WorldWideScience

Sample records for models conceptual uncertainty

  1. Can Bayesian Belief Networks help tackling conceptual model uncertainties in contaminated site risk assessment?

    DEFF Research Database (Denmark)

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

    different conceptual models may describe the same contaminated site equally well. In many cases, conceptual model uncertainty has been shown to be one of the dominant sources for uncertainty and is therefore essential to account for when quantifying uncertainties in risk assessments. We present here......A key component in risk assessment of contaminated sites is the formulation of a conceptual site model. The conceptual model is a simplified representation of reality and forms the basis for the mathematical modelling of contaminant fate and transport at the site. A conceptual model should...... a Bayesian Belief Network (BBN) approach for evaluating the uncertainty in risk assessment of groundwater contamination from contaminated sites. The approach accounts for conceptual model uncertainty by considering multiple conceptual models, each of which represents an alternative interpretation of the site...

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

    DEFF Research Database (Denmark)

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

    2012-01-01

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

  3. A conceptual precipitation-runoff modeling suite: Model selection, calibration and predictive uncertainty assessment

    Science.gov (United States)

    Tyler Jon Smith

    2008-01-01

    In Montana and much of the Rocky Mountain West, the single most important parameter in forecasting the controls on regional water resources is snowpack. Despite the heightened importance of snowpack, few studies have considered the representation of uncertainty in coupled snowmelt/hydrologic conceptual models. Uncertainty estimation provides a direct interpretation of...

  4. The treatment of conceptual model uncertainty for Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    Boak, J.M.; Flint, A.; Dockery, H.A.

    1995-01-01

    A reasonable risk assessment of radioactive waste disposals faces three main problems : 1) the ability of the conceptual model to account for the effective repository process ; 2) the boundary conditions ; 3) the parameters values that model the particular site. Yucca mountain Site Characterization Project deals with these problems through two major approaches that are described here : 1) the evolution of models for the recurrence rate of volcanism ; 2) the nominal hydrologic performance for the site. These two approaches are expected to lead to a reasonable demonstration of the suitability of the site. (D.L.). 13 refs., 2 figs

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

    Science.gov (United States)

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

    2012-04-01

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

  6. CAPTURING UNCERTAINTY IN UNSATURATED-ZONE FLOW USING DIFFERENT CONCEPTUAL MODELS OF FRACTURE-MATRIX INTERACTION

    International Nuclear Information System (INIS)

    SUSAN J. ALTMAN, MICHAEL L. WILSON, GUMUNDUR S. BODVARSSON

    1998-01-01

    Preliminary calculations show that the two different conceptual models of fracture-matrix interaction presented here yield different results pertinent to the performance of the potential repository at Yucca Mountain. Namely, each model produces different ranges of flow in the fractures, where radionuclide transport is thought to be most important. This method of using different flow models to capture both conceptual model and parameter uncertainty ensures that flow fields used in TSPA calculations will be reasonably calibrated to the available data while still capturing this uncertainty. This method also allows for the use of three-dimensional flow fields for the TSPA-VA calculations

  7. Tolerance of uncertainty: Conceptual analysis, integrative model, and implications for healthcare.

    Science.gov (United States)

    Hillen, Marij A; Gutheil, Caitlin M; Strout, Tania D; Smets, Ellen M A; Han, Paul K J

    2017-05-01

    Uncertainty tolerance (UT) is an important, well-studied phenomenon in health care and many other important domains of life, yet its conceptualization and measurement by researchers in various disciplines have varied substantially and its essential nature remains unclear. The objectives of this study were to: 1) analyze the meaning and logical coherence of UT as conceptualized by developers of UT measures, and 2) develop an integrative conceptual model to guide future empirical research regarding the nature, causes, and effects of UT. A narrative review and conceptual analysis of 18 existing measures of Uncertainty and Ambiguity Tolerance was conducted, focusing on how measure developers in various fields have defined both the "uncertainty" and "tolerance" components of UT-both explicitly through their writings and implicitly through the items constituting their measures. Both explicit and implicit conceptual definitions of uncertainty and tolerance vary substantially and are often poorly and inconsistently specified. A logically coherent, unified understanding or theoretical model of UT is lacking. To address these gaps, we propose a new integrative definition and multidimensional conceptual model that construes UT as the set of negative and positive psychological responses-cognitive, emotional, and behavioral-provoked by the conscious awareness of ignorance about particular aspects of the world. This model synthesizes insights from various disciplines and provides an organizing framework for future research. We discuss how this model can facilitate further empirical and theoretical research to better measure and understand the nature, determinants, and outcomes of UT in health care and other domains of life. Uncertainty tolerance is an important and complex phenomenon requiring more precise and consistent definition. An integrative definition and conceptual model, intended as a tentative and flexible point of departure for future research, adds needed breadth

  8. The treatment of conceptual model uncertainty in the Nagra programme: a few examples

    International Nuclear Information System (INIS)

    Zuidema, P.; Gautschi, A.; Smith, P.; Vomvoris, S.

    1995-01-01

    In this paper, a few examples are discussed which demonstrate how conceptual model uncertainty is treated within the Nagra programme. These examples cover geometric aspects, small-scale properties of the host rock and direction of the flow paths. Based on the examples, the pragmatic approach adopted in performance assessment is briefly discussed. (author). 3 refs., 7 figs

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

    Science.gov (United States)

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

    2014-10-01

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

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

    Science.gov (United States)

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

    2015-05-01

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

  11. Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations

    Science.gov (United States)

    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.

  12. A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

    Science.gov (United States)

    Thomsen, Nanna I.; Binning, Philip J.; McKnight, Ursula S.; Tuxen, Nina; Bjerg, Poul L.; Troldborg, Mads

    2016-05-01

    A key component in risk assessment of contaminated sites is in the formulation of a conceptual site model (CSM). A CSM is a simplified representation of reality and forms the basis for the mathematical modeling of contaminant fate and transport at the site. The CSM should therefore identify the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert opinion at different knowledge levels. The developed BBNs combine data from desktop studies and initial site investigations with expert opinion to assess which of the CSMs are more likely to reflect the actual site conditions. The method is demonstrated on a Danish field site, contaminated with chlorinated ethenes. Four different CSMs are developed by combining two contaminant source zone interpretations (presence or absence of a separate phase contamination) and two geological interpretations (fractured or unfractured clay till). The beliefs in each of the CSMs are assessed sequentially based on data from three investigation stages (a screening investigation, a more detailed investigation, and an expert consultation) to demonstrate that the belief can be updated as more information

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

    Science.gov (United States)

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

    2011-12-01

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

  14. Tolerance of uncertainty: Conceptual analysis, integrative model, and implications for healthcare

    NARCIS (Netherlands)

    Hillen, Marij A.; Gutheil, Caitlin M.; Strout, Tania D.; Smets, Ellen M. A.; Han, Paul K. J.

    2017-01-01

    Rationale: Uncertainty tolerance (UT) is an important, well-studied phenomenon in health care and many other important domains of life, yet its conceptualization and measurement by researchers in various disciplines have varied substantially and its essential nature remains unclear. Objective: The

  15. A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

    DEFF Research Database (Denmark)

    Thomsen, Nanna Isbak; Binning, Philip John; McKnight, Ursula S.

    2016-01-01

    the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found...... to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models...... that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert...

  16. Investigation of discrete-fracture network conceptual model uncertainty at Forsmark

    International Nuclear Information System (INIS)

    Geier, Joel

    2011-04-01

    In the present work a discrete fracture model has been further developed and implemented using the latest SKB site investigation data. The model can be used for analysing the fracture network and to model flow through the rock in Forsmark. The aim has been to study uncertainties in the hydrological discrete fracture network (DFN) for the repository model. More specifically the objective has been to study to which extent available data limits uncertainties in the DFN model and how data that can be obtained in future underground work can further limit these uncertainties. Moreover, the effects on deposition hole utilisation and placement have been investigated as well as the effects on the flow to deposition holes

  17. The influence of conceptual model uncertainty on management decisions for a groundwater-dependent ecosystem in karst

    DEFF Research Database (Denmark)

    Gondwe, Bibi Ruth Neuman; Merediz-Alonso, Gonzalo; Bauer-Gottwein, Peter

    2011-01-01

    abstractions and pollution threatens the fresh water resource, and consequently the ecosystem integrity of both Sian Ka’an and the adjacent coastal environment. Seven different catchment-scale conceptual models were implemented in a distributed hydrological modelling approach. Equivalent porous medium...... to preserve water resources and maintain ecosystem services. Multiple Model Simulation highlights the impact of model structure uncertainty on management decisions using several plausible conceptual models. Multiple Model Simulation was used for this purpose on the Yucatan Peninsula, which is one of the world......Groundwater management in karst is often based on limited hydrologic understanding of the aquifer. The geologic heterogeneities controlling the water flow are often insufficiently mapped. As karst aquifers are very vulnerable to pollution, groundwater protection and land use management are crucial...

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

    Directory of Open Access Journals (Sweden)

    A. E. Sikorska

    2012-04-01

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

  19. Information on Hydrologic Conceptual Models, Parameters, Uncertainty Analysis, and Data Sources for Dose Assessments at Decommissioning Sites

    International Nuclear Information System (INIS)

    Meyer, Philip D.; Gee, Glendon W.; Nicholson, Thomas J.

    1999-01-01

    This report addresses issues related to the analysis of uncertainty in dose assessments conducted as part of decommissioning analyses. The analysis is limited to the hydrologic aspects of the exposure pathway involving infiltration of water at the ground surface, leaching of contaminants, and transport of contaminants through the groundwater to a point of exposure. The basic conceptual models and mathematical implementations of three dose assessment codes are outlined along with the site-specific conditions under which the codes may provide inaccurate, potentially nonconservative results. In addition, the hydrologic parameters of the codes are identified and compared. A methodology for parameter uncertainty assessment is outlined that considers the potential data limitations and modeling needs of decommissioning analyses. This methodology uses generic parameter distributions based on national or regional databases, sensitivity analysis, probabilistic modeling, and Bayesian updating to incorporate site-specific information. Data sources for best-estimate parameter values and parameter uncertainty information are also reviewed. A follow-on report will illustrate the uncertainty assessment methodology using decommissioning test cases

  20. Information on Hydrologic Conceptual Models, Parameters, Uncertainty Analysis, and Data Sources for Dose Assessments at Decommissioning Sites

    International Nuclear Information System (INIS)

    Meyer D, Philip; Gee W, Glendon

    2000-01-01

    This report addresses issues related to the analysis of uncertainty in dose assessments conducted as part of decommissioning analyses. The analysis is limited to the hydrologic aspects of the exposure pathway involving infiltration of water at the ground surface, leaching of contaminants, and transport of contaminants through the groundwater to a point of exposure. The basic conceptual models and mathematical implementations of three dose assessment codes are outlined along with the site-specific conditions under which the codes may provide inaccurate, potentially nonconservative results. In addition, the hydrologic parameters of the codes are identified and compared. A methodology for parameter uncertainty assessment is outlined that considers the potential data limitations and modeling needs of decommissioning analyses. This methodology uses generic parameter distributions based on national or regional databases, sensitivity analysis, probabilistic modeling, and Bayesian updating to incorporate site-specific information. Data sources for best-estimate parameter values and parameter uncertainty information are also reviewed. A follow-on report will illustrate the uncertainty assessment methodology using decommissioning test cases

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

    NARCIS (Netherlands)

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

    2006-01-01

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

  2. Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty with Application to Uranium Transport at the Hanford Site 300 Area

    Energy Technology Data Exchange (ETDEWEB)

    Meyer, Philip D.; Ye, Ming; Rockhold, Mark L.; Neuman, Shlomo P.; Cantrell, Kirk J.

    2007-07-30

    This report to the Nuclear Regulatory Commission (NRC) describes the development and application of a methodology to systematically and quantitatively assess predictive uncertainty in groundwater flow and transport modeling that considers the combined impact of hydrogeologic uncertainties associated with the conceptual-mathematical basis of a model, model parameters, and the scenario to which the model is applied. The methodology is based on a n extension of a Maximum Likelihood implementation of Bayesian Model Averaging. Model uncertainty is represented by postulating a discrete set of alternative conceptual models for a site with associated prior model probabilities that reflect a belief about the relative plausibility of each model based on its apparent consistency with available knowledge and data. Posterior model probabilities are computed and parameter uncertainty is estimated by calibrating each model to observed system behavior; prior parameter estimates are optionally included. Scenario uncertainty is represented as a discrete set of alternative future conditions affecting boundary conditions, source/sink terms, or other aspects of the models, with associated prior scenario probabilities. A joint assessment of uncertainty results from combining model predictions computed under each scenario using as weight the posterior model and prior scenario probabilities. The uncertainty methodology was applied to modeling of groundwater flow and uranium transport at the Hanford Site 300 Area. Eight alternative models representing uncertainty in the hydrogeologic and geochemical properties as well as the temporal variability were considered. Two scenarios represent alternative future behavior of the Columbia River adjacent to the site were considered. The scenario alternatives were implemented in the models through the boundary conditions. Results demonstrate the feasibility of applying a comprehensive uncertainty assessment to large-scale, detailed groundwater flow

  3. Model uncertainty and probability

    International Nuclear Information System (INIS)

    Parry, G.W.

    1994-01-01

    This paper discusses the issue of model uncertainty. The use of probability as a measure of an analyst's uncertainty as well as a means of describing random processes has caused some confusion, even though the two uses are representing different types of uncertainty with respect to modeling a system. The importance of maintaining the distinction between the two types is illustrated with a simple example

  4. Uncertainty in biodiversity science, policy and management: a conceptual overview

    Directory of Open Access Journals (Sweden)

    Yrjö Haila

    2014-10-01

    Full Text Available The protection of biodiversity is a complex societal, political and ultimately practical imperative of current global society. The imperative builds upon scientific knowledge on human dependence on the life-support systems of the Earth. This paper aims at introducing main types of uncertainty inherent in biodiversity science, policy and management, as an introduction to a companion paper summarizing practical experiences of scientists and scholars (Haila et al. 2014. Uncertainty is a cluster concept: the actual nature of uncertainty is inherently context-bound. We use semantic space as a conceptual device to identify key dimensions of uncertainty in the context of biodiversity protection; these relate to [i] data; [ii] proxies; [iii] concepts; [iv] policy and management; and [v] normative goals. Semantic space offers an analytic perspective for drawing critical distinctions between types of uncertainty, identifying fruitful resonances that help to cope with the uncertainties, and building up collaboration between different specialists to support mutual social learning.

  5. Application of uncertainty analysis in conceptual fusion reactor design

    International Nuclear Information System (INIS)

    Wu, T.; Maynard, C.W.

    1979-01-01

    The theories of sensitivity and uncertainty analysis are described and applied to a new conceptual tokamak fusion reactor design--NUWMAK. The responses investigated in this study include the tritium breeding ratio, first wall Ti dpa and gas productions, nuclear heating in the blanket, energy leakage to the magnet, and the dpa rate in the superconducting magnet aluminum stabilizer. The sensitivities and uncertainties of these responses are calculated. The cost/benefit feature of proposed integral measurements is also studied through the uncertainty reductions of these responses

  6. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

    Science.gov (United States)

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

    2017-12-01

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

  7. Conceptual IT model

    Science.gov (United States)

    Arnaoudova, Kristina; Stanchev, Peter

    2015-11-01

    The business processes are the key asset for every organization. The design of the business process models is the foremost concern and target among an organization's functions. Business processes and their proper management are intensely dependent on the performance of software applications and technology solutions. The paper is attempt for definition of new Conceptual model of IT service provider, it could be examined as IT focused Enterprise model, part of Enterprise Architecture (EA) school.

  8. Essays on model uncertainty in financial models

    NARCIS (Netherlands)

    Li, Jing

    2018-01-01

    This dissertation studies model uncertainty, particularly in financial models. It consists of two empirical chapters and one theoretical chapter. The first empirical chapter (Chapter 2) classifies model uncertainty into parameter uncertainty and misspecification uncertainty. It investigates the

  9. Model uncertainty: Probabilities for models?

    International Nuclear Information System (INIS)

    Winkler, R.L.

    1994-01-01

    Like any other type of uncertainty, model uncertainty should be treated in terms of probabilities. The question is how to do this. The most commonly-used approach has a drawback related to the interpretation of the probabilities assigned to the models. If we step back and look at the big picture, asking what the appropriate focus of the model uncertainty question should be in the context of risk and decision analysis, we see that a different probabilistic approach makes more sense, although it raise some implementation questions. Current work that is underway to address these questions looks very promising

  10. Conceptual and computational basis for the quantification of margins and uncertainty

    International Nuclear Information System (INIS)

    Helton, Jon Craig

    2009-01-01

    In 2001, the National Nuclear Security Administration of the U.S. Department of Energy in conjunction with the national security laboratories (i.e, Los Alamos National Laboratory, Lawrence Livermore National Laboratory and Sandia National Laboratories) initiated development of a process designated Quantification of Margins and Uncertainty (QMU) for the use of risk assessment methodologies in the certification of the reliability and safety of the nation's nuclear weapons stockpile. This presentation discusses and illustrates the conceptual and computational basis of QMU in analyses that use computational models to predict the behavior of complex systems. Topics considered include (1) the role of aleatory and epistemic uncertainty in QMU, (2) the representation of uncertainty with probability, (3) the probabilistic representation of uncertainty in QMU analyses involving only epistemic uncertainty, (4) the probabilistic representation of uncertainty in QMU analyses involving aleatory and epistemic uncertainty, (5) procedures for sampling-based uncertainty and sensitivity analysis, (6) the representation of uncertainty with alternatives to probability such as interval analysis, possibility theory and evidence theory, (7) the representation of uncertainty with alternatives to probability in QMU analyses involving only epistemic uncertainty, and (8) the representation of uncertainty with alternatives to probability in QMU analyses involving aleatory and epistemic uncertainty. Concepts and computational procedures are illustrated with both notional examples and examples from reactor safety and radioactive waste disposal.

  11. Uncertainties in repository modeling

    Energy Technology Data Exchange (ETDEWEB)

    Wilson, J.R.

    1996-12-31

    The distant future is ver difficult to predict. Unfortunately, our regulators are being enchouraged to extend ther regulatory period form the standard 10,000 years to 1 million years. Such overconfidence is not justified due to uncertainties in dating, calibration, and modeling.

  12. Uncertainties in repository modeling

    International Nuclear Information System (INIS)

    Wilson, J.R.

    1996-01-01

    The distant future is ver difficult to predict. Unfortunately, our regulators are being enchouraged to extend ther regulatory period form the standard 10,000 years to 1 million years. Such overconfidence is not justified due to uncertainties in dating, calibration, and modeling

  13. Quantification of uncertainties of modeling and simulation

    International Nuclear Information System (INIS)

    Ma Zhibo; Yin Jianwei

    2012-01-01

    The principles of Modeling and Simulation (M and S) is interpreted by a functional relation, from which the total uncertainties of M and S are identified and sorted to three parts considered to vary along with the conceptual models' parameters. According to the idea of verification and validation, the space of the parameters is parted to verified and applied domains, uncertainties in the verified domain are quantified by comparison between numerical and standard results, and those in the applied domain are quantified by a newly developed extrapolating method. Examples are presented to demonstrate and qualify the ideas aimed to build a framework to quantify the uncertainties of M and S. (authors)

  14. Pragmatic aspects of uncertainty propagation: A conceptual review

    KAUST Repository

    Thacker, W.Carlisle; Iskandarani, Mohamad; Gonç alves, Rafael C.; Srinivasan, Ashwanth; Knio, Omar

    2015-01-01

    When quantifying the uncertainty of the response of a computationally costly oceanographic or meteorological model stemming from the uncertainty of its inputs, practicality demands getting the most information using the fewest simulations. It is widely recognized that, by interpolating the results of a small number of simulations, results of additional simulations can be inexpensively approximated to provide a useful estimate of the variability of the response. Even so, as computing the simulations to be interpolated remains the biggest expense, the choice of these simulations deserves attention. When making this choice, two requirement should be considered: (i) the nature of the interpolation and ii) the available information about input uncertainty. Examples comparing polynomial interpolation and Gaussian process interpolation are presented for three different views of input uncertainty.

  15. Pragmatic aspects of uncertainty propagation: A conceptual review

    KAUST Repository

    Thacker, W.Carlisle

    2015-09-11

    When quantifying the uncertainty of the response of a computationally costly oceanographic or meteorological model stemming from the uncertainty of its inputs, practicality demands getting the most information using the fewest simulations. It is widely recognized that, by interpolating the results of a small number of simulations, results of additional simulations can be inexpensively approximated to provide a useful estimate of the variability of the response. Even so, as computing the simulations to be interpolated remains the biggest expense, the choice of these simulations deserves attention. When making this choice, two requirement should be considered: (i) the nature of the interpolation and ii) the available information about input uncertainty. Examples comparing polynomial interpolation and Gaussian process interpolation are presented for three different views of input uncertainty.

  16. A commentary on model uncertainty

    International Nuclear Information System (INIS)

    Apostolakis, G.

    1994-01-01

    A framework is proposed for the identification of model and parameter uncertainties in risk assessment models. Two cases are distinguished; in the first case, a set of mutually exclusive and exhaustive hypotheses (models) can be formulated, while, in the second, only one reference model is available. The relevance of this formulation to decision making and the communication of uncertainties is discussed

  17. Chemical model reduction under uncertainty

    KAUST Repository

    Najm, Habib; Galassi, R. Malpica; Valorani, M.

    2016-01-01

    We outline a strategy for chemical kinetic model reduction under uncertainty. We present highlights of our existing deterministic model reduction strategy, and describe the extension of the formulation to include parametric uncertainty in the detailed mechanism. We discuss the utility of this construction, as applied to hydrocarbon fuel-air kinetics, and the associated use of uncertainty-aware measures of error between predictions from detailed and simplified models.

  18. Chemical model reduction under uncertainty

    KAUST Repository

    Najm, Habib

    2016-01-05

    We outline a strategy for chemical kinetic model reduction under uncertainty. We present highlights of our existing deterministic model reduction strategy, and describe the extension of the formulation to include parametric uncertainty in the detailed mechanism. We discuss the utility of this construction, as applied to hydrocarbon fuel-air kinetics, and the associated use of uncertainty-aware measures of error between predictions from detailed and simplified models.

  19. Representing uncertainty on model analysis plots

    Directory of Open Access Journals (Sweden)

    Trevor I. Smith

    2016-09-01

    Full Text Available Model analysis provides a mechanism for representing student learning as measured by standard multiple-choice surveys. The model plot contains information regarding both how likely students in a particular class are to choose the correct answer and how likely they are to choose an answer consistent with a well-documented conceptual model. Unfortunately, Bao’s original presentation of the model plot did not include a way to represent uncertainty in these measurements. I present details of a method to add error bars to model plots by expanding the work of Sommer and Lindell. I also provide a template for generating model plots with error bars.

  20. Uncertainty analysis of environmental models

    International Nuclear Information System (INIS)

    Monte, L.

    1990-01-01

    In the present paper an evaluation of the output uncertainty of an environmental model for assessing the transfer of 137 Cs and 131 I in the human food chain are carried out on the basis of a statistical analysis of data reported by the literature. The uncertainty analysis offers the oppotunity of obtaining some remarkable information about the uncertainty of models predicting the migration of non radioactive substances in the environment mainly in relation to the dry and wet deposition

  1. Model uncertainty in safety assessment

    International Nuclear Information System (INIS)

    Pulkkinen, U.; Huovinen, T.

    1996-01-01

    The uncertainty analyses are an essential part of any risk assessment. Usually the uncertainties of reliability model parameter values are described by probability distributions and the uncertainty is propagated through the whole risk model. In addition to the parameter uncertainties, the assumptions behind the risk models may be based on insufficient experimental observations and the models themselves may not be exact descriptions of the phenomena under analysis. The description and quantification of this type of uncertainty, model uncertainty, is the topic of this report. The model uncertainty is characterized and some approaches to model and quantify it are discussed. The emphasis is on so called mixture models, which have been applied in PSAs. Some of the possible disadvantages of the mixture model are addressed. In addition to quantitative analyses, also qualitative analysis is discussed shortly. To illustrate the models, two simple case studies on failure intensity and human error modeling are described. In both examples, the analysis is based on simple mixture models, which are observed to apply in PSA analyses. (orig.) (36 refs., 6 figs., 2 tabs.)

  2. Model uncertainty in safety assessment

    Energy Technology Data Exchange (ETDEWEB)

    Pulkkinen, U; Huovinen, T [VTT Automation, Espoo (Finland). Industrial Automation

    1996-01-01

    The uncertainty analyses are an essential part of any risk assessment. Usually the uncertainties of reliability model parameter values are described by probability distributions and the uncertainty is propagated through the whole risk model. In addition to the parameter uncertainties, the assumptions behind the risk models may be based on insufficient experimental observations and the models themselves may not be exact descriptions of the phenomena under analysis. The description and quantification of this type of uncertainty, model uncertainty, is the topic of this report. The model uncertainty is characterized and some approaches to model and quantify it are discussed. The emphasis is on so called mixture models, which have been applied in PSAs. Some of the possible disadvantages of the mixture model are addressed. In addition to quantitative analyses, also qualitative analysis is discussed shortly. To illustrate the models, two simple case studies on failure intensity and human error modeling are described. In both examples, the analysis is based on simple mixture models, which are observed to apply in PSA analyses. (orig.) (36 refs., 6 figs., 2 tabs.).

  3. Event-Based Conceptual Modeling

    DEFF Research Database (Denmark)

    Bækgaard, Lars

    2009-01-01

    The purpose of the paper is to obtain insight into and provide practical advice for event-based conceptual modeling. We analyze a set of event concepts and use the results to formulate a conceptual event model that is used to identify guidelines for creation of dynamic process models and static...... information models. We characterize events as short-duration processes that have participants, consequences, and properties, and that may be modeled in terms of information structures. The conceptual event model is used to characterize a variety of event concepts and it is used to illustrate how events can...... be used to integrate dynamic modeling of processes and static modeling of information structures. The results are unique in the sense that no other general event concept has been used to unify a similar broad variety of seemingly incompatible event concepts. The general event concept can be used...

  4. Uncertainties in Nuclear Proliferation Modeling

    International Nuclear Information System (INIS)

    Kim, Chul Min; Yim, Man-Sung; Park, Hyeon Seok

    2015-01-01

    There have been various efforts in the research community to understand the determinants of nuclear proliferation and develop quantitative tools to predict nuclear proliferation events. Such systematic approaches have shown the possibility to provide warning for the international community to prevent nuclear proliferation activities. However, there are still large debates for the robustness of the actual effect of determinants and projection results. Some studies have shown that several factors can cause uncertainties in previous quantitative nuclear proliferation modeling works. This paper analyzes the uncertainties in the past approaches and suggests future works in the view of proliferation history, analysis methods, and variable selection. The research community still lacks the knowledge for the source of uncertainty in current models. Fundamental problems in modeling will remain even other advanced modeling method is developed. Before starting to develop fancy model based on the time dependent proliferation determinants' hypothesis, using graph theory, etc., it is important to analyze the uncertainty of current model to solve the fundamental problems of nuclear proliferation modeling. The uncertainty from different proliferation history coding is small. Serious problems are from limited analysis methods and correlation among the variables. Problems in regression analysis and survival analysis cause huge uncertainties when using the same dataset, which decreases the robustness of the result. Inaccurate variables for nuclear proliferation also increase the uncertainty. To overcome these problems, further quantitative research should focus on analyzing the knowledge suggested on the qualitative nuclear proliferation studies

  5. Some remarks on modeling uncertainties

    International Nuclear Information System (INIS)

    Ronen, Y.

    1983-01-01

    Several topics related to the question of modeling uncertainties are considered. The first topic is related to the use of the generalized bias operator method for modeling uncertainties. The method is expanded to a more general form of operators. The generalized bias operator is also used in the inverse problem and applied to determine the anisotropic scattering law. The last topic discussed is related to the question of the limit to accuracy and how to establish its value. (orig.) [de

  6. Conceptual models of information processing

    Science.gov (United States)

    Stewart, L. J.

    1983-01-01

    The conceptual information processing issues are examined. Human information processing is defined as an active cognitive process that is analogous to a system. It is the flow and transformation of information within a human. The human is viewed as an active information seeker who is constantly receiving, processing, and acting upon the surrounding environmental stimuli. Human information processing models are conceptual representations of cognitive behaviors. Models of information processing are useful in representing the different theoretical positions and in attempting to define the limits and capabilities of human memory. It is concluded that an understanding of conceptual human information processing models and their applications to systems design leads to a better human factors approach.

  7. Bayesian analysis for uncertainty estimation of a canopy transpiration model

    Science.gov (United States)

    Samanta, S.; Mackay, D. S.; Clayton, M. K.; Kruger, E. L.; Ewers, B. E.

    2007-04-01

    A Bayesian approach was used to fit a conceptual transpiration model to half-hourly transpiration rates for a sugar maple (Acer saccharum) stand collected over a 5-month period and probabilistically estimate its parameter and prediction uncertainties. The model used the Penman-Monteith equation with the Jarvis model for canopy conductance. This deterministic model was extended by adding a normally distributed error term. This extension enabled using Markov chain Monte Carlo simulations to sample the posterior parameter distributions. The residuals revealed approximate conformance to the assumption of normally distributed errors. However, minor systematic structures in the residuals at fine timescales suggested model changes that would potentially improve the modeling of transpiration. Results also indicated considerable uncertainties in the parameter and transpiration estimates. This simple methodology of uncertainty analysis would facilitate the deductive step during the development cycle of deterministic conceptual models by accounting for these uncertainties while drawing inferences from data.

  8. Uncertainties in radioecological assessment models

    International Nuclear Information System (INIS)

    Hoffman, F.O.; Miller, C.W.; Ng, Y.C.

    1983-01-01

    Environmental radiological assessments rely heavily on the use of mathematical models. The predictions of these models are inherently uncertain because models are inexact representations of real systems. The major sources of this uncertainty are related to bias in model formulation and imprecision in parameter estimation. The magnitude of uncertainty is a function of the questions asked of the model and the specific radionuclides and exposure pathways of dominant importance. It is concluded that models developed as research tools should be distinguished from models developed for assessment applications. Furthermore, increased model complexity does not necessarily guarantee increased accuracy. To improve the realism of assessment modeling, stochastic procedures are recommended that translate uncertain parameter estimates into a distribution of predicted values. These procedures also permit the importance of model parameters to be ranked according to their relative contribution to the overall predicted uncertainty. Although confidence in model predictions can be improved through site-specific parameter estimation and increased model validation, health risk factors and internal dosimetry models will probably remain important contributors to the amount of uncertainty that is irreducible. 41 references, 4 figures, 4 tables

  9. A Bayesian approach to model uncertainty

    International Nuclear Information System (INIS)

    Buslik, A.

    1994-01-01

    A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given

  10. Numerical modeling of economic uncertainty

    DEFF Research Database (Denmark)

    Schjær-Jacobsen, Hans

    2007-01-01

    Representation and modeling of economic uncertainty is addressed by different modeling methods, namely stochastic variables and probabilities, interval analysis, and fuzzy numbers, in particular triple estimates. Focusing on discounted cash flow analysis numerical results are presented, comparisons...... are made between alternative modeling methods, and characteristics of the methods are discussed....

  11. Process generalization in conceptual models

    NARCIS (Netherlands)

    Wieringa, Roelf J.

    In conceptual modeling, the universe of discourse (UoD) is divided into classes which have a taxonomic structure. The classes are usually defined in terms of attributes (all objects in a class share attribute names) and possibly of events. For enmple, the class of employees is the set of objects to

  12. Model uncertainty in growth empirics

    NARCIS (Netherlands)

    Prüfer, P.

    2008-01-01

    This thesis applies so-called Bayesian model averaging (BMA) to three different economic questions substantially exposed to model uncertainty. Chapter 2 addresses a major issue of modern development economics: the analysis of the determinants of pro-poor growth (PPG), which seeks to combine high

  13. The conceptualization model problem—surprise

    Science.gov (United States)

    Bredehoeft, John

    2005-03-01

    The foundation of model analysis is the conceptual model. Surprise is defined as new data that renders the prevailing conceptual model invalid; as defined here it represents a paradigm shift. Limited empirical data indicate that surprises occur in 20-30% of model analyses. These data suggest that groundwater analysts have difficulty selecting the appropriate conceptual model. There is no ready remedy to the conceptual model problem other than (1) to collect as much data as is feasible, using all applicable methods—a complementary data collection methodology can lead to new information that changes the prevailing conceptual model, and (2) for the analyst to remain open to the fact that the conceptual model can change dramatically as more information is collected. In the final analysis, the hydrogeologist makes a subjective decision on the appropriate conceptual model. The conceptualization problem does not render models unusable. The problem introduces an uncertainty that often is not widely recognized. Conceptual model uncertainty is exacerbated in making long-term predictions of system performance. C'est le modèle conceptuel qui se trouve à base d'une analyse sur un modèle. On considère comme une surprise lorsque le modèle est invalidé par des données nouvelles; dans les termes définis ici la surprise est équivalente à un change de paradigme. Des données empiriques limitées indiquent que les surprises apparaissent dans 20 à 30% des analyses effectuées sur les modèles. Ces données suggèrent que l'analyse des eaux souterraines présente des difficultés lorsqu'il s'agit de choisir le modèle conceptuel approprié. Il n'existe pas un autre remède au problème du modèle conceptuel que: (1) rassembler autant des données que possible en utilisant toutes les méthodes applicables—la méthode des données complémentaires peut conduire aux nouvelles informations qui vont changer le modèle conceptuel, et (2) l'analyste doit rester ouvert au fait

  14. Uncertainty modeling and decision support

    International Nuclear Information System (INIS)

    Yager, Ronald R.

    2004-01-01

    We first formulate the problem of decision making under uncertainty. The importance of the representation of our knowledge about the uncertainty in formulating a decision process is pointed out. We begin with a brief discussion of the case of probabilistic uncertainty. Next, in considerable detail, we discuss the case of decision making under ignorance. For this case the fundamental role of the attitude of the decision maker is noted and its subjective nature is emphasized. Next the case in which a Dempster-Shafer belief structure is used to model our knowledge of the uncertainty is considered. Here we also emphasize the subjective choices the decision maker must make in formulating a decision function. The case in which the uncertainty is represented by a fuzzy measure (monotonic set function) is then investigated. We then return to the Dempster-Shafer belief structure and show its relationship to the fuzzy measure. This relationship allows us to get a deeper understanding of the formulation the decision function used Dempster- Shafer framework. We discuss how this deeper understanding allows a decision analyst to better make the subjective choices needed in the formulation of the decision function

  15. Uncertainty in hydrological change modelling

    DEFF Research Database (Denmark)

    Seaby, Lauren Paige

    applied at the grid scale. Flux and state hydrological outputs which integrate responses over time and space showed more sensitivity to precipitation mean spatial biases and less so on extremes. In the investigated catchments, the projected change of groundwater levels and basin discharge between current......Hydrological change modelling methodologies generally use climate models outputs to force hydrological simulations under changed conditions. There are nested sources of uncertainty throughout this methodology, including choice of climate model and subsequent bias correction methods. This Ph.......D. study evaluates the uncertainty of the impact of climate change in hydrological simulations given multiple climate models and bias correction methods of varying complexity. Three distribution based scaling methods (DBS) were developed and benchmarked against a more simplistic and commonly used delta...

  16. Uncertainty quantification for environmental models

    Science.gov (United States)

    Hill, Mary C.; Lu, Dan; Kavetski, Dmitri; Clark, Martyn P.; Ye, Ming

    2012-01-01

    Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10

  17. ADOxx Modelling Method Conceptualization Environment

    Directory of Open Access Journals (Sweden)

    Nesat Efendioglu

    2017-04-01

    Full Text Available The importance of Modelling Methods Engineering is equally rising with the importance of domain specific languages (DSL and individual modelling approaches. In order to capture the relevant semantic primitives for a particular domain, it is necessary to involve both, (a domain experts, who identify relevant concepts as well as (b method engineers who compose a valid and applicable modelling approach. This process consists of a conceptual design of formal or semi-formal of modelling method as well as a reliable, migratable, maintainable and user friendly software development of the resulting modelling tool. Modelling Method Engineering cycle is often under-estimated as both the conceptual architecture requires formal verification and the tool implementation requires practical usability, hence we propose a guideline and corresponding tools to support actors with different background along this complex engineering process. Based on practical experience in business, more than twenty research projects within the EU frame programmes and a number of bilateral research initiatives, this paper introduces the phases, corresponding a toolbox and lessons learned with the aim to support the engineering of a modelling method. ”The proposed approach is illustrated and validated within use cases from three different EU-funded research projects in the fields of (1 Industry 4.0, (2 e-learning and (3 cloud computing. The paper discusses the approach, the evaluation results and derived outlooks.

  18. Chemical model reduction under uncertainty

    KAUST Repository

    Malpica Galassi, Riccardo

    2017-03-06

    A general strategy for analysis and reduction of uncertain chemical kinetic models is presented, and its utility is illustrated in the context of ignition of hydrocarbon fuel–air mixtures. The strategy is based on a deterministic analysis and reduction method which employs computational singular perturbation analysis to generate simplified kinetic mechanisms, starting from a detailed reference mechanism. We model uncertain quantities in the reference mechanism, namely the Arrhenius rate parameters, as random variables with prescribed uncertainty factors. We propagate this uncertainty to obtain the probability of inclusion of each reaction in the simplified mechanism. We propose probabilistic error measures to compare predictions from the uncertain reference and simplified models, based on the comparison of the uncertain dynamics of the state variables, where the mixture entropy is chosen as progress variable. We employ the construction for the simplification of an uncertain mechanism in an n-butane–air mixture homogeneous ignition case, where a 176-species, 1111-reactions detailed kinetic model for the oxidation of n-butane is used with uncertainty factors assigned to each Arrhenius rate pre-exponential coefficient. This illustration is employed to highlight the utility of the construction, and the performance of a family of simplified models produced depending on chosen thresholds on importance and marginal probabilities of the reactions.

  19. Applied research in uncertainty modeling and analysis

    CERN Document Server

    Ayyub, Bilal

    2005-01-01

    Uncertainty has been a concern to engineers, managers, and scientists for many years. For a long time uncertainty has been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous. In the past forty years numerous tools that model uncertainty, above and beyond statistics, have been proposed by several engineers and scientists. The tool/method to model uncertainty in a specific context should really be chosen by considering the features of the phenomenon under consideration, not independent of what is known about the system and what causes uncertainty. In this fascinating overview of the field, the authors provide broad coverage of uncertainty analysis/modeling and its application. Applied Research in Uncertainty Modeling and Analysis presents the perspectives of various researchers and practitioners on uncertainty analysis and modeling outside their own fields and domain expertise. Rather than focusing explicitly on...

  20. Some illustrative examples of model uncertainty

    International Nuclear Information System (INIS)

    Bier, V.M.

    1994-01-01

    In this paper, we first discuss the view of model uncertainty proposed by Apostolakis. We then present several illustrative examples related to model uncertainty, some of which are not well handled by this formalism. Thus, Apostolakis' approach seems to be well suited to describing some types of model uncertainty, but not all. Since a comprehensive approach for characterizing and quantifying model uncertainty is not yet available, it is hoped that the examples presented here will service as a springboard for further discussion

  1. Modelling of Transport Projects Uncertainties

    DEFF Research Database (Denmark)

    Salling, Kim Bang; Leleur, Steen

    2009-01-01

    This paper proposes a new way of handling the uncertainties present in transport decision making based on infrastructure appraisals. The paper suggests to combine the principle of Optimism Bias, which depicts the historical tendency of overestimating transport related benefits and underestimating...... to supplement Optimism Bias and the associated Reference Class Forecasting (RCF) technique with a new technique that makes use of a scenario-grid. We tentatively introduce and refer to this as Reference Scenario Forecasting (RSF). The final RSF output from the CBA-DK model consists of a set of scenario......-based graphs which function as risk-related decision support for the appraised transport infrastructure project....

  2. Robust Bayesian Experimental Design for Conceptual Model Discrimination

    Science.gov (United States)

    Pham, H. V.; Tsai, F. T. C.

    2015-12-01

    A robust Bayesian optimal experimental design under uncertainty is presented to provide firm information for model discrimination, given the least number of pumping wells and observation wells. Firm information is the maximum information of a system can be guaranteed from an experimental design. The design is based on the Box-Hill expected entropy decrease (EED) before and after the experiment design and the Bayesian model averaging (BMA) framework. A max-min programming is introduced to choose the robust design that maximizes the minimal Box-Hill EED subject to that the highest expected posterior model probability satisfies a desired probability threshold. The EED is calculated by the Gauss-Hermite quadrature. The BMA method is used to predict future observations and to quantify future observation uncertainty arising from conceptual and parametric uncertainties in calculating EED. Monte Carlo approach is adopted to quantify the uncertainty in the posterior model probabilities. The optimal experimental design is tested by a synthetic 5-layer anisotropic confined aquifer. Nine conceptual groundwater models are constructed due to uncertain geological architecture and boundary condition. High-performance computing is used to enumerate all possible design solutions in order to identify the most plausible groundwater model. Results highlight the impacts of scedasticity in future observation data as well as uncertainty sources on potential pumping and observation locations.

  3. Uncertainty and its propagation in dynamics models

    International Nuclear Information System (INIS)

    Devooght, J.

    1994-01-01

    The purpose of this paper is to bring together some characteristics due to uncertainty when we deal with dynamic models and therefore to propagation of uncertainty. The respective role of uncertainty and inaccuracy is examined. A mathematical formalism based on Chapman-Kolmogorov equation allows to define a open-quotes subdynamicsclose quotes where the evolution equation takes the uncertainty into account. The problem of choosing or combining models is examined through a loss function associated to a decision

  4. Modelling of Transport Projects Uncertainties

    DEFF Research Database (Denmark)

    Salling, Kim Bang; Leleur, Steen

    2012-01-01

    This paper proposes a new way of handling the uncertainties present in transport decision making based on infrastructure appraisals. The paper suggests to combine the principle of Optimism Bias, which depicts the historical tendency of overestimating transport related benefits and underestimating...... to supplement Optimism Bias and the associated Reference Class Forecasting (RCF) technique with a new technique that makes use of a scenario-grid. We tentatively introduce and refer to this as Reference Scenario Forecasting (RSF). The final RSF output from the CBA-DK model consists of a set of scenario......-based graphs which functions as risk-related decision support for the appraised transport infrastructure project. The presentation of RSF is demonstrated by using an appraisal case concerning a new airfield in the capital of Greenland, Nuuk....

  5. Uncertainty and validation. Effect of model complexity on uncertainty estimates

    International Nuclear Information System (INIS)

    Elert, M.

    1996-09-01

    In the Model Complexity subgroup of BIOMOVS II, models of varying complexity have been applied to the problem of downward transport of radionuclides in soils. A scenario describing a case of surface contamination of a pasture soil was defined. Three different radionuclides with different environmental behavior and radioactive half-lives were considered: Cs-137, Sr-90 and I-129. The intention was to give a detailed specification of the parameters required by different kinds of model, together with reasonable values for the parameter uncertainty. A total of seven modelling teams participated in the study using 13 different models. Four of the modelling groups performed uncertainty calculations using nine different modelling approaches. The models used range in complexity from analytical solutions of a 2-box model using annual average data to numerical models coupling hydrology and transport using data varying on a daily basis. The complex models needed to consider all aspects of radionuclide transport in a soil with a variable hydrology are often impractical to use in safety assessments. Instead simpler models, often box models, are preferred. The comparison of predictions made with the complex models and the simple models for this scenario show that the predictions in many cases are very similar, e g in the predictions of the evolution of the root zone concentration. However, in other cases differences of many orders of magnitude can appear. One example is the prediction of the flux to the groundwater of radionuclides being transported through the soil column. Some issues that have come to focus in this study: There are large differences in the predicted soil hydrology and as a consequence also in the radionuclide transport, which suggests that there are large uncertainties in the calculation of effective precipitation and evapotranspiration. The approach used for modelling the water transport in the root zone has an impact on the predictions of the decline in root

  6. Uncertainty in a monthly water balance model using the generalized likelihood uncertainty estimation methodology

    Science.gov (United States)

    Rivera, Diego; Rivas, Yessica; Godoy, Alex

    2015-02-01

    Hydrological models are simplified representations of natural processes and subject to errors. Uncertainty bounds are a commonly used way to assess the impact of an input or model architecture uncertainty in model outputs. Different sets of parameters could have equally robust goodness-of-fit indicators, which is known as Equifinality. We assessed the outputs from a lumped conceptual hydrological model to an agricultural watershed in central Chile under strong interannual variability (coefficient of variability of 25%) by using the Equifinality concept and uncertainty bounds. The simulation period ran from January 1999 to December 2006. Equifinality and uncertainty bounds from GLUE methodology (Generalized Likelihood Uncertainty Estimation) were used to identify parameter sets as potential representations of the system. The aim of this paper is to exploit the use of uncertainty bounds to differentiate behavioural parameter sets in a simple hydrological model. Then, we analyze the presence of equifinality in order to improve the identification of relevant hydrological processes. The water balance model for Chillan River exhibits, at a first stage, equifinality. However, it was possible to narrow the range for the parameters and eventually identify a set of parameters representing the behaviour of the watershed (a behavioural model) in agreement with observational and soft data (calculation of areal precipitation over the watershed using an isohyetal map). The mean width of the uncertainty bound around the predicted runoff for the simulation period decreased from 50 to 20 m3s-1 after fixing the parameter controlling the areal precipitation over the watershed. This decrement is equivalent to decreasing the ratio between simulated and observed discharge from 5.2 to 2.5. Despite the criticisms against the GLUE methodology, such as the lack of statistical formality, it is identified as a useful tool assisting the modeller with the identification of critical parameters.

  7. Flood modelling : Parameterisation and inflow uncertainty

    NARCIS (Netherlands)

    Mukolwe, M.M.; Di Baldassarre, G.; Werner, M.; Solomatine, D.P.

    2014-01-01

    This paper presents an analysis of uncertainty in hydraulic modelling of floods, focusing on the inaccuracy caused by inflow errors and parameter uncertainty. In particular, the study develops a method to propagate the uncertainty induced by, firstly, application of a stage–discharge rating curve

  8. Uncertainty and validation. Effect of model complexity on uncertainty estimates

    Energy Technology Data Exchange (ETDEWEB)

    Elert, M. [Kemakta Konsult AB, Stockholm (Sweden)] [ed.

    1996-09-01

    In the Model Complexity subgroup of BIOMOVS II, models of varying complexity have been applied to the problem of downward transport of radionuclides in soils. A scenario describing a case of surface contamination of a pasture soil was defined. Three different radionuclides with different environmental behavior and radioactive half-lives were considered: Cs-137, Sr-90 and I-129. The intention was to give a detailed specification of the parameters required by different kinds of model, together with reasonable values for the parameter uncertainty. A total of seven modelling teams participated in the study using 13 different models. Four of the modelling groups performed uncertainty calculations using nine different modelling approaches. The models used range in complexity from analytical solutions of a 2-box model using annual average data to numerical models coupling hydrology and transport using data varying on a daily basis. The complex models needed to consider all aspects of radionuclide transport in a soil with a variable hydrology are often impractical to use in safety assessments. Instead simpler models, often box models, are preferred. The comparison of predictions made with the complex models and the simple models for this scenario show that the predictions in many cases are very similar, e g in the predictions of the evolution of the root zone concentration. However, in other cases differences of many orders of magnitude can appear. One example is the prediction of the flux to the groundwater of radionuclides being transported through the soil column. Some issues that have come to focus in this study: There are large differences in the predicted soil hydrology and as a consequence also in the radionuclide transport, which suggests that there are large uncertainties in the calculation of effective precipitation and evapotranspiration. The approach used for modelling the water transport in the root zone has an impact on the predictions of the decline in root

  9. Reusable launch vehicle model uncertainties impact analysis

    Science.gov (United States)

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

    2018-03-01

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

  10. Educational game models: conceptualization and evaluation ...

    African Journals Online (AJOL)

    Educational game models: conceptualization and evaluation. ... The Game Object Model (GOM), that marries educational theory and game design, forms the basis for the development of the Persona Outlining ... AJOL African Journals Online.

  11. Model Uncertainty for Bilinear Hysteretic Systems

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Thoft-Christensen, Palle

    1984-01-01

    . The statistical uncertainty -due to lack of information can e.g. be taken into account by describing the variables by predictive density functions, Veneziano [2). In general, model uncertainty is the uncertainty connected with mathematical modelling of the physical reality. When structural reliability analysis...... is related to the concept of a failure surface (or limit state surface) in the n-dimensional basic variable space then model uncertainty is at least due to the neglected variables, the modelling of the failure surface and the computational technique used. A more precise definition is given in section 2...

  12. Conceptual Models Core to Good Design

    CERN Document Server

    Johnson, Jeff

    2011-01-01

    People make use of software applications in their activities, applying them as tools in carrying out tasks. That this use should be good for people--easy, effective, efficient, and enjoyable--is a principal goal of design. In this book, we present the notion of Conceptual Models, and argue that Conceptual Models are core to achieving good design. From years of helping companies create software applications, we have come to believe that building applications without Conceptual Models is just asking for designs that will be confusing and difficult to learn, remember, and use. We show how Concept

  13. Template for Conceptual Model Construction: Model Review and Corps Applications

    National Research Council Canada - National Science Library

    Henderson, Jim E; O'Neil, L. J

    2007-01-01

    .... The template will expedite conceptual model construction by providing users with model parameters and potential model components, building on a study team's knowledge and experience, and promoting...

