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Model Sensitivity (model + sensitivity)
Selected AbstractsModeling and design of vapor-phase biofiltration for chlorinated volatile organic compoundsAICHE JOURNAL, Issue 9 2002Walter Den A mathematical model was developed for biofilter design and performance prediction with reference to the purification of contaminated gas streams. The model incorporated important aspects such as mass transfer, biodegradation, and adsorption processes. A systematic modeling protocol incorporated the development of a scale-up strategy based on dimensional analysis and similitude. Trichloroethylene (TCE) was employed as the model contaminant for biofiltration testing and model verification. The biokinetic and adsorption parameters for the contaminant were determined independently from a series of minibiofilter and miniadsorber column experiments, specifically designed to simulate the actual biofilter operational regimes in a miniature scale. Bench-scale biofilter experiments employing granular activated carbon columns indicated the good predictive capability of the model for the removal of TCE. Dynamic simulation studies were performed to assess the transient- and steady-state behavior of the model under various operating conditions. Model sensitivity was studied to evaluate the influence of adsorption equilibrium, transport and biological parameters on the biofilter dynamics. The results demonstrated that the biofilter performance was greatly influenced by the Monod coefficients and the biofilm thickness. [source] Joint full-waveform analysis of off-ground zero-offset ground penetrating radar and electromagnetic induction synthetic data for estimating soil electrical propertiesGEOPHYSICAL JOURNAL INTERNATIONAL, Issue 3 2010D. Moghadas SUMMARY A joint analysis of full-waveform information content in ground penetrating radar (GPR) and electromagnetic induction (EMI) synthetic data was investigated to reconstruct the electrical properties of multilayered media. The GPR and EMI systems operate in zero-offset, off-ground mode and are designed using vector network analyser technology. The inverse problem is formulated in the least-squares sense. We compared four approaches for GPR and EMI data fusion. The two first techniques consisted of defining a single objective function, applying different weighting methods. As a first approach, we weighted the EMI and GPR data using the inverse of the data variance. The ideal point method was also employed as a second weighting scenario. The third approach is the naive Bayesian method and the fourth technique corresponds to GPR,EMI and EMI,GPR sequential inversions. Synthetic GPR and EMI data were generated for the particular case of a two-layered medium. Analysis of the objective function response surfaces from the two first approaches demonstrated the benefit of combining the two sources of information. However, due to the variations of the GPR and EMI model sensitivities with respect to the medium electrical properties, the formulation of an optimal objective function based on the weighting methods is not straightforward. While the Bayesian method relies on assumptions with respect to the statistical distribution of the parameters, it may constitute a relevant alternative for GPR and EMI data fusion. Sequential inversions of different configurations for a two layered medium show that in the case of high conductivity or permittivity for the first layer, the inversion scheme can not fully retrieve the soil hydrogeophysical parameters. But in the case of low permittivity and conductivity for the first layer, GPR,EMI inversion provides proper estimation of values compared to the EMI,GPR inversion. [source] Evaluating and expressing the propagation of uncertainty in chemical fate and bioaccumulation modelsENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 4 2002Matthew MacLeod Abstract First-order analytical sensitivity and uncertainty analysis for environmental chemical fate models is described and applied to a regional contaminant fate model and a food web bioaccumulation model. By assuming linear relationships between inputs and outputs, independence, and log-normal distributions of input variables, a relationship between uncertainty in input parameters and uncertainty in output parameters can be derived, yielding results that are consistent with a Monte Carlo analysis with similar input assumptions. A graphical technique is devised for interpreting and communicating uncertainty propagation as a function of variance in input parameters and model sensitivity. The suggested approach is less calculationally intensive than Monte Carlo analysis and is appropriate for preliminary assessment of uncertainty when models are applied to generic environments or to large geographic areas or when detailed parameterization of input uncertainties is unwarranted or impossible. This approach is particularly useful as a starting point for identification of sensitive model inputs at the early stages of applying a generic contaminant fate model to a specific environmental scenario, as