Significance of settling model structures and parameter subsets in modelling WWTPs under wet-weather flow and filamentous bulking conditions.

PubMed

Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen; Plósz, Benedek Gy

2014-10-15

Current research focuses on predicting and mitigating the impacts of high hydraulic loadings on centralized wastewater treatment plants (WWTPs) under wet-weather conditions. The maximum permissible inflow to WWTPs depends not only on the settleability of activated sludge in secondary settling tanks (SSTs) but also on the hydraulic behaviour of SSTs. The present study investigates the impacts of ideal and non-ideal flow (dry and wet weather) and settling (good settling and bulking) boundary conditions on the sensitivity of WWTP model outputs to uncertainties intrinsic to the one-dimensional (1-D) SST model structures and parameters. We identify the critical sources of uncertainty in WWTP models through global sensitivity analysis (GSA) using the Benchmark simulation model No. 1 in combination with first- and second-order 1-D SST models. The results obtained illustrate that the contribution of settling parameters to the total variance of the key WWTP process outputs significantly depends on the influent flow and settling conditions. The magnitude of the impact is found to vary, depending on which type of 1-D SST model is used. Therefore, we identify and recommend potential parameter subsets for WWTP model calibration, and propose optimal choice of 1-D SST models under different flow and settling boundary conditions. Additionally, the hydraulic parameters in the second-order SST model are found significant under dynamic wet-weather flow conditions. These results highlight the importance of developing a more mechanistic based flow-dependent hydraulic sub-model in second-order 1-D SST models in the future. Copyright © 2014 Elsevier Ltd. All rights reserved.

Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis

USGS Publications Warehouse

Doherty, John E.; Hunt, Randall J.; Tonkin, Matthew J.

2010-01-01

Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty in those parameters. In fact, in many cases, enforcement of calibration constraints simply reduces the uncertainties associated with a number of broad-scale combinations of model parameters that collectively describe spatially averaged system properties. The uncertainties associated with other combinations of parameters, especially those that pertain to small-scale parameter heterogeneity, may not be reduced through the calibration process. To the extent that a prediction depends on system-property detail, its postcalibration variability may be reduced very little, if at all, by applying calibration constraints; knowledge constraints remain the only limits on the variability of predictions that depend on such detail. Regrettably, in many common modeling applications, these constraints are weak. Though the PEST software suite was initially developed as a tool for model calibration, recent developments have focused on the evaluation of model-parameter and predictive uncertainty. As a complement to functionality that it provides for highly parameterized inversion (calibration) by means of formal mathematical regularization techniques, the PEST suite provides utilities for linear and nonlinear error-variance and uncertainty analysis in these highly parameterized modeling contexts. Availability of these utilities is particularly important because, in many cases, a significant proportion of the uncertainty associated with model parameters-and the predictions that depend on them-arises from differences between the complex properties of the real world and the simplified representation of those properties that is expressed by the calibrated model. This report is intended to guide intermediate to advanced modelers in the use of capabilities available with the PEST suite of programs for evaluating model predictive error and uncertainty. A brief theoretical background is presented on sources of parameter and predictive uncertainty and on the means for evaluating this uncertainty. Applications of PEST tools are then discussed for overdetermined and underdetermined problems, both linear and nonlinear. PEST tools for calculating contributions to model predictive uncertainty, as well as optimization of data acquisition for reducing parameter and predictive uncertainty, are presented. The appendixes list the relevant PEST variables, files, and utilities required for the analyses described in the document.

Recharge characteristics of an unconfined aquifer from the rainfall-water table relationship

NASA Astrophysics Data System (ADS)

Viswanathan, M. N.

1984-02-01

The determination of recharge levels of unconfined aquifers, recharged entirely by rainfall, is done by developing a model for the aquifer that estimates the water-table levels from the history of rainfall observations and past water-table levels. In the present analysis, the model parameters that influence the recharge were not only assumed to be time dependent but also to have varying dependence rates for various parameters. Such a model is solved by the use of a recursive least-squares method. The variable-rate parameter variation is incorporated using a random walk model. From the field tests conducted at Tomago Sandbeds, Newcastle, Australia, it was observed that the assumption of variable rates of time dependency of recharge parameters produced better estimates of water-table levels compared to that with constant-recharge parameters. It was observed that considerable recharge due to rainfall occurred on the very same day of rainfall. The increase in water-table level was insignificant for subsequent days of rainfall. The level of recharge very much depends upon the intensity and history of rainfall. Isolated rainfalls, even of the order of 25 mm day -1, had no significant effect on the water-table levels.

Hygrothermal wave propagation in viscoelastic graphene under in-plane magnetic field based on nonlocal strain gradient theory

NASA Astrophysics Data System (ADS)

Karami, Behrouz; Shahsavari, Davood; Li, Li

2018-03-01

A size-dependent model is developed for the hygrothermal wave propagation analysis of an embedded viscoelastic single layer graphene sheet (SLGS) under the influence of in-plane magnetic field. The bi-Helmholtz nonlocal strain gradient theory involving three small scale parameters is introduced to account for the size-dependent effects. The size-dependent model is deduced based on Hamilton's principle. The closed-form solution of eigenfrequency relation between wave number and phase velocity is achieved. By studying the size-dependent effects on the flexural wave of SLGS, the dispersion relation predicted by the developed size-dependent model can show a good match with experimental data. The influence of in-plane magnetic field, temperature and moisture of environs, structural damping, damped substrate, lower and higher order nonlocal parameters and the material characteristic parameter on the phase velocity of SLGS is explored.

The structure and dynamics of tornado-like vortices

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

Nolan, D.S.; Farrell, B.F.

The structure and dynamics of axisymmetric tornado-like vortices are explored with a numerical model of axisymmetric incompressible flow based on recently developed numerical methods. The model is first shown to compare favorably with previous results and is then used to study the effects of varying the major parameters controlling the vortex: the strength of the convective forcing, the strength of the rotational forcing, and the magnitude of the model eddy viscosity. Dimensional analysis of the model problem indicates that the results must depend on only two dimensionless parameters. The natural choices for these two parameters are a convective Reynolds numbermore » (based on the velocity scale associated with the convective forcing) and a parameter analogous to the swirl ratio in laboratory models. However, by examining sets of simulations with different model parameters it is found that a dimensionless parameter known as the vortex Reynolds number, which is the ratio of the far-field circulation to the eddy viscosity, is more effective than the convention swirl ratio for predicting the structure of the vortex. The parameter space defined by the choices for model parameters is further explored with large sets of numerical simulations. For much of this parameter space it is confirmed that the vortex structure and time-dependent behavior depend strongly on the vortex Reynolds number and only weakly on the convective Reynolds number. The authors also find that for higher convective Reynolds numbers, the maximum possible wind speed increases, and the rotational forcing necessary to achieve that wind speed decreases. Physical reasoning is used to explain this behavior, and implications for tornado dynamics are discussed.« less

Model-based analysis of fatigued human knee extensors : Effects of isometrically induced fatigue on Hill-type model parameters and ballistic contractions.

PubMed

Penasso, Harald; Thaller, Sigrid

2018-05-05

This study investigated the effect of isometrically induced fatigue on Hill-type muscle model parameters and related task-dependent effects. Parameter identification methods were used to extract fatigue-related parameter trends from isometric and ballistic dynamic maximum voluntary knee extensions. Nine subjects, who completed ten fatiguing sets, each consisting of nine 3 s isometric maximum voluntary contractions with 3 s rest plus two ballistic contractions with different loads, were analyzed. Only at the isometric task, the identified optimized model parameter values of muscle activation rate and maximum force generating capacity of the contractile element decreased from [Formula: see text] to [Formula: see text] Hz and from [Formula: see text] to [Formula: see text] N, respectively. For all tasks, the maximum efficiency of the contractile element, mathematically related to the curvature of the force-velocity relation, increased from [Formula: see text] to [Formula: see text]. The model parameter maximum contraction velocity decreased from [Formula: see text] to [Formula: see text] m/s and the stiffness of the serial elastic element from [Formula: see text] to [Formula: see text] N/mm. Thus, models of fatigue should consider fatigue dependencies in active as well as in passive elements, and muscle activation dynamics should account for the task dependency of fatigue.

Revisiting gamma-ray burst afterglows with time-dependent parameters

NASA Astrophysics Data System (ADS)

Yang, Chao; Zou, Yuan-Chuan; Chen, Wei; Liao, Bin; Lei, Wei-Hua; Liu, Yu

2018-02-01

The relativistic external shock model of gamma-ray burst (GRB) afterglows has been established with five free parameters, i.e., the total kinetic energy E, the equipartition parameters for electrons {{ε }}{{e}} and for the magnetic field {{ε }}{{B}}, the number density of the environment n and the index of the power-law distribution of shocked electrons p. A lot of modified models have been constructed to consider the variety of GRB afterglows, such as: the wind medium environment by letting n change with radius, the energy injection model by letting kinetic energy change with time and so on. In this paper, by assuming all four parameters (except p) change with time, we obtain a set of formulas for the dynamics and radiation, which can be used as a reference for modeling GRB afterglows. Some interesting results are obtained. For example, in some spectral segments, the radiated flux density does not depend on the number density or the profile of the environment. As an application, through modeling the afterglow of GRB 060607A, we find that it can be interpreted in the framework of the time dependent parameter model within a reasonable range.

Synthesis and Characterization of Liquid Crystalline Epoxy Resins

DTIC Science & Technology

2014-01-01

Temperature dependence of the four parameters in the Burgers model. ......... 81 Figure 4.7 Dependence of creep compliance on creep time at different...Kinetic parameters for LCERs. ......................................................................... 65 Table 3.4 Kinetic parameters for non-LCERs...curing in a high strength magnetic field. The orientation was quantified by an orientation parameter determined with two-dimensional X-ray diffraction

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.

Dependence of tropical cyclone development on coriolis parameter: A theoretical model

NASA Astrophysics Data System (ADS)

Deng, Liyuan; Li, Tim; Bi, Mingyu; Liu, Jia; Peng, Melinda

2018-03-01

A simple theoretical model was formulated to investigate how tropical cyclone (TC) intensification depends on the Coriolis parameter. The theoretical framework includes a two-layer free atmosphere and an Ekman boundary layer at the bottom. The linkage between the free atmosphere and the boundary layer is through the Ekman pumping vertical velocity in proportion to the vorticity at the top of the boundary layer. The closure of this linear system assumes a simple relationship between the free atmosphere diabatic heating and the boundary layer moisture convergence. Under a set of realistic atmospheric parameter values, the model suggests that the most preferred latitude for TC development is around 5° without considering other factors. The theoretical result is confirmed by high-resolution WRF model simulations in a zero-mean flow and a constant SST environment on an f -plane with different Coriolis parameters. Given an initially balanced weak vortex, the TC-like vortex intensifies most rapidly at the reference latitude of 5°. Thus, the WRF model simulations confirm the f-dependent characteristics of TC intensification rate as suggested by the theoretical model.

Refractive indices of liquid crystal E7 depending on temperature and wavelengths

NASA Astrophysics Data System (ADS)

Ma, Mingjian; Li, Shuguang; Jing, Xili; Chen, Hailiang

2017-11-01

The dependence of refractive indices of liquid crystal (LC) on temperature is represented by the Haller approximation model, and its dependence on the wavelength is expressed by the extended Cauchy model. We derived the refractive indices expressions of nematic LC E7 depending on temperature and wavelength simultaneously by combining these two models. Based on the obtained expressions, one can acquire the refractive indices of E7 at arbitrary temperature and wavelengths. The birefringence, variation rate of refractive indices, macroscopic order parameter Q, and orientational order parameter ⟨P2⟩ of E7 were then discussed based on the expressions.

Development and parameter identification of a visco-hyperelastic model for the periodontal ligament.

PubMed

Huang, Huixiang; Tang, Wencheng; Tan, Qiyan; Yan, Bin

2017-04-01

The present study developed and implemented a new visco-hyperelastic model that is capable of predicting the time-dependent biomechanical behavior of the periodontal ligament. The constitutive model has been implemented into the finite element package ABAQUS by means of a user-defined material subroutine (UMAT). The stress response is decomposed into two constitutive parts in parallel which are a hyperelastic and a time-dependent viscoelastic stress response. In order to identify the model parameters, the indentation equation based on V-W hyperelastic model and the indentation creep model are developed. Then the parameters are determined by fitting them to the corresponding nanoindentation experimental data of the PDL. The nanoindentation experiment was simulated by finite element analysis to validate the visco-hyperelastic model. The simulated results are in good agreement with the experimental data, which demonstrates that the visco-hyperelastic model developed is able to accurately predict the time-dependent mechanical behavior of the PDL. Copyright © 2017 Elsevier Ltd. All rights reserved.

Stochastic Modeling and Analysis of Multiple Nonlinear Accelerated Degradation Processes through Information Fusion

PubMed Central

Sun, Fuqiang; Liu, Le; Li, Xiaoyang; Liao, Haitao

2016-01-01

Accelerated degradation testing (ADT) is an efficient technique for evaluating the lifetime of a highly reliable product whose underlying failure process may be traced by the degradation of the product’s performance parameters with time. However, most research on ADT mainly focuses on a single performance parameter. In reality, the performance of a modern product is usually characterized by multiple parameters, and the degradation paths are usually nonlinear. To address such problems, this paper develops a new s-dependent nonlinear ADT model for products with multiple performance parameters using a general Wiener process and copulas. The general Wiener process models the nonlinear ADT data, and the dependency among different degradation measures is analyzed using the copula method. An engineering case study on a tuner’s ADT data is conducted to demonstrate the effectiveness of the proposed method. The results illustrate that the proposed method is quite effective in estimating the lifetime of a product with s-dependent performance parameters. PMID:27509499

Stochastic Modeling and Analysis of Multiple Nonlinear Accelerated Degradation Processes through Information Fusion.

PubMed

Sun, Fuqiang; Liu, Le; Li, Xiaoyang; Liao, Haitao

2016-08-06

Accelerated degradation testing (ADT) is an efficient technique for evaluating the lifetime of a highly reliable product whose underlying failure process may be traced by the degradation of the product's performance parameters with time. However, most research on ADT mainly focuses on a single performance parameter. In reality, the performance of a modern product is usually characterized by multiple parameters, and the degradation paths are usually nonlinear. To address such problems, this paper develops a new s-dependent nonlinear ADT model for products with multiple performance parameters using a general Wiener process and copulas. The general Wiener process models the nonlinear ADT data, and the dependency among different degradation measures is analyzed using the copula method. An engineering case study on a tuner's ADT data is conducted to demonstrate the effectiveness of the proposed method. The results illustrate that the proposed method is quite effective in estimating the lifetime of a product with s-dependent performance parameters.

Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.

PubMed

De Haan-Rietdijk, Silvia; Gottman, John M; Bergeman, Cindy S; Hamaker, Ellen L

2016-03-01

Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions.

Constraints on a scale-dependent bias from galaxy clustering

NASA Astrophysics Data System (ADS)

Amendola, L.; Menegoni, E.; Di Porto, C.; Corsi, M.; Branchini, E.

2017-01-01

We forecast the future constraints on scale-dependent parametrizations of galaxy bias and their impact on the estimate of cosmological parameters from the power spectrum of galaxies measured in a spectroscopic redshift survey. For the latter we assume a wide survey at relatively large redshifts, similar to the planned Euclid survey, as the baseline for future experiments. To assess the impact of the bias we perform a Fisher matrix analysis, and we adopt two different parametrizations of scale-dependent bias. The fiducial models for galaxy bias are calibrated using mock catalogs of H α emitting galaxies mimicking the expected properties of the objects that will be targeted by the Euclid survey. In our analysis we have obtained two main results. First of all, allowing for a scale-dependent bias does not significantly increase the errors on the other cosmological parameters apart from the rms amplitude of density fluctuations, σ8 , and the growth index γ , whose uncertainties increase by a factor up to 2, depending on the bias model adopted. Second, we find that the accuracy in the linear bias parameter b0 can be estimated to within 1%-2% at various redshifts regardless of the fiducial model. The nonlinear bias parameters have significantly large errors that depend on the model adopted. Despite this, in the more realistic scenarios departures from the simple linear bias prescription can be detected with a ˜2 σ significance at each redshift explored. Finally, we use the Fisher matrix formalism to assess the impact od assuming an incorrect bias model and find that the systematic errors induced on the cosmological parameters are similar or even larger than the statistical ones.

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.

Non-parametric correlative uncertainty quantification and sensitivity analysis: Application to a Langmuir bimolecular adsorption model

NASA Astrophysics Data System (ADS)

Feng, Jinchao; Lansford, Joshua; Mironenko, Alexander; Pourkargar, Davood Babaei; Vlachos, Dionisios G.; Katsoulakis, Markos A.

2018-03-01

We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.

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

Climate modeling for Yamal territory using supercomputer atmospheric circulation model ECHAM5-wiso

NASA Astrophysics Data System (ADS)

Denisova, N. Y.; Gribanov, K. G.; Werner, M.; Zakharov, V. I.

2015-11-01

Dependences of monthly means of regional averages of model atmospheric parameters on initial and boundary condition remoteness in the past are the subject of the study. We used atmospheric general circulation model ECHAM5-wiso for simulation of monthly means of regional averages of climate parameters for Yamal region and different periods of premodeling. Time interval was varied from several months to 12 years. We present dependences of model monthly means of regional averages of surface temperature, 2 m air temperature and humidity for December of 2000 on duration of premodeling. Comparison of these results with reanalysis data showed that best coincidence with true parameters could be reached if duration of pre-modelling is approximately 10 years.

How Sensitive Are Transdermal Transport Predictions by Microscopic Stratum Corneum Models to Geometric and Transport Parameter Input?

PubMed

Wen, Jessica; Koo, Soh Myoung; Lape, Nancy

2018-02-01

While predictive models of transdermal transport have the potential to reduce human and animal testing, microscopic stratum corneum (SC) model output is highly dependent on idealized SC geometry, transport pathway (transcellular vs. intercellular), and penetrant transport parameters (e.g., compound diffusivity in lipids). Most microscopic models are limited to a simple rectangular brick-and-mortar SC geometry and do not account for variability across delivery sites, hydration levels, and populations. In addition, these models rely on transport parameters obtained from pure theory, parameter fitting to match in vivo experiments, and time-intensive diffusion experiments for each compound. In this work, we develop a microscopic finite element model that allows us to probe model sensitivity to variations in geometry, transport pathway, and hydration level. Given the dearth of experimentally-validated transport data and the wide range in theoretically-predicted transport parameters, we examine the model's response to a variety of transport parameters reported in the literature. Results show that model predictions are strongly dependent on all aforementioned variations, resulting in order-of-magnitude differences in lag times and permeabilities for distinct structure, hydration, and parameter combinations. This work demonstrates that universally predictive models cannot fully succeed without employing experimentally verified transport parameters and individualized SC structures. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

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.

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.

Finite element analysis of history-dependent damage in time-dependent fracture mechanics

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

Krishnaswamy, P.; Brust, F.W.; Ghadiali, N.D.

1993-11-01

The demands for structural systems to perform reliably under both severe and changing operating conditions continue to increase. Under these conditions time-dependent straining and history-dependent damage become extremely important. This work focuses on studying creep crack growth using finite element (FE) analysis. Two important issues, namely, (1) the use of history-dependent constitutive laws, and (2) the use of various fracture parameters in predicting creep crack growth, have both been addressed in this work. The constitutive model used here is the one developed by Murakami and Ohno and is based on the concept of a creep hardening surface. An implicit FEmore » algorithm for this model was first developed and verified for simple geometries and loading configurations. The numerical methodology developed here has been used to model stationary and growing cracks in CT specimens. Various fracture parameters such as the C[sub 1], C[sup *], T[sup *], J were used to compare the numerical predictions with experimental results available in the literature. A comparison of the values of these parameters as a function of time has been made for both stationary and growing cracks. The merit of using each of these parameters has also been discussed.« less

Social networks: Evolving graphs with memory dependent edges

NASA Astrophysics Data System (ADS)

Grindrod, Peter; Parsons, Mark

2011-10-01

The plethora of digital communication technologies, and their mass take up, has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the existence or otherwise of certain infinite products and series involving age dependent model parameters. We show how to differentiate between the alternatives based on a finite set of observations. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

Estimating riparian understory vegetation cover with beta regression and copula models

USGS Publications Warehouse

Eskelson, Bianca N.I.; Madsen, Lisa; Hagar, Joan C.; Temesgen, Hailemariam

2011-01-01

Understory vegetation communities are critical components of forest ecosystems. As a result, the importance of modeling understory vegetation characteristics in forested landscapes has become more apparent. Abundance measures such as shrub cover are bounded between 0 and 1, exhibit heteroscedastic error variance, and are often subject to spatial dependence. These distributional features tend to be ignored when shrub cover data are analyzed. The beta distribution has been used successfully to describe the frequency distribution of vegetation cover. Beta regression models ignoring spatial dependence (BR) and accounting for spatial dependence (BRdep) were used to estimate percent shrub cover as a function of topographic conditions and overstory vegetation structure in riparian zones in western Oregon. The BR models showed poor explanatory power (pseudo-R2 ≤ 0.34) but outperformed ordinary least-squares (OLS) and generalized least-squares (GLS) regression models with logit-transformed response in terms of mean square prediction error and absolute bias. We introduce a copula (COP) model that is based on the beta distribution and accounts for spatial dependence. A simulation study was designed to illustrate the effects of incorrectly assuming normality, equal variance, and spatial independence. It showed that BR, BRdep, and COP models provide unbiased parameter estimates, whereas OLS and GLS models result in slightly biased estimates for two of the three parameters. On the basis of the simulation study, 93–97% of the GLS, BRdep, and COP confidence intervals covered the true parameters, whereas OLS and BR only resulted in 84–88% coverage, which demonstrated the superiority of GLS, BRdep, and COP over OLS and BR models in providing standard errors for the parameter estimates in the presence of spatial dependence.

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS.

USGS Publications Warehouse

Cooley, Richard L.; Vecchia, Aldo V.

1987-01-01

A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that inclusion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.

Sensitivity of land surface modeling to parameters: An uncertainty quantification method applied to the Community Land Model

NASA Astrophysics Data System (ADS)

Ricciuto, D. M.; Mei, R.; Mao, J.; Hoffman, F. M.; Kumar, J.

2015-12-01

Uncertainties in land parameters could have important impacts on simulated water and energy fluxes and land surface states, which will consequently affect atmospheric and biogeochemical processes. Therefore, quantification of such parameter uncertainties using a land surface model is the first step towards better understanding of predictive uncertainty in Earth system models. In this study, we applied a random-sampling, high-dimensional model representation (RS-HDMR) method to analyze the sensitivity of simulated photosynthesis, surface energy fluxes and surface hydrological components to selected land parameters in version 4.5 of the Community Land Model (CLM4.5). Because of the large computational expense of conducting ensembles of global gridded model simulations, we used the results of a previous cluster analysis to select one thousand representative land grid cells for simulation. Plant functional type (PFT)-specific uniform prior ranges for land parameters were determined using expert opinion and literature survey, and samples were generated with a quasi-Monte Carlo approach-Sobol sequence. Preliminary analysis of 1024 simulations suggested that four PFT-dependent parameters (including slope of the conductance-photosynthesis relationship, specific leaf area at canopy top, leaf C:N ratio and fraction of leaf N in RuBisco) are the dominant sensitive parameters for photosynthesis, surface energy and water fluxes across most PFTs, but with varying importance rankings. On the other hand, for surface ans sub-surface runoff, PFT-independent parameters, such as the depth-dependent decay factors for runoff, play more important roles than the previous four PFT-dependent parameters. Further analysis by conditioning the results on different seasons and years are being conducted to provide guidance on how climate variability and change might affect such sensitivity. This is the first step toward coupled simulations including biogeochemical processes, atmospheric processes or both to determine the full range of sensitivity of Earth system modeling to land-surface parameters. This can facilitate sampling strategies in measurement campaigns targeted at reduction of climate modeling uncertainties and can also provide guidance on land parameter calibration for simulation optimization.

An analysis of sensitivity of CLIMEX parameters in mapping species potential distribution and the broad-scale changes observed with minor variations in parameters values: an investigation using open-field Solanum lycopersicum and Neoleucinodes elegantalis as an example

NASA Astrophysics Data System (ADS)

da Silva, Ricardo Siqueira; Kumar, Lalit; Shabani, Farzin; Picanço, Marcelo Coutinho

2018-04-01

A sensitivity analysis can categorize levels of parameter influence on a model's output. Identifying parameters having the most influence facilitates establishing the best values for parameters of models, providing useful implications in species modelling of crops and associated insect pests. The aim of this study was to quantify the response of species models through a CLIMEX sensitivity analysis. Using open-field Solanum lycopersicum and Neoleucinodes elegantalis distribution records, and 17 fitting parameters, including growth and stress parameters, comparisons were made in model performance by altering one parameter value at a time, in comparison to the best-fit parameter values. Parameters that were found to have a greater effect on the model results are termed "sensitive". Through the use of two species, we show that even when the Ecoclimatic Index has a major change through upward or downward parameter value alterations, the effect on the species is dependent on the selection of suitability categories and regions of modelling. Two parameters were shown to have the greatest sensitivity, dependent on the suitability categories of each species in the study. Results enhance user understanding of which climatic factors had a greater impact on both species distributions in our model, in terms of suitability categories and areas, when parameter values were perturbed by higher or lower values, compared to the best-fit parameter values. Thus, the sensitivity analyses have the potential to provide additional information for end users, in terms of improving management, by identifying the climatic variables that are most sensitive.

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.

A model for phase noise generation in amplifiers.

PubMed

Tomlin, T D; Fynn, K; Cantoni, A

2001-11-01

In this paper, a model is presented for predicting the phase modulation (PM) and amplitude modulation (AM) noise in bipolar junction transistor (BJT) amplifiers. The model correctly predicts the dependence of phase noise on the signal frequency (at a particular carrier offset frequency), explains the noise shaping of the phase noise about the signal frequency, and shows the functional dependence on the transistor parameters and the circuit parameters. Experimental studies on common emitter (CE) amplifiers have been used to validate the PM noise model at carrier frequencies between 10 and 100 MHz.

Investigation of Laser Parameters in Silicon Pulsed Laser Conduction Welding

NASA Astrophysics Data System (ADS)

Shayganmanesh, Mahdi; Khoshnoud, Afsaneh

2016-03-01

In this paper, laser welding of silicon in conduction mode is investigated numerically. In this study, the effects of laser beam characteristics on the welding have been studied. In order to model the welding process, heat conduction equation is solved numerically and laser beam energy is considered as a boundary condition. Time depended heat conduction equation is used in our calculations to model pulsed laser welding. Thermo-physical and optical properties of the material are considered to be temperature dependent in our calculations. Effects of spatial and temporal laser beam parameters such as laser beam spot size, laser beam quality, laser beam polarization, laser incident angle, laser pulse energy, laser pulse width, pulse repetition frequency and welding speed on the welding characteristics are assessed. The results show that how the temperature dependent thermo-physical and optical parameters of the material are important in laser welding modeling. Also the results show how the parameters of the laser beam influence the welding characteristics.

Anomalous solute transport in saturated porous media: Relating transport model parameters to electrical and nuclear magnetic resonance properties

USGS Publications Warehouse

Swanson, Ryan D; Binley, Andrew; Keating, Kristina; France, Samantha; Osterman, Gordon; Day-Lewis, Frederick D.; Singha, Kamini

2015-01-01

The advection-dispersion equation (ADE) fails to describe commonly observed non-Fickian solute transport in saturated porous media, necessitating the use of other models such as the dual-domain mass-transfer (DDMT) model. DDMT model parameters are commonly calibrated via curve fitting, providing little insight into the relation between effective parameters and physical properties of the medium. There is a clear need for material characterization techniques that can provide insight into the geometry and connectedness of pore spaces related to transport model parameters. Here, we consider proton nuclear magnetic resonance (NMR), direct-current (DC) resistivity, and complex conductivity (CC) measurements for this purpose, and assess these methods using glass beads as a control and two different samples of the zeolite clinoptilolite, a material that demonstrates non-Fickian transport due to intragranular porosity. We estimate DDMT parameters via calibration of a transport model to column-scale solute tracer tests, and compare NMR, DC resistivity, CC results, which reveal that grain size alone does not control transport properties and measured geophysical parameters; rather, volume and arrangement of the pore space play important roles. NMR cannot provide estimates of more-mobile and less-mobile pore volumes in the absence of tracer tests because these estimates depend critically on the selection of a material-dependent and flow-dependent cutoff time. Increased electrical connectedness from DC resistivity measurements are associated with greater mobile pore space determined from transport model calibration. CC was hypothesized to be related to length scales of mass transfer, but the CC response is unrelated to DDMT.

Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length

NASA Astrophysics Data System (ADS)

Ricciuto, Daniel M.; King, Anthony W.; Dragoni, D.; Post, Wilfred M.

2011-03-01

Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model-data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties are then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model-data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model-data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarly reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error.

Impact of the calibration period on the conceptual rainfall-runoff model parameter estimates

NASA Astrophysics Data System (ADS)

Todorovic, Andrijana; Plavsic, Jasna

2015-04-01

A conceptual rainfall-runoff model is defined by its structure and parameters, which are commonly inferred through model calibration. Parameter estimates depend on objective function(s), optimisation method, and calibration period. Model calibration over different periods may result in dissimilar parameter estimates, while model efficiency decreases outside calibration period. Problem of model (parameter) transferability, which conditions reliability of hydrologic simulations, has been investigated for decades. In this paper, dependence of the parameter estimates and model performance on calibration period is analysed. The main question that is addressed is: are there any changes in optimised parameters and model efficiency that can be linked to the changes in hydrologic or meteorological variables (flow, precipitation and temperature)? Conceptual, semi-distributed HBV-light model is calibrated over five-year periods shifted by a year (sliding time windows). Length of the calibration periods is selected to enable identification of all parameters. One water year of model warm-up precedes every simulation, which starts with the beginning of a water year. The model is calibrated using the built-in GAP optimisation algorithm. The objective function used for calibration is composed of Nash-Sutcliffe coefficient for flows and logarithms of flows, and volumetric error, all of which participate in the composite objective function with approximately equal weights. Same prior parameter ranges are used in all simulations. The model is calibrated against flows observed at the Slovac stream gauge on the Kolubara River in Serbia (records from 1954 to 2013). There are no trends in precipitation nor in flows, however, there is a statistically significant increasing trend in temperatures at this catchment. Parameter variability across the calibration periods is quantified in terms of standard deviations of normalised parameters, enabling detection of the most variable parameters. Correlation coefficients among optimised model parameters and total precipitation P, mean temperature T and mean flow Q are calculated to give an insight into parameter dependence on the hydrometeorological drivers. The results reveal high sensitivity of almost all model parameters towards calibration period. The highest variability is displayed by the refreezing coefficient, water holding capacity, and temperature gradient. The only statistically significant (decreasing) trend is detected in the evapotranspiration reduction threshold. Statistically significant correlation is detected between the precipitation gradient and precipitation depth, and between the time-area histogram base and flows. All other correlations are not statistically significant, implying that changes in optimised parameters cannot generally be linked to the changes in P, T or Q. As for the model performance, the model reproduces the observed runoff satisfactorily, though the runoff is slightly overestimated in wet periods. The Nash-Sutcliffe efficiency coefficient (NSE) ranges from 0.44 to 0.79. Higher NSE values are obtained over wetter periods, what is supported by statistically significant correlation between NSE and flows. Overall, no systematic variations in parameters or in model performance are detected. Parameter variability may therefore rather be attributed to errors in data or inadequacies in the model structure. Further research is required to examine the impact of the calibration strategy or model structure on the variability in optimised parameters in time.

Temperature and velocity conditions of air flow in vertical channel of hinged ventilated facade of a multistory building.

NASA Astrophysics Data System (ADS)

Statsenko, Elena; Ostrovaia, Anastasia; Pigurin, Andrey

2018-03-01

This article considers the influence of the building's tallness and the presence of mounting grooved lines on the parameters of heat transfer in the gap of a hinged ventilated facade. A numerical description of the processes occurring in a heat-gravitational flow is given. The average velocity and temperature of the heat-gravitational flow of a structure with open and sealed rusts are determined with unchanged geometric parameters of the gap. The dependence of the parameters influencing the thermomechanical characteristics of the enclosing structure is derived depending on the internal parameters of the system. Physical modeling of real multistory structures is performed by projecting actual parameters onto a reduced laboratory model (scaling).

Sensitivity of Dynamical Systems to Banach Space Parameters

DTIC Science & Technology

2005-02-13

We consider general nonlinear dynamical systems in a Banach space with dependence on parameters in a second Banach space. An abstract theoretical ... framework for sensitivity equations is developed. An application to measure dependent delay differential systems arising in a class of HIV models is presented.

A 2-DOF microstructure-dependent model for the coupled torsion/bending instability of rotational nanoscanner

NASA Astrophysics Data System (ADS)

Keivani, M.; Abadian, N.; Koochi, A.; Mokhtari, J.; Abadyan, M.

2016-10-01

It has been well established that the physical performance of nanodevices might be affected by the microstructure. Herein, a two-degree-of-freedom model base on the modified couple stress theory is developed to incorporate the impact of microstructure in the torsion/bending coupled instability of rotational nanoscanner. Effect of microstructure dependency on the instability parameters is determined as a function of the microstructure parameter, bending/torsion coupling ratio, van der Waals force parameter and geometrical dimensions. It is found that the bending/torsion coupling substantially affects the stable behavior of the scanners especially those with long rotational beam elements. Impact of microstructure on instability voltage of the nanoscanner depends on coupling ratio and the conquering bending mode over torsion mode. This effect is more highlighted for higher values of coupling ratio. Depending on the geometry and material characteristics, the presented model is able to simulate both hardening behavior (due to microstructure) and softening behavior (due to torsion/bending coupling) of the nanoscanners.

Electromagnetic sunscreen model: design of experiments on particle specifications.

PubMed

Lécureux, Marie; Deumié, Carole; Enoch, Stefan; Sergent, Michelle

2015-10-01

We report a numerical study on sunscreen design and optimization. Thanks to the combined use of electromagnetic modeling and design of experiments, we are able to screen the most relevant parameters of mineral filters and to optimize sunscreens. Several electromagnetic modeling methods are used depending on the type of particles, density of particles, etc. Both the sun protection factor (SPF) and the UVB/UVA ratio are considered. We show that the design of experiments' model should include interactions between materials and other parameters. We conclude that the material of the particles is a key parameter for the SPF and the UVB/UVA ratio. Among the materials considered, none is optimal for both. The SPF is also highly dependent on the size of the particles.

Determining Experimental Parameters for Thermal-Mechanical Forming Simulation considering Martensite Formation in Austenitic Stainless Steel

NASA Astrophysics Data System (ADS)

Schmid, Philipp; Liewald, Mathias

2011-08-01

The forming behavior of metastable austenitic stainless steel is mainly dominated by the temperature-dependent TRIP effect (transformation induced plasticity). Of course, the high dependency of material properties on the temperature level during forming means the temperature must be considered during the FE analysis. The strain-induced formation of α'-martensite from austenite can be represented by using finite element programs utilizing suitable models such as the Haensel-model. This paper discusses the determination of parameters for a completely thermal-mechanical forming simulation in LS-DYNA based on the material model of Haensel. The measurement of the martensite evolution in non-isothermal tensile tests was performed with metastable austenitic stainless steel EN 1.4301 at different rolling directions between 0° and 90 °. This allows an estimation of the influence of the rolling direction to the martensite formation. Of specific importance is the accuracy of the martensite content measured by magnetic induction methods (Feritscope). The observation of different factors, such as stress dependence of the magnetisation, blank thickness and numerous calibration curves discloses a substantial important influence on the parameter determination for the material models. The parameters obtained for use of Haensel model and temperature-dependent friction coefficients are used to simulate forming process of a real component and to validate its implementation in the commercial code LS-DYNA.

Radiation and polarization signatures of the 3D multizone time-dependent hadronic blazar model

DOE PAGES

Zhang, Haocheng; Diltz, Chris; Bottcher, Markus

2016-09-23

We present a newly developed time-dependent three-dimensional multizone hadronic blazar emission model. By coupling a Fokker–Planck-based lepto-hadronic particle evolution code, 3DHad, with a polarization-dependent radiation transfer code, 3DPol, we are able to study the time-dependent radiation and polarization signatures of a hadronic blazar model for the first time. Our current code is limited to parameter regimes in which the hadronic γ-ray output is dominated by proton synchrotron emission, neglecting pion production. Our results demonstrate that the time-dependent flux and polarization signatures are generally dominated by the relation between the synchrotron cooling and the light-crossing timescale, which is largely independent ofmore » the exact model parameters. We find that unlike the low-energy polarization signatures, which can vary rapidly in time, the high-energy polarization signatures appear stable. Lastly, future high-energy polarimeters may be able to distinguish such signatures from the lower and more rapidly variable polarization signatures expected in leptonic models.« less

A fluidized bed technique for estimating soil critical shear stress

USDA-ARS?s Scientific Manuscript database

Soil erosion models, depending on how they are formulated, always have erodibilitiy parameters in the erosion equations. For a process-based model like the Water Erosion Prediction Project (WEPP) model, the erodibility parameters include rill and interrill erodibility and critical shear stress. Thes...

Measurement and simulation of unmyelinated nerve electrostimulation: Lumbricus terrestris experiment and numerical model

NASA Astrophysics Data System (ADS)

Šarolić, A.; Živković, Z.; Reilly, J. P.

2016-06-01

The electrostimulation excitation threshold of a nerve depends on temporal and frequency parameters of the stimulus. These dependences were investigated in terms of: (1) strength-duration (SD) curve for a single monophasic rectangular pulse, and (2) frequency dependence of the excitation threshold for a continuous sinusoidal current. Experiments were performed on the single-axon measurement setup based on Lumbricus terrestris having unmyelinated nerve fibers. The simulations were performed using the well-established SENN model for a myelinated nerve. Although the unmyelinated experimental model differs from the myelinated simulation model, both refer to a single axon. Thus we hypothesized that the dependence on temporal and frequency parameters should be very similar. The comparison was made possible by normalizing each set of results to the SD time constant and the rheobase current of each model, yielding the curves that show the temporal and frequency dependencies regardless of the model differences. The results reasonably agree, suggesting that this experimental setup and method of comparison with SENN model can be used for further studies of waveform effect on nerve excitability, including unmyelinated neurons.

Measurement and simulation of unmyelinated nerve electrostimulation: Lumbricus terrestris experiment and numerical model.

