometry (DBT)1. Performed on a 18O-enriched water sample, at a wavelength of 1.39 µm, the experiment has provided a value for kB with a combined uncertainty of 24 parts over 106, which is the best result obtained so far, by using an optical method. In the spectral analysis procedure, the partially correlated speed-dependent hard-collision (pC-SDHC) model was adopted. The uncertainty budget has clearly revealed that the major contributions come from the statistical uncertainty (type A) and from the uncertainty associated to the line-shape model (type B)2. In the present work, we present the first results of a theoretical and numerical work aimed at reducing these uncertainty components. It is well known that molecular line shapes exhibit clear deviations from the time honoured Voigt profile. Even in the case of a well isolated spectral line, under the influence of binary collisions, in the Doppler regime, the shape can be quite complicated by the joint occurrence of velocity-change collisions and speed-dependent effects. The partially correlated speed-dependent Keilson-Storer profile (pC-SDKS) has been recently proposed as a very realistic model, capable of reproducing very accurately the absorption spectra for self-colliding water molecules, in the near infrared3. Unfortunately, the model is so complex that it cannot be implemented into a fitting routine for the analysis of experimental spectra. Therefore, we have developed a MATLAB code to simulate a variety of H218O spectra in thermodynamic conditions identical to the one of our DBT experiment, using the pC-SDKS model. The numerical calculations to determine such a profile have a very large computational cost, resulting from a very sophisticated iterative procedure. Hence, the numerically simulated spectra (with the addition of random noise) have been used to test the validity of simplified line shape models, such as the speed-dependent Galatry (SDG) profile and pC-SDHC model. In particular, we have used the global fitting procedure that is described in Amodio et al4. Such a procedure is very effective in reducing the uncertainty resulting from statistical correlation among free parameters. Therefore, the analysis of large amounts of simulated spectra has allowed us to study the influence of the choice of the model and quantify the achievable precision and accuracy levels, at the present value of the signal-to-noise ratio. freely redistributable under the GPL http://www.gnu.org.

Robust Flutter Margin Analysis that Incorporates Flight Data

NASA Technical Reports Server (NTRS)

Lind, Rick; Brenner, Martin J.

1998-01-01

An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, mu, computes a stability margin that directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The mu margins are robust margins that indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 Systems Research Aircraft using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.

Sensitivity-Uncertainty Techniques for Nuclear Criticality Safety

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Forrest B.; Rising, Michael Evan; Alwin, Jennifer Louise

2017-08-07

The sensitivity and uncertainty analysis course will introduce students to k eff sensitivity data, cross-section uncertainty data, how k eff sensitivity data and k eff uncertainty data are generated and how they can be used. Discussion will include how sensitivity/uncertainty data can be used to select applicable critical experiments, to quantify a defensible margin to cover validation gaps and weaknesses, and in development of upper subcritical limits.

Predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data

NASA Astrophysics Data System (ADS)

McCoy, Daniel T.; Bender, Frida A.-M.; Grosvenor, Daniel P.; Mohrmann, Johannes K.; Hartmann, Dennis L.; Wood, Robert; Field, Paul R.

2018-02-01

Cloud droplet number concentration (CDNC) is the key state variable that moderates the relationship between aerosol and the radiative forcing arising from aerosol-cloud interactions. Uncertainty related to the effect of anthropogenic aerosol on cloud properties represents the largest uncertainty in total anthropogenic radiative forcing. Here we show that regionally averaged time series of the Moderate-Resolution Imaging Spectroradiometer (MODIS) observed CDNC of low, liquid-topped clouds is well predicted by the MERRA2 reanalysis near-surface sulfate mass concentration over decadal timescales. A multiple linear regression between MERRA2 reanalyses masses of sulfate (SO4), black carbon (BC), organic carbon (OC), sea salt (SS), and dust (DU) shows that CDNC across many different regimes can be reproduced by a simple power-law fit to near-surface SO4, with smaller contributions from BC, OC, SS, and DU. This confirms previous work using a less sophisticated retrieval of CDNC on monthly timescales. The analysis is supported by an examination of remotely sensed sulfur dioxide (SO2) over maritime volcanoes and the east coasts of North America and Asia, revealing that maritime CDNC responds to changes in SO2 as observed by the ozone monitoring instrument (OMI). This investigation of aerosol reanalysis and top-down remote-sensing observations reveals that emission controls in Asia and North America have decreased CDNC in their maritime outflow on a decadal timescale.

Uncertainties in the governance of animal disease: an interdisciplinary framework for analysis

PubMed Central

Fish, Robert; Austin, Zoe; Christley, Robert; Haygarth, Philip M.; Heathwaite, Louise A.; Latham, Sophia; Medd, William; Mort, Maggie; Oliver, David M.; Pickup, Roger; Wastling, Jonathan M.; Wynne, Brian

2011-01-01

Uncertainty is an inherent feature of strategies to contain animal disease. In this paper, an interdisciplinary framework for representing strategies of containment, and analysing how uncertainties are embedded and propagated through them, is developed and illustrated. Analysis centres on persistent, periodic and emerging disease threats, with a particular focus on cryptosporidiosis, foot and mouth disease and avian influenza. Uncertainty is shown to be produced at strategic, tactical and operational levels of containment, and across the different arenas of disease prevention, anticipation and alleviation. The paper argues for more critically reflexive assessments of uncertainty in containment policy and practice. An interdisciplinary approach has an important contribution to make, but is absent from current real-world containment policy. PMID:21624922

'spup' - an R package for uncertainty propagation analysis in spatial environmental modelling

NASA Astrophysics Data System (ADS)

Sawicka, Kasia; Heuvelink, Gerard

2017-04-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Currently, advances in uncertainty propagation and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability and being able to deal with case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the 'spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques, as well as several uncertainty visualization functions. Uncertain environmental variables are represented in the package as objects whose attribute values may be uncertain and described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is also accommodated for. For uncertainty propagation the package has implemented the MC approach with efficient sampling algorithms, i.e. stratified random sampling and Latin hypercube sampling. The design includes facilitation of parallel computing to speed up MC computation. The MC realizations may be used as an input to the environmental models called from R, or externally. Selected visualization methods that are understandable by non-experts with limited background in statistics can be used to summarize and visualize uncertainty about the measured input, model parameters and output of the uncertainty propagation. We demonstrate that the 'spup' package is an effective and easy tool to apply and can be used in multi-disciplinary research and model-based decision support.

Uncertainty of climate change impact on groundwater reserves - Application to a chalk aquifer

NASA Astrophysics Data System (ADS)

Goderniaux, Pascal; Brouyère, Serge; Wildemeersch, Samuel; Therrien, René; Dassargues, Alain

2015-09-01

Recent studies have evaluated the impact of climate change on groundwater resources for different geographical and climatic contexts. However, most studies have either not estimated the uncertainty around projected impacts or have limited the analysis to the uncertainty related to climate models. In this study, the uncertainties around impact projections from several sources (climate models, natural variability of the weather, hydrological model calibration) are calculated and compared for the Geer catchment (465 km2) in Belgium. We use a surface-subsurface integrated model implemented using the finite element code HydroGeoSphere, coupled with climate change scenarios (2010-2085) and the UCODE_2005 inverse model, to assess the uncertainty related to the calibration of the hydrological model. This integrated model provides a more realistic representation of the water exchanges between surface and subsurface domains and constrains more the calibration with the use of both surface and subsurface observed data. Sensitivity and uncertainty analyses were performed on predictions. The linear uncertainty analysis is approximate for this nonlinear system, but it provides some measure of uncertainty for computationally demanding models. Results show that, for the Geer catchment, the most important uncertainty is related to calibration of the hydrological model. The total uncertainty associated with the prediction of groundwater levels remains large. By the end of the century, however, the uncertainty becomes smaller than the predicted decline in groundwater levels.

Uncertainty Modeling for Structural Control Analysis and Synthesis

NASA Technical Reports Server (NTRS)

Campbell, Mark E.; Crawley, Edward F.

1996-01-01

The development of an accurate model of uncertainties for the control of structures that undergo a change in operational environment, based solely on modeling and experimentation in the original environment is studied. The application used throughout this work is the development of an on-orbit uncertainty model based on ground modeling and experimentation. A ground based uncertainty model consisting of mean errors and bounds on critical structural parameters is developed. The uncertainty model is created using multiple data sets to observe all relevant uncertainties in the system. The Discrete Extended Kalman Filter is used as an identification/parameter estimation method for each data set, in addition to providing a covariance matrix which aids in the development of the uncertainty model. Once ground based modal uncertainties have been developed, they are localized to specific degrees of freedom in the form of mass and stiffness uncertainties. Two techniques are presented: a matrix method which develops the mass and stiffness uncertainties in a mathematical manner; and a sensitivity method which assumes a form for the mass and stiffness uncertainties in macroelements and scaling factors. This form allows the derivation of mass and stiffness uncertainties in a more physical manner. The mass and stiffness uncertainties of the ground based system are then mapped onto the on-orbit system, and projected to create an analogous on-orbit uncertainty model in the form of mean errors and bounds on critical parameters. The Middeck Active Control Experiment is introduced as experimental verification for the localization and projection methods developed. In addition, closed loop results from on-orbit operations of the experiment verify the use of the uncertainty model for control analysis and synthesis in space.

Uncertainty modelling and analysis of volume calculations based on a regular grid digital elevation model (DEM)

NASA Astrophysics Data System (ADS)

Li, Chang; Wang, Qing; Shi, Wenzhong; Zhao, Sisi

2018-05-01

The accuracy of earthwork calculations that compute terrain volume is critical to digital terrain analysis (DTA). The uncertainties in volume calculations (VCs) based on a DEM are primarily related to three factors: 1) model error (ME), which is caused by an adopted algorithm for a VC model, 2) discrete error (DE), which is usually caused by DEM resolution and terrain complexity, and 3) propagation error (PE), which is caused by the variables' error. Based on these factors, the uncertainty modelling and analysis of VCs based on a regular grid DEM are investigated in this paper. Especially, how to quantify the uncertainty of VCs is proposed by a confidence interval based on truncation error (TE). In the experiments, the trapezoidal double rule (TDR) and Simpson's double rule (SDR) were used to calculate volume, where the TE is the major ME, and six simulated regular grid DEMs with different terrain complexity and resolution (i.e. DE) were generated by a Gauss synthetic surface to easily obtain the theoretical true value and eliminate the interference of data errors. For PE, Monte-Carlo simulation techniques and spatial autocorrelation were used to represent DEM uncertainty. This study can enrich uncertainty modelling and analysis-related theories of geographic information science.

A comparative study of multivariable robustness analysis methods as applied to integrated flight and propulsion control

NASA Technical Reports Server (NTRS)

Schierman, John D.; Lovell, T. A.; Schmidt, David K.

1993-01-01

Three multivariable robustness analysis methods are compared and contrasted. The focus of the analysis is on system stability and performance robustness to uncertainty in the coupling dynamics between two interacting subsystems. Of particular interest is interacting airframe and engine subsystems, and an example airframe/engine vehicle configuration is utilized in the demonstration of these approaches. The singular value (SV) and structured singular value (SSV) analysis methods are compared to a method especially well suited for analysis of robustness to uncertainties in subsystem interactions. This approach is referred to here as the interacting subsystem (IS) analysis method. This method has been used previously to analyze airframe/engine systems, emphasizing the study of stability robustness. However, performance robustness is also investigated here, and a new measure of allowable uncertainty for acceptable performance robustness is introduced. The IS methodology does not require plant uncertainty models to measure the robustness of the system, and is shown to yield valuable information regarding the effects of subsystem interactions. In contrast, the SV and SSV methods allow for the evaluation of the robustness of the system to particular models of uncertainty, and do not directly indicate how the airframe (engine) subsystem interacts with the engine (airframe) subsystem.

Managing uncertainty in collaborative robotics engineering projects: The influence of task structure and peer interaction

NASA Astrophysics Data System (ADS)

Jordan, Michelle

Uncertainty is ubiquitous in life, and learning is an activity particularly likely to be fraught with uncertainty. Previous research suggests that students and teachers struggle in their attempts to manage the psychological experience of uncertainty and that students often fail to experience uncertainty when uncertainty may be warranted. Yet, few educational researchers have explicitly and systematically observed what students do, their behaviors and strategies, as they attempt to manage the uncertainty they experience during academic tasks. In this study I investigated how students in one fifth grade class managed uncertainty they experienced while engaged in collaborative robotics engineering projects, focusing particularly on how uncertainty management was influenced by task structure and students' interactions with their peer collaborators. The study was initiated at the beginning of instruction related to robotics engineering and preceded through the completion of several long-term collaborative robotics projects, one of which was a design project. I relied primarily on naturalistic observation of group sessions, semi-structured interviews, and collection of artifacts. My data analysis was inductive and interpretive, using qualitative discourse analysis techniques and methods of grounded theory. Three theoretical frameworks influenced the conception and design of this study: community of practice, distributed cognition, and complex adaptive systems theory. Uncertainty was a pervasive experience for the students collaborating in this instructional context. Students experienced uncertainty related to the project activity and uncertainty related to the social system as they collaborated to fulfill the requirements of their robotics engineering projects. They managed their uncertainty through a diverse set of tactics for reducing, ignoring, maintaining, and increasing uncertainty. Students experienced uncertainty from more different sources and used more and different types of uncertainty management strategies in the less structured task setting than in the more structured task setting. Peer interaction was influential because students relied on supportive social response to enact most of their uncertainty management strategies. When students could not garner socially supportive response from their peers, their options for managing uncertainty were greatly reduced.

Uncertainty Analysis via Failure Domain Characterization: Polynomial Requirement Functions

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Munoz, Cesar A.; Narkawicz, Anthony J.; Kenny, Sean P.; Giesy, Daniel P.

2011-01-01

This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. A Bernstein expansion approach is used to size hyper-rectangular subsets while a sum of squares programming approach is used to size quasi-ellipsoidal subsets. These methods are applicable to requirement functions whose functional dependency on the uncertainty is a known polynomial. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the uncertainty model assumed (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.

Uncertainty Analysis via Failure Domain Characterization: Unrestricted Requirement Functions

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2011-01-01

This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. The methods developed herein, which are based on nonlinear constrained optimization, are applicable to requirement functions whose functional dependency on the uncertainty is arbitrary and whose explicit form may even be unknown. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the assumed uncertainty model (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.

Uncertainty analysis on simple mass balance model to calculate critical loads for soil acidity.

PubMed

Li, Harbin; McNulty, Steven G

2007-10-01

Simple mass balance equations (SMBE) of critical acid loads (CAL) in forest soil were developed to assess potential risks of air pollutants to ecosystems. However, to apply SMBE reliably at large scales, SMBE must be tested for adequacy and uncertainty. Our goal was to provide a detailed analysis of uncertainty in SMBE so that sound strategies for scaling up CAL estimates to the national scale could be developed. Specifically, we wanted to quantify CAL uncertainty under natural variability in 17 model parameters, and determine their relative contributions in predicting CAL. Results indicated that uncertainty in CAL came primarily from components of base cation weathering (BC(w); 49%) and acid neutralizing capacity (46%), whereas the most critical parameters were BC(w) base rate (62%), soil depth (20%), and soil temperature (11%). Thus, improvements in estimates of these factors are crucial to reducing uncertainty and successfully scaling up SMBE for national assessments of CAL.

On different types of uncertainties in the context of the precautionary principle.

PubMed

Aven, Terje

2011-10-01

Few policies for risk management have created more controversy than the precautionary principle. A main problem is the extreme number of different definitions and interpretations. Almost all definitions of the precautionary principle identify "scientific uncertainties" as the trigger or criterion for its invocation; however, the meaning of this concept is not clear. For applying the precautionary principle it is not sufficient that the threats or hazards are uncertain. A stronger requirement is needed. This article provides an in-depth analysis of this issue. We question how the scientific uncertainties are linked to the interpretation of the probability concept, expected values, the results from probabilistic risk assessments, the common distinction between aleatory uncertainties and epistemic uncertainties, and the problem of establishing an accurate prediction model (cause-effect relationship). A new classification structure is suggested to define what scientific uncertainties mean. © 2011 Society for Risk Analysis.

Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Yi; Zhao, Yanxia; Wang, Chunyi; Chen, Sining

2017-11-01

Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and three emission scenarios representing the climate projections uncertainty, and two crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize ( Zea mays L.) yield at three locations (Benxi, Changling, and Hailun) across Northeast China (NEC) in periods 2010-2039 and 2040-2069, taking 1976-2005 as the baseline period. The multi-models ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5 % in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods.

Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar

2016-01-01

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

NASA Astrophysics Data System (ADS)

Wang, Z.

2015-12-01

For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

Towards extensive spatio-temporal reconstructions of North American land cover: a comparison of state-of-the-art pollen-vegetation models

NASA Astrophysics Data System (ADS)

Dawson, A.; Trachsel, M.; Goring, S. J.; Paciorek, C. J.; McLachlan, J. S.; Jackson, S. T.; Williams, J. W.

2017-12-01

Pollen records have been extensively used to reconstruct past changes in vegetation and study the underlying processes. However, developing the statistical techniques needed to accurately represent both data and process uncertainties is a formidable challenge. Recent advances in paleoecoinformatics (e.g. the Neotoma Paleoecology Database and the European Pollen Database), Bayesian age-depth models, and process-based pollen-vegetation models, and Bayesian hierarchical modeling have pushed paleovegetation reconstructions forward to a point where multiple sources of uncertainty can be incorporated into reconstructions, which in turn enables new hypotheses to be asked and more rigorous integration of paleovegetation data with earth system models and terrestrial ecosystem models. Several kinds of pollen-vegetation models have been developed, notably LOVE/REVEALS, STEPPS, and classical transfer functions such as the modern analog technique. LOVE/REVEALS has been adopted as the standard method for the LandCover6k effort to develop quantitative reconstructions of land cover for the Holocene, while STEPPS has been developed recently as part of the PalEON project and applied to reconstruct with uncertainty shifts in forest composition in New England and the upper Midwest during the late Holocene. Each PVM has different assumptions and structure and uses different input data, but few comparisons among approaches yet exist. Here, we present new reconstructions of land cover change in northern North America during the Holocene based on LOVE/REVEALS and data drawn from the Neotoma database and compare STEPPS-based reconstructions to those from LOVE/REVEALS. These parallel developments with LOVE/REVEALS provide an opportunity to compare and contrast models, and to begin to generate continental scale reconstructions, with explicit uncertainties, that can provide a base for interdisciplinary research within the biogeosciences. We show how STEPPS provides an important benchmark for past land-cover reconstruction, and how the LandCover 6k effort in North America advances our understanding of the past by allowing cross-continent comparisons using standardized methods and quantifying the impact of humans in the early Anthropocene.

Integrating model behavior, optimization, and sensitivity/uncertainty analysis: overview and application of the MOUSE software toolbox

USDA-ARS?s Scientific Manuscript database

This paper provides an overview of the Model Optimization, Uncertainty, and SEnsitivity Analysis (MOUSE) software application, an open-source, Java-based toolbox of visual and numerical analysis components for the evaluation of environmental models. MOUSE is based on the OPTAS model calibration syst...

Uncertainty of Polarized Parton Distributions

NASA Astrophysics Data System (ADS)

Hirai, M.; Goto, Y.; Horaguchi, T.; Kobayashi, H.; Kumano, S.; Miyama, M.; Saito, N.; Shibata, T.-A.

Polarized parton distribution functions are determined by a χ2 analysis of polarized deep inelastic experimental data. In this paper, uncertainty of obtained distribution functions is investigated by a Hessian method. We find that the uncertainty of the polarized gluon distribution is fairly large. Then, we estimate the gluon uncertainty by including the fake data which are generated from prompt photon process at RHIC. We observed that the uncertainty could be reduced with these data.

Uncertainty of quantitative microbiological methods of pharmaceutical analysis.

PubMed

Gunar, O V; Sakhno, N G

2015-12-30

The total uncertainty of quantitative microbiological methods, used in pharmaceutical analysis, consists of several components. The analysis of the most important sources of the quantitative microbiological methods variability demonstrated no effect of culture media and plate-count techniques in the estimation of microbial count while the highly significant effect of other factors (type of microorganism, pharmaceutical product and individual reading and interpreting errors) was established. The most appropriate method of statistical analysis of such data was ANOVA which enabled not only the effect of individual factors to be estimated but also their interactions. Considering all the elements of uncertainty and combining them mathematically the combined relative uncertainty of the test results was estimated both for method of quantitative examination of non-sterile pharmaceuticals and microbial count technique without any product. These data did not exceed 35%, appropriated for a traditional plate count methods. Copyright © 2015 Elsevier B.V. All rights reserved.