  14. Wastewater treatment modelling: dealing with uncertainties

    DEFF Research Database (Denmark)

    Belia, E.; Amerlinck, Y.; Benedetti, L.

    2009-01-01

    This paper serves as a problem statement of the issues surrounding uncertainty in wastewater treatment modelling. The paper proposes a structure for identifying the sources of uncertainty introduced during each step of an engineering project concerned with model-based design or optimisation...

  15. On Evaluation of Recharge Model Uncertainty: a Priori and a Posteriori

    International Nuclear Information System (INIS)

    Ming Ye; Karl Pohlmann; Jenny Chapman; David Shafer

    2006-01-01

    Hydrologic environments are open and complex, rendering them prone to multiple interpretations and mathematical descriptions. Hydrologic analyses typically rely on a single conceptual-mathematical model, which ignores conceptual model uncertainty and may result in bias in predictions and under-estimation of predictive uncertainty. This study is to assess conceptual model uncertainty residing in five recharge models developed to date by different researchers based on different theories for Nevada and Death Valley area, CA. A recently developed statistical method, Maximum Likelihood Bayesian Model Averaging (MLBMA), is utilized for this analysis. In a Bayesian framework, the recharge model uncertainty is assessed, a priori, using expert judgments collected through an expert elicitation in the form of prior probabilities of the models. The uncertainty is then evaluated, a posteriori, by updating the prior probabilities to estimate posterior model probability. The updating is conducted through maximum likelihood inverse modeling by calibrating the Death Valley Regional Flow System (DVRFS) model corresponding to each recharge model against observations of head and flow. Calibration results of DVRFS for the five recharge models are used to estimate three information criteria (AIC, BIC, and KIC) used to rank and discriminate these models. Posterior probabilities of the five recharge models, evaluated using KIC, are used as weights to average head predictions, which gives posterior mean and variance. The posterior quantities incorporate both parametric and conceptual model uncertainties

  16. Urban drainage models - making uncertainty analysis simple

    DEFF Research Database (Denmark)

    Vezzaro, Luca; Mikkelsen, Peter Steen; Deletic, Ana

    2012-01-01

    in each measured/observed datapoint; an issue which is commonly overlook in the uncertainty analysis of urban drainage models. This comparison allows the user to intuitively estimate the optimum number of simulations required to conduct uncertainty analyses. The output of the method includes parameter......There is increasing awareness about uncertainties in modelling of urban drainage systems and, as such, many new methods for uncertainty analyses have been developed. Despite this, all available methods have limitations which restrict their widespread application among practitioners. Here...

  17. A unifying conceptual model of entrepreneurial management

    DEFF Research Database (Denmark)

    Senderovitz, Martin

    This article offers a systematic analysis and synthesis of the area of entrepreneurial management. Through a presentation of two main perspectives on entrepreneurial management and a newly developed unifying conceptual entrepreneurial management model, the paper discusses a number of theoretical...

  18. Study on Uncertainty and Contextual Modelling

    Czech Academy of Sciences Publication Activity Database

    Klimešová, Dana; Ocelíková, E.

    2007-01-01

    Roč. 1, č. 1 (2007), s. 12-15 ISSN 1998-0140 Institutional research plan: CEZ:AV0Z10750506 Keywords : Knowledge * contextual modelling * temporal modelling * uncertainty * knowledge management Subject RIV: BD - Theory of Information

  19. Some concepts of model uncertainty for performance assessments of nuclear waste repositories

    International Nuclear Information System (INIS)

    Eisenberg, N.A.; Sagar, B.; Wittmeyer, G.W.

    1994-01-01

    Models of the performance of nuclear waste repositories will be central to making regulatory decisions regarding the safety of such facilities. The conceptual model of repository performance is represented by mathematical relationships, which are usually implemented as one or more computer codes. A geologic system may allow many conceptual models, which are consistent with the observations. These conceptual models may or may not have the same mathematical representation. Experiences in modeling the performance of a waste repository representation. Experiences in modeling the performance of a waste repository (which is, in part, a geologic system), show that this non-uniqueness of conceptual models is a significant source of model uncertainty. At the same time, each conceptual model has its own set of parameters and usually, it is not be possible to completely separate model uncertainty from parameter uncertainty for the repository system. Issues related to the origin of model uncertainty, its relation to parameter uncertainty, and its incorporation in safety assessments are discussed from a broad regulatory perspective. An extended example in which these issues are explored numerically is also provided

  20. Model uncertainties in top-quark physics

    CERN Document Server

    Seidel, Markus

    2014-01-01

    The ATLAS and CMS collaborations at the Large Hadron Collider (LHC) are studying the top quark in pp collisions at 7 and 8 TeV. Due to the large integrated luminosity, precision measurements of production cross-sections and properties are often limited by systematic uncertainties. An overview of the modeling uncertainties for simulated events is given in this report.

  1. Incorporating uncertainty in predictive species distribution modelling.

    Science.gov (United States)

    Beale, Colin M; Lennon, Jack J

    2012-01-19

    Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

  2. Representing Uncertainty on Model Analysis Plots

    Science.gov (United States)

    Smith, Trevor I.

    2016-01-01

    Model analysis provides a mechanism for representing student learning as measured by standard multiple-choice surveys. The model plot contains information regarding both how likely students in a particular class are to choose the correct answer and how likely they are to choose an answer consistent with a well-documented conceptual model.…

  3. OWL references in ORM conceptual modelling

    Science.gov (United States)

    Matula, Jiri; Belunek, Roman; Hunka, Frantisek

    2017-07-01

    Object Role Modelling methodology is the fact-based type of conceptual modelling. The aim of the paper is to emphasize a close connection to OWL documents and its possible mutual cooperation. The definition of entities or domain values is an indispensable part of the conceptual schema design procedure defined by the ORM methodology. Many of these entities are already defined in OWL documents. Therefore, it is not necessary to declare entities again, whereas it is possible to utilize references from OWL documents during modelling of information systems.

  4. Driver Performance Model: 1. Conceptual Framework

    National Research Council Canada - National Science Library

    Heimerl, Joseph

    2001-01-01

    ...'. At the present time, no such comprehensive model exists. This report discusses a conceptual framework designed to encompass the relationships, conditions, and constraints related to direct, indirect, and remote modes of driving and thus provides a guide or 'road map' for the construction and creation of a comprehensive driver performance model.

  5. Analysis of Subjective Conceptualizations Towards Collective Conceptual Modelling

    DEFF Research Database (Denmark)

    Glückstad, Fumiko Kano; Herlau, Tue; Schmidt, Mikkel Nørgaard

    2013-01-01

    This work is conducted as a preliminary study for a project where individuals' conceptualizations of domain knowledge will thoroughly be analyzed across 150 subjects from 6 countries. The project aims at investigating how humans' conceptualizations differ according to different types of mother la...

  6. Empirical Bayesian inference and model uncertainty

    International Nuclear Information System (INIS)

    Poern, K.

    1994-01-01

    This paper presents a hierarchical or multistage empirical Bayesian approach for the estimation of uncertainty concerning the intensity of a homogeneous Poisson process. A class of contaminated gamma distributions is considered to describe the uncertainty concerning the intensity. These distributions in turn are defined through a set of secondary parameters, the knowledge of which is also described and updated via Bayes formula. This two-stage Bayesian approach is an example where the modeling uncertainty is treated in a comprehensive way. Each contaminated gamma distributions, represented by a point in the 3D space of secondary parameters, can be considered as a specific model of the uncertainty about the Poisson intensity. Then, by the empirical Bayesian method each individual model is assigned a posterior probability

  7. Conceptual modelling of human resource evaluation process

    Directory of Open Access Journals (Sweden)

    Negoiţă Doina Olivia

    2017-01-01

    Full Text Available Taking into account the highly diverse tasks which employees have to fulfil due to complex requirements of nowadays consumers, the human resource within an enterprise has become a strategic element for developing and exploiting products which meet the market expectations. Therefore, organizations encounter difficulties when approaching the human resource evaluation process. Hence, the aim of the current paper is to design a conceptual model of the aforementioned process, which allows the enterprises to develop a specific methodology. In order to design the conceptual model, Business Process Modelling instruments were employed - Adonis Community Edition Business Process Management Toolkit using the ADONIS BPMS Notation. The conceptual model was developed based on an in-depth secondary research regarding the human resource evaluation process. The proposed conceptual model represents a generic workflow (sequential and/ or simultaneously activities, which can be extended considering the enterprise’s needs regarding their requirements when conducting a human resource evaluation process. Enterprises can benefit from using software instruments for business process modelling as they enable process analysis and evaluation (predefined / specific queries and also model optimization (simulations.

  8. Nuclear data requirements for the ADS conceptual design EFIT: Uncertainty and sensitivity study

    Energy Technology Data Exchange (ETDEWEB)

    Garcia-Herranz, N., E-mail: nuria@din.upm.e [Departamento de Ingenieria Nuclear, Universidad Politecnica de Madrid (Spain); Instituto de Fusion Nuclear, Universidad Politecnica de Madrid (Spain); Cabellos, O. [Departamento de Ingenieria Nuclear, Universidad Politecnica de Madrid (Spain); Instituto de Fusion Nuclear, Universidad Politecnica de Madrid (Spain); Alvarez-Velarde, F. [CIEMAT (Spain); Sanz, J. [Instituto de Fusion Nuclear, Universidad Politecnica de Madrid (Spain); Departamento de Ingenieria Energetica, UNED (Spain); Gonzalez-Romero, E.M. [CIEMAT (Spain); Juan, J. [Laboratorio de Estadistica, Universidad Politecnica de Madrid (Spain)

    2010-11-15

    In this paper, we assess the impact of activation cross-section uncertainties on relevant fuel cycle parameters for a conceptual design of a modular European Facility for Industrial Transmutation (EFIT) with a 'double strata' fuel cycle. Next, the nuclear data requirements are evaluated so that the parameters can meet the assigned design target accuracies. Different discharge burn-up levels are considered: a low burn-up, corresponding to the equilibrium cycle, and a high burn-up level, simulating the effects on the fuel of the multi-recycling scenario. In order to perform this study, we propose a methodology in two steps. Firstly, we compute the uncertainties on the system parameters by using a Monte Carlo simulation, as it is considered the most reliable approach to address this problem. Secondly, the analysis of the results is performed by a sensitivity technique, in order to identify the relevant reaction channels and prioritize the data improvement needs. Cross-section uncertainties are taken from the EAF-2007/UN library since it includes data for all the actinides potentially present in the irradiated fuel. Relevant uncertainties in some of the fuel cycle parameters have been obtained, and we conclude with recommendations for future nuclear data measurement programs, beyond the specific results obtained with the present nuclear data files and the limited available covariance information. A comparison with the uncertainty and accuracy analysis recently published by the WPEC-Subgroup26 of the OECD using BOLNA covariance matrices is performed. Despite the differences in the transmuter reactor used for the analysis, some conclusions obtained by Subgroup26 are qualitatively corroborated, and improvements for additional cross sections are suggested.

  9. Event-Based Conceptual Modeling

    DEFF Research Database (Denmark)

    Bækgaard, Lars

    The paper demonstrates that a wide variety of event-based modeling approaches are based on special cases of the same general event concept, and that the general event concept can be used to unify the otherwise unrelated fields of information modeling and process modeling. A set of event......-based modeling approaches are analyzed and the results are used to formulate a general event concept that can be used for unifying the seemingly unrelated event concepts. Events are characterized as short-duration processes that have participants, consequences, and properties, and that may be modeled in terms...... of information structures. The general event concept can be used to guide systems analysis and design and to improve modeling approaches....

  10. Analysis of uncertainty in modeling perceived risks

    International Nuclear Information System (INIS)

    Melnyk, R.; Sandquist, G.M.

    2005-01-01

    Expanding on a mathematical model developed for quantifying and assessing perceived risks, the distribution functions, variances, and uncertainties associated with estimating the model parameters are quantified. The analytical model permits the identification and assignment of any number of quantifiable risk perception factors that can be incorporated within standard risk methodology. Those risk perception factors associated with major technical issues are modeled using lognormal probability density functions to span the potentially large uncertainty variations associated with these risk perceptions. The model quantifies the logic of public risk perception and provides an effective means for measuring and responding to perceived risks. (authors)

  11. FRSAD conceptual modeling of aboutness

    CERN Document Server

    Zeng, Marcia; Žumer, Maja

    2012-01-01

    The first comprehensive exploration of the development and use of the International Federation of Library Associations and Institutions' (IFLA) newly released model for subject authority data, covering everything from the rationale for creating the model to practical steps for implementing it.

  12. Bayesian uncertainty analyses of probabilistic risk models

    International Nuclear Information System (INIS)

    Pulkkinen, U.

    1989-01-01

    Applications of Bayesian principles to the uncertainty analyses are discussed in the paper. A short review of the most important uncertainties and their causes is provided. An application of the principle of maximum entropy to the determination of Bayesian prior distributions is described. An approach based on so called probabilistic structures is presented in order to develop a method of quantitative evaluation of modelling uncertainties. The method is applied to a small example case. Ideas for application areas for the proposed method are discussed

  13. Assessing uncertainty in mechanistic models

    Science.gov (United States)

    Edwin J. Green; David W. MacFarlane; Harry T. Valentine

    2000-01-01

    Concern over potential global change has led to increased interest in the use of mechanistic models for predicting forest growth. The rationale for this interest is that empirical models may be of limited usefulness if environmental conditions change. Intuitively, we expect that mechanistic models, grounded as far as possible in an understanding of the biology of tree...

  14. Model Uncertainty Quantification Methods In Data Assimilation

    Science.gov (United States)

    Pathiraja, S. D.; Marshall, L. A.; Sharma, A.; Moradkhani, H.

    2017-12-01

    Data Assimilation involves utilising observations to improve model predictions in a seamless and statistically optimal fashion. Its applications are wide-ranging; from improving weather forecasts to tracking targets such as in the Apollo 11 mission. The use of Data Assimilation methods in high dimensional complex geophysical systems is an active area of research, where there exists many opportunities to enhance existing methodologies. One of the central challenges is in model uncertainty quantification; the outcome of any Data Assimilation study is strongly dependent on the uncertainties assigned to both observations and models. I focus on developing improved model uncertainty quantification methods that are applicable to challenging real world scenarios. These include developing methods for cases where the system states are only partially observed, where there is little prior knowledge of the model errors, and where the model error statistics are likely to be highly non-Gaussian.

  15. Modeling of uncertainties in statistical inverse problems

    International Nuclear Information System (INIS)

    Kaipio, Jari

    2008-01-01

    In all real world problems, the models that tie the measurements to the unknowns of interest, are at best only approximations for reality. While moderate modeling and approximation errors can be tolerated with stable problems, inverse problems are a notorious exception. Typical modeling errors include inaccurate geometry, unknown boundary and initial data, properties of noise and other disturbances, and simply the numerical approximations of the physical models. In principle, the Bayesian approach to inverse problems, in which all uncertainties are modeled as random variables, is capable of handling these uncertainties. Depending on the type of uncertainties, however, different strategies may be adopted. In this paper we give an overview of typical modeling errors and related strategies within the Bayesian framework.

  16. Assessing alternative conceptual models of fracture flow

    International Nuclear Information System (INIS)

    Ho, C.K.

    1995-01-01

    The numerical code TOUGH2 was used to assess alternative conceptual models of fracture flow. The models that were considered included the equivalent continuum model (ECM) and the dual permeability (DK) model. A one-dimensional, layered, unsaturated domain was studied with a saturated bottom boundary and a constant infiltration at the top boundary. Two different infiltration rates were used in the studies. In addition, the connection areas between the fracture and matrix elements in the dual permeability model were varied. Results showed that the two conceptual models of fracture flow produced different saturation and velocity profiles-even under steady-state conditions. The magnitudes of the discrepancies were sensitive to two parameters that affected the flux between the fractures and matrix in the dual permeability model: (1) the fracture-matrix connection areas and (2) the capillary pressure gradients between the fracture and matrix elements

  17. Uncertainty modeling process for semantic technology

    Directory of Open Access Journals (Sweden)

    Rommel N. Carvalho

    2016-08-01

    Full Text Available The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there remains little guidance in the knowledge engineering literature for how to design probabilistic ontologies. To address the gap, this paper presents the Uncertainty Modeling Process for Semantic Technology (UMP-ST, a new methodology for modeling probabilistic ontologies. To explain how the methodology works and to verify that it can be applied to different scenarios, this paper describes step-by-step the construction of a proof-of-concept probabilistic ontology. The resulting domain model can be used to support identification of fraud in public procurements in Brazil. While the case study illustrates the development of a probabilistic ontology in the PR-OWL probabilistic ontology language, the methodology is applicable to any ontology formalism that properly integrates uncertainty with domain semantics.

  18. Medieval Kingship: A Conceptual Model.

    Science.gov (United States)

    Renna, Thomas

    1982-01-01

    Presents a model which describes the theories of kingship in Germany, France, and England between 800-1380 A.D. The three royal functions were defense of the realm, defense of the church, and material and moral improvement of royal subjects. (AM)

  19. Logistics and Transport - a conceptual model

    DEFF Research Database (Denmark)

    Jespersen, Per Homann; Drewes, Lise

    2004-01-01

    This paper describes how the freight transport sector is influenced by logistical principles of production and distribution. It introduces new ways of understanding freight transport as an integrated part of the changing trends of mobility. By introducing a conceptual model for understanding...... the interaction between logistics and transport, it points at ways to over-come inherent methodological difficulties when studying this relation...

  20. Chemical model reduction under uncertainty

    KAUST Repository

    Malpica Galassi, Riccardo; Valorani, Mauro; Najm, Habib N.; Safta, Cosmin; Khalil, Mohammad; Ciottoli, Pietro P.

    2017-01-01

    A general strategy for analysis and reduction of uncertain chemical kinetic models is presented, and its utility is illustrated in the context of ignition of hydrocarbon fuel–air mixtures. The strategy is based on a deterministic analysis

  1. Evaluation of uncertainty in geological framework models at Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    Bagtzoglou, A.C.; Stirewalt, G.L.; Henderson, D.B.; Seida, S.B.

    1995-01-01

    The first step towards determining compliance with the performance objectives for both the repository system and the geologic setting at Yucca Mountain requires the development of detailed geostratigraphic models. This paper proposes an approach for the evaluation of the degree of uncertainty inherent in geologic maps and associated three-dimensional geological models. Following this approach, an assessment of accuracy and completeness of the data and evaluation of conceptual uncertainties in the geological framework models can be performed

  2. Modeling Uncertainty in Climate Change: A Multi-Model Comparison

    Energy Technology Data Exchange (ETDEWEB)

    Gillingham, Kenneth; Nordhaus, William; Anthoff, David; Blanford, Geoffrey J.; Bosetti, Valentina; Christensen, Peter; McJeon, Haewon C.; Reilly, J. M.; Sztorc, Paul

    2015-10-01

    The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity and estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insight on tail events.

  3. Large regional groundwater modeling - a sensitivity study of some selected conceptual descriptions and simplifications

    International Nuclear Information System (INIS)

    Ericsson, Lars O.; Holmen, Johan

    2010-12-01

    The primary aim of this report is: - To present a supplementary, in-depth evaluation of certain conceptual simplifications, descriptions and model uncertainties in conjunction with regional groundwater simulation, which in the first instance refer to model depth, topography, groundwater table level and boundary conditions. Implementation was based on geo-scientifically available data compilations from the Smaaland region but different conceptual assumptions have been analysed

  4. Estimating Coastal Digital Elevation Model (DEM) Uncertainty

    Science.gov (United States)

    Amante, C.; Mesick, S.

    2017-12-01

    Integrated bathymetric-topographic digital elevation models (DEMs) are representations of the Earth's solid surface and are fundamental to the modeling of coastal processes, including tsunami, storm surge, and sea-level rise inundation. Deviations in elevation values from the actual seabed or land surface constitute errors in DEMs, which originate from numerous sources, including: (i) the source elevation measurements (e.g., multibeam sonar, lidar), (ii) the interpolative gridding technique (e.g., spline, kriging) used to estimate elevations in areas unconstrained by source measurements, and (iii) the datum transformation used to convert bathymetric and topographic data to common vertical reference systems. The magnitude and spatial distribution of the errors from these sources are typically unknown, and the lack of knowledge regarding these errors represents the vertical uncertainty in the DEM. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) has developed DEMs for more than 200 coastal communities. This study presents a methodology developed at NOAA NCEI to derive accompanying uncertainty surfaces that estimate DEM errors at the individual cell-level. The development of high-resolution (1/9th arc-second), integrated bathymetric-topographic DEMs along the southwest coast of Florida serves as the case study for deriving uncertainty surfaces. The estimated uncertainty can then be propagated into the modeling of coastal processes that utilize DEMs. Incorporating the uncertainty produces more reliable modeling results, and in turn, better-informed coastal management decisions.

  5. An evaluation of uncertainties in radioecological models

    International Nuclear Information System (INIS)

    Hoffmann, F.O.; Little, C.A.; Miller, C.W.; Dunning, D.E. Jr.; Rupp, E.M.; Shor, R.W.; Schaeffer, D.L.; Baes, C.F. III

    1978-01-01

    The paper presents results of analyses for seven selected parameters commonly used in environmental radiological assessment models, assuming that the available data are representative of the true distribution of parameter values and that their respective distributions are lognormal. Estimates of the most probable, median, mean, and 99th percentile for each parameter are fiven and compared to U.S. NRC default values. The regulatory default values are generally greater than the median values for the selected parameters, but some are associated with percentiles significantly less than the 50th. The largest uncertainties appear to be associated with aquatic bioaccumulation factors for fresh water fish. Approximately one order of magnitude separates median values and values of the 99th percentile. The uncertainty is also estimated for the annual dose rate predicted by a multiplicative chain model for the transport of molecular iodine-131 via the air-pasture-cow-milk-child's thyroid pathway. The value for the 99th percentile is ten times larger than the median value of the predicted dose normalized for a given air concentration of 131 I 2 . About 72% of the uncertainty in this model is contributed by the dose conversion factor and the milk transfer coefficient. Considering the difficulties in obtaining a reliable quantification of the true uncertainties in model predictions, methods for taking these uncertainties into account when determining compliance with regulatory statutes are discussed. (orig./HP) [de

  6. Uncertainty quantification in wind farm flow models

    DEFF Research Database (Denmark)

    Murcia Leon, Juan Pablo

    uncertainties through a model chain are presented and applied to several wind energy related problems such as: annual energy production estimation, wind turbine power curve estimation, wake model calibration and validation, and estimation of lifetime equivalent fatigue loads on a wind turbine. Statistical...

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

    International Nuclear Information System (INIS)

    Sig Drellack, Lance Prothro

    2007-01-01

    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 of 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

  8. Conceptual geohydrological model of the separations area

    International Nuclear Information System (INIS)

    Root, R.W.; Marine, I.W.

    1977-01-01

    Subsurface drilling in and around the Separations Areas (F-Area and H-Area of the Savannah River Plant) is providing detailed information for a conceptual model of the geology and hydrology underlying these areas. This conceptual model will provide the framework needed for a mathematical model of groundwater movement beneath these areas. Existing information substantiates the presence of two areally extensive clay layers and several discontinuous clay and sandy-clay layers. These layers occur in and between beds of clayey and silty sand that make up most of the subsurface material. Within these sand beds are geologic units of differing hydraulic conductivity. For the present scale of the model, the subsurface information is considered adequate in H-Area, but additional drilling is planned in F-Area

  9. Uncertainty in biology a computational modeling approach

    CERN Document Server

    Gomez-Cabrero, David

    2016-01-01

    Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies.  Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process.  This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples.  This book is intended for graduate stude...

  10. A Conceptual Model of Military Recruitment

    Science.gov (United States)

    2009-10-01

    Hiring Expectancies – Expectancy (VIE) Theory ( Vroom , 1996) states individuals choose among a set of employment alternatives on the basis of the...A Conceptual Model of Military Recruitment Presented at NATO Technical Course HFM 180 – Strategies to Address Recruiting and Retention Issues in...the Military Fariya Syed October, 2009 Based on A Proposed Model Of Military Recruitment (Schreurs & Syed, 2007) Report Documentation Page

  11. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

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

  12. Uncertainties

    Indian Academy of Sciences (India)

    To reflect this uncertainty in the climate scenarios, the use of AOGCMs that explicitly simulate the carbon cycle and chemistry of all the substances are needed. The Hadley Centre has developed a version of the climate model that allows the effect of climate change on the carbon cycle and its feedback into climate, to be ...

  13. Uncertainty Analysis Framework - Hanford Site-Wide Groundwater Flow and Transport Model

    Energy Technology Data Exchange (ETDEWEB)

    Cole, Charles R.; Bergeron, Marcel P.; Murray, Christopher J.; Thorne, Paul D.; Wurstner, Signe K.; Rogers, Phillip M.

    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 an uncertainty analysis framework.

  14. Uncertainty in reactive transport geochemical modelling

    International Nuclear Information System (INIS)

    Oedegaard-Jensen, A.; Ekberg, C.

    2005-01-01

    Full text of publication follows: Geochemical modelling is one way of predicting the transport of i.e. radionuclides in a rock formation. In a rock formation there will be fractures in which water and dissolved species can be transported. The composition of the water and the rock can either increase or decrease the mobility of the transported entities. When doing simulations on the mobility or transport of different species one has to know the exact water composition, the exact flow rates in the fracture and in the surrounding rock, the porosity and which minerals the rock is composed of. The problem with simulations on rocks is that the rock itself it not uniform i.e. larger fractures in some areas and smaller in other areas which can give different water flows. The rock composition can be different in different areas. In additions to this variance in the rock there are also problems with measuring the physical parameters used in a simulation. All measurements will perturb the rock and this perturbation will results in more or less correct values of the interesting parameters. The analytical methods used are also encumbered with uncertainties which in this case are added to the uncertainty from the perturbation of the analysed parameters. When doing simulation the effect of the uncertainties must be taken into account. As the computers are getting faster and faster the complexity of simulated systems are increased which also increase the uncertainty in the results from the simulations. In this paper we will show how the uncertainty in the different parameters will effect the solubility and mobility of different species. Small uncertainties in the input parameters can result in large uncertainties in the end. (authors)

  15. Parametric uncertainty in optical image modeling

    Science.gov (United States)

    Potzick, James; Marx, Egon; Davidson, Mark

    2006-10-01

    Optical photomask feature metrology and wafer exposure process simulation both rely on optical image modeling for accurate results. While it is fair to question the accuracies of the available models, model results also depend on several input parameters describing the object and imaging system. Errors in these parameter values can lead to significant errors in the modeled image. These parameters include wavelength, illumination and objective NA's, magnification, focus, etc. for the optical system, and topography, complex index of refraction n and k, etc. for the object. In this paper each input parameter is varied over a range about its nominal value and the corresponding images simulated. Second order parameter interactions are not explored. Using the scenario of the optical measurement of photomask features, these parametric sensitivities are quantified by calculating the apparent change of the measured linewidth for a small change in the relevant parameter. Then, using reasonable values for the estimated uncertainties of these parameters, the parametric linewidth uncertainties can be calculated and combined to give a lower limit to the linewidth measurement uncertainty for those parameter uncertainties.

  16. Context, Experience, Expectation, and Action—Towards an Empirically Grounded, General Model for Analyzing Biographical Uncertainty

    Directory of Open Access Journals (Sweden)

    Herwig Reiter

    2010-01-01

    Full Text Available The article proposes a general, empirically grounded model for analyzing biographical uncertainty. The model is based on findings from a qualitative-explorative study of transforming meanings of unemployment among young people in post-Soviet Lithuania. In a first step, the particular features of the uncertainty puzzle in post-communist youth transitions are briefly discussed. A historical event like the collapse of state socialism in Europe, similar to the recent financial and economic crisis, is a generator of uncertainty par excellence: it undermines the foundations of societies and the taken-for-grantedness of related expectations. Against this background, the case of a young woman and how she responds to the novel threat of unemployment in the transition to the world of work is introduced. Her uncertainty management in the specific time perspective of certainty production is then conceptually rephrased by distinguishing three types or levels of biographical uncertainty: knowledge, outcome, and recognition uncertainty. Biographical uncertainty, it is argued, is empirically observable through the analysis of acting and projecting at the biographical level. The final part synthesizes the empirical findings and the conceptual discussion into a stratification model of biographical uncertainty as a general tool for the biographical analysis of uncertainty phenomena. URN: urn:nbn:de:0114-fqs100120

  17. Optical Model and Cross Section Uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    Herman,M.W.; Pigni, M.T.; Dietrich, F.S.; Oblozinsky, P.

    2009-10-05

    Distinct minima and maxima in the neutron total cross section uncertainties were observed in model calculations using spherical optical potential. We found this oscillating structure to be a general feature of quantum mechanical wave scattering. Specifically, we analyzed neutron interaction with 56Fe from 1 keV up to 65 MeV, and investigated physical origin of the minima.We discuss their potential importance for practical applications as well as the implications for the uncertainties in total and absorption cross sections.

  18. Uncertainty quantification and stochastic modeling with Matlab

    CERN Document Server

    Souza de Cursi, Eduardo

    2015-01-01

    Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does no

  19. The conceptual model of organization social responsibility

    OpenAIRE

    LUO, Lan; WEI, Jingfu

    2014-01-01

    With the developing of the research of CSR, people more and more deeply noticethat the corporate should take responsibility. Whether other organizations besides corporatesshould not take responsibilities beyond their field? This paper puts forward theconcept of organization social responsibility on the basis of the concept of corporate socialresponsibility and other theories. And the conceptual models are built based on theconception, introducing the OSR from three angles: the types of organi...

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

    Science.gov (United States)

    Jacquin, A. P.

    2012-04-01

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

  1. UNCERTAINTIES IN GALACTIC CHEMICAL EVOLUTION MODELS

    International Nuclear Information System (INIS)

    Côté, Benoit; Ritter, Christian; Herwig, Falk; O’Shea, Brian W.; Pignatari, Marco; Jones, Samuel; Fryer, Chris L.

    2016-01-01

    We use a simple one-zone galactic chemical evolution model to quantify the uncertainties generated by the input parameters in numerical predictions for a galaxy with properties similar to those of the Milky Way. We compiled several studies from the literature to gather the current constraints for our simulations regarding the typical value and uncertainty of the following seven basic parameters: the lower and upper mass limits of the stellar initial mass function (IMF), the slope of the high-mass end of the stellar IMF, the slope of the delay-time distribution function of Type Ia supernovae (SNe Ia), the number of SNe Ia per M ⊙ formed, the total stellar mass formed, and the final mass of gas. We derived a probability distribution function to express the range of likely values for every parameter, which were then included in a Monte Carlo code to run several hundred simulations with randomly selected input parameters. This approach enables us to analyze the predicted chemical evolution of 16 elements in a statistical manner by identifying the most probable solutions, along with their 68% and 95% confidence levels. Our results show that the overall uncertainties are shaped by several input parameters that individually contribute at different metallicities, and thus at different galactic ages. The level of uncertainty then depends on the metallicity and is different from one element to another. Among the seven input parameters considered in this work, the slope of the IMF and the number of SNe Ia are currently the two main sources of uncertainty. The thicknesses of the uncertainty bands bounded by the 68% and 95% confidence levels are generally within 0.3 and 0.6 dex, respectively. When looking at the evolution of individual elements as a function of galactic age instead of metallicity, those same thicknesses range from 0.1 to 0.6 dex for the 68% confidence levels and from 0.3 to 1.0 dex for the 95% confidence levels. The uncertainty in our chemical evolution model

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

    Science.gov (United States)

    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

  3. Uncertainty visualisation in the Model Web

    Science.gov (United States)

    Gerharz, L. E.; Autermann, C.; Hopmann, H.; Stasch, C.; Pebesma, E.

    2012-04-01

    Visualisation of geospatial data as maps is a common way to communicate spatially distributed information. If temporal and furthermore uncertainty information are included in the data, efficient visualisation methods are required. For uncertain spatial and spatio-temporal data, numerous visualisation methods have been developed and proposed, but only few tools for visualisation of data in a standardised way exist. Furthermore, usually they are realised as thick clients, and lack functionality of handling data coming from web services as it is envisaged in the Model Web. We present an interactive web tool for visualisation of uncertain spatio-temporal data developed in the UncertWeb project. The client is based on the OpenLayers JavaScript library. OpenLayers provides standard map windows and navigation tools, i.e. pan, zoom in/out, to allow interactive control for the user. Further interactive methods are implemented using jStat, a JavaScript library for statistics plots developed in UncertWeb, and flot. To integrate the uncertainty information into existing standards for geospatial data, the Uncertainty Markup Language (UncertML) was applied in combination with OGC Observations&Measurements 2.0 and JavaScript Object Notation (JSON) encodings for vector and NetCDF for raster data. The client offers methods to visualise uncertain vector and raster data with temporal information. Uncertainty information considered for the tool are probabilistic and quantified attribute uncertainties which can be provided as realisations or samples, full probability distributions functions and statistics. Visualisation is supported for uncertain continuous and categorical data. In the client, the visualisation is realised using a combination of different methods. Based on previously conducted usability studies, a differentiation between expert (in statistics or mapping) and non-expert users has been indicated as useful. Therefore, two different modes are realised together in the tool

  4. A Conceptual Methodology for Assessing Acquisition Requirements Robustness against Technology Uncertainties

    Science.gov (United States)

    Chou, Shuo-Ju

    2011-12-01

    -makers with the ability to assess or measure the robustness of program requirements against such uncertainties. A literature review of techniques for forecasting technology performance and development uncertainties and subsequent impacts on capability, budget, and schedule requirements resulted in the conclusion that an analysis process that coupled a probabilistic analysis technique such as Monte Carlo Simulations with quantitative and parametric models of technology performance impact and technology development time and cost requirements would allow the probabilities of meeting specific constraints of these requirements to be established. These probabilities of requirements success metrics can then be used as a quantitative and probabilistic measure of program requirements robustness against technology uncertainties. Combined with a Multi-Objective Genetic Algorithm optimization process and computer-based Decision Support System, critical information regarding requirements robustness against technology uncertainties can be captured and quantified for acquisition decision-makers. This results in a more informed and justifiable selection of program technologies during initial program definition as well as formulation of program development and risk management strategies. To meet the stated research objective, the ENhanced TEchnology Robustness Prediction and RISk Evaluation (ENTERPRISE) methodology was formulated to provide a structured and transparent process for integrating these enabling techniques to provide a probabilistic and quantitative assessment of acquisition program requirements robustness against technology performance and development uncertainties. In order to demonstrate the capabilities of the ENTERPRISE method and test the research Hypotheses, an demonstration application of this method was performed on a notional program for acquiring the Carrier-based Suppression of Enemy Air Defenses (SEAD) using Unmanned Combat Aircraft Systems (UCAS) and their enabling

  5. Uncertainty Quantification in Geomagnetic Field Modeling

    Science.gov (United States)

    Chulliat, A.; Nair, M. C.; Alken, P.; Meyer, B.; Saltus, R.; Woods, A.

    2017-12-01

    Geomagnetic field models are mathematical descriptions of the various sources of the Earth's magnetic field, and are generally obtained by solving an inverse problem. They are widely used in research to separate and characterize field sources, but also in many practical applications such as aircraft and ship navigation, smartphone orientation, satellite attitude control, and directional drilling. In recent years, more sophisticated models have been developed, thanks to the continuous availability of high quality satellite data and to progress in modeling techniques. Uncertainty quantification has become an integral part of model development, both to assess the progress made and to address specific users' needs. Here we report on recent advances made by our group in quantifying the uncertainty of geomagnetic field models. We first focus on NOAA's World Magnetic Model (WMM) and the International Geomagnetic Reference Field (IGRF), two reference models of the main (core) magnetic field produced every five years. We describe the methods used in quantifying the model commission error as well as the omission error attributed to various un-modeled sources such as magnetized rocks in the crust and electric current systems in the atmosphere and near-Earth environment. A simple error model was derived from this analysis, to facilitate usage in practical applications. We next report on improvements brought by combining a main field model with a high resolution crustal field model and a time-varying, real-time external field model, like in NOAA's High Definition Geomagnetic Model (HDGM). The obtained uncertainties are used by the directional drilling industry to mitigate health, safety and environment risks.

  6. Realising the Uncertainty Enabled Model Web

    Science.gov (United States)

    Cornford, D.; Bastin, L.; Pebesma, E. J.; Williams, M.; Stasch, C.; Jones, R.; Gerharz, L.

    2012-12-01

    The FP7 funded UncertWeb project aims to create the "uncertainty enabled model web". The central concept here is that geospatial models and data resources are exposed via standard web service interfaces, such as the Open Geospatial Consortium (OGC) suite of encodings and interface standards, allowing the creation of complex workflows combining both data and models. The focus of UncertWeb is on the issue of managing uncertainty in such workflows, and providing the standards, architecture, tools and software support necessary to realise the "uncertainty enabled model web". In this paper we summarise the developments in the first two years of UncertWeb, illustrating several key points with examples taken from the use case requirements that motivate the project. Firstly we address the issue of encoding specifications. We explain the usage of UncertML 2.0, a flexible encoding for representing uncertainty based on a probabilistic approach. This is designed to be used within existing standards such as Observations and Measurements (O&M) and data quality elements of ISO19115 / 19139 (geographic information metadata and encoding specifications) as well as more broadly outside the OGC domain. We show profiles of O&M that have been developed within UncertWeb and how UncertML 2.0 is used within these. We also show encodings based on NetCDF and discuss possible future directions for encodings in JSON. We then discuss the issues of workflow construction, considering discovery of resources (both data and models). We discuss why a brokering approach to service composition is necessary in a world where the web service interfaces remain relatively heterogeneous, including many non-OGC approaches, in particular the more mainstream SOAP and WSDL approaches. We discuss the trade-offs between delegating uncertainty management functions to the service interfaces themselves and integrating the functions in the workflow management system. We describe two utility services to address

  7. Spatial variability and parametric uncertainty in performance assessment models

    International Nuclear Information System (INIS)

    Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo

    2011-01-01

    The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)

  8. Parametric uncertainty modeling for robust control

    DEFF Research Database (Denmark)

    Rasmussen, K.H.; Jørgensen, Sten Bay

    1999-01-01

    The dynamic behaviour of a non-linear process can often be approximated with a time-varying linear model. In the presented methodology the dynamics is modeled non-conservatively as parametric uncertainty in linear lime invariant models. The obtained uncertainty description makes it possible...... to perform robustness analysis on a control system using the structured singular value. The idea behind the proposed method is to fit a rational function to the parameter variation. The parameter variation can then be expressed as a linear fractional transformation (LFT), It is discussed how the proposed...... point changes. It is shown that a diagonal PI control structure provides robust performance towards variations in feed flow rate or feed concentrations. However including both liquid and vapor flow delays robust performance specifications cannot be satisfied with this simple diagonal control structure...

  9. A conceptual model of political market orientation

    DEFF Research Database (Denmark)

    Ormrod, Robert P.

    2005-01-01

    . The remaining four constructs are attitudinal, designed to capture the awareness of members to the activities and importance of stakeholder groups in society, both internal and external to the organisation. The model not only allows the level of a party's political market orientation to be assessed, but also......This article proposes eight constructs of a conceptual model of political market orientation, taking inspiration from the business and political marketing literature. Four of the constructs are 'behavioural' in that they aim to describe the process of how information flows through the organisation...

  10. Conceptual models for cumulative risk assessment.

    Science.gov (United States)

    Linder, Stephen H; Sexton, Ken

    2011-12-01

    In the absence of scientific consensus on an appropriate theoretical framework, cumulative risk assessment and related research have relied on speculative conceptual models. We argue for the importance of theoretical backing for such models and discuss 3 relevant theoretical frameworks, each supporting a distinctive "family" of models. Social determinant models postulate that unequal health outcomes are caused by structural inequalities; health disparity models envision social and contextual factors acting through individual behaviors and biological mechanisms; and multiple stressor models incorporate environmental agents, emphasizing the intermediary role of these and other stressors. The conclusion is that more careful reliance on established frameworks will lead directly to improvements in characterizing cumulative risk burdens and accounting for disproportionate adverse health effects.

  11. Business Model Innovation: An Integrative Conceptual Framework

    Directory of Open Access Journals (Sweden)

    Bernd Wirtz

    2017-01-01

    Full Text Available Purpose: The point of departure of this exploratory study is the gap between the increasing importance of business model innovation (BMI in science and management and the limited conceptual assistance available. Therefore, the study identi es and explores scattered BMI insights and deduces them into an integrative framework to enhance our understanding about this phenomenon and to present a helpful guidance for researchers and practitioners. Design/Methodology/Approach: The study identi es BMI insights through a literature-based investigation and consolidates them into an integrative BMI framework that presents the key elements and dimensions of BMI as well as their presumed relationships. Findings: The study enhances our understanding about the key elements and dimensions of BMI, presents further conceptual insights into the BMI phenomenon, supplies implications for science and management, and may serve as a helpful guidance for future research. Practical Implications: The presented framework provides managers with a tool to identify critical BMI issues and can serve as a conceptual BMI guideline. Research limitations: Given the vast amount of academic journals, it is unlikely that every applicable scienti c publication is included in the analysis. The illustrative examples are descriptive in nature, and thus do not provide empirical validity. Several implications for future research are provided. Originality/Value: The study’s main contribution lies in the unifying approach of the dispersed BMI knowledge. Since our understanding of BMI is still limited, this study should provide the necessary insights and conceptual assistance to further develop the concept and guide its practical application.

  12. Conceptual Model of Dynamic Geographic Environment

    Directory of Open Access Journals (Sweden)

    Martínez-Rosales Miguel Alejandro

    2014-04-01

    Full Text Available In geographic environments, there are many and different types of geographic entities such as automobiles, trees, persons, buildings, storms, hurricanes, etc. These entities can be classified into two groups: geographic objects and geographic phenomena. By its nature, a geographic environment is dynamic, thus, it’s static modeling is not sufficient. Considering the dynamics of geographic environment, a new type of geographic entity called event is introduced. The primary target is a modeling of geographic environment as an event sequence, because in this case the semantic relations are much richer than in the case of static modeling. In this work, the conceptualization of this model is proposed. It is based on the idea to process each entity apart instead of processing the environment as a whole. After that, the so called history of each entity and its spatial relations to other entities are defined to describe the whole environment. The main goal is to model systems at a conceptual level that make use of spatial and temporal information, so that later it can serve as the semantic engine for such systems.

  13. Achievements and Problems of Conceptual Modelling

    Science.gov (United States)

    Thalheim, Bernhard

    Database and information systems technology has substantially changed. Nowadays, content management systems, (information-intensive) web services, collaborating systems, internet databases, OLAP databases etc. have become buzzwords. At the same time, object-relational technology has gained the maturity for being widely applied. Conceptual modelling has not (yet) covered all these novel topics. It has been concentrated for more than two decades around specification of structures. Meanwhile, functionality, interactivity and distribution must be included into conceptual modelling of information systems. Also, some of the open problems that have been already discussed in 1987 [15, 16] still remain to be open. At the same time, novel models such as object-relational models or XML-based models have been developed. They did not overcome all the problems but have been sharpening and extending the variety of open problems. The open problem presented are given for classical areas of database research, i.e., structuring and functionality. The entire are of distribution and interaction is currently an area of very intensive research.

  14. Physical and Model Uncertainty for Fatigue Design of Composite Material

    DEFF Research Database (Denmark)

    Toft, Henrik Stensgaard; Sørensen, John Dalsgaard

    The main aim of the present report is to establish stochastic models for the uncertainties related to fatigue design of composite materials. The uncertainties considered are the physical uncertainty related to the static and fatigue strength and the model uncertainty related to Miners rule...

  15. Conceptual Frameworks in the Doctoral Research Process: A Pedagogical Model

    Science.gov (United States)

    Berman, Jeanette; Smyth, Robyn

    2015-01-01

    This paper contributes to consideration of the role of conceptual frameworks in the doctoral research process. Through reflection on the two authors' own conceptual frameworks for their doctoral studies, a pedagogical model has been developed. The model posits the development of a conceptual framework as a core element of the doctoral…

  16. A Structural Equation Model of Conceptual Change in Physics

    Science.gov (United States)

    Taasoobshirazi, Gita; Sinatra, Gale M.

    2011-01-01

    A model of conceptual change in physics was tested on introductory-level, college physics students. Structural equation modeling was used to test hypothesized relationships among variables linked to conceptual change in physics including an approach goal orientation, need for cognition, motivation, and course grade. Conceptual change in physics…

  17. A conceptual model of referee efficacy

    Directory of Open Access Journals (Sweden)

    Félix eGuillén

    2011-02-01

    Full Text Available This paper presents a conceptual model of referee efficacy, defines the concept, proposes sources of referee specific efficacy information, and suggests consequences of having high or low referee efficacy. Referee efficacy is defined as the extent to which referees believe they have the capacity to perform successfully in their job. Referee efficacy beliefs are hypothesized to be influenced by mastery experiences, referee knowledge/education, support from significant others, physical/mental preparedness, environmental comfort, and perceived anxiety. In turn, referee efficacy beliefs are hypothesized to influence referee performance, referee stress, athlete rule violations, athlete satisfaction, and co-referee satisfaction.

  18. Sierra toolkit computational mesh conceptual model

    International Nuclear Information System (INIS)

    Baur, David G.; Edwards, Harold Carter; Cochran, William K.; Williams, Alan B.; Sjaardema, Gregory D.

    2010-01-01

    The Sierra Toolkit computational mesh is a software library intended to support massively parallel multi-physics computations on dynamically changing unstructured meshes. This domain of intended use is inherently complex due to distributed memory parallelism, parallel scalability, heterogeneity of physics, heterogeneous discretization of an unstructured mesh, and runtime adaptation of the mesh. Management of this inherent complexity begins with a conceptual analysis and modeling of this domain of intended use; i.e., development of a domain model. The Sierra Toolkit computational mesh software library is designed and implemented based upon this domain model. Software developers using, maintaining, or extending the Sierra Toolkit computational mesh library must be familiar with the concepts/domain model presented in this report.

  19. Probabilistic Radiological Performance Assessment Modeling and Uncertainty

    Science.gov (United States)

    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

  20. Application Of Global Sensitivity Analysis And Uncertainty Quantification In Dynamic Modelling Of Micropollutants In Stormwater Runoff

    DEFF Research Database (Denmark)

    Vezzaro, Luca; Mikkelsen, Peter Steen

    2012-01-01

    of uncertainty in a conceptual lumped dynamic stormwater runoff quality model that is used in a study catchment to estimate (i) copper loads, (ii) compliance with dissolved Cu concentration limits on stormwater discharge and (iii) the fraction of Cu loads potentially intercepted by a planned treatment facility...

  1. A Proposed Conceptual Model of Military Medical Readiness

    National Research Council Canada - National Science Library

    Van Hall, Brian M

    2007-01-01

    .... The basis for the proposed conceptual model builds on common and accepted latent variable and theoretical modeling techniques proposed by healthcare scholars, organizational theorists, mathematical...

  2. Intrinsic Uncertainties in Modeling Complex Systems.

    Energy Technology Data Exchange (ETDEWEB)

    Cooper, Curtis S; Bramson, Aaron L.; Ames, Arlo L.

    2014-09-01

    Models are built to understand and predict the behaviors of both natural and artificial systems. Because it is always necessary to abstract away aspects of any non-trivial system being modeled, we know models can potentially leave out important, even critical elements. This reality of the modeling enterprise forces us to consider the prospective impacts of those effects completely left out of a model - either intentionally or unconsidered. Insensitivity to new structure is an indication of diminishing returns. In this work, we represent a hypothetical unknown effect on a validated model as a finite perturba- tion whose amplitude is constrained within a control region. We find robustly that without further constraints, no meaningful bounds can be placed on the amplitude of a perturbation outside of the control region. Thus, forecasting into unsampled regions is a very risky proposition. We also present inherent difficulties with proper time discretization of models and representing in- herently discrete quantities. We point out potentially worrisome uncertainties, arising from math- ematical formulation alone, which modelers can inadvertently introduce into models of complex systems. Acknowledgements This work has been funded under early-career LDRD project #170979, entitled "Quantify- ing Confidence in Complex Systems Models Having Structural Uncertainties", which ran from 04/2013 to 09/2014. We wish to express our gratitude to the many researchers at Sandia who con- tributed ideas to this work, as well as feedback on the manuscript. In particular, we would like to mention George Barr, Alexander Outkin, Walt Beyeler, Eric Vugrin, and Laura Swiler for provid- ing invaluable advice and guidance through the course of the project. We would also like to thank Steven Kleban, Amanda Gonzales, Trevor Manzanares, and Sarah Burwell for their assistance in managing project tasks and resources.