PubMed

Šarolić, A; Živković, Z; Reilly, J P

2016-06-21

The electrostimulation excitation threshold of a nerve depends on temporal and frequency parameters of the stimulus. These dependences were investigated in terms of: (1) strength-duration (SD) curve for a single monophasic rectangular pulse, and (2) frequency dependence of the excitation threshold for a continuous sinusoidal current. Experiments were performed on the single-axon measurement setup based on Lumbricus terrestris having unmyelinated nerve fibers. The simulations were performed using the well-established SENN model for a myelinated nerve. Although the unmyelinated experimental model differs from the myelinated simulation model, both refer to a single axon. Thus we hypothesized that the dependence on temporal and frequency parameters should be very similar. The comparison was made possible by normalizing each set of results to the SD time constant and the rheobase current of each model, yielding the curves that show the temporal and frequency dependencies regardless of the model differences. The results reasonably agree, suggesting that this experimental setup and method of comparison with SENN model can be used for further studies of waveform effect on nerve excitability, including unmyelinated neurons.

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

N.D. Francis

The objective of this calculation is to develop a time dependent in-drift effective thermal conductivity parameter that will approximate heat conduction, thermal radiation, and natural convection heat transfer using a single mode of heat transfer (heat conduction). In order to reduce the physical and numerical complexity of the heat transfer processes that occur (and must be modeled) as a result of the emplacement of heat generating wastes, a single parameter will be developed that approximates all forms of heat transfer from the waste package surface to the drift wall (or from one surface exchanging heat with another). Subsequently, with thismore » single parameter, one heat transfer mechanism (e.g., conduction heat transfer) can be used in the models. The resulting parameter is to be used as input in the drift-scale process-level models applied in total system performance assessments for the site recommendation (TSPA-SR). The format of this parameter will be a time-dependent table for direct input into the thermal-hydrologic (TH) and the thermal-hydrologic-chemical (THC) models.« less

Density-dependent home-range size revealed by spatially explicit capture–recapture

USGS Publications Warehouse

Efford, M.G.; Dawson, Deanna K.; Jhala, Y.V.; Qureshi, Q.

2016-01-01

The size of animal home ranges often varies inversely with population density among populations of a species. This fact has implications for population monitoring using spatially explicit capture–recapture (SECR) models, in which both the scale of home-range movements σ and population density D usually appear as parameters, and both may vary among populations. It will often be appropriate to model a structural relationship between population-specific values of these parameters, rather than to assume independence. We suggest re-parameterizing the SECR model using kp = σp √Dp, where kp relates to the degree of overlap between home ranges and the subscript p distinguishes populations. We observe that kp is often nearly constant for populations spanning a range of densities. This justifies fitting a model in which the separate kp are replaced by the single parameter k and σp is a density-dependent derived parameter. Continuous density-dependent spatial variation in σ may also be modelled, using a scaled non-Euclidean distance between detectors and the locations of animals. We illustrate these methods with data from automatic photography of tigers (Panthera tigris) across India, in which the variation is among populations, from mist-netting of ovenbirds (Seiurus aurocapilla) in Maryland, USA, in which the variation is within a single population over time, and from live-trapping of brushtail possums (Trichosurus vulpecula) in New Zealand, modelling spatial variation within one population. Possible applications and limitations of the methods are discussed. A model in which kp is constant, while density varies, provides a parsimonious null model for SECR. The parameter k of the null model is a concise summary of the empirical relationship between home-range size and density that is useful in comparative studies. We expect deviations from this model, particularly the dependence of kp on covariates, to be biologically interesting.

Deformation analysis of polymers composites: rheological model involving time-based fractional derivative

NASA Astrophysics Data System (ADS)

Zhou, H. W.; Yi, H. Y.; Mishnaevsky, L.; Wang, R.; Duan, Z. Q.; Chen, Q.

2017-05-01

A modeling approach to time-dependent property of Glass Fiber Reinforced Polymers (GFRP) composites is of special interest for quantitative description of long-term behavior. An electronic creep machine is employed to investigate the time-dependent deformation of four specimens of dog-bond-shaped GFRP composites at various stress level. A negative exponent function based on structural changes is introduced to describe the damage evolution of material properties in the process of creep test. Accordingly, a new creep constitutive equation, referred to fractional derivative Maxwell model, is suggested to characterize the time-dependent behavior of GFRP composites by replacing Newtonian dashpot with the Abel dashpot in the classical Maxwell model. The analytic solution for the fractional derivative Maxwell model is given and the relative parameters are determined. The results estimated by the fractional derivative Maxwell model proposed in the paper are in a good agreement with the experimental data. It is shown that the new creep constitutive model proposed in the paper needs few parameters to represent various time-dependent behaviors.

Soil and vegetation parameter uncertainty on future terrestrial carbon sinks

NASA Astrophysics Data System (ADS)

Kothavala, Z.; Felzer, B. S.

2013-12-01

We examine the role of the terrestrial carbon cycle in a changing climate at the centennial scale using an intermediate complexity Earth system climate model that includes the effects of dynamic vegetation and the global carbon cycle. We present a series of ensemble simulations to evaluate the sensitivity of simulated terrestrial carbon sinks to three key model parameters: (a) The temperature dependence of soil carbon decomposition, (b) the upper temperature limits on the rate of photosynthesis, and (c) the nitrogen limitation of the maximum rate of carboxylation of Rubisco. We integrated the model in fully coupled mode for a 1200-year spin-up period, followed by a 300-year transient simulation starting at year 1800. Ensemble simulations were conducted varying each parameter individually and in combination with other variables. The results of the transient simulations show that terrestrial carbon uptake is very sensitive to the choice of model parameters. Changes in net primary productivity were most sensitive to the upper temperature limit on the rate of photosynthesis, which also had a dominant effect on overall land carbon trends; this is consistent with previous research that has shown the importance of climatic suppression of photosynthesis as a driver of carbon-climate feedbacks. Soil carbon generally decreased with increasing temperature, though the magnitude of this trend depends on both the net primary productivity changes and the temperature dependence of soil carbon decomposition. Vegetation carbon increased in some simulations, but this was not consistent across all configurations of model parameters. Comparing to global carbon budget observations, we identify the subset of model parameters which are consistent with observed carbon sinks; this serves to narrow considerably the future model projections of terrestrial carbon sink changes in comparison with the full model ensemble.

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

Dana L. Kelly

Typical engineering systems in applications with high failure consequences such as nuclear reactor plants often employ redundancy and diversity of equipment in an effort to lower the probability of failure and therefore risk. However, it has long been recognized that dependencies exist in these redundant and diverse systems. Some dependencies, such as common sources of electrical power, are typically captured in the logic structure of the risk model. Others, usually referred to as intercomponent dependencies, are treated implicitly by introducing one or more statistical parameters into the model. Such common-cause failure models have limitations in a simulation environment. In addition,more » substantial subjectivity is associated with parameter estimation for these models. This paper describes an approach in which system performance is simulated by drawing samples from the joint distributions of dependent variables. The approach relies on the notion of a copula distribution, a notion which has been employed by the actuarial community for ten years or more, but which has seen only limited application in technological risk assessment. The paper also illustrates how equipment failure data can be used in a Bayesian framework to estimate the parameter values in the copula model. This approach avoids much of the subjectivity required to estimate parameters in traditional common-cause failure models. Simulation examples are presented for failures in time. The open-source software package R is used to perform the simulations. The open-source software package WinBUGS is used to perform the Bayesian inference via Markov chain Monte Carlo sampling.« less

Estimation of infiltration and hydraulic resistance in furrow irrigation, with infiltration dependent on flow depth

USDA-ARS?s Scientific Manuscript database

The estimation of parameters of a flow-depth dependent furrow infiltration model and of hydraulic resistance, using irrigation evaluation data, was investigated. The estimated infiltration parameters are the saturated hydraulic conductivity and the macropore volume per unit area. Infiltration throu...

An Extension of the Concept of Specific Objectivity.

ERIC Educational Resources Information Center

Irtel, Hans

1995-01-01

Comparisons of subjects are specifically objective if they do not depend on the items involved. Such comparisons are not restricted to the one-parameter logistic latent trait model but may also be defined within ordinal independence models and even within the two-parameter logistic model. (Author)

Parameter Balancing in Kinetic Models of Cell Metabolism†

PubMed Central

2010-01-01

Kinetic modeling of metabolic pathways has become a major field of systems biology. It combines structural information about metabolic pathways with quantitative enzymatic rate laws. Some of the kinetic constants needed for a model could be collected from ever-growing literature and public web resources, but they are often incomplete, incompatible, or simply not available. We address this lack of information by parameter balancing, a method to complete given sets of kinetic constants. Based on Bayesian parameter estimation, it exploits the thermodynamic dependencies among different biochemical quantities to guess realistic model parameters from available kinetic data. Our algorithm accounts for varying measurement conditions in the input data (pH value and temperature). It can process kinetic constants and state-dependent quantities such as metabolite concentrations or chemical potentials, and uses prior distributions and data augmentation to keep the estimated quantities within plausible ranges. An online service and free software for parameter balancing with models provided in SBML format (Systems Biology Markup Language) is accessible at www.semanticsbml.org. We demonstrate its practical use with a small model of the phosphofructokinase reaction and discuss its possible applications and limitations. In the future, parameter balancing could become an important routine step in the kinetic modeling of large metabolic networks. PMID:21038890

The effects of intraspecific competition and stabilizing selection on a polygenic trait.

PubMed Central

Bürger, Reinhard; Gimelfarb, Alexander

2004-01-01

The equilibrium properties of an additive multilocus model of a quantitative trait under frequency- and density-dependent selection are investigated. Two opposing evolutionary forces are assumed to act: (i) stabilizing selection on the trait, which favors genotypes with an intermediate phenotype, and (ii) intraspecific competition mediated by that trait, which favors genotypes whose effect on the trait deviates most from that of the prevailing genotypes. Accordingly, fitnesses of genotypes have a frequency-independent component describing stabilizing selection and a frequency- and density-dependent component modeling competition. We study how the equilibrium structure, in particular, number, degree of polymorphism, and genetic variance of stable equilibria, is affected by the strength of frequency dependence, and what role the number of loci, the amount of recombination, and the demographic parameters play. To this end, we employ a statistical and numerical approach, complemented by analytical results, and explore how the equilibrium properties averaged over a large number of genetic systems with a given number of loci and average amount of recombination depend on the ecological and demographic parameters. We identify two parameter regions with a transitory region in between, in which the equilibrium properties of genetic systems are distinctively different. These regions depend on the strength of frequency dependence relative to pure stabilizing selection and on the demographic parameters, but not on the number of loci or the amount of recombination. We further study the shape of the fitness function observed at equilibrium and the extent to which the dynamics in this model are adaptive, and we present examples of equilibrium distributions of genotypic values under strong frequency dependence. Consequences for the maintenance of genetic variation, the detection of disruptive selection, and models of sympatric speciation are discussed. PMID:15280253

Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling

PubMed Central

Boos, Moritz; Seer, Caroline; Lange, Florian; Kopp, Bruno

2016-01-01

Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision. PMID:27303323

Numerical Simulation Of Cratering Effects In Adobe

DTIC Science & Technology

2013-07-01

DEVELOPMENT OF MATERIAL PARAMETERS .........................................................7 PROBLEM SETUP...37 PARAMETER ADJUSTMENTS ......................................................................................38 GLOSSARY...dependent yield surface with the Geological Yield Surface (GEO) modeled in CTH using well characterized adobe. By identifying key parameters that

Brownian motion model with stochastic parameters for asset prices

NASA Astrophysics Data System (ADS)

Ching, Soo Huei; Hin, Pooi Ah

2013-09-01

The Brownian motion model may not be a completely realistic model for asset prices because in real asset prices the drift μ and volatility σ may change over time. Presently we consider a model in which the parameter x = (μ,σ) is such that its value x (t + Δt) at a short time Δt ahead of the present time t depends on the value of the asset price at time t + Δt as well as the present parameter value x(t) and m-1 other parameter values before time t via a conditional distribution. The Malaysian stock prices are used to compare the performance of the Brownian motion model with fixed parameter with that of the model with stochastic parameter.

Towards adjoint-based inversion of time-dependent mantle convection with nonlinear viscosity

NASA Astrophysics Data System (ADS)

Li, Dunzhu; Gurnis, Michael; Stadler, Georg

2017-04-01

We develop and study an adjoint-based inversion method for the simultaneous recovery of initial temperature conditions and viscosity parameters in time-dependent mantle convection from the current mantle temperature and historic plate motion. Based on a realistic rheological model with temperature-dependent and strain-rate-dependent viscosity, we formulate the inversion as a PDE-constrained optimization problem. The objective functional includes the misfit of surface velocity (plate motion) history, the misfit of the current mantle temperature, and a regularization for the uncertain initial condition. The gradient of this functional with respect to the initial temperature and the uncertain viscosity parameters is computed by solving the adjoint of the mantle convection equations. This gradient is used in a pre-conditioned quasi-Newton minimization algorithm. We study the prospects and limitations of the inversion, as well as the computational performance of the method using two synthetic problems, a sinking cylinder and a realistic subduction model. The subduction model is characterized by the migration of a ridge toward a trench whereby both plate motions and subduction evolve. The results demonstrate: (1) for known viscosity parameters, the initial temperature can be well recovered, as in previous initial condition-only inversions where the effective viscosity was given; (2) for known initial temperature, viscosity parameters can be recovered accurately, despite the existence of trade-offs due to ill-conditioning; (3) for the joint inversion of initial condition and viscosity parameters, initial condition and effective viscosity can be reasonably recovered, but the high dimension of the parameter space and the resulting ill-posedness may limit recovery of viscosity parameters.

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

Post, Wilfred M; King, Anthony Wayne; Dragoni, Danilo

Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model-data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties aremore » then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model-data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model-data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarly reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error.« less

A temperature-dependent coarse-grained model for the thermoresponsive polymer poly(N-isopropylacrylamide).

PubMed

Abbott, Lauren J; Stevens, Mark J

2015-12-28

A coarse-grained (CG) model is developed for the thermoresponsive polymer poly(N-isopropylacrylamide) (PNIPAM), using a hybrid top-down and bottom-up approach. Nonbonded parameters are fit to experimental thermodynamic data following the procedures of the SDK (Shinoda, DeVane, and Klein) CG force field, with minor adjustments to provide better agreement with radial distribution functions from atomistic simulations. Bonded parameters are fit to probability distributions from atomistic simulations using multi-centered Gaussian-based potentials. The temperature-dependent potentials derived for the PNIPAM CG model in this work properly capture the coil-globule transition of PNIPAM single chains and yield a chain-length dependence consistent with atomistic simulations.

Multiplicity Control in Structural Equation Modeling: Incorporating Parameter Dependencies

ERIC Educational Resources Information Center

Smith, Carrie E.; Cribbie, Robert A.

2013-01-01

When structural equation modeling (SEM) analyses are conducted, significance tests for all important model relationships (parameters including factor loadings, covariances, etc.) are typically conducted at a specified nominal Type I error rate ([alpha]). Despite the fact that many significance tests are often conducted in SEM, rarely is…

Computer simulation of earthquakes

NASA Technical Reports Server (NTRS)

Cohen, S. C.

1976-01-01

Two computer simulation models of earthquakes were studied for the dependence of the pattern of events on the model assumptions and input parameters. Both models represent the seismically active region by mechanical blocks which are connected to one another and to a driving plate. The blocks slide on a friction surface. In the first model elastic forces were employed and time independent friction to simulate main shock events. The size, length, and time and place of event occurrence were influenced strongly by the magnitude and degree of homogeniety in the elastic and friction parameters of the fault region. Periodically reoccurring similar events were frequently observed in simulations with near homogeneous parameters along the fault, whereas, seismic gaps were a common feature of simulations employing large variations in the fault parameters. The second model incorporated viscoelastic forces and time-dependent friction to account for aftershock sequences. The periods between aftershock events increased with time and the aftershock region was confined to that which moved in the main event.

High-resolution time-frequency representation of EEG data using multi-scale wavelets

NASA Astrophysics Data System (ADS)

Li, Yang; Cui, Wei-Gang; Luo, Mei-Lin; Li, Ke; Wang, Lina

2017-09-01

An efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time-frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an application of the proposed method to a real EEG signal demonstrates the new approach can provide highly time-dependent spectral resolution capability.

Multidimensional extended spatial evolutionary games.

PubMed

Krześlak, Michał; Świerniak, Andrzej

2016-02-01

The goal of this paper is to study the classical hawk-dove model using mixed spatial evolutionary games (MSEG). In these games, played on a lattice, an additional spatial layer is introduced for dependence on more complex parameters and simulation of changes in the environment. Furthermore, diverse polymorphic equilibrium points dependent on cell reproduction, model parameters, and their simulation are discussed. Our analysis demonstrates the sensitivity properties of MSEGs and possibilities for further development. We discuss applications of MSEGs, particularly algorithms for modelling cell interactions during the development of tumours. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dependence of the multiplicities of secondary particles on the impact parameter in collisions of high-energy neon and iron nuclei with photoemulsion nuclei

NASA Technical Reports Server (NTRS)

Dudkin, V. E.; Kovalev, E. E.; Nefedov, N. A.; Antonchik, V. A.; Bogdanov, S. D.; Kosmach, V. F.; Likhachev, A. YU.; Benton, E. V.; Crawford, H. J.

1995-01-01

A method is proposed for finding the dependence of mean multiplicities of secondaries on the nucleus-collision impact parameter from the data on the total interaction ensemble. The impact parameter has been shown to completely define the mean characteristics of an individual interaction event. A difference has been found between experimental results and the data calculated in terms of the cascade-evaporation model at impact-parameter values below 3 fm.

A Comparison of Two Mathematical Modeling Frameworks for Evaluating Sexually Transmitted Infection Epidemiology.

PubMed

Johnson, Leigh F; Geffen, Nathan

2016-03-01

Different models of sexually transmitted infections (STIs) can yield substantially different conclusions about STI epidemiology, and it is important to understand how and why models differ. Frequency-dependent models make the simplifying assumption that STI incidence is proportional to STI prevalence in the population, whereas network models calculate STI incidence more realistically by classifying individuals according to their partners' STI status. We assessed a deterministic frequency-dependent model approximation to a microsimulation network model of STIs in South Africa. Sexual behavior and demographic parameters were identical in the 2 models. Six STIs were simulated using each model: HIV, herpes, syphilis, gonorrhea, chlamydia, and trichomoniasis. For all 6 STIs, the frequency-dependent model estimated a higher STI prevalence than the network model, with the difference between the 2 models being relatively large for the curable STIs. When the 2 models were fitted to the same STI prevalence data, the best-fitting parameters differed substantially between models, with the frequency-dependent model suggesting more immunity and lower transmission probabilities. The fitted frequency-dependent model estimated that the effects of a hypothetical elimination of concurrent partnerships and a reduction in commercial sex were both smaller than estimated by the fitted network model, whereas the latter model estimated a smaller impact of a reduction in unprotected sex in spousal relationships. The frequency-dependent assumption is problematic when modeling short-term STIs. Frequency-dependent models tend to underestimate the importance of high-risk groups in sustaining STI epidemics, while overestimating the importance of long-term partnerships and low-risk groups.

Maintenance of genetic variation with a frequency-dependent selection model as compared to the overdominant model.

PubMed

Hedrick, P W

1972-12-01

A frequency-dependent selection model proposed by Huang, Singh and Kojima (1971) was found to be more effective at maintaining genetic variation in a finite population than the overdominant model. The fourth moment parameter of the distribution of unfixed states showed that there was a more platykurtic distribution for the frequency-dependent model. This agreed well with the expected gene frequency change found for an infinite population.

Spectral structure of pressure measurements made in a combustion duct. [jet engine noise

NASA Technical Reports Server (NTRS)

Miles, J. H.; Raftopoulos, D. D.

1980-01-01

A model for acoustic plane wave propagation in a combustion duct through a confined, flowing gas containing soot particles is presented. The model takes into account only heat transfer between the gas and soot particles. As a result, the model depends on only a single parameter which can be written as the ratio of the soot particle thermal relaxation time to the soot particle mass fraction. The model yields expressions for the attenuation and dispersion of the plane wave which depends only on this single parameter. The model was used to calculate pressure spectra in a combustion duct. The results were compared with measured spectra. For particular values of the single free parameter, the calculated spectra resemble the measured spectra. Consequently, the model, to this extent, explains the experimental measurements and provides some insight into the number and type of particles.

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.

Effects of Ignoring Item Interaction on Item Parameter Estimation and Detection of Interacting Items

ERIC Educational Resources Information Center

Chen, Cheng-Te; Wang, Wen-Chung

2007-01-01

This study explores the effects of ignoring item interaction on item parameter estimation and the efficiency of using the local dependence index Q[subscript 3] and the SAS NLMIXED procedure to detect item interaction under the three-parameter logistic model and the generalized partial credit model. Through simulations, it was found that ignoring…

Parameter dependences of the separatrix density in nitrogen seeded ASDEX Upgrade H-mode discharges

NASA Astrophysics Data System (ADS)

Kallenbach, A.; Sun, H. J.; Eich, T.; Carralero, D.; Hobirk, J.; Scarabosio, A.; Siccinio, M.; ASDEX Upgrade Team; EUROfusion MST1 Team

2018-04-01

The upstream separatrix electron density is an important interface parameter for core performance and divertor power exhaust. It has been measured in ASDEX Upgrade H-mode discharges by means of Thomson scattering using a self-consistent estimate of the upstream electron temperature under the assumption of Spitzer-Härm electron conduction. Its dependence on various plasma parameters has been tested for different plasma conditions in H-mode. The leading parameter determining n e,sep was found to be the neutral divertor pressure, which can be considered as an engineering parameter since it is determined mainly by the gas puff rate and the pumping speed. The experimentally found parameter dependence of n e,sep, which is dominated by the divertor neutral pressure, could be approximately reconciled by 2-point modelling.

Density-dependence as a size-independent regulatory mechanism.

PubMed

de Vladar, Harold P

2006-01-21

The growth function of populations is central in biomathematics. The main dogma is the existence of density-dependence mechanisms, which can be modelled with distinct functional forms that depend on the size of the population. One important class of regulatory functions is the theta-logistic, which generalizes the logistic equation. Using this model as a motivation, this paper introduces a simple dynamical reformulation that generalizes many growth functions. The reformulation consists of two equations, one for population size, and one for the growth rate. Furthermore, the model shows that although population is density-dependent, the dynamics of the growth rate does not depend either on population size, nor on the carrying capacity. Actually, the growth equation is uncoupled from the population size equation, and the model has only two parameters, a Malthusian parameter rho and a competition coefficient theta. Distinct sign combinations of these parameters reproduce not only the family of theta-logistics, but also the van Bertalanffy, Gompertz and Potential Growth equations, among other possibilities. It is also shown that, except for two critical points, there is a general size-scaling relation that includes those appearing in the most important allometric theories, including the recently proposed Metabolic Theory of Ecology. With this model, several issues of general interest are discussed such as the growth of animal population, extinctions, cell growth and allometry, and the effect of environment over a population.

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

Determination of the Parameter Sets for the Best Performance of IPS-driven ENLIL Model

NASA Astrophysics Data System (ADS)

Yun, Jongyeon; Choi, Kyu-Cheol; Yi, Jonghyuk; Kim, Jaehun; Odstrcil, Dusan

2016-12-01

Interplanetary scintillation-driven (IPS-driven) ENLIL model was jointly developed by University of California, San Diego (UCSD) and National Aeronaucics and Space Administration/Goddard Space Flight Center (NASA/GSFC). The model has been in operation by Korean Space Weather Cetner (KSWC) since 2014. IPS-driven ENLIL model has a variety of ambient solar wind parameters and the results of the model depend on the combination of these parameters. We have conducted researches to determine the best combination of parameters to improve the performance of the IPS-driven ENLIL model. The model results with input of 1,440 combinations of parameters are compared with the Advanced Composition Explorer (ACE) observation data. In this way, the top 10 parameter sets showing best performance were determined. Finally, the characteristics of the parameter sets were analyzed and application of the results to IPS-driven ENLIL model was discussed.

Relaxation limit of a compressible gas-liquid model with well-reservoir interaction

NASA Astrophysics Data System (ADS)

Solem, Susanne; Evje, Steinar

2017-02-01

This paper deals with the relaxation limit of a two-phase compressible gas-liquid model which contains a pressure-dependent well-reservoir interaction term of the form q (P_r - P) where q>0 is the rate of the pressure-dependent influx/efflux of gas, P is the (unknown) wellbore pressure, and P_r is the (known) surrounding reservoir pressure. The model can be used to study gas-kick flow scenarios relevant for various wellbore operations. One extreme case is when the wellbore pressure P is largely dictated by the surrounding reservoir pressure P_r. Formally, this model is obtained by deriving the limiting system as the relaxation parameter q in the full model tends to infinity. The main purpose of this work is to understand to what extent this case can be represented by a well-defined mathematical model for a fixed global time T>0. Well-posedness of the full model has been obtained in Evje (SIAM J Math Anal 45(2):518-546, 2013). However, as the estimates for the full model are dependent on the relaxation parameter q, new estimates must be obtained for the equilibrium model to ensure existence of solutions. By means of appropriate a priori assumptions and some restrictions on the model parameters, necessary estimates (low order and higher order) are obtained. These estimates that depend on the global time T together with smallness assumptions on the initial data are then used to obtain existence of solutions in suitable Sobolev spaces.

Effect of time dependence on probabilistic seismic-hazard maps and deaggregation for the central Apennines, Italy

USGS Publications Warehouse

Akinci, A.; Galadini, F.; Pantosti, D.; Petersen, M.; Malagnini, L.; Perkins, D.

2009-01-01

We produce probabilistic seismic-hazard assessments for the central Apennines, Italy, using time-dependent models that are characterized using a Brownian passage time recurrence model. Using aperiodicity parameters, ?? of 0.3, 0.5, and 0.7, we examine the sensitivity of the probabilistic ground motion and its deaggregation to these parameters. For the seismic source model we incorporate both smoothed historical seismicity over the area and geological information on faults. We use the maximum magnitude model for the fault sources together with a uniform probability of rupture along the fault (floating fault model) to model fictitious faults to account for earthquakes that cannot be correlated with known geologic structural segmentation.

About influence of input rate random part of nonstationary queue system on statistical estimates of its macroscopic indicators

NASA Astrophysics Data System (ADS)

Korelin, Ivan A.; Porshnev, Sergey V.

2018-05-01

A model of the non-stationary queuing system (NQS) is described. The input of this model receives a flow of requests with input rate λ = λdet (t) + λrnd (t), where λdet (t) is a deterministic function depending on time; λrnd (t) is a random function. The parameters of functions λdet (t), λrnd (t) were identified on the basis of statistical information on visitor flows collected from various Russian football stadiums. The statistical modeling of NQS is carried out and the average statistical dependences are obtained: the length of the queue of requests waiting for service, the average wait time for the service, the number of visitors entered to the stadium on the time. It is shown that these dependencies can be characterized by the following parameters: the number of visitors who entered at the time of the match; time required to service all incoming visitors; the maximum value; the argument value when the studied dependence reaches its maximum value. The dependences of these parameters on the energy ratio of the deterministic and random component of the input rate are investigated.

Spatial dependence of extreme rainfall

NASA Astrophysics Data System (ADS)

Radi, Noor Fadhilah Ahmad; Zakaria, Roslinazairimah; Satari, Siti Zanariah; Azman, Muhammad Az-zuhri

2017-05-01

This study aims to model the spatial extreme daily rainfall process using the max-stable model. The max-stable model is used to capture the dependence structure of spatial properties of extreme rainfall. Three models from max-stable are considered namely Smith, Schlather and Brown-Resnick models. The methods are applied on 12 selected rainfall stations in Kelantan, Malaysia. Most of the extreme rainfall data occur during wet season from October to December of 1971 to 2012. This period is chosen to assure the available data is enough to satisfy the assumption of stationarity. The dependence parameters including the range and smoothness, are estimated using composite likelihood approach. Then, the bootstrap approach is applied to generate synthetic extreme rainfall data for all models using the estimated dependence parameters. The goodness of fit between the observed extreme rainfall and the synthetic data is assessed using the composite likelihood information criterion (CLIC). Results show that Schlather model is the best followed by Brown-Resnick and Smith models based on the smallest CLIC's value. Thus, the max-stable model is suitable to be used to model extreme rainfall in Kelantan. The study on spatial dependence in extreme rainfall modelling is important to reduce the uncertainties of the point estimates for the tail index. If the spatial dependency is estimated individually, the uncertainties will be large. Furthermore, in the case of joint return level is of interest, taking into accounts the spatial dependence properties will improve the estimation process.

Three-dimensional particle-particle simulations: Dependence of relaxation time on plasma parameter

NASA Astrophysics Data System (ADS)

Zhao, Yinjian

2018-05-01

A particle-particle simulation model is applied to investigate the dependence of the relaxation time on the plasma parameter in a three-dimensional unmagnetized plasma. It is found that the relaxation time increases linearly as the plasma parameter increases within the range of the plasma parameter from 2 to 10; when the plasma parameter equals 2, the relaxation time is independent of the total number of particles, but when the plasma parameter equals 10, the relaxation time slightly increases as the total number of particles increases, which indicates the transition of a plasma from collisional to collisionless. In addition, ions with initial Maxwell-Boltzmann (MB) distribution are found to stay in the MB distribution during the whole simulation time, and the mass of ions does not significantly affect the relaxation time of electrons. This work also shows the feasibility of the particle-particle model when using GPU parallel computing techniques.

Electrode performance parameters for a radioisotope-powered AMTEC for space power applications

NASA Technical Reports Server (NTRS)

Underwood, M. L.; O'Connor, D.; Williams, R. M.; Jeffries-Nakamura, B.; Ryan, M. A.; Bankston, C. P.

1992-01-01

The alkali metal thermoelastic converter (AMTEC) is a device for the direct conversion of heat to electricity. Recently a design of an AMTEC using a radioisotope heat source was described, but the optimum condenser temperature was hotter than the temperatures used in the laboratory to develop the electrode performance model. Now laboratory experiments have confirmed the dependence of two model parameters over a broader range of condenser and electrode temperatures for two candidate electrode compositions. One parameter, the electrochemical exchange current density at the reaction interface, is independent of the condenser temperature, and depends only upon the collision rate of sodium at the reaction zone. The second parameter, a morphological parameter, which measures the mass transport resistance through the electrode, is independent of condenser and electrode temperatures for molybdenum electrodes. For rhodium-tungsten electrodes, however, this parameter increases for decreasing electrode temperature, indicating an activated mass transport mechanism such as surface diffusion.

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.

Conditional probability of rainfall extremes across multiple durations

NASA Astrophysics Data System (ADS)

Le, Phuong Dong; Leonard, Michael; Westra, Seth

2017-04-01

The conditional probability that extreme rainfall will occur at one location given that it is occurring at another location is critical in engineering design and management circumstances including planning of evacuation routes and the sitting of emergency infrastructure. A challenge with this conditional simulation is that in many situations the interest is not so much the conditional distributions of rainfall of the same duration at two locations, but rather the conditional distribution of flooding in two neighbouring catchments, which may be influenced by rainfall of different critical durations. To deal with this challenge, a model that can consider both spatial and duration dependence of extremes is required. The aim of this research is to develop a model that can take account both spatial dependence and duration dependence into the dependence structure of extreme rainfalls. To achieve this aim, this study is a first attempt at combining extreme rainfall for multiple durations within a spatial extreme model framework based on max-stable process theory. Max-stable processes provide a general framework for modelling multivariate extremes with spatial dependence for just a single duration extreme rainfall. To achieve dependence across multiple timescales, this study proposes a new approach that includes addition elements representing duration dependence of extremes to the covariance matrix of max-stable model. To improve the efficiency of calculation, a re-parameterization proposed by Koutsoyiannis et al. (1998) is used to reduce the number of parameters necessary to be estimated. This re-parameterization enables the GEV parameters to be represented as a function of timescale. A stepwise framework has been adopted to achieve the overall aims of this research. Firstly, the re-parameterization is used to define a new set of common parameters for marginal distribution across multiple durations. Secondly, spatial interpolation of the new parameter set is used to estimate marginal parameters across the full spatial domain. Finally, spatial interpolation result is used as initial condition to estimate dependence parameters via a likelihood function of max-stable model for multiple durations. The Hawkesbury-Nepean catchment near Sydney in Australia was selected as case study for this research. This catchment has 25 sub-daily rain gauges with the minimum record length of 24 years over a region of 300 km × 300 km area. The re-parameterization was applied for each station for durations from 1 hour to 24 hours and then is evaluated by comparing with the at-site fitted GEV. The evaluation showed that the average R2 for all station is around 0.80 with the range from 0.26 to 1.0. The output of re-parameterization then was used to construct the spatial surface based on covariates including longitude, latitude, and elevation. The dependence model showed good agreements between empirical extremal coefficient and theoretical extremal coefficient for multiple durations. For the overall model, a leave-one-out cross-validation for all stations showed it works well for 20 out of 25 stations. The potential application of this model framework was illustrated through a conditional map of return period and return level across multiple durations, both of which are important for engineering design and management.

Assessing the sensitivity of a land-surface scheme to the parameter values using a single column model

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

Pitman, A.J.

The sensitivity of a land-surface scheme (the Biosphere Atmosphere Transfer Scheme, BATS) to its parameter values was investigated using a single column model. Identifying which parameters were important in controlling the turbulent energy fluxes, temperature, soil moisture, and runoff was dependent upon many factors. In the simulation of a nonmoisture-stressed tropical forest, results were dependent on a combination of reservoir terms (soil depth, root distribution), flux efficiency terms (roughness length, stomatal resistance), and available energy (albedo). If moisture became limited, the reservoir terms increased in importance because the total fluxes predicted depended on moisture availability and not on the ratemore » of transfer between the surface and the atmosphere. The sensitivity shown by BATS depended on which vegetation type was being simulated, which variable was used to determine sensitivity, the magnitude and sign of the parameter change, the climate regime (precipitation amount and frequency), and soil moisture levels and proximity to wilting. The interactions between these factors made it difficult to identify the most important parameters in BATS. Therefore, this paper does not argue that a particular set of parameters is important in BATS, rather it shows that no general ranking of parameters is possible. It is also emphasized that using `stand-alone` forcing to examine the sensitivity of a land-surface scheme to perturbations, in either parameters or the atmosphere, is unreliable due to the lack of surface-atmospheric feedbacks.« less

Important observations and parameters for a salt water intrusion model

USGS Publications Warehouse

Shoemaker, W.B.

2004-01-01

Sensitivity analysis with a density-dependent ground water flow simulator can provide insight and understanding of salt water intrusion calibration problems far beyond what is possible through intuitive analysis alone. Five simple experimental simulations presented here demonstrate this point. Results show that dispersivity is a very important parameter for reproducing a steady-state distribution of hydraulic head, salinity, and flow in the transition zone between fresh water and salt water in a coastal aquifer system. When estimating dispersivity, the following conclusions can be drawn about the data types and locations considered. (1) The "toe" of the transition zone is the most effective location for hydraulic head and salinity observations. (2) Areas near the coastline where submarine ground water discharge occurs are the most effective locations for flow observations. (3) Salinity observations are more effective than hydraulic head observations. (4) The importance of flow observations aligned perpendicular to the shoreline varies dramatically depending on distance seaward from the shoreline. Extreme parameter correlation can prohibit unique estimation of permeability parameters such as hydraulic conductivity and flow parameters such as recharge in a density-dependent ground water flow model when using hydraulic head and salinity observations. Adding flow observations perpendicular to the shoreline in areas where ground water is exchanged with the ocean body can reduce the correlation, potentially resulting in unique estimates of these parameter values. Results are expected to be directly applicable to many complex situations, and have implications for model development whether or not formal optimization methods are used in model calibration.

Important observations and parameters for a salt water intrusion model.

PubMed

Shoemaker, W Barclay

2004-01-01

Sensitivity analysis with a density-dependent ground water flow simulator can provide insight and understanding of salt water intrusion calibration problems far beyond what is possible through intuitive analysis alone. Five simple experimental simulations presented here demonstrate this point. Results show that dispersivity is a very important parameter for reproducing a steady-state distribution of hydraulic head, salinity, and flow in the transition zone between fresh water and salt water in a coastal aquifer system. When estimating dispersivity, the following conclusions can be drawn about the data types and locations considered. (1) The "toe" of the transition zone is the most effective location for hydraulic head and salinity observations. (2) Areas near the coastline where submarine ground water discharge occurs are the most effective locations for flow observations. (3) Salinity observations are more effective than hydraulic head observations. (4) The importance of flow observations aligned perpendicular to the shoreline varies dramatically depending on distance seaward from the shoreline. Extreme parameter correlation can prohibit unique estimation of permeability parameters such as hydraulic conductivity and flow parameters such as recharge in a density-dependent ground water flow model when using hydraulic head and salinity observations. Adding flow observations perpendicular to the shoreline in areas where ground water is exchanged with the ocean body can reduce the correlation, potentially resulting in unique estimates of these parameter values. Results are expected to be directly applicable to many complex situations, and have implications for model development whether or not formal optimization methods are used in model calibration.

Maintenance of Genetic Variation with a Frequency-Dependent Selection Model as Compared to the Overdominant Model

PubMed Central

Hedrick, Philip W.

1972-01-01

A frequency-dependent selection model proposed by Huang, Singh and Kojima (1971) was found to be more effective at maintaining genetic variation in a finite population than the overdominant model. The fourth moment parameter of the distribution of unfixed states showed that there was a more platykurtic distribution for the frequency-dependent model. This agreed well with the expected gene frequency change found for an infinite population. PMID:4652882

An IRT Model with a Parameter-Driven Process for Change

ERIC Educational Resources Information Center

Rijmen, Frank; De Boeck, Paul; van der Maas, Han L. J.

2005-01-01

An IRT model with a parameter-driven process for change is proposed. Quantitative differences between persons are taken into account by a continuous latent variable, as in common IRT models. In addition, qualitative inter-individual differences and auto-dependencies are accounted for by assuming within-subject variability with respect to the…

Comparative Analyses of MIRT Models and Software (BMIRT and flexMIRT)

ERIC Educational Resources Information Center

Yavuz, Guler; Hambleton, Ronald K.

2017-01-01

Application of MIRT modeling procedures is dependent on the quality of parameter estimates provided by the estimation software and techniques used. This study investigated model parameter recovery of two popular MIRT packages, BMIRT and flexMIRT, under some common measurement conditions. These packages were specifically selected to investigate the…

Event-scale power law recession analysis: quantifying methodological uncertainty

NASA Astrophysics Data System (ADS)

Dralle, David N.; Karst, Nathaniel J.; Charalampous, Kyriakos; Veenstra, Andrew; Thompson, Sally E.

