Hydrological model parameterization using NDVI values to account for the effects of land-cover change on the rainfall-runoff response

USDA-ARS?s Scientific Manuscript database

Classic rainfall-runoff models usually use historical data to estimate model parameters and mean values of parameters are considered for predictions. However, due to climate changes and human effects, the parameters of model change temporally. To overcome this problem, Normalized Difference Vegetati...

A framework for streamflow prediction in the world's most severely data-limited regions: Test of applicability and performance in a poorly-gauged region of China

NASA Astrophysics Data System (ADS)

Alipour, M. H.; Kibler, Kelly M.

2018-02-01

A framework methodology is proposed for streamflow prediction in poorly-gauged rivers located within large-scale regions of sparse hydrometeorologic observation. A multi-criteria model evaluation is developed to select models that balance runoff efficiency with selection of accurate parameter values. Sparse observed data are supplemented by uncertain or low-resolution information, incorporated as 'soft' data, to estimate parameter values a priori. Model performance is tested in two catchments within a data-poor region of southwestern China, and results are compared to models selected using alternative calibration methods. While all models perform consistently with respect to runoff efficiency (NSE range of 0.67-0.78), models selected using the proposed multi-objective method may incorporate more representative parameter values than those selected by traditional calibration. Notably, parameter values estimated by the proposed method resonate with direct estimates of catchment subsurface storage capacity (parameter residuals of 20 and 61 mm for maximum soil moisture capacity (Cmax), and 0.91 and 0.48 for soil moisture distribution shape factor (B); where a parameter residual is equal to the centroid of a soft parameter value minus the calibrated parameter value). A model more traditionally calibrated to observed data only (single-objective model) estimates a much lower soil moisture capacity (residuals of Cmax = 475 and 518 mm and B = 1.24 and 0.7). A constrained single-objective model also underestimates maximum soil moisture capacity relative to a priori estimates (residuals of Cmax = 246 and 289 mm). The proposed method may allow managers to more confidently transfer calibrated models to ungauged catchments for streamflow predictions, even in the world's most data-limited regions.

Constraints on a generalized deceleration parameter from cosmic chronometers

NASA Astrophysics Data System (ADS)

Mamon, Abdulla Al

2018-04-01

In this paper, we have proposed a generalized parametrization for the deceleration parameter q in order to study the evolutionary history of the universe. We have shown that the proposed model can reproduce three well known q-parametrized models for some specific values of the model parameter α. We have used the latest compilation of the Hubble parameter measurements obtained from the cosmic chronometer (CC) method (in combination with the local value of the Hubble constant H0) and the Type Ia supernova (SNIa) data to place constraints on the parameters of the model for different values of α. We have found that the resulting constraints on the deceleration parameter and the dark energy equation of state support the ΛCDM model within 1σ confidence level at the present epoch.

A trade-off solution between model resolution and covariance in surface-wave inversion

USGS Publications Warehouse

Xia, J.; Xu, Y.; Miller, R.D.; Zeng, C.

2010-01-01

Regularization is necessary for inversion of ill-posed geophysical problems. Appraisal of inverse models is essential for meaningful interpretation of these models. Because uncertainties are associated with regularization parameters, extra conditions are usually required to determine proper parameters for assessing inverse models. Commonly used techniques for assessment of a geophysical inverse model derived (generally iteratively) from a linear system are based on calculating the model resolution and the model covariance matrices. Because the model resolution and the model covariance matrices of the regularized solutions are controlled by the regularization parameter, direct assessment of inverse models using only the covariance matrix may provide incorrect results. To assess an inverted model, we use the concept of a trade-off between model resolution and covariance to find a proper regularization parameter with singular values calculated in the last iteration. We plot the singular values from large to small to form a singular value plot. A proper regularization parameter is normally the first singular value that approaches zero in the plot. With this regularization parameter, we obtain a trade-off solution between model resolution and model covariance in the vicinity of a regularized solution. The unit covariance matrix can then be used to calculate error bars of the inverse model at a resolution level determined by the regularization parameter. We demonstrate this approach with both synthetic and real surface-wave data. ?? 2010 Birkh??user / Springer Basel AG.

Modification of a rainfall-runoff model for distributed modeling in a GIS and its validation

NASA Astrophysics Data System (ADS)

Nyabeze, W. R.

A rainfall-runoff model, which can be inter-faced with a Geographical Information System (GIS) to integrate definition, measurement, calculating parameter values for spatial features, presents considerable advantages. The modification of the GWBasic Wits Rainfall-Runoff Erosion Model (GWBRafler) to enable parameter value estimation in a GIS (GISRafler) is presented in this paper. Algorithms are applied to estimate parameter values reducing the number of input parameters and the effort to populate them. The use of a GIS makes the relationship between parameter estimates and cover characteristics more evident. This paper has been produced as part of research to generalize the GWBRafler on a spatially distributed basis. Modular data structures are assumed and parameter values are weighted relative to the module area and centroid properties. Modifications to the GWBRafler enable better estimation of low flows, which are typical in drought conditions.

Evaluation, Calibration and Comparison of the Precipitation-Runoff Modeling System (PRMS) National Hydrologic Model (NHM) Using Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) Gridded Datasets

NASA Astrophysics Data System (ADS)

Norton, P. A., II; Haj, A. E., Jr.

2014-12-01

The United States Geological Survey is currently developing a National Hydrologic Model (NHM) to support and facilitate coordinated and consistent hydrologic modeling efforts at the scale of the continental United States. As part of this effort, the Geospatial Fabric (GF) for the NHM was created. The GF is a database that contains parameters derived from datasets that characterize the physical features of watersheds. The GF was used to aggregate catchments and flowlines defined in the National Hydrography Dataset Plus dataset for more than 100,000 hydrologic response units (HRUs), and to establish initial parameter values for input to the Precipitation-Runoff Modeling System (PRMS). Many parameter values are adjusted in PRMS using an automated calibration process. Using these adjusted parameter values, the PRMS model estimated variables such as evapotranspiration (ET), potential evapotranspiration (PET), snow-covered area (SCA), and snow water equivalent (SWE). In order to evaluate the effectiveness of parameter calibration, and model performance in general, several satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) gridded datasets including ET, PET, SCA, and SWE were compared to PRMS-simulated values. The MODIS and SNODAS data were spatially averaged for each HRU, and compared to PRMS-simulated ET, PET, SCA, and SWE values for each HRU in the Upper Missouri River watershed. Default initial GF parameter values and PRMS calibration ranges were evaluated. Evaluation results, and the use of MODIS and SNODAS datasets to update GF parameter values and PRMS calibration ranges, are presented and discussed.

The dimensional reduction method for identification of parameters that trade-off due to similar model roles.

PubMed

Davidson, Shaun M; Docherty, Paul D; Murray, Rua

2017-03-01

Parameter identification is an important and widely used process across the field of biomedical engineering. However, it is susceptible to a number of potential difficulties, such as parameter trade-off, causing premature convergence at non-optimal parameter values. The proposed Dimensional Reduction Method (DRM) addresses this issue by iteratively reducing the dimension of hyperplanes where trade off occurs, and running subsequent identification processes within these hyperplanes. The DRM was validated using clinical data to optimize 4 parameters of the widely used Bergman Minimal Model of glucose and insulin kinetics, as well as in-silico data to optimize 5 parameters of the Pulmonary Recruitment (PR) Model. Results were compared with the popular Levenberg-Marquardt (LMQ) Algorithm using a Monte-Carlo methodology, with both methods afforded equivalent computational resources. The DRM converged to a lower or equal residual value in all tests run using the Bergman Minimal Model and actual patient data. For the PR model, the DRM attained significantly lower overall median parameter error values and lower residuals in the vast majority of tests. This shows the DRM has potential to provide better resolution of optimum parameter values for the variety of biomedical models in which significant levels of parameter trade-off occur. Copyright © 2017 Elsevier Inc. All rights reserved.

NWP model forecast skill optimization via closure parameter variations

NASA Astrophysics Data System (ADS)

Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.

2012-04-01

We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.

Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method

DOE PAGES

Liu, Y.; Liu, Z.; Zhang, S.; ...

2014-05-29

Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. And for a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. An adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final globalmore » uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.« less

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

2011-12-01

This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

Scalable Online Network Modeling and Simulation

DTIC Science & Technology

2005-08-01

ONLINE NETWORK MODELING AND SIMULATION 6. AUTHOR(S) Boleslaw Szymanski , Shivkumar Kalyanaraman, Biplab Sikdar and Christopher Carothers 5...performance for a wide range of parameter values (parameter sensitivity), understanding of protocol stability and dynamics, and studying feature ...a wide range of parameter values (parameter sensitivity), understanding of protocol stability and dynamics, and studying feature interactions

Modeling polyvinyl chloride Plasma Modification by Neural Networks

NASA Astrophysics Data System (ADS)

Wang, Changquan

2018-03-01

Neural networks model were constructed to analyze the connection between dielectric barrier discharge parameters and surface properties of material. The experiment data were generated from polyvinyl chloride plasma modification by using uniform design. Discharge voltage, discharge gas gap and treatment time were as neural network input layer parameters. The measured values of contact angle were as the output layer parameters. A nonlinear mathematical model of the surface modification for polyvinyl chloride was developed based upon the neural networks. The optimum model parameters were obtained by the simulation evaluation and error analysis. The results of the optimal model show that the predicted value is very close to the actual test value. The prediction model obtained here are useful for discharge plasma surface modification analysis.

Thermodynamically consistent model calibration in chemical kinetics

PubMed Central

2011-01-01

Background The dynamics of biochemical reaction systems are constrained by the fundamental laws of thermodynamics, which impose well-defined relationships among the reaction rate constants characterizing these systems. Constructing biochemical reaction systems from experimental observations often leads to parameter values that do not satisfy the necessary thermodynamic constraints. This can result in models that are not physically realizable and may lead to inaccurate, or even erroneous, descriptions of cellular function. Results We introduce a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. By calculating thermodynamically feasible values for the kinetic parameters of a well-known model of the EGF/ERK signaling cascade, we demonstrate the qualitative and quantitative significance of imposing thermodynamic constraints on these parameters and the effectiveness of our method for accomplishing this important task. MATLAB software, using the Systems Biology Toolbox 2.1, can be accessed from http://www.cis.jhu.edu/~goutsias/CSS lab/software.html. An SBML file containing the thermodynamically feasible EGF/ERK signaling cascade model can be found in the BioModels database. Conclusions TCMC is a simple and flexible method for obtaining physically plausible values for the kinetic parameters of open biochemical reaction systems. It can be effectively used to recalculate a thermodynamically consistent set of parameter values for existing thermodynamically infeasible biochemical reaction models of cellular function as well as to estimate thermodynamically feasible values for the parameters of new models. Furthermore, TCMC can provide dimensionality reduction, better estimation performance, and lower computational complexity, and can help to alleviate the problem of data overfitting. PMID:21548948

Cognitive models of risky choice: parameter stability and predictive accuracy of prospect theory.

PubMed

Glöckner, Andreas; Pachur, Thorsten

2012-04-01

In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are individual differences as measured by model parameters stable enough to improve the ability to predict behavior as compared to modeling without adjustable parameters? We examined this issue in cumulative prospect theory (CPT), arguably the most widely used framework to model decisions under risk. Specifically, we examined (a) the temporal stability of CPT's parameters; and (b) how well different implementations of CPT, varying in the number of adjustable parameters, predict individual choice relative to models with no adjustable parameters (such as CPT with fixed parameters, expected value theory, and various heuristics). We presented participants with risky choice problems and fitted CPT to each individual's choices in two separate sessions (which were 1 week apart). All parameters were correlated across time, in particular when using a simple implementation of CPT. CPT allowing for individual variability in parameter values predicted individual choice better than CPT with fixed parameters, expected value theory, and the heuristics. CPT's parameters thus seem to pick up stable individual differences that need to be considered when predicting risky choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Sensitivity of NTCP parameter values against a change of dose calculation algorithm.

PubMed

Brink, Carsten; Berg, Martin; Nielsen, Morten

2007-09-01

Optimization of radiation treatment planning requires estimations of the normal tissue complication probability (NTCP). A number of models exist that estimate NTCP from a calculated dose distribution. Since different dose calculation algorithms use different approximations the dose distributions predicted for a given treatment will in general depend on the algorithm. The purpose of this work is to test whether the optimal NTCP parameter values change significantly when the dose calculation algorithm is changed. The treatment plans for 17 breast cancer patients have retrospectively been recalculated with a collapsed cone algorithm (CC) to compare the NTCP estimates for radiation pneumonitis with those obtained from the clinically used pencil beam algorithm (PB). For the PB calculations the NTCP parameters were taken from previously published values for three different models. For the CC calculations the parameters were fitted to give the same NTCP as for the PB calculations. This paper demonstrates that significant shifts of the NTCP parameter values are observed for three models, comparable in magnitude to the uncertainties of the published parameter values. Thus, it is important to quote the applied dose calculation algorithm when reporting estimates of NTCP parameters in order to ensure correct use of the models.

Sensitivity of NTCP parameter values against a change of dose calculation algorithm

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

Brink, Carsten; Berg, Martin; Nielsen, Morten

2007-09-15

Optimization of radiation treatment planning requires estimations of the normal tissue complication probability (NTCP). A number of models exist that estimate NTCP from a calculated dose distribution. Since different dose calculation algorithms use different approximations the dose distributions predicted for a given treatment will in general depend on the algorithm. The purpose of this work is to test whether the optimal NTCP parameter values change significantly when the dose calculation algorithm is changed. The treatment plans for 17 breast cancer patients have retrospectively been recalculated with a collapsed cone algorithm (CC) to compare the NTCP estimates for radiation pneumonitis withmore » those obtained from the clinically used pencil beam algorithm (PB). For the PB calculations the NTCP parameters were taken from previously published values for three different models. For the CC calculations the parameters were fitted to give the same NTCP as for the PB calculations. This paper demonstrates that significant shifts of the NTCP parameter values are observed for three models, comparable in magnitude to the uncertainties of the published parameter values. Thus, it is important to quote the applied dose calculation algorithm when reporting estimates of NTCP parameters in order to ensure correct use of the models.« less

The alfa and beta of tumours: a review of parameters of the linear-quadratic model, derived from clinical radiotherapy studies.

PubMed

van Leeuwen, C M; Oei, A L; Crezee, J; Bel, A; Franken, N A P; Stalpers, L J A; Kok, H P

2018-05-16

Prediction of radiobiological response is a major challenge in radiotherapy. Of several radiobiological models, the linear-quadratic (LQ) model has been best validated by experimental and clinical data. Clinically, the LQ model is mainly used to estimate equivalent radiotherapy schedules (e.g. calculate the equivalent dose in 2 Gy fractions, EQD 2 ), but increasingly also to predict tumour control probability (TCP) and normal tissue complication probability (NTCP) using logistic models. The selection of accurate LQ parameters α, β and α/β is pivotal for a reliable estimate of radiation response. The aim of this review is to provide an overview of published values for the LQ parameters of human tumours as a guideline for radiation oncologists and radiation researchers to select appropriate radiobiological parameter values for LQ modelling in clinical radiotherapy. We performed a systematic literature search and found sixty-four clinical studies reporting α, β and α/β for tumours. Tumour site, histology, stage, number of patients, type of LQ model, radiation type, TCP model, clinical endpoint and radiobiological parameter estimates were extracted. Next, we stratified by tumour site and by tumour histology. Study heterogeneity was expressed by the I 2 statistic, i.e. the percentage of variance in reported values not explained by chance. A large heterogeneity in LQ parameters was found within and between studies (I 2  > 75%). For the same tumour site, differences in histology partially explain differences in the LQ parameters: epithelial tumours have higher α/β values than adenocarcinomas. For tumour sites with different histologies, such as in oesophageal cancer, the α/β estimates correlate well with histology. However, many other factors contribute to the study heterogeneity of LQ parameters, e.g. tumour stage, type of LQ model, TCP model and clinical endpoint (i.e. survival, tumour control and biochemical control). The value of LQ parameters for tumours as published in clinical radiotherapy studies depends on many clinical and methodological factors. Therefore, for clinical use of the LQ model, LQ parameters for tumour should be selected carefully, based on tumour site, histology and the applied LQ model. To account for uncertainties in LQ parameter estimates, exploring a range of values is recommended.

AQUATOX Data Sources Documents

EPA Pesticide Factsheets

Contains the data sources for parameter values of the AQUATOX model including: a bibliography for the AQUATOX data libraries and the compendia of parameter values for US Army Corps of Engineers models.

Outdoor ground impedance models.

PubMed

Attenborough, Keith; Bashir, Imran; Taherzadeh, Shahram

2011-05-01

Many models for the acoustical properties of rigid-porous media require knowledge of parameter values that are not available for outdoor ground surfaces. The relationship used between tortuosity and porosity for stacked spheres results in five characteristic impedance models that require not more than two adjustable parameters. These models and hard-backed-layer versions are considered further through numerical fitting of 42 short range level difference spectra measured over various ground surfaces. For all but eight sites, slit-pore, phenomenological and variable porosity models yield lower fitting errors than those given by the widely used one-parameter semi-empirical model. Data for 12 of 26 grassland sites and for three beech wood sites are fitted better by hard-backed-layer models. Parameter values obtained by fitting slit-pore and phenomenological models to data for relatively low flow resistivity grounds, such as forest floors, porous asphalt, and gravel, are consistent with values that have been obtained non-acoustically. Three impedance models yield reasonable fits to a narrow band excess attenuation spectrum measured at short range over railway ballast but, if extended reaction is taken into account, the hard-backed-layer version of the slit-pore model gives the most reasonable parameter values.

Development of model for prediction of Leachate Pollution Index (LPI) in absence of leachate parameters.

PubMed

Lothe, Anjali G; Sinha, Alok

2017-05-01

Leachate pollution index (LPI) is an environmental index which quantifies the pollution potential of leachate generated in landfill site. Calculation of Leachate pollution index (LPI) is based on concentration of 18 parameters present in leachate. However, in case of non-availability of all 18 parameters evaluation of actual values of LPI becomes difficult. In this study, a model has been developed to predict the actual values of LPI in case of partial availability of parameters. This model generates eleven equations that helps in determination of upper and lower limit of LPI. The geometric mean of these two values results in LPI value. Application of this model to three landfill site results in LPI value with an error of ±20% for ∑ i n w i ⩾0.6. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Probabilistic Approach to Model Update

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Voracek, David F.

2001-01-01

Finite element models are often developed for load validation, structural certification, response predictions, and to study alternate design concepts. In rare occasions, models developed with a nominal set of parameters agree with experimental data without the need to update parameter values. Today, model updating is generally heuristic and often performed by a skilled analyst with in-depth understanding of the model assumptions. Parameter uncertainties play a key role in understanding the model update problem and therefore probabilistic analysis tools, developed for reliability and risk analysis, may be used to incorporate uncertainty in the analysis. In this work, probability analysis (PA) tools are used to aid the parameter update task using experimental data and some basic knowledge of potential error sources. Discussed here is the first application of PA tools to update parameters of a finite element model for a composite wing structure. Static deflection data at six locations are used to update five parameters. It is shown that while prediction of individual response values may not be matched identically, the system response is significantly improved with moderate changes in parameter values.

State and Parameter Estimation for a Coupled Ocean--Atmosphere Model

NASA Astrophysics Data System (ADS)

Ghil, M.; Kondrashov, D.; Sun, C.

2006-12-01

The El-Nino/Southern-Oscillation (ENSO) dominates interannual climate variability and plays, therefore, a key role in seasonal-to-interannual prediction. Much is known by now about the main physical mechanisms that give rise to and modulate ENSO, but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean--atmosphere model of ENSO. The coupled model consists of an upper-ocean, reduced-gravity model of the Tropical Pacific and a steady-state atmospheric response to the sea surface temperature (SST). The model errors are assumed to be mainly in the atmospheric wind stress, and assimilated data are equatorial Pacific SSTs. Model behavior is very sensitive to two key parameters: (i) μ, the ocean-atmosphere coupling coefficient between SST and wind stress anomalies; and (ii) δs, the surface-layer coefficient. Previous work has shown that δs determines the period of the model's self-sustained oscillation, while μ measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Estimation of these parameters is tested first on synthetic data and allows us to recover the delayed-oscillator mode starting from model parameter values that correspond to the westward-propagating case. Assimilation of SST data from the NCEP-NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean--atmosphere GCMs will be discussed.

Image Discrimination Models With Stochastic Channel Selection

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Beard, Bettina L.; Null, Cynthia H. (Technical Monitor)

1995-01-01

Many models of human image processing feature a large fixed number of channels representing cortical units varying in spatial position (visual field direction and eccentricity) and spatial frequency (radial frequency and orientation). The values of these parameters are usually sampled at fixed values selected to ensure adequate overlap considering the bandwidth and/or spread parameters, which are usually fixed. Even high levels of overlap does not always ensure that the performance of the model will vary smoothly with image translation or scale changes. Physiological measurements of bandwidth and/or spread parameters result in a broad distribution of estimated parameter values and the prediction of some psychophysical results are facilitated by the assumption that these parameters also take on a range of values. Selecting a sample of channels from a continuum of channels rather than using a fixed set can make model performance vary smoothly with changes in image position, scale, and orientation. It also facilitates the addition of spatial inhomogeneity, nonlinear feature channels, and focus of attention to channel models.

Calibration of infiltration parameters on hydrological tank model using runoff coefficient of rational method

NASA Astrophysics Data System (ADS)

Suryoputro, Nugroho; Suhardjono, Soetopo, Widandi; Suhartanto, Ery

2017-09-01

In calibrating hydrological models, there are generally two stages of activity: 1) determining realistic model initial parameters in representing natural component physical processes, 2) entering initial parameter values which are then processed by trial error or automatically to obtain optimal values. To determine a realistic initial value, it takes experience and user knowledge of the model. This is a problem for beginner model users. This paper will present another approach to estimate the infiltration parameters in the tank model. The parameters will be approximated by the runoff coefficient of rational method. The value approach of infiltration parameter is simply described as the result of the difference in the percentage of total rainfall minus the percentage of runoff. It is expected that the results of this research will accelerate the calibration process of tank model parameters. The research was conducted on the sub-watershed Kali Bango in Malang Regency with an area of 239,71 km2. Infiltration measurements were carried out in January 2017 to March 2017. Analysis of soil samples at Soil Physics Laboratory, Department of Soil Science, Faculty of Agriculture, Universitas Brawijaya. Rainfall and discharge data were obtained from UPT PSAWS Bango Gedangan in Malang. Temperature, evaporation, relative humidity, wind speed data was obtained from BMKG station of Karang Ploso, Malang. The results showed that the infiltration coefficient at the top tank outlet can be determined its initial value by using the approach of the coefficient of runoff rational method with good result.

Estimation of soil hydraulic properties with microwave techniques

NASA Technical Reports Server (NTRS)

Oneill, P. E.; Gurney, R. J.; Camillo, P. J.

1985-01-01

Useful quantitative information about soil properties may be obtained by calibrating energy and moisture balance models with remotely sensed data. A soil physics model solves heat and moisture flux equations in the soil profile and is driven by the surface energy balance. Model generated surface temperature and soil moisture and temperature profiles are then used in a microwave emission model to predict the soil brightness temperature. The model hydraulic parameters are varied until the predicted temperatures agree with the remotely sensed values. This method is used to estimate values for saturated hydraulic conductivity, saturated matrix potential, and a soil texture parameter. The conductivity agreed well with a value measured with an infiltration ring and the other parameters agreed with values in the literature.

An Extreme-Value Approach to Anomaly Vulnerability Identification

NASA Technical Reports Server (NTRS)

Everett, Chris; Maggio, Gaspare; Groen, Frank

2010-01-01

The objective of this paper is to present a method for importance analysis in parametric probabilistic modeling where the result of interest is the identification of potential engineering vulnerabilities associated with postulated anomalies in system behavior. In the context of Accident Precursor Analysis (APA), under which this method has been developed, these vulnerabilities, designated as anomaly vulnerabilities, are conditions that produce high risk in the presence of anomalous system behavior. The method defines a parameter-specific Parameter Vulnerability Importance measure (PVI), which identifies anomaly risk-model parameter values that indicate the potential presence of anomaly vulnerabilities, and allows them to be prioritized for further investigation. This entails analyzing each uncertain risk-model parameter over its credible range of values to determine where it produces the maximum risk. A parameter that produces high system risk for a particular range of values suggests that the system is vulnerable to the modeled anomalous conditions, if indeed the true parameter value lies in that range. Thus, PVI analysis provides a means of identifying and prioritizing anomaly-related engineering issues that at the very least warrant improved understanding to reduce uncertainty, such that true vulnerabilities may be identified and proper corrective actions taken.

Quantitative Diagnosis of Continuous-Valued, Stead-State Systems

NASA Technical Reports Server (NTRS)

Rouquette, N.

1995-01-01

Quantitative diagnosis involves numerically estimating the values of unobservable parameters that best explain the observed parameter values. We consider quantitative diagnosis for continuous, lumped- parameter, steady-state physical systems because such models are easy to construct and the diagnosis problem is considerably simpler than that for corresponding dynamic models. To further tackle the difficulties of numerically inverting a simulation model to compute a diagnosis, we propose to decompose a physical system model in terms of feedback loops. This decomposition reduces the dimension of the problem and consequently decreases the diagnosis search space. We illustrate this approach on a model of thermal control system studied in earlier research.

Development of genetic algorithm-based optimization module in WHAT system for hydrograph analysis and model application

NASA Astrophysics Data System (ADS)

Lim, Kyoung Jae; Park, Youn Shik; Kim, Jonggun; Shin, Yong-Chul; Kim, Nam Won; Kim, Seong Joon; Jeon, Ji-Hong; Engel, Bernard A.

2010-07-01

Many hydrologic and water quality computer models have been developed and applied to assess hydrologic and water quality impacts of land use changes. These models are typically calibrated and validated prior to their application. The Long-Term Hydrologic Impact Assessment (L-THIA) model was applied to the Little Eagle Creek (LEC) watershed and compared with the filtered direct runoff using BFLOW and the Eckhardt digital filter (with a default BFI max value of 0.80 and filter parameter value of 0.98), both available in the Web GIS-based Hydrograph Analysis Tool, called WHAT. The R2 value and the Nash-Sutcliffe coefficient values were 0.68 and 0.64 with BFLOW, and 0.66 and 0.63 with the Eckhardt digital filter. Although these results indicate that the L-THIA model estimates direct runoff reasonably well, the filtered direct runoff values using BFLOW and Eckhardt digital filter with the default BFI max and filter parameter values do not reflect hydrological and hydrogeological situations in the LEC watershed. Thus, a BFI max GA-Analyzer module (BFI max Genetic Algorithm-Analyzer module) was developed and integrated into the WHAT system for determination of the optimum BFI max parameter and filter parameter of the Eckhardt digital filter. With the automated recession curve analysis method and BFI max GA-Analyzer module of the WHAT system, the optimum BFI max value of 0.491 and filter parameter value of 0.987 were determined for the LEC watershed. The comparison of L-THIA estimates with filtered direct runoff using an optimized BFI max and filter parameter resulted in an R2 value of 0.66 and the Nash-Sutcliffe coefficient value of 0.63. However, L-THIA estimates calibrated with the optimized BFI max and filter parameter increased by 33% and estimated NPS pollutant loadings increased by more than 20%. This indicates L-THIA model direct runoff estimates can be incorrect by 33% and NPS pollutant loading estimation by more than 20%, if the accuracy of the baseflow separation method is not validated for the study watershed prior to model comparison. This study shows the importance of baseflow separation in hydrologic and water quality modeling using the L-THIA model.

Investigating the Metallicity–Mixing-length Relation

NASA Astrophysics Data System (ADS)

Viani, Lucas S.; Basu, Sarbani; Joel Ong J., M.; Bonaca, Ana; Chaplin, William J.

2018-05-01

Stellar models typically use the mixing-length approximation as a way to implement convection in a simplified manner. While conventionally the value of the mixing-length parameter, α, used is the solar-calibrated value, many studies have shown that other values of α are needed to properly model stars. This uncertainty in the value of the mixing-length parameter is a major source of error in stellar models and isochrones. Using asteroseismic data, we determine the value of the mixing-length parameter required to properly model a set of about 450 stars ranging in log g, {T}eff}, and [{Fe}/{{H}}]. The relationship between the value of α required and the properties of the star is then investigated. For Eddington atmosphere, non-diffusion models, we find that the value of α can be approximated by a linear model, in the form of α /{α }ȯ =5.426{--}0.101 {log}(g)-1.071 {log}({T}eff}) +0.437([{Fe}/{{H}}]). This process is repeated using a variety of model physics, as well as compared with previous studies and results from 3D convective simulations.

Detecting influential observations in nonlinear regression modeling of groundwater flow

USGS Publications Warehouse

Yager, Richard M.

1998-01-01

Nonlinear regression is used to estimate optimal parameter values in models of groundwater flow to ensure that differences between predicted and observed heads and flows do not result from nonoptimal parameter values. Parameter estimates can be affected, however, by observations that disproportionately influence the regression, such as outliers that exert undue leverage on the objective function. Certain statistics developed for linear regression can be used to detect influential observations in nonlinear regression if the models are approximately linear. This paper discusses the application of Cook's D, which measures the effect of omitting a single observation on a set of estimated parameter values, and the statistical parameter DFBETAS, which quantifies the influence of an observation on each parameter. The influence statistics were used to (1) identify the influential observations in the calibration of a three-dimensional, groundwater flow model of a fractured-rock aquifer through nonlinear regression, and (2) quantify the effect of omitting influential observations on the set of estimated parameter values. Comparison of the spatial distribution of Cook's D with plots of model sensitivity shows that influential observations correspond to areas where the model heads are most sensitive to certain parameters, and where predicted groundwater flow rates are largest. Five of the six discharge observations were identified as influential, indicating that reliable measurements of groundwater flow rates are valuable data in model calibration. DFBETAS are computed and examined for an alternative model of the aquifer system to identify a parameterization error in the model design that resulted in overestimation of the effect of anisotropy on horizontal hydraulic conductivity.

Modeling clear-sky solar radiation across a range of elevations in Hawai‘i: Comparing the use of input parameters at different temporal resolutions

NASA Astrophysics Data System (ADS)

Longman, Ryan J.; Giambelluca, Thomas W.; Frazier, Abby G.

2012-01-01

Estimates of clear sky global solar irradiance using the parametric model SPCTRAL2 were tested against clear sky radiation observations at four sites in Hawai`i using daily, mean monthly, and 1 year mean model parameter settings. Atmospheric parameters in SPCTRAL2 and similar models are usually set at site-specific values and are not varied to represent the effects of fluctuating humidity, aerosol amount and type, or ozone concentration, because time-dependent atmospheric parameter estimates are not available at most sites of interest. In this study, we sought to determine the added value of using time dependent as opposed to fixed model input parameter settings. At the AERONET site, Mauna Loa Observatory (MLO) on the island of Hawai`i, where daily measurements of atmospheric optical properties and hourly solar radiation observations are available, use of daily rather than 1 year mean aerosol parameter values reduced mean bias error (MBE) from 18 to 10 W m-2 and root mean square error from 25 to 17 W m-2. At three stations in the HaleNet climate network, located at elevations of 960, 1640, and 2590 m on the island of Maui, where aerosol-related parameter settings were interpolated from observed values for AERONET sites at MLO (3397 m) and Lāna`i (20 m), and precipitable water was estimated using radiosonde-derived humidity profiles from nearby Hilo, the model performed best when using constant 1 year mean parameter values. At HaleNet Station 152, for example, MBE was 18, 10, and 8 W m-2 for daily, monthly, and 1 year mean parameters, respectively.

On Interpreting the Model Parameters for the Three Parameter Logistic Model

ERIC Educational Resources Information Center

Maris, Gunter; Bechger, Timo

2009-01-01

This paper addresses two problems relating to the interpretability of the model parameters in the three parameter logistic model. First, it is shown that if the values of the discrimination parameters are all the same, the remaining parameters are nonidentifiable in a nontrivial way that involves not only ability and item difficulty, but also the…

Parameter extraction and transistor models

NASA Technical Reports Server (NTRS)

Rykken, Charles; Meiser, Verena; Turner, Greg; Wang, QI

1985-01-01

Using specified mathematical models of the MOSFET device, the optimal values of the model-dependent parameters were extracted from data provided by the Jet Propulsion Laboratory (JPL). Three MOSFET models, all one-dimensional were used. One of the models took into account diffusion (as well as convection) currents. The sensitivity of the models was assessed for variations of the parameters from their optimal values. Lines of future inquiry are suggested on the basis of the behavior of the devices, of the limitations of the proposed models, and of the complexity of the required numerical investigations.

The application of the pilot points in groundwater numerical inversion model

NASA Astrophysics Data System (ADS)

Hu, Bin; Teng, Yanguo; Cheng, Lirong

2015-04-01

Numerical inversion simulation of groundwater has been widely applied in groundwater. Compared to traditional forward modeling, inversion model has more space to study. Zones and inversing modeling cell by cell are conventional methods. Pilot points is a method between them. The traditional inverse modeling method often uses software dividing the model into several zones with a few parameters needed to be inversed. However, distribution is usually too simple for modeler and result of simulation deviation. Inverse cell by cell will get the most actual parameter distribution in theory, but it need computational complexity greatly and quantity of survey data for geological statistical simulation areas. Compared to those methods, pilot points distribute a set of points throughout the different model domains for parameter estimation. Property values are assigned to model cells by Kriging to ensure geological units within the parameters of heterogeneity. It will reduce requirements of simulation area geological statistics and offset the gap between above methods. Pilot points can not only save calculation time, increase fitting degree, but also reduce instability of numerical model caused by numbers of parameters and other advantages. In this paper, we use pilot point in a field which structure formation heterogeneity and hydraulics parameter was unknown. We compare inversion modeling results of zones and pilot point methods. With the method of comparative analysis, we explore the characteristic of pilot point in groundwater inversion model. First, modeler generates an initial spatially correlated field given a geostatistical model by the description of the case site with the software named Groundwater Vistas 6. Defining Kriging to obtain the value of the field functions over the model domain on the basis of their values at measurement and pilot point locations (hydraulic conductivity), then we assign pilot points to the interpolated field which have been divided into 4 zones. And add range of disturbance values to inversion targets to calculate the value of hydraulic conductivity. Third, after inversion calculation (PEST), the interpolated field will minimize an objective function measuring the misfit between calculated and measured data. It's an optimization problem to find the optimum value of parameters. After the inversion modeling, the following major conclusion can be found out: (1) In a field structure formation is heterogeneity, the results of pilot point method is more real: better fitting result of parameters, more stable calculation of numerical simulation (stable residual distribution). Compared to zones, it is better of reflecting the heterogeneity of study field. (2) Pilot point method ensures that each parameter is sensitive and not entirely dependent on other parameters. Thus it guarantees the relative independence and authenticity of parameters evaluation results. However, it costs more time to calculate than zones. Key words: groundwater; pilot point; inverse model; heterogeneity; hydraulic conductivity

Evaluation of a Silicone Membrane as an Alternative to Human Skin for Determining Skin Permeation Parameters of Chemical Compounds.

PubMed

Uchida, Takashi; Yakumaru, Masafumi; Nishioka, Keisuke; Higashi, Yoshihiro; Sano, Tomohiko; Todo, Hiroaki; Sugibayashi, Kenji

2016-01-01

We evaluated the effectiveness of a silicone membrane as an alternative to human skin using the skin permeation parameters of chemical compounds. An in vitro permeation study using 15 model compounds was conducted, and permeation parameters comprising permeability coefficient (P), diffusion parameter (DL(-2)), and partition parameter (KL) were calculated from each permeation profile. Significant correlations were obtained in log P, log DL(-2), and log KL values between the silicone membrane and human skin. DL(-2) values of model compounds, except flurbiprofen, in the silicone membrane were independent of the lipophilicity of the model compounds and were 100-fold higher than those in human skin. For antipyrine and caffeine, which are hydrophilic, KL values in the silicone membrane were 100-fold lower than those in human skin, and P values, calculated as the product of a DL(-2) and KL, were similar. For lipophilic compounds, such as n-butyl paraben and flurbiprofen, KL values for silicone were similar to or 10-fold higher than those in human skin, and P values for silicone were 100-fold higher than those in human skin. Furthermore, for amphiphilic compounds with log Ko/w values from 0.5 to 3.5, KL values in the silicone membrane were 10-fold lower than those in human skin, and P values for silicone were 10-fold higher than those in human skin. The silicone membrane was useful as a human skin alternative in an in vitro skin permeation study. However, depending on the lipophilicity of the model compounds, some parameters may be over- or underestimated.

Forecasting impact injuries of unrestrained occupants in railway vehicle passenger compartments.

PubMed

Xie, Suchao; Zhou, Hui

2014-01-01

In order to predict the injury parameters of the occupants corresponding to different experimental parameters and to determine impact injury indices conveniently and efficiently, a model forecasting occupant impact injury was established in this work. The work was based on finite experimental observation values obtained by numerical simulation. First, the various factors influencing the impact injuries caused by the interaction between unrestrained occupants and the compartment's internal structures were collated and the most vulnerable regions of the occupant's body were analyzed. Then, the forecast model was set up based on a genetic algorithm-back propagation (GA-BP) hybrid algorithm, which unified the individual characteristics of the back propagation-artificial neural network (BP-ANN) model and the genetic algorithm (GA). The model was well suited to studies of occupant impact injuries and allowed multiple-parameter forecasts of the occupant impact injuries to be realized assuming values for various influencing factors. Finally, the forecast results for three types of secondary collision were analyzed using forecasting accuracy evaluation methods. All of the results showed the ideal accuracy of the forecast model. When an occupant faced a table, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.0 percent and the average relative error (ARE) values did not exceed 3.0 percent. When an occupant faced a seat, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 5.2 percent and the ARE values did not exceed 3.1 percent. When the occupant faced another occupant, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.3 percent and the ARE values did not exceed 3.8 percent. The injury forecast model established in this article reduced repeat experiment times and improved the design efficiency of the internal compartment's structure parameters, and it provided a new way for assessing the safety performance of the interior structural parameters in existing, and newly designed, railway vehicle compartments.

Estimating parameter values of a socio-hydrological flood model

NASA Astrophysics Data System (ADS)

Holkje Barendrecht, Marlies; Viglione, Alberto; Kreibich, Heidi; Vorogushyn, Sergiy; Merz, Bruno; Blöschl, Günter

2018-06-01

Socio-hydrological modelling studies that have been published so far show that dynamic coupled human-flood models are a promising tool to represent the phenomena and the feedbacks in human-flood systems. So far these models are mostly generic and have not been developed and calibrated to represent specific case studies. We believe that applying and calibrating these type of models to real world case studies can help us to further develop our understanding about the phenomena that occur in these systems. In this paper we propose a method to estimate the parameter values of a socio-hydrological model and we test it by applying it to an artificial case study. We postulate a model that describes the feedbacks between floods, awareness and preparedness. After simulating hypothetical time series with a given combination of parameters, we sample few data points for our variables and try to estimate the parameters given these data points using Bayesian Inference. The results show that, if we are able to collect data for our case study, we would, in theory, be able to estimate the parameter values for our socio-hydrological flood model.

UCODE, a computer code for universal inverse modeling

USGS Publications Warehouse

Poeter, E.P.; Hill, M.C.

1999-01-01

This article presents the US Geological Survey computer program UCODE, which was developed in collaboration with the US Army Corps of Engineers Waterways Experiment Station and the International Ground Water Modeling Center of the Colorado School of Mines. UCODE performs inverse modeling, posed as a parameter-estimation problem, using nonlinear regression. Any application model or set of models can be used; the only requirement is that they have numerical (ASCII or text only) input and output files and that the numbers in these files have sufficient significant digits. Application models can include preprocessors and postprocessors as well as models related to the processes of interest (physical, chemical and so on), making UCODE extremely powerful for model calibration. Estimated parameters can be defined flexibly with user-specified functions. Observations to be matched in the regression can be any quantity for which a simulated equivalent value can be produced, thus simulated equivalent values are calculated using values that appear in the application model output files and can be manipulated with additive and multiplicative functions, if necessary. Prior, or direct, information on estimated parameters also can be included in the regression. The nonlinear regression problem is solved by minimizing a weighted least-squares objective function with respect to the parameter values using a modified Gauss-Newton method. Sensitivities needed for the method are calculated approximately by forward or central differences and problems and solutions related to this approximation are discussed. Statistics are calculated and printed for use in (1) diagnosing inadequate data or identifying parameters that probably cannot be estimated with the available data, (2) evaluating estimated parameter values, (3) evaluating the model representation of the actual processes and (4) quantifying the uncertainty of model simulated values. UCODE is intended for use on any computer operating system: it consists of algorithms programmed in perl, a freeware language designed for text manipulation and Fortran90, which efficiently performs numerical calculations.

Chaos control of Hastings-Powell model by combining chaotic motions.

PubMed

Danca, Marius-F; Chattopadhyay, Joydev

2016-04-01

In this paper, we propose a Parameter Switching (PS) algorithm as a new chaos control method for the Hastings-Powell (HP) system. The PS algorithm is a convergent scheme that switches the control parameter within a set of values while the controlled system is numerically integrated. The attractor obtained with the PS algorithm matches the attractor obtained by integrating the system with the parameter replaced by the averaged value of the switched parameter values. The switching rule can be applied periodically or randomly over a set of given values. In this way, every stable cycle of the HP system can be approximated if its underlying parameter value equalizes the average value of the switching values. Moreover, the PS algorithm can be viewed as a generalization of Parrondo's game, which is applied for the first time to the HP system, by showing that losing strategy can win: "losing + losing = winning." If "loosing" is replaced with "chaos" and, "winning" with "order" (as the opposite to "chaos"), then by switching the parameter value in the HP system within two values, which generate chaotic motions, the PS algorithm can approximate a stable cycle so that symbolically one can write "chaos + chaos = regular." Also, by considering a different parameter control, new complex dynamics of the HP model are revealed.

Chaos control of Hastings-Powell model by combining chaotic motions

NASA Astrophysics Data System (ADS)

Danca, Marius-F.; Chattopadhyay, Joydev

2016-04-01

In this paper, we propose a Parameter Switching (PS) algorithm as a new chaos control method for the Hastings-Powell (HP) system. The PS algorithm is a convergent scheme that switches the control parameter within a set of values while the controlled system is numerically integrated. The attractor obtained with the PS algorithm matches the attractor obtained by integrating the system with the parameter replaced by the averaged value of the switched parameter values. The switching rule can be applied periodically or randomly over a set of given values. In this way, every stable cycle of the HP system can be approximated if its underlying parameter value equalizes the average value of the switching values. Moreover, the PS algorithm can be viewed as a generalization of Parrondo's game, which is applied for the first time to the HP system, by showing that losing strategy can win: "losing + losing = winning." If "loosing" is replaced with "chaos" and, "winning" with "order" (as the opposite to "chaos"), then by switching the parameter value in the HP system within two values, which generate chaotic motions, the PS algorithm can approximate a stable cycle so that symbolically one can write "chaos + chaos = regular." Also, by considering a different parameter control, new complex dynamics of the HP model are revealed.

Approaches to highly parameterized inversion: Pilot-point theory, guidelines, and research directions

USGS Publications Warehouse

Doherty, John E.; Fienen, Michael N.; Hunt, Randall J.

2011-01-01

Pilot points have been used in geophysics and hydrogeology for at least 30 years as a means to bridge the gap between estimating a parameter value in every cell of a model and subdividing models into a small number of homogeneous zones. Pilot points serve as surrogate parameters at which values are estimated in the inverse-modeling process, and their values are interpolated onto the modeling domain in such a way that heterogeneity can be represented at a much lower computational cost than trying to estimate parameters in every cell of a model. Although the use of pilot points is increasingly common, there are few works documenting the mathematical implications of their use and even fewer sources of guidelines for their implementation in hydrogeologic modeling studies. This report describes the mathematics of pilot-point use, provides guidelines for their use in the parameter-estimation software suite (PEST), and outlines several research directions. Two key attributes for pilot-point definitions are highlighted. First, the difference between the information contained in the every-cell parameter field and the surrogate parameter field created using pilot points should be in the realm of parameters which are not informed by the observed data (the null space). Second, the interpolation scheme for projecting pilot-point values onto model cells ideally should be orthogonal. These attributes are informed by the mathematics and have important ramifications for both the guidelines and suggestions for future research.

Quantifying Groundwater Model Uncertainty

NASA Astrophysics Data System (ADS)

Hill, M. C.; Poeter, E.; Foglia, L.

2007-12-01

Groundwater models are characterized by the (a) processes simulated, (b) boundary conditions, (c) initial conditions, (d) method of solving the equation, (e) parameterization, and (f) parameter values. Models are related to the system of concern using data, some of which form the basis of observations used most directly, through objective functions, to estimate parameter values. Here we consider situations in which parameter values are determined by minimizing an objective function. Other methods of model development are not considered because their ad hoc nature generally prohibits clear quantification of uncertainty. Quantifying prediction uncertainty ideally includes contributions from (a) to (f). The parameter values of (f) tend to be continuous with respect to both the simulated equivalents of the observations and the predictions, while many aspects of (a) through (e) are discrete. This fundamental difference means that there are options for evaluating the uncertainty related to parameter values that generally do not exist for other aspects of a model. While the methods available for (a) to (e) can be used for the parameter values (f), the inferential methods uniquely available for (f) generally are less computationally intensive and often can be used to considerable advantage. However, inferential approaches require calculation of sensitivities. Whether the numerical accuracy and stability of the model solution required for accurate sensitivities is more broadly important to other model uses is an issue that needs to be addressed. Alternative global methods can require 100 or even 1,000 times the number of runs needed by inferential methods, though methods of reducing the number of needed runs are being developed and tested. Here we present three approaches for quantifying model uncertainty and investigate their strengths and weaknesses. (1) Represent more aspects as parameters so that the computationally efficient methods can be broadly applied. This approach is attainable through universal model analysis software such as UCODE-2005, PEST, and joint use of these programs, which allow many aspects of a model to be defined as parameters. (2) Use highly parameterized models to quantify aspects of (e). While promising, this approach implicitly includes parameterizations that may be considered unreasonable if investigated explicitly, so that resulting measures of uncertainty may be too large. (3) Use a combination of inferential and global methods that can be facilitated using the new software MMA (Multi-Model Analysis), which is constructed using the JUPITER API. Here we consider issues related to the model discrimination criteria calculated by MMA.

Estimation of pharmacokinetic parameters from non-compartmental variables using Microsoft Excel.

PubMed

Dansirikul, Chantaratsamon; Choi, Malcolm; Duffull, Stephen B

2005-06-01

This study was conducted to develop a method, termed 'back analysis (BA)', for converting non-compartmental variables to compartment model dependent pharmacokinetic parameters for both one- and two-compartment models. A Microsoft Excel spreadsheet was implemented with the use of Solver and visual basic functions. The performance of the BA method in estimating pharmacokinetic parameter values was evaluated by comparing the parameter values obtained to a standard modelling software program, NONMEM, using simulated data. The results show that the BA method was reasonably precise and provided low bias in estimating fixed and random effect parameters for both one- and two-compartment models. The pharmacokinetic parameters estimated from the BA method were similar to those of NONMEM estimation.

K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution

DOE PAGES

DeChant, Lawrence; Ray, Jaideep; Lefantzi, Sophia; ...

2017-06-09

The k-ε turbulence model has been described as perhaps “the most widely used complete turbulence model.” This family of heuristic Reynolds Averaged Navier-Stokes (RANS) turbulence closures is supported by a suite of model parameters that have been estimated by demanding the satisfaction of well-established canonical flows such as homogeneous shear flow, log-law behavior, etc. While this procedure does yield a set of so-called nominal parameters, it is abundantly clear that they do not provide a universally satisfactory turbulence model that is capable of simulating complex flows. Recent work on the Bayesian calibration of the k-ε model using jet-in-crossflow wind tunnelmore » data has yielded parameter estimates that are far more predictive than nominal parameter values. In this paper, we develop a self-similar asymptotic solution for axisymmetric jet-in-crossflow interactions and derive analytical estimates of the parameters that were inferred using Bayesian calibration. The self-similar method utilizes a near field approach to estimate the turbulence model parameters while retaining the classical far-field scaling to model flow field quantities. Our parameter values are seen to be far more predictive than the nominal values, as checked using RANS simulations and experimental measurements. They are also closer to the Bayesian estimates than the nominal parameters. A traditional simplified jet trajectory model is explicitly related to the turbulence model parameters and is shown to yield good agreement with measurement when utilizing the analytical derived turbulence model coefficients. Finally, the close agreement between the turbulence model coefficients obtained via Bayesian calibration and the analytically estimated coefficients derived in this paper is consistent with the contention that the Bayesian calibration approach is firmly rooted in the underlying physical description.« less

Posterior uncertainty of GEOS-5 L-band radiative transfer model parameters and brightness temperatures after calibration with SMOS observations

NASA Astrophysics Data System (ADS)

De Lannoy, G. J.; Reichle, R. H.; Vrugt, J. A.

2012-12-01

Simulated L-band (1.4 GHz) brightness temperatures are very sensitive to the values of the parameters in the radiative transfer model (RTM). We assess the optimum RTM parameter values and their (posterior) uncertainty in the Goddard Earth Observing System (GEOS-5) land surface model using observations of multi-angular brightness temperature over North America from the Soil Moisture Ocean Salinity (SMOS) mission. Two different parameter estimation methods are being compared: (i) a particle swarm optimization (PSO) approach, and (ii) an MCMC simulation procedure using the differential evolution adaptive Metropolis (DREAM) algorithm. Our results demonstrate that both methods provide similar "optimal" parameter values. Yet, DREAM exhibits better convergence properties, resulting in a reduced spread of the posterior ensemble. The posterior parameter distributions derived with both methods are used for predictive uncertainty estimation of brightness temperature. This presentation will highlight our model-data synthesis framework and summarize our initial findings.

System parameters for erythropoiesis control model: Comparison of normal values in human and mouse model

NASA Technical Reports Server (NTRS)

1979-01-01

The computer model for erythropoietic control was adapted to the mouse system by altering system parameters originally given for the human to those which more realistically represent the mouse. Parameter values were obtained from a variety of literature sources. Using the mouse model, the mouse was studied as a potential experimental model for spaceflight. Simulation studies of dehydration and hypoxia were performed. A comparison of system parameters for the mouse and human models is presented. Aside from the obvious differences expected in fluid volumes, blood flows and metabolic rates, larger differences were observed in the following: erythrocyte life span, erythropoietin half-life, and normal arterial pO2.

Modeling the bidirectional reflectance distribution function of mixed finite plant canopies and soil

NASA Technical Reports Server (NTRS)

Schluessel, G.; Dickinson, R. E.; Privette, J. L.; Emery, W. J.; Kokaly, R.

1994-01-01

An analytical model of the bidirectional reflectance for optically semi-infinite plant canopies has been extended to describe the reflectance of finite depth canopies contributions from the underlying soil. The model depends on 10 independent parameters describing vegetation and soil optical and structural properties. The model is inverted with a nonlinear minimization routine using directional reflectance data for lawn (leaf area index (LAI) is equal to 9.9), soybeans (LAI, 2.9) and simulated reflectance data (LAI, 1.0) from a numerical bidirectional reflectance distribution function (BRDF) model (Myneni et al., 1988). While the ten-parameter model results in relatively low rms differences for the BRDF, most of the retrieved parameters exhibit poor stability. The most stable parameter was the single-scattering albedo of the vegetation. Canopy albedo could be derived with an accuracy of less than 5% relative error in the visible and less than 1% in the near-infrared. Sensitivity were performed to determine which of the 10 parameters were most important and to assess the effects of Gaussian noise on the parameter retrievals. Out of the 10 parameters, three were identified which described most of the BRDF variability. At low LAI values the most influential parameters were the single-scattering albedos (both soil and vegetation) and LAI, while at higher LAI values (greater than 2.5) these shifted to the two scattering phase function parameters for vegetation and the single-scattering albedo of the vegetation. The three-parameter model, formed by fixing the seven least significant parameters, gave higher rms values but was less sensitive to noise in the BRDF than the full ten-parameter model. A full hemispherical reflectance data set for lawn was then interpolated to yield BRDF values corresponding to advanced very high resolution radiometer (AVHRR) scan geometries collected over a period of nine days. The resulting parameters and BRDFs are similar to those for the full sampling geometry, suggesting that the limited geometry of AVHRR measurements might be used to reliably retrieve BRDF and canopy albedo with this model.

Impact of the hard-coded parameters on the hydrologic fluxes of the land surface model Noah-MP

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Mai, Juliane; Samaniego, Luis; Clark, Martyn; Wulfmeyer, Volker; Attinger, Sabine; Thober, Stephan

2016-04-01

Land surface models incorporate a large number of processes, described by physical, chemical and empirical equations. The process descriptions contain a number of parameters that can be soil or plant type dependent and are typically read from tabulated input files. Land surface models may have, however, process descriptions that contain fixed, hard-coded numbers in the computer code, which are not identified as model parameters. Here we searched for hard-coded parameters in the computer code of the land surface model Noah with multiple process options (Noah-MP) to assess the importance of the fixed values on restricting the model's agility during parameter estimation. We found 139 hard-coded values in all Noah-MP process options, which are mostly spatially constant values. This is in addition to the 71 standard parameters of Noah-MP, which mostly get distributed spatially by given vegetation and soil input maps. We performed a Sobol' global sensitivity analysis of Noah-MP to variations of the standard and hard-coded parameters for a specific set of process options. 42 standard parameters and 75 hard-coded parameters were active with the chosen process options. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated. These sensitivities were evaluated at twelve catchments of the Eastern United States with very different hydro-meteorological regimes. Noah-MP's hydrologic output fluxes are sensitive to two thirds of its standard parameters. The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard-coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the calculation of hydrologic signatures of the runoff data. Latent heat and total runoff exhibit very similar sensitivities towards standard and hard-coded parameters in Noah-MP because of their tight coupling via the water balance. It should therefore be comparable to calibrate Noah-MP either against latent heat observations or against river runoff data. Latent heat and total runoff are sensitive to both, plant and soil parameters. Calibrating only a parameter sub-set of only soil parameters, for example, thus limits the ability to derive realistic model parameters. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites

NASA Astrophysics Data System (ADS)

Post, Hanna; Vrugt, Jasper A.; Fox, Andrew; Vereecken, Harry; Hendricks Franssen, Harrie-Jan

2017-03-01

The Community Land Model (CLM) contains many parameters whose values are uncertain and thus require careful estimation for model application at individual sites. Here we used Bayesian inference with the DiffeRential Evolution Adaptive Metropolis (DREAM(zs)) algorithm to estimate eight CLM v.4.5 ecosystem parameters using 1 year records of half-hourly net ecosystem CO2 exchange (NEE) observations of four central European sites with different plant functional types (PFTs). The posterior CLM parameter distributions of each site were estimated per individual season and on a yearly basis. These estimates were then evaluated using NEE data from an independent evaluation period and data from "nearby" FLUXNET sites at 600 km distance to the original sites. Latent variables (multipliers) were used to treat explicitly uncertainty in the initial carbon-nitrogen pools. The posterior parameter estimates were superior to their default values in their ability to track and explain the measured NEE data of each site. The seasonal parameter values reduced with more than 50% (averaged over all sites) the bias in the simulated NEE values. The most consistent performance of CLM during the evaluation period was found for the posterior parameter values of the forest PFTs, and contrary to the C3-grass and C3-crop sites, the latent variables of the initial pools further enhanced the quality-of-fit. The carbon sink function of the forest PFTs significantly increased with the posterior parameter estimates. We thus conclude that land surface model predictions of carbon stocks and fluxes require careful consideration of uncertain ecological parameters and initial states.

Influence of parameter values on the oscillation sensitivities of two p53-Mdm2 models.

PubMed

Cuba, Christian E; Valle, Alexander R; Ayala-Charca, Giancarlo; Villota, Elizabeth R; Coronado, Alberto M

2015-09-01

Biomolecular networks that present oscillatory behavior are ubiquitous in nature. While some design principles for robust oscillations have been identified, it is not well understood how these oscillations are affected when the kinetic parameters are constantly changing or are not precisely known, as often occurs in cellular environments. Many models of diverse complexity level, for systems such as circadian rhythms, cell cycle or the p53 network, have been proposed. Here we assess the influence of hundreds of different parameter sets on the sensitivities of two configurations of a well-known oscillatory system, the p53 core network. We show that, for both models and all parameter sets, the parameter related to the p53 positive feedback, i.e. self-promotion, is the only one that presents sizeable sensitivities on extrema, periods and delay. Moreover, varying the parameter set values to change the dynamical characteristics of the response is more restricted in the simple model, whereas the complex model shows greater tunability. These results highlight the importance of the presence of specific network patterns, in addition to the role of parameter values, when we want to characterize oscillatory biochemical systems.

Generic NICA-Donnan model parameters for metal-ion binding by humic substances.

PubMed

Milne, Christopher J; Kinniburgh, David G; van Riemsdijk, Willem H; Tipping, Edward

2003-03-01

A total of 171 datasets of literature and experimental data for metal-ion binding by fulvic and humic acids have been digitized and re-analyzed using the NICA-Donnan model. Generic parameter values have been derived that can be used for modeling in the absence of specific metalion binding measurements. These values complement the previously derived generic descriptions of proton binding. For ions where the ranges of pH, concentration, and ionic strength conditions are well covered by the available data,the generic parameters successfully describe the metalion binding behavior across a very wide range of conditions and for different humic and fulvic acids. Where published data for other metal ions are too sparse to constrain the model well, generic parameters have been estimated by interpolating trends observable in the parameter values of the well-defined data. Recommended generic NICA-Donnan model parameters are provided for 23 metal ions (Al, Am, Ba, Ca, Cd, Cm, Co, CrIII, Cu, Dy, Eu, FeII, FeIII, Hg, Mg, Mn, Ni, Pb, Sr, Thv, UVIO2, VIIIO, and Zn) for both fulvic and humic acids. These parameters probably represent the best NICA-Donnan description of metal-ion binding that can be achieved using existing data.

Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample

PubMed Central

Oakley, Jeremy E.; Brennan, Alan; Breeze, Penny

2015-01-01

Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a proposed research design can be quantified by its expected value of sample information (EVSI). EVSI is commonly estimated via a 2-level Monte Carlo procedure in which plausible data sets are generated in an outer loop, and then, conditional on these, the parameters of the decision model are updated via Bayes rule and sampled in an inner loop. At each iteration of the inner loop, the decision model is evaluated. This is computationally demanding and may be difficult if the posterior distribution of the model parameters conditional on sampled data is hard to sample from. We describe a fast nonparametric regression-based method for estimating per-patient EVSI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method avoids the need to sample from the posterior distributions of the parameters and avoids the need to rerun the model. The only requirement is that sample data sets can be generated. The method is applicable with a model of any complexity and with any specification of model parameter distribution. We demonstrate in a case study the superior efficiency of the regression method over the 2-level Monte Carlo method. PMID:25810269

The 4-parameter Compressible Packing Model (CPM) including a critical cavity size ratio

NASA Astrophysics Data System (ADS)

Roquier, Gerard

2017-06-01

The 4-parameter Compressible Packing Model (CPM) has been developed to predict the packing density of mixtures constituted by bidisperse spherical particles. The four parameters are: the wall effect and the loosening effect coefficients, the compaction index and a critical cavity size ratio. The two geometrical interactions have been studied theoretically on the basis of a spherical cell centered on a secondary class bead. For the loosening effect, a critical cavity size ratio, below which a fine particle can be inserted into a small cavity created by touching coarser particles, is introduced. This is the only parameter which requires adaptation to extend the model to other types of particles. The 4-parameter CPM demonstrates its efficiency on frictionless glass beads (300 values), spherical particles numerically simulated (20 values), round natural particles (125 values) and crushed particles (335 values) with correlation coefficients equal to respectively 99.0%, 98.7%, 97.8%, 96.4% and mean deviations equal to respectively 0.007, 0.006, 0.007, 0.010.

Applications of Monte Carlo method to nonlinear regression of rheological data

NASA Astrophysics Data System (ADS)

Kim, Sangmo; Lee, Junghaeng; Kim, Sihyun; Cho, Kwang Soo

2018-02-01

In rheological study, it is often to determine the parameters of rheological models from experimental data. Since both rheological data and values of the parameters vary in logarithmic scale and the number of the parameters is quite large, conventional method of nonlinear regression such as Levenberg-Marquardt (LM) method is usually ineffective. The gradient-based method such as LM is apt to be caught in local minima which give unphysical values of the parameters whenever the initial guess of the parameters is far from the global optimum. Although this problem could be solved by simulated annealing (SA), the Monte Carlo (MC) method needs adjustable parameter which could be determined in ad hoc manner. We suggest a simplified version of SA, a kind of MC methods which results in effective values of the parameters of most complicated rheological models such as the Carreau-Yasuda model of steady shear viscosity, discrete relaxation spectrum and zero-shear viscosity as a function of concentration and molecular weight.

Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

PubMed Central

Murakami, Yohei

2014-01-01

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor. PMID:25089832

Application of the precipitation-runoff model in the Warrior coal field, Alabama

USGS Publications Warehouse

Kidd, Robert E.; Bossong, C.R.

1987-01-01

A deterministic precipitation-runoff model, the Precipitation-Runoff Modeling System, was applied in two small basins located in the Warrior coal field, Alabama. Each basin has distinct geologic, hydrologic, and land-use characteristics. Bear Creek basin (15.03 square miles) is undisturbed, is underlain almost entirely by consolidated coal-bearing rocks of Pennsylvanian age (Pottsville Formation), and is drained by an intermittent stream. Turkey Creek basin (6.08 square miles) contains a surface coal mine and is underlain by both the Pottsville Formation and unconsolidated clay, sand, and gravel deposits of Cretaceous age (Coker Formation). Aquifers in the Coker Formation sustain flow through extended rainless periods. Preliminary daily and storm calibrations were developed for each basin. Initial parameter and variable values were determined according to techniques recommended in the user's manual for the modeling system and through field reconnaissance. Parameters with meaningful sensitivity were identified and adjusted to match hydrograph shapes and to compute realistic water year budgets. When the developed calibrations were applied to data exclusive of the calibration period as a verification exercise, results were comparable to those for the calibration period. The model calibrations included preliminary parameter values for the various categories of geology and land use in each basin. The parameter values for areas underlain by the Pottsville Formation in the Bear Creek basin were transferred directly to similar areas in the Turkey Creek basin, and these parameter values were held constant throughout the model calibration. Parameter values for all geologic and land-use categories addressed in the two calibrations can probably be used in ungaged basins where similar conditions exist. The parameter transfer worked well, as a good calibration was obtained for Turkey Creek basin.

The contribution of NOAA/CMDL ground-based measurements to understanding long-term stratospheric changes

NASA Astrophysics Data System (ADS)

Montzka, S. A.; Butler, J. H.; Dutton, G.; Thompson, T. M.; Hall, B.; Mondeel, D. J.; Elkins, J. W.

2005-05-01

The El-Nino/Southern-Oscillation (ENSO) dominates interannual climate variability and plays, therefore, a key role in seasonal-to-interannual prediction. Much is known by now about the main physical mechanisms that give rise to and modulate ENSO, but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean--atmosphere model of ENSO. The coupled model consists of an upper-ocean, reduced-gravity model of the Tropical Pacific and a steady-state atmospheric response to the sea surface temperature (SST). The model errors are assumed to be mainly in the atmospheric wind stress, and assimilated data are equatorial Pacific SSTs. Model behavior is very sensitive to two key parameters: (i) μ, the ocean-atmosphere coupling coefficient between SST and wind stress anomalies; and (ii) δs, the surface-layer coefficient. Previous work has shown that δs determines the period of the model's self-sustained oscillation, while μ measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Estimation of these parameters is tested first on synthetic data and allows us to recover the delayed-oscillator mode starting from model parameter values that correspond to the westward-propagating case. Assimilation of SST data from the NCEP-NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean--atmosphere GCMs will be discussed.

A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations

USGS Publications Warehouse

Ward, Adam S.; Kelleher, Christa A.; Mason, Seth J. K.; Wagener, Thorsten; McIntyre, Neil; McGlynn, Brian L.; Runkel, Robert L.; Payn, Robert A.

2017-01-01

Researchers and practitioners alike often need to understand and characterize how water and solutes move through a stream in terms of the relative importance of in-stream and near-stream storage and transport processes. In-channel and subsurface storage processes are highly variable in space and time and difficult to measure. Storage estimates are commonly obtained using transient-storage models (TSMs) of the experimentally obtained solute-tracer test data. The TSM equations represent key transport and storage processes with a suite of numerical parameters. Parameter values are estimated via inverse modeling, in which parameter values are iteratively changed until model simulations closely match observed solute-tracer data. Several investigators have shown that TSM parameter estimates can be highly uncertain. When this is the case, parameter values cannot be used reliably to interpret stream-reach functioning. However, authors of most TSM studies do not evaluate or report parameter certainty. Here, we present a software tool linked to the One-dimensional Transport with Inflow and Storage (OTIS) model that enables researchers to conduct uncertainty analyses via Monte-Carlo parameter sampling and to visualize uncertainty and sensitivity results. We demonstrate application of our tool to 2 case studies and compare our results to output obtained from more traditional implementation of the OTIS model. We conclude by suggesting best practices for transient-storage modeling and recommend that future applications of TSMs include assessments of parameter certainty to support comparisons and more reliable interpretations of transport processes.

Recommended direct simulation Monte Carlo collision model parameters for modeling ionized air transport processes

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

Swaminathan-Gopalan, Krishnan; Stephani, Kelly A., E-mail: ksteph@illinois.edu

2016-02-15

A systematic approach for calibrating the direct simulation Monte Carlo (DSMC) collision model parameters to achieve consistency in the transport processes is presented. The DSMC collision cross section model parameters are calibrated for high temperature atmospheric conditions by matching the collision integrals from DSMC against ab initio based collision integrals that are currently employed in the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) and Data Parallel Line Relaxation (DPLR) high temperature computational fluid dynamics solvers. The DSMC parameter values are computed for the widely used Variable Hard Sphere (VHS) and the Variable Soft Sphere (VSS) models using the collision-specific pairing approach.more » The recommended best-fit VHS/VSS parameter values are provided over a temperature range of 1000-20 000 K for a thirteen-species ionized air mixture. Use of the VSS model is necessary to achieve consistency in transport processes of ionized gases. The agreement of the VSS model transport properties with the transport properties as determined by the ab initio collision integral fits was found to be within 6% in the entire temperature range, regardless of the composition of the mixture. The recommended model parameter values can be readily applied to any gas mixture involving binary collisional interactions between the chemical species presented for the specified temperature range.« less

Chaos control of Hastings–Powell model by combining chaotic motions

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

Danca, Marius-F., E-mail: danca@rist.ro; Chattopadhyay, Joydev, E-mail: joydev@isical.ac.in

2016-04-15

In this paper, we propose a Parameter Switching (PS) algorithm as a new chaos control method for the Hastings–Powell (HP) system. The PS algorithm is a convergent scheme that switches the control parameter within a set of values while the controlled system is numerically integrated. The attractor obtained with the PS algorithm matches the attractor obtained by integrating the system with the parameter replaced by the averaged value of the switched parameter values. The switching rule can be applied periodically or randomly over a set of given values. In this way, every stable cycle of the HP system can bemore » approximated if its underlying parameter value equalizes the average value of the switching values. Moreover, the PS algorithm can be viewed as a generalization of Parrondo's game, which is applied for the first time to the HP system, by showing that losing strategy can win: “losing + losing = winning.” If “loosing” is replaced with “chaos” and, “winning” with “order” (as the opposite to “chaos”), then by switching the parameter value in the HP system within two values, which generate chaotic motions, the PS algorithm can approximate a stable cycle so that symbolically one can write “chaos + chaos = regular.” Also, by considering a different parameter control, new complex dynamics of the HP model are revealed.« less

Investigating the relationship between a soils classification and the spatial parameters of a conceptual catchment-scale hydrological model

NASA Astrophysics Data System (ADS)

Dunn, S. M.; Lilly, A.

2001-10-01

There are now many examples of hydrological models that utilise the capabilities of Geographic Information Systems to generate spatially distributed predictions of behaviour. However, the spatial variability of hydrological parameters relating to distributions of soils and vegetation can be hard to establish. In this paper, the relationship between a soil hydrological classification Hydrology of Soil Types (HOST) and the spatial parameters of a conceptual catchment-scale model is investigated. A procedure involving inverse modelling using Monte-Carlo simulations on two catchments is developed to identify relative values for soil related parameters of the DIY model. The relative values determine the internal variability of hydrological processes as a function of the soil type. For three out of the four soil parameters studied, the variability between HOST classes was found to be consistent across two catchments when tested independently. Problems in identifying values for the fourth 'fast response distance' parameter have highlighted a potential limitation with the present structure of the model. The present assumption that this parameter can be related simply to soil type rather than topography appears to be inadequate. With the exclusion of this parameter, calibrated parameter sets from one catchment can be converted into equivalent parameter sets for the alternate catchment on the basis of their HOST distributions, to give a reasonable simulation of flow. Following further testing on different catchments, and modifications to the definition of the fast response distance parameter, the technique provides a methodology whereby it is possible to directly derive spatial soil parameters for new catchments.

Resolving model parameter values from carbon and nitrogen stock measurements in a wide range of tropical mature forests using nonlinear inversion and regression trees

Treesearch

Shuguang Liua; Pamela Anderson; Guoyi Zhoud; Boone Kauffman; Flint Hughes; David Schimel; Vicente Watson; Joseph Tosi

2008-01-01

Objectively assessing the performance of a model and deriving model parameter values from observations are critical and challenging in landscape to regional modeling. In this paper, we applied a nonlinear inversion technique to calibrate the ecosystem model CENTURY against carbon (C) and nitrogen (N) stock measurements collected from 39 mature tropical forest sites in...