  3. Modeling of uncertainties in biochemical reactions.

    Science.gov (United States)

    Mišković, Ljubiša; Hatzimanikatis, Vassily

    2011-02-01

    Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual reactions. However, kinetic data acquired from experimental observations bring along uncertainties due to various experimental conditions and measurement methods. In this contribution, we propose a novel way to model the uncertainty in the enzyme kinetics and to predict quantitatively the responses of metabolic reactions to the changes in enzyme activities under uncertainty. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints, and is based on formalism from systems theory and metabolic control analysis. We achieve this by observing that kinetic responses of metabolic reactions depend: (i) on the distribution of the enzymes among their free form and all reactive states; (ii) on the equilibrium displacements of the overall reaction and that of the individual enzymatic steps; and (iii) on the net fluxes through the enzyme. Relying on this observation, we develop a novel, efficient Monte Carlo sampling procedure to generate all states within a metabolic reaction that satisfy imposed constrains. Thus, we derive the statistics of the expected responses of the metabolic reactions to changes in enzyme levels and activities, in the levels of metabolites, and in the values of the kinetic parameters. We present aspects of the proposed framework through an example of the fundamental three-step reversible enzymatic reaction mechanism. We demonstrate that the equilibrium displacements of the individual enzymatic steps have an important influence on kinetic responses of the enzyme. Furthermore, we derive the conditions that must be satisfied by a reversible three-step enzymatic reaction operating far away from the equilibrium in order to respond to

  4. Assessing scenario and parametric uncertainties in risk analysis: a model uncertainty audit

    International Nuclear Information System (INIS)

    Tarantola, S.; Saltelli, A.; Draper, D.

    1999-01-01

    In the present study a process of model audit is addressed on a computational model used for predicting maximum radiological doses to humans in the field of nuclear waste disposal. Global uncertainty and sensitivity analyses are employed to assess output uncertainty and to quantify the contribution of parametric and scenario uncertainties to the model output. These tools are of fundamental importance for risk analysis and decision making purposes

  5. Testing an integral conceptual model of frailty.

    Science.gov (United States)

    Gobbens, Robbert J; van Assen, Marcel A; Luijkx, Katrien G; Schols, Jos M

    2012-09-01

    This paper is a report of a study conducted to test three hypotheses derived from an integral conceptual model of frailty.   The integral model of frailty describes the pathway from life-course determinants to frailty to adverse outcomes. The model assumes that life-course determinants and the three domains of frailty (physical, psychological, social) affect adverse outcomes, the effect of disease(s) on adverse outcomes is mediated by frailty, and the effect of frailty on adverse outcomes depends on the life-course determinants. In June 2008 a questionnaire was sent to a sample of community-dwelling people, aged 75 years and older (n = 213). Life-course determinants and frailty were assessed using the Tilburg frailty indicator. Adverse outcomes were measured using the Groningen activity restriction scale, the WHOQOL-BREF and questions regarding healthcare utilization. The effect of seven self-reported chronic diseases was examined. Life-course determinants, chronic disease(s), and frailty together explain a moderate to large part of the variance of the seven continuous adverse outcomes (26-57%). All these predictors together explained a significant part of each of the five dichotomous adverse outcomes. The effect of chronic disease(s) on all 12 adverse outcomes was mediated at least partly by frailty. The effect of frailty domains on adverse outcomes did not depend on life-course determinants. Our finding that the adverse outcomes are differently and uniquely affected by the three domains of frailty (physical, psychological, social), and life-course determinants and disease(s), emphasizes the importance of an integral conceptual model of frailty. © 2011 Blackwell Publishing Ltd.

  6. Model uncertainty from a regulatory point of view

    International Nuclear Information System (INIS)

    Abramson, L.R.

    1994-01-01

    This paper discusses model uncertainty in the larger context of knowledge and random uncertainty. It explores some regulatory implications of model uncertainty and argues that, from a regulator's perspective, a conservative approach must be taken. As a consequence of this perspective, averaging over model results is ruled out

  7. Uncertainty Assessment in Urban Storm Water Drainage Modelling

    DEFF Research Database (Denmark)

    Thorndahl, Søren

    The object of this paper is to make an overall description of the author's PhD study, concerning uncertainties in numerical urban storm water drainage models. Initially an uncertainty localization and assessment of model inputs and parameters as well as uncertainties caused by different model...

  8. Uncertainty associated with selected environmental transport models

    International Nuclear Information System (INIS)

    Little, C.A.; Miller, C.W.

    1979-11-01

    A description is given of the capabilities of several models to predict accurately either pollutant concentrations in environmental media or radiological dose to human organs. The models are discussed in three sections: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations. This procedure is infeasible for food chain models and, therefore, the uncertainty embodied in the models input parameters, rather than the model output, is estimated. Aquatic transport models are divided into one-dimensional, longitudinal-vertical, and longitudinal-horizontal models. Several conclusions were made about the ability of the Gaussian plume atmospheric dispersion model to predict accurately downwind air concentrations from releases under several sets of conditions. It is concluded that no validation study has been conducted to test the predictions of either aquatic or terrestrial food chain models. Using the aquatic pathway from water to fish to an adult for 137 Cs as an example, a 95% one-tailed confidence limit interval for the predicted exposure is calculated by examining the distributions of the input parameters. Such an interval is found to be 16 times the value of the median exposure. A similar one-tailed limit for the air-grass-cow-milk-thyroid for 131 I and infants was 5.6 times the median dose. Of the three model types discussed in this report,the aquatic transport models appear to do the best job of predicting observed concentrations. However, this conclusion is based on many fewer aquatic validation data than were availaable for atmospheric model validation

  9. Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations

    Science.gov (United States)

    Feyen, Luc; Caers, Jef

    2006-06-01

    In this work, we address the problem of characterizing the heterogeneity and uncertainty of hydraulic properties for complex geological settings. Hereby, we distinguish between two scales of heterogeneity, namely the hydrofacies structure and the intrafacies variability of the hydraulic properties. We employ multiple-point geostatistics to characterize the hydrofacies architecture. The multiple-point statistics are borrowed from a training image that is designed to reflect the prior geological conceptualization. The intrafacies variability of the hydraulic properties is represented using conventional two-point correlation methods, more precisely, spatial covariance models under a multi-Gaussian spatial law. We address the different levels and sources of uncertainty in characterizing the subsurface heterogeneity, and explore their effect on groundwater flow and transport predictions. Typically, uncertainty is assessed by way of many images, termed realizations, of a fixed statistical model. However, in many cases, sampling from a fixed stochastic model does not adequately represent the space of uncertainty. It neglects the uncertainty related to the selection of the stochastic model and the estimation of its input parameters. We acknowledge the uncertainty inherent in the definition of the prior conceptual model of aquifer architecture and in the estimation of global statistics, anisotropy, and correlation scales. Spatial bootstrap is used to assess the uncertainty of the unknown statistical parameters. As an illustrative example, we employ a synthetic field that represents a fluvial setting consisting of an interconnected network of channel sands embedded within finer-grained floodplain material. For this highly non-stationary setting we quantify the groundwater flow and transport model prediction uncertainty for various levels of hydrogeological uncertainty. Results indicate the importance of accurately describing the facies geometry, especially for transport

  10. Methodology for characterizing modeling and discretization uncertainties in computational simulation

    Energy Technology Data Exchange (ETDEWEB)

    ALVIN,KENNETH F.; OBERKAMPF,WILLIAM L.; RUTHERFORD,BRIAN M.; DIEGERT,KATHLEEN V.

    2000-03-01

    This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis.

  11. A conceptual competitive intelligence quality assurance model

    Directory of Open Access Journals (Sweden)

    Tshilidzi Eric Nenzhelele

    2015-12-01

    Full Text Available Competitive Intelligence (CI improves the quality of product and service, decision-making and it improves quality of life. However, it has been established that decision makers are not happy about the quality of CI. This is because enterprises fail in quality assurance of CI. It has been concluded that most enterprises are clueless concerning CI quality assurance. Studies that previously attempted to resolve CI quality problem were limited in scope and focused too much on the quality of information than the overall CI quality. The purpose of this study is to propose a conceptual CI quality assurance model which will help in quality assurance of CI. The research was qualitative in nature and used content analysis.

  12. Event based uncertainty assessment in urban drainage modelling, applying the GLUE methodology

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Beven, K.J.; Jensen, Jacob Birk

    2008-01-01

    of combined sewer overflow. The GLUE methodology is used to test different conceptual setups in order to determine if one model setup gives a better goodness of fit conditional on the observations than the other. Moreover, different methodological investigations of GLUE are conducted in order to test......In the present paper an uncertainty analysis on an application of the commercial urban drainage model MOUSE is conducted. Applying the Generalized Likelihood Uncertainty Estimation (GLUE) methodology the model is conditioned on observation time series from two flow gauges as well as the occurrence...... if the uncertainty analysis is unambiguous. It is shown that the GLUE methodology is very applicable in uncertainty analysis of this application of an urban drainage model, although it was shown to be quite difficult of get good fits of the whole time series....

  13. A conceptual holding model for veterinary applications

    Directory of Open Access Journals (Sweden)

    Nicola Ferrè

    2014-05-01

    Full Text Available Spatial references are required when geographical information systems (GIS are used for the collection, storage and management of data. In the veterinary domain, the spatial component of a holding (of animals is usually defined by coordinates, and no other relevant information needs to be interpreted or used for manipulation of the data in the GIS environment provided. Users trying to integrate or reuse spatial data organised in such a way, frequently face the problem of data incompatibility and inconsistency. The root of the problem lies in differences with respect to syntax as well as variations in the semantic, spatial and temporal representations of the geographic features. To overcome these problems and to facilitate the inter-operability of different GIS, spatial data must be defined according to a “schema” that includes the definition, acquisition, analysis, access, presentation and transfer of such data between different users and systems. We propose an application “schema” of holdings for GIS applications in the veterinary domain according to the European directive framework (directive 2007/2/EC - INSPIRE. The conceptual model put forward has been developed at two specific levels to produce the essential and the abstract model, respectively. The former establishes the conceptual linkage of the system design to the real world, while the latter describes how the system or software works. The result is an application “schema” that formalises and unifies the information-theoretic foundations of how to spatially represent a holding in order to ensure straightforward information-sharing within the veterinary community.

  14. Implementation of Scientific Community Laboratories and Their Effect on Student Conceptual Learning, Attitudes, and Understanding of Uncertainty

    Science.gov (United States)

    Lark, Adam

    Scientific Community Laboratories, developed by The University of Maryland, have shown initial promise as laboratories meant to emulate the practice of doing physics. These laboratories have been re-created by incorporating their design elements with the University of Toledo course structure and resources. The laboratories have been titled the Scientific Learning Community (SLC) Laboratories. A comparative study between these SLC laboratories and the University of Toledo physics department's traditional laboratories was executed during the fall 2012 semester on first semester calculus-based physics students. Three tests were executed as pre-test and post-tests to capture the change in students' concept knowledge, attitudes, and understanding of uncertainty. The Force Concept Inventory (FCI) was used to evaluate students' conceptual changes through the semester and average normalized gains were compared between both traditional and SLC laboratories. The Colorado Learning Attitudes about Science Survey for Experimental Physics (E-CLASS) was conducted to elucidate students' change in attitudes through the course of each laboratory. Finally, interviews regarding data analysis and uncertainty were transcribed and coded to track changes in the way students understand uncertainty and data analysis in experimental physics after their participation in both laboratory type. Students in the SLC laboratories showed a notable an increase conceptual knowledge and attitudes when compared to traditional laboratories. SLC students' understanding of uncertainty showed most improvement, diverging completely from students in the traditional laboratories, who declined throughout the semester.

  15. Uncertainty in parameterisation and model structure affect simulation results in coupled ecohydrological models

    Directory of Open Access Journals (Sweden)

    S. Arnold

    2009-10-01

    Full Text Available In this paper we develop and apply a conceptual ecohydrological model to investigate the effects of model structure and parameter uncertainty on the simulation of vegetation structure and hydrological dynamics. The model is applied for a typical water limited riparian ecosystem along an ephemeral river: the middle section of the Kuiseb River in Namibia. We modelled this system by coupling an ecological model with a conceptual hydrological model. The hydrological model is storage based with stochastical forcing from the flood. The ecosystem is modelled with a population model, and represents three dominating riparian plant populations. In appreciation of uncertainty about population dynamics, we applied three model versions with increasing complexity. Population parameters were found by Latin hypercube sampling of the parameter space and with the constraint that three species should coexist as observed. Two of the three models were able to reproduce the observed coexistence. However, both models relied on different coexistence mechanisms, and reacted differently to change of long term memory in the flood forcing. The coexistence requirement strongly constrained the parameter space for both successful models. Only very few parameter sets (0.5% of 150 000 samples allowed for coexistence in a representative number of repeated simulations (at least 10 out of 100 and the success of the coexistence mechanism was controlled by the combination of population parameters. The ensemble statistics of average values of hydrologic variables like transpiration and depth to ground water were similar for both models, suggesting that they were mainly controlled by the applied hydrological model. The ensemble statistics of the fluctuations of depth to groundwater and transpiration, however, differed significantly, suggesting that they were controlled by the applied ecological model and coexistence mechanisms. Our study emphasizes that uncertainty about ecosystem

  16. Implications of model uncertainty for the practice of risk assessment

    International Nuclear Information System (INIS)

    Laskey, K.B.

    1994-01-01

    A model is a representation of a system that can be used to answer questions about the system's behavior. The term model uncertainty refers to problems in which there is no generally agreed upon, validated model that can be used as a surrogate for the system itself. Model uncertainty affects both the methodology appropriate for building models and how models should be used. This paper discusses representations of model uncertainty, methodologies for exercising and interpreting models in the presence of model uncertainty, and the appropriate use of fallible models for policy making

  17. On the relationship between aerosol model uncertainty and radiative forcing uncertainty.

    Science.gov (United States)

    Lee, Lindsay A; Reddington, Carly L; Carslaw, Kenneth S

    2016-05-24

    The largest uncertainty in the historical radiative forcing of climate is caused by the interaction of aerosols with clouds. Historical forcing is not a directly measurable quantity, so reliable assessments depend on the development of global models of aerosols and clouds that are well constrained by observations. However, there has been no systematic assessment of how reduction in the uncertainty of global aerosol models will feed through to the uncertainty in the predicted forcing. We use a global model perturbed parameter ensemble to show that tight observational constraint of aerosol concentrations in the model has a relatively small effect on the aerosol-related uncertainty in the calculated forcing between preindustrial and present-day periods. One factor is the low sensitivity of present-day aerosol to natural emissions that determine the preindustrial aerosol state. However, the major cause of the weak constraint is that the full uncertainty space of the model generates a large number of model variants that are equally acceptable compared to present-day aerosol observations. The narrow range of aerosol concentrations in the observationally constrained model gives the impression of low aerosol model uncertainty. However, these multiple "equifinal" models predict a wide range of forcings. To make progress, we need to develop a much deeper understanding of model uncertainty and ways to use observations to constrain it. Equifinality in the aerosol model means that tuning of a small number of model processes to achieve model-observation agreement could give a misleading impression of model robustness.

  18. Creating a Universe, a Conceptual Model

    Directory of Open Access Journals (Sweden)

    James R. Johnson

    2016-10-01

    Full Text Available Space is something. Space inherently contains laws of nature: universal rules (mathematics, space dimensions, types of forces, types of fields, and particle species, laws (relativity, quantum mechanics, thermodynamics, and electromagnetism and symmetries (Lorentz, Gauge, and symmetry breaking. We have significant knowledge about these laws of nature because all our scientific theories assume their presence. Their existence is critical for developing either a unique theory of our universe or more speculative multiverse theories. Scientists generally ignore the laws of nature because they “are what they are” and because visualizing different laws of nature challenges the imagination. This article defines a conceptual model separating space (laws of nature from the universe’s energy source (initial conditions and expansion (big bang. By considering the ramifications of changing the laws of nature, initial condition parameters, and two variables in the big bang theory, the model demonstrates that traditional fine tuning is not the whole story when creating a universe. Supporting the model, space and “nothing” are related to the laws of nature, mathematics and multiverse possibilities. Speculation on the beginning of time completes the model.

  19. A Conceptual Modeling Approach for OLAP Personalization

    Science.gov (United States)

    Garrigós, Irene; Pardillo, Jesús; Mazón, Jose-Norberto; Trujillo, Juan

    Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies. However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance. Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex. As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated. In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process. To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour. In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules. The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs. Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development.

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

    DEFF Research Database (Denmark)

    Hukkerikar, Amol; Sarup, Bent; Abildskov, Jens

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

  1. Classification and moral evaluation of uncertainties in engineering modeling.

    Science.gov (United States)

    Murphy, Colleen; Gardoni, Paolo; Harris, Charles E

    2011-09-01

    Engineers must deal with risks and uncertainties as a part of their professional work and, in particular, uncertainties are inherent to engineering models. Models play a central role in engineering. Models often represent an abstract and idealized version of the mathematical properties of a target. Using models, engineers can investigate and acquire understanding of how an object or phenomenon will perform under specified conditions. This paper defines the different stages of the modeling process in engineering, classifies the various sources of uncertainty that arise in each stage, and discusses the categories into which these uncertainties fall. The paper then considers the way uncertainty and modeling are approached in science and the criteria for evaluating scientific hypotheses, in order to highlight the very different criteria appropriate for the development of models and the treatment of the inherent uncertainties in engineering. Finally, the paper puts forward nine guidelines for the treatment of uncertainty in engineering modeling.

  2. The uncertainty analysis of model results a practical guide

    CERN Document Server

    Hofer, Eduard

    2018-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1995-12-31

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

  4. How to: understanding SWAT model uncertainty relative to measured results

    Science.gov (United States)

    Watershed models are being relied upon to contribute to most policy-making decisions of watershed management, and the demand for an accurate accounting of complete model uncertainty is rising. Generalized likelihood uncertainty estimation (GLUE) is a widely used method for quantifying uncertainty i...

  5. Meteorological Uncertainty of atmospheric Dispersion model results (MUD)

    DEFF Research Database (Denmark)

    Havskov Sørensen, Jens; Amstrup, Bjarne; Feddersen, Henrik

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario....

  6. Representing and managing uncertainty in qualitative ecological models

    NARCIS (Netherlands)

    Nuttle, T.; Bredeweg, B.; Salles, P.; Neumann, M.

    2009-01-01

    Ecologists and decision makers need ways to understand systems, test ideas, and make predictions and explanations about systems. However, uncertainty about causes and effects of processes and parameter values is pervasive in models of ecological systems. Uncertainty associated with incomplete

  7. COSMO: a conceptual framework for service modelling and refinement

    NARCIS (Netherlands)

    Quartel, Dick; Steen, Maarten W.A.; Pokraev, S.; van Sinderen, Marten J.

    This paper presents a conceptual framework for service modelling and refinement, called the COSMO (COnceptual Service MOdelling) framework. This framework provides concepts to model and reason about services, and to support operations, such as composition and discovery, which are performed on them

  8. Modelling of data uncertainties on hybrid computers

    Energy Technology Data Exchange (ETDEWEB)

    Schneider, Anke (ed.)

    2016-06-15

    The codes d{sup 3}f and r{sup 3}t are well established for modelling density-driven flow and nuclide transport in the far field of repositories for hazardous material in deep geological formations. They are applicable in porous media as well as in fractured rock or mudstone, for modelling salt- and heat transport as well as a free groundwater surface. Development of the basic framework of d{sup 3}f and r{sup 3}t had begun more than 20 years ago. Since that time significant advancements took place in the requirements for safety assessment as well as for computer hardware development. The period of safety assessment for a repository of high-level radioactive waste was extended to 1 million years, and the complexity of the models is steadily growing. Concurrently, the demands on accuracy increase. Additionally, model and parameter uncertainties become more and more important for an increased understanding of prediction reliability. All this leads to a growing demand for computational power that requires a considerable software speed-up. An effective way to achieve this is the use of modern, hybrid computer architectures which requires basically the set-up of new data structures and a corresponding code revision but offers a potential speed-up by several orders of magnitude. The original codes d{sup 3}f and r{sup 3}t were applications of the software platform UG /BAS 94/ whose development had begun in the early nineteennineties. However, UG had recently been advanced to the C++ based, substantially revised version UG4 /VOG 13/. To benefit also in the future from state-of-the-art numerical algorithms and to use hybrid computer architectures, the codes d{sup 3}f and r{sup 3}t were transferred to this new code platform. Making use of the fact that coupling between different sets of equations is natively supported in UG4, d{sup 3}f and r{sup 3}t were combined to one conjoint code d{sup 3}f++. A direct estimation of uncertainties for complex groundwater flow models with the

  9. Conceptual models in the field of library catalogues

    Directory of Open Access Journals (Sweden)

    Marija Petek

    2000-01-01

    Full Text Available The publishing world is changing quickly and so must also bibliographic control. It is tirne to re-examine cataloguing rules and MARC formats. This can be done by the method of conceptual modelling. Some conceptual models are presented; an IFLA study on the functional requirements for bibliographic records is described in detail.

  10. Uncertainty propagation in urban hydrology water quality modelling

    NARCIS (Netherlands)

    Torres Matallana, Arturo; Leopold, U.; Heuvelink, G.B.M.

    2016-01-01

    Uncertainty is often ignored in urban hydrology modelling. Engineering practice typically ignores uncertainties and uncertainty propagation. This can have large impacts, such as the wrong dimensioning of urban drainage systems and the inaccurate estimation of pollution in the environment caused

  11. Uncertainty in a spatial evacuation model

    Science.gov (United States)

    Mohd Ibrahim, Azhar; Venkat, Ibrahim; Wilde, Philippe De

    2017-08-01

    Pedestrian movements in crowd motion can be perceived in terms of agents who basically exhibit patient or impatient behavior. We model crowd motion subject to exit congestion under uncertainty conditions in a continuous space and compare the proposed model via simulations with the classical social force model. During a typical emergency evacuation scenario, agents might not be able to perceive with certainty the strategies of opponents (other agents) owing to the dynamic changes entailed by the neighborhood of opponents. In such uncertain scenarios, agents will try to update their strategy based on their own rules or their intrinsic behavior. We study risk seeking, risk averse and risk neutral behaviors of such agents via certain game theory notions. We found that risk averse agents tend to achieve faster evacuation time whenever the time delay in conflicts appears to be longer. The results of our simulations also comply with previous work and conform to the fact that evacuation time of agents becomes shorter once mutual cooperation among agents is achieved. Although the impatient strategy appears to be the rational strategy that might lead to faster evacuation times, our study scientifically shows that the more the agents are impatient, the slower is the egress time.

  12. Characterization uncertainty and its effects on models and performance

    International Nuclear Information System (INIS)

    Rautman, C.A.; Treadway, A.H.

    1991-01-01

    Geostatistical simulation is being used to develop multiple geologic models of rock properties at the proposed Yucca Mountain repository site. Because each replicate model contains the same known information, and is thus essentially indistinguishable statistically from others, the differences between models may be thought of as representing the uncertainty in the site description. The variability among performance measures, such as ground water travel time, calculated using these replicate models therefore quantifies the uncertainty in performance that arises from uncertainty in site characterization

  13. Conceptualizing Telehealth in Nursing Practice: Advancing a Conceptual Model to Fill a Virtual Gap.

    Science.gov (United States)

    Nagel, Daniel A; Penner, Jamie L

    2016-03-01

    Increasingly nurses use various telehealth technologies to deliver health care services; however, there has been a lag in research and generation of empirical knowledge to support nursing practice in this expanding field. One challenge to generating knowledge is a gap in development of a comprehensive conceptual model or theoretical framework to illustrate relationships of concepts and phenomena inherent to adoption of a broad range of telehealth technologies to holistic nursing practice. A review of the literature revealed eight published conceptual models, theoretical frameworks, or similar entities applicable to nursing practice. Many of these models focus exclusively on use of telephones and four were generated from qualitative studies, but none comprehensively reflect complexities of bridging nursing process and elements of nursing practice into use of telehealth. The purpose of this article is to present a review of existing conceptual models and frameworks, discuss predominant themes and features of these models, and present a comprehensive conceptual model for telehealth nursing practice synthesized from this literature for consideration and further development. This conceptual model illustrates characteristics of, and relationships between, dimensions of telehealth practice to guide research and knowledge development in provision of holistic person-centered care delivery to individuals by nurses through telehealth technologies. © The Author(s) 2015.

  14. Identification and communication of uncertainties of phenomenological models in PSA

    International Nuclear Information System (INIS)

    Pulkkinen, U.; Simola, K.

    2001-11-01

    This report aims at presenting a view upon uncertainty analysis of phenomenological models with an emphasis on the identification and documentation of various types of uncertainties and assumptions in the modelling of the phenomena. In an uncertainty analysis, it is essential to include and document all unclear issues, in order to obtain a maximal coverage of unresolved issues. This holds independently on their nature or type of the issues. The classification of uncertainties is needed in the decomposition of the problem and it helps in the identification of means for uncertainty reduction. Further, an enhanced documentation serves to evaluate the applicability of the results to various risk-informed applications. (au)

  15. Assessment of structural model and parameter uncertainty with a multi-model system for soil water balance models

    Science.gov (United States)

    Michalik, Thomas; Multsch, Sebastian; Frede, Hans-Georg; Breuer, Lutz

    2016-04-01

    ~100 mm for SWAP and <10 mm for AquaCrop. Both findings show the strong variety in model structures and the effects on simulation performance. In general, the results of this study indicate a greater impact of conceptual than parameter uncertainty and demonstrate the need for further research concerning water balance modeling for irrigation management.

  16. Hydrogeological conceptual model development and numerical modelling using CONNECTFLOW, Forsmark modelling stage 2.3

    Energy Technology Data Exchange (ETDEWEB)

    Follin, Sven (SF GeoLogic AB, Taeby (Sweden)); Hartley, Lee; Jackson, Peter; Roberts, David (Serco TAP (United Kingdom)); Marsic, Niko (Kemakta Konsult AB, Stockholm (Sweden))

    2008-05-15

    Three versions of a site descriptive model (SDM) have been completed for the Forsmark area. Version 0 established the state of knowledge prior to the start of the site investigation programme. Version 1.1 was essentially a training exercise and was completed during 2004. Version 1.2 was a preliminary site description and concluded the initial site investigation work (ISI) in June 2005. Three modelling stages are planned for the complete site investigation work (CSI). These are labelled stage 2.1, 2.2 and 2.3, respectively. An important component of each of these stages is to address and continuously try to resolve discipline-specific uncertainties of importance for repository engineering and safety assessment. Stage 2.1 included an updated geological model for Forsmark and aimed to provide a feedback from the modelling working group to the site investigation team to enable completion of the site investigation work. Stage 2.2 described the conceptual understanding and the numerical modelling of the bedrock hydrogeology in the Forsmark area based on data freeze 2.2. The present report describes the modelling based on data freeze 2.3, which is the final data freeze in Forsmark. In comparison, data freeze 2.3 is considerably smaller than data freeze 2.2. Therefore, stage 2.3 deals primarily with model confirmation and uncertainty analysis, e.g. verification of important hypotheses made in stage 2.2 and the role of parameter uncertainty in the numerical modelling. On the whole, the work reported here constitutes an addendum to the work reported in stage 2.2. Two changes were made to the CONNECTFLOW code in stage 2.3. These serve to: 1) improve the representation of the hydraulic properties of the regolith, and 2) improve the conditioning of transmissivity of the deformation zones against single-hole hydraulic tests. The changes to the modelling of the regolith were made to improve the consistency with models made with the MIKE SHE code, which involved the introduction

  17. Conceptual and Numerical Models for UZ Flow and Transport

    International Nuclear Information System (INIS)

    Liu, H.

    2000-01-01

    The purpose of this Analysis/Model Report (AMR) is to document the conceptual and numerical models used for modeling of unsaturated zone (UZ) fluid (water and air) flow and solute transport processes. This is in accordance with ''AMR Development Plan for U0030 Conceptual and Numerical Models for Unsaturated Zone (UZ) Flow and Transport Processes, Rev 00''. The conceptual and numerical modeling approaches described in this AMR are used for models of UZ flow and transport in fractured, unsaturated rock under ambient and thermal conditions, which are documented in separate AMRs. This AMR supports the UZ Flow and Transport Process Model Report (PMR), the Near Field Environment PMR, and the following models: Calibrated Properties Model; UZ Flow Models and Submodels; Mountain-Scale Coupled Processes Model; Thermal-Hydrologic-Chemical (THC) Seepage Model; Drift Scale Test (DST) THC Model; Seepage Model for Performance Assessment (PA); and UZ Radionuclide Transport Models

  18. Role of conceptual models in nuclear power plant operation

    International Nuclear Information System (INIS)

    Williams, M.D.; Moran, T.P.; Brown, J.S.

    1982-01-01

    A crucial objective in plant operation (and perhaps licensing) ought to be to explicitly train operators to develop, perhaps with computer aids, robust conceptual models of the plants they control. The question is whether we are actually able to develop robust conceptual models and validate their robustness. Cognitive science is just beginning to come to grips with this problem. This paper describes some of the evolving technology for building conceptual models of physical mechanisms and some of the implications of such models in the context of nuclear power plant operation

  19. National Identity: Conceptual models, discourses and political change

    DEFF Research Database (Denmark)

    Harder, Peter

    2014-01-01

    of conceptual models or discourses. This is especially important in cases that involve conflictive political issues such as national and ethnic identity. The article reports on a historical project with a linguistic dimension in my department (PI Stuart Ward, cf. Ward 2004), which aims to throw light......Cognitive Linguistics has demonstrated the applicability of a conceptual approach to the understanding of political issues, cf. Lakoff (2008) and many others. From a different perspective, critical discourse analysis has approached political concepts with a focus on issues involving potentially...... divisive features such as race, class, gender and ethnic identity. Although discourses are not identical to conceptual models, conceptual models are typically manifested in discourse, and discourses are typically reflections of conceptualizations, a theme explored e.g. in Hart and Lukes (2007). As argued...

  20. Toolkit for Conceptual Modeling (TCM): User's Guide and Reference

    NARCIS (Netherlands)

    Dehne, F.; Wieringa, Roelf J.

    1997-01-01

    The Toolkit for Conceptual Modeling (TCM) is a suite of graphical editors for a number of graphical notation systems that are used in software specification methods. The notations can be used to represent the conceptual structure of the software - hence the name of the suite. This manual describes

  1. Imprecision and Uncertainty in the UFO Database Model.

    Science.gov (United States)

    Van Gyseghem, Nancy; De Caluwe, Rita

    1998-01-01

    Discusses how imprecision and uncertainty are dealt with in the UFO (Uncertainty and Fuzziness in an Object-oriented) database model. Such information is expressed by means of possibility distributions, and modeled by means of the proposed concept of "role objects." The role objects model uncertain, tentative information about objects,…

  2. Utilizing Uncertainty Multidisciplinary Design Optimization for Conceptual Design of Space Systems

    NARCIS (Netherlands)

    Yao, W.; Guo, J.; Chen, X.; Van Tooren, M.

    2010-01-01

    With progress of space technology and increase of space mission demand, requirements for robustness and reliability of space systems are ever-increasing. For the whole space mission life cycle, the most important decisions are made in the conceptual design phase, so it is very crucial to take

  3. Multi-scenario modelling of uncertainty in stochastic chemical systems

    International Nuclear Information System (INIS)

    Evans, R. David; Ricardez-Sandoval, Luis A.

    2014-01-01

    Uncertainty analysis has not been well studied at the molecular scale, despite extensive knowledge of uncertainty in macroscale systems. The ability to predict the effect of uncertainty allows for robust control of small scale systems such as nanoreactors, surface reactions, and gene toggle switches. However, it is difficult to model uncertainty in such chemical systems as they are stochastic in nature, and require a large computational cost. To address this issue, a new model of uncertainty propagation in stochastic chemical systems, based on the Chemical Master Equation, is proposed in the present study. The uncertain solution is approximated by a composite state comprised of the averaged effect of samples from the uncertain parameter distributions. This model is then used to study the effect of uncertainty on an isomerization system and a two gene regulation network called a repressilator. The results of this model show that uncertainty in stochastic systems is dependent on both the uncertain distribution, and the system under investigation. -- Highlights: •A method to model uncertainty on stochastic systems was developed. •The method is based on the Chemical Master Equation. •Uncertainty in an isomerization reaction and a gene regulation network was modelled. •Effects were significant and dependent on the uncertain input and reaction system. •The model was computationally more efficient than Kinetic Monte Carlo

  4. Analytic uncertainty and sensitivity analysis of models with input correlations

    Science.gov (United States)

    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.

  5. Uncertainty models applied to the substation planning

    Energy Technology Data Exchange (ETDEWEB)

    Fontoura Filho, Roberto N [ELETROBRAS, Rio de Janeiro, RJ (Brazil); Aires, Joao Carlos O; Tortelly, Debora L.S. [Light Servicos de Eletricidade S.A., Rio de Janeiro, RJ (Brazil)

    1994-12-31

    The selection of the reinforcements for a power system expansion becomes a difficult task on an environment of uncertainties. These uncertainties can be classified according to their sources as exogenous and endogenous. The first one is associated to the elements of the generation, transmission and distribution systems. The exogenous uncertainly is associated to external aspects, as the financial resources, the time spent to build the installations, the equipment price and the load level. The load uncertainly is extremely sensible to the behaviour of the economic conditions. Although the impossibility to take out completely the uncertainty , the endogenous one can be convenient treated and the exogenous uncertainly can be compensated. This paper describes an uncertainty treatment methodology and a practical application to a group of substations belonging to LIGHT company, the Rio de Janeiro electric utility. The equipment performance uncertainty is treated by adopting a probabilistic approach. The uncertainly associated to the load increase is considered by using technical analysis of scenarios and choice criteria based on the Decision Theory. On this paper it was used the Savage Method and the Fuzzy Set Method, in order to select the best middle term reinforcements plan. (author) 7 refs., 4 figs., 6 tabs.

  6. Aspects of uncertainty analysis in accident consequence modeling

    International Nuclear Information System (INIS)

    Travis, C.C.; Hoffman, F.O.

    1981-01-01

    Mathematical models are frequently used to determine probable dose to man from an accidental release of radionuclides by a nuclear facility. With increased emphasis on the accuracy of these models, the incorporation of uncertainty analysis has become one of the most crucial and sensitive components in evaluating the significance of model predictions. In the present paper, we address three aspects of uncertainty in models used to assess the radiological impact to humans: uncertainties resulting from the natural variability in human biological parameters; the propagation of parameter variability by mathematical models; and comparison of model predictions to observational data

  7. Uncertainty and endogenous technical change in climate policy models

    International Nuclear Information System (INIS)

    Baker, Erin; Shittu, Ekundayo

    2008-01-01

    Until recently endogenous technical change and uncertainty have been modeled separately in climate policy models. In this paper, we review the emerging literature that considers both these elements together. Taken as a whole the literature indicates that explicitly including uncertainty has important quantitative and qualitative impacts on optimal climate change technology policy. (author)

  8. Appropriatie spatial scales to achieve model output uncertainty goals

    NARCIS (Netherlands)

    Booij, Martijn J.; Melching, Charles S.; Chen, Xiaohong; Chen, Yongqin; Xia, Jun; Zhang, Hailun

    2008-01-01

    Appropriate spatial scales of hydrological variables were determined using an existing methodology based on a balance in uncertainties from model inputs and parameters extended with a criterion based on a maximum model output uncertainty. The original methodology uses different relationships between

  9. Uncertainty

    International Nuclear Information System (INIS)

    Silva, T.A. da

    1988-01-01

    The comparison between the uncertainty method recommended by International Atomic Energy Agency (IAEA) and the and the International Weight and Measure Commitee (CIPM) are showed, for the calibration of clinical dosimeters in the secondary standard Dosimetry Laboratory (SSDL). (C.G.C.) [pt

  10. Urban drainage models simplifying uncertainty analysis for practitioners

    DEFF Research Database (Denmark)

    Vezzaro, Luca; Mikkelsen, Peter Steen; Deletic, Ana

    2013-01-01

    in each measured/observed datapoint; an issue that is commonly overlooked in the uncertainty analysis of urban drainage models. This comparison allows the user to intuitively estimate the optimum number of simulations required to conduct uncertainty analyses. The output of the method includes parameter......There is increasing awareness about uncertainties in the modelling of urban drainage systems and, as such, many new methods for uncertainty analyses have been developed. Despite this, all available methods have limitations which restrict their widespread application among practitioners. Here...

  11. Towards a Model of Technology Adoption: A Conceptual Model Proposition

    Science.gov (United States)

    Costello, Pat; Moreton, Rob

    A conceptual model for Information Communication Technology (ICT) adoption by Small Medium Enterprises (SMEs) is proposed. The research uses several ICT adoption models as its basis with theoretical underpinning provided by the Diffusion of Innovation theory and the Technology Acceptance Model (TAM). Taking an exploratory research approach the model was investigated amongst 200 SMEs whose core business is ICT. Evidence from this study demonstrates that these SMEs face the same issues as all other industry sectors. This work points out weaknesses in SMEs environments regarding ICT adoption and suggests what they may need to do to increase the success rate of any proposed adoption. The methodology for development of the framework is described and recommendations made for improved Government-led ICT adoption initiatives. Application of the general methodology has resulted in new opportunities to embed the ethos and culture surrounding the issues into the framework of new projects developed as a result of Government intervention. A conceptual model is proposed that may lead to a deeper understanding of the issues under consideration.

  12. A conceptual model for the development and management of the ...

    African Journals Online (AJOL)

    A conceptual model for the development and management of the Cape Flats ... rainfall; this rainfall pattern has implications for recharge and water management issues in ... Keywords: water resource management, Cape Town, water quality, ...

  13. Technologies for conceptual modelling and intelligent query formulation

    CSIR Research Space (South Africa)

    Alberts, R

    2008-11-01

    Full Text Available The aim of the project is to devise and evaluate algorithms, methodologies, techniques and interaction paradigms to build a tool for conceptual modelling and query management of complex data repositories based on a framework with solid formal...

  14. Extracting conceptual models from user stories with Visual Narrator

    NARCIS (Netherlands)

    Lucassen, Garm; Robeer, Marcel; Dalpiaz, Fabiano; van der Werf, Jan Martijn E. M.; Brinkkemper, Sjaak

    2017-01-01

    Extracting conceptual models from natural language requirements can help identify dependencies, redundancies, and conflicts between requirements via a holistic and easy-to-understand view that is generated from lengthy textual specifications. Unfortunately, existing approaches never gained traction

  15. Conceptual Model for Effective Sports Marketing in Nigeria | Akarah ...

    African Journals Online (AJOL)

    Conceptual Model for Effective Sports Marketing in Nigeria. ... that are influenced by the sports market mix and sports consumers that are influenced by psychological factors and notes that; ... EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT

  16. Southern marl prairies conceptual ecological model

    Science.gov (United States)

    Davis, S.M.; Loftus, W.F.; Gaiser, E.E.; Huffman, A.E.

    2005-01-01

    About 190,000 ha of higher-elevation marl prairies flank either side of Shark River Slough in the southern Everglades. Water levels typically drop below the ground surface each year in this landscape. Consequently, peat soil accretion is inhibited, and substrates consist either of calcitic marl produced by algal periphyton mats or exposed limestone bedrock. The southern marl prairies support complex mosaics of wet prairie, sawgrass sawgrass (Cladium jamaicense), tree islands, and tropical hammock communities and a high diversity of plant species. However, relatively short hydroperiods and annual dry downs provide stressful conditions for aquatic fauna, affecting survival in the dry season when surface water is absent. Here, we present a conceptual ecological model developed for this landscape through scientific concensus, use of empirical data, and modeling. The two major societal drivers affecting the southern marl prairies are water management practices and agricultural and urban development. These drivers lead to five groups of ecosystem stressors: loss of spatial extent and connectivity, shortened hydroperiod and increased drought severity, extended hydroperiod and drying pattern reversals, introduction and spread of non-native trees, and introduction and spread of non-native fishes. Major ecological attributes include periphyton mats, plant species diversity and community mosaic, Cape Sable seaside sparrow (Ammodramus maritimus mirabilis), marsh fishes and associated aquatic fauna prey base, American alligator (Alligator mississippiensis), and wading bird early dry season foraging. Water management and development are hypothesized to have a negative effect on the ecological attributes of the southern marl prairies in the following ways. Periphyton mats have decreased in cover in areas where hydroperiod has been significantly reduced and changed in community composition due to inverse responses to increased nutrient availability. Plant species diversity and

  17. Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor

    Directory of Open Access Journals (Sweden)

    Jae-Han Park

    2012-06-01

    Full Text Available This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

  18. Spatial uncertainty model for visual features using a Kinect™ sensor.

    Science.gov (United States)

    Park, Jae-Han; Shin, Yong-Deuk; Bae, Ji-Hun; Baeg, Moon-Hong

    2012-01-01

    This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

  19. Impact of petrophysical uncertainty on Bayesian hydrogeophysical inversion and model selection

    Science.gov (United States)

    Brunetti, Carlotta; Linde, Niklas

    2018-01-01

    Quantitative hydrogeophysical studies rely heavily on petrophysical relationships that link geophysical properties to hydrogeological properties and state variables. Coupled inversion studies are frequently based on the questionable assumption that these relationships are perfect (i.e., no scatter). Using synthetic examples and crosshole ground-penetrating radar (GPR) data from the South Oyster Bacterial Transport Site in Virginia, USA, we investigate the impact of spatially-correlated petrophysical uncertainty on inferred posterior porosity and hydraulic conductivity distributions and on Bayes factors used in Bayesian model selection. Our study shows that accounting for petrophysical uncertainty in the inversion (I) decreases bias of the inferred variance of hydrogeological subsurface properties, (II) provides more realistic uncertainty assessment and (III) reduces the overconfidence in the ability of geophysical data to falsify conceptual hydrogeological models.

  20. CHARACTERIZING AND PROPAGATING MODELING UNCERTAINTIES IN PHOTOMETRICALLY DERIVED REDSHIFT DISTRIBUTIONS

    International Nuclear Information System (INIS)

    Abrahamse, Augusta; Knox, Lloyd; Schmidt, Samuel; Thorman, Paul; Anthony Tyson, J.; Zhan Hu

    2011-01-01

    The uncertainty in the redshift distributions of galaxies has a significant potential impact on the cosmological parameter values inferred from multi-band imaging surveys. The accuracy of the photometric redshifts measured in these surveys depends not only on the quality of the flux data, but also on a number of modeling assumptions that enter into both the training set and spectral energy distribution (SED) fitting methods of photometric redshift estimation. In this work we focus on the latter, considering two types of modeling uncertainties: uncertainties in the SED template set and uncertainties in the magnitude and type priors used in a Bayesian photometric redshift estimation method. We find that SED template selection effects dominate over magnitude prior errors. We introduce a method for parameterizing the resulting ignorance of the redshift distributions, and for propagating these uncertainties to uncertainties in cosmological parameters.

  1. Conceptual Models and Guidelines for Clinical Assessment of Financial Capacity.

    Science.gov (United States)

    Marson, Daniel

    2016-09-01

    The ability to manage financial affairs is a life skill of critical importance, and neuropsychologists are increasingly asked to assess financial capacity across a variety of settings. Sound clinical assessment of financial capacity requires knowledge and appreciation of applicable clinical conceptual models and principles. However, the literature has presented relatively little conceptual guidance for clinicians concerning financial capacity and its assessment. This article seeks to address this gap. The article presents six clinical models of financial capacity : (1) the early gerontological IADL model of Lawton, (2) the clinical skills model and (3) related cognitive psychological model developed by Marson and colleagues, (4) a financial decision-making model adapting earlier decisional capacity work of Appelbaum and Grisso, (5) a person-centered model of financial decision-making developed by Lichtenberg and colleagues, and (6) a recent model of financial capacity in the real world developed through the Institute of Medicine. Accompanying presentation of the models is discussion of conceptual and practical perspectives they represent for clinician assessment. Based on the models, the article concludes by presenting a series of conceptually oriented guidelines for clinical assessment of financial capacity. In summary, sound assessment of financial capacity requires knowledge and appreciation of clinical conceptual models and principles. Awareness of such models, principles and guidelines will strengthen and advance clinical assessment of financial capacity. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  2. A Bayesian approach for quantification of model uncertainty

    International Nuclear Information System (INIS)

    Park, Inseok; Amarchinta, Hemanth K.; Grandhi, Ramana V.

    2010-01-01

    In most engineering problems, more than one model can be created to represent an engineering system's behavior. Uncertainty is inevitably involved in selecting the best model from among the models that are possible. Uncertainty in model selection cannot be ignored, especially when the differences between the predictions of competing models are significant. In this research, a methodology is proposed to quantify model uncertainty using measured differences between experimental data and model outcomes under a Bayesian statistical framework. The adjustment factor approach is used to propagate model uncertainty into prediction of a system response. A nonlinear vibration system is used to demonstrate the processes for implementing the adjustment factor approach. Finally, the methodology is applied on the engineering benefits of a laser peening process, and a confidence band for residual stresses is established to indicate the reliability of model prediction.

  3. Modeling uncertainty in requirements engineering decision support

    Science.gov (United States)

    Feather, Martin S.; Maynard-Zhang, Pedrito; Kiper, James D.

    2005-01-01

    One inherent characteristic of requrements engineering is a lack of certainty during this early phase of a project. Nevertheless, decisions about requirements must be made in spite of this uncertainty. Here we describe the context in which we are exploring this, and some initial work to support elicitation of uncertain requirements, and to deal with the combination of such information from multiple stakeholders.

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

    Science.gov (United States)

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

    2016-12-01

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

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

    Science.gov (United States)

    Jacquin, A. P.

    2012-04-01

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

  6. Assessing Groundwater Model Uncertainty for the Central Nevada Test Area

    International Nuclear Information System (INIS)

    Pohll, Greg; Pohlmann, Karl; Hassan, Ahmed; Chapman, Jenny; Mihevc, Todd

    2002-01-01

    The purpose of this study is to quantify the flow and transport model uncertainty for the Central Nevada Test Area (CNTA). Six parameters were identified as uncertain, including the specified head boundary conditions used in the flow model, the spatial distribution of the underlying welded tuff unit, effective porosity, sorption coefficients, matrix diffusion coefficient, and the geochemical release function which describes nuclear glass dissolution. The parameter uncertainty was described by assigning prior statistical distributions for each of these parameters. Standard Monte Carlo techniques were used to sample from the parameter distributions to determine the full prediction uncertainty. Additional analysis is performed to determine the most cost-beneficial characterization activities. The maximum radius of the tritium and strontium-90 contaminant boundary was used as the output metric for evaluation of prediction uncertainty. The results indicate that combining all of the uncertainty in the parameters listed above propagates to a prediction uncertainty in the maximum radius of the contaminant boundary of 234 to 308 m and 234 to 302 m, for tritium and strontium-90, respectively. Although the uncertainty in the input parameters is large, the prediction uncertainty in the contaminant boundary is relatively small. The relatively small prediction uncertainty is primarily due to the small transport velocities such that large changes in the uncertain input parameters causes small changes in the contaminant boundary. This suggests that the model is suitable in terms of predictive capability for the contaminant boundary delineation

  7. Reservoir management under geological uncertainty using fast model update

    NARCIS (Netherlands)

    Hanea, R.; Evensen, G.; Hustoft, L.; Ek, T.; Chitu, A.; Wilschut, F.

    2015-01-01

    Statoil is implementing "Fast Model Update (FMU)," an integrated and automated workflow for reservoir modeling and characterization. FMU connects all steps and disciplines from seismic depth conversion to prediction and reservoir management taking into account relevant reservoir uncertainty. FMU

  8. Incorporating parametric uncertainty into population viability analysis models

    Science.gov (United States)

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

    2011-01-01

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

  9. Discussion of OECD LWR Uncertainty Analysis in Modelling Benchmark

    International Nuclear Information System (INIS)

    Ivanov, K.; Avramova, M.; Royer, E.; Gillford, J.

    2013-01-01

    The demand for best estimate calculations in nuclear reactor design and safety evaluations has increased in recent years. Uncertainty quantification has been highlighted as part of the best estimate calculations. The modelling aspects of uncertainty and sensitivity analysis are to be further developed and validated on scientific grounds in support of their performance and application to multi-physics reactor simulations. The Organization for Economic Co-operation and Development (OECD) / Nuclear Energy Agency (NEA) Nuclear Science Committee (NSC) has endorsed the creation of an Expert Group on Uncertainty Analysis in Modelling (EGUAM). Within the framework of activities of EGUAM/NSC the OECD/NEA initiated the Benchmark for Uncertainty Analysis in Modelling for Design, Operation, and Safety Analysis of Light Water Reactor (OECD LWR UAM benchmark). The general objective of the benchmark is to propagate the predictive uncertainties of code results through complex coupled multi-physics and multi-scale simulations. The benchmark is divided into three phases with Phase I highlighting the uncertainty propagation in stand-alone neutronics calculations, while Phase II and III are focused on uncertainty analysis of reactor core and system respectively. This paper discusses the progress made in Phase I calculations, the Specifications for Phase II and the incoming challenges in defining Phase 3 exercises. The challenges of applying uncertainty quantification to complex code systems, in particular the time-dependent coupled physics models are the large computational burden and the utilization of non-linear models (expected due to the physics coupling). (authors)

  10. Modelling geological uncertainty for mine planning

    Energy Technology Data Exchange (ETDEWEB)

    Mitchell, M

    1980-07-01

    Geosimplan is an operational gaming approach used in testing a proposed mining strategy against uncertainty in geological disturbance. Geoplan is a technique which facilitates the preparation of summary analyses to give an impression of size, distribution and quality of reserves, and to assist in calculation of year by year output estimates. Geoplan concentrates on variations in seam properties and the interaction between geological information and marketing and output requirements.

