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PubMed1998

Propagation of experimental uncertainties using the Lipari-Szabo model-free analysis of protein dynamics.

In this paper we make use of the graphical procedure previously described [Jin, D. et al. (1997) J. Am. Chem. Soc., 119, 6923-6924] to analyze NMR relaxation data using the Lipari-Szabo model-free formalism. The graphical approach is advantageous in that it allows the direct visualization of the experimental uncertainties in the motional parameter space. Some general 'rules' describing the relationship between the precision of the relaxation measurements and the precision of the model-free parameters and how this relationship changes with the overall tumbling time (tau m) are summarized. The effect of the precision in the relaxation measurements on the detection of internal motions not close to the extreme narrowing limit is analyzed. We also show that multiple timescale internal motions may be obscured by experimental uncertainty, and that the collection of relaxation data at very high field strength can improve the ability to detect such deviations from the simple Lipari-Szabo model.

Models, Theoretical↗

PubMed1992

The basics of decision analysis.

Historically, decision analysis (DA) arose from economics, psychology, and statistics. Medical and dental applications have developed over the past two decades. While decision psychology explores how people make their decisions, the DA process involves construction of a model and development of insights into the strengths and uncertainties about recommendations derived from analysis of model outputs. Uncertainties are represented as probabilities and values are assigned to desirable or adverse outcomes according to preferences expressed by the decision maker. The model unifies probabilities and values by calculation of the expected value for each decision choice. The decision maker can improve his or her insight into uncertainties in the model by conducting sensitivity analyses, and can take action based on this improved insight. The DA process is illustrated using the decision to take or skip influenza vaccination. People's decision making behavior for this problem has also been analyzed using methods from decision psychology. Distinctions between clinical DA and cost-effectiveness analysis are given, as are caveats about especially complicated subtopics in decision analysis for medical problems. In closing, opportunities for further study of decision analysis are presented.

Cost-Benefit Analysis↗

PubMed2005

Sensor fault diagnosis for nonlinear processes with parametric uncertainties.

This paper addresses the problem of detecting, discriminating, and reconstructing sensor faults for nonlinear systems with known model structure but uncertainty in the parameters of the process. The convenience of the proposed technique lies in the fact that historical operational data and/or a priori fault information is not required to achieve accurate fault reconstruction except for fixed, short intervals. The overall fault diagnosis algorithm is composed of a series of nonlinear estimators, which estimates parameter and a fault isolation and identification filter. Parameter estimation and fault reconstruction cannot be performed accurately since faults and parametric uncertainty interact with each other. Therefore, these two tasks are performed at different time scales, where the fault diagnosis takes place at a more frequent rate than the parameter estimation. It is shown that the fault can be reconstructed under some realistic assumptions and the performance of the proposed methodology is evaluated on a simulated chemical process exhibiting nonlinear dynamic behavior.

Algorithms↗

PubMedDate not supplied

A population exposure model for particulate matter: case study results for PM(2.5) in Philadelphia, PA.

A population exposure model for particulate matter (PM), called the Stochastic Human Exposure and Dose Simulation (SHEDS-PM) model, has been developed and applied in a case study of daily PM(2.5) exposures for the population living in Philadelphia, PA. SHEDS-PM is a probabilistic model that estimates the population distribution of total PM exposures by randomly sampling from various input distributions. A mass balance equation is used to calculate indoor PM concentrations for the residential microenvironment from ambient outdoor PM concentrations and physical factor data (e.g., air exchange, penetration, deposition), as well as emission strengths for indoor PM sources (e.g., smoking, cooking). PM concentrations in nonresidential microenvironments are calculated using equations developed from regression analysis of available indoor and outdoor measurement data for vehicles, offices, schools, stores, and restaurants/bars. Additional model inputs include demographic data for the population being modeled and human activity pattern data from EPA's Consolidated Human Activity Database (CHAD). Model outputs include distributions of daily total PM exposures in various microenvironments (indoors, in vehicles, outdoors), and the contribution from PM of ambient origin to daily total PM exposures in these microenvironments. SHEDS-PM has been applied to the population of Philadelphia using spatially and temporally interpolated ambient PM(2.5) measurements from 1992-1993 and 1990 US Census data for each census tract in Philadelphia. The resulting distributions showed substantial variability in daily total PM(2.5) exposures for the population of Philadelphia (median=20 microg/m(3); 90th percentile=59 microg/m(3)). Variability in human activities, and the presence of indoor-residential sources in particular, contributed to the observed variability in total PM(2.5) exposures. The uncertainty in the estimated population distribution for total PM(2.5) exposures was highest at the upper end of the distribution and revealed the importance of including estimates of input uncertainty in population exposure models. The distributions of daily microenvironmental PM(2.5) exposures (exposures due to time spent in various microenvironments) indicated that indoor-residential PM(2.5) exposures (median=13 microg/m(3)) had the greatest influence on total PM(2.5) exposures compared to the other microenvironments. The distribution of daily exposures to PM(2.5) of ambient origin was less variable across the population than the distribution of daily total PM(2.5) exposures (median=7 microg/m(3); 90th percentile=18 microg/m(3)) and similar to the distribution of ambient outdoor PM(2.5) concentrations. This result suggests that human activity patterns did not have as strong an influence on ambient PM(2.5) exposures as was observed for exposure to other PM(2.5) sources. For most of the simulated population, exposure to PM(2.5) of ambient origin contributed a significant percent of the daily total PM(2.5) exposures (median=37.5%), especially for the segment of the population without exposure to environmental tobacco smoke in the residence (median=46.4%). Development of the SHEDS-PM model using the Philadelphia PM(2.5) case study also provided useful insights into the limitations of currently available data for use in population exposure models. In addition, data needs for improving inputs to the SHEDS-PM model, reducing uncertainty and further refinement of the model structure, were identified.

