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Results for “sensitivity analysis”

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PubMed2006

Sensitivity analysis of thermodynamic properties of liquid water: a general approach to improve empirical potentials.

A sensitivity analysis of bulk water thermodynamics is presented in an effort to understand the relation between qualitative features of molecular potentials and properties that they predict. The analysis is incorporated in molecular dynamics simulations and investigates the sensitivity of the Helmholtz free energy, internal energy, entropy, heat capacity, pressure, thermal pressure coefficient, and static dielectric constant to components of the potential rather than the parameters of a given functional form. The sensitivities of the properties are calculated with respect to the van der Waals repulsive and the attractive parts, plus short- and long-range Coulomb parts of three four site empirical water potentials: TIP4P, Dang-Chang and TTM2R. The polarization sensitivity is calculated for the polarizable Dang-Chang and TTM2R potentials. This new type of analysis allows direct comparisons of the sensitivities for different potentials that use different functional forms. The analysis indicates that all investigated properties are most sensitive to the van der Waals repulsive, the short-range Coulomb and the polarization components of the potentials. When polarization is included in the potentials, the magnitude of the sensitivity of the Helmholtz free energy, internal energy, and entropy with respect to this part of the potential is comparable in magnitude to the other electrostatic components. In addition similarities in trends of observed sensitivities for nonpolarizable and polarizable potentials lead to the conclusion that the complexity of the model is not of critical importance for the calculation of these thermodynamic properties for bulk water. The van der Waals attractive and the long-range Coulomb sensitivities are relatively small for the entropy, heat capacity, thermal pressure coefficient and the static dielectric constant, while small changes in any of the potential contributions will significantly affect the pressure. The analysis suggests a procedure for modification of the potentials to improve predictions of thermodynamic properties and we demonstrate this general approach for modifying potentials for one of the potentials.

Journal Article↗
PubMed2005

Photonic crystal device sensitivity analysis with Wannier basis gradients.

We present a powerful sensitivity analysis method for devices in a photonic crystal. The method is based on a Wannier basis field expansion and efficient matrix analysis techniques for finding eigenvalue and transmission gradients with respect to the perturbation. The method permits fast analysis of a large number of dielectric perturbation situations for multiple devices in a photonic crystal. We verify the method with finite-difference time-domain and plane-wave expansion calculations.

Journal Article↗
PubMed1994

Stochastic simulation and sensitivity analysis: estimating future demand for health resources in China.

A simulation model has been built to estimate the demand for hospital beds and doctors in China between 1990 and 2010. The model was used to compare deterministic sensitivity analysis and stochastic simulation in assessing inherent uncertainty in health projections. The stochastic simulation method uses information more efficiently, and produces a more reasonable average estimate and a more meaningful range of projections than deterministic sensitivity analysis. However, it may be preferable to combine the use of both approaches because they have different, complementary, advantages and disadvantages. The usefulness of 3 value estimates of input variables and the benefits of the triangular distribution for stochastic simulation should be emphasized in health projections.

China↗
PubMed2002

Sensitivity analysis of grating parameter estimation.

An optimization method for the sensitivity of diffraction efficiency measurements is presented. I define the sensitivity as the estimation precision of the grating parameters. The optimization method called sensitivity analysis for fitting scans all the possible measurement configurations and selects the configuration that yields the best sensitivity. The scan is made over the domain of the experimental parameters of the arrangement, such as the azimuth angle of the grating and the orientation angles of the analyzer and the polarizer. These parameters can be freely varied, and among the multitude of possible combinations there is one configuration that provides optimum sensitivity. Comparison with experimental results reveals a qualitative agreement between theory and practice.

Journal Article↗
PubMed2005

Sensitivity analysis applied to the construction of radial basis function networks.

Conventionally, a radial basis function (RBF) network is constructed by obtaining cluster centers of basis function by maximum likelihood learning. This paper proposes a novel learning algorithm for the construction of radial basis function using sensitivity analysis. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the output's sensitivity to the training data. In classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. Our experimental results show that our proposed sensitivity-based RBF classifier outperforms the conventional RBFs and is as accurate as support vector machine (SVM). Hence, sensitivity analysis is expected to be a new alternative way to the construction of RBF networks.

Algorithms↗
PubMed2006

Biological diagnosis for neuroblastoma using the combination of highly sensitive analysis of prognostic factors.