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components requiredmore » for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quanti cation, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.« less

Assessment of Uncertainties Related to Seismic Hazard Using Fuzzy Analysis

NASA Astrophysics Data System (ADS)

Jorjiashvili, N.; Yokoi, T.; Javakhishvili, Z.

2013-05-01

Seismic hazard analysis in last few decades has been become very important issue. Recently, new technologies and available data have been improved that helped many scientists to understand where and why earthquakes happen, physics of earthquakes, etc. They have begun to understand the role of uncertainty in Seismic hazard analysis. However, there is still significant problem how to handle existing uncertainty. The same lack of information causes difficulties to quantify uncertainty accurately. Usually attenuation curves are obtained in statistical way: regression analysis. Statistical and probabilistic analysis show overlapped results for the site coefficients. This overlapping takes place not only at the border between two neighboring classes, but also among more than three classes. Although the analysis starts from classifying sites using the geological terms, these site coefficients are not classified at all. In the present study, this problem is solved using Fuzzy set theory. Using membership functions the ambiguities at the border between neighboring classes can be avoided. Fuzzy set theory is performed for southern California by conventional way. In this study standard deviations that show variations between each site class obtained by Fuzzy set theory and classical way are compared. Results on this analysis show that when we have insufficient data for hazard assessment site classification based on Fuzzy set theory shows values of standard deviations less than obtained by classical way which is direct proof of less uncertainty.

Illness uncertainty and quality of life in children with cancer.

PubMed

Fortier, Michelle A; Batista, Melissa L; Wahi, Aditi; Kain, Alexandra; Strom, Suzanne; Sender, Leonard S

2013-07-01

Illness uncertainty is prevalent in children with cancer and has been associated with increased psychological distress. The relationship between illness uncertainty and quality of life in pediatric cancer patients remains unclear. The aim of the present study was to examine illness uncertainty as a predictor of health-related quality of life in children diagnosed with cancer. It was hypothesized that child-reported illness uncertainty would be negatively associated with child health-related quality of life. Children aged 8 to 18 years old and receiving treatment for cancer were recruited to participate in this study. One hundred twenty children and their parent(s) completed measures of illness uncertainty, pain, anxiety, and quality of life during a routine visit to the Cancer Center at Children's Hospital of Orange County. Illness uncertainty was significantly associated with child age (P=0.02), overall health-related (P<0.001) and cancer-related (P<0.001) quality of life, but not with treatment status (on/off chemotherapy) or demographic variables including sex and household income. Regression analyses statistically controlling for age, anxiety, and pain revealed that illness uncertainty significantly predicted child-reported cancer-related and health-related quality of life (P<0.01) as well as parent-reported cancer-specific quality of life (P<0.01). Illness uncertainty is prevalent and associated with lower quality of life in children diagnosed with cancer. Improved communication with children regarding disease state, treatment expectations, and prognosis may alleviate uncertainty and improve functioning in this vulnerable patient population.

Entropic uncertainty for spin-1/2 XXX chains in the presence of inhomogeneous magnetic fields and its steering via weak measurement reversals

NASA Astrophysics Data System (ADS)

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

2017-09-01

The uncertainty principle configures a low bound to the measuring precision for a pair of non-commuting observables, and hence is considerably nontrivial to quantum precision measurement in the field of quantum information theory. In this letter, we consider the entropic uncertainty relation (EUR) in the context of quantum memory in a two-qubit isotropic Heisenberg spin chain. Specifically, we explore the dynamics of EUR in a practical scenario, where two associated nodes of a one-dimensional XXX-spin chain, under an inhomogeneous magnetic field, are connected to a thermal entanglement. We show that the temperature and magnetic field effect can lead to the inflation of the measuring uncertainty, stemming from the reduction of systematic quantum correlation. Notably, we reveal that, firstly, the uncertainty is not fully dependent on the observed quantum correlation of the system; secondly, the dynamical behaviors of the measuring uncertainty are relatively distinct with respect to ferromagnetism and antiferromagnetism chains. Meanwhile, we deduce that the measuring uncertainty is dramatically correlated with the mixedness of the system, implying that smaller mixedness tends to reduce the uncertainty. Furthermore, we propose an effective strategy to control the uncertainty of interest by means of quantum weak measurement reversal. Therefore, our work may shed light on the dynamics of the measuring uncertainty in the Heisenberg spin chain, and thus be important to quantum precision measurement in various solid-state systems.

Guaranteeing robustness of structural condition monitoring to environmental variability

NASA Astrophysics Data System (ADS)

Van Buren, Kendra; Reilly, Jack; Neal, Kyle; Edwards, Harry; Hemez, François

2017-01-01

Advances in sensor deployment and computational modeling have allowed significant strides to be recently made in the field of Structural Health Monitoring (SHM). One widely used SHM strategy is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability, unknown model functional forms, and unknown values of model parameters. Not accounting for these sources of uncertainty can lead to false-positives or false-negatives in the structural condition assessment. To manage the uncertainty, we propose a robust SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes throughout time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM. (Publication approved for unlimited, public release on October-28-2015, LA-UR-15-28442, unclassified.)

The IAEA coordinated research programme on HTGR uncertainty analysis: Phase I status and Ex. I-1 prismatic reference results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bostelmann, Friederike; Strydom, Gerhard; Reitsma, Frederik

The quantification of uncertainties in design and safety analysis of reactors is today not only broadly accepted, but in many cases became the preferred way to replace traditional conservative analysis for safety and licensing analysis. The use of a more fundamental methodology is also consistent with the reliable high fidelity physics models and robust, efficient, and accurate codes available today. To facilitate uncertainty analysis applications a comprehensive approach and methodology must be developed and applied, in contrast to the historical approach where sensitivity analysis were performed and uncertainties then determined by a simplified statistical combination of a few important inputmore » parameters. New methodologies are currently under development in the OECD/NEA Light Water Reactor (LWR) Uncertainty Analysis in Best-Estimate Modelling (UAM) benchmark activity. High Temperature Gas-cooled Reactor (HTGR) designs require specific treatment of the double heterogeneous fuel design and large graphite quantities at high temperatures. The IAEA has therefore launched a Coordinated Research Project (CRP) on HTGR Uncertainty Analysis in Modelling (UAM) in 2013 to study uncertainty propagation specifically in the HTGR analysis chain. Two benchmark problems are defined, with the prismatic design represented by the General Atomics (GA) MHTGR-350 and a 250 MW modular pebble bed design similar to the Chinese HTR-PM. Work has started on the first phase and the current CRP status is reported in the paper. A comparison of the Serpent and SCALE/KENO-VI reference Monte Carlo results for Ex. I-1 of the MHTGR-350 design is also included. It was observed that the SCALE/KENO-VI Continuous Energy (CE) k ∞ values were 395 pcm (Ex. I-1a) to 803 pcm (Ex. I-1b) higher than the respective Serpent lattice calculations, and that within the set of the SCALE results, the KENO-VI 238 Multi-Group (MG) k ∞ values were up to 800 pcm lower than the KENO-VI CE values. The use of the latest ENDF-B-VII.1 cross section library in Serpent lead to ~180 pcm lower k ∞ values compared to the older ENDF-B-VII.0 dataset, caused by the modified graphite neutron capture cross section. Furthermore, the fourth beta release of SCALE 6.2 likewise produced lower CE k∞ values when compared to SCALE 6.1, and the improved performance of the new 252-group library available in SCALE 6.2 is especially noteworthy. A SCALE/TSUNAMI uncertainty analysis of the Hot Full Power variant for Ex. I-1a furthermore concluded that the 238U(n,γ) (capture) and 235U(View the MathML source) cross-section covariance matrices contributed the most to the total k ∞ uncertainty of 0.58%.« less

The IAEA coordinated research programme on HTGR uncertainty analysis: Phase I status and Ex. I-1 prismatic reference results

DOE PAGES

Bostelmann, Friederike; Strydom, Gerhard; Reitsma, Frederik; ...

2016-01-11

The quantification of uncertainties in design and safety analysis of reactors is today not only broadly accepted, but in many cases became the preferred way to replace traditional conservative analysis for safety and licensing analysis. The use of a more fundamental methodology is also consistent with the reliable high fidelity physics models and robust, efficient, and accurate codes available today. To facilitate uncertainty analysis applications a comprehensive approach and methodology must be developed and applied, in contrast to the historical approach where sensitivity analysis were performed and uncertainties then determined by a simplified statistical combination of a few important inputmore » parameters. New methodologies are currently under development in the OECD/NEA Light Water Reactor (LWR) Uncertainty Analysis in Best-Estimate Modelling (UAM) benchmark activity. High Temperature Gas-cooled Reactor (HTGR) designs require specific treatment of the double heterogeneous fuel design and large graphite quantities at high temperatures. The IAEA has therefore launched a Coordinated Research Project (CRP) on HTGR Uncertainty Analysis in Modelling (UAM) in 2013 to study uncertainty propagation specifically in the HTGR analysis chain. Two benchmark problems are defined, with the prismatic design represented by the General Atomics (GA) MHTGR-350 and a 250 MW modular pebble bed design similar to the Chinese HTR-PM. Work has started on the first phase and the current CRP status is reported in the paper. A comparison of the Serpent and SCALE/KENO-VI reference Monte Carlo results for Ex. I-1 of the MHTGR-350 design is also included. It was observed that the SCALE/KENO-VI Continuous Energy (CE) k ∞ values were 395 pcm (Ex. I-1a) to 803 pcm (Ex. I-1b) higher than the respective Serpent lattice calculations, and that within the set of the SCALE results, the KENO-VI 238 Multi-Group (MG) k ∞ values were up to 800 pcm lower than the KENO-VI CE values. The use of the latest ENDF-B-VII.1 cross section library in Serpent lead to ~180 pcm lower k ∞ values compared to the older ENDF-B-VII.0 dataset, caused by the modified graphite neutron capture cross section. Furthermore, the fourth beta release of SCALE 6.2 likewise produced lower CE k∞ values when compared to SCALE 6.1, and the improved performance of the new 252-group library available in SCALE 6.2 is especially noteworthy. A SCALE/TSUNAMI uncertainty analysis of the Hot Full Power variant for Ex. I-1a furthermore concluded that the 238U(n,γ) (capture) and 235U(View the MathML source) cross-section covariance matrices contributed the most to the total k ∞ uncertainty of 0.58%.« less

COMPUTATIONAL METHODS FOR SENSITIVITY AND UNCERTAINTY ANALYSIS FOR ENVIRONMENTAL AND BIOLOGICAL MODELS

EPA Science Inventory

This work introduces a computationally efficient alternative method for uncertainty propagation, the Stochastic Response Surface Method (SRSM). The SRSM approximates uncertainties in model outputs through a series expansion in normal random variables (polynomial chaos expansion)...

Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Weixuan; Lian, Jianming; Engel, Dave

2017-07-27

This paper presents a general uncertainty quantification (UQ) framework that provides a systematic analysis of the uncertainty involved in the modeling of a control system, and helps to improve the performance of a control strategy.

Uncertainty Quantification Techniques of SCALE/TSUNAMI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rearden, Bradley T; Mueller, Don

2011-01-01

The Standardized Computer Analysis for Licensing Evaluation (SCALE) code system developed at Oak Ridge National Laboratory (ORNL) includes Tools for Sensitivity and Uncertainty Analysis Methodology Implementation (TSUNAMI). The TSUNAMI code suite can quantify the predicted change in system responses, such as k{sub eff}, reactivity differences, or ratios of fluxes or reaction rates, due to changes in the energy-dependent, nuclide-reaction-specific cross-section data. Where uncertainties in the neutron cross-section data are available, the sensitivity of the system to the cross-section data can be applied to propagate the uncertainties in the cross-section data to an uncertainty in the system response. Uncertainty quantification ismore » useful for identifying potential sources of computational biases and highlighting parameters important to code validation. Traditional validation techniques often examine one or more average physical parameters to characterize a system and identify applicable benchmark experiments. However, with TSUNAMI correlation coefficients are developed by propagating the uncertainties in neutron cross-section data to uncertainties in the computed responses for experiments and safety applications through sensitivity coefficients. The bias in the experiments, as a function of their correlation coefficient with the intended application, is extrapolated to predict the bias and bias uncertainty in the application through trending analysis or generalized linear least squares techniques, often referred to as 'data adjustment.' Even with advanced tools to identify benchmark experiments, analysts occasionally find that the application models include some feature or material for which adequately similar benchmark experiments do not exist to support validation. For example, a criticality safety analyst may want to take credit for the presence of fission products in spent nuclear fuel. In such cases, analysts sometimes rely on 'expert judgment' to select an additional administrative margin to account for gap in the validation data or to conclude that the impact on the calculated bias and bias uncertainty is negligible. As a result of advances in computer programs and the evolution of cross-section covariance data, analysts can use the sensitivity and uncertainty analysis tools in the TSUNAMI codes to estimate the potential impact on the application-specific bias and bias uncertainty resulting from nuclides not represented in available benchmark experiments. This paper presents the application of methods described in a companion paper.« less

Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

NASA Astrophysics Data System (ADS)

Gorbunov, Michael E.; Kirchengast, Gottfried

2018-01-01

A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA) retrieval in the lower troposphere and introduce (1) an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2) the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT) amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and random uncertainties are propagated from excess phase to bending angle profiles, using a perturbation approach and the wave-optical method recently introduced by Gorbunov and Kirchengast (2015), starting with estimated excess phase uncertainties. The results are encouraging and this uncertainty propagation approach combined with BLB correction enables a robust reduction and quantification of the uncertainties of excess phases and bending angles in the lower troposphere.

Responses to clinical uncertainty in Australian general practice trainees: a cross-sectional analysis.

PubMed

Cooke, Georga; Tapley, Amanda; Holliday, Elizabeth; Morgan, Simon; Henderson, Kim; Ball, Jean; van Driel, Mieke; Spike, Neil; Kerr, Rohan; Magin, Parker

2017-12-01

Tolerance for ambiguity is essential for optimal learning and professional competence. General practice trainees must be, or must learn to be, adept at managing clinical uncertainty. However, few studies have examined associations of intolerance of uncertainty in this group. The aim of this study was to establish levels of tolerance of uncertainty in Australian general practice trainees and associations of uncertainty with demographic, educational and training practice factors. A cross-sectional analysis was performed on the Registrar Clinical Encounters in Training (ReCEnT) project, an ongoing multi-site cohort study. Scores on three of the four independent subscales of the Physicians' Reaction to Uncertainty (PRU) instrument were analysed as outcome variables in linear regression models with trainee and practice factors as independent variables. A total of 594 trainees contributed data on a total of 1209 occasions. Trainees in earlier training terms had higher scores for 'Anxiety due to uncertainty', 'Concern about bad outcomes' and 'Reluctance to disclose diagnosis/treatment uncertainty to patients'. Beyond this, findings suggest two distinct sets of associations regarding reaction to uncertainty. Firstly, affective aspects of uncertainty (the 'Anxiety' and 'Concern' subscales) were associated with female gender, less experience in hospital prior to commencing general practice training, and graduation overseas. Secondly, a maladaptive response to uncertainty (the 'Reluctance to disclose' subscale) was associated with urban practice, health qualifications prior to studying medicine, practice in an area of higher socio-economic status, and being Australian-trained. This study has established levels of three measures of trainees' responses to uncertainty and associations with these responses. The current findings suggest differing 'phenotypes' of trainees with high 'affective' responses to uncertainty and those reluctant to disclose uncertainty to patients. More research is needed to examine the relationship between clinical uncertainty and clinical outcomes, temporal changes in tolerance for uncertainty, and strategies that might assist physicians in developing adaptive responses to clinical uncertainty. © 2017 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models.

PubMed

Degeling, Koen; IJzerman, Maarten J; Koopman, Miriam; Koffijberg, Hendrik

2017-12-15

Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes. Two approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study. Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes. Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.

Modelling uncertainties and possible future trends of precipitation and temperature for 10 sub-basins in Columbia River Basin (CRB)

NASA Astrophysics Data System (ADS)

Ahmadalipour, A.; Rana, A.; Qin, Y.; Moradkhani, H.

2014-12-01

Trends and changes in future climatic parameters, such as, precipitation and temperature have been a central part of climate change studies. In the present work, we have analyzed the seasonal and yearly trends and uncertainties of prediction in all the 10 sub-basins of Columbia River Basin (CRB) for future time period of 2010-2099. The work is carried out using 2 different sets of statistically downscaled Global Climate Model (GCMs) projection datasets i.e. Bias correction and statistical downscaling (BCSD) generated at Portland State University and The Multivariate Adaptive Constructed Analogs (MACA) generated at University of Idaho. The analysis is done for with 10 GCM downscaled products each from CMIP5 daily dataset totaling to 40 different downscaled products for robust analysis. Summer, winter and yearly trend analysis is performed for all the 10 sub-basins using linear regression (significance tested by student t test) and Mann Kendall test (0.05 percent significance level), for precipitation (P), temperature maximum (Tmax) and temperature minimum (Tmin). Thereafter, all the parameters are modelled for uncertainty, across all models, in all the 10 sub-basins and across the CRB for future scenario periods. Results have indicated in varied degree of trends for all the sub-basins, mostly pointing towards a significant increase in all three climatic parameters, for all the seasons and yearly considerations. Uncertainty analysis have reveled very high change in all the parameters across models and sub-basins under consideration. Basin wide uncertainty analysis is performed to corroborate results from smaller, sub-basin scale. Similar trends and uncertainties are reported on the larger scale as well. Interestingly, both trends and uncertainties are higher during winter period than during summer, contributing to large part of the yearly change.

puma: a Bioconductor package for propagating uncertainty in microarray analysis.

PubMed

Pearson, Richard D; Liu, Xuejun; Sanguinetti, Guido; Milo, Marta; Lawrence, Neil D; Rattray, Magnus

2009-07-09

Most analyses of microarray data are based on point estimates of expression levels and ignore the uncertainty of such estimates. By determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analyses it has been shown that we can improve results of differential expression detection, principal component analysis and clustering. Previously, implementations of these uncertainty propagation methods have only been available as separate packages, written in different languages. Previous implementations have also suffered from being very costly to compute, and in the case of differential expression detection, have been limited in the experimental designs to which they can be applied. puma is a Bioconductor package incorporating a suite of analysis methods for use on Affymetrix GeneChip data. puma extends the differential expression detection methods of previous work from the 2-class case to the multi-factorial case. puma can be used to automatically create design and contrast matrices for typical experimental designs, which can be used both within the package itself but also in other Bioconductor packages. The implementation of differential expression detection methods has been parallelised leading to significant decreases in processing time on a range of computer architectures. puma incorporates the first R implementation of an uncertainty propagation version of principal component analysis, and an implementation of a clustering method based on uncertainty propagation. All of these techniques are brought together in a single, easy-to-use package with clear, task-based documentation. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. These methods can be used to improve results from more traditional analyses of microarray data. puma also offers improvements in terms of scope and speed of execution over previously available methods. puma is recommended for anyone working with the Affymetrix GeneChip platform for gene expression analysis and can also be applied more generally.

Efficient Data-Worth Analysis Using a Multilevel Monte Carlo Method Applied in Oil Reservoir Simulations

NASA Astrophysics Data System (ADS)

Lu, D.; Ricciuto, D. M.; Evans, K. J.

2017-12-01

Data-worth analysis plays an essential role in improving the understanding of the subsurface system, in developing and refining subsurface models, and in supporting rational water resources management. However, data-worth analysis is computationally expensive as it requires quantifying parameter uncertainty, prediction uncertainty, and both current and potential data uncertainties. Assessment of these uncertainties in large-scale stochastic subsurface simulations using standard Monte Carlo (MC) sampling or advanced surrogate modeling is extremely computationally intensive, sometimes even infeasible. In this work, we propose efficient Bayesian analysis of data-worth using a multilevel Monte Carlo (MLMC) method. Compared to the standard MC that requires a significantly large number of high-fidelity model executions to achieve a prescribed accuracy in estimating expectations, the MLMC can substantially reduce the computational cost with the use of multifidelity approximations. As the data-worth analysis involves a great deal of expectation estimations, the cost savings from MLMC in the assessment can be very outstanding. While the proposed MLMC-based data-worth analysis is broadly applicable, we use it to a highly heterogeneous oil reservoir simulation to select an optimal candidate data set that gives the largest uncertainty reduction in predicting mass flow rates at four production wells. The choices made by the MLMC estimation are validated by the actual measurements of the potential data, and consistent with the estimation obtained from the standard MC. But compared to the standard MC, the MLMC greatly reduces the computational costs in the uncertainty reduction estimation, with up to 600 days cost savings when one processor is used.

Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality

NASA Astrophysics Data System (ADS)

Keating, Elizabeth H.; Doherty, John; Vrugt, Jasper A.; Kang, Qinjun

2010-10-01

Highly parameterized and CPU-intensive groundwater models are increasingly being used to understand and predict flow and transport through aquifers. Despite their frequent use, these models pose significant challenges for parameter estimation and predictive uncertainty analysis algorithms, particularly global methods which usually require very large numbers of forward runs. Here we present a general methodology for parameter estimation and uncertainty analysis that can be utilized in these situations. Our proposed method includes extraction of a surrogate model that mimics key characteristics of a full process model, followed by testing and implementation of a pragmatic uncertainty analysis technique, called null-space Monte Carlo (NSMC), that merges the strengths of gradient-based search and parameter dimensionality reduction. As part of the surrogate model analysis, the results of NSMC are compared with a formal Bayesian approach using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Such a comparison has never been accomplished before, especially in the context of high parameter dimensionality. Despite the highly nonlinear nature of the inverse problem, the existence of multiple local minima, and the relatively large parameter dimensionality, both methods performed well and results compare favorably with each other. Experiences gained from the surrogate model analysis are then transferred to calibrate the full highly parameterized and CPU intensive groundwater model and to explore predictive uncertainty of predictions made by that model. The methodology presented here is generally applicable to any highly parameterized and CPU-intensive environmental model, where efficient methods such as NSMC provide the only practical means for conducting predictive uncertainty analysis.

The effect of uncertainties in distance-based ranking methods for multi-criteria decision making

NASA Astrophysics Data System (ADS)

Jaini, Nor I.; Utyuzhnikov, Sergei V.

2017-08-01

Data in the multi-criteria decision making are often imprecise and changeable. Therefore, it is important to carry out sensitivity analysis test for the multi-criteria decision making problem. The paper aims to present a sensitivity analysis for some ranking techniques based on the distance measures in multi-criteria decision making. Two types of uncertainties are considered for the sensitivity analysis test. The first uncertainty is related to the input data, while the second uncertainty is towards the Decision Maker preferences (weights). The ranking techniques considered in this study are TOPSIS, the relative distance and trade-off ranking methods. TOPSIS and the relative distance method measure a distance from an alternative to the ideal and antiideal solutions. In turn, the trade-off ranking calculates a distance of an alternative to the extreme solutions and other alternatives. Several test cases are considered to study the performance of each ranking technique in both types of uncertainties.

Uncertainty Analysis of the Grazing Flow Impedance Tube

NASA Technical Reports Server (NTRS)

Brown, Martha C.; Jones, Michael G.; Watson, Willie R.

2012-01-01

This paper outlines a methodology to identify the measurement uncertainty of NASA Langley s Grazing Flow Impedance Tube (GFIT) over its operating range, and to identify the parameters that most significantly contribute to the acoustic impedance prediction. Two acoustic liners are used for this study. The first is a single-layer, perforate-over-honeycomb liner that is nonlinear with respect to sound pressure level. The second consists of a wire-mesh facesheet and a honeycomb core, and is linear with respect to sound pressure level. These liners allow for evaluation of the effects of measurement uncertainty on impedances educed with linear and nonlinear liners. In general, the measurement uncertainty is observed to be larger for the nonlinear liners, with the largest uncertainty occurring near anti-resonance. A sensitivity analysis of the aerodynamic parameters (Mach number, static temperature, and static pressure) used in the impedance eduction process is also conducted using a Monte-Carlo approach. This sensitivity analysis demonstrates that the impedance eduction process is virtually insensitive to each of these parameters.

Development code for sensitivity and uncertainty analysis of input on the MCNPX for neutronic calculation in PWR core

NASA Astrophysics Data System (ADS)

Hartini, Entin; Andiwijayakusuma, Dinan

2014-09-01

This research was carried out on the development of code for uncertainty analysis is based on a statistical approach for assessing the uncertainty input parameters. In the butn-up calculation of fuel, uncertainty analysis performed for input parameters fuel density, coolant density and fuel temperature. This calculation is performed during irradiation using Monte Carlo N-Particle Transport. The Uncertainty method based on the probabilities density function. Development code is made in python script to do coupling with MCNPX for criticality and burn-up calculations. Simulation is done by modeling the geometry of PWR terrace, with MCNPX on the power 54 MW with fuel type UO2 pellets. The calculation is done by using the data library continuous energy cross-sections ENDF / B-VI. MCNPX requires nuclear data in ACE format. Development of interfaces for obtaining nuclear data in the form of ACE format of ENDF through special process NJOY calculation to temperature changes in a certain range.

Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework

NASA Astrophysics Data System (ADS)

Sadegh, Mojtaba; Ragno, Elisa; AghaKouchak, Amir

2017-06-01

We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which includes a wide range of copula families with different levels of complexity. MvCAT employs a Bayesian framework with a residual-based Gaussian likelihood function for inferring copula parameters and estimating the underlying uncertainties. The contribution of this paper is threefold: (a) providing a Bayesian framework to approximate the predictive uncertainties of fitted copulas, (b) introducing a hybrid-evolution Markov Chain Monte Carlo (MCMC) approach designed for numerical estimation of the posterior distribution of copula parameters, and (c) enabling the community to explore a wide range of copulas and evaluate them relative to the fitting uncertainties. We show that the commonly used local optimization methods for copula parameter estimation often get trapped in local minima. The proposed method, however, addresses this limitation and improves describing the dependence structure. MvCAT also enables evaluation of uncertainties relative to the length of record, which is fundamental to a wide range of applications such as multivariate frequency analysis.

Development code for sensitivity and uncertainty analysis of input on the MCNPX for neutronic calculation in PWR core

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hartini, Entin, E-mail: entin@batan.go.id; Andiwijayakusuma, Dinan, E-mail: entin@batan.go.id

2014-09-30

This research was carried out on the development of code for uncertainty analysis is based on a statistical approach for assessing the uncertainty input parameters. In the butn-up calculation of fuel, uncertainty analysis performed for input parameters fuel density, coolant density and fuel temperature. This calculation is performed during irradiation using Monte Carlo N-Particle Transport. The Uncertainty method based on the probabilities density function. Development code is made in python script to do coupling with MCNPX for criticality and burn-up calculations. Simulation is done by modeling the geometry of PWR terrace, with MCNPX on the power 54 MW with fuelmore » type UO2 pellets. The calculation is done by using the data library continuous energy cross-sections ENDF / B-VI. MCNPX requires nuclear data in ACE format. Development of interfaces for obtaining nuclear data in the form of ACE format of ENDF through special process NJOY calculation to temperature changes in a certain range.« less

Approach for validating actinide and fission product compositions for burnup credit criticality safety analyses

DOE PAGES

Radulescu, Georgeta; Gauld, Ian C.; Ilas, Germina; ...

2014-11-01

This paper describes a depletion code validation approach for criticality safety analysis using burnup credit for actinide and fission product nuclides in spent nuclear fuel (SNF) compositions. The technical basis for determining the uncertainties in the calculated nuclide concentrations is comparison of calculations to available measurements obtained from destructive radiochemical assay of SNF samples. Probability distributions developed for the uncertainties in the calculated nuclide concentrations were applied to the SNF compositions of a criticality safety analysis model by the use of a Monte Carlo uncertainty sampling method to determine bias and bias uncertainty in effective neutron multiplication factor. Application ofmore » the Monte Carlo uncertainty sampling approach is demonstrated for representative criticality safety analysis models of pressurized water reactor spent fuel pool storage racks and transportation packages using burnup-dependent nuclide concentrations calculated with SCALE 6.1 and the ENDF/B-VII nuclear data. Furthermore, the validation approach and results support a recent revision of the U.S. Nuclear Regulatory Commission Interim Staff Guidance 8.« less

Application of Monte Carlo Methods to Perform Uncertainty and Sensitivity Analysis on Inverse Water-Rock Reactions with NETPATH

DOE Office of Scientific and Technical Information (OSTI.GOV)

McGraw, David; Hershey, Ronald L.

Methods were developed to quantify uncertainty and sensitivity for NETPATH inverse water-rock reaction models and to calculate dissolved inorganic carbon, carbon-14 groundwater travel times. The NETPATH models calculate upgradient groundwater mixing fractions that produce the downgradient target water chemistry along with amounts of mineral phases that are either precipitated or dissolved. Carbon-14 groundwater travel times are calculated based on the upgradient source-water fractions, carbonate mineral phase changes, and isotopic fractionation. Custom scripts and statistical code were developed for this study to facilitate modifying input parameters, running the NETPATH simulations, extracting relevant output, postprocessing the results, and producing graphs and summaries.more » The scripts read userspecified values for each constituent’s coefficient of variation, distribution, sensitivity parameter, maximum dissolution or precipitation amounts, and number of Monte Carlo simulations. Monte Carlo methods for analysis of parametric uncertainty assign a distribution to each uncertain variable, sample from those distributions, and evaluate the ensemble output. The uncertainty in input affected the variability of outputs, namely source-water mixing, phase dissolution and precipitation amounts, and carbon-14 travel time. Although NETPATH may provide models that satisfy the constraints, it is up to the geochemist to determine whether the results are geochemically reasonable. Two example water-rock reaction models from previous geochemical reports were considered in this study. Sensitivity analysis was also conducted to evaluate the change in output caused by a small change in input, one constituent at a time. Results were standardized to allow for sensitivity comparisons across all inputs, which results in a representative value for each scenario. The approach yielded insight into the uncertainty in water-rock reactions and travel times. For example, there was little variation in source-water fraction between the deterministic and Monte Carlo approaches, and therefore, little variation in travel times between approaches. Sensitivity analysis proved very useful for identifying the most important input constraints (dissolved-ion concentrations), which can reveal the variables that have the most influence on source-water fractions and carbon-14 travel times. Once these variables are determined, more focused effort can be applied to determining the proper distribution for each constraint. Second, Monte Carlo results for water-rock reaction modeling showed discrete and nonunique results. The NETPATH models provide the solutions that satisfy the constraints of upgradient and downgradient water chemistry. There can exist multiple, discrete solutions for any scenario and these discrete solutions cause grouping of results. As a result, the variability in output may not easily be represented by a single distribution or a mean and variance and care should be taken in the interpretation and reporting of results.« less

Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin

NASA Astrophysics Data System (ADS)

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.

Water supply infrastructure planning under multiple uncertainties: A differentiated approach

NASA Astrophysics Data System (ADS)

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 desalination. Intense withdrawals for urban and agricultural use will lead to lowering of the water table in the aquifer at rapid but uncertain rates due to poor groundwater characterization. We assess the potential for additional groundwater data collection and a flexible infrastructure approach similar to that in Melbourne to mitigate risk.

Effects of stress on decisions under uncertainty: A meta-analysis.

PubMed

Starcke, Katrin; Brand, Matthias

2016-09-01

[Correction Notice: An Erratum for this article was reported in Vol 142(9) of Psychological Bulletin (see record 2016-39486-001). It should have been reported that the inverted u-shaped relationship between cortisol stress responses and decision-making performance was only observed in female, but not in male participants as suggested by the study by van den Bos, Harteveld, and Stoop (2009). Corrected versions of the affected sentences are provided.] The purpose of the present meta-analysis was to quantify the effects that stress has on decisions made under uncertainty. We hypothesized that stress increases reward seeking and risk taking through alterations of dopamine firing rates and reduces executive control by hindering optimal prefrontal cortex functioning. In certain decision situations, increased reward seeking and risk taking is dysfunctional, whereas in others, this is not the case. We also assumed that the type of stressor plays a role. In addition, moderating variables are analyzed, such as the hormonal stress response, the time between stress onset and decisions, and the participants' age and gender. We included studies in the meta-analysis that investigated decision making after a laboratory stress-induction versus a control condition (k = 32 datasets, N = 1829 participants). A random-effects model revealed that overall, stress conditions lead to decisions that can be described as more disadvantageous, more reward seeking, and more risk taking than nonstress conditions (d = .17). In those situations in which increased reward seeking and risk taking is disadvantageous, stress had significant effects (d = .26), whereas in other situations, no effects were observed (d = .01). Effects were observed under processive stressors (d = .19), but not under systemic ones (d = .09). Moderation analyses did not reveal any significant results. We concluded that stress deteriorates overall decision-making performance through the mechanisms proposed. The effects differ, depending on the decision situation and the type of stressor, but not on the characteristics of the individuals. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A first hazard analysis of the Harrat Ash Shamah volcanic field, Syria-Jordan Borderline

NASA Astrophysics Data System (ADS)

Cagnan, Zehra; Akkar, Sinan; Moghimi, Saed

2017-04-01

The northernmost part of the Saudi Cenozoic Volcanic Fields, the 100,000 km2 Harrat Ash Shamah has hosted some of the most recent volcanic eruptions along the Syria-Jordan borderline. With rapid growth of the cities in this region, exposure to any potential renewed volcanism increased considerably. We present here a first-order probabilistic hazard analysis related to new vent formation and subsequent lava flow from Harrat Ash Shamah. The 733 visible eruption vent sites were utilized to develop a probability density function for new eruption sites using Gaussian kernel smoothing. This revealed a NNW striking zone of high spatial hazard surrounding the cities Amman and Irbid in Jordan. The temporal eruption recurrence rate is estimated to be approximately one vent per 3500 years, but the temporal record of the field is so poorly constrained that the lower and upper bounds for the recurrence interval are 17,700 yrs and 70 yrs, respectively. A Poisson temporal model is employed within the scope of this study. In order to treat the uncertainties associated with the spatio-temporal models as well as size of the area affected by the lava flow, the logic tree approach is adopted. For the Syria-Jordan borderline, the spatial variation of volcanic hazard is computed as well as uncertainty associated with these estimates.

Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre- and Post-Processing in Sequential Data Assimilation

NASA Astrophysics Data System (ADS)

Wang, S.; Ancell, B. C.; Huang, G. H.; Baetz, B. W.

2018-03-01

Data assimilation using the ensemble Kalman filter (EnKF) has been increasingly recognized as a promising tool for probabilistic hydrologic predictions. However, little effort has been made to conduct the pre- and post-processing of assimilation experiments, posing a significant challenge in achieving the best performance of hydrologic predictions. This paper presents a unified data assimilation framework for improving the robustness of hydrologic ensemble predictions. Statistical pre-processing of assimilation experiments is conducted through the factorial design and analysis to identify the best EnKF settings with maximized performance. After the data assimilation operation, statistical post-processing analysis is also performed through the factorial polynomial chaos expansion to efficiently address uncertainties in hydrologic predictions, as well as to explicitly reveal potential interactions among model parameters and their contributions to the predictive accuracy. In addition, the Gaussian anamorphosis is used to establish a seamless bridge between data assimilation and uncertainty quantification of hydrologic predictions. Both synthetic and real data assimilation experiments are carried out to demonstrate feasibility and applicability of the proposed methodology in the Guadalupe River basin, Texas. Results suggest that statistical pre- and post-processing of data assimilation experiments provide meaningful insights into the dynamic behavior of hydrologic systems and enhance robustness of hydrologic ensemble predictions.

Uncertainty Analysis for Angle Calibrations Using Circle Closure

PubMed Central

Estler, W. Tyler

1998-01-01

We analyze two types of full-circle angle calibrations: a simple closure in which a single set of unknown angular segments is sequentially compared with an unknown reference angle, and a dual closure in which two divided circles are simultaneously calibrated by intercomparison. In each case, the constraint of circle closure provides auxiliary information that (1) enables a complete calibration process without reference to separately calibrated reference artifacts, and (2) serves to reduce measurement uncertainty. We derive closed-form expressions for the combined standard uncertainties of angle calibrations, following guidelines published by the International Organization for Standardization (ISO) and NIST. The analysis includes methods for the quantitative evaluation of the standard uncertainty of small angle measurement using electronic autocollimators, including the effects of calibration uncertainty and air turbulence. PMID:28009359

Uncertainty and sensitivity analysis of control strategies using the benchmark simulation model No1 (BSM1).

PubMed

Flores-Alsina, Xavier; Rodriguez-Roda, Ignasi; Sin, Gürkan; Gernaey, Krist V

2009-01-01

The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predictions, considering the ASM1 bio-kinetic parameters and influent fractions as input uncertainties while the Effluent Quality Index (EQI) and the Operating Cost Index (OCI) are focused on as model outputs. The resulting Monte Carlo simulations are presented using descriptive statistics indicating the degree of uncertainty in the predicted EQI and OCI. Next, the Standard Regression Coefficients (SRC) method is used for sensitivity analysis to identify which input parameters influence the uncertainty in the EQI predictions the most. The results show that control strategies including an ammonium (S(NH)) controller reduce uncertainty in both overall pollution removal and effluent total Kjeldahl nitrogen. Also, control strategies with an external carbon source reduce the effluent nitrate (S(NO)) uncertainty increasing both their economical cost and variability as a trade-off. Finally, the maximum specific autotrophic growth rate (micro(A)) causes most of the variance in the effluent for all the evaluated control strategies. The influence of denitrification related parameters, e.g. eta(g) (anoxic growth rate correction factor) and eta(h) (anoxic hydrolysis rate correction factor), becomes less important when a S(NO) controller manipulating an external carbon source addition is implemented.

Quantifying uncertainty in the measurement of arsenic in suspended particulate matter by Atomic Absorption Spectrometry with hydride generator

PubMed Central

2011-01-01

Arsenic is the toxic element, which creates several problems in human being specially when inhaled through air. So the accurate and precise measurement of arsenic in suspended particulate matter (SPM) is of prime importance as it gives information about the level of toxicity in the environment, and preventive measures could be taken in the effective areas. Quality assurance is equally important in the measurement of arsenic in SPM samples before making any decision. The quality and reliability of the data of such volatile elements depends upon the measurement of uncertainty of each step involved from sampling to analysis. The analytical results quantifying uncertainty gives a measure of the confidence level of the concerned laboratory. So the main objective of this study was to determine arsenic content in SPM samples with uncertainty budget and to find out various potential sources of uncertainty, which affects the results. Keeping these facts, we have selected seven diverse sites of Delhi (National Capital of India) for quantification of arsenic content in SPM samples with uncertainty budget following sampling by HVS to analysis by Atomic Absorption Spectrometer-Hydride Generator (AAS-HG). In the measurement of arsenic in SPM samples so many steps are involved from sampling to final result and we have considered various potential sources of uncertainties. The calculation of uncertainty is based on ISO/IEC17025: 2005 document and EURACHEM guideline. It has been found that the final results mostly depend on the uncertainty in measurement mainly due to repeatability, final volume prepared for analysis, weighing balance and sampling by HVS. After the analysis of data of seven diverse sites of Delhi, it has been concluded that during the period from 31st Jan. 2008 to 7th Feb. 2008 the arsenic concentration varies from 1.44 ± 0.25 to 5.58 ± 0.55 ng/m3 with 95% confidence level (k = 2). PMID:21466671

Uncertainty Propagation for Terrestrial Mobile Laser Scanner

NASA Astrophysics Data System (ADS)

Mezian, c.; Vallet, Bruno; Soheilian, Bahman; Paparoditis, Nicolas

2016-06-01

Laser scanners are used more and more in mobile mapping systems. They provide 3D point clouds that are used for object reconstruction and registration of the system. For both of those applications, uncertainty analysis of 3D points is of great interest but rarely investigated in the literature. In this paper we present a complete pipeline that takes into account all the sources of uncertainties and allows to compute a covariance matrix per 3D point. The sources of uncertainties are laser scanner, calibration of the scanner in relation to the vehicle and direct georeferencing system. We suppose that all the uncertainties follow the Gaussian law. The variances of the laser scanner measurements (two angles and one distance) are usually evaluated by the constructors. This is also the case for integrated direct georeferencing devices. Residuals of the calibration process were used to estimate the covariance matrix of the 6D transformation between scanner laser and the vehicle system. Knowing the variances of all sources of uncertainties, we applied uncertainty propagation technique to compute the variance-covariance matrix of every obtained 3D point. Such an uncertainty analysis enables to estimate the impact of different laser scanners and georeferencing devices on the quality of obtained 3D points. The obtained uncertainty values were illustrated using error ellipsoids on different datasets.