Aspen succession in the Intermountain West: A deterministic model

Treesearch

Dale L. Bartos; Frederick R. Ward; George S. Innis

1983-01-01

A deterministic model of succession in aspen forests was developed using existing data and intuition. The degree of uncertainty, which was determined by allowing the parameter values to vary at random within limits, was larger than desired. This report presents results of an analysis of model sensitivity to changes in parameter values. These results have indicated...

Distributed activation energy model parameters of some Turkish coals

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

Gunes, M.; Gunes, S.K.

2008-07-01

A multi-reaction model based on distributed activation energy has been applied to some Turkish coals. The kinetic parameters of distributed activation energy model were calculated via computer program developed for this purpose. It was observed that the values of mean of activation energy distribution vary between 218 and 248 kJ/mol, and the values of standard deviation of activation energy distribution vary between 32 and 70 kJ/mol. The correlations between kinetic parameters of the distributed activation energy model and certain properties of coal have been investigated.

Evaluation of confidence intervals for a steady-state leaky aquifer model

USGS Publications Warehouse

Christensen, S.; Cooley, R.L.

1999-01-01

The fact that dependent variables of groundwater models are generally nonlinear functions of model parameters is shown to be a potentially significant factor in calculating accurate confidence intervals for both model parameters and functions of the parameters, such as the values of dependent variables calculated by the model. The Lagrangian method of Vecchia and Cooley [Vecchia, A.V. and Cooley, R.L., Water Resources Research, 1987, 23(7), 1237-1250] was used to calculate nonlinear Scheffe-type confidence intervals for the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear) widths was not correct. Results show that nonlinear effects can cause the nonlinear intervals to be asymmetric and either larger or smaller than the linear approximations. Prior information on transmissivities helps reduce the size of the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.The fact that dependent variables of groundwater models are generally nonlinear functions of model parameters is shown to be a potentially significant factor in calculating accurate confidence intervals for both model parameters and functions of the parameters, such as the values of dependent variables calculated by the model. The Lagrangian method of Vecchia and Cooley was used to calculate nonlinear Scheffe-type confidence intervals for the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear) widths was not correct. Results show that nonlinear effects can cause the nonlinear intervals to be asymmetric and either larger or smaller than the linear approximations. Prior information on transmissivities helps reduce the size of the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.

Hyperbolic Discounting: Value and Time Processes of Substance Abusers and Non-Clinical Individuals in Intertemporal Choice

PubMed Central

2014-01-01

The single parameter hyperbolic model has been frequently used to describe value discounting as a function of time and to differentiate substance abusers and non-clinical participants with the model's parameter k. However, k says little about the mechanisms underlying the observed differences. The present study evaluates several alternative models with the purpose of identifying whether group differences stem from differences in subjective valuation, and/or time perceptions. Using three two-parameter models, plus secondary data analyses of 14 studies with 471 indifference point curves, results demonstrated that adding a valuation, or a time perception function led to better model fits. However, the gain in fit due to the flexibility granted by a second parameter did not always lead to a better understanding of the data patterns and corresponding psychological processes. The k parameter consistently indexed group and context (magnitude) differences; it is thus a mixed measure of person and task level effects. This was similar for a parameter meant to index payoff devaluation. A time perception parameter, on the other hand, fluctuated with contexts in a non-predicted fashion and the interpretation of its values was inconsistent with prior findings that supported enlarged perceived delays for substance abusers compared to controls. Overall, the results provide mixed support for hyperbolic models of intertemporal choice in terms of the psychological meaning afforded by their parameters. PMID:25390941

Estimation of Physical Properties and Chemical Reactivity Parameters of Organic Compounds for Environmental Modeling by SPARC

EPA Science Inventory

Mathematical models for predicting the transport and fate of pollutants in the environment require reactivity parameter values that is value of the physical and chemical constants that govern reactivity. Although empirical structure activity relationships have been developed th...

Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems

PubMed Central

Lomnitz, Jason G.; Savageau, Michael A.

2016-01-01

Mathematical models of biochemical systems provide a means to elucidate the link between the genotype, environment, and phenotype. A subclass of mathematical models, known as mechanistic models, quantitatively describe the complex non-linear mechanisms that capture the intricate interactions between biochemical components. However, the study of mechanistic models is challenging because most are analytically intractable and involve large numbers of system parameters. Conventional methods to analyze them rely on local analyses about a nominal parameter set and they do not reveal the vast majority of potential phenotypes possible for a given system design. We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy. Instead, this approach relies on architectural features of the model to identify the phenotypic repertoire and then predict values for the parameters that yield specific instances of the system that realize desired phenotypic characteristics. Here, we present a collection of software tools, the Design Space Toolbox V2 based on the System Design Space method, that automates (1) enumeration of the repertoire of model phenotypes, (2) prediction of values for the parameters for any model phenotype, and (3) analysis of model phenotypes through analytical and numerical methods. The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach. We illustrate the power of these new tools by applying them to a synthetic gene circuit that can exhibit multi-stability. We then predict values for the system parameters such that the design exhibits 2, 3, and 4 stable steady states. In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count. This example shows the power of these new automated methods to rapidly identify behaviors of interest and efficiently predict parameter values for their realization. These tools may be applied to understand complex natural circuitry and to aid in the rational design of synthetic circuits. PMID:27462346

Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water.

PubMed

Salari, Marjan; Salami Shahid, Esmaeel; Afzali, Seied Hosein; Ehteshami, Majid; Conti, Gea Oliveri; Derakhshan, Zahra; Sheibani, Solmaz Nikbakht

2018-04-22

Today, due to the increase in the population, the growth of industry and the variety of chemical compounds, the quality of drinking water has decreased. Five important river water quality properties such as: dissolved oxygen (DO), total dissolved solids (TDS), total hardness (TH), alkalinity (ALK) and turbidity (TU) were estimated by parameters such as: electric conductivity (EC), temperature (T), and pH that could be measured easily with almost no costs. Simulate water quality parameters were examined with two methods of modeling include mathematical and Artificial Neural Networks (ANN). Mathematical methods are based on polynomial fitting with least square method and ANN modeling algorithms are feed-forward networks. All conditions/circumstances covered by neural network modeling were tested for all parameters in this study, except for Alkalinity. All optimum ANN models developed to simulate water quality parameters had precision value as R-value close to 0.99. The ANN model extended to simulate alkalinity with R-value equals to 0.82. Moreover, Surface fitting techniques were used to refine data sets. Presented models and equations are reliable/useable tools for studying water quality parameters at similar rivers, as a proper replacement for traditional water quality measuring equipment's. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

PubMed Central

2014-01-01

This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD. PMID:24456676

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

PubMed Central

Serrancolí, Gil; Kinney, Allison L.; Fregly, Benjamin J.; Font-Llagunes, Josep M.

2016-01-01

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and −0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking. PMID:27210105

The heuristic value of redundancy models of aging.

PubMed

Boonekamp, Jelle J; Briga, Michael; Verhulst, Simon

2015-11-01

Molecular studies of aging aim to unravel the cause(s) of aging bottom-up, but linking these mechanisms to organismal level processes remains a challenge. We propose that complementary top-down data-directed modelling of organismal level empirical findings may contribute to developing these links. To this end, we explore the heuristic value of redundancy models of aging to develop a deeper insight into the mechanisms causing variation in senescence and lifespan. We start by showing (i) how different redundancy model parameters affect projected aging and mortality, and (ii) how variation in redundancy model parameters relates to variation in parameters of the Gompertz equation. Lifestyle changes or medical interventions during life can modify mortality rate, and we investigate (iii) how interventions that change specific redundancy parameters within the model affect subsequent mortality and actuarial senescence. Lastly, as an example of data-directed modelling and the insights that can be gained from this, (iv) we fit a redundancy model to mortality patterns observed by Mair et al. (2003; Science 301: 1731-1733) in Drosophila that were subjected to dietary restriction and temperature manipulations. Mair et al. found that dietary restriction instantaneously reduced mortality rate without affecting aging, while temperature manipulations had more transient effects on mortality rate and did affect aging. We show that after adjusting model parameters the redundancy model describes both effects well, and a comparison of the parameter values yields a deeper insight in the mechanisms causing these contrasting effects. We see replacement of the redundancy model parameters by more detailed sub-models of these parameters as a next step in linking demographic patterns to underlying molecular mechanisms. Copyright © 2015 Elsevier Inc. All rights reserved.

Basic research on design analysis methods for rotorcraft vibrations

NASA Technical Reports Server (NTRS)

Hanagud, S.

1991-01-01

The objective of the present work was to develop a method for identifying physically plausible finite element system models of airframe structures from test data. The assumed models were based on linear elastic behavior with general (nonproportional) damping. Physical plausibility of the identified system matrices was insured by restricting the identification process to designated physical parameters only and not simply to the elements of the system matrices themselves. For example, in a large finite element model the identified parameters might be restricted to the moduli for each of the different materials used in the structure. In the case of damping, a restricted set of damping values might be assigned to finite elements based on the material type and on the fabrication processes used. In this case, different damping values might be associated with riveted, bolted and bonded elements. The method itself is developed first, and several approaches are outlined for computing the identified parameter values. The method is applied first to a simple structure for which the 'measured' response is actually synthesized from an assumed model. Both stiffness and damping parameter values are accurately identified. The true test, however, is the application to a full-scale airframe structure. In this case, a NASTRAN model and actual measured modal parameters formed the basis for the identification of a restricted set of physically plausible stiffness and damping parameters.

Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.

PubMed

Alizadeh, Mohamad Javad; Kavianpour, Mohamad Reza

2015-09-15

The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Ocean, are taken under consideration. Different combinations of water quality parameters are applied as input variables to predict daily values of salinity, temperature and DO as well as hourly values of DO. The results demonstrate that the WNN models are superior to the ANN models. Also, the hourly models developed for DO prediction outperform the daily models of DO. For the daily models, the most accurate model has R equal to 0.96, while for the hourly model it reaches up to 0.98. Overall, the results show the ability of the model to monitor the ocean parameters, in condition with missing data, or when regular measurement and monitoring are impossible. Copyright © 2015 Elsevier Ltd. All rights reserved.

Understanding identifiability as a crucial step in uncertainty assessment

NASA Astrophysics Data System (ADS)

Jakeman, A. J.; Guillaume, J. H. A.; Hill, M. C.; Seo, L.

2016-12-01

The topic of identifiability analysis offers concepts and approaches to identify why unique model parameter values cannot be identified, and can suggest possible responses that either increase uniqueness or help to understand the effect of non-uniqueness on predictions. Identifiability analysis typically involves evaluation of the model equations and the parameter estimation process. Non-identifiability can have a number of undesirable effects. In terms of model parameters these effects include: parameters not being estimated uniquely even with ideal data; wildly different values being returned for different initialisations of a parameter optimisation algorithm; and parameters not being physically meaningful in a model attempting to represent a process. This presentation illustrates some of the drastic consequences of ignoring model identifiability analysis. It argues for a more cogent framework and use of identifiability analysis as a way of understanding model limitations and systematically learning about sources of uncertainty and their importance. The presentation specifically distinguishes between five sources of parameter non-uniqueness (and hence uncertainty) within the modelling process, pragmatically capturing key distinctions within existing identifiability literature. It enumerates many of the various approaches discussed in the literature. Admittedly, improving identifiability is often non-trivial. It requires thorough understanding of the cause of non-identifiability, and the time, knowledge and resources to collect or select new data, modify model structures or objective functions, or improve conditioning. But ignoring these problems is not a viable solution. Even simple approaches such as fixing parameter values or naively using a different model structure may have significant impacts on results which are too often overlooked because identifiability analysis is neglected.

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

PubMed Central

Ho, Qirong; Cipar, James; Cui, Henggang; Kim, Jin Kyu; Lee, Seunghak; Gibbons, Phillip B.; Gibson, Garth A.; Ganger, Gregory R.; Xing, Eric P.

2014-01-01

We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees. The parameter server provides an easy-to-use shared interface for read/write access to an ML model’s values (parameters and variables), and the SSP model allows distributed workers to read older, stale versions of these values from a local cache, instead of waiting to get them from a central storage. This significantly increases the proportion of time workers spend computing, as opposed to waiting. Furthermore, the SSP model ensures ML algorithm correctness by limiting the maximum age of the stale values. We provide a proof of correctness under SSP, as well as empirical results demonstrating that the SSP model achieves faster algorithm convergence on several different ML problems, compared to fully-synchronous and asynchronous schemes. PMID:25400488

Fractional Derivative Models for Ultrasonic Characterization of Polymer and Breast Tissue Viscoelasticity

PubMed Central

Coussot, Cecile; Kalyanam, Sureshkumar; Yapp, Rebecca; Insana, Michael F.

2009-01-01

The viscoelastic response of hydropolymers, which include glandular breast tissues, may be accurately characterized for some applications with as few as 3 rheological parameters by applying the Kelvin-Voigt fractional derivative (KVFD) modeling approach. We describe a technique for ultrasonic imaging of KVFD parameters in media undergoing unconfined, quasi-static, uniaxial compression. We analyze the KVFD parameter values in simulated and experimental echo data acquired from phantoms and show that the KVFD parameters may concisely characterize the viscoelastic properties of hydropolymers. We then interpret the KVFD parameter values for normal and cancerous breast tissues and hypothesize that this modeling approach may ultimately be applied to tumor differentiation. PMID:19406700

Empirical flow parameters - a tool for hydraulic model validity assessment : [summary].

DOT National Transportation Integrated Search

2013-10-01

Hydraulic modeling assembles models based on generalizations of parameter values from textbooks, professional literature, computer program documentation, and engineering experience. Actual measurements adjacent to the model location are seldom availa...

Macroscopically constrained Wang-Landau method for systems with multiple order parameters and its application to drawing complex phase diagrams

NASA Astrophysics Data System (ADS)

Chan, C. H.; Brown, G.; Rikvold, P. A.

2017-05-01

A generalized approach to Wang-Landau simulations, macroscopically constrained Wang-Landau, is proposed to simulate the density of states of a system with multiple macroscopic order parameters. The method breaks a multidimensional random-walk process in phase space into many separate, one-dimensional random-walk processes in well-defined subspaces. Each of these random walks is constrained to a different set of values of the macroscopic order parameters. When the multivariable density of states is obtained for one set of values of fieldlike model parameters, the density of states for any other values of these parameters can be obtained by a simple transformation of the total system energy. All thermodynamic quantities of the system can then be rapidly calculated at any point in the phase diagram. We demonstrate how to use the multivariable density of states to draw the phase diagram, as well as order-parameter probability distributions at specific phase points, for a model spin-crossover material: an antiferromagnetic Ising model with ferromagnetic long-range interactions. The fieldlike parameters in this model are an effective magnetic field and the strength of the long-range interaction.

The Easy Way of Finding Parameters in IBM (EWofFP-IBM)

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

Turkan, Nureddin

E2/M1 multipole mixing ratios of even-even nuclei in transitional region can be calculated as soon as B(E2) and B(M1) values by using the PHINT and/or NP-BOS codes. The correct calculations of energies must be obtained to produce such calculations. Also, the correct parameter values are needed to calculate the energies. The logic of the codes is based on the mathematical and physical Statements describing interacting boson model (IBM) which is one of the model of nuclear structure physics. Here, the big problem is to find the best fitted parameters values of the model. So, by using the Easy Way ofmore » Finding Parameters in IBM (EWofFP-IBM), the best parameter values of IBM Hamiltonian for {sup 102-110}Pd and {sup 102-110}Ru isotopes were firstly obtained and then the energies were calculated. At the end, it was seen that the calculated results are in good agreement with the experimental ones. In addition, it was carried out that the presented energy values obtained by using the EWofFP-IBM are dominantly better than the previous theoretical data.« less

Probability Analysis of the Wave-Slamming Pressure Values of the Horizontal Deck with Elastic Support

NASA Astrophysics Data System (ADS)

Zuo, Weiguang; Liu, Ming; Fan, Tianhui; Wang, Pengtao

2018-06-01

This paper presents the probability distribution of the slamming pressure from an experimental study of regular wave slamming on an elastically supported horizontal deck. The time series of the slamming pressure during the wave impact were first obtained through statistical analyses on experimental data. The exceeding probability distribution of the maximum slamming pressure peak and distribution parameters were analyzed, and the results show that the exceeding probability distribution of the maximum slamming pressure peak accords with the three-parameter Weibull distribution. Furthermore, the range and relationships of the distribution parameters were studied. The sum of the location parameter D and the scale parameter L was approximately equal to 1.0, and the exceeding probability was more than 36.79% when the random peak was equal to the sample average during the wave impact. The variation of the distribution parameters and slamming pressure under different model conditions were comprehensively presented, and the parameter values of the Weibull distribution of wave-slamming pressure peaks were different due to different test models. The parameter values were found to decrease due to the increased stiffness of the elastic support. The damage criterion of the structure model caused by the wave impact was initially discussed, and the structure model was destroyed when the average slamming time was greater than a certain value during the duration of the wave impact. The conclusions of the experimental study were then described.

LRS Bianchi type-I cosmological model with constant deceleration parameter in f(R,T) gravity

NASA Astrophysics Data System (ADS)

Bishi, Binaya K.; Pacif, S. K. J.; Sahoo, P. K.; Singh, G. P.

A spatially homogeneous anisotropic LRS Bianchi type-I cosmological model is studied in f(R,T) gravity with a special form of Hubble's parameter, which leads to constant deceleration parameter. The parameters involved in the considered form of Hubble parameter can be tuned to match, our models with the ΛCDM model. With the present observed value of the deceleration parameter, we have discussed physical and kinematical properties of a specific model. Moreover, we have discussed the cosmological distances for our model.

Practical identifiability analysis of a minimal cardiovascular system model.

PubMed

Pironet, Antoine; Docherty, Paul D; Dauby, Pierre C; Chase, J Geoffrey; Desaive, Thomas

2017-01-17

Parameters of mathematical models of the cardiovascular system can be used to monitor cardiovascular state, such as total stressed blood volume status, vessel elastance and resistance. To do so, the model parameters have to be estimated from data collected at the patient's bedside. This work considers a seven-parameter model of the cardiovascular system and investigates whether these parameters can be uniquely determined using indices derived from measurements of arterial and venous pressures, and stroke volume. An error vector defined the residuals between the simulated and reference values of the seven clinically available haemodynamic indices. The sensitivity of this error vector to each model parameter was analysed, as well as the collinearity between parameters. To assess practical identifiability of the model parameters, profile-likelihood curves were constructed for each parameter. Four of the seven model parameters were found to be practically identifiable from the selected data. The remaining three parameters were practically non-identifiable. Among these non-identifiable parameters, one could be decreased as much as possible. The other two non-identifiable parameters were inversely correlated, which prevented their precise estimation. This work presented the practical identifiability analysis of a seven-parameter cardiovascular system model, from limited clinical data. The analysis showed that three of the seven parameters were practically non-identifiable, thus limiting the use of the model as a monitoring tool. Slight changes in the time-varying function modeling cardiac contraction and use of larger values for the reference range of venous pressure made the model fully practically identifiable. Copyright © 2017. Published by Elsevier B.V.

Measures of GCM Performance as Functions of Model Parameters Affecting Clouds and Radiation

NASA Astrophysics Data System (ADS)

Jackson, C.; Mu, Q.; Sen, M.; Stoffa, P.

2002-05-01

This abstract is one of three related presentations at this meeting dealing with several issues surrounding optimal parameter and uncertainty estimation of model predictions of climate. Uncertainty in model predictions of climate depends in part on the uncertainty produced by model approximations or parameterizations of unresolved physics. Evaluating these uncertainties is computationally expensive because one needs to evaluate how arbitrary choices for any given combination of model parameters affects model performance. Because the computational effort grows exponentially with the number of parameters being investigated, it is important to choose parameters carefully. Evaluating whether a parameter is worth investigating depends on two considerations: 1) does reasonable choices of parameter values produce a large range in model response relative to observational uncertainty? and 2) does the model response depend non-linearly on various combinations of model parameters? We have decided to narrow our attention to selecting parameters that affect clouds and radiation, as it is likely that these parameters will dominate uncertainties in model predictions of future climate. We present preliminary results of ~20 to 30 AMIPII style climate model integrations using NCAR's CCM3.10 that show model performance as functions of individual parameters controlling 1) critical relative humidity for cloud formation (RHMIN), and 2) boundary layer critical Richardson number (RICR). We also explore various definitions of model performance that include some or all observational data sources (surface air temperature and pressure, meridional and zonal winds, clouds, long and short-wave cloud forcings, etc...) and evaluate in a few select cases whether the model's response depends non-linearly on the parameter values we have selected.

Models based on value and probability in health improve shared decision making.

PubMed

Ortendahl, Monica

2008-10-01

Diagnostic reasoning and treatment decisions are a key competence of doctors. A model based on values and probability provides a conceptual framework for clinical judgments and decisions, and also facilitates the integration of clinical and biomedical knowledge into a diagnostic decision. Both value and probability are usually estimated values in clinical decision making. Therefore, model assumptions and parameter estimates should be continually assessed against data, and models should be revised accordingly. Introducing parameter estimates for both value and probability, which usually pertain in clinical work, gives the model labelled subjective expected utility. Estimated values and probabilities are involved sequentially for every step in the decision-making process. Introducing decision-analytic modelling gives a more complete picture of variables that influence the decisions carried out by the doctor and the patient. A model revised for perceived values and probabilities by both the doctor and the patient could be used as a tool for engaging in a mutual and shared decision-making process in clinical work.

Model Update of a Micro Air Vehicle (MAV) Flexible Wing Frame with Uncertainty Quantification

NASA Technical Reports Server (NTRS)

Reaves, Mercedes C.; Horta, Lucas G.; Waszak, Martin R.; Morgan, Benjamin G.

2004-01-01

This paper describes a procedure to update parameters in the finite element model of a Micro Air Vehicle (MAV) to improve displacement predictions under aerodynamics loads. Because of fabrication, materials, and geometric uncertainties, a statistical approach combined with Multidisciplinary Design Optimization (MDO) is used to modify key model parameters. Static test data collected using photogrammetry are used to correlate with model predictions. Results show significant improvements in model predictions after parameters are updated; however, computed probabilities values indicate low confidence in updated values and/or model structure errors. Lessons learned in the areas of wing design, test procedures, modeling approaches with geometric nonlinearities, and uncertainties quantification are all documented.

Optimizing the parameters of the Lyman-Kutcher-Burman, Källman, and Logit+EUD models for the rectum - a comparison between normal tissue complication probability and clinical data

NASA Astrophysics Data System (ADS)

Trojková, Darina; Judas, Libor; Trojek, Tomáš

2014-11-01

Minimizing the late rectal toxicity of prostate cancer patients is a very important and widely-discussed topic. Normal tissue complication probability (NTCP) models can be used to evaluate competing treatment plans. In our work, the parameters of the Lyman-Kutcher-Burman (LKB), Källman, and Logit+EUD models are optimized by minimizing the Brier score for a group of 302 prostate cancer patients. The NTCP values are calculated and are compared with the values obtained using previously published values for the parameters. χ2 Statistics were calculated as a check of goodness of optimization.

Application of a parameter-estimation technique to modeling the regional aquifer underlying the eastern Snake River plain, Idaho

USGS Publications Warehouse

Garabedian, Stephen P.

1986-01-01

A nonlinear, least-squares regression technique for the estimation of ground-water flow model parameters was applied to the regional aquifer underlying the eastern Snake River Plain, Idaho. The technique uses a computer program to simulate two-dimensional, steady-state ground-water flow. Hydrologic data for the 1980 water year were used to calculate recharge rates, boundary fluxes, and spring discharges. Ground-water use was estimated from irrigated land maps and crop consumptive-use figures. These estimates of ground-water withdrawal, recharge rates, and boundary flux, along with leakance, were used as known values in the model calibration of transmissivity. Leakance values were adjusted between regression solutions by comparing model-calculated to measured spring discharges. In other simulations, recharge and leakance also were calibrated as prior-information regression parameters, which limits the variation of these parameters using a normalized standard error of estimate. Results from a best-fit model indicate a wide areal range in transmissivity from about 0.05 to 44 feet squared per second and in leakance from about 2.2x10 -9 to 6.0 x 10 -8 feet per second per foot. Along with parameter values, model statistics also were calculated, including the coefficient of correlation between calculated and observed head (0.996), the standard error of the estimates for head (40 feet), and the parameter coefficients of variation (about 10-40 percent). Additional boundary flux was added in some areas during calibration to achieve proper fit to ground-water flow directions. Model fit improved significantly when areas that violated model assumptions were removed. It also improved slightly when y-direction (northwest-southeast) transmissivity values were larger than x-direction (northeast-southwest) transmissivity values. The model was most sensitive to changes in recharge, and in some areas, to changes in transmissivity, particularly near the spring discharge area from Milner Dam to King Hill.

UCODE_2005 and six other computer codes for universal sensitivity analysis, calibration, and uncertainty evaluation constructed using the JUPITER API

USGS Publications Warehouse

Poeter, Eileen E.; Hill, Mary C.; Banta, Edward R.; Mehl, Steffen; Christensen, Steen

2006-01-01

This report documents the computer codes UCODE_2005 and six post-processors. Together the codes can be used with existing process models to perform sensitivity analysis, data needs assessment, calibration, prediction, and uncertainty analysis. Any process model or set of models can be used; the only requirements are that models have numerical (ASCII or text only) input and output files, that the numbers in these files have sufficient significant digits, that all required models can be run from a single batch file or script, and that simulated values are continuous functions of the parameter values. Process models can include pre-processors and post-processors as well as one or more models related to the processes of interest (physical, chemical, and so on), making UCODE_2005 extremely powerful. An estimated parameter can be a quantity that appears in the input files of the process model(s), or a quantity used in an equation that produces a value that appears in the input files. In the latter situation, the equation is user-defined. UCODE_2005 can compare observations and simulated equivalents. The simulated equivalents can be any simulated value written in the process-model output files or can be calculated from simulated values with user-defined equations. The quantities can be model results, or dependent variables. For example, for ground-water models they can be heads, flows, concentrations, and so on. Prior, or direct, information on estimated parameters also can be considered. Statistics are calculated to quantify the comparison of observations and simulated equivalents, including a weighted least-squares objective function. In addition, data-exchange files are produced that facilitate graphical analysis. UCODE_2005 can be used fruitfully in model calibration through its sensitivity analysis capabilities and its ability to estimate parameter values that result in the best possible fit to the observations. Parameters are estimated using nonlinear regression: a weighted least-squares objective function is minimized with respect to the parameter values using a modified Gauss-Newton method or a double-dogleg technique. Sensitivities needed for the method can be read from files produced by process models that can calculate sensitivities, such as MODFLOW-2000, or can be calculated by UCODE_2005 using a more general, but less accurate, forward- or central-difference perturbation technique. Problems resulting from inaccurate sensitivities and solutions related to the perturbation techniques are discussed in the report. Statistics are calculated and printed for use in (1) diagnosing inadequate data and identifying parameters that probably cannot be estimated; (2) evaluating estimated parameter values; and (3) evaluating how well the model represents the simulated processes. Results from UCODE_2005 and codes RESIDUAL_ANALYSIS and RESIDUAL_ANALYSIS_ADV can be used to evaluate how accurately the model represents the processes it simulates. Results from LINEAR_UNCERTAINTY can be used to quantify the uncertainty of model simulated values if the model is sufficiently linear. Results from MODEL_LINEARITY and MODEL_LINEARITY_ADV can be used to evaluate model linearity and, thereby, the accuracy of the LINEAR_UNCERTAINTY results. UCODE_2005 can also be used to calculate nonlinear confidence and predictions intervals, which quantify the uncertainty of model simulated values when the model is not linear. CORFAC_PLUS can be used to produce factors that allow intervals to account for model intrinsic nonlinearity and small-scale variations in system characteristics that are not explicitly accounted for in the model or the observation weighting. The six post-processing programs are independent of UCODE_2005 and can use the results of other programs that produce the required data-exchange files. UCODE_2005 and the other six codes are intended for use on any computer operating system. The programs con

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

A hybrid optimization approach to the estimation of distributed parameters in two-dimensional confined aquifers

USGS Publications Warehouse

Heidari, M.; Ranjithan, S.R.

1998-01-01

In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.

Two-dimensional advective transport in ground-water flow parameter estimation

USGS Publications Warehouse

Anderman, E.R.; Hill, M.C.; Poeter, E.P.

1996-01-01

Nonlinear regression is useful in ground-water flow parameter estimation, but problems of parameter insensitivity and correlation often exist given commonly available hydraulic-head and head-dependent flow (for example, stream and lake gain or loss) observations. To address this problem, advective-transport observations are added to the ground-water flow, parameter-estimation model MODFLOWP using particle-tracking methods. The resulting model is used to investigate the importance of advective-transport observations relative to head-dependent flow observations when either or both are used in conjunction with hydraulic-head observations in a simulation of the sewage-discharge plume at Otis Air Force Base, Cape Cod, Massachusetts, USA. The analysis procedure for evaluating the probable effect of new observations on the regression results consists of two steps: (1) parameter sensitivities and correlations calculated at initial parameter values are used to assess the model parameterization and expected relative contributions of different types of observations to the regression; and (2) optimal parameter values are estimated by nonlinear regression and evaluated. In the Cape Cod parameter-estimation model, advective-transport observations did not significantly increase the overall parameter sensitivity; however: (1) inclusion of advective-transport observations decreased parameter correlation enough for more unique parameter values to be estimated by the regression; (2) realistic uncertainties in advective-transport observations had a small effect on parameter estimates relative to the precision with which the parameters were estimated; and (3) the regression results and sensitivity analysis provided insight into the dynamics of the ground-water flow system, especially the importance of accurate boundary conditions. In this work, advective-transport observations improved the calibration of the model and the estimation of ground-water flow parameters, and use of regression and related techniques produced significant insight into the physical system.

Inverse estimation of parameters for an estuarine eutrophication model

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

Shen, J.; Kuo, A.Y.

1996-11-01

An inverse model of an estuarine eutrophication model with eight state variables is developed. It provides a framework to estimate parameter values of the eutrophication model by assimilation of concentration data of these state variables. The inverse model using the variational technique in conjunction with a vertical two-dimensional eutrophication model is general enough to be applicable to aid model calibration. The formulation is illustrated by conducting a series of numerical experiments for the tidal Rappahannock River, a western shore tributary of the Chesapeake Bay. The numerical experiments of short-period model simulations with different hypothetical data sets and long-period model simulationsmore » with limited hypothetical data sets demonstrated that the inverse model can be satisfactorily used to estimate parameter values of the eutrophication model. The experiments also showed that the inverse model is useful to address some important questions, such as uniqueness of the parameter estimation and data requirements for model calibration. Because of the complexity of the eutrophication system, degrading of speed of convergence may occur. Two major factors which cause degradation of speed of convergence are cross effects among parameters and the multiple scales involved in the parameter system.« less

Assessing the importance of rainfall uncertainty on hydrological models with different spatial and temporal scale

NASA Astrophysics Data System (ADS)

Nossent, Jiri; Pereira, Fernando; Bauwens, Willy

2015-04-01

Precipitation is one of the key inputs for hydrological models. As long as the values of the hydrological model parameters are fixed, a variation of the rainfall input is expected to induce a change in the model output. Given the increased awareness of uncertainty on rainfall records, it becomes more important to understand the impact of this input - output dynamic. Yet, modellers often still have the intention to mimic the observed flow, whatever the deviation of the employed records from the actual rainfall might be, by recklessly adapting the model parameter values. But is it actually possible to vary the model parameter values in such a way that a certain (observed) model output can be generated based on inaccurate rainfall inputs? Thus, how important is the rainfall uncertainty for the model output with respect to the model parameter importance? To address this question, we apply the Sobol' sensitivity analysis method to assess and compare the importance of the rainfall uncertainty and the model parameters on the output of the hydrological model. In order to be able to treat the regular model parameters and input uncertainty in the same way, and to allow a comparison of their influence, a possible approach is to represent the rainfall uncertainty by a parameter. To tackle the latter issue, we apply so called rainfall multipliers on hydrological independent storm events, as a probabilistic parameter representation of the possible rainfall variation. As available rainfall records are very often point measurements at a discrete time step (hourly, daily, monthly,…), they contain uncertainty due to a latent lack of spatial and temporal variability. The influence of the latter variability can also be different for hydrological models with different spatial and temporal scale. Therefore, we perform the sensitivity analyses on a semi-distributed model (SWAT) and a lumped model (NAM). The assessment and comparison of the importance of the rainfall uncertainty and the model parameters is achieved by considering different scenarios for the included parameters and the state of the models.

Accounting for uncertainty in model-based prevalence estimation: paratuberculosis control in dairy herds.

PubMed

Davidson, Ross S; McKendrick, Iain J; Wood, Joanna C; Marion, Glenn; Greig, Alistair; Stevenson, Karen; Sharp, Michael; Hutchings, Michael R

2012-09-10

A common approach to the application of epidemiological models is to determine a single (point estimate) parameterisation using the information available in the literature. However, in many cases there is considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood and highly variable, and for such infections there is a need to develop and apply statistical techniques which make maximal use of available data. A technique based on Latin hypercube sampling combined with a novel reweighting method was developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds which combines a continuous time stochastic algorithm with model features such as within herd variability in disease development and shedding, which have not been previously explored in paratuberculosis models. Generated sample parameter combinations were assigned a weight, determined by quantifying the model's resultant ability to reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios such as control options. To illustrate the utility of this approach these reweighted model outputs were used to compare standard test and cull control strategies both individually and in combination with simple husbandry practices that aim to reduce infection rates. The technique developed has been shown to be applicable to a complex model incorporating realistic control options. For models where parameters are not well known or subject to significant variability, the reweighting scheme allowed estimated distributions of parameter values to be combined with additional sources of information, such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for parameter uncertainty and combining different sources of information, and is thus expected to be useful in application to a large number of disease systems.

Normal Values for Heart Electrophysiology Parameters of Healthy Swine Determined on Electrophysiology Study.

PubMed

Noszczyk-Nowak, Agnieszka; Cepiel, Alicja; Janiszewski, Adrian; Pasławski, Robert; Gajek, Jacek; Pasławska, Urszula; Nicpoń, Józef

2016-01-01

Swine are a well-recognized animal model for human cardiovascular diseases. Despite the widespread use of porcine model in experimental electrophysiology, still no reference values for intracardiac electrical activity and conduction parameters determined during an invasive electrophysiology study (EPS) have been developed in this species thus far. The aim of the study was to develop a set of normal values for intracardiac electrical activity and conduction parameters determined during an invasive EPS of swine. The study included 36 healthy domestic swine (24-40 kg body weight). EPS was performed under a general anesthesia with midazolam, propofol and isoflurane. The reference values for intracardiac electrical activity and conduction parameters were calculated as arithmetic means ± 2 standard deviations. The reference values were determined for AH, HV and PA intervals, interatrial conduction time at its own and imposed rhythm, sinus node recovery time (SNRT), corrected sinus node recovery time (CSNRT), anterograde and retrograde Wenckebach points, atrial, atrioventricular node and ventricular refractory periods. No significant correlations were found between body weight and heart rate of the examined pigs and their electrophysiological parameters. The hereby presented reference values can be helpful in comparing the results of various studies, as well as in more accurately estimating the values of electrophysiological parameters that can be expected in a given experiment.

Optimizing the Determination of Roughness Parameters for Model Urban Canopies

NASA Astrophysics Data System (ADS)

Huq, Pablo; Rahman, Auvi

2018-05-01

We present an objective optimization procedure to determine the roughness parameters for very rough boundary-layer flow over model urban canopies. For neutral stratification the mean velocity profile above a model urban canopy is described by the logarithmic law together with the set of roughness parameters of displacement height d, roughness length z_0 , and friction velocity u_* . Traditionally, values of these roughness parameters are obtained by fitting the logarithmic law through (all) the data points comprising the velocity profile. The new procedure generates unique velocity profiles from subsets or combinations of the data points of the original velocity profile, after which all possible profiles are examined. Each of the generated profiles is fitted to the logarithmic law for a sequence of values of d, with the representative value of d obtained from the minima of the summed least-squares errors for all the generated profiles. The representative values of z_0 and u_* are identified by the peak in the bivariate histogram of z_0 and u_* . The methodology has been verified against laboratory datasets of flow above model urban canopies.

A time series model: First-order integer-valued autoregressive (INAR(1))

NASA Astrophysics Data System (ADS)

Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.

2017-07-01

Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.

A Note on the Item Information Function of the Four-Parameter Logistic Model

ERIC Educational Resources Information Center

Magis, David

2013-01-01

This article focuses on four-parameter logistic (4PL) model as an extension of the usual three-parameter logistic (3PL) model with an upper asymptote possibly different from 1. For a given item with fixed item parameters, Lord derived the value of the latent ability level that maximizes the item information function under the 3PL model. The…

Development of a Screening Model for Design and Costing of an Innovative Tailored Granular Activated Carbon Technology to Treat Perchlorate-Contaminated Water

DTIC Science & Technology

2007-03-01

column experiments were used to obtain model parameters . Cost data used in the model were based on conventional GAC installations, as modified to...43 Calculation of Parameters ...66 Determination of Parameter Values

Measurement of unsaturated hydraulic properties and evaluation of property-transfer models for deep sedimentary interbeds, Idaho National Laboratory, Idaho

USGS Publications Warehouse

Perkins, Kimberlie; Johnson, Brittany D.; Mirus, Benjamin B.

2014-01-01

During 2013–14, the USGS, in cooperation with the U.S. Department of Energy, focused on further characterization of the sedimentary interbeds below the future site of the proposed Remote Handled Low-Level Waste (RHLLW) facility, which is intended for the long-term storage of low-level radioactive waste. Twelve core samples from the sedimentary interbeds from a borehole near the proposed facility were collected for laboratory analysis of hydraulic properties, which also allowed further testing of the property-transfer modeling approach. For each core sample, the steady-state centrifuge method was used to measure relations between matric potential, saturation, and conductivity. These laboratory measurements were compared to water-retention and unsaturated hydraulic conductivity parameters estimated using the established property-transfer models. For each core sample obtained, the agreement between measured and estimated hydraulic parameters was evaluated quantitatively using the Pearson correlation coefficient (r). The highest correlation is for saturated hydraulic conductivity (Ksat) with an r value of 0.922. The saturated water content (qsat) also exhibits a strong linear correlation with an r value of 0.892. The curve shape parameter (λ) has a value of 0.731, whereas the curve scaling parameter (yo) has the lowest r value of 0.528. The r values demonstrate that model predictions correspond well to the laboratory measured properties for most parameters, which supports the value of extending this approach for quantifying unsaturated hydraulic properties at various sites throughout INL.

MODFLOW-2000, the U.S. Geological Survey modular ground-water model; user guide to the observation, sensitivity, and parameter-estimation processes and three post-processing programs

USGS Publications Warehouse

Hill, Mary C.; Banta, E.R.; Harbaugh, A.W.; Anderman, E.R.

2000-01-01

This report documents the Observation, Sensitivity, and Parameter-Estimation Processes of the ground-water modeling computer program MODFLOW-2000. The Observation Process generates model-calculated values for comparison with measured, or observed, quantities. A variety of statistics is calculated to quantify this comparison, including a weighted least-squares objective function. In addition, a number of files are produced that can be used to compare the values graphically. The Sensitivity Process calculates the sensitivity of hydraulic heads throughout the model with respect to specified parameters using the accurate sensitivity-equation method. These are called grid sensitivities. If the Observation Process is active, it uses the grid sensitivities to calculate sensitivities for the simulated values associated with the observations. These are called observation sensitivities. Observation sensitivities are used to calculate a number of statistics that can be used (1) to diagnose inadequate data, (2) to identify parameters that probably cannot be estimated by regression using the available observations, and (3) to evaluate the utility of proposed new data. The Parameter-Estimation Process uses a modified Gauss-Newton method to adjust values of user-selected input parameters in an iterative procedure to minimize the value of the weighted least-squares objective function. Statistics produced by the Parameter-Estimation Process can be used to evaluate estimated parameter values; statistics produced by the Observation Process and post-processing program RESAN-2000 can be used to evaluate how accurately the model represents the actual processes; statistics produced by post-processing program YCINT-2000 can be used to quantify the uncertainty of model simulated values. Parameters are defined in the Ground-Water Flow Process input files and can be used to calculate most model inputs, such as: for explicitly defined model layers, horizontal hydraulic conductivity, horizontal anisotropy, vertical hydraulic conductivity or vertical anisotropy, specific storage, and specific yield; and, for implicitly represented layers, vertical hydraulic conductivity. In addition, parameters can be defined to calculate the hydraulic conductance of the River, General-Head Boundary, and Drain Packages; areal recharge rates of the Recharge Package; maximum evapotranspiration of the Evapotranspiration Package; pumpage or the rate of flow at defined-flux boundaries of the Well Package; and the hydraulic head at constant-head boundaries. The spatial variation of model inputs produced using defined parameters is very flexible, including interpolated distributions that require the summation of contributions from different parameters. Observations can include measured hydraulic heads or temporal changes in hydraulic heads, measured gains and losses along head-dependent boundaries (such as streams), flows through constant-head boundaries, and advective transport through the system, which generally would be inferred from measured concentrations. MODFLOW-2000 is intended for use on any computer operating system. The program consists of algorithms programmed in Fortran 90, which efficiently performs numerical calculations and is fully compatible with the newer Fortran 95. The code is easily modified to be compatible with FORTRAN 77. Coordination for multiple processors is accommodated using Message Passing Interface (MPI) commands. The program is designed in a modular fashion that is intended to support inclusion of new capabilities.

On the applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion to the Community Land Model: Case studies at flux tower sites: SURROGATE-BASED MCMC FOR CLM

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

Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan

2016-07-04

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesianmore » model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically-average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. Analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.« less

On the applicability of surrogate-based MCMC-Bayesian inversion to the Community Land Model: Case studies at Flux tower sites

DOE PAGES

Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan; ...

2016-06-01

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesianmore » model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. As a result, analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.« less

On the applicability of surrogate-based MCMC-Bayesian inversion to the Community Land Model: Case studies at Flux tower sites

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

Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesianmore » model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. As a result, analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.« less

On the applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion to the Community Land Model: Case studies at flux tower sites

NASA Astrophysics Data System (ADS)

Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan; Ren, Huiying; Liu, Ying; Swiler, Laura

2016-07-01

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesian model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. Analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.

Dynamic behavior of the interaction between epidemics and cascades on heterogeneous networks

NASA Astrophysics Data System (ADS)

Jiang, Lurong; Jin, Xinyu; Xia, Yongxiang; Ouyang, Bo; Wu, Duanpo

2014-12-01

Epidemic spreading and cascading failure are two important dynamical processes on complex networks. They have been investigated separately for a long time. But in the real world, these two dynamics sometimes may interact with each other. In this paper, we explore a model combined with the SIR epidemic spreading model and a local load sharing cascading failure model. There exists a critical value of the tolerance parameter for which the epidemic with high infection probability can spread out and infect a fraction of the network in this model. When the tolerance parameter is smaller than the critical value, the cascading failure cuts off the abundance of paths and blocks the spreading of the epidemic locally. While the tolerance parameter is larger than the critical value, the epidemic spreads out and infects a fraction of the network. A method for estimating the critical value is proposed. In simulations, we verify the effectiveness of this method in the uncorrelated configuration model (UCM) scale-free networks.

Assessing the importance of self-regulating mechanisms in diamondback moth population dynamics: application of discrete mathematical models.

PubMed

Nedorezov, Lev V; Löhr, Bernhard L; Sadykova, Dinara L

2008-10-07

The applicability of discrete mathematical models for the description of diamondback moth (DBM) (Plutella xylostella L.) population dynamics was investigated. The parameter values for several well-known discrete time models (Skellam, Moran-Ricker, Hassell, Maynard Smith-Slatkin, and discrete logistic models) were estimated for an experimental time series from a highland cabbage-growing area in eastern Kenya. For all sets of parameters, boundaries of confidence domains were determined. Maximum calculated birth rates varied between 1.086 and 1.359 when empirical values were used for parameter estimation. After fitting of the models to the empirical trajectory, all birth rate values resulted considerably higher (1.742-3.526). The carrying capacity was determined between 13.0 and 39.9DBM/plant, after fitting of the models these values declined to 6.48-9.3, all values well within the range encountered empirically. The application of the Durbin-Watson criteria for comparison of theoretical and experimental population trajectories produced negative correlations with all models. A test of residual value groupings for randomness showed that their distribution is non-stochastic. In consequence, we conclude that DBM dynamics cannot be explained as a result of intra-population self-regulative mechanisms only (=by any of the models tested) and that more comprehensive models are required for the explanation of DBM population dynamics.

THE ON-SITE ON-LINE TOOL FOR SITE ASSESSMENT CALCULATIONS

EPA Science Inventory

State and Federal Agency personnel often receive modeling reports with undocumented parameter values. The reports give parameter values, but often no indication if the value was measured, taken from the literature, the result of calibration, or some type of estimate. Recent examp...

Bayesian Parameter Estimation for Heavy-Duty Vehicles

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

Miller, Eric; Konan, Arnaud; Duran, Adam

2017-03-28

Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses Monte Carlo to generate parameter sets which is fed to a variant of the road load equation. Modeled road load is then compared to measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the currentmore » state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters. Results confirm the method's ability to estimate reasonable parameter sets, and indicates an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.« less

Recovering Parameters of Johnson's SB Distribution

Treesearch

Bernard R. Parresol

2003-01-01

A new parameter recovery model for Johnson's SB distribution is developed. This latest alternative approach permits recovery of the range and both shape parameters. Previous models recovered only the two shape parameters. Also, a simple procedure for estimating the distribution minimum from sample values is presented. The new methodology...

Impact of orbit modeling on DORIS station position and Earth rotation estimates

NASA Astrophysics Data System (ADS)

Štěpánek, Petr; Rodriguez-Solano, Carlos Javier; Hugentobler, Urs; Filler, Vratislav

2014-04-01

The high precision of estimated station coordinates and Earth rotation parameters (ERP) obtained from satellite geodetic techniques is based on the precise determination of the satellite orbit. This paper focuses on the analysis of the impact of different orbit parameterizations on the accuracy of station coordinates and the ERPs derived from DORIS observations. In a series of experiments the DORIS data from the complete year 2011 were processed with different orbit model settings. First, the impact of precise modeling of the non-conservative forces on geodetic parameters was compared with results obtained with an empirical-stochastic modeling approach. Second, the temporal spacing of drag scaling parameters was tested. Third, the impact of estimating once-per-revolution harmonic accelerations in cross-track direction was analyzed. And fourth, two different approaches for solar radiation pressure (SRP) handling were compared, namely adjusting SRP scaling parameter or fixing it on pre-defined values. Our analyses confirm that the empirical-stochastic orbit modeling approach, which does not require satellite attitude information and macro models, results for most of the monitored station parameters in comparable accuracy as the dynamical model that employs precise non-conservative force modeling. However, the dynamical orbit model leads to a reduction of the RMS values for the estimated rotation pole coordinates by 17% for x-pole and 12% for y-pole. The experiments show that adjusting atmospheric drag scaling parameters each 30 min is appropriate for DORIS solutions. Moreover, it was shown that the adjustment of cross-track once-per-revolution empirical parameter increases the RMS of the estimated Earth rotation pole coordinates. With recent data it was however not possible to confirm the previously known high annual variation in the estimated geocenter z-translation series as well as its mitigation by fixing the SRP parameters on pre-defined values.

Characterization of Initial Parameter Information for Lifetime Prediction of Electronic Devices.

PubMed

Li, Zhigang; Liu, Boying; Yuan, Mengxiong; Zhang, Feifei; Guo, Jiaqiang

2016-01-01

Newly manufactured electronic devices are subject to different levels of potential defects existing among the initial parameter information of the devices. In this study, a characterization of electromagnetic relays that were operated at their optimal performance with appropriate and steady parameter values was performed to estimate the levels of their potential defects and to develop a lifetime prediction model. First, the initial parameter information value and stability were quantified to measure the performance of the electronics. In particular, the values of the initial parameter information were estimated using the probability-weighted average method, whereas the stability of the parameter information was determined by using the difference between the extrema and end points of the fitting curves for the initial parameter information. Second, a lifetime prediction model for small-sized samples was proposed on the basis of both measures. Finally, a model for the relationship of the initial contact resistance and stability over the lifetime of the sampled electromagnetic relays was proposed and verified. A comparison of the actual and predicted lifetimes of the relays revealed a 15.4% relative error, indicating that the lifetime of electronic devices can be predicted based on their initial parameter information.

Characterization of Initial Parameter Information for Lifetime Prediction of Electronic Devices

PubMed Central

Li, Zhigang; Liu, Boying; Yuan, Mengxiong; Zhang, Feifei; Guo, Jiaqiang

2016-01-01

Newly manufactured electronic devices are subject to different levels of potential defects existing among the initial parameter information of the devices. In this study, a characterization of electromagnetic relays that were operated at their optimal performance with appropriate and steady parameter values was performed to estimate the levels of their potential defects and to develop a lifetime prediction model. First, the initial parameter information value and stability were quantified to measure the performance of the electronics. In particular, the values of the initial parameter information were estimated using the probability-weighted average method, whereas the stability of the parameter information was determined by using the difference between the extrema and end points of the fitting curves for the initial parameter information. Second, a lifetime prediction model for small-sized samples was proposed on the basis of both measures. Finally, a model for the relationship of the initial contact resistance and stability over the lifetime of the sampled electromagnetic relays was proposed and verified. A comparison of the actual and predicted lifetimes of the relays revealed a 15.4% relative error, indicating that the lifetime of electronic devices can be predicted based on their initial parameter information. PMID:27907188

Parameter Estimation of Partial Differential Equation Models.

PubMed

Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab

2013-01-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.

Orbit Estimation of Non-Cooperative Maneuvering Spacecraft

DTIC Science & Technology

2015-06-01

only take on values that generate real sigma points; therefore, λ > −n. The additional weighting scheme is outlined in the following equations κ = α2...orbit shapes resulted in a similar model weighting. Additional cases of this orbit type also resulted in heavily weighting smaller η value models. It is...determined using both the symmetric and additional parameters UTs. The best values for the weighting parameters are then compared for each test case

Liver fibrosis: stretched exponential model outperforms mono-exponential and bi-exponential models of diffusion-weighted MRI.

PubMed

Seo, Nieun; Chung, Yong Eun; Park, Yung Nyun; Kim, Eunju; Hwang, Jinwoo; Kim, Myeong-Jin

2018-07-01

To compare the ability of diffusion-weighted imaging (DWI) parameters acquired from three different models for the diagnosis of hepatic fibrosis (HF). Ninety-five patients underwent DWI using nine b values at 3 T magnetic resonance. The hepatic apparent diffusion coefficient (ADC) from a mono-exponential model, the true diffusion coefficient (D t ), pseudo-diffusion coefficient (D p ) and perfusion fraction (f) from a biexponential model, and the distributed diffusion coefficient (DDC) and intravoxel heterogeneity index (α) from a stretched exponential model were compared with the pathological HF stage. For the stretched exponential model, parameters were also obtained using a dataset of six b values (DDC # , α # ). The diagnostic performances of the parameters for HF staging were evaluated with Obuchowski measures and receiver operating characteristics (ROC) analysis. The measurement variability of DWI parameters was evaluated using the coefficient of variation (CoV). Diagnostic accuracy for HF staging was highest for DDC # (Obuchowski measures, 0.770 ± 0.03), and it was significantly higher than that of ADC (0.597 ± 0.05, p < 0.001), D t (0.575 ± 0.05, p < 0.001) and f (0.669 ± 0.04, p = 0.035). The parameters from stretched exponential DWI and D p showed higher areas under the ROC curve (AUCs) for determining significant fibrosis (≥F2) and cirrhosis (F = 4) than other parameters. However, D p showed significantly higher measurement variability (CoV, 74.6%) than DDC # (16.1%, p < 0.001) and α # (15.1%, p < 0.001). Stretched exponential DWI is a promising method for HF staging with good diagnostic performance and fewer b-value acquisitions, allowing shorter acquisition time. • Stretched exponential DWI provides a precise and accurate model for HF staging. • Stretched exponential DWI parameters are more reliable than D p from bi-exponential DWI model • Acquisition of six b values is sufficient to obtain accurate DDC and α.

Time Domain Estimation of Arterial Parameters using the Windkessel Model and the Monte Carlo Method

NASA Astrophysics Data System (ADS)

Gostuski, Vladimir; Pastore, Ignacio; Rodriguez Palacios, Gaspar; Vaca Diez, Gustavo; Moscoso-Vasquez, H. Marcela; Risk, Marcelo

2016-04-01

Numerous parameter estimation techniques exist for characterizing the arterial system using electrical circuit analogs. However, they are often limited by their requirements and usually high computational burdain. Therefore, a new method for estimating arterial parameters based on Monte Carlo simulation is proposed. A three element Windkessel model was used to represent the arterial system. The approach was to reduce the error between the calculated and physiological aortic pressure by randomly generating arterial parameter values, while keeping constant the arterial resistance. This last value was obtained for each subject using the arterial flow, and was a necessary consideration in order to obtain a unique set of values for the arterial compliance and peripheral resistance. The estimation technique was applied to in vivo data containing steady beats in mongrel dogs, and it reliably estimated Windkessel arterial parameters. Further, this method appears to be computationally efficient for on-line time-domain estimation of these parameters.

A flexible model of foraging by a honey bee colony: the effects of individual behaviour on foraging success.

PubMed

Cox, Melissa D; Myerscough, Mary R

2003-07-21

This paper develops and explores a model of foraging in honey bee colonies. The model may be applied to forage sources with various properties, and to colonies with different foraging-related parameters. In particular, we examine the effect of five foraging-related parameters on the foraging response and consequent nectar intake of a homogeneous colony. The parameters investigated affect different quantities critical to the foraging cycle--visit rate (affected by g), probability of dancing (mpd and bpd), duration of dancing (mcirc), or probability of abandonment (A). We show that one parameter, A, affects nectar intake in a nonlinear way. Further, we show that colonies with a midrange value of any foraging parameter perform better than the average of colonies with high- and low-range values, when profitable sources are available. Together these observations suggest that a heterogeneous colony, in which a range of parameter values are present, may perform better than a homogeneous colony. We modify the model to represent heterogeneous colonies and use it to show that the most important effect of heterogeneous foraging behaviour within the colony is to reduce the variance in the average quantity of nectar collected by heterogeneous colonies.

Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models.

PubMed

Cotten, Cameron; Reed, Jennifer L

2013-01-30

Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used. In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism. This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets.

Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models

PubMed Central

2013-01-01

Background Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used. Results In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism. Conclusions This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets. PMID:23360254

Local sensitivity analysis for inverse problems solved by singular value decomposition

USGS Publications Warehouse

Hill, M.C.; Nolan, B.T.

2010-01-01

Local sensitivity analysis provides computationally frugal ways to evaluate models commonly used for resource management, risk assessment, and so on. This includes diagnosing inverse model convergence problems caused by parameter insensitivity and(or) parameter interdependence (correlation), understanding what aspects of the model and data contribute to measures of uncertainty, and identifying new data likely to reduce model uncertainty. Here, we consider sensitivity statistics relevant to models in which the process model parameters are transformed using singular value decomposition (SVD) to create SVD parameters for model calibration. The statistics considered include the PEST identifiability statistic, and combined use of the process-model parameter statistics composite scaled sensitivities and parameter correlation coefficients (CSS and PCC). The statistics are complimentary in that the identifiability statistic integrates the effects of parameter sensitivity and interdependence, while CSS and PCC provide individual measures of sensitivity and interdependence. PCC quantifies correlations between pairs or larger sets of parameters; when a set of parameters is intercorrelated, the absolute value of PCC is close to 1.00 for all pairs in the set. The number of singular vectors to include in the calculation of the identifiability statistic is somewhat subjective and influences the statistic. To demonstrate the statistics, we use the USDA’s Root Zone Water Quality Model to simulate nitrogen fate and transport in the unsaturated zone of the Merced River Basin, CA. There are 16 log-transformed process-model parameters, including water content at field capacity (WFC) and bulk density (BD) for each of five soil layers. Calibration data consisted of 1,670 observations comprising soil moisture, soil water tension, aqueous nitrate and bromide concentrations, soil nitrate concentration, and organic matter content. All 16 of the SVD parameters could be estimated by regression based on the range of singular values. Identifiability statistic results varied based on the number of SVD parameters included. Identifiability statistics calculated for four SVD parameters indicate the same three most important process-model parameters as CSS/PCC (WFC1, WFC2, and BD2), but the order differed. Additionally, the identifiability statistic showed that BD1 was almost as dominant as WFC1. The CSS/PCC analysis showed that this results from its high correlation with WCF1 (-0.94), and not its individual sensitivity. Such distinctions, combined with analysis of how high correlations and(or) sensitivities result from the constructed model, can produce important insights into, for example, the use of sensitivity analysis to design monitoring networks. In conclusion, the statistics considered identified similar important parameters. They differ because (1) with CSS/PCC can be more awkward because sensitivity and interdependence are considered separately and (2) identifiability requires consideration of how many SVD parameters to include. A continuing challenge is to understand how these computationally efficient methods compare with computationally demanding global methods like Markov-Chain Monte Carlo given common nonlinear processes and the often even more nonlinear models.

Global Sensitivity Analysis of Environmental Models: Convergence, Robustness and Validation

NASA Astrophysics Data System (ADS)

Sarrazin, Fanny; Pianosi, Francesca; Khorashadi Zadeh, Farkhondeh; Van Griensven, Ann; Wagener, Thorsten

2015-04-01

Global Sensitivity Analysis aims to characterize the impact that variations in model input factors (e.g. the parameters) have on the model output (e.g. simulated streamflow). In sampling-based Global Sensitivity Analysis, the sample size has to be chosen carefully in order to obtain reliable sensitivity estimates while spending computational resources efficiently. Furthermore, insensitive parameters are typically identified through the definition of a screening threshold: the theoretical value of their sensitivity index is zero but in a sampling-base framework they regularly take non-zero values. There is little guidance available for these two steps in environmental modelling though. The objective of the present study is to support modellers in making appropriate choices, regarding both sample size and screening threshold, so that a robust sensitivity analysis can be implemented. We performed sensitivity analysis for the parameters of three hydrological models with increasing level of complexity (Hymod, HBV and SWAT), and tested three widely used sensitivity analysis methods (Elementary Effect Test or method of Morris, Regional Sensitivity Analysis, and Variance-Based Sensitivity Analysis). We defined criteria based on a bootstrap approach to assess three different types of convergence: the convergence of the value of the sensitivity indices, of the ranking (the ordering among the parameters) and of the screening (the identification of the insensitive parameters). We investigated the screening threshold through the definition of a validation procedure. The results showed that full convergence of the value of the sensitivity indices is not necessarily needed to rank or to screen the model input factors. Furthermore, typical values of the sample sizes that are reported in the literature can be well below the sample sizes that actually ensure convergence of ranking and screening.

Computer-Based Model Calibration and Uncertainty Analysis: Terms and Concepts

DTIC Science & Technology

2015-07-01

uncertainty analyses throughout the lifecycle of planning, designing, and operating of Civil Works flood risk management projects as described in...value 95% of the time. In the frequentist approach to PE, model parameters area regarded as having true values, and their estimate is based on the...in catchment models. 1. Evaluating parameter uncertainty. Water Resources Research 19(5):1151–1172. Lee, P. M. 2012. Bayesian statistics: An

Analysis of the Large Urban Fire Environment. Part II. Parametric Analysis and Model City Simulations.

DTIC Science & Technology

1982-11-01

algorithm for turning-region boundary value problem -70- d. Program control parameters: ALPHA (Qq) max’ maximum value of Qq in present coding. BETA, BLOSS...Parameters available for either system descrip- tion or program control . (These parameters are currently unused, so they are set equal to zero.) IGUESS...Parameter that controls the initial choices of first-shoot values along y = 0. IGUESS = 1: Discretized versions of P(r, 0), T(r, 0), and u(r, 0) must

Monitoring the injured brain: registered, patient specific atlas models to improve accuracy of recovered brain saturation values

NASA Astrophysics Data System (ADS)

Clancy, Michael; Belli, Antonio; Davies, David; Lucas, Samuel J. E.; Su, Zhangjie; Dehghani, Hamid

2015-07-01

The subject of superficial contamination and signal origins remains a widely debated topic in the field of Near Infrared Spectroscopy (NIRS), yet the concept of using the technology to monitor an injured brain, in a clinical setting, poses additional challenges concerning the quantitative accuracy of recovered parameters. Using high density diffuse optical tomography probes, quantitatively accurate parameters from different layers (skin, bone and brain) can be recovered from subject specific reconstruction models. This study assesses the use of registered atlas models for situations where subject specific models are not available. Data simulated from subject specific models were reconstructed using the 8 registered atlas models implementing a regional (layered) parameter recovery in NIRFAST. A 3-region recovery based on the atlas model yielded recovered brain saturation values which were accurate to within 4.6% (percentage error) of the simulated values, validating the technique. The recovered saturations in the superficial regions were not quantitatively accurate. These findings highlight differences in superficial (skin and bone) layer thickness between the subject and atlas models. This layer thickness mismatch was propagated through the reconstruction process decreasing the parameter accuracy.

Sample Size and Item Parameter Estimation Precision When Utilizing the One-Parameter "Rasch" Model

ERIC Educational Resources Information Center

Custer, Michael

2015-01-01

This study examines the relationship between sample size and item parameter estimation precision when utilizing the one-parameter model. Item parameter estimates are examined relative to "true" values by evaluating the decline in root mean squared deviation (RMSD) and the number of outliers as sample size increases. This occurs across…

Diagnosing ΛHDE model with statefinder hierarchy and fractional growth parameter

NASA Astrophysics Data System (ADS)

Zhou, LanJun; Wang, Shuang

2016-07-01

Recently, a new dark energy model called ΛHDE was proposed. In this model, dark energy consists of two parts: cosmological constant Λ and holographic dark energy (HDE). Two key parameters of this model are the fractional density of cosmological constant ΩΛ0, and the dimensionless HDE parameter c. Since these two parameters determine the dynamical properties of DE and the destiny of universe, it is important to study the impacts of different values of ΩΛ0 and c on the ΛHDE model. In this paper, we apply various DE diagnostic tools to diagnose ΛHDE models with different values of ΩΛ0 and c; these tools include statefinder hierarchy {S 3 (1) , S 4 (1) }, fractional growth parameter ɛ, and composite null diagnostic (CND), which is a combination of {S 3 (1) , S 4 (1) } and ɛ. We find that: (1) adopting different values of ΩΛ0 only has quantitative impacts on the evolution of the ΛHDE model, while adopting different c has qualitative impacts; (2) compared with S 3 (1) , S 4 (1) can give larger differences among the cosmic evolutions of the ΛHDE model associated with different ΩΛ0 or different c; (3) compared with the case of using a single diagnostic, adopting a CND pair has much stronger ability to diagnose the ΛHDE model.

Methods for using groundwater model predictions to guide hydrogeologic data collection, with application to the Death Valley regional groundwater flow system

USGS Publications Warehouse

Tiedeman, C.R.; Hill, M.C.; D'Agnese, F. A.; Faunt, C.C.

2003-01-01

Calibrated models of groundwater systems can provide substantial information for guiding data collection. This work considers using such models to guide hydrogeologic data collection for improving model predictions by identifying model parameters that are most important to the predictions. Identification of these important parameters can help guide collection of field data about parameter values and associated flow system features and can lead to improved predictions. Methods for identifying parameters important to predictions include prediction scaled sensitivities (PSS), which account for uncertainty on individual parameters as well as prediction sensitivity to parameters, and a new "value of improved information" (VOII) method presented here, which includes the effects of parameter correlation in addition to individual parameter uncertainty and prediction sensitivity. In this work, the PSS and VOII methods are demonstrated and evaluated using a model of the Death Valley regional groundwater flow system. The predictions of interest are advective transport paths originating at sites of past underground nuclear testing. Results show that for two paths evaluated the most important parameters include a subset of five or six of the 23 defined model parameters. Some of the parameters identified as most important are associated with flow system attributes that do not lie in the immediate vicinity of the paths. Results also indicate that the PSS and VOII methods can identify different important parameters. Because the methods emphasize somewhat different criteria for parameter importance, it is suggested that parameters identified by both methods be carefully considered in subsequent data collection efforts aimed at improving model predictions.

Physiological Parameters Database for PBPK Modeling (External Review Draft)

EPA Science Inventory

EPA released for public comment a physiological parameters database (created using Microsoft ACCESS) intended to be used in PBPK modeling. The database contains physiological parameter values for humans from early childhood through senescence. It also contains similar data for an...

Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Massonnet, F.; Goosse, H.; Fichefet, T.; Counillon, F.

2014-07-01

The choice of parameter values is crucial in the course of sea ice model development, since parameters largely affect the modeled mean sea ice state. Manual tuning of parameters will soon become impractical, as sea ice models will likely include more parameters to calibrate, leading to an exponential increase of the number of possible combinations to test. Objective and automatic methods for parameter calibration are thus progressively called on to replace the traditional heuristic, "trial-and-error" recipes. Here a method for calibration of parameters based on the ensemble Kalman filter is implemented, tested and validated in the ocean-sea ice model NEMO-LIM3. Three dynamic parameters are calibrated: the ice strength parameter P*, the ocean-sea ice drag parameter Cw, and the atmosphere-sea ice drag parameter Ca. In twin, perfect-model experiments, the default parameter values are retrieved within 1 year of simulation. Using 2007-2012 real sea ice drift data, the calibration of the ice strength parameter P* and the oceanic drag parameter Cw improves clearly the Arctic sea ice drift properties. It is found that the estimation of the atmospheric drag Ca is not necessary if P* and Cw are already estimated. The large reduction in the sea ice speed bias with calibrated parameters comes with a slight overestimation of the winter sea ice areal export through Fram Strait and a slight improvement in the sea ice thickness distribution. Overall, the estimation of parameters with the ensemble Kalman filter represents an encouraging alternative to manual tuning for ocean-sea ice models.

Equal Area Logistic Estimation for Item Response Theory

NASA Astrophysics Data System (ADS)

Lo, Shih-Ching; Wang, Kuo-Chang; Chang, Hsin-Li

2009-08-01

Item response theory (IRT) models use logistic functions exclusively as item response functions (IRFs). Applications of IRT models require obtaining the set of values for logistic function parameters that best fit an empirical data set. However, success in obtaining such set of values does not guarantee that the constructs they represent actually exist, for the adequacy of a model is not sustained by the possibility of estimating parameters. In this study, an equal area based two-parameter logistic model estimation algorithm is proposed. Two theorems are given to prove that the results of the algorithm are equivalent to the results of fitting data by logistic model. Numerical results are presented to show the stability and accuracy of the algorithm.

Probing Reliability of Transport Phenomena Based Heat Transfer and Fluid Flow Analysis in Autogeneous Fusion Welding Process

NASA Astrophysics Data System (ADS)

Bag, S.; de, A.

2010-09-01

The transport phenomena based heat transfer and fluid flow calculations in weld pool require a number of input parameters. Arc efficiency, effective thermal conductivity, and viscosity in weld pool are some of these parameters, values of which are rarely known and difficult to assign a priori based on the scientific principles alone. The present work reports a bi-directional three-dimensional (3-D) heat transfer and fluid flow model, which is integrated with a real number based genetic algorithm. The bi-directional feature of the integrated model allows the identification of the values of a required set of uncertain model input parameters and, next, the design of process parameters to achieve a target weld pool dimension. The computed values are validated with measured results in linear gas-tungsten-arc (GTA) weld samples. Furthermore, a novel methodology to estimate the overall reliability of the computed solutions is also presented.

Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models.

PubMed

Shah, A A; Xing, W W; Triantafyllidis, V

2017-04-01

In this paper, we develop reduced-order models for dynamic, parameter-dependent, linear and nonlinear partial differential equations using proper orthogonal decomposition (POD). The main challenges are to accurately and efficiently approximate the POD bases for new parameter values and, in the case of nonlinear problems, to efficiently handle the nonlinear terms. We use a Bayesian nonlinear regression approach to learn the snapshots of the solutions and the nonlinearities for new parameter values. Computational efficiency is ensured by using manifold learning to perform the emulation in a low-dimensional space. The accuracy of the method is demonstrated on a linear and a nonlinear example, with comparisons with a global basis approach.

Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models

PubMed Central

Xing, W. W.; Triantafyllidis, V.

2017-01-01

In this paper, we develop reduced-order models for dynamic, parameter-dependent, linear and nonlinear partial differential equations using proper orthogonal decomposition (POD). The main challenges are to accurately and efficiently approximate the POD bases for new parameter values and, in the case of nonlinear problems, to efficiently handle the nonlinear terms. We use a Bayesian nonlinear regression approach to learn the snapshots of the solutions and the nonlinearities for new parameter values. Computational efficiency is ensured by using manifold learning to perform the emulation in a low-dimensional space. The accuracy of the method is demonstrated on a linear and a nonlinear example, with comparisons with a global basis approach. PMID:28484327

Sensitivity analysis of conservative and reactive stream transient storage models applied to field data from multiple-reach experiments

USGS Publications Warehouse

Gooseff, M.N.; Bencala, K.E.; Scott, D.T.; Runkel, R.L.; McKnight, Diane M.

2005-01-01

The transient storage model (TSM) has been widely used in studies of stream solute transport and fate, with an increasing emphasis on reactive solute transport. In this study we perform sensitivity analyses of a conservative TSM and two different reactive solute transport models (RSTM), one that includes first-order decay in the stream and the storage zone, and a second that considers sorption of a reactive solute on streambed sediments. Two previously analyzed data sets are examined with a focus on the reliability of these RSTMs in characterizing stream and storage zone solute reactions. Sensitivities of simulations to parameters within and among reaches, parameter coefficients of variation, and correlation coefficients are computed and analyzed. Our results indicate that (1) simulated values have the greatest sensitivity to parameters within the same reach, (2) simulated values are also sensitive to parameters in reaches immediately upstream and downstream (inter-reach sensitivity), (3) simulated values have decreasing sensitivity to parameters in reaches farther downstream, and (4) in-stream reactive solute data provide adequate data to resolve effective storage zone reaction parameters, given the model formulations. Simulations of reactive solutes are shown to be equally sensitive to transport parameters and effective reaction parameters of the model, evidence of the control of physical transport on reactive solute dynamics. Similar to conservative transport analysis, reactive solute simulations appear to be most sensitive to data collected during the rising and falling limb of the concentration breakthrough curve. ?? 2005 Elsevier Ltd. All rights reserved.