BACKGROUND/PURPOSE: To select the optimal treatment according to the degree of malignancy of neuroblastoma, it is essential to accurately and rapidly identify any genetic abnormalities associated with the prognosis. This study aims to assess the correlation between the combination of prognostic factors and the biologic findings of neuroblastoma using a highly sensitive analysis of prognostic factors. METHODS: In 44 neuroblastoma primary samples, we determined the gene dosages of MYCN and Survivin (as the target of 17q gain) and the expression levels of MYCN, Survivin, and BIN1 using highly sensitive analysis (the quantitative polymerase chain reaction method); furthermore, we assessed the correlation between the combination of their prognostic factors and the biology of neuroblastoma. RESULTS: The gene dosage of MYCN or Survivin was significantly associated with all known prognostic factors. The expression level of MYCN or Survivin was not significantly associated with any prognostic factors, whereas the expression level of BIN1 was significantly associated with 5 of 6 prognostic factors. Regarding the combination of MYCN amplification and 17q gain (the gene dosage of Survivin), and the low expression of BIN1, the rates of advanced stages (stage III or IV) were 100% for the cases with 3 factors, 63% for the cases with 2 factors, 42% for the cases with 1 factor, and 0% for the cases with null factor. Furthermore, the survival rates were 20% for the cases with 3 factors, 50% for the cases with 2 factors, 100% for the cases with 1 factor, and 100% for the cases with null factor. CONCLUSION: The combination of gene dosages of MYCN and Survivin and the expression level of BIN1 using the quantitative polymerase chain reaction method was significantly correlated with the clinical stage and the patients' outcome. This combination of biologic factors may enhance the accuracy to the conventional criteria, but this would have to be shown in a much larger study that is adequately powered to detect such an advantage.

Adaptor Proteins, Signal Transducing↗
PubMed1990

Sensitivity analysis applied to Coburn-Forster-Kane models of carboxyhemoglobin formation.

When mathematical model predictions disagree with the behavior of the physiological system modeled, blame is generally placed on the inadequacy of the model. It was shown using the Coburn-Forster-Kane (CFK) models of carboxyhemoglobin (COHb) formation as illustrations, that a sensitivity analysis of the model can provide estimates of the effects of data variability and inaccuracy on model predictions. Sensitivity functions were derived for each variable in the model, and families of them were plotted as functions of time with work level as a parameter. The sensitivity plots identify the variables which can contribute the most to disparities between model and system behavior and illustrate how the relative importance of the error in each variable changes with both time and work level. For example, with exposure to a constant concentration of carbon monoxide (CO) at a constant level of exercise, errors in blood volume determination, initial [COHb], and total hemoglobin concentration do not affect the calculated equilibrium value of blood [COHb]; neither inspired concentration of carbon monoxide nor endogenous production rate affect the rate at which equilibrium is achieved; and all other variables affect both the equilibrium value of blood [COHb] and the rate at which it is achieved. The sensitivity analysis provides a link between model output variability and input or data variability which can be used to assess the value of efforts to reduce data error and to estimate the overall uncertainty of model predictions.

Carbon Monoxide↗
PubMed1990

Sensitivity analysis of respiratory parameter uncertainties: impact of criterion function form and constraints.

A sensitivity analysis based on weighted least-squares regression is presented to evaluate alternative methods for fitting lumped-parameter models to respiratory impedance data. The goal is to maintain parameter accuracy simultaneously with practical experiment design. The analysis focuses on predicting parameter uncertainties using a linearized approximation for joint confidence regions. Applications are with four-element parallel and viscoelastic models for 0.125- to 4-Hz data and a six-element model with separate tissue and airway properties for input and transfer impedance data from 2-64 Hz. The criterion function form was evaluated by comparing parameter uncertainties when data are fit as magnitude and phase, dynamic resistance and compliance, or real and imaginary parts of input impedance. The proper choice of weighting can make all three criterion variables comparable. For the six-element model, parameter uncertainties were predicted when both input impedance and transfer impedance are acquired and fit simultaneously. A fit to both data sets from 4 to 64 Hz could reduce parameter estimate uncertainties considerably from those achievable by fitting either alone. For the four-element models, use of an independent, but noisy, measure of static compliance was assessed as a constraint on model parameters. This may allow acceptable parameter uncertainties for a minimum frequency of 0.275-0.375 Hz rather than 0.125 Hz. This reduces data acquisition requirements from a 16- to a 5.33- to 8-s breath holding period. These results are approximations, and the impact of using the linearized approximation for the confidence regions is discussed.

Airway Resistance↗
PubMed2004

Parametric sensitivity analysis of leachate transport simulations at landfills.