Performance and Reliability Optimization for Aerospace Systems subject to Uncertainty and Degradation

NASA Technical Reports Server (NTRS)

Miller, David W.; Uebelhart, Scott A.; Blaurock, Carl

2004-01-01

This report summarizes work performed by the Space Systems Laboratory (SSL) for NASA Langley Research Center in the field of performance optimization for systems subject to uncertainty. The objective of the research is to develop design methods and tools to the aerospace vehicle design process which take into account lifecycle uncertainties. It recognizes that uncertainty between the predictions of integrated models and data collected from the system in its operational environment is unavoidable. Given the presence of uncertainty, the goal of this work is to develop means of identifying critical sources of uncertainty, and to combine these with the analytical tools used with integrated modeling. In this manner, system uncertainty analysis becomes part of the design process, and can motivate redesign. The specific program objectives were: 1. To incorporate uncertainty modeling, propagation and analysis into the integrated (controls, structures, payloads, disturbances, etc.) design process to derive the error bars associated with performance predictions. 2. To apply modern optimization tools to guide in the expenditure of funds in a way that most cost-effectively improves the lifecycle productivity of the system by enhancing the subsystem reliability and redundancy. The results from the second program objective are described. This report describes the work and results for the first objective: uncertainty modeling, propagation, and synthesis with integrated modeling.

A Framework for Quantifying Measurement Uncertainties and Uncertainty Propagation in HCCI/LTGC Engine Experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Petitpas, Guillaume; McNenly, Matthew J.; Whitesides, Russell A.

In this study, a framework for estimating experimental measurement uncertainties for a Homogenous Charge Compression Ignition (HCCI)/Low-Temperature Gasoline Combustion (LTGC) engine testing facility is presented. Detailed uncertainty quantification is first carried out for the measurement of the in-cylinder pressure, whose variations during the cycle provide most of the information for performance evaluation. Standard uncertainties of other measured quantities, such as the engine geometry and speed, the air and fuel flow rate and the intake/exhaust dry molar fractions are also estimated. Propagating those uncertainties using a Monte Carlo simulation and Bayesian inference methods then allows for estimation of uncertainties of themore » mass-average temperature and composition at IVC and throughout the cycle; and also of the engine performances such as gross Integrated Mean Effective Pressure, Heat Release and Ringing Intensity. Throughout the analysis, nominal values for uncertainty inputs were taken from a well-characterized engine test facility. However, the analysis did not take into account the calibration practice of experiments run in that facility and the resulting uncertainty values are therefore not indicative of the expected accuracy of those experimental results. A future study will employ the methodology developed here to explore the effects of different calibration methods on the different uncertainty values in order to evaluate best practices for accurate engine measurements.« less

A Framework for Quantifying Measurement Uncertainties and Uncertainty Propagation in HCCI/LTGC Engine Experiments

DOE PAGES

Petitpas, Guillaume; McNenly, Matthew J.; Whitesides, Russell A.

2017-03-28

In this study, a framework for estimating experimental measurement uncertainties for a Homogenous Charge Compression Ignition (HCCI)/Low-Temperature Gasoline Combustion (LTGC) engine testing facility is presented. Detailed uncertainty quantification is first carried out for the measurement of the in-cylinder pressure, whose variations during the cycle provide most of the information for performance evaluation. Standard uncertainties of other measured quantities, such as the engine geometry and speed, the air and fuel flow rate and the intake/exhaust dry molar fractions are also estimated. Propagating those uncertainties using a Monte Carlo simulation and Bayesian inference methods then allows for estimation of uncertainties of themore » mass-average temperature and composition at IVC and throughout the cycle; and also of the engine performances such as gross Integrated Mean Effective Pressure, Heat Release and Ringing Intensity. Throughout the analysis, nominal values for uncertainty inputs were taken from a well-characterized engine test facility. However, the analysis did not take into account the calibration practice of experiments run in that facility and the resulting uncertainty values are therefore not indicative of the expected accuracy of those experimental results. A future study will employ the methodology developed here to explore the effects of different calibration methods on the different uncertainty values in order to evaluate best practices for accurate engine measurements.« less

CASL L1 Milestone report : CASL.P4.01, sensitivity and uncertainty analysis for CIPS with VIPRE-W and BOA.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sung, Yixing; Adams, Brian M.; Secker, Jeffrey R.

2011-12-01

The CASL Level 1 Milestone CASL.P4.01, successfully completed in December 2011, aimed to 'conduct, using methodologies integrated into VERA, a detailed sensitivity analysis and uncertainty quantification of a crud-relevant problem with baseline VERA capabilities (ANC/VIPRE-W/BOA).' The VUQ focus area led this effort, in partnership with AMA, and with support from VRI. DAKOTA was coupled to existing VIPRE-W thermal-hydraulics and BOA crud/boron deposit simulations representing a pressurized water reactor (PWR) that previously experienced crud-induced power shift (CIPS). This work supports understanding of CIPS by exploring the sensitivity and uncertainty in BOA outputs with respect to uncertain operating and model parameters. Thismore » report summarizes work coupling the software tools, characterizing uncertainties, and analyzing the results of iterative sensitivity and uncertainty studies. These studies focused on sensitivity and uncertainty of CIPS indicators calculated by the current version of the BOA code used in the industry. Challenges with this kind of analysis are identified to inform follow-on research goals and VERA development targeting crud-related challenge problems.« less

Aeroservoelastic Model Validation and Test Data Analysis of the F/A-18 Active Aeroelastic Wing

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Prazenica, Richard J.

2003-01-01

Model validation and flight test data analysis require careful consideration of the effects of uncertainty, noise, and nonlinearity. Uncertainty prevails in the data analysis techniques and results in a composite model uncertainty from unmodeled dynamics, assumptions and mechanics of the estimation procedures, noise, and nonlinearity. A fundamental requirement for reliable and robust model development is an attempt to account for each of these sources of error, in particular, for model validation, robust stability prediction, and flight control system development. This paper is concerned with data processing procedures for uncertainty reduction in model validation for stability estimation and nonlinear identification. F/A-18 Active Aeroelastic Wing (AAW) aircraft data is used to demonstrate signal representation effects on uncertain model development, stability estimation, and nonlinear identification. Data is decomposed using adaptive orthonormal best-basis and wavelet-basis signal decompositions for signal denoising into linear and nonlinear identification algorithms. Nonlinear identification from a wavelet-based Volterra kernel procedure is used to extract nonlinear dynamics from aeroelastic responses, and to assist model development and uncertainty reduction for model validation and stability prediction by removing a class of nonlinearity from the uncertainty.

Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations

NASA Astrophysics Data System (ADS)

Mockler, E. M.; Chun, K. P.; Sapriza-Azuri, G.; Bruen, M.; Wheater, H. S.

2016-11-01

Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make including model and criteria selection can have a significant impact on the magnitude and distribution of the output uncertainty. Hydrological modellers are tasked with understanding and minimising the uncertainty surrounding streamflow predictions before communicating the overall uncertainty to decision makers. Parameter uncertainty in conceptual rainfall-runoff models has been widely investigated, and model structural uncertainty and forcing data have been receiving increasing attention. This study aimed to assess uncertainties in streamflow predictions due to forcing data and the identification of behavioural parameter sets in 31 Irish catchments. By combining stochastic rainfall ensembles and multiple parameter sets for three conceptual rainfall-runoff models, an analysis of variance model was used to decompose the total uncertainty in streamflow simulations into contributions from (i) forcing data, (ii) identification of model parameters and (iii) interactions between the two. The analysis illustrates that, for our subjective choices, hydrological model selection had a greater contribution to overall uncertainty, while performance criteria selection influenced the relative intra-annual uncertainties in streamflow predictions. Uncertainties in streamflow predictions due to the method of determining parameters were relatively lower for wetter catchments, and more evenly distributed throughout the year when the Nash-Sutcliffe Efficiency of logarithmic values of flow (lnNSE) was the evaluation criterion.

Public Perception of Uncertainties Within Climate Change Science.

PubMed

Visschers, Vivianne H M

2018-01-01

Climate change is a complex, multifaceted problem involving various interacting systems and actors. Therefore, the intensities, locations, and timeframes of the consequences of climate change are hard to predict and cause uncertainties. Relatively little is known about how the public perceives this scientific uncertainty and how this relates to their concern about climate change. In this article, an online survey among 306 Swiss people is reported that investigated whether people differentiate between different types of uncertainty in climate change research. Also examined was the way in which the perception of uncertainty is related to people's concern about climate change, their trust in science, their knowledge about climate change, and their political attitude. The results of a principal component analysis showed that respondents differentiated between perceived ambiguity in climate research, measurement uncertainty, and uncertainty about the future impact of climate change. Using structural equation modeling, it was found that only perceived ambiguity was directly related to concern about climate change, whereas measurement uncertainty and future uncertainty were not. Trust in climate science was strongly associated with each type of uncertainty perception and was indirectly associated with concern about climate change. Also, more knowledge about climate change was related to less strong perceptions of each type of climate science uncertainty. Hence, it is suggested that to increase public concern about climate change, it may be especially important to consider the perceived ambiguity about climate research. Efforts that foster trust in climate science also appear highly worthwhile. © 2017 Society for Risk Analysis.

Uncertainty Analysis of Air Radiation for Lunar Return Shock Layers

NASA Technical Reports Server (NTRS)

Kleb, Bil; Johnston, Christopher O.

2008-01-01

By leveraging a new uncertainty markup technique, two risk analysis methods are used to compute the uncertainty of lunar-return shock layer radiation predicted by the High temperature Aerothermodynamic Radiation Algorithm (HARA). The effects of epistemic uncertainty, or uncertainty due to a lack of knowledge, is considered for the following modeling parameters: atomic line oscillator strengths, atomic line Stark broadening widths, atomic photoionization cross sections, negative ion photodetachment cross sections, molecular bands oscillator strengths, and electron impact excitation rates. First, a simplified shock layer problem consisting of two constant-property equilibrium layers is considered. The results of this simplified problem show that the atomic nitrogen oscillator strengths and Stark broadening widths in both the vacuum ultraviolet and infrared spectral regions, along with the negative ion continuum, are the dominant uncertainty contributors. Next, three variable property stagnation-line shock layer cases are analyzed: a typical lunar return case and two Fire II cases. For the near-equilibrium lunar return and Fire 1643-second cases, the resulting uncertainties are very similar to the simplified case. Conversely, the relatively nonequilibrium 1636-second case shows significantly larger influence from electron impact excitation rates of both atoms and molecules. For all cases, the total uncertainty in radiative heat flux to the wall due to epistemic uncertainty in modeling parameters is 30% as opposed to the erroneously-small uncertainty levels (plus or minus 6%) found when treating model parameter uncertainties as aleatory (due to chance) instead of epistemic (due to lack of knowledge).

"I Don't Want to Be an Ostrich": Managing Mothers' Uncertainty during BRCA1/2 Genetic Counseling.

PubMed

Fisher, Carla L; Roccotagliata, Thomas; Rising, Camella J; Kissane, David W; Glogowski, Emily A; Bylund, Carma L

2017-06-01

Families who face genetic disease risk must learn how to grapple with complicated uncertainties about their health and future on a long-term basis. Women who undergo BRCA 1/2 genetic testing describe uncertainty related to personal risk as well as their loved ones', particularly daughters', risk. The genetic counseling setting is a prime opportunity for practitioners to help mothers manage uncertainty in the moment but also once they leave a session. Uncertainty Management Theory (UMT) helps to illuminate the various types of uncertainty women encounter and the important role of communication in uncertainty management. Informed by UMT, we conducted a thematic analysis of 16 genetic counseling sessions between practitioners and mothers at risk for, or carriers of, a BRCA1/2 mutation. Five themes emerged that represent communication strategies used to manage uncertainty: 1) addresses myths, misunderstandings, or misconceptions; 2) introduces uncertainty related to science; 3) encourages information seeking or sharing about family medical history; 4) reaffirms or validates previous behavior or decisions; and 5) minimizes the probability of personal risk or family members' risk. Findings illustrate the critical role of genetic counseling for families in managing emotionally challenging risk-related uncertainty. The analysis may prove beneficial to not only genetic counseling practice but generations of families at high risk for cancer who must learn strategic approaches to managing a complex web of uncertainty that can challenge them for a lifetime.

Aeroservoelastic Uncertainty Model Identification from Flight Data

NASA Technical Reports Server (NTRS)

Brenner, Martin J.

2001-01-01

Uncertainty modeling is a critical element in the estimation of robust stability margins for stability boundary prediction and robust flight control system development. There has been a serious deficiency to date in aeroservoelastic data analysis with attention to uncertainty modeling. Uncertainty can be estimated from flight data using both parametric and nonparametric identification techniques. The model validation problem addressed in this paper is to identify aeroservoelastic models with associated uncertainty structures from a limited amount of controlled excitation inputs over an extensive flight envelope. The challenge to this problem is to update analytical models from flight data estimates while also deriving non-conservative uncertainty descriptions consistent with the flight data. Multisine control surface command inputs and control system feedbacks are used as signals in a wavelet-based modal parameter estimation procedure for model updates. Transfer function estimates are incorporated in a robust minimax estimation scheme to get input-output parameters and error bounds consistent with the data and model structure. Uncertainty estimates derived from the data in this manner provide an appropriate and relevant representation for model development and robust stability analysis. This model-plus-uncertainty identification procedure is applied to aeroservoelastic flight data from the NASA Dryden Flight Research Center F-18 Systems Research Aircraft.

INSPECTION SHOP: PLAN TO PROVIDE UNCERTAINTY ANALYSIS WITH MEASUREMENTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nederbragt, W W

The LLNL inspection shop is chartered to make dimensional measurements of components for critical programmatic experiments. These measurements ensure that components are within tolerance and provide geometric details that can be used to further refine simulations. For these measurements to be useful, they must be significantly more accurate than the tolerances that are being checked. For example, if a part has a specified dimension of 100 millimeters and a tolerance of 1 millimeter, then the precision and/or accuracy of the measurement should be less than 1 millimeter. Using the ''10-to-1 gaugemaker's rule of thumb'', the desired precision of the measurementmore » should be less than 100 micrometers. Currently, the process for associating measurement uncertainty with data is not standardized, nor is the uncertainty based on a thorough uncertainty analysis. The goal of this project is to begin providing measurement uncertainty statements with critical measurements performed in the inspection shop. To accomplish this task, comprehensive knowledge about the underlying sources of uncertainty for measurement instruments need to be understood and quantified. Moreover, measurements of elemental uncertainties for each physical source need to be combined in a meaningful way to obtain an overall measurement uncertainty.« less

Competition-Colonization Trade-Offs, Competitive Uncertainty, and the Evolutionary Assembly of Species

PubMed Central

Pillai, Pradeep; Guichard, Frédéric

2012-01-01

We utilize a standard competition-colonization metapopulation model in order to study the evolutionary assembly of species. Based on earlier work showing how models assuming strict competitive hierarchies will likely lead to runaway evolution and self-extinction for all species, we adopt a continuous competition function that allows for levels of uncertainty in the outcome of competition. We then, by extending the standard patch-dynamic metapopulation model in order to include evolutionary dynamics, allow for the coevolution of species into stable communities composed of species with distinct limiting similarities. Runaway evolution towards stochastic extinction then becomes a limiting case controlled by the level of competitive uncertainty. We demonstrate how intermediate competitive uncertainty maximizes the equilibrium species richness as well as maximizes the adaptive radiation and self-assembly of species under adaptive dynamics with mutations of non-negligible size. By reconciling competition-colonization tradeoff theory with co-evolutionary dynamics, our results reveal the importance of intermediate levels of competitive uncertainty for the evolutionary assembly of species. PMID:22448253

Evaluating uncertainties in modelling the snow hydrology of the Fraser River Basin, British Columbia, Canada

NASA Astrophysics Data System (ADS)

Islam, Siraj Ul; Déry, Stephen J.

2017-03-01

This study evaluates predictive uncertainties in the snow hydrology of the Fraser River Basin (FRB) of British Columbia (BC), Canada, using the Variable Infiltration Capacity (VIC) model forced with several high-resolution gridded climate datasets. These datasets include the Canadian Precipitation Analysis and the thin-plate smoothing splines (ANUSPLIN), North American Regional Reanalysis (NARR), University of Washington (UW) and Pacific Climate Impacts Consortium (PCIC) gridded products. Uncertainties are evaluated at different stages of the VIC implementation, starting with the driving datasets, optimization of model parameters, and model calibration during cool and warm phases of the Pacific Decadal Oscillation (PDO). The inter-comparison of the forcing datasets (precipitation and air temperature) and their VIC simulations (snow water equivalent - SWE - and runoff) reveals widespread differences over the FRB, especially in mountainous regions. The ANUSPLIN precipitation shows a considerable dry bias in the Rocky Mountains, whereas the NARR winter air temperature is 2 °C warmer than the other datasets over most of the FRB. In the VIC simulations, the elevation-dependent changes in the maximum SWE (maxSWE) are more prominent at higher elevations of the Rocky Mountains, where the PCIC-VIC simulation accumulates too much SWE and ANUSPLIN-VIC yields an underestimation. Additionally, at each elevation range, the day of maxSWE varies from 10 to 20 days between the VIC simulations. The snow melting season begins early in the NARR-VIC simulation, whereas the PCIC-VIC simulation delays the melting, indicating seasonal uncertainty in SWE simulations. When compared with the observed runoff for the Fraser River main stem at Hope, BC, the ANUSPLIN-VIC simulation shows considerable underestimation of runoff throughout the water year owing to reduced precipitation in the ANUSPLIN forcing dataset. The NARR-VIC simulation yields more winter and spring runoff and earlier decline of flows in summer due to a nearly 15-day earlier onset of the FRB springtime snowmelt. Analysis of the parametric uncertainty in the VIC calibration process shows that the choice of the initial parameter range plays a crucial role in defining the model hydrological response for the FRB. Furthermore, the VIC calibration process is biased toward cool and warm phases of the PDO and the choice of proper calibration and validation time periods is important for the experimental setup. Overall the VIC hydrological response is prominently influenced by the uncertainties involved in the forcing datasets rather than those in its parameter optimization and experimental setups.

Uncertainty quantification and sensitivity analysis with CASL Core Simulator VERA-CS

DOE PAGES

Brown, C. S.; Zhang, Hongbin

2016-05-24

Uncertainty quantification and sensitivity analysis are important for nuclear reactor safety design and analysis. A 2x2 fuel assembly core design was developed and simulated by the Virtual Environment for Reactor Applications, Core Simulator (VERA-CS) coupled neutronics and thermal-hydraulics code under development by the Consortium for Advanced Simulation of Light Water Reactors (CASL). An approach to uncertainty quantification and sensitivity analysis with VERA-CS was developed and a new toolkit was created to perform uncertainty quantification and sensitivity analysis with fourteen uncertain input parameters. Furthermore, the minimum departure from nucleate boiling ratio (MDNBR), maximum fuel center-line temperature, and maximum outer clad surfacemore » temperature were chosen as the selected figures of merit. Pearson, Spearman, and partial correlation coefficients were considered for all of the figures of merit in sensitivity analysis and coolant inlet temperature was consistently the most influential parameter. We used parameters as inputs to the critical heat flux calculation with the W-3 correlation were shown to be the most influential on the MDNBR, maximum fuel center-line temperature, and maximum outer clad surface temperature.« less

Application of the JENDL-4.0 nuclear data set for uncertainty analysis of the prototype FBR Monju

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tamagno, P.; Van Rooijen, W. F. G.; Takeda, T.