  11. IAEA CRP on HTGR Uncertainties in Modeling: Assessment of Phase I Lattice to Core Model Uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    Rouxelin, Pascal Nicolas [Idaho National Lab. (INL), Idaho Falls, ID (United States); Strydom, Gerhard [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2016-09-01

    Best-estimate plus uncertainty analysis of reactors is replacing the traditional conservative (stacked uncertainty) method for safety and licensing analysis. To facilitate uncertainty analysis applications, a comprehensive approach and methodology must be developed and applied. High temperature gas cooled reactors (HTGRs) have several features that require techniques not used in light-water reactor analysis (e.g., coated-particle design and large graphite quantities at high temperatures). The International Atomic Energy Agency has therefore launched the Coordinated Research Project on HTGR Uncertainty Analysis in Modeling to study uncertainty propagation in the HTGR analysis chain. The benchmark problem defined for the prismatic design is represented by the General Atomics Modular HTGR 350. The main focus of this report is the compilation and discussion of the results obtained for various permutations of Exercise I 2c and the use of the cross section data in Exercise II 1a of the prismatic benchmark, which is defined as the last and first steps of the lattice and core simulation phases, respectively. The report summarizes the Idaho National Laboratory (INL) best estimate results obtained for Exercise I 2a (fresh single-fuel block), Exercise I 2b (depleted single-fuel block), and Exercise I 2c (super cell) in addition to the first results of an investigation into the cross section generation effects for the super-cell problem. The two dimensional deterministic code known as the New ESC based Weighting Transport (NEWT) included in the Standardized Computer Analyses for Licensing Evaluation (SCALE) 6.1.2 package was used for the cross section evaluation, and the results obtained were compared to the three dimensional stochastic SCALE module KENO VI. The NEWT cross section libraries were generated for several permutations of the current benchmark super-cell geometry and were then provided as input to the Phase II core calculation of the stand alone neutronics Exercise

  12. Development of a Prototype Model-Form Uncertainty Knowledge Base

    Science.gov (United States)

    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.

  13. Meteorological Uncertainty of atmospheric Dispersion model results (MUD)

    DEFF Research Database (Denmark)

    Havskov Sørensen, Jens; Amstrup, Bjarne; Feddersen, Henrik

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the ‘most likely’ di...

  14. A Model-Free Definition of Increasing Uncertainty

    NARCIS (Netherlands)

    Grant, S.; Quiggin, J.

    2001-01-01

    We present a definition of increasing uncertainty, independent of any notion of subjective probabilities, or of any particular model of preferences.Our notion of an elementary increase in the uncertainty of any act corresponds to the addition of an 'elementary bet' which increases consumption by a

  15. Improved Wave-vessel Transfer Functions by Uncertainty Modelling

    DEFF Research Database (Denmark)

    Nielsen, Ulrik Dam; Fønss Bach, Kasper; Iseki, Toshio

    2016-01-01

    This paper deals with uncertainty modelling of wave-vessel transfer functions used to calculate or predict wave-induced responses of a ship in a seaway. Although transfer functions, in theory, can be calculated to exactly reflect the behaviour of the ship when exposed to waves, uncertainty in inp...

  16. Uncertainties in environmental radiological assessment models and their implications

    International Nuclear Information System (INIS)

    Hoffman, F.O.; Miller, C.W.

    1983-01-01

    Environmental radiological assessments rely heavily on the use of mathematical models. The predictions of these models are inherently uncertain because these models are inexact representations of real systems. The major sources of this uncertainty are related to biases in model formulation and parameter estimation. The best approach for estimating the actual extent of over- or underprediction is model validation, a procedure that requires testing over the range of the intended realm of model application. Other approaches discussed are the use of screening procedures, sensitivity and stochastic analyses, and model comparison. The magnitude of uncertainty in model predictions is a function of the questions asked of the model and the specific radionuclides and exposure pathways of dominant importance. Estimates are made of the relative magnitude of uncertainty for situations requiring predictions of individual and collective risks for both chronic and acute releases of radionuclides. It is concluded that models developed as research tools should be distinguished from models developed for assessment applications. Furthermore, increased model complexity does not necessarily guarantee increased accuracy. To improve the realism of assessment modeling, stochastic procedures are recommended that translate uncertain parameter estimates into a distribution of predicted values. These procedures also permit the importance of model parameters to be ranked according to their relative contribution to the overall predicted uncertainty. Although confidence in model predictions can be improved through site-specific parameter estimation and increased model validation, risk factors and internal dosimetry models will probably remain important contributors to the amount of uncertainty that is irreducible

  17. Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment

    Science.gov (United States)

    Koskela, J. J.; Croke, B. W. F.; Koivusalo, H.; Jakeman, A. J.; Kokkonen, T.

    2012-11-01

    Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.

  18. Bayesian models for comparative analysis integrating phylogenetic uncertainty

    Directory of Open Access Journals (Sweden)

    Villemereuil Pierre de

    2012-06-01

    Full Text Available Abstract Background Uncertainty in comparative analyses can come from at least two sources: a phylogenetic uncertainty in the tree topology or branch lengths, and b uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow and inflated significance in hypothesis testing (e.g. p-values will be too small. Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. Methods We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. Results We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Conclusions Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible

  19. Bayesian models for comparative analysis integrating phylogenetic uncertainty

    Science.gov (United States)

    2012-01-01

    Background Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. Methods We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. Results We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Conclusions Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible general purpose tool for

  20. Sensitivity and uncertainty analyses for performance assessment modeling

    International Nuclear Information System (INIS)

    Doctor, P.G.

    1988-08-01

    Sensitivity and uncertainty analyses methods for computer models are being applied in performance assessment modeling in the geologic high level radioactive waste repository program. The models used in performance assessment tend to be complex physical/chemical models with large numbers of input variables. There are two basic approaches to sensitivity and uncertainty analyses: deterministic and statistical. The deterministic approach to sensitivity analysis involves numerical calculation or employs the adjoint form of a partial differential equation to compute partial derivatives; the uncertainty analysis is based on Taylor series expansions of the input variables propagated through the model to compute means and variances of the output variable. The statistical approach to sensitivity analysis involves a response surface approximation to the model with the sensitivity coefficients calculated from the response surface parameters; the uncertainty analysis is based on simulation. The methods each have strengths and weaknesses. 44 refs

  1. Factoring uncertainty into restoration modeling of in-situ leach uranium mines

    Science.gov (United States)

    Johnson, Raymond H.; Friedel, Michael J.

    2009-01-01

    Postmining restoration is one of the greatest concerns for uranium in-situ leach (ISL) mining operations. The ISL-affected aquifer needs to be returned to conditions specified in the mining permit (either premining or other specified conditions). When uranium ISL operations are completed, postmining restoration is usually achieved by injecting reducing agents into the mined zone. The objective of this process is to restore the aquifer to premining conditions by reducing the solubility of uranium and other metals in the ground water. Reactive transport modeling is a potentially useful method for simulating the effectiveness of proposed restoration techniques. While reactive transport models can be useful, they are a simplification of reality that introduces uncertainty through the model conceptualization, parameterization, and calibration processes. For this reason, quantifying the uncertainty in simulated temporal and spatial hydrogeochemistry is important for postremedial risk evaluation of metal concentrations and mobility. Quantifying the range of uncertainty in key predictions (such as uranium concentrations at a specific location) can be achieved using forward Monte Carlo or other inverse modeling techniques (trial-and-error parameter sensitivity, calibration constrained Monte Carlo). These techniques provide simulated values of metal concentrations at specified locations that can be presented as nonlinear uncertainty limits or probability density functions. Decisionmakers can use these results to better evaluate environmental risk as future metal concentrations with a limited range of possibilities, based on a scientific evaluation of uncertainty.

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

    International Nuclear Information System (INIS)

    Kirchner, G.; Steiner, M.

    2008-01-01

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

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

    Science.gov (United States)

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

    2014-12-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-05-15

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

  5. Sensitivity and uncertainty analysis of the PATHWAY radionuclide transport model

    International Nuclear Information System (INIS)

    Otis, M.D.

    1983-01-01

    Procedures were developed for the uncertainty and sensitivity analysis of a dynamic model of radionuclide transport through human food chains. Uncertainty in model predictions was estimated by propagation of parameter uncertainties using a Monte Carlo simulation technique. Sensitivity of model predictions to individual parameters was investigated using the partial correlation coefficient of each parameter with model output. Random values produced for the uncertainty analysis were used in the correlation analysis for sensitivity. These procedures were applied to the PATHWAY model which predicts concentrations of radionuclides in foods grown in Nevada and Utah and exposed to fallout during the period of atmospheric nuclear weapons testing in Nevada. Concentrations and time-integrated concentrations of iodine-131, cesium-136, and cesium-137 in milk and other foods were investigated. 9 figs., 13 tabs

  6. A conceptual model for determining career choice of CHROME alumna based on farmer's conceptual models

    Science.gov (United States)

    Moore, Lisa Simmons

    This qualitative program evaluation examines the career decision-making processes and career choices of nine, African American women who participated in the Cooperating Hampton Roads Organization for Minorities in Engineering (CHROME) and who graduated from urban, rural or suburban high schools in the year 2000. The CHROME program is a nonprofit, pre-college intervention program that encourages underrepresented minority and female students to enter science, technically related, engineering, and math (STEM) career fields. The study describes career choices and decisions made by each participant over a five-year period since high school graduation. Data was collected through an Annual Report, Post High School Questionnaires, Environmental Support Questionnaires, Career Choice Questionnaires, Senior Reports, and standardized open-ended interviews. Data was analyzed using a model based on Helen C. Farmer's Conceptual Models, John Ogbu's Caste Theory and Feminist Theory. The CHROME program, based on its stated goals and tenets, was also analyzed against study findings. Findings indicated that participants received very low levels of support from counselors and teachers to pursue STEM careers and high levels of support from parents and family, the CHROME program and financial backing. Findings of this study also indicated that the majority of CHROME alumna persisted in STEM careers. The most successful participants, in terms of undergraduate degree completion and occupational prestige, were the African American women who remained single, experienced no critical incidents, came from a middle class to upper middle class socioeconomic background, and did not have children.

  7. Identifying students’ mental models of sound propagation: The role of conceptual blending in understanding conceptual change

    Directory of Open Access Journals (Sweden)

    Zdeslav Hrepic

    2010-09-01

    Full Text Available We investigated introductory physics students’ mental models of sound propagation. We used a phenomenographic method to analyze the data in the study. In addition to the scientifically accepted Wave model, students used the “Entity” model to describe the propagation of sound. In this latter model sound is a self-standing entity, different from the medium through which it propagates. All other observed alternative models contain elements of both Entity and Wave models, but at the same time are distinct from each of the constituent models. We called these models “hybrid” or “blend” models. We discuss how students use these models in various contexts before and after instruction and how our findings contribute to the understanding of conceptual change. Implications of our findings for teaching are summarized.

  8. Conceptual adsorption models and open issues pertaining to performance assessment

    International Nuclear Information System (INIS)

    Serne, R.J.

    1992-01-01

    Recently several articles have been published that question the appropriateness of the distribution coefficient, Rd, concept to quantify radionuclide migration. Several distinct issues surrounding the modeling of nuclide retardation. The first section defines adsorption terminology and discusses various adsorption processes. The next section describes five commonly used adsorption conceptual models, specifically emphasizing what attributes that affect adsorption are explicitly accommodated in each model. I also review efforts to incorporate each adsorption model into performance assessment transport computer codes. The five adsorption conceptual models are (1) the constant Rd model, (2) the parametric Rd model, (3) isotherm adsorption models, (4) mass action adsorption models, and (5) surface-complexation with electrostatics models. The final section discusses the adequacy of the distribution ratio concept, the adequacy of transport calculations that rely on constant retardation factors and the status of incorporating sophisticated adsorption models into transport codes. 86 refs., 1 fig., 1 tab

  9. Conceptual adsorption models and open issues pertaining to performance assessment

    International Nuclear Information System (INIS)

    Serne, R.J.

    1991-10-01

    Recently several articles have been published that question the appropriateness of the distribution coefficient, Rd, concept to quantify radionuclide migration. Several distinct issues are raised by various critics. In this paper I provide some perspective on issues surrounding the modeling of nuclide retardation. The first section defines adsorption terminology and discusses various adsorption processes. The next section describes five commonly used adsorption conceptual models, specifically emphasizing what attributes that affect adsorption are explicitly accommodated in each model. I also review efforts to incorporate each adsorption model into performance assessment transport computer codes. The five adsorption conceptual models are (1) the constant Rd model, (2) the parametric Rd model, (3) isotherm adsorption models, (4) mass-action adsorption models, and (5) surface-complexation with electrostatics models. The final section discusses the adequacy of the distribution ratio concept, the adequacy of transport calculations that rely on constant retardation factors and the status of incorporating sophisticated adsorption models into transport codes

  10. Guide for developing conceptual models for ecological risk assessments

    International Nuclear Information System (INIS)

    Suter, G.W., II.

    1996-05-01

    Ecological conceptual models are the result of the problem formulation phase of an ecological risk assessment, which is an important component of the Remedial Investigation process. They present hypotheses of how the site contaminants might affect the site ecology. The contaminant sources, routes, media, routes, and endpoint receptors are presented in the form of a flow chart. This guide is for preparing the conceptual models; use of this guide will standardize the models so that they will be of high quality, useful to the assessment process, and sufficiently consistent so that connections between sources of exposure and receptors can be extended across operable units (OU). Generic conceptual models are presented for source, aquatic integrator, groundwater integrator, and terrestrial OUs

  11. Modeling theoretical uncertainties in phenomenological analyses for particle physics

    Energy Technology Data Exchange (ETDEWEB)

    Charles, Jerome [CNRS, Aix-Marseille Univ, Universite de Toulon, CPT UMR 7332, Marseille Cedex 9 (France); Descotes-Genon, Sebastien [CNRS, Univ. Paris-Sud, Universite Paris-Saclay, Laboratoire de Physique Theorique (UMR 8627), Orsay Cedex (France); Niess, Valentin [CNRS/IN2P3, UMR 6533, Laboratoire de Physique Corpusculaire, Aubiere Cedex (France); Silva, Luiz Vale [CNRS, Univ. Paris-Sud, Universite Paris-Saclay, Laboratoire de Physique Theorique (UMR 8627), Orsay Cedex (France); Univ. Paris-Sud, CNRS/IN2P3, Universite Paris-Saclay, Groupe de Physique Theorique, Institut de Physique Nucleaire, Orsay Cedex (France); J. Stefan Institute, Jamova 39, P. O. Box 3000, Ljubljana (Slovenia)

    2017-04-15

    The determination of the fundamental parameters of the Standard Model (and its extensions) is often limited by the presence of statistical and theoretical uncertainties. We present several models for the latter uncertainties (random, nuisance, external) in the frequentist framework, and we derive the corresponding p values. In the case of the nuisance approach where theoretical uncertainties are modeled as biases, we highlight the important, but arbitrary, issue of the range of variation chosen for the bias parameters. We introduce the concept of adaptive p value, which is obtained by adjusting the range of variation for the bias according to the significance considered, and which allows us to tackle metrology and exclusion tests with a single and well-defined unified tool, which exhibits interesting frequentist properties. We discuss how the determination of fundamental parameters is impacted by the model chosen for theoretical uncertainties, illustrating several issues with examples from quark flavor physics. (orig.)

  12. A sliding mode observer for hemodynamic characterization under modeling uncertainties

    KAUST Repository

    Zayane, Chadia; Laleg-Kirati, Taous-Meriem

    2014-01-01

    This paper addresses the case of physiological states reconstruction in a small region of the brain under modeling uncertainties. The misunderstood coupling between the cerebral blood volume and the oxygen extraction fraction has lead to a partial

  13. Uncertainty modelling of critical column buckling for reinforced ...

    Indian Academy of Sciences (India)

    for columns, having major importance to a building's safety, are considered stability limits. ... Various research works have been carried out for uncertainty analysis in ... need appropriate material models, advanced structural simulation tools.

  14. Uncertainty of Modal Parameters Estimated by ARMA Models

    DEFF Research Database (Denmark)

    Jensen, Jacob Laigaard; Brincker, Rune; Rytter, Anders

    1990-01-01

    In this paper the uncertainties of identified modal parameters such as eidenfrequencies and damping ratios are assed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the parameters...... by simulation study of a lightly damped single degree of freedom system. Identification by ARMA models has been choosen as system identification method. It is concluded that both the sampling interval and number of sampled points may play a significant role with respect to the statistical errors. Furthermore......, it is shown that the model errors may also contribute significantly to the uncertainty....

  15. Innovative supply chain optimization models with multiple uncertainty factors

    DEFF Research Database (Denmark)

    Choi, Tsan Ming; Govindan, Kannan; Li, Xiang

    2017-01-01

    Uncertainty is an inherent factor that affects all dimensions of supply chain activities. In today’s business environment, initiatives to deal with one specific type of uncertainty might not be effective since other types of uncertainty factors and disruptions may be present. These factors relate...... to supply chain competition and coordination. Thus, to achieve a more efficient and effective supply chain requires the deployment of innovative optimization models and novel methods. This preface provides a concise review of critical research issues regarding innovative supply chain optimization models...

  16. Modelling and propagation of uncertainties in the German Risk Study

    International Nuclear Information System (INIS)

    Hofer, E.; Krzykacz, B.

    1982-01-01

    Risk assessments are generally subject to uncertainty considerations. This is because of the various estimates that are involved. The paper points out those estimates in the so-called phase A of the German Risk Study, for which uncertainties were quantified. It explains the probabilistic models applied in the assessment to their impact on the findings of the study. Finally the resulting subjective confidence intervals of the study results are presented and their sensitivity to these probabilistic models is investigated

  17. Modelling ecosystem service flows under uncertainty with stochiastic SPAN

    Science.gov (United States)

    Johnson, Gary W.; Snapp, Robert R.; Villa, Ferdinando; Bagstad, Kenneth J.

    2012-01-01

    Ecosystem service models are increasingly in demand for decision making. However, the data required to run these models are often patchy, missing, outdated, or untrustworthy. Further, communication of data and model uncertainty to decision makers is often either absent or unintuitive. In this work, we introduce a systematic approach to addressing both the data gap and the difficulty in communicating uncertainty through a stochastic adaptation of the Service Path Attribution Networks (SPAN) framework. The SPAN formalism assesses ecosystem services through a set of up to 16 maps, which characterize the services in a study area in terms of flow pathways between ecosystems and human beneficiaries. Although the SPAN algorithms were originally defined deterministically, we present them here in a stochastic framework which combines probabilistic input data with a stochastic transport model in order to generate probabilistic spatial outputs. This enables a novel feature among ecosystem service models: the ability to spatially visualize uncertainty in the model results. The stochastic SPAN model can analyze areas where data limitations are prohibitive for deterministic models. Greater uncertainty in the model inputs (including missing data) should lead to greater uncertainty expressed in the model’s output distributions. By using Bayesian belief networks to fill data gaps and expert-provided trust assignments to augment untrustworthy or outdated information, we can account for uncertainty in input data, producing a model that is still able to run and provide information where strictly deterministic models could not. Taken together, these attributes enable more robust and intuitive modelling of ecosystem services under uncertainty.

  18. Conceptual Modeling of Time-Varying Information

    DEFF Research Database (Denmark)

    Gregersen, Heidi; Jensen, Christian S.

    2004-01-01

    A wide range of database applications manage information that varies over time. Many of the underlying database schemas of these were designed using the Entity-Relationship (ER) model. In the research community as well as in industry, it is common knowledge that the temporal aspects of the mini......-world are important, but difficult to capture using the ER model. Several enhancements to the ER model have been proposed in an attempt to support the modeling of temporal aspects of information. Common to the existing temporally extended ER models, few or no specific requirements to the models were given...

  19. Model-specification uncertainty in future forest pest outbreak.

    Science.gov (United States)

    Boulanger, Yan; Gray, David R; Cooke, Barry J; De Grandpré, Louis

    2016-04-01

    Climate change will modify forest pest outbreak characteristics, although there are disagreements regarding the specifics of these changes. A large part of this variability may be attributed to model specifications. As a case study, we developed a consensus model predicting spruce budworm (SBW, Choristoneura fumiferana [Clem.]) outbreak duration using two different predictor data sets and six different correlative methods. The model was used to project outbreak duration and the uncertainty associated with using different data sets and correlative methods (=model-specification uncertainty) for 2011-2040, 2041-2070 and 2071-2100, according to three forcing scenarios (RCP 2.6, RCP 4.5 and RCP 8.5). The consensus model showed very high explanatory power and low bias. The model projected a more important northward shift and decrease in outbreak duration under the RCP 8.5 scenario. However, variation in single-model projections increases with time, making future projections highly uncertain. Notably, the magnitude of the shifts in northward expansion, overall outbreak duration and the patterns of outbreaks duration at the southern edge were highly variable according to the predictor data set and correlative method used. We also demonstrated that variation in forcing scenarios contributed only slightly to the uncertainty of model projections compared with the two sources of model-specification uncertainty. Our approach helped to quantify model-specification uncertainty in future forest pest outbreak characteristics. It may contribute to sounder decision-making by acknowledging the limits of the projections and help to identify areas where model-specification uncertainty is high. As such, we further stress that this uncertainty should be strongly considered when making forest management plans, notably by adopting adaptive management strategies so as to reduce future risks. © 2015 Her Majesty the Queen in Right of Canada Global Change Biology © 2015 Published by John

  20. A Conceptual Framework of Business Model Emerging Resilience

    OpenAIRE

    Goumagias, Nik; Fernandes, Kiran; Cabras, Ignazio; Li, Feng; Shao, Jianhao; Devlin, Sam; Hodge, Victoria Jane; Cowling, Peter Ivan; Kudenko, Daniel

    2016-01-01

    In this paper we introduce an environmentally driven conceptual framework of Business Model change. Business models acquired substantial momentum in academic literature during the past decade. Several studies focused on what exactly constitutes a Business Model (role model, recipe, architecture etc.) triggering a theoretical debate about the Business Model’s components and their corresponding dynamics and relationships. In this paper, we argue that for Business Models as cognitive structures,...

  1. Comparison of two conceptual models of flow using the TSA

    International Nuclear Information System (INIS)

    Wilson, M.L.

    1992-01-01

    Several new capabilities have been added to the Total-System Analyzer (TSA), including a new model of unsaturated flow and transport, two new models of source releases, a different computational method for saturated transport, and gas-release capability. In this paper these new capabilities are described, and a comparison is made of results from the two different conceptual models of unsaturated flow that are now part of the TSA, a composite-porosity model and a simple fracture-flow model

  2. Uncertainty analysis for a field-scale P loss model

    Science.gov (United States)

    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 assessed the effect of model input error on predic...

  3. Conceptual language models for domain-specific retrieval

    NARCIS (Netherlands)

    Meij, E.; Trieschnigg, D.; de Rijke, M.; Kraaij, W.

    2010-01-01

    Over the years, various meta-languages have been used to manually enrich documents with conceptual knowledge of some kind. Examples include keyword assignment to citations or, more recently, tags to websites. In this paper we propose generative concept models as an extension to query modeling within

  4. River City High School Guidance Services: A Conceptual Model.

    Science.gov (United States)

    American Coll. Testing Program, Iowa City, IA.

    This model describes how the guidance staff at a hypothetical high school communicated the effectiveness of the guidance program to students, parents, teachers, and administrators. A description of the high school is presented, and guidance services and personnel are described. A conceptual model responding to student needs is outlined along with…

  5. Conceptual basis for developing of trainig models in complex ...

    African Journals Online (AJOL)

    This paper presents conceptual basis for developing of training models of interactive assembling system for automatic building of application software systems, ... software generation, such as: program module compatibility, formalization of computer interaction and choosing of formal model for human machine interface.

  6. Regionalising Parameters of a Conceptual Rainfall-Runoff Model for ...

    African Journals Online (AJOL)

    IHACRES, a lumped conceptual rainfall-runoff model, was calibrated to six catchments ranging in size from 49km2 to 600 km2 within the upper Tana River basin to obtain a set of model parameters that characterise the hydrological behaviour within the region. Physical catchment attributes indexing topography, soil and ...

  7. Conceptual Model of Artifacts for Design Science Research

    DEFF Research Database (Denmark)

    Bækgaard, Lars

    2015-01-01

    We present a conceptual model of design science research artifacts. The model views an artifact at three levels. At the artifact level a selected artifact is viewed as a combination of material and immaterial aspects and a set of representations hereof. At the design level the selected artifact...

  8. Menthor Editor: An Ontology-Driven Conceptual Modeling Platform

    NARCIS (Netherlands)

    Moreira, João Luiz; Sales, Tiago Prince; Guerson, John; Braga, Bernardo F.B; Brasileiro, Freddy; Sobral, Vinicius

    2016-01-01

    The lack of well-founded constructs in ontology tools can lead to the construction of non-intended models. In this demonstration we present the Menthor Editor, an ontology-driven conceptual modelling platform which incorporates the theories of the Unified Foundational Ontology (UFO). We illustrate

  9. Virtual Business Collaboration Conceptual Knowledge Model (VBCKM)

    OpenAIRE

    Morcous Massoud Yassa; Fatama A Omara; Hesham A Hassan

    2012-01-01

    Within the context of virtual business collaboration modeling, many pervious works have been accepted to consider some essential virtual business collaborative models. A practical dynamic virtual organization may be a combination of those models and some other elemental features with some modifications to meet the business opportunity requirements. Therefore, some guidelines and rules are needed to help in constructing a practical collaboration model. This work aims to determine the essential...

  10. Meteorological uncertainty of atmospheric dispersion model results (MUD)

    Energy Technology Data Exchange (ETDEWEB)

    Havskov Soerensen, J.; Amstrup, B.; Feddersen, H. [Danish Meteorological Institute, Copenhagen (Denmark)] [and others

    2013-08-15

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for long-range atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing e.g. the initial state of an NWP model run in agreement with the available observational data, an ensemble of meteorological forecasts is produced from which uncertainties in the various meteorological parameters are estimated, e.g. probabilities for rain. Corresponding ensembles of atmospheric dispersion can now be computed from which uncertainties of predicted radionuclide concentration and deposition patterns can be derived. (Author)

  11. Meteorological uncertainty of atmospheric dispersion model results (MUD)

    International Nuclear Information System (INIS)

    Havskov Soerensen, J.; Amstrup, B.; Feddersen, H.

    2013-08-01

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for long-range atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing e.g. the initial state of an NWP model run in agreement with the available observational data, an ensemble of meteorological forecasts is produced from which uncertainties in the various meteorological parameters are estimated, e.g. probabilities for rain. Corresponding ensembles of atmospheric dispersion can now be computed from which uncertainties of predicted radionuclide concentration and deposition patterns can be derived. (Author)

  12. The explicit treatment of model uncertainties in the presence of aleatory and epistemic parameter uncertainties in risk and reliability analysis

    International Nuclear Information System (INIS)

    Ahn, Kwang Il; Yang, Joon Eon

    2003-01-01

    In the risk and reliability analysis of complex technological systems, the primary concern of formal uncertainty analysis is to understand why uncertainties arise, and to evaluate how they impact the results of the analysis. In recent times, many of the uncertainty analyses have focused on parameters of the risk and reliability analysis models, whose values are uncertain in an aleatory or an epistemic way. As the field of parametric uncertainty analysis matures, however, more attention is being paid to the explicit treatment of uncertainties that are addressed in the predictive model itself as well as the accuracy of the predictive model. The essential steps for evaluating impacts of these model uncertainties in the presence of parameter uncertainties are to determine rigorously various sources of uncertainties to be addressed in an underlying model itself and in turn model parameters, based on our state-of-knowledge and relevant evidence. Answering clearly the question of how to characterize and treat explicitly the forgoing different sources of uncertainty is particularly important for practical aspects such as risk and reliability optimization of systems as well as more transparent risk information and decision-making under various uncertainties. The main purpose of this paper is to provide practical guidance for quantitatively treating various model uncertainties that would often be encountered in the risk and reliability modeling process of complex technological systems

  13. Uncertainty analysis and validation of environmental models. The empirically based uncertainty analysis

    International Nuclear Information System (INIS)

    Monte, Luigi; Hakanson, Lars; Bergstroem, Ulla; Brittain, John; Heling, Rudie

    1996-01-01

    The principles of Empirically Based Uncertainty Analysis (EBUA) are described. EBUA is based on the evaluation of 'performance indices' that express the level of agreement between the model and sets of empirical independent data collected in different experimental circumstances. Some of these indices may be used to evaluate the confidence limits of the model output. The method is based on the statistical analysis of the distribution of the index values and on the quantitative relationship of these values with the ratio 'experimental data/model output'. Some performance indices are described in the present paper. Among these, the so-called 'functional distance' (d) between the logarithm of model output and the logarithm of the experimental data, defined as d 2 =Σ n 1 ( ln M i - ln O i ) 2 /n where M i is the i-th experimental value, O i the corresponding model evaluation and n the number of the couplets 'experimental value, predicted value', is an important tool for the EBUA method. From the statistical distribution of this performance index, it is possible to infer the characteristics of the distribution of the ratio 'experimental data/model output' and, consequently to evaluate the confidence limits for the model predictions. This method was applied to calculate the uncertainty level of a model developed to predict the migration of radiocaesium in lacustrine systems. Unfortunately, performance indices are affected by the uncertainty of the experimental data used in validation. Indeed, measurement results of environmental levels of contamination are generally associated with large uncertainty due to the measurement and sampling techniques and to the large variability in space and time of the measured quantities. It is demonstrated that this non-desired effect, in some circumstances, may be corrected by means of simple formulae

  14. Application of a Novel Dose-Uncertainty Model for Dose-Uncertainty Analysis in Prostate Intensity-Modulated Radiotherapy

    International Nuclear Information System (INIS)

    Jin Hosang; Palta, Jatinder R.; Kim, You-Hyun; Kim, Siyong

    2010-01-01

    Purpose: To analyze dose uncertainty using a previously published dose-uncertainty model, and to assess potential dosimetric risks existing in prostate intensity-modulated radiotherapy (IMRT). Methods and Materials: The dose-uncertainty model provides a three-dimensional (3D) dose-uncertainty distribution in a given confidence level. For 8 retrospectively selected patients, dose-uncertainty maps were constructed using the dose-uncertainty model at the 95% CL. In addition to uncertainties inherent to the radiation treatment planning system, four scenarios of spatial errors were considered: machine only (S1), S1 + intrafraction, S1 + interfraction, and S1 + both intrafraction and interfraction errors. To evaluate the potential risks of the IMRT plans, three dose-uncertainty-based plan evaluation tools were introduced: confidence-weighted dose-volume histogram, confidence-weighted dose distribution, and dose-uncertainty-volume histogram. Results: Dose uncertainty caused by interfraction setup error was more significant than that of intrafraction motion error. The maximum dose uncertainty (95% confidence) of the clinical target volume (CTV) was smaller than 5% of the prescribed dose in all but two cases (13.9% and 10.2%). The dose uncertainty for 95% of the CTV volume ranged from 1.3% to 2.9% of the prescribed dose. Conclusions: The dose uncertainty in prostate IMRT could be evaluated using the dose-uncertainty model. Prostate IMRT plans satisfying the same plan objectives could generate a significantly different dose uncertainty because a complex interplay of many uncertainty sources. The uncertainty-based plan evaluation contributes to generating reliable and error-resistant treatment plans.

  15. Partitioning uncertainty in streamflow projections under nonstationary model conditions

    Science.gov (United States)

    Chawla, Ila; Mujumdar, P. P.

    2018-02-01

    Assessing the impacts of Land Use (LU) and climate change on future streamflow projections is necessary for efficient management of water resources. However, model projections are burdened with significant uncertainty arising from various sources. Most of the previous studies have considered climate models and scenarios as major sources of uncertainty, but uncertainties introduced by land use change and hydrologic model assumptions are rarely investigated. In this paper an attempt is made to segregate the contribution from (i) general circulation models (GCMs), (ii) emission scenarios, (iii) land use scenarios, (iv) stationarity assumption of the hydrologic model, and (v) internal variability of the processes, to overall uncertainty in streamflow projections using analysis of variance (ANOVA) approach. Generally, most of the impact assessment studies are carried out with unchanging hydrologic model parameters in future. It is, however, necessary to address the nonstationarity in model parameters with changing land use and climate. In this paper, a regression based methodology is presented to obtain the hydrologic model parameters with changing land use and climate scenarios in future. The Upper Ganga Basin (UGB) in India is used as a case study to demonstrate the methodology. The semi-distributed Variable Infiltration Capacity (VIC) model is set-up over the basin, under nonstationary conditions. Results indicate that model parameters vary with time, thereby invalidating the often-used assumption of model stationarity. The streamflow in UGB under the nonstationary model condition is found to reduce in future. The flows are also found to be sensitive to changes in land use. Segregation results suggest that model stationarity assumption and GCMs along with their interactions with emission scenarios, act as dominant sources of uncertainty. This paper provides a generalized framework for hydrologists to examine stationarity assumption of models before considering them

  16. Motivation to Improve Work through Learning: A Conceptual Model

    Directory of Open Access Journals (Sweden)

    Kueh Hua Ng

    2014-12-01

    Full Text Available This study aims to enhance our current understanding of the transfer of training by proposing a conceptual model that supports the mediating role of motivation to improve work through learning about the relationship between social support and the transfer of training. The examination of motivation to improve work through motivation to improve work through a learning construct offers a holistic view pertaining to a learner's profile in a workplace setting, which emphasizes learning for the improvement of work performance. The proposed conceptual model is expected to benefit human resource development theory building, as well as field practitioners by emphasizing the motivational aspects crucial for successful transfer of training.

  17. A Conceptual Model for Leadership Transition

    Science.gov (United States)

    Manderscheid, Steven V.; Ardichvili, Alexandre

    2008-01-01

    The purpose of this study was to develop a model of leadership transition based on an integrative review of literature. The article establishes a compelling case for focusing on leadership transitions as an area for study and leadership development practitioner intervention. The proposed model in this study identifies important success factors…

  18. Compilation of information on uncertainties involved in deposition modeling

    International Nuclear Information System (INIS)

    Lewellen, W.S.; Varma, A.K.; Sheng, Y.P.

    1985-04-01

    The current generation of dispersion models contains very simple parameterizations of deposition processes. The analysis here looks at the physical mechanisms governing these processes in an attempt to see if more valid parameterizations are available and what level of uncertainty is involved in either these simple parameterizations or any more advanced parameterization. The report is composed of three parts. The first, on dry deposition model sensitivity, provides an estimate of the uncertainty existing in current estimates of the deposition velocity due to uncertainties in independent variables such as meteorological stability, particle size, surface chemical reactivity and canopy structure. The range of uncertainty estimated for an appropriate dry deposition velocity for a plume generated by a nuclear power plant accident is three orders of magnitude. The second part discusses the uncertainties involved in precipitation scavenging rates for effluents resulting from a nuclear reactor accident. The conclusion is that major uncertainties are involved both as a result of the natural variability of the atmospheric precipitation process and due to our incomplete understanding of the underlying process. The third part involves a review of the important problems associated with modeling the interaction between the atmosphere and a forest. It gives an indication of the magnitude of the problem involved in modeling dry deposition in such environments. Separate analytics have been done for each section and are contained in the EDB

  19. Including model uncertainty in risk-informed decision making

    International Nuclear Information System (INIS)

    Reinert, Joshua M.; Apostolakis, George E.

    2006-01-01

    Model uncertainties can have a significant impact on decisions regarding licensing basis changes. We present a methodology to identify basic events in the risk assessment that have the potential to change the decision and are known to have significant model uncertainties. Because we work with basic event probabilities, this methodology is not appropriate for analyzing uncertainties that cause a structural change to the model, such as success criteria. We use the risk achievement worth (RAW) importance measure with respect to both the core damage frequency (CDF) and the change in core damage frequency (ΔCDF) to identify potentially important basic events. We cross-check these with generically important model uncertainties. Then, sensitivity analysis is performed on the basic event probabilities, which are used as a proxy for the model parameters, to determine how much error in these probabilities would need to be present in order to impact the decision. A previously submitted licensing basis change is used as a case study. Analysis using the SAPHIRE program identifies 20 basic events as important, four of which have model uncertainties that have been identified in the literature as generally important. The decision is fairly insensitive to uncertainties in these basic events. In three of these cases, one would need to show that model uncertainties would lead to basic event probabilities that would be between two and four orders of magnitude larger than modeled in the risk assessment before they would become important to the decision. More detailed analysis would be required to determine whether these higher probabilities are reasonable. Methods to perform this analysis from the literature are reviewed and an example is demonstrated using the case study

  20. Sensitivities and uncertainties of modeled ground temperatures in mountain environments

    Directory of Open Access Journals (Sweden)

    S. Gubler

    2013-08-01

    Full Text Available Model evaluation is often performed at few locations due to the lack of spatially distributed data. Since the quantification of model sensitivities and uncertainties can be performed independently from ground truth measurements, these analyses are suitable to test the influence of environmental variability on model evaluation. In this study, the sensitivities and uncertainties of a physically based mountain permafrost model are quantified within an artificial topography. The setting consists of different elevations and exposures combined with six ground types characterized by porosity and hydraulic properties. The analyses are performed for a combination of all factors, that allows for quantification of the variability of model sensitivities and uncertainties within a whole modeling domain. We found that model sensitivities and uncertainties vary strongly depending on different input factors such as topography or different soil types. The analysis shows that model evaluation performed at single locations may not be representative for the whole modeling domain. For example, the sensitivity of modeled mean annual ground temperature to ground albedo ranges between 0.5 and 4 °C depending on elevation, aspect and the ground type. South-exposed inclined locations are more sensitive to changes in ground albedo than north-exposed slopes since they receive more solar radiation. The sensitivity to ground albedo increases with decreasing elevation due to shorter duration of the snow cover. The sensitivity in the hydraulic properties changes considerably for different ground types: rock or clay, for instance, are not sensitive to uncertainties in the hydraulic properties, while for gravel or peat, accurate estimates of the hydraulic properties significantly improve modeled ground temperatures. The discretization of ground, snow and time have an impact on modeled mean annual ground temperature (MAGT that cannot be neglected (more than 1 °C for several

  1. The ACTIVE conceptual framework as a structural equation model

    Science.gov (United States)

    Gross, Alden L.; Payne, Brennan R.; Casanova, Ramon; Davoudzadeh, Pega; Dzierzewski, Joseph M.; Farias, Sarah; Giovannetti, Tania; Ip, Edward H.; Marsiske, Michael; Rebok, George W.; Schaie, K. Warner; Thomas, Kelsey; Willis, Sherry; Jones, Richard N.

    2018-01-01

    Background/Study Context Conceptual frameworks are analytic models at a high level of abstraction. Their operationalization can inform randomized trial design and sample size considerations. Methods The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) conceptual framework was empirically tested using structural equation modeling (N=2,802). ACTIVE was guided by a conceptual framework for cognitive training in which proximal cognitive abilities (memory, inductive reasoning, speed of processing) mediate treatment-related improvement in primary outcomes (everyday problem-solving, difficulty with activities of daily living, everyday speed, driving difficulty), which in turn lead to improved secondary outcomes (health-related quality of life, health service utilization, mobility). Measurement models for each proximal, primary, and secondary outcome were developed and tested using baseline data. Each construct was then combined in one model to evaluate fit (RMSEA, CFI, normalized residuals of each indicator). To expand the conceptual model and potentially inform future trials, evidence of modification of structural model parameters was evaluated by age, years of education, sex, race, and self-rated health status. Results Preconceived measurement models for memory, reasoning, speed of processing, everyday problem-solving, instrumental activities of daily living (IADL) difficulty, everyday speed, driving difficulty, and health-related quality of life each fit well to the data (all RMSEA .95). Fit of the full model was excellent (RMSEA = .038; CFI = .924). In contrast with previous findings from ACTIVE regarding who benefits from training, interaction testing revealed associations between proximal abilities and primary outcomes are stronger on average by nonwhite race, worse health, older age, and less education (p conceptual model. Findings suggest that the types of people who show intervention effects on cognitive performance potentially may be

  2. The ACTIVE conceptual framework as a structural equation model.

    Science.gov (United States)

    Gross, Alden L; Payne, Brennan R; Casanova, Ramon; Davoudzadeh, Pega; Dzierzewski, Joseph M; Farias, Sarah; Giovannetti, Tania; Ip, Edward H; Marsiske, Michael; Rebok, George W; Schaie, K Warner; Thomas, Kelsey; Willis, Sherry; Jones, Richard N

    2018-01-01

    Background/Study Context: Conceptual frameworks are analytic models at a high level of abstraction. Their operationalization can inform randomized trial design and sample size considerations. The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) conceptual framework was empirically tested using structural equation modeling (N=2,802). ACTIVE was guided by a conceptual framework for cognitive training in which proximal cognitive abilities (memory, inductive reasoning, speed of processing) mediate treatment-related improvement in primary outcomes (everyday problem-solving, difficulty with activities of daily living, everyday speed, driving difficulty), which in turn lead to improved secondary outcomes (health-related quality of life, health service utilization, mobility). Measurement models for each proximal, primary, and secondary outcome were developed and tested using baseline data. Each construct was then combined in one model to evaluate fit (RMSEA, CFI, normalized residuals of each indicator). To expand the conceptual model and potentially inform future trials, evidence of modification of structural model parameters was evaluated by age, years of education, sex, race, and self-rated health status. Preconceived measurement models for memory, reasoning, speed of processing, everyday problem-solving, instrumental activities of daily living (IADL) difficulty, everyday speed, driving difficulty, and health-related quality of life each fit well to the data (all RMSEA .95). Fit of the full model was excellent (RMSEA = .038; CFI = .924). In contrast with previous findings from ACTIVE regarding who benefits from training, interaction testing revealed associations between proximal abilities and primary outcomes are stronger on average by nonwhite race, worse health, older age, and less education (p conceptual model. Findings suggest that the types of people who show intervention effects on cognitive performance potentially may be different from

  3. Uncertainty calculation in transport models and forecasts

    DEFF Research Database (Denmark)

    Manzo, Stefano; Prato, Carlo Giacomo

    Transport projects and policy evaluations are often based on transport model output, i.e. traffic flows and derived effects. However, literature has shown that there is often a considerable difference between forecasted and observed traffic flows. This difference causes misallocation of (public...... implemented by using an approach based on stochastic techniques (Monte Carlo simulation and Bootstrap re-sampling) or scenario analysis combined with model sensitivity tests. Two transport models are used as case studies: the Næstved model and the Danish National Transport Model. 3 The first paper...... in a four-stage transport model related to different variable distributions (to be used in a Monte Carlo simulation procedure), assignment procedures and levels of congestion, at both the link and the network level. The analysis used as case study the Næstved model, referring to the Danish town of Næstved2...

  4. Multilevel Models: Conceptual Framework and Applicability

    Directory of Open Access Journals (Sweden)

    Roxana-Otilia-Sonia Hrițcu

    2015-10-01

    Full Text Available Individuals and the social or organizational groups they belong to can be viewed as a hierarchical system situated on different levels. Individuals are situated on the first level of the hierarchy and they are nested together on the higher levels. Individuals interact with the social groups they belong to and are influenced by these groups. Traditional methods that study the relationships between data, like simple regression, do not take into account the hierarchical structure of the data and the effects of a group membership and, hence, results may be invalidated. Unlike standard regression modelling, the multilevel approach takes into account the individuals as well as the groups to which they belong. To take advantage of the multilevel analysis it is important that we recognize the multilevel characteristics of the data. In this article we introduce the outlines of multilevel data and we describe the models that work with such data. We introduce the basic multilevel model, the two-level model: students can be nested into classes, individuals into countries and the general two-level model can be extended very easily to several levels. Multilevel analysis has begun to be extensively used in many research areas. We present the most frequent study areas where multilevel models are used, such as sociological studies, education, psychological research, health studies, demography, epidemiology, biology, environmental studies and entrepreneurship. We support the idea that since hierarchies exist everywhere, multilevel data should be recognized and analyzed properly by using multilevel modelling.

  5. UNCERTAINTY SUPPLY CHAIN MODEL AND TRANSPORT IN ITS DEPLOYMENTS

    Directory of Open Access Journals (Sweden)

    Fabiana Lucena Oliveira

    2014-05-01

    Full Text Available This article discusses the Model Uncertainty of Supply Chain, and proposes a matrix with their transportation modes best suited to their chains. From the detailed analysis of the matrix of uncertainty, it is suggested transportation modes best suited to the management of these chains, so that transport is the most appropriate optimization of the gains previously proposed by the original model, particularly when supply chains are distant from suppliers of raw materials and / or supplies.Here we analyze in detail Agile Supply Chains, which is a result of Uncertainty Supply Chain Model, with special attention to Manaus Industrial Center. This research was done at Manaus Industrial Pole, which is a model of industrial agglomerations, based in Manaus, State of Amazonas (Brazil, which contemplates different supply chains and strategies sharing same infrastructure of transport, handling and storage and clearance process and uses inbound for suppliers of raw material.  The state of art contemplates supply chain management, uncertainty supply chain model, agile supply chains, Manaus Industrial Center (MIC and Brazilian legislation, as a business case, and presents concepts and features, of each one. The main goal is to present and discuss how transport is able to support Uncertainty Supply Chain Model, in order to complete management model. The results obtained confirms the hypothesis of integrated logistics processes are able to guarantee attractivity for industrial agglomerations, and open discussions when the suppliers are far from the manufacturer center, in a logistics management.

  6. Uncertainty quantification in Rothermel's Model using an efficient sampling method

    Science.gov (United States)

    Edwin Jimenez; M. Yousuff Hussaini; Scott L. Goodrick

    2007-01-01

    The purpose of the present work is to quantify parametric uncertainty in Rothermel’s wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread models in the United States. This model consists of a nonlinear system of equations that relates environmental variables (input parameter...

  7. Model Uncertainty and Robustness: A Computational Framework for Multimodel Analysis

    Science.gov (United States)

    Young, Cristobal; Holsteen, Katherine

    2017-01-01

    Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all…

  8. Impact of inherent meteorology uncertainty on air quality model predictions

    Science.gov (United States)

    It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is impor...

  9. Uncertainty management in integrated modelling, the IMAGE case

    International Nuclear Information System (INIS)

    Van der Sluijs, J.P.

    1995-01-01

    Integrated assessment models of global environmental problems play an increasingly important role in decision making. This use demands a good insight regarding the reliability of these models. In this paper we analyze uncertainty management in the IMAGE-project (Integrated Model to Assess the Greenhouse Effect). We use a classification scheme comprising type and source of uncertainty. Our analysis shows reliability analysis as main area for improvement. We briefly review a recently developed methodology, NUSAP (Numerical, Unit, Spread, Assessment and Pedigree), that systematically addresses the strength of data in terms of spread, reliability and scientific status (pedigree) of information. This approach is being tested through interviews with model builders. 3 tabs., 20 refs

  10. Robustness for slope stability modelling under deep uncertainty

    Science.gov (United States)

    Almeida, Susana; Holcombe, Liz; Pianosi, Francesca; Wagener, Thorsten

    2015-04-01

    Landslides can have large negative societal and economic impacts, such as loss of life and damage to infrastructure. However, the ability of slope stability assessment to guide management is limited by high levels of uncertainty in model predictions. Many of these uncertainties cannot be easily quantified, such as those linked to climate change and other future socio-economic conditions, restricting the usefulness of traditional decision analysis tools. Deep uncertainty can be managed more effectively by developing robust, but not necessarily optimal, policies that are expected to perform adequately under a wide range of future conditions. Robust strategies are particularly valuable when the consequences of taking a wrong decision are high as is often the case of when managing natural hazard risks such as landslides. In our work a physically based numerical model of hydrologically induced slope instability (the Combined Hydrology and Stability Model - CHASM) is applied together with robust decision making to evaluate the most important uncertainties (storm events, groundwater conditions, surface cover, slope geometry, material strata and geotechnical properties) affecting slope stability. Specifically, impacts of climate change on long-term slope stability are incorporated, accounting for the deep uncertainty in future climate projections. Our findings highlight the potential of robust decision making to aid decision support for landslide hazard reduction and risk management under conditions of deep uncertainty.

  11. Uncertainty and Complexity in Mathematical Modeling

    Science.gov (United States)

    Cannon, Susan O.; Sanders, Mark

    2017-01-01

    Modeling is an effective tool to help students access mathematical concepts. Finding a math teacher who has not drawn a fraction bar or pie chart on the board would be difficult, as would finding students who have not been asked to draw models and represent numbers in different ways. In this article, the authors will discuss: (1) the properties of…

  12. Model Uncertainty and Exchange Rate Forecasting

    NARCIS (Netherlands)

    Kouwenberg, R.; Markiewicz, A.; Verhoeks, R.; Zwinkels, R.C.J.

    2017-01-01

    Exchange rate models with uncertain and incomplete information predict that investors focus on a small set of fundamentals that changes frequently over time. We design a model selection rule that captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample tests show

  13. An independent verification and validation of the Future Theater Level Model conceptual model

    Energy Technology Data Exchange (ETDEWEB)

    Hartley, D.S. III; Kruse, K.L.; Martellaro, A.J.; Packard, S.L.; Thomas, B. Jr.; Turley, V.K.

    1994-08-01

    This report describes the methodology and results of independent verification and validation performed on a combat model in its design stage. The combat model is the Future Theater Level Model (FTLM), under development by The Joint Staff/J-8. J-8 has undertaken its development to provide an analysis tool that addresses the uncertainties of combat more directly than previous models and yields more rapid study results. The methodology adopted for this verification and validation consisted of document analyses. Included were detailed examination of the FTLM design documents (at all stages of development), the FTLM Mission Needs Statement, and selected documentation for other theater level combat models. These documents were compared to assess the FTLM as to its design stage, its purpose as an analytical combat model, and its capabilities as specified in the Mission Needs Statement. The conceptual design passed those tests. The recommendations included specific modifications as well as a recommendation for continued development. The methodology is significant because independent verification and validation have not been previously reported as being performed on a combat model in its design stage. The results are significant because The Joint Staff/J-8 will be using the recommendations from this study in determining whether to proceed with develop of the model.