a tool to support refinements of the model and the uncertainty analysis for site-specific scenarios, or for examining defined end points. The analysis identifies those input parameters that contribute significantly to uncertainty in outputs, enabling attention to be focused on defining median values and more appropriate distributions to describe these variables. [source] Time-distributed effect of exposure and infectious outbreaksENVIRONMETRICS, Issue 3 2009Elena N. Naumova Abstract Extreme weather affects the timing and intensity of infectious outbreaks, the resurgence and redistribution of infections, and it causes disturbances in human-environment interactions. Environmental stressors with high thermoregulatory demands require susceptible populations to undergo physiological adaptive processes potentially compromising immune function and increasing susceptibility to infection. In assessing associations between environmental exposures and infectious diseases, failure to account for a latent period between time of exposure and time of disease manifestation may lead to severe underestimation of the effects. In a population, health effects of an episode of exposure are distributed over a range of time lags. To consider such time-distributed lags is a challenging task given that the length of a latent period varies from hours to months and depends on the type of pathogen, individual susceptibility to the pathogen, dose of exposure, route of transmission, and many other factors. The two main objectives of this communication are to introduce an approach to modeling time-distributed effect of exposures to infection cases and to demonstrate this approach in an analysis of the association between high ambient temperature and daily incidence of enterically transmitted infections. The study is supplemented with extensive simulations to examine model sensitivity to response magnitude, exposure frequency, and extent of latent period. Copyright © 2008 John Wiley & Sons, Ltd. [source] MODFLOW 2000 Head Uncertainty, a First-Order Second Moment MethodGROUND WATER, Issue 3 2003Harry S. Glasgow A computationally efficient method to estimate the variance and covariance in piezometric head results computed through MODFLOW 2000 using a first-order second moment (FOSM) approach is presented. This methodology employs a first-order Taylor series expansion to combine model sensitivity with uncertainty in geologic data. MOD-FLOW 2000 is used to calculate both the ground water head and the sensitivity of head to changes in input data. From a limited number of samples, geologic data are extrapolated and their associated uncertainties are computed through a conditional probability calculation. Combining the spatially related sensitivity and input uncertainty produces the variance-covariance matrix, the diagonal of which is used to yield the standard deviation in MODFLOW 2000 head. The variance in piezometric head can be used for calibrating the model, estimating confidence intervals, directing exploration, and evaluating the reliability of a design. A case study illustrates the approach, where aquifer transmis-sivity is the spatially related uncertain geologic input data. The FOSM methodology is shown to be applicable for calculating output uncertainty for (1) spatially related input and output data, and (2) multiple input parameters (trans-missivity and recharge). [source] The Impact of Damage Caps on Malpractice Claims: Randomization Inference with Difference-in-DifferencesJOURNAL OF EMPIRICAL LEGAL STUDIES, Issue 1 2007John J. Donohue III We use differences-in-differences (DID) to assess the impact of damage caps on medical malpractice claims for states adopting caps between 1991,2004. We find that conventional DID estimators exhibit acute model sensitivity. As a solution, we offer (nonparametric) covariance-adjusted randomization inference, which incorporates information about cap adoption more directly and reduces model sensitivity. We find no evidence that caps affect the number of malpractice claims against physicians. [source] Adiponectin is independently associated with insulin sensitivity in women with polycystic ovary syndromeCLINICAL ENDOCRINOLOGY, Issue 6 2004Joachim Spranger Summary objective, The polycystic ovary syndrome (PCOS) is associated with obesity and insulin resistance predisposing to diabetes mellitus type 2 and atherosclerosis. Adiponectin is a recently discovered adipocytokine with insulin-sensitizing and putative antiatherosclerotic properties. The aim of the study was to elucidate determinants of circulating adiponectin levels and to investigate the potential role of adiponectin in insulin resistance in PCOS women. patients and measurements, Plasma adiponectin and parameters of obesity, insulin resistance and hyperandrogenism were measured In 62 women with PCOS and in 35 healthy female controls. results, Both in PCOS and controls, adiponectin levels were lower in overweight or obese women than in normal-weight women, without any difference between PCOS and controls after adjustment for body mass index (BMI). In PCOS and in