Viscous cosmology in new holographic dark energy model and the cosmic acceleration

NASA Astrophysics Data System (ADS)

Singh, C. P.; Srivastava, Milan

2018-03-01

In this work, we study a flat Friedmann-Robertson-Walker universe filled with dark matter and viscous new holographic dark energy. We present four possible solutions of the model depending on the choice of the viscous term. We obtain the evolution of the cosmological quantities such as scale factor, deceleration parameter and transition redshift to observe the effect of viscosity in the evolution. We also emphasis upon the two independent geometrical diagnostics for our model, namely the statefinder and the Om diagnostics. In the first case we study new holographic dark energy model without viscous and obtain power-law expansion of the universe which gives constant deceleration parameter and statefinder parameters. In the limit of the parameter, the model approaches to Λ CDM model. In new holographic dark energy model with viscous, the bulk viscous coefficient is assumed as ζ =ζ 0+ζ 1H, where ζ 0 and ζ 1 are constants, and H is the Hubble parameter. In this model, we obtain all possible solutions with viscous term and analyze the expansion history of the universe. We draw the evolution graphs of the scale factor and deceleration parameter. It is observed that the universe transits from deceleration to acceleration for small values of ζ in late time. However, it accelerates very fast from the beginning for large values of ζ . By illustrating the evolutionary trajectories in r-s and r-q planes, we find that our model behaves as an quintessence like for small values of viscous coefficient and a Chaplygin gas like for large values of bulk viscous coefficient at early stage. However, model has close resemblance to that of the Λ CDM cosmology in late time. The Om has positive and negative curvatures for phantom and quintessence models, respectively depending on ζ . Our study shows that the bulk viscosity plays very important role in the expansion history of the universe.

Small field models with gravitational wave signature supported by CMB data

PubMed Central

Brustein, Ramy

2018-01-01

We study scale dependence of the cosmic microwave background (CMB) power spectrum in a class of small, single-field models of inflation which lead to a high value of the tensor to scalar ratio. The inflaton potentials that we consider are degree 5 polynomials, for which we precisely calculate the power spectrum, and extract the cosmological parameters: the scalar index ns, the running of the scalar index nrun and the tensor to scalar ratio r. We find that for non-vanishing nrun and for r as small as r = 0.001, the precisely calculated values of ns and nrun deviate significantly from what the standard analytic treatment predicts. We study in detail, and discuss the probable reasons for such deviations. As such, all previously considered models (of this kind) are based upon inaccurate assumptions. We scan the possible values of potential parameters for which the cosmological parameters are within the allowed range by observations. The 5 parameter class is able to reproduce all of the allowed values of ns and nrun for values of r that are as high as 0.001. Subsequently this study at once refutes previous such models built using the analytical Stewart-Lyth term, and revives the small field brand, by building models that do yield an appreciable r while conforming to known CMB observables. PMID:29795608

Bayesian inference for OPC modeling

NASA Astrophysics Data System (ADS)

Burbine, Andrew; Sturtevant, John; Fryer, David; Smith, Bruce W.

2016-03-01

The use of optical proximity correction (OPC) demands increasingly accurate models of the photolithographic process. Model building and inference techniques in the data science community have seen great strides in the past two decades which make better use of available information. This paper aims to demonstrate the predictive power of Bayesian inference as a method for parameter selection in lithographic models by quantifying the uncertainty associated with model inputs and wafer data. Specifically, the method combines the model builder's prior information about each modelling assumption with the maximization of each observation's likelihood as a Student's t-distributed random variable. Through the use of a Markov chain Monte Carlo (MCMC) algorithm, a model's parameter space is explored to find the most credible parameter values. During parameter exploration, the parameters' posterior distributions are generated by applying Bayes' rule, using a likelihood function and the a priori knowledge supplied. The MCMC algorithm used, an affine invariant ensemble sampler (AIES), is implemented by initializing many walkers which semiindependently explore the space. The convergence of these walkers to global maxima of the likelihood volume determine the parameter values' highest density intervals (HDI) to reveal champion models. We show that this method of parameter selection provides insights into the data that traditional methods do not and outline continued experiments to vet the method.

Musings on cosmological relaxation and the hierarchy problem

NASA Astrophysics Data System (ADS)

Jaeckel, Joerg; Mehta, Viraf M.; Witkowski, Lukas T.

2016-03-01

Recently Graham, Kaplan and Rajendran proposed cosmological relaxation as a mechanism for generating a hierarchically small Higgs vacuum expectation value. Inspired by this we collect some thoughts on steps towards a solution to the electroweak hierarchy problem and apply them to the original model of cosmological relaxation [Phys. Rev. Lett. 115, 221801 (2015)]. To do so, we study the dynamics of the model and determine the relation between the fundamental input parameters and the electroweak vacuum expectation value. Depending on the input parameters the model exhibits three qualitatively different regimes, two of which allow for hierarchically small Higgs vacuum expectation values. One leads to standard electroweak symmetry breaking whereas in the other regime electroweak symmetry is mainly broken by a Higgs source term. While the latter is not acceptable in a model based on the QCD axion, in non-QCD models this may lead to new and interesting signatures in Higgs observables. Overall, we confirm that cosmological relaxation can successfully give rise to a hierarchically small Higgs vacuum expectation value if (at least) one model parameter is chosen sufficiently small. However, we find that the required level of tuning for achieving this hierarchy in relaxation models can be much more severe than in the Standard Model.

Exact Scheffé-type confidence intervals for output from groundwater flow models: 2. Combined use of hydrogeologic information and calibration data

USGS Publications Warehouse

Cooley, Richard L.

1993-01-01

Calibration data (observed values corresponding to model-computed values of dependent variables) are incorporated into a general method of computing exact Scheffé-type confidence intervals analogous to the confidence intervals developed in part 1 (Cooley, this issue) for a function of parameters derived from a groundwater flow model. Parameter uncertainty is specified by a distribution of parameters conditioned on the calibration data. This distribution was obtained as a posterior distribution by applying Bayes' theorem to the hydrogeologically derived prior distribution of parameters from part 1 and a distribution of differences between the calibration data and corresponding model-computed dependent variables. Tests show that the new confidence intervals can be much smaller than the intervals of part 1 because the prior parameter variance-covariance structure is altered so that combinations of parameters that give poor model fit to the data are unlikely. The confidence intervals of part 1 and the new confidence intervals can be effectively employed in a sequential method of model construction whereby new information is used to reduce confidence interval widths at each stage.

Parameter estimation of multivariate multiple regression model using bayesian with non-informative Jeffreys’ prior distribution

NASA Astrophysics Data System (ADS)

Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.

2018-05-01

Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.

The Model Human Processor and the Older Adult: Parameter Estimation and Validation within a Mobile Phone Task

ERIC Educational Resources Information Center

Jastrzembski, Tiffany S.; Charness, Neil

2007-01-01

The authors estimate weighted mean values for nine information processing parameters for older adults using the Card, Moran, and Newell (1983) Model Human Processor model. The authors validate a subset of these parameters by modeling two mobile phone tasks using two different phones and comparing model predictions to a sample of younger (N = 20;…

Optimization-Based Inverse Identification of the Parameters of a Concrete Cap Material Model

NASA Astrophysics Data System (ADS)

Král, Petr; Hokeš, Filip; Hušek, Martin; Kala, Jiří; Hradil, Petr

2017-10-01

Issues concerning the advanced numerical analysis of concrete building structures in sophisticated computing systems currently require the involvement of nonlinear mechanics tools. The efforts to design safer, more durable and mainly more economically efficient concrete structures are supported via the use of advanced nonlinear concrete material models and the geometrically nonlinear approach. The application of nonlinear mechanics tools undoubtedly presents another step towards the approximation of the real behaviour of concrete building structures within the framework of computer numerical simulations. However, the success rate of this application depends on having a perfect understanding of the behaviour of the concrete material models used and having a perfect understanding of the used material model parameters meaning. The effective application of nonlinear concrete material models within computer simulations often becomes very problematic because these material models very often contain parameters (material constants) whose values are difficult to obtain. However, getting of the correct values of material parameters is very important to ensure proper function of a concrete material model used. Today, one possibility, which permits successful solution of the mentioned problem, is the use of optimization algorithms for the purpose of the optimization-based inverse material parameter identification. Parameter identification goes hand in hand with experimental investigation while it trying to find parameter values of the used material model so that the resulting data obtained from the computer simulation will best approximate the experimental data. This paper is focused on the optimization-based inverse identification of the parameters of a concrete cap material model which is known under the name the Continuous Surface Cap Model. Within this paper, material parameters of the model are identified on the basis of interaction between nonlinear computer simulations, gradient based and nature inspired optimization algorithms and experimental data, the latter of which take the form of a load-extension curve obtained from the evaluation of uniaxial tensile test results. The aim of this research was to obtain material model parameters corresponding to the quasi-static tensile loading which may be further used for the research involving dynamic and high-speed tensile loading. Based on the obtained results it can be concluded that the set goal has been reached.

Uncertainty analyses of the calibrated parameter values of a water quality model

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.; Lindenschmidt, K.-E.

2003-04-01

For river basin management water quality models are increasingly used for the analysis and evaluation of different management measures. However substantial uncertainties exist in parameter values depending on the available calibration data. In this paper an uncertainty analysis for a water quality model is presented, which considers the impact of available model calibration data and the variance of input variables. The investigation was conducted based on four extensive flowtime related longitudinal surveys in the River Elbe in the years 1996 to 1999 with varying discharges and seasonal conditions. For the model calculations the deterministic model QSIM of the BfG (Germany) was used. QSIM is a one dimensional water quality model and uses standard algorithms for hydrodynamics and phytoplankton dynamics in running waters, e.g. Michaelis Menten/Monod kinetics, which are used in a wide range of models. The multi-objective calibration of the model was carried out with the nonlinear parameter estimator PEST. The results show that for individual flow time related measuring surveys very good agreements between model calculation and measured values can be obtained. If these parameters are applied to deviating boundary conditions, substantial errors in model calculation can occur. These uncertainties can be decreased with an increased calibration database. More reliable model parameters can be identified, which supply reasonable results for broader boundary conditions. The extension of the application of the parameter set on a wider range of water quality conditions leads to a slight reduction of the model precision for the specific water quality situation. Moreover the investigations show that highly variable water quality variables like the algal biomass always allow a smaller forecast accuracy than variables with lower coefficients of variation like e.g. nitrate.

Hierarchial mark-recapture models: a framework for inference about demographic processes

USGS Publications Warehouse

Link, W.A.; Barker, R.J.

2004-01-01

The development of sophisticated mark-recapture models over the last four decades has provided fundamental tools for the study of wildlife populations, allowing reliable inference about population sizes and demographic rates based on clearly formulated models for the sampling processes. Mark-recapture models are now routinely described by large numbers of parameters. These large models provide the next challenge to wildlife modelers: the extraction of signal from noise in large collections of parameters. Pattern among parameters can be described by strong, deterministic relations (as in ultrastructural models) but is more flexibly and credibly modeled using weaker, stochastic relations. Trend in survival rates is not likely to be manifest by a sequence of values falling precisely on a given parametric curve; rather, if we could somehow know the true values, we might anticipate a regression relation between parameters and explanatory variables, in which true value equals signal plus noise. Hierarchical models provide a useful framework for inference about collections of related parameters. Instead of regarding parameters as fixed but unknown quantities, we regard them as realizations of stochastic processes governed by hyperparameters. Inference about demographic processes is based on investigation of these hyperparameters. We advocate the Bayesian paradigm as a natural, mathematically and scientifically sound basis for inference about hierarchical models. We describe analysis of capture-recapture data from an open population based on hierarchical extensions of the Cormack-Jolly-Seber model. In addition to recaptures of marked animals, we model first captures of animals and losses on capture, and are thus able to estimate survival probabilities w (i.e., the complement of death or permanent emigration) and per capita growth rates f (i.e., the sum of recruitment and immigration rates). Covariation in these rates, a feature of demographic interest, is explicitly described in the model.

Converting HAZUS capacity curves to seismic hazard-compatible building fragility functions: effect of hysteretic models

USGS Publications Warehouse

Ryu, Hyeuk; Luco, Nicolas; Baker, Jack W.; Karaca, Erdem

2008-01-01

A methodology was recently proposed for the development of hazard-compatible building fragility models using parameters of capacity curves and damage state thresholds from HAZUS (Karaca and Luco, 2008). In the methodology, HAZUS curvilinear capacity curves were used to define nonlinear dynamic SDOF models that were subjected to the nonlinear time history analysis instead of the capacity spectrum method. In this study, we construct a multilinear capacity curve with negative stiffness after an ultimate (capping) point for the nonlinear time history analysis, as an alternative to the curvilinear model provided in HAZUS. As an illustration, here we propose parameter values of the multilinear capacity curve for a moderate-code low-rise steel moment resisting frame building (labeled S1L in HAZUS). To determine the final parameter values, we perform nonlinear time history analyses of SDOF systems with various parameter values and investigate their effects on resulting fragility functions through sensitivity analysis. The findings improve capacity curves and thereby fragility and/or vulnerability models for generic types of structures.

Parameter estimation of Monod model by the Least-Squares method for microalgae Botryococcus Braunii sp

NASA Astrophysics Data System (ADS)

See, J. J.; Jamaian, S. S.; Salleh, R. M.; Nor, M. E.; Aman, F.

2018-04-01

This research aims to estimate the parameters of Monod model of microalgae Botryococcus Braunii sp growth by the Least-Squares method. Monod equation is a non-linear equation which can be transformed into a linear equation form and it is solved by implementing the Least-Squares linear regression method. Meanwhile, Gauss-Newton method is an alternative method to solve the non-linear Least-Squares problem with the aim to obtain the parameters value of Monod model by minimizing the sum of square error ( SSE). As the result, the parameters of the Monod model for microalgae Botryococcus Braunii sp can be estimated by the Least-Squares method. However, the estimated parameters value obtained by the non-linear Least-Squares method are more accurate compared to the linear Least-Squares method since the SSE of the non-linear Least-Squares method is less than the linear Least-Squares method.

Inverse modeling with RZWQM2 to predict water quality

USGS Publications Warehouse

Nolan, Bernard T.; Malone, Robert W.; Ma, Liwang; Green, Christopher T.; Fienen, Michael N.; Jaynes, Dan B.

2011-01-01

This chapter presents guidelines for autocalibration of the Root Zone Water Quality Model (RZWQM2) by inverse modeling using PEST parameter estimation software (Doherty, 2010). Two sites with diverse climate and management were considered for simulation of N losses by leaching and in drain flow: an almond [Prunus dulcis (Mill.) D.A. Webb] orchard in the San Joaquin Valley, California and the Walnut Creek watershed in central Iowa, which is predominantly in corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation. Inverse modeling provides an objective statistical basis for calibration that involves simultaneous adjustment of model parameters and yields parameter confidence intervals and sensitivities. We describe operation of PEST in both parameter estimation and predictive analysis modes. The goal of parameter estimation is to identify a unique set of parameters that minimize a weighted least squares objective function, and the goal of predictive analysis is to construct a nonlinear confidence interval for a prediction of interest by finding a set of parameters that maximizes or minimizes the prediction while maintaining the model in a calibrated state. We also describe PEST utilities (PAR2PAR, TSPROC) for maintaining ordered relations among model parameters (e.g., soil root growth factor) and for post-processing of RZWQM2 outputs representing different cropping practices at the Iowa site. Inverse modeling provided reasonable fits to observed water and N fluxes and directly benefitted the modeling through: (i) simultaneous adjustment of multiple parameters versus one-at-a-time adjustment in manual approaches; (ii) clear indication by convergence criteria of when calibration is complete; (iii) straightforward detection of nonunique and insensitive parameters, which can affect the stability of PEST and RZWQM2; and (iv) generation of confidence intervals for uncertainty analysis of parameters and model predictions. Composite scaled sensitivities, which reflect the total information provided by the observations for a parameter, indicated that most of the RZWQM2 parameters at the California study site (CA) and Iowa study site (IA) could be reliably estimated by regression. Correlations obtained in the CA case indicated that all model parameters could be uniquely estimated by inverse modeling. Although water content at field capacity was highly correlated with bulk density (−0.94), the correlation is less than the threshold for nonuniqueness (0.95, absolute value basis). Additionally, we used truncated singular value decomposition (SVD) at CA to mitigate potential problems with highly correlated and insensitive parameters. Singular value decomposition estimates linear combinations (eigenvectors) of the original process-model parameters. Parameter confidence intervals (CIs) at CA indicated that parameters were reliably estimated with the possible exception of an organic pool transfer coefficient (R45), which had a comparatively wide CI. However, the 95% confidence interval for R45 (0.03–0.35) is mostly within the range of values reported for this parameter. Predictive analysis at CA generated confidence intervals that were compared with independently measured annual water flux (groundwater recharge) and median nitrate concentration in a collocated monitoring well as part of model evaluation. Both the observed recharge (42.3 cm yr−1) and nitrate concentration (24.3 mg L−1) were within their respective 90% confidence intervals, indicating that overall model error was within acceptable limits.

Multi-response calibration of a conceptual hydrological model in the semiarid catchment of Wadi al Arab, Jordan

NASA Astrophysics Data System (ADS)

Rödiger, T.; Geyer, S.; Mallast, U.; Merz, R.; Krause, P.; Fischer, C.; Siebert, C.

2014-02-01

A key factor for sustainable management of groundwater systems is the accurate estimation of groundwater recharge. Hydrological models are common tools for such estimations and widely used. As such models need to be calibrated against measured values, the absence of adequate data can be problematic. We present a nested multi-response calibration approach for a semi-distributed hydrological model in the semi-arid catchment of Wadi al Arab in Jordan, with sparsely available runoff data. The basic idea of the calibration approach is to use diverse observations in a nested strategy, in which sub-parts of the model are calibrated to various observation data types in a consecutive manner. First, the available different data sources have to be screened for information content of processes, e.g. if data sources contain information on mean values, spatial or temporal variability etc. for the entire catchment or only sub-catchments. In a second step, the information content has to be mapped to relevant model components, which represent these processes. Then the data source is used to calibrate the respective subset of model parameters, while the remaining model parameters remain unchanged. This mapping is repeated for other available data sources. In that study the gauged spring discharge (GSD) method, flash flood observations and data from the chloride mass balance (CMB) are used to derive plausible parameter ranges for the conceptual hydrological model J2000g. The water table fluctuation (WTF) method is used to validate the model. Results from modelling using a priori parameter values from literature as a benchmark are compared. The estimated recharge rates of the calibrated model deviate less than ±10% from the estimates derived from WTF method. Larger differences are visible in the years with high uncertainties in rainfall input data. The performance of the calibrated model during validation produces better results than applying the model with only a priori parameter values. The model with a priori parameter values from literature tends to overestimate recharge rates with up to 30%, particular in the wet winter of 1991/1992. An overestimation of groundwater recharge and hence available water resources clearly endangers reliable water resource managing in water scarce region. The proposed nested multi-response approach may help to better predict water resources despite data scarcity.

Global Sensitivity Analysis and Parameter Calibration for an Ecosystem Carbon Model

NASA Astrophysics Data System (ADS)

Safta, C.; Ricciuto, D. M.; Sargsyan, K.; Najm, H. N.; Debusschere, B.; Thornton, P. E.

2013-12-01

We present uncertainty quantification results for a process-based ecosystem carbon model. The model employs 18 parameters and is driven by meteorological data corresponding to years 1992-2006 at the Harvard Forest site. Daily Net Ecosystem Exchange (NEE) observations were available to calibrate the model parameters and test the performance of the model. Posterior distributions show good predictive capabilities for the calibrated model. A global sensitivity analysis was first performed to determine the important model parameters based on their contribution to the variance of NEE. We then proceed to calibrate the model parameters in a Bayesian framework. The daily discrepancies between measured and predicted NEE values were modeled as independent and identically distributed Gaussians with prescribed daily variance according to the recorded instrument error. All model parameters were assumed to have uninformative priors with bounds set according to expert opinion. The global sensitivity results show that the rate of leaf fall (LEAFALL) is responsible for approximately 25% of the total variance in the average NEE for 1992-2005. A set of 4 other parameters, Nitrogen use efficiency (NUE), base rate for maintenance respiration (BR_MR), growth respiration fraction (RG_FRAC), and allocation to plant stem pool (ASTEM) contribute between 5% and 12% to the variance in average NEE, while the rest of the parameters have smaller contributions. The posterior distributions, sampled with a Markov Chain Monte Carlo algorithm, exhibit significant correlations between model parameters. However LEAFALL, the most important parameter for the average NEE, is not informed by the observational data, while less important parameters show significant updates between their prior and posterior densities. The Fisher information matrix values, indicating which parameters are most informed by the experimental observations, are examined to augment the comparison between the calibration and global sensitivity analysis results.

Determination of remodeling parameters for a strain-adaptive finite element model of the distal ulna.

PubMed

Neuert, Mark A C; Dunning, Cynthia E

2013-09-01

Strain energy-based adaptive material models are used to predict bone resorption resulting from stress shielding induced by prosthetic joint implants. Generally, such models are governed by two key parameters: a homeostatic strain-energy state (K) and a threshold deviation from this state required to initiate bone reformation (s). A refinement procedure has been performed to estimate these parameters in the femur and glenoid; this study investigates the specific influences of these parameters on resulting density distributions in the distal ulna. A finite element model of a human ulna was created using micro-computed tomography (µCT) data, initialized to a homogeneous density distribution, and subjected to approximate in vivo loading. Values for K and s were tested, and the resulting steady-state density distribution compared with values derived from µCT images. The sensitivity of these parameters to initial conditions was examined by altering the initial homogeneous density value. The refined model parameters selected were then applied to six additional human ulnae to determine their performance across individuals. Model accuracy using the refined parameters was found to be comparable with that found in previous studies of the glenoid and femur, and gross bone structures, such as the cortical shell and medullary canal, were reproduced. The model was found to be insensitive to initial conditions; however, a fair degree of variation was observed between the six specimens. This work represents an important contribution to the study of changes in load transfer in the distal ulna following the implementation of commercial orthopedic implants.

Assessing the quality of life history information in publicly available databases.

PubMed

Thorson, James T; Cope, Jason M; Patrick, Wesley S

2014-01-01

Single-species life history parameters are central to ecological research and management, including the fields of macro-ecology, fisheries science, and ecosystem modeling. However, there has been little independent evaluation of the precision and accuracy of the life history values in global and publicly available databases. We therefore develop a novel method based on a Bayesian errors-in-variables model that compares database entries with estimates from local experts, and we illustrate this process by assessing the accuracy and precision of entries in FishBase, one of the largest and oldest life history databases. This model distinguishes biases among seven life history parameters, two types of information available in FishBase (i.e., published values and those estimated from other parameters), and two taxa (i.e., bony and cartilaginous fishes) relative to values from regional experts in the United States, while accounting for additional variance caused by sex- and region-specific life history traits. For published values in FishBase, the model identifies a small positive bias in natural mortality and negative bias in maximum age, perhaps caused by unacknowledged mortality caused by fishing. For life history values calculated by FishBase, the model identified large and inconsistent biases. The model also demonstrates greatest precision for body size parameters, decreased precision for values derived from geographically distant populations, and greatest between-sex differences in age at maturity. We recommend that our bias and precision estimates be used in future errors-in-variables models as a prior on measurement errors. This approach is broadly applicable to global databases of life history traits and, if used, will encourage further development and improvements in these databases.

Assessing Disparities of Dengue Virus Transmission Risk across the US-Mexican Border Using a Climate Driven Vector-Epidemiological Model

NASA Technical Reports Server (NTRS)

Morin, Cory; Monaghan, Andrew; Quattrochi, Dale; Crosson, William; Hayden, Mary; Ernst, Kacey

2015-01-01

Dengue fever is a mosquito-borne viral disease reemerging throughout much of the tropical Americas. Dengue virus transmission is explicitly influenced by climate and the environment through its primary vector, Aedes aegypti. Temperature regulates Ae. aegypti development, survival, and replication rates as well as the incubation period of the virus within the mosquito. Precipitation provides water for many of the preferred breeding habitats of the mosquito, including buckets, old tires, and other places water can collect. Although transmission regularly occurs along the border region in Mexico, dengue virus transmission in bordering Arizona has not occurred. Using NASA's TRMM (Tropical Rainfall Measuring Mission) satellite for precipitation input and Daymet for temperature and supplemental precipitation input, we modeled dengue transmission along a US-Mexico transect using a dynamic dengue transmission model that includes interacting vector ecology and epidemiological components. Model runs were performed for 5 cities in Sonora, Mexico and southern Arizona. Employing a Monte Carlo approach, we performed ensembles of several thousands of model simulations in order to resolve the model uncertainty arising from using different combinations of parameter values that are not well known. For cities with reported dengue case data, the top model simulations that best reproduced dengue case numbers were retained and their parameter values were extracted for comparison. These parameter values were used to run simulations in areas where dengue virus transmission does not occur or where dengue fever case data was unavailable. Additional model runs were performed to reveal how changes in climate or parameter values could alter transmission risk along the transect. The relative influence of climate variability and model parameters on dengue virus transmission is assessed to help public health workers prepare location specific infection prevention strategies.

Second derivative in the model of classical binary system

NASA Astrophysics Data System (ADS)

Abubekerov, M. K.; Gostev, N. Yu.

2016-06-01

We have obtained an analytical expression for the second derivatives of the light curve with respect to geometric parameters in the model of eclipsing classical binary systems. These expressions are essentially efficient algorithm to calculate the numerical values of these second derivatives for all physical values of geometric parameters. Knowledge of the values of second derivatives of the light curve at some point provides additional information about asymptotical behaviour of the function near this point and can significantly improve the search for the best-fitting light curve through the use of second-order optimization method. We write the expression for the second derivatives in a form which is most compact and uniform for all values of the geometric parameters and so make it easy to write a computer program to calculate the values of these derivatives.

Sensitivity Analysis of the Bone Fracture Risk Model

NASA Technical Reports Server (NTRS)

Lewandowski, Beth; Myers, Jerry; Sibonga, Jean Diane

2017-01-01

Introduction: The probability of bone fracture during and after spaceflight is quantified to aid in mission planning, to determine required astronaut fitness standards and training requirements and to inform countermeasure research and design. Probability is quantified with a probabilistic modeling approach where distributions of model parameter values, instead of single deterministic values, capture the parameter variability within the astronaut population and fracture predictions are probability distributions with a mean value and an associated uncertainty. Because of this uncertainty, the model in its current state cannot discern an effect of countermeasures on fracture probability, for example between use and non-use of bisphosphonates or between spaceflight exercise performed with the Advanced Resistive Exercise Device (ARED) or on devices prior to installation of ARED on the International Space Station. This is thought to be due to the inability to measure key contributors to bone strength, for example, geometry and volumetric distributions of bone mass, with areal bone mineral density (BMD) measurement techniques. To further the applicability of model, we performed a parameter sensitivity study aimed at identifying those parameter uncertainties that most effect the model forecasts in order to determine what areas of the model needed enhancements for reducing uncertainty. Methods: The bone fracture risk model (BFxRM), originally published in (Nelson et al) is a probabilistic model that can assess the risk of astronaut bone fracture. This is accomplished by utilizing biomechanical models to assess the applied loads; utilizing models of spaceflight BMD loss in at-risk skeletal locations; quantifying bone strength through a relationship between areal BMD and bone failure load; and relating fracture risk index (FRI), the ratio of applied load to bone strength, to fracture probability. There are many factors associated with these calculations including environmental factors, factors associated with the fall event, mass and anthropometric values of the astronaut, BMD characteristics, characteristics of the relationship between BMD and bone strength and bone fracture characteristics. The uncertainty in these factors is captured through the use of parameter distributions and the fracture predictions are probability distributions with a mean value and an associated uncertainty. To determine parameter sensitivity, a correlation coefficient is found between the sample set of each model parameter and the calculated fracture probabilities. Each parameters contribution to the variance is found by squaring the correlation coefficients, dividing by the sum of the squared correlation coefficients, and multiplying by 100. Results: Sensitivity analyses of BFxRM simulations of preflight, 0 days post-flight and 365 days post-flight falls onto the hip revealed a subset of the twelve factors within the model which cause the most variation in the fracture predictions. These factors include the spring constant used in the hip biomechanical model, the midpoint FRI parameter within the equation used to convert FRI to fracture probability and preflight BMD values. Future work: Plans are underway to update the BFxRM by incorporating bone strength information from finite element models (FEM) into the bone strength portion of the BFxRM. Also, FEM bone strength information along with fracture outcome data will be incorporated into the FRI to fracture probability.

An optimal generic model for multi-parameters and big data optimizing: a laboratory experimental study

NASA Astrophysics Data System (ADS)

Utama, D. N.; Ani, N.; Iqbal, M. M.

2018-03-01

Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.

Seasonal variability of the parameters of the Ball-Berry model of stomatal conductance in maize (Zea mays L.) and sunflower (Helianthus annuus L.) under well-watered and water-stressed conditions.

PubMed

Miner, Grace L; Bauerle, William L

2017-09-01

The Ball-Berry (BB) model of stomatal conductance (g s ) is frequently coupled with a model of assimilation to estimate water and carbon exchanges in plant canopies. The empirical slope (m) and 'residual' g s (g 0 ) parameters of the BB model influence transpiration estimates, but the time-intensive nature of measurement limits species-specific data on seasonal and stress responses. We measured m and g 0 seasonally and under different water availability for maize and sunflower. The statistical method used to estimate parameters impacted values nominally when inter-plant variability was low, but had substantial impact with larger inter-plant variability. Values for maize (m = 4.53 ± 0.65; g 0  = 0.017 ± 0.016 mol m -2 s -1 ) were 40% higher than other published values. In maize, we found no seasonal changes in m or g 0 , supporting the use of constant seasonal values, but water stress reduced both parameters. In sunflower, inter-plant variability of m and g 0 was large (m = 8.84 ± 3.77; g 0  = 0.354 ± 0.226 mol m -2 s -1 ), presenting a challenge to clear interpretation of seasonal and water stress responses - m values were stable seasonally, even as g 0 values trended downward, and m values trended downward with water stress while g 0 values declined substantially. © 2017 John Wiley & Sons Ltd.

Application of maximum entropy to statistical inference for inversion of data from a single track segment.

PubMed

Stotts, Steven A; Koch, Robert A

2017-08-01

In this paper an approach is presented to estimate the constraint required to apply maximum entropy (ME) for statistical inference with underwater acoustic data from a single track segment. Previous algorithms for estimating the ME constraint require multiple source track segments to determine the constraint. The approach is relevant for addressing model mismatch effects, i.e., inaccuracies in parameter values determined from inversions because the propagation model does not account for all acoustic processes that contribute to the measured data. One effect of model mismatch is that the lowest cost inversion solution may be well outside a relatively well-known parameter value's uncertainty interval (prior), e.g., source speed from track reconstruction or towed source levels. The approach requires, for some particular parameter value, the ME constraint to produce an inferred uncertainty interval that encompasses the prior. Motivating this approach is the hypothesis that the proposed constraint determination procedure would produce a posterior probability density that accounts for the effect of model mismatch on inferred values of other inversion parameters for which the priors might be quite broad. Applications to both measured and simulated data are presented for model mismatch that produces minimum cost solutions either inside or outside some priors.

Estimation of Filling and Afterload Conditions by Pump Intrinsic Parameters in a Pulsatile Total Artificial Heart.

PubMed

Cuenca-Navalon, Elena; Laumen, Marco; Finocchiaro, Thomas; Steinseifer, Ulrich

2016-07-01

A physiological control algorithm is being developed to ensure an optimal physiological interaction between the ReinHeart total artificial heart (TAH) and the circulatory system. A key factor for that is the long-term, accurate determination of the hemodynamic state of the cardiovascular system. This study presents a method to determine estimation models for predicting hemodynamic parameters (pump chamber filling and afterload) from both left and right cardiovascular circulations. The estimation models are based on linear regression models that correlate filling and afterload values with pump intrinsic parameters derived from measured values of motor current and piston position. Predictions for filling lie in average within 5% from actual values, predictions for systemic afterload (AoPmean , AoPsys ) and mean pulmonary afterload (PAPmean ) lie in average within 9% from actual values. Predictions for systolic pulmonary afterload (PAPsys ) present an average deviation of 14%. The estimation models show satisfactory prediction and confidence intervals and are thus suitable to estimate hemodynamic parameters. This method and derived estimation models are a valuable alternative to implanted sensors and are an essential step for the development of a physiological control algorithm for a fully implantable TAH. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Validation and upgrading of physically based mathematical models

NASA Technical Reports Server (NTRS)

Duval, Ronald

1992-01-01

The validation of the results of physically-based mathematical models against experimental results was discussed. Systematic techniques are used for: (1) isolating subsets of the simulator mathematical model and comparing the response of each subset to its experimental response for the same input conditions; (2) evaluating the response error to determine whether it is the result of incorrect parameter values, incorrect structure of the model subset, or unmodeled external effects of cross coupling; and (3) modifying and upgrading the model and its parameter values to determine the most physically appropriate combination of changes.

Derivation of WECC Distributed PV System Model Parameters from Quasi-Static Time-Series Distribution System Simulations

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

Mather, Barry A; Boemer, Jens C.; Vittal, Eknath

The response of low voltage networks with high penetration of PV systems to transmission network faults will, in the future, determine the overall power system performance during certain hours of the year. The WECC distributed PV system model (PVD1) is designed to represent small-scale distribution-connected systems. Although default values are provided by WECC for the model parameters, tuning of those parameters seems to become important in order to accurately estimate the partial loss of distributed PV systems for bulk system studies. The objective of this paper is to describe a new methodology to determine the WECC distributed PV system (PVD1)more » model parameters and to derive parameter sets obtained for six distribution circuits of a Californian investor-owned utility with large amounts of distributed PV systems. The results indicate that the parameters for the partial loss of distributed PV systems may differ significantly from the default values provided by WECC.« less

Monte Carlo Solution to Find Input Parameters in Systems Design Problems

NASA Astrophysics Data System (ADS)

Arsham, Hossein

2013-06-01

Most engineering system designs, such as product, process, and service design, involve a framework for arriving at a target value for a set of experiments. This paper considers a stochastic approximation algorithm for estimating the controllable input parameter within a desired accuracy, given a target value for the performance function. Two different problems, what-if and goal-seeking problems, are explained and defined in an auxiliary simulation model, which represents a local response surface model in terms of a polynomial. A method of constructing this polynomial by a single run simulation is explained. An algorithm is given to select the design parameter for the local response surface model. Finally, the mean time to failure (MTTF) of a reliability subsystem is computed and compared with its known analytical MTTF value for validation purposes.

Estimating model parameters for an impact-produced shock-wave simulation: Optimal use of partial data with the extended Kalman filter

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

Kao, Jim; Flicker, Dawn; Ide, Kayo

2006-05-20

This paper builds upon our recent data assimilation work with the extended Kalman filter (EKF) method [J. Kao, D. Flicker, R. Henninger, S. Frey, M. Ghil, K. Ide, Data assimilation with an extended Kalman filter for an impact-produced shock-wave study, J. Comp. Phys. 196 (2004) 705-723.]. The purpose is to test the capability of EKF in optimizing a model's physical parameters. The problem is to simulate the evolution of a shock produced through a high-speed flyer plate. In the earlier work, we have showed that the EKF allows one to estimate the evolving state of the shock wave from amore » single pressure measurement, assuming that all model parameters are known. In the present paper, we show that imperfectly known model parameters can also be estimated accordingly, along with the evolving model state, from the same single measurement. The model parameter optimization using the EKF can be achieved through a simple modification of the original EKF formalism by including the model parameters into an augmented state variable vector. While the regular state variables are governed by both deterministic and stochastic forcing mechanisms, the parameters are only subject to the latter. The optimally estimated model parameters are thus obtained through a unified assimilation operation. We show that improving the accuracy of the model parameters also improves the state estimate. The time variation of the optimized model parameters results from blending the data and the corresponding values generated from the model and lies within a small range, of less than 2%, from the parameter values of the original model. The solution computed with the optimized parameters performs considerably better and has a smaller total variance than its counterpart using the original time-constant parameters. These results indicate that the model parameters play a dominant role in the performance of the shock-wave hydrodynamic code at hand.« less

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

PubMed

Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J

2014-01-01

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

The Effect of Roughness Model on Scattering Properties of Ice Crystals.

NASA Technical Reports Server (NTRS)

Geogdzhayev, Igor V.; Van Diedenhoven, Bastiaan

2016-01-01

We compare stochastic models of microscale surface roughness assuming uniform and Weibull distributions of crystal facet tilt angles to calculate scattering by roughened hexagonal ice crystals using the geometric optics (GO) approximation. Both distributions are determined by similar roughness parameters, while the Weibull model depends on the additional shape parameter. Calculations were performed for two visible wavelengths (864 nm and 410 nm) for roughness values between 0.2 and 0.7 and Weibull shape parameters between 0 and 1.0 for crystals with aspect ratios of 0.21, 1 and 4.8. For this range of parameters we find that, for a given roughness level, varying the Weibull shape parameter can change the asymmetry parameter by up to about 0.05. The largest effect of the shape parameter variation on the phase function is found in the backscattering region, while the degree of linear polarization is most affected at the side-scattering angles. For high roughness, scattering properties calculated using the uniform and Weibull models are in relatively close agreement for a given roughness parameter, especially when a Weibull shape parameter of 0.75 is used. For smaller roughness values, a shape parameter close to unity provides a better agreement. Notable differences are observed in the phase function over the scattering angle range from 5deg to 20deg, where the uniform roughness model produces a plateau while the Weibull model does not.

Evaluation of Different Dose-Response Models for High Hydrostatic Pressure Inactivation of Microorganisms

PubMed Central

2017-01-01

Modeling of microbial inactivation by high hydrostatic pressure (HHP) requires a plot of the log microbial count or survival ratio versus time data under a constant pressure and temperature. However, at low pressure and temperature values, very long holding times are needed to obtain measurable inactivation. Since the time has a significant effect on the cost of HHP processing it may be reasonable to fix the time at an appropriate value and quantify the inactivation with respect to pressure. Such a plot is called dose-response curve and it may be more beneficial than the traditional inactivation modeling since short holding times with different pressure values can be selected and used for the modeling of HHP inactivation. For this purpose, 49 dose-response curves (with at least 4 log10 reduction and ≥5 data points including the atmospheric pressure value (P = 0.1 MPa), and with holding time ≤10 min) for HHP inactivation of microorganisms obtained from published studies were fitted with four different models, namely the Discrete model, Shoulder model, Fermi equation, and Weibull model, and the pressure value needed for 5 log10 (P5) inactivation was calculated for all the models above. The Shoulder model and Fermi equation produced exactly the same parameter and P5 values, while the Discrete model produced similar or sometimes the exact same parameter values as the Fermi equation. The Weibull model produced the worst fit (had the lowest adjusted determination coefficient (R2adj) and highest mean square error (MSE) values), while the Fermi equation had the best fit (the highest R2adj and lowest MSE values). Parameters of the models and also P5 values of each model can be useful for the further experimental design of HHP processing and also for the comparison of the pressure resistance of different microorganisms. Further experiments can be done to verify the P5 values at given conditions. The procedure given in this study can also be extended for enzyme inactivation by HHP. PMID:28880255

The "covariation method" for estimating the parameters of the standard Dynamic Energy Budget model II: Properties and preliminary patterns

NASA Astrophysics Data System (ADS)

Lika, Konstadia; Kearney, Michael R.; Kooijman, Sebastiaan A. L. M.

2011-11-01

The covariation method for estimating the parameters of the standard Dynamic Energy Budget (DEB) model provides a single-step method of accessing all the core DEB parameters from commonly available empirical data. In this study, we assess the robustness of this parameter estimation procedure and analyse the role of pseudo-data using elasticity coefficients. In particular, we compare the performance of Maximum Likelihood (ML) vs. Weighted Least Squares (WLS) approaches and find that the two approaches tend to converge in performance as the number of uni-variate data sets increases, but that WLS is more robust when data sets comprise single points (zero-variate data). The efficiency of the approach is shown to be high, and the prior parameter estimates (pseudo-data) have very little influence if the real data contain information about the parameter values. For instance, the effects of the pseudo-value for the allocation fraction κ is reduced when there is information for both growth and reproduction, that for the energy conductance is reduced when information on age at birth and puberty is given, and the effects of the pseudo-value for the maturity maintenance rate coefficient are insignificant. The estimation of some parameters (e.g., the zoom factor and the shape coefficient) requires little information, while that of others (e.g., maturity maintenance rate, puberty threshold and reproduction efficiency) require data at several food levels. The generality of the standard DEB model, in combination with the estimation of all of its parameters, allows comparison of species on the basis of parameter values. We discuss a number of preliminary patterns emerging from the present collection of parameter estimates across a wide variety of taxa. We make the observation that the estimated value of the fraction κ of mobilised reserve that is allocated to soma is far away from the value that maximises reproduction. We recognise this as the reason why two very different parameter sets must exist that fit most data set reasonably well, and give arguments why, in most cases, the set with the large value of κ should be preferred. The continued development of a parameter database through the estimation procedures described here will provide a strong basis for understanding evolutionary patterns in metabolic organisation across the diversity of life.

Efficient Screening of Climate Model Sensitivity to a Large Number of Perturbed Input Parameters [plus supporting information

DOE PAGES

Covey, Curt; Lucas, Donald D.; Tannahill, John; ...

2013-07-01

Modern climate models contain numerous input parameters, each with a range of possible values. Since the volume of parameter space increases exponentially with the number of parameters N, it is generally impossible to directly evaluate a model throughout this space even if just 2-3 values are chosen for each parameter. Sensitivity screening algorithms, however, can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination.This can aid both model development and the uncertainty quantification (UQ) process. Here we report results from a parameter sensitivity screening algorithm hitherto untested in climate modeling,more » the Morris one-at-a-time (MOAT) method. This algorithm drastically reduces the computational cost of estimating sensitivities in a high dimensional parameter space because the sample size grows linearly rather than exponentially with N. It nevertheless samples over much of the N-dimensional volume and allows assessment of parameter interactions, unlike traditional elementary one-at-a-time (EOAT) parameter variation. We applied both EOAT and MOAT to the Community Atmosphere Model (CAM), assessing CAM’s behavior as a function of 27 uncertain input parameters related to the boundary layer, clouds, and other subgrid scale processes. For radiation balance at the top of the atmosphere, EOAT and MOAT rank most input parameters similarly, but MOAT identifies a sensitivity that EOAT underplays for two convection parameters that operate nonlinearly in the model. MOAT’s ranking of input parameters is robust to modest algorithmic variations, and it is qualitatively consistent with model development experience. Supporting information is also provided at the end of the full text of the article.« less

Soil Parameters for Representing a Karst Geologic Terrain in the Noah Land-Surface Model over Tennessee and Kentucky

NASA Astrophysics Data System (ADS)

Sullivan, Z.; Fan, X.

2015-12-01

Currently, the Noah Land-Surface Model (Noah-LSM) coupled with the Weather Research and Forecasting (WRF) model does not have a representation of the physical behavior of a karst terrain found in a large area of Tennessee and Kentucky and 25% of land area worldwide. The soluble nature of the bedrock within a karst geologic terrains allows for the formation of caverns, joints, fissures, sinkholes, and underground streams which affect the hydrological behavior of the region. The Highland Rim of Tennessee and the Pennyroyal Plateau and Bluegrass region of Kentucky make up a larger karst area known as the Interior Low Plateau. The highly weathered upper portion of the karst terrain, known as the epikarst, allows for more rapid transport of water through the system. For this study, hydrological aspects, such as bedrock porosity and the hydraulic conductivity, were chosen within this region in order to determine the most representative subsurface parameters for the Noah-LSM. These values along with the use of similar proxy values were chosen to calculate and represent the remaining eight parameters within the SOILPARM.TBL for the WRF model. Hydraulic conductivity values show a variation ranging from around 10-7 and 10-5 ms-1 for the karst bedrock within this region. A sand and clay soil type was used along with bedrock parameters to determine an average soil parameter type for the epikarst bedrock located within this region. Results from this study show parameters for an epikarst bedrock type displaying higher water transport through the system, similar to that of a sandy soil type with a water retention similar to that of a loam type soil. The physical nature of epikarst may lead to a decrease in latent heat values over this region and increase sensible heat values. This, in turn, may effect boundary layer growth which could lead to convective development. Future modeling work can be conducted using these values by way of coupling the soil parameters with the karst regions of the Tennessee/Kentucky area.

Exchangeability, extreme returns and Value-at-Risk forecasts

NASA Astrophysics Data System (ADS)

Huang, Chun-Kai; North, Delia; Zewotir, Temesgen

2017-07-01

In this paper, we propose a new approach to extreme value modelling for the forecasting of Value-at-Risk (VaR). In particular, the block maxima and the peaks-over-threshold methods are generalised to exchangeable random sequences. This caters for the dependencies, such as serial autocorrelation, of financial returns observed empirically. In addition, this approach allows for parameter variations within each VaR estimation window. Empirical prior distributions of the extreme value parameters are attained by using resampling procedures. We compare the results of our VaR forecasts to that of the unconditional extreme value theory (EVT) approach and the conditional GARCH-EVT model for robust conclusions.

Automated evaluation of liver fibrosis in thioacetamide, carbon tetrachloride, and bile duct ligation rodent models using second-harmonic generation/two-photon excited fluorescence microscopy.

PubMed

Liu, Feng; Chen, Long; Rao, Hui-Ying; Teng, Xiao; Ren, Ya-Yun; Lu, Yan-Qiang; Zhang, Wei; Wu, Nan; Liu, Fang-Fang; Wei, Lai

2017-01-01

Animal models provide a useful platform for developing and testing new drugs to treat liver fibrosis. Accordingly, we developed a novel automated system to evaluate liver fibrosis in rodent models. This system uses second-harmonic generation (SHG)/two-photon excited fluorescence (TPEF) microscopy to assess a total of four mouse and rat models, using chemical treatment with either thioacetamide (TAA) or carbon tetrachloride (CCl 4 ), and a surgical method, bile duct ligation (BDL). The results obtained by the new technique were compared with that using Ishak fibrosis scores and two currently used quantitative methods for determining liver fibrosis: the collagen proportionate area (CPA) and measurement of hydroxyproline (HYP) content. We show that 11 shared morphological parameters faithfully recapitulate Ishak fibrosis scores in the models, with high area under the receiver operating characteristic (ROC) curve (AUC) performance. The AUC values of 11 shared parameters were greater than that of the CPA (TAA: 0.758-0.922 vs 0.752-0.908; BDL: 0.874-0.989 vs 0.678-0.966) in the TAA mice and BDL rat models and similar to that of the CPA in the TAA rat and CCl 4 mouse models. Similarly, based on the trends in these parameters at different time points, 9, 10, 7, and 2 model-specific parameters were selected for the TAA rats, TAA mice, CCl 4 mice, and BDL rats, respectively. These parameters identified differences among the time points in the four models, with high AUC accuracy, and the corresponding AUC values of these parameters were greater compared with those of the CPA in the TAA rat and mouse models (rats: 0.769-0.894 vs 0.64-0.799; mice: 0.87-0.93 vs 0.739-0.836) and similar to those of the CPA in the CCl 4 mouse and BDL rat models. Similarly, the AUC values of 11 shared parameters and model-specific parameters were greater than those of HYP in the TAA rats, TAA mice, and CCl 4 mouse models and were similar to those of HYP in the BDL rat models. The automated evaluation system, combined with 11 shared parameters and model-specific parameters, could specifically, accurately, and quantitatively stage liver fibrosis in animal models.

Robust linear parameter-varying control of blood pressure using vasoactive drugs

NASA Astrophysics Data System (ADS)

Luspay, Tamas; Grigoriadis, Karolos

2015-10-01

Resuscitation of emergency care patients requires fast restoration of blood pressure to a target value to achieve hemodynamic stability and vital organ perfusion. A robust control design methodology is presented in this paper for regulating the blood pressure of hypotensive patients by means of the closed-loop administration of vasoactive drugs. To this end, a dynamic first-order delay model is utilised to describe the vasoactive drug response with varying parameters that represent intra-patient and inter-patient variability. The proposed framework consists of two components: first, an online model parameter estimation is carried out using a multiple-model extended Kalman-filter. Second, the estimated model parameters are used for continuously scheduling a robust linear parameter-varying (LPV) controller. The closed-loop behaviour is characterised by parameter-varying dynamic weights designed to regulate the mean arterial pressure to a target value. Experimental data of blood pressure response of anesthetised pigs to phenylephrine injection are used for validating the LPV blood pressure models. Simulation studies are provided to validate the online model estimation and the LPV blood pressure control using phenylephrine drug injection models representing patients showing sensitive, nominal and insensitive response to the drug.

A mathematical model of physiological processes and its application to the study of aging

NASA Technical Reports Server (NTRS)

Hibbs, A. R.; Walford, R. L.

1989-01-01

The behavior of a physiological system which, after displacement, returns by homeostatic mechanisms to its original condition can be described by a simple differential equation in which the "recovery time" is a parameter. Two such systems, which influence one another, can be linked mathematically by the use of "coupling" or "feedback" coefficients. These concepts are the basis for many mathematical models of physiological behavior, and we describe the general nature of such models. Next, we introduce the concept of a "fatal limit" for the displacement of a physiological system, and show how measures of such limits can be included in mathematical models. We show how the numerical values of such limits depend on the values of other system parameters, i.e., recovery times and coupling coefficients, and suggest ways of measuring all these parameters experimentally, for example by monitoring changes induced by X-irradiation. Next, we discuss age-related changes in these parameters, and show how the parameters of mortality statistics, such as the famous Gompertz parameters, can be derived from experimentally measurable changes. Concepts of onset-of-aging, critical or fatal limits, equilibrium value (homeostasis), recovery times and coupling constants are involved. Illustrations are given using published data from mouse and rat populations. We believe that this method of deriving survival patterns from model that is experimentally testable is unique.

A systematic approach to parameter selection for CAD-virtual reality data translation using response surface methodology and MOGA-II.

PubMed

Abidi, Mustufa Haider; Al-Ahmari, Abdulrahman; Ahmad, Ali

2018-01-01

Advanced graphics capabilities have enabled the use of virtual reality as an efficient design technique. The integration of virtual reality in the design phase still faces impediment because of issues linked to the integration of CAD and virtual reality software. A set of empirical tests using the selected conversion parameters was found to yield properly represented virtual reality models. The reduced model yields an R-sq (pred) value of 72.71% and an R-sq (adjusted) value of 86.64%, indicating that 86.64% of the response variability can be explained by the model. The R-sq (pred) is 67.45%, which is not very high, indicating that the model should be further reduced by eliminating insignificant terms. The reduced model yields an R-sq (pred) value of 73.32% and an R-sq (adjusted) value of 79.49%, indicating that 79.49% of the response variability can be explained by the model. Using the optimization software MODE Frontier (Optimization, MOGA-II, 2014), four types of response surfaces for the three considered response variables were tested for the data of DOE. The parameter values obtained using the proposed experimental design methodology result in better graphics quality, and other necessary design attributes.

Validating proposed migration equation and parameters' values as a tool to reproduce and predict 137Cs vertical migration activity in Spanish soils.

PubMed

Olondo, C; Legarda, F; Herranz, M; Idoeta, R

2017-04-01

This paper shows the procedure performed to validate the migration equation and the migration parameters' values presented in a previous paper (Legarda et al., 2011) regarding the migration of 137 Cs in Spanish mainland soils. In this paper, this model validation has been carried out checking experimentally obtained activity concentration values against those predicted by the model. This experimental data come from the measured vertical activity profiles of 8 new sampling points which are located in northern Spain. Before testing predicted values of the model, the uncertainty of those values has been assessed with the appropriate uncertainty analysis. Once establishing the uncertainty of the model, both activity concentration values, experimental versus model predicted ones, have been compared. Model validation has been performed analyzing its accuracy, studying it as a whole and also at different depth intervals. As a result, this model has been validated as a tool to predict 137 Cs behaviour in a Mediterranean environment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modelling of intermittent microwave convective drying: parameter sensitivity

NASA Astrophysics Data System (ADS)

Zhang, Zhijun; Qin, Wenchao; Shi, Bin; Gao, Jingxin; Zhang, Shiwei

2017-06-01

The reliability of the predictions of a mathematical model is a prerequisite to its utilization. A multiphase porous media model of intermittent microwave convective drying is developed based on the literature. The model considers the liquid water, gas and solid matrix inside of food. The model is simulated by COMSOL software. Its sensitivity parameter is analysed by changing the parameter values by ±20%, with the exception of several parameters. The sensitivity analysis of the process of the microwave power level shows that each parameter: ambient temperature, effective gas diffusivity, and evaporation rate constant, has significant effects on the process. However, the surface mass, heat transfer coefficient, relative and intrinsic permeability of the gas, and capillary diffusivity of water do not have a considerable effect. The evaporation rate constant has minimal parameter sensitivity with a ±20% value change, until it is changed 10-fold. In all results, the temperature and vapour pressure curves show the same trends as the moisture content curve. However, the water saturation at the medium surface and in the centre show different results. Vapour transfer is the major mass transfer phenomenon that affects the drying process.

Examining lag effects between industrial land development and regional economic changes: The Netherlands experience.

PubMed

Ustaoglu, Eda; Lavalle, Carlo

2017-01-01

In most empirical applications, forecasting models for the analysis of industrial land focus on the relationship between current values of economic parameters and industrial land use. This paper aims to test this assumption by focusing on the dynamic relationship between current and lagged values of the 'economic fundamentals' and industrial land development. Not much effort has yet been attributed to develop land forecasting models to predict the demand for industrial land except those applying static regressions or other statistical measures. In this research, we estimated a dynamic panel data model across 40 regions from 2000 to 2008 for the Netherlands to uncover the relationship between current and lagged values of economic parameters and industrial land development. Land-use regulations such as land zoning policies, and other land-use restrictions like natural protection areas, geographical limitations in the form of water bodies or sludge areas are expected to affect supply of land, which will in turn be reflected in industrial land market outcomes. Our results suggest that gross domestic product (GDP), industrial employment, gross value added (GVA), property price, and other parameters representing demand and supply conditions in the industrial market explain industrial land developments with high significance levels. It is also shown that contrary to the current values, lagged values of the economic parameters have more sound relationships with the industrial developments in the Netherlands. The findings suggest use of lags between selected economic parameters and industrial land use in land forecasting applications.

Examining lag effects between industrial land development and regional economic changes: The Netherlands experience

PubMed Central

Ustaoglu, Eda; Lavalle, Carlo

2017-01-01

In most empirical applications, forecasting models for the analysis of industrial land focus on the relationship between current values of economic parameters and industrial land use. This paper aims to test this assumption by focusing on the dynamic relationship between current and lagged values of the ‘economic fundamentals’ and industrial land development. Not much effort has yet been attributed to develop land forecasting models to predict the demand for industrial land except those applying static regressions or other statistical measures. In this research, we estimated a dynamic panel data model across 40 regions from 2000 to 2008 for the Netherlands to uncover the relationship between current and lagged values of economic parameters and industrial land development. Land-use regulations such as land zoning policies, and other land-use restrictions like natural protection areas, geographical limitations in the form of water bodies or sludge areas are expected to affect supply of land, which will in turn be reflected in industrial land market outcomes. Our results suggest that gross domestic product (GDP), industrial employment, gross value added (GVA), property price, and other parameters representing demand and supply conditions in the industrial market explain industrial land developments with high significance levels. It is also shown that contrary to the current values, lagged values of the economic parameters have more sound relationships with the industrial developments in the Netherlands. The findings suggest use of lags between selected economic parameters and industrial land use in land forecasting applications. PMID:28877204

Precise measurement of renal filtration and vascular parameters using a two-compartment model for dynamic contrast-enhanced MRI of the kidney gives realistic normal values.

PubMed

Tofts, Paul S; Cutajar, Marica; Mendichovszky, Iosif A; Peters, A Michael; Gordon, Isky

2012-06-01

To model the uptake phase of T(1)-weighted DCE-MRI data in normal kidneys and to demonstrate that the fitted physiological parameters correlate with published normal values. The model incorporates delay and broadening of the arterial vascular peak as it appears in the capillary bed, two distinct compartments for renal intravascular and extravascular Gd tracer, and uses a small-vessel haematocrit value of 24%. Four physiological parameters can be estimated: regional filtration K ( trans ) (ml min(-1) [ml tissue](-1)), perfusion F (ml min(-1) [100 ml tissue](-1)), blood volume v ( b ) (%) and mean residence time MRT (s). From these are found the filtration fraction (FF; %) and total GFR (ml min(-1)). Fifteen healthy volunteers were imaged twice using oblique coronal slices every 2.5 s to determine the reproducibility. Using parenchymal ROIs, group mean values for renal biomarkers all agreed with published values: K ( trans ): 0.25; F: 219; v ( b ): 34; MRT: 5.5; FF: 15; GFR: 115. Nominally cortical ROIs consistently underestimated total filtration (by ~50%). Reproducibility was 7-18%. Sensitivity analysis showed that these fitted parameters are most vulnerable to errors in the fixed parameters kidney T(1), flip angle, haematocrit and relaxivity. These renal biomarkers can potentially measure renal physiology in diagnosis and treatment. • Dynamic contrast-enhanced magnetic resonance imaging can measure renal function. • Filtration and perfusion values in healthy volunteers agree with published normal values. • Precision measured in healthy volunteers is between 7 and 15%.

Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics

PubMed Central

Welch, Stephen M.; White, Jeffrey W.; Thorp, Kelly R.; Bello, Nora M.

2018-01-01

Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP’s as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<10−6919) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP’s becomes a robust means to help solve the genotype-to-phenotype problem in crops. PMID:29672629

Modelling Accuracy of a Car Steering Mechanism with Rack and Pinion and McPherson Suspension

NASA Astrophysics Data System (ADS)

Knapczyk, J.; Kucybała, P.

2016-08-01

Modelling accuracy of a car steering mechanism with a rack and pinion and McPherson suspension is analyzed. Geometrical parameters of the model are described by using the coordinates of centers of spherical joints, directional unit vectors and axis points of revolute, cylindrical and prismatic joints. Modelling accuracy is assumed as the differences between the values of the wheel knuckle position and orientation coordinates obtained using a simulation model and the corresponding measured values. The sensitivity analysis of the parameters on the model accuracy is illustrated by two numerical examples.

EPR, optical and superposition model study of Mn2+ doped L+ glutamic acid

NASA Astrophysics Data System (ADS)

Kripal, Ram; Singh, Manju

2015-12-01

Electron paramagnetic resonance (EPR) study of Mn2+ doped L+ glutamic acid single crystal is done at room temperature. Four interstitial sites are observed and the spin Hamiltonian parameters are calculated with the help of large number of resonant lines for various angular positions of external magnetic field. The optical absorption study is also done at room temperature. The energy values for different orbital levels are calculated, and observed bands are assigned as transitions from 6A1g(s) ground state to various excited states. With the help of these assigned bands, Racah inter-electronic repulsion parameters B = 869 cm-1, C = 2080 cm-1 and cubic crystal field splitting parameter Dq = 730 cm-1 are calculated. Zero field splitting (ZFS) parameters D and E are calculated by the perturbation formulae and crystal field parameters obtained using superposition model. The calculated values of ZFS parameters are in good agreement with the experimental values obtained by EPR.

Influence of structural parameters of deep groove ball bearings on vibration

NASA Astrophysics Data System (ADS)

Yu, Guangwei; Wu, Rui; Xia, Wei

2018-04-01

Taking 6201 bearing as the research object, a dynamic model of 4 degrees of freedom is established to solve the vibration characteristics such as the displacement, velocity and acceleration of deep groove ball bearings by MATLAB and Runge-Kutta method. By calculating the theoretical value of the frequency of the rolling element passing through the outer ring and the simulation value of the model, it can be known that the theoretical calculation value and the simulation value have good consistency. By the experiments, the measured values and simulation values are consistent. Using the mathematical model, the effect of structural parameters on vibration is obtained. The method in the paper is testified to be feasible and the results can be used as references for the design, manufacturing and testing of deep groove ball bearings.

Environment Modeling Using Runtime Values for JPF-Android

NASA Technical Reports Server (NTRS)

van der Merwe, Heila; Tkachuk, Oksana; Nel, Seal; van der Merwe, Brink; Visser, Willem

2015-01-01

Software applications are developed to be executed in a specific environment. This environment includes external native libraries to add functionality to the application and drivers to fire the application execution. For testing and verification, the environment of an application is simplified abstracted using models or stubs. Empty stubs, returning default values, are simple to generate automatically, but they do not perform well when the application expects specific return values. Symbolic execution is used to find input parameters for drivers and return values for library stubs, but it struggles to detect the values of complex objects. In this work-in-progress paper, we explore an approach to generate drivers and stubs based on values collected during runtime instead of using default values. Entry-points and methods that need to be modeled are instrumented to log their parameters and return values. The instrumented applications are then executed using a driver and instrumented libraries. The values collected during runtime are used to generate driver and stub values on- the-fly that improve coverage during verification by enabling the execution of code that previously crashed or was missed. We are implementing this approach to improve the environment model of JPF-Android, our model checking and analysis tool for Android applications.

Visual Basic, Excel-based fish population modeling tool - The pallid sturgeon example

USGS Publications Warehouse

Moran, Edward H.; Wildhaber, Mark L.; Green, Nicholas S.; Albers, Janice L.

2016-02-10

The model presented in this report is a spreadsheet-based model using Visual Basic for Applications within Microsoft Excel (http://dx.doi.org/10.5066/F7057D0Z) prepared in cooperation with the U.S. Army Corps of Engineers and U.S. Fish and Wildlife Service. It uses the same model structure and, initially, parameters as used by Wildhaber and others (2015) for pallid sturgeon. The difference between the model structure used for this report and that used by Wildhaber and others (2015) is that variance is not partitioned. For the model of this report, all variance is applied at the iteration and time-step levels of the model. Wildhaber and others (2015) partition variance into parameter variance (uncertainty about the value of a parameter itself) applied at the iteration level and temporal variance (uncertainty caused by random environmental fluctuations with time) applied at the time-step level. They included implicit individual variance (uncertainty caused by differences between individuals) within the time-step level.The interface developed for the model of this report is designed to allow the user the flexibility to change population model structure and parameter values and uncertainty separately for every component of the model. This flexibility makes the modeling tool potentially applicable to any fish species; however, the flexibility inherent in this modeling tool makes it possible for the user to obtain spurious outputs. The value and reliability of the model outputs are only as good as the model inputs. Using this modeling tool with improper or inaccurate parameter values, or for species for which the structure of the model is inappropriate, could lead to untenable management decisions. By facilitating fish population modeling, this modeling tool allows the user to evaluate a range of management options and implications. The goal of this modeling tool is to be a user-friendly modeling tool for developing fish population models useful to natural resource managers to inform their decision-making processes; however, as with all population models, caution is needed, and a full understanding of the limitations of a model and the veracity of user-supplied parameters should always be considered when using such model output in the management of any species.

Two statistics for evaluating parameter identifiability and error reduction

USGS Publications Warehouse

Doherty, John; Hunt, Randall J.

2009-01-01

Two statistics are presented that can be used to rank input parameters utilized by a model in terms of their relative identifiability based on a given or possible future calibration dataset. Identifiability is defined here as the capability of model calibration to constrain parameters used by a model. Both statistics require that the sensitivity of each model parameter be calculated for each model output for which there are actual or presumed field measurements. Singular value decomposition (SVD) of the weighted sensitivity matrix is then undertaken to quantify the relation between the parameters and observations that, in turn, allows selection of calibration solution and null spaces spanned by unit orthogonal vectors. The first statistic presented, "parameter identifiability", is quantitatively defined as the direction cosine between a parameter and its projection onto the calibration solution space. This varies between zero and one, with zero indicating complete non-identifiability and one indicating complete identifiability. The second statistic, "relative error reduction", indicates the extent to which the calibration process reduces error in estimation of a parameter from its pre-calibration level where its value must be assigned purely on the basis of prior expert knowledge. This is more sophisticated than identifiability, in that it takes greater account of the noise associated with the calibration dataset. Like identifiability, it has a maximum value of one (which can only be achieved if there is no measurement noise). Conceptually it can fall to zero; and even below zero if a calibration problem is poorly posed. An example, based on a coupled groundwater/surface-water model, is included that demonstrates the utility of the statistics. ?? 2009 Elsevier B.V.

Acceptable Tolerances for Matching Icing Similarity Parameters in Scaling Applications

NASA Technical Reports Server (NTRS)

Anderson, David N.

2003-01-01

This paper reviews past work and presents new data to evaluate how changes in similarity parameters affect ice shapes and how closely scale values of the parameters should match reference values. Experimental ice shapes presented are from tests by various researchers in the NASA Glenn Icing Research Tunnel. The parameters reviewed are the modified inertia parameter (which determines the stagnation collection efficiency), accumulation parameter, freezing fraction, Reynolds number, and Weber number. It was demonstrated that a good match of scale and reference ice shapes could sometimes be achieved even when values of the modified inertia parameter did not match precisely. Consequently, there can be some flexibility in setting scale droplet size, which is the test condition determined from the modified inertia parameter. A recommended guideline is that the modified inertia parameter be chosen so that the scale stagnation collection efficiency is within 10 percent of the reference value. The scale accumulation parameter and freezing fraction should also be within 10 percent of their reference values. The Weber number based on droplet size and water properties appears to be a more important scaling parameter than one based on model size and air properties. Scale values of both the Reynolds and Weber numbers need to be in the range of 60 to 160 percent of the corresponding reference values. The effects of variations in other similarity parameters have yet to be established.

Basal glycogenolysis in mouse skeletal muscle: in vitro model predicts in vivo fluxes

NASA Technical Reports Server (NTRS)

Lambeth, Melissa J.; Kushmerick, Martin J.; Marcinek, David J.; Conley, Kevin E.

2002-01-01

A previously published mammalian kinetic model of skeletal muscle glycogenolysis, consisting of literature in vitro parameters, was modified by substituting mouse specific Vmax values. The model demonstrates that glycogen breakdown to lactate is under ATPase control. Our criteria to test whether in vitro parameters could reproduce in vivo dynamics was the ability of the model to fit phosphocreatine (PCr) and inorganic phosphate (Pi) dynamic NMR data from ischemic basal mouse hindlimbs and predict biochemically-assayed lactate concentrations. Fitting was accomplished by optimizing four parameters--the ATPase rate coefficient, fraction of activated glycogen phosphorylase, and the equilibrium constants of creatine kinase and adenylate kinase (due to the absence of pH in the model). The optimized parameter values were physiologically reasonable, the resultant model fit the [PCr] and [Pi] timecourses well, and the model predicted the final measured lactate concentration. This result demonstrates that additional features of in vivo enzyme binding are not necessary for quantitative description of glycogenolytic dynamics.

A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling.

PubMed

Sumner, T; Shephard, E; Bogle, I D L

2012-09-07

One of the main challenges in the development of mathematical and computational models of biological systems is the precise estimation of parameter values. Understanding the effects of uncertainties in parameter values on model behaviour is crucial to the successful use of these models. Global sensitivity analysis (SA) can be used to quantify the variability in model predictions resulting from the uncertainty in multiple parameters and to shed light on the biological mechanisms driving system behaviour. We present a new methodology for global SA in systems biology which is computationally efficient and can be used to identify the key parameters and their interactions which drive the dynamic behaviour of a complex biological model. The approach combines functional principal component analysis with established global SA techniques. The methodology is applied to a model of the insulin signalling pathway, defects of which are a major cause of type 2 diabetes and a number of key features of the system are identified.

Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo.

PubMed

Sharifi, Soroosh; Murthy, Sudhir; Takács, Imre; Massoudieh, Arash

2014-03-01

One of the most important challenges in making activated sludge models (ASMs) applicable to design problems is identifying the values of its many stoichiometric and kinetic parameters. When wastewater characteristics data from full-scale biological treatment systems are used for parameter estimation, several sources of uncertainty, including uncertainty in measured data, external forcing (e.g. influent characteristics), and model structural errors influence the value of the estimated parameters. This paper presents a Bayesian hierarchical modeling framework for the probabilistic estimation of activated sludge process parameters. The method provides the joint probability density functions (JPDFs) of stoichiometric and kinetic parameters by updating prior information regarding the parameters obtained from expert knowledge and literature. The method also provides the posterior correlations between the parameters, as well as a measure of sensitivity of the different constituents with respect to the parameters. This information can be used to design experiments to provide higher information content regarding certain parameters. The method is illustrated using the ASM1 model to describe synthetically generated data from a hypothetical biological treatment system. The results indicate that data from full-scale systems can narrow down the ranges of some parameters substantially whereas the amount of information they provide regarding other parameters is small, due to either large correlations between some of the parameters or a lack of sensitivity with respect to the parameters. Copyright © 2013 Elsevier Ltd. All rights reserved.

Path loss variation of on-body UWB channel in the frequency bands of IEEE 802.15.6 standard.

PubMed

Goswami, Dayananda; Sarma, Kanak C; Mahanta, Anil

2016-06-01

The wireless body area network (WBAN) has gaining tremendous attention among researchers and academicians for its envisioned applications in healthcare service. Ultra wideband (UWB) radio technology is considered as excellent air interface for communication among body area network devices. Characterisation and modelling of channel parameters are utmost prerequisite for the development of reliable communication system. The path loss of on-body UWB channel for each frequency band defined in IEEE 802.15.6 standard is experimentally determined. The parameters of path loss model are statistically determined by analysing measurement data. Both the line-of-sight and non-line-of-sight channel conditions are considered in the measurement. Variations of parameter values with the size of human body are analysed along with the variation of parameter values with the surrounding environments. It is observed that the parameters of the path loss model vary with the frequency band as well as with the body size and surrounding environment. The derived parameter values are specific to the particular frequency bands of IEEE 802.15.6 standard, which will be useful for the development of efficient UWB WBAN system.