Activities of Daily Living↗

PubMed1993

Capitation funding: population, age, and mortality adjustments for regional and district health authorities in England.

OBJECTIVE: To assess the effect of each of the components of the national capitation funding formula--population projections and age and mortality weighting--at regional and district level. DESIGN: Application of age-cost and mortality weights to the projected 1997 populations of regions and districts, based on the Department of Health public health common dataset. SETTING: Regional and district health authorities in England. RESULTS: The application of the age-cost and mortality weights to projected populations resulted in greater changes in the shares of weighted populations relative to the estimated 1991 population at district level (mean 0, range -17% to 28%) than at regional level (mean 0, range -9% to 6%). At district level mortality weights had less scope for influence (mean 0, range -9% to 14%) than population projections (mean 0, range -16% to 31%) or age weights (mean 0, range -20% to 30%). CONCLUSIONS: The adjustments to the 1991 population shares due to the application of the national capitation funding formula depend on the interaction of three elements: the projected population by age group, the age-cost weight, and the mortality weight. Since each is open to uncertainty, either in terms of measurement (projected population, particularly for births and the over 85s) or method (derivation of the age-cost and mortality weights), the formula should be implemented cautiously at district level. Ways should be considered of incorporating elements of uncertainty into the model. Further research is required on the elements and degree of uncertainty in each of these components, as well as on the relative health needs of different populations.

Adolescent↗

PubMed2003

Influences of parameter uncertainties within the ICRP-66 respiratory tract model: particle clearance.

Quantifying radiological risk following the inhalation of radioactive aerosols entails not only an assessment of particle deposition within respiratory tract regions but a full accounting of clearance mechanisms whereby particles may be translocated to adjacent respiratory tissue regions, absorbed to blood, or released to the gastrointestinal tract. The model outlined in ICRP Publication 66 represents to date one of the most complete overall descriptions of particle deposition and clearance, as well as localized radiation dosimetry, within the respiratory tract. In this study, a previous review of the ICRP-66 deposition model is extended to the study of the subsequent clearance model. A systematic review of the clearance component within the ICRP 66 respiratory tract model was conducted in which probability density functions were assigned to all input parameters for both 239PuO2 and 238UO2/238U3O8. These distributions were subsequently incorporated within a computer code LUDUC (Lung Dose Uncertainty Code) in which Latin hypercube sampling techniques are used to generate multiple (e.g., 1,000) sets of input vectors (i.e., trials) for all model parameters needed to assess mechanical clearance and particle dissolution/absorption. Integral numbers of nuclear disintegrations, U(s), in various lung regions were shown to be well-described by lognormal probability distributions. Of the four extrathoracic clearance compartments of the respiratory tract, uncertainties in U(s), expressed as the ratio of its 95% to 5% confidence levels, were highest within the LN(ET) tissues for 239PuO2 (ratio of 50 to 130) and within the ET(seq) tissues for 238UO2/238U3O8 (ratio of 12 to 50). Peak uncertainties in U(s) in these respiratory regions occurred at particle sizes of approximately 0.5-0.6 microm where uncertainties in ET2 particle deposition fractions accounted for only approximately 10% of the total U(s) uncertainty for 239PuO2, and only approximately 30% of the total U(s) uncertainty for 238UO2/238U3O8 (the remainder is attributed to the clearance model alone). Of the eight clearance compartments within the thoracic regions of the respiratory tract, and for particle sizes below approximately 5 microm, uncertainties in U(s) were highest within the LN(TH) tissues for 239PuO2 (ratio of 60 to 80) and within the BB(seq) tissues for 238UO2/238U3O8 (ratio of 20 and 60). At particle sizes exceeding approximately 5 microm in aerodynamic diameter, peak uncertainties in U(s) are noted for the AI, bb(seq), and bb1 clearance compartments. As the particle size approaches 10 microm in size, uncertainties in U(s) within these three thoracic tissue regions approach a factor of 1,000 and are dominated by corresponding uncertainties in particle deposition.