2017-01-01

The study of single streamflow recession events is receiving increasing attention following the presentation of novel theoretical explanations for the emergence of power law forms of the recession relationship, and drivers of its variability. Individually characterizing streamflow recessions often involves describing the similarities and differences between model parameters fitted to each recession time series. Significant methodological sensitivity has been identified in the fitting and parameterization of models that describe populations of many recessions, but the dependence of estimated model parameters on methodological choices has not been evaluated for event-by-event forms of analysis. Here, we use daily streamflow data from 16 catchments in northern California and southern Oregon to investigate how combinations of commonly used streamflow recession definitions and fitting techniques impact parameter estimates of a widely used power law recession model. Results are relevant to watersheds that are relatively steep, forested, and rain-dominated. The highly seasonal mediterranean climate of northern California and southern Oregon ensures study catchments explore a wide range of recession behaviors and wetness states, ideal for a sensitivity analysis. In such catchments, we show the following: (i) methodological decisions, including ones that have received little attention in the literature, can impact parameter value estimates and model goodness of fit; (ii) the central tendencies of event-scale recession parameter probability distributions are largely robust to methodological choices, in the sense that differing methods rank catchments similarly according to the medians of these distributions; (iii) recession parameter distributions are method-dependent, but roughly catchment-independent, such that changing the choices made about a particular method affects a given parameter in similar ways across most catchments; and (iv) the observed correlative relationship between the power-law recession scale parameter and catchment antecedent wetness varies depending on recession definition and fitting choices. Considering study results, we recommend a combination of four key methodological decisions to maximize the quality of fitted recession curves, and to minimize bias in the related populations of fitted recession parameters.

Elastic Scattering of 65 MeV Protons from Several Nuclei between 16O and 209Bi

NASA Astrophysics Data System (ADS)

Ahmed, Syed; Akther, Parvin; Ferdous, Nasima; Begum, Amena; Gupta, Hiranmay

1997-10-01

Elastic scattering of 65 MeV polarized protons from twenty five nuclei ranging from 16O to 209Bi have been analysed within the framework of the nine parameter optical model. A set of optical model parameters has been obtained which shows the systematic behaviour of the target mass dependence of the real potential, volume integral and the r.m.s. radius. The isotopic spin dependence of the real potential has also been studied. Parameters obtained by fitting the elastic scattering data have been able to reproduce the pickup and stripping reaction cross sections as studied in a few cases.

Size-density scaling in protists and the links between consumer-resource interaction parameters.

PubMed

DeLong, John P; Vasseur, David A

2012-11-01

Recent work indicates that the interaction between body-size-dependent demographic processes can generate macroecological patterns such as the scaling of population density with body size. In this study, we evaluate this possibility for grazing protists and also test whether demographic parameters in these models are correlated after controlling for body size. We compiled data on the body-size dependence of consumer-resource interactions and population density for heterotrophic protists grazing algae in laboratory studies. We then used nested dynamic models to predict both the height and slope of the scaling relationship between population density and body size for these protists. We also controlled for consumer size and assessed links between model parameters. Finally, we used the models and the parameter estimates to assess the individual- and population-level dependence of resource use on body-size and prey-size selection. The predicted size-density scaling for all models matched closely to the observed scaling, and the simplest model was sufficient to predict the pattern. Variation around the mean size-density scaling relationship may be generated by variation in prey productivity and area of capture, but residuals are relatively insensitive to variation in prey size selection. After controlling for body size, many consumer-resource interaction parameters were correlated, and a positive correlation between residual prey size selection and conversion efficiency neutralizes the apparent fitness advantage of taking large prey. Our results indicate that widespread community-level patterns can be explained with simple population models that apply consistently across a range of sizes. They also indicate that the parameter space governing the dynamics and the steady states in these systems is structured such that some parts of the parameter space are unlikely to represent real systems. Finally, predator-prey size ratios represent a kind of conundrum, because they are widely observed but apparently have little influence on population size and fitness, at least at this level of organization. © 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.

Influence of primary fragment excitation energy and spin distributions on fission observables

NASA Astrophysics Data System (ADS)

Litaize, Olivier; Thulliez, Loïc; Serot, Olivier; Chebboubi, Abdelaziz; Tamagno, Pierre

2018-03-01

Fission observables in the case of 252Cf(sf) are investigated by exploring several models involved in the excitation energy sharing and spin-parity assignment between primary fission fragments. In a first step the parameters used in the FIFRELIN Monte Carlo code "reference route" are presented: two parameters for the mass dependent temperature ratio law and two constant spin cut-off parameters for light and heavy fragment groups respectively. These parameters determine the initial fragment entry zone in excitation energy and spin-parity (E*, Jπ). They are chosen to reproduce the light and heavy average prompt neutron multiplicities. When these target observables are achieved all other fission observables can be predicted. We show here the influence of input parameters on the saw-tooth curve and we discuss the influence of a mass and energy-dependent spin cut-off model on gamma-rays related fission observables. The part of the model involving level densities, neutron transmission coefficients or photon strength functions remains unchanged.

Modeling of Density-Dependent Flow based on the Thermodynamically Constrained Averaging Theory

NASA Astrophysics Data System (ADS)

Weigand, T. M.; Schultz, P. B.; Kelley, C. T.; Miller, C. T.; Gray, W. G.

2016-12-01

The thermodynamically constrained averaging theory (TCAT) has been used to formulate general classes of porous medium models, including new models for density-dependent flow. The TCAT approach provides advantages that include a firm connection between the microscale, or pore scale, and the macroscale; a thermodynamically consistent basis; explicit inclusion of factors such as a diffusion that arises from gradients associated with pressure and activity and the ability to describe both high and low concentration displacement. The TCAT model is presented and closure relations for the TCAT model are postulated based on microscale averages and a parameter estimation is performed on a subset of the experimental data. Due to the sharpness of the fronts, an adaptive moving mesh technique was used to ensure grid independent solutions within the run time constraints. The optimized parameters are then used for forward simulations and compared to the set of experimental data not used for the parameter estimation.

A temperature-dependent coarse-grained model for the thermoresponsive polymer poly(N-isopropylacrylamide)

DOE PAGES

Abbott, Lauren J.; Stevens, Mark J.

2015-12-22

In this study, a coarse-grained (CG) model is developed for the thermoresponsive polymer poly(N-isopropylacrylamide) (PNIPAM), using a hybrid top-down and bottom-up approach. Nonbonded parameters are fit to experimental thermodynamic data following the procedures of the SDK (Shinoda, DeVane, and Klein) CG force field, with minor adjustments to provide better agreement with radial distribution functions from atomistic simulations. Bonded parameters are fit to probability distributions from atomistic simulations using multi-centered Gaussian-based potentials. The temperature-dependent potentials derived for the PNIPAM CG model in this work properly capture the coil–globule transition of PNIPAM single chains and yield a chain-length dependence consistent with atomisticmore » simulations.« less

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

Lis, Jakub

In this paper, we investigate the Q-ball Ansatz in the baby Skyrme model. First, the appearance of peakons, i.e. solutions with extremely large absolute values of the second derivative at maxima, is analyzed. It is argued that such solutions are intrinsic to the baby Skyrme model and do not depend on the detailed form of a potential used in calculations. Next, we concentrate on compact nonspinning Q-balls. We show the failure of a small parameter expansion in this case. Finally, we explore the existence and parameter dependence of Q-ball solutions.

Aerodynamic Parameters of High Performance Aircraft Estimated from Wind Tunnel and Flight Test Data

NASA Technical Reports Server (NTRS)

Klein, Vladislav; Murphy, Patrick C.

1998-01-01

A concept of system identification applied to high performance aircraft is introduced followed by a discussion on the identification methodology. Special emphasis is given to model postulation using time invariant and time dependent aerodynamic parameters, model structure determination and parameter estimation using ordinary least squares an mixed estimation methods, At the same time problems of data collinearity detection and its assessment are discussed. These parts of methodology are demonstrated in examples using flight data of the X-29A and X-31A aircraft. In the third example wind tunnel oscillatory data of the F-16XL model are used. A strong dependence of these data on frequency led to the development of models with unsteady aerodynamic terms in the form of indicial functions. The paper is completed by concluding remarks.

Effect of correlated observation error on parameters, predictions, and uncertainty

USGS Publications Warehouse

Tiedeman, Claire; Green, Christopher T.

2013-01-01

Correlations among observation errors are typically omitted when calculating observation weights for model calibration by inverse methods. We explore the effects of omitting these correlations on estimates of parameters, predictions, and uncertainties. First, we develop a new analytical expression for the difference in parameter variance estimated with and without error correlations for a simple one-parameter two-observation inverse model. Results indicate that omitting error correlations from both the weight matrix and the variance calculation can either increase or decrease the parameter variance, depending on the values of error correlation (ρ) and the ratio of dimensionless scaled sensitivities (rdss). For small ρ, the difference in variance is always small, but for large ρ, the difference varies widely depending on the sign and magnitude of rdss. Next, we consider a groundwater reactive transport model of denitrification with four parameters and correlated geochemical observation errors that are computed by an error-propagation approach that is new for hydrogeologic studies. We compare parameter estimates, predictions, and uncertainties obtained with and without the error correlations. Omitting the correlations modestly to substantially changes parameter estimates, and causes both increases and decreases of parameter variances, consistent with the analytical expression. Differences in predictions for the models calibrated with and without error correlations can be greater than parameter differences when both are considered relative to their respective confidence intervals. These results indicate that including observation error correlations in weighting for nonlinear regression can have important effects on parameter estimates, predictions, and their respective uncertainties.

The predictive consequences of parameterization

NASA Astrophysics Data System (ADS)

White, J.; Hughes, J. D.; Doherty, J. E.

2013-12-01

In numerical groundwater modeling, parameterization is the process of selecting the aspects of a computer model that will be allowed to vary during history matching. This selection process is dependent on professional judgment and is, therefore, inherently subjective. Ideally, a robust parameterization should be commensurate with the spatial and temporal resolution of the model and should include all uncertain aspects of the model. Limited computing resources typically require reducing the number of adjustable parameters so that only a subset of the uncertain model aspects are treated as estimable parameters; the remaining aspects are treated as fixed parameters during history matching. We use linear subspace theory to develop expressions for the predictive error incurred by fixing parameters. The predictive error is comprised of two terms. The first term arises directly from the sensitivity of a prediction to fixed parameters. The second term arises from prediction-sensitive adjustable parameters that are forced to compensate for fixed parameters during history matching. The compensation is accompanied by inappropriate adjustment of otherwise uninformed, null-space parameter components. Unwarranted adjustment of null-space components away from prior maximum likelihood values may produce bias if a prediction is sensitive to those components. The potential for subjective parameterization choices to corrupt predictions is examined using a synthetic model. Several strategies are evaluated, including use of piecewise constant zones, use of pilot points with Tikhonov regularization and use of the Karhunen-Loeve transformation. The best choice of parameterization (as defined by minimum error variance) is strongly dependent on the types of predictions to be made by the model.

An implicit adaptation algorithm for a linear model reference control system

NASA Technical Reports Server (NTRS)

Mabius, L.; Kaufman, H.

1975-01-01

This paper presents a stable implicit adaptation algorithm for model reference control. The constraints for stability are found using Lyapunov's second method and do not depend on perfect model following between the system and the reference model. Methods are proposed for satisfying these constraints without estimating the parameters on which the constraints depend.

Parameter identification of process simulation models as a means for knowledge acquisition and technology transfer

NASA Astrophysics Data System (ADS)

Batzias, Dimitris F.; Ifanti, Konstantina

2012-12-01

Process simulation models are usually empirical, therefore there is an inherent difficulty in serving as carriers for knowledge acquisition and technology transfer, since their parameters have no physical meaning to facilitate verification of the dependence on the production conditions; in such a case, a 'black box' regression model or a neural network might be used to simply connect input-output characteristics. In several cases, scientific/mechanismic models may be proved valid, in which case parameter identification is required to find out the independent/explanatory variables and parameters, which each parameter depends on. This is a difficult task, since the phenomenological level at which each parameter is defined is different. In this paper, we have developed a methodological framework under the form of an algorithmic procedure to solve this problem. The main parts of this procedure are: (i) stratification of relevant knowledge in discrete layers immediately adjacent to the layer that the initial model under investigation belongs to, (ii) design of the ontology corresponding to these layers, (iii) elimination of the less relevant parts of the ontology by thinning, (iv) retrieval of the stronger interrelations between the remaining nodes within the revised ontological network, and (v) parameter identification taking into account the most influential interrelations revealed in (iv). The functionality of this methodology is demonstrated by quoting two representative case examples on wastewater treatment.

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

DOE PAGES

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

2016-07-20

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

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

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

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.

Anti Submarine Warfare Search Models

DTIC Science & Technology

2016-09-01

worthwhile to send a helicopter out to search for the target? The answer to this operational question depends on the probability of finding the target and...fist,” and “lungs” of the ASW weapon. This balance certainly depends upon the mission and the tactical parameters of the associated scenario. For...effectiveness of search models depends on the scenario and assumptions made, and one can never perfectly model an operational scenario. Each chapter

Dynamics in the Parameter Space of a Neuron Model

NASA Astrophysics Data System (ADS)

Paulo, C. Rech

2012-06-01

Some two-dimensional parameter-space diagrams are numerically obtained by considering the largest Lyapunov exponent for a four-dimensional thirteen-parameter Hindmarsh—Rose neuron model. Several different parameter planes are considered, and it is shown that depending on the combination of parameters, a typical scenario can be preserved: for some choice of two parameters, the parameter plane presents a comb-shaped chaotic region embedded in a large periodic region. It is also shown that there exist regions close to these comb-shaped chaotic regions, separated by the comb teeth, organizing themselves in period-adding bifurcation cascades.

Real-time individualization of the unified model of performance.

PubMed

Liu, Jianbo; Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Balkin, Thomas J; Reifman, Jaques

2017-12-01

Existing mathematical models for predicting neurobehavioural performance are not suited for mobile computing platforms because they cannot adapt model parameters automatically in real time to reflect individual differences in the effects of sleep loss. We used an extended Kalman filter to develop a computationally efficient algorithm that continually adapts the parameters of the recently developed Unified Model of Performance (UMP) to an individual. The algorithm accomplishes this in real time as new performance data for the individual become available. We assessed the algorithm's performance by simulating real-time model individualization for 18 subjects subjected to 64 h of total sleep deprivation (TSD) and 7 days of chronic sleep restriction (CSR) with 3 h of time in bed per night, using psychomotor vigilance task (PVT) data collected every 2 h during wakefulness. This UMP individualization process produced parameter estimates that progressively approached the solution produced by a post-hoc fitting of model parameters using all data. The minimum number of PVT measurements needed to individualize the model parameters depended upon the type of sleep-loss challenge, with ~30 required for TSD and ~70 for CSR. However, model individualization depended upon the overall duration of data collection, yielding increasingly accurate model parameters with greater number of days. Interestingly, reducing the PVT sampling frequency by a factor of two did not notably hamper model individualization. The proposed algorithm facilitates real-time learning of an individual's trait-like responses to sleep loss and enables the development of individualized performance prediction models for use in a mobile computing platform. © 2017 European Sleep Research Society.

Determination of the turbulence integral model parameters for a case of a coolant angular flow in regular rod-bundle

NASA Astrophysics Data System (ADS)

Bayaskhalanov, M. V.; Vlasov, M. N.; Korsun, A. S.; Merinov, I. G.; Philippov, M. Ph

2017-11-01

Research results of “k-ε” turbulence integral model (TIM) parameters dependence on the angle of a coolant flow in regular smooth cylindrical rod-bundle are presented. TIM is intended for the definition of efficient impulse and heat transport coefficients in the averaged equations of a heat and mass transfer in the regular rod structures in an anisotropic porous media approximation. The TIM equations are received by volume-averaging of the “k-ε” turbulence model equations on periodic cell of rod-bundle. The water flow across rod-bundle under angles from 15 to 75 degrees was simulated by means of an ANSYS CFX code. Dependence of the TIM parameters on flow angle was as a result received.

Inverse problem of HIV cell dynamics using Genetic Algorithms

NASA Astrophysics Data System (ADS)

González, J. A.; Guzmán, F. S.

2017-01-01

In order to describe the cell dynamics of T-cells in a patient infected with HIV, we use a flavour of Perelson's model. This is a non-linear system of Ordinary Differential Equations that describes the evolution of healthy, latently infected, infected T-cell concentrations and the free viral cells. Different parameters in the equations give different dynamics. Considering the concentration of these types of cells is known for a particular patient, the inverse problem consists in estimating the parameters in the model. We solve this inverse problem using a Genetic Algorithm (GA) that minimizes the error between the solutions of the model and the data from the patient. These errors depend on the parameters of the GA, like mutation rate and population, although a detailed analysis of this dependence will be described elsewhere.

Model of optical phantoms thermal response upon irradiation with 975 nm dermatological laser

NASA Astrophysics Data System (ADS)

Wróbel, M. S.; Bashkatov, A. N.; Yakunin, A. N.; Avetisyan, Yu. A.; Genina, E. A.; Galla, S.; Sekowska, A.; Truchanowicz, D.; Cenian, A.; Jedrzejewska-Szczerska, M.; Tuchin, V. V.

2018-04-01

We have developed a numerical model describing the optical and thermal behavior of optical tissue phantoms upon laser irradiation. According to our previous studies, the phantoms can be used as substitute of real skin from the optical, as well as thermal point of view. However, the thermal parameters are not entirely similar to those of real tissues thus there is a need to develop mathematical model, describing the thermal and optical response of such materials. This will facilitate the correction factors, which would be invaluable in translation between measurements on skin phantom to real tissues, and gave a good representation of a real case application. Here, we present the model dependent on the data of our optical phantoms fabricated and measured in our previous preliminary study. The ambiguity between the modeling and the thermal measurements depend on lack of accurate knowledge of material's thermal properties and some exact parameters of the laser beam. Those parameters were varied in the simulation, to provide an overview of possible parameters' ranges and the magnitude of thermal response.

Linear and nonlinear equivalent circuit modeling of CMUTs.

PubMed

Lohfink, Annette; Eccardt, Peter-Christian

2005-12-01

Using piston radiator and plate capacitance theory capacitive micromachined ultrasound transducers (CMUT) membrane cells can be described by one-dimensional (1-D) model parameters. This paper describes in detail a new method, which derives a 1-D model for CMUT arrays from finite-element methods (FEM) simulations. A few static and harmonic FEM analyses of a single CMUT membrane cell are sufficient to derive the mechanical and electrical parameters of an equivalent piston as the moving part of the cell area. For an array of parallel-driven cells, the acoustic parameters are derived as a complex mechanical fluid impedance, depending on the membrane shape form. As a main advantage, the nonlinear behavior of the CMUT can be investigated much easier and faster compared to FEM simulations, e.g., for a design of the maximum applicable voltage depending on the input signal. The 1-D parameter model allows an easy description of the CMUT behavior in air and fluids and simplifies the investigation of wave propagation within the connecting fluid represented by FEM or transmission line matrix (TLM) models.

Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

NASA Astrophysics Data System (ADS)

Adams, Matthew P.; Collier, Catherine J.; Uthicke, Sven; Ow, Yan X.; Langlois, Lucas; O'Brien, Katherine R.

2017-01-01

When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (Topt) for maximum photosynthetic rate (Pmax). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.

Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species.

PubMed

Adams, Matthew P; Collier, Catherine J; Uthicke, Sven; Ow, Yan X; Langlois, Lucas; O'Brien, Katherine R

2017-01-04

When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (T opt ) for maximum photosynthetic rate (P max ). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.

Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

PubMed Central

Adams, Matthew P.; Collier, Catherine J.; Uthicke, Sven; Ow, Yan X.; Langlois, Lucas; O’Brien, Katherine R.

2017-01-01

When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (Topt) for maximum photosynthetic rate (Pmax). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike. PMID:28051123

Novel scheme for rapid parallel parameter estimation of gravitational waves from compact binary coalescences

NASA Astrophysics Data System (ADS)

Pankow, C.; Brady, P.; Ochsner, E.; O'Shaughnessy, R.

2015-07-01

We introduce a highly parallelizable architecture for estimating parameters of compact binary coalescence using gravitational-wave data and waveform models. Using a spherical harmonic mode decomposition, the waveform is expressed as a sum over modes that depend on the intrinsic parameters (e.g., masses) with coefficients that depend on the observer dependent extrinsic parameters (e.g., distance, sky position). The data is then prefiltered against those modes, at fixed intrinsic parameters, enabling efficiently evaluation of the likelihood for generic source positions and orientations, independent of waveform length or generation time. We efficiently parallelize our intrinsic space calculation by integrating over all extrinsic parameters using a Monte Carlo integration strategy. Since the waveform generation and prefiltering happens only once, the cost of integration dominates the procedure. Also, we operate hierarchically, using information from existing gravitational-wave searches to identify the regions of parameter space to emphasize in our sampling. As proof of concept and verification of the result, we have implemented this algorithm using standard time-domain waveforms, processing each event in less than one hour on recent computing hardware. For most events we evaluate the marginalized likelihood (evidence) with statistical errors of ≲5 %, and even smaller in many cases. With a bounded runtime independent of the waveform model starting frequency, a nearly unchanged strategy could estimate neutron star (NS)-NS parameters in the 2018 advanced LIGO era. Our algorithm is usable with any noise curve and existing time-domain model at any mass, including some waveforms which are computationally costly to evolve.

Developing population models with data from marked individuals

USGS Publications Warehouse

Hae Yeong Ryu,; Kevin T. Shoemaker,; Eva Kneip,; Anna Pidgeon,; Patricia Heglund,; Brooke Bateman,; Thogmartin, Wayne E.; Reşit Akçakaya,

2016-01-01

Population viability analysis (PVA) is a powerful tool for biodiversity assessments, but its use has been limited because of the requirements for fully specified population models such as demographic structure, density-dependence, environmental stochasticity, and specification of uncertainties. Developing a fully specified population model from commonly available data sources – notably, mark–recapture studies – remains complicated due to lack of practical methods for estimating fecundity, true survival (as opposed to apparent survival), natural temporal variability in both survival and fecundity, density-dependence in the demographic parameters, and uncertainty in model parameters. We present a general method that estimates all the key parameters required to specify a stochastic, matrix-based population model, constructed using a long-term mark–recapture dataset. Unlike standard mark–recapture analyses, our approach provides estimates of true survival rates and fecundities, their respective natural temporal variabilities, and density-dependence functions, making it possible to construct a population model for long-term projection of population dynamics. Furthermore, our method includes a formal quantification of parameter uncertainty for global (multivariate) sensitivity analysis. We apply this approach to 9 bird species and demonstrate the feasibility of using data from the Monitoring Avian Productivity and Survivorship (MAPS) program. Bias-correction factors for raw estimates of survival and fecundity derived from mark–recapture data (apparent survival and juvenile:adult ratio, respectively) were non-negligible, and corrected parameters were generally more biologically reasonable than their uncorrected counterparts. Our method allows the development of fully specified stochastic population models using a single, widely available data source, substantially reducing the barriers that have until now limited the widespread application of PVA. This method is expected to greatly enhance our understanding of the processes underlying population dynamics and our ability to analyze viability and project trends for species of conservation concern.

Amplitude and Phase Characteristics of Signals at the Output of Spatially Separated Antennas for Paths with Scattering

NASA Astrophysics Data System (ADS)

Anikin, A. S.

2018-06-01

Conditional statistical characteristics of the phase difference are considered depending on the ratio of instantaneous output signal amplitudes of spatially separated weakly directional antennas for the normal field model for paths with radio-wave scattering. The dependences obtained are related to the physical processes on the radio-wave propagation path. The normal model parameters are established at which the statistical characteristics of the phase difference depend on the ratio of the instantaneous amplitudes and hence can be used to measure the phase difference. Using Shannon's formula, the amount of information on the phase difference of signals contained in the ratio of their amplitudes is calculated depending on the parameters of the normal field model. Approaches are suggested to reduce the shift of phase difference measured for paths with radio-wave scattering. A comparison with results of computer simulation by the Monte Carlo method is performed.

Method and device for predicting wavelength dependent radiation influences in thermal systems

DOEpatents

Kee, Robert J.; Ting, Aili

1996-01-01

A method and apparatus for predicting the spectral (wavelength-dependent) radiation transport in thermal systems including interaction by the radiation with partially transmitting medium. The predicted model of the thermal system is used to design and control the thermal system. The predictions are well suited to be implemented in design and control of rapid thermal processing (RTP) reactors. The method involves generating a spectral thermal radiation transport model of an RTP reactor. The method also involves specifying a desired wafer time dependent temperature profile. The method further involves calculating an inverse of the generated model using the desired wafer time dependent temperature to determine heating element parameters required to produce the desired profile. The method also involves controlling the heating elements of the RTP reactor in accordance with the heating element parameters to heat the wafer in accordance with the desired profile.

A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters

NASA Astrophysics Data System (ADS)

Zhu, Gaofeng; Li, Xin; Ma, Jinzhu; Wang, Yunquan; Liu, Shaomin; Huang, Chunlin; Zhang, Kun; Hu, Xiaoli

2018-04-01

Sequential Monte Carlo (SMC) samplers have become increasing popular for estimating the posterior parameter distribution with the non-linear dependency structures and multiple modes often present in hydrological models. However, the explorative capabilities and efficiency of the sampler depends strongly on the efficiency in the move step of SMC sampler. In this paper we presented a new SMC sampler entitled the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which is well suited to handle unknown static parameters of hydrologic model. The PEM-SMC sampler is inspired by the works of Liang and Wong (2001) and operates by incorporating the strengths of the genetic algorithm, differential evolution algorithm and Metropolis-Hasting algorithm into the framework of SMC. We also prove that the sampler admits the target distribution to be a stationary distribution. Two case studies including a multi-dimensional bimodal normal distribution and a conceptual rainfall-runoff hydrologic model by only considering parameter uncertainty and simultaneously considering parameter and input uncertainty show that PEM-SMC sampler is generally superior to other popular SMC algorithms in handling the high dimensional problems. The study also indicated that it may be important to account for model structural uncertainty by using multiplier different hydrological models in the SMC framework in future study.

The Active Fault Parameters for Time-Dependent Earthquake Hazard Assessment in Taiwan

NASA Astrophysics Data System (ADS)

Lee, Y.; Cheng, C.; Lin, P.; Shao, K.; Wu, Y.; Shih, C.

2011-12-01

Taiwan is located at the boundary between the Philippine Sea Plate and the Eurasian Plate, with a convergence rate of ~ 80 mm/yr in a ~N118E direction. The plate motion is so active that earthquake is very frequent. In the Taiwan area, disaster-inducing earthquakes often result from active faults. For this reason, it's an important subject to understand the activity and hazard of active faults. The active faults in Taiwan are mainly located in the Western Foothills and the Eastern longitudinal valley. Active fault distribution map published by the Central Geological Survey (CGS) in 2010 shows that there are 31 active faults in the island of Taiwan and some of which are related to earthquake. Many researchers have investigated these active faults and continuously update new data and results, but few people have integrated them for time-dependent earthquake hazard assessment. In this study, we want to gather previous researches and field work results and then integrate these data as an active fault parameters table for time-dependent earthquake hazard assessment. We are going to gather the seismic profiles or earthquake relocation of a fault and then combine the fault trace on land to establish the 3D fault geometry model in GIS system. We collect the researches of fault source scaling in Taiwan and estimate the maximum magnitude from fault length or fault area. We use the characteristic earthquake model to evaluate the active fault earthquake recurrence interval. In the other parameters, we will collect previous studies or historical references and complete our parameter table of active faults in Taiwan. The WG08 have done the time-dependent earthquake hazard assessment of active faults in California. They established the fault models, deformation models, earthquake rate models, and probability models and then compute the probability of faults in California. Following these steps, we have the preliminary evaluated probability of earthquake-related hazards in certain faults in Taiwan. By accomplishing active fault parameters table in Taiwan, we would apply it in time-dependent earthquake hazard assessment. The result can also give engineers a reference for design. Furthermore, it can be applied in the seismic hazard map to mitigate disasters.

Dependence of growth of the phases of multiphase binary systems on the diffusion parameters

NASA Astrophysics Data System (ADS)

Molokhina, L. A.; Rogalin, V. E.; Filin, S. A.; Kaplunov, I. A.

2017-12-01

A mathematical model of the diffusion interaction of a binary system with several phases on the equilibrium phase diagram is presented. The theoretical and calculated dependences of the layer thickness of each phase in the multiphase diffusion zone on the isothermal annealing time and the ratio of the diffusion parameters in the neighboring phases with an unlimited supply of both components were constructed. The phase formation and growth in the diffusion zone during "reactive" diffusion corresponds to the equilibrium state diagram for two components, and the order of their appearance in the diffusion zone depends only on the ratio of the diffusion parameters in the phases themselves and on the duration of the incubation periods. The dependence of phase appearance on the incubation periods, annealing time, and difference in the movement rates of the components across the interface boundaries was obtained. An example of the application of the model for processing the experimental data on phase growth in a two-component three-phase system was given.

Periodic and chaotic psychological stress variations as predicted by a social support buffered response model

NASA Astrophysics Data System (ADS)

Field, Richard J.; Gallas, Jason A. C.; Schuldberg, David

2017-08-01

Recent work has introduced social dynamic models of people's stress-related processes, some including amelioration of stress symptoms by support from others. The effects of support may be ;direct;, depending only on the level of support, or ;buffering;, depending on the product of the level of support and level of stress. We focus here on the nonlinear buffering term and use a model involving three variables (and 12 control parameters), including stress as perceived by the individual, physical and psychological symptoms, and currently active social support. This model is quantified by a set of three nonlinear differential equations governing its stationary-state stability, temporal evolution (sometimes oscillatory), and how each variable affects the others. Chaos may appear with periodic forcing of an environmental stress parameter. Here we explore this model carefully as the strength and amplitude of this forcing, and an important psychological parameter relating to self-kindling in the stress response, are varied. Three significant observations are made: 1. There exist many complex but orderly regions of periodicity and chaos, 2. there are nested regions of increasing number of peaks per cycle that may cascade to chaos, and 3. there are areas where more than one state, e.g., a period-2 oscillation and chaos, coexist for the same parameters; which one is reached depends on initial conditions.

Analysis of signal-dependent sensor noise on JPEG 2000-compressed Sentinel-2 multi-spectral images

NASA Astrophysics Data System (ADS)

Uss, M.; Vozel, B.; Lukin, V.; Chehdi, K.

2017-10-01

The processing chain of Sentinel-2 MultiSpectral Instrument (MSI) data involves filtering and compression stages that modify MSI sensor noise. As a result, noise in Sentinel-2 Level-1C data distributed to users becomes processed. We demonstrate that processed noise variance model is bivariate: noise variance depends on image intensity (caused by signal-dependency of photon counting detectors) and signal-to-noise ratio (SNR; caused by filtering/compression). To provide information on processed noise parameters, which is missing in Sentinel-2 metadata, we propose to use blind noise parameter estimation approach. Existing methods are restricted to univariate noise model. Therefore, we propose extension of existing vcNI+fBm blind noise parameter estimation method to multivariate noise model, mvcNI+fBm, and apply it to each band of Sentinel-2A data. Obtained results clearly demonstrate that noise variance is affected by filtering/compression for SNR less than about 15. Processed noise variance is reduced by a factor of 2 - 5 in homogeneous areas as compared to noise variance for high SNR values. Estimate of noise variance model parameters are provided for each Sentinel-2A band. Sentinel-2A MSI Level-1C noise models obtained in this paper could be useful for end users and researchers working in a variety of remote sensing applications.

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.

Relativistic elliptic matrix tops and finite Fourier transformations

NASA Astrophysics Data System (ADS)

Zotov, A.

2017-10-01

We consider a family of classical elliptic integrable systems including (relativistic) tops and their matrix extensions of different types. These models can be obtained from the “off-shell” Lax pairs, which do not satisfy the Lax equations in general case but become true Lax pairs under various conditions (reductions). At the level of the off-shell Lax matrix, there is a natural symmetry between the spectral parameter z and relativistic parameter η. It is generated by the finite Fourier transformation, which we describe in detail. The symmetry allows one to consider z and η on an equal footing. Depending on the type of integrable reduction, any of the parameters can be chosen to be the spectral one. Then another one is the relativistic deformation parameter. As a by-product, we describe the model of N2 interacting GL(M) matrix tops and/or M2 interacting GL(N) matrix tops depending on a choice of the spectral parameter.

Green-ampt infiltration parameters in riparian buffers

Treesearch

L.M. Stahr; D.E. Eisenhauer; M.J. Helmers; Mike G. Dosskey; T.G. Franti

2004-01-01

Riparian buffers can improve surface water quality by filtering contaminants from runoff before they enter streams. Infiltration is an important process in riparian buffers. Computer models are often used to assess the performance of riparian buffers. Accurate prediction of infiltration by these models is dependent upon accurate estimates of infiltration parameters....

Impact of the time scale of model sensitivity response on coupled model parameter estimation

NASA Astrophysics Data System (ADS)

Liu, Chang; Zhang, Shaoqing; Li, Shan; Liu, Zhengyu

2017-11-01

That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter estimation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being estimated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency determined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.

Content dependent selection of image enhancement parameters for mobile displays

NASA Astrophysics Data System (ADS)

Lee, Yoon-Gyoo; Kang, Yoo-Jin; Kim, Han-Eol; Kim, Ka-Hee; Kim, Choon-Woo

2011-01-01

Mobile devices such as cellular phones and portable multimedia player with capability of playing terrestrial digital multimedia broadcasting (T-DMB) contents have been introduced into consumer market. In this paper, content dependent image quality enhancement method for sharpness and colorfulness and noise reduction is presented to improve perceived image quality on mobile displays. Human visual experiments are performed to analyze viewers' preference. Relationship between the objective measures and the optimal values of image control parameters are modeled by simple lookup tables based on the results of human visual experiments. Content dependent values of image control parameters are determined based on the calculated measures and predetermined lookup tables. Experimental results indicate that dynamic selection of image control parameters yields better image quality.

Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study.

PubMed

Fan, Ming; Kuwahara, Hiroyuki; Wang, Xiaolei; Wang, Suojin; Gao, Xin

2015-11-01

Parameter estimation is a challenging computational problem in the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter estimation of gene circuit models from such time-series mRNA data has become an important method for quantitatively dissecting the regulation of gene expression. By focusing on the modeling of gene circuits, we examine here the performance of three types of state-of-the-art parameter estimation methods: population-based methods, online methods and model-decomposition-based methods. Our results show that certain population-based methods are able to generate high-quality parameter solutions. The performance of these methods, however, is heavily dependent on the size of the parameter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, online methods and model decomposition-based methods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fast methods with local search as a subsequent refinement procedure can substantially increase the quality of their parameter estimates to the level on par with the best solution obtained from the population-based methods while maintaining high computational speed. These suggest that such hybrid methods can be a promising alternative to the more commonly used population-based methods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatory mechanisms makes the size of the parameter search space vastly large. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

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.

Study of Quantum Chaos in the Framework of Triaxial Rotator Models

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

Proskurins, J.; Bavrins, K.; Andrejevs, A.

2009-01-28

Dynamical quantum chaos criteria--a perturbed wave function entropy W({psi}{sub i}) and a fragmentation width {kappa}({phi}{sub k}) of basis states were studied in two cases of nuclear rigid triaxial rotator models. The first model is characterized by deformation angle {gamma} only, while the second model depends on both quadrupole deformation parameters ({beta},{gamma}). The degree of chaoticity has been determined in the studies of the dependence of criteria W({psi}{sub i}) and {kappa}({phi}{sub k}) from nuclear spin values up to I{<=}101 for model parameters {gamma} and ({beta},{gamma}) correspondingly. The transition from librational to rotational type energy spectra has been considered for both modelsmore » as well.« less

Towards an Analytical Age-Dependent Model of Contrast Sensitivity Functions for an Ageing Society

PubMed Central

Joulan, Karine; Brémond, Roland

2015-01-01

The Contrast Sensitivity Function (CSF) describes how the visibility of a grating depends on the stimulus spatial frequency. Many published CSF data have demonstrated that contrast sensitivity declines with age. However, an age-dependent analytical model of the CSF is not available to date. In this paper, we propose such an analytical CSF model based on visual mechanisms, taking into account the age factor. To this end, we have extended an existing model from Barten (1999), taking into account the dependencies of this model's optical and physiological parameters on age. Age-dependent models of the cones and ganglion cells densities, the optical and neural MTF, and optical and neural noise are proposed, based on published data. The proposed age-dependent CSF is finally tested against available experimental data, with fair results. Such an age-dependent model may be beneficial when designing real-time age-dependent image coding and display applications. PMID:26078994

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

The effect of zero-point energy differences on the isotope dependence of the formation of ozone: a classical trajectory study.

PubMed

Schinke, Reinhard; Fleurat-Lessard, Paul

2005-03-01

The effect of zero-point energy differences (DeltaZPE) between the possible fragmentation channels of highly excited O(3) complexes on the isotope dependence of the formation of ozone is investigated by means of classical trajectory calculations and a strong-collision model. DeltaZPE is incorporated in the calculations in a phenomenological way by adjusting the potential energy surface in the product channels so that the correct exothermicities and endothermicities are matched. The model contains two parameters, the frequency of stabilizing collisions omega and an energy dependent parameter Delta(damp), which favors the lower energies in the Maxwell-Boltzmann distribution. The stabilization frequency is used to adjust the pressure dependence of the absolute formation rate while Delta(damp) is utilized to control its isotope dependence. The calculations for several isotope combinations of oxygen atoms show a clear dependence of relative formation rates on DeltaZPE. The results are similar to those of Gao and Marcus [J. Chem. Phys. 116, 137 (2002)] obtained within a statistical model. In particular, like in the statistical approach an ad hoc parameter eta approximately 1.14, which effectively reduces the formation rates of the symmetric ABA ozone molecules, has to be introduced in order to obtain good agreement with the measured relative rates of Janssen et al. [Phys. Chem. Chem. Phys. 3, 4718 (2001)]. The temperature dependence of the recombination rate is also addressed.

Electrode performance parameters for a radioisotope-powered AMTEC for space power applications

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

Underwood, M.L.; O'Connor, D.; Williams, R.M.