Synthetic calibration of a Rainfall-Runoff Model

USGS Publications Warehouse

Thompson, David B.; Westphal, Jerome A.; ,

1990-01-01

A method for synthetically calibrating storm-mode parameters for the U.S. Geological Survey's Precipitation-Runoff Modeling System is described. Synthetic calibration is accomplished by adjusting storm-mode parameters to minimize deviations between the pseudo-probability disributions represented by regional regression equations and actual frequency distributions fitted to model-generated peak discharge and runoff volume. Results of modeling storm hydrographs using synthetic and analytic storm-mode parameters are presented. Comparisons are made between model results from both parameter sets and between model results and observed hydrographs. Although mean storm runoff is reproducible to within about 26 percent of the observed mean storm runoff for five or six parameter sets, runoff from individual storms is subject to large disparities. Predicted storm runoff volume ranged from 2 percent to 217 percent of commensurate observed values. Furthermore, simulation of peak discharges was poor. Predicted peak discharges from individual storm events ranged from 2 percent to 229 percent of commensurate observed values. The model was incapable of satisfactorily executing storm-mode simulations for the study watersheds. This result is not considered a particular fault of the model, but instead is indicative of deficiencies in similar conceptual models.

Optimisation of process parameters on thin shell part using response surface methodology (RSM)

NASA Astrophysics Data System (ADS)

Faiz, J. M.; Shayfull, Z.; Nasir, S. M.; Fathullah, M.; Rashidi, M. M.

2017-09-01

This study is carried out to focus on optimisation of process parameters by simulation using Autodesk Moldflow Insight (AMI) software. The process parameters are taken as the input in order to analyse the warpage value which is the output in this study. There are some significant parameters that have been used which are melt temperature, mould temperature, packing pressure, and cooling time. A plastic part made of Polypropylene (PP) has been selected as the study part. Optimisation of process parameters is applied in Design Expert software with the aim to minimise the obtained warpage value. Response Surface Methodology (RSM) has been applied in this study together with Analysis of Variance (ANOVA) in order to investigate the interactions between parameters that are significant to the warpage value. Thus, the optimised warpage value can be obtained using the model designed using RSM due to its minimum error value. This study comes out with the warpage value improved by using RSM.

40 CFR 80.49 - Fuels to be used in augmenting the complex emission model through vehicle testing.

Code of Federal Regulations, 2010 CFR

2010-07-01

... shall be within the blending tolerances defined in this paragraph (a)(4) relative to the values... be within the blending tolerances defined in this paragraph (c) relative to the values specified in... “candidate” level of the parameter shall refer to the most extreme value of the parameter, relative to...

Reference values of clinical chemistry and hematology parameters in rhesus monkeys (Macaca mulatta).

PubMed

Chen, Younan; Qin, Shengfang; Ding, Yang; Wei, Lingling; Zhang, Jie; Li, Hongxia; Bu, Hong; Lu, Yanrong; Cheng, Jingqiu

2009-01-01

Rhesus monkey models are valuable to the studies of human biology. Reference values for clinical chemistry and hematology parameters of rhesus monkeys are required for proper data interpretation. Whole blood was collected from 36 healthy Chinese rhesus monkeys (Macaca mulatta) of either sex, 3 to 5 yr old. Routine chemistry and hematology parameters, and some special coagulation parameters including thromboelastograph and activities of coagulation factors were tested. We presented here the baseline values of clinical chemistry and hematology parameters in normal Chinese rhesus monkeys. These data may provide valuable information for veterinarians and investigators using rhesus monkeys in experimental studies.

A Phenomenological Model for Circadian and Sleep Allostatic Modulation of Plasma Cortisol Concentration

DTIC Science & Technology

2012-09-25

amplitudes of the model’s produc- tion parameters (w, , s) and degradation parameters (kp, dc) because the estimates for all of these parameters... degradation parameters (kp, dc), because the estimates for all of these parameters are higher for group A than for group C. E1194 A MODEL OF...values of both production and degradation parameters (Table 3), but there is significant variability between subjects that is caused by underlying

State and parameter estimation of the heat shock response system using Kalman and particle filters.

PubMed

Liu, Xin; Niranjan, Mahesan

2012-06-01

Traditional models of systems biology describe dynamic biological phenomena as solutions to ordinary differential equations, which, when parameters in them are set to correct values, faithfully mimic observations. Often parameter values are tweaked by hand until desired results are achieved, or computed from biochemical experiments carried out in vitro. Of interest in this article, is the use of probabilistic modelling tools with which parameters and unobserved variables, modelled as hidden states, can be estimated from limited noisy observations of parts of a dynamical system. Here we focus on sequential filtering methods and take a detailed look at the capabilities of three members of this family: (i) extended Kalman filter (EKF), (ii) unscented Kalman filter (UKF) and (iii) the particle filter, in estimating parameters and unobserved states of cellular response to sudden temperature elevation of the bacterium Escherichia coli. While previous literature has studied this system with the EKF, we show that parameter estimation is only possible with this method when the initial guesses are sufficiently close to the true values. The same turns out to be true for the UKF. In this thorough empirical exploration, we show that the non-parametric method of particle filtering is able to reliably estimate parameters and states, converging from initial distributions relatively far away from the underlying true values. Software implementation of the three filters on this problem can be freely downloaded from http://users.ecs.soton.ac.uk/mn/HeatShock

Robust design of configurations and parameters of adaptable products

NASA Astrophysics Data System (ADS)

Zhang, Jian; Chen, Yongliang; Xue, Deyi; Gu, Peihua

2014-03-01

An adaptable product can satisfy different customer requirements by changing its configuration and parameter values during the operation stage. Design of adaptable products aims at reducing the environment impact through replacement of multiple different products with single adaptable ones. Due to the complex architecture, multiple functional requirements, and changes of product configurations and parameter values in operation, impact of uncertainties to the functional performance measures needs to be considered in design of adaptable products. In this paper, a robust design approach is introduced to identify the optimal design configuration and parameters of an adaptable product whose functional performance measures are the least sensitive to uncertainties. An adaptable product in this paper is modeled by both configurations and parameters. At the configuration level, methods to model different product configuration candidates in design and different product configuration states in operation to satisfy design requirements are introduced. At the parameter level, four types of product/operating parameters and relations among these parameters are discussed. A two-level optimization approach is developed to identify the optimal design configuration and its parameter values of the adaptable product. A case study is implemented to illustrate the effectiveness of the newly developed robust adaptable design method.

Influence of the "surface effect" on the segregation parameters of S in Fe(100): A multi-scale modelling and Auger Electron Spectroscopy study

NASA Astrophysics Data System (ADS)

Barnard, P. E.; Terblans, J. J.; Swart, H. C.

2015-12-01

The article takes a new look at the process of atomic segregation by considering the influence of surface relaxation on the segregation parameters; the activation energy (Q), segregation energy (ΔG), interaction parameter (Ω) and the pre-exponential factor (D0). Computational modelling, namely Density Functional Theory (DFT) and the Modified Darken Model (MDM) in conjunction with Auger Electron Spectroscopy (AES) was utilized to study the variation of the segregation parameters for S in the surface region of Fe(100). Results indicate a variation in each of the segregation parameters as a function of the atomic layer under consideration. Values of the segregation parameters varied more dramatically as the surface layer is approached, with atomic layer 2 having the largest deviations in comparison to the bulk values. This atomic layer had the highest Q value and formed the rate limiting step for the segregation of S towards the Fe(100) surface. It was found that the segregation process is influenced by two sets of segregation parameters, those of the surface region formed by atomic layer 2, and those in the bulk material. This article is the first to conduct a full scale investigation on the influence of surface relaxation on segregation and labelled it the "surface effect".

Channel Characterization for Free-Space Optical Communications

DTIC Science & Technology

2012-07-01

parameters. From the path- average parameters, a 2nC profile model, called the HAP model, was constructed so that the entire channel from air to ground...SR), both of which are required to estimate the Power in the Bucket (PIB) and Power in the Fiber (PIF) associated with the FOENEX data beam. UCF was...of the path-average values of 2nC , the resulting HAP 2nC profile model led to values of ground level 2 nC that compared very well with actual

Simulation of nutrient and sediment concentrations and loads in the Delaware inland bays watershed: Extension of the hydrologic and water-quality model to ungaged segments

USGS Publications Warehouse

Gutierrez-Magness, Angelica L.

2006-01-01

Rapid population increases, agriculture, and industrial practices have been identified as important sources of excessive nutrients and sediments in the Delaware Inland Bays watershed. The amount and effect of excessive nutrients and sediments in the Inland Bays watershed have been well documented by the Delaware Geological Survey, the Delaware Department of Natural Resources and Environmental Control, the U.S. Environmental Protection Agency's National Estuary Program, the Delaware Center for Inland Bays, the University of Delaware, and other agencies. This documentation and data previously were used to develop a hydrologic and water-quality model of the Delaware Inland Bays watershed to simulate nutrients and sediment concentrations and loads, and to calibrate the model by comparing concentrations and streamflow data at six stations in the watershed over a limited period of time (October 1998 through April 2000). Although the model predictions of nutrient and sediment concentrations for the calibrated segments were fairly accurate, the predictions for the 28 ungaged segments located near tidal areas, where stream data were not available, were above the range of values measured in the area. The cooperative study established in 2000 by the Delaware Department of Natural Resources and Environmental Control, the Delaware Geological Survey, and the U.S. Geological Survey was extended to evaluate the model predictions in ungaged segments and to ensure that the model, developed as a planning and management tool, could accurately predict nutrient and sediment concentrations within the measured range of values in the area. The evaluation of the predictions was limited to the period of calibration (1999) of the 2003 model. To develop estimates on ungaged watersheds, parameter values from calibrated segments are transferred to the ungaged segments; however, accurate predictions are unlikely where parameter transference is subject to error. The unexpected nutrient and sediment concentrations simulated with the 2003 model were likely the result of inappropriate criteria for the transference of parameter values. From a model-simulation perspective, it is a common practice to transfer parameter values based on the similarity of soils or the similarity of land-use proportions between segments. For the Inland Bays model, the similarity of soils between segments was used as the basis to transfer parameter values. An alternative approach, which is documented in this report, is based on the similarity of the spatial distribution of the land use between segments and the similarity of land-use proportions, as these can be important factors for the transference of parameter values in lumped models. Previous work determined that the difference in the variation of runoff due to various spatial distributions of land use within a watershed can cause substantialloss of accuracy in the model predictions. The incorporation of the spatial distribution of land use to transfer parameter values from calibrated to uncalibrated segments provided more consistent and rational predictions of flow, especially during the summer, and consequently, predictions of lower nutrient concentrations during the same period. For the segments where the similarity of spatial distribution of land use was not clearly established with a calibrated segment, the similarity of the location of the most impervious areas was also used as a criterion for the transference of parameter values. The model predictions from the 28 ungaged segments were verified through comparison with measured in-stream concentrations from local and nearby streams provided by the Delaware Department of Natural Resources and Environmental Control. Model results indicated that the predicted edge-of-stream total suspended solids loads in the Inland Bays watershed were low in comparison to loads reported for the Eastern Shore of Maryland from the Chesapeake Bay watershed model. The flatness of the ter

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

2010-10-01

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

Distribution-centric 3-parameter thermodynamic models of partition gas chromatography.

PubMed

Blumberg, Leonid M

2017-03-31

If both parameters (the entropy, ΔS, and the enthalpy, ΔH) of the classic van't Hoff model of dependence of distribution coefficients (K) of analytes on temperature (T) are treated as the temperature-independent constants then the accuracy of the model is known to be insufficient for the needed accuracy of retention time prediction. A more accurate 3-parameter Clarke-Glew model offers a way to treat ΔS and ΔH as functions, ΔS(T) and ΔH(T), of T. A known T-centric construction of these functions is based on relating them to the reference values (ΔS ref and ΔH ref ) corresponding to a predetermined reference temperature (T ref ). Choosing a single T ref for all analytes in a complex sample or in a large database might lead to practically irrelevant values of ΔS ref and ΔH ref for those analytes that have too small or too large retention factors at T ref . Breaking all analytes in several subsets each with its own T ref leads to discontinuities in the analyte parameters. These problems are avoided in the K-centric modeling where ΔS(T) and ΔS(T) and other analyte parameters are described in relation to their values corresponding to a predetermined reference distribution coefficient (K Ref ) - the same for all analytes. In this report, the mathematics of the K-centric modeling are described and the properties of several types of K-centric parameters are discussed. It has been shown that the earlier introduced characteristic parameters of the analyte-column interaction (the characteristic temperature, T char , and the characteristic thermal constant, θ char ) are a special chromatographically convenient case of the K-centric parameters. Transformations of T-centric parameters into K-centric ones and vice-versa as well as the transformations of one set of K-centric parameters into another set and vice-versa are described. Copyright © 2017 Elsevier B.V. All rights reserved.

How robust are the natural history parameters used in chlamydia transmission dynamic models? A systematic review.

PubMed

Davies, Bethan; Anderson, Sarah-Jane; Turner, Katy M E; Ward, Helen

2014-01-30

Transmission dynamic models linked to economic analyses often form part of the decision making process when introducing new chlamydia screening interventions. Outputs from these transmission dynamic models can vary depending on the values of the parameters used to describe the infection. Therefore these values can have an important influence on policy and resource allocation. The risk of progression from infection to pelvic inflammatory disease has been extensively studied but the parameters which govern the transmission dynamics are frequently neglected. We conducted a systematic review of transmission dynamic models linked to economic analyses of chlamydia screening interventions to critically assess the source and variability of the proportion of infections that are asymptomatic, the duration of infection and the transmission probability. We identified nine relevant studies in Pubmed, Embase and the Cochrane database. We found that there is a wide variation in their natural history parameters, including an absolute difference in the proportion of asymptomatic infections of 25% in women and 75% in men, a six-fold difference in the duration of asymptomatic infection and a four-fold difference in the per act transmission probability. We consider that much of this variation can be explained by a lack of consensus in the literature. We found that a significant proportion of parameter values were referenced back to the early chlamydia literature, before the introduction of nucleic acid modes of diagnosis and the widespread testing of asymptomatic individuals. In conclusion, authors should use high quality contemporary evidence to inform their parameter values, clearly document their assumptions and make appropriate use of sensitivity analysis. This will help to make models more transparent and increase their utility to policy makers.

Parameter sensitivity analysis and optimization for a satellite-based evapotranspiration model across multiple sites using Moderate Resolution Imaging Spectroradiometer and flux data

NASA Astrophysics Data System (ADS)

Zhang, Kun; Ma, Jinzhu; Zhu, Gaofeng; Ma, Ting; Han, Tuo; Feng, Li Li

2017-01-01

Global and regional estimates of daily evapotranspiration are essential to our understanding of the hydrologic cycle and climate change. In this study, we selected the radiation-based Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) model and assessed it at a daily time scale by using 44 flux towers. These towers distributed in a wide range of ecological systems: croplands, deciduous broadleaf forest, evergreen broadleaf forest, evergreen needleleaf forest, grasslands, mixed forests, savannas, and shrublands. A regional land surface evapotranspiration model with a relatively simple structure, the PT-JPL model largely uses ecophysiologically-based formulation and parameters to relate potential evapotranspiration to actual evapotranspiration. The results using the original model indicate that the model always overestimates evapotranspiration in arid regions. This likely results from the misrepresentation of water limitation and energy partition in the model. By analyzing physiological processes and determining the sensitive parameters, we identified a series of parameter sets that can increase model performance. The model with optimized parameters showed better performance (R2 = 0.2-0.87; Nash-Sutcliffe efficiency (NSE) = 0.1-0.87) at each site than the original model (R2 = 0.19-0.87; NSE = -12.14-0.85). The results of the optimization indicated that the parameter β (water control of soil evaporation) was much lower in arid regions than in relatively humid regions. Furthermore, the optimized value of parameter m1 (plant control of canopy transpiration) was mostly between 1 to 1.3, slightly lower than the original value. Also, the optimized parameter Topt correlated well to the actual environmental temperature at each site. We suggest that using optimized parameters with the PT-JPL model could provide an efficient way to improve the model performance.

Optimization of Bleaching Parameters in Refining Process of Kenaf Seed Oil with a Central Composite Design Model.

PubMed

Chew, Sook Chin; Tan, Chin Ping; Nyam, Kar Lin

2017-07-01

Kenaf seed oil has been suggested to be used as nutritious edible oil due to its unique fatty acid composition and nutritional value. The objective of this study was to optimize the bleaching parameters of the chemical refining process for kenaf seed oil, namely concentration of bleaching earth (0.5 to 2.5% w/w), temperature (30 to 110 °C) and time (5 to 65 min) based on the responses of total oxidation value (TOTOX) and color reduction using response surface methodology. The results indicated that the corresponding response surface models were highly statistical significant (P < 0.0001) and sufficient to describe and predict TOTOX value and color reduction with R 2 of 0.9713 and 0.9388, respectively. The optimal parameters in the bleaching stage of kenaf seed oil were: 1.5% w/w of the concentration of bleaching earth, temperature of 70 °C, and time of 40 min. These optimum parameters produced bleached kenaf seed oil with TOTOX value of 8.09 and color reduction of 32.95%. There were no significant differences (P > 0.05) between experimental and predicted values, indicating the adequacy of the fitted models. © 2017 Institute of Food Technologists®.

AmapSim: a structural whole-plant simulator based on botanical knowledge and designed to host external functional models.

PubMed

Barczi, Jean-François; Rey, Hervé; Caraglio, Yves; de Reffye, Philippe; Barthélémy, Daniel; Dong, Qiao Xue; Fourcaud, Thierry

2008-05-01

AmapSim is a tool that implements a structural plant growth model based on a botanical theory and simulates plant morphogenesis to produce accurate, complex and detailed plant architectures. This software is the result of more than a decade of research and development devoted to plant architecture. New advances in the software development have yielded plug-in external functions that open up the simulator to functional processes. The simulation of plant topology is based on the growth of a set of virtual buds whose activity is modelled using stochastic processes. The geometry of the resulting axes is modelled by simple descriptive functions. The potential growth of each bud is represented by means of a numerical value called physiological age, which controls the value for each parameter in the model. The set of possible values for physiological ages is called the reference axis. In order to mimic morphological and architectural metamorphosis, the value allocated for the physiological age of buds evolves along this reference axis according to an oriented finite state automaton whose occupation and transition law follows a semi-Markovian function. Simulations were performed on tomato plants to demonstrate how the AmapSim simulator can interface external modules, e.g. a GREENLAB growth model and a radiosity model. The algorithmic ability provided by AmapSim, e.g. the reference axis, enables unified control to be exercised over plant development parameter values, depending on the biological process target: how to affect the local pertinent process, i.e. the pertinent parameter(s), while keeping the rest unchanged. This opening up to external functions also offers a broadened field of applications and thus allows feedback between plant growth and the physical environment.

AmapSim: A Structural Whole-plant Simulator Based on Botanical Knowledge and Designed to Host External Functional Models

PubMed Central

Barczi, Jean-François; Rey, Hervé; Caraglio, Yves; de Reffye, Philippe; Barthélémy, Daniel; Dong, Qiao Xue; Fourcaud, Thierry

2008-01-01

Background and Aims AmapSim is a tool that implements a structural plant growth model based on a botanical theory and simulates plant morphogenesis to produce accurate, complex and detailed plant architectures. This software is the result of more than a decade of research and development devoted to plant architecture. New advances in the software development have yielded plug-in external functions that open up the simulator to functional processes. Methods The simulation of plant topology is based on the growth of a set of virtual buds whose activity is modelled using stochastic processes. The geometry of the resulting axes is modelled by simple descriptive functions. The potential growth of each bud is represented by means of a numerical value called physiological age, which controls the value for each parameter in the model. The set of possible values for physiological ages is called the reference axis. In order to mimic morphological and architectural metamorphosis, the value allocated for the physiological age of buds evolves along this reference axis according to an oriented finite state automaton whose occupation and transition law follows a semi-Markovian function. Key Results Simulations were performed on tomato plants to demostrate how the AmapSim simulator can interface external modules, e.g. a GREENLAB growth model and a radiosity model. Conclusions The algorithmic ability provided by AmapSim, e.g. the reference axis, enables unified control to be exercised over plant development parameter values, depending on the biological process target: how to affect the local pertinent process, i.e. the pertinent parameter(s), while keeping the rest unchanged. This opening up to external functions also offers a broadened field of applications and thus allows feedback between plant growth and the physical environment. PMID:17766310

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

NASA Astrophysics Data System (ADS)

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. In this study, first order and total effects of the group of precipitation factors FP1- FP4, and the precipitation factor FP5, are calculated separately. First order and total effects of the group FP1- FP4 are much higher than first order and total effects of the factor FP5, which are negligible This situation is due to the fact that the actual value taken by FP5 does not have much influence in the contribution of the glacier zone to the catchment's output discharge, mainly limited by incident solar radiation. In addition to this, first order effects indicate that, in average, nearly 25% of predictive uncertainty could be reduced if the true values of the precipitation factors FPi could be known, but no information was available on the appropriate values for the remaining model parameters. Finally, the total effects of the precipitation factors FP1- FP4 are close to 41% in average, implying that even if the appropriate values for the remaining model parameters could be fixed, predictive uncertainty would be still quite high if the spatial distribution of precipitation remains unknown. Acknowledgements: This research was funded by FONDECYT, Research Project 1110279.

Method and system for monitoring and displaying engine performance parameters

NASA Technical Reports Server (NTRS)

Abbott, Terence S. (Inventor); Person, Jr., Lee H. (Inventor)

1991-01-01

The invention is a method and system for monitoring and directly displaying the actual thrust produced by a jet aircraft engine under determined operating conditions and the available thrust and predicted (commanded) thrust of a functional model of an ideal engine under the same determined operating conditions. A first set of actual value output signals representative of a plurality of actual performance parameters of the engine under the determined operating conditions is generated and compared with a second set of predicted value output signals representative of the predicted value of corresponding performance parameters of a functional model of the engine under the determined operating conditions to produce a third set of difference value output signals within a range of normal, caution, or warning limit values. A thrust indicator displays when any one of the actual value output signals is in the warning range while shaping function means shape each of the respective difference output signals as each approaches the limit of the respective normal, caution, and warning range limits.

Spatially constrained incoherent motion method improves diffusion-weighted MRI signal decay analysis in the liver and spleen

PubMed Central

Taimouri, Vahid; Afacan, Onur; Perez-Rossello, Jeannette M.; Callahan, Michael J.; Mulkern, Robert V.; Warfield, Simon K.; Freiman, Moti

2015-01-01

Purpose: To evaluate the effect of the spatially constrained incoherent motion (SCIM) method on improving the precision and robustness of fast and slow diffusion parameter estimates from diffusion-weighted MRI in liver and spleen in comparison to the independent voxel-wise intravoxel incoherent motion (IVIM) model. Methods: We collected diffusion-weighted MRI (DW-MRI) data of 29 subjects (5 healthy subjects and 24 patients with Crohn’s disease in the ileum). We evaluated parameters estimates’ robustness against different combinations of b-values (i.e., 4 b-values and 7 b-values) by comparing the variance of the estimates obtained with the SCIM and the independent voxel-wise IVIM model. We also evaluated the improvement in the precision of parameter estimates by comparing the coefficient of variation (CV) of the SCIM parameter estimates to that of the IVIM. Results: The SCIM method was more robust compared to IVIM (up to 70% in liver and spleen) for different combinations of b-values. Also, the CV values of the parameter estimations using the SCIM method were significantly lower compared to repeated acquisition and signal averaging estimated using IVIM, especially for the fast diffusion parameter in liver (CVIV IM = 46.61 ± 11.22, CVSCIM = 16.85 ± 2.160, p < 0.001) and spleen (CVIV IM = 95.15 ± 19.82, CVSCIM = 52.55 ± 1.91, p < 0.001). Conclusions: The SCIM method characterizes fast and slow diffusion more precisely compared to the independent voxel-wise IVIM model fitting in the liver and spleen. PMID:25832079

Quantification of tumor perfusion using dynamic contrast-enhanced ultrasound: impact of mathematical modeling

NASA Astrophysics Data System (ADS)

Doury, Maxime; Dizeux, Alexandre; de Cesare, Alain; Lucidarme, Olivier; Pellot-Barakat, Claire; Bridal, S. Lori; Frouin, Frédérique

2017-02-01

Dynamic contrast-enhanced ultrasound has been proposed to monitor tumor therapy, as a complement to volume measurements. To assess the variability of perfusion parameters in ideal conditions, four consecutive test-retest studies were acquired in a mouse tumor model, using controlled injections. The impact of mathematical modeling on parameter variability was then investigated. Coefficients of variation (CV) of tissue blood volume (BV) and tissue blood flow (BF) based-parameters were estimated inside 32 sub-regions of the tumors, comparing the log-normal (LN) model with a one-compartment model fed by an arterial input function (AIF) and improved by the introduction of a time delay parameter. Relative perfusion parameters were also estimated by normalization of the LN parameters and normalization of the one-compartment parameters estimated with the AIF, using a reference tissue (RT) region. A direct estimation (rRTd) of relative parameters, based on the one-compartment model without using the AIF, was also obtained by using the kinetics inside the RT region. Results of test-retest studies show that absolute regional parameters have high CV, whatever the approach, with median values of about 30% for BV, and 40% for BF. The positive impact of normalization was established, showing a coherent estimation of relative parameters, with reduced CV (about 20% for BV and 30% for BF using the rRTd approach). These values were significantly lower (p  <  0.05) than the CV of absolute parameters. The rRTd approach provided the smallest CV and should be preferred for estimating relative perfusion parameters.

Chaos and Localization in Dieterich-Ruina Friction

NASA Astrophysics Data System (ADS)

Erickson, B. A.; Birnir, B.; Lavallee, D.

2009-12-01

We consider two models derived from a 1-D Burridge-Knopoff chain of spring connected blocks subject to the Dieterich-Ruina (D-R) friction law. We analyze both the discrete ordinary differential equations, as well as the continuum model. Preliminary investigation into the ODEs shows evidence of the Dieterich-Ruina law exhibiting chaos, dependent on the size of the system. Periodic behavior occurs when considering chains of 3 or 5 blocks, while a chain of 10 blocks with the same parameter values results in chaotic motion. The continuum model (PDE) undergoes a transition to chaos when a specific parameter is increased and the chaotic regime is reached for smaller critical values than in the case of a single block (see Erickson et. al. 2008). This parameter, epsilon is the ratio of the stress parameters (B-A) and A in the D-R friction law. The parameter A is a measure of the direct velocity dependence (sometimes called the "direct effect") while (A-B) is a measure of the steady-state velocity dependence. When compared to the slip weakening friction law, the parameter (B-A) plays a role of a stress drop while A corresponds to the strength excess. In the case of a single block, transitions to chaos occur when epsilon = 11, a value too high for applications in seismology. For the continuum model however, the chaotic regime is reached for epsilon = 1. That the transition to chaos ensues for smaller parameter values than in the case of a single block may also be an indication that a careful rescaling of the friction law is necessary, similar to the conclusions made by Schmittbuhl et. al. (1996) who studied a "hierarchical array of blocks" and found that velocity weakening friction was scale dependent. We also observe solutions to both the discrete and the continuous model where the slip remains localized in space, suggesting the presence of solitonic behavior. Initial data in the form of a gaussian pulse tends to remain localized under certain parameter values and we explore the space of values for which this occurs. These solitonic or localized solutions can be understood as proxy for the propagation of the rupture across the fault during an earthquake. Under the Dieterich-Ruina law we may have discovered only a small subset of solutions to both the discrete and the continuous model, but there is no question that even in one spatial dimension, a rich phenomenology of dynamics exists.

Development of a predictive model for lead, cadmium and fluorine soil-water partition coefficients using sparse multiple linear regression analysis.

PubMed

Nakamura, Kengo; Yasutaka, Tetsuo; Kuwatani, Tatsu; Komai, Takeshi

2017-11-01

In this study, we applied sparse multiple linear regression (SMLR) analysis to clarify the relationships between soil properties and adsorption characteristics for a range of soils across Japan and identify easily-obtained physical and chemical soil properties that could be used to predict K and n values of cadmium, lead and fluorine. A model was first constructed that can easily predict the K and n values from nine soil parameters (pH, cation exchange capacity, specific surface area, total carbon, soil organic matter from loss on ignition and water holding capacity, the ratio of sand, silt and clay). The K and n values of cadmium, lead and fluorine of 17 soil samples were used to verify the SMLR models by the root mean square error values obtained from 512 combinations of soil parameters. The SMLR analysis indicated that fluorine adsorption to soil may be associated with organic matter, whereas cadmium or lead adsorption to soil is more likely to be influenced by soil pH, IL. We found that an accurate K value can be predicted from more than three soil parameters for most soils. Approximately 65% of the predicted values were between 33 and 300% of their measured values for the K value; 76% of the predicted values were within ±30% of their measured values for the n value. Our findings suggest that adsorption properties of lead, cadmium and fluorine to soil can be predicted from the soil physical and chemical properties using the presented models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mathematical Model of Three Species Food Chain Interaction with Mixed Functional Response

NASA Astrophysics Data System (ADS)

Ws, Mada Sanjaya; Mohd, Ismail Bin; Mamat, Mustafa; Salleh, Zabidin

In this paper, we study mathematical model of ecology with a tritrophic food chain composed of a classical Lotka-Volterra functional response for prey and predator, and a Holling type-III functional response for predator and super predator. There are two equilibrium points of the system. In the parameter space, there are passages from instability to stability, which are called Hopf bifurcation points. For the first equilibrium point, it is possible to find bifurcation points analytically and to prove that the system has periodic solutions around these points. Furthermore the dynamical behaviors of this model are investigated. Models for biologically reasonable parameter values, exhibits stable, unstable periodic and limit cycles. The dynamical behavior is found to be very sensitive to parameter values as well as the parameters of the practical life. Computer simulations are carried out to explain the analytical findings.

The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model

NASA Astrophysics Data System (ADS)

Thober, S.; Cuntz, M.; Mai, J.; Samaniego, L. E.; Clark, M. P.; Branch, O.; Wulfmeyer, V.; Attinger, S.

2016-12-01

Land surface models incorporate a large number of processes, described by physical, chemical and empirical equations. The agility of the models to react to different meteorological conditions is artificially constrained by having hard-coded parameters in their equations. Here we searched for hard-coded parameters in the computer code of the land surface model Noah with multiple process options (Noah-MP) to assess the model's agility during parameter estimation. We found 139 hard-coded values in all Noah-MP process options in addition to the 71 standard parameters. We performed a Sobol' global sensitivity analysis to variations of the standard and hard-coded parameters. The sensitivities of the hydrologic output fluxes latent heat and total runoff, their component fluxes, as well as photosynthesis and sensible heat were evaluated at twelve catchments of the Eastern United States with very different hydro-meteorological regimes. Noah-MP's output fluxes are sensitive to two thirds of its standard parameters. The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for evaporation, which proved to be oversensitive in other land surface models as well. Latent heat and total runoff show very similar sensitivities towards standard and hard-coded parameters. They are sensitive to both soil and plant parameters, which means that model calibrations of hydrologic or land surface models should take both soil and plant parameters into account. Sensible and latent heat exhibit almost the same sensitivities so that calibration or sensitivity analysis can be performed with either of the two. Photosynthesis has almost the same sensitivities as transpiration, which are different from the sensitivities of latent heat. Including photosynthesis and latent heat in model calibration might therefore be beneficial. Surface runoff is sensitive to almost all hard-coded snow parameters. These sensitivities get, however, diminished in total runoff. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

Assessment of Optimum Value for Dip Angle and Locking Rate Parameters in Makran Subduction Zone

NASA Astrophysics Data System (ADS)

Safari, A.; Abolghasem, A. M.; Abedini, N.; Mousavi, Z.

2017-09-01

Makran subduction zone is one of the convergent areas that have been studied by spatial geodesy. Makran zone is located in the South Eastern of Iran and South of Pakistan forming the part of Eurasian-Arabian plate's border where oceanic crust in the Arabian plate (or in Oman Sea) subducts under the Eurasian plate ( Farhoudi and Karig, 1977). Due to lack of historical and modern tools in the area, a sampling of sparse measurements of the permanent GPS stations and temporary stations (campaign) has been conducted in the past decade. Makran subduction zone from different perspectives has unusual behaviour: For example, the Eastern and Western parts of the region have very different seismicity and also dip angle of subducted plate is in about 2 to 8 degrees that this value due to the dip angle in other subduction zone is very low. In this study, we want to find the best possible value for parameters that differs Makran subduction zone from other subduction zones. Rigid block modelling method was used to determine these parameters. From the velocity vectors calculated from GPS observations in this area, block model is formed. These observations are obtained from GPS stations that a number of them are located in South Eastern Iran and South Western Pakistan and a station located in North Eastern Oman. According to previous studies in which the locking depth of Makran subduction zone is 38km (Frohling, 2016), in the preparation of this model, parameter value of at least 38 km is considered. With this function, the amount of 2 degree value is the best value for dip angle but for the locking rate there is not any specified amount. Because the proposed model is not sensitive to this parameter. So we can not expect big earthquakes in West of Makran or a low seismicity activity in there but the proposed model definitely shows the Makran subduction layer is locked.

A simulation of water pollution model parameter estimation

NASA Technical Reports Server (NTRS)

Kibler, J. F.

1976-01-01

A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.

Application of genetic algorithm in modeling on-wafer inductors for up to 110 Ghz

NASA Astrophysics Data System (ADS)

Liu, Nianhong; Fu, Jun; Liu, Hui; Cui, Wenpu; Liu, Zhihong; Liu, Linlin; Zhou, Wei; Wang, Quan; Guo, Ao

2018-05-01

In this work, the genetic algorithm has been introducted into parameter extraction for on-wafer inductors for up to 110 GHz millimeter-wave operations, and nine independent parameters of the equivalent circuit model are optimized together. With the genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the preliminary parameters without optimization. Especially, the fitting accuracy of the Q value achieves a significant improvement after the optimization.

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

NASA Astrophysics Data System (ADS)

Luke, Adam; Vrugt, Jasper A.; AghaKouchak, Amir; Matthew, Richard; Sanders, Brett F.

2017-07-01

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

Statistical Bayesian method for reliability evaluation based on ADT data

NASA Astrophysics Data System (ADS)

Lu, Dawei; Wang, Lizhi; Sun, Yusheng; Wang, Xiaohong

2018-05-01

Accelerated degradation testing (ADT) is frequently conducted in the laboratory to predict the products’ reliability under normal operating conditions. Two kinds of methods, degradation path models and stochastic process models, are utilized to analyze degradation data and the latter one is the most popular method. However, some limitations like imprecise solution process and estimation result of degradation ratio still exist, which may affect the accuracy of the acceleration model and the extrapolation value. Moreover, the conducted solution of this problem, Bayesian method, lose key information when unifying the degradation data. In this paper, a new data processing and parameter inference method based on Bayesian method is proposed to handle degradation data and solve the problems above. First, Wiener process and acceleration model is chosen; Second, the initial values of degradation model and parameters of prior and posterior distribution under each level is calculated with updating and iteration of estimation values; Third, the lifetime and reliability values are estimated on the basis of the estimation parameters; Finally, a case study is provided to demonstrate the validity of the proposed method. The results illustrate that the proposed method is quite effective and accuracy in estimating the lifetime and reliability of a product.

Linear Regression between CIE-Lab Color Parameters and Organic Matter in Soils of Tea Plantations

NASA Astrophysics Data System (ADS)

Chen, Yonggen; Zhang, Min; Fan, Dongmei; Fan, Kai; Wang, Xiaochang

2018-02-01

To quantify the relationship between the soil organic matter and color parameters using the CIE-Lab system, 62 soil samples (0-10 cm, Ferralic Acrisols) from tea plantations were collected from southern China. After air-drying and sieving, numerical color information and reflectance spectra of soil samples were measured under laboratory conditions using an UltraScan VIS (HunterLab) spectrophotometer equipped with CIE-Lab color models. We found that soil total organic carbon (TOC) and nitrogen (TN) contents were negatively correlated with the L* value (lightness) ( r = -0.84 and -0.80, respectively), a* value (correlation coefficient r = -0.51 and -0.46, respectively) and b* value ( r = -0.76 and -0.70, respectively). There were also linear regressions between TOC and TN contents with the L* value and b* value. Results showed that color parameters from a spectrophotometer equipped with CIE-Lab color models can predict TOC contents well for soils in tea plantations. The linear regression model between color values and soil organic carbon contents showed it can be used as a rapid, cost-effective method to evaluate content of soil organic matter in Chinese tea plantations.

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

DOE PAGES

Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; ...

2015-12-04

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less

Estimation of key parameters in adaptive neuron model according to firing patterns based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yuan, Chunhua; Wang, Jiang; Yi, Guosheng

2017-03-01

Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.

Stochastic and Perturbed Parameter Representations of Model Uncertainty in Convection Parameterization

NASA Astrophysics Data System (ADS)

Christensen, H. M.; Moroz, I.; Palmer, T.

2015-12-01

It is now acknowledged that representing model uncertainty in atmospheric simulators is essential for the production of reliable probabilistic ensemble forecasts, and a number of different techniques have been proposed for this purpose. Stochastic convection parameterization schemes use random numbers to represent the difference between a deterministic parameterization scheme and the true atmosphere, accounting for the unresolved sub grid-scale variability associated with convective clouds. An alternative approach varies the values of poorly constrained physical parameters in the model to represent the uncertainty in these parameters. This study presents new perturbed parameter schemes for use in the European Centre for Medium Range Weather Forecasts (ECMWF) convection scheme. Two types of scheme are developed and implemented. Both schemes represent the joint uncertainty in four of the parameters in the convection parametrisation scheme, which was estimated using the Ensemble Prediction and Parameter Estimation System (EPPES). The first scheme developed is a fixed perturbed parameter scheme, where the values of uncertain parameters are changed between ensemble members, but held constant over the duration of the forecast. The second is a stochastically varying perturbed parameter scheme. The performance of these schemes was compared to the ECMWF operational stochastic scheme, Stochastically Perturbed Parametrisation Tendencies (SPPT), and to a model which does not represent uncertainty in convection. The skill of probabilistic forecasts made using the different models was evaluated. While the perturbed parameter schemes improve on the stochastic parametrisation in some regards, the SPPT scheme outperforms the perturbed parameter approaches when considering forecast variables that are particularly sensitive to convection. Overall, SPPT schemes are the most skilful representations of model uncertainty due to convection parametrisation. Reference: H. M. Christensen, I. M. Moroz, and T. N. Palmer, 2015: Stochastic and Perturbed Parameter Representations of Model Uncertainty in Convection Parameterization. J. Atmos. Sci., 72, 2525-2544.

Analytic proof of the existence of the Lorenz attractor in the extended Lorenz model

NASA Astrophysics Data System (ADS)

Ovsyannikov, I. I.; Turaev, D. V.

2017-01-01

We give an analytic (free of computer assistance) proof of the existence of a classical Lorenz attractor for an open set of parameter values of the Lorenz model in the form of Yudovich-Morioka-Shimizu. The proof is based on detection of a homoclinic butterfly with a zero saddle value and rigorous verification of one of the Shilnikov criteria for the birth of the Lorenz attractor; we also supply a proof for this criterion. The results are applied in order to give an analytic proof for the existence of a robust, pseudohyperbolic strange attractor (the so-called discrete Lorenz attractor) for an open set of parameter values in a 4-parameter family of 3D Henon-like diffeomorphisms.

Exploration of DGVM Parameter Solution Space Using Simulated Annealing: Implications for Forecast Uncertainties

NASA Astrophysics Data System (ADS)

Wells, J. R.; Kim, J. B.

2011-12-01

Parameters in dynamic global vegetation models (DGVMs) are thought to be weakly constrained and can be a significant source of errors and uncertainties. DGVMs use between 5 and 26 plant functional types (PFTs) to represent the average plant life form in each simulated plot, and each PFT typically has a dozen or more parameters that define the way it uses resource and responds to the simulated growing environment. Sensitivity analysis explores how varying parameters affects the output, but does not do a full exploration of the parameter solution space. The solution space for DGVM parameter values are thought to be complex and non-linear; and multiple sets of acceptable parameters may exist. In published studies, PFT parameters are estimated from published literature, and often a parameter value is estimated from a single published value. Further, the parameters are "tuned" using somewhat arbitrary, "trial-and-error" methods. BIOMAP is a new DGVM created by fusing MAPSS biogeography model with Biome-BGC. It represents the vegetation of North America using 26 PFTs. We are using simulated annealing, a global search method, to systematically and objectively explore the solution space for the BIOMAP PFTs and system parameters important for plant water use. We defined the boundaries of the solution space by obtaining maximum and minimum values from published literature, and where those were not available, using +/-20% of current values. We used stratified random sampling to select a set of grid cells representing the vegetation of the conterminous USA. Simulated annealing algorithm is applied to the parameters for spin-up and a transient run during the historical period 1961-1990. A set of parameter values is considered acceptable if the associated simulation run produces a modern potential vegetation distribution map that is as accurate as one produced by trial-and-error calibration. We expect to confirm that the solution space is non-linear and complex, and that multiple acceptable parameter sets exist. Further we expect to demonstrate that the multiple parameter sets produce significantly divergent future forecasts in NEP, C storage, and ET and runoff; and thereby identify a highly important source of DGVM uncertainty

Computer simulation of storm runoff for three watersheds in Albuquerque, New Mexico

USGS Publications Warehouse

Knutilla, R.L.; Veenhuis, J.E.

1994-01-01

Rainfall-runoff data from three watersheds were selected for calibration and verification of the U.S. Geological Survey's Distributed Routing Rainfall-Runoff Model. The watersheds chosen are residentially developed. The conceptually based model uses an optimization process that adjusts selected parameters to achieve the best fit between measured and simulated runoff volumes and peak discharges. Three of these optimization parameters represent soil-moisture conditions, three represent infiltration, and one accounts for effective impervious area. Each watershed modeled was divided into overland-flow segments and channel segments. The overland-flow segments were further subdivided to reflect pervious and impervious areas. Each overland-flow and channel segment was assigned representative values of area, slope, percentage of imperviousness, and roughness coefficients. Rainfall-runoff data for each watershed were separated into two sets for use in calibration and verification. For model calibration, seven input parameters were optimized to attain a best fit of the data. For model verification, parameter values were set using values from model calibration. The standard error of estimate for calibration of runoff volumes ranged from 19 to 34 percent, and for peak discharge calibration ranged from 27 to 44 percent. The standard error of estimate for verification of runoff volumes ranged from 26 to 31 percent, and for peak discharge verification ranged from 31 to 43 percent.

Permutation on hybrid natural inflation

NASA Astrophysics Data System (ADS)

Carone, Christopher D.; Erlich, Joshua; Ramos, Raymundo; Sher, Marc

2014-09-01

We analyze a model of hybrid natural inflation based on the smallest non-Abelian discrete group S3. Leading invariant terms in the scalar potential have an accidental global symmetry that is spontaneously broken, providing a pseudo-Goldstone boson that is identified as the inflaton. The S3 symmetry restricts both the form of the inflaton potential and the couplings of the inflaton field to the waterfall fields responsible for the end of inflation. We identify viable points in the model parameter space. Although the power in tensor modes is small in most of the parameter space of the model, we identify parameter choices that yield potentially observable values of r without super-Planckian initial values of the inflaton field.

MLBCD: a machine learning tool for big clinical data.

PubMed

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

Non-minimally coupled f(R) cosmology

NASA Astrophysics Data System (ADS)

Thakur, Shruti; Sen, Anjan A.; Seshadri, T. R.

2011-02-01

We investigate the consequences of non-minimal gravitational coupling to matter and study how it differs from the case of minimal coupling by choosing certain simple forms for the nature of coupling. The values of the parameters are specified at z=0 (present epoch) and the equations are evolved backwards to calculate the evolution of cosmological parameters. We find that the Hubble parameter evolves more slowly in non-minimal coupling case as compared to the minimal coupling case. In both the cases, the universe accelerates around present time, and enters the decelerating regime in the past. Using the latest Union2 dataset for supernova Type Ia observations as well as the data for baryon acoustic oscillation (BAO) from SDSS observations, we constraint the parameters of Linder exponential model in the two different approaches. We find that there is an upper bound on model parameter in minimal coupling. But for non-minimal coupling case, there is range of allowed values for the model parameter.

On the treatment of evapotranspiration, soil moisture accounting, and aquifer recharge in monthly water balance models

USGS Publications Warehouse

Alley, William M.

1984-01-01

Several two- to six-parameter regional water balance models are examined by using 50-year records of monthly streamflow at 10 sites in New Jersey. These models include variants of the Thornthwaite-Mather model, the Palmer model, and the more recent Thomas abcd model. Prediction errors are relatively similar among the models. However, simulated values of state variables such as soil moisture storage differ substantially among the models, and fitted parameter values for different models sometimes indicated an entirely different type of basin response to precipitation. Some problems in parameter identification are noted, including difficulties in identifying an appropriate time lag factor for the Thornthwaite-Mather-type model for basins with little groundwater storage, very high correlations between upper and lower storages in the Palmer-type model, and large sensitivity of parameter a of the abcd model to bias in estimates of precipitation and potential evapotranspiration. Modifications to the threshold concept of the Thornthwaite-Mather model were statistically valid for the six stations in northern New Jersey. The abcd model resulted in a simulated seasonal cycle of groundwater levels similar to fluctuations observed in nearby wells but with greater persistence. These results suggest that extreme caution should be used in attaching physical significance to model parameters and in using the state variables of the models in indices of drought and basin productivity.

Evaluation of different mathematical models and different b-value ranges of diffusion-weighted imaging in peripheral zone prostate cancer detection using b-value up to 4500 s/mm2

PubMed Central

Feng, Zhaoyan; Min, Xiangde; Margolis, Daniel J. A.; Duan, Caohui; Chen, Yuping; Sah, Vivek Kumar; Chaudhary, Nabin; Li, Basen; Ke, Zan; Zhang, Peipei; Wang, Liang

2017-01-01

Objectives To evaluate the diagnostic performance of different mathematical models and different b-value ranges of diffusion-weighted imaging (DWI) in peripheral zone prostate cancer (PZ PCa) detection. Methods Fifty-six patients with histologically proven PZ PCa who underwent DWI-magnetic resonance imaging (MRI) using 21 b-values (0–4500 s/mm2) were included. The mean signal intensities of the regions of interest (ROIs) placed in benign PZs and cancerous tissues on DWI images were fitted using mono-exponential, bi-exponential, stretched-exponential, and kurtosis models. The b-values were divided into four ranges: 0–1000, 0–2000, 0–3200, and 0–4500 s/mm2, grouped as A, B, C, and D, respectively. ADC, , D*, f, DDC, α, Dapp, and Kapp were estimated for each group. The adjusted coefficient of determination (R2) was calculated to measure goodness-of-fit. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic performance of the parameters. Results All parameters except D* showed significant differences between cancerous tissues and benign PZs in each group. The area under the curve values (AUCs) of ADC were comparable in groups C and D (p = 0.980) and were significantly higher than those in groups A and B (p< 0.05 for all). The AUCs of ADC and Kapp in groups B and C were similar (p = 0.07 and p = 0.954), and were significantly higher than the other parameters (p< 0.001 for all). The AUCs of ADC in group D was slightly higher than Kapp (p = 0.002), and both were significantly higher than the other parameters (p< 0.001 for all). Conclusions ADC derived from conventional mono-exponential high b-value (3200 s/mm2) models is an optimal parameter for PZ PCa detection. PMID:28199367

EPR, optical and modeling of Mn(2+) doped sarcosinium oxalate monohydrate.

PubMed

Kripal, Ram; Singh, Manju

2015-01-25

Electron paramagnetic resonance (EPR) study of Mn(2+) ions doped in sarcosinium oxalate monohydrate (SOM) single crystal is done at liquid nitrogen temperature (LNT). EPR spectrum shows a bunch of five fine structure lines and further they split into six hyperfine components. Only one interstitial site was observed. With the help of EPR spectra the spin Hamiltonian parameters including zero field splitting (ZFS) parameters are evaluated. The optical absorption study at room temperature is also done in the wavelength range 195-1100 nm. From this study cubic crystal field splitting parameter, Dq=730 cm(-1) and Racah inter-electronic repulsion parameters B=792 cm(-1), C=2278 cm(-1) are determined. ZFS parameters D and E are also calculated using crystal field parameters from superposition model and microscopic spin Hamiltonian theory. The calculated ZFS parameter values are in good match with the experimental values obtained by EPR. Copyright © 2014 Elsevier B.V. All rights reserved.

Parameterization of aquatic ecosystem functioning and its natural variation: Hierarchical Bayesian modelling of plankton food web dynamics

NASA Astrophysics Data System (ADS)

Norros, Veera; Laine, Marko; Lignell, Risto; Thingstad, Frede

2017-10-01

Methods for extracting empirically and theoretically sound parameter values are urgently needed in aquatic ecosystem modelling to describe key flows and their variation in the system. Here, we compare three Bayesian formulations for mechanistic model parameterization that differ in their assumptions about the variation in parameter values between various datasets: 1) global analysis - no variation, 2) separate analysis - independent variation and 3) hierarchical analysis - variation arising from a shared distribution defined by hyperparameters. We tested these methods, using computer-generated and empirical data, coupled with simplified and reasonably realistic plankton food web models, respectively. While all methods were adequate, the simulated example demonstrated that a well-designed hierarchical analysis can result in the most accurate and precise parameter estimates and predictions, due to its ability to combine information across datasets. However, our results also highlighted sensitivity to hyperparameter prior distributions as an important caveat of hierarchical analysis. In the more complex empirical example, hierarchical analysis was able to combine precise identification of parameter values with reasonably good predictive performance, although the ranking of the methods was less straightforward. We conclude that hierarchical Bayesian analysis is a promising tool for identifying key ecosystem-functioning parameters and their variation from empirical datasets.

Improvement of shallow landslide prediction accuracy using soil parameterisation for a granite area in South Korea

NASA Astrophysics Data System (ADS)

Kim, M. S.; Onda, Y.; Kim, J. K.

2015-01-01

SHALSTAB model applied to shallow landslides induced by rainfall to evaluate soil properties related with the effect of soil depth for a granite area in Jinbu region, Republic of Korea. Soil depth measured by a knocking pole test and two soil parameters from direct shear test (a and b) as well as one soil parameters from a triaxial compression test (c) were collected to determine the input parameters for the model. Experimental soil data were used for the first simulation (Case I) and, soil data represented the effect of measured soil depth and average soil depth from soil data of Case I were used in the second (Case II) and third simulations (Case III), respectively. All simulations were analysed using receiver operating characteristic (ROC) analysis to determine the accuracy of prediction. ROC analysis results for first simulation showed the low ROC values under 0.75 may be due to the internal friction angle and particularly the cohesion value. Soil parameters calculated from a stochastic hydro-geomorphological model were applied to the SHALSTAB model. The accuracy of Case II and Case III using ROC analysis showed higher accuracy values rather than first simulation. Our results clearly demonstrate that the accuracy of shallow landslide prediction can be improved when soil parameters represented the effect of soil thickness.

[Monitoring of occupational activities under the risk of heat stress: use of mathematical models in the prediction of physiological parameters].

PubMed

Terzi, R; Catenacci, G; Marcaletti, G

1985-01-01

Some authors proposed mathematical models that, starting from standardized conditions of environmental microclimate parameters, thermal impedance of the clothing, and energetic expenditure allowed the forecast of the body temperature and heart rate variations in respect to the basal values in subjects standing in the same environment. In the present work we verify the usefulness of these models applied to the working tasks characterized by standardized job made under unfavourable thermal conditions. In subject working in an electric power station the values of the body temperature and heart rate are registered and compared with the values obtained by the application of the studied models. The results are discussed in view of the practical use.

Editor’s message: Groundwater modeling fantasies - Part 1, adrift in the details

USGS Publications Warehouse

Voss, Clifford I.

2011-01-01

Fools ignore complexity. Pragmatists suffer it. Some can avoid it. Geniuses remove it. …Simplicity does not precede complexity, but follows it. (Epigrams in Programming by Alan Perlis, a computer scientist; Perlis 1982).A doctoral student creating a groundwater model of a regional aquifer put individual circular regions around data points where he had hydraulic head measurements, so that each region’s parameter values could be adjusted to get perfect fit with the measurement at that point. Nearly every measurement point had its own parameter-value region. After calibration, the student was satisfied because his model correctly reproduced all of his data. Did he really get the true field values of parameters in this manner? Did this approach result in a realistic, meaningful and useful groundwater model?—truly doubtful. Is this story a sign of a common style of educating hydrogeology students these days? Where this is the case, major changes are needed to add back ‘common-sense hydrogeology’ to the curriculum. Worse, this type of modeling approach has become an industry trend in application of groundwater models to real systems, encouraged by the advent of automatic model calibration software that has no problem providing numbers for as many parameter value estimates as desired. Just because a computer program can easily create such values does not mean that they are in any sense useful—but unquestioning practitioners are happy to follow such software developments, perhaps because of an implied promise that highly parameterized models, here referred to as ‘complex’, are somehow superior. This and other fallacies are implicit in groundwater modeling studies, most usually not acknowledged when presenting results. This two-part Editor’s Message deals with the state of groundwater modeling: part 1 (here) focuses on problems and part 2 (Voss 2011) on prospects.

Variable frame rate transmission - A review of methodology and application to narrow-band LPC speech coding

NASA Astrophysics Data System (ADS)

Viswanathan, V. R.; Makhoul, J.; Schwartz, R. M.; Huggins, A. W. F.

1982-04-01

The variable frame rate (VFR) transmission methodology developed, implemented, and tested in the years 1973-1978 for efficiently transmitting linear predictive coding (LPC) vocoder parameters extracted from the input speech at a fixed frame rate is reviewed. With the VFR method, parameters are transmitted only when their values have changed sufficiently over the interval since their preceding transmission. Two distinct approaches to automatic implementation of the VFR method are discussed. The first bases the transmission decisions on comparisons between the parameter values of the present frame and the last transmitted frame. The second, which is based on a functional perceptual model of speech, compares the parameter values of all the frames that lie in the interval between the present frame and the last transmitted frame against a linear model of parameter variation over that interval. Also considered is the application of VFR transmission to the design of narrow-band LPC speech coders with average bit rates of 2000-2400 bts/s.

MMA, A Computer Code for Multi-Model Analysis

USGS Publications Warehouse

Poeter, Eileen P.; Hill, Mary C.

2007-01-01

This report documents the Multi-Model Analysis (MMA) computer code. MMA can be used to evaluate results from alternative models of a single system using the same set of observations for all models. As long as the observations, the observation weighting, and system being represented are the same, the models can differ in nearly any way imaginable. For example, they may include different processes, different simulation software, different temporal definitions (for example, steady-state and transient models could be considered), and so on. The multiple models need to be calibrated by nonlinear regression. Calibration of the individual models needs to be completed before application of MMA. MMA can be used to rank models and calculate posterior model probabilities. These can be used to (1) determine the relative importance of the characteristics embodied in the alternative models, (2) calculate model-averaged parameter estimates and predictions, and (3) quantify the uncertainty of parameter estimates and predictions in a way that integrates the variations represented by the alternative models. There is a lack of consensus on what model analysis methods are best, so MMA provides four default methods. Two are based on Kullback-Leibler information, and use the AIC (Akaike Information Criterion) or AICc (second-order-bias-corrected AIC) model discrimination criteria. The other two default methods are the BIC (Bayesian Information Criterion) and the KIC (Kashyap Information Criterion) model discrimination criteria. Use of the KIC criterion is equivalent to using the maximum-likelihood Bayesian model averaging (MLBMA) method. AIC, AICc, and BIC can be derived from Frequentist or Bayesian arguments. The default methods based on Kullback-Leibler information have a number of theoretical advantages, including that they tend to favor more complicated models as more data become available than do the other methods, which makes sense in many situations. Many applications of MMA will be well served by the default methods provided. To use the default methods, the only required input for MMA is a list of directories where the files for the alternate models are located. Evaluation and development of model-analysis methods are active areas of research. To facilitate exploration and innovation, MMA allows the user broad discretion to define alternatives to the default procedures. For example, MMA allows the user to (a) rank models based on model criteria defined using a wide range of provided and user-defined statistics in addition to the default AIC, AICc, BIC, and KIC criteria, (b) create their own criteria using model measures available from the code, and (c) define how each model criterion is used to calculate related posterior model probabilities. The default model criteria rate models are based on model fit to observations, the number of observations and estimated parameters, and, for KIC, the Fisher information matrix. In addition, MMA allows the analysis to include an evaluation of estimated parameter values. This is accomplished by allowing the user to define unreasonable estimated parameter values or relative estimated parameter values. An example of the latter is that it may be expected that one parameter value will be less than another, as might be the case if two parameters represented the hydraulic conductivity of distinct materials such as fine and coarse sand. Models with parameter values that violate the user-defined conditions are excluded from further consideration by MMA. Ground-water models are used as examples in this report, but MMA can be used to evaluate any set of models for which the required files have been produced. MMA needs to read files from a separate directory for each alternative model considered. The needed files are produced when using the Sensitivity-Analysis or Parameter-Estimation mode of UCODE_2005, or, possibly, the equivalent capability of another program. MMA is constructed using

Nitrous oxide emissions from cropland: a procedure for calibrating the DayCent biogeochemical model using inverse modelling

USGS Publications Warehouse

Rafique, Rashad; Fienen, Michael N.; Parkin, Timothy B.; Anex, Robert P.

2013-01-01

DayCent is a biogeochemical model of intermediate complexity widely used to simulate greenhouse gases (GHG), soil organic carbon and nutrients in crop, grassland, forest and savannah ecosystems. Although this model has been applied to a wide range of ecosystems, it is still typically parameterized through a traditional “trial and error” approach and has not been calibrated using statistical inverse modelling (i.e. algorithmic parameter estimation). The aim of this study is to establish and demonstrate a procedure for calibration of DayCent to improve estimation of GHG emissions. We coupled DayCent with the parameter estimation (PEST) software for inverse modelling. The PEST software can be used for calibration through regularized inversion as well as model sensitivity and uncertainty analysis. The DayCent model was analysed and calibrated using N2O flux data collected over 2 years at the Iowa State University Agronomy and Agricultural Engineering Research Farms, Boone, IA. Crop year 2003 data were used for model calibration and 2004 data were used for validation. The optimization of DayCent model parameters using PEST significantly reduced model residuals relative to the default DayCent parameter values. Parameter estimation improved the model performance by reducing the sum of weighted squared residual difference between measured and modelled outputs by up to 67 %. For the calibration period, simulation with the default model parameter values underestimated mean daily N2O flux by 98 %. After parameter estimation, the model underestimated the mean daily fluxes by 35 %. During the validation period, the calibrated model reduced sum of weighted squared residuals by 20 % relative to the default simulation. Sensitivity analysis performed provides important insights into the model structure providing guidance for model improvement.

Exact solution of three-dimensional transport problems using one-dimensional models. [in semiconductor devices

NASA Technical Reports Server (NTRS)

Misiakos, K.; Lindholm, F. A.

1986-01-01

Several parameters of certain three-dimensional semiconductor devices including diodes, transistors, and solar cells can be determined without solving the actual boundary-value problem. The recombination current, transit time, and open-circuit voltage of planar diodes are emphasized here. The resulting analytical expressions enable determination of the surface recombination velocity of shallow planar diodes. The method involves introducing corresponding one-dimensional models having the same values of these parameters.

Comparative Analyses of Creep Models of a Solid Propellant

NASA Astrophysics Data System (ADS)

Zhang, J. B.; Lu, B. J.; Gong, S. F.; Zhao, S. P.

2018-05-01

The creep experiments of a solid propellant samples under five different stresses are carried out at 293.15 K and 323.15 K. In order to express the creep properties of this solid propellant, the viscoelastic model i.e. three Parameters solid, three Parameters fluid, four Parameters solid, four Parameters fluid and exponential model are involved. On the basis of the principle of least squares fitting, and different stress of all the parameters for the models, the nonlinear fitting procedure can be used to analyze the creep properties. The study shows that the four Parameters solid model can best express the behavior of creep properties of the propellant samples. However, the three Parameters solid and exponential model cannot very well reflect the initial value of the creep process, while the modified four Parameters models are found to agree well with the acceleration characteristics of the creep process.

Single neuron modeling and data assimilation in BNST neurons

NASA Astrophysics Data System (ADS)

Farsian, Reza

Neurons, although tiny in size, are vastly complicated systems, which are responsible for the most basic yet essential functions of any nervous system. Even the most simple models of single neurons are usually high dimensional, nonlinear, and contain many parameters and states which are unobservable in a typical neurophysiological experiment. One of the most fundamental problems in experimental neurophysiology is the estimation of these parameters and states, since knowing their values is essential in identification, model construction, and forward prediction of biological neurons. Common methods of parameter and state estimation do not perform well for neural models due to their high dimensionality and nonlinearity. In this dissertation, two alternative approaches for parameters and state estimation of biological neurons have been demonstrated: dynamical parameter estimation (DPE) and a Markov Chain Monte Carlo (MCMC) method. The first method uses elements of chaos control and synchronization theory for parameter and state estimation. MCMC is a statistical approach which uses a path integral formulation to evaluate a mean and an error bound for these unobserved parameters and states. These methods have been applied to biological system of neurons in Bed Nucleus of Stria Termialis neurons (BNST) of rats. State and parameters of neurons in both systems were estimated, and their value were used for recreating a realistic model and predicting the behavior of the neurons successfully. The knowledge of biological parameters can ultimately provide a better understanding of the internal dynamics of a neuron in order to build robust models of neuron networks.

Convergence in parameters and predictions using computational experimental design.

PubMed

Hagen, David R; White, Jacob K; Tidor, Bruce

2013-08-06

Typically, biological models fitted to experimental data suffer from significant parameter uncertainty, which can lead to inaccurate or uncertain predictions. One school of thought holds that accurate estimation of the true parameters of a biological system is inherently problematic. Recent work, however, suggests that optimal experimental design techniques can select sets of experiments whose members probe complementary aspects of a biochemical network that together can account for its full behaviour. Here, we implemented an experimental design approach for selecting sets of experiments that constrain parameter uncertainty. We demonstrated with a model of the epidermal growth factor-nerve growth factor pathway that, after synthetically performing a handful of optimal experiments, the uncertainty in all 48 parameters converged below 10 per cent. Furthermore, the fitted parameters converged to their true values with a small error consistent with the residual uncertainty. When untested experimental conditions were simulated with the fitted models, the predicted species concentrations converged to their true values with errors that were consistent with the residual uncertainty. This paper suggests that accurate parameter estimation is achievable with complementary experiments specifically designed for the task, and that the resulting parametrized models are capable of accurate predictions.

EFFECT ON PERFUSION VALUES OF SAMPLING INTERVAL OF CT PERFUSION ACQUISITIONS IN NEUROENDOCRINE LIVER METASTASES AND NORMAL LIVER

PubMed Central

Ng, Chaan S.; Hobbs, Brian P.; Wei, Wei; Anderson, Ella F.; Herron, Delise H.; Yao, James C.; Chandler, Adam G.

2014-01-01

Objective To assess the effects of sampling interval (SI) of CT perfusion acquisitions on CT perfusion values in normal liver and liver metastases from neuroendocrine tumors. Methods CT perfusion in 16 patients with neuroendocrine liver metastases were analyzed by distributed parameter modeling to yield tissue blood flow, blood volume, mean transit time, permeability, and hepatic arterial fraction, for tumor and normal liver. CT perfusion values for the reference sampling interval of 0.5s (SI0.5) were compared with those of SI datasets of 1s, 2s, 3s and 4s, using mixed-effects model analyses. Results Increases in SI beyond 1s were associated with significant and increasing departures of CT perfusion parameters from reference values at SI0.5 (p≤0.0009). CT perfusion values deviated from reference with increasing uncertainty with increasing SIs. Findings for normal liver were concordant. Conclusion Increasing SIs beyond 1s yield significantly different CT perfusion parameter values compared to reference values at SI0.5. PMID:25626401

Calibration process of highly parameterized semi-distributed hydrological model

NASA Astrophysics Data System (ADS)

Vidmar, Andrej; Brilly, Mitja

2017-04-01

Hydrological phenomena take place in the hydrological system, which is governed by nature, and are essentially stochastic. These phenomena are unique, non-recurring, and changeable across space and time. Since any river basin with its own natural characteristics and any hydrological event therein, are unique, this is a complex process that is not researched enough. Calibration is a procedure of determining the parameters of a model that are not known well enough. Input and output variables and mathematical model expressions are known, while only some parameters are unknown, which are determined by calibrating the model. The software used for hydrological modelling nowadays is equipped with sophisticated algorithms for calibration purposes without possibility to manage process by modeler. The results are not the best. We develop procedure for expert driven process of calibration. We use HBV-light-CLI hydrological model which has command line interface and coupling it with PEST. PEST is parameter estimation tool which is used widely in ground water modeling and can be used also on surface waters. Process of calibration managed by expert directly, and proportionally to the expert knowledge, affects the outcome of the inversion procedure and achieves better results than if the procedure had been left to the selected optimization algorithm. First step is to properly define spatial characteristic and structural design of semi-distributed model including all morphological and hydrological phenomena, like karstic area, alluvial area and forest area. This step includes and requires geological, meteorological, hydraulic and hydrological knowledge of modeler. Second step is to set initial parameter values at their preferred values based on expert knowledge. In this step we also define all parameter and observation groups. Peak data are essential in process of calibration if we are mainly interested in flood events. Each Sub Catchment in the model has own observations group. Third step is to set appropriate bounds to parameters in their range of realistic values. Fourth step is to use of singular value decomposition (SVD) ensures that PEST maintains numerical stability, regardless of how ill-posed is the inverse problem Fifth step is to run PWTADJ1. This creates a new PEST control file in which weights are adjusted such that the contribution made to the total objective function by each observation group is the same. This prevents the information content of any group from being invisible to the inversion process. Sixth step is to add Tikhonov regularization to the PEST control file by running the ADDREG1 utility (Doherty, J, 2013). In adding regularization to the PEST control file ADDREG1 automatically provides a prior information equation for each parameter in which the preferred value of that parameter is equated to its initial value. Last step is to run PEST. We run BeoPEST which a parallel version of PEST and can be run on multiple computers in parallel in same time on TCP communications and this speedup process of calibrations. The case study with results of calibration and validation of the model will be presented.

Quantifying uncertainty and sensitivity in sea ice models

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

Urrego Blanco, Jorge Rolando; Hunke, Elizabeth Clare; Urban, Nathan Mark

The Los Alamos Sea Ice model has a number of input parameters for which accurate values are not always well established. We conduct a variance-based sensitivity analysis of hemispheric sea ice properties to 39 input parameters. The method accounts for non-linear and non-additive effects in the model.

Thermodynamic characterization of tandem mismatches found in naturally occurring RNA

PubMed Central

Christiansen, Martha E.; Znosko, Brent M.

2009-01-01

Although all sequence symmetric tandem mismatches and some sequence asymmetric tandem mismatches have been thermodynamically characterized and a model has been proposed to predict the stability of previously unmeasured sequence asymmetric tandem mismatches [Christiansen,M.E. and Znosko,B.M. (2008) Biochemistry, 47, 4329–4336], experimental thermodynamic data for frequently occurring tandem mismatches is lacking. Since experimental data is preferred over a predictive model, the thermodynamic parameters for 25 frequently occurring tandem mismatches were determined. These new experimental values, on average, are 1.0 kcal/mol different from the values predicted for these mismatches using the previous model. The data for the sequence asymmetric tandem mismatches reported here were then combined with the data for 72 sequence asymmetric tandem mismatches that were published previously, and the parameters used to predict the thermodynamics of previously unmeasured sequence asymmetric tandem mismatches were updated. The average absolute difference between the measured values and the values predicted using these updated parameters is 0.5 kcal/mol. This updated model improves the prediction for tandem mismatches that were predicted rather poorly by the previous model. This new experimental data and updated predictive model allow for more accurate calculations of the free energy of RNA duplexes containing tandem mismatches, and, furthermore, should allow for improved prediction of secondary structure from sequence. PMID:19509311

Application of Markov chain Monte Carlo analysis to biomathematical modeling of respirable dust in US and UK coal miners

PubMed Central

Sweeney, Lisa M.; Parker, Ann; Haber, Lynne T.; Tran, C. Lang; Kuempel, Eileen D.

2015-01-01

A biomathematical model was previously developed to describe the long-term clearance and retention of particles in the lungs of coal miners. The model structure was evaluated and parameters were estimated in two data sets, one from the United States and one from the United Kingdom. The three-compartment model structure consists of deposition of inhaled particles in the alveolar region, competing processes of either clearance from the alveolar region or translocation to the lung interstitial region, and very slow, irreversible sequestration of interstitialized material in the lung-associated lymph nodes. Point estimates of model parameter values were estimated separately for the two data sets. In the current effort, Bayesian population analysis using Markov chain Monte Carlo simulation was used to recalibrate the model while improving assessments of parameter variability and uncertainty. When model parameters were calibrated simultaneously to the two data sets, agreement between the derived parameters for the two groups was very good, and the central tendency values were similar to those derived from the deterministic approach. These findings are relevant to the proposed update of the ICRP human respiratory tract model with revisions to the alveolar-interstitial region based on this long-term particle clearance and retention model. PMID:23454101

A calibration protocol of a one-dimensional moving bed bioreactor (MBBR) dynamic model for nitrogen removal.