This paper presents a case study in simulating leachate generation and transport at a 2000 ton/day landfill facility and assesses leachate migration away from the landfill in order to control associated environmental impacts, particularly on groundwater wells down gradient of the site. The site offers unique characteristics in that it is a former quarry converted to a landfill and is planned to have refuse depths that could reach 100 m, making it one of the deepest in the world. Leachate quantity and potential percolation into the subsurface are estimated using the Hydrologic Evaluation of Landfill Performance (HELP) model. A three-dimensional subsurface model (PORFLOW) was adopted to simulate ground water flow and contaminant transport away from the site. A comprehensive sensitivity analysis to leachate transport control parameters was also conducted. Sensitivity analysis suggests that changes in partition coefficient, source strength, aquifer hydraulic conductivity, and dispersivity have the most significant impact on model output indicating that these parameters should be carefully selected when similar modeling studies are performed.

Geological Phenomena↗
PubMed2004

Investigation of biological and fouling characteristics of submerged membrane bioreactor process for wastewater treatment by model sensitivity analysis.

In this study, a mathematical model for the submerged membrane bioreactor (SMBR) was developed. The activated sludge model No. 1 (ASM1) was modified to be suitable for describing the characteristics of the SMBR, and the resistance-in-series model was integrated into the ASM1 to describe membrane fouling. Using the newly developed model, the biological and fouling characteristics of the submerged membrane bioreactor process for wastewater treatment was investigated by sensitivity analysis. The sensitivity of effluent COD and nitrogen, TSS in the reactor and membrane flux with respect to each parameter (K(h), mu(H), K(S), K(NHH), K(NOH), b(H), Y(H), mu(A), K(NHA), b(A), Y(A), K(m) and alpha) was investigated by model simulation. As a result, the most important factors affecting membrane fouling were hydrolysis rate constant (K(h)) and cross-flow effect coefficient (K(m)). Heterotrophic yield coefficient (Y(H)) had a great influence on effluent quality. Effluent quality was also somewhat sensitive to K(h). Peculiar operating conditions of the SMBR such as long solids retention time (SRT), absolute retention of solids by membrane and high biomass concentration in bioreactor could explain these model simulation results. The model developed in this study would be very helpful to optimize operating conditions as well as design parameters for a SMBR system.

Biofilms↗
PubMedDate not supplied

Uncertainty in the economic evaluation of health care technologies: the role of sensitivity analysis.

Given the increased use of economic evaluation to inform decision making in the health service, it is particularly important that the research methods used are critically assessed and, where possible, improved. The systematic handling of uncertainty in economic evaluation is an important area that remains methodologically underdeveloped. With the increased use of the clinical trial as a vehicle for economic evaluation, there has been recent interest in how the statistical methods routinely employed to handle uncertainty in clinical research might be applied to economic evaluation. This paper reviews the types of uncertainty that exist in economic evaluation and argues that some forms of uncertainty are not amenable to statistical methods. Sensitivity analysis is not a single approach but can take a number of different forms. The different types of sensitivity analysis are reviewed, with an indication of their strengths and weaknesses in relation to the different types of uncertainty in economic evaluation.

Cost-Benefit Analysis↗
PubMed2005

Building consensus in environmental impact assessment through multicriteria modeling and sensitivity analysis.

Multicriteria decision analysis (MCDA) increasingly is being applied in environmental impact assessment (EIA). In this article, two MCDA techniques, stochastic analytic hierarchy process and compromise programming, are combined to ascertain the environmental impacts of and to rank two alternative sites for Mexico City's new airport. Extensive sensitivity analyses were performed to determine the probability of changes in rank ordering given uncertainty in the hierarchy structure, decision criteria weights, and decision criteria performances. Results demonstrate that sensitivity analysis is fundamental for attaining consensus among members of interdisciplinary teams and for settling debates in controversial projects. It was concluded that sensitivity analysis is critical for achieving a transparent and technically defensible MCDA implementation in controversial EIA.

Aircraft↗
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↗
PubMedDate not supplied

A comparison of solute-transport solution techniques and their effect on sensitivity analysis and inverse modeling results.

Five common numerical techniques for solving the advection-dispersion equation (finite difference, predictor corrector, total variation diminishing, method of characteristics, and modified method of characteristics) were tested using simulations of a controlled conservative tracer-test experiment through a heterogeneous, two-dimensional sand tank. The experimental facility was constructed using discrete, randomly distributed, homogeneous blocks of five sand types. This experimental model provides an opportunity to compare the solution techniques: the heterogeneous hydraulic-conductivity distribution of known structure can be accurately represented by a numerical model, and detailed measurements can be compared with simulated concentrations and total flow through the tank. The present work uses this opportunity to investigate how three common types of results--simulated breakthrough curves, sensitivity analysis, and calibrated parameter values--change in this heterogeneous situation given the different methods of simulating solute transport. The breakthrough curves show that simulated peak concentrations, even at very fine grid spacings, varied between the techniques because of different amounts of numerical dispersion. Sensitivity-analysis results revealed: (1) a high correlation between hydraulic conductivity and porosity given the concentration and flow observations used, so that both could not be estimated; and (2) that the breakthrough curve data did not provide enough information to estimate individual values of dispersivity for the five sands. This study demonstrates that the choice of assigned dispersivity and the amount of numerical dispersion present in the solution technique influence estimated hydraulic conductivity values to a surprising degree.