1992-08-01

The alkali metal thermoelastic converter (AMTEC) is a device for the direct conversion of heat to electricity. Recently a design of an AMTEC using a radioisotope heat source was described, but the optimum condenser temperature was hotter than the temperatures used in the laboratory to develop the electrode performance model. Now laboratory experiments have confirmed the dependence of two model parameters over a broader range of condenser and electrode temperatures for two candidate electrode compositions. One parameter, the electrochemical exchange current density at the reaction interface, is independent of the condenser temperature, and depends only upon the collision rate ofmore » sodium at the reaction zone. The second parameter, a morphological parameter, which measures the mass transport resistance through the electrode, is independent of condenser and electrode temperatures for molybdenum electrodes. For rhodium-tungsten electrodes, however, this parameter increases for decreasing electrode temperature, indicating an activated mass transport mechanism such as surface diffusion. 21 refs.« less

Leading temperature dependence of the conductance in Kondo-correlated quantum dots.

PubMed

Aligia, A A

2018-04-18

Using renormalized perturbation theory in the Coulomb repulsion, we derive an analytical expression for the leading term in the temperature dependence of the conductance through a quantum dot described by the impurity Anderson model, in terms of the renormalized parameters of the model. Taking these parameters from the literature, we compare the results with published ones calculated using the numerical renormalization group obtaining a very good agreement. The approach is superior to alternative perturbative treatments. We compare in particular to the results of a simple interpolative perturbation approach.

Pressure dependence of axisymmetric vortices in superfluid 3B

NASA Astrophysics Data System (ADS)

Fetter, Alexander L.

1985-06-01

The pressure dependence of the vortex core in rotating 3B is studied in the Ginzburg-Landau formalism with two distinct models of the strong-coupling corrections. The parametrization of Sauls and Serene [Phys. Rev. B 24, 183 (1981)] predicts a transition from a core with large magnetic moment below ~10 bars to one with small magnetic moment for higher pressures, in qualitative agreement with experiments. The earlier one-parameter model of Brinkman, Serene, and Anderson predicts no such transition, with the core having a large moment for all values of the parameter δ.

Finite-sample and asymptotic sign-based tests for parameters of non-linear quantile regression with Markov noise

NASA Astrophysics Data System (ADS)

Sirenko, M. A.; Tarasenko, P. F.; Pushkarev, M. I.

2017-01-01

One of the most noticeable features of sign-based statistical procedures is an opportunity to build an exact test for simple hypothesis testing of parameters in a regression model. In this article, we expanded a sing-based approach to the nonlinear case with dependent noise. The examined model is a multi-quantile regression, which makes it possible to test hypothesis not only of regression parameters, but of noise parameters as well.

Oscillating in synchrony with a metronome: serial dependence, limit cycle dynamics, and modeling.

PubMed

Torre, Kjerstin; Balasubramaniam, Ramesh; Delignières, Didier

2010-07-01

We analyzed serial dependencies in periods and asynchronies collected during oscillations performed in synchrony with a metronome. Results showed that asynchronies contain 1/f fluctuations, and the series of periods contain antipersistent dependence. The analysis of the phase portrait revealed a specific asymmetry induced by synchronization. We propose a hybrid limit cycle model including a cycle-dependent stiffness parameter provided with fractal properties, and a parametric driving function based on velocity. This model accounts for most experimentally evidenced statistical features, including serial dependence and limit cycle dynamics. We discuss the results and modeling choices within the framework of event-based and emergent timing.

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.

General Multimechanism Reversible-Irreversible Time-Dependent Constitutive Deformation Model Being Developed

NASA Technical Reports Server (NTRS)

Saleeb, A. F.; Arnold, Steven M.

2001-01-01

Since most advanced material systems (for example metallic-, polymer-, and ceramic-based systems) being currently researched and evaluated are for high-temperature airframe and propulsion system applications, the required constitutive models must account for both reversible and irreversible time-dependent deformations. Furthermore, since an integral part of continuum-based computational methodologies (be they microscale- or macroscale-based) is an accurate and computationally efficient constitutive model to describe the deformation behavior of the materials of interest, extensive research efforts have been made over the years on the phenomenological representations of constitutive material behavior in the inelastic analysis of structures. From a more recent and comprehensive perspective, the NASA Glenn Research Center in conjunction with the University of Akron has emphasized concurrently addressing three important and related areas: that is, 1) Mathematical formulation; 2) Algorithmic developments for updating (integrating) the external (e.g., stress) and internal state variables; 3) Parameter estimation for characterizing the model. This concurrent perspective to constitutive modeling has enabled the overcoming of the two major obstacles to fully utilizing these sophisticated time-dependent (hereditary) constitutive models in practical engineering analysis. These obstacles are: 1) Lack of efficient and robust integration algorithms; 2) Difficulties associated with characterizing the large number of required material parameters, particularly when many of these parameters lack obvious or direct physical interpretations.

Comparison of two laryngeal tissue fiber constitutive models

NASA Astrophysics Data System (ADS)

Hunter, Eric J.; Palaparthi, Anil Kumar Reddy; Siegmund, Thomas; Chan, Roger W.

2014-02-01

Biological tissues are complex time-dependent materials, and the best choice of the appropriate time-dependent constitutive description is not evident. This report reviews two constitutive models (a modified Kelvin model and a two-network Ogden-Boyce model) in the characterization of the passive stress-strain properties of laryngeal tissue under tensile deformation. The two models are compared, as are the automated methods for parameterization of tissue stress-strain data (a brute force vs. a common optimization method). Sensitivity (error curves) of parameters from both models and the optimized parameter set are calculated and contrast by optimizing to the same tissue stress-strain data. Both models adequately characterized empirical stress-strain datasets and could be used to recreate a good likeness of the data. Nevertheless, parameters in both models were sensitive to measurement errors or uncertainties in stress-strain, which would greatly hinder the confidence in those parameters. The modified Kelvin model emerges as a potential better choice for phonation models which use a tissue model as one component, or for general comparisons of the mechanical properties of one type of tissue to another (e.g., axial stress nonlinearity). In contrast, the Ogden-Boyce model would be more appropriate to provide a basic understanding of the tissue's mechanical response with better insights into the tissue's physical characteristics in terms of standard engineering metrics such as shear modulus and viscosity.

In-medium effects via nuclear stopping in asymmetric colliding nuclei

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

Kaur, Mandeep

2016-05-06

The nuclear stopping is studied using isospin-dependent quantum molecular dynamics (IQMD) model in asymmetric colliding nuclei by varying mass asymmetry. The calculations have been done at incident energies varying between 50 and 400 MeV/nucleon for different impact parameters. We investigate the relative role of constant scaled and density-dependent scaled cross-sections. Our study reveals that nuclear stopping depends on the mass asymmetry, incident energy and impact parameter, however, it is independent of the way of scaling the cross-section.

Influence of grain boundaries on the distribution of components in binary alloys

NASA Astrophysics Data System (ADS)

L'vov, P. E.; Svetukhin, V. V.

2017-12-01

Based on the free-energy density functional method (the Cahn-Hilliard equation), a phenomenological model that describes the influence of grain boundaries on the distribution of components in binary alloys has been developed. The model is built on the assumption of the difference between the interaction parameters of solid solution components in the bulk and at the grain boundary. The difference scheme based on the spectral method is proposed to solve the Cahn-Hilliard equation with interaction parameters depending on coordinates. Depending on the ratio between the interaction parameters in the bulk and at the grain boundary, temperature, and alloy composition, the model can give rise to different types of distribution of a dissolved component, namely, either depletion or enrichment of the grain-boundary area, preferential grainboundary precipitation, competitive precipitation in the bulk and at the grain boundary, etc.

Temperature-dependence laws of absorption line shape parameters of the CO2 ν3 band

NASA Astrophysics Data System (ADS)

Wilzewski, J. S.; Birk, M.; Loos, J.; Wagner, G.

2018-02-01

To improve the understanding of temperature-dependence laws of spectral line shape parameters, spectra of the ν3 rovibrational band of CO2 perturbed by 10, 30, 100, 300 and 1000 mbar of N2 were recorded at nine temperatures between 190 K and 330 K using a 22 cm long single-pass absorption cell in a Bruker IFS125 HR Fourier Transform spectrometer. The spectra were fitted employing a quadratic speed-dependent hard collision model in the Hartmann-Tran implementation extended to account for line mixing in the Rosenkranz approximation by means of a multispectrum fitting approach developed at DLR. This enables high accuracy parameter retrievals to reproduce the spectra down to noise level and we present the behavior of line widths, shifts, speed-dependence-, collisional narrowing- and line mixing-parameters over this 140 K temperature range.

Synergistic effects in threshold models on networks.

PubMed

Juul, Jonas S; Porter, Mason A

2018-01-01

Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can-depending on a parameter-either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

Box-modeling of bone and tooth phosphate oxygen isotope compositions as a function of environmental and physiological parameters.

PubMed

Langlois, C; Simon, L; Lécuyer, Ch

2003-12-01

A time-dependent box model is developed to calculate oxygen isotope compositions of bone phosphate as a function of environmental and physiological parameters. Input and output oxygen fluxes related to body water and bone reservoirs are scaled to the body mass. The oxygen fluxes are evaluated by stoichiometric scaling to the calcium accretion and resorption rates, assuming a pure hydroxylapatite composition for the bone and tooth mineral. The model shows how the diet composition, body mass, ambient relative humidity and temperature may control the oxygen isotope composition of bone phosphate. The model also computes how bones and teeth record short-term variations in relative humidity, air temperature and delta18O of drinking water, depending on body mass. The documented diversity of oxygen isotope fractionation equations for vertebrates is accounted for by our model when for each specimen the physiological and diet parameters are adjusted in the living range of environmental conditions.

Dynamic Computation of Change Operations in Version Management of Business Process Models

NASA Astrophysics Data System (ADS)

Küster, Jochen Malte; Gerth, Christian; Engels, Gregor

Version management of business process models requires that changes can be resolved by applying change operations. In order to give a user maximal freedom concerning the application order of change operations, position parameters of change operations must be computed dynamically during change resolution. In such an approach, change operations with computed position parameters must be applicable on the model and dependencies and conflicts of change operations must be taken into account because otherwise invalid models can be constructed. In this paper, we study the concept of partially specified change operations where parameters are computed dynamically. We provide a formalization for partially specified change operations using graph transformation and provide a concept for their applicability. Based on this, we study potential dependencies and conflicts of change operations and show how these can be taken into account within change resolution. Using our approach, a user can resolve changes of business process models without being unnecessarily restricted to a certain order.

Microbial models with data-driven parameters predict stronger soil carbon responses to climate change.

PubMed

Hararuk, Oleksandra; Smith, Matthew J; Luo, Yiqi

2015-06-01

Long-term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon (SOC). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However, major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into C cycle model formulations has shown potential to improve model predictive performance when assessed against global SOC databases. This study aimed to data-constrained parameters of two soil microbial models, evaluate the improvements in performance of those calibrated models in predicting contemporary carbon stocks, and compare the SOC responses to climate change and their uncertainties between microbial and conventional models. Microbial models with calibrated parameters explained 51% of variability in the observed total SOC, whereas a calibrated conventional model explained 41%. The microbial models, when forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project (CMIP5), produced stronger soil C responses to 95 years of climate change than any of the 11 CMIP5 models. The calibrated microbial models predicted between 8% (2-pool model) and 11% (4-pool model) soil C losses compared with CMIP5 model projections which ranged from a 7% loss to a 22.6% gain. Lastly, we observed unrealistic oscillatory SOC dynamics in the 2-pool microbial model. The 4-pool model also produced oscillations, but they were less prominent and could be avoided, depending on the parameter values. © 2014 John Wiley & Sons Ltd.

A composite likelihood approach for spatially correlated survival data

PubMed Central

Paik, Jane; Ying, Zhiliang

2013-01-01

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory. PMID:24223450

A composite likelihood approach for spatially correlated survival data.

PubMed

Paik, Jane; Ying, Zhiliang

2013-01-01

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.

Engineering frequency-dependent superfluidity in Bose-Fermi mixtures

NASA Astrophysics Data System (ADS)

Arzamasovs, Maksims; Liu, Bo

2018-04-01

Unconventional superconductivity and superfluidity are among the most exciting and fascinating quantum phenomena in condensed-matter physics. Usually such states are characterized by nontrivial spin or spatial symmetry of the pairing order parameter, such as "spin triplet" or "p wave." However, besides spin and spatial dependence the order parameter may have unconventional frequency dependence which is also permitted by Fermi-Dirac statistics. Odd-frequency fermionic pairing is an exciting paradigm when discussing exotic superfluidity or superconductivity and is yet to be realized in experiments. In this paper we propose a symmetry-based method of controlling frequency dependence of the pairing order parameter via manipulating the inversion symmetry of the system. First, a toy model is introduced to illustrate that frequency dependence of the order parameter can be achieved through our proposed approach. Second, by taking advantage of recent rapid developments in producing spin-orbit-coupled dispersions in ultracold gases, we propose a Bose-Fermi mixture to realize such frequency-dependent superfluid. The key idea is introducing the frequency-dependent attraction between fermions mediated by Bogoliubov phonons with asymmetric dispersion. Our proposal should pave an alternative way for exploring frequency-dependent superfluids with cold atoms.

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

Calculation of Optical Parameters of Liquid Crystals

NASA Astrophysics Data System (ADS)

Kumar, A.

2007-12-01

Validation of a modified four-parameter model describing temperature effect on liquid crystal refractive indices is being reported in the present article. This model is based upon the Vuks equation. Experimental data of ordinary and extraordinary refractive indices for two liquid crystal samples MLC-9200-000 and MLC-6608 are used to validate the above-mentioned theoretical model. Using these experimental data, birefringence, order parameter, normalized polarizabilities, and the temperature gradient of refractive indices are determined. Two methods: directly using birefringence measurements and using Haller's extrapolation procedure are adopted for the determination of order parameter. Both approches of order parameter calculation are compared. The temperature dependences of all these parameters are discussed. A close agreement between theory and experiment is obtained.

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

Kinematics of our Galaxy from the PMA and TGAS catalogues

NASA Astrophysics Data System (ADS)

Velichko, Anna B.; Akhmetov, Volodymyr S.; Fedorov, Peter N.

2018-04-01

We derive and compare kinematic parameters of the Galaxy using the PMA and Gaia TGAS data. Two methods are used in calculations: evaluation of the Ogorodnikov-Milne model (OMM) parameters by the least square method (LSM) and a decomposition on a set of vector spherical harmonics (VSH). We trace dependencies on the distance of the derived parameters including the Oort constants A and B and the rotational velocity of the Galaxy V rot at the Solar distance for the common sample of stars of mixed spectral composition of the PMA and TGAS catalogues. The distances were obtained from the TGAS parallaxes or from reduced proper motions for fainter stars. The A, B and V rot parameters derived from proper motions of both catalogues used show identical behaviour but the values are systematically shifted by about 0.5 mas/yr. The Oort B parameter derived from the PMA sample of red giants shows gradual decrease with increasing the distance while the Oort A has a minimum at about 2 kpc and then gradually increases. As for models chosen for calculations, first, we confirm conclusions of other authors about the existence of extra-model harmonics in the stellar velocity field. Secondly, not all parameters of the OMM are statistically significant, and the set of parameters depends on the stellar sample used.

Inferring the temperature dependence of population parameters: the effects of experimental design and inference algorithm

PubMed Central

Palamara, Gian Marco; Childs, Dylan Z; Clements, Christopher F; Petchey, Owen L; Plebani, Marco; Smith, Matthew J

2014-01-01

Understanding and quantifying the temperature dependence of population parameters, such as intrinsic growth rate and carrying capacity, is critical for predicting the ecological responses to environmental change. Many studies provide empirical estimates of such temperature dependencies, but a thorough investigation of the methods used to infer them has not been performed yet. We created artificial population time series using a stochastic logistic model parameterized with the Arrhenius equation, so that activation energy drives the temperature dependence of population parameters. We simulated different experimental designs and used different inference methods, varying the likelihood functions and other aspects of the parameter estimation methods. Finally, we applied the best performing inference methods to real data for the species Paramecium caudatum. The relative error of the estimates of activation energy varied between 5% and 30%. The fraction of habitat sampled played the most important role in determining the relative error; sampling at least 1% of the habitat kept it below 50%. We found that methods that simultaneously use all time series data (direct methods) and methods that estimate population parameters separately for each temperature (indirect methods) are complementary. Indirect methods provide a clearer insight into the shape of the functional form describing the temperature dependence of population parameters; direct methods enable a more accurate estimation of the parameters of such functional forms. Using both methods, we found that growth rate and carrying capacity of Paramecium caudatum scale with temperature according to different activation energies. Our study shows how careful choice of experimental design and inference methods can increase the accuracy of the inferred relationships between temperature and population parameters. The comparison of estimation methods provided here can increase the accuracy of model predictions, with important implications in understanding and predicting the effects of temperature on the dynamics of populations. PMID:25558365

The dependence of the strength and thickness of field-aligned currents on solar wind and ionospheric parameters

PubMed Central

Johnson, Jay R.; Wing, Simon

2017-01-01

Sheared plasma flows at the low-latitude boundary layer (LLBL) correlate well with early afternoon auroral arcs and upward field-aligned currents. We present a simple analytic model that relates solar wind and ionospheric parameters to the strength and thickness of field-aligned currents (Λ) in a region of sheared velocity, such as the LLBL. We compare the predictions of the model with DMSP observations and find remarkably good scaling of the upward region 1 currents with solar wind and ionospheric parameters in region located at the boundary layer or open field lines at 1100–1700 magnetic local time. We demonstrate that Λ~nsw−0.5 and Λ ~ L when Λ/L < 5 where L is the auroral electrostatic scale length. The sheared boundary layer thickness (Δm) is inferred to be around 3000 km, which appears to have weak dependence on Vsw. J‖ has dependencies on Δm, Σp, nsw, and Vsw. The analytic model provides a simple way to organize data and to infer boundary layer structures from ionospheric data. PMID:29057194

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.

Quark matter at high density based on an extended confined isospin-density-dependent mass model

NASA Astrophysics Data System (ADS)

Qauli, A. I.; Sulaksono, A.

2016-01-01

We investigate the effect of the inclusion of relativistic Coulomb terms in a confined-isospin-density-dependent-mass (CIDDM) model of strange quark matter (SQM). We found that if we include the Coulomb term in scalar density form, the SQM equation of state (EOS) at high densities is stiffer but if we include the Coulomb term in vector density form it is softer than that of the standard CIDDM model. We also investigate systematically the role of each term of the extended CIDDM model. Compared with what was reported by Chu and Chen [Astrophys. J. 780, 135 (2014)], we found the stiffness of SQM EOS is controlled by the interplay among the oscillator harmonic, isospin asymmetry and Coulomb contributions depending on the parameter's range of these terms. We have found that the absolute stable condition of SQM and the mass of 2 M⊙ pulsars can constrain the parameter of oscillator harmonic κ1≈0.53 in the case the Coulomb term is excluded. If the Coulomb term is included, for the models with their parameters are consistent with SQM absolute stability condition, the 2.0 M⊙ constraint more prefers the maximum mass prediction of the model with the scalar Coulomb term than that of the model with the vector Coulomb term. On the contrary, the high densities EOS predicted by the model with the vector Coulomb is more compatible with the recent perturbative quantum chromodynamics result [1] than that predicted by the model with the scalar Coulomb. Furthermore, we also observed the quark composition in a very high density region depends quite sensitively on the kind of Coulomb term used.

Edge Modeling by Two Blur Parameters in Varying Contrasts.

PubMed

Seo, Suyoung

2018-06-01

This paper presents a method of modeling edge profiles with two blur parameters, and estimating and predicting those edge parameters with varying brightness combinations and camera-to-object distances (COD). First, the validity of the edge model is proven mathematically. Then, it is proven experimentally with edges from a set of images captured for specifically designed target sheets and with edges from natural images. Estimation of the two blur parameters for each observed edge profile is performed with a brute-force method to find parameters that produce global minimum errors. Then, using the estimated blur parameters, actual blur parameters of edges with arbitrary brightness combinations are predicted using a surface interpolation method (i.e., kriging). The predicted surfaces show that the two blur parameters of the proposed edge model depend on both dark-side edge brightness and light-side edge brightness following a certain global trend. This is similar across varying CODs. The proposed edge model is compared with a one-blur parameter edge model using experiments of the root mean squared error for fitting the edge models to each observed edge profile. The comparison results suggest that the proposed edge model has superiority over the one-blur parameter edge model in most cases where edges have varying brightness combinations.

SU-E-T-248: An Extended Generalized Equivalent Uniform Dose Accounting for Dose-Range Dependency of Radio-Biological Parameters.

PubMed

Troeller, A; Soehn, M; Yan, D

2012-06-01

Introducing an extended, phenomenological, generalized equivalent uniform dose (eEUD) that incorporates multiple volume-effect parameters for different dose-ranges. The generalized EUD (gEUD) was introduced as an estimate of the EUD that incorporates a single, tissue-specific parameter - the volume-effect-parameter (VEP) 'a'. As a purely phenomenological concept, its radio-biological equivalency to a given inhomogeneous dose distribution is not a priori clear and mechanistic models based on radio-biological parameters are assumed to better resemble the underlying biology. However, for normal organs mechanistic models are hard to derive, since the structural organization of the tissue plays a significant role. Consequently, phenomenological approaches might be especially useful in order to describe dose-response for normal tissues. However, the single parameter used to estimate the gEUD may not suffice in accurately representing more complex biological effects that have been discussed in the literature. For instance, radio-biological parameters and hence the effects of fractionation are known to be dose-range dependent. Therefore, we propose an extended phenomenological eEUD formula that incorporates multiple VEPs accounting for dose-range dependency. The eEUD introduced is a piecewise polynomial expansion of the gEUD formula. In general, it allows for an arbitrary number of VEPs, each valid for a certain dose-range. We proved that the formula fulfills required mathematical and physical criteria such as invertibility of the underlying dose-effect and continuity in dose. Furthermore, it contains the gEUD as a special case, if all VEPs are equal to 'a' from the gEUD model. The eEUD is a concept that expands the gEUD such that it can theoretically represent dose-range dependent effects. Its practicality, however, remains to be shown. As a next step, this will be done by estimating the eEUD from patient data using maximum-likelihood based NTCP modelling in the same way it is commonly done for the gEUD. © 2012 American Association of Physicists in Medicine.

A model for the relative biological effectiveness of protons: the tissue specific parameter α/β of photons is a predictor for the sensitivity to LET changes.

PubMed

Wedenberg, Minna; Lind, Bengt K; Hårdemark, Björn

2013-04-01

The biological effects of particles are often expressed in relation to that of photons through the concept of relative biological effectiveness, RBE. In proton radiotherapy, a constant RBE of 1.1 is usually assumed. However, there is experimental evidence that RBE depends on various factors. The aim of this study is to develop a model to predict the RBE based on linear energy transfer (LET), dose, and the tissue specific parameter α/β of the linear-quadratic model for the reference radiation. Moreover, the model should capture the basic features of the RBE using a minimum of assumptions, each supported by experimental data. The α and β parameters for protons were studied with respect to their dependence on LET. An RBE model was proposed where the dependence of LET is affected by the (α/β)phot ratio of photons. Published cell survival data with a range of well-defined LETs and cell types were selected for model evaluation rendering a total of 10 cell lines and 24 RBE values. A statistically significant relation was found between α for protons and LET. Moreover, the strength of that relation varied significantly with (α/β)phot. In contrast, no significant relation between β and LET was found. On the whole, the resulting RBE model provided a significantly improved fit (p-value < 0.01) to the experimental data compared to the standard constant RBE. By accounting for the α/β ratio of photons, clearer trends between RBE and LET of protons were found, and our results suggest that late responding tissues are more sensitive to LET changes than early responding tissues and most tumors. An advantage with the proposed RBE model in optimization and evaluation of treatment plans is that it only requires dose, LET, and (α/β)phot as input parameters. Hence, no proton specific biological parameters are needed.

Comprehensive Analyses of Ventricular Myocyte Models Identify Targets Exhibiting Favorable Rate Dependence

PubMed Central

Bugana, Marco; Severi, Stefano; Sobie, Eric A.

2014-01-01

Reverse rate dependence is a problematic property of antiarrhythmic drugs that prolong the cardiac action potential (AP). The prolongation caused by reverse rate dependent agents is greater at slow heart rates, resulting in both reduced arrhythmia suppression at fast rates and increased arrhythmia risk at slow rates. The opposite property, forward rate dependence, would theoretically overcome these parallel problems, yet forward rate dependent (FRD) antiarrhythmics remain elusive. Moreover, there is evidence that reverse rate dependence is an intrinsic property of perturbations to the AP. We have addressed the possibility of forward rate dependence by performing a comprehensive analysis of 13 ventricular myocyte models. By simulating populations of myocytes with varying properties and analyzing population results statistically, we simultaneously predicted the rate-dependent effects of changes in multiple model parameters. An average of 40 parameters were tested in each model, and effects on AP duration were assessed at slow (0.2 Hz) and fast (2 Hz) rates. The analysis identified a variety of FRD ionic current perturbations and generated specific predictions regarding their mechanisms. For instance, an increase in L-type calcium current is FRD when this is accompanied by indirect, rate-dependent changes in slow delayed rectifier potassium current. A comparison of predictions across models identified inward rectifier potassium current and the sodium-potassium pump as the two targets most likely to produce FRD AP prolongation. Finally, a statistical analysis of results from the 13 models demonstrated that models displaying minimal rate-dependent changes in AP shape have little capacity for FRD perturbations, whereas models with large shape changes have considerable FRD potential. This can explain differences between species and between ventricular cell types. Overall, this study provides new insights, both specific and general, into the determinants of AP duration rate dependence, and illustrates a strategy for the design of potentially beneficial antiarrhythmic drugs. PMID:24675446

Comprehensive analyses of ventricular myocyte models identify targets exhibiting favorable rate dependence.

PubMed

Cummins, Megan A; Dalal, Pavan J; Bugana, Marco; Severi, Stefano; Sobie, Eric A

2014-03-01

Reverse rate dependence is a problematic property of antiarrhythmic drugs that prolong the cardiac action potential (AP). The prolongation caused by reverse rate dependent agents is greater at slow heart rates, resulting in both reduced arrhythmia suppression at fast rates and increased arrhythmia risk at slow rates. The opposite property, forward rate dependence, would theoretically overcome these parallel problems, yet forward rate dependent (FRD) antiarrhythmics remain elusive. Moreover, there is evidence that reverse rate dependence is an intrinsic property of perturbations to the AP. We have addressed the possibility of forward rate dependence by performing a comprehensive analysis of 13 ventricular myocyte models. By simulating populations of myocytes with varying properties and analyzing population results statistically, we simultaneously predicted the rate-dependent effects of changes in multiple model parameters. An average of 40 parameters were tested in each model, and effects on AP duration were assessed at slow (0.2 Hz) and fast (2 Hz) rates. The analysis identified a variety of FRD ionic current perturbations and generated specific predictions regarding their mechanisms. For instance, an increase in L-type calcium current is FRD when this is accompanied by indirect, rate-dependent changes in slow delayed rectifier potassium current. A comparison of predictions across models identified inward rectifier potassium current and the sodium-potassium pump as the two targets most likely to produce FRD AP prolongation. Finally, a statistical analysis of results from the 13 models demonstrated that models displaying minimal rate-dependent changes in AP shape have little capacity for FRD perturbations, whereas models with large shape changes have considerable FRD potential. This can explain differences between species and between ventricular cell types. Overall, this study provides new insights, both specific and general, into the determinants of AP duration rate dependence, and illustrates a strategy for the design of potentially beneficial antiarrhythmic drugs.

Electrostatic potential jump across fast-mode collisionless shocks

NASA Technical Reports Server (NTRS)

Mandt, M. E.; Kan, J. R.

1991-01-01

The electrostatic potential jump across fast-mode collisionless shocks is examined by comparing published observations, hybrid simulations, and a simple model, in order to better characterize its dependence on the various shock parameters. In all three, it is assumed that the electrons can be described by an isotropic power-law equation of state. The observations show that the cross-shock potential jump correlates well with the shock strength but shows very little correlation with other shock parameters. Assuming that the electrons obey an isotropic power law equation of state, the correlation of the potential jump with the shock strength follows naturally from the increased shock compression and an apparent dependence of the power law exponent on the Mach number which the observations indicate. It is found that including a Mach number dependence for the power law exponent in the electron equation of state in the simple model produces a potential jump which better fits the observations. On the basis of the simulation results and theoretical estimates of the cross-shock potential, it is discussed how the cross-shock potential might be expected to depend on the other shock parameters.

Determination of spatially dependent diffusion parameters in bovine bone using Kalman filter.

PubMed

Shokry, Abdallah; Ståhle, Per; Svensson, Ingrid

2015-11-07

Although many studies have been made for homogenous constant diffusion, bone is an inhomogeneous material. It has been suggested that bone porosity decreases from the inner boundaries to the outer boundaries of the long bones. The diffusivity of substances in the bone matrix is believed to increase as the bone porosity increases. In this study, an experimental set up is used where bovine bone samples, saturated with potassium chloride (KCl), were put into distilled water and the conductivity of the water was followed. Chloride ions in the bone samples escaped out in the water through diffusion and the increase of the conductivity was measured. A one-dimensional, spatially dependent mathematical model describing the diffusion process is used. The diffusion parameters in the model are determined using a Kalman filter technique. The parameters for spatially dependent at endosteal and periosteal surfaces are found to be (12.8 ± 4.7) × 10(-11) and (5 ± 3.5) × 10(-11)m(2)/s respectively. The mathematical model function using the obtained diffusion parameters fits very well with the experimental data with mean square error varies from 0.06 × 10(-6) to 0.183 × 10(-6) (μS/m)(2). Copyright © 2015 Elsevier Ltd. All rights reserved.

Interpersonal distance modeling during fighting activities.

PubMed

Dietrich, Gilles; Bredin, Jonathan; Kerlirzin, Yves

2010-10-01

The aim of this article is to elaborate a general framework for modeling dual opposition activities, or more generally, dual interaction. The main hypothesis is that opposition behavior can be measured directly from a global variable and that the relative distance between the two subjects can be this parameter. Moreover, this parameter should be considered as multidimensional parameter depending not only on the dynamics of the subjects but also on the "internal" parameters of the subjects, such as sociological and/or emotional states. Standard and simple mechanical formalization will be used to model this multifactorial distance. To illustrate such a general modeling methodology, this model was compared with actual data from an opposition activity like Japanese fencing (kendo). This model captures not only coupled coordination, but more generally interaction in two-subject activities.

Integrable Time-Dependent Quantum Hamiltonians

NASA Astrophysics Data System (ADS)

Sinitsyn, Nikolai A.; Yuzbashyan, Emil A.; Chernyak, Vladimir Y.; Patra, Aniket; Sun, Chen

2018-05-01

We formulate a set of conditions under which the nonstationary Schrödinger equation with a time-dependent Hamiltonian is exactly solvable analytically. The main requirement is the existence of a non-Abelian gauge field with zero curvature in the space of system parameters. Known solvable multistate Landau-Zener models satisfy these conditions. Our method provides a strategy to incorporate time dependence into various quantum integrable models while maintaining their integrability. We also validate some prior conjectures, including the solution of the driven generalized Tavis-Cummings model.

A size-structured model of bacterial growth and reproduction.

PubMed

Ellermeyer, S F; Pilyugin, S S

2012-01-01

We consider a size-structured bacterial population model in which the rate of cell growth is both size- and time-dependent and the average per capita reproduction rate is specified as a model parameter. It is shown that the model admits classical solutions. The population-level and distribution-level behaviours of these solutions are then determined in terms of the model parameters. The distribution-level behaviour is found to be different from that found in similar models of bacterial population dynamics. Rather than convergence to a stable size distribution, we find that size distributions repeat in cycles. This phenomenon is observed in similar models only under special assumptions on the functional form of the size-dependent growth rate factor. Our main results are illustrated with examples, and we also provide an introductory study of the bacterial growth in a chemostat within the framework of our model.

A microphysical parameterization of aqSOA and sulfate formation in clouds

NASA Astrophysics Data System (ADS)

McVay, Renee; Ervens, Barbara

2017-07-01

Sulfate and secondary organic aerosol (cloud aqSOA) can be chemically formed in cloud water. Model implementation of these processes represents a computational burden due to the large number of microphysical and chemical parameters. Chemical mechanisms have been condensed by reducing the number of chemical parameters. Here an alternative is presented to reduce the number of microphysical parameters (number of cloud droplet size classes). In-cloud mass formation is surface and volume dependent due to surface-limited oxidant uptake and/or size-dependent pH. Box and parcel model simulations show that using the effective cloud droplet diameter (proportional to total volume-to-surface ratio) reproduces sulfate and aqSOA formation rates within ≤30% as compared to full droplet distributions; other single diameters lead to much greater deviations. This single-class approach reduces computing time significantly and can be included in models when total liquid water content and effective diameter are available.

Mapping the Chevallier-Polarski-Linder parametrization onto physical dark energy Models

NASA Astrophysics Data System (ADS)

Scherrer, Robert J.

2015-08-01

We examine the Chevallier-Polarski-Linder (CPL) parametrization, in the context of quintessence and barotropic dark energy models, to determine the subset of such models to which it can provide a good fit. The CPL parametrization gives the equation of state parameter w for the dark energy as a linear function of the scale factor a , namely w =w0+wa(1 -a ). In the case of quintessence models, we find that over most of the w0, wa parameter space the CPL parametrization maps onto a fairly narrow form of behavior for the potential V (ϕ ), while a one-dimensional subset of parameter space, for which wa=κ (1 +w0) , with κ constant, corresponds to a wide range of functional forms for V (ϕ ). For barotropic models, we show that the functional dependence of the pressure on the density, up to a multiplicative constant, depends only on wi=wa+w0 and not on w0 and wa separately. Our results suggest that the CPL parametrization may not be optimal for testing either type of model.

Study on reservoir time-varying design flood of inflow based on Poisson process with time-dependent parameters

NASA Astrophysics Data System (ADS)

Li, Jiqing; Huang, Jing; Li, Jianchang

2018-06-01

The time-varying design flood can make full use of the measured data, which can provide the reservoir with the basis of both flood control and operation scheduling. This paper adopts peak over threshold method for flood sampling in unit periods and Poisson process with time-dependent parameters model for simulation of reservoirs time-varying design flood. Considering the relationship between the model parameters and hypothesis, this paper presents the over-threshold intensity, the fitting degree of Poisson distribution and the design flood parameters are the time-varying design flood unit period and threshold discriminant basis, deduced Longyangxia reservoir time-varying design flood process at 9 kinds of design frequencies. The time-varying design flood of inflow is closer to the reservoir actual inflow conditions, which can be used to adjust the operating water level in flood season and make plans for resource utilization of flood in the basin.

Vacuum-induced Berry phases in single-mode Jaynes-Cummings models

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

Liu, Yu; Wei, L. F.; Jia, W. Z.

2010-10-15

Motivated by work [Phys. Rev. Lett. 89, 220404 (2002)] for detecting the vacuum-induced Berry phases with two-mode Jaynes-Cummings models (JCMs), we show here that, for a parameter-dependent single-mode JCM, certain atom-field states also acquired photon-number-dependent Berry phases after the parameter slowly changed and eventually returned to its initial value. This geometric effect related to the field quantization still exists, even if the field is kept in its vacuum state. Specifically, a feasible Ramsey interference experiment with a cavity quantum electrodynamics system is designed to detect the vacuum-induced Berry phase.

Analysis and numerical simulation of a laboratory analog of radiatively induced cloud-top entrainment.

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

Kerstein, Alan R.; Sayler, Bentley J.; Wunsch, Scott Edward

2010-11-01

Numerical simulations using the One-Dimensional-Turbulence model are compared to water-tank measurements [B. J. Sayler and R. E. Breidenthal, J. Geophys. Res. 103 (D8), 8827 (1998)] emulating convection and entrainment in stratiform clouds driven by cloud-top cooling. Measured dependences of the entrainment rate on Richardson number, molecular transport coefficients, and other experimental parameters are reproduced. Additional parameter variations suggest more complicated dependences of the entrainment rate than previously anticipated. A simple algebraic model indicates the ways in which laboratory and cloud entrainment behaviors might be similar and different.

Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States.

PubMed

Eggo, Rosalind M; Cauchemez, Simon; Ferguson, Neil M

2011-02-06

There is still limited understanding of key determinants of spatial spread of influenza. The 1918 pandemic provides an opportunity to elucidate spatial determinants of spread on a large scale. To better characterize the spread of the 1918 major wave, we fitted a range of city-to-city transmission models to mortality data collected for 246 population centres in England and Wales and 47 cities in the US. Using a gravity model for city-to-city contacts, we explored the effect of population size and distance on the spread of disease and tested assumptions regarding density dependence in connectivity between cities. We employed Bayesian Markov Chain Monte Carlo methods to estimate parameters of the model for population, infectivity, distance and density dependence. We inferred the most likely transmission trees for both countries. For England and Wales, a model that estimated the degree of density dependence in connectivity between cities was preferable by deviance information criterion comparison. Early in the major wave, long distance infective interactions predominated, with local infection events more likely as the epidemic became widespread. For the US, with fewer more widely dispersed cities, statistical power was lacking to estimate population size dependence or the degree of density dependence, with the preferred model depending on distance only. We find that parameters estimated from the England and Wales dataset can be applied to the US data with no likelihood penalty.

Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States

PubMed Central

Eggo, Rosalind M.; Cauchemez, Simon; Ferguson, Neil M.

2011-01-01

There is still limited understanding of key determinants of spatial spread of influenza. The 1918 pandemic provides an opportunity to elucidate spatial determinants of spread on a large scale. To better characterize the spread of the 1918 major wave, we fitted a range of city-to-city transmission models to mortality data collected for 246 population centres in England and Wales and 47 cities in the US. Using a gravity model for city-to-city contacts, we explored the effect of population size and distance on the spread of disease and tested assumptions regarding density dependence in connectivity between cities. We employed Bayesian Markov Chain Monte Carlo methods to estimate parameters of the model for population, infectivity, distance and density dependence. We inferred the most likely transmission trees for both countries. For England and Wales, a model that estimated the degree of density dependence in connectivity between cities was preferable by deviance information criterion comparison. Early in the major wave, long distance infective interactions predominated, with local infection events more likely as the epidemic became widespread. For the US, with fewer more widely dispersed cities, statistical power was lacking to estimate population size dependence or the degree of density dependence, with the preferred model depending on distance only. We find that parameters estimated from the England and Wales dataset can be applied to the US data with no likelihood penalty. PMID:20573630

Can a simple lumped parameter model simulate complex transit time distributions? Benchmarking experiments in a virtual watershed.

NASA Astrophysics Data System (ADS)

Wilusz, D. C.; Maxwell, R. M.; Buda, A. R.; Ball, W. P.; Harman, C. J.