PubMed

Barry, U; Choubert, J-M; Canler, J-P; Héduit, A; Robin, L; Lessard, P

2012-01-01

This work suggests a procedure to correctly calibrate the parameters of a one-dimensional MBBR dynamic model in nitrification treatment. The study deals with the MBBR configuration with two reactors in series, one for carbon treatment and the other for nitrogen treatment. Because of the influence of the first reactor on the second one, the approach needs a specific calibration strategy. Firstly, a comparison between measured values and simulated ones obtained with default parameters has been carried out. Simulated values of filtered COD, NH(4)-N and dissolved oxygen are underestimated and nitrates are overestimated compared with observed data. Thus, nitrifying rate and oxygen transfer into the biofilm are overvalued. Secondly, a sensitivity analysis was carried out for parameters and for COD fractionation. It revealed three classes of sensitive parameters: physical, diffusional and kinetic. Then a calibration protocol of the MBBR dynamic model was proposed. It was successfully tested on data recorded at a pilot-scale plant and a calibrated set of values was obtained for four parameters: the maximum biofilm thickness, the detachment rate, the maximum autotrophic growth rate and the oxygen transfer rate.

Numerical modeling of the transmission dynamics of drug-sensitive and drug-resistant HSV-2

NASA Astrophysics Data System (ADS)

Gumel, A. B.

2001-03-01

A competitive finite-difference method will be constructed and used to solve a modified deterministic model for the spread of herpes simplex virus type-2 (HSV-2) within a given population. The model monitors the transmission dynamics and control of drug-sensitive and drug-resistant HSV-2. Unlike the fourth-order Runge-Kutta method (RK4), which fails when the discretization parameters exceed certain values, the novel numerical method to be developed in this paper gives convergent results for all parameter values.

An Improved Statistical Solution for Global Seismicity by the HIST-ETAS Approach

NASA Astrophysics Data System (ADS)

Chu, A.; Ogata, Y.; Katsura, K.

2010-12-01

For long-term global seismic model fitting, recent work by Chu et al. (2010) applied the spatial-temporal ETAS model (Ogata 1998) and analyzed global data partitioned into tectonic zones based on geophysical characteristics (Bird 2003), and it has shown tremendous improvements of model fitting compared with one overall global model. While the ordinary ETAS model assumes constant parameter values across the complete region analyzed, the hierarchical space-time ETAS model (HIST-ETAS, Ogata 2004) is a newly introduced approach by proposing regional distinctions of the parameters for more accurate seismic prediction. As the HIST-ETAS model has been fit to regional data of Japan (Ogata 2010), our work applies the model to describe global seismicity. Employing the Akaike's Bayesian Information Criterion (ABIC) as an assessment method, we compare the MLE results with zone divisions considered to results obtained by an overall global model. Location dependent parameters of the model and Gutenberg-Richter b-values are optimized, and seismological interpretations are discussed.

Strange stars in f( R) theories of gravity in the Palatini formalism

NASA Astrophysics Data System (ADS)

Panotopoulos, Grigoris

2017-05-01

In the present work we study strange stars in f( R) theories of gravity in the Palatini formalism. We consider two concrete well-known cases, namely the R+R^2/(6 M^2) model as well as the R-μ ^4/R model for two different values of the mass parameter M or μ . We integrate the modified Tolman-Oppenheimer-Volkoff equations numerically, and we show the mass-radius diagram for each model separately. The standard case corresponding to the General Relativity is also shown in the same figure for comparison. Our numerical results show that the interior solution can be vastly different depending on the model and/or the value of the parameter of each model. In addition, our findings imply that (i) for the cosmologically interesting values of the mass scales M,μ the effect of modified gravity on strange stars is negligible, while (ii) for the values predicting an observable effect, the modified gravity models discussed here would be ruled out by their cosmological effects.

Quantification of dental prostheses on cone‐beam CT images by the Taguchi method

PubMed Central

Kuo, Rong‐Fu; Fang, Kwang‐Ming; TY, Wong

2016-01-01

The gray values accuracy of dental cone‐beam computed tomography (CBCT) is affected by dental metal prostheses. The distortion of dental CBCT gray values could lead to inaccuracies of orthodontic and implant treatment. The aim of this study was to quantify the effect of scanning parameters and dental metal prostheses on the accuracy of dental cone‐beam computed tomography (CBCT) gray values using the Taguchi method. Eight dental model casts of an upper jaw including prostheses, and a ninth prosthesis‐free dental model cast, were scanned by two dental CBCT devices. The mean gray value of the selected circular regions of interest (ROIs) were measured using dental CBCT images of eight dental model casts and were compared with those measured from CBCT images of the prosthesis‐free dental model cast. For each image set, four consecutive slices of gingiva were selected. The seven factors (CBCTs, occlusal plane canting, implant connection, prosthesis position, coping material, coping thickness, and types of dental restoration) were used to evaluate scanning parameter and dental prostheses effects. Statistical methods of signal to noise ratio (S/N) and analysis of variance (ANOVA) with 95% confidence were applied to quantify the effects of scanning parameters and dental prostheses on dental CBCT gray values accuracy. For ROIs surrounding dental prostheses, the accuracy of CBCT gray values were affected primarily by implant connection (42%), followed by type of restoration (29%), prostheses position (19%), coping material (4%), and coping thickness (4%). For a single crown prosthesis (without support of implants) placed in dental model casts, gray value differences for ROIs 1–9 were below 12% and gray value differences for ROIs 13–18 away from prostheses were below 10%. We found the gray value differences set to be between 7% and 8% for regions next to a single implant‐supported titanium prosthesis, and between 46% and 59% for regions between double implant‐supported, nickel‐chromium alloys (Ni‐Cr) prostheses. Quantification of the effect of prostheses and scanning parameters on dental CBCT gray values was assessed. PACS numbers: 87.59.bd, 87.57Q PMID:26894354

Modeling Active Contraction and Relaxation of Left Ventricle Using Different Zero-load Diastole and Systole Geometries for Better Material Parameter Estimation and Stress/Strain Calculations

PubMed Central

Fan, Longling; Yao, Jing; Yang, Chun; Xu, Di; Tang, Dalin

2018-01-01

Modeling ventricle active contraction based on in vivo data is extremely challenging because of complex ventricle geometry, dynamic heart motion and active contraction where the reference geometry (zero-stress geometry) changes constantly. A new modeling approach using different diastole and systole zero-load geometries was introduced to handle the changing zero-load geometries for more accurate stress/strain calculations. Echo image data were acquired from 5 patients with infarction (Infarct Group) and 10 without (Non-Infarcted Group). Echo-based computational two-layer left ventricle models using one zero-load geometry (1G) and two zero-load geometries (2G) were constructed. Material parameter values in Mooney-Rivlin models were adjusted to match echo volume data. Effective Young’s moduli (YM) were calculated for easy comparison. For diastole phase, begin-filling (BF) mean YM value in the fiber direction (YMf) was 738% higher than its end-diastole (ED) value (645.39 kPa vs. 76.97 kPa, p=3.38E-06). For systole phase, end-systole (ES) YMf was 903% higher than its begin-ejection (BE) value (1025.10 kPa vs. 102.11 kPa, p=6.10E-05). Comparing systolic and diastolic material properties, ES YMf was 59% higher than its BF value (1025.10 kPa vs. 645.39 kPa. p=0.0002). BE mean stress value was 514% higher than its ED value (299.69 kPa vs. 48.81 kPa, p=3.39E-06), while BE mean strain value was 31.5% higher than its ED value (0.9417 vs. 0.7162, p=0.004). Similarly, ES mean stress value was 562% higher than its BF value (19.74 kPa vs. 2.98 kPa, p=6.22E-05), and ES mean strain value was 264% higher than its BF value (0.1985 vs. 0.0546, p=3.42E-06). 2G models improved over 1G model limitations and may provide better material parameter estimation and stress/strain calculations. PMID:29399004

Modeling Active Contraction and Relaxation of Left Ventricle Using Different Zero-load Diastole and Systole Geometries for Better Material Parameter Estimation and Stress/Strain Calculations.

PubMed

Fan, Longling; Yao, Jing; Yang, Chun; Xu, Di; Tang, Dalin

2016-01-01

Modeling ventricle active contraction based on in vivo data is extremely challenging because of complex ventricle geometry, dynamic heart motion and active contraction where the reference geometry (zero-stress geometry) changes constantly. A new modeling approach using different diastole and systole zero-load geometries was introduced to handle the changing zero-load geometries for more accurate stress/strain calculations. Echo image data were acquired from 5 patients with infarction (Infarct Group) and 10 without (Non-Infarcted Group). Echo-based computational two-layer left ventricle models using one zero-load geometry (1G) and two zero-load geometries (2G) were constructed. Material parameter values in Mooney-Rivlin models were adjusted to match echo volume data. Effective Young's moduli (YM) were calculated for easy comparison. For diastole phase, begin-filling (BF) mean YM value in the fiber direction (YM f ) was 738% higher than its end-diastole (ED) value (645.39 kPa vs. 76.97 kPa, p=3.38E-06). For systole phase, end-systole (ES) YM f was 903% higher than its begin-ejection (BE) value (1025.10 kPa vs. 102.11 kPa, p=6.10E-05). Comparing systolic and diastolic material properties, ES YM f was 59% higher than its BF value (1025.10 kPa vs. 645.39 kPa. p=0.0002). BE mean stress value was 514% higher than its ED value (299.69 kPa vs. 48.81 kPa, p=3.39E-06), while BE mean strain value was 31.5% higher than its ED value (0.9417 vs. 0.7162, p=0.004). Similarly, ES mean stress value was 562% higher than its BF value (19.74 kPa vs. 2.98 kPa, p=6.22E-05), and ES mean strain value was 264% higher than its BF value (0.1985 vs. 0.0546, p=3.42E-06). 2G models improved over 1G model limitations and may provide better material parameter estimation and stress/strain calculations.

Sensitivity analysis of a multilayer, finite-difference model of the Southeastern Coastal Plain regional aquifer system; Mississippi, Alabama, Georgia, and South Carolina

USGS Publications Warehouse

Pernik, Meribeth

1987-01-01

The sensitivity of a multilayer finite-difference regional flow model was tested by changing the calibrated values for five parameters in the steady-state model and one in the transient-state model. The parameters that changed under the steady-state condition were those that had been routinely adjusted during the calibration process as part of the effort to match pre-development potentiometric surfaces, and elements of the water budget. The tested steady-state parameters include: recharge, riverbed conductance, transmissivity, confining unit leakance, and boundary location. In the transient-state model, the storage coefficient was adjusted. The sensitivity of the model to changes in the calibrated values of these parameters was evaluated with respect to the simulated response of net base flow to the rivers, and the mean value of the absolute head residual. To provide a standard measurement of sensitivity from one parameter to another, the standard deviation of the absolute head residual was calculated. The steady-state model was shown to be most sensitive to changes in rates of recharge. When the recharge rate was held constant, the model was more sensitive to variations in transmissivity. Near the rivers, the riverbed conductance becomes the dominant parameter in controlling the heads. Changes in confining unit leakance had little effect on simulated base flow, but greatly affected head residuals. The model was relatively insensitive to changes in the location of no-flow boundaries and to moderate changes in the altitude of constant head boundaries. The storage coefficient was adjusted under transient conditions to illustrate the model 's sensitivity to changes in storativity. The model is less sensitive to an increase in storage coefficient than it is to a decrease in storage coefficient. As the storage coefficient decreased, the aquifer drawdown increases, the base flow decreased. The opposite response occurred when the storage coefficient was increased. (Author 's abstract)

Sensitivity analysis of pulse pileup model parameter in photon counting detectors

NASA Astrophysics Data System (ADS)

Shunhavanich, Picha; Pelc, Norbert J.

2017-03-01

Photon counting detectors (PCDs) may provide several benefits over energy-integrating detectors (EIDs), including spectral information for tissue characterization and the elimination of electronic noise. PCDs, however, suffer from pulse pileup, which distorts the detected spectrum and degrades the accuracy of material decomposition. Several analytical models have been proposed to address this problem. The performance of these models are dependent on the assumptions used, including the estimated pulse shape whose parameter values could differ from the actual physical ones. As the incident flux increases and the corrections become more significant the needed parameter value accuracy may be more crucial. In this work, the sensitivity of model parameter accuracies is analyzed for the pileup model of Taguchi et al. The spectra distorted by pileup at different count rates are simulated using either the model or Monte Carlo simulations, and the basis material thicknesses are estimated by minimizing the negative log-likelihood with Poisson or multivariate Gaussian distributions. From simulation results, we find that the accuracy of the deadtime, the height of pulse negative tail, and the timing to the end of the pulse are more important than most other parameters, and they matter more with increasing count rate. This result can help facilitate further work on parameter calibrations.

Comparison of ionospheric profile parameters with IRI-2012 model over Jicamarca

NASA Astrophysics Data System (ADS)

Bello, S. A.; Abdullah, M.; Hamid, N. S. A.; Reinisch, B. W.

2017-05-01

We used the hourly ionogram data obtained from Jicamarca station (12° S, 76.9° W, dip latitude: 1.0° N) an equatorial region to study the variation of the electron density profile parameters: maximum height of F2-layer (hmF2), bottomside thickness (B0) and shape (B1) parameter of F-layer. The period of study is for the year 2010 (solar minimum period).The diurnal monthly averages of these parameters are compared with the updated IRI-2012 model. The results show that hmF2 is highest during the daytime than nighttime. The variation in hmF2 was observed to modulate the thickness of the bottomside F2-layer. The observed hmF2 and B0 post-sunset peak is as result of the upward drift velocity of ionospheric plasma. We found a close agreement between IRI-CCIR hmF2 model and observed hmF2 during 0000-0700 LT while outside this period the model predictions deviate significantly with the observational values. Significant discrepancies are observed between the IRI model options for B0 and the observed B0 values. Specifically, the modeled values do not show B0 post-sunset peak. A fairly good agreement was observed between the observed B1 and IRI model options (ABT-2009 and Bill 2000) for B1.

Validation of a mathematical model of the bovine estrous cycle for cows with different estrous cycle characteristics.

PubMed

Boer, H M T; Butler, S T; Stötzel, C; Te Pas, M F W; Veerkamp, R F; Woelders, H

2017-11-01

A recently developed mechanistic mathematical model of the bovine estrous cycle was parameterized to fit empirical data sets collected during one estrous cycle of 31 individual cows, with the main objective to further validate the model. The a priori criteria for validation were (1) the resulting model can simulate the measured data correctly (i.e. goodness of fit), and (2) this is achieved without needing extreme, probably non-physiological parameter values. We used a least squares optimization procedure to identify parameter configurations for the mathematical model to fit the empirical in vivo measurements of follicle and corpus luteum sizes, and the plasma concentrations of progesterone, estradiol, FSH and LH for each cow. The model was capable of accommodating normal variation in estrous cycle characteristics of individual cows. With the parameter sets estimated for the individual cows, the model behavior changed for 21 cows, with improved fit of the simulated output curves for 18 of these 21 cows. Moreover, the number of follicular waves was predicted correctly for 18 of the 25 two-wave and three-wave cows, without extreme parameter value changes. Estimation of specific parameters confirmed results of previous model simulations indicating that parameters involved in luteolytic signaling are very important for regulation of general estrous cycle characteristics, and are likely responsible for differences in estrous cycle characteristics between cows.

Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter.

PubMed

Lord, Dominique

2006-07-01

There has been considerable research conducted on the development of statistical models for predicting crashes on highway facilities. Despite numerous advancements made for improving the estimation tools of statistical models, the most common probabilistic structure used for modeling motor vehicle crashes remains the traditional Poisson and Poisson-gamma (or Negative Binomial) distribution; when crash data exhibit over-dispersion, the Poisson-gamma model is usually the model of choice most favored by transportation safety modelers. Crash data collected for safety studies often have the unusual attributes of being characterized by low sample mean values. Studies have shown that the goodness-of-fit of statistical models produced from such datasets can be significantly affected. This issue has been defined as the "low mean problem" (LMP). Despite recent developments on methods to circumvent the LMP and test the goodness-of-fit of models developed using such datasets, no work has so far examined how the LMP affects the fixed dispersion parameter of Poisson-gamma models used for modeling motor vehicle crashes. The dispersion parameter plays an important role in many types of safety studies and should, therefore, be reliably estimated. The primary objective of this research project was to verify whether the LMP affects the estimation of the dispersion parameter and, if it is, to determine the magnitude of the problem. The secondary objective consisted of determining the effects of an unreliably estimated dispersion parameter on common analyses performed in highway safety studies. To accomplish the objectives of the study, a series of Poisson-gamma distributions were simulated using different values describing the mean, the dispersion parameter, and the sample size. Three estimators commonly used by transportation safety modelers for estimating the dispersion parameter of Poisson-gamma models were evaluated: the method of moments, the weighted regression, and the maximum likelihood method. In an attempt to complement the outcome of the simulation study, Poisson-gamma models were fitted to crash data collected in Toronto, Ont. characterized by a low sample mean and small sample size. The study shows that a low sample mean combined with a small sample size can seriously affect the estimation of the dispersion parameter, no matter which estimator is used within the estimation process. The probability the dispersion parameter becomes unreliably estimated increases significantly as the sample mean and sample size decrease. Consequently, the results show that an unreliably estimated dispersion parameter can significantly undermine empirical Bayes (EB) estimates as well as the estimation of confidence intervals for the gamma mean and predicted response. The paper ends with recommendations about minimizing the likelihood of producing Poisson-gamma models with an unreliable dispersion parameter for modeling motor vehicle crashes.

Molecular-dynamics simulation of mutual diffusion in nonideal liquid mixtures

NASA Astrophysics Data System (ADS)

Rowley, R. L.; Stoker, J. M.; Giles, N. F.

1991-05-01

The mutual-diffusion coefficients, D 12, of n-hexane, n-heptane, and n-octane in chloroform were modeled using equilibrium molecular-dynamics (MD) simulations of simple Lennard-Jones (LJ) fluids. Pure-component LJ parameters were obtained by comparison of simulations to experimental self-diffusion coefficients. While values of “effective” LJ parameters are not expected to simulate accurately diverse thermophysical properties over a wide range of conditions, it was recently shown that effective parameters obtained from pure self-diffusion coefficients can accurately model mutual diffusion in ideal, liquid mixtures. In this work, similar simulations are used to model diffusion in nonideal mixtures. The same combining rules used in the previous study for the cross-interaction parameters were found to be adequate to represent the composition dependence of D 12. The effect of alkane chain length on D 12 is also correctly predicted by the simulations. A commonly used assumption in empirical correlations of D 12, that its kinetic portion is a simple, compositional average of the intradiffusion coefficients, is inconsistent with the simulation results. In fact, the value of the kinetic portion of D 12 was often outside the range of values bracketed by the two intradiffusion coefficients for the nonideal system modeled here.

Perennial rhizomatous grasses as bioenergy feedstock in SWAT: parameter development and model improvement

DOE PAGES

Trybula, Elizabeth M.; Cibin, Raj; Burks, Jennifer L.; ...

2014-06-13

The Soil and Water Assessment Tool (SWAT) is increasingly used to quantify hydrologic and water quality impacts of bioenergy production, but crop-growth parameters for candidate perennial rhizomatous grasses (PRG) Miscanthus × giganteus and upland ecotypes of Panicum virgatum (switchgrass) are limited by the availability of field data. Crop-growth parameter ranges and suggested values were developed in this study using agronomic and weather data collected at the Purdue University Water Quality Field Station in northwestern Indiana. During the process of parameterization, the comparison of measured data with conceptual representation of PRG growth in the model led to three changes in themore » SWAT 2009 code: the harvest algorithm was modified to maintain belowground biomass over winter, plant respiration was extended via modified-DLAI to better reflect maturity and leaf senescence, and nutrient uptake algorithms were revised to respond to temperature, water, and nutrient stress. Parameter values and changes to the model resulted in simulated biomass yield and leaf area index consistent with reported values for the region. Code changes in the SWAT model improved nutrient storage during dormancy period and nitrogen and phosphorus uptake by both switchgrass and Miscanthus.« less

Optimisation of dispersion parameters of Gaussian plume model for CO₂ dispersion.

PubMed

Liu, Xiong; Godbole, Ajit; Lu, Cheng; Michal, Guillaume; Venton, Philip

2015-11-01

The carbon capture and storage (CCS) and enhanced oil recovery (EOR) projects entail the possibility of accidental release of carbon dioxide (CO2) into the atmosphere. To quantify the spread of CO2 following such release, the 'Gaussian' dispersion model is often used to estimate the resulting CO2 concentration levels in the surroundings. The Gaussian model enables quick estimates of the concentration levels. However, the traditionally recommended values of the 'dispersion parameters' in the Gaussian model may not be directly applicable to CO2 dispersion. This paper presents an optimisation technique to obtain the dispersion parameters in order to achieve a quick estimation of CO2 concentration levels in the atmosphere following CO2 blowouts. The optimised dispersion parameters enable the Gaussian model to produce quick estimates of CO2 concentration levels, precluding the necessity to set up and run much more complicated models. Computational fluid dynamics (CFD) models were employed to produce reference CO2 dispersion profiles in various atmospheric stability classes (ASC), different 'source strengths' and degrees of ground roughness. The performance of the CFD models was validated against the 'Kit Fox' field measurements, involving dispersion over a flat horizontal terrain, both with low and high roughness regions. An optimisation model employing a genetic algorithm (GA) to determine the best dispersion parameters in the Gaussian plume model was set up. Optimum values of the dispersion parameters for different ASCs that can be used in the Gaussian plume model for predicting CO2 dispersion were obtained.

Brute force meets Bruno force in parameter optimisation: introduction of novel constraints for parameter accuracy improvement by symbolic computation.

PubMed

Nakatsui, M; Horimoto, K; Lemaire, F; Ürgüplü, A; Sedoglavic, A; Boulier, F

2011-09-01

Recent remarkable advances in computer performance have enabled us to estimate parameter values by the huge power of numerical computation, the so-called 'Brute force', resulting in the high-speed simultaneous estimation of a large number of parameter values. However, these advancements have not been fully utilised to improve the accuracy of parameter estimation. Here the authors review a novel method for parameter estimation using symbolic computation power, 'Bruno force', named after Bruno Buchberger, who found the Gröbner base. In the method, the objective functions combining the symbolic computation techniques are formulated. First, the authors utilise a symbolic computation technique, differential elimination, which symbolically reduces an equivalent system of differential equations to a system in a given model. Second, since its equivalent system is frequently composed of large equations, the system is further simplified by another symbolic computation. The performance of the authors' method for parameter accuracy improvement is illustrated by two representative models in biology, a simple cascade model and a negative feedback model in comparison with the previous numerical methods. Finally, the limits and extensions of the authors' method are discussed, in terms of the possible power of 'Bruno force' for the development of a new horizon in parameter estimation.

Traveltime inversion and error analysis for layered anisotropy

NASA Astrophysics Data System (ADS)

Jiang, Fan; Zhou, Hua-wei

2011-02-01

While tilted transverse isotropy (TTI) is a good approximation of the velocity structure for many dipping and fractured strata, it is still challenging to estimate anisotropic depth models even when the tilted angle is known. With the assumption of weak anisotropy, we present a TTI traveltime inversion approach for models consisting of several thickness-varying layers where the anisotropic parameters are constant for each layer. For each model layer the inversion variables consist of the anisotropic parameters ɛ and δ, the tilted angle φ of its symmetry axis, layer velocity along the symmetry axis, and thickness variation of the layer. Using this method and synthetic data, we evaluate the effects of errors in some of the model parameters on the inverted values of the other parameters in crosswell and Vertical Seismic Profile (VSP) acquisition geometry. The analyses show that the errors in the layer symmetry axes sensitively affect the inverted values of other parameters, especially δ. However, the impact of errors in δ on the inversion of other parameters is much less than the impact on δ from the errors in other parameters. Hence, a practical strategy is first to invert for the most error-tolerant parameter layer velocity, then progressively invert for ɛ in crosswell geometry or δ in VSP geometry.

WE-FG-206-06: Dual-Input Tracer Kinetic Modeling and Its Analog Implementation for Dynamic Contrast-Enhanced (DCE-) MRI of Malignant Mesothelioma (MPM)

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

Lee, S; Rimner, A; Hayes, S

Purpose: To use dual-input tracer kinetic modeling of the lung for mapping spatial heterogeneity of various kinetic parameters in malignant MPM Methods: Six MPM patients received DCE-MRI as part of their radiation therapy simulation scan. 5 patients had the epitheloid subtype of MPM, while one was biphasic. A 3D fast-field echo sequence with TR/TE/Flip angle of 3.62ms/1.69ms/15° was used for DCE-MRI acquisition. The scan was collected for 5 minutes with a temporal resolution of 5-9 seconds depending on the spatial extent of the tumor. A principal component analysis-based groupwise deformable registration was used to co-register all the DCE-MRI series formore » motion compensation. All the images were analyzed using five different dual-input tracer kinetic models implemented in analog continuous-time formalism: the Tofts-Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH), and distributed parameter (DP) models. The following parameters were computed for each model: total blood flow (BF), pulmonary flow fraction (γ), pulmonary blood flow (BF-pa), systemic blood flow (BF-a), blood volume (BV), mean transit time (MTT), permeability-surface area product (PS), fractional interstitial volume (vi), extraction fraction (E), volume transfer constant (Ktrans) and efflux rate constant (kep). Results: Although the majority of patients had epitheloid histologies, kinetic parameter values varied across different models. One patient showed a higher total BF value in all models among the epitheloid histologies, although the γ value was varying among these different models. In one tumor with a large area of necrosis, the TK and ETK models showed higher E, Ktrans, and kep values and lower interstitial volume as compared to AATH and DP and 2CX models. Kinetic parameters such as BF-pa, BF-a, PS, Ktrans values were higher in surviving group compared to non-surviving group across most models. Conclusion: Dual-input tracer kinetic modeling is feasible in determining micro-vascular characteristics of MPM. This project was supported from Cycle for Survival and MSK Imaging and radiation science (IMRAS) grants.« less

Validation of systems biology derived molecular markers of renal donor organ status associated with long term allograft function.

PubMed

Perco, Paul; Heinzel, Andreas; Leierer, Johannes; Schneeberger, Stefan; Bösmüller, Claudia; Oberhuber, Rupert; Wagner, Silvia; Engler, Franziska; Mayer, Gert

2018-05-03

Donor organ quality affects long term outcome after renal transplantation. A variety of prognostic molecular markers is available, yet their validity often remains undetermined. A network-based molecular model reflecting donor kidney status based on transcriptomics data and molecular features reported in scientific literature to be associated with chronic allograft nephropathy was created. Significantly enriched biological processes were identified and representative markers were selected. An independent kidney pre-implantation transcriptomics dataset of 76 organs was used to predict estimated glomerular filtration rate (eGFR) values twelve months after transplantation using available clinical data and marker expression values. The best-performing regression model solely based on the clinical parameters donor age, donor gender, and recipient gender explained 17% of variance in post-transplant eGFR values. The five molecular markers EGF, CD2BP2, RALBP1, SF3B1, and DDX19B representing key molecular processes of the constructed renal donor organ status molecular model in addition to the clinical parameters significantly improved model performance (p-value = 0.0007) explaining around 33% of the variability of eGFR values twelve months after transplantation. Collectively, molecular markers reflecting donor organ status significantly add to prediction of post-transplant renal function when added to the clinical parameters donor age and gender.

The Routine Fitting of Kinetic Data to Models

PubMed Central

Berman, Mones; Shahn, Ezra; Weiss, Marjory F.

1962-01-01

A mathematical formalism is presented for use with digital computers to permit the routine fitting of data to physical and mathematical models. Given a set of data, the mathematical equations describing a model, initial conditions for an experiment, and initial estimates for the values of model parameters, the computer program automatically proceeds to obtain a least squares fit of the data by an iterative adjustment of the values of the parameters. When the experimental measures are linear combinations of functions, the linear coefficients for a least squares fit may also be calculated. The values of both the parameters of the model and the coefficients for the sum of functions may be unknown independent variables, unknown dependent variables, or known constants. In the case of dependence, only linear dependencies are provided for in routine use. The computer program includes a number of subroutines, each one of which performs a special task. This permits flexibility in choosing various types of solutions and procedures. One subroutine, for example, handles linear differential equations, another, special non-linear functions, etc. The use of analytic or numerical solutions of equations is possible. PMID:13867975

Image analysis and green tea color change kinetics during thin-layer drying.

PubMed

Shahabi, Mohammad; Rafiee, Shahin; Mohtasebi, Seyed Saeid; Hosseinpour, Soleiman

2014-09-01

This study was conducted to investigate the effect of air temperature and air flow velocity on kinetics of color parameter changes during hot-air drying of green tea, to obtain the best model for hot-air drying of green tea, to apply a computer vision system and to study the color changes during drying. In the proposed computer vision system system, at first RGB values of the images were converted into XYZ values and then to Commission International d'Eclairage L*a*b* color coordinates. The obtained color parameters of L*, a* and b* were calibrated with Hunter-Lab colorimeter. These values were also used for calculation of the color difference, chroma, hue angle and browning index. The values of L* and b* decreased, while the values of a* and color difference (ΔE*ab ) increased during hot-air drying. Drying data were fitted to three kinetic models. Zero, first-order and fractional conversion models were utilized to describe the color changes of green tea. The suitability of fitness was determined using the coefficient of determination (R (2)) and root-mean-square error. Results showed that the fraction conversion model had more acceptable fitness than the other two models in most of color parameters. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

An approach to adjustment of relativistic mean field model parameters

NASA Astrophysics Data System (ADS)

Bayram, Tuncay; Akkoyun, Serkan

2017-09-01

The Relativistic Mean Field (RMF) model with a small number of adjusted parameters is powerful tool for correct predictions of various ground-state nuclear properties of nuclei. Its success for describing nuclear properties of nuclei is directly related with adjustment of its parameters by using experimental data. In the present study, the Artificial Neural Network (ANN) method which mimics brain functionality has been employed for improvement of the RMF model parameters. In particular, the understanding capability of the ANN method for relations between the RMF model parameters and their predictions for binding energies (BEs) of 58Ni and 208Pb have been found in agreement with the literature values.

Analysis of a model of gambiense sleeping sickness in humans and cattle.

PubMed

Ndondo, A M; Munganga, J M W; Mwambakana, J N; Saad-Roy, C M; van den Driessche, P; Walo, R O

2016-01-01

Human African Trypanosomiasis (HAT) and Nagana in cattle, commonly called sleeping sickness, is caused by trypanosome protozoa transmitted by bites of infected tsetse flies. We present a deterministic model for the transmission of HAT caused by Trypanosoma brucei gambiense between human hosts, cattle hosts and tsetse flies. The model takes into account the growth of the tsetse fly, from its larval stage to the adult stage. Disease in the tsetse fly population is modeled by three compartments, and both the human and cattle populations are modeled by four compartments incorporating the two stages of HAT. We provide a rigorous derivation of the basic reproduction number R0. For R0 < 1, the disease free equilibrium is globally asymptotically stable, thus HAT dies out; whereas (assuming no return to susceptibility) for R0 >1, HAT persists. Elasticity indices for R0 with respect to different parameters are calculated with baseline parameter values appropriate for HAT in West Africa; indicating parameters that are important for control strategies to bring R0 below 1. Numerical simulations with R0 > 1 show values for the infected populations at the endemic equilibrium, and indicate that with certain parameter values, HAT could not persist in the human population in the absence of cattle.

Developing a non-point source P loss indicator in R and its parameter uncertainty assessment using GLUE: a case study in northern China.

PubMed

Su, Jingjun; Du, Xinzhong; Li, Xuyong

2018-05-16

Uncertainty analysis is an important prerequisite for model application. However, the existing phosphorus (P) loss indexes or indicators were rarely evaluated. This study applied generalized likelihood uncertainty estimation (GLUE) method to assess the uncertainty of parameters and modeling outputs of a non-point source (NPS) P indicator constructed in R language. And the influences of subjective choices of likelihood formulation and acceptability threshold of GLUE on model outputs were also detected. The results indicated the following. (1) Parameters RegR 2 , RegSDR 2 , PlossDP fer , PlossDP man , DPDR, and DPR were highly sensitive to overall TP simulation and their value ranges could be reduced by GLUE. (2) Nash efficiency likelihood (L 1 ) seemed to present better ability in accentuating high likelihood value simulations than the exponential function (L 2 ) did. (3) The combined likelihood integrating the criteria of multiple outputs acted better than single likelihood in model uncertainty assessment in terms of reducing the uncertainty band widths and assuring the fitting goodness of whole model outputs. (4) A value of 0.55 appeared to be a modest choice of threshold value to balance the interests between high modeling efficiency and high bracketing efficiency. Results of this study could provide (1) an option to conduct NPS modeling under one single computer platform, (2) important references to the parameter setting for NPS model development in similar regions, (3) useful suggestions for the application of GLUE method in studies with different emphases according to research interests, and (4) important insights into the watershed P management in similar regions.

Modeling and Optimization of NLDH/PVDF Ultrafiltration Nanocomposite Membrane Using Artificial Neural Network-Genetic Algorithm Hybrid.

PubMed

Arefi-Oskoui, Samira; Khataee, Alireza; Vatanpour, Vahid

2017-07-10

In this research, MgAl-CO 3 2- nanolayered double hydroxide (NLDH) was synthesized through a facile coprecipitation method, followed by a hydrothermal treatment. The prepared NLDHs were used as a hydrophilic nanofiller for improving the performance of the PVDF-based ultrafiltration membranes. The main objective of this research was to obtain the optimized formula of NLDH/PVDF nanocomposite membrane presenting the best performance using computational techniques as a cost-effective method. For this aim, an artificial neural network (ANN) model was developed for modeling and expressing the relationship between the performance of the nanocomposite membrane (pure water flux, protein flux and flux recovery ratio) and the affecting parameters including the NLDH, PVP 29000 and polymer concentrations. The effects of the mentioned parameters and the interaction between the parameters were investigated using the contour plot predicted with the developed model. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and water contact angle techniques were applied to characterize the nanocomposite membranes and to interpret the predictions of the ANN model. The developed ANN model was introduced to genetic algorithm (GA) as a bioinspired optimizer to determine the optimum values of input parameters leading to high pure water flux, protein flux, and flux recovery ratio. The optimum values for NLDH, PVP 29000 and the PVDF concentration were determined to be 0.54, 1, and 18 wt %, respectively. The performance of the nanocomposite membrane prepared using the optimum values proposed by GA was investigated experimentally, in which the results were in good agreement with the values predicted by ANN model with error lower than 6%. This good agreement confirmed that the nanocomposite membranes prformance could be successfully modeled and optimized by ANN-GA system.

An eigensystem realization algorithm using data correlations (ERA/DC) for modal parameter identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Cooper, J. E.; Wright, J. R.

1987-01-01

A modification to the Eigensystem Realization Algorithm (ERA) for modal parameter identification is presented in this paper. The ERA minimum order realization approach using singular value decomposition is combined with the philosophy of the Correlation Fit method in state space form such that response data correlations rather than actual response values are used for modal parameter identification. This new method, the ERA using data correlations (ERA/DC), reduces bias errors due to noise corruption significantly without the need for model overspecification. This method is tested using simulated five-degree-of-freedom system responses corrupted by measurement noise. It is found for this case that, when model overspecification is permitted and a minimum order solution obtained via singular value truncation, the results from the two methods are of similar quality.

Method for Predicting and Optimizing System Parameters for Electrospinning System

NASA Technical Reports Server (NTRS)

Wincheski, Russell A. (Inventor)

2011-01-01

An electrospinning system using a spinneret and a counter electrode is first operated for a fixed amount of time at known system and operational parameters to generate a fiber mat having a measured fiber mat width associated therewith. Next, acceleration of the fiberizable material at the spinneret is modeled to determine values of mass, drag, and surface tension associated with the fiberizable material at the spinneret output. The model is then applied in an inversion process to generate predicted values of an electric charge at the spinneret output and an electric field between the spinneret and electrode required to fabricate a selected fiber mat design. The electric charge and electric field are indicative of design values for system and operational parameters needed to fabricate the selected fiber mat design.

Value of eddy-covariance data for individual-based, forest gap models

NASA Astrophysics Data System (ADS)

Roedig, Edna; Cuntz, Matthias; Huth, Andreas

2014-05-01

Individual-based forest gap models simulate tree growth and carbon fluxes on large time scales. They are a well established tool to predict forest dynamics and successions. However, the effect of climatic variables on processes of such individual-based models is uncertain (e.g. the effect of temperature or soil moisture on the gross primary production (GPP)). Commonly, functional relationships and parameter values that describe the effect of climate variables on the model processes are gathered from various vegetation models of different spatial scales. Though, their accuracies and parameter values have not been validated for the specific model scales of individual-based forest gap models. In this study, we address this uncertainty by linking Eddy-covariance (EC) data and a forest gap model. The forest gap model FORMIND is applied on the Norwegian spruce monoculture forest at Wetzstein in Thuringia, Germany for the years 2003-2008. The original parameterizations of climatic functions are adapted according to the EC-data. The time step of the model is reduced to one day in order to adapt to the high resolution EC-data. The FORMIND model uses functional relationships on an individual level, whereas the EC-method measures eco-physiological responses at the ecosystem level. However, we assume that in homogeneous stands as in our study, functional relationships for both methods are comparable. The model is then validated at the spruce forest Waldstein, Germany. Results show that the functional relationships used in the model, are similar to those observed with the EC-method. The temperature reduction curve is well reflected in the EC-data, though parameter values differ from the originally expected values. For example at the freezing point, the observed GPP is 30% higher than predicted by the forest gap model. The response of observed GPP to soil moisture shows that the permanent wilting point is 7 vol-% lower than the value derived from the literature. The light response curve, integrated over the canopy and the forest stand, is underestimated compared to the measured data. The EC-method measures a yearly carbon balance of 13 mol(CO2)m-2 for the Wetzstein site. The model with the original parameterization overestimates the yearly carbon balance by nearly 5 mol(CO2)m-2 while the model with an EC-based parameterization fits the measured data very well. The parameter values derived from EC-data are applied on the spruce forest Waldstein and clearly improve estimates of the carbon balance.

Influence of different dose calculation algorithms on the estimate of NTCP for lung complications.

PubMed

Hedin, Emma; Bäck, Anna

2013-09-06

Due to limitations and uncertainties in dose calculation algorithms, different algorithms can predict different dose distributions and dose-volume histograms for the same treatment. This can be a problem when estimating the normal tissue complication probability (NTCP) for patient-specific dose distributions. Published NTCP model parameters are often derived for a different dose calculation algorithm than the one used to calculate the actual dose distribution. The use of algorithm-specific NTCP model parameters can prevent errors caused by differences in dose calculation algorithms. The objective of this work was to determine how to change the NTCP model parameters for lung complications derived for a simple correction-based pencil beam dose calculation algorithm, in order to make them valid for three other common dose calculation algorithms. NTCP was calculated with the relative seriality (RS) and Lyman-Kutcher-Burman (LKB) models. The four dose calculation algorithms used were the pencil beam (PB) and collapsed cone (CC) algorithms employed by Oncentra, and the pencil beam convolution (PBC) and anisotropic analytical algorithm (AAA) employed by Eclipse. Original model parameters for lung complications were taken from four published studies on different grades of pneumonitis, and new algorithm-specific NTCP model parameters were determined. The difference between original and new model parameters was presented in relation to the reported model parameter uncertainties. Three different types of treatments were considered in the study: tangential and locoregional breast cancer treatment and lung cancer treatment. Changing the algorithm without the derivation of new model parameters caused changes in the NTCP value of up to 10 percentage points for the cases studied. Furthermore, the error introduced could be of the same magnitude as the confidence intervals of the calculated NTCP values. The new NTCP model parameters were tabulated as the algorithm was varied from PB to PBC, AAA, or CC. Moving from the PB to the PBC algorithm did not require new model parameters; however, moving from PB to AAA or CC did require a change in the NTCP model parameters, with CC requiring the largest change. It was shown that the new model parameters for a given algorithm are different for the different treatment types.

SBRML: a markup language for associating systems biology data with models.

PubMed

Dada, Joseph O; Spasić, Irena; Paton, Norman W; Mendes, Pedro

2010-04-01

Research in systems biology is carried out through a combination of experiments and models. Several data standards have been adopted for representing models (Systems Biology Markup Language) and various types of relevant experimental data (such as FuGE and those of the Proteomics Standards Initiative). However, until now, there has been no standard way to associate a model and its entities to the corresponding datasets, or vice versa. Such a standard would provide a means to represent computational simulation results as well as to frame experimental data in the context of a particular model. Target applications include model-driven data analysis, parameter estimation, and sharing and archiving model simulations. We propose the Systems Biology Results Markup Language (SBRML), an XML-based language that associates a model with several datasets. Each dataset is represented as a series of values associated with model variables, and their corresponding parameter values. SBRML provides a flexible way of indexing the results to model parameter values, which supports both spreadsheet-like data and multidimensional data cubes. We present and discuss several examples of SBRML usage in applications such as enzyme kinetics, microarray gene expression and various types of simulation results. The XML Schema file for SBRML is available at http://www.comp-sys-bio.org/SBRML under the Academic Free License (AFL) v3.0.

Designing occupancy studies when false-positive detections occur

USGS Publications Warehouse

Clement, Matthew

2016-01-01

1.Recently, estimators have been developed to estimate occupancy probabilities when false-positive detections occur during presence-absence surveys. Some of these estimators combine different types of survey data to improve estimates of occupancy. With these estimators, there is a tradeoff between the number of sample units surveyed, and the number and type of surveys at each sample unit. Guidance on efficient design of studies when false positives occur is unavailable. 2.For a range of scenarios, I identified survey designs that minimized the mean square error of the estimate of occupancy. I considered an approach that uses one survey method and two observation states and an approach that uses two survey methods. For each approach, I used numerical methods to identify optimal survey designs when model assumptions were met and parameter values were correctly anticipated, when parameter values were not correctly anticipated, and when the assumption of no unmodelled detection heterogeneity was violated. 3.Under the approach with two observation states, false positive detections increased the number of recommended surveys, relative to standard occupancy models. If parameter values could not be anticipated, pessimism about detection probabilities avoided poor designs. Detection heterogeneity could require more or fewer repeat surveys, depending on parameter values. If model assumptions were met, the approach with two survey methods was inefficient. However, with poor anticipation of parameter values, with detection heterogeneity, or with removal sampling schemes, combining two survey methods could improve estimates of occupancy. 4.Ignoring false positives can yield biased parameter estimates, yet false positives greatly complicate the design of occupancy studies. Specific guidance for major types of false-positive occupancy models, and for two assumption violations common in field data, can conserve survey resources. This guidance can be used to design efficient monitoring programs and studies of species occurrence, species distribution, or habitat selection, when false positives occur during surveys.

Comparison of Regression Analysis and Transfer Function in Estimating the Parameters of Central Pulse Waves from Brachial Pulse Wave.

PubMed

Chai, Rui; Xu, Li-Sheng; Yao, Yang; Hao, Li-Ling; Qi, Lin

2017-01-01

This study analyzed ascending branch slope (A_slope), dicrotic notch height (Hn), diastolic area (Ad) and systolic area (As) diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), subendocardial viability ratio (SEVR), waveform parameter (k), stroke volume (SV), cardiac output (CO), and peripheral resistance (RS) of central pulse wave invasively and non-invasively measured. Invasively measured parameters were compared with parameters measured from brachial pulse waves by regression model and transfer function model. Accuracy of parameters estimated by regression and transfer function model, was compared too. Findings showed that k value, central pulse wave and brachial pulse wave parameters invasively measured, correlated positively. Regression model parameters including A_slope, DBP, SEVR, and transfer function model parameters had good consistency with parameters invasively measured. They had same effect of consistency. SBP, PP, SV, and CO could be calculated through the regression model, but their accuracies were worse than that of transfer function model.

High Throughput pharmacokinetic modeling using computationally predicted parameter values: dissociation constants (TDS)

EPA Science Inventory

Estimates of the ionization association and dissociation constant (pKa) are vital to modeling the pharmacokinetic behavior of chemicals in vivo. Methodologies for the prediction of compound sequestration in specific tissues using partition coefficients require a parameter that ch...

Modeling actual evapotranspiration with routine meteorological variables in the data-scarce region of the Tibetan Plateau: Comparisons and implications

NASA Astrophysics Data System (ADS)

Ma, Ning; Zhang, Yinsheng; Xu, Chong-Yu; Szilagyi, Jozsef

2015-08-01

Quantitative estimation of actual evapotranspiration (ETa) by in situ measurements and mathematical modeling is a fundamental task for physical understanding of ETa as well as the feedback mechanisms between land and the ambient atmosphere. However, the ETa information in the Tibetan Plateau (TP) has been greatly impeded by the extremely sparse ground observation network in the region. Approaches for estimating ETa solely from routine meteorological variables are therefore important for investigating spatiotemporal variations of ETa in the data-scarce region of the TP. Motivated by this need, the complementary relationship (CR) and Penman-Monteith approaches were evaluated against in situ measurements of ETa on a daily basis in an alpine steppe region of the TP. The former includes the Nonlinear Complementary Relationship (Nonlinear-CR) as well as the Complementary Relationship Areal Evapotranspiration (CRAE) models, while the latter involves the Katerji-Perrier and the Todorovic models. Results indicate that the Nonlinear-CR, CRAE, and Katerji-Perrier models are all capable of efficiently simulating daily ETa, provided their parameter values were appropriately calibrated. The Katerji-Perrier model performed best since its site-specific parameters take the soil water status into account. The Nonlinear-CR model also performed well with the advantage of not requiring the user to choose between a symmetric and asymmetric CR. The CRAE model, even with a relatively low Nash-Sutcliffe efficiency (NSE) value, is also an acceptable approach in this data-scarce region as it does not need information of wind speed and ground surface conditions. In contrast, application of the Todorovic model was found to be inappropriate in the dry regions of the TP due to its significant overestimation of ETa as it neglects the effect of water stress on the bulk surface resistance. Sensitivity analysis of the parameter values demonstrated the relative importance of each parameter in the corresponding model. Overall, the Nonlinear-CR model is recommended in the absence of measured ETa for local calibration of the model parameter values.

Exponential parameter and tracking error convergence guarantees for adaptive controllers without persistency of excitation

NASA Astrophysics Data System (ADS)

Chowdhary, Girish; Mühlegg, Maximilian; Johnson, Eric

2014-08-01

In model reference adaptive control (MRAC) the modelling uncertainty is often assumed to be parameterised with time-invariant unknown ideal parameters. The convergence of parameters of the adaptive element to these ideal parameters is beneficial, as it guarantees exponential stability, and makes an online learned model of the system available. Most MRAC methods, however, require persistent excitation of the states to guarantee that the adaptive parameters converge to the ideal values. Enforcing PE may be resource intensive and often infeasible in practice. This paper presents theoretical analysis and illustrative examples of an adaptive control method that leverages the increasing ability to record and process data online by using specifically selected and online recorded data concurrently with instantaneous data for adaptation. It is shown that when the system uncertainty can be modelled as a combination of known nonlinear bases, simultaneous exponential tracking and parameter error convergence can be guaranteed if the system states are exciting over finite intervals such that rich data can be recorded online; PE is not required. Furthermore, the rate of convergence is directly proportional to the minimum singular value of the matrix containing online recorded data. Consequently, an online algorithm to record and forget data is presented and its effects on the resulting switched closed-loop dynamics are analysed. It is also shown that when radial basis function neural networks (NNs) are used as adaptive elements, the method guarantees exponential convergence of the NN parameters to a compact neighbourhood of their ideal values without requiring PE. Flight test results on a fixed-wing unmanned aerial vehicle demonstrate the effectiveness of the method.

Effects of molecular and particle scatterings on the model parameter for remote-sensing reflectance.

PubMed

Lee, ZhongPing; Carder, Kendall L; Du, KePing

2004-09-01

For optically deep waters, remote-sensing reflectance (r(rs)) is traditionally expressed as the ratio of the backscattering coefficient (b(b)) to the sum of absorption and backscattering coefficients (a + b(b)) that multiples a model parameter (g, or the so-called f'/Q). Parameter g is further expressed as a function of b(b)/(a + b(b)) (or b(b)/a) to account for its variation that is due to multiple scattering. With such an approach, the same g value will be derived for different a and b(b) values that provide the same ratio. Because g is partially a measure of the angular distribution of upwelling light, and the angular distribution from molecular scattering is quite different from that of particle scattering; g values are expected to vary with different scattering distributions even if the b(b)/a ratios are the same. In this study, after numerically demonstrating the effects of molecular and particle scatterings on the values of g, an innovative r(rs) model is developed. This new model expresses r(rs) in two separate terms: one governed by the phase function of molecular scattering and one governed by the phase function of particle scattering, with a model parameter introduced for each term. In this way the phase function effects from molecular and particle scatterings are explicitly separated and accounted for. This new model provides an analytical tool to understand and quantify the phase-function effects on r(rs), and a platform to calculate r(rs) spectrum quickly and accurately that is required for remote-sensing applications.

Simulating settlement during waste placement at a landfill with waste lifts placed under frozen conditions.

PubMed

Van Geel, Paul J; Murray, Kathleen E

2015-12-01

Twelve instrument bundles were placed within two waste profiles as waste was placed in an operating landfill in Ste. Sophie, Quebec, Canada. The settlement data were simulated using a three-component model to account for primary or instantaneous compression, secondary compression or mechanical creep and biodegradation induced settlement. The regressed model parameters from the first waste layer were able to predict the settlement of the remaining four waste layers with good agreement. The model parameters were compared to values published in the literature. A MSW landfill scenario referenced in the literature was used to illustrate how the parameter values from the different studies predicted settlement. The parameters determined in this study and other studies with total waste heights between 15 and 60 m provided similar estimates of total settlement in the long term while the settlement rates and relative magnitudes of the three components varied. The parameters determined based on studies with total waste heights less than 15m resulted in larger secondary compression indices and lower biodegradation induced settlements. When these were applied to a MSW landfill scenario with a total waste height of 30 m, the settlement was overestimated and provided unrealistic values. This study concludes that more field studies are needed to measure waste settlement during the filling stage of landfill operations and more field data are needed to assess different settlement models and their respective parameters. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modelling biological invasions: species traits, species interactions, and habitat heterogeneity.

PubMed

Cannas, Sergio A; Marco, Diana E; Páez, Sergio A

2003-05-01

In this paper we explore the integration of different factors to understand, predict and control ecological invasions, through a general cellular automaton model especially developed. The model includes life history traits of several species in a modular structure interacting multiple cellular automata. We performed simulations using field values corresponding to the exotic Gleditsia triacanthos and native co-dominant trees in a montane area. Presence of G. triacanthos juvenile bank was a determinant condition for invasion success. Main parameters influencing invasion velocity were mean seed dispersal distance and minimum reproductive age. Seed production had a small influence on the invasion velocity. Velocities predicted by the model agreed well with estimations from field data. Values of population density predicted matched field values closely. The modular structure of the model, the explicit interaction between the invader and the native species, and the simplicity of parameters and transition rules are novel features of the model.

Failure analysis of parameter-induced simulation crashes in climate models

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Klein, R.; Tannahill, J.; Ivanova, D.; Brandon, S.; Domyancic, D.; Zhang, Y.

2013-01-01

Simulations using IPCC-class climate models are subject to fail or crash for a variety of reasons. Quantitative analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation crashes within the Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4). About 8.5% of our CCSM4 simulations failed for numerical reasons at combinations of POP2 parameter values. We apply support vector machine (SVM) classification from machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicts model failures in an independent validation ensemble, as assessed by the area under the receiver operating characteristic (ROC) curve metric (AUC > 0.96). The causes of the simulation failures are determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations are the major sources of the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations across the relevant parameters. Our method can also be used to quantify, predict, and understand simulation crashes in other complex geoscientific models.

Failure analysis of parameter-induced simulation crashes in climate models

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Klein, R.; Tannahill, J.; Ivanova, D.; Brandon, S.; Domyancic, D.; Zhang, Y.

2013-08-01

Simulations using IPCC (Intergovernmental Panel on Climate Change)-class climate models are subject to fail or crash for a variety of reasons. Quantitative analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation crashes within the Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4). About 8.5% of our CCSM4 simulations failed for numerical reasons at combinations of POP2 parameter values. We applied support vector machine (SVM) classification from machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicted model failures in an independent validation ensemble, as assessed by the area under the receiver operating characteristic (ROC) curve metric (AUC > 0.96). The causes of the simulation failures were determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations were the major sources of the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations across the relevant parameters. Our method can also be used to quantify, predict, and understand simulation crashes in other complex geoscientific models.

Improving the Fit of a Land-Surface Model to Data Using its Adjoint

NASA Astrophysics Data System (ADS)

Raoult, Nina; Jupp, Tim; Cox, Peter; Luke, Catherine

2016-04-01

Land-surface models (LSMs) are crucial components of the Earth System Models (ESMs) which are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. In this study, JULES is automatically differentiated using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed, to search for locally optimum parameter sets by calibrating against observations. We present an introduction to the adJULES system and demonstrate its ability to improve the model-data fit using eddy covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the 5 Plant Functional Types (PFTS) in JULES. The optimised PFT-specific parameters improve the performance of JULES over 90% of the FLUXNET sites used in the study. These reductions in error are shown and compared to reductions found due to site-specific optimisations. Finally, we show that calculation of the 2nd derivative of JULES allows us to produce posterior probability density functions of the parameters and how knowledge of parameter values is constrained by observations.

Maximum entropy approach to statistical inference for an ocean acoustic waveguide.

PubMed

Knobles, D P; Sagers, J D; Koch, R A

2012-02-01

A conditional probability distribution suitable for estimating the statistical properties of ocean seabed parameter values inferred from acoustic measurements is derived from a maximum entropy principle. The specification of the expectation value for an error function constrains the maximization of an entropy functional. This constraint determines the sensitivity factor (β) to the error function of the resulting probability distribution, which is a canonical form that provides a conservative estimate of the uncertainty of the parameter values. From the conditional distribution, marginal distributions for individual parameters can be determined from integration over the other parameters. The approach is an alternative to obtaining the posterior probability distribution without an intermediary determination of the likelihood function followed by an application of Bayes' rule. In this paper the expectation value that specifies the constraint is determined from the values of the error function for the model solutions obtained from a sparse number of data samples. The method is applied to ocean acoustic measurements taken on the New Jersey continental shelf. The marginal probability distribution for the values of the sound speed ratio at the surface of the seabed and the source levels of a towed source are examined for different geoacoustic model representations. © 2012 Acoustical Society of America

Mapping an operator's perception of a parameter space

NASA Technical Reports Server (NTRS)

Pew, R. W.; Jagacinski, R. J.

1972-01-01

Operators monitored the output of two versions of the crossover model having a common random input. Their task was to make discrete, real-time adjustments of the parameters k and tau of one of the models to make its output time history converge to that of the other, fixed model. A plot was obtained of the direction of parameter change as a function of position in the (tau, k) parameter space relative to the nominal value. The plot has a great deal of structure and serves as one form of representation of the operator's perception of the parameter space.

Comparing Exponential and Exponentiated Models of Drug Demand in Cocaine Users

PubMed Central

Strickland, Justin C.; Lile, Joshua A.; Rush, Craig R.; Stoops, William W.

2016-01-01

Drug purchase tasks provide rapid and efficient measurement of drug demand. Zero values (i.e., prices with zero consumption) present a quantitative challenge when using exponential demand models that exponentiated models may resolve. We aimed to replicate and advance the utility of using an exponentiated model by demonstrating construct validity (i.e., association with real-world drug use) and generalizability across drug commodities. Participants (N = 40 cocaine-using adults) completed Cocaine, Alcohol, and Cigarette Purchase Tasks evaluating hypothetical consumption across changes in price. Exponentiated and exponential models were fit to these data using different treatments of zero consumption values, including retaining zeros or replacing them with 0.1, 0.01, 0.001. Excellent model fits were observed with the exponentiated model. Means and precision fluctuated with different replacement values when using the exponential model, but were consistent for the exponentiated model. The exponentiated model provided the strongest correlation between derived demand intensity (Q0) and self-reported free consumption in all instances (Cocaine r = .88; Alcohol r = .97; Cigarette r = .91). Cocaine demand elasticity was positively correlated with alcohol and cigarette elasticity. Exponentiated parameters were associated with real-world drug use (e.g., weekly cocaine use), whereas these correlations were less consistent for exponential parameters. Our findings show that selection of zero replacement values impact demand parameters and their association with drug-use outcomes when using the exponential model, but not the exponentiated model. This work supports the adoption of the exponentiated demand model by replicating improved fit and consistency, in addition to demonstrating construct validity and generalizability. PMID:27929347

Comparing exponential and exponentiated models of drug demand in cocaine users.

PubMed

Strickland, Justin C; Lile, Joshua A; Rush, Craig R; Stoops, William W

2016-12-01

Drug purchase tasks provide rapid and efficient measurement of drug demand. Zero values (i.e., prices with zero consumption) present a quantitative challenge when using exponential demand models that exponentiated models may resolve. We aimed to replicate and advance the utility of using an exponentiated model by demonstrating construct validity (i.e., association with real-world drug use) and generalizability across drug commodities. Participants (N = 40 cocaine-using adults) completed Cocaine, Alcohol, and Cigarette Purchase Tasks evaluating hypothetical consumption across changes in price. Exponentiated and exponential models were fit to these data using different treatments of zero consumption values, including retaining zeros or replacing them with 0.1, 0.01, or 0.001. Excellent model fits were observed with the exponentiated model. Means and precision fluctuated with different replacement values when using the exponential model but were consistent for the exponentiated model. The exponentiated model provided the strongest correlation between derived demand intensity (Q0) and self-reported free consumption in all instances (Cocaine r = .88; Alcohol r = .97; Cigarette r = .91). Cocaine demand elasticity was positively correlated with alcohol and cigarette elasticity. Exponentiated parameters were associated with real-world drug use (e.g., weekly cocaine use) whereas these correlations were less consistent for exponential parameters. Our findings show that selection of zero replacement values affects demand parameters and their association with drug-use outcomes when using the exponential model but not the exponentiated model. This work supports the adoption of the exponentiated demand model by replicating improved fit and consistency and demonstrating construct validity and generalizability. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Evaluation of a Mysis bioenergetics model

USGS Publications Warehouse

Chipps, S.R.; Bennett, D.H.

2002-01-01

Direct approaches for estimating the feeding rate of the opossum shrimp Mysis relicta can be hampered by variable gut residence time (evacuation rate models) and non-linear functional responses (clearance rate models). Bioenergetics modeling provides an alternative method, but the reliability of this approach needs to be evaluated using independent measures of growth and food consumption. In this study, we measured growth and food consumption for M. relicta and compared experimental results with those predicted from a Mysis bioenergetics model. For Mysis reared at 10??C, model predictions were not significantly different from observed values. Moreover, decomposition of mean square error indicated that 70% of the variation between model predictions and observed values was attributable to random error. On average, model predictions were within 12% of observed values. A sensitivity analysis revealed that Mysis respiration and prey energy density were the most sensitive parameters affecting model output. By accounting for uncertainty (95% CLs) in Mysis respiration, we observed a significant improvement in the accuracy of model output (within 5% of observed values), illustrating the importance of sensitive input parameters for model performance. These findings help corroborate the Mysis bioenergetics model and demonstrate the usefulness of this approach for estimating Mysis feeding rate.

Human Resource Scheduling in Performing a Sequence of Discrete Responses

DTIC Science & Technology

2009-02-28

each is a graph comparing simulated results of each respective model with data from Experiment 3b. As described below the parameters of the model...initiated in parallel with ongoing Central operations on another. To fix model parameters we estimated the range of times to perform the sum of the...standard deviation for each parameter was set to 50% of mean value. Initial simulations found no meaningful differences between setting the standard

Analysis of sensitivity of simulated recharge to selected parameters for seven watersheds modeled using the precipitation-runoff modeling system

USGS Publications Warehouse

Ely, D. Matthew

2006-01-01

Recharge is a vital component of the ground-water budget and methods for estimating it range from extremely complex to relatively simple. The most commonly used techniques, however, are limited by the scale of application. One method that can be used to estimate ground-water recharge includes process-based models that compute distributed water budgets on a watershed scale. These models should be evaluated to determine which model parameters are the dominant controls in determining ground-water recharge. Seven existing watershed models from different humid regions of the United States were chosen to analyze the sensitivity of simulated recharge to model parameters. Parameter sensitivities were determined using a nonlinear regression computer program to generate a suite of diagnostic statistics. The statistics identify model parameters that have the greatest effect on simulated ground-water recharge and that compare and contrast the hydrologic system responses to those parameters. Simulated recharge in the Lost River and Big Creek watersheds in Washington State was sensitive to small changes in air temperature. The Hamden watershed model in west-central Minnesota was developed to investigate the relations that wetlands and other landscape features have with runoff processes. Excess soil moisture in the Hamden watershed simulation was preferentially routed to wetlands, instead of to the ground-water system, resulting in little sensitivity of any parameters to recharge. Simulated recharge in the North Fork Pheasant Branch watershed, Wisconsin, demonstrated the greatest sensitivity to parameters related to evapotranspiration. Three watersheds were simulated as part of the Model Parameter Estimation Experiment (MOPEX). Parameter sensitivities for the MOPEX watersheds, Amite River, Louisiana and Mississippi, English River, Iowa, and South Branch Potomac River, West Virginia, were similar and most sensitive to small changes in air temperature and a user-defined flow routing parameter. Although the primary objective of this study was to identify, by geographic region, the importance of the parameter value to the simulation of ground-water recharge, the secondary objectives proved valuable for future modeling efforts. The value of a rigorous sensitivity analysis can (1) make the calibration process more efficient, (2) guide additional data collection, (3) identify model limitations, and (4) explain simulated results.

Influence of dose calculation algorithms on the predicted dose distribution and NTCP values for NSCLC patients.

PubMed

Nielsen, Tine B; Wieslander, Elinore; Fogliata, Antonella; Nielsen, Morten; Hansen, Olfred; Brink, Carsten

2011-05-01

To investigate differences in calculated doses and normal tissue complication probability (NTCP) values between different dose algorithms. Six dose algorithms from four different treatment planning systems were investigated: Eclipse AAA, Oncentra MasterPlan Collapsed Cone and Pencil Beam, Pinnacle Collapsed Cone and XiO Multigrid Superposition, and Fast Fourier Transform Convolution. Twenty NSCLC patients treated in the period 2001-2006 at the same accelerator were included and the accelerator used for treatments were modeled in the different systems. The treatment plans were recalculated with the same number of monitor units and beam arrangements across the dose algorithms. Dose volume histograms of the GTV, PTV, combined lungs (excluding the GTV), and heart were exported and evaluated. NTCP values for heart and lungs were calculated using the relative seriality model and the LKB model, respectively. Furthermore, NTCP for the lungs were calculated from two different model parameter sets. Calculations and evaluations were performed both including and excluding density corrections. There are found statistical significant differences between the calculated dose to heart, lung, and targets across the algorithms. Mean lung dose and V20 are not very sensitive to change between the investigated dose calculation algorithms. However, the different dose levels for the PTV averaged over the patient population are varying up to 11%. The predicted NTCP values for pneumonitis vary between 0.20 and 0.24 or 0.35 and 0.48 across the investigated dose algorithms depending on the chosen model parameter set. The influence of the use of density correction in the dose calculation on the predicted NTCP values depends on the specific dose calculation algorithm and the model parameter set. For fixed values of these, the changes in NTCP can be up to 45%. Calculated NTCP values for pneumonitis are more sensitive to the choice of algorithm than mean lung dose and V20 which are also commonly used for plan evaluation. The NTCP values for heart complication are, in this study, not very sensitive to the choice of algorithm. Dose calculations based on density corrections result in quite different NTCP values than calculations without density corrections. It is therefore important when working with NTCP planning to use NTCP parameter values based on calculations and treatments similar to those for which the NTCP is of interest.

The predicted influence of climate change on lesser prairie-chicken reproductive parameters

USGS Publications Warehouse

Grisham, Blake A.; Boal, Clint W.; Haukos, David A.; Davis, D.; Boydston, Kathy K.; Dixon, Charles; Heck, Willard R.

2013-01-01

The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001-2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter's linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Nina events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival.

The predicted influence of climate change on lesser prairie-chicken reproductive parameters.

PubMed

Grisham, Blake A; Boal, Clint W; Haukos, David A; Davis, Dawn M; Boydston, Kathy K; Dixon, Charles; Heck, Willard R

2013-01-01

The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001-2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter's linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Niña events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival.

Investigation of uncertainty in CO 2 reservoir models: A sensitivity analysis of relative permeability parameter values

DOE PAGES

Yoshida, Nozomu; Levine, Jonathan S.; Stauffer, Philip H.

2016-03-22

Numerical reservoir models of CO 2 injection in saline formations rely on parameterization of laboratory-measured pore-scale processes. Here, we have performed a parameter sensitivity study and Monte Carlo simulations to determine the normalized change in total CO 2 injected using the finite element heat and mass-transfer code (FEHM) numerical reservoir simulator. Experimentally measured relative permeability parameter values were used to generate distribution functions for parameter sampling. The parameter sensitivity study analyzed five different levels for each of the relative permeability model parameters. All but one of the parameters changed the CO 2 injectivity by <10%, less than the geostatistical uncertainty that applies to all large subsurface systems due to natural geophysical variability and inherently small sample sizes. The exception was the end-point CO 2 relative permeability, kmore » $$0\\atop{r}$$ CO2, the maximum attainable effective CO 2 permeability during CO 2 invasion, which changed CO2 injectivity by as much as 80%. Similarly, Monte Carlo simulation using 1000 realizations of relative permeability parameters showed no relationship between CO 2 injectivity and any of the parameters but k$$0\\atop{r}$$ CO2, which had a very strong (R 2 = 0.9685) power law relationship with total CO 2 injected. Model sensitivity to k$$0\\atop{r}$$ CO2 points to the importance of accurate core flood and wettability measurements.« less

Investigation of uncertainty in CO 2 reservoir models: A sensitivity analysis of relative permeability parameter values

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

Yoshida, Nozomu; Levine, Jonathan S.; Stauffer, Philip H.

Numerical reservoir models of CO 2 injection in saline formations rely on parameterization of laboratory-measured pore-scale processes. Here, we have performed a parameter sensitivity study and Monte Carlo simulations to determine the normalized change in total CO 2 injected using the finite element heat and mass-transfer code (FEHM) numerical reservoir simulator. Experimentally measured relative permeability parameter values were used to generate distribution functions for parameter sampling. The parameter sensitivity study analyzed five different levels for each of the relative permeability model parameters. All but one of the parameters changed the CO 2 injectivity by <10%, less than the geostatistical uncertainty that applies to all large subsurface systems due to natural geophysical variability and inherently small sample sizes. The exception was the end-point CO 2 relative permeability, kmore » $$0\\atop{r}$$ CO2, the maximum attainable effective CO 2 permeability during CO 2 invasion, which changed CO2 injectivity by as much as 80%. Similarly, Monte Carlo simulation using 1000 realizations of relative permeability parameters showed no relationship between CO 2 injectivity and any of the parameters but k$$0\\atop{r}$$ CO2, which had a very strong (R 2 = 0.9685) power law relationship with total CO 2 injected. Model sensitivity to k$$0\\atop{r}$$ CO2 points to the importance of accurate core flood and wettability measurements.« less

Application of modern radiative transfer tools to model laboratory quartz emissivity

NASA Astrophysics Data System (ADS)

Pitman, Karly M.; Wolff, Michael J.; Clayton, Geoffrey C.

2005-08-01

Planetary remote sensing of regolith surfaces requires use of theoretical models for interpretation of constituent grain physical properties. In this work, we review and critically evaluate past efforts to strengthen numerical radiative transfer (RT) models with comparison to a trusted set of nadir incidence laboratory quartz emissivity spectra. By first establishing a baseline statistical metric to rate successful model-laboratory emissivity spectral fits, we assess the efficacy of hybrid computational solutions (Mie theory + numerically exact RT algorithm) to calculate theoretical emissivity values for micron-sized α-quartz particles in the thermal infrared (2000-200 cm-1) wave number range. We show that Mie theory, a widely used but poor approximation to irregular grain shape, fails to produce the single scattering albedo and asymmetry parameter needed to arrive at the desired laboratory emissivity values. Through simple numerical experiments, we show that corrections to single scattering albedo and asymmetry parameter values generated via Mie theory become more necessary with increasing grain size. We directly compare the performance of diffraction subtraction and static structure factor corrections to the single scattering albedo, asymmetry parameter, and emissivity for dense packing of grains. Through these sensitivity studies, we provide evidence that, assuming RT methods work well given sufficiently well-quantified inputs, assumptions about the scatterer itself constitute the most crucial aspect of modeling emissivity values.

Statistical Parameter Study of the Time Interval Distribution for Nonparalyzable, Paralyzable, and Hybrid Dead Time Models

NASA Astrophysics Data System (ADS)

Syam, Nur Syamsi; Maeng, Seongjin; Kim, Myo Gwang; Lim, Soo Yeon; Lee, Sang Hoon

2018-05-01

A large dead time of a Geiger Mueller (GM) detector may cause a large count loss in radiation measurements and consequently may cause distortion of the Poisson statistic of radiation events into a new distribution. The new distribution will have different statistical parameters compared to the original distribution. Therefore, the variance, skewness, and excess kurtosis in association with the observed count rate of the time interval distribution for well-known nonparalyzable, paralyzable, and nonparalyzable-paralyzable hybrid dead time models of a Geiger Mueller detector were studied using Monte Carlo simulation (GMSIM). These parameters were then compared with the statistical parameters of a perfect detector to observe the change in the distribution. The results show that the behaviors of the statistical parameters for the three dead time models were different. The values of the skewness and the excess kurtosis of the nonparalyzable model are equal or very close to those of the perfect detector, which are ≅2 for skewness, and ≅6 for excess kurtosis, while the statistical parameters in the paralyzable and hybrid model obtain minimum values that occur around the maximum observed count rates. The different trends of the three models resulting from the GMSIM simulation can be used to distinguish the dead time behavior of a GM counter; i.e. whether the GM counter can be described best by using the nonparalyzable, paralyzable, or hybrid model. In a future study, these statistical parameters need to be analyzed further to determine the possibility of using them to determine a dead time for each model, particularly for paralyzable and hybrid models.