  14. Evaluation of uncertainties in selected environmental dispersion models

    International Nuclear Information System (INIS)

    Little, C.A.; Miller, C.W.

    1979-01-01

    Compliance with standards of radiation dose to the general public has necessitated the use of dispersion models to predict radionuclide concentrations in the environment due to releases from nuclear facilities. Because these models are only approximations of reality and because of inherent variations in the input parameters used in these models, their predictions are subject to uncertainty. Quantification of this uncertainty is necessary to assess the adequacy of these models for use in determining compliance with protection standards. This paper characterizes the capabilities of several dispersion models to predict accurately pollutant concentrations in environmental media. Three types of models are discussed: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations

  15. Immersive Data Comprehension: Visualizing Uncertainty in Measurable Models

    Directory of Open Access Journals (Sweden)

    Pere eBrunet

    2015-09-01

    Full Text Available Recent advances in 3D scanning technologies have opened new possibilities in a broad range of applications includingcultural heritage, medicine, civil engineering and urban planning. Virtual Reality systems can provide new tools toprofessionals that want to understand acquired 3D models. In this paper, we review the concept of data comprehension with an emphasis on visualization and inspection tools on immersive setups. We claim that in most application fields, data comprehension requires model measurements which in turn should be based on the explicit visualization of uncertainty. As 3D digital representations are not faithful, information on their fidelity at local level should be included in the model itself as uncertainty bounds. We propose the concept of Measurable 3D Models as digital models that explicitly encode local uncertainty bounds related to their quality. We claim that professionals and experts can strongly benefit from immersive interaction through new specific, fidelity-aware measurement tools which can facilitate 3D data comprehension. Since noise and processing errors are ubiquitous in acquired datasets, we discuss the estimation, representation and visualization of data uncertainty. We show that, based on typical user requirements in Cultural Heritage and other domains, application-oriented measuring tools in 3D models must consider uncertainty and local error bounds. We also discuss the requirements of immersive interaction tools for the comprehension of huge 3D and nD datasets acquired from real objects.

  16. Modelling students' knowledge organisation: Genealogical conceptual networks

    Science.gov (United States)

    Koponen, Ismo T.; Nousiainen, Maija

    2018-04-01

    Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students' concept networks.

  17. A Conceptualized Investment Model of Crowdfunding

    DEFF Research Database (Denmark)

    Tomczak, A.; Brem, Alexander

    2013-01-01

    Crowdfunding is growing in popularity as a new form of both investment opportunity and source of venture capital. This article takes a view on whether crowdfunding is a replacement or an addition to traditional seed capital sources in the early stages of a new venture. With access to angel...... investment decreasing since the financial crisis of 2008, crowdfunding is of great importance to start-ups seeking starting capital. However, little effort has been made to define the investment model of crowdfunding with both crowdfunder and crowdfundee in mind. Drawing on an in-depth review of current...... literature on crowdfunding, this article creates an investment model of crowdfunding with various reward models available to investor and investee in mind. This article provides an extensive survey of the environment of crowdfunding based on current literature. It offers a jumping off point and a thorough...

  18. Numerical Modelling of Structures with Uncertainties

    Directory of Open Access Journals (Sweden)

    Kahsin Maciej

    2017-04-01

    Full Text Available The nature of environmental interactions, as well as large dimensions and complex structure of marine offshore objects, make designing, building and operation of these objects a great challenge. This is the reason why a vast majority of investment cases of this type include structural analysis, performed using scaled laboratory models and complemented by extended computer simulations. The present paper focuses on FEM modelling of the offshore wind turbine supporting structure. Then problem is studied using the modal analysis, sensitivity analysis, as well as the design of experiment (DOE and response surface model (RSM methods. The results of modal analysis based simulations were used for assessing the quality of the FEM model against the data measured during the experimental modal analysis of the scaled laboratory model for different support conditions. The sensitivity analysis, in turn, has provided opportunities for assessing the effect of individual FEM model parameters on the dynamic response of the examined supporting structure. The DOE and RSM methods allowed to determine the effect of model parameter changes on the supporting structure response.

  19. A Conceptual Model of Investor Behavior

    NARCIS (Netherlands)

    M. Lovric (Milan); U. Kaymak (Uzay); J. Spronk (Jaap)

    2008-01-01

    textabstractBased on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is

  20. A conceptual model of investor behavior

    NARCIS (Netherlands)

    Lovric, M.; Kaymak, U.; Spronk, J.; Nefti, S.; Gray, J.O.

    2010-01-01

    Behavioral finance is a subdiscipline of finance that uses insights from cogni tive and social psychology to enrich our knowledge of how investors make their financial decisions. Agent-based artificial financial markets are bottomup models of financial markets that start from the micro level of

  1. Learning strategies: a synthesis and conceptual model

    Science.gov (United States)

    Hattie, John A. C.; Donoghue, Gregory M.

    2016-08-01

    The purpose of this article is to explore a model of learning that proposes that various learning strategies are powerful at certain stages in the learning cycle. The model describes three inputs and outcomes (skill, will and thrill), success criteria, three phases of learning (surface, deep and transfer) and an acquiring and consolidation phase within each of the surface and deep phases. A synthesis of 228 meta-analyses led to the identification of the most effective strategies. The results indicate that there is a subset of strategies that are effective, but this effectiveness depends on the phase of the model in which they are implemented. Further, it is best not to run separate sessions on learning strategies but to embed the various strategies within the content of the subject, to be clearer about developing both surface and deep learning, and promoting their associated optimal strategies and to teach the skills of transfer of learning. The article concludes with a discussion of questions raised by the model that need further research.

  2. Developing rural palliative care: validating a conceptual model.

    Science.gov (United States)

    Kelley, Mary Lou; Williams, Allison; DeMiglio, Lily; Mettam, Hilary

    2011-01-01

    The purpose of this research was to validate a conceptual model for developing palliative care in rural communities. This model articulates how local rural healthcare providers develop palliative care services according to four sequential phases. The model has roots in concepts of community capacity development, evolves from collaborative, generalist rural practice, and utilizes existing health services infrastructure. It addresses how rural providers manage challenges, specifically those related to: lack of resources, minimal community understanding of palliative care, health professionals' resistance, the bureaucracy of the health system, and the obstacles of providing services in rural environments. Seven semi-structured focus groups were conducted with interdisciplinary health providers in 7 rural communities in two Canadian provinces. Using a constant comparative analysis approach, focus group data were analyzed by examining participants' statements in relation to the model and comparing emerging themes in the development of rural palliative care to the elements of the model. The data validated the conceptual model as the model was able to theoretically predict and explain the experiences of the 7 rural communities that participated in the study. New emerging themes from the data elaborated existing elements in the model and informed the requirement for minor revisions. The model was validated and slightly revised, as suggested by the data. The model was confirmed as being a useful theoretical tool for conceptualizing the development of rural palliative care that is applicable in diverse rural communities.

  3. Geological-structural models used in SR 97. Uncertainty analysis

    Energy Technology Data Exchange (ETDEWEB)

    Saksa, P.; Nummela, J. [FINTACT Oy (Finland)

    1998-10-01

    The uncertainty of geological-structural models was studied for the three sites in SR 97, called Aberg, Beberg and Ceberg. The evaluation covered both regional and site scale models, the emphasis being placed on fracture zones in the site scale. Uncertainty is a natural feature of all geoscientific investigations. It originates from measurements (errors in data, sampling limitations, scale variation) and conceptualisation (structural geometries and properties, ambiguous geometric or parametric solutions) to name the major ones. The structures of A-, B- and Ceberg are fracture zones of varying types. No major differences in the conceptualisation between the sites were noted. One source of uncertainty in the site models is the non-existence of fracture and zone information in the scale from 10 to 300 - 1000 m. At Aberg the development of the regional model has been performed very thoroughly. At the site scale one major source of uncertainty is that a clear definition of the target area is missing. Structures encountered in the boreholes are well explained and an interdisciplinary approach in interpretation have taken place. Beberg and Ceberg regional models contain relatively large uncertainties due to the investigation methodology and experience available at that time. In site scale six additional structures were proposed both to Beberg and Ceberg to variant analysis of these sites. Both sites include uncertainty in the form of many non-interpreted fractured sections along the boreholes. Statistical analysis gives high occurrences of structures for all three sites: typically 20 - 30 structures/km{sup 3}. Aberg has highest structural frequency, Beberg comes next and Ceberg has the lowest. The borehole configuration, orientations and surveying goals were inspected to find whether preferences or factors causing bias were present. Data from Aberg supports the conclusion that Aespoe sub volume would be an anomalously fractured, tectonised unit of its own. This means that

  4. Geological-structural models used in SR 97. Uncertainty analysis

    International Nuclear Information System (INIS)

    Saksa, P.; Nummela, J.

    1998-10-01

    The uncertainty of geological-structural models was studied for the three sites in SR 97, called Aberg, Beberg and Ceberg. The evaluation covered both regional and site scale models, the emphasis being placed on fracture zones in the site scale. Uncertainty is a natural feature of all geoscientific investigations. It originates from measurements (errors in data, sampling limitations, scale variation) and conceptualisation (structural geometries and properties, ambiguous geometric or parametric solutions) to name the major ones. The structures of A-, B- and Ceberg are fracture zones of varying types. No major differences in the conceptualisation between the sites were noted. One source of uncertainty in the site models is the non-existence of fracture and zone information in the scale from 10 to 300 - 1000 m. At Aberg the development of the regional model has been performed very thoroughly. At the site scale one major source of uncertainty is that a clear definition of the target area is missing. Structures encountered in the boreholes are well explained and an interdisciplinary approach in interpretation have taken place. Beberg and Ceberg regional models contain relatively large uncertainties due to the investigation methodology and experience available at that time. In site scale six additional structures were proposed both to Beberg and Ceberg to variant analysis of these sites. Both sites include uncertainty in the form of many non-interpreted fractured sections along the boreholes. Statistical analysis gives high occurrences of structures for all three sites: typically 20 - 30 structures/km 3 . Aberg has highest structural frequency, Beberg comes next and Ceberg has the lowest. The borehole configuration, orientations and surveying goals were inspected to find whether preferences or factors causing bias were present. Data from Aberg supports the conclusion that Aespoe sub volume would be an anomalously fractured, tectonised unit of its own. This means that the

  5. Quantifying and Visualizing Uncertainties in Molecular Models

    OpenAIRE

    Rasheed, Muhibur; Clement, Nathan; Bhowmick, Abhishek; Bajaj, Chandrajit

    2015-01-01

    Computational molecular modeling and visualization has seen significant progress in recent years with sev- eral molecular modeling and visualization software systems in use today. Nevertheless the molecular biology community lacks techniques and tools for the rigorous analysis, quantification and visualization of the associated errors in molecular structure and its associated properties. This paper attempts at filling this vacuum with the introduction of a systematic statistical framework whe...

  6. A conceptual model specification language (CMSL Version 2)

    NARCIS (Netherlands)

    Wieringa, Roelf J.

    1992-01-01

    Version 2 of a language (CMSL) to specify conceptual models is defined. CMSL consists of two parts, the value specification language VSL and the object spercification language OSL. There is a formal semantics and an inference system for CMSL but research on this still continues. A method for

  7. Conceptual model for reinforced grass on inner dike slopes

    NARCIS (Netherlands)

    Verhagen, H.J.; ComCoast

    2005-01-01

    A desk study has been carried out in order to develop a conceptual model for the erosion of inner dike slopes with reinforced grass cover. Based on the results the following can be concluded: The presence of a geosynthetic in a grass slope can be taken into account in the EPM method by increasing

  8. LCM 3.0: A Language for describing Conceptual Models

    NARCIS (Netherlands)

    Feenstra, Remco; Wieringa, Roelf J.

    1993-01-01

    The syntax of the conceptual model specification language LCM is defined. LCM uses equational logic to specify data types and order-sorted dynamic logic to specify objects with identity and mutable state. LCM specifies database transactions as finite sets of atomic object transitions.

  9. A New Conceptual Model for Understanding International Students' College Needs

    Science.gov (United States)

    Alfattal, Eyad

    2016-01-01

    This study concerns the theory and practice of international marketing in higher education with the purpose of exploring a conceptual model for understanding international students' needs in the context of a four-year college in the United States. A transcendental phenomenological design was employed to investigate the essence of international…

  10. Designing Public Library Websites for Teens: A Conceptual Model

    Science.gov (United States)

    Naughton, Robin Amanda

    2012-01-01

    The main goal of this research study was to develop a conceptual model for the design of public library websites for teens (TLWs) that would enable designers and librarians to create library websites that better suit teens' information needs and practices. It bridges a gap in the research literature between user interface design in human-computer…

  11. A Conceptual Model of the World of Work.

    Science.gov (United States)

    VanRooy, William H.

    The conceptual model described in this paper resulted from the need to organize a body of knowledge related to the world of work which would enable curriculum developers to prepare accurate, realistic instructional materials. The world of work is described by applying Malinowski's scientific study of the structural components of culture. It is…

  12. Conceptual models for waste tank mechanistic analysis

    International Nuclear Information System (INIS)

    Allemann, R.T.; Antoniak, Z.I.; Eyler, L.L.; Liljegren, L.M.; Roberts, J.S.

    1992-02-01

    Pacific Northwest Laboratory (PNL) is conducting a study for Westinghouse Hanford Company (Westinghouse Hanford), a contractor for the US Department of Energy (DOE). The purpose of the work is to study possible mechanisms and fluid dynamics contributing to the periodic release of gases from double-shell waste storage tanks at the Hanford Site in Richland, Washington. This interim report emphasizing the modeling work follows two other interim reports, Mechanistic Analysis of Double-Shell Tank Gas Release Progress Report -- November 1990 and Collection and Analysis of Existing Data for Waste Tank Mechanistic Analysis Progress Report -- December 1990, that emphasized data correlation and mechanisms. The approach in this study has been to assemble and compile data that are pertinent to the mechanisms, analyze the data, evaluate physical properties and parameters, evaluate hypothetical mechanisms, and develop mathematical models of mechanisms

  13. An educational model for ensemble streamflow simulation and uncertainty analysis

    Directory of Open Access Journals (Sweden)

    A. AghaKouchak

    2013-02-01

    Full Text Available This paper presents the hands-on modeling toolbox, HBV-Ensemble, designed as a complement to theoretical hydrology lectures, to teach hydrological processes and their uncertainties. The HBV-Ensemble can be used for in-class lab practices and homework assignments, and assessment of students' understanding of hydrological processes. Using this modeling toolbox, students can gain more insights into how hydrological processes (e.g., precipitation, snowmelt and snow accumulation, soil moisture, evapotranspiration and runoff generation are interconnected. The educational toolbox includes a MATLAB Graphical User Interface (GUI and an ensemble simulation scheme that can be used for teaching uncertainty analysis, parameter estimation, ensemble simulation and model sensitivity. HBV-Ensemble was administered in a class for both in-class instruction and a final project, and students submitted their feedback about the toolbox. The results indicate that this educational software had a positive impact on students understanding and knowledge of uncertainty in hydrological modeling.

  14. A Conceptual Model of Investor Behavior

    OpenAIRE

    Lovric, M.; Kaymak, U.; Spronk, J.

    2008-01-01

    textabstractBased on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exh...

  15. Conceptual model of integrated apiarian consultancy

    OpenAIRE

    Bodescu, Dan; Stefan, Gavril; Paveliuc Olariu, Codrin; Magdici, Maria

    2010-01-01

    The socio-economic field researches have indicated the necessity of realizing an integrated consultancy service for beekeepers that will supply technical-economic solutions with a practical character for ensuring the lucrativeness and viability of the apiaries. Consequently, an integrated apiarian consultancy model has been built holding the following features: it realizes the diagnosis of the meliferous resources and supplies solutions for its optimal administration; it realizes the technica...

  16. Enhancing uncertainty tolerance in the modelling creep of ligaments

    International Nuclear Information System (INIS)

    Taha, M M Reda; Lucero, J

    2006-01-01

    The difficulty in performing biomechanical tests and the scarcity of biomechanical experimental databases necessitate extending the current knowledge base to allow efficient modelling using limited data sets. This study suggests a framework to reduce uncertainties in biomechanical systems using limited data sets. The study also shows how sparse data and epistemic input can be exploited using fuzzy logic to represent biomechanical relations. An example application to model collagen fibre recruitment in the medial collateral ligaments during time-dependent deformation under cyclic loading (creep) is presented. The study suggests a quality metric that can be employed to observe and enhance uncertainty tolerance in the modelling process

  17. Integration of inaccurate data into model building and uncertainty assessment

    Energy Technology Data Exchange (ETDEWEB)

    Coleou, Thierry

    1998-12-31

    Model building can be seen as integrating numerous measurements and mapping through data points considered as exact. As the exact data set is usually sparse, using additional non-exact data improves the modelling and reduces the uncertainties. Several examples of non-exact data are discussed and a methodology to honor them in a single pass, along with the exact data is presented. This automatic procedure is valid for both ``base case`` model building and stochastic simulations for uncertainty analysis. 5 refs., 3 figs.

  18. Uncertainty the soul of modeling, probability & statistics

    CERN Document Server

    Briggs, William

    2016-01-01

    This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, suc...

  19. Estimation and uncertainty of reversible Markov models.

    Science.gov (United States)

    Trendelkamp-Schroer, Benjamin; Wu, Hao; Paul, Fabian; Noé, Frank

    2015-11-07

    Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software--http://pyemma.org--as of version 2.0.

  20. River meander modeling and confronting uncertainty.

    Energy Technology Data Exchange (ETDEWEB)

    Posner, Ari J. (University of Arizona Tucson, AZ)

    2011-05-01

    This study examines the meandering phenomenon as it occurs in media throughout terrestrial, glacial, atmospheric, and aquatic environments. Analysis of the minimum energy principle, along with theories of Coriolis forces (and random walks to explain the meandering phenomenon) found that these theories apply at different temporal and spatial scales. Coriolis forces might induce topological changes resulting in meandering planforms. The minimum energy principle might explain how these forces combine to limit the sinuosity to depth and width ratios that are common throughout various media. The study then compares the first order analytical solutions for flow field by Ikeda, et al. (1981) and Johannesson and Parker (1989b). Ikeda's et al. linear bank erosion model was implemented to predict the rate of bank erosion in which the bank erosion coefficient is treated as a stochastic variable that varies with physical properties of the bank (e.g., cohesiveness, stratigraphy, or vegetation density). The developed model was used to predict the evolution of meandering planforms. Then, the modeling results were analyzed and compared to the observed data. Since the migration of a meandering channel consists of downstream translation, lateral expansion, and downstream or upstream rotations several measures are formulated in order to determine which of the resulting planforms is closest to the experimental measured one. Results from the deterministic model highly depend on the calibrated erosion coefficient. Since field measurements are always limited, the stochastic model yielded more realistic predictions of meandering planform evolutions. Due to the random nature of bank erosion coefficient, the meandering planform evolution is a stochastic process that can only be accurately predicted by a stochastic model.

  1. Incorporating model parameter uncertainty into inverse treatment planning

    International Nuclear Information System (INIS)

    Lian Jun; Xing Lei

    2004-01-01

    Radiobiological treatment planning depends not only on the accuracy of the models describing the dose-response relation of different tumors and normal tissues but also on the accuracy of tissue specific radiobiological parameters in these models. Whereas the general formalism remains the same, different sets of model parameters lead to different solutions and thus critically determine the final plan. Here we describe an inverse planning formalism with inclusion of model parameter uncertainties. This is made possible by using a statistical analysis-based frameset developed by our group. In this formalism, the uncertainties of model parameters, such as the parameter a that describes tissue-specific effect in the equivalent uniform dose (EUD) model, are expressed by probability density function and are included in the dose optimization process. We found that the final solution strongly depends on distribution functions of the model parameters. Considering that currently available models for computing biological effects of radiation are simplistic, and the clinical data used to derive the models are sparse and of questionable quality, the proposed technique provides us with an effective tool to minimize the effect caused by the uncertainties in a statistical sense. With the incorporation of the uncertainties, the technique has potential for us to maximally utilize the available radiobiology knowledge for better IMRT treatment

  2. Eigenspace perturbations for structural uncertainty estimation of turbulence closure models

    Science.gov (United States)

    Jofre, Lluis; Mishra, Aashwin; Iaccarino, Gianluca

    2017-11-01

    With the present state of computational resources, a purely numerical resolution of turbulent flows encountered in engineering applications is not viable. Consequently, investigations into turbulence rely on various degrees of modeling. Archetypal amongst these variable resolution approaches would be RANS models in two-equation closures, and subgrid-scale models in LES. However, owing to the simplifications introduced during model formulation, the fidelity of all such models is limited, and therefore the explicit quantification of the predictive uncertainty is essential. In such scenario, the ideal uncertainty estimation procedure must be agnostic to modeling resolution, methodology, and the nature or level of the model filter. The procedure should be able to give reliable prediction intervals for different Quantities of Interest, over varied flows and flow conditions, and at diametric levels of modeling resolution. In this talk, we present and substantiate the Eigenspace perturbation framework as an uncertainty estimation paradigm that meets these criteria. Commencing from a broad overview, we outline the details of this framework at different modeling resolution. Thence, using benchmark flows, along with engineering problems, the efficacy of this procedure is established. This research was partially supported by NNSA under the Predictive Science Academic Alliance Program (PSAAP) II, and by DARPA under the Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) project (technical monitor: Dr Fariba Fahroo).

  3. Modeling in transport phenomena a conceptual approach

    CERN Document Server

    Tosun, Ismail

    2007-01-01

    Modeling in Transport Phenomena, Second Edition presents and clearly explains with example problems the basic concepts and their applications to fluid flow, heat transfer, mass transfer, chemical reaction engineering and thermodynamics. A balanced approach is presented between analysis and synthesis, students will understand how to use the solution in engineering analysis. Systematic derivations of the equations and the physical significance of each term are given in detail, for students to easily understand and follow up the material. There is a strong incentive in science and engineering to

  4. Wound care clinical pathway: a conceptual model.

    Science.gov (United States)

    Barr, J E; Cuzzell, J

    1996-08-01

    A clinical pathway is a written sequence of clinical processes or events that guides a patient with a defined problem toward an expected outcome. Clinical pathways are tools to assist with the cost-effective management of clinical outcomes related to specific problems or disease processes. The primary obstacles to developing clinical pathways for wound care are the chronic natures of some wounds and the many variables that can delay healing. The pathway introduced in this article was modeled upon the three phases of tissue repair: inflammatory, proliferative, and maturation. This physiology-based model allows clinicians to identify and monitor outcomes based on observable and measurable clinical parameters. The pathway design, which also includes educational and behavioral outcomes, allows the clinician to individualize the expected timeframe for outcome achievement based on individual patient criteria and expert judgement. Integral to the pathway are the "4P's" which help standardize the clinical processes by wound type: Protocols, Policies, Procedures, and Patient education tools. Four categories into which variances are categorized based on the cause of the deviation from the norm are patient, process/system, practitioner, and planning/discharge. Additional research is warranted to support the value of this clinical pathway in the clinical arena.

  5. Conceptual design interpretations, mindset and models

    CERN Document Server

    Andreasen, Mogens Myrup; Cash, Philip

    2015-01-01

    Maximising reader insights into the theory, models, methods and fundamental reasoning of design, this book addresses design activities in industrial settings, as well as the actors involved. This approach offers readers a new understanding of design activities and related functions, properties and dispositions. Presenting a ‘design mindset’ that seeks to empower students, researchers, and practitioners alike, it features a strong focus on how designers create new concepts to be developed into products, and how they generate new business and satisfy human needs.   Employing a multi-faceted perspective, the book supplies the reader with a comprehensive worldview of design in the form of a proposed model that will empower their activities as student, researcher or practitioner. We draw the reader into the core role of design conceptualisation for society, for the development of industry, for users and buyers of products, and for citizens in relation to public systems. The book also features original con...

  6. Large regional groundwater modeling - a sensitivity study of some selected conceptual descriptions and simplifications; Storregional grundvattenmodellering - en kaenslighetsstudie av naagra utvalda konceptuella beskrivningar och foerenklingar

    Energy Technology Data Exchange (ETDEWEB)

    Ericsson, Lars O. (Lars O. Ericsson Consulting AB (Sweden)); Holmen, Johan (Golder Associates (Sweden))

    2010-12-15

    The primary aim of this report is: - To present a supplementary, in-depth evaluation of certain conceptual simplifications, descriptions and model uncertainties in conjunction with regional groundwater simulation, which in the first instance refer to model depth, topography, groundwater table level and boundary conditions. Implementation was based on geo-scientifically available data compilations from the Smaaland region but different conceptual assumptions have been analysed

  7. Conceptual Models as Tools for Communication Across Disciplines

    Directory of Open Access Journals (Sweden)

    Marieke Heemskerk

    2003-12-01

    Full Text Available To better understand and manage complex social-ecological systems, social scientists and ecologists must collaborate. However, issues related to language and research approaches can make it hard for researchers in different fields to work together. This paper suggests that researchers can improve interdisciplinary science through the use of conceptual models as a communication tool. The authors share lessons from a workshop in which interdisciplinary teams of young scientists developed conceptual models of social-ecological systems using data sets and metadata from Long-Term Ecological Research sites across the United States. Both the process of model building and the models that were created are discussed. The exercise revealed that the presence of social scientists in a group influenced the place and role of people in the models. This finding suggests that the participation of both ecologists and social scientists in the early stages of project development may produce better questions and more accurate models of interactions between humans and ecosystems. Although the participants agreed that a better understanding of human intentions and behavior would advance ecosystem science, they felt that interdisciplinary research might gain more by training strong disciplinarians than by merging ecology and social sciences into a new field. It is concluded that conceptual models can provide an inspiring point of departure and a guiding principle for interdisciplinary group discussions. Jointly developing a model not only helped the participants to formulate questions, clarify system boundaries, and identify gaps in existing data, but also revealed the thoughts and assumptions of fellow scientists. Although the use of conceptual models will not serve all purposes, the process of model building can help scientists, policy makers, and resource managers discuss applied problems and theory among themselves and with those in other areas.

  8. Modeling multibody systems with uncertainties. Part II: Numerical applications

    International Nuclear Information System (INIS)

    Sandu, Corina; Sandu, Adrian; Ahmadian, Mehdi

    2006-01-01

    This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper 'Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects .In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set. The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties

  9. Modeling multibody systems with uncertainties. Part II: Numerical applications

    Energy Technology Data Exchange (ETDEWEB)

    Sandu, Corina, E-mail: csandu@vt.edu; Sandu, Adrian; Ahmadian, Mehdi [Virginia Polytechnic Institute and State University, Mechanical Engineering Department (United States)

    2006-04-15

    This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper 'Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects .In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set. The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties.

  10. Uncertainty propagation through dynamic models of assemblies of mechanical structures

    International Nuclear Information System (INIS)

    Daouk, Sami

    2016-01-01

    When studying the behaviour of mechanical systems, mathematical models and structural parameters are usually considered deterministic. Return on experience shows however that these elements are uncertain in most cases, due to natural variability or lack of knowledge. Therefore, quantifying the quality and reliability of the numerical model of an industrial assembly remains a major question in low-frequency dynamics. The purpose of this thesis is to improve the vibratory design of bolted assemblies through setting up a dynamic connector model that takes account of different types and sources of uncertainty on stiffness parameters, in a simple, efficient and exploitable in industrial context. This work has been carried out in the framework of the SICODYN project, led by EDF R and D, that aims to characterise and quantify, numerically and experimentally, the uncertainties in the dynamic behaviour of bolted industrial assemblies. Comparative studies of several numerical methods of uncertainty propagation demonstrate the advantage of using the Lack-Of-Knowledge theory. An experimental characterisation of uncertainties in bolted structures is performed on a dynamic test rig and on an industrial assembly. The propagation of many small and large uncertainties through different dynamic models of mechanical assemblies leads to the assessment of the efficiency of the Lack-Of-Knowledge theory and its applicability in an industrial environment. (author)

  11. Conceptual model innovation management: market orientation

    Directory of Open Access Journals (Sweden)

    L.Ya. Maljuta

    2015-06-01

    Full Text Available The article highlights issues that determine the beginning of the innovation process. Determined that until recently in Ukraine at all levels of innovation management (regional, sectoral, institutional dominated grocery orientation innovation that focus on production innovation and found that the transition to a market economy, the restructuring of production and complexity of social needs led to the strengthening of the consumer. It is proved that innovation itself – not the ultimate goal, but only a means of satisfying consumer needs. It proved that changing production conditions, complications of social needs and the need to improve the competitiveness of innovations require finding new forms of innovation. In this regard, proposed to allocate such basic scheme (model of innovation in small businesses, individual entrepreneurs, venture capital firms, eksplerents, patients, violents and commutants, spin-offs and spin-out company, network (or shell company and a network of small businesses.

  12. Collaborative Rural Healthcare Network: A Conceptual Model

    Directory of Open Access Journals (Sweden)

    U. Raja

    2011-07-01

    Full Text Available Healthcare is a critical issue in rural communities throughout the world. Provision of timely and cost effective health care in these communities is a challenge since it is coupled with a lack of adequate infrastructure and manpower support. Twenty percent of the United States of America‘s population resides in rural communities, i.e., 59 million people; however, only nine percent of the nation’s physicians practice in rural communities. Shortage of health care personnel and the lack of equipment and facilities often force rural residents to travel long distances to receive needed medical treatment. Researchers and practitioners are in search of solutions to address these unique challenges. In this research, we present a proposed collaborative model of a health information system for rural communities and the challenges and opportunities of this global issue.

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

    Science.gov (United States)

    Drexler, M.

    2016-02-01

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

  14. A Conceptual Modeling for a GoldSim Program for Safety Assessment of an LILW Repository

    International Nuclear Information System (INIS)

    Lee, Youn Myoung; Hwang, Yong Soo; Kang, Chul Hyung; Lee, Sung Ho

    2009-12-01

    Modeling study and development of a total system performance assessment (TSPA) program, by which an assessment of safety and performance for a low- and intermediate-level radioactive waste disposal repository with normal or abnormal nuclide release cases associated with the various FEPs involved in the performance of the proposed repository could be made has been carrying out by utilizing GoldSim under contract with KRMC. The report deals with a detailed conceptual modeling scheme by which a GoldSim program modules, all of which are integrated into a TSPA program as well as the input data set currently available. In-depth system models that are conceptually and rather practically described and then ready for implementing into a GoldSim program are introduced with plenty of illustrative conceptual models and sketches. The GoldSim program that will be finally developed through this project is expected to be successfully applied to the post closure safety assessment required both for the LILW repository and pyro processed repository by the regulatory body with both increased practicality and much reduced uncertainty

  15. Conceptual model of male military sexual trauma.

    Science.gov (United States)

    Elder, William B; Domino, Jessica L; Rentz, Timothy O; Mata-Galán, Emma L

    2017-08-01

    Male sexual trauma is understudied, leaving much to be known about the unique mental health needs of male survivors. This study examined veteran men's perceptions of the effects of military sexual trauma. Military sexual trauma was defined as physically forced, verbally coerced, or substance-incapacitated acts experienced during military service. Interviews were conducted with 21 male veterans who reported experiencing military sexual trauma. Data were drawn together using a grounded theory methodology. Three categories emerged from data analysis, including (a) types of military sexual trauma (being touched in a sexual way against their will [N = 18]; sexual remarks directed at them [N = 15]; being physically forced to have sex [N = 13]); (b) negative life effects (difficulty trusting others [N = 18]; fear of abandonment [N = 17]; substance use [N = 13]; fear of interpersonal violence [N = 12]; conduct and vocational problems [N = 11]; irritability/aggression [N = 8]; insecurity about sexual performance [N = 8]; difficulty managing anger [N = 8]); and (c) posttraumatic growth (N = 15). Results from this study suggest sexual trauma in the military context may affect systems of self-organization, specifically problems in affective, self-concept, and relational domains, similar to symptoms of those who have experienced prolonged traumatic stressors. This model can be used by clinicians to select treatments that specifically target these symptoms and promote posttraumatic growth. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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

    Science.gov (United States)

    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...

  17. Tumor heterogeneity and progression: conceptual foundations for modeling.

    Science.gov (United States)

    Greller, L D; Tobin, F L; Poste, G

    1996-01-01

    A conceptual foundation for modeling tumor progression, growth, and heterogeneity is presented. The purpose of such models is to aid understanding, test ideas, formulate experiments, and to model cancer 'in machina' to address the dynamic features of tumor cell heterogeneity, progression, and growth. The descriptive capabilities of such an approach provides a consistent language for qualitatively reasoning about tumor behavior. This approach provides a schema for building conceptual models that combine three key phenomenological driving elements: growth, progression, and genetic instability. The growth element encompasses processes contributing to changes in tumor bulk and is distinct from progression per se. The progression element subsumes a broad collection of processes underlying phenotypic progression. The genetics elements represents heritable changes which potentially affect tumor character and behavior. Models, conceptual and mathematical, can be built for different tumor situations by drawing upon the interaction of these three distinct driving elements. These models can be used as tools to explore a diversity of hypotheses concerning dynamic changes in cellular populations during tumor progression, including the generation of intratumor heterogeneity. Such models can also serve to guide experimentation and to gain insight into dynamic aspects of complex tumor behavior.

  18. Estimation of a multivariate mean under model selection uncertainty

    Directory of Open Access Journals (Sweden)

    Georges Nguefack-Tsague

    2014-05-01

    Full Text Available Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty.  When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference. In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.

  19. Modeling flow in fractured medium. Uncertainty analysis with stochastic continuum approach

    International Nuclear Information System (INIS)

    Niemi, A.

    1994-01-01

    For modeling groundwater flow in formation-scale fractured media, no general method exists for scaling the highly heterogeneous hydraulic conductivity data to model parameters. The deterministic approach is limited in representing the heterogeneity of a medium and the application of fracture network models has both conceptual and practical limitations as far as site-scale studies are concerned. The study investigates the applicability of stochastic continuum modeling at the scale of data support. No scaling of the field data is involved, and the original variability is preserved throughout the modeling. Contributions of various aspects to the total uncertainty in the modeling prediction can also be determined with this approach. Data from five crystalline rock sites in Finland are analyzed. (107 refs., 63 figs., 7 tabs.)

  20. BIM-enabled Conceptual Modelling and Representation of Building Circulation

    OpenAIRE

    Lee, Jin Kook; Kim, Mi Jeong

    2014-01-01

    This paper describes how a building information modelling (BIM)-based approach for building circulation enables us to change the process of building design in terms of its computational representation and processes, focusing on the conceptual modelling and representation of circulation within buildings. BIM has been designed for use by several BIM authoring tools, in particular with the widely known interoperable industry foundation classes (IFCs), which follow an object-oriented data modelli...

  1. Advertisement Effectiveness for Print Media: A Conceptual Model

    OpenAIRE

    Prateek Maheshwari; Nitin Seth; Anoop Kumar Gupta

    2015-01-01

    The objective of present research paper is to highlight the importance of measuring advertisement effectiveness in print media and to develop a conceptual model for advertisement effectiveness. The developed model is based on dimensions on which advertisement effectiveness depends and on the dimensions which are used to measure the effectiveness. An in-depth and extensive literature review is carried out to understand the concept of advertisement effectiveness and its var...

  2. A conceptual framework for measuring airline business model convergence

    OpenAIRE

    Daft, Jost; Albers, Sascha

    2012-01-01

    This paper develops a measurement framework that synthesizes the airline and strategy literature to identify relevant dimensions and elements of airline business models. The applicability of this framework for describing airline strategies and structures and, based on this conceptualization, for assessing the potential convergence of airline business models over time is then illustrated using a small sample of five German passenger airlines. For this sample, the perception of a rapprochement ...

  3. A possibilistic uncertainty model in classical reliability theory

    International Nuclear Information System (INIS)

    De Cooman, G.; Capelle, B.

    1994-01-01

    The authors argue that a possibilistic uncertainty model can be used to represent linguistic uncertainty about the states of a system and of its components. Furthermore, the basic properties of the application of this model to classical reliability theory are studied. The notion of the possibilistic reliability of a system or a component is defined. Based on the concept of a binary structure function, the important notion of a possibilistic function is introduced. It allows to calculate the possibilistic reliability of a system in terms of the possibilistic reliabilities of its components

  4. Wind energy: Overcoming inadequate wind and modeling uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    Kane, Vivek

    2010-09-15

    'Green Energy' is the call of the day, and significance of Wind Energy can never be overemphasized. But the key question here is - What if the wind resources are inadequate? Studies reveal that the probability of finding favorable wind at a given place on land is only 15%. Moreover, there are inherent uncertainties associated with wind business. Can we overcome inadequate wind resources? Can we scientifically quantify uncertainty and model it to make business sense? This paper proposes a solution, by way of break-through Wind Technologies, combined with advanced tools for Financial Modeling, enabling vital business decisions.

  5. Dealing with uncertainty in modeling intermittent water supply

    Science.gov (United States)

    Lieb, A. M.; Rycroft, C.; Wilkening, J.

    2015-12-01

    Intermittency in urban water supply affects hundreds of millions of people in cities around the world, impacting water quality and infrastructure. Building on previous work to dynamically model the transient flows in water distribution networks undergoing frequent filling and emptying, we now consider the hydraulic implications of uncertain input data. Water distribution networks undergoing intermittent supply are often poorly mapped, and household metering frequently ranges from patchy to nonexistent. In the face of uncertain pipe material, pipe slope, network connectivity, and outflow, we investigate how uncertainty affects dynamical modeling results. We furthermore identify which parameters exert the greatest influence on uncertainty, helping to prioritize data collection.

  6. Elements of a flexible approach for conceptual hydrological modeling : 1. Motivation and theoretical development

    NARCIS (Netherlands)

    Fenicia, F.; Kavetski, D.; Savenije, H.H.G.

    2011-01-01

    This paper introduces a flexible framework for conceptual hydrological modeling, with two related objectives: (1) generalize and systematize the currently fragmented field of conceptual models and (2) provide a robust platform for understanding and modeling hydrological systems. In contrast to

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

    Science.gov (United States)

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

    2016-01-01

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

  8. Linear models in the mathematics of uncertainty

    CERN Document Server

    Mordeson, John N; Clark, Terry D; Pham, Alex; Redmond, Michael A

    2013-01-01

    The purpose of this book is to present new mathematical techniques for modeling global issues. These mathematical techniques are used to determine linear equations between a dependent variable and one or more independent variables in cases where standard techniques such as linear regression are not suitable. In this book, we examine cases where the number of data points is small (effects of nuclear warfare), where the experiment is not repeatable (the breakup of the former Soviet Union), and where the data is derived from expert opinion (how conservative is a political party). In all these cases the data  is difficult to measure and an assumption of randomness and/or statistical validity is questionable.  We apply our methods to real world issues in international relations such as  nuclear deterrence, smart power, and cooperative threat reduction. We next apply our methods to issues in comparative politics such as successful democratization, quality of life, economic freedom, political stability, and fail...

  9. Uncertainty Quantification for Large-Scale Ice Sheet Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Ghattas, Omar [Univ. of Texas, Austin, TX (United States)

    2016-02-05

    This report summarizes our work to develop advanced forward and inverse solvers and uncertainty quantification capabilities for a nonlinear 3D full Stokes continental-scale ice sheet flow model. The components include: (1) forward solver: a new state-of-the-art parallel adaptive scalable high-order-accurate mass-conservative Newton-based 3D nonlinear full Stokes ice sheet flow simulator; (2) inverse solver: a new adjoint-based inexact Newton method for solution of deterministic inverse problems governed by the above 3D nonlinear full Stokes ice flow model; and (3) uncertainty quantification: a novel Hessian-based Bayesian method for quantifying uncertainties in the inverse ice sheet flow solution and propagating them forward into predictions of quantities of interest such as ice mass flux to the ocean.

  10. Sensitivity of wildlife habitat models to uncertainties in GIS data

    Science.gov (United States)

    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.

  11. Conceptual Commitments of the LIDA Model of Cognition

    Science.gov (United States)

    Franklin, Stan; Strain, Steve; McCall, Ryan; Baars, Bernard

    2013-06-01

    Significant debate on fundamental issues remains in the subfields of cognitive science, including perception, memory, attention, action selection, learning, and others. Psychology, neuroscience, and artificial intelligence each contribute alternative and sometimes conflicting perspectives on the supervening problem of artificial general intelligence (AGI). Current efforts toward a broad-based, systems-level model of minds cannot await theoretical convergence in each of the relevant subfields. Such work therefore requires the formulation of tentative hypotheses, based on current knowledge, that serve to connect cognitive functions into a theoretical framework for the study of the mind. We term such hypotheses "conceptual commitments" and describe the hypotheses underlying one such model, the Learning Intelligent Distribution Agent (LIDA) Model. Our intention is to initiate a discussion among AGI researchers about which conceptual commitments are essential, or particularly useful, toward creating AGI agents.

  12. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

    Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

    2001-01-01

    Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

  13. Bayesian uncertainty quantification in linear models for diffusion MRI.

    Science.gov (United States)

    Sjölund, Jens; Eklund, Anders; Özarslan, Evren; Herberthson, Magnus; Bånkestad, Maria; Knutsson, Hans

    2018-03-29

    Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification. Copyright © 2018 Elsevier Inc. All rights reserved.

  14. Simulation of shallow groundwater levels: Comparison of a data-driven and a conceptual model

    Science.gov (United States)

    Fahle, Marcus; Dietrich, Ottfried; Lischeid, Gunnar

    2015-04-01

    Despite an abundance of models aimed at simulating shallow groundwater levels, application of such models is often hampered by a lack of appropriate input data. Difficulties especially arise with regard to soil data, which are typically hard to obtain and prone to spatial variability, eventually leading to uncertainties in the model results. Modelling approaches relying entirely on easily measured quantities are therefore an alternative to encourage the applicability of models. We present and compare two models for calculating 1-day-ahead predictions of the groundwater level that are only based on measurements of potential evapotranspiration, precipitation and groundwater levels. The first model is a newly developed conceptual model that is parametrized using the White method (which estimates the actual evapotranspiration on basis of diurnal groundwater fluctuations) and a rainfall-response ratio. Inverted versions of the two latter approaches are then used to calculate the predictions of the groundwater level. Furthermore, as a completely data-driven alternative, a simple feed-forward multilayer perceptron neural network was trained based on the same inputs and outputs. Data of 4 growing periods (April to October) from a study site situated in the Spreewald wetland in North-east Germany were taken to set-up the models and compare their performance. In addition, response surfaces that relate model outputs to combinations of different input variables are used to reveal those aspects in which the two approaches coincide and those in which they differ. Finally, it will be evaluated whether the conceptual approach can be enhanced by extracting knowledge of the neural network. This is done by replacing in the conceptual model the default function that relates groundwater recharge and groundwater level, which is assumed to be linear, by the non-linear function extracted from the neural network.

  15. Uncertainty Quantification in Control Problems for Flocking Models

    Directory of Open Access Journals (Sweden)

    Giacomo Albi

    2015-01-01

    Full Text Available The optimal control of flocking models with random inputs is investigated from a numerical point of view. The effect of uncertainty in the interaction parameters is studied for a Cucker-Smale type model using a generalized polynomial chaos (gPC approach. Numerical evidence of threshold effects in the alignment dynamic due to the random parameters is given. The use of a selective model predictive control permits steering of the system towards the desired state even in unstable regimes.

  16. Model complexity in carbon sequestration:A design of experiment and response surface uncertainty analysis

    Science.gov (United States)

    Zhang, Y.; Li, S.

    2014-12-01

    Geologic carbon sequestration (GCS) is proposed for the Nugget Sandstone in Moxa Arch, a regional saline aquifer with a large storage potential. For a proposed storage site, this study builds a suite of increasingly complex conceptual "geologic" model families, using subsets of the site characterization data: a homogeneous model family, a stationary petrophysical model family, a stationary facies model family with sub-facies petrophysical variability, and a non-stationary facies model family (with sub-facies variability) conditioned to soft data. These families, representing alternative conceptual site models built with increasing data, were simulated with the same CO2 injection test (50 years at 1/10 Mt per year), followed by 2950 years of monitoring. Using the Design of Experiment, an efficient sensitivity analysis (SA) is conducted for all families, systematically varying uncertain input parameters. Results are compared among the families to identify parameters that have 1st order impact on predicting the CO2 storage ratio (SR) at both end of injection and end of monitoring. At this site, geologic modeling factors do not significantly influence the short-term prediction of the storage ratio, although they become important over monitoring time, but only for those families where such factors are accounted for. Based on the SA, a response surface analysis is conducted to generate prediction envelopes of the storage ratio, which are compared among the families at both times. Results suggest a large uncertainty in the predicted storage ratio given the uncertainties in model parameters and modeling choices: SR varies from 5-60% (end of injection) to 18-100% (end of monitoring), although its variation among the model families is relatively minor. Moreover, long-term leakage risk is considered small at the proposed site. In the lowest-SR scenarios, all families predict gravity-stable supercritical CO2 migrating toward the bottom of the aquifer. In the highest

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

    African Journals Online (AJOL)

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

  18. Geostatistical modeling of groundwater properties and assessment of their uncertainties

    International Nuclear Information System (INIS)

    Honda, Makoto; Yamamoto, Shinya; Sakurai, Hideyuki; Suzuki, Makoto; Sanada, Hiroyuki; Matsui, Hiroya; Sugita, Yutaka

    2010-01-01

    The distribution of groundwater properties is important for understanding of the deep underground hydrogeological environments. This paper proposes a geostatistical system for modeling the groundwater properties which have a correlation with the ground resistivity data obtained from widespread and exhaustive survey. That is, the methodology for the integration of resistivity data measured by various methods and the methodology for modeling the groundwater properties using the integrated resistivity data has been developed. The proposed system has also been validated using the data obtained in the Horonobe Underground Research Laboratory project. Additionally, the quantification of uncertainties in the estimated model has been tried by numerical simulations based on the data. As a result, the uncertainties of the proposal model have been estimated lower than other traditional model's. (author)

  19. Evaluating the uncertainty of input quantities in measurement models

    Science.gov (United States)

    Possolo, Antonio; Elster, Clemens

    2014-06-01

    The Guide to the Expression of Uncertainty in Measurement (GUM) gives guidance about how values and uncertainties should be assigned to the input quantities that appear in measurement models. This contribution offers a concrete proposal for how that guidance may be updated in light of the advances in the evaluation and expression of measurement uncertainty that were made in the course of the twenty years that have elapsed since the publication of the GUM, and also considering situations that the GUM does not yet contemplate. Our motivation is the ongoing conversation about a new edition of the GUM. While generally we favour a Bayesian approach to uncertainty evaluation, we also recognize the value that other approaches may bring to the problems considered here, and focus on methods for uncertainty evaluation and propagation that are widely applicable, including to cases that the GUM has not yet addressed. In addition to Bayesian methods, we discuss maximum-likelihood estimation, robust statistical methods, and measurement models where values of nominal properties play the same role that input quantities play in traditional models. We illustrate these general-purpose techniques in concrete examples, employing data sets that are realistic but that also are of conveniently small sizes. The supplementary material available online lists the R computer code that we have used to produce these examples (stacks.iop.org/Met/51/3/339/mmedia). Although we strive to stay close to clause 4 of the GUM, which addresses the evaluation of uncertainty for input quantities, we depart from it as we review the classes of measurement models that we believe are generally useful in contemporary measurement science. We also considerably expand and update the treatment that the GUM gives to Type B evaluations of uncertainty: reviewing the state-of-the-art, disciplined approach to the elicitation of expert knowledge, and its encapsulation in probability distributions that are usable in

  20. Identification of Chemistry Learning Problems Viewed From Conceptual Change Model

    OpenAIRE

    Redhana, I. W; Sudria, I. B. N; Hidayat, I; Merta, L. M

    2017-01-01

    This study aimed at describing and explaining chemistry learning problems viewed from conceptual change model and misconceptions of students. The study was qualitative research of case study type conducted in one class of SMAN 1 Singaraja. Subjects of the study were a chemistry teacher and students. Data were obtained through classroom observation, interviews, and conception tests. The chemistry learning problems were grouped based on aspects of necessity, intelligibility, plausibility, and f...

  1. Nuclear Physical Uncertainties in Modeling X-Ray Bursts

    Science.gov (United States)

    Regis, Eric; Amthor, A. Matthew

    2017-09-01

    Type I x-ray bursts occur when a neutron star accretes material from the surface of another star in a compact binary star system. For certain accretion rates and material compositions, much of the nuclear material is burned in short, explosive bursts. Using a one-dimensional stellar model, Kepler, and a comprehensive nuclear reaction rate library, ReacLib, we have simulated chains of type I x-ray bursts. Unfortunately, there are large remaining uncertainties in the nuclear reaction rates involved, since many of the isotopes reacting are unstable and have not yet been studied experimentally. Some individual reactions, when varied within their estimated uncertainty, alter the light curves dramatically. This limits our ability to understand the structure of the neutron star. Previous studies have looked at the effects of individual reaction rate uncertainties. We have applied a Monte Carlo method ``-simultaneously varying a set of reaction rates'' -in order to probe the expected uncertainty in x-ray burst behaviour due to the total uncertainty in all nuclear reaction rates. Furthermore, we aim to discover any nonlinear effects due to the coupling between different reaction rates. Early results show clear non-linear effects. This research was made possible by NSF-DUE Grant 1317446, BUScholars Program.