2016-12-01

The catchment transit-time distribution (TTD) is the time-varying, probabilistic distribution of water travel times through a watershed. The TTD is increasingly recognized as a useful descriptor of a catchment's flow and transport processes. However, TTDs are temporally complex and cannot be observed directly at watershed scale. Estimates of TTDs depend on available environmental tracers (such as stable water isotopes) and an assumed model whose parameters can be inverted from tracer data. All tracers have limitations though, such as (typically) short periods of observation or non-conservative behavior. As a result, models that faithfully simulate tracer observations may nonetheless yield TTD estimates with significant errors at certain times and water ages, conditioned on the tracer data available and the model structure. Recent advances have shown that time-varying catchment TTDs can be parsimoniously modeled by the lumped parameter rank StorAge Selection (rSAS) model, in which an rSAS function relates the distribution of water ages in outflows to the composition of age-ranked water in storage. Like other TTD models, rSAS is calibrated and evaluated against environmental tracer data, and the relative influence of tracer-dependent and model-dependent error on its TTD estimates is poorly understood. The purpose of this study is to benchmark the ability of different rSAS formulations to simulate TTDs in a complex, synthetic watershed where the lumped model can be calibrated and directly compared to a virtually "true" TTD. This experimental design allows for isolation of model-dependent error from tracer-dependent error. The integrated hydrologic model ParFlow with SLIM-FAST particle tracking code is used to simulate the watershed and its true TTD. To add field intelligence, the ParFlow model is populated with over forty years of hydrometric and physiographic data from the WE-38 subwatershed of the USDA's Mahantango Creek experimental catchment in PA, USA. The results are intended to give practical insight into tradeoffs between rSAS model structure and skill, and define a new performance benchmark to which other transit time models can be compared.

Difference-based ridge-type estimator of parameters in restricted partial linear model with correlated errors.

PubMed

Wu, Jibo

2016-01-01

In this article, a generalized difference-based ridge estimator is proposed for the vector parameter in a partial linear model when the errors are dependent. It is supposed that some additional linear constraints may hold to the whole parameter space. Its mean-squared error matrix is compared with the generalized restricted difference-based estimator. Finally, the performance of the new estimator is explained by a simulation study and a numerical example.

Beth-Uhlenbeck approach for repulsive interactions between baryons in a hadron gas

NASA Astrophysics Data System (ADS)

Vovchenko, Volodymyr; Motornenko, Anton; Gorenstein, Mark I.; Stoecker, Horst

2018-03-01

The quantum mechanical Beth-Uhlenbeck (BU) approach for repulsive hard-core interactions between baryons is applied to the thermodynamics of a hadron gas. The second virial coefficient a2—the "excluded volume" parameter—calculated within the BU approach is found to be temperature dependent, and it differs dramatically from the classical excluded volume (EV) model result. At temperatures T =100 -200 MeV, the widely used classical EV model underestimates the EV parameter for nucleons at a given value of the nucleon hard-core radius by large factors of 3-4. Previous studies, which employed the hard-core radii of hadrons as an input into the classical EV model, have to be re-evaluated using the appropriately rescaled EV parameters. The BU approach is used to model the repulsive baryonic interactions in the hadron resonance gas (HRG) model. Lattice data for the second- and fourth-order net baryon susceptibilities are described fairly well when the temperature dependent BU baryonic excluded volume parameter corresponds to nucleon hard-core radii of rc=0.25 -0.3 fm. Role of the attractive baryonic interactions is also considered. It is argued that HRG model with a constant baryon-baryon EV parameter vN N≃1 fm3 provides a simple yet efficient description of baryon-baryon interaction in the crossover temperature region.

Kink dynamics in a parametric Φ 6 system: a model with controllably many internal modes

DOE PAGES

Demirkaya, A.; Decker, R.; Kevrekidis, P. G.; ...

2017-12-14

We explore a variant of the Φ 6 model originally proposed in Phys. Rev.D 12 (1975) 1606 as a prototypical, so-called, “bag” model in which domain walls play the role of quarks within hadrons. We examine the steady state of the model, namely an apparent bound state of two kink structures. We explore its linearization, and we find that, as a function of a parameter controlling the curvature of the potential, an effectively arbitrary number of internal modes may arise in the point spectrum of the linearization about the domain wall profile. We explore some of the key characteristics ofmore » kink-antikink collisions, such as the critical velocity and the multi-bounce windows, and how they depend on the principal parameter of the model. We find that the critical velocity exhibits a non-monotonic dependence on the parameter controlling the curvature of the potential. For the multi-bounce windows, we find that their range and complexity decrease as the relevant parameter decreases (and as the number of internal modes in the model increases). We use a modified collective coordinates method [in the spirit of recent works such as Phys. Rev.D 94 (2016) 085008] in order to capture the relevant phenomenology in a semi-analytical manner.« less

Kink dynamics in a parametric Φ 6 system: a model with controllably many internal modes

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

Demirkaya, A.; Decker, R.; Kevrekidis, P. G.

We explore a variant of the Φ 6 model originally proposed in Phys. Rev.D 12 (1975) 1606 as a prototypical, so-called, “bag” model in which domain walls play the role of quarks within hadrons. We examine the steady state of the model, namely an apparent bound state of two kink structures. We explore its linearization, and we find that, as a function of a parameter controlling the curvature of the potential, an effectively arbitrary number of internal modes may arise in the point spectrum of the linearization about the domain wall profile. We explore some of the key characteristics ofmore » kink-antikink collisions, such as the critical velocity and the multi-bounce windows, and how they depend on the principal parameter of the model. We find that the critical velocity exhibits a non-monotonic dependence on the parameter controlling the curvature of the potential. For the multi-bounce windows, we find that their range and complexity decrease as the relevant parameter decreases (and as the number of internal modes in the model increases). We use a modified collective coordinates method [in the spirit of recent works such as Phys. Rev.D 94 (2016) 085008] in order to capture the relevant phenomenology in a semi-analytical manner.« less

Kink dynamics in a parametric ϕ 6 system: a model with controllably many internal modes

NASA Astrophysics Data System (ADS)

Demirkaya, A.; Decker, R.; Kevrekidis, P. G.; Christov, I. C.; Saxena, A.

2017-12-01

We explore a variant of the ϕ 6 model originally proposed in Phys. Rev. D 12 (1975) 1606 as a prototypical, so-called, "bag" model in which domain walls play the role of quarks within hadrons. We examine the steady state of the model, namely an apparent bound state of two kink structures. We explore its linearization, and we find that, as a function of a parameter controlling the curvature of the potential, an effectively arbitrary number of internal modes may arise in the point spectrum of the linearization about the domain wall profile. We explore some of the key characteristics of kink-antikink collisions, such as the critical velocity and the multi-bounce windows, and how they depend on the principal parameter of the model. We find that the critical velocity exhibits a non-monotonic dependence on the parameter controlling the curvature of the potential. For the multi-bounce windows, we find that their range and complexity decrease as the relevant parameter decreases (and as the number of internal modes in the model increases). We use a modified collective coordinates method [in the spirit of recent works such as Phys. Rev. D 94 (2016) 085008] in order to capture the relevant phenomenology in a semi-analytical manner.

Weighted-density functionals for cavity formation and dispersion energies in continuum solvation models

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

Sundararaman, Ravishankar; Gunceler, Deniz; Arias, T. A.

2014-10-07

Continuum solvation models enable efficient first principles calculations of chemical reactions in solution, but require extensive parametrization and fitting for each solvent and class of solute systems. Here, we examine the assumptions of continuum solvation models in detail and replace empirical terms with physical models in order to construct a minimally-empirical solvation model. Specifically, we derive solvent radii from the nonlocal dielectric response of the solvent from ab initio calculations, construct a closed-form and parameter-free weighted-density approximation for the free energy of the cavity formation, and employ a pair-potential approximation for the dispersion energy. We show that the resulting modelmore » with a single solvent-independent parameter: the electron density threshold (n c), and a single solvent-dependent parameter: the dispersion scale factor (s 6), reproduces solvation energies of organic molecules in water, chloroform, and carbon tetrachloride with RMS errors of 1.1, 0.6 and 0.5 kcal/mol, respectively. We additionally show that fitting the solvent-dependent s 6 parameter to the solvation energy of a single non-polar molecule does not substantially increase these errors. Parametrization of this model for other solvents, therefore, requires minimal effort and is possible without extensive databases of experimental solvation free energies.« less

Weighted-density functionals for cavity formation and dispersion energies in continuum solvation models

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

Sundararaman, Ravishankar; Gunceler, Deniz; Arias, T. A.

2014-10-07

Continuum solvation models enable efficient first principles calculations of chemical reactions in solution, but require extensive parametrization and fitting for each solvent and class of solute systems. Here, we examine the assumptions of continuum solvation models in detail and replace empirical terms with physical models in order to construct a minimally-empirical solvation model. Specifically, we derive solvent radii from the nonlocal dielectric response of the solvent from ab initio calculations, construct a closed-form and parameter-free weighted-density approximation for the free energy of the cavity formation, and employ a pair-potential approximation for the dispersion energy. We show that the resulting modelmore » with a single solvent-independent parameter: the electron density threshold (n{sub c}), and a single solvent-dependent parameter: the dispersion scale factor (s{sub 6}), reproduces solvation energies of organic molecules in water, chloroform, and carbon tetrachloride with RMS errors of 1.1, 0.6 and 0.5 kcal/mol, respectively. We additionally show that fitting the solvent-dependent s{sub 6} parameter to the solvation energy of a single non-polar molecule does not substantially increase these errors. Parametrization of this model for other solvents, therefore, requires minimal effort and is possible without extensive databases of experimental solvation free energies.« less

Solution of magnetohydrodynamic flow and heat transfer of radiative viscoelastic fluid with temperature dependent viscosity in wire coating analysis

PubMed Central

Khan, Muhammad Altaf; Siddiqui, Nasir; Ullah, Murad; Shah, Qayyum

2018-01-01

Wire coating process is a continuous extrusion process for primary insulation of conducting wires with molten polymers for mechanical strength and protection in aggressive environments. In the present study, radiative melt polymer satisfying third grade fluid model is used for wire coating process. The effect of magnetic parameter, thermal radiation parameter and temperature dependent viscosity on wire coating analysis has been investigated. Reynolds model and Vogel’s models have been incorporated for variable viscosity. The governing equations characterizing the flow and heat transfer phenomena are solved analytically by utilizing homotopy analysis method (HAM). The computed results are also verified by ND-Solve method (Numerical technique) and Adomian Decomposition Method (ADM). The effect of pertinent parameters is shown graphically. In addition, the instability of the flow in the flows of the wall of the extrusion die is well marked in the case of the Vogel model as pointed by Nhan-Phan-Thien. PMID:29596448

An improved numerical model suggests potential differences of wind-blown sand between on Earth and Mars

NASA Astrophysics Data System (ADS)

Bo, T. L.; Fu, L. T.; Liu, L.; Zheng, X. J.

2017-06-01

The studies on wind-blown sand are crucial for understanding the change of climate and landscape on Mars. However, the disadvantages of the saltation models may result in unreliable predictions. In this paper, the saltation model has been improved from two main aspects, the aerodynamic surface roughness and the lift-off parameters. The aerodynamic surface roughness is expressed as function of particle size, wind strength, air density, and air dynamic viscosity. The lift-off parameters are improved through including the dependence of restitution coefficient on incident parameters and the correlation between saltating speed and angle. The improved model proved to be capable of reproducing the observed data well in both stable stage and evolution process. The modeling of wind-blown sand is promoted by all improved aspects, and the dependence of restitution coefficient on incident parameters could not be ignored. The constant restitution coefficient and uncorrelated lift-off parameter distributions would lead to both the overestimation of the sand transport rate and apparent surface roughness and the delay of evolution process. The distribution of lift-off speed and the evolution of lift-off parameters on Mars are found to be different from those on Earth. This may thus suggest that it is inappropriate to predict the evolution of wind-blown sand by using the lift-off velocity obtained in steady state saltation. And it also may be problematic to predict the wind-blown sand on Mars through applying the lift-off velocity obtained upon terrestrial conditions directly.

Estimating outflow facility through pressure dependent pathways of the human eye

PubMed Central

Gardiner, Bruce S.

2017-01-01

We develop and test a new theory for pressure dependent outflow from the eye. The theory comprises three main parameters: (i) a constant hydraulic conductivity, (ii) an exponential decay constant and (iii) a no-flow intraocular pressure, from which the total pressure dependent outflow, average outflow facilities and local outflow facilities for the whole eye may be evaluated. We use a new notation to specify precisely the meaning of model parameters and so model outputs. Drawing on a range of published data, we apply the theory to animal eyes, enucleated eyes and in vivo human eyes, and demonstrate how to evaluate model parameters. It is shown that the theory can fit high quality experimental data remarkably well. The new theory predicts that outflow facilities and total pressure dependent outflow for the whole eye are more than twice as large as estimates based on the Goldman equation and fluorometric analysis of anterior aqueous outflow. It appears likely that this discrepancy can be largely explained by pseudofacility and aqueous flow through the retinal pigmented epithelium, while any residual discrepancy may be due to pathological processes in aged eyes. The model predicts that if the hydraulic conductivity is too small, or the exponential decay constant is too large, then intraocular eye pressure may become unstable when subjected to normal circadian changes in aqueous production. The model also predicts relationships between variables that may be helpful when planning future experiments, and the model generates many novel testable hypotheses. With additional research, the analysis described here may find application in the differential diagnosis, prognosis and monitoring of glaucoma. PMID:29261696

Chaos synchronization and Nelder-Mead search for parameter estimation in nonlinear pharmacological systems: Estimating tumor antigenicity in a model of immunotherapy.

PubMed

Pillai, Nikhil; Craig, Morgan; Dokoumetzidis, Aristeidis; Schwartz, Sorell L; Bies, Robert; Freedman, Immanuel

2018-06-19

In mathematical pharmacology, models are constructed to confer a robust method for optimizing treatment. The predictive capability of pharmacological models depends heavily on the ability to track the system and to accurately determine parameters with reference to the sensitivity in projected outcomes. To closely track chaotic systems, one may choose to apply chaos synchronization. An advantageous byproduct of this methodology is the ability to quantify model parameters. In this paper, we illustrate the use of chaos synchronization combined with Nelder-Mead search to estimate parameters of the well-known Kirschner-Panetta model of IL-2 immunotherapy from noisy data. Chaos synchronization with Nelder-Mead search is shown to provide more accurate and reliable estimates than Nelder-Mead search based on an extended least squares (ELS) objective function. Our results underline the strength of this approach to parameter estimation and provide a broader framework of parameter identification for nonlinear models in pharmacology. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

Jassal, Anjali Rao; Vadawale, Santosh V.; Mithun, N. P. S.

Low-frequency quasi-periodic oscillations (QPOs) are commonly observed during the hard states of black hole binaries. Several studies have established various observational/empirical correlations between spectral parameters and QPO properties, indicating a close link between the two. However, the exact mechanism of generation of QPOs is not yet well understood. In this paper, we present our attempts to comprehend the connection between the spectral components and the low-frequency QPO (LFQPO) observed in GRS 1915+105 using the data from NuSTAR. Detailed spectral modeling as well as the presence of the LFQPO and its energy dependence during this observation have been reported by Millermore » et al. and Zhang et al., respectively. We investigate the compatibility of the spectral model and the energy dependence of the QPO by simulating light curves in various energy bands for small variation of the spectral parameters. The basic concept here is to establish the connection, if any, between the QPO and the variation of either a spectral component or a specific parameter, which in turn can shed some light on the origin of the QPO. We begin with the best-fit spectral model of Miller et al. and simulate the light curve by varying the spectral parameters at frequencies close to the observed QPO frequency in order to generate the simulated QPO. Furthermore we simulate similar light curves in various energy bands in order to reproduce the observed energy dependence of the rms amplitude of the QPO. We find that the observed trend of increasing rms amplitude with energy can be reproduced qualitatively if the spectral index is assumed to be varying with the phases of the QPO. Variation of any other spectral parameter does not reproduce the observed energy dependence.« less

The three-point function as a probe of models for large-scale structure

NASA Astrophysics Data System (ADS)

Frieman, Joshua A.; Gaztanaga, Enrique

1994-04-01

We analyze the consequences of models of structure formation for higher order (n-point) galaxy correlation functions in the mildly nonlinear regime. Several variations of the standard Omega = 1 cold dark matter model with scale-invariant primordial perturbations have recently been introduced to obtain more power on large scales, Rp is approximately 20/h Mpc, e.g., low matter-density (nonzero cosmological constant) models, 'tilted' primordial spectra, and scenarios with a mixture of cold and hot dark matter. They also include models with an effective scale-dependent bias, such as the cooperative galaxy formation scenario of Bower et al. We show that higher-order (n-point) galaxy correlation functions can provide a useful test of such models and can discriminate between models with true large-scale power in the density field and those where the galaxy power arises from scale-dependent bias: a bias with rapid scale dependence leads to a dramatic decrease of the the hierarchical amplitudes QJ at large scales, r is greater than or approximately Rp. Current observational constraints on the three-point amplitudes Q3 and S3 can place limits on the bias parameter(s) and appear to disfavor, but not yet rule out, the hypothesis that scale-dependent bias is responsible for the extra power observed on large scales.

Applying the relaxation model of interfacial heat transfer to calculate the liquid outflow with supercritical initial parameters

NASA Astrophysics Data System (ADS)

Alekseev, M. V.; Vozhakov, I. S.; Lezhnin, S. I.; Pribaturin, N. A.

2017-09-01

A comparative numerical simulation of the supercritical fluid outflow on the thermodynamic equilibrium and non-equilibrium relaxation models of phase transition for different times of relaxation has been performed. The model for the fixed relaxation time based on the experimentally determined radius of liquid droplets was compared with the model of dynamically changing relaxation time, calculated by the formula (7) and depending on local parameters. It is shown that the relaxation time varies significantly depending on the thermodynamic conditions of the two-phase medium in the course of outflowing. The application of the proposed model with dynamic relaxation time leads to qualitatively correct results. The model can be used for both vaporization and condensation processes. It is shown that the model can be improved on the basis of processing experimental data on the distribution of the droplet sizes formed during the breaking up of the liquid jet.

Inflow, Outflow, Yields, and Stellar Population Mixing in Chemical Evolution Models

NASA Astrophysics Data System (ADS)

Andrews, Brett H.; Weinberg, David H.; Schönrich, Ralph; Johnson, Jennifer A.

2017-02-01

Chemical evolution models are powerful tools for interpreting stellar abundance surveys and understanding galaxy evolution. However, their predictions depend heavily on the treatment of inflow, outflow, star formation efficiency (SFE), the stellar initial mass function, the SN Ia delay time distribution, stellar yields, and stellar population mixing. Using flexCE, a flexible one-zone chemical evolution code, we investigate the effects of and trade-offs between parameters. Two critical parameters are SFE and the outflow mass-loading parameter, which shift the knee in [O/Fe]-[Fe/H] and the equilibrium abundances that the simulations asymptotically approach, respectively. One-zone models with simple star formation histories follow narrow tracks in [O/Fe]-[Fe/H] unlike the observed bimodality (separate high-α and low-α sequences) in this plane. A mix of one-zone models with inflow timescale and outflow mass-loading parameter variations, motivated by the inside-out galaxy formation scenario with radial mixing, reproduces the two sequences better than a one-zone model with two infall epochs. We present [X/Fe]-[Fe/H] tracks for 20 elements assuming three different supernova yield models and find some significant discrepancies with solar neighborhood observations, especially for elements with strongly metallicity-dependent yields. We apply principal component abundance analysis to the simulations and existing data to reveal the main correlations among abundances and quantify their contributions to variation in abundance space. For the stellar population mixing scenario, the abundances of α-elements and elements with metallicity-dependent yields dominate the first and second principal components, respectively, and collectively explain 99% of the variance in the model. flexCE is a python package available at https://github.com/bretthandrews/flexCE.

Inflow, Outflow, Yields, and Stellar Population Mixing in Chemical Evolution Models

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

Andrews, Brett H.; Weinberg, David H.; Schönrich, Ralph

Chemical evolution models are powerful tools for interpreting stellar abundance surveys and understanding galaxy evolution. However, their predictions depend heavily on the treatment of inflow, outflow, star formation efficiency (SFE), the stellar initial mass function, the SN Ia delay time distribution, stellar yields, and stellar population mixing. Using flexCE, a flexible one-zone chemical evolution code, we investigate the effects of and trade-offs between parameters. Two critical parameters are SFE and the outflow mass-loading parameter, which shift the knee in [O/Fe]–[Fe/H] and the equilibrium abundances that the simulations asymptotically approach, respectively. One-zone models with simple star formation histories follow narrow tracksmore » in [O/Fe]–[Fe/H] unlike the observed bimodality (separate high- α and low- α sequences) in this plane. A mix of one-zone models with inflow timescale and outflow mass-loading parameter variations, motivated by the inside-out galaxy formation scenario with radial mixing, reproduces the two sequences better than a one-zone model with two infall epochs. We present [X/Fe]–[Fe/H] tracks for 20 elements assuming three different supernova yield models and find some significant discrepancies with solar neighborhood observations, especially for elements with strongly metallicity-dependent yields. We apply principal component abundance analysis to the simulations and existing data to reveal the main correlations among abundances and quantify their contributions to variation in abundance space. For the stellar population mixing scenario, the abundances of α -elements and elements with metallicity-dependent yields dominate the first and second principal components, respectively, and collectively explain 99% of the variance in the model. flexCE is a python package available at https://github.com/bretthandrews/flexCE.« less

The in vivo plantar soft tissue mechanical property under the metatarsal head: implications of tissues׳ joint-angle dependent response in foot finite element modeling.

PubMed

Chen, Wen-Ming; Lee, Sung-Jae; Lee, Peter Vee Sin

2014-12-01

Material properties of the plantar soft tissue have not been well quantified in vivo (i.e., from life subjects) nor for areas other than the heel pad. This study explored an in vivo investigation of the plantar soft tissue material behavior under the metatarsal head (MTH). We used a novel device collecting indentation data at controlled metatarsophalangeal joint angles. Combined with inverse analysis, tissues׳ joint-angle dependent material properties were identified. The results showed that the soft tissue under MTH exhibited joint-angle dependent material responses, and the computed parameters using the Ogden material model were 51.3% and 30.9% larger in the dorsiflexed than in the neutral positions, respectively. Using derived parameters in subject-specific foot finite element models revealed only those models that used tissues׳ joint-dependent responses could reproduce the known plantar pressure pattern under the MTH. It is suggested that, to further improve specificity of the personalized foot finite element models, quantitative mechanical properties of the tissue inclusive of the effects of metatarsophalangeal joint dorsiflexion are needed. Copyright © 2014 Elsevier Ltd. All rights reserved.

A critical state model for mudrock behavior at high stress levels

NASA Astrophysics Data System (ADS)

Heidari, M.; Nikolinakou, M. A.; Flemings, P. B.

2016-12-01

Recent experimental work has documented that the compression behavior, friction angle, and lateral stress ratio (k0) of mudrocks vary over the stress range of 1 to 100 MPa. We integrate these observations into a critical state model. The internal friction angle and the slope of the compression curve are key parameters in a mudrock critical state model. Published models assume that these parameters do not depend on the stress level, and hence predict lateral stress and normalized strength ratios that do not change with the stress level. However, recent experimental data on resedimented mudrock samples from Eugene Island, Gulf of Mexico, demonstrate that all these parameters vary considerably with the stress level (Casey and Germaine, 2013; Casey et al., 2015). To represent these variations, we develop an enhanced critical state model that uses a stress-level-dependent friction angle and a curvilinear compression curve. We show that this enhanced model predicts the observed variations of the lateral stress and strength ratios. The successful performance of our model indicates that the critical state theory developed for soil can predict mudrock nonlinear behavior at high stress levels and thus can be used in modeling geologic systems. Casey, B., Germaine, J., 2013. Stress Dependence of Shear Strength in Fine-Grained Soils and Correlations with Liquid Limit. J. Geotech. Geoenviron. Eng. 139, 1709-1717. Casey, B., Germaine, J., Flemings, P.B., Fahy, B.P., 2015. Estimating horizontal stresses for mudrocks under one-dimensional compression. Mar. Pet. Geol. 65, 178-186.

Bonded-cell model for particle fracture.

PubMed

Nguyen, Duc-Hanh; Azéma, Emilien; Sornay, Philippe; Radjai, Farhang

2015-02-01

Particle degradation and fracture play an important role in natural granular flows and in many applications of granular materials. We analyze the fracture properties of two-dimensional disklike particles modeled as aggregates of rigid cells bonded along their sides by a cohesive Mohr-Coulomb law and simulated by the contact dynamics method. We show that the compressive strength scales with tensile strength between cells but depends also on the friction coefficient and a parameter describing cell shape distribution. The statistical scatter of compressive strength is well described by the Weibull distribution function with a shape parameter varying from 6 to 10 depending on cell shape distribution. We show that this distribution may be understood in terms of percolating critical intercellular contacts. We propose a random-walk model of critical contacts that leads to particle size dependence of the compressive strength in good agreement with our simulation data.

Bayesian methods for characterizing unknown parameters of material models

DOE PAGES

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

2016-02-04

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less

Bayesian methods for characterizing unknown parameters of material models

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

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less

A path-dependent fatigue crack propagation model under non-proportional modes I and III loading conditions

DOE PAGES

Mei, J.; Dong, P.; Kalnaus, S.; ...

2017-07-21

It has been well established that fatigue damage process is load-path dependent under non-proportional multi-axial loading conditions. Most of studies to date have been focusing on interpretation of S-N based test data by constructing a path-dependent fatigue damage model. Our paper presents a two-parameter mixed-mode fatigue crack growth model which takes into account of crack growth dependency on both load path traversed and a maximum effective stress intensity attained in a stress intensity factor plane (e.g.,KI-KIII plane). Furthermore, by taking advantage of a path-dependent maximum range (PDMR) cycle definition (Dong et al., 2010; Wei and Dong, 2010), the two parametersmore » are formulated by introducing a moment of load path (MLP) based equivalent stress intensity factor range (ΔKNP) and a maximum effective stress intensity parameter KMax incorporating an interaction term KI·KIII. To examine the effectiveness of the proposed model, two sets of crack growth rate test data are considered. The first set is obtained as a part of this study using 304 stainless steel disk specimens subjected to three combined non-proportional modes I and III loading conditions (i.e., with a phase angle of 0°, 90°, and 180°). The second set was obtained by Feng et al. (2007) using 1070 steel disk specimens subjected to similar types of non-proportional mixed-mode conditions. Once the proposed two-parameter non-proportional mixed-mode crack growth model is used, it is shown that a good correlation can be achieved for both sets of the crack growth rate test data.« less

A path-dependent fatigue crack propagation model under non-proportional modes I and III loading conditions

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

Mei, J.; Dong, P.; Kalnaus, S.

It has been well established that fatigue damage process is load-path dependent under non-proportional multi-axial loading conditions. Most of studies to date have been focusing on interpretation of S-N based test data by constructing a path-dependent fatigue damage model. Our paper presents a two-parameter mixed-mode fatigue crack growth model which takes into account of crack growth dependency on both load path traversed and a maximum effective stress intensity attained in a stress intensity factor plane (e.g.,KI-KIII plane). Furthermore, by taking advantage of a path-dependent maximum range (PDMR) cycle definition (Dong et al., 2010; Wei and Dong, 2010), the two parametersmore » are formulated by introducing a moment of load path (MLP) based equivalent stress intensity factor range (ΔKNP) and a maximum effective stress intensity parameter KMax incorporating an interaction term KI·KIII. To examine the effectiveness of the proposed model, two sets of crack growth rate test data are considered. The first set is obtained as a part of this study using 304 stainless steel disk specimens subjected to three combined non-proportional modes I and III loading conditions (i.e., with a phase angle of 0°, 90°, and 180°). The second set was obtained by Feng et al. (2007) using 1070 steel disk specimens subjected to similar types of non-proportional mixed-mode conditions. Once the proposed two-parameter non-proportional mixed-mode crack growth model is used, it is shown that a good correlation can be achieved for both sets of the crack growth rate test data.« less

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

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

Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual modelmore » has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.« less

Vibroacoustic optimization using a statistical energy analysis model

NASA Astrophysics Data System (ADS)

Culla, Antonio; D`Ambrogio, Walter; Fregolent, Annalisa; Milana, Silvia

2016-08-01

In this paper, an optimization technique for medium-high frequency dynamic problems based on Statistical Energy Analysis (SEA) method is presented. Using a SEA model, the subsystem energies are controlled by internal loss factors (ILF) and coupling loss factors (CLF), which in turn depend on the physical parameters of the subsystems. A preliminary sensitivity analysis of subsystem energy to CLF's is performed to select CLF's that are most effective on subsystem energies. Since the injected power depends not only on the external loads but on the physical parameters of the subsystems as well, it must be taken into account under certain conditions. This is accomplished in the optimization procedure, where approximate relationships between CLF's, injected power and physical parameters are derived. The approach is applied on a typical aeronautical structure: the cabin of a helicopter.

Diffuse reflectance of TiO 2 pigmented paints: Spectral dependence of the average pathlength parameter and the forward scattering ratio

NASA Astrophysics Data System (ADS)

Vargas, William E.; Amador, Alvaro; Niklasson, Gunnar A.

2006-05-01

Diffuse reflectance spectra of paint coatings with different pigment concentrations, normally illuminated with unpolarized radiation, have been measured. A four-flux radiative transfer approach is used to model the diffuse reflectance of TiO2 (rutile) pigmented coatings through the solar spectral range. The spectral dependence of the average pathlength parameter and of the forward scattering ratio for diffuse radiation, are explicitly incorporated into this four-flux model from two novel approximations. The size distribution of the pigments has been taken into account to obtain the averages of the four-flux parameters: scattering and absorption cross sections, forward scattering ratios for collimated and isotropic diffuse radiation, and coefficients involved in the expansion of the single particle phase function in terms of Legendre polynomials.

Performance analysis of wideband data and television channels. [space shuttle communications

NASA Technical Reports Server (NTRS)

Geist, J. M.

1975-01-01

Several aspects are discussed of space shuttle communications, including the return link (shuttle-to-ground) relayed through a satellite repeater (TDRS). The repeater exhibits nonlinear amplification and an amplitude-dependent phase shift. Models were developed for various link configurations, and computer simulation programs based on these models are described. Certain analytical results on system performance were also obtained. For the system parameters assumed, the results indicate approximately 1 db degradation relative to a link employing a linear repeater. While this degradation is dependent upon the repeater, filter bandwidths, and modulation parameters used, the programs can accommodate changes to any of these quantities. Thus the programs can be applied to determine the performance with any given set of parameters, or used as an aid in link design.

A two-column formalism for time-dependent modelling of stellar convection. I. Description of the method

NASA Astrophysics Data System (ADS)

Stökl, A.

2008-11-01

Context: In spite of all the advances in multi-dimensional hydrodynamics, investigations of stellar evolution and stellar pulsations still depend on one-dimensional computations. This paper devises an alternative to the mixing-length theory or turbulence models usually adopted in modelling convective transport in such studies. Aims: The present work attempts to develop a time-dependent description of convection, which reflects the essential physics of convection and that is only moderately dependent on numerical parameters and far less time consuming than existing multi-dimensional hydrodynamics computations. Methods: Assuming that the most extensive convective patterns generate the majority of convective transport, the convective velocity field is described using two parallel, radial columns to represent up- and downstream flows. Horizontal exchange, in the form of fluid flow and radiation, over their connecting interface couples the two columns and allows a simple circulating motion. The main parameters of this convective description have straightforward geometrical meanings, namely the diameter of the columns (corresponding to the size of the convective cells) and the ratio of the cross-section between up- and downdrafts. For this geometrical setup, the time-dependent solution of the equations of radiation hydrodynamics is computed from an implicit scheme that has the advantage of being unaffected by the Courant-Friedrichs-Lewy time-step limit. This implementation is part of the TAPIR-Code (short for The adaptive, implicit RHD-Code). Results: To demonstrate the approach, results for convection zones in Cepheids are presented. The convective energy transport and convective velocities agree with expectations for Cepheids and the scheme reproduces both the kinetic energy flux and convective overshoot. A study of the parameter influence shows that the type of solution derived for these stars is in fact fairly robust with respect to the constitutive numerical parameters.

Scale-dependent CMB power asymmetry from primordial speed of sound and a generalized δ N formalism

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

Wang, Dong-Gang; Cai, Yi-Fu; Zhao, Wen

2016-02-01

We explore a plausible mechanism that the hemispherical power asymmetry in the CMB is produced by the spatial variation of the primordial sound speed parameter. We suggest that in a generalized approach of the δ N formalism the local e-folding number may depend on some other primordial parameters besides the initial values of inflaton. Here the δ N formalism is extended by considering the effects of a spatially varying sound speed parameter caused by a super-Hubble perturbation of a light field. Using this generalized δ N formalism, we systematically calculate the asymmetric primordial spectrum in the model of multi-speed inflation by taking intomore » account the constraints of primordial non-Gaussianities. We further discuss specific model constraints, and the corresponding asymmetry amplitudes are found to be scale-dependent, which can accommodate current observations of the power asymmetry at different length scales.« less

Observation model and parameter partials for the JPL VLBI parameter estimation software MASTERFIT-1987

NASA Technical Reports Server (NTRS)

Sovers, O. J.; Fanselow, J. L.

1987-01-01

This report is a revision of the document of the same title (1986), dated August 1, which it supersedes. Model changes during 1986 and 1987 included corrections for antenna feed rotation, refraction in modelling antenna axis offsets, and an option to employ improved values of the semiannual and annual nutation amplitudes. Partial derivatives of the observables with respect to an additional parameter (surface temperature) are now available. New versions of two figures representing the geometric delay are incorporated. The expressions for the partial derivatives with respect to the nutation parameters have been corrected to include contributions from the dependence of UTI on nutation. The authors hope to publish revisions of this document in the future, as modeling improvements warrant.

Observation model and parameter partials for the JPL VLBI parameter estimation software MASTERFIT-1987

NASA Astrophysics Data System (ADS)

Sovers, O. J.; Fanselow, J. L.

1987-12-01

This report is a revision of the document of the same title (1986), dated August 1, which it supersedes. Model changes during 1986 and 1987 included corrections for antenna feed rotation, refraction in modelling antenna axis offsets, and an option to employ improved values of the semiannual and annual nutation amplitudes. Partial derivatives of the observables with respect to an additional parameter (surface temperature) are now available. New versions of two figures representing the geometric delay are incorporated. The expressions for the partial derivatives with respect to the nutation parameters have been corrected to include contributions from the dependence of UTI on nutation. The authors hope to publish revisions of this document in the future, as modeling improvements warrant.

Spatiotemporal pattern formation in a prey-predator model under environmental driving forces

NASA Astrophysics Data System (ADS)

Sirohi, Anuj Kumar; Banerjee, Malay; Chakraborti, Anirban

2015-09-01

Many existing studies on pattern formation in the reaction-diffusion systems rely on deterministic models. However, environmental noise is often a major factor which leads to significant changes in the spatiotemporal dynamics. In this paper, we focus on the spatiotemporal patterns produced by the predator-prey model with ratio-dependent functional response and density dependent death rate of predator. We get the reaction-diffusion equations incorporating the self-diffusion terms, corresponding to random movement of the individuals within two dimensional habitats, into the growth equations for the prey and predator population. In order to have the noise added model, small amplitude heterogeneous perturbations to the linear intrinsic growth rates are introduced using uncorrelated Gaussian white noise terms. For the noise added system, we then observe spatial patterns for the parameter values lying outside the Turing instability region. With thorough numerical simulations we characterize the patterns corresponding to Turing and Turing-Hopf domain and study their dependence on different system parameters like noise-intensity, etc.

Modeling of Internet Influence on Group Emotion

NASA Astrophysics Data System (ADS)

Czaplicka, Agnieszka; Hołyst, Janusz A.

Long-range interactions are introduced to a two-dimensional model of agents with time-dependent internal variables ei = 0, ±1 corresponding to valencies of agent emotions. Effects of spontaneous emotion emergence and emotional relaxation processes are taken into account. The valence of agent i depends on valencies of its four nearest neighbors but it is also influenced by long-range interactions corresponding to social relations developed for example by Internet contacts to a randomly chosen community. Two types of such interactions are considered. In the first model the community emotional influence depends only on the sign of its temporary emotion. When the coupling parameter approaches a critical value a phase transition takes place and as result for larger coupling constants the mean group emotion of all agents is nonzero over long time periods. In the second model the community influence is proportional to magnitude of community average emotion. The ordered emotional phase was here observed for a narrow set of system parameters.

Kinetic model for dependence of thin film stress on growth rate, temperature, and microstructure

NASA Astrophysics Data System (ADS)

Chason, E.; Shin, J. W.; Hearne, S. J.; Freund, L. B.

2012-04-01

During deposition, many thin films go through a range of stress states, changing from compressive to tensile and back again. In addition, the stress depends strongly on the processing and material parameters. We have developed a simple analytical model to describe the stress evolution in terms of a kinetic competition between different mechanisms of stress generation and relaxation at the triple junction where the surface and grain boundary intersect. The model describes how the steady state stress scales with the dimensionless parameter D/LR where D is the diffusivity, R is the growth rate, and L is the grain size. It also explains the transition from tensile to compressive stress as the microstructure evolves from isolated islands to a continuous film. We compare calculations from the model with measurements of the stress dependence on grain size and growth rate in the steady state regime and of the evolution of stress with thickness for different temperatures.

Using discharge data to reduce structural deficits in a hydrological model with a Bayesian inference approach and the implications for the prediction of critical source areas

NASA Astrophysics Data System (ADS)

Frey, M. P.; Stamm, C.; Schneider, M. K.; Reichert, P.

2011-12-01

A distributed hydrological model was used to simulate the distribution of fast runoff formation as a proxy for critical source areas for herbicide pollution in a small agricultural catchment in Switzerland. We tested to what degree predictions based on prior knowledge without local measurements could be improved upon relying on observed discharge. This learning process consisted of five steps: For the prior prediction (step 1), knowledge of the model parameters was coarse and predictions were fairly uncertain. In the second step, discharge data were used to update the prior parameter distribution. Effects of uncertainty in input data and model structure were accounted for by an autoregressive error model. This step decreased the width of the marginal distributions of parameters describing the lower boundary (percolation rates) but hardly affected soil hydraulic parameters. Residual analysis (step 3) revealed model structure deficits. We modified the model, and in the subsequent Bayesian updating (step 4) the widths of the posterior marginal distributions were reduced for most parameters compared to those of the prior. This incremental procedure led to a strong reduction in the uncertainty of the spatial prediction. Thus, despite only using spatially integrated data (discharge), the spatially distributed effect of the improved model structure can be expected to improve the spatially distributed predictions also. The fifth step consisted of a test with independent spatial data on herbicide losses and revealed ambiguous results. The comparison depended critically on the ratio of event to preevent water that was discharged. This ratio cannot be estimated from hydrological data only. The results demonstrate that the value of local data is strongly dependent on a correct model structure. An iterative procedure of Bayesian updating, model testing, and model modification is suggested.