2012-07-01

This paper deals with uncertainty analysis of the Monju reactor using JENDL-4.0 and the ERANOS code 1. In 2010 the Japan Atomic Energy Agency - JAEA - released the JENDL-4.0 nuclear data set. This new evaluation contains improved values of cross-sections and emphasizes accurate covariance matrices. Also in 2010, JAEA restarted the sodium-cooled fast reactor prototype Monju after about 15 years of shutdown. The long shutdown time resulted in a build-up of {sup 241}Am by natural decay from the initially loaded Pu. As well as improved covariance matrices, JENDL-4.0 is announced to contain improved data for minor actinides 2. Themore » choice of Monju reactor as an application of the new evaluation seems then even more relevant. The uncertainty analysis requires the determination of sensitivity coefficients. The well-established ERANOS code was chosen because of its integrated modules that allow users to perform sensitivity and uncertainty analysis. A JENDL-4.0 cross-sections library is not available for ERANOS. Therefor a cross-sections library had to be made from the original ENDF files for the ECCO cell code (part of ERANOS). For confirmation of the newly made library, calculations of a benchmark core were performed. These calculations used the MZA and MZB benchmarks and showed consistent results with other libraries. Calculations for the Monju reactor were performed using hexagonal 3D geometry and PN transport theory. However, the ERANOS sensitivity modules cannot use the resulting fluxes, as these modules require finite differences based fluxes, obtained from RZ SN-transport or 3D diffusion calculations. The corresponding geometrical models have been made and the results verified with Monju restart experimental data 4. Uncertainty analysis was performed using the RZ model. JENDL-4.0 uncertainty analysis showed a significant reduction of the uncertainty related to the fission cross-section of Pu along with an increase of the uncertainty related to the capture cross-section of {sup 238}U compared with the previous JENDL-3.3 version. Covariance data recently added in JENDL-4.0 for {sup 241}Am appears to have a non-negligible contribution. (authors)« less

[Transformer winding's temperature rising and an analysis of its uncertainty].

PubMed

Wang, Pei-Lian; Chen, Yu-En; Zhong, Sheng-Kui

2007-09-01

This paper introduces the temperature rising experimental process and some matters needing attention when the transformer is normally loading. And an analysis of the uncertainty for transformer's temperature rising is also made based on the practical examples' data.

The costs of production of alternative jet fuel: A harmonized stochastic assessment.

PubMed

Bann, Seamus J; Malina, Robert; Staples, Mark D; Suresh, Pooja; Pearlson, Matthew; Tyner, Wallace E; Hileman, James I; Barrett, Steven

2017-03-01

This study quantifies and compares the costs of production for six alternative jet fuel pathways using consistent financial and technical assumptions. Uncertainty was propagated through the analysis using Monte Carlo simulations. The six processes assessed were HEFA, advanced fermentation, Fischer-Tropsch, aqueous phase processing, hydrothermal liquefaction, and fast pyrolysis. The results indicate that none of the six processes would be profitable in the absence of government incentives, with HEFA using yellow grease, HEFA using tallow, and FT revealing the lowest mean jet fuel prices at $0.91/liter ($0.66/liter-$1.24/liter), $1.06/liter ($0.79/liter-$1.42/liter), and $1.15/liter ($0.95/liter-$1.39/liter), respectively. This study also quantifies plant performance in the United States with a Renewable Fuel Standard policy analysis. Results indicate that some pathways could achieve positive NPV with relatively high likelihood under existing policy supports, with HEFA and FPH revealing the highest probability of positive NPV at 94.9% and 99.7%, respectively, in the best-case scenario. Copyright © 2016 Elsevier Ltd. All rights reserved.

Rainfall or parameter uncertainty? The power of sensitivity analysis on grouped factors

NASA Astrophysics Data System (ADS)

Nossent, Jiri; Pereira, Fernando; Bauwens, Willy

2017-04-01

Hydrological models are typically used to study and represent (a part of) the hydrological cycle. In general, the output of these models mostly depends on their input rainfall and parameter values. Both model parameters and input precipitation however, are characterized by uncertainties and, therefore, lead to uncertainty on the model output. Sensitivity analysis (SA) allows to assess and compare the importance of the different factors for this output uncertainty. Hereto, the rainfall uncertainty can be incorporated in the SA by representing it as a probabilistic multiplier. Such multiplier can be defined for the entire time series, or several of these factors can be determined for every recorded rainfall pulse or for hydrological independent storm events. As a consequence, the number of parameters included in the SA related to the rainfall uncertainty can be (much) lower or (much) higher than the number of model parameters. Although such analyses can yield interesting results, it remains challenging to determine which type of uncertainty will affect the model output most due to the different weight both types will have within the SA. In this study, we apply the variance based Sobol' sensitivity analysis method to two different hydrological simulators (NAM and HyMod) for four diverse watersheds. Besides the different number of model parameters (NAM: 11 parameters; HyMod: 5 parameters), the setup of our sensitivity and uncertainty analysis-combination is also varied by defining a variety of scenarios including diverse numbers of rainfall multipliers. To overcome the issue of the different number of factors and, thus, the different weights of the two types of uncertainty, we build on one of the advantageous properties of the Sobol' SA, i.e. treating grouped parameters as a single parameter. The latter results in a setup with a single factor for each uncertainty type and allows for a straightforward comparison of their importance. In general, the results show a clear influence of the weights in the different SA scenarios. However, working with grouped factors resolves this issue and leads to clear importance results.

Planning for robust reserve networks using uncertainty analysis

USGS Publications Warehouse

Moilanen, A.; Runge, M.C.; Elith, Jane; Tyre, A.; Carmel, Y.; Fegraus, E.; Wintle, B.A.; Burgman, M.; Ben-Haim, Y.

2006-01-01

Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence?absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data?erroneous species presence?absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.

Uncertainty characterization approaches for risk assessment of DBPs in drinking water: a review.

PubMed

Chowdhury, Shakhawat; Champagne, Pascale; McLellan, P James

2009-04-01

The management of risk from disinfection by-products (DBPs) in drinking water has become a critical issue over the last three decades. The areas of concern for risk management studies include (i) human health risk from DBPs, (ii) disinfection performance, (iii) technical feasibility (maintenance, management and operation) of treatment and disinfection approaches, and (iv) cost. Human health risk assessment is typically considered to be the most important phase of the risk-based decision-making or risk management studies. The factors associated with health risk assessment and other attributes are generally prone to considerable uncertainty. Probabilistic and non-probabilistic approaches have both been employed to characterize uncertainties associated with risk assessment. The probabilistic approaches include sampling-based methods (typically Monte Carlo simulation and stratified sampling) and asymptotic (approximate) reliability analysis (first- and second-order reliability methods). Non-probabilistic approaches include interval analysis, fuzzy set theory and possibility theory. However, it is generally accepted that no single method is suitable for the entire spectrum of problems encountered in uncertainty analyses for risk assessment. Each method has its own set of advantages and limitations. In this paper, the feasibility and limitations of different uncertainty analysis approaches are outlined for risk management studies of drinking water supply systems. The findings assist in the selection of suitable approaches for uncertainty analysis in risk management studies associated with DBPs and human health risk.

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ettehadtavakkol, Amin, E-mail: amin.ettehadtavakkol@ttu.edu; Jablonowski, Christopher; Lake, Larry

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum designmore » concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.« less

Phylogenetic analysis of molecular and morphological data highlights uncertainty in the relationships of fossil and living species of Elopomorpha (Actinopterygii: Teleostei).

PubMed

Dornburg, Alex; Friedman, Matt; Near, Thomas J

2015-08-01

Elopomorpha is one of the three main clades of living teleost fishes and includes a range of disparate lineages including eels, tarpons, bonefishes, and halosaurs. Elopomorphs were among the first groups of fishes investigated using Hennigian phylogenetic methods and continue to be the object of intense phylogenetic scrutiny due to their economic significance, diversity, and crucial evolutionary status as the sister group of all other teleosts. While portions of the phylogenetic backbone for Elopomorpha are consistent between studies, the relationships among Albula, Pterothrissus, Notacanthiformes, and Anguilliformes remain contentious and difficult to evaluate. This lack of phylogenetic resolution is problematic as fossil lineages are often described and placed taxonomically based on an assumed sister group relationship between Albula and Pterothrissus. In addition, phylogenetic studies using morphological data that sample elopomorph fossil lineages often do not include notacanthiform or anguilliform lineages, potentially introducing a bias toward interpreting fossils as members of the common stem of Pterothrissus and Albula. Here we provide a phylogenetic analysis of DNA sequences sampled from multiple nuclear genes that include representative taxa from Albula, Pterothrissus, Notacanthiformes and Anguilliformes. We integrate our molecular dataset with a morphological character matrix that spans both living and fossil elopomorph lineages. Our results reveal substantial uncertainty in the placement of Pterothrissus as well as all sampled fossil lineages, questioning the stability of the taxonomy of fossil Elopomorpha. However, despite topological uncertainty, our integration of fossil lineages into a Bayesian time calibrated framework provides divergence time estimates for the clade that are consistent with previously published age estimates based on the elopomorph fossil record and molecular estimates resulting from traditional node-dating methods. Copyright © 2015 Elsevier Inc. All rights reserved.

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

NASA Astrophysics Data System (ADS)

Yi, Yonghong; Kimball, John S.; Chen, Richard H.; Moghaddam, Mahta; Reichle, Rolf H.; Mishra, Umakant; Zona, Donatella; Oechel, Walter C.

2018-01-01

An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modeling and analysis framework combining field observations, local-scale ( ˜ 50 m resolution) active layer thickness (ALT) and soil moisture maps derived from low-frequency (L + P-band) airborne radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modeled ALT results show good correspondence with in situ measurements in higher-permafrost-probability (PP ≥ 70 %) areas (n = 33; R = 0.60; mean bias = 1.58 cm; RMSE = 20.32 cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend = 0.32±1.18 cm yr-1) and much larger increases (> 3 cm yr-1) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R = 0.60 ± 0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modeling framework across a larger domain.

Predictive Uncertainty And Parameter Sensitivity Of A Sediment-Flux Model: Nitrogen Flux and Sediment Oxygen Demand

EPA Science Inventory

Estimating model predictive uncertainty is imperative to informed environmental decision making and management of water resources. This paper applies the Generalized Sensitivity Analysis (GSA) to examine parameter sensitivity and the Generalized Likelihood Uncertainty Estimation...

Uncertainties in Earthquake Loss Analysis: A Case Study From Southern California

NASA Astrophysics Data System (ADS)

Mahdyiar, M.; Guin, J.

2005-12-01

Probabilistic earthquake hazard and loss analyses play important roles in many areas of risk management, including earthquake related public policy and insurance ratemaking. Rigorous loss estimation for portfolios of properties is difficult since there are various types of uncertainties in all aspects of modeling and analysis. It is the objective of this study to investigate the sensitivity of earthquake loss estimation to uncertainties in regional seismicity, earthquake source parameters, ground motions, and sites' spatial correlation on typical property portfolios in Southern California. Southern California is an attractive region for such a study because it has a large population concentration exposed to significant levels of seismic hazard. During the last decade, there have been several comprehensive studies of most regional faults and seismogenic sources. There have also been detailed studies on regional ground motion attenuations and regional and local site responses to ground motions. This information has been used by engineering seismologists to conduct regional seismic hazard and risk analysis on a routine basis. However, one of the more difficult tasks in such studies is the proper incorporation of uncertainties in the analysis. From the hazard side, there are uncertainties in the magnitudes, rates and mechanisms of the seismic sources and local site conditions and ground motion site amplifications. From the vulnerability side, there are considerable uncertainties in estimating the state of damage of buildings under different earthquake ground motions. From an analytical side, there are challenges in capturing the spatial correlation of ground motions and building damage, and integrating thousands of loss distribution curves with different degrees of correlation. In this paper we propose to address some of these issues by conducting loss analyses of a typical small portfolio in southern California, taking into consideration various source and ground motion uncertainties. The approach is designed to integrate loss distribution functions with different degrees of correlation for portfolio analysis. The analysis is based on USGS 2002 regional seismicity model.

Robust Adaptation? Assessing the sensitivity of safety margins in flood defences to uncertainty in future simulations - a case study from Ireland.

NASA Astrophysics Data System (ADS)

Murphy, Conor; Bastola, Satish; Sweeney, John

2013-04-01

Climate change impact and adaptation assessments have traditionally adopted a 'top-down' scenario based approach, where information from different Global Climate Models (GCMs) and emission scenarios are employed to develop impacts led adaptation strategies. Due to the tradeoffs in the computational cost and need to include a wide range of GCMs for fuller characterization of uncertainties, scenarios are better used for sensitivity testing and adaptation options appraisal. One common approach to adaptation that has been defined as robust is the use of safety margins. In this work the sensitivity of safety margins that have been adopted by the agency responsible for flood risk management in Ireland, to the uncertainty in future projections are examined. The sensitivity of fluvial flood risk to climate change is assessed for four Irish catchments using a large number of GCMs (17) forced with three emissions scenarios (SRES A1B, A2, B1) as input to four hydrological models. Both uncertainty within and between hydrological models is assessed using the GLUE framework. Regionalisation is achieved using a change factor method to infer changes in the parameters of a weather generator using monthly output from the GCMs, while flood frequency analysis is conducted using the method of probability weighted moments to fit the Generalised Extreme Value distribution to ~20,000 annual maxima series. The sensitivity of design margins to the uncertainty space considered is visualised using risk response surfaces. The hydrological sensitivity is measured as the percentage change in flood peak for specified recurrence intervals. Results indicate that there is a considerable residual risk associated with allowances of +20% when uncertainties are accounted for and that the risk of exceedence of design allowances is greatest for more extreme, low frequency events with considerable implication for critical infrastructure, e.g., culverts, bridges, flood defences whose designs are normally associated with such return periods. Sensitivity results show that the impact of climate change is not as great for flood peaks with higher return periods. The average width of the uncertainty range and the size of the range for each catchment reveals that the uncertainties in low frequency events are greater than high frequency events. In addition, the uncertainty interval, estimated as the average width of the uncertainty range of flow for the five return periods, grows wider with a decrease in the runoff coefficient and wetness index of each catchment, both of which tend to increase the nonlinearity in the rainfall response. A key management question that emerges is the acceptability of residual risk where high exposure of vulnerable populations and/or critical infrastructure coincide with high costs of additional capacity in safety margins.

The Relationship between National Culture and the Usability of an E-Learning System

ERIC Educational Resources Information Center

Downey, Steve; Wentling, Rose Mary; Wentling, Tim; Wadsworth, Andrew

2004-01-01

This study sought to measure the relationship between national culture and the usability of an e-Learning system by using Hofstede's cultural dimensions and Nielson's usability attributes. The study revealed that high uncertainty avoidance cultures found the system more frustrating to use. The study also revealed that individuals from cultures…

Influence of sub-surface damage evolution on low-energy-plasma-driven deuterium permeation through tungsten

NASA Astrophysics Data System (ADS)

Kapser, Stefan; Balden, Martin; Fiorini da Silva, Tiago; Elgeti, Stefan; Manhard, Armin; Schmid, Klaus; Schwarz-Selinger, Thomas; von Toussaint, Udo

2018-05-01

Low-energy-plasma-driven deuterium permeation through tungsten at 300 K and 450 K has been investigated. Microstructural analysis by scanning electron microscopy, assisted by focused ion beam, revealed sub-surface damage evolution only at 300 K. This damage evolution was correlated with a significant evolution of the deuterium amount retained below the plasma-exposed surface. Although both of these phenomena were observed for 300 K exposure temperature only, the deuterium permeation flux at both exposure temperatures was indistinguishable within the experimental uncertainty. The permeation flux was used to estimate the maximum ratio of solute-deuterium to tungsten atoms during deuterium-plasma exposure at both temperatures and thus in the presence and absence of damage evolution. Diffusion-trapping simulations revealed the proximity of damage evolution to the implantation surface as the reason for an only insignificant decrease of the permeation flux.

How the public engages with global warming: A social representations approach.

PubMed

Smith, Nicholas; Joffe, Helene

2013-01-01

The present study utilises social representations theory to explore common sense conceptualisations of global warming risk using an in-depth, qualitative methodology. Fifty-six members of a British, London-based 2008 public were initially asked to draw or write four spontaneous "first thoughts or feelings" about global warming. These were then explored via an open-ended, exploratory interview. The analysis revealed that first thoughts, either drawn or written, often mirrored the images used by the British press to depict global warming visually. Thus in terms of media framings, it was their visual rather than their textual content that was spontaneously available for their audiences. Furthermore, an in-depth exploration of interview data revealed that global warming was structured around three themata: self/other, natural/unnatural and certainty/uncertainty, reflecting the complex and often contradictory nature of common sense thinking in relation to risk issues.

Governance Structures for Open Innovation: A Preliminary Framework

NASA Astrophysics Data System (ADS)

Feller, Joseph; Finnegan, Patrick; Hayes, Jeremy; O'Reilly, Philip

This research-in-progress paper presents a preliminary framework of four open innovation governance structures. The study seeks to describe four distinct ways in which firms utilize hierarchical relationships, organizational intermediaries, and the market system to supply and acquire intellectual property and/or innovation capabilities from sources external to the firm. This paper reports on phase one of the study, which involved an analysis of six open innovation exemplars based on public data. This phase of the study reveals that governance structures for open innovation can be categorized based on whether they (1) are mediated or direct or (2) seek to acquire intellectual property or innovation capability. We analyze the differences in four governance structures along seven dimensions, and reveal the importance of knowledge dispersion and uncertainty to the use of open innovation hierarchies, brokerages, and markets. The paper concludes by examining the implications of the findings and outlining the next phase of the study.

A systematic uncertainty analysis of an evaluative fate and exposure model.

PubMed

Hertwich, E G; McKone, T E; Pease, W S

2000-08-01

Multimedia fate and exposure models are widely used to regulate the release of toxic chemicals, to set cleanup standards for contaminated sites, and to evaluate emissions in life-cycle assessment. CalTOX, one of these models, is used to calculate the potential dose, an outcome that is combined with the toxicity of the chemical to determine the Human Toxicity Potential (HTP), used to aggregate and compare emissions. The comprehensive assessment of the uncertainty in the potential dose calculation in this article serves to provide the information necessary to evaluate the reliability of decisions based on the HTP A framework for uncertainty analysis in multimedia risk assessment is proposed and evaluated with four types of uncertainty. Parameter uncertainty is assessed through Monte Carlo analysis. The variability in landscape parameters is assessed through a comparison of potential dose calculations for different regions in the United States. Decision rule uncertainty is explored through a comparison of the HTP values under open and closed system boundaries. Model uncertainty is evaluated through two case studies, one using alternative formulations for calculating the plant concentration and the other testing the steady state assumption for wet deposition. This investigation shows that steady state conditions for the removal of chemicals from the atmosphere are not appropriate and result in an underestimate of the potential dose for 25% of the 336 chemicals evaluated.

Preliminary analysis of Dione Regio, Venus: The final Magellan regional imaging gap

NASA Technical Reports Server (NTRS)

Keddie, S. T.

1993-01-01

In Sep. 1992, the Magellan spacecraft filled the final large gap in its coverage of Venus when it imaged an area west of Alpha Regio. F-BIDR's and some test MIDR's of parts of this area were available as of late December. Dione Regio was imaged by the Arecibo observatory and a preliminary investigation of Magellan images supports the interpretations made based on these earlier images: Dione Regio is a regional highland on which is superposed three large, very distinct volcanic edifices. The superior resolution and different viewing geometry of the Magellan images also clarified some uncertainties and revealed fascinating details about this region.

How do clinicians and suicide attempters understand suicide attempt impulsivity? A qualitative study.

PubMed

Rimkeviciene, Jurgita; O'Gorman, John; De Leo, Diego

2016-01-01

Inconsistencies in the definition of impulsive suicide attempts hamper research integration. To expand the currently limited data on how this construct is used in clinical practice, researchers interviewed eight suicide attempters to create timelines of their suicide process, then had seven experienced clinicians review these timelines. Thematic analysis of the patient and clinician data revealed three themes: "thinking out," build-up, and unclear intentionality. The results imply that assessing build-up of agitation and exhaustion symptoms can contribute to understanding acuteness of suicide risk. In addition, uncertainty about one's intentions during the attempt should not be equated to low intent to die.

Reducing the extinction risk of stochastic populations via nondemographic noise

NASA Astrophysics Data System (ADS)

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.

Potential uncertainty reduction in model-averaged benchmark dose estimates informed by an additional dose study.

PubMed

Shao, Kan; Small, Mitchell J

2011-10-01

A methodology is presented for assessing the information value of an additional dosage experiment in existing bioassay studies. The analysis demonstrates the potential reduction in the uncertainty of toxicity metrics derived from expanded studies, providing insights for future studies. Bayesian methods are used to fit alternative dose-response models using Markov chain Monte Carlo (MCMC) simulation for parameter estimation and Bayesian model averaging (BMA) is used to compare and combine the alternative models. BMA predictions for benchmark dose (BMD) are developed, with uncertainty in these predictions used to derive the lower bound BMDL. The MCMC and BMA results provide a basis for a subsequent Monte Carlo analysis that backcasts the dosage where an additional test group would have been most beneficial in reducing the uncertainty in the BMD prediction, along with the magnitude of the expected uncertainty reduction. Uncertainty reductions are measured in terms of reduced interval widths of predicted BMD values and increases in BMDL values that occur as a result of this reduced uncertainty. The methodology is illustrated using two existing data sets for TCDD carcinogenicity, fitted with two alternative dose-response models (logistic and quantal-linear). The example shows that an additional dose at a relatively high value would have been most effective for reducing the uncertainty in BMA BMD estimates, with predicted reductions in the widths of uncertainty intervals of approximately 30%, and expected increases in BMDL values of 5-10%. The results demonstrate that dose selection for studies that subsequently inform dose-response models can benefit from consideration of how these models will be fit, combined, and interpreted. © 2011 Society for Risk Analysis.

Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groen, E.A., E-mail: Evelyne.Groen@gmail.com; Heijungs, R.; Leiden University, Einsteinweg 2, Leiden 2333 CC

Life cycle assessment (LCA) is an established tool to quantify the environmental impact of a product. A good assessment of uncertainty is important for making well-informed decisions in comparative LCA, as well as for correctly prioritising data collection efforts. Under- or overestimation of output uncertainty (e.g. output variance) will lead to incorrect decisions in such matters. The presence of correlations between input parameters during uncertainty propagation, can increase or decrease the the output variance. However, most LCA studies that include uncertainty analysis, ignore correlations between input parameters during uncertainty propagation, which may lead to incorrect conclusions. Two approaches to include correlationsmore » between input parameters during uncertainty propagation and global sensitivity analysis were studied: an analytical approach and a sampling approach. The use of both approaches is illustrated for an artificial case study of electricity production. Results demonstrate that both approaches yield approximately the same output variance and sensitivity indices for this specific case study. Furthermore, we demonstrate that the analytical approach can be used to quantify the risk of ignoring correlations between input parameters during uncertainty propagation in LCA. We demonstrate that: (1) we can predict if including correlations among input parameters in uncertainty propagation will increase or decrease output variance; (2) we can quantify the risk of ignoring correlations on the output variance and the global sensitivity indices. Moreover, this procedure requires only little data. - Highlights: • Ignoring correlation leads to under- or overestimation of the output variance. • We demonstrated that the risk of ignoring correlation can be quantified. • The procedure proposed is generally applicable in life cycle assessment. • In some cases, ignoring correlation has a minimal effect on decision-making tools.« less

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

USGS Publications Warehouse

Zajac, Zuzanna; Stith, Bradley M.; Bowling, Andrea C.; Langtimm, Catherine A.; Swain, Eric D.

2015-01-01

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions.

Synthesis of instrumentally and historically recorded earthquakes and studying their spatial statistical relationship (A case study: Dasht-e-Biaz, Eastern Iran)

NASA Astrophysics Data System (ADS)

Jalali, Mohammad; Ramazi, Hamidreza

2018-06-01

Earthquake catalogues are the main source of statistical seismology for the long term studies of earthquake occurrence. Therefore, studying the spatiotemporal problems is important to reduce the related uncertainties in statistical seismology studies. A statistical tool, time normalization method, has been determined to revise time-frequency relationship in one of the most active regions of Asia, Eastern Iran and West of Afghanistan, (a and b were calculated around 8.84 and 1.99 in the exponential scale, not logarithmic scale). Geostatistical simulation method has been further utilized to reduce the uncertainties in the spatial domain. A geostatistical simulation produces a representative, synthetic catalogue with 5361 events to reduce spatial uncertainties. The synthetic database is classified using a Geographical Information System, GIS, based on simulated magnitudes to reveal the underlying seismicity patterns. Although some regions with highly seismicity correspond to known faults, significantly, as far as seismic patterns are concerned, the new method highlights possible locations of interest that have not been previously identified. It also reveals some previously unrecognized lineation and clusters in likely future strain release.

Efficient use of single molecule time traces to resolve kinetic rates, models and uncertainties

NASA Astrophysics Data System (ADS)

Schmid, Sonja; Hugel, Thorsten

2018-03-01

Single molecule time traces reveal the time evolution of unsynchronized kinetic systems. Especially single molecule Förster resonance energy transfer (smFRET) provides access to enzymatically important time scales, combined with molecular distance resolution and minimal interference with the sample. Yet the kinetic analysis of smFRET time traces is complicated by experimental shortcomings—such as photo-bleaching and noise. Here we recapitulate the fundamental limits of single molecule fluorescence that render the classic, dwell-time based kinetic analysis unsuitable. In contrast, our Single Molecule Analysis of Complex Kinetic Sequences (SMACKS) considers every data point and combines the information of many short traces in one global kinetic rate model. We demonstrate the potential of SMACKS by resolving the small kinetic effects caused by different ionic strengths in the chaperone protein Hsp90. These results show an unexpected interrelation between conformational dynamics and ATPase activity in Hsp90.

Efficient design and verification of diagnostics for impurity transport experiments.

PubMed

Chilenski, M A; Greenwald, M J; Marzouk, Y M; Rice, J E; White, A E

2018-01-01

Recent attempts to measure impurity transport in Alcator C-Mod using an x-ray imaging crystal spectrometer and laser blow-off impurity injector have failed to yield unique reconstructions of the transport coefficient profiles. This paper presents a fast, linearized model which was constructed to estimate diagnostic requirements for impurity transport experiments. The analysis shows that the spectroscopic diagnostics on Alcator C-Mod should be capable of inferring simple profiles of impurity diffusion D Z and convection V Z accurate to better than ±10% uncertainty, suggesting that the failure to infer unique D Z and V Z from experimental data is attributable to an inadequate analysis procedure rather than the result of insufficient diagnostics. Furthermore, the analysis reveals that even a modest spatial resolution can overcome a low time resolution. This approach can be adapted to design and verify diagnostics for transport experiments on any magnetic confinement device.

Climate change uncertainty and risk assessment in Iran during twenty-first century: evapotranspiration and green water deficit analysis

NASA Astrophysics Data System (ADS)

Karandish, Fatemeh; Mousavi, Seyed-Saeed

2018-01-01

For a 120-year period, the projected effects of climate change on annual, seasonal, and monthly potential evapotranspiration (ETo) and green water deficit (GWD) were analyzed involving the associated uncertainties for five climatic zones of Iran. Analysis was carried out using data obtained from 15 general circulation models (GCMs) under three SRES scenarios of A1B, A2, and B1 which were downscaled using LARS-WG for 52 synoptic stations up to 2100. The majority of GCMs as well as the median of the ensemble for each scenario project a positive change in both ETo and GWD. A total of 5.8-19.8 % increase in annual ETo, drier than normal wet seasons, as well as 2.3-56.4 % increase in ETo during December-March period well represent a probable increase in the hydrological water requirement in Iran under global warming. Regarding GWD, the country will experience more arid years requiring 113.7 × 103-576.8 × 103 Mm3 more water to supply annual atmospheric water demand. Semi-arid and Mediterranean regions, principal agricultural producer areas of Iran, will be the most vulnerable part of the country due to 1-38.6 % increase in annual GWD under climate change. In addition, water scarcity for irrigated agriculture will enhance in all climatic zones due to 0.9-41 % increase GWD in June-August. However, rain-fed agriculture might be less affected in the hyper-humid and Mediterranean regions because of 1.1-105.3 % reduction in GWD during wet season. Nevertheless, uncertainty analysis revealed that given results for monthly timescale as well as those for times and regions with lower ETo will be the most uncertain. Based on the results, suitable adaptation solutions are highly required to be undertaken to relieve the extra pressure on the decreased blue water resources in the future.

Reducing uncertainty on satellite image classification through spatiotemporal reasoning

NASA Astrophysics Data System (ADS)

Partsinevelos, Panagiotis; Nikolakaki, Natassa; Psillakis, Periklis; Miliaresis, George; Xanthakis, Michail

2014-05-01

The natural habitat constantly endures both inherent natural and human-induced influences. Remote sensing has been providing monitoring oriented solutions regarding the natural Earth surface, by offering a series of tools and methodologies which contribute to prudent environmental management. Processing and analysis of multi-temporal satellite images for the observation of the land changes include often classification and change-detection techniques. These error prone procedures are influenced mainly by the distinctive characteristics of the study areas, the remote sensing systems limitations and the image analysis processes. The present study takes advantage of the temporal continuity of multi-temporal classified images, in order to reduce classification uncertainty, based on reasoning rules. More specifically, pixel groups that temporally oscillate between classes are liable to misclassification or indicate problematic areas. On the other hand, constant pixel group growth indicates a pressure prone area. Computational tools are developed in order to disclose the alterations in land use dynamics and offer a spatial reference to the pressures that land use classes endure and impose between them. Moreover, by revealing areas that are susceptible to misclassification, we propose specific target site selection for training during the process of supervised classification. The underlying objective is to contribute to the understanding and analysis of anthropogenic and environmental factors that influence land use changes. The developed algorithms have been tested upon Landsat satellite image time series, depicting the National Park of Ainos in Kefallinia, Greece, where the unique in the world Abies cephalonica grows. Along with the minor changes and pressures indicated in the test area due to harvesting and other human interventions, the developed algorithms successfully captured fire incidents that have been historically confirmed. Overall, the results have shown that the use of the suggested procedures can contribute to the reduction of the classification uncertainty and support the existing knowledge regarding the pressure among land-use changes.

STATISTICAL ANALYSIS OF TANK 18F FLOOR SAMPLE RESULTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harris, S.

2010-09-02

Representative sampling has been completed for characterization of the residual material on the floor of Tank 18F as per the statistical sampling plan developed by Shine [1]. Samples from eight locations have been obtained from the tank floor and two of the samples were archived as a contingency. Six samples, referred to in this report as the current scrape samples, have been submitted to and analyzed by SRNL [2]. This report contains the statistical analysis of the floor sample analytical results to determine if further data are needed to reduce uncertainty. Included are comparisons with the prior Mantis samples resultsmore » [3] to determine if they can be pooled with the current scrape samples to estimate the upper 95% confidence limits (UCL{sub 95%}) for concentration. Statistical analysis revealed that the Mantis and current scrape sample results are not compatible. Therefore, the Mantis sample results were not used to support the quantification of analytes in the residual material. Significant spatial variability among the current sample results was not found. Constituent concentrations were similar between the North and South hemispheres as well as between the inner and outer regions of the tank floor. The current scrape sample results from all six samples fall within their 3-sigma limits. In view of the results from numerous statistical tests, the data were pooled from all six current scrape samples. As such, an adequate sample size was provided for quantification of the residual material on the floor of Tank 18F. The uncertainty is quantified in this report by an upper 95% confidence limit (UCL{sub 95%}) on each analyte concentration. The uncertainty in analyte concentration was calculated as a function of the number of samples, the average, and the standard deviation of the analytical results. The UCL{sub 95%} was based entirely on the six current scrape sample results (each averaged across three analytical determinations).« less

STATISTICAL ANALYSIS OF TANK 19F FLOOR SAMPLE RESULTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harris, S.

2010-09-02

Representative sampling has been completed for characterization of the residual material on the floor of Tank 19F as per the statistical sampling plan developed by Harris and Shine. Samples from eight locations have been obtained from the tank floor and two of the samples were archived as a contingency. Six samples, referred to in this report as the current scrape samples, have been submitted to and analyzed by SRNL. This report contains the statistical analysis of the floor sample analytical results to determine if further data are needed to reduce uncertainty. Included are comparisons with the prior Mantis samples resultsmore » to determine if they can be pooled with the current scrape samples to estimate the upper 95% confidence limits (UCL95%) for concentration. Statistical analysis revealed that the Mantis and current scrape sample results are not compatible. Therefore, the Mantis sample results were not used to support the quantification of analytes in the residual material. Significant spatial variability among the current scrape sample results was not found. Constituent concentrations were similar between the North and South hemispheres as well as between the inner and outer regions of the tank floor. The current scrape sample results from all six samples fall within their 3-sigma limits. In view of the results from numerous statistical tests, the data were pooled from all six current scrape samples. As such, an adequate sample size was provided for quantification of the residual material on the floor of Tank 19F. The uncertainty is quantified in this report by an UCL95% on each analyte concentration. The uncertainty in analyte concentration was calculated as a function of the number of samples, the average, and the standard deviation of the analytical results. The UCL95% was based entirely on the six current scrape sample results (each averaged across three analytical determinations).« less

Autonomous Mission Design in Extreme Orbit Environments

NASA Astrophysics Data System (ADS)

Surovik, David Allen

An algorithm for autonomous online mission design at asteroids, comets, and small moons is developed to meet the novel challenges of their complex non-Keplerian orbit environments, which render traditional methods inapplicable. The core concept of abstract reachability analysis, in which a set of impulsive maneuvering options is mapped onto a space of high-level mission outcomes, is applied to enable goal-oriented decision-making with robustness to uncertainty. These nuanced analyses are efficiently computed by utilizing a heuristic-based adaptive sampling scheme that either maximizes an objective function for autonomous planning or resolves details of interest for preliminary analysis and general study. Illustrative examples reveal the chaotic nature of small body systems through the structure of various families of reachable orbits, such as those that facilitate close-range observation of targeted surface locations or achieve soft impact upon them. In order to fulfill extensive sets of observation tasks, the single-maneuver design method is implemented in a receding-horizon framework such that a complete mission is constructed on-the-fly one piece at a time. Long-term performance and convergence are assured by augmenting the objective function with a prospect heuristic, which approximates the likelihood that a reachable end-state will benefit the subsequent planning horizon. When state and model uncertainty produce larger trajectory deviations than were anticipated, the next control horizon is advanced to allow for corrective action -- a low-frequency form of feedback control. Through Monte Carlo analysis, the planning algorithm is ultimately demonstrated to produce mission profiles that vary drastically in their physical paths but nonetheless consistently complete all goals, suggesting a high degree of flexibility. It is further shown that the objective function can be tuned to preferentially minimize fuel cost or mission duration, as well as to optimize performance under different levels of uncertainty by appropriately balancing the mitigation paradigms of robust planning and reactive execution.

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

PubMed Central

Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González

2016-01-01

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering. PMID:27872840

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation.

PubMed

Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González

2016-01-01

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis

USGS Publications Warehouse

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 these highly parameterized modeling contexts. Availability of these utilities is particularly important because, in many cases, a significant proportion of the uncertainty associated with model parameters-and the predictions that depend on them-arises from differences between the complex properties of the real world and the simplified representation of those properties that is expressed by the calibrated model. This report is intended to guide intermediate to advanced modelers in the use of capabilities available with the PEST suite of programs for evaluating model predictive error and uncertainty. A brief theoretical background is presented on sources of parameter and predictive uncertainty and on the means for evaluating this uncertainty. Applications of PEST tools are then discussed for overdetermined and underdetermined problems, both linear and nonlinear. PEST tools for calculating contributions to model predictive uncertainty, as well as optimization of data acquisition for reducing parameter and predictive uncertainty, are presented. The appendixes list the relevant PEST variables, files, and utilities required for the analyses described in the document.

10 CFR 50.48 - Fire protection.

Code of Federal Regulations, 2011 CFR

2011-01-01

... suppression systems; and (iii) The means to limit fire damage to structures, systems, or components important...) Standard 805, “Performance-Based Standard for Fire Protection for Light Water Reactor Electric Generating... pressurized-water reactors (PWRs) is not permitted. (iv) Uncertainty analysis. An uncertainty analysis...

10 CFR 50.48 - Fire protection.

Code of Federal Regulations, 2010 CFR

2010-01-01

... suppression systems; and (iii) The means to limit fire damage to structures, systems, or components important...) Standard 805, “Performance-Based Standard for Fire Protection for Light Water Reactor Electric Generating... pressurized-water reactors (PWRs) is not permitted. (iv) Uncertainty analysis. An uncertainty analysis...

UNCERTAINTY ANALYSIS OF TCE USING THE DOSE EXPOSURE ESTIMATING MODEL (DEEM) IN ACSL

EPA Science Inventory

The ACSL-based Dose Exposure Estimating Model(DEEM) under development by EPA is used to perform art uncertainty analysis of a physiologically based pharmacokinetic (PSPK) model of trichloroethylene (TCE). This model involves several circulating metabolites such as trichloroacet...

Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment

NASA Technical Reports Server (NTRS)

Britton, Paul; Al Hassan, Mohammad; Ring, Robert

2017-01-01

Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This paper will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

Assessment of the impact of climate change on spatiotemporal variability of blue and green water resources under CMIP3 and CMIP5 models in a highly mountainous watershed

NASA Astrophysics Data System (ADS)

Fazeli Farsani, Iman; Farzaneh, M. R.; Besalatpour, A. A.; Salehi, M. H.; Faramarzi, M.

2018-04-01

The variability and uncertainty of water resources associated with climate change are critical issues in arid and semi-arid regions. In this study, we used the soil and water assessment tool (SWAT) to evaluate the impact of climate change on the spatial and temporal variability of water resources in the Bazoft watershed, Iran. The analysis was based on changes of blue water flow, green water flow, and green water storage for a future period (2010-2099) compared to a historical period (1992-2008). The r-factor, p-factor, R 2, and Nash-Sutcliff coefficients for discharge were 1.02, 0.89, 0.80, and 0.80 for the calibration period and 1.03, 0.76, 0.57, and 0.59 for the validation period, respectively. General circulation models (GCMs) under 18 emission scenarios from the IPCC's Fourth (AR4) and Fifth (AR5) Assessment Reports were fed into the SWAT model. At the sub-basin level, blue water tended to decrease, while green water flow tended to increase in the future scenario, and green water storage was predicted to continue its historical trend into the future. At the monthly time scale, the 95% prediction uncertainty bands (95PPUs) of blue and green water flows varied widely in the watershed. A large number (18) of climate change scenarios fell within the estimated uncertainty band of the historical period. The large differences among scenarios indicated high levels of uncertainty in the watershed. Our results reveal that the spatial patterns of water resource components and their uncertainties in the context of climate change are notably different between IPCC AR4 and AR5 in the Bazoft watershed. This study provides a strong basis for water supply-demand analyses, and the general analytical framework can be applied to other study areas with similar challenges.

Methane emissions from global wetlands: An assessment of the uncertainty associated with various wetland extent data sets

NASA Astrophysics Data System (ADS)

Zhang, Bowen; Tian, Hanqin; Lu, Chaoqun; Chen, Guangsheng; Pan, Shufen; Anderson, Christopher; Poulter, Benjamin

2017-09-01

A wide range of estimates on global wetland methane (CH4) fluxes has been reported during the recent two decades. This gives rise to urgent needs to clarify and identify the uncertainty sources, and conclude a reconciled estimate for global CH4 fluxes from wetlands. Most estimates by using bottom-up approach rely on wetland data sets, but these data sets show largely inconsistent in terms of both wetland extent and spatiotemporal distribution. A quantitative assessment of uncertainties associated with these discrepancies among wetland data sets has not been well investigated yet. By comparing the five widely used global wetland data sets (GISS, GLWD, Kaplan, GIEMS and SWAMPS-GLWD), it this study, we found large differences in the wetland extent, ranging from 5.3 to 10.2 million km2, as well as their spatial and temporal distributions among the five data sets. These discrepancies in wetland data sets resulted in large bias in model-estimated global wetland CH4 emissions as simulated by using the Dynamic Land Ecosystem Model (DLEM). The model simulations indicated that the mean global wetland CH4 emissions during 2000-2007 were 177.2 ± 49.7 Tg CH4 yr-1, based on the five different data sets. The tropical regions contributed the largest portion of estimated CH4 emissions from global wetlands, but also had the largest discrepancy. Among six continents, the largest uncertainty was found in South America. Thus, the improved estimates of wetland extent and CH4 emissions in the tropical regions and South America would be a critical step toward an accurate estimate of global CH4 emissions. This uncertainty analysis also reveals an important need for our scientific community to generate a global scale wetland data set with higher spatial resolution and shorter time interval, by integrating multiple sources of field and satellite data with modeling approaches, for cross-scale extrapolation.

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

NASA Astrophysics Data System (ADS)

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 decision making under uncertainty methods from the state of the art. We will compare the efficiency of alternative approaches to the two case studies. Finally, we will present a hybrid decision analytic tool to address the synthesis of uncertainties.

"It's not if I get cancer, it's when I get cancer": BRCA-positive patients' (un)certain health experiences regarding hereditary breast and ovarian cancer risk.