Application and optimization of input parameter spaces in mass flow modelling: a case study with r.randomwalk and r.ranger

NASA Astrophysics Data System (ADS)

Krenn, Julia; Zangerl, Christian; Mergili, Martin

2017-04-01

r.randomwalk is a GIS-based, multi-functional, conceptual open source model application for forward and backward analyses of the propagation of mass flows. It relies on a set of empirically derived, uncertain input parameters. In contrast to many other tools, r.randomwalk accepts input parameter ranges (or, in case of two or more parameters, spaces) in order to directly account for these uncertainties. Parameter spaces represent a possibility to withdraw from discrete input values which in most cases are likely to be off target. r.randomwalk automatically performs multiple calculations with various parameter combinations in a given parameter space, resulting in the impact indicator index (III) which denotes the fraction of parameter value combinations predicting an impact on a given pixel. Still, there is a need to constrain the parameter space used for a certain process type or magnitude prior to performing forward calculations. This can be done by optimizing the parameter space in terms of bringing the model results in line with well-documented past events. As most existing parameter optimization algorithms are designed for discrete values rather than for ranges or spaces, the necessity for a new and innovative technique arises. The present study aims at developing such a technique and at applying it to derive guiding parameter spaces for the forward calculation of rock avalanches through back-calculation of multiple events. In order to automatize the work flow we have designed r.ranger, an optimization and sensitivity analysis tool for parameter spaces which can be directly coupled to r.randomwalk. With r.ranger we apply a nested approach where the total value range of each parameter is divided into various levels of subranges. All possible combinations of subranges of all parameters are tested for the performance of the associated pattern of III. Performance indicators are the area under the ROC curve (AUROC) and the factor of conservativeness (FoC). This strategy is best demonstrated for two input parameters, but can be extended arbitrarily. We use a set of small rock avalanches from western Austria, and some larger ones from Canada and New Zealand, to optimize the basal friction coefficient and the mass-to-drag ratio of the two-parameter friction model implemented with r.randomwalk. Thereby we repeat the optimization procedure with conservative and non-conservative assumptions of a set of complementary parameters and with different raster cell sizes. Our preliminary results indicate that the model performance in terms of AUROC achieved with broad parameter spaces is hardly surpassed by the performance achieved with narrow parameter spaces. However, broad spaces may result in very conservative or very non-conservative predictions. Therefore, guiding parameter spaces have to be (i) broad enough to avoid the risk of being off target; and (ii) narrow enough to ensure a reasonable level of conservativeness of the results. The next steps will consist in (i) extending the study to other types of mass flow processes in order to support forward calculations using r.randomwalk; and (ii) in applying the same strategy to the more complex, dynamic model r.avaflow.

Determining Hypocentral Parameters for Local Earthquakes in 1-D Using a Genetic Algorithm and Two-point ray tracing

NASA Astrophysics Data System (ADS)

Kim, W.; Hahm, I.; Ahn, S. J.; Lim, D. H.

2005-12-01

This paper introduces a powerful method for determining hypocentral parameters for local earthquakes in 1-D using a genetic algorithm (GA) and two-point ray tracing. Using existing algorithms to determine hypocentral parameters is difficult, because these parameters can vary based on initial velocity models. We developed a new method to solve this problem by applying a GA to an existing algorithm, HYPO-71 (Lee and Larh, 1975). The original HYPO-71 algorithm was modified by applying two-point ray tracing and a weighting factor with respect to the takeoff angle at the source to reduce errors from the ray path and hypocenter depth. Artificial data, without error, were generated by computer using two-point ray tracing in a true model, in which velocity structure and hypocentral parameters were known. The accuracy of the calculated results was easily determined by comparing calculated and actual values. We examined the accuracy of this method for several cases by changing the true and modeled layer numbers and thicknesses. The computational results show that this method determines nearly exact hypocentral parameters without depending on initial velocity models. Furthermore, accurate and nearly unique hypocentral parameters were obtained, although the number of modeled layers and thicknesses differed from those in the true model. Therefore, this method can be a useful tool for determining hypocentral parameters in regions where reliable local velocity values are unknown. This method also provides the basic a priori information for 3-D studies. KEY -WORDS: hypocentral parameters, genetic algorithm (GA), two-point ray tracing

Analysis of Mathematical Modelling on Potentiometric Biosensors

PubMed Central

Mehala, N.; Rajendran, L.

2014-01-01

A mathematical model of potentiometric enzyme electrodes for a nonsteady condition has been developed. The model is based on the system of two coupled nonlinear time-dependent reaction diffusion equations for Michaelis-Menten formalism that describes the concentrations of substrate and product within the enzymatic layer. Analytical expressions for the concentration of substrate and product and the corresponding flux response have been derived for all values of parameters using the new homotopy perturbation method. Furthermore, the complex inversion formula is employed in this work to solve the boundary value problem. The analytical solutions obtained allow a full description of the response curves for only two kinetic parameters (unsaturation/saturation parameter and reaction/diffusion parameter). Theoretical descriptions are given for the two limiting cases (zero and first order kinetics) and relatively simple approaches for general cases are presented. All the analytical results are compared with simulation results using Scilab/Matlab program. The numerical results agree with the appropriate theories. PMID:25969765

Analysis of mathematical modelling on potentiometric biosensors.

PubMed

Mehala, N; Rajendran, L

2014-01-01

A mathematical model of potentiometric enzyme electrodes for a nonsteady condition has been developed. The model is based on the system of two coupled nonlinear time-dependent reaction diffusion equations for Michaelis-Menten formalism that describes the concentrations of substrate and product within the enzymatic layer. Analytical expressions for the concentration of substrate and product and the corresponding flux response have been derived for all values of parameters using the new homotopy perturbation method. Furthermore, the complex inversion formula is employed in this work to solve the boundary value problem. The analytical solutions obtained allow a full description of the response curves for only two kinetic parameters (unsaturation/saturation parameter and reaction/diffusion parameter). Theoretical descriptions are given for the two limiting cases (zero and first order kinetics) and relatively simple approaches for general cases are presented. All the analytical results are compared with simulation results using Scilab/Matlab program. The numerical results agree with the appropriate theories.

Simulation of runoff and nutrient export from a typical small watershed in China using the Hydrological Simulation Program-Fortran.

PubMed

Li, Zhaofu; Liu, Hongyu; Luo, Chuan; Li, Yan; Li, Hengpeng; Pan, Jianjun; Jiang, Xiaosan; Zhou, Quansuo; Xiong, Zhengqin

2015-05-01

The Hydrological Simulation Program-Fortran (HSPF), which is a hydrological and water-quality computer model that was developed by the United States Environmental Protection Agency, was employed to simulate runoff and nutrient export from a typical small watershed in a hilly eastern monsoon region of China. First, a parameter sensitivity analysis was performed to assess how changes in the model parameters affect runoff and nutrient export. Next, the model was calibrated and validated using measured runoff and nutrient concentration data. The Nash-Sutcliffe efficiency (E NS ) values of the yearly runoff were 0.87 and 0.69 for the calibration and validation periods, respectively. For storms runoff events, the E NS values were 0.93 for the calibration period and 0.47 for the validation period. Antecedent precipitation and soil moisture conditions can affect the simulation accuracy of storm event flow. The E NS values for the total nitrogen (TN) export were 0.58 for the calibration period and 0.51 for the validation period. In addition, the correlation coefficients between the observed and simulated TN concentrations were 0.84 for the calibration period and 0.74 for the validation period. For phosphorus export, the E NS values were 0.89 for the calibration period and 0.88 for the validation period. In addition, the correlation coefficients between the observed and simulated orthophosphate concentrations were 0.96 and 0.94 for the calibration and validation periods, respectively. The nutrient simulation results are generally satisfactory even though the parameter-lumped HSPF model cannot represent the effects of the spatial pattern of land cover on nutrient export. The model parameters obtained in this study could serve as reference values for applying the model to similar regions. In addition, HSPF can properly describe the characteristics of water quantity and quality processes in this area. After adjustment, calibration, and validation of the parameters, the HSPF model is suitable for hydrological and water-quality simulations in watershed planning and management and for designing best management practices.

Further comments on sensitivities, parameter estimation, and sampling design in one-dimensional analysis of solute transport in porous media

USGS Publications Warehouse

Knopman, Debra S.; Voss, Clifford I.

1988-01-01

Sensitivities of solute concentration to parameters associated with first-order chemical decay, boundary conditions, initial conditions, and multilayer transport are examined in one-dimensional analytical models of transient solute transport in porous media. A sensitivity is a change in solute concentration resulting from a change in a model parameter. Sensitivity analysis is important because minimum information required in regression on chemical data for the estimation of model parameters by regression is expressed in terms of sensitivities. Nonlinear regression models of solute transport were tested on sets of noiseless observations from known models that exceeded the minimum sensitivity information requirements. Results demonstrate that the regression models consistently converged to the correct parameters when the initial sets of parameter values substantially deviated from the correct parameters. On the basis of the sensitivity analysis, several statements may be made about design of sampling for parameter estimation for the models examined: (1) estimation of parameters associated with solute transport in the individual layers of a multilayer system is possible even when solute concentrations in the individual layers are mixed in an observation well; (2) when estimating parameters in a decaying upstream boundary condition, observations are best made late in the passage of the front near a time chosen by adding the inverse of an hypothesized value of the source decay parameter to the estimated mean travel time at a given downstream location; (3) estimation of a first-order chemical decay parameter requires observations to be made late in the passage of the front, preferably near a location corresponding to a travel time of √2 times the half-life of the solute; and (4) estimation of a parameter relating to spatial variability in an initial condition requires observations to be made early in time relative to passage of the solute front.

Probability Distribution Estimated From the Minimum, Maximum, and Most Likely Values: Applied to Turbine Inlet Temperature Uncertainty

NASA Technical Reports Server (NTRS)

Holland, Frederic A., Jr.

2004-01-01

Modern engineering design practices are tending more toward the treatment of design parameters as random variables as opposed to fixed, or deterministic, values. The probabilistic design approach attempts to account for the uncertainty in design parameters by representing them as a distribution of values rather than as a single value. The motivations for this effort include preventing excessive overdesign as well as assessing and assuring reliability, both of which are important for aerospace applications. However, the determination of the probability distribution is a fundamental problem in reliability analysis. A random variable is often defined by the parameters of the theoretical distribution function that gives the best fit to experimental data. In many cases the distribution must be assumed from very limited information or data. Often the types of information that are available or reasonably estimated are the minimum, maximum, and most likely values of the design parameter. For these situations the beta distribution model is very convenient because the parameters that define the distribution can be easily determined from these three pieces of information. Widely used in the field of operations research, the beta model is very flexible and is also useful for estimating the mean and standard deviation of a random variable given only the aforementioned three values. However, an assumption is required to determine the four parameters of the beta distribution from only these three pieces of information (some of the more common distributions, like the normal, lognormal, gamma, and Weibull distributions, have two or three parameters). The conventional method assumes that the standard deviation is a certain fraction of the range. The beta parameters are then determined by solving a set of equations simultaneously. A new method developed in-house at the NASA Glenn Research Center assumes a value for one of the beta shape parameters based on an analogy with the normal distribution (ref.1). This new approach allows for a very simple and direct algebraic solution without restricting the standard deviation. The beta parameters obtained by the new method are comparable to the conventional method (and identical when the distribution is symmetrical). However, the proposed method generally produces a less peaked distribution with a slightly larger standard deviation (up to 7 percent) than the conventional method in cases where the distribution is asymmetric or skewed. The beta distribution model has now been implemented into the Fast Probability Integration (FPI) module used in the NESSUS computer code for probabilistic analyses of structures (ref. 2).

Study of some chaotic inflationary models in f(R) gravity

NASA Astrophysics Data System (ADS)

Sharif, M.; Nawazish, Iqra

2018-04-01

In this paper, we discuss an inflationary scenario via scalar field and fluid cosmology for an anisotropic homogeneous universe model in f(R) gravity. We consider an equation of state which corresponds to a quasi-de Sitter expansion and investigate the effect of the anisotropy parameter for different values of the deviation parameter. We evaluate potential models like linear, quadratic and quartic models which correspond to chaotic inflation. We construct the observational parameters for a power-law model of f(R) gravity and construct the graphical analysis of tensor-scalar ratio and spectral index which indicates the consistency of these parameters with Planck 2015 data.

Parameters Identification of Interface Friction Model for Ceramic Matrix Composites Based on Stress-Strain Response

NASA Astrophysics Data System (ADS)

Han, Xiao; Gao, Xiguang; Song, Yingdong

2017-10-01

An approach to identify parameters of interface friction model for Ceramic Matrix composites based on stress-strain response was developed. The stress distribution of fibers in the interface slip region and intact region of the damaged composite was determined by adopting the interface friction model. The relation between maximum strain, secant moduli of hysteresis loop and interface shear stress, interface de-bonding stress was established respectively with the method of symbolic-graphic combination. By comparing the experimental strain, secant moduli of hysteresis loop with computation values, the interface shear stress and interface de-bonding stress corresponding to first cycle were identified. Substituting the identification of parameters into interface friction model, the stress-strain curves were predicted and the predicted results fit experiments well. Besides, the influence of number of data points on identifying the value of interface parameters was discussed. And the approach was compared with the method based on the area of hysteresis loop.

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

PubMed Central

Walter, Jonathan P.; Kinney, Allison L.; Banks, Scott A.; D'Lima, Darryl D.; Besier, Thor F.; Lloyd, David G.; Fregly, Benjamin J.

2014-01-01

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values. PMID:24402438

Muscle synergies may improve optimization prediction of knee contact forces during walking.

PubMed

Walter, Jonathan P; Kinney, Allison L; Banks, Scott A; D'Lima, Darryl D; Besier, Thor F; Lloyd, David G; Fregly, Benjamin J

2014-02-01

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

Perceiving while producing: Modeling the dynamics of phonological planning

PubMed Central

Roon, Kevin D.; Gafos, Adamantios I.

2016-01-01

We offer a dynamical model of phonological planning that provides a formal instantiation of how the speech production and perception systems interact during online processing. The model is developed on the basis of evidence from an experimental task that requires concurrent use of both systems, the so-called response-distractor task in which speakers hear distractor syllables while they are preparing to produce required responses. The model formalizes how ongoing response planning is affected by perception and accounts for a range of results reported across previous studies. It does so by explicitly addressing the setting of parameter values in representations. The key unit of the model is that of the dynamic field, a distribution of activation over the range of values associated with each representational parameter. The setting of parameter values takes place by the attainment of a stable distribution of activation over the entire field, stable in the sense that it persists even after the response cue in the above experiments has been removed. This and other properties of representations that have been taken as axiomatic in previous work are derived by the dynamics of the proposed model. PMID:27440947

Model Adaptation in Parametric Space for POD-Galerkin Models

NASA Astrophysics Data System (ADS)

Gao, Haotian; Wei, Mingjun

2017-11-01

The development of low-order POD-Galerkin models is largely motivated by the expectation to use the model developed with a set of parameters at their native values to predict the dynamic behaviors of the same system under different parametric values, in other words, a successful model adaptation in parametric space. However, most of time, even small deviation of parameters from their original value may lead to large deviation or unstable results. It has been shown that adding more information (e.g. a steady state, mean value of a different unsteady state, or an entire different set of POD modes) may improve the prediction of flow with other parametric states. For a simple case of the flow passing a fixed cylinder, an orthogonal mean mode at a different Reynolds number may stabilize the POD-Galerkin model when Reynolds number is changed. For a more complicated case of the flow passing an oscillatory cylinder, a global POD-Galerkin model is first applied to handle the moving boundaries, then more information (e.g. more POD modes) is required to predicate the flow under different oscillatory frequencies. Supported by ARL.

Design of robust systems by means of the numerical optimization with harmonic changing of the model parameters

NASA Astrophysics Data System (ADS)

Zhmud, V. A.; Reva, I. L.; Dimitrov, L. V.

2017-01-01

The design of robust feedback systems by means of the numerical optimization method is mostly accomplished with modeling of the several systems simultaneously. In each such system, regulators are similar. But the object models are different. It includes all edge values from the possible variants of the object model parameters. With all this, not all possible sets of model parameters are taken into account. Hence, the regulator can be not robust, i. e. it can not provide system stability in some cases, which were not tested during the optimization procedure. The paper proposes an alternative method. It consists in sequent changing of all parameters according to harmonic low. The frequencies of changing of each parameter are aliquant. It provides full covering of the parameters space.

Finding identifiable parameter combinations in nonlinear ODE models and the rational reparameterization of their input-output equations.

PubMed

Meshkat, Nicolette; Anderson, Chris; Distefano, Joseph J

2011-09-01

When examining the structural identifiability properties of dynamic system models, some parameters can take on an infinite number of values and yet yield identical input-output data. These parameters and the model are then said to be unidentifiable. Finding identifiable combinations of parameters with which to reparameterize the model provides a means for quantitatively analyzing the model and computing solutions in terms of the combinations. In this paper, we revisit and explore the properties of an algorithm for finding identifiable parameter combinations using Gröbner Bases and prove useful theoretical properties of these parameter combinations. We prove a set of M algebraically independent identifiable parameter combinations can be found using this algorithm and that there exists a unique rational reparameterization of the input-output equations over these parameter combinations. We also demonstrate application of the procedure to a nonlinear biomodel. Copyright © 2011 Elsevier Inc. All rights reserved.

Comparative Sensitivity Analysis of Muscle Activation Dynamics

PubMed Central

Günther, Michael; Götz, Thomas

2015-01-01

We mathematically compared two models of mammalian striated muscle activation dynamics proposed by Hatze and Zajac. Both models are representative for a broad variety of biomechanical models formulated as ordinary differential equations (ODEs). These models incorporate parameters that directly represent known physiological properties. Other parameters have been introduced to reproduce empirical observations. We used sensitivity analysis to investigate the influence of model parameters on the ODE solutions. In addition, we expanded an existing approach to treating initial conditions as parameters and to calculating second-order sensitivities. Furthermore, we used a global sensitivity analysis approach to include finite ranges of parameter values. Hence, a theoretician striving for model reduction could use the method for identifying particularly low sensitivities to detect superfluous parameters. An experimenter could use it for identifying particularly high sensitivities to improve parameter estimation. Hatze's nonlinear model incorporates some parameters to which activation dynamics is clearly more sensitive than to any parameter in Zajac's linear model. Other than Zajac's model, Hatze's model can, however, reproduce measured shifts in optimal muscle length with varied muscle activity. Accordingly we extracted a specific parameter set for Hatze's model that combines best with a particular muscle force-length relation. PMID:26417379

Control and Diagnostic Model of Brushless Dc Motor

NASA Astrophysics Data System (ADS)

Abramov, Ivan V.; Nikitin, Yury R.; Abramov, Andrei I.; Sosnovich, Ella V.; Božek, Pavol

2014-09-01

A simulation model of brushless DC motor (BLDC) control and diagnostics is considered. The model has been developed using a freeware complex "Modeling in technical devices". Faults and diagnostic parameters of BLDC are analyzed. A logicallinguistic diagnostic model of BLDC has been developed on basis of fuzzy logic. The calculated rules determine dependence of technical condition on diagnostic parameters, their trends and utilized lifetime of BLDC. Experimental results of BLDC technical condition diagnostics are discussed. It is shown that in the course of BLDC degradation the motor condition change depends on diagnostic parameter values

Sensitivity of ecological soil-screening levels for metals to exposure model parameterization and toxicity reference values.

PubMed

Sample, Bradley E; Fairbrother, Anne; Kaiser, Ashley; Law, Sheryl; Adams, Bill

2014-10-01

Ecological soil-screening levels (Eco-SSLs) were developed by the United States Environmental Protection Agency (USEPA) for the purposes of setting conservative soil screening values that can be used to eliminate the need for further ecological assessment for specific analytes at a given site. Ecological soil-screening levels for wildlife represent a simplified dietary exposure model solved in terms of soil concentrations to produce exposure equal to a no-observed-adverse-effect toxicity reference value (TRV). Sensitivity analyses were performed for 6 avian and mammalian model species, and 16 metals/metalloids for which Eco-SSLs have been developed. The relative influence of model parameters was expressed as the absolute value of the range of variation observed in the resulting soil concentration when exposure is equal to the TRV. Rank analysis of variance was used to identify parameters with greatest influence on model output. For both birds and mammals, soil ingestion displayed the broadest overall range (variability), although TRVs consistently had the greatest influence on calculated soil concentrations; bioavailability in food was consistently the least influential parameter, although an important site-specific variable. Relative importance of parameters differed by trophic group. Soil ingestion ranked 2nd for carnivores and herbivores, but was 4th for invertivores. Different patterns were exhibited, depending on which parameter, trophic group, and analyte combination was considered. The approach for TRV selection was also examined in detail, with Cu as the representative analyte. The underlying assumption that generic body-weight-normalized TRVs can be used to derive protective levels for any species is not supported by the data. Whereas the use of site-, species-, and analyte-specific exposure parameters is recommended to reduce variation in exposure estimates (soil protection level), improvement of TRVs is more problematic. © 2014 The Authors. Environmental Toxicology and Chemistry Published by Wiley Periodicals, Inc.

Sensitivity of ecological soil-screening levels for metals to exposure model parameterization and toxicity reference values

PubMed Central

Sample, Bradley E; Fairbrother, Anne; Kaiser, Ashley; Law, Sheryl; Adams, Bill

2014-01-01

Ecological soil-screening levels (Eco-SSLs) were developed by the United States Environmental Protection Agency (USEPA) for the purposes of setting conservative soil screening values that can be used to eliminate the need for further ecological assessment for specific analytes at a given site. Ecological soil-screening levels for wildlife represent a simplified dietary exposure model solved in terms of soil concentrations to produce exposure equal to a no-observed-adverse-effect toxicity reference value (TRV). Sensitivity analyses were performed for 6 avian and mammalian model species, and 16 metals/metalloids for which Eco-SSLs have been developed. The relative influence of model parameters was expressed as the absolute value of the range of variation observed in the resulting soil concentration when exposure is equal to the TRV. Rank analysis of variance was used to identify parameters with greatest influence on model output. For both birds and mammals, soil ingestion displayed the broadest overall range (variability), although TRVs consistently had the greatest influence on calculated soil concentrations; bioavailability in food was consistently the least influential parameter, although an important site-specific variable. Relative importance of parameters differed by trophic group. Soil ingestion ranked 2nd for carnivores and herbivores, but was 4th for invertivores. Different patterns were exhibited, depending on which parameter, trophic group, and analyte combination was considered. The approach for TRV selection was also examined in detail, with Cu as the representative analyte. The underlying assumption that generic body-weight–normalized TRVs can be used to derive protective levels for any species is not supported by the data. Whereas the use of site-, species-, and analyte-specific exposure parameters is recommended to reduce variation in exposure estimates (soil protection level), improvement of TRVs is more problematic. Environ Toxicol Chem 2014;33:2386–2398. PMID:24944000

Comparison of results from simple expressions for MOSFET parameter extraction

NASA Technical Reports Server (NTRS)

Buehler, M. G.; Lin, Y.-S.

1988-01-01

In this paper results are compared from a parameter extraction procedure applied to the linear, saturation, and subthreshold regions for enhancement-mode MOSFETs fabricated in a 3-micron CMOS process. The results indicate that the extracted parameters differ significantly depending on the extraction algorithm and the distribution of I-V data points. It was observed that KP values vary by 30 percent, VT values differ by 50 mV, and Delta L values differ by 1 micron. Thus for acceptance of wafers from foundries and for modeling purposes, the extraction method and data point distribution must be specified. In this paper measurement and extraction procedures that will allow a consistent evaluation of measured parameters are discussed.

An automatic and effective parameter optimization method for model tuning

NASA Astrophysics Data System (ADS)

Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.

2015-05-01

Physical parameterizations in General Circulation Models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.

Calibration of HEC-Ras hydrodynamic model using gauged discharge data and flood inundation maps

NASA Astrophysics Data System (ADS)

Tong, Rui; Komma, Jürgen

2017-04-01

The estimation of flood is essential for disaster alleviation. Hydrodynamic models are implemented to predict the occurrence and variance of flood in different scales. In practice, the calibration of hydrodynamic models aims to search the best possible parameters for the representation the natural flow resistance. Recent years have seen the calibration of hydrodynamic models being more actual and faster following the advance of earth observation products and computer based optimization techniques. In this study, the Hydrologic Engineering River Analysis System (HEC-Ras) model was set up with high-resolution digital elevation model from Laser scanner for the river Inn in Tyrol, Austria. 10 largest flood events from 19 hourly discharge gauges and flood inundation maps were selected to calibrate the HEC-Ras model. Manning roughness values and lateral inflow factors as parameters were automatically optimized with the Shuffled complex with Principal component analysis (SP-UCI) algorithm developed from the Shuffled Complex Evolution (SCE-UA). Different objective functions (Nash-Sutcliffe model efficiency coefficient, the timing of peak, peak value and Root-mean-square deviation) were used in single or multiple way. It was found that the lateral inflow factor was the most sensitive parameter. SP-UCI algorithm could avoid the local optimal and achieve efficient and effective parameters in the calibration of HEC-Ras model using flood extension images. As results showed, calibration by means of gauged discharge data and flood inundation maps, together with objective function of Nash-Sutcliffe model efficiency coefficient, was very robust to obtain more reliable flood simulation, and also to catch up with the peak value and the timing of peak.

Adaptation of an urban land surface model to a tropical suburban area: Offline evaluation, sensitivity analysis, and optimization of TEB/ISBA (SURFEX)

NASA Astrophysics Data System (ADS)

Harshan, Suraj

The main objective of the present thesis is the improvement of the TEB/ISBA (SURFEX) urban land surface model (ULSM) through comprehensive evaluation, sensitivity analysis, and optimization experiments using energy balance and radiative and air temperature data observed during 11 months at a tropical sub-urban site in Singapore. Overall the performance of the model is satisfactory, with a small underestimation of net radiation and an overestimation of sensible heat flux. Weaknesses in predicting the latent heat flux are apparent with smaller model values during daytime and the model also significantly underpredicts both the daytime peak and nighttime storage heat. Surface temperatures of all facets are generally overpredicted. Significant variation exists in the model behaviour between dry and wet seasons. The vegetation parametrization used in the model is inadequate to represent the moisture dynamics, producing unrealistically low latent heat fluxes during a particularly dry period. The comprehensive evaluation of the USLM shows the need for accurate estimation of input parameter values for present site. Since obtaining many of these parameters through empirical methods is not feasible, the present study employed a two step approach aimed at providing information about the most sensitive parameters and an optimized parameter set from model calibration. Two well established sensitivity analysis methods (global: Sobol and local: Morris) and a state-of-the-art multiobjective evolutionary algorithm (Borg) were employed for sensitivity analysis and parameter estimation. Experiments were carried out for three different weather periods. The analysis indicates that roof related parameters are the most important ones in controlling the behaviour of the sensible heat flux and net radiation flux, with roof and road albedo as the most influential parameters. Soil moisture initialization parameters are important in controlling the latent heat flux. The built (town) fraction has a significant influence on all fluxes considered. Comparison between the Sobol and Morris methods shows similar sensitivities, indicating the robustness of the present analysis and that the Morris method can be employed as a computationally cheaper alternative of Sobol's method. Optimization as well as the sensitivity experiments for the three periods (dry, wet and mixed), show a noticeable difference in parameter sensitivity and parameter convergence, indicating inadequacies in model formulation. Existence of a significant proportion of less sensitive parameters might be indicating an over-parametrized model. Borg MOEA showed great promise in optimizing the input parameters set. The optimized model modified using the site specific values for thermal roughness length parametrization shows an improvement in the performances of outgoing longwave radiation flux, overall surface temperature, heat storage flux and sensible heat flux.

Modeling sugar cane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values

NASA Astrophysics Data System (ADS)

Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Huth, N.; Marin, F.; Martiné, J.-F.

2014-01-01

Agro-Land Surface Models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil-vegetation-atmosphere continuum. When developing agro-LSM models, a particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of Agro-LSM models is related to their usually large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE-STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS' phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte-Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugar cane cultivation in Australia and Brazil. Ten parameters driving most of the uncertainty in the ORCHIDEE-STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting climate-mediated different sensitivities of modeled sugar cane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

Modeling sugarcane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values

NASA Astrophysics Data System (ADS)

Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Caubel, A.; Huth, N.; Marin, F.; Martiné, J.-F.

2014-06-01

Agro-land surface models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil-vegetation-atmosphere continuum. When developing agro-LSM models, particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of agro-LSM models is related to their usually large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugarcane biomass production with the agro-LSM ORCHIDEE-STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE-STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte Carlo sampling method associated with the calculation of partial ranked correlation coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugarcane cultivation in Australia and Brazil. The ten parameters driving most of the uncertainty in the ORCHIDEE-STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting different climate-mediated sensitivities of modeled sugarcane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

Using Active Learning for Speeding up Calibration in Simulation Models.

PubMed

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

A Numerical Method for Calculating Stellar Occultation Light Curves from an Arbitrary Atmospheric Model

NASA Technical Reports Server (NTRS)

Chamberlain, D. M.; Elliot, J. L.

1997-01-01

We present a method for speeding up numerical calculations of a light curve for a stellar occultation by a planetary atmosphere with an arbitrary atmospheric model that has spherical symmetry. This improved speed makes least-squares fitting for model parameters practical. Our method takes as input several sets of values for the first two radial derivatives of the refractivity at different values of model parameters, and interpolates to obtain the light curve at intermediate values of one or more model parameters. It was developed for small occulting bodies such as Pluto and Triton, but is applicable to planets of all sizes. We also present the results of a series of tests showing that our method calculates light curves that are correct to an accuracy of 10(exp -4) of the unocculted stellar flux. The test benchmarks are (i) an atmosphere with a l/r dependence of temperature, which yields an analytic solution for the light curve, (ii) an atmosphere that produces an exponential refraction angle, and (iii) a small-planet isothermal model. With our method, least-squares fits to noiseless data also converge to values of parameters with fractional errors of no more than 10(exp -4), with the largest errors occurring in small planets. These errors are well below the precision of the best stellar occultation data available. Fits to noisy data had formal errors consistent with the level of synthetic noise added to the light curve. We conclude: (i) one should interpolate refractivity derivatives and then form light curves from the interpolated values, rather than interpolating the light curves themselves; (ii) for the most accuracy, one must specify the atmospheric model for radii many scale heights above half light; and (iii) for atmospheres with smoothly varying refractivity with altitude, light curves can be sampled as coarsely as two points per scale height.

Potential application of item-response theory to interpretation of medical codes in electronic patient records

PubMed Central

2011-01-01

Background Electronic patient records are generally coded using extensive sets of codes but the significance of the utilisation of individual codes may be unclear. Item response theory (IRT) models are used to characterise the psychometric properties of items included in tests and questionnaires. This study asked whether the properties of medical codes in electronic patient records may be characterised through the application of item response theory models. Methods Data were provided by a cohort of 47,845 participants from 414 family practices in the UK General Practice Research Database (GPRD) with a first stroke between 1997 and 2006. Each eligible stroke code, out of a set of 202 OXMIS and Read codes, was coded as either recorded or not recorded for each participant. A two parameter IRT model was fitted using marginal maximum likelihood estimation. Estimated parameters from the model were considered to characterise each code with respect to the latent trait of stroke diagnosis. The location parameter is referred to as a calibration parameter, while the slope parameter is referred to as a discrimination parameter. Results There were 79,874 stroke code occurrences available for analysis. Utilisation of codes varied between family practices with intraclass correlation coefficients of up to 0.25 for the most frequently used codes. IRT analyses were restricted to 110 Read codes. Calibration and discrimination parameters were estimated for 77 (70%) codes that were endorsed for 1,942 stroke patients. Parameters were not estimated for the remaining more frequently used codes. Discrimination parameter values ranged from 0.67 to 2.78, while calibration parameters values ranged from 4.47 to 11.58. The two parameter model gave a better fit to the data than either the one- or three-parameter models. However, high chi-square values for about a fifth of the stroke codes were suggestive of poor item fit. Conclusion The application of item response theory models to coded electronic patient records might potentially contribute to identifying medical codes that offer poor discrimination or low calibration. This might indicate the need for improved coding sets or a requirement for improved clinical coding practice. However, in this study estimates were only obtained for a small proportion of participants and there was some evidence of poor model fit. There was also evidence of variation in the utilisation of codes between family practices raising the possibility that, in practice, properties of codes may vary for different coders. PMID:22176509

Optimization of a Thermodynamic Model Using a Dakota Toolbox Interface

NASA Astrophysics Data System (ADS)

Cyrus, J.; Jafarov, E. E.; Schaefer, K. M.; Wang, K.; Clow, G. D.; Piper, M.; Overeem, I.

2016-12-01

Scientific modeling of the Earth physical processes is an important driver of modern science. The behavior of these scientific models is governed by a set of input parameters. It is crucial to choose accurate input parameters that will also preserve the corresponding physics being simulated in the model. In order to effectively simulate real world processes the models output data must be close to the observed measurements. To achieve this optimal simulation, input parameters are tuned until we have minimized the objective function, which is the error between the simulation model outputs and the observed measurements. We developed an auxiliary package, which serves as a python interface between the user and DAKOTA. The package makes it easy for the user to conduct parameter space explorations, parameter optimizations, as well as sensitivity analysis while tracking and storing results in a database. The ability to perform these analyses via a Python library also allows the users to combine analysis techniques, for example finding an approximate equilibrium with optimization then immediately explore the space around it. We used the interface to calibrate input parameters for the heat flow model, which is commonly used in permafrost science. We performed optimization on the first three layers of the permafrost model, each with two thermal conductivity coefficients input parameters. Results of parameter space explorations indicate that the objective function not always has a unique minimal value. We found that gradient-based optimization works the best for the objective functions with one minimum. Otherwise, we employ more advanced Dakota methods such as genetic optimization and mesh based convergence in order to find the optimal input parameters. We were able to recover 6 initially unknown thermal conductivity parameters within 2% accuracy of their known values. Our initial tests indicate that the developed interface for the Dakota toolbox could be used to perform analysis and optimization on a `black box' scientific model more efficiently than using just Dakota.

Population Pharmacokinetic/Pharmacodynamic Analysis of Alirocumab in Healthy Volunteers or Hypercholesterolemic Subjects Using an Indirect Response Model to Predict Low-Density Lipoprotein Cholesterol Lowering: Support for a Biologics License Application Submission: Part II.

PubMed

Nicolas, Xavier; Djebli, Nassim; Rauch, Clémence; Brunet, Aurélie; Hurbin, Fabrice; Martinez, Jean-Marie; Fabre, David

2018-05-03

Alirocumab, a human monoclonal antibody against proprotein convertase subtilisin/kexin type 9 (PCSK9), significantly lowers low-density lipoprotein cholesterol levels. This analysis aimed to develop and qualify a population pharmacokinetic/pharmacodynamic model for alirocumab based on pooled data obtained from 13 phase I/II/III clinical trials. From a dataset of 2799 individuals (14,346 low-density lipoprotein-cholesterol values), individual pharmacokinetic parameters from the population pharmacokinetic model presented in Part I of this series were used to estimate alirocumab concentrations. As a second step, we then developed the current population pharmacokinetic/pharmacodynamic model using an indirect response model with a Hill coefficient, parameterized with increasing low-density lipoprotein cholesterol elimination, to relate alirocumab concentrations to low-density lipoprotein cholesterol values. The population pharmacokinetic/pharmacodynamic model allowed the characterization of the pharmacokinetic/pharmacodynamic properties of alirocumab in the target population and estimation of individual low-density lipoprotein cholesterol levels and derived pharmacodynamic parameters (the maximum decrease in low-density lipoprotein cholesterol values from baseline and the difference between baseline low-density lipoprotein cholesterol and the pre-dose value before the next alirocumab dose). Significant parameter-covariate relationships were retained in the model, with a total of ten covariates (sex, age, weight, free baseline PCSK9, total time-varying PCSK9, concomitant statin administration, total baseline PCSK9, co-administration of high-dose statins, disease status) included in the final population pharmacokinetic/pharmacodynamic model to explain between-subject variability. Nevertheless, the high number of covariates included in the model did not have a clinically meaningful impact on model-derived pharmacodynamic parameters. This model successfully allowed the characterization of the population pharmacokinetic/pharmacodynamic properties of alirocumab in its target population and the estimation of individual low-density lipoprotein cholesterol levels.

Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver

NASA Astrophysics Data System (ADS)

Kang, Ling; Zhou, Liwei

2018-02-01

Abstract . The Muskingum model is an effective flood routing technology in hydrology and water resources Engineering. With the development of optimization technology, more and more variable-parameter Muskingum models were presented to improve effectiveness of the Muskingum model in recent decades. A variable-parameter nonlinear Muskingum model (NVPNLMM) was proposed in this paper. According to the results of two real and frequently-used case studies by various models, the NVPNLMM could obtain better values of evaluation criteria, which are used to describe the superiority of the estimated outflows and compare the accuracies of flood routing using various models, and the optimal estimated outflows by the NVPNLMM were closer to the observed outflows than the ones by other models.

Parameter estimation and sensitivity analysis for a mathematical model with time delays of leukemia

NASA Astrophysics Data System (ADS)

Cândea, Doina; Halanay, Andrei; Rǎdulescu, Rodica; Tǎlmaci, Rodica

2017-01-01

We consider a system of nonlinear delay differential equations that describes the interaction between three competing cell populations: healthy, leukemic and anti-leukemia T cells involved in Chronic Myeloid Leukemia (CML) under treatment with Imatinib. The aim of this work is to establish which model parameters are the most important in the success or failure of leukemia remission under treatment using a sensitivity analysis of the model parameters. For the most significant parameters of the model which affect the evolution of CML disease during Imatinib treatment we try to estimate the realistic values using some experimental data. For these parameters, steady states are calculated and their stability is analyzed and biologically interpreted.

Curve Number Application in Continuous Runoff Models: An Exercise in Futility?

NASA Astrophysics Data System (ADS)

Lamont, S. J.; Eli, R. N.

2006-12-01

The suitability of applying the NRCS (Natural Resource Conservation Service) Curve Number (CN) to continuous runoff prediction is examined by studying the dependence of CN on several hydrologic variables in the context of a complex nonlinear hydrologic model. The continuous watershed model Hydrologic Simulation Program-FORTRAN (HSPF) was employed using a simple theoretical watershed in two numerical procedures designed to investigate the influence of soil type, soil depth, storm depth, storm distribution, and initial abstraction ratio value on the calculated CN value. This study stems from a concurrent project involving the design of a hydrologic modeling system to support the Cumulative Hydrologic Impact Assessments (CHIA) of over 230 coal-mined watersheds throughout West Virginia. Because of the large number of watersheds and limited availability of data necessary for HSPF calibration, it was initially proposed that predetermined CN values be used as a surrogate for those HSPF parameters controlling direct runoff. A soil physics model was developed to relate CN values to those HSPF parameters governing soil moisture content and infiltration behavior, with the remaining HSPF parameters being adopted from previous calibrations on real watersheds. A numerical procedure was then adopted to back-calculate CN values from the theoretical watershed using antecedent moisture conditions equivalent to the NRCS Antecedent Runoff Condition (ARC) II. This procedure used the direct runoff produced from a cyclic synthetic storm event time series input to HSPF. A second numerical method of CN determination, using real time series rainfall data, was used to provide a comparison to those CN values determined using the synthetic storm event time series. It was determined that the calculated CN values resulting from both numerical methods demonstrated a nonlinear dependence on all of the computational variables listed above. It was concluded that the use of the Curve Number as a surrogate for the selected subset of HPSF parameters could not be justified. These results suggest that use of the Curve Number in other complex continuous time series hydrologic models may not be appropriate, given the limitations inherent in the definition of the NRCS CN method.

An automatic and effective parameter optimization method for model tuning

NASA Astrophysics Data System (ADS)

Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.

2015-11-01

Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks

PubMed Central

Liao, Shuohao; Vejchodský, Tomáš; Erban, Radek

2015-01-01

Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org. PMID:26063822

Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks.

PubMed

Liao, Shuohao; Vejchodský, Tomáš; Erban, Radek

2015-07-06

Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.

Recommended Parameter Values for GENII Modeling of Radionuclides in Routine Air and Water Releases

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

Snyder, Sandra F.; Arimescu, Carmen; Napier, Bruce A.

The GENII v2 code is used to estimate dose to individuals or populations from the release of radioactive materials into air or water. Numerous parameter values are required for input into this code. User-defined parameters cover the spectrum from chemical data, meteorological data, agricultural data, and behavioral data. This document is a summary of parameter values that reflect conditions in the United States. Reasonable regional and age-dependent data is summarized. Data availability and quality varies. The set of parameters described address scenarios for chronic air emissions or chronic releases to public waterways. Considerations for the special tritium and carbon-14 modelsmore » are briefly addressed. GENIIv2.10.0 is the current software version that this document supports.« less

Perceptual control models of pursuit manual tracking demonstrate individual specificity and parameter consistency.

PubMed

Parker, Maximilian G; Tyson, Sarah F; Weightman, Andrew P; Abbott, Bruce; Emsley, Richard; Mansell, Warren

2017-11-01

Computational models that simulate individuals' movements in pursuit-tracking tasks have been used to elucidate mechanisms of human motor control. Whilst there is evidence that individuals demonstrate idiosyncratic control-tracking strategies, it remains unclear whether models can be sensitive to these idiosyncrasies. Perceptual control theory (PCT) provides a unique model architecture with an internally set reference value parameter, and can be optimized to fit an individual's tracking behavior. The current study investigated whether PCT models could show temporal stability and individual specificity over time. Twenty adults completed three blocks of 15 1-min, pursuit-tracking trials. Two blocks (training and post-training) were completed in one session and the third was completed after 1 week (follow-up). The target moved in a one-dimensional, pseudorandom pattern. PCT models were optimized to the training data using a least-mean-squares algorithm, and validated with data from post-training and follow-up. We found significant inter-individual variability (partial η 2 : .464-.697) and intra-individual consistency (Cronbach's α: .880-.976) in parameter estimates. Polynomial regression revealed that all model parameters, including the reference value parameter, contribute to simulation accuracy. Participants' tracking performances were significantly more accurately simulated by models developed from their own tracking data than by models developed from other participants' data. We conclude that PCT models can be optimized to simulate the performance of an individual and that the test-retest reliability of individual models is a necessary criterion for evaluating computational models of human performance.

Nonlinear mathematical modeling and sensitivity analysis of hydraulic drive unit

NASA Astrophysics Data System (ADS)

Kong, Xiangdong; Yu, Bin; Quan, Lingxiao; Ba, Kaixian; Wu, Liujie

2015-09-01

The previous sensitivity analysis researches are not accurate enough and also have the limited reference value, because those mathematical models are relatively simple and the change of the load and the initial displacement changes of the piston are ignored, even experiment verification is not conducted. Therefore, in view of deficiencies above, a nonlinear mathematical model is established in this paper, including dynamic characteristics of servo valve, nonlinear characteristics of pressure-flow, initial displacement of servo cylinder piston and friction nonlinearity. The transfer function block diagram is built for the hydraulic drive unit closed loop position control, as well as the state equations. Through deriving the time-varying coefficient items matrix and time-varying free items matrix of sensitivity equations respectively, the expression of sensitivity equations based on the nonlinear mathematical model are obtained. According to structure parameters of hydraulic drive unit, working parameters, fluid transmission characteristics and measured friction-velocity curves, the simulation analysis of hydraulic drive unit is completed on the MATLAB/Simulink simulation platform with the displacement step 2 mm, 5 mm and 10 mm, respectively. The simulation results indicate that the developed nonlinear mathematical model is sufficient by comparing the characteristic curves of experimental step response and simulation step response under different constant load. Then, the sensitivity function time-history curves of seventeen parameters are obtained, basing on each state vector time-history curve of step response characteristic. The maximum value of displacement variation percentage and the sum of displacement variation absolute values in the sampling time are both taken as sensitivity indexes. The sensitivity indexes values above are calculated and shown visually in histograms under different working conditions, and change rules are analyzed. Then the sensitivity indexes values of four measurable parameters, such as supply pressure, proportional gain, initial position of servo cylinder piston and load force, are verified experimentally on test platform of hydraulic drive unit, and the experimental research shows that the sensitivity analysis results obtained through simulation are approximate to the test results. This research indicates each parameter sensitivity characteristics of hydraulic drive unit, the performance-affected main parameters and secondary parameters are got under different working conditions, which will provide the theoretical foundation for the control compensation and structure optimization of hydraulic drive unit.

A Global Picture of the Gamma-Ricker Map: A Flexible Discrete-Time Model with Factors of Positive and Negative Density Dependence.

PubMed

Liz, Eduardo

2018-02-01

The gamma-Ricker model is one of the more flexible and general discrete-time population models. It is defined on the basis of the Ricker model, introducing an additional parameter [Formula: see text]. For some values of this parameter ([Formula: see text], population is overcompensatory, and the introduction of an additional parameter gives more flexibility to fit the stock-recruitment curve to field data. For other parameter values ([Formula: see text]), the gamma-Ricker model represents populations whose per-capita growth rate combines both negative density dependence and positive density dependence. The former can lead to overcompensation and dynamic instability, and the latter can lead to a strong Allee effect. We study the impact of the cooperation factor in the dynamics and provide rigorous conditions under which increasing the Allee effect strength stabilizes or destabilizes population dynamics, promotes or prevents population extinction, and increases or decreases population size. Our theoretical results also include new global stability criteria and a description of the possible bifurcations.

Structural and Practical Identifiability Issues of Immuno-Epidemiological Vector-Host Models with Application to Rift Valley Fever.

PubMed

Tuncer, Necibe; Gulbudak, Hayriye; Cannataro, Vincent L; Martcheva, Maia

2016-09-01

In this article, we discuss the structural and practical identifiability of a nested immuno-epidemiological model of arbovirus diseases, where host-vector transmission rate, host recovery, and disease-induced death rates are governed by the within-host immune system. We incorporate the newest ideas and the most up-to-date features of numerical methods to fit multi-scale models to multi-scale data. For an immunological model, we use Rift Valley Fever Virus (RVFV) time-series data obtained from livestock under laboratory experiments, and for an epidemiological model we incorporate a human compartment to the nested model and use the number of human RVFV cases reported by the CDC during the 2006-2007 Kenya outbreak. We show that the immunological model is not structurally identifiable for the measurements of time-series viremia concentrations in the host. Thus, we study the non-dimensionalized and scaled versions of the immunological model and prove that both are structurally globally identifiable. After fixing estimated parameter values for the immunological model derived from the scaled model, we develop a numerical method to fit observable RVFV epidemiological data to the nested model for the remaining parameter values of the multi-scale system. For the given (CDC) data set, Monte Carlo simulations indicate that only three parameters of the epidemiological model are practically identifiable when the immune model parameters are fixed. Alternatively, we fit the multi-scale data to the multi-scale model simultaneously. Monte Carlo simulations for the simultaneous fitting suggest that the parameters of the immunological model and the parameters of the immuno-epidemiological model are practically identifiable. We suggest that analytic approaches for studying the structural identifiability of nested models are a necessity, so that identifiable parameter combinations can be derived to reparameterize the nested model to obtain an identifiable one. This is a crucial step in developing multi-scale models which explain multi-scale data.

EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model

NASA Astrophysics Data System (ADS)

Holoien, Thomas W.-S.; Marshall, Philip J.; Wechsler, Risa H.

2017-06-01

We describe two new open-source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program that uses Gaussian mixtures to perform density estimation of noisy data using extreme deconvolution (XD) algorithms. Additionally, it has functionality not available in other XD tools. It allows the user to select between the AstroML and Bovy et al. fitting methods and is compatible with scikit-learn machine learning algorithms. Most crucially, it allows the user to condition a model based on the known values of a subset of parameters. This gives the user the ability to produce a tool that can predict unknown parameters based on a model that is conditioned on known values of other parameters. EmpiriciSN is an exemplary application of this functionality, which can be used to fit an XDGMM model to observed supernova/host data sets and predict likely supernova parameters using a model conditioned on observed host properties. It is primarily intended to simulate realistic supernovae for LSST data simulations based on empirical galaxy properties.

The validation of a generalized Hooke's law for coronary arteries.

PubMed

Wang, Chong; Zhang, Wei; Kassab, Ghassan S

2008-01-01

The exponential form of constitutive model is widely used in biomechanical studies of blood vessels. There are two main issues, however, with this model: 1) the curve fits of experimental data are not always satisfactory, and 2) the material parameters may be oversensitive. A new type of strain measure in a generalized Hooke's law for blood vessels was recently proposed by our group to address these issues. The new model has one nonlinear parameter and six linear parameters. In this study, the stress-strain equation is validated by fitting the model to experimental data of porcine coronary arteries. Material constants of left anterior descending artery and right coronary artery for the Hooke's law were computed with a separable nonlinear least-squares method with an excellent goodness of fit. A parameter sensitivity analysis shows that the stability of material constants is improved compared with the exponential model and a biphasic model. A boundary value problem was solved to demonstrate that the model prediction can match the measured arterial deformation under experimental loading conditions. The validated constitutive relation will serve as a basis for the solution of various boundary value problems of cardiovascular biomechanics.

EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model

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

Holoien, Thomas W. -S.; Marshall, Philip J.; Wechsler, Risa H.

We describe two new open-source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program that uses Gaussian mixtures to perform density estimation of noisy data using extreme deconvolution (XD) algorithms. Additionally, it has functionality not available in other XD tools. It allows the user to select between the AstroML and Bovy et al. fitting methods and is compatible with scikit-learn machine learning algorithms. Most crucially, it allows the user to condition a model based on the known values of amore » subset of parameters. This gives the user the ability to produce a tool that can predict unknown parameters based on a model that is conditioned on known values of other parameters. EmpiriciSN is an exemplary application of this functionality, which can be used to fit an XDGMM model to observed supernova/host data sets and predict likely supernova parameters using a model conditioned on observed host properties. It is primarily intended to simulate realistic supernovae for LSST data simulations based on empirical galaxy properties.« less

Parameter optimisation for a better representation of drought by LSMs: inverse modelling vs. sequential data assimilation

NASA Astrophysics Data System (ADS)

Dewaele, Hélène; Munier, Simon; Albergel, Clément; Planque, Carole; Laanaia, Nabil; Carrer, Dominique; Calvet, Jean-Christophe

2017-09-01

Soil maximum available water content (MaxAWC) is a key parameter in land surface models (LSMs). However, being difficult to measure, this parameter is usually uncertain. This study assesses the feasibility of using a 15-year (1999-2013) time series of satellite-derived low-resolution observations of leaf area index (LAI) to estimate MaxAWC for rainfed croplands over France. LAI interannual variability is simulated using the CO2-responsive version of the Interactions between Soil, Biosphere and Atmosphere (ISBA) LSM for various values of MaxAWC. Optimal value is then selected by using (1) a simple inverse modelling technique, comparing simulated and observed LAI and (2) a more complex method consisting in integrating observed LAI in ISBA through a land data assimilation system (LDAS) and minimising LAI analysis increments. The evaluation of the MaxAWC estimates from both methods is done using simulated annual maximum above-ground biomass (Bag) and straw cereal grain yield (GY) values from the Agreste French agricultural statistics portal, for 45 administrative units presenting a high proportion of straw cereals. Significant correlations (p value < 0.01) between Bag and GY are found for up to 36 and 53 % of the administrative units for the inverse modelling and LDAS tuning methods, respectively. It is found that the LDAS tuning experiment gives more realistic values of MaxAWC and maximum Bag than the inverse modelling experiment. Using undisaggregated LAI observations leads to an underestimation of MaxAWC and maximum Bag in both experiments. Median annual maximum values of disaggregated LAI observations are found to correlate very well with MaxAWC.

CALCULATING PHYSICAL PROPERTIES OF ORGANIC COMPOUNDS FOR ENVIRONMENTAL MODELING FROM MOLECULAR STRUCTURE

EPA Science Inventory

Mathematical models for predicting the transport and fate of pollutants in the environment require reactivity parameter values-- that is value of the physical and chemical constants that govern reactivity. Although empirical structure activity relationships have been developed t...

Comparison of the WSA-ENLIL model with three CME cone types

NASA Astrophysics Data System (ADS)

Jang, Soojeong; Moon, Y.; Na, H.

2013-07-01

We have made a comparison of the CME-associated shock propagation based on the WSA-ENLIL model with three cone types using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters as well as their associated interplanetary (IP) shocks. For this study we consider three different cone types (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine 3-D CME parameters (radial velocity, angular width and source location), which are the input values of the WSA-ENLIL model. The mean absolute error (MAE) of the arrival times for the asymmetric cone model is 10.6 hours, which is about 1 hour smaller than those of the other models. Their ensemble average of MAE is 9.5 hours. However, this value is still larger than that (8.7 hours) of the empirical model of Kim et al. (2007). We will compare their IP shock velocities and densities with those from ACE in-situ measurements and discuss them in terms of the prediction of geomagnetic storms.Abstract (2,250 Maximum Characters): We have made a comparison of the CME-associated shock propagation based on the WSA-ENLIL model with three cone types using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters as well as their associated interplanetary (IP) shocks. For this study we consider three different cone types (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine 3-D CME parameters (radial velocity, angular width and source location), which are the input values of the WSA-ENLIL model. The mean absolute error (MAE) of the arrival times for the asymmetric cone model is 10.6 hours, which is about 1 hour smaller than those of the other models. Their ensemble average of MAE is 9.5 hours. However, this value is still larger than that (8.7 hours) of the empirical model of Kim et al. (2007). We will compare their IP shock velocities and densities with those from ACE in-situ measurements and discuss them in terms of the prediction of geomagnetic storms.

Uncertainty Quantification of Equilibrium Climate Sensitivity in CCSM4

NASA Astrophysics Data System (ADS)

Covey, C. C.; Lucas, D. D.; Tannahill, J.; Klein, R.

2013-12-01

Uncertainty in the global mean equilibrium surface warming due to doubled atmospheric CO2, as computed by a "slab ocean" configuration of the Community Climate System Model version 4 (CCSM4), is quantified using 1,039 perturbed-input-parameter simulations. The slab ocean configuration reduces the model's e-folding time when approaching an equilibrium state to ~5 years. This time is much less than for the full ocean configuration, consistent with the shallow depth of the upper well-mixed layer of the ocean represented by the "slab." Adoption of the slab ocean configuration requires the assumption of preset values for the convergence of ocean heat transport beneath the upper well-mixed layer. A standard procedure for choosing these values maximizes agreement with the full ocean version's simulation of the present-day climate when input parameters assume their default values. For each new set of input parameter values, we computed the change in ocean heat transport implied by a "Phase 1" model run in which sea surface temperatures and sea ice concentrations were set equal to present-day values. The resulting total ocean heat transport (= standard value + change implied by Phase 1 run) was then input into "Phase 2" slab ocean runs with varying values of atmospheric CO2. Our uncertainty estimate is based on Latin Hypercube sampling over expert-provided uncertainty ranges of N = 36 adjustable parameters in the atmosphere (CAM4) and sea ice (CICE4) components of CCSM4. Two-dimensional projections of our sampling distribution for the N(N-1)/2 possible pairs of input parameters indicate full coverage of the N-dimensional parameter space, including edges. We used a machine learning-based support vector regression (SVR) statistical model to estimate the probability density function (PDF) of equilibrium warming. This fitting procedure produces a PDF that is qualitatively consistent with the raw histogram of our CCSM4 results. Most of the values from the SVR statistical model are within ~0.1 K of the raw results, well below the inter-decile range inferred below. Independent validation of the fit indicates residual errors that are distributed about zero with a standard deviation of 0.17 K. Analysis of variance shows that the equilibrium warming in CCSM4 is mainly linear in parameter changes. Thus, in accord with the Central Limit Theorem of statistics, the PDF of the warming is approximately Gaussian, i.e. symmetric about its mean value (3.0 K). Since SVR allows for highly nonlinear fits, the symmetry is not an artifact of the fitting procedure. The 10-90 percentile range of the PDF is 2.6-3.4 K, consistent with earlier estimates from CCSM4 but narrower than estimates from other models, which sometimes produce a high-temperature asymmetric tail in the PDF. This work was performed under auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, and was funded by LLNL's Uncertainty Quantification Strategic Initiative (Laboratory Directed Research and Development Project 10-SI-013).

A Hierarchical Bayesian Model for Calibrating Estimates of Species Divergence Times

PubMed Central

Heath, Tracy A.

2012-01-01

In Bayesian divergence time estimation methods, incorporating calibrating information from the fossil record is commonly done by assigning prior densities to ancestral nodes in the tree. Calibration prior densities are typically parametric distributions offset by minimum age estimates provided by the fossil record. Specification of the parameters of calibration densities requires the user to quantify his or her prior knowledge of the age of the ancestral node relative to the age of its calibrating fossil. The values of these parameters can, potentially, result in biased estimates of node ages if they lead to overly informative prior distributions. Accordingly, determining parameter values that lead to adequate prior densities is not straightforward. In this study, I present a hierarchical Bayesian model for calibrating divergence time analyses with multiple fossil age constraints. This approach applies a Dirichlet process prior as a hyperprior on the parameters of calibration prior densities. Specifically, this model assumes that the rate parameters of exponential prior distributions on calibrated nodes are distributed according to a Dirichlet process, whereby the rate parameters are clustered into distinct parameter categories. Both simulated and biological data are analyzed to evaluate the performance of the Dirichlet process hyperprior. Compared with fixed exponential prior densities, the hierarchical Bayesian approach results in more accurate and precise estimates of internal node ages. When this hyperprior is applied using Markov chain Monte Carlo methods, the ages of calibrated nodes are sampled from mixtures of exponential distributions and uncertainty in the values of calibration density parameters is taken into account. PMID:22334343

Linear regression metamodeling as a tool to summarize and present simulation model results.

PubMed

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Hippocampal effective synchronization values are not pre-seizure indicator without considering the state of the onset channels

PubMed Central

Shayegh, Farzaneh; Sadri, Saeed; Amirfattahi, Rassoul; Ansari-Asl, Karim; Bellanger, Jean-Jacques; Senhadji, Lotfi

2014-01-01

In this paper, a model-based approach is presented to quantify the effective synchrony between hippocampal areas from depth-EEG signals. This approach is based on the parameter identification procedure of a realistic Multi-Source/Multi-Channel (MSMC) hippocampal model that simulates the function of different areas of hippocampus. In the model it is supposed that the observed signals recorded using intracranial electrodes are generated by some hidden neuronal sources, according to some parameters. An algorithm is proposed to extract the intrinsic (solely relative to one hippocampal area) and extrinsic (coupling coefficients between two areas) model parameters, simultaneously, by a Maximum Likelihood (ML) method. Coupling coefficients are considered as the measure of effective synchronization. This work can be considered as an application of Dynamic Causal Modeling (DCM) that enables us to understand effective synchronization changes during transition from inter-ictal to pre -ictal state. The algorithm is first validated by using some synthetic datasets. Then by extracting the coupling coefficients of real depth-EEG signals by the proposed approach, it is observed that the coupling values show no significant difference between ictal, pre-ictal and inter-ictal states, i.e., either the increase or decrease of coupling coefficients has been observed in all states. However, taking the value of intrinsic parameters into account, pre-seizure state can be distinguished from inter-ictal state. It is claimed that seizures start to appear when there are seizure-related physiological parameters on the onset channel, and its coupling coefficient toward other channels increases simultaneously. As a result of considering both intrinsic and extrinsic parameters as the feature vector, inter-ictal, pre-ictal and ictal activities are discriminated from each other with an accuracy of 91.33% accuracy. PMID:25061815

A stochastic multicloud convective parameterization in the NCEP Climate Forecast System (CFSv2) : implementation and calibration.

NASA Astrophysics Data System (ADS)

Goswami, B. B.; Khouider, B.; Krishna, R. P. M.; Mukhopadhyay, P.; Majda, A.

2017-12-01

A stochastic multicloud (SMCM) cumulus parameterization is implemented in the National Centres for Environmental Predictions (NCEP) Climate Forecast System version 2 (CFSv2) model, named as the CFSsmcm model. We present here results from a systematic attempt to understand the CFSsmcm model's sensitivity to the SMCM parameters. To asses the model-sentivity to the different SMCM parameters, we have analized a set of 14 5-year long climate simulations produced by the CFSsmcm model. The model is found to be resilient to minor changes in the parameter values. The middle tropospheric dryness (MTD) and the stratiform cloud decay timescale are found to be most crucial parameters in the SMCM formulation in the CFSsmcm model.

Kinetic model for microbial growth and desulphurisation with Enterobacter sp.

PubMed

Liu, Long; Guo, Zhiguo; Lu, Jianjiang; Xu, Xiaolin

2015-02-01

Biodesulphurisation was investigated by using Enterobacter sp. D4, which can selectively desulphurise and convert dibenzothiophene into 2-hydroxybiphenyl (2-HBP). The experimental values of growth, substrate consumption and product generation were obtained at 95 % confidence level of the fitted values using three models: Hinshelwood equation, Luedeking-Piret and Luedeking-Piret-like equations. The average error values between experimental values and fitted values were less than 10 %. These kinetic models describe all the experimental data with good statistical parameters. The production of 2-HBP in Enterobacter sp. was by "coupled growth".

Modelling duodenum radiotherapy toxicity using cohort dose-volume-histogram data.

PubMed

Holyoake, Daniel L P; Aznar, Marianne; Mukherjee, Somnath; Partridge, Mike; Hawkins, Maria A

2017-06-01

Gastro-intestinal toxicity is dose-limiting in abdominal radiotherapy and correlated with duodenum dose-volume parameters. We aimed to derive updated NTCP model parameters using published data and prospective radiotherapy quality-assured cohort data. A systematic search identified publications providing duodenum dose-volume histogram (DVH) statistics for clinical studies of conventionally-fractionated radiotherapy. Values for the Lyman-Kutcher-Burman (LKB) NTCP model were derived through sum-squared-error minimisation and using leave-one-out cross-validation. Data were corrected for fraction size and weighted according to patient numbers, and the model refined using individual patient DVH data for two further cohorts from prospective clinical trials. Six studies with published DVH data were utilised, and with individual patient data included outcomes for 531 patients in total (median follow-up 16months). Observed gastro-intestinal toxicity rates ranged from 0% to 14% (median 8%). LKB parameter values for unconstrained fit to published data were: n=0.070, m=0.46, TD 50(1) [Gy]=183.8, while the values for the model incorporating the individual patient data were n=0.193, m=0.51, TD 50(1) [Gy]=299.1. LKB parameters derived using published data are shown to be consistent to those previously obtained using individual patient data, supporting a small volume-effect and dependence on exposure to high threshold dose. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Characterization of Titan surface scenarios combining Cassini SAR images and radiometric data

NASA Astrophysics Data System (ADS)

Ventura, B.; Notarnicola, C.; Casarano, D.; Janssen, M.; Posa, F.; Cassini RADAR Science Team

2009-04-01

A great amount of data and images was provided by the radar on Cassini probe, thus opening and suggesting new scenarios about Titan's formation and evolution. An important result was the detection, among the peculiar and heterogeneous Titan's surface features, of lakes most likely constituted by liquid hydrocarbons, thus supporting the hypothesis of a methane cycle similar to water cycle on Earth.These areas, which resemble terrestrial lakes, seem to be sprinkled all over the high latitudes surrounding Titan's pole. The abundant methane in Titan's atmosphere combined with the low temperature, 94 K, lead scientists to interpret them as lakes of liquid methane or ethane. In this work, scattering models and a Bayesian inversion algorithm are applied in order to characterize lake and land surfaces. The possibility of combining the SAR data with radiometric ones on both lakes and neighboring land areas is also presented. Radar backscattering from lakes is described in terms of a double layer model, consisting of Bragg or facets scattering for the upper liquid layer and the Integral Equation Model (IEM) model for the lower solid surface. Furthermore, by means of a gravity-capillary wave model (Donelan-Pierson), the wave spectra of liquid hydrocarbons surfaces are introduced as a function of wind speed and direction. Theoretical radar backscattering coefficient values are compared with the experimental ones collected by the radar in order to estimate physical and morphological surface parameters, and to evaluate their compatibility with the expected constituents for Titan surfaces. This electromagnetic analysis is the starting point for a statistical inversion algorithm which allows determining limits on the parameters values, especially on the optical thickness and wind speed of the lakes. The physical surface parameters inferred by using the inversion algorithm are used as input for a forward radiative transfer model calculation to obtain simulated brightness temperatures. The radiometric model has been introduced to further verify the values ranges for the different parameters. In fact the same parameters derived from the radar data analysis have been used as input for the radiometric model. The comparison between the observed and computed brightness temperatures has been performed in order to address the consistency of the observations from the two instruments and to determine the coarse characteristics of the surface parameters. For both radar and radiometric data the soil medium is horizontally stratified into 2 layers. Each layer can be characterized by different absorption coefficients depending on the optical thickness, dielectric constant and physical temperature. In this algorithm, the starting point is the map of optical thickness derived from the SAR images. The simulated brightness temperature is calculated by applying the forward radiative transfer model to the optical thickness map with the same hypotheses assumed to derive it. The simulation is also carried out on the neighboring land areas by considering a double layer model including a contribution of volume scattering. Each layer is described in terms of dielectric constant values, albedo and roughness parameters with the hypothesis of water ice ammonia on layers of solid hydrocarbons and organic compounds like tholins. The analysis is applied to the areas detected on flybys 25 and 30. One important result arises from the analysis of the inverted optical thickness on deep lakes. In this case, found values of optical thickness can be considered limit values because, beyond these values, a complete attenuation can be considered. This limit value is important as it is stable even if the other parameters vary. Starting from this point, posing the condition of a complete attenuation of the second layer, i.e. fixing the value of the optical thickness, the algorithm can be used to estimate the wind speed. The retrieved values vary between 0.2 to 0.5 m/s. The first results also show a good agreement between the simulated data and the measured brightness temperature for both the liquid surface and the surrounding areas. In the last case, a good agreement is obtained only if the contribution from volume scattering is included in the model

Bending analysis of agglomerated carbon nanotube-reinforced beam resting on two parameters modified Vlasov model foundation

NASA Astrophysics Data System (ADS)

Ghorbanpour Arani, A.; Zamani, M. H.

2018-06-01

The present work deals with bending behavior of nanocomposite beam resting on two parameters modified Vlasov model foundation (MVMF), with consideration of agglomeration and distribution of carbon nanotubes (CNTs) in beam matrix. Equivalent fiber based on Eshelby-Mori-Tanaka approach is employed to determine influence of CNTs aggregation on elastic properties of CNT-reinforced beam. The governing equations are deduced using the principle of minimum potential energy under assumption of the Euler-Bernoulli beam theory. The MVMF required the estimation of γ parameter; to this purpose, unique iterative technique based on variational principles is utilized to compute value of the γ and subsequently fourth-order differential equation is solved analytically. Eventually, the transverse displacements and bending stresses are obtained and compared for different agglomeration parameters, various boundary conditions simultaneously and variant elastic foundation without requirement to instate values for foundation parameters.

A modified Leslie-Gower predator-prey interaction model and parameter identifiability

NASA Astrophysics Data System (ADS)

Tripathi, Jai Prakash; Meghwani, Suraj S.; Thakur, Manoj; Abbas, Syed

2018-01-01

In this work, bifurcation and a systematic approach for estimation of identifiable parameters of a modified Leslie-Gower predator-prey system with Crowley-Martin functional response and prey refuge is discussed. Global asymptotic stability is discussed by applying fluctuation lemma. The system undergoes into Hopf bifurcation with respect to parameters intrinsic growth rate of predators (s) and prey reserve (m). The stability of Hopf bifurcation is also discussed by calculating Lyapunov number. The sensitivity analysis of the considered model system with respect to all variables is performed which also supports our theoretical study. To estimate the unknown parameter from the data, an optimization procedure (pseudo-random search algorithm) is adopted. System responses and phase plots for estimated parameters are also compared with true noise free data. It is found that the system dynamics with true set of parametric values is similar to the estimated parametric values. Numerical simulations are presented to substantiate the analytical findings.

A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

PubMed

Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

2015-09-01

Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Simulation-based sensitivity analysis for non-ignorably missing data.

PubMed

Yin, Peng; Shi, Jian Q

2017-01-01

Sensitivity analysis is popular in dealing with missing data problems particularly for non-ignorable missingness, where full-likelihood method cannot be adopted. It analyses how sensitively the conclusions (output) may depend on assumptions or parameters (input) about missing data, i.e. missing data mechanism. We call models with the problem of uncertainty sensitivity models. To make conventional sensitivity analysis more useful in practice we need to define some simple and interpretable statistical quantities to assess the sensitivity models and make evidence based analysis. We propose a novel approach in this paper on attempting to investigate the possibility of each missing data mechanism model assumption, by comparing the simulated datasets from various MNAR models with the observed data non-parametrically, using the K-nearest-neighbour distances. Some asymptotic theory has also been provided. A key step of this method is to plug in a plausibility evaluation system towards each sensitivity parameter, to select plausible values and reject unlikely values, instead of considering all proposed values of sensitivity parameters as in the conventional sensitivity analysis method. The method is generic and has been applied successfully to several specific models in this paper including meta-analysis model with publication bias, analysis of incomplete longitudinal data and mean estimation with non-ignorable missing data.

Validation of DYSTOOL for unsteady aerodynamic modeling of 2D airfoils

NASA Astrophysics Data System (ADS)

González, A.; Gomez-Iradi, S.; Munduate, X.

2014-06-01

From the point of view of wind turbine modeling, an important group of tools is based on blade element momentum (BEM) theory using 2D aerodynamic calculations on the blade elements. Due to the importance of this sectional computation of the blades, the National Renewable Wind Energy Center of Spain (CENER) developed DYSTOOL, an aerodynamic code for 2D airfoil modeling based on the Beddoes-Leishman model. The main focus here is related to the model parameters, whose values depend on the airfoil or the operating conditions. In this work, the values of the parameters are adjusted using available experimental or CFD data. The present document is mainly related to the validation of the results of DYSTOOL for 2D airfoils. The results of the computations have been compared with unsteady experimental data of the S809 and NACA0015 profiles. Some of the cases have also been modeled using the CFD code WMB (Wind Multi Block), within the framework of a collaboration with ACCIONA Windpower. The validation has been performed using pitch oscillations with different reduced frequencies, Reynolds numbers, amplitudes and mean angles of attack. The results have shown a good agreement using the methodology of adjustment for the value of the parameters. DYSTOOL have demonstrated to be a promising tool for 2D airfoil unsteady aerodynamic modeling.