A Violation of the Conditional Independence Assumption in the Two-High-Threshold Model of Recognition Memory

ERIC Educational Resources Information Center

Chen, Tina; Starns, Jeffrey J.; Rotello, Caren M.

2015-01-01

The 2-high-threshold (2HT) model of recognition memory assumes that test items result in distinct internal states: they are either detected or not, and the probability of responding at a particular confidence level that an item is "old" or "new" depends on the state-response mapping parameters. The mapping parameters are…

Improved Model for Predicting the Free Energy Contribution of Dinucleotide Bulges to RNA Duplex Stability.

PubMed

Tomcho, Jeremy C; Tillman, Magdalena R; Znosko, Brent M

2015-09-01

Predicting the secondary structure of RNA is an intermediate in predicting RNA three-dimensional structure. Commonly, determining RNA secondary structure from sequence uses free energy minimization and nearest neighbor parameters. Current algorithms utilize a sequence-independent model to predict free energy contributions of dinucleotide bulges. To determine if a sequence-dependent model would be more accurate, short RNA duplexes containing dinucleotide bulges with different sequences and nearest neighbor combinations were optically melted to derive thermodynamic parameters. These data suggested energy contributions of dinucleotide bulges were sequence-dependent, and a sequence-dependent model was derived. This model assigns free energy penalties based on the identity of nucleotides in the bulge (3.06 kcal/mol for two purines, 2.93 kcal/mol for two pyrimidines, 2.71 kcal/mol for 5'-purine-pyrimidine-3', and 2.41 kcal/mol for 5'-pyrimidine-purine-3'). The predictive model also includes a 0.45 kcal/mol penalty for an A-U pair adjacent to the bulge and a -0.28 kcal/mol bonus for a G-U pair adjacent to the bulge. The new sequence-dependent model results in predicted values within, on average, 0.17 kcal/mol of experimental values, a significant improvement over the sequence-independent model. This model and new experimental values can be incorporated into algorithms that predict RNA stability and secondary structure from sequence.

Effect of the cation model on the equilibrium structure of poly-L-glutamate in aqueous sodium chloride solution

NASA Astrophysics Data System (ADS)

Marchand, Gabriel; Soetens, Jean-Christophe; Jacquemin, Denis; Bopp, Philippe A.

2015-12-01

We demonstrate that different sets of Lennard-Jones parameters proposed for the Na+ ion, in conjunction with the empirical combining rules routinely used in simulation packages, can lead to essentially different equilibrium structures for a deprotonated poly-L-glutamic acid molecule (poly-L-glutamate) dissolved in a 0.3M aqueous NaCl solution. It is, however, difficult to discriminate a priori between these model potentials; when investigating the structure of the Na+-solvation shell in bulk NaCl solution, all parameter sets lead to radial distribution functions and solvation numbers in broad agreement with the available experimental data. We do not find any such dependency of the equilibrium structure on the parameters associated with the Cl- ion. This work does not aim at recommending a particular set of parameters for any particular purpose. Instead, it stresses the model dependence of simulation results for complex systems such as biomolecules in solution and thus the difficulties if simulations are to be used for unbiased predictions, or to discriminate between contradictory experiments. However, this opens the possibility of validating a model specifically in view of analyzing experimental data believed to be reliable.

On the impact of reducing global geophysical fluid model deformations in SLR data processing

NASA Astrophysics Data System (ADS)

Weigelt, Matthias; Thaller, Daniela

2016-04-01

Mass redistributions in the atmosphere, oceans and the continental hydrology cause elastic loading deformations of the Earth's crust and thus systematically influence Earth-bound observation systems such as VLBI, GNSS or SLR. Causing non-linear station variations, these loading deformations have a direct impact on the estimated station coordinates and an indirect impact on other parameters of global space-geodetic solutions, e.g. Earth orientation parameters, geocenter coordinates, satellite orbits or troposphere parameters. Generally, the impact can be mitigated by co-parameterisation or by reducing deformations derived from global geophysical fluid models. Here, we focus on the latter approach. A number of data sets modelling the (non-tidal) loading deformations are generated by various groups. They show regionally and locally significant differences and consequently the impact on the space-geodetic solutions heavily depends on the available network geometry. We present and discuss the differences between these models and choose SLR as the speace-geodetic technique of interest in order to discuss the impact of atmospheric, oceanic and hydrological loading on the parameters of space-geodetic solutions when correcting for the global geophysical fluid models at the observation level. Special emphasis is given to a consistent usage of models for geometric and gravimetric corrections during the data processing. We quantify the impact of the different deformation models on the station coordinates and discuss the improvement in the Earth orientation parameters and the geocenter motion. We also show that a significant reduction in the RMS of the station coordinates can be achieved depending on the model of choice.

Validation of the alternating conditional estimation algorithm for estimation of flexible extensions of Cox's proportional hazards model with nonlinear constraints on the parameters.

PubMed

Wynant, Willy; Abrahamowicz, Michal

2016-11-01

Standard optimization algorithms for maximizing likelihood may not be applicable to the estimation of those flexible multivariable models that are nonlinear in their parameters. For applications where the model's structure permits separating estimation of mutually exclusive subsets of parameters into distinct steps, we propose the alternating conditional estimation (ACE) algorithm. We validate the algorithm, in simulations, for estimation of two flexible extensions of Cox's proportional hazards model where the standard maximum partial likelihood estimation does not apply, with simultaneous modeling of (1) nonlinear and time-dependent effects of continuous covariates on the hazard, and (2) nonlinear interaction and main effects of the same variable. We also apply the algorithm in real-life analyses to estimate nonlinear and time-dependent effects of prognostic factors for mortality in colon cancer. Analyses of both simulated and real-life data illustrate good statistical properties of the ACE algorithm and its ability to yield new potentially useful insights about the data structure. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A simple parameter can switch between different weak-noise-induced phenomena in a simple neuron model

NASA Astrophysics Data System (ADS)

Yamakou, Marius E.; Jost, Jürgen

2017-10-01

In recent years, several, apparently quite different, weak-noise-induced resonance phenomena have been discovered. Here, we show that at least two of them, self-induced stochastic resonance (SISR) and inverse stochastic resonance (ISR), can be related by a simple parameter switch in one of the simplest models, the FitzHugh-Nagumo (FHN) neuron model. We consider a FHN model with a unique fixed point perturbed by synaptic noise. Depending on the stability of this fixed point and whether it is located to either the left or right of the fold point of the critical manifold, two distinct weak-noise-induced phenomena, either SISR or ISR, may emerge. SISR is more robust to parametric perturbations than ISR, and the coherent spike train generated by SISR is more robust than that generated deterministically. ISR also depends on the location of initial conditions and on the time-scale separation parameter of the model equation. Our results could also explain why real biological neurons having similar physiological features and synaptic inputs may encode very different information.

Voronoi Cell Patterns: theoretical model and application to submonolayer growth

NASA Astrophysics Data System (ADS)

González, Diego Luis; Einstein, T. L.

2012-02-01

We use a simple fragmentation model to describe the statistical behavior of the Voronoi cell patterns generated by a homogeneous and isotropic set of points in 1D and in 2D. In particular, we are interested in the distribution of sizes of these Voronoi cells. Our model is completely defined by two probability distributions in 1D and again in 2D, the probability to add a new point inside an existing cell and the probability that this new point is at a particular position relative to the preexisting point inside this cell. In 1D the first distribution depends on a single parameter while the second distribution is defined through a fragmentation kernel; in 2D both distributions depend on a single parameter. The fragmentation kernel and the control parameters are closely related to the physical properties of the specific system under study. We apply our model to describe the Voronoi cell patterns of island nucleation for critical island sizes i=0,1,2,3. Experimental results for the Voronoi cells of InAs/GaAs quantum dots are also described by our model.

An experimental study of dependence of hydro turbine vibration parameters on pressure pulsations in the flow path

NASA Astrophysics Data System (ADS)

Dekterev, D.; Maslennikova, A.; Abramov, A.

2017-09-01

The operation modes of the hydraulic power plant water turbine with the formation of a precessing vortex core were studied on the hydrodynamic set-up with the model of hydraulic unit. The dependence of low-frequency vibrations on flow pressure pulsations in the hydraulic unit was established. The results of the air injection effect on the vibrational parameters of the hydrodynamic set-up were presented.

Predictions from star formation in the multiverse

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

Bousso, Raphael; Leichenauer, Stefan

2010-03-15

We compute trivariate probability distributions in the landscape, scanning simultaneously over the cosmological constant, the primordial density contrast, and spatial curvature. We consider two different measures for regulating the divergences of eternal inflation, and three different models for observers. In one model, observers are assumed to arise in proportion to the entropy produced by stars; in the others, they arise at a fixed time (5 or 10x10{sup 9} years) after star formation. The star formation rate, which underlies all our observer models, depends sensitively on the three scanning parameters. We employ a recently developed model of star formation in themore » multiverse, a considerable refinement over previous treatments of the astrophysical and cosmological properties of different pocket universes. For each combination of observer model and measure, we display all single and bivariate probability distributions, both with the remaining parameter(s) held fixed and marginalized. Our results depend only weakly on the observer model but more strongly on the measure. Using the causal diamond measure, the observed parameter values (or bounds) lie within the central 2{sigma} of nearly all probability distributions we compute, and always within 3{sigma}. This success is encouraging and rather nontrivial, considering the large size and dimension of the parameter space. The causal patch measure gives similar results as long as curvature is negligible. If curvature dominates, the causal patch leads to a novel runaway: it prefers a negative value of the cosmological constant, with the smallest magnitude available in the landscape.« less

Estimating varying coefficients for partial differential equation models.

PubMed

Zhang, Xinyu; Cao, Jiguo; Carroll, Raymond J

2017-09-01

Partial differential equations (PDEs) are used to model complex dynamical systems in multiple dimensions, and their parameters often have important scientific interpretations. In some applications, PDE parameters are not constant but can change depending on the values of covariates, a feature that we call varying coefficients. We propose a parameter cascading method to estimate varying coefficients in PDE models from noisy data. Our estimates of the varying coefficients are shown to be consistent and asymptotically normally distributed. The performance of our method is evaluated by a simulation study and by an empirical study estimating three varying coefficients in a PDE model arising from LIDAR data. © 2017, The International Biometric Society.

Joint coverage probability in a simulation study on Continuous-Time Markov Chain parameter estimation.

PubMed

Benoit, Julia S; Chan, Wenyaw; Doody, Rachelle S

2015-01-01

Parameter dependency within data sets in simulation studies is common, especially in models such as Continuous-Time Markov Chains (CTMC). Additionally, the literature lacks a comprehensive examination of estimation performance for the likelihood-based general multi-state CTMC. Among studies attempting to assess the estimation, none have accounted for dependency among parameter estimates. The purpose of this research is twofold: 1) to develop a multivariate approach for assessing accuracy and precision for simulation studies 2) to add to the literature a comprehensive examination of the estimation of a general 3-state CTMC model. Simulation studies are conducted to analyze longitudinal data with a trinomial outcome using a CTMC with and without covariates. Measures of performance including bias, component-wise coverage probabilities, and joint coverage probabilities are calculated. An application is presented using Alzheimer's disease caregiver stress levels. Comparisons of joint and component-wise parameter estimates yield conflicting inferential results in simulations from models with and without covariates. In conclusion, caution should be taken when conducting simulation studies aiming to assess performance and choice of inference should properly reflect the purpose of the simulation.

A micromechanical interpretation of the temperature dependence of Beremin model parameters for french RPV steel

NASA Astrophysics Data System (ADS)

Mathieu, Jean-Philippe; Inal, Karim; Berveiller, Sophie; Diard, Olivier

2010-11-01

Local approach to brittle fracture for low-alloyed steels is discussed in this paper. A bibliographical introduction intends to highlight general trends and consensual points of the topic and evokes debatable aspects. French RPV steel 16MND5 (equ. ASTM A508 Cl.3), is then used as a model material to study the influence of temperature on brittle fracture. A micromechanical modelling of brittle fracture at the elementary volume scale already used in previous work is then recalled. It involves a multiscale modelling of microstructural plasticity which has been tuned on experimental inter-phase and inter-granular stresses heterogeneities measurements. Fracture probability of the elementary volume can then be computed using a randomly attributed defect size distribution based on realistic carbides repartition. This defect distribution is then deterministically correlated to stress heterogeneities simulated within the microstructure using a weakest-link hypothesis on the elementary volume, which results in a deterministic stress to fracture. Repeating the process allows to compute Weibull parameters on the elementary volume. This tool is then used to investigate the physical mechanisms that could explain the already experimentally observed temperature dependence of Beremin's parameter for 16MND5 steel. It is showed that, assuming that the hypothesis made in this work about cleavage micro-mechanisms are correct, effective equivalent surface energy (i.e. surface energy plus plastically dissipated energy when blunting the crack tip) for propagating a crack has to be temperature dependent to explain Beremin's parameters temperature evolution.

Integrated Logistics Support Analysis of the International Space Station Alpha, Background and Summary of Mathematical Modeling and Failure Density Distributions Pertaining to Maintenance Time Dependent Parameters

NASA Technical Reports Server (NTRS)

Sepehry-Fard, F.; Coulthard, Maurice H.

1995-01-01

The process of predicting the values of maintenance time dependent variable parameters such as mean time between failures (MTBF) over time must be one that will not in turn introduce uncontrolled deviation in the results of the ILS analysis such as life cycle costs, spares calculation, etc. A minor deviation in the values of the maintenance time dependent variable parameters such as MTBF over time will have a significant impact on the logistics resources demands, International Space Station availability and maintenance support costs. There are two types of parameters in the logistics and maintenance world: a. Fixed; b. Variable Fixed parameters, such as cost per man hour, are relatively easy to predict and forecast. These parameters normally follow a linear path and they do not change randomly. However, the variable parameters subject to the study in this report such as MTBF do not follow a linear path and they normally fall within the distribution curves which are discussed in this publication. The very challenging task then becomes the utilization of statistical techniques to accurately forecast the future non-linear time dependent variable arisings and events with a high confidence level. This, in turn, shall translate in tremendous cost savings and improved availability all around.

The effects of ionic strength and organic matter on virus inactivation at low temperatures: general likelihood uncertainty estimation (GLUE) as an alternative to least-squares parameter optimization for the fitting of virus inactivation models

NASA Astrophysics Data System (ADS)

Mayotte, Jean-Marc; Grabs, Thomas; Sutliff-Johansson, Stacy; Bishop, Kevin

2017-06-01

This study examined how the inactivation of bacteriophage MS2 in water was affected by ionic strength (IS) and dissolved organic carbon (DOC) using static batch inactivation experiments at 4 °C conducted over a period of 2 months. Experimental conditions were characteristic of an operational managed aquifer recharge (MAR) scheme in Uppsala, Sweden. Experimental data were fit with constant and time-dependent inactivation models using two methods: (1) traditional linear and nonlinear least-squares techniques; and (2) a Monte-Carlo based parameter estimation technique called generalized likelihood uncertainty estimation (GLUE). The least-squares and GLUE methodologies gave very similar estimates of the model parameters and their uncertainty. This demonstrates that GLUE can be used as a viable alternative to traditional least-squares parameter estimation techniques for fitting of virus inactivation models. Results showed a slight increase in constant inactivation rates following an increase in the DOC concentrations, suggesting that the presence of organic carbon enhanced the inactivation of MS2. The experiment with a high IS and a low DOC was the only experiment which showed that MS2 inactivation may have been time-dependent. However, results from the GLUE methodology indicated that models of constant inactivation were able to describe all of the experiments. This suggested that inactivation time-series longer than 2 months were needed in order to provide concrete conclusions regarding the time-dependency of MS2 inactivation at 4 °C under these experimental conditions.

3-D ballistic transport of ellipsoidal volcanic projectiles considering horizontal wind field and variable shape-dependent drag coefficients

NASA Astrophysics Data System (ADS)

Bertin, Daniel

2017-02-01

An innovative 3-D numerical model for the dynamics of volcanic ballistic projectiles is presented here. The model focuses on ellipsoidal particles and improves previous approaches by considering horizontal wind field, virtual mass forces, and drag forces subjected to variable shape-dependent drag coefficients. Modeling suggests that the projectile's launch velocity and ejection angle are first-order parameters influencing ballistic trajectories. The projectile's density and minor radius are second-order factors, whereas both intermediate and major radii of the projectile are of third order. Comparing output parameters, assuming different input data, highlights the importance of considering a horizontal wind field and variable shape-dependent drag coefficients in ballistic modeling, which suggests that they should be included in every ballistic model. On the other hand, virtual mass forces should be discarded since they almost do not contribute to ballistic trajectories. Simulation results were used to constrain some crucial input parameters (launch velocity, ejection angle, wind speed, and wind azimuth) of the block that formed the biggest and most distal ballistic impact crater during the 1984-1993 eruptive cycle of Lascar volcano, Northern Chile. Subsequently, up to 106 simulations were performed, whereas nine ejection parameters were defined by a Latin-hypercube sampling approach. Simulation results were summarized as a quantitative probabilistic hazard map for ballistic projectiles. Transects were also done in order to depict aerial hazard zones based on the same probabilistic procedure. Both maps combined can be used as a hazard prevention tool for ground and aerial transits nearby unresting volcanoes.

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

NASA Astrophysics Data System (ADS)

Domanskyi, Sergii; Schilling, Joshua E.; Gorshkov, Vyacheslav; Libert, Sergiy; Privman, Vladimir

2016-09-01

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of "stiff" equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

NASA Astrophysics Data System (ADS)

Domanskyi, Sergii; Schilling, Joshua; Gorshkov, Vyacheslav; Libert, Sergiy; Privman, Vladimir

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of ``stiff'' equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.

Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling

USGS Publications Warehouse

Necpálová, Magdalena; Anex, Robert P.; Fienen, Michael N.; Del Grosso, Stephen J.; Castellano, Michael J.; Sawyer, John E.; Iqbal, Javed; Pantoja, Jose L.; Barker, Daniel W.

2015-01-01

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil3NO− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil 3NO− and 4NH+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent.

Analysis of Brown camera distortion model

NASA Astrophysics Data System (ADS)

Nowakowski, Artur; Skarbek, Władysław

2013-10-01

Contemporary image acquisition devices introduce optical distortion into image. It results in pixel displacement and therefore needs to be compensated for many computer vision applications. The distortion is usually modeled by the Brown distortion model, which parameters can be included in camera calibration task. In this paper we describe original model, its dependencies and analyze orthogonality with regard to radius for its decentering distortion component. We also report experiments with camera calibration algorithm included in OpenCV library, especially a stability of distortion parameters estimation is evaluated.

Bringing metabolic networks to life: convenience rate law and thermodynamic constraints

PubMed Central

Liebermeister, Wolfram; Klipp, Edda

2006-01-01

Background Translating a known metabolic network into a dynamic model requires rate laws for all chemical reactions. The mathematical expressions depend on the underlying enzymatic mechanism; they can become quite involved and may contain a large number of parameters. Rate laws and enzyme parameters are still unknown for most enzymes. Results We introduce a simple and general rate law called "convenience kinetics". It can be derived from a simple random-order enzyme mechanism. Thermodynamic laws can impose dependencies on the kinetic parameters. Hence, to facilitate model fitting and parameter optimisation for large networks, we introduce thermodynamically independent system parameters: their values can be varied independently, without violating thermodynamical constraints. We achieve this by expressing the equilibrium constants either by Gibbs free energies of formation or by a set of independent equilibrium constants. The remaining system parameters are mean turnover rates, generalised Michaelis-Menten constants, and constants for inhibition and activation. All parameters correspond to molecular energies, for instance, binding energies between reactants and enzyme. Conclusion Convenience kinetics can be used to translate a biochemical network – manually or automatically - into a dynamical model with plausible biological properties. It implements enzyme saturation and regulation by activators and inhibitors, covers all possible reaction stoichiometries, and can be specified by a small number of parameters. Its mathematical form makes it especially suitable for parameter estimation and optimisation. Parameter estimates can be easily computed from a least-squares fit to Michaelis-Menten values, turnover rates, equilibrium constants, and other quantities that are routinely measured in enzyme assays and stored in kinetic databases. PMID:17173669

Rate heterogeneity across Squamata, misleading ancestral state reconstruction and the importance of proper null model specification.

PubMed

Harrington, S; Reeder, T W

2017-02-01

The binary-state speciation and extinction (BiSSE) model has been used in many instances to identify state-dependent diversification and reconstruct ancestral states. However, recent studies have shown that the standard procedure of comparing the fit of the BiSSE model to constant-rate birth-death models often inappropriately favours the BiSSE model when diversification rates vary in a state-independent fashion. The newly developed HiSSE model enables researchers to identify state-dependent diversification rates while accounting for state-independent diversification at the same time. The HiSSE model also allows researchers to test state-dependent models against appropriate state-independent null models that have the same number of parameters as the state-dependent models being tested. We reanalyse two data sets that originally used BiSSE to reconstruct ancestral states within squamate reptiles and reached surprising conclusions regarding the evolution of toepads within Gekkota and viviparity across Squamata. We used this new method to demonstrate that there are many shifts in diversification rates across squamates. We then fit various HiSSE submodels and null models to the state and phylogenetic data and reconstructed states under these models. We found that there is no single, consistent signal for state-dependent diversification associated with toepads in gekkotans or viviparity across all squamates. Our reconstructions show limited support for the recently proposed hypotheses that toepads evolved multiple times independently in Gekkota and that transitions from viviparity to oviparity are common in Squamata. Our results highlight the importance of considering an adequate pool of models and null models when estimating diversification rate parameters and reconstructing ancestral states. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Modelling leaf photosynthetic and transpiration temperature-dependent responses in Vitis vinifera cv. Semillon grapevines growing in hot, irrigated vineyard conditions

PubMed Central

Greer, Dennis H.

2012-01-01

Background and aims Grapevines growing in Australia are often exposed to very high temperatures and the question of how the gas exchange processes adjust to these conditions is not well understood. The aim was to develop a model of photosynthesis and transpiration in relation to temperature to quantify the impact of the growing conditions on vine performance. Methodology Leaf gas exchange was measured along the grapevine shoots in accordance with their growth and development over several growing seasons. Using a general linear statistical modelling approach, photosynthesis and transpiration were modelled against leaf temperature separated into bands and the model parameters and coefficients applied to independent datasets to validate the model. Principal results Photosynthesis, transpiration and stomatal conductance varied along the shoot, with early emerging leaves having the highest rates, but these declined as later emerging leaves increased their gas exchange capacities in accordance with development. The general linear modelling approach applied to these data revealed that photosynthesis at each temperature was additively dependent on stomatal conductance, internal CO2 concentration and photon flux density. The temperature-dependent coefficients for these parameters applied to other datasets gave a predicted rate of photosynthesis that was linearly related to the measured rates, with a 1 : 1 slope. Temperature-dependent transpiration was multiplicatively related to stomatal conductance and the leaf to air vapour pressure deficit and applying the coefficients also showed a highly linear relationship, with a 1 : 1 slope between measured and modelled rates, when applied to independent datasets. Conclusions The models developed for the grapevines were relatively simple but accounted for much of the seasonal variation in photosynthesis and transpiration. The goodness of fit in each case demonstrated that explicitly selecting leaf temperature as a model parameter, rather than including temperature intrinsically as is usually done in more complex models, was warranted. PMID:22567220

Lattice parameter evolution in Pt nanoparticles during photo-thermally induced sintering and grain growth

DOE PAGES

Kelly, B.G.; Loether, A.; DiChiara, A. D.; ...

2017-04-20

An in-situ optical pump/x-ray probe technique has been used to study the size dependent lattice parameter of Pt nanoparticles subjected to picosecond duration optical laser pulses. The as-prepared Pt nanoparticles exhibited a contracted lattice parameter consistent with the response of an isolated elastic sphere to a compressive surface stress. During photo-thermally induced sintering and grain growth, however, the Pt lattice parameter did not evolve with the inverse particle size dependence predicted by simple surface stress models. Lastly, the observed behavior could be attributed to the combined effects of a compressive surface/interface stress and a tensile stress arising from intergranular material.

Lattice parameter evolution in Pt nanoparticles during photo-thermally induced sintering and grain growth

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

Kelly, B.G.; Loether, A.; DiChiara, A. D.

An in-situ optical pump/x-ray probe technique has been used to study the size dependent lattice parameter of Pt nanoparticles subjected to picosecond duration optical laser pulses. The as-prepared Pt nanoparticles exhibited a contracted lattice parameter consistent with the response of an isolated elastic sphere to a compressive surface stress. During photo-thermally induced sintering and grain growth, however, the Pt lattice parameter did not evolve with the inverse particle size dependence predicted by simple surface stress models. Lastly, the observed behavior could be attributed to the combined effects of a compressive surface/interface stress and a tensile stress arising from intergranular material.

Measurement of myocardial blood flow by cardiovascular magnetic resonance perfusion: comparison of distributed parameter and Fermi models with single and dual bolus.

PubMed

Papanastasiou, Giorgos; Williams, Michelle C; Kershaw, Lucy E; Dweck, Marc R; Alam, Shirjel; Mirsadraee, Saeed; Connell, Martin; Gray, Calum; MacGillivray, Tom; Newby, David E; Semple, Scott Ik

2015-02-17

Mathematical modeling of cardiovascular magnetic resonance perfusion data allows absolute quantification of myocardial blood flow. Saturation of left ventricle signal during standard contrast administration can compromise the input function used when applying these models. This saturation effect is evident during application of standard Fermi models in single bolus perfusion data. Dual bolus injection protocols have been suggested to eliminate saturation but are much less practical in the clinical setting. The distributed parameter model can also be used for absolute quantification but has not been applied in patients with coronary artery disease. We assessed whether distributed parameter modeling might be less dependent on arterial input function saturation than Fermi modeling in healthy volunteers. We validated the accuracy of each model in detecting reduced myocardial blood flow in stenotic vessels versus gold-standard invasive methods. Eight healthy subjects were scanned using a dual bolus cardiac perfusion protocol at 3T. We performed both single and dual bolus analysis of these data using the distributed parameter and Fermi models. For the dual bolus analysis, a scaled pre-bolus arterial input function was used. In single bolus analysis, the arterial input function was extracted from the main bolus. We also performed analysis using both models of single bolus data obtained from five patients with coronary artery disease and findings were compared against independent invasive coronary angiography and fractional flow reserve. Statistical significance was defined as two-sided P value < 0.05. Fermi models overestimated myocardial blood flow in healthy volunteers due to arterial input function saturation in single bolus analysis compared to dual bolus analysis (P < 0.05). No difference was observed in these volunteers when applying distributed parameter-myocardial blood flow between single and dual bolus analysis. In patients, distributed parameter modeling was able to detect reduced myocardial blood flow at stress (<2.5 mL/min/mL of tissue) in all 12 stenotic vessels compared to only 9 for Fermi modeling. Comparison of single bolus versus dual bolus values suggests that distributed parameter modeling is less dependent on arterial input function saturation than Fermi modeling. Distributed parameter modeling showed excellent accuracy in detecting reduced myocardial blood flow in all stenotic vessels.

Damping behavior of nano-fibrous composites with viscous interface in anti-plane shear

NASA Astrophysics Data System (ADS)

Wang, Xu

2017-06-01

By using the composite cylinder assemblage model, we derive an explicit expression of the specific damping capacity of nano-fibrous composite with viscous interface when subjected to time-harmonic anti-plane shear loads. The fiber and the matrix are first endowed with separate and distinct Gurtin-Murdoch surface elasticities, and rate-dependent sliding occurs on the fiber-matrix interface. Our analysis indicates that the effective damping of the composite depends on five dimensionless parameters: the fiber volume fraction, the stiffness ratio, two parameters arising from surface elasticity and one parameter due to interface sliding.

Regional flow simulation in fractured aquifers using stress-dependent parameters.

PubMed

Preisig, Giona; Joel Cornaton, Fabien; Perrochet, Pierre

2012-01-01

A model function relating effective stress to fracture permeability is developed from Hooke's law, implemented in the tensorial form of Darcy's law, and used to evaluate discharge rates and pressure distributions at regional scales. The model takes into account elastic and statistical fracture parameters, and is able to simulate real stress-dependent permeabilities from laboratory to field studies. This modeling approach gains in phenomenology in comparison to the classical ones because the permeability tensors may vary in both strength and principal directions according to effective stresses. Moreover this method allows evaluation of the fracture porosity changes, which are then translated into consolidation of the medium. © 2011, The Author(s). Ground Water © 2011, National Ground Water Association.

An Analytic Formulation of the 21 cm Signal from the Early Phase of the Epoch of Reionization

NASA Astrophysics Data System (ADS)

Raste, Janakee; Sethi, Shiv

2018-06-01

We present an analytic formulation to model the fluctuating component of the H I signal from the epoch of reionization during the phase of partial heating. During this phase, we assume self-ionized regions, whose size distribution can be computed using excursion set formalism, to be surrounded by heated regions. We model the evolution of the heating profile around these regions (near zone) and their merger into the time-dependent background (far zone). We develop a formalism to compute the two-point correlation function for this topology, taking into account the heating autocorrelation and heating-ionization cross-correlation. We model the ionization and X-ray heating using four parameters: efficiency of ionization, ζ number of X-ray photons per stellar baryon, N heat; spectral index of X-ray photons, α and minimum frequency of X-ray photons, ν min. We compute the H I signal in the redshift range 10 < z < 20 for the ΛCDM model for a set of these parameters. We show that the H I signal for a range of scales 1–8 Mpc shows a peak strength of 100–1000 (mK)2 during the partially heated era. The redshift at which the signal makes a transition to a uniformly heated universe depends on the modeling parameters; e.g., if ν min is changed from 100 eV to 1 keV, this transition moves from z ≃ 15 to z ≃ 12. This result, along with the dependence of the H I signal on the modeling parameters, is in reasonable agreement with existing results from N-body simulations.

Parameter estimation of history-dependent leaky integrate-and-fire neurons using maximum-likelihood methods

PubMed Central

Dong, Yi; Mihalas, Stefan; Russell, Alexander; Etienne-Cummings, Ralph; Niebur, Ernst

2012-01-01

When a neuronal spike train is observed, what can we say about the properties of the neuron that generated it? A natural way to answer this question is to make an assumption about the type of neuron, select an appropriate model for this type, and then to choose the model parameters as those that are most likely to generate the observed spike train. This is the maximum likelihood method. If the neuron obeys simple integrate and fire dynamics, Paninski, Pillow, and Simoncelli (2004) showed that its negative log-likelihood function is convex and that its unique global minimum can thus be found by gradient descent techniques. The global minimum property requires independence of spike time intervals. Lack of history dependence is, however, an important constraint that is not fulfilled in many biological neurons which are known to generate a rich repertoire of spiking behaviors that are incompatible with history independence. Therefore, we expanded the integrate and fire model by including one additional variable, a variable threshold (Mihalas & Niebur, 2009) allowing for history-dependent firing patterns. This neuronal model produces a large number of spiking behaviors while still being linear. Linearity is important as it maintains the distribution of the random variables and still allows for maximum likelihood methods to be used. In this study we show that, although convexity of the negative log-likelihood is not guaranteed for this model, the minimum of the negative log-likelihood function yields a good estimate for the model parameters, in particular if the noise level is treated as a free parameter. Furthermore, we show that a nonlinear function minimization method (r-algorithm with space dilation) frequently reaches the global minimum. PMID:21851282

Generalized model of electromigration with 1:1 (analyte:selector) complexation stoichiometry: part I. Theory.

PubMed

Dubský, Pavel; Müllerová, Ludmila; Dvořák, Martin; Gaš, Bohuslav

2015-03-06

The model of electromigration of a multivalent weak acidic/basic/amphoteric analyte that undergoes complexation with a mixture of selectors is introduced. The model provides an extension of the series of models starting with the single-selector model without dissociation by Wren and Rowe in 1992, continuing with the monovalent weak analyte/single-selector model by Rawjee, Williams and Vigh in 1993 and that by Lelièvre in 1994, and ending with the multi-selector overall model without dissociation developed by our group in 2008. The new multivalent analyte multi-selector model shows that the effective mobility of the analyte obeys the original Wren and Row's formula. The overall complexation constant, mobility of the free analyte and mobility of complex can be measured and used in a standard way. The mathematical expressions for the overall parameters are provided. We further demonstrate mathematically that the pH dependent parameters for weak analytes can be simply used as an input into the multi-selector overall model and, in reverse, the multi-selector overall parameters can serve as an input into the pH-dependent models for the weak analytes. These findings can greatly simplify the rationale method development in analytical electrophoresis, specifically enantioseparations. Copyright © 2015 Elsevier B.V. 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.

Nonlinear, discrete flood event models, 1. Bayesian estimation of parameters

NASA Astrophysics Data System (ADS)

Bates, Bryson C.; Townley, Lloyd R.

1988-05-01

In this paper (Part 1), a Bayesian procedure for parameter estimation is applied to discrete flood event models. The essence of the procedure is the minimisation of a sum of squares function for models in which the computed peak discharge is nonlinear in terms of the parameters. This objective function is dependent on the observed and computed peak discharges for several storms on the catchment, information on the structure of observation error, and prior information on parameter values. The posterior covariance matrix gives a measure of the precision of the estimated parameters. The procedure is demonstrated using rainfall and runoff data from seven Australian catchments. It is concluded that the procedure is a powerful alternative to conventional parameter estimation techniques in situations where a number of floods are available for parameter estimation. Parts 2 and 3 will discuss the application of statistical nonlinearity measures and prediction uncertainty analysis to calibrated flood models. Bates (this volume) and Bates and Townley (this volume).

Functional modeling of the human auditory brainstem response to broadband stimulationa)

PubMed Central

Verhulst, Sarah; Bharadwaj, Hari M.; Mehraei, Golbarg; Shera, Christopher A.; Shinn-Cunningham, Barbara G.

2015-01-01

Population responses such as the auditory brainstem response (ABR) are commonly used for hearing screening, but the relationship between single-unit physiology and scalp-recorded population responses are not well understood. Computational models that integrate physiologically realistic models of single-unit auditory-nerve (AN), cochlear nucleus (CN) and inferior colliculus (IC) cells with models of broadband peripheral excitation can be used to simulate ABRs and thereby link detailed knowledge of animal physiology to human applications. Existing functional ABR models fail to capture the empirically observed 1.2–2 ms ABR wave-V latency-vs-intensity decrease that is thought to arise from level-dependent changes in cochlear excitation and firing synchrony across different tonotopic sections. This paper proposes an approach where level-dependent cochlear excitation patterns, which reflect human cochlear filter tuning parameters, drive AN fibers to yield realistic level-dependent properties of the ABR wave-V. The number of free model parameters is minimal, producing a model in which various sources of hearing-impairment can easily be simulated on an individualized and frequency-dependent basis. The model fits latency-vs-intensity functions observed in human ABRs and otoacoustic emissions while maintaining rate-level and threshold characteristics of single-unit AN fibers. The simulations help to reveal which tonotopic regions dominate ABR waveform peaks at different stimulus intensities. PMID:26428802

Dynamic multipopulation and density dependent evolutionary games related to replicator dynamics. A metasimplex concept.

PubMed

Argasinski, Krzysztof

2006-07-01

This paper contains the basic extensions of classical evolutionary games (multipopulation and density dependent models). It is shown that classical bimatrix approach is inconsistent with other approaches because it does not depend on proportion between populations. The main conclusion is that interspecific proportion parameter is important and must be considered in multipopulation models. The paper provides a synthesis of both extensions (a metasimplex concept) which solves the problem intrinsic in the bimatrix model. It allows us to model interactions among any number of subpopulations including density dependence effects. We prove that all modern approaches to evolutionary games are closely related. All evolutionary models (except classical bimatrix approaches) can be reduced to a single population general model by a simple change of variables. Differences between classic bimatrix evolutionary games and a new model which is dependent on interspecific proportion are shown by examples.

Economic inequality and mobility in kinetic models for social sciences

NASA Astrophysics Data System (ADS)

Letizia Bertotti, Maria; Modanese, Giovanni

2016-10-01

Statistical evaluations of the economic mobility of a society are more difficult than measurements of the income distribution, because they require to follow the evolution of the individuals' income for at least one or two generations. In micro-to-macro theoretical models of economic exchanges based on kinetic equations, the income distribution depends only on the asymptotic equilibrium solutions, while mobility estimates also involve the detailed structure of the transition probabilities of the model, and are thus an important tool for assessing its validity. Empirical data show a remarkably general negative correlation between economic inequality and mobility, whose explanation is still unclear. It is therefore particularly interesting to study this correlation in analytical models. In previous work we investigated the behavior of the Gini inequality index in kinetic models in dependence on several parameters which define the binary interactions and the taxation and redistribution processes: saving propensity, taxation rates gap, tax evasion rate, welfare means-testing etc. Here, we check the correlation of mobility with inequality by analyzing the mobility dependence from the same parameters. According to several numerical solutions, the correlation is confirmed to be negative.

Role of temperature-dependent O-p-Fe-d hybridization parameter in the metal-insulator transition of Fe3O4: a theoretical study

NASA Astrophysics Data System (ADS)

Fauzi, A. D.; Majidi, M. A.; Rusydi, A.

2017-04-01

We propose a simple tight-binding based model for Fe3O4 that captures the preference of ferrimagnetic over ferromagnetic spin configuration of the system. Our model is consistent with previous theoretical and experimental studies suggesting that the system is half metallic, in which spin polarized electrons hop only among the Fe B sites. To address the metal-insulator transition (MIT) we propose that the strong correlation among electrons, which may also be influenced by the electron-phonon interactions, manifest as the temperature-dependence of the O-p-Fe-d hybridization parameter, particularly Fe-d belonging to one of the Fe B sites (denoted as {t}{{FeB}-{{O}}}(2)). By proposing that this parameter increases as the temperature decreases, our density-of-states calculation successfully captures a gap opening at the Fermi level, transforming the system from half metal to insulator. Within this model along with the corresponding choice of parameters and a certain profile of the temperature dependence of {t}{{FeB}-{{O}}}(2), we calculate the resistivity of the system as a function of temperature. Our calculation result reveals the drastic uprising trend of the resistivity profile as the temperature decreases, with the MIT transition temperature located around 100 K, which is in agreement with experimental data.