controls there was a significant correlation of adiponectin with BMI (r = ,0·516, P < 0·001), fasting insulin (r = ,0·404, P < 0·001), homeostasis model sensitivity (HOMA %S) (r = ,0·424, P < 0·001) and testosterone (r = ,0·279, P < 0·01), but no correlation with androstenedione (r = ,0·112, P = 0·325), 17-OH-progesterone (r =,0·031, P = 0·784) or the LH/FSH ratio (r =,0·033, P = 0·753). Multiple linear regression analysis revealed that BMI and HOMA %S but not testosterone were independently associated with adiponectin plasma levels, explaining 16% (BMI) and 13% (HOMA %S) of the variability of adiponectin, respectively. In PCOS patients insulin sensitivity, as indicated by continuous infusion of glucose with model assessment (CIGMA %S) was significantly correlated with adiponectin (r = 0·55; P < 0·001), BMI (r =,0·575; P < 0·001), waist-to-hip ratio (WHR) (r =,0·48; P = 0·001), body fat mass assessed by dual-energy X-ray-absorptiometry (DEXA) [Dexa-fat (total) (r = ,0·61; P < 0·001) and Dexa-fat (trunk) (r = ,0·59; P < 0·001)] and with testosterone (r = ,0·42; P = 0·001). Multiple linear regression analysis demonstrated that markers of obesity such as BMI, total or truncal fat mass, age and adiponectin were independently associated with CIGMA %S, and that circulating adiponectin accounted for about 18% of the degree of insulin resistance in PCOS. By contrast, testosterone was not a significant factor, suggesting that PCOS per se did not affect insulin sensitivity independent from obesity, age and adiponectin. Metformin treatment for 6 months in insulin-resistant PCOS women (n = 9) had no effect on plasma adiponectin (P = 0·59) despite significant loss of weight and fat mass and improvement in hyperandrogenaemia. conclusions, PCOS per se is not associated with decreased levels of plasma adiponectin. However, circulating adiponectin is independently associated with the degree of insulin resistance in PCOS women and may contribute to the development and/or maintenance of insulin resistance independent from adiposity. [source] Modelling rainfall interception loss in forest restoration trials in PanamaECOHYDROLOGY, Issue 3 2010Darryl E. Carlyle-Moses Abstract A modified Liu analytical model of rainfall interception (Ic) by tree canopies was evaluated using rainfall, throughfall and stemflow data collected from forest restoration trials in the Republic of Panama. The model uses an introduced approach to estimating the water storage capacities of tree boles, which has a more realistic physical basis than earlier iterations of the Liu model. Study species (Acacia mangium, Gliricidia sepium, Guazuma ulmifolia, Ochroma pyramidale, and Pachira quinata) were selected on the basis of differing leaf size and crown characteristics. Significant interspecific differences in both observed and simulated cumulative interception loss were found, with A. mangium intercepting more rainfall than other species. Errors between calculated and modelled cumulative Ic ranged from + 6·3% to + 30·5%, with modelled Ic always being the larger term. During-event evaporation rates from the study trees were positively related to tree height, crown area, and basal diameter. Crown area and the storage capacity of tree boles were negatively correlated. The results of a sensitivity analysis suggested that the modified model was most sensitive to variations in during-event evaporation rate. The implications of the model's sensitivity to during-event evaporation and the importance of this mechanism of interception loss are discussed, while suggestions are provided that may lead to further improvements to the analytical model. Copyright © 2010 John Wiley & Sons, Ltd. [source] A mechanistic model of the enzymatic hydrolysis of celluloseBIOTECHNOLOGY & BIOENGINEERING, Issue 1 2010Seth E. Levine Abstract A detailed mechanistic model of enzymatic cellulose hydrolysis has been developed. The behavior of individual cellulase enzymes and parameters describing the cellulose surface properties are included. Results obtained for individual enzymes (T. reesei endoglucanase 2 and cellobiohydrolase I) and systems with both enzymes present are compared with experimental literature data. The model was sensitive to cellulase-accessible surface area; the EG2,CBHI synergy observed experimentally was only predicted at a sufficiently high cellulose surface area. Enzyme crowding, which is more apparent at low surface areas, resulted in differences between predicted and experimental rates of hydrolysis. Model predictions also indicated that the observed decrease in hydrolysis rates following the initial rate of rapid hydrolysis is not solely caused by product inhibition and/or thermal deactivation. Surface heterogeneities, which are not accounted for in this work, may play a role in decreasing the hydrolysis rate. The importance of separating the enzyme adsorption and complexation steps is illustrated by the model's sensitivity to the rate of formation of enzyme,substrate complexes on the cellulose surface. Biotechnol. Bioeng. 2010;107: 37,51. © 2010 Wiley Periodicals, Inc. [source] | |||||||||||||