PubMed

Dean, Marleah

2016-08-01

Women with a harmful mutation in the BReast CAncer (BRCA) gene are at significantly increased risk of developing hereditary breast and ovarian cancer (HBOC) during their lifetime, compared to those without. Such patients-with a genetic predisposition to develop cancer but who have not yet been diagnosed with cancer-live in a constant state of uncertainty and wonder not if they might get cancer but when. Framed by uncertainty management theory, the purpose of this study was to explore BRCA-positive patients' health experiences after testing positive for the BRCA genetic mutation, specifically identifying their sources of uncertainty. Thirty-four, qualitative interviews were conducted with female patients. Participants responded to online research postings on the non-profit organization Facing Our Risk of Cancer Empowered's (FORCE) message board and social media pages as well as HBOC-specific Facebook groups. The interview data were coded using the constant comparison method. Two major themes representing BRCA-positive patients' sources of uncertainty regarding their genetic predisposition and health experiences emerged from the data. Medical uncertainty included the following three subthemes: the unknown future, medical appointments, and personal cancer scares. Familial uncertainty encompassed the subthemes traumatic family cancer memories and motherhood. Overall, the study supports and extends existing research on uncertainty-revealing uncertainty is inherent in BRCA-positive patients' health experiences-and offers new insight regarding uncertainty management and HBOC risk. Copyright © 2016 Elsevier Ltd. All rights reserved.

Climate change impacts on extreme events in the United States: an uncertainty analysis

EPA Science Inventory

Extreme weather and climate events, such as heat waves, droughts and severe precipitation events, have substantial impacts on ecosystems and the economy. However, future climate simulations display large uncertainty in mean changes. As a result, the uncertainty in future changes ...

Robustness Analysis of Integrated LPV-FDI Filters and LTI-FTC System for a Transport Aircraft

NASA Technical Reports Server (NTRS)

Khong, Thuan H.; Shin, Jong-Yeob

2007-01-01

This paper proposes an analysis framework for robustness analysis of a nonlinear dynamics system that can be represented by a polynomial linear parameter varying (PLPV) system with constant bounded uncertainty. The proposed analysis framework contains three key tools: 1) a function substitution method which can convert a nonlinear system in polynomial form into a PLPV system, 2) a matrix-based linear fractional transformation (LFT) modeling approach, which can convert a PLPV system into an LFT system with the delta block that includes key uncertainty and scheduling parameters, 3) micro-analysis, which is a well known robust analysis tool for linear systems. The proposed analysis framework is applied to evaluating the performance of the LPV-fault detection and isolation (FDI) filters of the closed-loop system of a transport aircraft in the presence of unmodeled actuator dynamics and sensor gain uncertainty. The robustness analysis results are compared with nonlinear time simulations.

Uncertainty in flood damage estimates and its potential effect on investment decisions

NASA Astrophysics Data System (ADS)

Wagenaar, D. J.; de Bruijn, K. M.; Bouwer, L. M.; de Moel, H.

2016-01-01

This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage functions and maximum damages can have large effects on flood damage estimates. This explanation is then used to quantify the uncertainty in the damage estimates with a Monte Carlo analysis. The Monte Carlo analysis uses a damage function library with 272 functions from seven different flood damage models. The paper shows that the resulting uncertainties in estimated damages are in the order of magnitude of a factor of 2 to 5. The uncertainty is typically larger for flood events with small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

Uncertainty Analysis in Humidity Measurements by the Psychrometer Method

PubMed Central

Chen, Jiunyuan; Chen, Chiachung

2017-01-01

The most common and cheap indirect technique to measure relative humidity is by using psychrometer based on a dry and a wet temperature sensor. In this study, the measurement uncertainty of relative humidity was evaluated by this indirect method with some empirical equations for calculating relative humidity. Among the six equations tested, the Penman equation had the best predictive ability for the dry bulb temperature range of 15–50 °C. At a fixed dry bulb temperature, an increase in the wet bulb depression increased the error. A new equation for the psychrometer constant was established by regression analysis. This equation can be computed by using a calculator. The average predictive error of relative humidity was <0.1% by this new equation. The measurement uncertainty of the relative humidity affected by the accuracy of dry and wet bulb temperature and the numeric values of measurement uncertainty were evaluated for various conditions. The uncertainty of wet bulb temperature was the main factor on the RH measurement uncertainty. PMID:28216599

Uncertainty Analysis in Humidity Measurements by the Psychrometer Method.

PubMed

Chen, Jiunyuan; Chen, Chiachung

2017-02-14

The most common and cheap indirect technique to measure relative humidity is by using psychrometer based on a dry and a wet temperature sensor. In this study, the measurement uncertainty of relative humidity was evaluated by this indirect method with some empirical equations for calculating relative humidity. Among the six equations tested, the Penman equation had the best predictive ability for the dry bulb temperature range of 15-50 °C. At a fixed dry bulb temperature, an increase in the wet bulb depression increased the error. A new equation for the psychrometer constant was established by regression analysis. This equation can be computed by using a calculator. The average predictive error of relative humidity was <0.1% by this new equation. The measurement uncertainty of the relative humidity affected by the accuracy of dry and wet bulb temperature and the numeric values of measurement uncertainty were evaluated for various conditions. The uncertainty of wet bulb temperature was the main factor on the RH measurement uncertainty.

Development of the X-33 Aerodynamic Uncertainty Model

NASA Technical Reports Server (NTRS)

Cobleigh, Brent R.

1998-01-01

An aerodynamic uncertainty model for the X-33 single-stage-to-orbit demonstrator aircraft has been developed at NASA Dryden Flight Research Center. The model is based on comparisons of historical flight test estimates to preflight wind-tunnel and analysis code predictions of vehicle aerodynamics documented during six lifting-body aircraft and the Space Shuttle Orbiter flight programs. The lifting-body and Orbiter data were used to define an appropriate uncertainty magnitude in the subsonic and supersonic flight regions, and the Orbiter data were used to extend the database to hypersonic Mach numbers. The uncertainty data consist of increments or percentage variations in the important aerodynamic coefficients and derivatives as a function of Mach number along a nominal trajectory. The uncertainty models will be used to perform linear analysis of the X-33 flight control system and Monte Carlo mission simulation studies. Because the X-33 aerodynamic uncertainty model was developed exclusively using historical data rather than X-33 specific characteristics, the model may be useful for other lifting-body studies.

Examination of the uncertainty in contaminant fate and transport modeling: a case study in the Venice Lagoon.

PubMed

Sommerfreund, J; Arhonditsis, G B; Diamond, M L; Frignani, M; Capodaglio, G; Gerino, M; Bellucci, L; Giuliani, S; Mugnai, C

2010-03-01

A Monte Carlo analysis is used to quantify environmental parametric uncertainty in a multi-segment, multi-chemical model of the Venice Lagoon. Scientific knowledge, expert judgment and observational data are used to formulate prior probability distributions that characterize the uncertainty pertaining to 43 environmental system parameters. The propagation of this uncertainty through the model is then assessed by a comparative analysis of the moments (central tendency, dispersion) of the model output distributions. We also apply principal component analysis in combination with correlation analysis to identify the most influential parameters, thereby gaining mechanistic insights into the ecosystem functioning. We found that modeled concentrations of Cu, Pb, OCDD/F and PCB-180 varied by up to an order of magnitude, exhibiting both contaminant- and site-specific variability. These distributions generally overlapped with the measured concentration ranges. We also found that the uncertainty of the contaminant concentrations in the Venice Lagoon was characterized by two modes of spatial variability, mainly driven by the local hydrodynamic regime, which separate the northern and central parts of the lagoon and the more isolated southern basin. While spatial contaminant gradients in the lagoon were primarily shaped by hydrology, our analysis also shows that the interplay amongst the in-place historical pollution in the central lagoon, the local suspended sediment concentrations and the sediment burial rates exerts significant control on the variability of the contaminant concentrations. We conclude that the probabilistic analysis presented herein is valuable for quantifying uncertainty and probing its cause in over-parameterized models, while some of our results can be used to dictate where additional data collection efforts should focus on and the directions that future model refinement should follow. (c) 2009 Elsevier Inc. All rights reserved.

Probabilistic Methods for Uncertainty Propagation Applied to Aircraft Design

NASA Technical Reports Server (NTRS)

Green, Lawrence L.; Lin, Hong-Zong; Khalessi, Mohammad R.

2002-01-01

Three methods of probabilistic uncertainty propagation and quantification (the method of moments, Monte Carlo simulation, and a nongradient simulation search method) are applied to an aircraft analysis and conceptual design program to demonstrate design under uncertainty. The chosen example problems appear to have discontinuous design spaces and thus these examples pose difficulties for many popular methods of uncertainty propagation and quantification. However, specific implementation features of the first and third methods chosen for use in this study enable successful propagation of small uncertainties through the program. Input uncertainties in two configuration design variables are considered. Uncertainties in aircraft weight are computed. The effects of specifying required levels of constraint satisfaction with specified levels of input uncertainty are also demonstrated. The results show, as expected, that the designs under uncertainty are typically heavier and more conservative than those in which no input uncertainties exist.

Form measurement of a 0.1 mm diameter wire with a chromatic confocal sensor, with associated uncertainty evaluation

NASA Astrophysics Data System (ADS)

Sanz, Claude; Giusca, Claudiu; Morantz, Paul; Marin, Antonio; Chérif, Ahmed; Schneider, Jürgen; Mainaud-Durand, Hélène; Shore, Paul; Steffens, Norbert

2018-07-01

The accurate characterisation of a copper–beryllium wire with a diameter of 0.1 mm is one of the steps to increase the precision of future accelerators’ pre-alignment. Novelties in measuring the wire properties were found in order to overcome the difficulties brought by its small size. This paper focuses on an implementation of a chromatic-confocal sensor leading to a sub-micrometric uncertainty on the form measurements. Hence, this text reveals a high-accuracy metrology technique applicable to objects with small diameters: it details the methodology, describes a validation by comparison with a reference and specifies the uncertainty budget of this technique.

Ramp time synchronization. [for NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Hietzke, W.

1979-01-01

A new method of intercontinental clock synchronization has been developed and proposed for possible use by NASA's Deep Space Network (DSN), using a two-way/three-way radio link with a spacecraft. Analysis of preliminary data indicates that the real-time method has an uncertainty of 0.6 microsec, and it is very likely that further work will decrease the uncertainty. Also, the method is compatible with a variety of nonreal-time analysis techniques, which may reduce the uncertainty down to the tens of nanosecond range.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Yannan; Hou, Zhangshuan; Meng, Da

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.

Sources of ambient volatile organic compounds and their contributions to photochemical ozone formation at a site in the Pearl River Delta, southern China.

PubMed

Ling, Z H; Guo, H; Cheng, H R; Yu, Y F

2011-10-01

The Positive Matrix Factorization (PMF) receptor model and the Observation Based Model (OBM) were combined to analyze volatile organic compound (VOC) data collected at a suburban site (WQS) in the PRD region. The purposes are to estimate the VOC source apportionment and investigate the contributions of these sources and species of these sources to the O(3) formation in PRD. Ten VOC sources were identified. We further applied the PMF-extracted concentrations of these 10 sources into the OBM and found "solvent usage 1", "diesel vehicular emissions" and "biomass/biofuel burning" contributed most to the O(3) formation at WQS. Among these three sources, higher Relative Incremental Reactivity (RIR)-weighted values of ethene, toluene and m/p-xylene indicated that they were mainly responsible for local O(3) formation in the region. Sensitivity analysis revealed that the sources of "diesel vehicular emissions", "biomass/biofuel burning" and "solvent usage 1" had low uncertainties whereas "gasoline evaporation" showed the highest uncertainty. Copyright © 2011 Elsevier Ltd. All rights reserved.

Statistical uncertainty analysis applied to the DRAGONv4 code lattice calculations and based on JENDL-4 covariance data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hernandez-Solis, A.; Demaziere, C.; Ekberg, C.

2012-07-01

In this paper, multi-group microscopic cross-section uncertainty is propagated through the DRAGON (Version 4) lattice code, in order to perform uncertainty analysis on k{infinity} and 2-group homogenized macroscopic cross-sections predictions. A statistical methodology is employed for such purposes, where cross-sections of certain isotopes of various elements belonging to the 172 groups DRAGLIB library format, are considered as normal random variables. This library is based on JENDL-4 data, because JENDL-4 contains the largest amount of isotopic covariance matrixes among the different major nuclear data libraries. The aim is to propagate multi-group nuclide uncertainty by running the DRAGONv4 code 500 times, andmore » to assess the output uncertainty of a test case corresponding to a 17 x 17 PWR fuel assembly segment without poison. The chosen sampling strategy for the current study is Latin Hypercube Sampling (LHS). The quasi-random LHS allows a much better coverage of the input uncertainties than simple random sampling (SRS) because it densely stratifies across the range of each input probability distribution. Output uncertainty assessment is based on the tolerance limits concept, where the sample formed by the code calculations infers to cover 95% of the output population with at least a 95% of confidence. This analysis is the first attempt to propagate parameter uncertainties of modern multi-group libraries, which are used to feed advanced lattice codes that perform state of the art resonant self-shielding calculations such as DRAGONv4. (authors)« less

Stochastic Simulation and Forecast of Hydrologic Time Series Based on Probabilistic Chaos Expansion

NASA Astrophysics Data System (ADS)

Li, Z.; Ghaith, M.

2017-12-01

Hydrological processes are characterized by many complex features, such as nonlinearity, dynamics and uncertainty. How to quantify and address such complexities and uncertainties has been a challenging task for water engineers and managers for decades. To support robust uncertainty analysis, an innovative approach for the stochastic simulation and forecast of hydrologic time series is developed is this study. Probabilistic Chaos Expansions (PCEs) are established through probabilistic collocation to tackle uncertainties associated with the parameters of traditional hydrological models. The uncertainties are quantified in model outputs as Hermite polynomials with regard to standard normal random variables. Sequentially, multivariate analysis techniques are used to analyze the complex nonlinear relationships between meteorological inputs (e.g., temperature, precipitation, evapotranspiration, etc.) and the coefficients of the Hermite polynomials. With the established relationships between model inputs and PCE coefficients, forecasts of hydrologic time series can be generated and the uncertainties in the future time series can be further tackled. The proposed approach is demonstrated using a case study in China and is compared to a traditional stochastic simulation technique, the Markov-Chain Monte-Carlo (MCMC) method. Results show that the proposed approach can serve as a reliable proxy to complicated hydrological models. It can provide probabilistic forecasting in a more computationally efficient manner, compared to the traditional MCMC method. This work provides technical support for addressing uncertainties associated with hydrological modeling and for enhancing the reliability of hydrological modeling results. Applications of the developed approach can be extended to many other complicated geophysical and environmental modeling systems to support the associated uncertainty quantification and risk analysis.

Effect of monthly areal rainfall uncertainty on streamflow simulation

NASA Astrophysics Data System (ADS)

Ndiritu, J. G.; Mkhize, N.

2017-08-01

Areal rainfall is mostly obtained from point rainfall measurements that are sparsely located and several studies have shown that this results in large areal rainfall uncertainties at the daily time step. However, water resources assessment is often carried out a monthly time step and streamflow simulation is usually an essential component of this assessment. This study set out to quantify monthly areal rainfall uncertainties and assess their effect on streamflow simulation. This was achieved by; i) quantifying areal rainfall uncertainties and using these to generate stochastic monthly areal rainfalls, and ii) finding out how the quality of monthly streamflow simulation and streamflow variability change if stochastic areal rainfalls are used instead of historic areal rainfalls. Tests on monthly rainfall uncertainty were carried out using data from two South African catchments while streamflow simulation was confined to one of them. A non-parametric model that had been applied at a daily time step was used for stochastic areal rainfall generation and the Pitman catchment model calibrated using the SCE-UA optimizer was used for streamflow simulation. 100 randomly-initialised calibration-validation runs using 100 stochastic areal rainfalls were compared with 100 runs obtained using the single historic areal rainfall series. By using 4 rain gauges alternately to obtain areal rainfall, the resulting differences in areal rainfall averaged to 20% of the mean monthly areal rainfall and rainfall uncertainty was therefore highly significant. Pitman model simulations obtained coefficient of efficiencies averaging 0.66 and 0.64 in calibration and validation using historic rainfalls while the respective values using stochastic areal rainfalls were 0.59 and 0.57. Average bias was less than 5% in all cases. The streamflow ranges using historic rainfalls averaged to 29% of the mean naturalised flow in calibration and validation and the respective average ranges using stochastic monthly rainfalls were 86 and 90% of the mean naturalised streamflow. In calibration, 33% of the naturalised flow located within the streamflow ranges with historic rainfall simulations and using stochastic rainfalls increased this to 66%. In validation the respective percentages of naturalised flows located within the simulated streamflow ranges were 32 and 72% respectively. The analysis reveals that monthly areal rainfall uncertainty is significant and incorporating it into streamflow simulation would add validity to the results.

A polynomial chaos approach to the analysis of vehicle dynamics under uncertainty

NASA Astrophysics Data System (ADS)

Kewlani, Gaurav; Crawford, Justin; Iagnemma, Karl

2012-05-01

The ability of ground vehicles to quickly and accurately analyse their dynamic response to a given input is critical to their safety and efficient autonomous operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this uncertainty must be considered in the analysis of vehicle motion dynamics. Here, polynomial chaos approaches that explicitly consider parametric uncertainty during modelling of vehicle dynamics are presented. They are shown to be computationally more efficient than the standard Monte Carlo scheme, and experimental results compared with the simulation results performed on ANVEL (a vehicle simulator) indicate that the method can be utilised for efficient and accurate prediction of vehicle motion in realistic scenarios.

Uncertainty in low-flow data from three streamflow-gaging stations on the upper Verde River, Arizona

USGS Publications Warehouse

Anning, D.W.; ,

2004-01-01

The evaluation of uncertainty in low-flow data collected from three streamflow-gaging stations on the upper Verde River, Arizona, was presented. In downstream order, the stations are Verde River near Paulden, Verde River near Clarkdale, and Verde River near Camp Verde. A monitoring objective of the evaluation was to characterize discharge of the lower flow regime through a variety of procedures such as frequency analysis and base-flow analysis. For Verde River near Paulden and near Camp Verde, the uncertainty of daily low flows can be reduced by decreasing the uncertainty of discharge-measurement frequency, or building an artificial control that would have a stable stage-discharge relation over time.

Uncertainty Analysis Framework - Hanford Site-Wide Groundwater Flow and Transport Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cole, Charles R.; Bergeron, Marcel P.; Murray, Christopher J.

2001-11-09

Pacific Northwest National Laboratory (PNNL) embarked on a new initiative to strengthen the technical defensibility of the predictions being made with a site-wide groundwater flow and transport model at the U.S. Department of Energy Hanford Site in southeastern Washington State. In FY 2000, the focus of the initiative was on the characterization of major uncertainties in the current conceptual model that would affect model predictions. The long-term goals of the initiative are the development and implementation of an uncertainty estimation methodology in future assessments and analyses using the site-wide model. This report focuses on the development and implementation of anmore » uncertainty analysis framework.« less

The deuteron-radius puzzle is alive: A new analysis of nuclear structure uncertainties

NASA Astrophysics Data System (ADS)

Hernandez, O. J.; Ekström, A.; Nevo Dinur, N.; Ji, C.; Bacca, S.; Barnea, N.

2018-03-01

To shed light on the deuteron radius puzzle we analyze the theoretical uncertainties of the nuclear structure corrections to the Lamb shift in muonic deuterium. We find that the discrepancy between the calculated two-photon exchange correction and the corresponding experimentally inferred value by Pohl et al. [1] remain. The present result is consistent with our previous estimate, although the discrepancy is reduced from 2.6 σ to about 2 σ. The error analysis includes statistic as well as systematic uncertainties stemming from the use of nucleon-nucleon interactions derived from chiral effective field theory at various orders. We therefore conclude that nuclear theory uncertainty is more likely not the source of the discrepancy.

Landmark based localization in urban environment

NASA Astrophysics Data System (ADS)

Qu, Xiaozhi; Soheilian, Bahman; Paparoditis, Nicolas

2018-06-01

A landmark based localization with uncertainty analysis based on cameras and geo-referenced landmarks is presented in this paper. The system is developed to adapt different camera configurations for six degree-of-freedom pose estimation. Local bundle adjustment is applied for optimization and the geo-referenced landmarks are integrated to reduce the drift. In particular, the uncertainty analysis is taken into account. On the one hand, we estimate the uncertainties of poses to predict the precision of localization. On the other hand, uncertainty propagation is considered for matching, tracking and landmark registering. The proposed method is evaluated on both KITTI benchmark and the data acquired by a mobile mapping system. In our experiments, decimeter level accuracy can be reached.