Modeling Spatial Dependence of Rainfall Extremes Across Multiple Durations

NASA Astrophysics Data System (ADS)

Le, Phuong Dong; Leonard, Michael; Westra, Seth

2018-03-01

Determining the probability of a flood event in a catchment given that another flood has occurred in a nearby catchment is useful in the design of infrastructure such as road networks that have multiple river crossings. These conditional flood probabilities can be estimated by calculating conditional probabilities of extreme rainfall and then transforming rainfall to runoff through a hydrologic model. Each catchment's hydrological response times are unlikely to be the same, so in order to estimate these conditional probabilities one must consider the dependence of extreme rainfall both across space and across critical storm durations. To represent these types of dependence, this study proposes a new approach for combining extreme rainfall across different durations within a spatial extreme value model using max-stable process theory. This is achieved in a stepwise manner. The first step defines a set of common parameters for the marginal distributions across multiple durations. The parameters are then spatially interpolated to develop a spatial field. Storm-level dependence is represented through the max-stable process for rainfall extremes across different durations. The dependence model shows a reasonable fit between the observed pairwise extremal coefficients and the theoretical pairwise extremal coefficient function across all durations. The study demonstrates how the approach can be applied to develop conditional maps of the return period and return level across different durations.

Comparison of 2-3D convection models with parameterized thermal evolution models: Application to Mars

NASA Astrophysics Data System (ADS)

Thiriet, M.; Plesa, A. C.; Breuer, D.; Michaut, C.

2017-12-01

To model the thermal evolution of terrestrial planets, 1D parametrized models are often used as 2 or 3D mantle convection codes are very time-consuming. In these parameterized models, scaling laws that describe the convective heat transfer rate as a function of the convective parameters are derived from 2-3D steady state convection models. However, so far there has been no comprehensive comparison whether they can be applied to model the thermal evolution of a cooling planet. Here we compare 2D and 3D thermal evolution models in the stagnant lid regime with 1D parametrized models and use parameters representing the cooling of the Martian mantle. For the 1D parameterized models, we use the approach of Grasset and Parmentier (1998) and treat the stagnant lid and the convecting layer separately. In the convecting layer, the scaling law for a fluid with constant viscosity is valid with Nu (Ra/Rac) ?, with Rac the critical Rayleigh number at which the thermal boundary layers (TBL) - top or bottom - destabilize. ? varies between 1/3 and 1/4 depending on the heating mode and previous studies have proposed intermediate values of b 0.28-0.32 according to their model set-up. The base of the stagnant lid is defined by the temperature at which the mantle viscosity has increased by a factor of 10; it thus depends on the rate of viscosity change with temperature multiplied by a factor? , whose value appears to vary depending on the geometry and convection conditions. In applying Monte Carlo simulations, we search for the best fit to temperature profiles and heat flux using three free parameters, i.e. ? of the upper TBL, ? and the Rac of the lower TBL. We find that depending on the definition of the stagnant lid thickness in the 2-3D models several combinations of ? and ? for the upper TBL can retrieve suitable fits. E.g. combinations of ? = 0.329 and ? = 2.19 but also ? = 0.295 and ? = 2.97 are possible; Rac of the lower TBL is 10 for all best fits. The results show that although the heating conditions change from bottom to mainly internally heating as a function of time, the thermal evolution can be represented by one set of parameters.

Determination of Spatio-Temporal Characteristics of D-region Electron Density during Annular Solar Eclipse from VLF Network Observations

NASA Astrophysics Data System (ADS)

Basak, T.; Hobara, Y.

2015-12-01

A major part of the path of the annular solar eclipse of May 20, 2012 (magnitude 0.9439) was over southern Japan. The D-region ionospheric changes associated with that eclipse, led to several degree of observable perturbations of sub-ionospheric very low frequency (VLF) radio signal. The University of Electro-Communications (UEC) operates VLF observation network over Japan. The solar eclipse associated signal changes were recorded in several receiving stations (Rx) simultaneously for the VLF signals coming from NWC/19.8kHz, JJI/22.2kHz, JJY/40.0kHz, NLK/24.8kHz and other VLF transmitters (Tx). These temporal dependences of VLF signal perturbation have been analyzed and the spatio-temporal characteristics of respective sub-ionospheric perturbations has already been studied by earlier workers using 2D-Finite Difference Time Domain method of simulation. In this work, we determine the spatial scale, depth and temporal dependence of lower ionospheric perturbation in consistence with umbral and penumbral motion. We considered the 2-parameter D-region ionospheric model with exponential electron density profile. To model the solar obscuration effect over it, we assumed a generalized space-time dependent 2-dimensional elliptical Gaussian distribution for ionospheric parameters, such as, effective reflection height (h') and sharpness factor (β). The depth (△hmax, △βmax), center of shadow (lato(t), lono(t)) and spatial scale (σlat,lon) of that Gaussian distribution are used as model parameters. In the vicinity of the eclipse zone, we compute the VLF signal perturbations using Long Wave Propagation Capability (LWPC) code for several signal propagation paths. The propagation path characteristics, such as, ground and water conductivity and geomagnetic effect on ionosphere are considered from standard LWPC prescriptions. The model parameters are tuned to set an optimum agreement between our computation and observed positive and negative type of VLF perturbations. Thus, appropriate set of parameters lead us to the possible determination of spatial scale, depth and temporal dependence of eclipse associated D-region electron density perturbation solely from the VLF-network observations coupled with theoretical modeling.

A new approach to modeling aerosol effects on East Asian climate: Parametric uncertainties associated with emissions, cloud microphysics, and their interactions: AEROSOL EFFECTS ON EAST ASIAN CLIMATE

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

Yan, Huiping; Qian, Yun; Zhao, Chun

2015-09-09

In this study, we adopt a parametric sensitivity analysis framework that integrates the quasi-Monte Carlo parameter sampling approach and a surrogate model to examine aerosol effects on the East Asian Monsoon climate simulated in the Community Atmosphere Model (CAM5). A total number of 256 CAM5 simulations are conducted to quantify the model responses to the uncertain parameters associated with cloud microphysics parameterizations and aerosol (e.g., sulfate, black carbon (BC), and dust) emission factors and their interactions. Results show that the interaction terms among parameters are important for quantifying the sensitivity of fields of interest, especially precipitation, to the parameters. Themore » relative importance of cloud-microphysics parameters and emission factors (strength) depends on evaluation metrics or the model fields we focused on, and the presence of uncertainty in cloud microphysics imposes an additional challenge in quantifying the impact of aerosols on cloud and climate. Due to their different optical and microphysical properties and spatial distributions, sulfate, BC, and dust aerosols have very different impacts on East Asian Monsoon through aerosol-cloud-radiation interactions. The climatic effects of aerosol do not always have a monotonic response to the change of emission factors. The spatial patterns of both sign and magnitude of aerosol-induced changes in radiative fluxes, cloud, and precipitation could be different, depending on the aerosol types, when parameters are sampled in different ranges of values. We also identify the different cloud microphysical parameters that show the most significant impact on climatic effect induced by sulfate, BC and dust, respectively, in East Asia.« less

Models for estimating photosynthesis parameters from in situ production profiles

NASA Astrophysics Data System (ADS)

Kovač, Žarko; Platt, Trevor; Sathyendranath, Shubha; Antunović, Suzana

2017-12-01

The rate of carbon assimilation in phytoplankton primary production models is mathematically prescribed with photosynthesis irradiance functions, which convert a light flux (energy) into a material flux (carbon). Information on this rate is contained in photosynthesis parameters: the initial slope and the assimilation number. The exactness of parameter values is crucial for precise calculation of primary production. Here we use a model of the daily production profile based on a suite of photosynthesis irradiance functions and extract photosynthesis parameters from in situ measured daily production profiles at the Hawaii Ocean Time-series station Aloha. For each function we recover parameter values, establish parameter distributions and quantify model skill. We observe that the choice of the photosynthesis irradiance function to estimate the photosynthesis parameters affects the magnitudes of parameter values as recovered from in situ profiles. We also tackle the problem of parameter exchange amongst the models and the effect it has on model performance. All models displayed little or no bias prior to parameter exchange, but significant bias following parameter exchange. The best model performance resulted from using optimal parameter values. Model formulation was extended further by accounting for spectral effects and deriving a spectral analytical solution for the daily production profile. The daily production profile was also formulated with time dependent growing biomass governed by a growth equation. The work on parameter recovery was further extended by exploring how to extract photosynthesis parameters from information on watercolumn production. It was demonstrated how to estimate parameter values based on a linearization of the full analytical solution for normalized watercolumn production and from the solution itself, without linearization. The paper complements previous works on photosynthesis irradiance models by analysing the skill and consistency of photosynthesis irradiance functions and parameters for modeling in situ production profiles. In light of the results obtained in this work we argue that the choice of the primary production model should reflect the available data and these models should be data driven regarding parameter estimation.

Sub-poissonian photon statistics in the coherent state Jaynes-Cummings model in non-resonance

NASA Astrophysics Data System (ADS)

Zhang, Jia-tai; Fan, An-fu

1992-03-01

We study a model with a two-level atom (TLA) non-resonance interacting with a single-mode quantized cavity field (QCF). The photon number probability function, the mean photon number and Mandel's fluctuation parameter are calculated. The sub-Poissonian distributions of the photon statistics are obtained in non-resonance interaction. This statistical properties are strongly dependent on the detuning parameters.

Analyzing Environmental Policies for Chlorinated Solvents with a Model of Markets and Regulations

DTIC Science & Technology

1991-01-01

electronics, aerospace, fabricated metal products, and dry cleaning depend heavily on chlorinated solvents in their production processes . For example...production processes . The second of the model’s components is a group of economic equations that represents all of the solvent substitutions in...Instead, the process for numerically specifying the substitution parameters involves eliciting expert judgments and then normalizing the parameters

Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches.

PubMed

Chan, Jennifer S K

2016-05-01

Dropouts are common in longitudinal study. If the dropout probability depends on the missing observations at or after dropout, this type of dropout is called informative (or nonignorable) dropout (ID). Failure to accommodate such dropout mechanism into the model will bias the parameter estimates. We propose a conditional autoregressive model for longitudinal binary data with an ID model such that the probabilities of positive outcomes as well as the drop-out indicator in each occasion are logit linear in some covariates and outcomes. This model adopting a marginal model for outcomes and a conditional model for dropouts is called a selection model. To allow for the heterogeneity and clustering effects, the outcome model is extended to incorporate mixture and random effects. Lastly, the model is further extended to a novel model that models the outcome and dropout jointly such that their dependency is formulated through an odds ratio function. Parameters are estimated by a Bayesian approach implemented using the user-friendly Bayesian software WinBUGS. A methadone clinic dataset is analyzed to illustrate the proposed models. Result shows that the treatment time effect is still significant but weaker after allowing for an ID process in the data. Finally the effect of drop-out on parameter estimates is evaluated through simulation studies. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modeling the focusing efficiency of lobster-eye optics for image shifting depending on the soft x-ray wavelength.

PubMed

Su, Luning; Li, Wei; Wu, Mingxuan; Su, Yun; Guo, Chongling; Ruan, Ningjuan; Yang, Bingxin; Yan, Feng

2017-08-01

Lobster-eye optics is widely applied to space x-ray detection missions and x-ray security checks for its wide field of view and low weight. This paper presents a theoretical model to obtain spatial distribution of focusing efficiency based on lobster-eye optics in a soft x-ray wavelength. The calculations reveal the competition mechanism of contributions to the focusing efficiency between the geometrical parameters of lobster-eye optics and the reflectivity of the iridium film. In addition, the focusing efficiency image depending on x-ray wavelengths further explains the influence of different geometrical parameters of lobster-eye optics and different soft x-ray wavelengths on focusing efficiency. These results could be beneficial to optimize parameters of lobster-eye optics in order to realize maximum focusing efficiency.

Magnetic field induced switching of the antiferromagnetic order parameter in thin films of magnetoelectric chromia

NASA Astrophysics Data System (ADS)

Fallarino, Lorenzo; Berger, Andreas; Binek, Christian

2015-02-01

A Landau-theoretical approach is utilized to model the magnetic field induced reversal of the antiferromagnetic order parameter in thin films of magnetoelectric antiferromagnets. A key ingredient of this peculiar switching phenomenon is the presence of a robust spin polarized state at the surface of the antiferromagnetic films. Surface or boundary magnetization is symmetry allowed in magnetoelectric antiferromagnets and experimentally established for chromia thin films. It couples rigidly to the antiferromagnetic order parameter and its Zeeman energy creates a pathway to switch the antiferromagnet via magnetic field application. In the framework of a minimalist Landau free energy expansion, the temperature dependence of the switching field and the field dependence of the transition width are derived. Least-squares fits to magnetometry data of (0001 ) textured chromia thin films strongly support this model of the magnetic reversal mechanism.

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

Furui, Shun’ya; Fukazawa, Yasushi; Ohno, Masanori

We construct an X-ray spectral model of reprocessing by a torus in an active galactic nucleus (AGN) with the Monte Carlo simulation framework MONACO. Two torus geometries of smooth and clumpy cases are considered and compared. In order to reproduce a Compton shoulder accurately, MONACO includes not only free electron scattering but also bound electron scattering. Raman and Rayleigh scattering are also treated, and scattering cross sections dependent on chemical states of hydrogen and helium are included. Doppler broadening by turbulence velocity can be implemented. Our model gives results consistent with other available models, such as MYTorus, except for differencesmore » due to different physical parameters and assumptions. We studied the dependence on torus parameters for a Compton shoulder, and found that a intensity ratio of a Compton shoulder to the line core mainly depends on column density, inclination angle, and metal abundance. For instance, an increase of metal abundance makes a Compton shoulder relatively weak. Also, the shape of a Compton shoulder depends on the column density. Furthermore, these dependences become different between smooth and clumpy cases. Then, we discuss the possibility of ASTRO-H/SXS spectroscopy of Compton shoulders in AGN reflection spectra.« less

Limited Bandwidth Recognition of Collective Behaviors in Bio-Inspired Swarms

DTIC Science & Technology

2014-05-09

collective? Some swarm models exhibit multiple emergent behaviors from the same parameters. This provides increased expressivity at the cost of...swarms, namely, how do you know what the swarm is doing if you can’t ob- serve every agent in the collective? Some swarm models exhibit multiple ...flocking [15, 21, 12] or cyclic behavior [8, 7], and in some cases can exhibit multiple group behaviors depending on the model parameters used [6, 3, 17

Phase-Angle Dependence of Determinations of Diameter, Albedo, and Taxonomy: A Case Study of NEO 3691 Bede

NASA Technical Reports Server (NTRS)

Wooden, Diane H.; Lederer, Susan M.; Jehin, Emmanuel; Howell, Ellen S.; Fernandez, Yan; Harker, David E.; Ryan, Erin; Lovell, Amy; Woodward, Charles E.; Benner, Lance A.

2015-01-01

Parameters important for NEO risk assessment and mitigation include Near-Earth Object diameter and taxonomic classification, which translates to surface composition. Diameters of NEOs are derived from the thermal fluxes measured by WISE, NEOWISE, Spitzer Warm Mission and ground-based telescopes including the IRTF and UKIRT. Diameter and its coupled parameters Albedo and IR beaming parameter (a proxy for thermal inertia and/or surface roughness) are dependent upon the phase angle, which is the Sun-target-observer angle. Orbit geometries of NEOs, however, typically provide for observations at phase angles greater than 20 degrees. At higher phase angles, the observed thermal emission is sampling both the day and night sides of the NEO. We compare thermal models for NEOs that exclude (NEATM) and include (NESTM) night-side emission. We present a case study of NEO 3691 Bede, which is a higher albedo object, X (Ec) or Cgh taxonomy, to highlight the range of H magnitudes for this object (depending on the albedo and phase function slope parameter G), and to examine at different phase angles the taxonomy and thermal model fits for this NEO. Observations of 3691 Bede include our observations with IRTF+SpeX and with the 10 micrometer UKIRT+Michelle instrument, as well as WISE and Spitzer Warm mission data. By examining 3691 Bede as a case study, we highlight the interplay between the derivation of basic physical parameters and observing geometry, and we discuss the uncertainties in H magnitude, taxonomy assignment amongst the X-class (P, M, E), and diameter determinations. Systematic dependencies in the derivation of basic characterization parameters of H-magnitude, diameter, albedo and taxonomy with observing geometry are important to understand. These basic characterization parameters affect the statistical assessments of the NEO population, which in turn, affects the assignment of statistically-assessed basic parameters to discovered but yet-to-be-fully-characterized NEOs.

Concentration dependence of the cell membrane permeability to cryoprotectant and water and implications for design of methods for post-thaw washing of human erythrocytes.

PubMed

Lahmann, John M; Benson, James D; Higgins, Adam Z

2018-02-01

For more than fifty years the human red blood cell (RBC) has been a widely studied model for transmembrane mass transport. Existing literature spans myriad experimental designs with varying results and physiologic interpretations. In this review, we examine the kinetics and mechanisms of membrane transport in the context of RBC cryopreservation. We include a discussion of the pathways for water and glycerol permeation through the cell membrane and the implications for mathematical modeling of the membrane transport process. In particular, we examine the concentration dependence of water and glycerol transport and provide equations for estimating permeability parameters as a function of concentration based on a synthesis of literature data. This concentration-dependent transport model may allow for design of improved methods for post-thaw removal of glycerol from cryopreserved blood. More broadly, the consideration of the concentration dependence of membrane permeability parameters may be important for other cell types as well, especially for design of methods for equilibration with the highly concentrated solutions used for vitrification. Copyright © 2017 Elsevier Inc. All rights reserved.

Global Sensitivity Analysis for Identifying Important Parameters of Nitrogen Nitrification and Denitrification under Model and Scenario Uncertainties

NASA Astrophysics Data System (ADS)

Ye, M.; Chen, Z.; Shi, L.; Zhu, Y.; Yang, J.

2017-12-01

Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. While global sensitivity analysis is a vital tool for identifying the parameters important to nitrogen reactive transport, conventional global sensitivity analysis only considers parametric uncertainty. This may result in inaccurate selection of important parameters, because parameter importance may vary under different models and modeling scenarios. By using a recently developed variance-based global sensitivity analysis method, this paper identifies important parameters with simultaneous consideration of parametric uncertainty, model uncertainty, and scenario uncertainty. In a numerical example of nitrogen reactive transport modeling, a combination of three scenarios of soil temperature and two scenarios of soil moisture leads to a total of six scenarios. Four alternative models are used to evaluate reduction functions used for calculating actual rates of nitrification and denitrification. The model uncertainty is tangled with scenario uncertainty, as the reduction functions depend on soil temperature and moisture content. The results of sensitivity analysis show that parameter importance varies substantially between different models and modeling scenarios, which may lead to inaccurate selection of important parameters if model and scenario uncertainties are not considered. This problem is avoided by using the new method of sensitivity analysis in the context of model averaging and scenario averaging. The new method of sensitivity analysis can be applied to other problems of contaminant transport modeling when model uncertainty and/or scenario uncertainty are present.

Majority-Vote Model on Opinion-Dependent Network

NASA Astrophysics Data System (ADS)

Lima, F. W. S.

2013-09-01

We study a nonequilibrium model with up-down symmetry and a noise parameter q known as majority-vote model (MVM) of Oliveira 1992 on opinion-dependent network or Stauffer-Hohnisch-Pittnauer (SHP) networks. By Monte Carlo (MC) simulations and finite-size scaling relations the critical exponents β/ν, γ/ν and 1/ν and points qc and U* are obtained. After extensive simulations, we obtain β/ν = 0.230(3), γ/ν = 0.535(2) and 1/ν = 0.475(8). The calculated values of the critical noise parameter and Binder cumulant are qc = 0.166(3) and U* = 0.288(3). Within the error bars, the exponents obey the relation 2β/ν + γ/ν = 1 and the results presented here demonstrate that the MVM belongs to a different universality class than the equilibrium Ising model on SHP networks, but to the same class as majority-vote models on some other networks.

Effects of Differing Energy Dependences in Three Level-Density Models on Calculated Cross Sections

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

Fu, C.Y.

2000-07-15

Three level-density formalisms commonly used for cross-section calculations are examined. Residual nuclides in neutron interaction with {sup 58}Ni are chosen to quantify the well-known differences in the energy dependences of the three formalisms. Level-density parameters for the Gilbert and Cameron model are determined from experimental information. Parameters for the back-shifted Fermi-gas and generalized superfluid models are obtained by fitting their level densities at two selected energies for each nuclide to those of the Gilbert and Cameron model, forcing the level densities of the three models to be as close as physically allowed. The remaining differences are in their energy dependencesmore » that, it is shown, can change the calculated cross sections and particle emission spectra significantly, in some cases or energy ranges by a factor of 2.« less

A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors.

PubMed

El-Diasty, Mohammed; Pagiatakis, Spiros

2009-01-01

In this paper, we examine the effect of changing the temperature points on MEMS-based inertial sensor random error. We collect static data under different temperature points using a MEMS-based inertial sensor mounted inside a thermal chamber. Rigorous stochastic models, namely Autoregressive-based Gauss-Markov (AR-based GM) models are developed to describe the random error behaviour. The proposed AR-based GM model is initially applied to short stationary inertial data to develop the stochastic model parameters (correlation times). It is shown that the stochastic model parameters of a MEMS-based inertial unit, namely the ADIS16364, are temperature dependent. In addition, field kinematic test data collected at about 17 °C are used to test the performance of the stochastic models at different temperature points in the filtering stage using Unscented Kalman Filter (UKF). It is shown that the stochastic model developed at 20 °C provides a more accurate inertial navigation solution than the ones obtained from the stochastic models developed at -40 °C, -20 °C, 0 °C, +40 °C, and +60 °C. The temperature dependence of the stochastic model is significant and should be considered at all times to obtain optimal navigation solution for MEMS-based INS/GPS integration.

Spatio-temporal modelling of wind speed variations and extremes in the Caribbean and the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Rychlik, Igor; Mao, Wengang

2018-02-01

The wind speed variability in the North Atlantic has been successfully modelled using a spatio-temporal transformed Gaussian field. However, this type of model does not correctly describe the extreme wind speeds attributed to tropical storms and hurricanes. In this study, the transformed Gaussian model is further developed to include the occurrence of severe storms. In this new model, random components are added to the transformed Gaussian field to model rare events with extreme wind speeds. The resulting random field is locally stationary and homogeneous. The localized dependence structure is described by time- and space-dependent parameters. The parameters have a natural physical interpretation. To exemplify its application, the model is fitted to the ECMWF ERA-Interim reanalysis data set. The model is applied to compute long-term wind speed distributions and return values, e.g., 100- or 1000-year extreme wind speeds, and to simulate random wind speed time series at a fixed location or spatio-temporal wind fields around that location.

Geometric dependence of the parasitic components and thermal properties of HEMTs

NASA Astrophysics Data System (ADS)

Vun, Peter V.; Parker, Anthony E.; Mahon, Simon J.; Fattorini, Anthony

2007-12-01

For integrated circuit design up to 50GHz and beyond accurate models of the transistor access structures and intrinsic structures are necessary for prediction of circuit performance. The circuit design process relies on optimising transistor geometry parameters such as unit gate width, number of gates, number of vias and gate-to-gate spacing. So the relationship between electrical and thermal parasitic components in transistor access structures, and transistor geometry is important to understand when developing models for transistors of differing geometries. Current approaches to describing the geometric dependence of models are limited to empirical methods which only describe a finite set of geometries and only include unit gate width and number of gates as variables. A better understanding of the geometric dependence is seen as a way to provide scalable models that remain accurate for continuous variation of all geometric parameters. Understanding the distribution of parasitic elements between the manifold, the terminal fingers, and the reference plane discontinuities is an issue identified as important in this regard. Examination of dc characteristics and thermal images indicates that gate-to-gate thermal coupling and increased thermal conductance at the gate ends, affects the device total thermal conductance. Consequently, a distributed thermal model is proposed which accounts for these effects. This work is seen as a starting point for developing comprehensive scalable models that will allow RF circuit designers to optimise circuit performance parameters such as total die area, maximum output power, power-added-efficiency (PAE) and channel temperature/lifetime.

Local tsunamis and earthquake source parameters

USGS Publications Warehouse

Geist, Eric L.; Dmowska, Renata; Saltzman, Barry

1999-01-01

This chapter establishes the relationship among earthquake source parameters and the generation, propagation, and run-up of local tsunamis. In general terms, displacement of the seafloor during the earthquake rupture is modeled using the elastic dislocation theory for which the displacement field is dependent on the slip distribution, fault geometry, and the elastic response and properties of the medium. Specifically, nonlinear long-wave theory governs the propagation and run-up of tsunamis. A parametric study is devised to examine the relative importance of individual earthquake source parameters on local tsunamis, because the physics that describes tsunamis from generation through run-up is complex. Analysis of the source parameters of various tsunamigenic earthquakes have indicated that the details of the earthquake source, namely, nonuniform distribution of slip along the fault plane, have a significant effect on the local tsunami run-up. Numerical methods have been developed to address the realistic bathymetric and shoreline conditions. The accuracy of determining the run-up on shore is directly dependent on the source parameters of the earthquake, which provide the initial conditions used for the hydrodynamic models.

The three-point function as a probe of models for large-scale structure

NASA Technical Reports Server (NTRS)

Frieman, Joshua A.; Gaztanaga, Enrique

1993-01-01

The consequences of models of structure formation for higher-order (n-point) galaxy correlation functions in the mildly non-linear regime are analyzed. Several variations of the standard Omega = 1 cold dark matter model with scale-invariant primordial perturbations were recently introduced to obtain more power on large scales, R(sub p) is approximately 20 h(sup -1) Mpc, e.g., low-matter-density (non-zero cosmological constant) models, 'tilted' primordial spectra, and scenarios with a mixture of cold and hot dark matter. They also include models with an effective scale-dependent bias, such as the cooperative galaxy formation scenario of Bower, etal. It is shown that higher-order (n-point) galaxy correlation functions can provide a useful test of such models and can discriminate between models with true large-scale power in the density field and those where the galaxy power arises from scale-dependent bias: a bias with rapid scale-dependence leads to a dramatic decrease of the hierarchical amplitudes Q(sub J) at large scales, r is approximately greater than R(sub p). Current observational constraints on the three-point amplitudes Q(sub 3) and S(sub 3) can place limits on the bias parameter(s) and appear to disfavor, but not yet rule out, the hypothesis that scale-dependent bias is responsible for the extra power observed on large scales.

Investigation on the effects of temperature dependency of material parameters on a thermoelastic loading problem

NASA Astrophysics Data System (ADS)

Kumar, Anil; Mukhopadhyay, Santwana

2017-08-01

The present work is concerned with the investigation of thermoelastic interactions inside a spherical shell with temperature-dependent material parameters. We employ the heat conduction model with a single delay term. The problem is studied by considering three different kinds of time-dependent temperature and stress distributions applied at the inner and outer surfaces of the shell. The problem is formulated by considering that the thermal properties vary as linear function of temperature that yield nonlinear governing equations. The problem is solved by applying Kirchhoff transformation along with integral transform technique. The numerical results of the field variables are shown in the different graphs to study the influence of temperature-dependent thermal parameters in various cases. It has been shown that the temperature-dependent effect is more prominent in case of stress distribution as compared to other fields and also the effect is significant in case of thermal shock applied at the two boundary surfaces of the spherical shell.

A study of the kinematic dynamo equation with time-dependent coefficients

NASA Technical Reports Server (NTRS)

Ko, Chung-Ming

1990-01-01

During an active star formation epoch the interstellar medium of a galaxy is in a hyperactive state, and the average turbulent velocity is higher than in the long periods between star formation epochs. The galactic magnetic field generated by dynamo action depends strongly on the turbulent velocity, so that generation of magnetic field should vary with star formation activity. This paper is a preliminary study of the kinematic dynamo equation with time-dependent coefficients simulating the time dependence of the star formation activities. Ko and Parker argued in a simple model that the thickness of the dynamo region is the most sensitive dynamo parameter. The present work shows that the effect of inflating the galactic disk suddenly is to transform a stationary magnetic field into a growing field while keeping the profile more or less intact. Plane wave solutions for a dynamo with power-law time-dependent parameters show that the field may decay first and then grow, and vice versa, which is quite different from a constant parameter dynamo.

Nature of size effects in compact models of field effect transistors

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

Torkhov, N. A., E-mail: trkf@mail.ru; Scientific-Research Institute of Semiconductor Devices, Tomsk 634050; Tomsk State University of Control Systems and Radioelectronics, Tomsk 634050

Investigations have shown that in the local approximation (for sizes L < 100 μm), AlGaN/GaN high electron mobility transistor (HEMT) structures satisfy to all properties of chaotic systems and can be described in the language of fractal geometry of fractional dimensions. For such objects, values of their electrophysical characteristics depend on the linear sizes of the examined regions, which explain the presence of the so-called size effects—dependences of the electrophysical and instrumental characteristics on the linear sizes of the active elements of semiconductor devices. In the present work, a relationship has been established for the linear model parameters of themore » equivalent circuit elements of internal transistors with fractal geometry of the heteroepitaxial structure manifested through a dependence of its relative electrophysical characteristics on the linear sizes of the examined surface areas. For the HEMTs, this implies dependences of their relative static (A/mm, mA/V/mm, Ω/mm, etc.) and microwave characteristics (W/mm) on the width d of the sink-source channel and on the number of sections n that leads to a nonlinear dependence of the retrieved parameter values of equivalent circuit elements of linear internal transistor models on n and d. Thus, it has been demonstrated that the size effects in semiconductors determined by the fractal geometry must be taken into account when investigating the properties of semiconductor objects on the levels less than the local approximation limit and designing and manufacturing field effect transistors. In general, the suggested approach allows a complex of problems to be solved on designing, optimizing, and retrieving the parameters of equivalent circuits of linear and nonlinear models of not only field effect transistors but also any arbitrary semiconductor devices with nonlinear instrumental characteristics.« less

Robust estimation procedure in panel data model

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

Shariff, Nurul Sima Mohamad; Hamzah, Nor Aishah

2014-06-19

The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectional dependence and outliers. Even though there are few methods that take into consideration the presence of cross sectional dependence in the panel, the methods may provide inconsistent parameter estimates and inferences when outliers occur in the panel. As such, an alternative method that is robust to outliers and cross sectional dependencemore » is introduced in this paper. The properties and construction of the confidence interval for the parameter estimates are also considered in this paper. The robustness of the procedure is investigated and comparisons are made to the existing method via simulation studies. Our results have shown that robust approach is able to produce an accurate and reliable parameter estimates under the condition considered.« less

A general model for attitude determination error analysis

NASA Technical Reports Server (NTRS)

Markley, F. Landis; Seidewitz, ED; Nicholson, Mark

1988-01-01

An overview is given of a comprehensive approach to filter and dynamics modeling for attitude determination error analysis. The models presented include both batch least-squares and sequential attitude estimation processes for both spin-stabilized and three-axis stabilized spacecraft. The discussion includes a brief description of a dynamics model of strapdown gyros, but it does not cover other sensor models. Model parameters can be chosen to be solve-for parameters, which are assumed to be estimated as part of the determination process, or consider parameters, which are assumed to have errors but not to be estimated. The only restriction on this choice is that the time evolution of the consider parameters must not depend on any of the solve-for parameters. The result of an error analysis is an indication of the contributions of the various error sources to the uncertainties in the determination of the spacecraft solve-for parameters. The model presented gives the uncertainty due to errors in the a priori estimates of the solve-for parameters, the uncertainty due to measurement noise, the uncertainty due to dynamic noise (also known as process noise or measurement noise), the uncertainty due to the consider parameters, and the overall uncertainty due to all these sources of error.

Isotopic dependence of fusion enhancement of various heavy ion systems using energy dependent Woods-Saxon potential

NASA Astrophysics Data System (ADS)

Gautam, Manjeet Singh

2015-01-01

In the present work, the fusion of symmetric and asymmetric projectile-target combinations are deeply analyzed within the framework of energy dependent Woods-Saxon potential model (EDWSP model) in conjunction with one dimensional Wong formula and the coupled channel code CCFULL. The neutron transfer channels and the inelastic surface excitations of collision partners are dominating mode of couplings and the coupling of relative motion of colliding nuclei to such relevant internal degrees of freedom produces a significant fusion enhancement at sub-barrier energies. It is quite interesting that the effects of dominant intrinsic degrees of freedom such as multi-phonon vibrational states, neutron transfer channels and proton transfer channels can be simulated by introducing the energy dependence in the nucleus-nucleus potential (EDWSP model). In the EDWSP model calculations, a wide range of diffuseness parameter ranging from a = 0.85 fm to a = 0.97 fm, which is much larger than a value (a = 0.65 fm) extracted from the elastic scattering data, is needed to reproduce sub-barrier fusion data. However, such diffuseness anomaly, which might be an artifact of some dynamical effects, has been resolved by trajectory fluctuation dissipation (TFD) model wherein the resulting nucleus-nucleus potential possesses normal diffuseness parameter.

Optical components damage parameters database system

NASA Astrophysics Data System (ADS)

Tao, Yizheng; Li, Xinglan; Jin, Yuquan; Xie, Dongmei; Tang, Dingyong

2012-10-01

Optical component is the key to large-scale laser device developed by one of its load capacity is directly related to the device output capacity indicators, load capacity depends on many factors. Through the optical components will damage parameters database load capacity factors of various digital, information technology, for the load capacity of optical components to provide a scientific basis for data support; use of business processes and model-driven approach, the establishment of component damage parameter information model and database systems, system application results that meet the injury test optical components business processes and data management requirements of damage parameters, component parameters of flexible, configurable system is simple, easy to use, improve the efficiency of the optical component damage test.

TRPM8-Dependent Dynamic Response in a Mathematical Model of Cold Thermoreceptor

PubMed Central

Olivares, Erick; Salgado, Simón; Maidana, Jean Paul; Herrera, Gaspar; Campos, Matías; Madrid, Rodolfo; Orio, Patricio

2015-01-01

Cold-sensitive nerve terminals (CSNTs) encode steady temperatures with regular, rhythmic temperature-dependent firing patterns that range from irregular tonic firing to regular bursting (static response). During abrupt temperature changes, CSNTs show a dynamic response, transiently increasing their firing frequency as temperature decreases and silencing when the temperature increases (dynamic response). To date, mathematical models that simulate the static response are based on two depolarizing/repolarizing pairs of membrane ionic conductance (slow and fast kinetics). However, these models fail to reproduce the dynamic response of CSNTs to rapid changes in temperature and notoriously they lack a specific cold-activated conductance such as the TRPM8 channel. We developed a model that includes TRPM8 as a temperature-dependent conductance with a calcium-dependent desensitization. We show by computer simulations that it appropriately reproduces the dynamic response of CSNTs from mouse cornea, while preserving their static response behavior. In this model, the TRPM8 conductance is essential to display a dynamic response. In agreement with experimental results, TRPM8 is also needed for the ongoing activity in the absence of stimulus (i.e. neutral skin temperature). Free parameters of the model were adjusted by an evolutionary optimization algorithm, allowing us to find different solutions. We present a family of possible parameters that reproduce the behavior of CSNTs under different temperature protocols. The detection of temperature gradients is associated to a homeostatic mechanism supported by the calcium-dependent desensitization. PMID:26426259

Voronoi cell patterns: Theoretical model and applications

NASA Astrophysics Data System (ADS)

González, Diego Luis; Einstein, T. L.

2011-11-01

We use a simple fragmentation model to describe the statistical behavior of the Voronoi cell patterns generated by a homogeneous and isotropic set of points in 1D and in 2D. In particular, we are interested in the distribution of sizes of these Voronoi cells. Our model is completely defined by two probability distributions in 1D and again in 2D, the probability to add a new point inside an existing cell and the probability that this new point is at a particular position relative to the preexisting point inside this cell. In 1D the first distribution depends on a single parameter while the second distribution is defined through a fragmentation kernel; in 2D both distributions depend on a single parameter. The fragmentation kernel and the control parameters are closely related to the physical properties of the specific system under study. We use our model to describe the Voronoi cell patterns of several systems. Specifically, we study the island nucleation with irreversible attachment, the 1D car-parking problem, the formation of second-level administrative divisions, and the pattern formed by the Paris Métro stations.

The four fixed points of scale invariant single field cosmological models

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

Xue, BingKan, E-mail: bxue@princeton.edu

2012-10-01

We introduce a new set of flow parameters to describe the time dependence of the equation of state and the speed of sound in single field cosmological models. A scale invariant power spectrum is produced if these flow parameters satisfy specific dynamical equations. We analyze the flow of these parameters and find four types of fixed points that encompass all known single field models. Moreover, near each fixed point we uncover new models where the scale invariance of the power spectrum relies on having simultaneously time varying speed of sound and equation of state. We describe several distinctive new modelsmore » and discuss constraints from strong coupling and superluminality.« less

Model-independent constraints on modified gravity from current data and from the Euclid and SKA future surveys

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

Taddei, Laura; Martinelli, Matteo; Amendola, Luca, E-mail: taddei@thphys.uni-heidelberg.de, E-mail: martinelli@lorentz.leidenuniv.nl, E-mail: amendola@thphys.uni-heidelberg.de

2016-12-01

The aim of this paper is to constrain modified gravity with redshift space distortion observations and supernovae measurements. Compared with a standard ΛCDM analysis, we include three additional free parameters, namely the initial conditions of the matter perturbations, the overall perturbation normalization, and a scale-dependent modified gravity parameter modifying the Poisson equation, in an attempt to perform a more model-independent analysis. First, we constrain the Poisson parameter Y (also called G {sub eff}) by using currently available f σ{sub 8} data and the recent SN catalog JLA. We find that the inclusion of the additional free parameters makes the constraintsmore » significantly weaker than when fixing them to the standard cosmological value. Second, we forecast future constraints on Y by using the predicted growth-rate data for Euclid and SKA missions. Here again we point out the weakening of the constraints when the additional parameters are included. Finally, we adopt as modified gravity Poisson parameter the specific Horndeski form, and use scale-dependent forecasts to build an exclusion plot for the Yukawa potential akin to the ones realized in laboratory experiments, both for the Euclid and the SKA surveys.« less

Comparative study of the requantization of the time-dependent mean field for the dynamics of nuclear pairing

NASA Astrophysics Data System (ADS)

Ni, Fang; Nakatsukasa, Takashi

2018-04-01

To describe quantal collective phenomena, it is useful to requantize the time-dependent mean-field dynamics. We study the time-dependent Hartree-Fock-Bogoliubov (TDHFB) theory for the two-level pairing Hamiltonian, and compare results of different quantization methods. The one constructing microscopic wave functions, using the TDHFB trajectories fulfilling the Einstein-Brillouin-Keller quantization condition, turns out to be the most accurate. The method is based on the stationary-phase approximation to the path integral. We also examine the performance of the collective model which assumes that the pairing gap parameter is the collective coordinate. The applicability of the collective model is limited for the nuclear pairing with a small number of single-particle levels, because the pairing gap parameter represents only a half of the pairing collective space.