  2. Robust nonlinear control of nuclear reactors under model uncertainty

    International Nuclear Information System (INIS)

    Park, Moon Ghu

    1993-02-01

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

  3. Using Annotated Conceptual Models to Derive Information System Implementations

    Directory of Open Access Journals (Sweden)

    Anthony Berglas

    1994-05-01

    Full Text Available Producing production quality information systems from conceptual descriptions is a time consuming process that employs many of the world's programmers. Although most of this programming is fairly routine, the process has not been amenable to simple automation because conceptual models do not provide sufficient parameters to make all the implementation decisions that are required, and numerous special cases arise in practice. Most commercial CASE tools address these problems by essentially implementing a waterfall model in which the development proceeds from analysis through design, layout and coding phases in a partially automated manner, but the analyst/programmer must heavily edit each intermediate stage. This paper demonstrates that by recognising the nature of information systems, it is possible to specify applications completely using a conceptual model that has een annotated with additional parameters that guide automated implementation. More importantly, it will be argued that a manageable number of annotations are sufficient to implement realistic applications, and techniques will be described that enabled the author's commercial CASE tool, the Intelligent Develope to automated implementation without requiring complex theorem proving technology.

  4. Conceptual Model of Climate Change Impacts at LANL

    Energy Technology Data Exchange (ETDEWEB)

    Dewart, Jean Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-17

    Goal 9 of the LANL FY15 Site Sustainability Plan (LANL 2014a) addresses Climate Change Adaptation. As part of Goal 9, the plan reviews many of the individual programs the Laboratory has initiated over the past 20 years to address climate change impacts to LANL (e.g. Wildland Fire Management Plan, Forest Management Plan, etc.). However, at that time, LANL did not yet have a comprehensive approach to climate change adaptation. To fill this gap, the FY15 Work Plan for the LANL Long Term Strategy for Environmental Stewardship and Sustainability (LANL 2015) included a goal of (1) establishing a comprehensive conceptual model of climate change impacts at LANL and (2) establishing specific climate change indices to measure climate change and impacts at Los Alamos. Establishing a conceptual model of climate change impacts will demonstrate that the Laboratory is addressing climate change impacts in a comprehensive manner. This paper fulfills the requirement of goal 1. The establishment of specific indices of climate change at Los Alamos (goal 2), will improve our ability to determine climate change vulnerabilities and assess risk. Future work will include prioritizing risks, evaluating options/technologies/costs, and where appropriate, taking actions. To develop a comprehensive conceptual model of climate change impacts, we selected the framework provided in the National Oceanic and Atmospheric Administration (NOAA) Climate Resilience Toolkit (http://toolkit.climate.gov/).

  5. Bayesian uncertainty analysis with applications to turbulence modeling

    International Nuclear Information System (INIS)

    Cheung, Sai Hung; Oliver, Todd A.; Prudencio, Ernesto E.; Prudhomme, Serge; Moser, Robert D.

    2011-01-01

    In this paper, we apply Bayesian uncertainty quantification techniques to the processes of calibrating complex mathematical models and predicting quantities of interest (QoI's) with such models. These techniques also enable the systematic comparison of competing model classes. The processes of calibration and comparison constitute the building blocks of a larger validation process, the goal of which is to accept or reject a given mathematical model for the prediction of a particular QoI for a particular scenario. In this work, we take the first step in this process by applying the methodology to the analysis of the Spalart-Allmaras turbulence model in the context of incompressible, boundary layer flows. Three competing model classes based on the Spalart-Allmaras model are formulated, calibrated against experimental data, and used to issue a prediction with quantified uncertainty. The model classes are compared in terms of their posterior probabilities and their prediction of QoI's. The model posterior probability represents the relative plausibility of a model class given the data. Thus, it incorporates the model's ability to fit experimental observations. Alternatively, comparing models using the predicted QoI connects the process to the needs of decision makers that use the results of the model. We show that by using both the model plausibility and predicted QoI, one has the opportunity to reject some model classes after calibration, before subjecting the remaining classes to additional validation challenges.

  6. A simplified model of choice behavior under uncertainty

    Directory of Open Access Journals (Sweden)

    Ching-Hung Lin

    2016-08-01

    Full Text Available The Iowa Gambling Task (IGT has been standardized as a clinical assessment tool (Bechara, 2007. Nonetheless, numerous research groups have attempted to modify IGT models to optimize parameters for predicting the choice behavior of normal controls and patients. A decade ago, most researchers considered the expected utility (EU model (Busemeyer and Stout, 2002 to be the optimal model for predicting choice behavior under uncertainty. However, in recent years, studies have demonstrated the prospect utility (PU models (Ahn et al., 2008 to be more effective than the EU models in the IGT. Nevertheless, after some preliminary tests, we propose that Ahn et al. (2008 PU model is not optimal due to some incompatible results between our behavioral and modeling data. This study aims to modify Ahn et al. (2008 PU model to a simplified model and collected 145 subjects’ IGT performance as the benchmark data for comparison. In our simplified PU model, the best goodness-of-fit was found mostly while α approaching zero. More specifically, we retested the key parameters α, λ , and A in the PU model. Notably, the power of influence of the parameters α, λ, and A has a hierarchical order in terms of manipulating the goodness-of-fit in the PU model. Additionally, we found that the parameters λ and A may be ineffective when the parameter α is close to zero in the PU model. The present simplified model demonstrated that decision makers mostly adopted the strategy of gain-stay-loss-shift rather than foreseeing the long-term outcome. However, there still have other behavioral variables that are not well revealed under these dynamic uncertainty situations. Therefore, the optimal behavioral models may not have been found. In short, the best model for predicting choice behavior under dynamic-uncertainty situations should be further evaluated.

  7. Conceptual Model of IT Infrastructure Capability and Its Empirical Justification

    Institute of Scientific and Technical Information of China (English)

    QI Xianfeng; LAN Boxiong; GUO Zhenwei

    2008-01-01

    Increasing importance has been attached to the value of information technology (IT) infrastructure in today's organizations. The development of efficacious IT infrastructure capability enhances business performance and brings sustainable competitive advantage. This study analyzed the IT infrastructure capability in a holistic way and then presented a concept model of IT capability. IT infrastructure capability was categorized into sharing capability, service capability, and flexibility. This study then empirically tested the model using a set of survey data collected from 145 firms. Three factors emerge from the factor analysis as IT flexibility, IT service capability, and IT sharing capability, which agree with those in the conceptual model built in this study.

  8. Conceptual Model of Quantities, Units, Dimensions, and Values

    Science.gov (United States)

    Rouquette, Nicolas F.; DeKoenig, Hans-Peter; Burkhart, Roger; Espinoza, Huascar

    2011-01-01

    JPL collaborated with experts from industry and other organizations to develop a conceptual model of quantities, units, dimensions, and values based on the current work of the ISO 80000 committee revising the International System of Units & Quantities based on the International Vocabulary of Metrology (VIM). By providing support for ISO 80000 in SysML via the International Vocabulary of Metrology (VIM), this conceptual model provides, for the first time, a standard-based approach for addressing issues of unit coherence and dimensional analysis into the practice of systems engineering with SysML-based tools. This conceptual model provides support for two kinds of analyses specified in the International Vocabulary of Metrology (VIM): coherence of units as well as of systems of units, and dimension analysis of systems of quantities. To provide a solid and stable foundation, the model for defining quantities, units, dimensions, and values in SysML is explicitly based on the concepts defined in VIM. At the same time, the model library is designed in such a way that extensions to the ISQ (International System of Quantities) and SI Units (Systeme International d Unites) can be represented, as well as any alternative systems of quantities and units. The model library can be used to support SysML user models in various ways. A simple approach is to define and document libraries of reusable systems of units and quantities for reuse across multiple projects, and to link units and quantity kinds from these libraries to Unit and QuantityKind stereotypes defined in SysML user models.

  9. Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework

    Science.gov (United States)

    Tyler Jon Smith; Lucy Amanda Marshall

    2010-01-01

    Model selection is an extremely important aspect of many hydrologic modeling studies because of the complexity, variability, and uncertainty that surrounds the current understanding of watershed-scale systems. However, development and implementation of a complete precipitation-runoff modeling framework, from model selection to calibration and uncertainty analysis, are...

  10. Numerical solution of dynamic equilibrium models under Poisson uncertainty

    DEFF Research Database (Denmark)

    Posch, Olaf; Trimborn, Timo

    2013-01-01

    We propose a simple and powerful numerical algorithm to compute the transition process in continuous-time dynamic equilibrium models with rare events. In this paper we transform the dynamic system of stochastic differential equations into a system of functional differential equations of the retar...... solution to Lucas' endogenous growth model under Poisson uncertainty are used to compute the exact numerical error. We show how (potential) catastrophic events such as rare natural disasters substantially affect the economic decisions of households....

  11. Formal modeling of a system of chemical reactions under uncertainty.

    Science.gov (United States)

    Ghosh, Krishnendu; Schlipf, John

    2014-10-01

    We describe a novel formalism representing a system of chemical reactions, with imprecise rates of reactions and concentrations of chemicals, and describe a model reduction method, pruning, based on the chemical properties. We present two algorithms, midpoint approximation and interval approximation, for construction of efficient model abstractions with uncertainty in data. We evaluate computational feasibility by posing queries in computation tree logic (CTL) on a prototype of extracellular-signal-regulated kinase (ERK) pathway.

  12. Approximating prediction uncertainty for random forest regression models

    Science.gov (United States)

    John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

    2016-01-01

    Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

  13. Conceptual Models in Health Informatics Research: A Literature Review and Suggestions for Development.

    Science.gov (United States)

    Gray, Kathleen; Sockolow, Paulina

    2016-02-24

    Contributing to health informatics research means using conceptual models that are integrative and explain the research in terms of the two broad domains of health science and information science. However, it can be hard for novice health informatics researchers to find exemplars and guidelines in working with integrative conceptual models. The aim of this paper is to support the use of integrative conceptual models in research on information and communication technologies in the health sector, and to encourage discussion of these conceptual models in scholarly forums. A two-part method was used to summarize and structure ideas about how to work effectively with conceptual models in health informatics research that included (1) a selective review and summary of the literature of conceptual models; and (2) the construction of a step-by-step approach to developing a conceptual model. The seven-step methodology for developing conceptual models in health informatics research explained in this paper involves (1) acknowledging the limitations of health science and information science conceptual models; (2) giving a rationale for one's choice of integrative conceptual model; (3) explicating a conceptual model verbally and graphically; (4) seeking feedback about the conceptual model from stakeholders in both the health science and information science domains; (5) aligning a conceptual model with an appropriate research plan; (6) adapting a conceptual model in response to new knowledge over time; and (7) disseminating conceptual models in scholarly and scientific forums. Making explicit the conceptual model that underpins a health informatics research project can contribute to increasing the number of well-formed and strongly grounded health informatics research projects. This explication has distinct benefits for researchers in training, research teams, and researchers and practitioners in information, health, and other disciplines.

  14. Uncertainties in soil-plant interactions in advanced models for long-timescale dose assessment

    Energy Technology Data Exchange (ETDEWEB)

    Klos, R. [Aleksandria Sciences Ltd. (United Kingdom); Limer, L. [Limer Scientific Ltd. (United Kingdom); Perez-Sanchez, D. [Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas - CIEMAT (Spain); Xu, S.; Andersson, P. [Swedish Radiation Safty Authority (Sweden)

    2014-07-01

    Traditional models for long-timescale dose assessment are generally conceptually straightforward, featuring one, two or three spatial compartments in the soil column and employing data based on annually averaged parameters for climate characteristics. The soil-plant system is usually modelled using concentration ratios. The justification for this approach is that the timescales relevant to the geologic disposal of radioactive waste are so long that simple conceptual models are necessary to account for the inherent uncertainties over the timescale of the dose assessment. In the past few years, attention has been given to more detailed 'advanced' models for use dose assessment that have a high degree of site-specific detail. These recognise more features, events and processes since they have higher spatial and temporal resolution. This modelling approach has been developed to account for redox sensitive radionuclides, variability of the water table position and accumulation in non-agricultural ecosystems prior to conversion to an agricultural ecosystem. The models feature higher spatial and temporal resolution in the soil column (up to ten layers with spatially varying k{sub d}s dependent on soil conditions) and monthly rather than annually averaged parameters. Soil-plant interaction is treated as a dynamic process, allowing for root uptake as a function of time and depth, according to the root profile. Uncertainty in dose assessment models associated with the treatment of prior accumulations in agricultural soils has demonstrated the importance of the model's representation of the soil-plant interaction. The treatment of root uptake as a dynamic process as opposed to a simple concentration ratio implies a potentially important difference despite the dynamic soil-plant transfer rate being based on established concentration ratio values. These discrepancies have also appeared in the results from the higher spatio-temporal resolution models. This paper

  15. Verification and Uncertainty Reduction of Amchitka Underground Nuclear Testing Models

    Energy Technology Data Exchange (ETDEWEB)

    Ahmed Hassan; Jenny Chapman

    2006-02-01

    The modeling of Amchitka underground nuclear tests conducted in 2002 is verified and uncertainty in model input parameters, as well as predictions, has been reduced using newly collected data obtained by the summer 2004 field expedition of CRESP. Newly collected data that pertain to the groundwater model include magnetotelluric (MT) surveys conducted on the island to determine the subsurface salinity and porosity structure of the subsurface, and bathymetric surveys to determine the bathymetric maps of the areas offshore from the Long Shot and Cannikin Sites. Analysis and interpretation of the MT data yielded information on the location of the transition zone, and porosity profiles showing porosity values decaying with depth. These new data sets are used to verify the original model in terms of model parameters, model structure, and model output verification. In addition, by using the new data along with the existing data (chemistry and head data), the uncertainty in model input and output is decreased by conditioning on all the available data. A Markov Chain Monte Carlo (MCMC) approach is adapted for developing new input parameter distributions conditioned on prior knowledge and new data. The MCMC approach is a form of Bayesian conditioning that is constructed in such a way that it produces samples of the model parameters that eventually converge to a stationary posterior distribution. The Bayesian MCMC approach enhances probabilistic assessment. Instead of simply propagating uncertainty forward from input parameters into model predictions (i.e., traditional Monte Carlo approach), MCMC propagates uncertainty backward from data onto parameters, and then forward from parameters into predictions. Comparisons between new data and the original model, and conditioning on all available data using MCMC method, yield the following results and conclusions: (1) Model structure is verified at Long Shot and Cannikin where the high-resolution bathymetric data collected by CRESP

  16. A conceptual and disease model framework for osteoporotic kyphosis.

    Science.gov (United States)

    Bayliss, M; Miltenburger, C; White, M; Alvares, L

    2013-09-01

    This paper presents a multi-method research project to develop a conceptual framework for measuring outcomes in studies of osteoporotic kyphosis. The research involved literature research and qualitative interviews among clinicians who treat patients with kyphosis and among patients with the condition. Kyphosis due to at least one vertebral compression fracture is prevalent among osteoporotic patients, resulting in well-documented symptoms and impact on functioning and well-being. A three-part study led to development of a conceptual measurement framework for comprehensive assessment of symptoms, impact, and treatment benefit for kyphosis. A literature-based disease model (DM) was developed and tested with physicians (n = 10) and patients (n = 10), and FDA guidelines were used to develop a final disease model and a conceptual framework. The DM included signs, symptoms, causes/triggers, exacerbations, and functional status associated with kyphosis. The DM was largely confirmed, but physicians and patients added several concepts related to impact on functioning, and some concepts were not confirmed and removed from the DM. This study confirms the need for more comprehensive assessment of health outcomes in kyphosis, as most current studies omit key concepts.

  17. Conceptual Modeling in the Time of the Revolution: Part II

    Science.gov (United States)

    Mylopoulos, John

    Conceptual Modeling was a marginal research topic at the very fringes of Computer Science in the 60s and 70s, when the discipline was dominated by topics focusing on programs, systems and hardware architectures. Over the years, however, the field has moved to centre stage and has come to claim a central role both in Computer Science research and practice in diverse areas, such as Software Engineering, Databases, Information Systems, the Semantic Web, Business Process Management, Service-Oriented Computing, Multi-Agent Systems, Knowledge Management, and more. The transformation was greatly aided by the adoption of standards in modeling languages (e.g., UML), and model-based methodologies (e.g., Model-Driven Architectures) by the Object Management Group (OMG) and other standards organizations. We briefly review the history of the field over the past 40 years, focusing on the evolution of key ideas. We then note some open challenges and report on-going research, covering topics such as the representation of variability in conceptual models, capturing model intentions, and models of laws.

  18. Operations and support cost modeling of conceptual space vehicles

    Science.gov (United States)

    Ebeling, Charles

    1994-01-01

    The University of Dayton is pleased to submit this annual report to the National Aeronautics and Space Administration (NASA) Langley Research Center which documents the development of an operations and support (O&S) cost model as part of a larger life cycle cost (LCC) structure. It is intended for use during the conceptual design of new launch vehicles and spacecraft. This research is being conducted under NASA Research Grant NAG-1-1327. This research effort changes the focus from that of the first two years in which a reliability and maintainability model was developed to the initial development of an operations and support life cycle cost model. Cost categories were initially patterned after NASA's three axis work breakdown structure consisting of a configuration axis (vehicle), a function axis, and a cost axis. A revised cost element structure (CES), which is currently under study by NASA, was used to established the basic cost elements used in the model. While the focus of the effort was on operations and maintenance costs and other recurring costs, the computerized model allowed for other cost categories such as RDT&E and production costs to be addressed. Secondary tasks performed concurrent with the development of the costing model included support and upgrades to the reliability and maintainability (R&M) model. The primary result of the current research has been a methodology and a computer implementation of the methodology to provide for timely operations and support cost analysis during the conceptual design activities.

  19. Detailed modeling of the statistical uncertainty of Thomson scattering measurements

    International Nuclear Information System (INIS)

    Morton, L A; Parke, E; Hartog, D J Den

    2013-01-01

    The uncertainty of electron density and temperature fluctuation measurements is determined by statistical uncertainty introduced by multiple noise sources. In order to quantify these uncertainties precisely, a simple but comprehensive model was made of the noise sources in the MST Thomson scattering system and of the resulting variance in the integrated scattered signals. The model agrees well with experimental and simulated results. The signal uncertainties are then used by our existing Bayesian analysis routine to find the most likely electron temperature and density, with confidence intervals. In the model, photonic noise from scattered light and plasma background light is multiplied by the noise enhancement factor (F) of the avalanche photodiode (APD). Electronic noise from the amplifier and digitizer is added. The amplifier response function shapes the signal and induces correlation in the noise. The data analysis routine fits a characteristic pulse to the digitized signals from the amplifier, giving the integrated scattered signals. A finite digitization rate loses information and can cause numerical integration error. We find a formula for the variance of the scattered signals in terms of the background and pulse amplitudes, and three calibration constants. The constants are measured easily under operating conditions, resulting in accurate estimation of the scattered signals' uncertainty. We measure F ≈ 3 for our APDs, in agreement with other measurements for similar APDs. This value is wavelength-independent, simplifying analysis. The correlated noise we observe is reproduced well using a Gaussian response function. Numerical integration error can be made negligible by using an interpolated characteristic pulse, allowing digitization rates as low as the detector bandwidth. The effect of background noise is also determined

  20. Statistical approach for uncertainty quantification of experimental modal model parameters

    DEFF Research Database (Denmark)

    Luczak, M.; Peeters, B.; Kahsin, M.

    2014-01-01

    Composite materials are widely used in manufacture of aerospace and wind energy structural components. These load carrying structures are subjected to dynamic time-varying loading conditions. Robust structural dynamics identification procedure impose tight constraints on the quality of modal models...... represent different complexity levels ranging from coupon, through sub-component up to fully assembled aerospace and wind energy structural components made of composite materials. The proposed method is demonstrated on two application cases of a small and large wind turbine blade........ This paper aims at a systematic approach for uncertainty quantification of the parameters of the modal models estimated from experimentally obtained data. Statistical analysis of modal parameters is implemented to derive an assessment of the entire modal model uncertainty measure. Investigated structures...

  1. Another two dark energy models motivated from Karolyhazy uncertainty relation

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Cheng-Yi; Yang, Wen-Li; Song, Yu. [Northwest University, Institute of Modern Physics, Xian (China); Yue, Rui-Hong [Ningbo University, Faculty of Science, Ningbo (China)

    2012-03-15

    The Karolyhazy uncertainty relation indicates that there exists a minimal detectable cell {delta}t{sup 3} over the region t{sup 3} in Minkowski space-time. Due to the energy-time uncertainty relation, the energy of the cell {delta}t {sup 3} cannot be less {delta}t{sup -1}. Then we get a new energy density of metric fluctuations of Minkowski spacetime as {delta}t{sup -4}. Motivated by the energy density, we propose two new dark-energy models. One model is characterized by the age of the universe and the other is characterized by the conformal age of the universe. We find that in the two models, the dark energy mimics a cosmological constant in the late time. (orig.)

  2. How much expert knowledge is it worth to put in conceptual hydrological models?

    Science.gov (United States)

    Antonetti, Manuel; Zappa, Massimiliano

    2017-04-01

    Both modellers and experimentalists agree on using expert knowledge to improve our conceptual hydrological simulations on ungauged basins. However, they use expert knowledge differently for both hydrologically mapping the landscape and parameterising a given hydrological model. Modellers use generally very simplified (e.g. topography-based) mapping approaches and put most of the knowledge for constraining the model by defining parameter and process relational rules. In contrast, experimentalists tend to invest all their detailed and qualitative knowledge about processes to obtain a spatial distribution of areas with different dominant runoff generation processes (DRPs) as realistic as possible, and for defining plausible narrow value ranges for each model parameter. Since, most of the times, the modelling goal is exclusively to simulate runoff at a specific site, even strongly simplified hydrological classifications can lead to satisfying results due to equifinality of hydrological models, overfitting problems and the numerous uncertainty sources affecting runoff simulations. Therefore, to test to which extent expert knowledge can improve simulation results under uncertainty, we applied a typical modellers' modelling framework relying on parameter and process constraints defined based on expert knowledge to several catchments on the Swiss Plateau. To map the spatial distribution of the DRPs, mapping approaches with increasing involvement of expert knowledge were used. Simulation results highlighted the potential added value of using all the expert knowledge available on a catchment. Also, combinations of event types and landscapes, where even a simplified mapping approach can lead to satisfying results, were identified. Finally, the uncertainty originated by the different mapping approaches was compared with the one linked to meteorological input data and catchment initial conditions.

  3. Effects of input uncertainty on cross-scale crop modeling

    Science.gov (United States)

    Waha, Katharina; Huth, Neil; Carberry, Peter

    2014-05-01

    The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input

  4. A Conceptual Model of the Pasadena Housing System

    Science.gov (United States)

    Hirshberg, Alan S.; Barber, Thomas A.

    1971-01-01

    During the last 5 years, there have been several attempts at applying systems analysis to complex urban problems. This paper describes one such attempt by a multidisciplinary team of students, engineers, professors, and community representatives. The Project organization is discussed and the interaction of the different disciplines (the process) described. The two fundamental analysis questions posed by the Project were: "Why do houses deteriorate?" and "Why do people move?" The analysis of these questions led to the development of a conceptual system model of housing in Pasadena. The major elements of this model are described, and several conclusions drawn from it are presented.

  5. A Conceptual Model of eLearning Adoption

    Directory of Open Access Journals (Sweden)

    Muneer Abbad

    2011-05-01

    Full Text Available Internet-based learning systems are being used in many universities and firms but their adoption requires a solid understanding of the user acceptance processes. The technology acceptance model (TAM has been used to test the acceptance of various technologies and software within an e-learning context. This research aims to discuss the main factors of a successful e-learning adoption by students. A conceptual research framework of e-learning adoption is proposed based on the TAM model.

  6. Geomorphic dam-break flows. Part I: conceptual model

    OpenAIRE

    Leal, JGAB; Ferreira, RML; Cardoso, AH

    2010-01-01

    Proceedings of the Institution of Civil Engineers - Water Management 163 Issue WM6 This paper presents a one-dimensional conceptual model for simulating geomorphic dam-break flows. The model is based on conservation laws drawn from continuum mixture theory that are integrated over the flow depth,assuming that the f10w is composed of two transport layers. Closure equations were derived from the review and reanalysis of previous studies on granular flow,debris f10w and sheet flow. The sedime...

  7. A Conceptual Model for Multidimensional Analysis of Documents

    Science.gov (United States)

    Ravat, Franck; Teste, Olivier; Tournier, Ronan; Zurlfluh, Gilles

    Data warehousing and OLAP are mainly used for the analysis of transactional data. Nowadays, with the evolution of Internet, and the development of semi-structured data exchange format (such as XML), it is possible to consider entire fragments of data such as documents as analysis sources. As a consequence, an adapted multidimensional analysis framework needs to be provided. In this paper, we introduce an OLAP multidimensional conceptual model without facts. This model is based on the unique concept of dimensions and is adapted for multidimensional document analysis. We also provide a set of manipulation operations.

  8. Conceptual models of microseismicity induced by fluid injection

    Science.gov (United States)

    Baro Urbea, J.; Lord-May, C.; Eaton, D. W. S.; Joern, D.

    2017-12-01

    Variations in the pore pressure due to fluid invasion are accountable for microseismic activity recorded in geothermal systems and during hydraulic fracturing operations. To capture this phenomenon on a conceptual level, invasion percolation models have been suggested to represent the flow network of fluids within a porous media and seismic activity is typically considered to be directly related to the expansion of the percolated area. Although such models reproduce scale-free frequency-magnitude distributions, the associated b-values of the Gutenberg-Richter relation do not align with observed data. Here, we propose an alternative conceptual invasion percolation model that decouples the fluid propagation from the microseismic events. Instead of a uniform pressure, the pressure is modeled to decay along the distance from the injection site. Wet fracture events are simulated with a stochastic spring block model exhibiting stick-slip dynamics as a result of the variations of the pore pressure. We show that the statistics of the stick-slip events are scale-free, but now the b-values depend on the level of heterogeneity in the local static friction coefficients. Thus, this model is able to reproduce the wide spectrum of b-values observed in field catalogs associated with fluid induced microseismicity. Moreover, the spatial distribution of microseismic events is also consistent with observations.

  9. A Conceptual Culture Model for Design Science Research

    Directory of Open Access Journals (Sweden)

    Thomas Richter

    2016-03-01

    Full Text Available The aim of design science research (DSR in information systems is the user-centred creation of IT-artifacts with regard to specific social environments. For culture research in the field, which is necessary for a proper localization of IT-artifacts, models and research approaches from social sciences usually are adopted. Descriptive dimension-based culture models most commonly are applied for this purpose, which assume culture being a national phenomenon and tend to reduce it to basic values. Such models are useful for investigations in behavioural culture research because it aims to isolate, describe and explain culture-specific attitudes and characteristics within a selected society. In contrast, with the necessity to deduce concrete decisions for artifact-design, research results from DSR need to go beyond this aim. As hypothesis, this contribution generally questions the applicability of such generic culture dimensions’ models for DSR and focuses on their theoretical foundation, which goes back to Hofstede’s conceptual Onion Model of Culture. The herein applied literature-based analysis confirms the hypothesis. Consequently, an alternative conceptual culture model is being introduced and discussed as theoretical foundation for culture research in DSR.

  10. A simple conceptual model of abrupt glacial climate events

    Directory of Open Access Journals (Sweden)

    H. Braun

    2007-11-01

    Full Text Available Here we use a very simple conceptual model in an attempt to reduce essential parts of the complex nonlinearity of abrupt glacial climate changes (the so-called Dansgaard-Oeschger events to a few simple principles, namely (i the existence of two different climate states, (ii a threshold process and (iii an overshooting in the stability of the system at the start and the end of the events, which is followed by a millennial-scale relaxation. By comparison with a so-called Earth system model of intermediate complexity (CLIMBER-2, in which the events represent oscillations between two climate states corresponding to two fundamentally different modes of deep-water formation in the North Atlantic, we demonstrate that the conceptual model captures fundamental aspects of the nonlinearity of the events in that model. We use the conceptual model in order to reproduce and reanalyse nonlinear resonance mechanisms that were already suggested in order to explain the characteristic time scale of Dansgaard-Oeschger events. In doing so we identify a new form of stochastic resonance (i.e. an overshooting stochastic resonance and provide the first explicitly reported manifestation of ghost resonance in a geosystem, i.e. of a mechanism which could be relevant for other systems with thresholds and with multiple states of operation. Our work enables us to explicitly simulate realistic probability measures of Dansgaard-Oeschger events (e.g. waiting time distributions, which are a prerequisite for statistical analyses on the regularity of the events by means of Monte-Carlo simulations. We thus think that our study is an important advance in order to develop more adequate methods to test the statistical significance and the origin of the proposed glacial 1470-year climate cycle.

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

    International Nuclear Information System (INIS)

    Roselle, S.J.

    1992-06-01

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

  12. Model Uncertainties for Valencia RPA Effect for MINERvA

    Energy Technology Data Exchange (ETDEWEB)

    Gran, Richard [Univ. of Minnesota, Duluth, MN (United States)

    2017-05-08

    This technical note describes the application of the Valencia RPA multi-nucleon effect and its uncertainty to QE reactions from the GENIE neutrino event generator. The analysis of MINERvA neutrino data in Rodrigues et al. PRL 116 071802 (2016) paper makes clear the need for an RPA suppression, especially at very low momentum and energy transfer. That published analysis does not constrain the magnitude of the effect; it only tests models with and without the effect against the data. Other MINERvA analyses need an expression of the model uncertainty in the RPA effect. A well-described uncertainty can be used for systematics for unfolding, for model errors in the analysis of non-QE samples, and as input for fitting exercises for model testing or constraining backgrounds. This prescription takes uncertainties on the parameters in the Valencia RPA model and adds a (not-as-tight) constraint from muon capture data. For MINERvA we apply it as a 2D ($q_0$,$q_3$) weight to GENIE events, in lieu of generating a full beyond-Fermi-gas quasielastic events. Because it is a weight, it can be applied to the generated and fully Geant4 simulated events used in analysis without a special GENIE sample. For some limited uses, it could be cast as a 1D $Q^2$ weight without much trouble. This procedure is a suitable starting point for NOvA and DUNE where the energy dependence is modest, but probably not adequate for T2K or MicroBooNE.

  13. Uncertainty propagation in a multiscale model of nanocrystalline plasticity

    International Nuclear Information System (INIS)

    Koslowski, M.; Strachan, Alejandro

    2011-01-01

    We characterize how uncertainties propagate across spatial and temporal scales in a physics-based model of nanocrystalline plasticity of fcc metals. Our model combines molecular dynamics (MD) simulations to characterize atomic-level processes that govern dislocation-based-plastic deformation with a phase field approach to dislocation dynamics (PFDD) that describes how an ensemble of dislocations evolve and interact to determine the mechanical response of the material. We apply this approach to a nanocrystalline Ni specimen of interest in micro-electromechanical (MEMS) switches. Our approach enables us to quantify how internal stresses that result from the fabrication process affect the properties of dislocations (using MD) and how these properties, in turn, affect the yield stress of the metallic membrane (using the PFMM model). Our predictions show that, for a nanocrystalline sample with small grain size (4 nm), a variation in residual stress of 20 MPa (typical in today's microfabrication techniques) would result in a variation on the critical resolved shear yield stress of approximately 15 MPa, a very small fraction of the nominal value of approximately 9 GPa. - Highlights: → Quantify how fabrication uncertainties affect yield stress in a microswitch component. → Propagate uncertainties in a multiscale model of single crystal plasticity. → Molecular dynamics quantifies how fabrication variations affect dislocations. → Dislocation dynamics relate variations in dislocation properties to yield stress.

  14. Uncertainty Model for Total Solar Irradiance Estimation on Australian Rooftops

    Science.gov (United States)

    Al-Saadi, Hassan; Zivanovic, Rastko; Al-Sarawi, Said

    2017-11-01

    The installations of solar panels on Australian rooftops have been in rise for the last few years, especially in the urban areas. This motivates academic researchers, distribution network operators and engineers to accurately address the level of uncertainty resulting from grid-connected solar panels. The main source of uncertainty is the intermittent nature of radiation, therefore, this paper presents a new model to estimate the total radiation incident on a tilted solar panel. Where a probability distribution factorizes clearness index, the model is driven upon clearness index with special attention being paid for Australia with the utilization of best-fit-correlation for diffuse fraction. The assessment of the model validity is achieved with the adoption of four goodness-of-fit techniques. In addition, the Quasi Monte Carlo and sparse grid methods are used as sampling and uncertainty computation tools, respectively. High resolution data resolution of solar irradiations for Adelaide city were used for this assessment, with an outcome indicating a satisfactory agreement between actual data variation and model.

  15. Towards Smart and Resilient City: A Conceptual Model

    Science.gov (United States)

    Arafah, Y.; Winarso, H.; Suroso, D. S. A.

    2018-05-01

    This paper aims to compare five smart city models selected based on a number of specific criteria. Following the comparison and assessment performed, we draw conclusions and further linkages identifying the components and characters found in resilient cities. The purpose of this analysis is to produce a new approach and concept: the “smart and resilient city.” Through in-depth literature study, this paper analyzes five conceptual smart city models deemed to have a background, point of view, and benchmark towards software group, as they focus on welfare, inclusion, social equality, and competitiveness. Analyzing the strategies, methods, and techniques of five smart city models, this paper concludes that there has been no inclusion of resilience concepts in the assessment, especially in the context of natural disasters. Basically, the models are also interrelated and there are some things that overlap. As a recommendation, there is a model that tries to combine the components and character of smart city and resilient city into one entity that is embedded as a whole in a conceptual picture towards the new concept, the “smart and resilient city”. The concept of smart city and resilient city go hand in hand with each other and thus are interrelated. Therefore, it is imperative to study that concept deeper, in this case primarily in the context of disaster.

  16. Our evolving conceptual model of the coastal eutrophication problem

    Science.gov (United States)

    Cloern, James E.

    2001-01-01

    A primary focus of coastal science during the past 3 decades has been the question: How does anthropogenic nutrient enrichment cause change in the structure or function of nearshore coastal ecosystems? This theme of environmental science is recent, so our conceptual model of the coastal eutrophication problem continues to change rapidly. In this review, I suggest that the early (Phase I) conceptual model was strongly influenced by limnologists, who began intense study of lake eutrophication by the 1960s. The Phase I model emphasized changing nutrient input as a signal, and responses to that signal as increased phytoplankton biomass and primary production, decomposition of phytoplankton-derived organic matter, and enhanced depletion of oxygen from bottom waters. Coastal research in recent decades has identified key differences in the responses of lakes and coastal-estuarine ecosystems to nutrient enrichment. The contemporary (Phase II) conceptual model reflects those differences and includes explicit recognition of (1) system-specific attributes that act as a filter to modulate the responses to enrichment (leading to large differences among estuarine-coastal systems in their sensitivity to nutrient enrichment); and (2) a complex suite of direct and indirect responses including linked changes in: water transparency, distribution of vascular plants and biomass of macroalgae, sediment biogeochemistry and nutrient cycling, nutrient ratios and their regulation of phytoplankton community composition, frequency of toxic/harmful algal blooms, habitat quality for metazoans, reproduction/growth/survival of pelagic and benthic invertebrates, and subtle changes such as shifts in the seasonality of ecosystem functions. Each aspect of the Phase II model is illustrated here with examples from coastal ecosystems around the world. In the last section of this review I present one vision of the next (Phase III) stage in the evolution of our conceptual model, organized around 5

  17. Uncertainty and sensitivity analysis of environmental transport models

    International Nuclear Information System (INIS)

    Margulies, T.S.; Lancaster, L.E.

    1985-01-01

    An uncertainty and sensitivity analysis has been made of the CRAC-2 (Calculations of Reactor Accident Consequences) atmospheric transport and deposition models. Robustness and uncertainty aspects of air and ground deposited material and the relative contribution of input and model parameters were systematically studied. The underlying data structures were investigated using a multiway layout of factors over specified ranges generated via a Latin hypercube sampling scheme. The variables selected in our analysis include: weather bin, dry deposition velocity, rain washout coefficient/rain intensity, duration of release, heat content, sigma-z (vertical) plume dispersion parameter, sigma-y (crosswind) plume dispersion parameter, and mixing height. To determine the contributors to the output variability (versus distance from the site) step-wise regression analyses were performed on transformations of the spatial concentration patterns simulated. 27 references, 2 figures, 3 tables

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

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  19. Antineutrinos from Earth: A reference model and its uncertainties

    International Nuclear Information System (INIS)

    Mantovani, Fabio; Carmignani, Luigi; Fiorentini, Gianni; Lissia, Marcello

    2004-01-01

    We predict geoneutrino fluxes in a reference model based on a detailed description of Earth's crust and mantle and using the best available information on the abundances of uranium, thorium, and potassium inside Earth's layers. We estimate the uncertainties of fluxes corresponding to the uncertainties of the element abundances. In addition to distance integrated fluxes, we also provide the differential fluxes as a function of distance from several sites of experimental interest. Event yields at several locations are estimated and their dependence on the neutrino oscillation parameters is discussed. At Kamioka we predict N(U+Th)=35±6 events for 10 32 proton yr and 100% efficiency assuming sin 2 (2θ)=0.863 and δm 2 =7.3x10 -5 eV 2 . The maximal prediction is 55 events, obtained in a model with fully radiogenic production of the terrestrial heat flow

  20. A CONCEPTUAL MODEL FOR EFFECTIVE DISTANCE LEARNING IN HIGHER A CONCEPTUAL MODEL FOR EFFECTIVE DISTANCE LEARNING IN HIGHER A CONCEPTUAL MODEL FOR EFFECTIVE DISTANCE LEARNING IN HIGHER EDUCATION

    Directory of Open Access Journals (Sweden)

    Mehran FARAJOLLAHI

    2010-07-01

    Full Text Available The present research aims at presenting a conceptual model for effective distance learning in higher education. Findings of this research shows that an understanding of the technological capabilities and learning theories especially constructive theory and independent learning theory and communicative and interaction theory in Distance learning is an efficient factor in the planning of effective Distance learning in higher education. Considering the theoretical foundations of the present research, in the effective distance learning model, the learner is situated at the center of learning environment. For this purpose, the learner needs to be ready for successful learning and the teacher has to be ready to design the teaching- learning activities when they initially enter the environment. In the present model, group and individual active teaching-learning approach, timely feedback, using IT and eight types of interactions have been designed with respect to theoretical foundations and current university missions. From among the issues emphasized in this model, one can refer to the Initial, Formative and Summative evaluations. In an effective distance learning environment, evaluation should be part of the learning process and the feedback resulting from it should be used to improve learning. For validating the specified features, the opinions of Distance learning experts in Payame Noor, Shiraz, Science and Technology and Amirkabir Universities have been used which verified a high percentage of the statistical sample of the above mentioned features.

  1. Hydrological model uncertainty due to spatial evapotranspiration estimation methods

    Science.gov (United States)

    Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub

    2016-05-01

    Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

  2. Discriminative Random Field Models for Subsurface Contamination Uncertainty Quantification

    Science.gov (United States)

    Arshadi, M.; Abriola, L. M.; Miller, E. L.; De Paolis Kaluza, C.

    2017-12-01

    Application of flow and transport simulators for prediction of the release, entrapment, and persistence of dense non-aqueous phase liquids (DNAPLs) and associated contaminant plumes is a computationally intensive process that requires specification of a large number of material properties and hydrologic/chemical parameters. Given its computational burden, this direct simulation approach is particularly ill-suited for quantifying both the expected performance and uncertainty associated with candidate remediation strategies under real field conditions. Prediction uncertainties primarily arise from limited information about contaminant mass distributions, as well as the spatial distribution of subsurface hydrologic properties. Application of direct simulation to quantify uncertainty would, thus, typically require simulating multiphase flow and transport for a large number of permeability and release scenarios to collect statistics associated with remedial effectiveness, a computationally prohibitive process. The primary objective of this work is to develop and demonstrate a methodology that employs measured field data to produce equi-probable stochastic representations of a subsurface source zone that capture the spatial distribution and uncertainty associated with key features that control remediation performance (i.e., permeability and contamination mass). Here we employ probabilistic models known as discriminative random fields (DRFs) to synthesize stochastic realizations of initial mass distributions consistent with known, and typically limited, site characterization data. Using a limited number of full scale simulations as training data, a statistical model is developed for predicting the distribution of contaminant mass (e.g., DNAPL saturation and aqueous concentration) across a heterogeneous domain. Monte-Carlo sampling methods are then employed, in conjunction with the trained statistical model, to generate realizations conditioned on measured borehole data

  3. An Adaptation Dilemma Caused by Impacts-Modeling Uncertainty

    Science.gov (United States)

    Frieler, K.; Müller, C.; Elliott, J. W.; Heinke, J.; Arneth, A.; Bierkens, M. F.; Ciais, P.; Clark, D. H.; Deryng, D.; Doll, P. M.; Falloon, P.; Fekete, B. M.; Folberth, C.; Friend, A. D.; Gosling, S. N.; Haddeland, I.; Khabarov, N.; Lomas, M. R.; Masaki, Y.; Nishina, K.; Neumann, K.; Oki, T.; Pavlick, R.; Ruane, A. C.; Schmid, E.; Schmitz, C.; Stacke, T.; Stehfest, E.; Tang, Q.; Wisser, D.

    2013-12-01

    Ensuring future well-being for a growing population under either strong climate change or an aggressive mitigation strategy requires a subtle balance of potentially conflicting response measures. In the case of competing goals, uncertainty in impact estimates plays a central role when high confidence in achieving a primary objective (such as food security) directly implies an increased probability of uncertainty induced failure with regard to a competing target (such as climate protection). We use cross sectoral consistent multi-impact model simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP, www.isi-mip.org) to illustrate this uncertainty dilemma: RCP projections from 7 global crop, 11 hydrological, and 7 biomes models are combined to analyze irrigation and land use changes as possible responses to climate change and increasing crop demand due to population growth and economic development. We show that - while a no-regrets option with regard to climate protection - additional irrigation alone is not expected to balance the demand increase by 2050. In contrast, a strong expansion of cultivated land closes the projected production-demand gap in some crop models. However, it comes at the expense of a loss of natural carbon sinks of order 50%. Given the large uncertainty of state of the art crop model projections even these strong land use changes would not bring us ';on the safe side' with respect to food supply. In a world where increasing carbon emissions continue to shrink the overall solution space, we demonstrate that current impacts-modeling uncertainty is a luxury we cannot afford. ISI-MIP is intended to provide cross sectoral consistent impact projections for model intercomparison and improvement as well as cross-sectoral integration. The results presented here were generated within the first Fast-Track phase of the project covering global impact projections. The second phase will also include regional projections. It is the aim

  4. Development and validation of a mass casualty conceptual model.

    Science.gov (United States)

    Culley, Joan M; Effken, Judith A

    2010-03-01

    To develop and validate a conceptual model that provides a framework for the development and evaluation of information systems for mass casualty events. The model was designed based on extant literature and existing theoretical models. A purposeful sample of 18 experts validated the model. Open-ended questions, as well as a 7-point Likert scale, were used to measure expert consensus on the importance of each construct and its relationship in the model and the usefulness of the model to future research. Computer-mediated applications were used to facilitate a modified Delphi technique through which a panel of experts provided validation for the conceptual model. Rounds of questions continued until consensus was reached, as measured by an interquartile range (no more than 1 scale point for each item); stability (change in the distribution of responses less than 15% between rounds); and percent agreement (70% or greater) for indicator questions. Two rounds of the Delphi process were needed to satisfy the criteria for consensus or stability related to the constructs, relationships, and indicators in the model. The panel reached consensus or sufficient stability to retain all 10 constructs, 9 relationships, and 39 of 44 indicators. Experts viewed the model as useful (mean of 5.3 on a 7-point scale). Validation of the model provides the first step in understanding the context in which mass casualty events take place and identifying variables that impact outcomes of care. This study provides a foundation for understanding the complexity of mass casualty care, the roles that nurses play in mass casualty events, and factors that must be considered in designing and evaluating information-communication systems to support effective triage under these conditions.

  5. Robust Optimization Model for Production Planning Problem under Uncertainty

    Directory of Open Access Journals (Sweden)

    Pembe GÜÇLÜ

    2017-01-01

    Full Text Available Conditions of businesses change very quickly. To take into account the uncertainty engendered by changes has become almost a rule while planning. Robust optimization techniques that are methods of handling uncertainty ensure to produce less sensitive results to changing conditions. Production planning, is to decide from which product, when and how much will be produced, with a most basic definition. Modeling and solution of the Production planning problems changes depending on structure of the production processes, parameters and variables. In this paper, it is aimed to generate and apply scenario based robust optimization model for capacitated two-stage multi-product production planning problem under parameter and demand uncertainty. With this purpose, production planning problem of a textile company that operate in İzmir has been modeled and solved, then deterministic scenarios’ and robust method’s results have been compared. Robust method has provided a production plan that has higher cost but, will result close to feasible and optimal for most of the different scenarios in the future.

  6. A conceptual model of psychological contracts in construction projects

    Directory of Open Access Journals (Sweden)

    Yongjian Ke

    2016-09-01

    Full Text Available The strategic importance of relationship style contracting is recognised in the construction industry. Both public and private sector clients are stipulating more integrated and collaborative forms of procurement. Despite relationship and integrated contractual arrangement being available for some time, it is clear that construction firms have been slow to adopt them. Hence it is timely to examine how social exchanges, via unwritten agreement and behaviours, are being nurtured in construction projects. This paper adopted the concept of Psychological Contracts (PC to describe unwritten agreement and behaviours. A conceptual model of the PC is developed and validated using the results from a questionnaire survey administered to construction professionals in Australia. The results uncovered the relationships that existed amongst relational conditions and relational benefits, the PC and the partners’ satisfaction. The results show that all the hypotheses in the conceptual model of the PC are supported, suggesting the PC model is important and may have an effect on project performance and relationship quality among contracting parties. A validated model of the PC in construction was then developed based on the correlations among each component. The managerial implications are that past relationships and relationship characteristics should be taken into account in the selection of procurement partners and the promise of future resources, support and tangible relational outcomes are also vital. It is important for contracting parties to pay attention to unwritten agreements (the PC and behaviours when managing construction projects.

  7. Hydrological model parameter dimensionality is a weak measure of prediction uncertainty

    Science.gov (United States)

    Pande, S.; Arkesteijn, L.; Savenije, H.; Bastidas, L. A.

    2015-04-01

    This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting) and its simplified version SIXPAR (Six Parameter Model), are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters) does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.

  8. Conceptual geoinformation model of natural hazards risk assessment

    Science.gov (United States)

    Kulygin, Valerii

    2016-04-01

    Natural hazards are the major threat to safe interactions between nature and society. The assessment of the natural hazards impacts and their consequences is important in spatial planning and resource management. Today there is a challenge to advance our understanding of how socio-economical and climate changes will affect the frequency and magnitude of hydro-meteorological hazards and associated risks. However, the impacts from different types of natural hazards on various marine and coastal economic activities are not of the same type. In this study, the conceptual geomodel of risk assessment is presented to highlight the differentiation by the type of economic activities in extreme events risk assessment. The marine and coastal ecosystems are considered as the objects of management, on the one hand, and as the place of natural hazards' origin, on the other hand. One of the key elements in describing of such systems is the spatial characterization of their components. Assessment of ecosystem state is based on ecosystem indicators (indexes). They are used to identify the changes in time. The scenario approach is utilized to account for the spatio-temporal dynamics and uncertainty factors. Two types of scenarios are considered: scenarios of using ecosystem services by economic activities and scenarios of extreme events and related hazards. The reported study was funded by RFBR, according to the research project No. 16-35-60043 mol_a_dk.

  9. Conceptual Model of Offshore Wind Environmental Risk Evaluation System

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Richard M.; Copping, Andrea E.; Van Cleve, Frances B.; Unwin, Stephen D.; Hamilton, Erin L.

    2010-06-01

    In this report we describe the development of the Environmental Risk Evaluation System (ERES), a risk-informed analytical process for estimating the environmental risks associated with the construction and operation of offshore wind energy generation projects. The development of ERES for offshore wind is closely allied to a concurrent process undertaken to examine environmental effects of marine and hydrokinetic (MHK) energy generation, although specific risk-relevant attributes will differ between the MHK and offshore wind domains. During FY10, a conceptual design of ERES for offshore wind will be developed. The offshore wind ERES mockup described in this report will provide a preview of the functionality of a fully developed risk evaluation system that will use risk assessment techniques to determine priority stressors on aquatic organisms and environments from specific technology aspects, identify key uncertainties underlying high-risk issues, compile a wide-range of data types in an innovative and flexible data organizing scheme, and inform planning and decision processes with a transparent and technically robust decision-support tool. A fully functional version of ERES for offshore wind will be developed in a subsequent phase of the project.

  10. MISCO: A Conceptual Model for MIS Implementation in SMEs

    Directory of Open Access Journals (Sweden)

    R.Bali

    1999-01-01

    Full Text Available Information Technology ('IT' has made a prolific impact, both in sociological and commercial terms. In the business world, the pursuit of new technology and working practices has often been at the expense of equal regard for the correct methods to manage the new technology. Contemporary IT techniques and methods include Management Information Systems ('MIS' which are normally implemented on a company-wide basis. However, MIS implementation has major cultural and organisational implications which will form the main focus of this paper. A conceptual model is proposed for successful MIS implementation which combines established research findings with ethnographically-informed data from a small, UK-based, business.