The Model Optimization, Uncertainty, and SEnsitivity analysis (MOUSE) toolbox: overview and application

USDA-ARS?s Scientific Manuscript database

For several decades, optimization and sensitivity/uncertainty analysis of environmental models has been the subject of extensive research. Although much progress has been made and sophisticated methods developed, the growing complexity of environmental models to represent real-world systems makes it...

FORMAL UNCERTAINTY ANALYSIS OF A LAGRANGIAN PHOTOCHEMICAL AIR POLLUTION MODEL. (R824792)

EPA Science Inventory

This study applied Monte Carlo analysis with Latin
hypercube sampling to evaluate the effects of uncertainty
in air parcel trajectory paths, emissions, rate constants,
deposition affinities, mixing heights, and atmospheric stability
on predictions from a vertically...

Uncertainty Estimate for the Outdoor Calibration of Solar Pyranometers: A Metrologist Perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reda, I.; Myers, D.; Stoffel, T.

2008-12-01

Pyranometers are used outdoors to measure solar irradiance. By design, this type of radiometer can measure the; total hemispheric (global) or diffuse (sky) irradiance when the detector is unshaded or shaded from the sun disk, respectively. These measurements are used in a variety of applications including solar energy conversion, atmospheric studies, agriculture, and materials science. Proper calibration of pyranometers is essential to ensure measurement quality. This paper describes a step-by-step method for calculating and reporting the uncertainty of the calibration, using the guidelines of the ISO 'Guide to the Expression of Uncertainty in Measurement' or GUM, that is applied tomore » the pyranometer; calibration procedures used at the National Renewable Energy Laboratory (NREL). The NREL technique; characterizes a responsivity function of a pyranometer as a function of the zenith angle, as well as reporting a single; calibration responsivity value for a zenith angle of 45 ..deg... The uncertainty analysis shows that a lower uncertainty can be achieved by using the response function of a pyranometer determined as a function of zenith angle, in lieu of just using; the average value at 45..deg... By presenting the contribution of each uncertainty source to the total uncertainty; users will be able to troubleshoot and improve their calibration process. The uncertainty analysis method can also be used to determine the uncertainty of different calibration techniques and applications, such as deriving the uncertainty of field measurements.« less

Using a Software Tool in Forecasting: a Case Study of Sales Forecasting Taking into Account Data Uncertainty

NASA Astrophysics Data System (ADS)

Fabianová, Jana; Kačmáry, Peter; Molnár, Vieroslav; Michalik, Peter

2016-10-01

Forecasting is one of the logistics activities and a sales forecast is the starting point for the elaboration of business plans. Forecast accuracy affects the business outcomes and ultimately may significantly affect the economic stability of the company. The accuracy of the prediction depends on the suitability of the use of forecasting methods, experience, quality of input data, time period and other factors. The input data are usually not deterministic but they are often of random nature. They are affected by uncertainties of the market environment, and many other factors. Taking into account the input data uncertainty, the forecast error can by reduced. This article deals with the use of the software tool for incorporating data uncertainty into forecasting. Proposals are presented of a forecasting approach and simulation of the impact of uncertain input parameters to the target forecasted value by this case study model. The statistical analysis and risk analysis of the forecast results is carried out including sensitivity analysis and variables impact analysis.

Classifying the Sizes of Explosive Eruptions using Tephra Deposits: The Advantages of a Numerical Inversion Approach

NASA Astrophysics Data System (ADS)

Connor, C.; Connor, L.; White, J.

2015-12-01

Explosive volcanic eruptions are often classified by deposit mass and eruption column height. How well are these eruption parameters determined in older deposits, and how well can we reduce uncertainty using robust numerical and statistical methods? We describe an efficient and effective inversion and uncertainty quantification approach for estimating eruption parameters given a dataset of tephra deposit thickness and granulometry. The inversion and uncertainty quantification is implemented using the open-source PEST++ code. Inversion with PEST++ can be used with a variety of forward models and here is applied using Tephra2, a code that simulates advective and dispersive tephra transport and deposition. The Levenburg-Marquardt algorithm is combined with formal Tikhonov and subspace regularization to invert eruption parameters; a linear equation for conditional uncertainty propagation is used to estimate posterior parameter uncertainty. Both the inversion and uncertainty analysis support simultaneous analysis of the full eruption and wind-field parameterization. The combined inversion/uncertainty-quantification approach is applied to the 1992 eruption of Cerro Negro (Nicaragua), the 2011 Kirishima-Shinmoedake (Japan), and the 1913 Colima (Mexico) eruptions. These examples show that although eruption mass uncertainty is reduced by inversion against tephra isomass data, considerable uncertainty remains for many eruption and wind-field parameters, such as eruption column height. Supplementing the inversion dataset with tephra granulometry data is shown to further reduce the uncertainty of most eruption and wind-field parameters. We think the use of such robust models provides a better understanding of uncertainty in eruption parameters, and hence eruption classification, than is possible with more qualitative methods that are widely used.

The critical role of uncertainty in projections of hydrological extremes

NASA Astrophysics Data System (ADS)

Meresa, Hadush K.; Romanowicz, Renata J.

2017-08-01

This paper aims to quantify the uncertainty in projections of future hydrological extremes in the Biala Tarnowska River at Koszyce gauging station, south Poland. The approach followed is based on several climate projections obtained from the EURO-CORDEX initiative, raw and bias-corrected realizations of catchment precipitation, and flow simulations derived using multiple hydrological model parameter sets. The projections cover the 21st century. Three sources of uncertainty are considered: one related to climate projection ensemble spread, the second related to the uncertainty in hydrological model parameters and the third related to the error in fitting theoretical distribution models to annual extreme flow series. The uncertainty of projected extreme indices related to hydrological model parameters was conditioned on flow observations from the reference period using the generalized likelihood uncertainty estimation (GLUE) approach, with separate criteria for high- and low-flow extremes. Extreme (low and high) flow quantiles were estimated using the generalized extreme value (GEV) distribution at different return periods and were based on two different lengths of the flow time series. A sensitivity analysis based on the analysis of variance (ANOVA) shows that the uncertainty introduced by the hydrological model parameters can be larger than the climate model variability and the distribution fit uncertainty for the low-flow extremes whilst for the high-flow extremes higher uncertainty is observed from climate models than from hydrological parameter and distribution fit uncertainties. This implies that ignoring one of the three uncertainty sources may cause great risk to future hydrological extreme adaptations and water resource planning and management.

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

PubMed

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.

Parameter uncertainty analysis for the annual phosphorus loss estimator (APLE) model

USDA-ARS?s Scientific Manuscript database

Technical abstract: Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, we conduct an uncertainty analys...

Uncertainty of rotating shadowband irradiometers and Si-pyranometers including the spectral irradiance error

NASA Astrophysics Data System (ADS)

Wilbert, Stefan; Kleindiek, Stefan; Nouri, Bijan; Geuder, Norbert; Habte, Aron; Schwandt, Marko; Vignola, Frank

2016-05-01

Concentrating solar power projects require accurate direct normal irradiance (DNI) data including uncertainty specifications for plant layout and cost calculations. Ground measured data are necessary to obtain the required level of accuracy and are often obtained with Rotating Shadowband Irradiometers (RSI) that use photodiode pyranometers and correction functions to account for systematic effects. The uncertainty of Si-pyranometers has been investigated, but so far basically empirical studies were published or decisive uncertainty influences had to be estimated based on experience in analytical studies. One of the most crucial estimated influences is the spectral irradiance error because Si-photodiode-pyranometers only detect visible and color infrared radiation and have a spectral response that varies strongly within this wavelength interval. Furthermore, analytic studies did not discuss the role of correction functions and the uncertainty introduced by imperfect shading. In order to further improve the bankability of RSI and Si-pyranometer data, a detailed uncertainty analysis following the Guide to the Expression of Uncertainty in Measurement (GUM) has been carried out. The study defines a method for the derivation of the spectral error and spectral uncertainties and presents quantitative values of the spectral and overall uncertainties. Data from the PSA station in southern Spain was selected for the analysis. Average standard uncertainties for corrected 10 min data of 2 % for global horizontal irradiance (GHI), and 2.9 % for DNI (for GHI and DNI over 300 W/m²) were found for the 2012 yearly dataset when separate GHI and DHI calibration constants were used. Also the uncertainty in 1 min resolution was analyzed. The effect of correction functions is significant. The uncertainties found in this study are consistent with results of previous empirical studies.

Uncertainty of Rotating Shadowband Irradiometers and Si-Pyranometers Including the Spectral Irradiance Error

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilbert, Stefan; Kleindiek, Stefan; Nouri, Bijan

2016-05-31

Concentrating solar power projects require accurate direct normal irradiance (DNI) data including uncertainty specifications for plant layout and cost calculations. Ground measured data are necessary to obtain the required level of accuracy and are often obtained with Rotating Shadowband Irradiometers (RSI) that use photodiode pyranometers and correction functions to account for systematic effects. The uncertainty of Si-pyranometers has been investigated, but so far basically empirical studies were published or decisive uncertainty influences had to be estimated based on experience in analytical studies. One of the most crucial estimated influences is the spectral irradiance error because Si-photodiode-pyranometers only detect visible andmore » color infrared radiation and have a spectral response that varies strongly within this wavelength interval. Furthermore, analytic studies did not discuss the role of correction functions and the uncertainty introduced by imperfect shading. In order to further improve the bankability of RSI and Si-pyranometer data, a detailed uncertainty analysis following the Guide to the Expression of Uncertainty in Measurement (GUM) has been carried out. The study defines a method for the derivation of the spectral error and spectral uncertainties and presents quantitative values of the spectral and overall uncertainties. Data from the PSA station in southern Spain was selected for the analysis. Average standard uncertainties for corrected 10 min data of 2% for global horizontal irradiance (GHI), and 2.9% for DNI (for GHI and DNI over 300 W/m2) were found for the 2012 yearly dataset when separate GHI and DHI calibration constants were used. Also the uncertainty in 1 min resolution was analyzed. The effect of correction functions is significant. The uncertainties found in this study are consistent with results of previous empirical studies.« less

Consistency of Estimated Global Water Cycle Variations Over the Satellite Era

NASA Technical Reports Server (NTRS)

Robertson, F. R.; Bosilovich, M. G.; Roberts, J. B.; Reichle, R. H.; Adler, R.; Ricciardulli, L.; Berg, W.; Huffman, G. J.

2013-01-01

Motivated by the question of whether recent indications of decadal climate variability and a possible "climate shift" may have affected the global water balance, we examine evaporation minus precipitation (E-P) variability integrated over the global oceans and global land from three points of view-remotely sensed retrievals / objective analyses over the oceans, reanalysis vertically-integrated moisture convergence (MFC) over land, and land surface models forced with observations-based precipitation, radiation and near-surface meteorology. Because monthly variations in area-averaged atmospheric moisture storage are small and the global integral of moisture convergence must approach zero, area-integrated E-P over ocean should essentially equal precipitation minus evapotranspiration (P-ET) over land (after adjusting for ocean and land areas). Our analysis reveals considerable uncertainty in the decadal variations of ocean evaporation when integrated to global scales. This is due to differences among datasets in 10m wind speed and near-surface atmospheric specific humidity (2m qa) used in bulk aerodynamic retrievals. Precipitation variations, all relying substantially on passive microwave retrievals over ocean, still have uncertainties in decadal variability, but not to the degree present with ocean evaporation estimates. Reanalysis MFC and P-ET over land from several observationally forced diagnostic and land surface models agree best on interannual variations. However, upward MFC (i.e. P-ET) reanalysis trends are likely related in part to observing system changes affecting atmospheric assimilation models. While some evidence for a low-frequency E-P maximum near 2000 is found, consistent with a recent apparent pause in sea-surface temperature (SST) rise, uncertainties in the datasets used here remain significant. Prospects for further reducing uncertainties are discussed. The results are interpreted in the context of recent climate variability (Pacific Decadal Oscillation, Atlantic Meridional Overturning), and efforts to distinguish these modes from longer-term trends.

Uncertainty analysis for effluent trading planning using a Bayesian estimation-based simulation-optimization modeling approach.

PubMed

Zhang, J L; Li, Y P; Huang, G H; Baetz, B W; Liu, J

2017-06-01

In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic-possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision alternatives. Copyright © 2017 Elsevier Ltd. All rights reserved.

Simulation-based optimization framework for reuse of agricultural drainage water in irrigation.

PubMed

Allam, A; Tawfik, A; Yoshimura, C; Fleifle, A

2016-05-01

A simulation-based optimization framework for agricultural drainage water (ADW) reuse has been developed through the integration of a water quality model (QUAL2Kw) and a genetic algorithm. This framework was applied to the Gharbia drain in the Nile Delta, Egypt, in summer and winter 2012. First, the water quantity and quality of the drain was simulated using the QUAL2Kw model. Second, uncertainty analysis and sensitivity analysis based on Monte Carlo simulation were performed to assess QUAL2Kw's performance and to identify the most critical variables for determination of water quality, respectively. Finally, a genetic algorithm was applied to maximize the total reuse quantity from seven reuse locations with the condition not to violate the standards for using mixed water in irrigation. The water quality simulations showed that organic matter concentrations are critical management variables in the Gharbia drain. The uncertainty analysis showed the reliability of QUAL2Kw to simulate water quality and quantity along the drain. Furthermore, the sensitivity analysis showed that the 5-day biochemical oxygen demand, chemical oxygen demand, total dissolved solids, total nitrogen and total phosphorous are highly sensitive to point source flow and quality. Additionally, the optimization results revealed that the reuse quantities of ADW can reach 36.3% and 40.4% of the available ADW in the drain during summer and winter, respectively. These quantities meet 30.8% and 29.1% of the drainage basin requirements for fresh irrigation water in the respective seasons. Copyright © 2016 Elsevier Ltd. All rights reserved.

Operational Implementation of a Pc Uncertainty Construct for Conjunction Assessment Risk Analysis

NASA Technical Reports Server (NTRS)

Newman, Lauri K.; Hejduk, Matthew D.; Johnson, Lauren C.

2016-01-01

Earlier this year the NASA Conjunction Assessment and Risk Analysis (CARA) project presented the theoretical and algorithmic aspects of a method to include the uncertainties in the calculation inputs when computing the probability of collision (Pc) between two space objects, principally uncertainties in the covariances and the hard-body radius. The output of this calculation approach is to produce rather than a single Pc value an entire probability density function that will represent the range of possible Pc values given the uncertainties in the inputs and bring CA risk analysis methodologies more in line with modern risk management theory. The present study provides results from the exercise of this method against an extended dataset of satellite conjunctions in order to determine the effect of its use on the evaluation of conjunction assessment (CA) event risk posture. The effects are found to be considerable: a good number of events are downgraded from or upgraded to a serious risk designation on the basis of consideration of the Pc uncertainty. The findings counsel the integration of the developed methods into NASA CA operations.

Chemical purity using quantitative 1H-nuclear magnetic resonance: a hierarchical Bayesian approach for traceable calibrations

NASA Astrophysics Data System (ADS)

Toman, Blaza; Nelson, Michael A.; Lippa, Katrice A.

2016-10-01

Chemical purity assessment using quantitative 1H-nuclear magnetic resonance spectroscopy is a method based on ratio references of mass and signal intensity of the analyte species to that of chemical standards of known purity. As such, it is an example of a calculation using a known measurement equation with multiple inputs. Though multiple samples are often analyzed during purity evaluations in order to assess measurement repeatability, the uncertainty evaluation must also account for contributions from inputs to the measurement equation. Furthermore, there may be other uncertainty components inherent in the experimental design, such as independent implementation of multiple calibration standards. As such, the uncertainty evaluation is not purely bottom up (based on the measurement equation) or top down (based on the experimental design), but inherently contains elements of both. This hybrid form of uncertainty analysis is readily implemented with Bayesian statistical analysis. In this article we describe this type of analysis in detail and illustrate it using data from an evaluation of chemical purity and its uncertainty for a folic acid material.

DECISION-COMPONENTS OF NICE'S TECHNOLOGY APPRAISALS ASSESSMENT FRAMEWORK.

PubMed

de Folter, Joost; Trusheim, Mark; Jonsson, Pall; Garner, Sarah

2018-01-01

Value assessment frameworks have gained prominence recently in the context of U.S. healthcare. Such frameworks set out a series of factors that are considered in funding decisions. The UK's National Institute of Health and Care Excellence (NICE) is an established health technology assessment (HTA) agency. We present a novel application of text analysis that characterizes NICE's Technology Appraisals in the context of the newer assessment frameworks and present the results in a visual way. A total of 243 documents of NICE's medicines guidance from 2007 to 2016 were analyzed. Text analysis was used to identify a hierarchical set of decision factors considered in the assessments. The frequency of decision factors stated in the documents was determined and their association with terms related to uncertainty. The results were incorporated into visual representations of hierarchical factors. We identified 125 decision factors, and hierarchically grouped these into eight domains: Clinical Effectiveness, Cost Effectiveness, Condition, Current Practice, Clinical Need, New Treatment, Studies, and Other Factors. Textual analysis showed all domains appeared consistently in the guidance documents. Many factors were commonly associated with terms relating to uncertainty. A series of visual representations was created. This study reveals the complexity and consistency of NICE's decision-making processes and demonstrates that cost effectiveness is not the only decision-criteria. The study highlights the importance of processes and methodology that can take both quantitative and qualitative information into account. Visualizations can help effectively communicate this complex information during the decision-making process and subsequently to stakeholders.

Overview of the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design

NASA Technical Reports Server (NTRS)

Benek, John A.; Luckring, James M.

2017-01-01

A NATO symposium held in 2008 identified many promising sensitivity analysis and un-certainty quantification technologies, but the maturity and suitability of these methods for realistic applications was not known. The STO Task Group AVT-191 was established to evaluate the maturity and suitability of various sensitivity analysis and uncertainty quantification methods for application to realistic problems of interest to NATO. The program ran from 2011 to 2015, and the work was organized into four discipline-centric teams: external aerodynamics, internal aerodynamics, aeroelasticity, and hydrodynamics. This paper presents an overview of the AVT-191 program content.

Summary Findings from the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design

NASA Technical Reports Server (NTRS)

Benek, John A.; Luckring, James M.

2017-01-01

A NATO symposium held in Greece in 2008 identified many promising sensitivity analysis and uncertainty quantification technologies, but the maturity and suitability of these methods for realistic applications was not clear. The NATO Science and Technology Organization, Task Group AVT-191 was established to evaluate the maturity and suitability of various sensitivity analysis and uncertainty quantification methods for application to realistic vehicle development problems. The program ran from 2011 to 2015, and the work was organized into four discipline-centric teams: external aerodynamics, internal aerodynamics, aeroelasticity, and hydrodynamics. This paper summarizes findings and lessons learned from the task group.

Considering Risk and Resilience in Decision-Making

NASA Technical Reports Server (NTRS)

Torres-Pomales, Wilfredo

2015-01-01

This paper examines the concepts of decision-making, risk analysis, uncertainty and resilience analysis. The relation between risk, vulnerability, and resilience is analyzed. The paper describes how complexity, uncertainty, and ambiguity are the most critical factors in the definition of the approach and criteria for decision-making. Uncertainty in its various forms is what limits our ability to offer definitive answers to questions about the outcomes of alternatives in a decision-making process. It is shown that, although resilience-informed decision-making would seem fundamentally different from risk-informed decision-making, this is not the case as resilience-analysis can be easily incorporated within existing analytic-deliberative decision-making frameworks.

Detailed Uncertainty Analysis for Ares I Ascent Aerodynamics Wind Tunnel Database

NASA Technical Reports Server (NTRS)

Hemsch, Michael J.; Hanke, Jeremy L.; Walker, Eric L.; Houlden, Heather P.

2008-01-01

A detailed uncertainty analysis for the Ares I ascent aero 6-DOF wind tunnel database is described. While the database itself is determined using only the test results for the latest configuration, the data used for the uncertainty analysis comes from four tests on two different configurations at the Boeing Polysonic Wind Tunnel in St. Louis and the Unitary Plan Wind Tunnel at NASA Langley Research Center. Four major error sources are considered: (1) systematic errors from the balance calibration curve fits and model + balance installation, (2) run-to-run repeatability, (3) boundary-layer transition fixing, and (4) tunnel-to-tunnel reproducibility.

Socializing Identity Through Practice: A Mixed Methods Approach to Family Medicine Resident Perspectives on Uncertainty.

PubMed

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.