Bromides↗
PubMed2006

Sensitivity analysis of misclassification: a graphical and a Bayesian approach.

PURPOSE: Misclassification can produce bias in measures of association. Sensitivity analyses have been suggested to explore the impact of such bias, but do not supply formally justified interval estimates. METHODS: To account for exposure misclassification, recently developed Bayesian approaches were extended to incorporate prior uncertainty and correlation of sensitivity and specificity. Under nondifferential misclassification, a contour plot is used to depict relations among the corrected odds ratio, sensitivity, and specificity. RESULTS: Methods are illustrated by application to a case-control study of cigarette smoking and invasive pneumococcal disease while varying the distributional assumptions about sensitivity and specificity. Results are compared with those of conventional methods, which do not account for misclassification, and a sensitivity analysis, which assumes fixed sensitivity and specificity. CONCLUSION: By using Bayesian methods, investigators can incorporate uncertainty about misclassification into probabilistic inferences.

Bayes Theorem↗
PubMed1976

Parametric sensitivity analysis of a homeomorphic model for saccadic and vergence eye movements.

A non-linear sixth order homeomorphic model, fitted with parameters based on eye movements and physiological data, was tuned so that it provided good simulations for the shapes of the magnitude, velocity and acceleration trajectories. Excellent quantitative agreement was obtained in terms of the Main Sequence diagrams for human eye movements. Parametric sensitivity analysis was done for a saccade of ten degree amplitude, a physiologically normal magnitude at which experimental data is both abundant and relatively noise free. Among the many useful results of this sensitivity analysis are that pulse width (PW) and pulse height (PH) were confirmed as the two controlling parameters for the human eye movement model. Output behavior was relatively insensitive to variations of the passive elements of the plant. This analysis also pointed out that more physiological data are needed to understand the role of the non-linear force-velocity relationship of the extraocular muscles.

Computers↗
PubMed1998

Sensitivity analysis of the stimulus-response function of a static nonlinear accommodation model.

The effect of parameter variation of a nonlinear static feedback control model of the accommodation system was investigated. Simulations of a MATLAB/SIMULINK model showed a nonlinear relationship between stimulus and response in which the response curve was above the 1:1 line in the region to the left of the crossover and below the 1:1 line in the region to the right of the crossover. At the crossover, the response curve exhibited an inflection that was constant and equal to the tonic accommodation value (ABIAS). Sensitivity analysis showed that increasing depth of focus (deadspace range between +/- DSP) increased the separation between the boundary lines of the deadspace region, with a larger separation associated with late-onset myopia and congenital nystagmus. Increasing accommodative controller gain (ACG) increased the slope of the function on either side of the deadspace, with lower ACG values corresponding to lower slopes that indicated an amblyopic deficit. Increasing ABIAS increased the accommodative level at the inflection region. In addition, the saturation level of the accommodative response decreased with increasing age, while the slope remained the same, which was consistent with the Hess-Gullstrand theory of presbyopia. The accuracy and relative simplicity of the model indicated that it could serve as a basis for further comprehensive investigation of the basic and clinical aspects of the accommodation system.

Accommodation, Ocular↗
PubMed2006

Sensitivity analysis to evaluate the impact of uncertain factors in a scenario tree model for classical swine fever introduction.

Introduction of classical swine fever virus (CSFV) is a continuing threat to the pig production sector in the European Union. A scenario tree model was developed to obtain more insight into the main risk factors determining the probability of CSFV introduction (P(CSFV)). As this model contains many uncertain input parameters, sensitivity analysis was used to indicate which of these parameters influence model results most. Group screening combined with the statistical techniques of design of experiments and meta-modeling was applied to detect the most important uncertain input parameters among a total of 257 parameters. The response variable chosen was the annual P(CSFV) into the Netherlands. Only 128 scenario calculations were needed to specify the final meta-model. A consecutive one-at-a-time sensitivity analysis was performed with the main effects of this meta-model to explore their impact on the ranking of risk factors contributing most to the annual P(CSFV). The results indicated that model outcome is most sensitive to the uncertain input parameters concerning the expected number of classical swine fever epidemics in Germany, Belgium, and the United Kingdom and the probability that CSFV survives in an empty livestock truck traveling over a distance of 0-900 km.

Animals↗