  11. Penetration Testing Professional Ethics: a conceptual model and taxonomy

    Directory of Open Access Journals (Sweden)

    Justin Pierce

    2006-05-01

    Full Text Available In an environment where commercial software is continually patched to correct security flaws, penetration testing can provide organisations with a realistic assessment of their security posture. Penetration testing uses the same principles as criminal hackers to penetrate corporate networks and thereby verify the presence of software vulnerabilities. Network administrators can use the results of a penetration test to correct flaws and improve overall security. The use of hacking techniques, however, raises several ethical questions that centre on the integrity of the tester to maintain professional distance and uphold the profession. This paper discusses the ethics of penetration testing and presents our conceptual model and revised taxonomy.

  12. Conceptual Development af a 3D Product Configuration Model

    DEFF Research Database (Denmark)

    Skauge, Jørn

    2006-01-01

    in the development of IT-systems that support the procedures in companies and in the building industry. In other words, it is a knowledge-based system that helps companies in their daily work. The aim of the project has been to develop and examine conceptual ideas about 3D modelling configurator used in the company......’s production of steel fire sliding doors. The development of the 3D digital model is based on practical rather than theoretical research. The result of the research is a prototype digital 3D model to be presented live.......Paper. This project deals with 3D product configuration of a digital building element which has been developed as a prototype in cooperation between a product manufacturer and a research institution in Denmark. The project falls within the concept of product modelling which is more and more used...

  13. Uncertainties in modelling the climate impact of irrigation

    Science.gov (United States)

    de Vrese, Philipp; Hagemann, Stefan

    2017-11-01

    Irrigation-based agriculture constitutes an essential factor for food security as well as fresh water resources and has a distinct impact on regional and global climate. Many issues related to irrigation's climate impact are addressed in studies that apply a wide range of models. These involve substantial uncertainties related to differences in the model's structure and its parametrizations on the one hand and the need for simplifying assumptions for the representation of irrigation on the other hand. To address these uncertainties, we used the Max Planck Institute for Meteorology's Earth System model into which a simple irrigation scheme was implemented. In order to estimate possible uncertainties with regard to the model's more general structure, we compared the climate impact of irrigation between three simulations that use different schemes for the land-surface-atmosphere coupling. Here, it can be shown that the choice of coupling scheme does not only affect the magnitude of possible impacts but even their direction. For example, when using a scheme that does not explicitly resolve spatial subgrid scale heterogeneity at the surface, irrigation reduces the atmospheric water content, even in heavily irrigated regions. Contrarily, in simulations that use a coupling scheme that resolves heterogeneity at the surface or even within the lowest layers of the atmosphere, irrigation increases the average atmospheric specific humidity. A second experiment targeted possible uncertainties related to the representation of irrigation characteristics. Here, in four simulations the irrigation effectiveness (controlled by the target soil moisture and the non-vegetated fraction of the grid box that receives irrigation) and the timing of delivery were varied. The second experiment shows that uncertainties related to the modelled irrigation characteristics, especially the irrigation effectiveness, are also substantial. In general the impact of irrigation on the state of the land

  14. Conceptual model for deriving the repository source term

    International Nuclear Information System (INIS)

    Alexander, D.H.; Apted, M.J.; Liebetrau, A.M.; Van Luik, A.E.; Williford, R.E.; Doctor, P.G.; Pacific Northwest Lab., Richland, WA; Roy F. Weston, Inc./Rogers and Assoc. Engineering Corp., Rockville, MD)

    1984-01-01

    Part of a strategy for evaluating the compliance of geologic repositories with Federal regulations is a modeling approach that would provide realistic release estimates for a particular configuration of the engineered-barrier system. The objective is to avoid worst-case bounding assumptions that are physically impossible or excessively conservative and to obtain probabilitistic estimates of (1) the penetration time for metal barriers and (2) radionuclide-release rates for individually simulated waste packages after penetration has occurred. The conceptual model described in this paper will assume that release rates are explicitly related to such time-dependent processes as mass transfer, dissolution and precipitation, radionuclide decay, and variations in the geochemical environment. The conceptual model will take into account the reduction in the rates of waste-form dissolution and metal corrosion due to a buildup of chemical reaction products. The sorptive properties of the metal-barrier corrosion products in proximity to the waste form surface will also be included. Cumulative released from the engineered-barrier system will be calculated by summing the releases from a probabilistically generated population of individual waste packages. 14 refs., 7 figs

  15. Conceptual model for deriving the repository source term

    International Nuclear Information System (INIS)

    Alexander, D.H.; Apted, M.J.; Liebetrau, A.M.; Doctor, P.G.; Williford, R.E.; Van Luik, A.E.

    1984-11-01

    Part of a strategy for evaluating the compliance of geologic repositories with federal regulations is a modeling approach that would provide realistic release estimates for a particular configuration of the engineered-barrier system. The objective is to avoid worst-case bounding assumptions that are physically impossible or excessively conservative and to obtain probabilistic estimates of (1) the penetration time for metal barriers and (2) radionuclide-release rates for individually simulated waste packages after penetration has occurred. The conceptual model described in this paper will assume that release rates are explicitly related to such time-dependent processes as mass transfer, dissolution and precipitation, radionuclide decay, and variations in the geochemical environment. The conceptual model will take into account the reduction in the rates of waste-form dissolution and metal corrosion due to a buildup of chemical reaction products. The sorptive properties of the metal-barrier corrosion products in proximity to the waste form surface will also be included. Cumulative releases from the engineered-barrier system will be calculated by summing the releases from a probabilistically generated population of individual waste packages. 14 refs., 7 figs

  16. A python framework for environmental model uncertainty analysis

    Science.gov (United States)

    White, Jeremy; Fienen, Michael N.; Doherty, John E.

    2016-01-01

    We have developed pyEMU, a python framework for Environmental Modeling Uncertainty analyses, open-source tool that is non-intrusive, easy-to-use, computationally efficient, and scalable to highly-parameterized inverse problems. The framework implements several types of linear (first-order, second-moment (FOSM)) and non-linear uncertainty analyses. The FOSM-based analyses can also be completed prior to parameter estimation to help inform important modeling decisions, such as parameterization and objective function formulation. Complete workflows for several types of FOSM-based and non-linear analyses are documented in example notebooks implemented using Jupyter that are available in the online pyEMU repository. Example workflows include basic parameter and forecast analyses, data worth analyses, and error-variance analyses, as well as usage of parameter ensemble generation and management capabilities. These workflows document the necessary steps and provides insights into the results, with the goal of educating users not only in how to apply pyEMU, but also in the underlying theory of applied uncertainty quantification.

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

    Science.gov (United States)

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

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

    Science.gov (United States)

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

  19. Uncertainty and Preference Modelling for Multiple Criteria Vehicle Evaluation

    Directory of Open Access Journals (Sweden)

    Qiuping Yang

    2010-12-01

    Full Text Available A general framework for vehicle assessment is proposed based on both mass survey information and the evidential reasoning (ER approach. Several methods for uncertainty and preference modeling are developed within the framework, including the measurement of uncertainty caused by missing information, the estimation of missing information in original surveys, the use of nonlinear functions for data mapping, and the use of nonlinear functions as utility function to combine distributed assessments into a single index. The results of the investigation show that various measures can be used to represent the different preferences of decision makers towards the same feedback from respondents. Based on the ER approach, credible and informative analysis can be conducted through the complete understanding of the assessment problem in question and the full exploration of available information.

  20. Type-2 fuzzy elliptic membership functions for modeling uncertainty

    DEFF Research Database (Denmark)

    Kayacan, Erdal; Sarabakha, Andriy; Coupland, Simon

    2018-01-01

    Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make extensive analysis in terms of the capability of type-2 elliptic fuzzy MFs...... in modeling uncertainty. Having decoupled parameters for its support and width, elliptic MFs are unique amongst existing type-2 fuzzy MFs. In this investigation, the uncertainty distribution along the elliptic MF support is studied, and a detailed analysis is given to compare and contrast its performance...... advantages mentioned above, elliptic MFs have comparable prediction results when compared to Gaussian and triangular MFs. Finally, in order to test the performance of fuzzy logic controller with elliptic interval type-2 MFs, extensive real-time experiments are conducted for the 3D trajectory tracking problem...

  1. Structural reliability in context of statistical uncertainties and modelling discrepancies

    International Nuclear Information System (INIS)

    Pendola, Maurice

    2000-01-01

    Structural reliability methods have been largely improved during the last years and have showed their ability to deal with uncertainties during the design stage or to optimize the functioning and the maintenance of industrial installations. They are based on a mechanical modeling of the structural behavior according to the considered failure modes and on a probabilistic representation of input parameters of this modeling. In practice, only limited statistical information is available to build the probabilistic representation and different sophistication levels of the mechanical modeling may be introduced. Thus, besides the physical randomness, other uncertainties occur in such analyses. The aim of this work is triple: 1. at first, to propose a methodology able to characterize the statistical uncertainties due to the limited number of data in order to take them into account in the reliability analyses. The obtained reliability index measures the confidence in the structure considering the statistical information available. 2. Then, to show a methodology leading to reliability results evaluated from a particular mechanical modeling but by using a less sophisticated one. The objective is then to decrease the computational efforts required by the reference modeling. 3. Finally, to propose partial safety factors that are evolving as a function of the number of statistical data available and as a function of the sophistication level of the mechanical modeling that is used. The concepts are illustrated in the case of a welded pipe and in the case of a natural draught cooling tower. The results show the interest of the methodologies in an industrial context. [fr

  2. A sliding mode observer for hemodynamic characterization under modeling uncertainties

    KAUST Repository

    Zayane, Chadia

    2014-06-01

    This paper addresses the case of physiological states reconstruction in a small region of the brain under modeling uncertainties. The misunderstood coupling between the cerebral blood volume and the oxygen extraction fraction has lead to a partial knowledge of the so-called balloon model describing the hemodynamic behavior of the brain. To overcome this difficulty, a High Order Sliding Mode observer is applied to the balloon system, where the unknown coupling is considered as an internal perturbation. The effectiveness of the proposed method is illustrated through a set of synthetic data that mimic fMRI experiments.

  3. A conceptual gamma shield design using the DRP model computation

    Energy Technology Data Exchange (ETDEWEB)

    Ahmad, E E [Reactor Department, Nuclear Research Center, Atomic Energy Authority, Cairo (Egypt); Rahman, F A [National Center of Nuclear Safety and Radiation Control, Atomic Energy Authority, Cairo (Egypt)

    1997-12-31

    The purpose of this investigation is to assess basic areas of concern in the development of reactor shielding conceptual design calculations. A spherical shield model composed of low carbon steel and lead have been constructed to surround a Co-60 gamma point source. two alternative configurations have been considered in the model computation. The numerical calculations have been performed using both the ANISN code and DRP model computation together with the DLC 75-Bugle 80 data library. A resume of results for deep penetration in different shield materials with different packing densities is presented and analysed. The results showed that the gamma fluxes attenuation is increased with increasing distribution the packing density of the shield material which reflects its importance of considering it as a safety parameter in shielding design. 3 figs.

  4. A Bayesian statistical method for quantifying model form uncertainty and two model combination methods

    International Nuclear Information System (INIS)

    Park, Inseok; Grandhi, Ramana V.

    2014-01-01

    Apart from parametric uncertainty, model form uncertainty as well as prediction error may be involved in the analysis of engineering system. Model form uncertainty, inherently existing in selecting the best approximation from a model set cannot be ignored, especially when the predictions by competing models show significant differences. In this research, a methodology based on maximum likelihood estimation is presented to quantify model form uncertainty using the measured differences of experimental and model outcomes, and is compared with a fully Bayesian estimation to demonstrate its effectiveness. While a method called the adjustment factor approach is utilized to propagate model form uncertainty alone into the prediction of a system response, a method called model averaging is utilized to incorporate both model form uncertainty and prediction error into it. A numerical problem of concrete creep is used to demonstrate the processes for quantifying model form uncertainty and implementing the adjustment factor approach and model averaging. Finally, the presented methodology is applied to characterize the engineering benefits of a laser peening process

  5. Selected examples of practical approaches for the assessment of model reliability - parameter uncertainty analysis

    International Nuclear Information System (INIS)

    Hofer, E.; Hoffman, F.O.

    1987-02-01

    The uncertainty analysis of model predictions has to discriminate between two fundamentally different types of uncertainty. The presence of stochastic variability (Type 1 uncertainty) necessitates the use of a probabilistic model instead of the much simpler deterministic one. Lack of knowledge (Type 2 uncertainty), however, applies to deterministic as well as to probabilistic model predictions and often dominates over uncertainties of Type 1. The term ''probability'' is interpreted differently in the probabilistic analysis of either type of uncertainty. After these discriminations have been explained the discussion centers on the propagation of parameter uncertainties through the model, the derivation of quantitative uncertainty statements for model predictions and the presentation and interpretation of the results of a Type 2 uncertainty analysis. Various alternative approaches are compared for a very simple deterministic model

  6. Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation

    Science.gov (United States)

    Pathiraja, S.; Moradkhani, H.; Marshall, L.; Sharma, A.; Geenens, G.

    2018-02-01

    The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real-world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data-driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low-dimensional chaotic dynamics and a real hydrologic experiment for one-day-ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.

  7. Comparison of two conceptual models of flow using the TSA

    International Nuclear Information System (INIS)

    Wilson, M.L.

    1992-01-01

    As part of the performance-assessment task for the potential repository site at Yucca Mountain, Nevada, Sandia National Laboratories is developing a set of programs called the Total-System Analyzer (TSA). The TSA is one of the tools being used in the current effort to provide a systematic preliminary estimate the total-system performance of the Yucca Mountain site. The purposes of this paper are twofold: (1) to describe capabilities that have been added to the TSA in the last year; and (2) to present a comparison of two conceptual models of unsaturated-zone flow and transport, in terms of the performance measure specified by the Environmental Protection Agency (EPA) in 40 CFR Part 191. The conceptual-model comparison is intended to demonstrate the new TSA capabilities and at the same time shed some light on the performance implications of fracture flow at Yucca Mountain. Unsaturated fracture flow is not yet well understood, and it is of great importance in determining the performance of Yucca Mountain

  8. Conceptual models in man-machine design verification

    International Nuclear Information System (INIS)

    Rasmussen, J.

    1985-01-01

    The need for systematic methods for evaluation of design concepts for new man-machine systems has been rapidly increasing in consequence of the introduction of modern information technology. Direct empirical methods are difficult to apply when functions during rare conditions and support of operator decisions during emergencies are to be evaluated. In this paper, the problems of analytical evaluations based on conceptual models of the man-machine interaction are discussed, and the relations to system design and analytical risk assessment are considered. Finally, a conceptual framework for analytical evaluation is proposed, including several domains of description: 1. The problem space, in the form of a means-end hierarchy; 2. The structure of the decision process; 3. The mental strategies and heuristics used by operators; 4. The levels of cognitive control and the mechanisms related to human errors. Finally, the need for models representing operators' subjective criteria for choosing among available mental strategies and for accepting advice from intelligent interfaces is discussed

  9. Dynamics Under Location Uncertainty: Model Derivation, Modified Transport and Uncertainty Quantification

    Science.gov (United States)

    Resseguier, V.; Memin, E.; Chapron, B.; Fox-Kemper, B.

    2017-12-01

    In order to better observe and predict geophysical flows, ensemble-based data assimilation methods are of high importance. In such methods, an ensemble of random realizations represents the variety of the simulated flow's likely behaviors. For this purpose, randomness needs to be introduced in a suitable way and physically-based stochastic subgrid parametrizations are promising paths. This talk will propose a new kind of such a parametrization referred to as modeling under location uncertainty. The fluid velocity is decomposed into a resolved large-scale component and an aliased small-scale one. The first component is possibly random but time-correlated whereas the second is white-in-time but spatially-correlated and possibly inhomogeneous and anisotropic. With such a velocity, the material derivative of any - possibly active - tracer is modified. Three new terms appear: a correction of the large-scale advection, a multiplicative noise and a possibly heterogeneous and anisotropic diffusion. This parameterization naturally ensures attractive properties such as energy conservation for each realization. Additionally, this stochastic material derivative and the associated Reynolds' transport theorem offer a systematic method to derive stochastic models. In particular, we will discuss the consequences of the Quasi-Geostrophic assumptions in our framework. Depending on the turbulence amount, different models with different physical behaviors are obtained. Under strong turbulence assumptions, a simplified diagnosis of frontolysis and frontogenesis at the surface of the ocean is possible in this framework. A Surface Quasi-Geostrophic (SQG) model with a weaker noise influence has also been simulated. A single realization better represents small scales than a deterministic SQG model at the same resolution. Moreover, an ensemble accurately predicts extreme events, bifurcations as well as the amplitudes and the positions of the simulation errors. Figure 1 highlights this last

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

    Science.gov (United States)

    Liang, D.; Liu, X.

    2017-12-01

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

  11. Multiscale Modeling and Uncertainty Quantification for Nuclear Fuel Performance

    Energy Technology Data Exchange (ETDEWEB)

    Estep, Donald [Colorado State Univ., Fort Collins, CO (United States); El-Azab, Anter [Florida State Univ., Tallahassee, FL (United States); Pernice, Michael [Idaho National Lab. (INL), Idaho Falls, ID (United States); Peterson, John W. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Polyakov, Peter [Univ. of Wyoming, Laramie, WY (United States); Tavener, Simon [Colorado State Univ., Fort Collins, CO (United States); Xiu, Dongbin [Purdue Univ., West Lafayette, IN (United States); Univ. of Utah, Salt Lake City, UT (United States)

    2017-03-23

    In this project, we will address the challenges associated with constructing high fidelity multiscale models of nuclear fuel performance. We (*) propose a novel approach for coupling mesoscale and macroscale models, (*) devise efficient numerical methods for simulating the coupled system, and (*) devise and analyze effective numerical approaches for error and uncertainty quantification for the coupled multiscale system. As an integral part of the project, we will carry out analysis of the effects of upscaling and downscaling, investigate efficient methods for stochastic sensitivity analysis of the individual macroscale and mesoscale models, and carry out a posteriori error analysis for computed results. We will pursue development and implementation of solutions in software used at Idaho National Laboratories on models of interest to the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program.

  12. Uncertainty identification for robust control using a nuclear power plant model

    International Nuclear Information System (INIS)

    Power, M.; Edwards, R.M.

    1995-01-01

    An on-line technique which identifies the uncertainty between a lower order and a higher order nuclear power plant model is presented. The uncertainty identifier produces a hard upper bound in H ∞ on the additive uncertainty. This additive uncertainty description can be used for the design of H infinity or μ-synthesis controllers

  13. Making Invasion models useful for decision makers; incorporating uncertainty, knowledge gaps, and decision-making preferences

    Science.gov (United States)

    Denys Yemshanov; Frank H Koch; Mark Ducey

    2015-01-01

    Uncertainty is inherent in model-based forecasts of ecological invasions. In this chapter, we explore how the perceptions of that uncertainty can be incorporated into the pest risk assessment process. Uncertainty changes a decision maker’s perceptions of risk; therefore, the direct incorporation of uncertainty may provide a more appropriate depiction of risk. Our...

  14. Quantifying uncertainty, variability and likelihood for ordinary differential equation models

    LENUS (Irish Health Repository)

    Weisse, Andrea Y

    2010-10-28

    Abstract Background In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space. Results The partial differential equation that describes the evolution of this probability density function has a form that is particularly amenable to application of the well-known method of characteristics. The value of the density at some point in time is directly accessible by the solution of the original ODE extended by a single extra dimension (for the value of the density). This leads to simple methods for studying uncertainty, variability and likelihood, with significant advantages over more traditional Monte Carlo and related approaches especially when studying regions with low probability. Conclusions While such approaches based on the method of characteristics are common practice in other disciplines, their advantages for the study of biological systems have so far remained unrecognized. Several examples illustrate performance and accuracy of the approach and its limitations.

  15. Quantification of structural uncertainties in multi-scale models; case study of the Lublin Basin, Poland

    Science.gov (United States)

    Małolepszy, Zbigniew; Szynkaruk, Ewa

    2015-04-01

    same degrees of generalization shall be applied to uncertainties. However, approach for uncertainty assessment and quantification may vary depending on the scale of the model. In small scale regional and sub-regional models deterministic modelling methods are used, while stochastic algorithms can be applied for uncertainty modelling at large scale multi-prospect and field models. We believe that the 3D multiscale modelling describing geological architecture with quantified structure uncertainties, presented on standard deviation maps and grids, will allow us to outline exploration opportunities as well as to refine existing and build new conceptual models. As the tectonic setting of the area is the subject of long-term dispute, the model depicting at different resolutions both structures and gaps in geological knowledge shall allow to confirm some of the concepts related to geological history of the Lublin Basin and reject or modify the others.

  16. Quantifying uncertainty in Bayesian calibrated animal-to-human PBPK models with informative prior distributions

    Science.gov (United States)

    Understanding and quantifying the uncertainty of model parameters and predictions has gained more interest in recent years with the increased use of computational models in chemical risk assessment. Fully characterizing the uncertainty in risk metrics derived from linked quantita...

  17. Improving default risk prediction using Bayesian model uncertainty techniques.

    Science.gov (United States)

    Kazemi, Reza; Mosleh, Ali

    2012-11-01

    Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

  18. Selection of Representative Models for Decision Analysis Under Uncertainty

    Science.gov (United States)

    Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.

    2016-03-01

    The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.

  19. Sustainable infrastructure system modeling under uncertainties and dynamics

    Science.gov (United States)

    Huang, Yongxi

    potential risks caused by feedstock seasonality and demand uncertainty. Facility spatiality, time variation of feedstock yields, and demand uncertainty are integrated into a two-stage stochastic programming (SP) framework. In the study of Transitional Energy System Modeling under Uncertainty, a multistage stochastic dynamic programming is established to optimize the process of building and operating fuel production facilities during the transition. Dynamics due to the evolving technologies and societal changes and uncertainty due to demand fluctuations are the major issues to be addressed.

  20. A conceptual model for translating omic data into clinical action

    Directory of Open Access Journals (Sweden)

    Timothy M Herr

    2015-01-01

    Full Text Available Genomic, proteomic, epigenomic, and other "omic" data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate such data into patient care. Here, we outline a conceptual model for the application of omic data in the clinical context, called "the omic funnel." This model parallels the classic "Data, Information, Knowledge, Wisdom pyramid" and adds context for how to move between each successive layer. Its goal is to allow informaticians, researchers, and clinicians to approach the problem of translating omic data from bench to bedside, by using discrete steps with clearly defined needs. Such an approach can facilitate the development of modular and interoperable software that can bring precision medicine into widespread practice.

  1. GEOQUIMICO : an interactive tool for comparing sorption conceptual models (surface complexation modeling versus K[D])

    International Nuclear Information System (INIS)

    Hammond, Glenn E.; Cygan, Randall Timothy

    2007-01-01

    Within reactive geochemical transport, several conceptual models exist for simulating sorption processes in the subsurface. Historically, the K D approach has been the method of choice due to ease of implementation within a reactive transport model and straightforward comparison with experimental data. However, for modeling complex sorption phenomenon (e.g. sorption of radionuclides onto mineral surfaces), this approach does not systematically account for variations in location, time, or chemical conditions, and more sophisticated methods such as a surface complexation model (SCM) must be utilized. It is critical to determine which conceptual model to use; that is, when the material variation becomes important to regulatory decisions. The geochemical transport tool GEOQUIMICO has been developed to assist in this decision-making process. GEOQUIMICO provides a user-friendly framework for comparing the accuracy and performance of sorption conceptual models. The model currently supports the K D and SCM conceptual models. The code is written in the object-oriented Java programming language to facilitate model development and improve code portability. The basic theory underlying geochemical transport and the sorption conceptual models noted above is presented in this report. Explanations are provided of how these physicochemical processes are instrumented in GEOQUIMICO and a brief verification study comparing GEOQUIMICO results to data found in the literature is given

  2. Workshop on Model Uncertainty and its Statistical Implications

    CERN Document Server

    1988-01-01

    In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.

  3. Analysis of coupled model uncertainties in source-to-dose modeling of human exposures to ambient air pollution: A PM 2.5 case study

    Science.gov (United States)

    Özkaynak, Halûk; Frey, H. Christopher; Burke, Janet; Pinder, Robert W.

    Quantitative assessment of human exposures and health effects due to air pollution involve detailed characterization of impacts of air quality on exposure and dose. A key challenge is to integrate these three components on a consistent spatial and temporal basis taking into account linkages and feedbacks. The current state-of-practice for such assessments is to exercise emission, meteorology, air quality, exposure, and dose models separately, and to link them together by using the output of one model as input to the subsequent downstream model. Quantification of variability and uncertainty has been an important topic in the exposure assessment community for a number of years. Variability refers to differences in the value of a quantity (e.g., exposure) over time, space, or among individuals. Uncertainty refers to lack of knowledge regarding the true value of a quantity. An emerging challenge is how to quantify variability and uncertainty in integrated assessments over the source-to-dose continuum by considering contributions from individual as well as linked components. For a case study of fine particulate matter (PM 2.5) in North Carolina during July 2002, we characterize variability and uncertainty associated with each of the individual concentration, exposure and dose models that are linked, and use a conceptual framework to quantify and evaluate the implications of coupled model uncertainties. We find that the resulting overall uncertainties due to combined effects of both variability and uncertainty are smaller (usually by a factor of 3-4) than the crudely multiplied model-specific overall uncertainty ratios. Future research will need to examine the impact of potential dependencies among the model components by conducting a truly coupled modeling analysis.

  4. [Active ageing and success: A brief history of conceptual models].

    Science.gov (United States)

    Petretto, Donatella Rita; Pili, Roberto; Gaviano, Luca; Matos López, Cristina; Zuddas, Carlo

    2016-01-01

    The aim of this paper is to analyse and describe different conceptual models of successful ageing, active and healthy ageing developed in Europe and in America in the 20° century, starting from Rowe and Kahn's original model (1987, 1997). A narrative review was conducted on the literature on successful ageing. Our review included definition of successful ageing from European and American scholars. Models were found that aimed to describe indexes of active and healthy ageing, models devoted to describe processes involved in successful ageing, and additional views that emphasise subjective and objective perception of successful ageing. A description is also given of critiques on previous models and remedies according to Martin et al. (2014) and strategies for successful ageing according to Jeste and Depp (2014). The need is discussed for the enhancement of Rowe and Kahn's model and other models with a more inclusive, universal description of ageing, incorporating scientific evidence regarding active ageing. Copyright © 2015 SEGG. Publicado por Elsevier España, S.L.U. All rights reserved.

  5. Model structures amplify uncertainty in predicted soil carbon responses to climate change.

    Science.gov (United States)

    Shi, Zheng; Crowell, Sean; Luo, Yiqi; Moore, Berrien

    2018-06-04

    Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.

  6. Simulation modelling of fynbos ecosystems: Systems analysis and conceptual models

    CSIR Research Space (South Africa)

    Kruger, FJ

    1985-03-01

    Full Text Available -animal interactions. An additional two models, which expand aspects of the FYNBOS model, are described: a model for simulating canopy processes; and a Fire Recovery Simulator. The canopy process model will simulate ecophysiological processes in more detail than FYNBOS...

  7. A study on modeling customer preferences for conceptual design

    International Nuclear Information System (INIS)

    Han, Soon Young; Seo, Seok Hoon; Choi, Hae Jin

    2015-01-01

    In this paper, we propose a concept selection method to evaluate future market performances of concept candidates, and to choose the best concept among those. The main and interaction effects of product performance factors, economic factors, and time on a market performance are modeled using a Bayesian framework-based Artificial neural network (ANN). The Bayesian framework is employed to measure the potential risk of wrong selection in using a trained ANN model. Based on the measured uncertainty bounds in the predicted future market performance, the most promising and robust concept may be selected. To validate our concept-selection method, we employed an automobile concept selection problem in the U.S. market. Seventeen concepts were assumed to compete in 2013, and the future market share with error bounds was predicted using the trained model based on sale data

  8. A study on modeling customer preferences for conceptual design

    Energy Technology Data Exchange (ETDEWEB)

    Han, Soon Young; Seo, Seok Hoon; Choi, Hae Jin [Chung-Ang University, Seoul (Korea, Republic of)

    2015-11-15

    In this paper, we propose a concept selection method to evaluate future market performances of concept candidates, and to choose the best concept among those. The main and interaction effects of product performance factors, economic factors, and time on a market performance are modeled using a Bayesian framework-based Artificial neural network (ANN). The Bayesian framework is employed to measure the potential risk of wrong selection in using a trained ANN model. Based on the measured uncertainty bounds in the predicted future market performance, the most promising and robust concept may be selected. To validate our concept-selection method, we employed an automobile concept selection problem in the U.S. market. Seventeen concepts were assumed to compete in 2013, and the future market share with error bounds was predicted using the trained model based on sale data.

  9. Transient Inverse Calibration of Site-Wide Groundwater Model to Hanford Operational Impacts from 1943 to 1996-Alternative Conceptual Model Considering Interaction with Uppermost Basalt Confined Aquifer; FINAL

    International Nuclear Information System (INIS)

    Vermeul, Vince R; Cole, Charles R; Bergeron, Marcel P; Thorne, Paul D; Wurstner, Signe K

    2001-01-01

    The baseline three-dimensional transient inverse model for the estimation of site-wide scale flow parameters, including their uncertainties, using data on the transient behavior of the unconfined aquifer system over the entire historical period of Hanford operations, has been modified to account for the effects of basalt intercommunication between the Hanford unconfined aquifer and the underlying upper basalt confined aquifer. Both the baseline and alternative conceptual models (ACM-1) considered only the groundwater flow component and corresponding observational data in the 3-Dl transient inverse calibration efforts. Subsequent efforts will examine both groundwater flow and transport. Comparisons of goodness of fit measures and parameter estimation results for the ACM-1 transient inverse calibrated model with those from previous site-wide groundwater modeling efforts illustrate that the new 3-D transient inverse model approach will strengthen the technical defensibility of the final model(s) and provide the ability to incorporate uncertainty in predictions related to both conceptual model and parameter uncertainty

  10. A conceptual model to improve performance in virtual teams

    Directory of Open Access Journals (Sweden)

    Shopee Dube

    2016-09-01

    Full Text Available Background: The vast improvement in communication technologies and sophisticated project management tools, methods and techniques has allowed geographically and culturally diverse groups to operate and function in a virtual environment. To succeed in this virtual environment where time and space are becoming increasingly irrelevant, organisations must define new ways of implementing initiatives. This virtual environment phenomenon has brought about the formation of virtual project teams that allow organisations to harness the skills and knowhow of the best resources, irrespective of their location. Objectives: The aim of this article was to investigate performance criteria and develop a conceptual model which can be applied to enhance the success of virtual project teams. There are no clear guidelines of the performance criteria in managing virtual project teams. Method: A qualitative research methodology was used in this article. The purpose of content analysis was to explore the literature to understand the concept of performance in virtual project teams and to summarise the findings of the literature reviewed. Results: The research identified a set of performance criteria for the virtual project teams as follows: leadership, trust, communication, team cooperation, reliability, motivation, comfort and social interaction. These were used to conceptualise the model. Conclusion: The conceptual model can be used in a holistic way to determine the overall performance of the virtual project team, but each factor can be analysed individually to determine the impact on the overall performance. The knowledge of performance criteria for virtual project teams could aid project managers in enhancing the success of these teams and taking a different approach to better manage and coordinate them.

  11. Uncertainty Analysis of Multi-Model Flood Forecasts

    Directory of Open Access Journals (Sweden)

    Erich J. Plate

    2015-12-01

    Full Text Available This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf, calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.

  12. The structure of conceptual models with application to the Aespoe HRL project

    International Nuclear Information System (INIS)

    Olsson, Olle; Baeckblom, G.; Wikberg, P.; Gustafson, G.; Stanfors, R.

    1994-05-01

    In performance assessment a sequence of models is used to describe the function of the geological barrier. This report proposes a general structure and terminology for description of these models. A model description consists of the following components: A conceptual model which defines the geometric framework in which the problem is solved, the dimensions of the modelled volume, descriptions of the processes included in the model, and the boundary conditions; Data which are introduced into the conceptual model, and a mathematical or numerical tool used to produce output data. Contradictory to common practice in geohydrologic modelling it is proposed that the term conceptual model is restricted to define in what way the model is constructed, and that this is separated from any specific application of the conceptual model. Hence, the conceptual model should not include any specific data. 5 refs, 2 figs, 4 tabs

  13. GARUSO - Version 1.0. Uncertainty model for multipath ultrasonic transit time gas flow meters

    Energy Technology Data Exchange (ETDEWEB)

    Lunde, Per; Froeysa, Kjell-Eivind; Vestrheim, Magne

    1997-09-01

    This report describes an uncertainty model for ultrasonic transit time gas flow meters configured with parallel chords, and a PC program, GARUSO Version 1.0, implemented for calculation of the meter`s relative expanded uncertainty. The program, which is based on the theoretical uncertainty model, is used to carry out a simplified and limited uncertainty analysis for a 12`` 4-path meter, where examples of input and output uncertainties are given. The model predicts a relative expanded uncertainty for the meter at a level which further justifies today`s increasing tendency to use this type of instruments for fiscal metering of natural gas. 52 refs., 15 figs., 11 tabs.

  14. Climate stability and sensitivity in some simple conceptual models

    Energy Technology Data Exchange (ETDEWEB)

    Bates, J. Ray [University College Dublin, Meteorology and Climate Centre, School of Mathematical Sciences, Dublin (Ireland)

    2012-02-15

    A theoretical investigation of climate stability and sensitivity is carried out using three simple linearized models based on the top-of-the-atmosphere energy budget. The simplest is the zero-dimensional model (ZDM) commonly used as a conceptual basis for climate sensitivity and feedback studies. The others are two-zone models with tropics and extratropics of equal area; in the first of these (Model A), the dynamical heat transport (DHT) between the zones is implicit, in the second (Model B) it is explicitly parameterized. It is found that the stability and sensitivity properties of the ZDM and Model A are very similar, both depending only on the global-mean radiative response coefficient and the global-mean forcing. The corresponding properties of Model B are more complex, depending asymmetrically on the separate tropical and extratropical values of these quantities, as well as on the DHT coefficient. Adopting Model B as a benchmark, conditions are found under which the validity of the ZDM and Model A as climate sensitivity models holds. It is shown that parameter ranges of physical interest exist for which such validity may not hold. The 2 x CO{sub 2} sensitivities of the simple models are studied and compared. Possible implications of the results for sensitivities derived from GCMs and palaeoclimate data are suggested. Sensitivities for more general scenarios that include negative forcing in the tropics (due to aerosols, inadvertent or geoengineered) are also studied. Some unexpected outcomes are found in this case. These include the possibility of a negative global-mean temperature response to a positive global-mean forcing, and vice versa. (orig.)

  15. System convergence in transport models: algorithms efficiency and output uncertainty

    DEFF Research Database (Denmark)

    Rich, Jeppe; Nielsen, Otto Anker

    2015-01-01

    of this paper is to analyse convergence performance for the external loop and to illustrate how an improper linkage between the converging parts can lead to substantial uncertainty in the final output. Although this loop is crucial for the performance of large-scale transport models it has not been analysed...... much in the literature. The paper first investigates several variants of the Method of Successive Averages (MSA) by simulation experiments on a toy-network. It is found that the simulation experiments produce support for a weighted MSA approach. The weighted MSA approach is then analysed on large......-scale in the Danish National Transport Model (DNTM). It is revealed that system convergence requires that either demand or supply is without random noise but not both. In that case, if MSA is applied to the model output with random noise, it will converge effectively as the random effects are gradually dampened...

  16. Incentive salience attribution under reward uncertainty: A Pavlovian model.

    Science.gov (United States)

    Anselme, Patrick

    2015-02-01

    There is a vast literature on the behavioural effects of partial reinforcement in Pavlovian conditioning. Compared with animals receiving continuous reinforcement, partially rewarded animals typically show (a) a slower development of the conditioned response (CR) early in training and (b) a higher asymptotic level of the CR later in training. This phenomenon is known as the partial reinforcement acquisition effect (PRAE). Learning models of Pavlovian conditioning fail to account for it. In accordance with the incentive salience hypothesis, it is here argued that incentive motivation (or 'wanting') plays a more direct role in controlling behaviour than does learning, and reward uncertainty is shown to have an excitatory effect on incentive motivation. The psychological origin of that effect is discussed and a computational model integrating this new interpretation is developed. Many features of CRs under partial reinforcement emerge from this model. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Plasticity models of material variability based on uncertainty quantification techniques

    Energy Technology Data Exchange (ETDEWEB)

    Jones, Reese E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Rizzi, Francesco [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Boyce, Brad [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Templeton, Jeremy Alan [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ostien, Jakob [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

    2017-11-01

    The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

  18. A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design--part I. Model development.

    Science.gov (United States)

    He, L; Huang, G H; Lu, H W

    2010-04-15

    Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the "true" ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes. 2009 Elsevier B.V. All rights reserved.

  19. A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design-Part I. Model development

    Energy Technology Data Exchange (ETDEWEB)

    He, L., E-mail: li.he@ryerson.ca [Department of Civil Engineering, Faculty of Engineering, Architecture and Science, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3 (Canada); Huang, G.H. [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); College of Urban Environmental Sciences, Peking University, Beijing 100871 (China); Lu, H.W. [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada)

    2010-04-15

    Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the 'true' ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes.

  20. Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil

    Directory of Open Access Journals (Sweden)

    Paula Mendes Luz

    2003-10-01

    Full Text Available Dengue fever is currently the most important arthropod-borne viral disease in Brazil. Mathematical modeling of disease dynamics is a very useful tool for the evaluation of control measures. To be used in decision-making, however, a mathematical model must be carefully parameterized and validated with epidemiological and entomological data. In this work, we developed a simple dengue model to answer three questions: (i which parameters are worth pursuing in the field in order to develop a dengue transmission model for Brazilian cities; (ii how vector density spatial heterogeneity influences control efforts; (iii with a degree of uncertainty, what is the invasion potential of dengue virus type 4 (DEN-4 in Rio de Janeiro city. Our model consists of an expression for the basic reproductive number (R0 that incorporates vector density spatial heterogeneity. To deal with the uncertainty regarding parameter values, we parameterized the model using a priori probability density functions covering a range of plausible values for each parameter. Using the Latin Hypercube Sampling procedure, values for the parameters were generated. We conclude that, even in the presence of vector spatial heterogeneity, the two most important entomological parameters to be estimated in the field are the mortality rate and the extrinsic incubation period. The spatial heterogeneity of the vector population increases the risk of epidemics and makes the control strategies more complex. At last, we conclude that Rio de Janeiro is at risk of a DEN-4 invasion. Finally, we stress the point that epidemiologists, mathematicians, and entomologists need to interact more to find better approaches to the measuring and interpretation of the transmission dynamics of arthropod-borne diseases.

  1. Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil

    Directory of Open Access Journals (Sweden)

    Luz Paula Mendes

    2003-01-01

    Full Text Available Dengue fever is currently the most important arthropod-borne viral disease in Brazil. Mathematical modeling of disease dynamics is a very useful tool for the evaluation of control measures. To be used in decision-making, however, a mathematical model must be carefully parameterized and validated with epidemiological and entomological data. In this work, we developed a simple dengue model to answer three questions: (i which parameters are worth pursuing in the field in order to develop a dengue transmission model for Brazilian cities; (ii how vector density spatial heterogeneity influences control efforts; (iii with a degree of uncertainty, what is the invasion potential of dengue virus type 4 (DEN-4 in Rio de Janeiro city. Our model consists of an expression for the basic reproductive number (R0 that incorporates vector density spatial heterogeneity. To deal with the uncertainty regarding parameter values, we parameterized the model using a priori probability density functions covering a range of plausible values for each parameter. Using the Latin Hypercube Sampling procedure, values for the parameters were generated. We conclude that, even in the presence of vector spatial heterogeneity, the two most important entomological parameters to be estimated in the field are the mortality rate and the extrinsic incubation period. The spatial heterogeneity of the vector population increases the risk of epidemics and makes the control strategies more complex. At last, we conclude that Rio de Janeiro is at risk of a DEN-4 invasion. Finally, we stress the point that epidemiologists, mathematicians, and entomologists need to interact more to find better approaches to the measuring and interpretation of the transmission dynamics of arthropod-borne diseases.

  2. CONCEPTUAL MODEL OF CONSUMERS TRUST TO ONLINE SHOPS

    Directory of Open Access Journals (Sweden)

    T. Dubovyk

    2014-06-01

    Full Text Available In the article the conceptual model of the major factors that influence consumers trust in online shop: reliability of online store, reliable information system for making purchases online, factors of ethic interactiveness (security, third-party certification, internet-marketing communications of online-shop and other factors – that is divided enterprises of trade and consumers (demographic variables, psychological perception of internet-marketing communications, experience of purchase of commodities are in the Internet. The degree of individual customer trust propensity which reflects the personality traits, culture and previous experience. An implement signs of consumer confidence due to site elements online shop – graphic design, structured design, design of content, design harmonized with perception of target audience.

  3. Conceptual Models of the Individual Public Service Provider

    DEFF Research Database (Denmark)

    Andersen, Lotte Bøgh; Pedersen, Lene Holm; Bhatti, Yosef

    are used to gain insight on the motivation of public service providers; namely principal-agent theory, self-determination theory and public service motivation theory. We situate the theoretical discussions in the context of public service providers being transferred to private organizations......Individual public service providers’ motivation can be conceptualized as either extrinsic, autonomous or prosocial, and the question is how we can best theoretically understand this complexity without losing too much coherence and parsimony. Drawing on Allison’s approach (1969), three perspectives...... theoretical – to develop a coherent model of individual public service providers – but the empirical illustration also contributes to our understanding of motivation in the context of public sector outsourcing....

  4. CONCEPTUAL MODEL OF INFORMATION SYSTEM OF THE AGRICULTURAL ENTERPRISES

    Directory of Open Access Journals (Sweden)

    Uladzimir Buts

    2017-02-01

    Full Text Available Abstract. Research subject represented by the theoretical and practical issues use of information resources in the agricultural business. Research aim is to formation of a conceptual model of information system of agricultural enterprises according to the requirements of sustainable development. Research methods. The work is prepared on basis of several scientific methods and approaches including monographic, analytical, computational and constructive methods of mathematical and structural logic simulation of information systems. Research results. Based on the assessment of the results of research information systems in agribusiness, as reflected in the theoretical review, the author designed principles of the information system for the agricultural enterprise for sustainable development of agribusiness. Sphere of application of the research results. State and regional authorities of economic regulation. Agricultural enterprises and farmers.

  5. A Framework for Conceptual Modeling of Geographic Data Quality

    DEFF Research Database (Denmark)

    Friis-Christensen, Anders; Christensen, J.V.; Jensen, Christian Søndergaard

    2004-01-01

    Sustained advances in wireless communications, geo-positioning, and consumer electronics pave the way to a kind of location-based service that relies on the tracking of the continuously changing positions of an entire population of service users. This type of service is characterized by large...... an object is moving. Empirical performance studies based on a real road network and GPS logs from cars areThe notion of data quality is of particular importance to geographic data. One reason is that such data is often inherently imprecise. Another is that the usability of the data is in large part...... determined by how "good" the data is, as different applications of geographic data require different qualities of the data are met. Such qualities concern the object level as well as the attribute level of the data. This paper presents a systematic and integrated approach to the conceptual modeling...

  6. Recruiting Transcultural Qualitative Research Participants: A Conceptual Model

    Directory of Open Access Journals (Sweden)

    Phyllis Eide

    2005-06-01

    Full Text Available Working with diverse populations poses many challenges to the qualitative researcher who is a member of the dominant culture. Traditional methods of recruitment and selection (such as flyers and advertisements are often unproductive, leading to missed contributions from potential participants who were not recruited and researcher frustration. In this article, the authors explore recruitment issues related to the concept of personal knowing based on experiences with Aboriginal Hawai'ian and Micronesian populations, wherein knowing and being known are crucial to successful recruitment of participants. They present a conceptual model that incorporates key concepts of knowing the other, cultural context, and trust to guide other qualitative transcultural researchers. They also describe challenges, implications, and concrete suggestions for recruitment of participants.

  7. Conceptual hydrologic model of flow in the unsaturated zone, Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    Montazer, P.; Wilson, W.E.

    1984-01-01

    The purpose of this report is to propose a conceptual hydrologic model that reasonably describes the flow of fluids through the unsaturated zone at Yucca Mountain, for use as a basis for preliminary site-performance assessment and as a guide to further investigations. Scott and others (1983) presented an initial conceptual hydrogeologic model for the unsaturated zone at Yucca Mountain, based on detailed geologic, but very limited hydrologic, information. In this report, some of their concepts are examined and either supported or modified, and new concepts are developed. The model describes the manner in which flow probably occurs at Yucca Mountain and is based on: (1) current understanding of the hydrogeologic framework; (2) application of the principles of unsaturated flow; and (3) interpretation of some preliminary data from ongoing field and laboratory investigations. Included are extensive geologic information but relatively few hydrologic data that currently exist from the unsaturated zone in the Yucca Mountain area. Many uncertainties remain to be resolved concerning hydrologic conditions and processes. As a result, most of the concepts presented are intentionally descriptive and conjectural, with little quantitative basis provided. However, for the sake of directness and simplicity of expression, the model is presented as if it were a true expression of the facts. The authors recognize, and the reader should be aware, that the proposed model probably is not the only reasonable description that could be made at this point, and it certainly is subject to revision and quantification as more data become available. Although various alternative models probably could be developed, the one described in this report seems to fit current understanding of the unsaturated flow through a section of layered, fractured-rock formations with contrasting hydrologic properties, such as occurs at Yucca Mountain. 41 refs., 14 figs., 1 tab

  8. Fatigue in fibromyalgia: a conceptual model informed by patient interviews

    Directory of Open Access Journals (Sweden)

    Humphrey Louise

    2010-09-01

    Full Text Available Abstract Background Fatigue is increasingly recognized as an important symptom in fibromyalgia (FM. Unknown however is how fatigue is experienced by individuals in the context of FM. We conducted qualitative research in order to better understand aspects of fatigue that might be unique to FM as well as the impact it has on patients' lives. The data obtained informed the development of a conceptual model of fatigue in FM. Methods Open-ended interviews were conducted with 40 individuals with FM (US [n = 20], Germany [n = 10] and France [n = 10]. Transcripts were analyzed using qualitative methods based upon grounded theory to identify key themes and concepts. Results Participants were mostly female (70% with a mean age of 48.7 years (range: 25-79. Thirty-one individuals (i.e., 77.5% spontaneously described experiencing tiredness/lack of energy/fatigue due to FM. Participants discussed FM fatigue as being more severe, constant/persistent and unpredictable than normal tiredness. The conceptual model depicts the key elements of fatigue in FM from a patient perspective. This includes: an overwhelming feeling of tiredness (n = 17, 42.5%, not relieved by resting/sleeping (n = 15, 37.5%, not proportional to effort exerted (n = 25, 62.5%, associated with a feeling of weakness/heaviness (n = 20, 50%, interferes with motivation (n = 22, 55%, interferes with desired activities (n = 27, 67.5%, prolongs tasks (n = 15, 37.5%, and makes it difficult to concentrate (n = 21, 52.5%, think clearly (n = 12, 30% or remember things (n = 9, 22.5%. Conclusion The majority of individuals with FM who participated in this study experience fatigue and describe it as more severe than normal tiredness.

  9. A model for optimization of process integration investments under uncertainty

    International Nuclear Information System (INIS)

    Svensson, Elin; Stroemberg, Ann-Brith; Patriksson, Michael

    2011-01-01

    The long-term economic outcome of energy-related industrial investment projects is difficult to evaluate because of uncertain energy market conditions. In this article, a general, multistage, stochastic programming model for the optimization of investments in process integration and industrial energy technologies is proposed. The problem is formulated as a mixed-binary linear programming model where uncertainties are modelled using a scenario-based approach. The objective is to maximize the expected net present value of the investments which enables heat savings and decreased energy imports or increased energy exports at an industrial plant. The proposed modelling approach enables a long-term planning of industrial, energy-related investments through the simultaneous optimization of immediate and later decisions. The stochastic programming approach is also suitable for modelling what is possibly complex process integration constraints. The general model formulation presented here is a suitable basis for more specialized case studies dealing with optimization of investments in energy efficiency. -- Highlights: → Stochastic programming approach to long-term planning of process integration investments. → Extensive mathematical model formulation. → Multi-stage investment decisions and scenario-based modelling of uncertain energy prices. → Results illustrate how investments made now affect later investment and operation opportunities. → Approach for evaluation of robustness with respect to variations in probability distribution.

  10. Multi-Fidelity Uncertainty Propagation for Cardiovascular Modeling

    Science.gov (United States)

    Fleeter, Casey; Geraci, Gianluca; Schiavazzi, Daniele; Kahn, Andrew; Marsden, Alison

    2017-11-01

    Hemodynamic models are successfully employed in the diagnosis and treatment of cardiovascular disease with increasing frequency. However, their widespread adoption is hindered by our inability to account for uncertainty stemming from multiple sources, including boundary conditions, vessel material properties, and model geometry. In this study, we propose a stochastic framework which leverages three cardiovascular model fidelities: 3D, 1D and 0D models. 3D models are generated from patient-specific medical imaging (CT and MRI) of aortic and coronary anatomies using the SimVascular open-source platform, with fluid structure interaction simulations and Windkessel boundary conditions. 1D models consist of a simplified geometry automatically extracted from the 3D model, while 0D models are obtained from equivalent circuit representations of blood flow in deformable vessels. Multi-level and multi-fidelity estimators from Sandia's open-source DAKOTA toolkit are leveraged to reduce the variance in our estimated output quantities of interest while maintaining a reasonable computational cost. The performance of these estimators in terms of computational cost reductions is investigated for a variety of output quantities of interest, including global and local hemodynamic indicators. Sandia National Labs is a multimission laboratory managed and operated by NTESS, LLC, for the U.S. DOE under contract DE-NA0003525. Funding for this project provided by NIH-NIBIB R01 EB018302.