Estimating effective soil properties of heterogeneous areas for modeling infiltration and redistribution

USDA-ARS?s Scientific Manuscript database

Field scale water infiltration and soil-water and solute transport models require spatially-averaged “effective” soil hydraulic parameters to represent the average flux and storage. The values of these effective parameters vary for different conditions, processes, and component soils in a field. For...

Anisotropic strange star with Tolman V potential

NASA Astrophysics Data System (ADS)

Shee, Dibyendu; Deb, Debabrata; Ghosh, Shounak; Ray, Saibal; Guha, B. K.

In this paper, we present a strange stellar model using Tolman V-type metric potential employing simplest form of the MIT bag equation of state (EOS) for the quark matter. We consider that the stellar system is spherically symmetric, compact and made of an anisotropic fluid. Choosing different values of n we obtain exact solutions of the Einstein field equations and finally conclude that for a specific value of the parameter n = 1/2, we find physically acceptable features of the stellar object. Further, we conduct different physical tests, viz., the energy condition, generalized Tolman-Oppeheimer-Volkoff (TOV) equation, Herrera’s cracking concept, etc., to confirm the physical validity of the presented model. Matching conditions provide expressions for different constants whereas maximization of the anisotropy parameter provides bag constant. By using the observed data of several compact stars, we derive exact values of some of the physical parameters and exhibit their features in tabular form. It is to note that our predicted value of the bag constant satisfies the report of CERN-SPS and RHIC.

A model for filtered backprojection reconstruction artifacts due to time-varying attenuation values in perfusion C-arm CT.

PubMed

Fieselmann, Andreas; Dennerlein, Frank; Deuerling-Zheng, Yu; Boese, Jan; Fahrig, Rebecca; Hornegger, Joachim

2011-06-21

Filtered backprojection is the basis for many CT reconstruction tasks. It assumes constant attenuation values of the object during the acquisition of the projection data. Reconstruction artifacts can arise if this assumption is violated. For example, contrast flow in perfusion imaging with C-arm CT systems, which have acquisition times of several seconds per C-arm rotation, can cause this violation. In this paper, we derived and validated a novel spatio-temporal model to describe these kinds of artifacts. The model separates the temporal dynamics due to contrast flow from the scan and reconstruction parameters. We introduced derivative-weighted point spread functions to describe the spatial spread of the artifacts. The model allows prediction of reconstruction artifacts for given temporal dynamics of the attenuation values. Furthermore, it can be used to systematically investigate the influence of different reconstruction parameters on the artifacts. We have shown that with optimized redundancy weighting function parameters the spatial spread of the artifacts around a typical arterial vessel can be reduced by about 70%. Finally, an inversion of our model could be used as the basis for novel dynamic reconstruction algorithms that further minimize these artifacts.

Censored Hurdle Negative Binomial Regression (Case Study: Neonatorum Tetanus Case in Indonesia)

NASA Astrophysics Data System (ADS)

Yuli Rusdiana, Riza; Zain, Ismaini; Wulan Purnami, Santi

2017-06-01

Hurdle negative binomial model regression is a method that can be used for discreate dependent variable, excess zero and under- and overdispersion. It uses two parts approach. The first part estimates zero elements from dependent variable is zero hurdle model and the second part estimates not zero elements (non-negative integer) from dependent variable is called truncated negative binomial models. The discrete dependent variable in such cases is censored for some values. The type of censor that will be studied in this research is right censored. This study aims to obtain the parameter estimator hurdle negative binomial regression for right censored dependent variable. In the assessment of parameter estimation methods used Maximum Likelihood Estimator (MLE). Hurdle negative binomial model regression for right censored dependent variable is applied on the number of neonatorum tetanus cases in Indonesia. The type data is count data which contains zero values in some observations and other variety value. This study also aims to obtain the parameter estimator and test statistic censored hurdle negative binomial model. Based on the regression results, the factors that influence neonatorum tetanus case in Indonesia is the percentage of baby health care coverage and neonatal visits.

Delineation and Analysis of Uncertainty of Contributing Areas to Wells at the Southbury Training School, Southbury, Connecticut

USGS Publications Warehouse

Starn, J. Jeffrey; Stone, Janet Radway; Mullaney, John R.

2000-01-01

Contributing areas to public-supply wells at the Southbury Training School in Southbury, Connecticut, were mapped by simulating ground-water flow in stratified glacial deposits in the lower Transylvania Brook watershed. The simulation used nonlinear regression methods and informational statistics to estimate parameters of a ground-water flow model using drawdown data from an aquifer test. The goodness of fit of the model and the uncertainty associated with model predictions were statistically measured. A watershed-scale model, depicting large-scale ground-water flow in the Transylvania Brook watershed, was used to estimate the distribution of groundwater recharge. Estimates of recharge from 10 small basins in the watershed differed on the basis of the drainage characteristics of each basin. Small basins having well-defined stream channels contributed less ground-water recharge than basins having no defined channels because potential ground-water recharge was carried away in the stream channel. Estimates of ground-water recharge were used in an aquifer-scale parameter-estimation model. Seven variations of the ground-water-flow system were posed, each representing the ground-water-flow system in slightly different but realistic ways. The model that most closely reproduced measured hydraulic heads and flows with realistic parameter values was selected as the most representative of the ground-water-flow system and was used to delineate boundaries of the contributing areas. The model fit revealed no systematic model error, which indicates that the model is likely to represent the major characteristics of the actual system. Parameter values estimated during the simulation are as follows: horizontal hydraulic conductivity of coarse-grained deposits, 154 feet per day; vertical hydraulic conductivity of coarse-grained deposits, 0.83 feet per day; horizontal hydraulic conductivity of fine-grained deposits, 29 feet per day; specific yield, 0.007; specific storage, 1.6E-05. Average annual recharge was estimated using the watershed-scale model with no parameter estimation and was determined to be 24 inches per year in the valley areas and 9 inches per year in the upland areas. The parameter estimates produced in the model are similar to expected values, with two exceptions. The estimated specific yield of the stratified glacial deposits is lower than expected, which could be caused by the layered nature of the deposits. The recharge estimate produced by the model was also lower?about 32 percent of the average annual rate. This could be caused by the timing of the aquifer test with respect to the annual cycle of ground-water recharge, and by some of the expected recharge going to parts of the flow system that were not simulated. The data used in the calibration were collected during an aquifer test from October 30 to November 4, 1996. The model fit was very good, as indicated by the correlation coefficient (0.999) between the weighted simulated values and weighted observed values. The model also reproduced the general rise in ground-water levels caused by ground-water recharge and the cyclic fluctuations caused by pumping prior to the aquifer test. Contributing areas were delineated using a particle-tracking procedure. Hypothetical particles of water were introduced at each model cell in the top layer and were tracked to determine whether or not they reached the pumped well. A deterministic contributing area was calculated using the calibrated model, and a probabilistic contributing area was calculated using a Monte Carlo approach along with the calibrated model. The Monte Carlo simulation was done, using the parameter variance/covariance matrix generated by the regression model, to estimate probabilities associated with the contributing area to the wells. The probabilities arise from uncertainty in the estimated parameter values, which in turn arise from the adequacy of the data available to comprehensively describe the groundwater-flow sy

Evaluation of the geomorphometric results and residual values of a robust plane fitting method applied to different DTMs of various scales and accuracy

NASA Astrophysics Data System (ADS)

Koma, Zsófia; Székely, Balázs; Dorninger, Peter; Kovács, Gábor

2013-04-01

Due to the need for quantitative analysis of various geomorphological landforms, the importance of fast and effective automatic processing of the different kind of digital terrain models (DTMs) is increasing. The robust plane fitting (segmentation) method, developed at the Institute of Photogrammetry and Remote Sensing at Vienna University of Technology, allows the processing of large 3D point clouds (containing millions of points), performs automatic detection of the planar elements of the surface via parameter estimation, and provides a considerable data reduction for the modeled area. Its geoscientific application allows the modeling of different landforms with the fitted planes as planar facets. In our study we aim to analyze the accuracy of the resulting set of fitted planes in terms of accuracy, model reliability and dependence on the input parameters. To this end we used DTMs of different scales and accuracy: (1) artificially generated 3D point cloud model with different magnitudes of error; (2) LiDAR data with 0.1 m error; (3) SRTM (Shuttle Radar Topography Mission) DTM database with 5 m accuracy; (4) DTM data from HRSC (High Resolution Stereo Camera) of the planet Mars with 10 m error. The analysis of the simulated 3D point cloud with normally distributed errors comprised different kinds of statistical tests (for example Chi-square and Kolmogorov-Smirnov tests) applied on the residual values and evaluation of dependence of the residual values on the input parameters. These tests have been repeated on the real data supplemented with the categorization of the segmentation result depending on the input parameters, model reliability and the geomorphological meaning of the fitted planes. The simulation results show that for the artificially generated data with normally distributed errors the null hypothesis can be accepted based on the residual value distribution being also normal, but in case of the test on the real data the residual value distribution is often mixed or unknown. The residual values are found to be dependent on two input parameters (standard deviation and maximum point-plane distance both defining distance thresholds for assigning points to a segment) mainly and the curvature of the surface affected mostly the distributions. The results of the analysis helped to decide which parameter set is the best for further modelling and provides the highest accuracy. With these results in mind the success of quasi-automatic modelling of the planar (for example plateau-like) features became more successful and often provided more accuracy. These studies were carried out partly in the framework of TMIS.ascrea project (Nr. 2001978) financed by the Austrian Research Promotion Agency (FFG); the contribution of ZsK was partly funded by Campus Hungary Internship TÁMOP-424B1.

Particle swarm optimization algorithm based parameters estimation and control of epileptiform spikes in a neural mass model

NASA Astrophysics Data System (ADS)

Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan

2016-07-01

This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.

The Value of Information in Decision-Analytic Modeling for Malaria Vector Control in East Africa.

PubMed

Kim, Dohyeong; Brown, Zachary; Anderson, Richard; Mutero, Clifford; Miranda, Marie Lynn; Wiener, Jonathan; Kramer, Randall

2017-02-01

Decision analysis tools and mathematical modeling are increasingly emphasized in malaria control programs worldwide to improve resource allocation and address ongoing challenges with sustainability. However, such tools require substantial scientific evidence, which is costly to acquire. The value of information (VOI) has been proposed as a metric for gauging the value of reduced model uncertainty. We apply this concept to an evidenced-based Malaria Decision Analysis Support Tool (MDAST) designed for application in East Africa. In developing MDAST, substantial gaps in the scientific evidence base were identified regarding insecticide resistance in malaria vector control and the effectiveness of alternative mosquito control approaches, including larviciding. We identify four entomological parameters in the model (two for insecticide resistance and two for larviciding) that involve high levels of uncertainty and to which outputs in MDAST are sensitive. We estimate and compare a VOI for combinations of these parameters in evaluating three policy alternatives relative to a status quo policy. We find having perfect information on the uncertain parameters could improve program net benefits by up to 5-21%, with the highest VOI associated with jointly eliminating uncertainty about reproductive speed of malaria-transmitting mosquitoes and initial efficacy of larviciding at reducing the emergence of new adult mosquitoes. Future research on parameter uncertainty in decision analysis of malaria control policy should investigate the VOI with respect to other aspects of malaria transmission (such as antimalarial resistance), the costs of reducing uncertainty in these parameters, and the extent to which imperfect information about these parameters can improve payoffs. © 2016 Society for Risk Analysis.

Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation.

PubMed

Albert, Carlo; Ulzega, Simone; Stoop, Ruedi

2016-04-01

Parameter inference is a fundamental problem in data-driven modeling. Given observed data that is believed to be a realization of some parameterized model, the aim is to find parameter values that are able to explain the observed data. In many situations, the dominant sources of uncertainty must be included into the model for making reliable predictions. This naturally leads to stochastic models. Stochastic models render parameter inference much harder, as the aim then is to find a distribution of likely parameter values. In Bayesian statistics, which is a consistent framework for data-driven learning, this so-called posterior distribution can be used to make probabilistic predictions. We propose a novel, exact, and very efficient approach for generating posterior parameter distributions for stochastic differential equation models calibrated to measured time series. The algorithm is inspired by reinterpreting the posterior distribution as a statistical mechanics partition function of an object akin to a polymer, where the measurements are mapped on heavier beads compared to those of the simulated data. To arrive at distribution samples, we employ a Hamiltonian Monte Carlo approach combined with a multiple time-scale integration. A separation of time scales naturally arises if either the number of measurement points or the number of simulation points becomes large. Furthermore, at least for one-dimensional problems, we can decouple the harmonic modes between measurement points and solve the fastest part of their dynamics analytically. Our approach is applicable to a wide range of inference problems and is highly parallelizable.

Mapping the parameter space of a T2-dependent model of water diffusion MR in brain tissue.

PubMed

Hansen, Brian; Vestergaard-Poulsen, Peter

2006-10-01

We present a new model for describing the diffusion-weighted (DW) proton nuclear magnetic resonance signal obtained from normal grey matter. Our model is analytical and, in some respects, is an extension of earlier model schemes. We model tissue as composed of three separate compartments with individual properties of diffusion and transverse relaxation. Our study assumes slow exchange between compartments. We attempt to take cell morphology into account, along with its effect on water diffusion in tissues. Using this model, we simulate diffusion-sensitive MR signals and compare model output to experimental data from human grey matter. In doing this comparison, we perform a global search for good fits in the parameter space of the model. The characteristic nonmonoexponential behavior of the signal as a function of experimental b value is reproduced quite well, along with established values for tissue-specific parameters such as volume fraction, tortuosity and apparent diffusion coefficient. We believe that the presented approach to modeling diffusion in grey matter adds new aspects to the treatment of a longstanding problem.

DETERMINATION OF KOW VALUES FOR A SERIES OF ARYL GLUCURONIDES

EPA Science Inventory

An important perameter in toxicokinetic modeling is the octanol/water partition coefficient (Kow). This parameter has often been used to predict the accumulation of contaminants from water to fish (Klamer and Beekman 1995); however, few Kow values are available for modeling the b...

Space-dependent perfusion coefficient estimation in a 2D bioheat transfer problem

NASA Astrophysics Data System (ADS)

Bazán, Fermín S. V.; Bedin, Luciano; Borges, Leonardo S.

2017-05-01

In this work, a method for estimating the space-dependent perfusion coefficient parameter in a 2D bioheat transfer model is presented. In the method, the bioheat transfer model is transformed into a time-dependent semidiscrete system of ordinary differential equations involving perfusion coefficient values as parameters, and the estimation problem is solved through a nonlinear least squares technique. In particular, the bioheat problem is solved by the method of lines based on a highly accurate pseudospectral approach, and perfusion coefficient values are estimated by the regularized Gauss-Newton method coupled with a proper regularization parameter. The performance of the method on several test problems is illustrated numerically.

Use of system identification techniques for improving airframe finite element models using test data

NASA Technical Reports Server (NTRS)

Hanagud, Sathya V.; Zhou, Weiyu; Craig, James I.; Weston, Neil J.

1991-01-01

A method for using system identification techniques to improve airframe finite element models was developed and demonstrated. The method uses linear sensitivity matrices to relate changes in selected physical parameters to changes in total system matrices. The values for these physical parameters were determined using constrained optimization with singular value decomposition. The method was confirmed using both simple and complex finite element models for which pseudo-experimental data was synthesized directly from the finite element model. The method was then applied to a real airframe model which incorporated all the complexities and details of a large finite element model and for which extensive test data was available. The method was shown to work, and the differences between the identified model and the measured results were considered satisfactory.

Strategies for Efficient Computation of the Expected Value of Partial Perfect Information

PubMed Central

Madan, Jason; Ades, Anthony E.; Price, Malcolm; Maitland, Kathryn; Jemutai, Julie; Revill, Paul; Welton, Nicky J.

2014-01-01

Expected value of information methods evaluate the potential health benefits that can be obtained from conducting new research to reduce uncertainty in the parameters of a cost-effectiveness analysis model, hence reducing decision uncertainty. Expected value of partial perfect information (EVPPI) provides an upper limit to the health gains that can be obtained from conducting a new study on a subset of parameters in the cost-effectiveness analysis and can therefore be used as a sensitivity analysis to identify parameters that most contribute to decision uncertainty and to help guide decisions around which types of study are of most value to prioritize for funding. A common general approach is to use nested Monte Carlo simulation to obtain an estimate of EVPPI. This approach is computationally intensive, can lead to significant sampling bias if an inadequate number of inner samples are obtained, and incorrect results can be obtained if correlations between parameters are not dealt with appropriately. In this article, we set out a range of methods for estimating EVPPI that avoid the need for nested simulation: reparameterization of the net benefit function, Taylor series approximations, and restricted cubic spline estimation of conditional expectations. For each method, we set out the generalized functional form that net benefit must take for the method to be valid. By specifying this functional form, our methods are able to focus on components of the model in which approximation is required, avoiding the complexities involved in developing statistical approximations for the model as a whole. Our methods also allow for any correlations that might exist between model parameters. We illustrate the methods using an example of fluid resuscitation in African children with severe malaria. PMID:24449434

Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm.

PubMed

Tan, Xia; Ji, Zhong; Zhang, Yadan

2018-04-25

Non-invasive continuous blood pressure monitoring can provide an important reference and guidance for doctors wishing to analyze the physiological and pathological status of patients and to prevent and diagnose cardiovascular diseases in the clinical setting. Therefore, it is very important to explore a more accurate method of non-invasive continuous blood pressure measurement. To address the shortcomings of existing blood pressure measurement models based on pulse wave transit time or pulse wave parameters, a new method of non-invasive continuous blood pressure measurement - the GA-MIV-BP neural network model - is presented. The mean impact value (MIV) method is used to select the factors that greatly influence blood pressure from the extracted pulse wave transit time and pulse wave parameters. These factors are used as inputs, and the actual blood pressure values as outputs, to train the BP neural network model. The individual parameters are then optimized using a genetic algorithm (GA) to establish the GA-MIV-BP neural network model. Bland-Altman consistency analysis indicated that the measured and predicted blood pressure values were consistent and interchangeable. Therefore, this algorithm is of great significance to promote the clinical application of a non-invasive continuous blood pressure monitoring method.

Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model

PubMed Central

Sui, Yi; Wang, He; Liu, Guanzhong; Damen, Frederick W.; Wanamaker, Christian; Li, Yuhua

2015-01-01

Purpose To demonstrate that a new set of parameters (D, β, and μ) from a fractional order calculus (FROC) diffusion model can be used to improve the accuracy of MR imaging for differentiating among low- and high-grade pediatric brain tumors. Materials and Methods The institutional review board of the performing hospital approved this study, and written informed consent was obtained from the legal guardians of pediatric patients. Multi-b-value diffusion-weighted magnetic resonance (MR) imaging was performed in 67 pediatric patients with brain tumors. Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity), and a microstructural quantity μ were calculated by fitting the multi-b-value diffusion-weighted images to an FROC model. D, β, and μ values were measured in solid tumor regions, as well as in normal-appearing gray matter as a control. These values were compared between the low- and high-grade tumor groups by using the Mann-Whitney U test. The performance of FROC parameters for differentiating among patient groups was evaluated with receiver operating characteristic (ROC) analysis. Results None of the FROC parameters exhibited significant differences in normal-appearing gray matter (P ≥ .24), but all showed a significant difference (P < .002) between low- (D, 1.53 μm2/msec ± 0.47; β, 0.87 ± 0.06; μ, 8.67 μm ± 0.95) and high-grade (D, 0.86 μm2/msec ± 0.23; β, 0.73 ± 0.06; μ, 7.8 μm ± 0.70) brain tumor groups. The combination of D and β produced the largest area under the ROC curve (0.962) in the ROC analysis compared with individual parameters (β, 0.943; D,0.910; and μ, 0.763), indicating an improved performance for tumor differentiation. Conclusion The FROC parameters can be used to differentiate between low- and high-grade pediatric brain tumor groups. The combination of FROC parameters or individual parameters may serve as in vivo, noninvasive, and quantitative imaging markers for classifying pediatric brain tumors. © RSNA, 2015 PMID:26035586

Sampling of Stochastic Input Parameters for Rockfall Calculations and for Structural Response Calculations Under Vibratory Ground Motion

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

M. Gross

2004-09-01

The purpose of this scientific analysis is to define the sampled values of stochastic (random) input parameters for (1) rockfall calculations in the lithophysal and nonlithophysal zones under vibratory ground motions, and (2) structural response calculations for the drip shield and waste package under vibratory ground motions. This analysis supplies: (1) Sampled values of ground motion time history and synthetic fracture pattern for analysis of rockfall in emplacement drifts in nonlithophysal rock (Section 6.3 of ''Drift Degradation Analysis'', BSC 2004 [DIRS 166107]); (2) Sampled values of ground motion time history and rock mechanical properties category for analysis of rockfall inmore » emplacement drifts in lithophysal rock (Section 6.4 of ''Drift Degradation Analysis'', BSC 2004 [DIRS 166107]); (3) Sampled values of ground motion time history and metal to metal and metal to rock friction coefficient for analysis of waste package and drip shield damage to vibratory motion in ''Structural Calculations of Waste Package Exposed to Vibratory Ground Motion'' (BSC 2004 [DIRS 167083]) and in ''Structural Calculations of Drip Shield Exposed to Vibratory Ground Motion'' (BSC 2003 [DIRS 163425]). The sampled values are indices representing the number of ground motion time histories, number of fracture patterns and rock mass properties categories. These indices are translated into actual values within the respective analysis and model reports or calculations. This report identifies the uncertain parameters and documents the sampled values for these parameters. The sampled values are determined by GoldSim V6.04.007 [DIRS 151202] calculations using appropriate distribution types and parameter ranges. No software development or model development was required for these calculations. The calculation of the sampled values allows parameter uncertainty to be incorporated into the rockfall and structural response calculations that support development of the seismic scenario for the Total System Performance Assessment for the License Application (TSPA-LA). The results from this scientific analysis also address project requirements related to parameter uncertainty, as specified in the acceptance criteria in ''Yucca Mountain Review Plan, Final Report'' (NRC 2003 [DIRS 163274]). This document was prepared under the direction of ''Technical Work Plan for: Regulatory Integration Modeling of Drift Degradation, Waste Package and Drip Shield Vibratory Motion and Seismic Consequences'' (BSC 2004 [DIRS 170528]) which directed the work identified in work package ARTM05. This document was prepared under procedure AP-SIII.9Q, ''Scientific Analyses''. There are no specific known limitations to this analysis.« less

Influence of different dose calculation algorithms on the estimate of NTCP for lung complications

PubMed Central

Bäck, Anna

2013-01-01

Due to limitations and uncertainties in dose calculation algorithms, different algorithms can predict different dose distributions and dose‐volume histograms for the same treatment. This can be a problem when estimating the normal tissue complication probability (NTCP) for patient‐specific dose distributions. Published NTCP model parameters are often derived for a different dose calculation algorithm than the one used to calculate the actual dose distribution. The use of algorithm‐specific NTCP model parameters can prevent errors caused by differences in dose calculation algorithms. The objective of this work was to determine how to change the NTCP model parameters for lung complications derived for a simple correction‐based pencil beam dose calculation algorithm, in order to make them valid for three other common dose calculation algorithms. NTCP was calculated with the relative seriality (RS) and Lyman‐Kutcher‐Burman (LKB) models. The four dose calculation algorithms used were the pencil beam (PB) and collapsed cone (CC) algorithms employed by Oncentra, and the pencil beam convolution (PBC) and anisotropic analytical algorithm (AAA) employed by Eclipse. Original model parameters for lung complications were taken from four published studies on different grades of pneumonitis, and new algorithm‐specific NTCP model parameters were determined. The difference between original and new model parameters was presented in relation to the reported model parameter uncertainties. Three different types of treatments were considered in the study: tangential and locoregional breast cancer treatment and lung cancer treatment. Changing the algorithm without the derivation of new model parameters caused changes in the NTCP value of up to 10 percentage points for the cases studied. Furthermore, the error introduced could be of the same magnitude as the confidence intervals of the calculated NTCP values. The new NTCP model parameters were tabulated as the algorithm was varied from PB to PBC, AAA, or CC. Moving from the PB to the PBC algorithm did not require new model parameters; however, moving from PB to AAA or CC did require a change in the NTCP model parameters, with CC requiring the largest change. It was shown that the new model parameters for a given algorithm are different for the different treatment types. PACS numbers: 87.53.‐j, 87.53.Kn, 87.55.‐x, 87.55.dh, 87.55.kd PMID:24036865

Agreement Between Institutional Measurements and Treatment Planning System Calculations for Basic Dosimetric Parameters as Measured by the Imaging and Radiation Oncology Core-Houston

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

Kerns, James R.; Followill, David S.; Imaging and Radiation Oncology Core-Houston, The University of Texas Health Science Center-Houston, Houston, Texas

Purpose: To compare radiation machine measurement data collected by the Imaging and Radiation Oncology Core at Houston (IROC-H) with institutional treatment planning system (TPS) values, to identify parameters with large differences in agreement; the findings will help institutions focus their efforts to improve the accuracy of their TPS models. Methods and Materials: Between 2000 and 2014, IROC-H visited more than 250 institutions and conducted independent measurements of machine dosimetric data points, including percentage depth dose, output factors, off-axis factors, multileaf collimator small fields, and wedge data. We compared these data with the institutional TPS values for the same points bymore » energy, class, and parameter to identify differences and similarities using criteria involving both the medians and standard deviations for Varian linear accelerators. Distributions of differences between machine measurements and institutional TPS values were generated for basic dosimetric parameters. Results: On average, intensity modulated radiation therapy–style and stereotactic body radiation therapy–style output factors and upper physical wedge output factors were the most problematic. Percentage depth dose, jaw output factors, and enhanced dynamic wedge output factors agreed best between the IROC-H measurements and the TPS values. Although small differences were shown between 2 common TPS systems, neither was superior to the other. Parameter agreement was constant over time from 2000 to 2014. Conclusions: Differences in basic dosimetric parameters between machine measurements and TPS values vary widely depending on the parameter, although agreement does not seem to vary by TPS and has not changed over time. Intensity modulated radiation therapy–style output factors, stereotactic body radiation therapy–style output factors, and upper physical wedge output factors had the largest disagreement and should be carefully modeled to ensure accuracy.« less

Ring rolling process simulation for geometry optimization

NASA Astrophysics Data System (ADS)

Franchi, Rodolfo; Del Prete, Antonio; Donatiello, Iolanda; Calabrese, Maurizio

2017-10-01

Ring Rolling is a complex hot forming process where different rolls are involved in the production of seamless rings. Since each roll must be independently controlled, different speed laws must be set; usually, in the industrial environment, a milling curve is introduced to monitor the shape of the workpiece during the deformation in order to ensure the correct ring production. In the present paper a ring rolling process has been studied and optimized in order to obtain anular components to be used in aerospace applications. In particular, the influence of process input parameters (feed rate of the mandrel and angular speed of main roll) on geometrical features of the final ring has been evaluated. For this purpose, a three-dimensional finite element model for HRR (Hot Ring Rolling) has been implemented in SFTC DEFORM V11. The FEM model has been used to formulate a proper optimization problem. The optimization procedure has been implemented in the commercial software DS ISight in order to find the combination of process parameters which allows to minimize the percentage error of each obtained dimension with respect to its nominal value. The software allows to find the relationship between input and output parameters applying Response Surface Methodology (RSM), by using the exact values of output parameters in the control points of the design space explored through FEM simulation. Once this relationship is known, the values of the output parameters can be calculated for each combination of the input parameters. After the calculation of the response surfaces for the selected output parameters, an optimization procedure based on Genetic Algorithms has been applied. At the end, the error between each obtained dimension and its nominal value has been minimized. The constraints imposed were the maximum values of standard deviations of the dimensions obtained for the final ring.

Using Bayesian regression to test hypotheses about relationships between parameters and covariates in cognitive models.

PubMed

Boehm, Udo; Steingroever, Helen; Wagenmakers, Eric-Jan

2018-06-01

An important tool in the advancement of cognitive science are quantitative models that represent different cognitive variables in terms of model parameters. To evaluate such models, their parameters are typically tested for relationships with behavioral and physiological variables that are thought to reflect specific cognitive processes. However, many models do not come equipped with the statistical framework needed to relate model parameters to covariates. Instead, researchers often revert to classifying participants into groups depending on their values on the covariates, and subsequently comparing the estimated model parameters between these groups. Here we develop a comprehensive solution to the covariate problem in the form of a Bayesian regression framework. Our framework can be easily added to existing cognitive models and allows researchers to quantify the evidential support for relationships between covariates and model parameters using Bayes factors. Moreover, we present a simulation study that demonstrates the superiority of the Bayesian regression framework to the conventional classification-based approach.

Dynamical Analysis of an SEIT Epidemic Model with Application to Ebola Virus Transmission in Guinea.

PubMed

Li, Zhiming; Teng, Zhidong; Feng, Xiaomei; Li, Yingke; Zhang, Huiguo

2015-01-01

In order to investigate the transmission mechanism of the infectious individual with Ebola virus, we establish an SEIT (susceptible, exposed in the latent period, infectious, and treated/recovery) epidemic model. The basic reproduction number is defined. The mathematical analysis on the existence and stability of the disease-free equilibrium and endemic equilibrium is given. As the applications of the model, we use the recognized infectious and death cases in Guinea to estimate parameters of the model by the least square method. With suitable parameter values, we obtain the estimated value of the basic reproduction number and analyze the sensitivity and uncertainty property by partial rank correlation coefficients.

INDIVIDUALIZED FETAL GROWTH ASSESSMENT: CRITICAL EVALUATION OF KEY CONCEPTS IN THE SPECIFICATION OF THIRD TRIMESTER GROWTH TRAJECTORIES

PubMed Central

Deter, Russell L.; Lee, Wesley; Yeo, Lami; Romero, Roberto

2012-01-01

Objectives To characterize 2nd and 3rd trimester fetal growth using Individualized Growth Assessment in a large cohort of fetuses with normal growth outcomes. Methods A prospective longitudinal study of 119 pregnancies was carried out from 18 weeks, MA, to delivery. Measurements of eleven fetal growth parameters were obtained from 3D scans at 3–4 week intervals. Regression analyses were used to determine Start Points [SP] and Rossavik model [P = c (t) k + st] coefficients c, k and s for each parameter in each fetus. Second trimester growth model specification functions were re-established. These functions were used to generate individual growth models and determine predicted s and s-residual [s = pred s + s-resid] values. Actual measurements were compared to predicted growth trajectories obtained from the growth models and Percent Deviations [% Dev = {{actual − predicted}/predicted} × 100] calculated. Age-specific reference standards for this statistic were defined using 2-level statistical modeling for the nine directly measured parameters and estimated weight. Results Rossavik models fit the data for all parameters very well [R2: 99%], with SP’s and k values similar to those found in a much smaller cohort. The c values were strongly related to the 2nd trimester slope [R2: 97%] as was predicted s to estimated c [R2: 95%]. The latter was negative for skeletal parameters and positive for soft tissue parameters. The s-residuals were unrelated to estimated c’s [R2: 0%], and had mean values of zero. Rossavik models predicted 3rd trimester growth with systematic errors close to 0% and random errors [95% range] of 5.7 – 10.9% and 20.0 – 24.3% for one and three dimensional parameters, respectively. Moderate changes in age-specific variability were seen in the 3rd trimester.. Conclusions IGA procedures for evaluating 2nd and 3rd trimester growth are now established based on a large cohort [4–6 fold larger than those used previously], thus permitting more reliable growth assessment with each fetus acting as its own control. New, more rigorously defined, age-specific standards for the evaluation of 3rd trimester growth deviations are now available for 10 anatomical parameters. Our results are also consistent with the predicted s and s-residual being representatives of growth controllers operating through the insulin-like growth factor [IGF] axis. PMID:23962305

Evaluation of weather-based rice yield models in India.

PubMed

Sudharsan, D; Adinarayana, J; Reddy, D Raji; Sreenivas, G; Ninomiya, S; Hirafuji, M; Kiura, T; Tanaka, K; Desai, U B; Merchant, S N

2013-01-01

The objective of this study was to compare two different rice simulation models--standalone (Decision Support System for Agrotechnology Transfer [DSSAT]) and web based (SImulation Model for RIce-Weather relations [SIMRIW])--with agrometeorological data and agronomic parameters for estimation of rice crop production in southern semi-arid tropics of India. Studies were carried out on the BPT5204 rice variety to evaluate two crop simulation models. Long-term experiments were conducted in a research farm of Acharya N G Ranga Agricultural University (ANGRAU), Hyderabad, India. Initially, the results were obtained using 4 years (1994-1997) of data with weather parameters from a local weather station to evaluate DSSAT simulated results with observed values. Linear regression models used for the purpose showed a close relationship between DSSAT and observed yield. Subsequently, yield comparisons were also carried out with SIMRIW and DSSAT, and validated with actual observed values. Realizing the correlation coefficient values of SIMRIW simulation values in acceptable limits, further rice experiments in monsoon (Kharif) and post-monsoon (Rabi) agricultural seasons (2009, 2010 and 2011) were carried out with a location-specific distributed sensor network system. These proximal systems help to simulate dry weight, leaf area index and potential yield by the Java based SIMRIW on a daily/weekly/monthly/seasonal basis. These dynamic parameters are useful to the farming community for necessary decision making in a ubiquitous manner. However, SIMRIW requires fine tuning for better results/decision making.

Analysis of Seasonal Chlorophyll-a Using An Adjoint Three-Dimensional Ocean Carbon Cycle Model

NASA Astrophysics Data System (ADS)

Tjiputra, J.; Winguth, A.; Polzin, D.

2004-12-01

The misfit between numerical ocean model and observations can be reduced using data assimilation. This can be achieved by optimizing the model parameter values using adjoint model. The adjoint model minimizes the model-data misfit by estimating the sensitivity or gradient of the cost function with respect to initial condition, boundary condition, or parameters. The adjoint technique was used to assimilate seasonal chlorophyll-a data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite to a marine biogeochemical model HAMOCC5.1. An Identical Twin Experiment (ITE) was conducted to test the robustness of the model and the non-linearity level of the forward model. The ITE experiment successfully recovered most of the perturbed parameter to their initial values, and identified the most sensitive ecosystem parameters, which contribute significantly to model-data bias. The regional assimilations of SeaWiFS chlorophyll-a data into the model were able to reduce the model-data misfit (i.e. the cost function) significantly. The cost function reduction mostly occurred in the high latitudes (e.g. the model-data misfit in the northern region during summer season was reduced by 54%). On the other hand, the equatorial regions appear to be relatively stable with no strong reduction in cost function. The optimized parameter set is used to forecast the carbon fluxes between marine ecosystem compartments (e.g. Phytoplankton, Zooplankton, Nutrients, Particulate Organic Carbon, and Dissolved Organic Carbon). The a posteriori model run using the regional best-fit parameterization yields approximately 36 PgC/yr of global net primary productions in the euphotic zone.

Transfer-function-parameter estimation from frequency response data: A FORTRAN program

NASA Technical Reports Server (NTRS)

Seidel, R. C.

1975-01-01

A FORTRAN computer program designed to fit a linear transfer function model to given frequency response magnitude and phase data is presented. A conjugate gradient search is used that minimizes the integral of the absolute value of the error squared between the model and the data. The search is constrained to insure model stability. A scaling of the model parameters by their own magnitude aids search convergence. Efficient computer algorithms result in a small and fast program suitable for a minicomputer. A sample problem with different model structures and parameter estimates is reported.

VizieR Online Data Catalog: A catalog of exoplanet physical parameters (Foreman-Mackey+, 2014)

NASA Astrophysics Data System (ADS)

Foreman-Mackey, D.; Hogg, D. W.; Morton, T. D.

2017-05-01

The first ingredient for any probabilistic inference is a likelihood function, a description of the probability of observing a specific data set given a set of model parameters. In this particular project, the data set is a catalog of exoplanet measurements and the model parameters are the values that set the shape and normalization of the occurrence rate density. (2 data files).

Algorithmic detectability threshold of the stochastic block model

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro

2018-03-01

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation-maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

Challenges for MSSM Higgs searches at hadron colliders

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

Carena, Marcela S.; /Fermilab; Menon, A.

2007-04-01

In this article we analyze the impact of B-physics and Higgs physics at LEP on standard and non-standard Higgs bosons searches at the Tevatron and the LHC, within the framework of minimal flavor violating supersymmetric models. The B-physics constraints we consider come from the experimental measurements of the rare B-decays b {yields} s{gamma} and B{sub u} {yields} {tau}{nu} and the experimental limit on the B{sub s} {yields} {mu}{sup +}{mu}{sup -} branching ratio. We show that these constraints are severe for large values of the trilinear soft breaking parameter A{sub t}, rendering the non-standard Higgs searches at hadron colliders less promising.more » On the contrary these bounds are relaxed for small values of A{sub t} and large values of the Higgsino mass parameter {mu}, enhancing the prospects for the direct detection of non-standard Higgs bosons at both colliders. We also consider the available ATLAS and CMS projected sensitivities in the standard model Higgs search channels, and we discuss the LHC's ability in probing the whole MSSM parameter space. In addition we also consider the expected Tevatron collider sensitivities in the standard model Higgs h {yields} b{bar b} channel to show that it may be able to find 3 {sigma} evidence in the B-physics allowed regions for small or moderate values of the stop mixing parameter.« less

Estimation of parameter uncertainty for an activated sludge model using Bayesian inference: a comparison with the frequentist method.

PubMed

Zonta, Zivko J; Flotats, Xavier; Magrí, Albert

2014-08-01

The procedure commonly used for the assessment of the parameters included in activated sludge models (ASMs) relies on the estimation of their optimal value within a confidence region (i.e. frequentist inference). Once optimal values are estimated, parameter uncertainty is computed through the covariance matrix. However, alternative approaches based on the consideration of the model parameters as probability distributions (i.e. Bayesian inference), may be of interest. The aim of this work is to apply (and compare) both Bayesian and frequentist inference methods when assessing uncertainty for an ASM-type model, which considers intracellular storage and biomass growth, simultaneously. Practical identifiability was addressed exclusively considering respirometric profiles based on the oxygen uptake rate and with the aid of probabilistic global sensitivity analysis. Parameter uncertainty was thus estimated according to both the Bayesian and frequentist inferential procedures. Results were compared in order to evidence the strengths and weaknesses of both approaches. Since it was demonstrated that Bayesian inference could be reduced to a frequentist approach under particular hypotheses, the former can be considered as a more generalist methodology. Hence, the use of Bayesian inference is encouraged for tackling inferential issues in ASM environments.

Utility of coupling nonlinear optimization methods with numerical modeling software

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

Murphy, M.J.

1996-08-05

Results of using GLO (Global Local Optimizer), a general purpose nonlinear optimization software package for investigating multi-parameter problems in science and engineering is discussed. The package consists of the modular optimization control system (GLO), a graphical user interface (GLO-GUI), a pre-processor (GLO-PUT), a post-processor (GLO-GET), and nonlinear optimization software modules, GLOBAL & LOCAL. GLO is designed for controlling and easy coupling to any scientific software application. GLO runs the optimization module and scientific software application in an iterative loop. At each iteration, the optimization module defines new values for the set of parameters being optimized. GLO-PUT inserts the new parametermore » values into the input file of the scientific application. GLO runs the application with the new parameter values. GLO-GET determines the value of the objective function by extracting the results of the analysis and comparing to the desired result. GLO continues to run the scientific application over and over until it finds the ``best`` set of parameters by minimizing (or maximizing) the objective function. An example problem showing the optimization of material model is presented (Taylor cylinder impact test).« less

Using polarizable POSSIM force field and fuzzy-border continuum solvent model to calculate pK(a) shifts of protein residues.

PubMed

Sharma, Ity; Kaminski, George A

2017-01-15

Our Fuzzy-Border (FB) continuum solvent model has been extended and modified to produce hydration parameters for small molecules using POlarizable Simulations Second-order Interaction Model (POSSIM) framework with an average error of 0.136 kcal/mol. It was then used to compute pK a shifts for carboxylic and basic residues of the turkey ovomucoid third domain (OMTKY3) protein. The average unsigned errors in the acid and base pK a values were 0.37 and 0.4 pH units, respectively, versus 0.58 and 0.7 pH units as calculated with a previous version of polarizable protein force field and Poisson Boltzmann continuum solvent. This POSSIM/FB result is produced with explicit refitting of the hydration parameters to the pK a values of the carboxylic and basic residues of the OMTKY3 protein; thus, the values of the acidity constants can be viewed as additional fitting target data. In addition to calculating pK a shifts for the OMTKY3 residues, we have studied aspartic acid residues of Rnase Sa. This was done without any further refitting of the parameters and agreement with the experimental pK a values is within an average unsigned error of 0.65 pH units. This result included the Asp79 residue that is buried and thus has a high experimental pK a value of 7.37 units. Thus, the presented model is capable or reproducing pK a results for residues in an environment that is significantly different from the solvated protein surface used in the fitting. Therefore, the POSSIM force field and the FB continuum solvent parameters have been demonstrated to be sufficiently robust and transferable. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

An extension of the standard model with a single coupling parameter

NASA Astrophysics Data System (ADS)

Atance, Mario; Cortés, José Luis; Irastorza, Igor G.

1997-02-01

We show that it is possible to find an extension of the matter content of the standard model with a unification of gauge and Yukawa couplings reproducing their known values. The perturbative renormalizability of the model with a single coupling and the requirement to accommodate the known properties of the standard model fix the masses and couplings of the additional particles. The implications on the parameters of the standard model are discussed.

Automated dynamic analytical model improvement for damped structures

NASA Technical Reports Server (NTRS)

Fuh, J. S.; Berman, A.

1985-01-01

A method is described to improve a linear nonproportionally damped analytical model of a structure. The procedure finds the smallest changes in the analytical model such that the improved model matches the measured modal parameters. Features of the method are: (1) ability to properly treat complex valued modal parameters of a damped system; (2) applicability to realistically large structural models; and (3) computationally efficiency without involving eigensolutions and inversion of a large matrix.

Estimation of environment-related properties of chemicals for design of sustainable processes: development of group-contribution+ (GC+) property models and uncertainty analysis.

PubMed

Hukkerikar, Amol Shivajirao; Kalakul, Sawitree; Sarup, Bent; Young, Douglas M; Sin, Gürkan; Gani, Rafiqul

2012-11-26

The aim of this work is to develop group-contribution(+) (GC(+)) method (combined group-contribution (GC) method and atom connectivity index (CI) method) based property models to provide reliable estimations of environment-related properties of organic chemicals together with uncertainties of estimated property values. For this purpose, a systematic methodology for property modeling and uncertainty analysis is used. The methodology includes a parameter estimation step to determine parameters of property models and an uncertainty analysis step to establish statistical information about the quality of parameter estimation, such as the parameter covariance, the standard errors in predicted properties, and the confidence intervals. For parameter estimation, large data sets of experimentally measured property values of a wide range of chemicals (hydrocarbons, oxygenated chemicals, nitrogenated chemicals, poly functional chemicals, etc.) taken from the database of the US Environmental Protection Agency (EPA) and from the database of USEtox is used. For property modeling and uncertainty analysis, the Marrero and Gani GC method and atom connectivity index method have been considered. In total, 22 environment-related properties, which include the fathead minnow 96-h LC(50), Daphnia magna 48-h LC(50), oral rat LD(50), aqueous solubility, bioconcentration factor, permissible exposure limit (OSHA-TWA), photochemical oxidation potential, global warming potential, ozone depletion potential, acidification potential, emission to urban air (carcinogenic and noncarcinogenic), emission to continental rural air (carcinogenic and noncarcinogenic), emission to continental fresh water (carcinogenic and noncarcinogenic), emission to continental seawater (carcinogenic and noncarcinogenic), emission to continental natural soil (carcinogenic and noncarcinogenic), and emission to continental agricultural soil (carcinogenic and noncarcinogenic) have been modeled and analyzed. The application of the developed property models for the estimation of environment-related properties and uncertainties of the estimated property values is highlighted through an illustrative example. The developed property models provide reliable estimates of environment-related properties needed to perform process synthesis, design, and analysis of sustainable chemical processes and allow one to evaluate the effect of uncertainties of estimated property values on the calculated performance of processes giving useful insights into quality and reliability of the design of sustainable processes.

Charge relaxation and dynamics in organic semiconductors

NASA Astrophysics Data System (ADS)

Kwok, H. L.

2006-08-01

Charge relaxation in dispersive materials is often described in terms of the stretched exponential function (Kohlrausch law). The process can be explained using a "hopping" model which in principle, also applies to charge transport such as current conduction. This work analyzed reported transient photoconductivity data on functionalized pentacene single crystals using a geometric hopping model developed by B. Sturman et al and extracted values (or range of values) on the materials parameters relevant to charge relaxation as well as charge transport. Using the correlated disorder model (CDM), we estimated values of the carrier mobility for the pentacene samples. From these results, we observed the following: i) the transport site density appeared to be of the same order of magnitude as the carrier density; ii) it was possible to extract lower bound values on the materials parameters linked to the transport process; and iii) by matching the simulated charge decay to the transient photoconductivity data, we were able to refine estimates on the materials parameters. The data also allowed us to simulate the stretched exponential decay. Our observations suggested that the stretching index and the carrier mobility were related. Physically, such interdependence would allow one to demarcate between localized molecular interactions and distant coulomb interactions.

A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

NASA Astrophysics Data System (ADS)

Zhang, Shaojie; Zhao, Luqiang; Delgado-Tellez, Ricardo; Bao, Hongjun

2018-03-01

Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs < 1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

Volume effects of late term normal tissue toxicity in prostate cancer radiotherapy

NASA Astrophysics Data System (ADS)

Bonta, Dacian Viorel

Modeling of volume effects for treatment toxicity is paramount for optimization of radiation therapy. This thesis proposes a new model for calculating volume effects in gastro-intestinal and genito-urinary normal tissue complication probability (NTCP) following radiation therapy for prostate carcinoma. The radiobiological and the pathological basis for this model and its relationship to other models are detailed. A review of the radiobiological experiments and published clinical data identified salient features and specific properties a biologically adequate model has to conform to. The new model was fit to a set of actual clinical data. In order to verify the goodness of fit, two established NTCP models and a non-NTCP measure for complication risk were fitted to the same clinical data. The method of fit for the model parameters was maximum likelihood estimation. Within the framework of the maximum likelihood approach I estimated the parameter uncertainties for each complication prediction model. The quality-of-fit was determined using the Aikaike Information Criterion. Based on the model that provided the best fit, I identified the volume effects for both types of toxicities. Computer-based bootstrap resampling of the original dataset was used to estimate the bias and variance for the fitted parameter values. Computer simulation was also used to estimate the population size that generates a specific uncertainty level (3%) in the value of predicted complication probability. The same method was used to estimate the size of the patient population needed for accurate choice of the model underlying the NTCP. The results indicate that, depending on the number of parameters of a specific NTCP model, 100 (for two parameter models) and 500 patients (for three parameter models) are needed for accurate parameter fit. Correlation of complication occurrence in patients was also investigated. The results suggest that complication outcomes are correlated in a patient, although the correlation coefficient is rather small.

The Threshold Bias Model: A Mathematical Model for the Nomothetic Approach of Suicide

PubMed Central

Folly, Walter Sydney Dutra

2011-01-01

Background Comparative and predictive analyses of suicide data from different countries are difficult to perform due to varying approaches and the lack of comparative parameters. Methodology/Principal Findings A simple model (the Threshold Bias Model) was tested for comparative and predictive analyses of suicide rates by age. The model comprises of a six parameter distribution that was applied to the USA suicide rates by age for the years 2001 and 2002. Posteriorly, linear extrapolations are performed of the parameter values previously obtained for these years in order to estimate the values corresponding to the year 2003. The calculated distributions agreed reasonably well with the aggregate data. The model was also used to determine the age above which suicide rates become statistically observable in USA, Brazil and Sri Lanka. Conclusions/Significance The Threshold Bias Model has considerable potential applications in demographic studies of suicide. Moreover, since the model can be used to predict the evolution of suicide rates based on information extracted from past data, it will be of great interest to suicidologists and other researchers in the field of mental health. PMID:21909431

The threshold bias model: a mathematical model for the nomothetic approach of suicide.

PubMed

Folly, Walter Sydney Dutra

2011-01-01

Comparative and predictive analyses of suicide data from different countries are difficult to perform due to varying approaches and the lack of comparative parameters. A simple model (the Threshold Bias Model) was tested for comparative and predictive analyses of suicide rates by age. The model comprises of a six parameter distribution that was applied to the USA suicide rates by age for the years 2001 and 2002. Posteriorly, linear extrapolations are performed of the parameter values previously obtained for these years in order to estimate the values corresponding to the year 2003. The calculated distributions agreed reasonably well with the aggregate data. The model was also used to determine the age above which suicide rates become statistically observable in USA, Brazil and Sri Lanka. The Threshold Bias Model has considerable potential applications in demographic studies of suicide. Moreover, since the model can be used to predict the evolution of suicide rates based on information extracted from past data, it will be of great interest to suicidologists and other researchers in the field of mental health.

A mathematical model for mixed convective flow of chemically reactive Oldroyd-B fluid between isothermal stretching disks

NASA Astrophysics Data System (ADS)

Hashmi, M. S.; Khan, N.; Ullah Khan, Sami; Rashidi, M. M.

In this study, we have constructed a mathematical model to investigate the heat source/sink effects in mixed convection axisymmetric flow of an incompressible, electrically conducting Oldroyd-B fluid between two infinite isothermal stretching disks. The effects of viscous dissipation and Joule heating are also considered in the heat equation. The governing partial differential equations are converted into ordinary differential equations by using appropriate similarity variables. The series solution of these dimensionless equations is constructed by using homotopy analysis method. The convergence of the obtained solution is carefully examined. The effects of various involved parameters on pressure, velocity and temperature profiles are comprehensively studied. A graphical analysis has been presented for various values of problem parameters. The numerical values of wall shear stress and Nusselt number are computed at both upper and lower disks. Moreover, a graphical and tabular explanation for critical values of Frank-Kamenetskii regarding other flow parameters.

Incorporating rainfall uncertainty in a SWAT model: the river Zenne basin (Belgium) case study

NASA Astrophysics Data System (ADS)

Tolessa Leta, Olkeba; Nossent, Jiri; van Griensven, Ann; Bauwens, Willy

2013-04-01

The European Union Water Framework Directive (EU-WFD) called its member countries to achieve a good ecological status for all inland and coastal water bodies by 2015. According to recent studies, the river Zenne (Belgium) is far from this objective. Therefore, an interuniversity and multidisciplinary project "Towards a Good Ecological Status in the river Zenne (GESZ)" was launched to evaluate the effects of wastewater management plans on the river. In this project, different models have been developed and integrated using the Open Modelling Interface (OpenMI). The hydrologic, semi-distributed Soil and Water Assessment Tool (SWAT) is hereby used as one of the model components in the integrated modelling chain in order to model the upland catchment processes. The assessment of the uncertainty of SWAT is an essential aspect of the decision making process, in order to design robust management strategies that take the predicted uncertainties into account. Model uncertainty stems from the uncertainties on the model parameters, the input data (e.g, rainfall), the calibration data (e.g., stream flows) and on the model structure itself. The objective of this paper is to assess the first three sources of uncertainty in a SWAT model of the river Zenne basin. For the assessment of rainfall measurement uncertainty, first, we identified independent rainfall periods, based on the daily precipitation and stream flow observations and using the Water Engineering Time Series PROcessing tool (WETSPRO). Secondly, we assigned a rainfall multiplier parameter for each of the independent rainfall periods, which serves as a multiplicative input error corruption. Finally, we treated these multipliers as latent parameters in the model optimization and uncertainty analysis (UA). For parameter uncertainty assessment, due to the high number of parameters of the SWAT model, first, we screened out its most sensitive parameters using the Latin Hypercube One-factor-At-a-Time (LH-OAT) technique. Subsequently, we only considered the most sensitive parameters for parameter optimization and UA. To explicitly account for the stream flow uncertainty, we assumed that the stream flow measurement error increases linearly with the stream flow value. To assess the uncertainty and infer posterior distributions of the parameters, we used a Markov Chain Monte Carlo (MCMC) sampler - differential evolution adaptive metropolis (DREAM) that uses sampling from an archive of past states to generate candidate points in each individual chain. It is shown that the marginal posterior distributions of the rainfall multipliers vary widely between individual events, as a consequence of rainfall measurement errors and the spatial variability of the rain. Only few of the rainfall events are well defined. The marginal posterior distributions of the SWAT model parameter values are well defined and identified by DREAM, within their prior ranges. The posterior distributions of output uncertainty parameter values also show that the stream flow data is highly uncertain. The approach of using rainfall multipliers to treat rainfall uncertainty for a complex model has an impact on the model parameter marginal posterior distributions and on the model results Corresponding author: Tel.: +32 (0)2629 3027; fax: +32(0)2629 3022. E-mail: otolessa@vub.ac.be

Poster — Thur Eve — 44: Linearization of Compartmental Models for More Robust Estimates of Regional Hemodynamic, Metabolic and Functional Parameters using DCE-CT/PET Imaging

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

Blais, AR; Dekaban, M; Lee, T-Y

2014-08-15

Quantitative analysis of dynamic positron emission tomography (PET) data usually involves minimizing a cost function with nonlinear regression, wherein the choice of starting parameter values and the presence of local minima affect the bias and variability of the estimated kinetic parameters. These nonlinear methods can also require lengthy computation time, making them unsuitable for use in clinical settings. Kinetic modeling of PET aims to estimate the rate parameter k{sub 3}, which is the binding affinity of the tracer to a biological process of interest and is highly susceptible to noise inherent in PET image acquisition. We have developed linearized kineticmore » models for kinetic analysis of dynamic contrast enhanced computed tomography (DCE-CT)/PET imaging, including a 2-compartment model for DCE-CT and a 3-compartment model for PET. Use of kinetic parameters estimated from DCE-CT can stabilize the kinetic analysis of dynamic PET data, allowing for more robust estimation of k{sub 3}. Furthermore, these linearized models are solved with a non-negative least squares algorithm and together they provide other advantages including: 1) only one possible solution and they do not require a choice of starting parameter values, 2) parameter estimates are comparable in accuracy to those from nonlinear models, 3) significantly reduced computational time. Our simulated data show that when blood volume and permeability are estimated with DCE-CT, the bias of k{sub 3} estimation with our linearized model is 1.97 ± 38.5% for 1,000 runs with a signal-to-noise ratio of 10. In summary, we have developed a computationally efficient technique for accurate estimation of k{sub 3} from noisy dynamic PET data.« less

A mechanistic modeling and data assimilation framework for Mojave Desert ecohydrology

USGS Publications Warehouse

Ng, Gene-Hua Crystal.; Bedford, David; Miller, David

2014-01-01

This study demonstrates and addresses challenges in coupled ecohydrological modeling in deserts, which arise due to unique plant adaptations, marginal growing conditions, slow net primary production rates, and highly variable rainfall. We consider model uncertainty from both structural and parameter errors and present a mechanistic model for the shrub Larrea tridentata (creosote bush) under conditions found in the Mojave National Preserve in southeastern California (USA). Desert-specific plant and soil features are incorporated into the CLM-CN model by Oleson et al. (2010). We then develop a data assimilation framework using the ensemble Kalman filter (EnKF) to estimate model parameters based on soil moisture and leaf-area index observations. A new implementation procedure, the “multisite loop EnKF,” tackles parameter estimation difficulties found to affect desert ecohydrological applications. Specifically, the procedure iterates through data from various observation sites to alleviate adverse filter impacts from non-Gaussianity in small desert vegetation state values. It also readjusts inconsistent parameters and states through a model spin-up step that accounts for longer dynamical time scales due to infrequent rainfall in deserts. Observation error variance inflation may also be needed to help prevent divergence of estimates from true values. Synthetic test results highlight the importance of adequate observations for reducing model uncertainty, which can be achieved through data quality or quantity.

Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model

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

Wang, Jiali; Han, Yuefeng; Stein, Michael L.

2016-02-10

The Weather Research and Forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximummore » temperature through comparison with North American Regional Reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting bootstrap resampling. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.« less

Autocalibration of a one-dimensional hydrodynamic-ecological model (DYRESM 4.0-CAEDYM 3.1) using a Monte Carlo approach: simulations of hypoxic events in a polymictic lake

NASA Astrophysics Data System (ADS)

Luo, Liancong; Hamilton, David; Lan, Jia; McBride, Chris; Trolle, Dennis

2018-03-01

Automated calibration of complex deterministic water quality models with a large number of biogeochemical parameters can reduce time-consuming iterative simulations involving empirical judgements of model fit. We undertook autocalibration of the one-dimensional hydrodynamic-ecological lake model DYRESM-CAEDYM, using a Monte Carlo sampling (MCS) method, in order to test the applicability of this procedure for shallow, polymictic Lake Rotorua (New Zealand). The calibration procedure involved independently minimizing the root-mean-square error (RMSE), maximizing the Pearson correlation coefficient (r) and Nash-Sutcliffe efficient coefficient (Nr) for comparisons of model state variables against measured data. An assigned number of parameter permutations was used for 10 000 simulation iterations. The "optimal" temperature calibration produced a RMSE of 0.54 °C, Nr value of 0.99, and r value of 0.98 through the whole water column based on comparisons with 540 observed water temperatures collected between 13 July 2007 and 13 January 2009. The modeled bottom dissolved oxygen concentration (20.5 m below surface) was compared with 467 available observations. The calculated RMSE of the simulations compared with the measurements was 1.78 mg L-1, the Nr value was 0.75, and the r value was 0.87. The autocalibrated model was further tested for an independent data set by simulating bottom-water hypoxia events from 15 January 2009 to 8 June 2011 (875 days). This verification produced an accurate simulation of five hypoxic events corresponding to DO < 2 mg L-1 during summer of 2009-2011. The RMSE was 2.07 mg L-1, Nr value 0.62, and r value of 0.81, based on the available data set of 738 days. The autocalibration software of DYRESM-CAEDYM developed here is substantially less time-consuming and more efficient in parameter optimization than traditional manual calibration which has been the standard tool practiced for similar complex water quality models.

Auto-calibration of a one-dimensional hydrodynamic-ecological model using a Monte Carlo approach: simulation of hypoxic events in a polymictic lake

NASA Astrophysics Data System (ADS)

Luo, L.

2011-12-01

Automated calibration of complex deterministic water quality models with a large number of biogeochemical parameters can reduce time-consuming iterative simulations involving empirical judgements of model fit. We undertook auto-calibration of the one-dimensional hydrodynamic-ecological lake model DYRESM-CAEDYM, using a Monte Carlo sampling (MCS) method, in order to test the applicability of this procedure for shallow, polymictic Lake Rotorua (New Zealand). The calibration procedure involved independently minimising the root-mean-square-error (RMSE), maximizing the Pearson correlation coefficient (r) and Nash-Sutcliffe efficient coefficient (Nr) for comparisons of model state variables against measured data. An assigned number of parameter permutations was used for 10,000 simulation iterations. The 'optimal' temperature calibration produced a RMSE of 0.54 °C, Nr-value of 0.99 and r-value of 0.98 through the whole water column based on comparisons with 540 observed water temperatures collected between 13 July 2007 - 13 January 2009. The modeled bottom dissolved oxygen concentration (20.5 m below surface) was compared with 467 available observations. The calculated RMSE of the simulations compared with the measurements was 1.78 mg L-1, the Nr-value was 0.75 and the r-value was 0.87. The autocalibrated model was further tested for an independent data set by simulating bottom-water hypoxia events for the period 15 January 2009 to 8 June 2011 (875 days). This verification produced an accurate simulation of five hypoxic events corresponding to DO < 2 mg L-1 during summer of 2009-2011. The RMSE was 2.07 mg L-1, Nr-value 0.62 and r-value of 0.81, based on the available data set of 738 days. The auto-calibration software of DYRESM-CAEDYM developed here is substantially less time-consuming and more efficient in parameter optimisation than traditional manual calibration which has been the standard tool practiced for similar complex water quality models.

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

Towards simplification of hydrologic modeling: Identification of dominant processes

USGS Publications Warehouse

Markstrom, Steven; Hay, Lauren E.; Clark, Martyn P.

2016-01-01

The Precipitation–Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many

Investigations of respiratory control systems simulation

NASA Technical Reports Server (NTRS)

Gallagher, R. R.

1973-01-01

The Grodins' respiratory control model was investigated and it was determined that the following modifications were necessary before the model would be adaptable for current research efforts: (1) the controller equation must be modified to allow for integration of the respiratory system model with other physiological systems; (2) the system must be more closely correlated to the salient physiological functionings; (3) the respiratory frequency and the heart rate should be expanded to illustrate other physiological relationships and dependencies; and (4) the model should be adapted to particular individuals through a better defined set of initial parameter values in addition to relating these parameter values to the desired environmental conditions. Several of Milhorn's respiratory control models were also investigated in hopes of using some of their features as modifications for Grodins' model.

A parameters optimization method for planar joint clearance model and its application for dynamics simulation of reciprocating compressor

NASA Astrophysics Data System (ADS)

Hai-yang, Zhao; Min-qiang, Xu; Jin-dong, Wang; Yong-bo, Li

2015-05-01

In order to improve the accuracy of dynamics response simulation for mechanism with joint clearance, a parameter optimization method for planar joint clearance contact force model was presented in this paper, and the optimized parameters were applied to the dynamics response simulation for mechanism with oversized joint clearance fault. By studying the effect of increased clearance on the parameters of joint clearance contact force model, the relation of model parameters between different clearances was concluded. Then the dynamic equation of a two-stage reciprocating compressor with four joint clearances was developed using Lagrange method, and a multi-body dynamic model built in ADAMS software was used to solve this equation. To obtain a simulated dynamic response much closer to that of experimental tests, the parameters of joint clearance model, instead of using the designed values, were optimized by genetic algorithms approach. Finally, the optimized parameters were applied to simulate the dynamics response of model with oversized joint clearance fault according to the concluded parameter relation. The dynamics response of experimental test verified the effectiveness of this application.

Evaluating alternate models to estimate genetic parameters of calving traits in United Kingdom Holstein-Friesian dairy cattle.

PubMed

Eaglen, Sophie A E; Coffey, Mike P; Woolliams, John A; Wall, Eileen

2012-07-28

The focus in dairy cattle breeding is gradually shifting from production to functional traits and genetic parameters of calving traits are estimated more frequently. However, across countries, various statistical models are used to estimate these parameters. This study evaluates different models for calving ease and stillbirth in United Kingdom Holstein-Friesian cattle. Data from first and later parity records were used. Genetic parameters for calving ease, stillbirth and gestation length were estimated using the restricted maximum likelihood method, considering different models i.e. sire (-maternal grandsire), animal, univariate and bivariate models. Gestation length was fitted as a correlated indicator trait and, for all three traits, genetic correlations between first and later parities were estimated. Potential bias in estimates was avoided by acknowledging a possible environmental direct-maternal covariance. The total heritable variance was estimated for each trait to discuss its theoretical importance and practical value. Prediction error variances and accuracies were calculated to compare the models. On average, direct and maternal heritabilities for calving traits were low, except for direct gestation length. Calving ease in first parity had a significant and negative direct-maternal genetic correlation. Gestation length was maternally correlated to stillbirth in first parity and directly correlated to calving ease in later parities. Multi-trait models had a slightly greater predictive ability than univariate models, especially for the lowly heritable traits. The computation time needed for sire (-maternal grandsire) models was much smaller than for animal models with only small differences in accuracy. The sire (-maternal grandsire) model was robust when additional genetic components were estimated, while the equivalent animal model had difficulties reaching convergence. For the evaluation of calving traits, multi-trait models show a slight advantage over univariate models. Extended sire models (-maternal grandsire) are more practical and robust than animal models. Estimated genetic parameters for calving traits of UK Holstein cattle are consistent with literature. Calculating an aggregate estimated breeding value including direct and maternal values should encourage breeders to consider both direct and maternal effects in selection decisions.

Evaluating alternate models to estimate genetic parameters of calving traits in United Kingdom Holstein-Friesian dairy cattle

PubMed Central

2012-01-01

Background The focus in dairy cattle breeding is gradually shifting from production to functional traits and genetic parameters of calving traits are estimated more frequently. However, across countries, various statistical models are used to estimate these parameters. This study evaluates different models for calving ease and stillbirth in United Kingdom Holstein-Friesian cattle. Methods Data from first and later parity records were used. Genetic parameters for calving ease, stillbirth and gestation length were estimated using the restricted maximum likelihood method, considering different models i.e. sire (−maternal grandsire), animal, univariate and bivariate models. Gestation length was fitted as a correlated indicator trait and, for all three traits, genetic correlations between first and later parities were estimated. Potential bias in estimates was avoided by acknowledging a possible environmental direct-maternal covariance. The total heritable variance was estimated for each trait to discuss its theoretical importance and practical value. Prediction error variances and accuracies were calculated to compare the models. Results and discussion On average, direct and maternal heritabilities for calving traits were low, except for direct gestation length. Calving ease in first parity had a significant and negative direct-maternal genetic correlation. Gestation length was maternally correlated to stillbirth in first parity and directly correlated to calving ease in later parities. Multi-trait models had a slightly greater predictive ability than univariate models, especially for the lowly heritable traits. The computation time needed for sire (−maternal grandsire) models was much smaller than for animal models with only small differences in accuracy. The sire (−maternal grandsire) model was robust when additional genetic components were estimated, while the equivalent animal model had difficulties reaching convergence. Conclusions For the evaluation of calving traits, multi-trait models show a slight advantage over univariate models. Extended sire models (−maternal grandsire) are more practical and robust than animal models. Estimated genetic parameters for calving traits of UK Holstein cattle are consistent with literature. Calculating an aggregate estimated breeding value including direct and maternal values should encourage breeders to consider both direct and maternal effects in selection decisions. PMID:22839757

[Application of classical isothermal adsorption models in heavy metal ions/ diatomite system and related problems].

PubMed

Zhu, Jian; Wu, Qing-Ding; Wang, Ping; Li, Ke-Lin; Lei, Ming-Jing; Zhang, Wei-Li

2013-11-01

In order to fully understand adsorption nature of Cu2+, Zn2+, Pb2+, Cd2+, Mn2+, Fe3+ onto natural diatomite, and to find problems of classical isothermal adsorption models' application in liquid/solid system, a series of isothermal adsorption tests were conducted. As results indicate, the most suitable isotherm models for describing adsorption of Pb2+, Cd2+, Cu2+, Zn2+, Mn2+, Fe3+ onto natural diatomite are Tenkin, Tenkin, Langmuir, Tenkin, Freundlich and Freundlich, respectively, the adsorption of each ion onto natural diatomite is mainly a physical process, and the adsorption reaction is favorable. It also can be found that, when using classical isothermal adsorption models to fit the experimental data in liquid/solid system, the equilibrium adsorption amount q(e) is not a single function of ion equilibrium concentration c(e), while is a function of two variables, namely c(e) and the adsorbent concentration W0, q(e) only depends on c(e)/W(0). Results also show that the classical isothermal adsorption models have a significant adsorbent effect, and their parameter values are unstable, the simulation values of parameter differ greatly from the measured values, which is unhelpful for practical use. The tests prove that four-adsorption-components model can be used for describing adsorption behavior of single ion in nature diatomite-liquid system, its parameters k and q(m) have constant values, which is favorable for practical quantitative calculation in a given system.

Scaling properties of a rice-pile model: inertia and friction effects.

PubMed

Khfifi, M; Loulidi, M

2008-11-01

We present a rice-pile cellular automaton model that includes inertial and friction effects. This model is studied in one dimension, where the updating of metastable sites is done according to a stochastic dynamics governed by a probabilistic toppling parameter p that depends on the accumulated energy of moving grains. We investigate the scaling properties of the model using finite-size scaling analysis. The avalanche size, the lifetime, and the residence time distributions exhibit a power-law behavior. Their corresponding critical exponents, respectively, tau, y, and yr, are not universal. They present continuous variation versus the parameters of the system. The maximal value of the critical exponent tau that our model gives is very close to the experimental one, tau=2.02 [Frette, Nature (London) 379, 49 (1996)], and the probability distribution of the residence time is in good agreement with the experimental results. We note that the critical behavior is observed only in a certain range of parameter values of the system which correspond to low inertia and high friction.

Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation

NASA Astrophysics Data System (ADS)

Reis, D. S.; Stedinger, J. R.; Martins, E. S.

2005-10-01

This paper develops a Bayesian approach to analysis of a generalized least squares (GLS) regression model for regional analyses of hydrologic data. The new approach allows computation of the posterior distributions of the parameters and the model error variance using a quasi-analytic approach. Two regional skew estimation studies illustrate the value of the Bayesian GLS approach for regional statistical analysis of a shape parameter and demonstrate that regional skew models can be relatively precise with effective record lengths in excess of 60 years. With Bayesian GLS the marginal posterior distribution of the model error variance and the corresponding mean and variance of the parameters can be computed directly, thereby providing a simple but important extension of the regional GLS regression procedures popularized by Tasker and Stedinger (1989), which is sensitive to the likely values of the model error variance when it is small relative to the sampling error in the at-site estimator.

Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation

NASA Astrophysics Data System (ADS)

Li, Shan; Zhang, Shaoqing; Liu, Zhengyu; Lu, Lv; Zhu, Jiang; Zhang, Xuefeng; Wu, Xinrong; Zhao, Ming; Vecchi, Gabriel A.; Zhang, Rong-Hua; Lin, Xiaopei

2018-04-01

Parametric uncertainty in convection parameterization is one major source of model errors that cause model climate drift. Convection parameter tuning has been widely studied in atmospheric models to help mitigate the problem. However, in a fully coupled general circulation model (CGCM), convection parameters which impact the ocean as well as the climate simulation may have different optimal values. This study explores the possibility of estimating convection parameters with an ensemble coupled data assimilation method in a CGCM. Impacts of the convection parameter estimation on climate analysis and forecast are analyzed. In a twin experiment framework, five convection parameters in the GFDL coupled model CM2.1 are estimated individually and simultaneously under both perfect and imperfect model regimes. Results show that the ensemble data assimilation method can help reduce the bias in convection parameters. With estimated convection parameters, the analyses and forecasts for both the atmosphere and the ocean are generally improved. It is also found that information in low latitudes is relatively more important for estimating convection parameters. This study further suggests that when important parameters in appropriate physical parameterizations are identified, incorporating their estimation into traditional ensemble data assimilation procedure could improve the final analysis and climate prediction.

Suitability of parametric models to describe the hydraulic properties of an unsaturated coarse sand and gravel

USGS Publications Warehouse

Mace, Andy; Rudolph, David L.; Kachanoski , R. Gary

1998-01-01

The performance of parametric models used to describe soil water retention (SWR) properties and predict unsaturated hydraulic conductivity (K) as a function of volumetric water content (θ) is examined using SWR and K(θ) data for coarse sand and gravel sediments. Six 70 cm long, 10 cm diameter cores of glacial outwash were instrumented at eight depths with porous cup ten-siometers and time domain reflectometry probes to measure soil water pressure head (h) and θ, respectively, for seven unsaturated and one saturated steady-state flow conditions. Forty-two θ(h) and K(θ) relationships were measured from the infiltration tests on the cores. Of the four SWR models compared in the analysis, the van Genuchten (1980) equation with parameters m and n restricted according to the Mualem (m = 1 - 1/n) criterion is best suited to describe the θ(h) relationships. The accuracy of two models that predict K(θ) using parameter values derived from the SWR models was also evaluated. The model developed by van Genuchten (1980) based on the theoretical expression of Mualem (1976) predicted K(θ) more accurately than the van Genuchten (1980) model based on the theory of Burdine (1953). A sensitivity analysis shows that more accurate predictions of K(θ) are achieved using SWR model parameters derived with residual water content (θr) specified according to independent measurements of θ at values of h where θ/h ∼ 0 rather than model-fit θr values. The accuracy of the model K(θ) function improves markedly when at least one value of unsaturated K is used to scale the K(θ) function predicted using the saturated K. The results of this investigation indicate that the hydraulic properties of coarse-grained sediments can be accurately described using the parametric models. In addition, data collection efforts should focus on measuring at least one value of unsaturated hydraulic conductivity and as complete a set of SWR data as possible, particularly in the dry range.

Zener Diode Compact Model Parameter Extraction Using Xyce-Dakota Optimization.

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

Buchheit, Thomas E.; Wilcox, Ian Zachary; Sandoval, Andrew J

This report presents a detailed process for compact model parameter extraction for DC circuit Zener diodes. Following the traditional approach of Zener diode parameter extraction, circuit model representation is defined and then used to capture the different operational regions of a real diode's electrical behavior. The circuit model contains 9 parameters represented by resistors and characteristic diodes as circuit model elements. The process of initial parameter extraction, the identification of parameter values for the circuit model elements, is presented in a way that isolates the dependencies between certain electrical parameters and highlights both the empirical nature of the extraction andmore » portions of the real diode physical behavior which of the parameters are intended to represent. Optimization of the parameters, a necessary part of a robost parameter extraction process, is demonstrated using a 'Xyce-Dakota' workflow, discussed in more detail in the report. Among other realizations during this systematic approach of electrical model parameter extraction, non-physical solutions are possible and can be difficult to avoid because of the interdependencies between the different parameters. The process steps described are fairly general and can be leveraged for other types of semiconductor device model extractions. Also included in the report are recommendations for experiment setups for generating optimum dataset for model extraction and the Parameter Identification and Ranking Table (PIRT) for Zener diodes.« less

Bouc-Wen hysteresis model identification using Modified Firefly Algorithm

NASA Astrophysics Data System (ADS)

Zaman, Mohammad Asif; Sikder, Urmita

2015-12-01

The parameters of Bouc-Wen hysteresis model are identified using a Modified Firefly Algorithm. The proposed algorithm uses dynamic process control parameters to improve its performance. The algorithm is used to find the model parameter values that results in the least amount of error between a set of given data points and points obtained from the Bouc-Wen model. The performance of the algorithm is compared with the performance of conventional Firefly Algorithm, Genetic Algorithm and Differential Evolution algorithm in terms of convergence rate and accuracy. Compared to the other three optimization algorithms, the proposed algorithm is found to have good convergence rate with high degree of accuracy in identifying Bouc-Wen model parameters. Finally, the proposed method is used to find the Bouc-Wen model parameters from experimental data. The obtained model is found to be in good agreement with measured data.

Modeling spatially-varying landscape change points in species occurrence thresholds

USGS Publications Warehouse

Wagner, Tyler; Midway, Stephen R.

2014-01-01

Predicting species distributions at scales of regions to continents is often necessary, as large-scale phenomena influence the distributions of spatially structured populations. Land use and land cover are important large-scale drivers of species distributions, and landscapes are known to create species occurrence thresholds, where small changes in a landscape characteristic results in abrupt changes in occurrence. The value of the landscape characteristic at which this change occurs is referred to as a change point. We present a hierarchical Bayesian threshold model (HBTM) that allows for estimating spatially varying parameters, including change points. Our model also allows for modeling estimated parameters in an effort to understand large-scale drivers of variability in land use and land cover on species occurrence thresholds. We use range-wide detection/nondetection data for the eastern brook trout (Salvelinus fontinalis), a stream-dwelling salmonid, to illustrate our HBTM for estimating and modeling spatially varying threshold parameters in species occurrence. We parameterized the model for investigating thresholds in landscape predictor variables that are measured as proportions, and which are therefore restricted to values between 0 and 1. Our HBTM estimated spatially varying thresholds in brook trout occurrence for both the proportion agricultural and urban land uses. There was relatively little spatial variation in change point estimates, although there was spatial variability in the overall shape of the threshold response and associated uncertainty. In addition, regional mean stream water temperature was correlated to the change point parameters for the proportion of urban land use, with the change point value increasing with increasing mean stream water temperature. We present a framework for quantify macrosystem variability in spatially varying threshold model parameters in relation to important large-scale drivers such as land use and land cover. Although the model presented is a logistic HBTM, it can easily be extended to accommodate other statistical distributions for modeling species richness or abundance.

MODFLOW-2000, the U.S. Geological Survey Modular Ground-Water Model -Documentation of the Hydrogeologic-Unit Flow (HUF) Package

USGS Publications Warehouse

Anderman, E.R.; Hill, M.C.

2000-01-01

This report documents the Hydrogeologic-Unit Flow (HUF) Package for the groundwater modeling computer program MODFLOW-2000. The HUF Package is an alternative internal flow package that allows the vertical geometry of the system hydrogeology to be defined explicitly within the model using hydrogeologic units that can be different than the definition of the model layers. The HUF Package works with all the processes of MODFLOW-2000. For the Ground-Water Flow Process, the HUF Package calculates effective hydraulic properties for the model layers based on the hydraulic properties of the hydrogeologic units, which are defined by the user using parameters. The hydraulic properties are used to calculate the conductance coefficients and other terms needed to solve the ground-water flow equation. The sensitivity of the model to the parameters defined within the HUF Package input file can be calculated using the Sensitivity Process, using observations defined with the Observation Process. Optimal values of the parameters can be estimated by using the Parameter-Estimation Process. The HUF Package is nearly identical to the Layer-Property Flow (LPF) Package, the major difference being the definition of the vertical geometry of the system hydrogeology. Use of the HUF Package is illustrated in two test cases, which also serve to verify the performance of the package by showing that the Parameter-Estimation Process produces the true parameter values when exact observations are used.

Good Models Gone Bad: Quantifying and Predicting Parameter-Induced Climate Model Simulation Failures

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Klein, R.; Tannahill, J.; Brandon, S.; Covey, C. C.; Domyancic, D.; Ivanova, D. P.

2012-12-01

Simulations using IPCC-class climate models are subject to fail or crash for a variety of reasons. Statistical analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation failures of the Parallel Ocean Program (POP2). About 8.5% of our POP2 runs failed for numerical reasons at certain combinations of parameter values. We apply support vector machine (SVM) classification from the fields of pattern recognition and machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. The SVM classifiers readily predict POP2 failures in an independent validation ensemble, and are subsequently used to determine the causes of the failures via a global sensitivity analysis. Four parameters related to ocean mixing and viscosity are identified as the major sources of POP2 failures. Our method can be used to improve the robustness of complex scientific models to parameter perturbations and to better steer UQ ensembles. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the Uncertainty Quantification Strategic Initiative Laboratory Directed Research and Development Project at LLNL under project tracking code 10-SI-013 (UCRL LLNL-ABS-569112).

Parameter estimation for lithium ion batteries

NASA Astrophysics Data System (ADS)

Santhanagopalan, Shriram

With an increase in the demand for lithium based batteries at the rate of about 7% per year, the amount of effort put into improving the performance of these batteries from both experimental and theoretical perspectives is increasing. There exist a number of mathematical models ranging from simple empirical models to complicated physics-based models to describe the processes leading to failure of these cells. The literature is also rife with experimental studies that characterize the various properties of the system in an attempt to improve the performance of lithium ion cells. However, very little has been done to quantify the experimental observations and relate these results to the existing mathematical models. In fact, the best of the physics based models in the literature show as much as 20% discrepancy when compared to experimental data. The reasons for such a big difference include, but are not limited to, numerical complexities involved in extracting parameters from experimental data and inconsistencies in interpreting directly measured values for the parameters. In this work, an attempt has been made to implement simplified models to extract parameter values that accurately characterize the performance of lithium ion cells. The validity of these models under a variety of experimental conditions is verified using a model discrimination procedure. Transport and kinetic properties are estimated using a non-linear estimation procedure. The initial state of charge inside each electrode is also maintained as an unknown parameter, since this value plays a significant role in accurately matching experimental charge/discharge curves with model predictions and is not readily known from experimental data. The second part of the dissertation focuses on parameters that change rapidly with time. For example, in the case of lithium ion batteries used in Hybrid Electric Vehicle (HEV) applications, the prediction of the State of Charge (SOC) of the cell under a variety of road conditions is important. An algorithm to predict the SOC in time intervals as small as 5 ms is of critical demand. In such cases, the conventional non-linear estimation procedure is not time-effective. There exist methodologies in the literature, such as those based on fuzzy logic; however, these techniques require a lot of computational storage space. Consequently, it is not possible to implement such techniques on a micro-chip for integration as a part of a real-time device. The Extended Kalman Filter (EKF) based approach presented in this work is a first step towards developing an efficient method to predict online, the State of Charge of a lithium ion cell based on an electrochemical model. The final part of the dissertation focuses on incorporating uncertainty in parameter values into electrochemical models using the polynomial chaos theory (PCT).

Parameter Stability of the Functional–Structural Plant Model GREENLAB as Affected by Variation within Populations, among Seasons and among Growth Stages

PubMed Central

Ma, Yuntao; Li, Baoguo; Zhan, Zhigang; Guo, Yan; Luquet, Delphine; de Reffye, Philippe; Dingkuhn, Michael

2007-01-01

Background and Aims It is increasingly accepted that crop models, if they are to simulate genotype-specific behaviour accurately, should simulate the morphogenetic process generating plant architecture. A functional–structural plant model, GREENLAB, was previously presented and validated for maize. The model is based on a recursive mathematical process, with parameters whose values cannot be measured directly and need to be optimized statistically. This study aims at evaluating the stability of GREENLAB parameters in response to three types of phenotype variability: (1) among individuals from a common population; (2) among populations subjected to different environments (seasons); and (3) among different development stages of the same plants. Methods Five field experiments were conducted in the course of 4 years on irrigated fields near Beijing, China. Detailed observations were conducted throughout the seasons on the dimensions and fresh biomass of all above-ground plant organs for each metamer. Growth stage-specific target files were assembled from the data for GREENLAB parameter optimization. Optimization was conducted for specific developmental stages or the entire growth cycle, for individual plants (replicates), and for different seasons. Parameter stability was evaluated by comparing their CV with that of phenotype observation for the different sources of variability. A reduced data set was developed for easier model parameterization using one season, and validated for the four other seasons. Key Results and Conclusions The analysis of parameter stability among plants sharing the same environment and among populations grown in different environments indicated that the model explains some of the inter-seasonal variability of phenotype (parameters varied less than the phenotype itself), but not inter-plant variability (parameter and phenotype variability were similar). Parameter variability among developmental stages was small, indicating that parameter values were largely development-stage independent. The authors suggest that the high level of parameter stability observed in GREENLAB can be used to conduct comparisons among genotypes and, ultimately, genetic analyses. PMID:17158141

Turbulent eddy diffusion models in exposure assessment - Determination of the eddy diffusion coefficient.

PubMed

Shao, Yuan; Ramachandran, Sandhya; Arnold, Susan; Ramachandran, Gurumurthy

2017-03-01

The use of the turbulent eddy diffusion model and its variants in exposure assessment is limited due to the lack of knowledge regarding the isotropic eddy diffusion coefficient, D T . But some studies have suggested a possible relationship between D T and the air changes per hour (ACH) through a room. The main goal of this study was to accurately estimate D T for a range of ACH values by minimizing the difference between the concentrations measured and predicted by eddy diffusion model. We constructed an experimental chamber with a spatial concentration gradient away from the contaminant source, and conducted 27 3-hr long experiments using toluene and acetone under different air flow conditions (0.43-2.89 ACHs). An eddy diffusion model accounting for chamber boundary, general ventilation, and advection was developed. A mathematical expression for the slope based on the geometrical parameters of the ventilation system was also derived. There is a strong linear relationship between D T and ACH, providing a surrogate parameter for estimating D T in real-life settings. For the first time, a mathematical expression for the relationship between D T and ACH has been derived that also corrects for non-ideal conditions, and the calculated value of the slope between these two parameters is very close to the experimentally determined value. The values of D T obtained from the experiments are generally consistent with values reported in the literature. They are also independent of averaging time of measurements, allowing for comparison of values obtained from different measurement settings. These findings make the use of turbulent eddy diffusion models for exposure assessment in workplace/indoor environments more practical.

Estimation of k-ε parameters using surrogate models and jet-in-crossflow data

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

Lefantzi, Sophia; Ray, Jaideep; Arunajatesan, Srinivasan

2014-11-01

We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS (Reynolds Av- eraged Navier Stokes) models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the parameter estimates. Methods such as Markov chain Monte Carlo (MCMC) estimate the PDF by sampling, with each sample requiring a run of the RANS model. Consequently a quick-running surrogate is used instead to the RANS simulator. The surrogate can be very difficult to design if the model's response i.e., the dependence of themore » calibration variable (the observable) on the parameter being estimated is complex. We show how the training data used to construct the surrogate can be employed to isolate a promising and physically realistic part of the parameter space, within which the response is well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the "well-behaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k - ε parameters ( C μ, C ε2 , C ε1 ) from experimental data of a transonic jet-in-crossflow interaction. The robustness of the calibration is investigated by checking its predictions of variables not included in the cal- ibration data. We also check the limit of applicability of the calibration by testing at off-calibration flow regimes. We find that calibration yield turbulence model parameters which predict the flowfield far better than when the nomi- nal values of the parameters are used. Substantial improvements are still obtained when we use the calibrated RANS model to predict jet-in-crossflow at Mach numbers and jet strengths quite different from those used to generate the ex- perimental (calibration) data. Thus the primary reason for poor predictive skill of RANS, when using nominal values of the turbulence model parameters, was parametric uncertainty, which was rectified by calibration. Post-calibration, the dominant contribution to model inaccuraries are due to the structural errors in RANS.« less

Small-signal modeling with direct parameter extraction for impact ionization effect in high-electron-mobility transistors

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

Guan, He; Lv, Hongliang; Guo, Hui, E-mail: hguan@stu.xidian.edu.cn

2015-11-21

Impact ionization affects the radio-frequency (RF) behavior of high-electron-mobility transistors (HEMTs), which have narrow-bandgap semiconductor channels, and this necessitates complex parameter extraction procedures for HEMT modeling. In this paper, an enhanced small-signal equivalent circuit model is developed to investigate the impact ionization, and an improved method is presented in detail for direct extraction of intrinsic parameters using two-step measurements in low-frequency and high-frequency regimes. The practicability of the enhanced model and the proposed direct parameter extraction method are verified by comparing the simulated S-parameters with published experimental data from an InAs/AlSb HEMT operating over a wide frequency range. The resultsmore » demonstrate that the enhanced model with optimal intrinsic parameter values that were obtained by the direct extraction approach can effectively characterize the effects of impact ionization on the RF performance of HEMTs.« less

Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Jamaluddin; Siringoringo, Rimbun

2017-12-01

Fuzzy k-Nearest Neighbor (FkNN) is one of the most powerful classification methods. The presence of fuzzy concepts in this method successfully improves its performance on almost all classification issues. The main drawbackof FKNN is that it is difficult to determine the parameters. These parameters are the number of neighbors (k) and fuzzy strength (m). Both parameters are very sensitive. This makes it difficult to determine the values of ‘m’ and ‘k’, thus making FKNN difficult to control because no theories or guides can deduce how proper ‘m’ and ‘k’ should be. This study uses Modified Particle Swarm Optimization (MPSO) to determine the best value of ‘k’ and ‘m’. MPSO is focused on the Constriction Factor Method. Constriction Factor Method is an improvement of PSO in order to avoid local circumstances optima. The model proposed in this study was tested on the German Credit Dataset. The test of the data/The data test has been standardized by UCI Machine Learning Repository which is widely applied to classification problems. The application of MPSO to the determination of FKNN parameters is expected to increase the value of classification performance. Based on the experiments that have been done indicating that the model offered in this research results in a better classification performance compared to the Fk-NN model only. The model offered in this study has an accuracy rate of 81%, while. With using Fk-NN model, it has the accuracy of 70%. At the end is done comparison of research model superiority with 2 other classification models;such as Naive Bayes and Decision Tree. This research model has a better performance level, where Naive Bayes has accuracy 75%, and the decision tree model has 70%

STEWB - Simplified Transient Estimation of the Water Budget

NASA Astrophysics Data System (ADS)

Meyer, P. D.; Simmons, C. S.; Cady, R. E.; Gee, G. W.

2001-12-01

A simplified model describing the transient water budget of a shallow unsaturated soil profile is presented. This model was developed for the U.S. Nuclear Regulatory Commission to provide estimates of the time-varying net infiltration at sites containing residual levels of radioactive materials. Ease of use, computational efficiency, and use of standard parameters and available data were requirements of the model. The model's conceptualization imposes the following simplifications: a uniform soil profile, instantaneous redistribution of infiltrated water, drainage under a unit hydraulic gradient, and no drainage from the soil profile during infiltration. The model's formulation is a revision of that originally presented by Kim et al. [WRR, 32(12):3475-3484, 1996]. Daily meteorological data are required as input. Random durations for precipitation events are generated based on an estimate of the average number of exceedances per year for the specific daily rainfall depth observed. Snow accumulation and melt are described using empirical relationships. During precipitation or snowmelt, runoff is described using an infiltration equation for ponded conditions. When no water is being applied to the profile, evapotranspiration (ET) and drainage occur. The ET rate equals the potential evapotranspiration rate, PET, above a critical value of saturation, SC. Below this critical value, ET = PET*(S/SC)**p, where S is saturation and p is an empirical parameter. Drainage flux from the profile equals the hydraulic conductivity as represented by the Brooks-Corey model. The model has been implemented with an easy-to-use graphical interface and is available at http://nrc-hydro-uncert.pnl.gov/code.htm. Comparison of the model results with lysimeter measurements will be shown, including a 50-year record from the ARS-Coshocton site in Ohio. The interpretation of parameters and the sensitivity of the model to parameter values will be discussed.

Experimentally valid predictions of muscle force and EMG in models of motor-unit function are most sensitive to neural properties.

PubMed

Keenan, Kevin G; Valero-Cuevas, Francisco J

2007-09-01

Computational models of motor-unit populations are the objective implementations of the hypothesized mechanisms by which neural and muscle properties give rise to electromyograms (EMGs) and force. However, the variability/uncertainty of the parameters used in these models--and how they affect predictions--confounds assessing these hypothesized mechanisms. We perform a large-scale computational sensitivity analysis on the state-of-the-art computational model of surface EMG, force, and force variability by combining a comprehensive review of published experimental data with Monte Carlo simulations. To exhaustively explore model performance and robustness, we ran numerous iterative simulations each using a random set of values for nine commonly measured motor neuron and muscle parameters. Parameter values were sampled across their reported experimental ranges. Convergence after 439 simulations found that only 3 simulations met our two fitness criteria: approximating the well-established experimental relations for the scaling of EMG amplitude and force variability with mean force. An additional 424 simulations preferentially sampling the neighborhood of those 3 valid simulations converged to reveal 65 additional sets of parameter values for which the model predictions approximate the experimentally known relations. We find the model is not sensitive to muscle properties but very sensitive to several motor neuron properties--especially peak discharge rates and recruitment ranges. Therefore to advance our understanding of EMG and muscle force, it is critical to evaluate the hypothesized neural mechanisms as implemented in today's state-of-the-art models of motor unit function. We discuss experimental and analytical avenues to do so as well as new features that may be added in future implementations of motor-unit models to improve their experimental validity.

Sensitivity analysis of a data assimilation technique for hindcasting and forecasting hydrodynamics of a complex coastal water body

NASA Astrophysics Data System (ADS)

Ren, Lei; Hartnett, Michael

2017-02-01

Accurate forecasting of coastal surface currents is of great economic importance due to marine activities such as marine renewable energy and fish farms in coastal regions in recent twenty years. Advanced oceanographic observation systems such as satellites and radars can provide many parameters of interest, such as surface currents and waves, with fine spatial resolution in near real time. To enhance modelling capability, data assimilation (DA) techniques which combine the available measurements with the hydrodynamic models have been used since the 1990s in oceanography. Assimilating measurements into hydrodynamic models makes the original model background states follow the observation trajectory, then uses it to provide more accurate forecasting information. Galway Bay is an open, wind dominated water body on which two coastal radars are deployed. An efficient and easy to implement sequential DA algorithm named Optimal Interpolation (OI) was used to blend radar surface current data into a three-dimensional Environmental Fluid Dynamics Code (EFDC) model. Two empirical parameters, horizontal correlation length and DA cycle length (CL), are inherent within OI. No guidance has previously been published regarding selection of appropriate values of these parameters or how sensitive OI DA is to variations in their values. Detailed sensitivity analysis has been performed on both of these parameters and results presented. Appropriate value of DA CL was examined and determined on producing the minimum Root-Mean-Square-Error (RMSE) between radar data and model background states. Analysis was performed to evaluate assimilation index (AI) of using an OI DA algorithm in the model. AI of the half-day forecasting mean vectors' directions was over 50% in the best assimilation model. The ability of using OI to improve model forecasts was also assessed and is reported upon.

Uncertainty and the Social Cost of Methane Using Bayesian Constrained Climate Models

NASA Astrophysics Data System (ADS)

Errickson, F. C.; Anthoff, D.; Keller, K.

2016-12-01

Social cost estimates of greenhouse gases are important for the design of sound climate policies and are also plagued by uncertainty. One major source of uncertainty stems from the simplified representation of the climate system used in the integrated assessment models that provide these social cost estimates. We explore how uncertainty over the social cost of methane varies with the way physical processes and feedbacks in the methane cycle are modeled by (i) coupling three different methane models to a simple climate model, (ii) using MCMC to perform a Bayesian calibration of the three coupled climate models that simulates direct sampling from the joint posterior probability density function (pdf) of model parameters, and (iii) producing probabilistic climate projections that are then used to calculate the Social Cost of Methane (SCM) with the DICE and FUND integrated assessment models. We find that including a temperature feedback in the methane cycle acts as an additional constraint during the calibration process and results in a correlation between the tropospheric lifetime of methane and several climate model parameters. This correlation is not seen in the models lacking this feedback. Several of the estimated marginal pdfs of the model parameters also exhibit different distributional shapes and expected values depending on the methane model used. As a result, probabilistic projections of the climate system out to the year 2300 exhibit different levels of uncertainty and magnitudes of warming for each of the three models under an RCP8.5 scenario. We find these differences in climate projections result in differences in the distributions and expected values for our estimates of the SCM. We also examine uncertainty about the SCM by performing a Monte Carlo analysis using a distribution for the climate sensitivity while holding all other climate model parameters constant. Our SCM estimates using the Bayesian calibration are lower and exhibit less uncertainty about extremely high values in the right tail of the distribution compared to the Monte Carlo approach. This finding has important climate policy implications and suggests previous work that accounts for climate model uncertainty by only varying the climate sensitivity parameter may overestimate the SCM.

Homeostatic enhancement of active mechanotransduction

NASA Astrophysics Data System (ADS)

Milewski, Andrew; O'Maoiléidigh, Dáibhid; Hudspeth, A. J.

2018-05-01

Our sense of hearing boasts exquisite sensitivity to periodic signals. Experiments and modeling imply, however, that the auditory system achieves this performance for only a narrow range of parameter values. As a result, small changes in these values could compromise the ability of the mechanosensory hair cells to detect stimuli. We propose that, rather than exerting tight control over parameters, the auditory system employs a homeostatic mechanism that ensures the robustness of its operation to variation in parameter values. Through analytical techniques and computer simulations we investigate whether a homeostatic mechanism renders the hair bundle's signal-detection ability more robust to alterations in experimentally accessible parameters. When homeostasis is enforced, the range of values for which the bundle's sensitivity exceeds a threshold can increase by more than an order of magnitude. The robustness of cochlear function based on somatic motility or hair bundle motility may be achieved by employing the approach we describe here.

SU-F-BRD-16: Relative Biological Effectiveness of Double-Strand Break Induction for Modeling Cell Survival in Pristine Proton Beams of Different Dose-Averaged Linear Energy Transfers

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

Peeler, C; Bronk, L; UT Graduate School of Biomedical Sciences at Houston, Houston, TX

2015-06-15

Purpose: High throughput in vitro experiments assessing cell survival following proton radiation indicate that both the alpha and the beta parameters of the linear quadratic model increase with increasing proton linear energy transfer (LET). We investigated the relative biological effectiveness (RBE) of double-strand break (DSB) induction as a means of explaining the experimental results. Methods: Experiments were performed with two lung cancer cell lines and a range of proton LET values (0.94 – 19.4 keV/µm) using an experimental apparatus designed to irradiate cells in a 96 well plate such that each column encounters protons of different dose-averaged LET (LETd). Traditionalmore » linear quadratic survival curve fitting was performed, and alpha, beta, and RBE values obtained. Survival curves were also fit with a model incorporating RBE of DSB induction as the sole fit parameter. Fitted values of the RBE of DSB induction were then compared to values obtained using Monte Carlo Damage Simulation (MCDS) software and energy spectra calculated with Geant4. Other parameters including alpha, beta, and number of DSBs were compared to those obtained from traditional fitting. Results: Survival curve fitting with RBE of DSB induction yielded alpha and beta parameters that increase with proton LETd, which follows from the standard method of fitting; however, relying on a single fit parameter provided more consistent trends. The fitted values of RBE of DSB induction increased beyond what is predicted from MCDS data above proton LETd of approximately 10 keV/µm. Conclusion: In order to accurately model in vitro proton irradiation experiments performed with high throughput methods, the RBE of DSB induction must increase more rapidly than predicted by MCDS above LETd of 10 keV/µm. This can be explained by considering the increased complexity of DSBs or the nature of intra-track pairwise DSB interactions in this range of LETd values. NIH Grant 2U19CA021239-35.« less

Regression analysis and transfer function in estimating the parameters of central pulse waves from brachial pulse wave.

PubMed

Chai Rui; Li Si-Man; Xu Li-Sheng; Yao Yang; Hao Li-Ling

2017-07-01

This study mainly analyzed the parameters such as ascending branch slope (A_slope), dicrotic notch height (Hn), diastolic area (Ad) and systolic area (As) diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), subendocardial viability ratio (SEVR), waveform parameter (k), stroke volume (SV), cardiac output (CO) and peripheral resistance (RS) of central pulse wave invasively and non-invasively measured. These parameters extracted from the central pulse wave invasively measured were compared with the parameters measured from the brachial pulse waves by a regression model and a transfer function model. The accuracy of the parameters which were estimated by the regression model and the transfer function model was compared too. Our findings showed that in addition to the k value, the above parameters of the central pulse wave and the brachial pulse wave invasively measured had positive correlation. Both the regression model parameters including A_slope, DBP, SEVR and the transfer function model parameters had good consistency with the parameters invasively measured, and they had the same effect of consistency. The regression equations of the three parameters were expressed by Y'=a+bx. The SBP, PP, SV, CO of central pulse wave could be calculated through the regression model, but their accuracies were worse than that of transfer function model.

A Novel Scale Up Model for Prediction of Pharmaceutical Film Coating Process Parameters.

PubMed

Suzuki, Yasuhiro; Suzuki, Tatsuya; Minami, Hidemi; Terada, Katsuhide

2016-01-01

In the pharmaceutical tablet film coating process, we clarified that a difference in exhaust air relative humidity can be used to detect differences in process parameters values, the relative humidity of exhaust air was different under different atmospheric air humidity conditions even though all setting values of the manufacturing process parameters were the same, and the water content of tablets was correlated with the exhaust air relative humidity. Based on this experimental data, the exhaust air relative humidity index (EHI), which is an empirical equation that includes as functional parameters the pan coater type, heated air flow rate, spray rate of coating suspension, saturated water vapor pressure at heated air temperature, and partial water vapor pressure at atmospheric air pressure, was developed. The predictive values of exhaust relative humidity using EHI were in good correlation with the experimental data (correlation coefficient of 0.966) in all datasets. EHI was verified using the date of seven different drug products of different manufacturing scales. The EHI model will support formulation researchers by enabling them to set film coating process parameters when the batch size or pan coater type changes, and without the time and expense of further extensive testing.

An automatic scaling method for obtaining the trace and parameters from oblique ionogram based on hybrid genetic algorithm

NASA Astrophysics Data System (ADS)

Song, Huan; Hu, Yaogai; Jiang, Chunhua; Zhou, Chen; Zhao, Zhengyu; Zou, Xianjian

2016-12-01

Scaling oblique ionogram plays an important role in obtaining ionospheric structure at the midpoint of oblique sounding path. The paper proposed an automatic scaling method to extract the trace and parameters of oblique ionogram based on hybrid genetic algorithm (HGA). The extracted 10 parameters come from F2 layer and Es layer, such as maximum observation frequency, critical frequency, and virtual height. The method adopts quasi-parabolic (QP) model to describe F2 layer's electron density profile that is used to synthesize trace. And it utilizes secant theorem, Martyn's equivalent path theorem, image processing technology, and echoes' characteristics to determine seven parameters' best fit values, and three parameter's initial values in QP model to set up their searching spaces which are the needed input data of HGA. Then HGA searches the three parameters' best fit values from their searching spaces based on the fitness between the synthesized trace and the real trace. In order to verify the performance of the method, 240 oblique ionograms are scaled and their results are compared with manual scaling results and the inversion results of the corresponding vertical ionograms. The comparison results show that the scaling results are accurate or at least adequate 60-90% of the time.

Ring rolling process simulation for microstructure optimization

NASA Astrophysics Data System (ADS)

Franchi, Rodolfo; Del Prete, Antonio; Donatiello, Iolanda; Calabrese, Maurizio

2017-10-01

Metal undergoes complicated microstructural evolution during Hot Ring Rolling (HRR), which determines the quality, mechanical properties and life of the ring formed. One of the principal microstructure properties which mostly influences the structural performances of forged components, is the value of the average grain size. In the present paper a ring rolling process has been studied and optimized in order to obtain anular components to be used in aerospace applications. In particular, the influence of process input parameters (feed rate of the mandrel and angular velocity of driver roll) on microstructural and on geometrical features of the final ring has been evaluated. For this purpose, a three-dimensional finite element model for HRR has been developed in SFTC DEFORM V11, taking into account also microstructural development of the material used (the nickel superalloy Waspalloy). The Finite Element (FE) model has been used to formulate a proper optimization problem. The optimization procedure has been developed in order to find the combination of process parameters which allows to minimize the average grain size. The Response Surface Methodology (RSM) has been used to find the relationship between input and output parameters, by using the exact values of output parameters in the control points of a design space explored through FEM simulation. Once this relationship is known, the values of the output parameters can be calculated for each combination of the input parameters. Then, an optimization procedure based on Genetic Algorithms has been applied. At the end, the minimum value of average grain size with respect to the input parameters has been found.

Value of Information Analysis Applied to the Economic Evaluation of Interventions Aimed at Reducing Juvenile Delinquency: An Illustration.

PubMed

Eeren, Hester V; Schawo, Saskia J; Scholte, Ron H J; Busschbach, Jan J V; Hakkaart, Leona

2015-01-01

To investigate whether a value of information analysis, commonly applied in health care evaluations, is feasible and meaningful in the field of crime prevention. Interventions aimed at reducing juvenile delinquency are increasingly being evaluated according to their cost-effectiveness. Results of cost-effectiveness models are subject to uncertainty in their cost and effect estimates. Further research can reduce that parameter uncertainty. The value of such further research can be estimated using a value of information analysis, as illustrated in the current study. We built upon an earlier published cost-effectiveness model that demonstrated the comparison of two interventions aimed at reducing juvenile delinquency. Outcomes were presented as costs per criminal activity free year. At a societal willingness-to-pay of €71,700 per criminal activity free year, further research to eliminate parameter uncertainty was valued at €176 million. Therefore, in this illustrative analysis, the value of information analysis determined that society should be willing to spend a maximum of €176 million in reducing decision uncertainty in the cost-effectiveness of the two interventions. Moreover, the results suggest that reducing uncertainty in some specific model parameters might be more valuable than in others. Using a value of information framework to assess the value of conducting further research in the field of crime prevention proved to be feasible. The results were meaningful and can be interpreted according to health care evaluation studies. This analysis can be helpful in justifying additional research funds to further inform the reimbursement decision in regard to interventions for juvenile delinquents.

Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature.

PubMed

Van Derlinden, E; Bernaerts, K; Van Impe, J F

2010-05-21

Optimal experiment design for parameter estimation (OED/PE) has become a popular tool for efficient and accurate estimation of kinetic model parameters. When the kinetic model under study encloses multiple parameters, different optimization strategies can be constructed. The most straightforward approach is to estimate all parameters simultaneously from one optimal experiment (single OED/PE strategy). However, due to the complexity of the optimization problem or the stringent limitations on the system's dynamics, the experimental information can be limited and parameter estimation convergence problems can arise. As an alternative, we propose to reduce the optimization problem to a series of two-parameter estimation problems, i.e., an optimal experiment is designed for a combination of two parameters while presuming the other parameters known. Two different approaches can be followed: (i) all two-parameter optimal experiments are designed based on identical initial parameter estimates and parameters are estimated simultaneously from all resulting experimental data (global OED/PE strategy), and (ii) optimal experiments are calculated and implemented sequentially whereby the parameter values are updated intermediately (sequential OED/PE strategy). This work exploits OED/PE for the identification of the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993). This kinetic model describes the effect of temperature on the microbial growth rate and encloses four parameters. The three OED/PE strategies are considered and the impact of the OED/PE design strategy on the accuracy of the CTMI parameter estimation is evaluated. Based on a simulation study, it is observed that the parameter values derived from the sequential approach deviate more from the true parameters than the single and global strategy estimates. The single and global OED/PE strategies are further compared based on experimental data obtained from design implementation in a bioreactor. Comparable estimates are obtained, but global OED/PE estimates are, in general, more accurate and reliable. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Parameter Estimation for Thurstone Choice Models

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

Vojnovic, Milan; Yun, Seyoung

We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one ormore » more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.« less

Testable solution of the cosmological constant and coincidence problems

NASA Astrophysics Data System (ADS)

Shaw, Douglas J.; Barrow, John D.

2011-02-01

We present a new solution to the cosmological constant (CC) and coincidence problems in which the observed value of the CC, Λ, is linked to other observable properties of the Universe. This is achieved by promoting the CC from a parameter that must be specified, to a field that can take many possible values. The observed value of Λ≈(9.3Gyrs)-2 [≈10-120 in Planck units] is determined by a new constraint equation which follows from the application of a causally restricted variation principle. When applied to our visible Universe, the model makes a testable prediction for the dimensionless spatial curvature of Ωk0=-0.0056(ζb/0.5), where ζb˜1/2 is a QCD parameter. Requiring that a classical history exist, our model determines the probability of observing a given Λ. The observed CC value, which we successfully predict, is typical within our model even before the effects of anthropic selection are included. When anthropic selection effects are accounted for, we find that the observed coincidence between tΛ=Λ-1/2 and the age of the Universe, tU, is a typical occurrence in our model. In contrast to multiverse explanations of the CC problems, our solution is independent of the choice of a prior weighting of different Λ values and does not rely on anthropic selection effects. Our model includes no unnatural small parameters and does not require the introduction of new dynamical scalar fields or modifications to general relativity, and it can be tested by astronomical observations in the near future.

Full-envelope aerodynamic modeling of the Harrier aircraft

NASA Technical Reports Server (NTRS)

Mcnally, B. David

1986-01-01

A project to identify a full-envelope model of the YAV-8B Harrier using flight-test and parameter identification techniques is described. As part of the research in advanced control and display concepts for V/STOL aircraft, a full-envelope aerodynamic model of the Harrier is identified, using mathematical model structures and parameter identification methods. A global-polynomial model structure is also used as a basis for the identification of the YAV-8B aerodynamic model. State estimation methods are used to ensure flight data consistency prior to parameter identification.Equation-error methods are used to identify model parameters. A fixed-base simulator is used extensively to develop flight test procedures and to validate parameter identification software. Using simple flight maneuvers, a simulated data set was created covering the YAV-8B flight envelope from about 0.3 to 0.7 Mach and about -5 to 15 deg angle of attack. A singular value decomposition implementation of the equation-error approach produced good parameter estimates based on this simulated data set.

Use of system identification techniques for improving airframe finite element models using test data

NASA Technical Reports Server (NTRS)

Hanagud, Sathya V.; Zhou, Weiyu; Craig, James I.; Weston, Neil J.

1993-01-01

A method for using system identification techniques to improve airframe finite element models using test data was developed and demonstrated. The method uses linear sensitivity matrices to relate changes in selected physical parameters to changes in the total system matrices. The values for these physical parameters were determined using constrained optimization with singular value decomposition. The method was confirmed using both simple and complex finite element models for which pseudo-experimental data was synthesized directly from the finite element model. The method was then applied to a real airframe model which incorporated all of the complexities and details of a large finite element model and for which extensive test data was available. The method was shown to work, and the differences between the identified model and the measured results were considered satisfactory.

Modeling annual extreme temperature using generalized extreme value distribution: A case study in Malaysia

NASA Astrophysics Data System (ADS)

Hasan, Husna; Salam, Norfatin; Kassim, Suraiya

2013-04-01

Extreme temperature of several stations in Malaysia is modeled by fitting the annual maximum to the Generalized Extreme Value (GEV) distribution. The Augmented Dickey Fuller (ADF) and Phillips Perron (PP) tests are used to detect stochastic trends among the stations. The Mann-Kendall (MK) test suggests a non-stationary model. Three models are considered for stations with trend and the Likelihood Ratio test is used to determine the best-fitting model. The results show that Subang and Bayan Lepas stations favour a model which is linear for the location parameters while Kota Kinabalu and Sibu stations are suitable with a model in the logarithm of the scale parameters. The return level is the level of events (maximum temperature) which is expected to be exceeded once, on average, in a given number of years, is obtained.

Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models.

PubMed

White, L J; Mandl, J N; Gomes, M G M; Bodley-Tickell, A T; Cane, P A; Perez-Brena, P; Aguilar, J C; Siqueira, M M; Portes, S A; Straliotto, S M; Waris, M; Nokes, D J; Medley, G F

2007-09-01

The nature and role of re-infection and partial immunity are likely to be important determinants of the transmission dynamics of human respiratory syncytial virus (hRSV). We propose a single model structure that captures four possible host responses to infection and subsequent reinfection: partial susceptibility, altered infection duration, reduced infectiousness and temporary immunity (which might be partial). The magnitude of these responses is determined by four homotopy parameters, and by setting some of these parameters to extreme values we generate a set of eight nested, deterministic transmission models. In order to investigate hRSV transmission dynamics, we applied these models to incidence data from eight international locations. Seasonality is included as cyclic variation in transmission. Parameters associated with the natural history of the infection were assumed to be independent of geographic location, while others, such as those associated with seasonality, were assumed location specific. Models incorporating either of the two extreme assumptions for immunity (none or solid and lifelong) were unable to reproduce the observed dynamics. Model fits with either waning or partial immunity to disease or both were visually comparable. The best fitting structure was a lifelong partial immunity to both disease and infection. Observed patterns were reproduced by stochastic simulations using the parameter values estimated from the deterministic models.

Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

NASA Astrophysics Data System (ADS)

Yang, Bin

2017-07-01

Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately

A Morphological Analysis of Gamma-Ray Burst Early-optical Afterglows

NASA Astrophysics Data System (ADS)

Gao, He; Wang, Xiang-Gao; Mészáros, Peter; Zhang, Bing

2015-09-01

Within the framework of the external shock model of gamma-ray burst (GRB) afterglows, we perform a morphological analysis of the early-optical light curves to directly constrain model parameters. We define four morphological types, i.e., the reverse shock-dominated cases with/without the emergence of the forward shock peak (Type I/Type II), and the forward shock-dominated cases without/with νm crossing the band (Type III/IV). We systematically investigate all of the Swift GRBs that have optical detection earlier than 500 s and find 3/63 Type I bursts (4.8%), 12/63 Type II bursts (19.0%), 30/63 Type III bursts (47.6%), 8/63 Type IV bursts (12.7%), and 10/63 Type III/IV bursts (15.9%). We perform Monte Carlo simulations to constrain model parameters in order to reproduce the observations. We find that the favored value of the magnetic equipartition parameter in the forward shock ({ɛ }B{{f}}) ranges from 10-6 to 10-2, and the reverse-to-forward ratio of ɛB ({{R}}B) is about 100. The preferred electron equipartition parameter {ɛ }{{e}}{{r},{{f}}} value is 0.01, which is smaller than the commonly assumed value, e.g., 0.1. This could mitigate the so-called “efficiency problem” for the internal shock model, if ɛe during the prompt emission phase (in the internal shocks) is large (say, ˜0.1). The preferred {{R}}B value is in agreement with the results in previous works that indicate a moderately magnetized baryonic jet for GRBs.

Stochastic mechanical model of vocal folds for producing jitter and for identifying pathologies through real voices.

PubMed

Cataldo, E; Soize, C

2018-06-06

Jitter, in voice production applications, is a random phenomenon characterized by the deviation of the glottal cycle length with respect to a mean value. Its study can help in identifying pathologies related to the vocal folds according to the values obtained through the different ways to measure it. This paper aims to propose a stochastic model, considering three control parameters, to generate jitter based on a deterministic one-mass model for the dynamics of the vocal folds and to identify parameters from the stochastic model taking into account real voice signals experimentally obtained. To solve the corresponding stochastic inverse problem, the cost function used is based on the distance between probability density functions of the random variables associated with the fundamental frequencies obtained by the experimental voices and the simulated ones, and also on the distance between features extracted from the voice signals, simulated and experimental, to calculate jitter. The results obtained show that the model proposed is valid and some samples of voices are synthesized considering the identified parameters for normal and pathological cases. The strategy adopted is also a novelty and mainly because a solution was obtained. In addition to the use of three parameters to construct the model of jitter, it is the discussion of a parameter related to the bandwidth of the power spectral density function of the stochastic process to measure the quality of the signal generated. A study about the influence of all the main parameters is also performed. The identification of the parameters of the model considering pathological cases is maybe of all novelties introduced by the paper the most interesting. Copyright © 2018 Elsevier Ltd. All rights reserved.

Estimation of dynamic rotor loads for the rotor systems research aircraft: Methodology development and validation

NASA Technical Reports Server (NTRS)

Duval, R. W.; Bahrami, M.

1985-01-01

The Rotor Systems Research Aircraft uses load cells to isolate the rotor/transmission systm from the fuselage. A mathematical model relating applied rotor loads and inertial loads of the rotor/transmission system to the load cell response is required to allow the load cells to be used to estimate rotor loads from flight data. Such a model is derived analytically by applying a force and moment balance to the isolated rotor/transmission system. The model is tested by comparing its estimated values of applied rotor loads with measured values obtained from a ground based shake test. Discrepancies in the comparison are used to isolate sources of unmodeled external loads. Once the structure of the mathematical model has been validated by comparison with experimental data, the parameters must be identified. Since the parameters may vary with flight condition it is desirable to identify the parameters directly from the flight data. A Maximum Likelihood identification algorithm is derived for this purpose and tested using a computer simulation of load cell data. The identification is found to converge within 10 samples. The rapid convergence facilitates tracking of time varying parameters of the load cell model in flight.

Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.

PubMed

Rácz, A; Bajusz, D; Héberger, K

2015-01-01

Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.

A Simulation Model for Studying Effects of Pollution and Freshwater Inflow on Secondary Productivity in an Ecosystem. Ph.D. Thesis - North Carolina State Univ.

NASA Technical Reports Server (NTRS)

Johnson, R. W.

1974-01-01

A mathematical model of an ecosystem is developed. Secondary productivity is evaluated in terms of man related and controllable factors. Information from an existing physical parameters model is used as well as pertinent biological measurements. Predictive information of value to estuarine management is presented. Biological, chemical, and physical parameters measured in order to develop models of ecosystems are identified.

Optimal solutions for a bio mathematical model for the evolution of smoking habit

NASA Astrophysics Data System (ADS)

Sikander, Waseem; Khan, Umar; Ahmed, Naveed; Mohyud-Din, Syed Tauseef

In this study, we apply Variation of Parameter Method (VPM) coupled with an auxiliary parameter to obtain the approximate solutions for the epidemic model for the evolution of smoking habit in a constant population. Convergence of the developed algorithm, namely VPM with an auxiliary parameter is studied. Furthermore, a simple way is considered for obtaining an optimal value of auxiliary parameter via minimizing the total residual error over the domain of problem. Comparison of the obtained results with standard VPM shows that an auxiliary parameter is very feasible and reliable in controlling the convergence of approximate solutions.

Examples of Nonconservatism in the CARE 3 Program

NASA Technical Reports Server (NTRS)

Dotson, Kelly J.

1988-01-01

This paper presents parameter regions in the CARE 3 (Computer-Aided Reliability Estimation version 3) computer program where the program overestimates the reliability of a modeled system without warning the user. Five simple models of fault-tolerant computer systems are analyzed; and, the parameter regions where reliability is overestimated are given. The source of the error in the reliability estimates for models which incorporate transient fault occurrences was not readily apparent. However, the source of much of the error for models with permanent and intermittent faults can be attributed to the choice of values for the run-time parameters of the program.

Effects of reaction-kinetic parameters on modeling reaction pathways in GaN MOVPE growth

NASA Astrophysics Data System (ADS)

Zhang, Hong; Zuo, Ran; Zhang, Guoyi

2017-11-01

In the modeling of the reaction-transport process in GaN MOVPE growth, the selections of kinetic parameters (activation energy Ea and pre-exponential factor A) for gas reactions are quite uncertain, which cause uncertainties in both gas reaction path and growth rate. In this study, numerical modeling of the reaction-transport process for GaN MOVPE growth in a vertical rotating disk reactor is conducted with varying kinetic parameters for main reaction paths. By comparisons of the molar concentrations of major Ga-containing species and the growth rates, the effects of kinetic parameters on gas reaction paths are determined. The results show that, depending on the values of the kinetic parameters, the gas reaction path may be dominated either by adduct/amide formation path, or by TMG pyrolysis path, or by both. Although the reaction path varies with different kinetic parameters, the predicted growth rates change only slightly because the total transport rate of Ga-containing species to the substrate changes slightly with reaction paths. This explains why previous authors using different chemical models predicted growth rates close to the experiment values. By varying the pre-exponential factor for the amide trimerization, it is found that the more trimers are formed, the lower the growth rates are than the experimental value, which indicates that trimers are poor growth precursors, because of thermal diffusion effect caused by high temperature gradient. The effective order for the contribution of major species to growth rate is found as: pyrolysis species > amides > trimers. The study also shows that radical reactions have little effect on gas reaction path because of the generation and depletion of H radicals in the chain reactions when NH2 is considered as the end species.

A Simple Model of Global Aerosol Indirect Effects

NASA Technical Reports Server (NTRS)

Ghan, Steven J.; Smith, Steven J.; Wang, Minghuai; Zhang, Kai; Pringle, Kirsty; Carslaw, Kenneth; Pierce, Jeffrey; Bauer, Susanne; Adams, Peter

2013-01-01

Most estimates of the global mean indirect effect of anthropogenic aerosol on the Earth's energy balance are from simulations by global models of the aerosol lifecycle coupled with global models of clouds and the hydrologic cycle. Extremely simple models have been developed for integrated assessment models, but lack the flexibility to distinguish between primary and secondary sources of aerosol. Here a simple but more physically based model expresses the aerosol indirect effect (AIE) using analytic representations of cloud and aerosol distributions and processes. Although the simple model is able to produce estimates of AIEs that are comparable to those from some global aerosol models using the same global mean aerosol properties, the estimates by the simple model are sensitive to preindustrial cloud condensation nuclei concentration, preindustrial accumulation mode radius, width of the accumulation mode, size of primary particles, cloud thickness, primary and secondary anthropogenic emissions, the fraction of the secondary anthropogenic emissions that accumulates on the coarse mode, the fraction of the secondary mass that forms new particles, and the sensitivity of liquid water path to droplet number concentration. Estimates of present-day AIEs as low as 5 W/sq m and as high as 0.3 W/sq m are obtained for plausible sets of parameter values. Estimates are surprisingly linear in emissions. The estimates depend on parameter values in ways that are consistent with results from detailed global aerosol-climate simulation models, which adds to understanding of the dependence on AIE uncertainty on uncertainty in parameter values.

Comparison of interplanetary CME arrival times and solar wind parameters based on the WSA-ENLIL model with three cone types and observations

NASA Astrophysics Data System (ADS)

Jang, Soojeong; Moon, Y.-J.; Lee, Jae-Ok; Na, Hyeonock

2014-09-01

We have made a comparison between coronal mass ejection (CME)-associated shock propagations based on the Wang-Sheeley-Arge (WSA)-ENLIL model using three cone types and in situ observations. For this we use 28 full-halo CMEs, whose cone parameters are determined and their corresponding interplanetary shocks were observed at the Earth, from 2001 to 2002. We consider three different cone types (an asymmetric cone model, an ice cream cone model, and an elliptical cone model) to determine 3-D CME cone parameters (radial velocity, angular width, and source location), which are the input values of the WSA-ENLIL model. The mean absolute error of the CME-associated shock travel times for the WSA-ENLIL model using the ice-cream cone model is 9.9 h, which is about 1 h smaller than those of the other models. We compare the peak values and profiles of solar wind parameters (speed and density) with in situ observations. We find that the root-mean-square errors of solar wind peak speed and density for the ice cream and asymmetric cone model are about 190 km/s and 24/cm3, respectively. We estimate the cross correlations between the models and observations within the time lag of ± 2 days from the shock travel time. The correlation coefficients between the solar wind speeds from the WSA-ENLIL model using three cone types and in situ observations are approximately 0.7, which is larger than those of solar wind density (cc ˜0.6). Our preliminary investigations show that the ice cream cone model seems to be better than the other cone models in terms of the input parameters of the WSA-ENLIL model.

Black hole complementarity in gravity's rainbow

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

Gim, Yongwan; Kim, Wontae, E-mail: yongwan89@sogang.ac.kr, E-mail: wtkim@sogang.ac.kr

2015-05-01

To see how the gravity's rainbow works for black hole complementary, we evaluate the required energy for duplication of information in the context of black hole complementarity by calculating the critical value of the rainbow parameter in the certain class of the rainbow Schwarzschild black hole. The resultant energy can be written as the well-defined limit for the vanishing rainbow parameter which characterizes the deformation of the relativistic dispersion relation in the freely falling frame. It shows that the duplication of information in quantum mechanics could not be allowed below a certain critical value of the rainbow parameter; however, itmore » might be possible above the critical value of the rainbow parameter, so that the consistent formulation in our model requires additional constraints or any other resolutions for the latter case.« less

A generic hydrological model for a green roof drainage layer.

PubMed

Vesuviano, Gianni; Stovin, Virginia

2013-01-01

A rainfall simulator of length 5 m and width 1 m was used to supply constant intensity and largely spatially uniform water inflow events to 100 different configurations of commercially available green roof drainage layer and protection mat. The runoff from each inflow event was collected and sampled at one-second intervals. Time-series runoff responses were subsequently produced for each of the tested configurations, using the average response of three repeat tests. Runoff models, based on storage routing (dS/dt = I-Q) and a power-law relationship between storage and runoff (Q = kS(n)), and incorporating a delay parameter, were created. The parameters k, n and delay were optimized to best fit each of the runoff responses individually. The range and pattern of optimized parameter values was analysed with respect to roof and event configuration. An analysis was performed to determine the sensitivity of the shape of the runoff profile to changes in parameter values. There appears to be potential to consolidate values of n by roof slope and drainage component material.

Observational constraints on Hubble parameter in viscous generalized Chaplygin gas

NASA Astrophysics Data System (ADS)

Thakur, P.

2018-04-01

Cosmological model with viscous generalized Chaplygin gas (in short, VGCG) is considered here to determine observational constraints on its equation of state parameters (in short, EoS) from background data. These data consists of H(z)-z (OHD) data, Baryonic Acoustic Oscillations peak parameter, CMB shift parameter and SN Ia data (Union 2.1). Best-fit values of the EoS parameters including present Hubble parameter (H0) and their acceptable range at different confidence limits are determined. In this model the permitted range for the present Hubble parameter and the transition redshift (zt) at 1σ confidence limits are H0= 70.24^{+0.34}_{-0.36} and zt=0.76^{+0.07}_{-0.07} respectively. These EoS parameters are then compared with those of other models. Present age of the Universe (t0) have also been determined here. Akaike information criterion and Bayesian information criterion for the model selection have been adopted for comparison with other models. It is noted that VGCG model satisfactorily accommodates the present accelerating phase of the Universe.

Exemplifying the Effects of Parameterization Shortcomings in the Numerical Simulation of Geological Energy and Mass Storage

NASA Astrophysics Data System (ADS)

Dethlefsen, Frank; Tilmann Pfeiffer, Wolf; Schäfer, Dirk

2016-04-01

Numerical simulations of hydraulic, thermal, geomechanical, or geochemical (THMC-) processes in the subsurface have been conducted for decades. Often, such simulations are commenced by applying a parameter set that is as realistic as possible. Then, a base scenario is calibrated on field observations. Finally, scenario simulations can be performed, for instance to forecast the system behavior after varying input data. In the context of subsurface energy and mass storage, however, these model calibrations based on field data are often not available, as these storage actions have not been carried out so far. Consequently, the numerical models merely rely on the parameter set initially selected, and uncertainties as a consequence of a lack of parameter values or process understanding may not be perceivable, not mentioning quantifiable. Therefore, conducting THMC simulations in the context of energy and mass storage deserves a particular review of the model parameterization with its input data, and such a review so far hardly exists to the required extent. Variability or aleatory uncertainty exists for geoscientific parameter values in general, and parameters for that numerous data points are available, such as aquifer permeabilities, may be described statistically thereby exhibiting statistical uncertainty. In this case, sensitivity analyses for quantifying the uncertainty in the simulation resulting from varying this parameter can be conducted. There are other parameters, where the lack of data quantity and quality implies a fundamental changing of ongoing processes when such a parameter value is varied in numerical scenario simulations. As an example for such a scenario uncertainty, varying the capillary entry pressure as one of the multiphase flow parameters can either allow or completely inhibit the penetration of an aquitard by gas. As the last example, the uncertainty of cap-rock fault permeabilities and consequently potential leakage rates of stored gases into shallow compartments are regarded as recognized ignorance by the authors of this study, as no realistic approach exists to determine this parameter and values are best guesses only. In addition to these aleatory uncertainties, an equivalent classification is possible for rating epistemic uncertainties describing the degree of understanding processes such as the geochemical and hydraulic effects following potential gas intrusions from deeper reservoirs into shallow aquifers. As an outcome of this grouping of uncertainties, prediction errors of scenario simulations can be calculated by sensitivity analyses, if the uncertainties are identified as statistical. However, if scenario uncertainties exist or even recognized ignorance has to be attested to a parameter or a process in question, the outcomes of simulations mainly depend on the decision of the modeler by choosing parameter values or by interpreting the occurring of processes. In that case, the informative value of numerical simulations is limited by ambiguous simulation results, which cannot be refined without improving the geoscientific database through laboratory or field studies on a longer term basis, so that the effects of the subsurface use may be predicted realistically. This discussion, amended by a compilation of available geoscientific data to parameterize such simulations, will be presented in this study.

Adaptive control of servo system based on LuGre model

NASA Astrophysics Data System (ADS)

Jin, Wang; Niancong, Liu; Jianlong, Chen; Weitao, Geng

2018-03-01

This paper established a mechanical model of feed system based on LuGre model. In order to solve the influence of nonlinear factors on the system running stability, a nonlinear single observer is designed to estimate the parameter z in the LuGre model and an adaptive friction compensation controller is designed. Simulink simulation results show that the control method can effectively suppress the adverse effects of friction and external disturbances. The simulation show that the adaptive parameter kz is between 0.11-0.13, and the value of gamma1 is between 1.9-2.1. Position tracking error reaches level 10-3 and is stabilized near 0 values within 0.3 seconds, the compensation method has better tracking accuracy and robustness.

Evaluation of calibration efficacy under different levels of uncertainty

DOE PAGES

Heo, Yeonsook; Graziano, Diane J.; Guzowski, Leah; ...

2014-06-10

This study examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty.We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data withmore » differing levels of detail in building design, usage, and operation.« less

Inverse models: A necessary next step in ground-water modeling

USGS Publications Warehouse

Poeter, E.P.; Hill, M.C.

1997-01-01

Inverse models using, for example, nonlinear least-squares regression, provide capabilities that help modelers take full advantage of the insight available from ground-water models. However, lack of information about the requirements and benefits of inverse models is an obstacle to their widespread use. This paper presents a simple ground-water flow problem to illustrate the requirements and benefits of the nonlinear least-squares repression method of inverse modeling and discusses how these attributes apply to field problems. The benefits of inverse modeling include: (1) expedited determination of best fit parameter values; (2) quantification of the (a) quality of calibration, (b) data shortcomings and needs, and (c) confidence limits on parameter estimates and predictions; and (3) identification of issues that are easily overlooked during nonautomated calibration.Inverse models using, for example, nonlinear least-squares regression, provide capabilities that help modelers take full advantage of the insight available from ground-water models. However, lack of information about the requirements and benefits of inverse models is an obstacle to their widespread use. This paper presents a simple ground-water flow problem to illustrate the requirements and benefits of the nonlinear least-squares regression method of inverse modeling and discusses how these attributes apply to field problems. The benefits of inverse modeling include: (1) expedited determination of best fit parameter values; (2) quantification of the (a) quality of calibration, (b) data shortcomings and needs, and (c) confidence limits on parameter estimates and predictions; and (3) identification of issues that are easily overlooked during nonautomated calibration.

The Evonik-Mainz Eye Care-Study (EMECS): Development of an Expert System for Glaucoma Risk Detection in a Working Population

PubMed Central

Wahl, Jochen; Barleon, Lorenz; Morfeld, Peter; Lichtmeß, Andrea; Haas-Brähler, Sibylle; Pfeiffer, Norbert

2016-01-01

Purpose To develop an expert system for glaucoma screening in a working population based on a human expert procedure using images of optic nerve head (ONH), visual field (frequency doubling technology, FDT) and intraocular pressure (IOP). Methods 4167 of 13037 (32%) employees between 40 and 65 years of Evonik Industries were screened. An experienced glaucoma expert (JW) assessed papilla parameters and evaluated all individual screening results. His classification into “no glaucoma”, “possible glaucoma” and “probable glaucoma” was defined as “gold standard”. A screening model was developed which was tested versus the gold-standard. This model took into account the assessment of the ONH. Values and relationships of CDR and IOP and the FDT were considered additionally and a glaucoma score was generated. The structure of the screening model was specified a priori whereas values of the parameters were chosen post-hoc to optimize sensitivity and specificity of the algorithm. Simple screening models based on IOP and / or FDT were investigated for comparison. Results 111 persons (2.66%) were classified as glaucoma suspects, thereof 13 (0.31%) as probable and 98 (2.35%) as possible glaucoma suspects by the expert. Re-evaluation by the screening model revealed a sensitivity of 83.8% and a specificity of 99.6% for all glaucoma suspects. The positive predictive value of the model was 80.2%, the negative predictive value 99.6%. Simple screening models showed insufficient diagnostic accuracy. Conclusion Adjustment of ONH and symmetry parameters with respect to excavation and IOP in an expert system produced sufficiently satisfying diagnostic accuracy. This screening model seems to be applicable in such a working population with relatively low age and low glaucoma prevalence. Different experts should validate the model in different populations. PMID:27479301

The Evonik-Mainz Eye Care-Study (EMECS): Development of an Expert System for Glaucoma Risk Detection in a Working Population.

PubMed

Wahl, Jochen; Barleon, Lorenz; Morfeld, Peter; Lichtmeß, Andrea; Haas-Brähler, Sibylle; Pfeiffer, Norbert

2016-01-01

To develop an expert system for glaucoma screening in a working population based on a human expert procedure using images of optic nerve head (ONH), visual field (frequency doubling technology, FDT) and intraocular pressure (IOP). 4167 of 13037 (32%) employees between 40 and 65 years of Evonik Industries were screened. An experienced glaucoma expert (JW) assessed papilla parameters and evaluated all individual screening results. His classification into "no glaucoma", "possible glaucoma" and "probable glaucoma" was defined as "gold standard". A screening model was developed which was tested versus the gold-standard. This model took into account the assessment of the ONH. Values and relationships of CDR and IOP and the FDT were considered additionally and a glaucoma score was generated. The structure of the screening model was specified a priori whereas values of the parameters were chosen post-hoc to optimize sensitivity and specificity of the algorithm. Simple screening models based on IOP and / or FDT were investigated for comparison. 111 persons (2.66%) were classified as glaucoma suspects, thereof 13 (0.31%) as probable and 98 (2.35%) as possible glaucoma suspects by the expert. Re-evaluation by the screening model revealed a sensitivity of 83.8% and a specificity of 99.6% for all glaucoma suspects. The positive predictive value of the model was 80.2%, the negative predictive value 99.6%. Simple screening models showed insufficient diagnostic accuracy. Adjustment of ONH and symmetry parameters with respect to excavation and IOP in an expert system produced sufficiently satisfying diagnostic accuracy. This screening model seems to be applicable in such a working population with relatively low age and low glaucoma prevalence. Different experts should validate the model in different populations.

Modeling of Light Reflection from Human Skin

NASA Astrophysics Data System (ADS)

Delgado, J. A.; Cornejo, A.; Rivas-Silva, J. F.; Rodríguez, E. E.

2006-09-01

In this work a two-layer model is used to simulate the spectral reflectance of adult human skin. We report and discuss diffuse reflectance spectra of this model for three values of the volume fraction of melanosomes fme, namely a) lightly pigmented skin fme = 4%, b) moderately pigmented skin fme = 14% and c) heavily pigmented skin fme = 30% at a volume fraction of blood fbl = 0.2%. We also considered the modeling of reflectance spectra for two values of fbl (0.2% and 1%) with fme = 4%. Both simulations were done in the 400-700 nm spectral range using the Monte Carlo simulation code MCML in standard C. Results showed that the principal signatures of human skin reflectance spectrum are obtained with this model and that it could be of valuable use to made predictions of diffuse reflectance of human skin for different values of the parameters related to skin characterization. These parameters can be associated to distinct medical conditions, such as erythema, jaundice, etc.

Dependence of subject-specific parameters for a fast helical CT respiratory motion model on breathing rate: an animal study

NASA Astrophysics Data System (ADS)

O'Connell, Dylan; Thomas, David H.; Lamb, James M.; Lewis, John H.; Dou, Tai; Sieren, Jered P.; Saylor, Melissa; Hofmann, Christian; Hoffman, Eric A.; Lee, Percy P.; Low, Daniel A.

2018-02-01

To determine if the parameters relating lung tissue displacement to a breathing surrogate signal in a previously published respiratory motion model vary with the rate of breathing during image acquisition. An anesthetized pig was imaged using multiple fast helical scans to sample the breathing cycle with simultaneous surrogate monitoring. Three datasets were collected while the animal was mechanically ventilated with different respiratory rates: 12 bpm (breaths per minute), 17 bpm, and 24 bpm. Three sets of motion model parameters describing the correspondences between surrogate signals and tissue displacements were determined. The model error was calculated individually for each dataset, as well asfor pairs of parameters and surrogate signals from different experiments. The values of one model parameter, a vector field denoted α which related tissue displacement to surrogate amplitude, determined for each experiment were compared. The mean model error of the three datasets was 1.00  ±  0.36 mm with a 95th percentile value of 1.69 mm. The mean error computed from all combinations of parameters and surrogate signals from different datasets was 1.14  ±  0.42 mm with a 95th percentile of 1.95 mm. The mean difference in α over all pairs of experiments was 4.7%  ±  5.4%, and the 95th percentile was 16.8%. The mean angle between pairs of α was 5.0  ±  4.0 degrees, with a 95th percentile of 13.2 mm. The motion model parameters were largely unaffected by changes in the breathing rate during image acquisition. The mean error associated with mismatched sets of parameters and surrogate signals was 0.14 mm greater than the error achieved when using parameters and surrogate signals acquired with the same breathing rate, while maximum respiratory motion was 23.23 mm on average.

Simple model for piezoelectric ceramic/polymer 1-3 composites used in ultrasonic transducer applications.

PubMed

Chan, H W; Unsworth, J

1989-01-01

A theoretical model is presented for combining parameters of 1-3 ultrasonic composite materials in order to predict ultrasonic characteristics such as velocity, acoustic impedance, electromechanical coupling factor, and piezoelectric coefficients. Hence, the model allows the estimation of resonance frequencies of 1-3 composite transducers. This model has been extended to cover more material parameters, and they are compared to experimental results up to PZT volume fraction nu of 0.8. The model covers calculation of piezoelectric charge constants d(33) and d(31). Values are found to be in good agreement with experimental results obtained for PZT 7A/Araldite D 1-3 composites. The acoustic velocity, acoustic impedance, and electromechanical coupling factor are predicted and found to be close to the values determined experimentally.

Effects of climatological parameters in modeling and forecasting seasonal influenza transmission in Abidjan, Cote d'Ivoire.

PubMed

N'gattia, A K; Coulibaly, D; Nzussouo, N Talla; Kadjo, H A; Chérif, D; Traoré, Y; Kouakou, B K; Kouassi, P D; Ekra, K D; Dagnan, N S; Williams, T; Tiembré, I

2016-09-13

In temperate regions, influenza epidemics occur in the winter and correlate with certain climatological parameters. In African tropical regions, the effects of climatological parameters on influenza epidemics are not well defined. This study aims to identify and model the effects of climatological parameters on seasonal influenza activity in Abidjan, Cote d'Ivoire. We studied the effects of weekly rainfall, humidity, and temperature on laboratory-confirmed influenza cases in Abidjan from 2007 to 2010. We used the Box-Jenkins method with the autoregressive integrated moving average (ARIMA) process to create models using data from 2007-2010 and to assess the predictive value of best model on data from 2011 to 2012. The weekly number of influenza cases showed significant cross-correlation with certain prior weeks for both rainfall, and relative humidity. The best fitting multivariate model (ARIMAX (2,0,0) _RF) included the number of influenza cases during 1-week and 2-weeks prior, and the rainfall during the current week and 5-weeks prior. The performance of this model showed an increase of >3 % for Akaike Information Criterion (AIC) and 2.5 % for Bayesian Information Criterion (BIC) compared to the reference univariate ARIMA (2,0,0). The prediction of the weekly number of influenza cases during 2011-2012 with the best fitting multivariate model (ARIMAX (2,0,0) _RF), showed that the observed values were within the 95 % confidence interval of the predicted values during 97 of 104 weeks. Including rainfall increases the performances of fitted and predicted models. The timing of influenza in Abidjan can be partially explained by rainfall influence, in a setting with little change in temperature throughout the year. These findings can help clinicians to anticipate influenza cases during the rainy season by implementing preventive measures.

Cluster kinetics model for mixtures of glassformers

NASA Astrophysics Data System (ADS)

Brenskelle, Lisa A.; McCoy, Benjamin J.

2007-10-01

For glassformers we propose a binary mixture relation for parameters in a cluster kinetics model previously shown to represent pure compound data for viscosity and dielectric relaxation as functions of either temperature or pressure. The model parameters are based on activation energies and activation volumes for cluster association-dissociation processes. With the mixture parameters, we calculated dielectric relaxation times and compared the results to experimental values for binary mixtures. Mixtures of sorbitol and glycerol (seven compositions), sorbitol and xylitol (three compositions), and polychloroepihydrin and polyvinylmethylether (three compositions) were studied.

Energy conditions in f (T, TG) gravity

NASA Astrophysics Data System (ADS)

Jawad, Abdul

2015-05-01

This paper is devoted to study the energy conditions in f( T, T G ) gravity for the FRW universe with perfect fluid, where T is the torsion scalar and T G is the quartic torsion scalar. We construct the energy conditions in this theory and discuss them for two specific f( T, T G ) models. These models are and , which represent viability through some cosmological scenarios. We consider cosmographic parameters to simplify the energy condition expressions. The present-day values of these parameters are assumed to check the constraints on model parameters through energy condition inequalities.