Aerosols↗

PubMedDate not supplied

Ground water model calibration using pilot points and regularization.

Use of nonlinear parameter estimation techniques is now commonplace in ground water model calibration. However, there is still ample room for further development of these techniques in order to enable them to extract more information from calibration datasets, to more thoroughly explore the uncertainty associated with model predictions, and to make them easier to implement in various modeling contexts. This paper describes the use of "pilot points" as a methodology for spatial hydraulic property characterization. When used in conjunction with nonlinear parameter estimation software that incorporates advanced regularization functionality (such as PEST), use of pilot points can add a great deal of flexibility to the calibration process at the same time as it makes this process easier to implement. Pilot points can be used either as a substitute for zones of piecewise parameter uniformity, or in conjunction with such zones. In either case, they allow the disposition of areas of high and low hydraulic property value to be inferred through the calibration process, without the need for the modeler to guess the geometry of such areas prior to estimating the parameters that pertain to them. Pilot points and regularization can also be used as an adjunct to geostatistically based stochastic parameterization methods. Using the techniques described herein, a series of hydraulic property fields can be generated, all of which recognize the stochastic characterization of an area at the same time that they satisfy the constraints imposed on hydraulic property values by the need to ensure that model outputs match field measurements. Model predictions can then be made using all of these fields as a mechanism for exploring predictive uncertainty.

Calibration↗

PubMed1989

The propagation of uncertainty in human mortality processes operating in stochastic environments.

This paper presents a model describing how the uncertainty due to influential exogenous processes combines with stochasticity intrinsic to physiological aging processes and propagates through time to generate uncertainty about the future physiological state of the population. Variance expressions are derived for (a) the future values of the physiological variables under the assumption that external factors evolve under a linear stochastic diffusion process, and (b) the cohort survival functions and cohort life expectancies which reflect the uncertainty in the future values of the physiological variables. The model implies that a major component of uncertainty in forecasts of the physiological characteristics of a closed cohort is due to differential rates of survival associated with different realizations of the external process. This suggests that the limits to forecasting may be different in physiological systems subject to systematic mortality than in physical systems such as weather where the concepts of closed cohorts and of mortality selection have no simple analog.

Aging↗

PubMedDate not supplied

Radionuclide transport simulation and uncertainty analyses with the saturated-zone site-scale model at Yucca Mountain, Nevada.

Evaluation of radionuclide transport in the saturated zone (SZ) to the accessible environment is an important component of performance assessment for the proposed radioactive waste repository at Yucca Mountain. Simulations of radionuclide migration in the SZ have been performed using the calibrated three-dimensional (3-D) SZ site-scale flow and transport model. An innovative particle-tracking method was used to simulate transport that includes the processes of advection, dispersion, matrix diffusion, and sorption. The uncertainties in groundwater flow and radionuclide transport were quantitatively evaluated to develop uncertainty distributions for key model parameters, and multiple realizations of the SZ system were simulated using the SZ site-scale model. The results of multiple realizations of radionuclide transport indicate significant aggregate uncertainty in transport times through the SZ. The simulated radionuclide mass breakthrough curves in the SZ have been coupled with other components of the repository system in Total System Performance Assessment (TSPA) analyses and constitute the means by which uncertainty in the SZ is incorporated into regulatory analyses. Regression analysis has been used to determine the sensitivity of radionuclide transport simulation results to the uncertainty of individual model input parameters. Results of the sensitivity analysis indicate that median radionuclide transport times were dominantly controlled by uncertainty in the specific discharge in the SZ, with sorption and retardation in the alluvium playing important roles for some radionuclides.

Forecasting↗

PubMed2006

Application of uncertainty and sensitivity analysis in river basin management.