  11. Conceptual understanding of climate change with a globally resolved energy balance model

    Energy Technology Data Exchange (ETDEWEB)

    Dommenget, Dietmar [Monash University, School of Mathematical Sciences, Melbourne, VIC (Australia); Floeter, Janine [Leibniz Institute for Marine Sciences, Kiel (Germany)

    2011-12-15

    The future climate change projections are essentially based on coupled general circulation model (CGCM) simulations, which give a distinct global warming pattern with arctic winter amplification, an equilibrium land-sea warming contrast and an inter-hemispheric warming gradient. While these simulations are the most important tool of the Intergovernmental Panel on Climate Change (IPCC) predictions, the conceptual understanding of these predicted structures of climate change and the causes of their uncertainties is very difficult to reach if only based on these highly complex CGCM simulations. In the study presented here we will introduce a very simple, globally resolved energy balance (GREB) model, which is capable of simulating the main characteristics of global warming. The model shall give a bridge between the strongly simplified energy balance models and the fully coupled 4-dimensional complex CGCMs. It provides a fast tool for the conceptual understanding and development of hypotheses for climate change studies, which shall build a basis or starting point for more detailed studies of observations and CGCM simulations. It is based on the surface energy balance by very simple representations of solar and thermal radiation, the atmospheric hydrological cycle, sensible turbulent heat flux, transport by the mean atmospheric circulation and heat exchange with the deeper ocean. Despite some limitations in the representations of the basic processes, the models climate sensitivity and the spatial structure of the warming pattern are within the uncertainties of the IPCC models simulations. It is capable of simulating aspects of the arctic winter amplification, the equilibrium land-sea warming contrast and the inter-hemispheric warming gradient with good agreement to the IPCC models in amplitude and structure. The results give some insight into the understanding of the land-sea contrast and the polar amplification. The GREB model suggests that the regional inhomogeneous

  12. Assessment of private hospital portals: A conceptual model

    Directory of Open Access Journals (Sweden)

    Mehdi Alipour-Hafezi

    2016-01-01

    Full Text Available Introduction: Hospital portals, as the first virtual entry, play an important role in connecting people with hospital and also presenting hospital virtual services. The main purpose of this article was to suggest a conceptual model to improve Tehran private hospital portals. The suggested model can be used by all the health portals that are in the same circumstances and all the health portals which are in progress. Method: This is a practical research, using evaluative survey research method. Research population includes all the private hospital portals in Tehran, 34 portals, and ten top international hospital portals. Data gathering tool used in this research was a researcher-made checklist including 14 criteria and 77 sub-criteria with their weight score. In fact, objective observation with the mentioned checklist was used to gather information. Descriptive statistics were used to analyze the data and tables and graphs were used to present the organized data. Also, data were analyzed using independent t-test. Conceptual modeling technique was used to design the model and demonstration method was used to evaluate the proposed model. In this regard, SPSS statistical software was used to perform the tests. Results:The comparative study between the two groups of portals, TPH and WTH, in the 14 main criteria showed that the value of t-test in contact information criteria was 0.862, portal page specification was -1.378, page design criteria -1.527, updating pages -0.322, general information and access roads -3.161, public services -7.302, patient services -4.154, patient data -8.703, research and education -9.155, public relationship -3.009, page technical specifications -4.726, telemedicine -7.488, pharmaceutical services -6.183, and financial services -2.782. Finally, the findings demonstrated that Tehran private hospital portals in criterion of contact information were favorable; page design criteria were relatively favorable; page technical

  13. Modelling in Primary School: Constructing Conceptual Models and Making Sense of Fractions

    Science.gov (United States)

    Shahbari, Juhaina Awawdeh; Peled, Irit

    2017-01-01

    This article describes sixth-grade students' engagement in two model-eliciting activities offering students the opportunity to construct mathematical models. The findings show that students utilized their knowledge of fractions including conceptual and procedural knowledge in constructing mathematical models for the given situations. Some students…

  14. An Uncertainty Structure Matrix for Models and Simulations

    Science.gov (United States)

    Green, Lawrence L.; Blattnig, Steve R.; Hemsch, Michael J.; Luckring, James M.; Tripathi, Ram K.

    2008-01-01

    Software that is used for aerospace flight control and to display information to pilots and crew is expected to be correct and credible at all times. This type of software is typically developed under strict management processes, which are intended to reduce defects in the software product. However, modeling and simulation (M&S) software may exhibit varying degrees of correctness and credibility, depending on a large and complex set of factors. These factors include its intended use, the known physics and numerical approximations within the M&S, and the referent data set against which the M&S correctness is compared. The correctness and credibility of an M&S effort is closely correlated to the uncertainty management (UM) practices that are applied to the M&S effort. This paper describes an uncertainty structure matrix for M&S, which provides a set of objective descriptions for the possible states of UM practices within a given M&S effort. The columns in the uncertainty structure matrix contain UM elements or practices that are common across most M&S efforts, and the rows describe the potential levels of achievement in each of the elements. A practitioner can quickly look at the matrix to determine where an M&S effort falls based on a common set of UM practices that are described in absolute terms that can be applied to virtually any M&S effort. The matrix can also be used to plan those steps and resources that would be needed to improve the UM practices for a given M&S effort.

  15. A conceptual model for the fuel oxidation of defective fuel

    International Nuclear Information System (INIS)

    Higgs, J.D.; Lewis, B.J.; Thompson, W.T.; He, Z.

    2007-01-01

    A mechanistic conceptual model has been developed to predict the fuel oxidation behaviour in operating defective fuel elements for water-cooled nuclear reactors. This theoretical work accounts for gas-phase transport and sheath reactions in the fuel-to-sheath gap to determine the local oxygen potential. An improved thermodynamic analysis has also been incorporated into the model to describe the equilibrium state of the oxidized fuel. The fuel oxidation kinetics treatment accounts for multi-phase transport including normal diffusion and thermodiffusion for interstitial oxygen migration in the solid, as well as gas-phase transport in the fuel pellet cracks. The fuel oxidation treatment is further coupled to a heat conduction equation. A numerical solution of the coupled transport equations is obtained by a finite-element technique with the FEMLAB 3.1 software package. The model is able to provide radial-axial profiles of the oxygen-to-uranium ratio and the fuel temperatures as a function of time in the defective element for a wide range of element powers and defect sizes. The model results are assessed against coulometric titration measurements of the oxygen-to-metal profile for pellet samples taken from ten spent defective elements discharged from the National Research Universal Reactor at the Chalk River Laboratories and commercial reactors

  16. Internal Models, Vestibular Cognition, and Mental Imagery: Conceptual Considerations.

    Science.gov (United States)

    Mast, Fred W; Ellis, Andrew W

    2015-01-01

    Vestibular cognition has recently gained attention. Despite numerous experimental and clinical demonstrations, it is not yet clear what vestibular cognition really is. For future research in vestibular cognition, adopting a computational approach will make it easier to explore the underlying mechanisms. Indeed, most modeling approaches in vestibular science include a top-down or a priori component. We review recent Bayesian optimal observer models, and discuss in detail the conceptual value of prior assumptions, likelihood and posterior estimates for research in vestibular cognition. We then consider forward models in vestibular processing, which are required in order to distinguish between sensory input that is induced by active self-motion, and sensory input that is due to passive self-motion. We suggest that forward models are used not only in the service of estimating sensory states but they can also be drawn upon in an offline mode (e.g., spatial perspective transformations), in which interaction with sensory input is not desired. A computational approach to vestibular cognition will help to discover connections across studies, and it will provide a more coherent framework for investigating vestibular cognition.

  17. BIM-Enabled Conceptual Modelling and Representation of Building Circulation

    Directory of Open Access Journals (Sweden)

    Jin Kook Lee

    2014-08-01

    Full Text Available This paper describes how a building information modelling (BIM-based approach for building circulation enables us to change the process of building design in terms of its computational representation and processes, focusing on the conceptual modelling and representation of circulation within buildings. BIM has been designed for use by several BIM authoring tools, in particular with the widely known interoperable industry foundation classes (IFCs, which follow an object-oriented data modelling methodology. Advances in BIM authoring tools, using space objects and their relations defined in an IFC's schema, have made it possible to model, visualize and analyse circulation within buildings prior to their construction. Agent-based circulation has long been an interdisciplinary topic of research across several areas, including design computing, computer science, architectural morphology, human behaviour and environmental psychology. Such conventional approaches to building circulation are centred on navigational knowledge about built environments, and represent specific circulation paths and regulations. This paper, however, places emphasis on the use of ‘space objects’ in BIM-enabled design processes rather than on circulation agents, the latter of which are not defined in the IFCs' schemas. By introducing and reviewing some associated research and projects, this paper also surveys how such a circulation representation is applicable to the analysis of building circulation-related rules.

  18. A CONCEPTUAL MODEL OF THE LIFE CYCLE OF THE PROGRAM

    Directory of Open Access Journals (Sweden)

    Марія Костянтинівна СУХОНОС

    2016-02-01

    Full Text Available A conceptual model of the life cycle of the program is proposed. This model is based on the value approach. As a resulting index, it uses a category of complex structural value. This model renders the process of the life cycle of the program in the context of time/result. It assumes the presence of four basic phases of the life cycle, namely, initiation, planning, executing and closing. Also, this model formalizes interconnection of management processes of integration of program and management of its community and subprocesses. Selection of a value approach for the forming of a resulting index of a program determines by a variety of results of the program. This is a result of its variety and complexity in the process of finding a criterion for evaluation. Worked out a mechanism for assessing the value of the program. It consists of four steps and involves using of conventional methods (decomposition and expert estimates. As a unit of measurement assumes to use points and rating scale with the maximum score a hundred points. A complex value, which is evaluated at one hundred points, is a result of the program. It is critically important in the process of current and final evaluation of the program.

  19. A CONCEPTUAL MODEL FOR IMPROVED PROJECT SELECTION AND PRIORITISATION

    Directory of Open Access Journals (Sweden)

    P. J. Viljoen

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: Project portfolio management processes are often designed and operated as a series of stages (or project phases and gates. However, the flow of such a process is often slow, characterised by queues waiting for a gate decision and by repeated work from previous stages waiting for additional information or for re-processing. In this paper the authors propose a conceptual model that applies supply chain and constraint management principles to the project portfolio management process. An advantage of the proposed model is that it provides the ability to select and prioritise projects without undue changes to project schedules. This should result in faster flow through the system.

    AFRIKAANSE OPSOMMING: Prosesse om portefeuljes van projekte te bestuur word normaalweg ontwerp en bedryf as ’n reeks fases en hekke. Die vloei deur so ’n proses is dikwels stadig en word gekenmerk deur toue wat wag vir besluite by die hekke en ook deur herwerk van vorige fases wat wag vir verdere inligting of vir herprosessering. In hierdie artikel word ‘n konseptuele model voorgestel. Die model berus op die beginsels van voorsieningskettings sowel as van beperkingsbestuur, en bied die voordeel dat projekte geselekteer en geprioritiseer kan word sonder onnodige veranderinge aan projekskedules. Dit behoort te lei tot versnelde vloei deur die stelsel.

  20. High-Throughput Thermodynamic Modeling and Uncertainty Quantification for ICME

    Science.gov (United States)

    Otis, Richard A.; Liu, Zi-Kui

    2017-05-01

    One foundational component of the integrated computational materials engineering (ICME) and Materials Genome Initiative is the computational thermodynamics based on the calculation of phase diagrams (CALPHAD) method. The CALPHAD method pioneered by Kaufman has enabled the development of thermodynamic, atomic mobility, and molar volume databases of individual phases in the full space of temperature, composition, and sometimes pressure for technologically important multicomponent engineering materials, along with sophisticated computational tools for using the databases. In this article, our recent efforts will be presented in terms of developing new computational tools for high-throughput modeling and uncertainty quantification based on high-throughput, first-principles calculations and the CALPHAD method along with their potential propagations to downstream ICME modeling and simulations.

  1. Insights from a history of seasonal inflow forecasting with a conceptual hydrologic model

    Science.gov (United States)

    Druce, Donald J.

    2001-08-01

    BC Hydro has used a conceptual hydrologic model for forecasting the seasonal inflows to its Mica project, on the Columbia River, for more than 20 years. The model estimates the snowpack on the forecast date using recently observed weather data and then calculates the runoff response to approximately 30 historical weather sequences over the remainder of the year. The ensemble of equally likely seasonal inflows makes up the forecast. Forecasts are issued as of the first of each month from January to August. The same model and modeller have made all of such forecasts for the Mica project. While some may see this as a failure to progress, others may see it as an unique opportunity to learn how well an older hydrologic model has performed, in practice, over the long term. Comments on both perspectives are offered, after the analyses of the forecasts have been presented. The forecasts have been analysed, as of each of the monthly forecast dates, to determine the accuracy of the mean and to establish any differences between ex post and ex ante measures of uncertainty. Results are then compared with those from a regression model that has also been used for forecasting the seasonal inflows to the Mica project over the same period of record.

  2. Estimation of spatial uncertainties of tomographic velocity models

    Energy Technology Data Exchange (ETDEWEB)

    Jordan, M.; Du, Z.; Querendez, E. [SINTEF Petroleum Research, Trondheim (Norway)

    2012-12-15

    This research project aims to evaluate the possibility of assessing the spatial uncertainties in tomographic velocity model building in a quantitative way. The project is intended to serve as a test of whether accurate and specific uncertainty estimates (e.g., in meters) can be obtained. The project is based on Monte Carlo-type perturbations of the velocity model as obtained from the tomographic inversion guided by diagonal and off-diagonal elements of the resolution and the covariance matrices. The implementation and testing of this method was based on the SINTEF in-house stereotomography code, using small synthetic 2D data sets. To test the method the calculation and output of the covariance and resolution matrices was implemented, and software to perform the error estimation was created. The work included the creation of 2D synthetic data sets, the implementation and testing of the software to conduct the tests (output of the covariance and resolution matrices which are not implicitly provided by stereotomography), application to synthetic data sets, analysis of the test results, and creating the final report. The results show that this method can be used to estimate the spatial errors in tomographic images quantitatively. The results agree with' the known errors for our synthetic models. However, the method can only be applied to structures in the model, where the change of seismic velocity is larger than the predicted error of the velocity parameter amplitudes. In addition, the analysis is dependent on the tomographic method, e.g., regularization and parameterization. The conducted tests were very successful and we believe that this method could be developed further to be applied to third party tomographic images.

  3. Uncertainty analysis of a low flow model for the Rhine River

    NARCIS (Netherlands)

    Demirel, M.C.; Booij, Martijn J.

    2011-01-01

    It is widely recognized that hydrological models are subject to parameter uncertainty. However, little attention has been paid so far to the uncertainty in parameters of the data-driven models like weights in neural networks. This study aims at applying a structured uncertainty analysis to a

  4. Uncertainty analysis in WWTP model applications: a critical discussion using an example from design

    DEFF Research Database (Denmark)

    Sin, Gürkan; Gernaey, Krist; Neumann, Marc B.

    2009-01-01

    of design performance criteria differs significantly. The implication for the practical applications of uncertainty analysis in the wastewater industry is profound: (i) as the uncertainty analysis results are specific to the framing used, the results must be interpreted within the context of that framing......This study focuses on uncertainty analysis of WWTP models and analyzes the issue of framing and how it affects the interpretation of uncertainty analysis results. As a case study, the prediction of uncertainty involved in model-based design of a wastewater treatment plant is studied. The Monte...... to stoichiometric, biokinetic and influent parameters; (2) uncertainty due to hydraulic behaviour of the plant and mass transfer parameters; (3) uncertainty due to the combination of (1) and (2). The results demonstrate that depending on the way the uncertainty analysis is framed, the estimated uncertainty...

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

    International Nuclear Information System (INIS)

    Fenwick, John D.; Nahum, Alan E.

    2001-01-01

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

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

    Science.gov (United States)

    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.

  7. Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction.

    Science.gov (United States)

    Soleimani, Hossein; Hensman, James; Saria, Suchi

    2017-08-21

    Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations. These approaches, however, make strong parametric assumptions and do not easily scale to multivariate signals with many observations. Our proposed approach consists of several key innovations. First, we develop a flexible and scalable joint model based upon sparse multiple-output Gaussian processes. Unlike state-of-the-art joint models, the proposed model can explain highly challenging structure including non-Gaussian noise while scaling to large data. Second, we derive an optimal policy for predicting events using the distribution of the event occurrence estimated by the joint model. The derived policy trades-off the cost of a delayed detection versus incorrect assessments and abstains from making decisions when the estimated event probability does not satisfy the derived confidence criteria. Experiments on a large dataset show that the proposed framework significantly outperforms state-of-the-art techniques in event prediction.

  8. Quantile uncertainty and value-at-risk model risk.

    Science.gov (United States)

    Alexander, Carol; Sarabia, José María

    2012-08-01

    This article develops a methodology for quantifying model risk in quantile risk estimates. The application of quantile estimates to risk assessment has become common practice in many disciplines, including hydrology, climate change, statistical process control, insurance and actuarial science, and the uncertainty surrounding these estimates has long been recognized. Our work is particularly important in finance, where quantile estimates (called Value-at-Risk) have been the cornerstone of banking risk management since the mid 1980s. A recent amendment to the Basel II Accord recommends additional market risk capital to cover all sources of "model risk" in the estimation of these quantiles. We provide a novel and elegant framework whereby quantile estimates are adjusted for model risk, relative to a benchmark which represents the state of knowledge of the authority that is responsible for model risk. A simulation experiment in which the degree of model risk is controlled illustrates how to quantify Value-at-Risk model risk and compute the required regulatory capital add-on for banks. An empirical example based on real data shows how the methodology can be put into practice, using only two time series (daily Value-at-Risk and daily profit and loss) from a large bank. We conclude with a discussion of potential applications to nonfinancial risks. © 2012 Society for Risk Analysis.

  9. Quantifying uncertainty in LCA-modelling of waste management systems

    DEFF Research Database (Denmark)

    Clavreul, Julie; Guyonnet, D.; Christensen, Thomas Højlund

    2012-01-01

    Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste management, various methods have been implemented but a systematic method for uncertainty analysis of waste-LCA studies is lacking. The objective of this paper is (1) to present...... the sources of uncertainty specifically inherent to waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a sequence of four steps combining...

  10. Testing a Conceptual Change Model Framework for Visual Data

    Science.gov (United States)

    Finson, Kevin D.; Pedersen, Jon E.

    2015-01-01

    An emergent data analysis technique was employed to test the veracity of a conceptual framework constructed around visual data use and instruction in science classrooms. The framework incorporated all five key components Vosniadou (2007a, 2007b) described as existing in a learner's schema: framework theory, presuppositions, conceptual domains,…

  11. Addressing model uncertainty in dose-response: The case of chloroform

    International Nuclear Information System (INIS)

    Evans, J.S.

    1994-01-01

    This paper discusses the issues involved in addressing model uncertainty in the analysis of dose-response relationships. A method for addressing model uncertainty is described and applied to characterize the uncertainty in estimates of the carcinogenic potency of chloroform. The approach, which is rooted in Bayesian concepts of subjective probability, uses probability trees and formally-elicited expert judgments to address model uncertainty. It is argued that a similar approach could be used to improve the characterization of model uncertainty in the dose-response relationships for health effects from ionizing radiation

  12. Model uncertainty in financial markets : Long run risk and parameter uncertainty

    NARCIS (Netherlands)

    de Roode, F.A.

    2014-01-01

    Uncertainty surrounding key parameters of financial markets, such as the in- flation and equity risk premium, constitute a major risk for institutional investors with long investment horizons. Hedging the investors’ inflation exposure can be challenging due to the lack of domestic inflation-linked

  13. Impact of AMS-02 Measurements on Reducing GCR Model Uncertainties

    Science.gov (United States)

    Slaba, T. C.; O'Neill, P. M.; Golge, S.; Norbury, J. W.

    2015-01-01

    For vehicle design, shield optimization, mission planning, and astronaut risk assessment, the exposure from galactic cosmic rays (GCR) poses a significant and complex problem both in low Earth orbit and in deep space. To address this problem, various computational tools have been developed to quantify the exposure and risk in a wide range of scenarios. Generally, the tool used to describe the ambient GCR environment provides the input into subsequent computational tools and is therefore a critical component of end-to-end procedures. Over the past few years, several researchers have independently and very carefully compared some of the widely used GCR models to more rigorously characterize model differences and quantify uncertainties. All of the GCR models studied rely heavily on calibrating to available near-Earth measurements of GCR particle energy spectra, typically over restricted energy regions and short time periods. In this work, we first review recent sensitivity studies quantifying the ions and energies in the ambient GCR environment of greatest importance to exposure quantities behind shielding. Currently available measurements used to calibrate and validate GCR models are also summarized within this context. It is shown that the AMS-II measurements will fill a critically important gap in the measurement database. The emergence of AMS-II measurements also provides a unique opportunity to validate existing models against measurements that were not used to calibrate free parameters in the empirical descriptions. Discussion is given regarding rigorous approaches to implement the independent validation efforts, followed by recalibration of empirical parameters.

  14. Calibration under uncertainty for finite element models of masonry monuments

    Energy Technology Data Exchange (ETDEWEB)

    Atamturktur, Sezer,; Hemez, Francois,; Unal, Cetin

    2010-02-01

    Historical unreinforced masonry buildings often include features such as load bearing unreinforced masonry vaults and their supporting framework of piers, fill, buttresses, and walls. The masonry vaults of such buildings are among the most vulnerable structural components and certainly among the most challenging to analyze. The versatility of finite element (FE) analyses in incorporating various constitutive laws, as well as practically all geometric configurations, has resulted in the widespread use of the FE method for the analysis of complex unreinforced masonry structures over the last three decades. However, an FE model is only as accurate as its input parameters, and there are two fundamental challenges while defining FE model input parameters: (1) material properties and (2) support conditions. The difficulties in defining these two aspects of the FE model arise from the lack of knowledge in the common engineering understanding of masonry behavior. As a result, engineers are unable to define these FE model input parameters with certainty, and, inevitably, uncertainties are introduced to the FE model.

  15. Defining the end-point of mastication: A conceptual model.

    Science.gov (United States)

    Gray-Stuart, Eli M; Jones, Jim R; Bronlund, John E

    2017-10-01

    The great risks of swallowing are choking and aspiration of food into the lungs. Both are rare in normal functioning humans, which is remarkable given the diversity of foods and the estimated 10 million swallows performed in a lifetime. Nevertheless, it remains a major challenge to define the food properties that are necessary to ensure a safe swallow. Here, the mouth is viewed as a well-controlled processor where mechanical sensory assessment occurs throughout the occlusion-circulation cycle of mastication. Swallowing is a subsequent action. It is proposed here that, during mastication, temporal maps of interfacial property data are generated, which the central nervous system compares against a series of criteria in order to be sure that the bolus is safe to swallow. To determine these criteria, an engineering hazard analysis tool, alongside an understanding of fluid and particle mechanics, is used to deduce the mechanisms by which food may deposit or become stranded during swallowing. These mechanisms define the food properties that must be avoided. By inverting the thinking, from hazards to ensuring safety, six criteria arise which are necessary for a safe-to-swallow bolus. A new conceptual model is proposed to define when food is safe to swallow during mastication. This significantly advances earlier mouth models. The conceptual model proposed in this work provides a framework of decision-making to define when food is safe to swallow. This will be of interest to designers of dietary foods, foods for dysphagia sufferers and will aid the further development of mastication robots for preparation of artificial boluses for digestion research. It enables food designers to influence the swallow-point properties of their products. For example, a product may be designed to satisfy five of the criteria for a safe-to-swallow bolus, which means the sixth criterion and its attendant food properties define the swallow-point. Alongside other organoleptic factors, these

  16. Towards methodical modelling: Differences between the structure and output dynamics of multiple conceptual models

    Science.gov (United States)

    Knoben, Wouter; Woods, Ross; Freer, Jim

    2016-04-01

    Conceptual hydrologic models consist of a certain arrangement of spatial and temporal dynamics consisting of stores, fluxes and transformation functions, depending on the modeller's choices and intended use. They have the advantages of being computationally efficient, being relatively easy model structures to reconfigure and having relatively low input data demands. This makes them well-suited for large-scale and large-sample hydrology, where appropriately representing the dominant hydrologic functions of a catchment is a main concern. Given these requirements, the number of parameters in the model cannot be too high, to avoid equifinality and identifiability issues. This limits the number and level of complexity of dominant hydrologic processes the model can represent. Specific purposes and places thus require a specific model and this has led to an abundance of conceptual hydrologic models. No structured overview of these models exists and there is no clear method to select appropriate model structures for different catchments. This study is a first step towards creating an overview of the elements that make up conceptual models, which may later assist a modeller in finding an appropriate model structure for a given catchment. To this end, this study brings together over 30 past and present conceptual models. The reviewed model structures are simply different configurations of three basic model elements (stores, fluxes and transformation functions), depending on the hydrologic processes the models are intended to represent. Differences also exist in the inner workings of the stores, fluxes and transformations, i.e. the mathematical formulations that describe each model element's intended behaviour. We investigate the hypothesis that different model structures can produce similar behavioural simulations. This can clarify the overview of model elements by grouping elements which are similar, which can improve model structure selection.

  17. Uncertainty estimation of the mass discharge from a contaminated site using a fully Bayesian framework

    DEFF Research Database (Denmark)

    Troldborg, Mads; Nowak, W.; Binning, Philip John

    2010-01-01

    with an uncertain geostatistical model and iii) measurement uncertainty. The method is tested on a TCE contaminated site for which four different conceptual models were set up. The mass discharge and the associated uncertainty are hereby determined. It is discussed which of the conceptual models is most likely...

  18. Conceptual Modeling Framework for E-Area PA HELP Infiltration Model Simulations

    Energy Technology Data Exchange (ETDEWEB)

    Dyer, J. A. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

    2017-11-30

    A conceptual modeling framework based on the proposed E-Area Low-Level Waste Facility (LLWF) closure cap design is presented for conducting Hydrologic Evaluation of Landfill Performance (HELP) model simulations of intact and subsided cap infiltration scenarios for the next E-Area Performance Assessment (PA).

  19. Hemispheric Asymmetry of Global Warming Explained by a Conceptual Model

    Science.gov (United States)

    Funke, C. S.; Alexeev, V. A.

    2017-12-01

    Polar Amplification, the process of amplified warming at high latitudes, manifests itself differently in the Arctic and Antarctic. Not only is the temperature increase in the Arctic more pronounced than in the Antarctic but the dramatic sea ice decline in the Arctic over the last few decades also contrasts sharply with trendless to weak positive trend of Antarctic sea ice throughout the same period. This asymmetric behavior is often partly attributed to the differences in configuration of continents in the Arctic and Antarctic: the Arctic Ocean is surrounded by land while the Southern Ocean has a continent in the middle. A simple conceptual energy balance model of Budyko-Sellers type, accounting for differences between the Northern and Southern hemispheres, is applied to study the mechanisms of climate sensitivity to a variety of forcings. Asymmetry in major modes of variability is explained using an eigenmode analysis of the linearized model. Negative forcings over Antarctica such as from ozone depletion were found to have an amplified effect on southern hemisphere climate and may be an important cause of the muted warming and slightly positive Antarctic sea ice trend.

  20. Developing a conceptual model for selecting and evaluating online markets

    Directory of Open Access Journals (Sweden)

    Sadegh Feizollahi

    2013-04-01

    Full Text Available There are many evidences, which emphasis on the benefits of using new technologies of information and communication in international business and many believe that E-Commerce can help satisfy customer explicit and implicit requirements. Internet shopping is a concept developed after the introduction of electronic commerce. Information technology (IT and its applications, specifically in the realm of the internet and e-mail promoted the development of e-commerce in terms of advertising, motivating and information. However, with the development of new technologies, credit and financial exchange on the internet websites were constructed so to facilitate e-commerce. The proposed study sends a total of 200 questionnaires to the target group (teachers - students - professionals - managers of commercial web sites and it manages to collect 130 questionnaires for final evaluation. Cronbach's alpha test is used for measuring reliability and to evaluate the validity of measurement instruments (questionnaires, and to assure construct validity, confirmatory factor analysis is employed. In addition, in order to analyze the research questions based on the path analysis method and to determine markets selection models, a regular technique is implemented. In the present study, after examining different aspects of e-commerce, we provide a conceptual model for selecting and evaluating online marketing in Iran. These findings provide a consistent, targeted and holistic framework for the development of the Internet market in the country.

  1. Cognitive balanced model: a conceptual scheme of diagnostic decision making.

    Science.gov (United States)

    Lucchiari, Claudio; Pravettoni, Gabriella

    2012-02-01

    Diagnostic reasoning is a critical aspect of clinical performance, having a high impact on quality and safety of care. Although diagnosis is fundamental in medicine, we still have a poor understanding of the factors that determine its course. According to traditional understanding, all information used in diagnostic reasoning is objective and logically driven. However, these conditions are not always met. Although we would be less likely to make an inaccurate diagnosis when following rational decision making, as described by normative models, the real diagnostic process works in a different way. Recent work has described the major cognitive biases in medicine as well as a number of strategies for reducing them, collectively called debiasing techniques. However, advances have encountered obstacles in achieving implementation into clinical practice. While traditional understanding of clinical reasoning has failed to consider contextual factors, most debiasing techniques seem to fail in raising sound and safer medical praxis. Technological solutions, being data driven, are fundamental in increasing care safety, but they need to consider human factors. Thus, balanced models, cognitive driven and technology based, are needed in day-to-day applications to actually improve the diagnostic process. The purpose of this article, then, is to provide insight into cognitive influences that have resulted in wrong, delayed or missed diagnosis. Using a cognitive approach, we describe the basis of medical error, with particular emphasis on diagnostic error. We then propose a conceptual scheme of the diagnostic process by the use of fuzzy cognitive maps. © 2011 Blackwell Publishing Ltd.

  2. Uncertainty in dual permeability model parameters for structured soils

    Science.gov (United States)

    Arora, B.; Mohanty, B. P.; McGuire, J. T.

    2012-01-01

    Successful application of dual permeability models (DPM) to predict contaminant transport is contingent upon measured or inversely estimated soil hydraulic and solute transport parameters. The difficulty in unique identification of parameters for the additional macropore- and matrix-macropore interface regions, and knowledge about requisite experimental data for DPM has not been resolved to date. Therefore, this study quantifies uncertainty in dual permeability model parameters of experimental soil columns with different macropore distributions (single macropore, and low- and high-density multiple macropores). Uncertainty evaluation is conducted using adaptive Markov chain Monte Carlo (AMCMC) and conventional Metropolis-Hastings (MH) algorithms while assuming 10 out of 17 parameters to be uncertain or random. Results indicate that AMCMC resolves parameter correlations and exhibits fast convergence for all DPM parameters while MH displays large posterior correlations for various parameters. This study demonstrates that the choice of parameter sampling algorithms is paramount in obtaining unique DPM parameters when information on covariance structure is lacking, or else additional information on parameter correlations must be supplied to resolve the problem of equifinality of DPM parameters. This study also highlights the placement and significance of matrix-macropore interface in flow experiments of soil columns with different macropore densities. Histograms for certain soil hydraulic parameters display tri-modal characteristics implying that macropores are drained first followed by the interface region and then by pores of the matrix domain in drainage experiments. Results indicate that hydraulic properties and behavior of the matrix-macropore interface is not only a function of saturated hydraulic conductivity of the macroporematrix interface (Ksa) and macropore tortuosity (lf) but also of other parameters of the matrix and macropore domains.

  3. A novel dose uncertainty model and its application for dose verification

    International Nuclear Information System (INIS)

    Jin Hosang; Chung Heetaek; Liu Chihray; Palta, Jatinder; Suh, Tae-Suk; Kim, Siyong

    2005-01-01

    Based on statistical approach, a novel dose uncertainty model was introduced considering both nonspatial and spatial dose deviations. Non-space-oriented uncertainty is mainly caused by dosimetric uncertainties, and space-oriented dose uncertainty is the uncertainty caused by all spatial displacements. Assuming these two parts are independent, dose difference between measurement and calculation is a linear combination of nonspatial and spatial dose uncertainties. Two assumptions were made: (1) the relative standard deviation of nonspatial dose uncertainty is inversely proportional to the dose standard deviation σ, and (2) the spatial dose uncertainty is proportional to the gradient of dose. The total dose uncertainty is a quadratic sum of the nonspatial and spatial uncertainties. The uncertainty model provides the tolerance dose bound for comparison between calculation and measurement. In the statistical uncertainty model based on a Gaussian distribution, a confidence level of 3σ theoretically confines 99.74% of measurements within the bound. By setting the confidence limit, the tolerance bound for dose comparison can be made analogous to that of existing dose comparison methods (e.g., a composite distribution analysis, a γ test, a χ evaluation, and a normalized agreement test method). However, the model considers the inherent dose uncertainty characteristics of the test points by taking into account the space-specific history of dose accumulation, while the previous methods apply a single tolerance criterion to the points, although dose uncertainty at each point is significantly different from others. Three types of one-dimensional test dose distributions (a single large field, a composite flat field made by two identical beams, and three-beam intensity-modulated fields) were made to verify the robustness of the model. For each test distribution, the dose bound predicted by the uncertainty model was compared with simulated measurements. The simulated

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

    International Nuclear Information System (INIS)

    Riley, Matthew E.

    2015-01-01

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

  5. Dynamic knowledge representation using agent-based modeling: ontology instantiation and verification of conceptual models.

    Science.gov (United States)

    An, Gary

    2009-01-01

    The sheer volume of biomedical research threatens to overwhelm the capacity of individuals to effectively process this information. Adding to this challenge is the multiscale nature of both biological systems and the research community as a whole. Given this volume and rate of generation of biomedical information, the research community must develop methods for robust representation of knowledge in order for individuals, and the community as a whole, to "know what they know." Despite increasing emphasis on "data-driven" research, the fact remains that researchers guide their research using intuitively constructed conceptual models derived from knowledge extracted from publications, knowledge that is generally qualitatively expressed using natural language. Agent-based modeling (ABM) is a computational modeling method that is suited to translating the knowledge expressed in biomedical texts into dynamic representations of the conceptual models generated by researchers. The hierarchical object-class orientation of ABM maps well to biomedical ontological structures, facilitating the translation of ontologies into instantiated models. Furthermore, ABM is suited to producing the nonintuitive behaviors that often "break" conceptual models. Verification in this context is focused at determining the plausibility of a particular conceptual model, and qualitative knowledge representation is often sufficient for this goal. Thus, utilized in this fashion, ABM can provide a powerful adjunct to other computational methods within the research process, as well as providing a metamodeling framework to enhance the evolution of biomedical ontologies.

  6. Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

    Science.gov (United States)

    Wang, W.; Rinke, A.; Moore, J. C.; Cui, X.; Ji, D.; Li, Q.; Zhang, N.; Wang, C.; Zhang, S.; Lawrence, D. M.; McGuire, A. D.; Zhang, W.; Delire, C.; Koven, C.; Saito, K.; MacDougall, A.; Burke, E.; Decharme, B.

    2016-02-01

    We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135 × 104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 × 104 km2). However the uncertainty (1 to 128 × 104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for

  7. The Value of Conceptual Models in Coping with Complexity and Interdisciplinarity in Environmental Sciences Education

    Science.gov (United States)

    Fortuin, Karen P. J.; van Koppen, C. S. A.; Leemans, Rik

    2011-01-01

    Conceptual models are useful for facing the challenges of environmental sciences curriculum and course developers and students. These challenges are inherent to the interdisciplinary and problem-oriented character of environmental sciences curricula. In this article, we review the merits of conceptual models in facing these challenges. These…

  8. A conceptual and calculational model for gas formation from impure calcined plutonium oxides

    International Nuclear Information System (INIS)

    Lyman, John L.; Eller, P. Gary

    2000-01-01

    Safe transport and storage of pure and impure plutonium oxides requires an understanding of processes that may generate or consume gases in a confined storage vessel. We have formulated conceptual and calculational models for gas formation from calcined materials. The conceptual model for impure calcined plutonium oxides is based on the data collected to date

  9. Teacher Emotion Research: Introducing a Conceptual Model to Guide Future Research

    Science.gov (United States)

    Fried, Leanne; Mansfield, Caroline; Dobozy, Eva

    2015-01-01

    This article reports on the development of a conceptual model of teacher emotion through a review of teacher emotion research published between 2003 and 2013. By examining 82 publications regarding teacher emotion, the main aim of the review was to identify how teacher emotion was conceptualised in the literature and develop a conceptual model to…

  10. Non-monotonic reasoning in conceptual modeling and ontology design: A proposal

    CSIR Research Space (South Africa)

    Casini, G

    2013-06-01

    Full Text Available -1 2nd International Workshop on Ontologies and Conceptual Modeling (Onto.Com 2013), Valencia, Spain, 17-21 June 2013 Non-monotonic reasoning in conceptual modeling and ontology design: A proposal Giovanni Casini1 and Alessandro Mosca2 1...

  11. A note on the translation of conceptual data models into description logics: disjointness and covering assumptions

    CSIR Research Space (South Africa)

    Casini, G

    2012-10-01

    Full Text Available possibilities for conceptual data modeling. It also raises the question of how existing conceptual models using ER, UML or ORM could be translated into Description Logics (DLs), a family of logics that have proved to be particularly appropriate for formalizing...

  12. Uncertainties in (E)UV model atmosphere fluxes

    Science.gov (United States)

    Rauch, T.

    2008-04-01

    Context: During the comparison of synthetic spectra calculated with two NLTE model atmosphere codes, namely TMAP and TLUSTY, we encounter systematic differences in the EUV fluxes due to the treatment of level dissolution by pressure ionization. Aims: In the case of Sirius B, we demonstrate an uncertainty in modeling the EUV flux reliably in order to challenge theoreticians to improve the theory of level dissolution. Methods: We calculated synthetic spectra for hot, compact stars using state-of-the-art NLTE model-atmosphere techniques. Results: Systematic differences may occur due to a code-specific cutoff frequency of the H I Lyman bound-free opacity. This is the case for TMAP and TLUSTY. Both codes predict the same flux level at wavelengths lower than about 1500 Å for stars with effective temperatures (T_eff) below about 30 000 K only, if the same cutoff frequency is chosen. Conclusions: The theory of level dissolution in high-density plasmas, which is available for hydrogen only should be generalized to all species. Especially, the cutoff frequencies for the bound-free opacities should be defined in order to make predictions of UV fluxes more reliable.

  13. A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty

    Science.gov (United States)

    Friedel, Michael J.

    2011-01-01

    This study demonstrates the novel application of genetic programming to evolve nonlinear post-fire debris-flow volume equations from variables associated with a data-driven conceptual model of the western United States. The search space is constrained using a multi-component objective function that simultaneously minimizes root-mean squared and unit errors for the evolution of fittest equations. An optimization technique is then used to estimate the limits of nonlinear prediction uncertainty associated with the debris-flow equations. In contrast to a published multiple linear regression three-variable equation, linking basin area with slopes greater or equal to 30 percent, burn severity characterized as area burned moderate plus high, and total storm rainfall, the data-driven approach discovers many nonlinear and several dimensionally consistent equations that are unbiased and have less prediction uncertainty. Of the nonlinear equations, the best performance (lowest prediction uncertainty) is achieved when using three variables: average basin slope, total burned area, and total storm rainfall. Further reduction in uncertainty is possible for the nonlinear equations when dimensional consistency is not a priority and by subsequently applying a gradient solver to the fittest solutions. The data-driven modeling approach can be applied to nonlinear multivariate problems in all fields of study.

  14. Theory analysis of the Dental Hygiene Human Needs Conceptual Model.

    Science.gov (United States)

    MacDonald, L; Bowen, D M

    2017-11-01

    Theories provide a structural knowing about concept relationships, practice intricacies, and intuitions and thus shape the distinct body of the profession. Capturing ways of knowing and being is essential to any professions' practice, education and research. This process defines the phenomenon of the profession - its existence or experience. Theory evaluation is a systematic criterion-based assessment of a specific theory. This study presents a theory analysis of the Dental Hygiene Human Needs Conceptual Model (DH HNCM). Using the Walker and Avant Theory Analysis, a seven-step process, the DH HNCM, was analysed and evaluated for its meaningfulness and contribution to dental hygiene. The steps include the following: (i) investigate the origins; (ii) examine relationships of the theory's concepts; (iii) assess the logic of the theory's structure; (iv) consider the usefulness to practice; (v) judge the generalizability; (vi) evaluate the parsimony; and (vii) appraise the testability of the theory. Human needs theory in nursing and Maslow's Hierarchy of Need Theory prompted this theory's development. The DH HNCM depicts four concepts based on the paradigm concepts of the profession: client, health/oral health, environment and dental hygiene actions, and includes validated eleven human needs that evolved overtime to eight. It is logical, simplistic, allows scientific predictions and testing, and provides a unique lens for the dental hygiene practitioner. With this model, dental hygienists have entered practice, knowing they enable clients to meet their human needs. For the DH HNCM, theory analysis affirmed that the model is reasonable and insightful and adds to the dental hygiene professions' epistemology and ontology. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  15. Conceptual Model As The Tool For Managing Bank Services Quality

    Directory of Open Access Journals (Sweden)

    Kornelija Severović

    2009-07-01

    Full Text Available Quality has become basic factor of economic efficiency and basic principle of business activities of successful organizations. Its consequence is revolution on the area of quality that has comprised all kinds of products and services and so the bank services as well. To understand the present and future needs of clients and to know how to fulfill and try to exceed their expectations is the task of each efficient economy. Therefore, the banks in the developed economies try to re-orientate organizationally, technologically and informatically their business activities placing the client in the core of this business activity. Significant indicators of quality services that banks offer is measured by the waiting time of clients for the offer of the desirable service and the number of clients who give up to enter the bank due to the long waiting queues. Dissatisfied client is the worst work result and business activity of banks. Following the stated, the great effort is made to improve service qualities, which means professionalism and communication of personnel with whom the clients come in contact, and giving punctual and clear information and short waiting period of standing in the lines. The aim of this work is to present and describe the functioning of bank system under the conditions of establishing quality in offering services to clients and to recognize basic guidelines for quality increase in the work of sub branches. Since the banking is very dynamic and complex system, the conceptual model is carried out for the purpose of optimization of the stated quality parameters for the bank business activity; this model, in further research, will serve for the development of simulation model.

  16. Assessing uncertainty in SRTM elevations for global flood modelling

    Science.gov (United States)

    Hawker, L. P.; Rougier, J.; Neal, J. C.; Bates, P. D.

    2017-12-01

    The SRTM DEM is widely used as the topography input to flood models in data-sparse locations. Understanding spatial error in the SRTM product is crucial in constraining uncertainty about elevations and assessing the impact of these upon flood prediction. Assessment of SRTM error was carried out by Rodriguez et al (2006), but this did not explicitly quantify the spatial structure of vertical errors in the DEM, and nor did it distinguish between errors over different types of landscape. As a result, there is a lack of information about spatial structure of vertical errors of the SRTM in the landscape that matters most to flood models - the floodplain. Therefore, this study attempts this task by comparing SRTM, an error corrected SRTM product (The MERIT DEM of Yamazaki et al., 2017) and near truth LIDAR elevations for 3 deltaic floodplains (Mississippi, Po, Wax Lake) and a large lowland region (the Fens, UK). Using the error covariance function, calculated by comparing SRTM elevations to the near truth LIDAR, perturbations of the 90m SRTM DEM were generated, producing a catalogue of plausible DEMs. This allows modellers to simulate a suite of plausible DEMs at any aggregated block size above native SRTM resolution. Finally, the generated DEM's were input into a hydrodynamic model of the Mekong Delta, built using the LISFLOOD-FP hydrodynamic model, to assess how DEM error affects the hydrodynamics and inundation extent across the domain. The end product of this is an inundation map with the probability of each pixel being flooded based on the catalogue of DEMs. In a world of increasing computer power, but a lack of detailed datasets, this powerful approach can be used throughout natural hazard modelling to understand how errors in the SRTM DEM can impact the hazard assessment.

  17. Conceptualizing women's attitudes toward condom use with the tripartite model.

    Science.gov (United States)

    Hood, Kristina B; Shook, Natalie J

    2013-01-01

    The Centers for Disease Control and Prevention (2011) estimated that women represent 24% of HIV diagnoses in the United States, with most infections resulting from heterosexual contact. However, consistent condom use is highly effective in preventing the spread of HIV. The current study examined women's attitudes toward condom use and potential inconsistencies related to the conceptualization and measurement of attitudes. Data were collected from October 2009 through March 2010. Researchers included 556 female undergraduate students from the Southeast region of the United States. Exploratory and confirmatory factor analyses were used to determine whether women's condom use attitudes were composed of an affective and a cognitive component. Evidence for a two-factor model of condom use attitudes consisting of an affective and cognitive component was found, with participants reporting slightly negative feelings toward condom use but favorable beliefs about using condoms. Affect accounted for more variance (42%) than cognition (8%) in condom use attitudes. Notably, affect and cognition were differentially associated with past behavior and intentions to use condoms. Understanding the structure of women's attitudes toward using condoms can aid in the creation of appropriate HIV prevention and condom use messaging targeted toward promoting positive attitudes and normative change. Changing women's attitudes in this manner could enhance the effectiveness of condom use interventions.

  18. The Performative Gift: A Feminist Materialist Conceptual Model

    Directory of Open Access Journals (Sweden)

    Jörgen Skågeby

    2013-09-01

    Full Text Available Gifting (or gift-giving is a particularly interesting form of communication that envelops both material and social dimensions. Objects are transformed into gifts through particular socio-material practices. While these practices are, of course, interesting in themselves, this paper will take a step back and revisit attempts to define and theorize the gift as a concept. In a time when the gift economy is often called upon as a potential candidate for more “participatory alternatives to capitalist totality”, particularly in relation to theorizing of labour on and through the Internet, theories of gifting provide an important foundation for discussing the boundaries of alternative futures and economies. So far, little effort has been taken to advance gift theory into a new materialist or posthumanist thinking. In an attempt to take that first step, this paper provides two contributions. First, it highlights how feminist theorizing of the gift comprises interesting forerunners in a new materialist conception of the gift. Second, it explores the analytical traction that can be gained from interlocking theories of the gift, feminist materialism and digital media, the result being a conceptual model that addresses the gift as a form of virtual-digital-material communication.

  19. Facets of private practice nursing: a conceptual model.

    Science.gov (United States)

    Wilson, Anne; Averis, Andrea

    2002-04-01

    This paper critically examines the literature relating to private practice nursing. Particular attention is given to the reasons nurses choose private practice and the major issues involved. A conceptual model has been developed based on this information. Nurses' roles are expanding into different work domains. Private practice nursing is one of the advanced practice options available. It also requires the nurse to develop business knowledge and skills. A literature search was conducted of Pub-Med, Cinahl, Medline and InfoTrac databases using the terms 'private practice', 'nurse entrepreneur', 'nurses in business', Inurse practitioners', 'self-employed nurse', 'advanced practice' and 'clinical nurse specialist'. Further relevant articles were identified from the reference lists of papers detected by this literature search. In addition, conference proceedings were examined for any other material on this topic. A thorough search of the existing literature revealed one unpublished theoretically based study which examined limited aspects of private practice nursing in Victoria. A reasonable number of articles and publications that provided anecdotal and personal accounts of being a nurse in business were identified. This review highlights the need for further theoretically based research in this area of nursing, so as to expand nursing knowledge. Suggestions are given for further research in this topical area. Existing research into private practice nursing is limited and not sufficient to inform changes to policy and nurse education. More research is needed.

  20. Stakeholder Participation in System Change: A New Conceptual Model.

    Science.gov (United States)

    O'Rourke, Tammy; Higuchi, Kathryn S; Hogg, William

    2016-08-01

    A recent change in Canada's primary care system led to the introduction of Nurse Practitioner-Led clinics. The literature suggests that stakeholders can influence system change initiatives. However, very little is known about healthcare stakeholder motivations, particularly stakeholders who are seen as resistors to change. To examine stakeholder participation in the system change process that led to the introduction of the first Nurse Practitioner-Led clinic in Ontario. This single case study included two site visits, semistructured individual tape-recorded interviews, and the examination of relevant public documents. Qualitative content analysis was used to analyze the data. Sixteen individuals from different healthcare sectors and professions participated in the interviews and 20 documents were reviewed. Six key themes emerged from the data. Linking Evidence to Action The findings from the study present a new perspective on stakeholder participation that includes both those who supported the proposed change and those who advocated for a different change. The findings identify stakeholder activities used to shape, share, and protect their visions for system change. The conceptual model presented in this study adds to the understanding of challenges and complexities involved in healthcare system change. Understanding why and how stakeholders participate in change can help healthcare leaders in planning activities to enhance stakeholder involvement in healthcare system change. © 2016 Sigma Theta Tau International.