Considering uncertainty in the decision-making process in river basin management is important because uncertainty is regarded as one of the main obstacles to sound decision-making. In case of high uncertainty, the risks of making a wrong decision could be quite high, which may have severe consequences. This paper applies a screening sensitivity analysis method, the Morris method, to investigate the propagation of uncertainty from factors in a flood damage model into the model outputs and explores the importance of factors based on the sensitivity analysis. Uncertainty reduction in the most influential factors identified by the Morris method is proposed as a means to reduce the uncertainty in model outputs. In this way the risks of making a wrong decision could be reduced. The results in this paper show that the Morris method is an efficient approach to help reduce the uncertainty in model outputs.

Conservation of Natural Resources↗

PubMed2005

Robust interpretive optimisation in high-performance liquid chromatography considering uncertainties in peak position.

In the context of interpretive chromatographic optimisation, robustness is usually calculated by introducing deliberated shifts in the nominal optimal conditions and evaluating their effects on the monitored objective function, mimicking thus the experimental procedures used in method validation. However, such strategy ignores a major source of error: the uncertainties associated to the modelling step, that may give rise to deceiving results when conditions that were expected to yield baseline separation are reproduced in the chromatograph. Two approaches, based on the peak purity concept, are here proposed to evaluate the robustness of the objective function under the perspective of measurement errors and modelling. The first approach implements these uncertainties as an extra band broadening for each chromatographic peak. The second one implements them as peak fluctuations in simulated replicated assays, which gives rise to a distribution of peak purities, easily computed through Monte-Carlo simulations. Both approaches predict satisfactorily a decreased separation capability, with respect to the conventional approach, for those situations where the uncertainties in peak position make the objective function critical. The first approach is less optimistic and formally less rigorous than the second one, but its computation is simpler. It can be used to map the critical resolution regions, to be comprehensively appraised further by the slower, although more rigorous, Monte-Carlo approach.

Amino Acids↗

PubMed2002

Spatial-frequency uncertainty and cuing effects on psychometric functions for contrast detection.

In the present study, the effects of spatial-frequency uncertainty and cuing on psychometric functions for contrast detection of sinusoidal gratings are examined. For this purpose, psychometric functions were collected from 4 subjects under fixed-frequency, randomized-frequency, and cued-frequency conditions. The experiment was conducted with a temporal two-alternative forced-choice task, and five spatial frequencies in the range of 0.5 and 8.0 c/deg and seven contrast levels for each frequency were used. The results showed that the psychometric functions for the randomized-frequency condition were shallower than those for the fixed-frequency condition, supporting the single-band model for the uncertainty effects (Hübner, 1993a, 1993b). For the cued-frequency condition, the slopes of the functions were not clearly different from those for the randomized condition. These results clearly differ from those of Hübner (1996b), which showed, in the spatial two-alternative forced-choice task, steeper psychometric functions for the randomized-frequency condition than those for the fixed- and cued-frequency conditions, supporting the multiple-band model (Hübner, 1993a, 1993b). The difference suggests that the single-band model applies to the uncertainty effects in the temporal forced-choice task, whereas the multiple-band model does so in the spatial forced-choice task.

Adult↗

PubMed2006

Inferring resource distributions from Atlantic bluefin tuna movements: an analysis based on net displacement and length of track.

We use observed movement tracks of Atlantic bluefin tuna in the Gulf of Maine and mathematical modeling of this movement to identify possible resource patches. We infer bounds on the overall sizes and distribution of such patches, even though they are difficult to quantify by direct observation in situ. To do so, we segment individual fish tracks into intervals of distinct motion types based on the ratio of net displacement to length of track (DeltaD/DeltaL) over a time window Deltat. To find the best segmentation, we optimize the fit of a random-walk movement model to each motion type. We compare results from two distinct movement models: biased turning and biased speed, to check the model-dependence of our inferences, and find that uncertainty in choice of movement model dominates the uncertainties of our conclusions. We find that our data are best described using two motion types: "localized" (DeltaD/DeltaL small) and "long-ranged" (DeltaD/DeltaL large). The biased turning model leads to significantly better resolution of localized movement intervals than the biased speed model. We hypothesize that localized movement corresponds to exploitation of resource patches. Comparison with visual behavior observations made during tracking suggests that many inferred intervals of localized motion do indeed correspond to feeding activity. From our analysis, we estimate that, on average, bluefin tuna in the Gulf of Maine encounter a resource patch every 2h, that those patches have an average radius of 0.7-1.2 km, and that, overall, there are at most 5-9 such patches per 100 km(2) in the region studied.

Animals↗

PubMed2002

Constraints on radiative forcing and future climate change from observations and climate model ensembles.

The assessment of uncertainties in global warming projections is often based on expert judgement, because a number of key variables in climate change are poorly quantified. In particular, the sensitivity of climate to changing greenhouse-gas concentrations in the atmosphere and the radiative forcing effects by aerosols are not well constrained, leading to large uncertainties in global warming simulations. Here we present a Monte Carlo approach to produce probabilistic climate projections, using a climate model of reduced complexity. The uncertainties in the input parameters and in the model itself are taken into account, and past observations of oceanic and atmospheric warming are used to constrain the range of realistic model responses. We obtain a probability density function for the present-day total radiative forcing, giving 1.4 to 2.4 W m-2 for the 5-95 per cent confidence range, narrowing the global-mean indirect aerosol effect to the range of 0 to -1.2 W m-2. Ensemble simulations for two illustrative emission scenarios suggest a 40 per cent probability that global-mean surface temperature increase will exceed the range predicted by the Intergovernmental Panel on Climate Change (IPCC), but only a 5 per cent probability that warming will fall below that range.

Journal Article↗

PubMed2002

Analysis and predictive models of stormwater runoff volumes, loads, and pollutant concentrations from watersheds in the Twin Cities metropolitan area, Minnesota, USA.

Urban nonpoint source pollution is a significant contributor to water quality degradation. Watershed planners need to be able to estimate nonpoint source loads to lakes and streams if they are to plan effective management strategies. To meet this need for the twin cities metropolitan area, a large database of urban and suburban runoff data was compiled. Stormwater runoff loads and concentrations of 10 common constituents (six N and P forms, TSS, VSS, COD, Pb) were characterized, and effects of season and land use were analyzed. Relationships between runoff variables and storm and watershed characteristics were examined. The best regression equation to predict runoff volume for rain events was based on rainfall amount, drainage area, and percent impervious area (R2 = 0.78). Median event-mean concentrations (EMCs) tended to be higher in snowmelt runoff than in rainfall runoff, and significant seasonal differences were found in yields (kg/ha) and EMCs for most constituents. Simple correlations between explanatory variables and stormwater loads and EMCs were weak. Rainfall amount and intensity and drainage area were the most important variables in multiple linear regression models to predict event loads, but uncertainty was high in models developed with the pooled data set. The most accurate models for EMCs generally were found when sites were grouped according to common land use and size.

Forecasting↗

PubMed1999

Modelling radionuclide distribution and transport in the environment.

Mathematical models of radionuclide distribution and transport in the environment have been developed to assess the impact on people of routine and accidental releases of radioactivity from a variety of nuclear activities, including: weapons development, production, and testing; power production; and waste disposal. The models are used to estimate human exposures and doses in situations where measurements have not been made or would be impossible or impractical to make. Model results are used to assess whether nuclear facilities are operated in compliance with regulatory requirements, to determine the need for remediation of contaminated sites, to estimate the effects on human health of past releases, and to predict the potential effects of accidental releases or new facilities. This paper describes the various applications and types of models currently used to represent the distribution and transport of radionuclides in the terrestrial and aquatic environments, as well as integrated global models for selected radionuclides and special issues in the fields of solid radioactive waste disposal and dose reconstruction. Particular emphasis is placed on the issue of improving confidence in the model results, including the importance of uncertainty analysis and of model verification and validation.

Journal Article↗

PubMed2000

Begging and bleating: the evolution of parent-offspring signalling.

The evolution of biological signalling in the face of evolutionary conflicts of interest is an active area of evolutionary ecology, and one to which Maynard Smith has made important contributions. We explore the major theoretical challenges in the field, concentrating largely on how offspring signal to their parents when there is the potential for parent-offspring conflict. Costly offspring solicitation (begging etc.) has been interpreted in terms of a Zahavi Grafen honest handicap signal, but this has been challenged on the grounds of' the costs of signalling. We review this controversy and also explore the issue of pooling versus separating signalling equilibrium. An alternative explanation for costly begging is that it is due to sibling competition, and we discuss the relationship between these ideas and signalling models in families with more than one offspring. Finally we consider signal uncertainty, how signalling models can be made dynamic, and briefly how they may be tested experimentally.

Animal Communication↗

PubMed1989

Modelling medical decision-making under uncertainty.

This study investigates the heuristic knowledge which underlies expertise in a medical decision-making task. Data are obtained from a process tracing study of a consultant physician. A production system is developed to simulate his decision-making, tested on new cases, and compared with the performance of a second consultant physician and a representative sample of medical students. The results indicate that the production system performs at an effective level, and that representing expertise in such terms would make this difficult task more tractable for learners.

Decision Making↗