Waldmeier's Rules in the Solar and Stellar Dynamos

NASA Astrophysics Data System (ADS)

Pipin, Valery; Kosovichev, Alexander

2015-08-01

The Waldmeier's rules [1] establish important empirical relations between the general parameters of magnetic cycles (such as the amplitude, period, growth rate and time profile) on the Sun and solar-type stars [2]. Variations of the magnetic cycle parameters depend on properties of the global dynamo processes operating in the stellar convection zones. We employ nonlinear mean-field axisymmetric dynamo models [3] and calculate of the magnetic cycle parameters, such as the dynamo cycle period, total magnetic and Poynting fluxes for the Sun and solar-type stars with rotational periods from 15 to 30 days. We consider two types of the dynamo models: 1) distributed (D-type) models employing the standard α - effect distributed in the whole convection zone, and 2) Babcock-Leighton (BL-type) models with a non-local α - effect. The dynamo models take into account the principal mechanisms of the nonlinear dynamo generation and saturation, including the magnetic helicity conservation, magnetic buoyancy effects, and the feedback on the angular momentum balance inside the convection zones. Both types of models show that the dynamo generated magnetic flux increases with the increase of the rotation rate. This corresponds to stronger brightness variations. The distributed dynamo model reproduces the observed dependence of the cycle period on the rotation rate for the Sun analogs better than the BL-type model. For the solar-type stars rotating more rapidly than the Sun we find dynamo regimes with multiple periods. Such stars with multiple cycles form a separate branch in the variability-rotation diagram.1. Waldmeier, M., Prognose für das nächste Sonnenfleckenmaximum, 1936, Astron. Nachrichten, 259,262. Soon,W.H., Baliunas,S.L., Zhang,Q.,An interpretation of cycle periods of stellar chromospheric activity, 1993, ApJ, 414,333. Pipin,V.V., Dependence of magnetic cycle parameters on period of rotation in nonlinear solar-type dynamos, 2015, astro-ph: 14125284

SEPARABLE FACTOR ANALYSIS WITH APPLICATIONS TO MORTALITY DATA

PubMed Central

Fosdick, Bailey K.; Hoff, Peter D.

2014-01-01

Human mortality data sets can be expressed as multiway data arrays, the dimensions of which correspond to categories by which mortality rates are reported, such as age, sex, country and year. Regression models for such data typically assume an independent error distribution or an error model that allows for dependence along at most one or two dimensions of the data array. However, failing to account for other dependencies can lead to inefficient estimates of regression parameters, inaccurate standard errors and poor predictions. An alternative to assuming independent errors is to allow for dependence along each dimension of the array using a separable covariance model. However, the number of parameters in this model increases rapidly with the dimensions of the array and, for many arrays, maximum likelihood estimates of the covariance parameters do not exist. In this paper, we propose a submodel of the separable covariance model that estimates the covariance matrix for each dimension as having factor analytic structure. This model can be viewed as an extension of factor analysis to array-valued data, as it uses a factor model to estimate the covariance along each dimension of the array. We discuss properties of this model as they relate to ordinary factor analysis, describe maximum likelihood and Bayesian estimation methods, and provide a likelihood ratio testing procedure for selecting the factor model ranks. We apply this methodology to the analysis of data from the Human Mortality Database, and show in a cross-validation experiment how it outperforms simpler methods. Additionally, we use this model to impute mortality rates for countries that have no mortality data for several years. Unlike other approaches, our methodology is able to estimate similarities between the mortality rates of countries, time periods and sexes, and use this information to assist with the imputations. PMID:25489353

Dynamic properties in the four-state haploid coupled discrete-time mutation-selection model with an infinite population limit

NASA Astrophysics Data System (ADS)

Lee, Kyu Sang; Gill, Wonpyong

2017-11-01

The dynamic properties, such as the crossing time and time-dependence of the relative density of the four-state haploid coupled discrete-time mutation-selection model, were calculated with the assumption that μ ij = μ ji , where μ ij denotes the mutation rate between the sequence elements, i and j. The crossing time for s = 0 and r 23 = r 42 = 1 in the four-state model became saturated at a large fitness parameter when r 12 > 1, was scaled as a power law in the fitness parameter when r 12 = 1, and diverged when the fitness parameter approached the critical fitness parameter when r 12 < 1, where r ij = μ ij / μ 14.

Determining fundamental properties of matter created in ultrarelativistic heavy-ion collisions

NASA Astrophysics Data System (ADS)

Novak, J.; Novak, K.; Pratt, S.; Vredevoogd, J.; Coleman-Smith, C. E.; Wolpert, R. L.

2014-03-01

Posterior distributions for physical parameters describing relativistic heavy-ion collisions, such as the viscosity of the quark-gluon plasma, are extracted through a comparison of hydrodynamic-based transport models to experimental results from 100AGeV+100AGeV Au +Au collisions at the Relativistic Heavy Ion Collider. By simultaneously varying six parameters and by evaluating several classes of observables, we are able to explore the complex intertwined dependencies of observables on model parameters. The methods provide a full multidimensional posterior distribution for the model output, including a range of acceptable values for each parameter, and reveal correlations between them. The breadth of observables and the number of parameters considered here go beyond previous studies in this field. The statistical tools, which are based upon Gaussian process emulators, are tested in detail and should be extendable to larger data sets and a higher number of parameters.

Four-parameter model for polarization-resolved rough-surface BRDF.

PubMed

Renhorn, Ingmar G E; Hallberg, Tomas; Bergström, David; Boreman, Glenn D

2011-01-17

A modeling procedure is demonstrated, which allows representation of polarization-resolved BRDF data using only four parameters: the real and imaginary parts of an effective refractive index with an added parameter taking grazing incidence absorption into account and an angular-scattering parameter determined from the BRDF measurement of a chosen angle of incidence, preferably close to normal incidence. These parameters allow accurate predictions of s- and p-polarized BRDF for a painted rough surface, over three decades of variation in BRDF magnitude. To characterize any particular surface of interest, the measurements required to determine these four parameters are the directional hemispherical reflectance (DHR) for s- and p-polarized input radiation and the BRDF at a selected angle of incidence. The DHR data describes the angular and polarization dependence, as well as providing the overall normalization constraint. The resulting model conserves energy and fulfills the reciprocity criteria.

Theory of concentration dependence in drag reduction by polymers and of the maximum drag reduction asymptote.

PubMed

Benzi, Roberto; Ching, Emily S C; Horesh, Nizan; Procaccia, Itamar

2004-02-20

A simple model of the effect of polymer concentration on the amount of drag reduction in turbulence is presented, simulated, and analyzed. The qualitative phase diagram of drag coefficient versus Reynolds number (Re) is recaptured in this model, including the theoretically elusive onset of drag reduction and the maximum drag reduction (MDR) asymptote. The Re-dependent drag and the MDR are analytically explained, and the dependence of the amount of drag on material parameters is rationalized.

Precision calculations for h → WW/ZZ → 4 fermions in the Two-Higgs-Doublet Model with Prophecy4f

NASA Astrophysics Data System (ADS)

Altenkamp, Lukas; Dittmaier, Stefan; Rzehak, Heidi

2018-03-01

We have calculated the next-to-leading-order electroweak and QCD corrections to the decay processes h → WW/ZZ → 4 fermions of the light CP-even Higgs boson h of various types of Two-Higgs-Doublet Models (Types I and II, "lepton-specific" and "flipped" models). The input parameters are defined in four different renormalization schemes, where parameters that are not directly accessible by experiments are defined in the \\overline{MS} scheme. Numerical results are presented for the corrections to partial decay widths for various benchmark scenarios previously motivated in the literature, where we investigate the dependence on the \\overline{MS} renormalization scale and on the choice of the renormalization scheme in detail. We find that it is crucial to be precise with these issues in parameter analyses, since parameter conversions between different schemes can involve sizeable or large corrections, especially in scenarios that are close to experimental exclusion limits or theoretical bounds. It even turns out that some renormalization schemes are not applicable in specific regions of parameter space. Our investigation of differential distributions shows that corrections beyond the Standard Model are mostly constant offsets induced by the mixing between the light and heavy CP-even Higgs bosons, so that differential analyses of h→4 f decay observables do not help to identify Two-Higgs-Doublet Models. Moreover, the decay widths do not significantly depend on the specific type of those models. The calculations are implemented in the public Monte Carlo generator Prophecy4f and ready for application.

Influence of anisotropy on anomalous scaling of a passive scalar advected by the Navier-Stokes velocity field.

PubMed

Jurcisinová, E; Jurcisin, M; Remecký, R

2009-10-01

The influence of weak uniaxial small-scale anisotropy on the stability of the scaling regime and on the anomalous scaling of the single-time structure functions of a passive scalar advected by the velocity field governed by the stochastic Navier-Stokes equation is investigated by the field theoretic renormalization group and operator-product expansion within one-loop approximation of a perturbation theory. The explicit analytical expressions for coordinates of the corresponding fixed point of the renormalization-group equations as functions of anisotropy parameters are found, the stability of the three-dimensional Kolmogorov-like scaling regime is demonstrated, and the dependence of the borderline dimension d(c) is an element of (2,3] between stable and unstable scaling regimes is found as a function of the anisotropy parameters. The dependence of the turbulent Prandtl number on the anisotropy parameters is also briefly discussed. The influence of weak small-scale anisotropy on the anomalous scaling of the structure functions of a passive scalar field is studied by the operator-product expansion and their explicit dependence on the anisotropy parameters is present. It is shown that the anomalous dimensions of the structure functions, which are the same (universal) for the Kraichnan model, for the model with finite time correlations of the velocity field, and for the model with the advection by the velocity field driven by the stochastic Navier-Stokes equation in the isotropic case, can be distinguished by the assumption of the presence of the small-scale anisotropy in the systems even within one-loop approximation. The corresponding comparison of the anisotropic anomalous dimensions for the present model with that obtained within the Kraichnan rapid-change model is done.

SPOTting Model Parameters Using a Ready-Made Python Package

NASA Astrophysics Data System (ADS)

Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz

2017-04-01

The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.

SPOTting Model Parameters Using a Ready-Made Python Package.

PubMed

Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz

2015-01-01

The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.

SPOTting Model Parameters Using a Ready-Made Python Package

PubMed Central

Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz

2015-01-01

The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function. PMID:26680783

Transitions induced by speed in self-propelled particles system with attractive interactions

NASA Astrophysics Data System (ADS)

Cambui, Dorilson. S.; Rosas, Alexandre

2018-05-01

In this work, we consider a system of self-propelled particles with attractive interactions in two dimensions. The model presents an order-disorder transition with the speed playing the role of the control parameter. In order to characterize the transition, we investigate the behavior of the order parameter and the Binder cumulant as a function of the speed. Our main finding is that the transition can be either continuous or discontinuous depending on two parameter of the model: the strength of the noise and the radius of attraction.

Mad cows and computer models: the U.S. response to BSE.

PubMed

Ackerman, Frank; Johnecheck, Wendy A

2008-01-01

The proportion of slaughtered cattle tested for BSE is much smaller in the U.S. than in Europe and Japan, leaving the U.S. heavily dependent on statistical models to estimate both the current prevalence and the spread of BSE. We examine the models relied on by USDA, finding that the prevalence model provides only a rough estimate, due to limited data availability. Reassuring forecasts from the model of the spread of BSE depend on the arbitrary constraint that worst-case values are assumed by only one of 17 key parameters at a time. In three of the six published scenarios with multiple worst-case parameter values, there is at least a 25% probability that BSE will spread rapidly. In public policy terms, reliance on potentially flawed models can be seen as a gamble that no serious BSE outbreak will occur. Statistical modeling at this level of abstraction, with its myriad, compound uncertainties, is no substitute for precautionary policies to protect public health against the threat of epidemics such as BSE.

Neuromodulation impact on nonlinear firing behavior of a reduced model motoneuron with the active dendrite

PubMed Central

Kim, Hojeong; Heckman, C. J.

2014-01-01

Neuromodulatory inputs from brainstem systems modulate the normal function of spinal motoneurons by altering the activation properties of persistent inward currents (PICs) in their dendrites. However, the effect of the PIC on firing outputs also depends on its location in the dendritic tree. To investigate the interaction between PIC neuromodulation and PIC location dependence, we used a two-compartment model that was biologically realistic in that it retains directional and frequency-dependent electrical coupling between the soma and the dendrites, as seen in multi-compartment models based on full anatomical reconstructions of motoneurons. Our two-compartment approach allowed us to systematically vary the coupling parameters between the soma and the dendrite to accurately reproduce the effect of location of the dendritic PIC on the generation of nonlinear (hysteretic) motoneuron firing patterns. Our results show that as a single parameter value for PIC activation was either increased or decreased by 20% from its default value, the solution space of the coupling parameter values for nonlinear firing outputs was drastically reduced by approximately 80%. As a result, the model tended to fire only in a linear mode at the majority of dendritic PIC sites. The same results were obtained when all parameters for the PIC activation simultaneously changed only by approximately ±10%. Our results suggest the democratization effect of neuromodulation: the neuromodulation by the brainstem systems may play a role in switching the motoneurons with PICs at different dendritic locations to a similar mode of firing by reducing the effect of the dendritic location of PICs on the firing behavior. PMID:25309410

Temperature dependence of photoluminescence peaks of porous silicon structures

NASA Astrophysics Data System (ADS)

Brunner, Róbert; Pinčík, Emil; Kučera, Michal; Greguš, Ján; Vojtek, Pavel; Zábudlá, Zuzana

2017-12-01

Evaluation of photoluminescence spectra of porous silicon (PS) samples prepared by electrochemical etching is presented. The samples were measured at temperatures 30, 70 and 150 K. Peak parameters (energy, intensity and width) were calculated. The PL spectrum was approximated by a set of Gaussian peaks. Their parameters were fixed using fitting a procedure in which the optimal number of peeks included into the model was estimated using the residuum of the approximation. The weak thermal dependence of the spectra indicates the strong influence of active defects.

Comparison of Multivariate Spatial Dependence Structures of DPIL and Flowmeter Hydraulic Conductivity Data Sets at the MADE Site

NASA Astrophysics Data System (ADS)

Xiao, B.; Haslauer, C. P.; Bohling, G. C.; Bárdossy, A.

2017-12-01

The spatial arrangement of hydraulic conductivity (K) determines water flow and solute transport behaviour in groundwater systems. This presentation demonstrates three advances over commonly used geostatistical methods by integrating measurements from novel measurement techniques and novel multivariate non-Gaussian dependence models: The spatial dependence structure of K was analysed using both data sets of K. Previously encountered similarities were confirmed in low-dimensional dependence. These similarities become less stringent and deviate more from symmetric Gaussian dependence in dimensions larger than two. Measurements of small and large K values are more uncertain than medium K values due to decreased sensitivity of the measurement devices at both ends of the K scale. Nevertheless, these measurements contain useful information that we include in the estimation of the marginal distribution and the spatial dependence structure as ``censored measurements'' that are estimated jointly without the common assumption of independence. The spatial dependence structure of the two data sets and their cross-covariances are used to infer the spatial dependence and the amount of the bias between the two data sets. By doing so, one spatial model for K is constructed that is used for simulation and that reflects the characteristics of both measurement techniques. The concept of the presented methodology is to use all available information for the estimation of a stochastic model of the primary parameter (K) at the highly heterogeneous Macrodispersion Experiment (MADE) site. The primary parameter has been measured by two independent measurement techniques whose sets of locations do not overlap. This site offers the unique opportunity of large quantities of measurements of K (31123 direct push injection logging based measurements and 2611 flowmeter based measurements). This improved dependence structure of K will be included into the estimated non-Gaussian dependence models and is expected to reproduce observed solute concentrations at the site better than existing dependence models of K.

Vacuum phase transition solves the H0 tension

NASA Astrophysics Data System (ADS)

Di Valentino, Eleonora; Linder, Eric V.; Melchiorri, Alessandro

2018-02-01

Taking the Planck cosmic microwave background data and the more direct Hubble constant measurement data as unaffected by systematic offsets, the values of the Hubble constant H0 interpreted within the Λ CDM cosmological constant and cold dark matter cosmological model are in ˜3.3 σ tension. We show that the Parker vacuum metamorphosis (VM) model, physically motivated by quantum gravitational effects and with the same number of parameters as Λ CDM , can remove the H0 tension and can give an improved fit to data (up to a mean Δ χ2=-7.5 ). It also ameliorates tensions with weak lensing data and the high redshift Lyman alpha forest data. Considering Bayesian evidence, we found in the case of the Planck data set alone positive evidence for a VM model against a cosmological constant both in the six- and nine-parameter framework. When the R16 data set is also considered, we found a strong evidence for the VM model against a cosmological constant in nine-parameter space. We separately consider a scale-dependent scaling of the gravitational lensing amplitude, such as provided by modified gravity, neutrino mass, or cold dark energy, motivated by the somewhat different cosmological parameter estimates for low and high CMB multipoles. We find that no such scale dependence is preferred.

Rough parameter dependence in climate models and the role of Ruelle-Pollicott resonances.

PubMed

Chekroun, Mickaël David; Neelin, J David; Kondrashov, Dmitri; McWilliams, James C; Ghil, Michael

2014-02-04

Despite the importance of uncertainties encountered in climate model simulations, the fundamental mechanisms at the origin of sensitive behavior of long-term model statistics remain unclear. Variability of turbulent flows in the atmosphere and oceans exhibits recurrent large-scale patterns. These patterns, while evolving irregularly in time, manifest characteristic frequencies across a large range of time scales, from intraseasonal through interdecadal. Based on modern spectral theory of chaotic and dissipative dynamical systems, the associated low-frequency variability may be formulated in terms of Ruelle-Pollicott (RP) resonances. RP resonances encode information on the nonlinear dynamics of the system, and an approach for estimating them--as filtered through an observable of the system--is proposed. This approach relies on an appropriate Markov representation of the dynamics associated with a given observable. It is shown that, within this representation, the spectral gap--defined as the distance between the subdominant RP resonance and the unit circle--plays a major role in the roughness of parameter dependences. The model statistics are the most sensitive for the smallest spectral gaps; such small gaps turn out to correspond to regimes where the low-frequency variability is more pronounced, whereas autocorrelations decay more slowly. The present approach is applied to analyze the rough parameter dependence encountered in key statistics of an El-Niño-Southern Oscillation model of intermediate complexity. Theoretical arguments, however, strongly suggest that such links between model sensitivity and the decay of correlation properties are not limited to this particular model and could hold much more generally.

Rough parameter dependence in climate models and the role of Ruelle-Pollicott resonances

PubMed Central

Chekroun, Mickaël David; Neelin, J. David; Kondrashov, Dmitri; McWilliams, James C.; Ghil, Michael

2014-01-01

Despite the importance of uncertainties encountered in climate model simulations, the fundamental mechanisms at the origin of sensitive behavior of long-term model statistics remain unclear. Variability of turbulent flows in the atmosphere and oceans exhibits recurrent large-scale patterns. These patterns, while evolving irregularly in time, manifest characteristic frequencies across a large range of time scales, from intraseasonal through interdecadal. Based on modern spectral theory of chaotic and dissipative dynamical systems, the associated low-frequency variability may be formulated in terms of Ruelle-Pollicott (RP) resonances. RP resonances encode information on the nonlinear dynamics of the system, and an approach for estimating them—as filtered through an observable of the system—is proposed. This approach relies on an appropriate Markov representation of the dynamics associated with a given observable. It is shown that, within this representation, the spectral gap—defined as the distance between the subdominant RP resonance and the unit circle—plays a major role in the roughness of parameter dependences. The model statistics are the most sensitive for the smallest spectral gaps; such small gaps turn out to correspond to regimes where the low-frequency variability is more pronounced, whereas autocorrelations decay more slowly. The present approach is applied to analyze the rough parameter dependence encountered in key statistics of an El-Niño–Southern Oscillation model of intermediate complexity. Theoretical arguments, however, strongly suggest that such links between model sensitivity and the decay of correlation properties are not limited to this particular model and could hold much more generally. PMID:24443553

An asymptotic solution to a passive biped walker model

NASA Astrophysics Data System (ADS)

Yudaev, Sergey A.; Rachinskii, Dmitrii; Sobolev, Vladimir A.

2017-02-01

We consider a simple model of a passive dynamic biped robot walker with point feet and legs without knee. The model is a switched system, which includes an inverted double pendulum. Robot’s gait and its stability depend on parameters such as the slope of the ramp, the length of robot’s legs, and the mass distribution along the legs. We present an asymptotic solution of the model. The first correction to the zero order approximation is shown to agree with the numerical solution for a limited parameter range.

Constraints on running vacuum model with H(z) and f σ8

NASA Astrophysics Data System (ADS)

Geng, Chao-Qiang; Lee, Chung-Chi; Yin, Lu

2017-08-01

We examine the running vacuum model with Λ (H) = 3 ν H2 + Λ0, where ν is the model parameter and Λ0 is the cosmological constant. From the data of the cosmic microwave background radiation, weak lensing and baryon acoustic oscillation along with the time dependent Hubble parameter H(z) and weighted linear growth f (z)σ8(z) measurements, we find that ν=(1.37+0.72-0.95)× 10-4 with the best fitted χ2 value slightly smaller than that in the ΛCDM model.

Level density inputs in nuclear reaction codes and the role of the spin cutoff parameter

DOE PAGES

Voinov, A. V.; Grimes, S. M.; Brune, C. R.; ...

2014-09-03

Here, the proton spectrum from the 57Fe(α,p) reaction has been measured and analyzed with the Hauser-Feshbach model of nuclear reactions. Different input level density models have been tested. It was found that the best description is achieved with either Fermi-gas or constant temperature model functions obtained by fitting them to neutron resonance spacing and to discrete levels and using the spin cutoff parameter with much weaker excitation energy dependence than it is predicted by the Fermi-gas model.

Multiple robustness in factorized likelihood models.

PubMed

Molina, J; Rotnitzky, A; Sued, M; Robins, J M

2017-09-01

We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finite-dimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.

The effect of the magnetic nanoparticle's size dependence of the relaxation time constant on the specific loss power of magnetic nanoparticle hyperthermia

NASA Astrophysics Data System (ADS)

Harabech, Mariem; Leliaert, Jonathan; Coene, Annelies; Crevecoeur, Guillaume; Van Roost, Dirk; Dupré, Luc

2017-03-01

Magnetic nanoparticle hyperthermia is a cancer treatment in which magnetic nanoparticles (MNPs) are subjected to an alternating magnetic field to induce heat in the tumor. The generated heat of MNPs is characterized by the specific loss power (SLP) due to relaxation phenomena of the MNP. Up to now, several models have been proposed to predict the SLP, one of which is the Linear Response Theory. One parameter in this model is the relaxation time constant. In this contribution, we employ a macrospin model based on the Landau-Lifshitz-Gilbert equation to investigate the relation between the Gilbert damping parameter and the relaxation time constant. This relaxation time has a pre-factor τ0 which is often taken as a fixed value ranging between 10-8 and 10-12 s. However, in reality it has small size dependence. Here, the influence of this size dependence on the calculation of the SLP is demonstrated, consequently improving the accuracy of this estimate.

Stability margin of linear systems with parameters described by fuzzy numbers.

PubMed

Husek, Petr

2011-10-01

This paper deals with the linear systems with uncertain parameters described by fuzzy numbers. The problem of determining the stability margin of those systems with linear affine dependence of the coefficients of a characteristic polynomial on system parameters is studied. Fuzzy numbers describing the system parameters are allowed to be characterized by arbitrary nonsymmetric membership functions. An elegant solution, graphical in nature, based on generalization of the Tsypkin-Polyak plot is presented. The advantage of the presented approach over the classical robust concept is demonstrated on a control of the Fiat Dedra engine model and a control of the quarter car suspension model.

Bayesian hierarchical model for large-scale covariance matrix estimation.

PubMed

Zhu, Dongxiao; Hero, Alfred O

2007-12-01

Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.

Identification of a thermo-elasto-viscoplastic behavior law for the simulation of thermoforming of high impact polystyrene

NASA Astrophysics Data System (ADS)

Atmani, O.; Abbès, B.; Abbès, F.; Li, Y. M.; Batkam, S.

2018-05-01

Thermoforming of high impact polystyrene sheets (HIPS) requires technical knowledge on material behavior, mold type, mold material, and process variables. Accurate thermoforming simulations are needed in the optimization process. Determining the behavior of the material under thermoforming conditions is one of the key parameters for an accurate simulation. The aim of this work is to identify the thermomechanical behavior of HIPS in the thermoforming conditions. HIPS behavior is highly dependent on temperature and strain rate. In order to reproduce the behavior of such material, a thermo-elasto-viscoplastic constitutive law was implement in the finite element code ABAQUS. The proposed model parameters are considered as thermo-dependent. The strain-dependence effect is introduced using Prony series. Tensile tests were carried out at different temperatures and strain rates. The material parameters were then identified using a NSGA-II algorithm. To validate the rheological model, experimental blowing tests were carried out on a thermoforming pilot machine. To compare the numerical results with the experimental ones the thickness distribution and the bubble shape were investigated.

Sloppy-model universality class and the Vandermonde matrix.

PubMed

Waterfall, Joshua J; Casey, Fergal P; Gutenkunst, Ryan N; Brown, Kevin S; Myers, Christopher R; Brouwer, Piet W; Elser, Veit; Sethna, James P

2006-10-13

In a variety of contexts, physicists study complex, nonlinear models with many unknown or tunable parameters to explain experimental data. We explain why such systems so often are sloppy: the system behavior depends only on a few "stiff" combinations of the parameters and is unchanged as other "sloppy" parameter combinations vary by orders of magnitude. We observe that the eigenvalue spectra for the sensitivity of sloppy models have a striking, characteristic form with a density of logarithms of eigenvalues which is roughly constant over a large range. We suggest that the common features of sloppy models indicate that they may belong to a common universality class. In particular, we motivate focusing on a Vandermonde ensemble of multiparameter nonlinear models and show in one limit that they exhibit the universal features of sloppy models.

A new Bayesian recursive technique for parameter estimation

NASA Astrophysics Data System (ADS)

Kaheil, Yasir H.; Gill, M. Kashif; McKee, Mac; Bastidas, Luis

2006-08-01

The performance of any model depends on how well its associated parameters are estimated. In the current application, a localized Bayesian recursive estimation (LOBARE) approach is devised for parameter estimation. The LOBARE methodology is an extension of the Bayesian recursive estimation (BARE) method. It is applied in this paper on two different types of models: an artificial intelligence (AI) model in the form of a support vector machine (SVM) application for forecasting soil moisture and a conceptual rainfall-runoff (CRR) model represented by the Sacramento soil moisture accounting (SAC-SMA) model. Support vector machines, based on statistical learning theory (SLT), represent the modeling task as a quadratic optimization problem and have already been used in various applications in hydrology. They require estimation of three parameters. SAC-SMA is a very well known model that estimates runoff. It has a 13-dimensional parameter space. In the LOBARE approach presented here, Bayesian inference is used in an iterative fashion to estimate the parameter space that will most likely enclose a best parameter set. This is done by narrowing the sampling space through updating the "parent" bounds based on their fitness. These bounds are actually the parameter sets that were selected by BARE runs on subspaces of the initial parameter space. The new approach results in faster convergence toward the optimal parameter set using minimum training/calibration data and fewer sets of parameter values. The efficacy of the localized methodology is also compared with the previously used BARE algorithm.

Application of Bayesian model averaging to measurements of the primordial power spectrum

NASA Astrophysics Data System (ADS)

Parkinson, David; Liddle, Andrew R.

2010-11-01

Cosmological parameter uncertainties are often stated assuming a particular model, neglecting the model uncertainty, even when Bayesian model selection is unable to identify a conclusive best model. Bayesian model averaging is a method for assessing parameter uncertainties in situations where there is also uncertainty in the underlying model. We apply model averaging to the estimation of the parameters associated with the primordial power spectra of curvature and tensor perturbations. We use CosmoNest and MultiNest to compute the model evidences and posteriors, using cosmic microwave data from WMAP, ACBAR, BOOMERanG, and CBI, plus large-scale structure data from the SDSS DR7. We find that the model-averaged 95% credible interval for the spectral index using all of the data is 0.940

Maxwell boundary condition and velocity dependent accommodation coefficient

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

Struchtrup, Henning, E-mail: struchtr@uvic.ca

2013-11-15

A modification of Maxwell's boundary condition for the Boltzmann equation is developed that allows to incorporate velocity dependent accommodation coefficients into the microscopic description. As a first example, it is suggested to consider the wall-particle interaction as a thermally activated process with three parameters. A simplified averaging procedure leads to jump and slip boundary conditions for hydrodynamics. Coefficients for velocity slip, temperature jump, and thermal transpiration flow are identified and compared with those resulting from the original Maxwell model and the Cercignani-Lampis model. An extension of the model leads to temperature dependent slip and jump coefficients.

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.

Experimental Study of Temperature-Dependence Laws of Non-Voigt Absorption Line Shape Parameters

NASA Astrophysics Data System (ADS)

Wilzewski, Jonas; Birk, Manfred; Loos, Joep; Wagner, Georg

2017-06-01

To improve the understanding of temperature-dependence laws of spectral line shape parameters, spectra of the ν_3 rovibrational band of CO_2 perturbed by 10, 30, 100, 300 and 1000 mbar of N_2 were measured at nine temperatures between 190 K and 330 K using a 22 cm long single-pass absorption cell in a Bruker IFS125 HR Fourier Transform spectrometer. The spectra were fitted employing a quadratic speed-dependent hard collision model in the Hartmann-Tran implementation extended to account for line mixing in the Rosenkranz approximation by means of a multispectrum fitting approach developed at DLR This enables high accuracy parameter retrievals to reproduce the spectra down to noise level and we will present the behavior of line widths, shifts, speed-dependence-, collisional narrowing- and line mixing-parameters over this 140 K temperature range. Tran et al. JQSRT 129, 199-203 (2013); JQSRT 134, 104 (2014). Loos et al., 2014; http://doi.org/10.5281/zenodo.11156. Ngo et al. JQSRT 29, 89-100 (2013); JQSRT 134, 105 (2014).

Analytical results for a stochastic model of gene expression with arbitrary partitioning of proteins

NASA Astrophysics Data System (ADS)

Tschirhart, Hugo; Platini, Thierry

2018-05-01

In biophysics, the search for analytical solutions of stochastic models of cellular processes is often a challenging task. In recent work on models of gene expression, it was shown that a mapping based on partitioning of Poisson arrivals (PPA-mapping) can lead to exact solutions for previously unsolved problems. While the approach can be used in general when the model involves Poisson processes corresponding to creation or degradation, current applications of the method and new results derived using it have been limited to date. In this paper, we present the exact solution of a variation of the two-stage model of gene expression (with time dependent transition rates) describing the arbitrary partitioning of proteins. The methodology proposed makes full use of the PPA-mapping by transforming the original problem into a new process describing the evolution of three biological switches. Based on a succession of transformations, the method leads to a hierarchy of reduced models. We give an integral expression of the time dependent generating function as well as explicit results for the mean, variance, and correlation function. Finally, we discuss how results for time dependent parameters can be extended to the three-stage model and used to make inferences about models with parameter fluctuations induced by hidden stochastic variables.

Fractional time-dependent apparent viscosity model for semisolid foodstuffs

NASA Astrophysics Data System (ADS)

Yang, Xu; Chen, Wen; Sun, HongGuang

2017-10-01

The difficulty in the description of thixotropic behaviors in semisolid foodstuffs is the time dependent nature of apparent viscosity under constant shear rate. In this study, we propose a novel theoretical model via fractional derivative to address the high demand by industries. The present model adopts the critical parameter of fractional derivative order α to describe the corresponding time-dependent thixotropic behavior. More interestingly, the parameter α provides a quantitative insight into discriminating foodstuffs. With the re-exploration of three groups of experimental data (tehineh, balangu, and natillas), the proposed methodology is validated in good applicability and efficiency. The results show that the present fractional apparent viscosity model performs successfully for tested foodstuffs in the shear rate range of 50-150 s^{ - 1}. The fractional order α decreases with the increase of temperature at low temperature, below 50 °C, but increases with growing shear rate. While the ideal initial viscosity k decreases with the increase of temperature, shear rate, and ingredient content. It is observed that the magnitude of α is capable of characterizing the thixotropy of semisolid foodstuffs.

Postseismic viscoelastic deformation and stress. Part 2: Stress theory and computation; dependence of displacement, strain, and stress on fault parameters

NASA Technical Reports Server (NTRS)

Cohen, S. C.

1979-01-01

A viscoelastic model for deformation and stress associated with earthquakes is reported. The model consists of a rectangular dislocation (strike slip fault) in a viscoelastic layer (lithosphere) lying over a viscoelastic half space (asthenosphere). The time dependent surface stresses are analyzed. The model predicts that near the fault a significant fraction of the stress that was reduced during the earthquake is recovered by viscoelastic softening of the lithosphere. By contrast, the strain shows very little change near the fault. The model also predicts that the stress changes associated with asthenospheric flow extend over a broader region than those associated with lithospheric relaxation even though the peak value is less. The dependence of the displacements, stresses on fault parameters studied. Peak values of strain and stress drop increase with increasing fault height and decrease with fault depth. Under many circumstances postseismic strains and stresses show an increase with decreasing depth to the lithosphere-asthenosphere boundary. Values of the strain and stress at distant points from the fault increase with fault area but are relatively insensitive to fault depth.

A simple framework for modelling the dependence of bulk Comptonization by turbulence on accretion disc parameters

NASA Astrophysics Data System (ADS)

Kaufman, J.; Blaes, O. M.; Hirose, S.

2018-06-01

Warm Comptonization models for the soft X-ray excess in active galactic nuclei (AGN) do not self-consistently explain the relationship between the Comptonizing medium and the underlying accretion disc. Because of this, they cannot directly connect the fitted Comptonization temperatures and optical depths to accretion disc parameters. Since bulk velocities exceed thermal velocities in highly radiation pressure dominated discs, in these systems bulk Comptonization by turbulence may provide a physical basis in the disc itself for warm Comptonization models. We model the dependence of bulk Comptonization on fundamental accretion disc parameters, such as mass, luminosity, radius, spin, inner boundary condition, and α. In addition to constraining warm Comptonization models, our model can help distinguish contributions from bulk Comptonization to the soft X-ray excess from those due to other physical mechanisms, such as absorption and reflection. By linking the time variability of bulk Comptonization to fluctuations in the disc vertical structure due to magnetorotational instability (MRI) turbulence, our results show that observations of the soft X-ray excess can be used to study disc turbulence in the radiation pressure dominated regime. Because our model connects bulk Comptonization to 1D vertical structure temperature profiles in a physically intuitive way, it will be useful for understanding this effect in future simulations run in new regimes.

Toward a general physiologically-based pharmacokinetic model for intravenously injected nanoparticles.

PubMed

Carlander, Ulrika; Li, Dingsheng; Jolliet, Olivier; Emond, Claude; Johanson, Gunnar

2016-01-01

To assess the potential toxicity of nanoparticles (NPs), information concerning their uptake and disposition (biokinetics) is essential. Experience with industrial chemicals and pharmaceutical drugs reveals that biokinetics can be described and predicted accurately by physiologically-based pharmacokinetic (PBPK) modeling. The nano PBPK models developed to date all concern a single type of NP. Our aim here was to extend a recent model for pegylated polyacrylamide NP in order to develop a more general PBPK model for nondegradable NPs injected intravenously into rats. The same model and physiological parameters were applied to pegylated polyacrylamide, uncoated polyacrylamide, gold, and titanium dioxide NPs, whereas NP-specific parameters were chosen on the basis of the best fit to the experimental time-courses of NP accumulation in various tissues. Our model describes the biokinetic behavior of all four types of NPs adequately, despite extensive differences in this behavior as well as in their physicochemical properties. In addition, this simulation demonstrated that the dose exerts a profound impact on the biokinetics, since saturation of the phagocytic cells at higher doses becomes a major limiting step. The fitted model parameters that were most dependent on NP type included the blood:tissue coefficients of permeability and the rate constant for phagocytic uptake. Since only four types of NPs with several differences in characteristics (dose, size, charge, shape, and surface properties) were used, the relationship between these characteristics and the NP-dependent model parameters could not be elucidated and more experimental data are required in this context. In this connection, intravenous biodistribution studies with associated PBPK analyses would provide the most insight.

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.

Growth-rate dependent global effects on gene expression in bacteria

PubMed Central

Klumpp, Stefan; Zhang, Zhongge; Hwa, Terence

2010-01-01

Summary Bacterial gene expression depends not only on specific regulations but also directly on bacterial growth, because important global parameters such as the abundance of RNA polymerases and ribosomes are all growth-rate dependent. Understanding these global effects is necessary for a quantitative understanding of gene regulation and for the robust design of synthetic genetic circuits. The observed growth-rate dependence of constitutive gene expression can be explained by a simple model using the measured growth-rate dependence of the relevant cellular parameters. More complex growth dependences for genetic circuits involving activators, repressors and feedback control were analyzed, and salient features were verified experimentally using synthetic circuits. The results suggest a novel feedback mechanism mediated by general growth-dependent effects and not requiring explicit gene regulation, if the expressed protein affects cell growth. This mechanism can lead to growth bistability and promote the acquisition of important physiological functions such as antibiotic resistance and tolerance (persistence). PMID:20064380

From global to local: exploring the relationship between parameters and behaviors in models of electrical excitability.

PubMed

Fletcher, Patrick; Bertram, Richard; Tabak, Joel

2016-06-01

Models of electrical activity in excitable cells involve nonlinear interactions between many ionic currents. Changing parameters in these models can produce a variety of activity patterns with sometimes unexpected effects. Further more, introducing new currents will have different effects depending on the initial parameter set. In this study we combined global sampling of parameter space and local analysis of representative parameter sets in a pituitary cell model to understand the effects of adding K (+) conductances, which mediate some effects of hormone action on these cells. Global sampling ensured that the effects of introducing K (+) conductances were captured across a wide variety of contexts of model parameters. For each type of K (+) conductance we determined the types of behavioral transition that it evoked. Some transitions were counterintuitive, and may have been missed without the use of global sampling. In general, the wide range of transitions that occurred when the same current was applied to the model cell at different locations in parameter space highlight the challenge of making accurate model predictions in light of cell-to-cell heterogeneity. Finally, we used bifurcation analysis and fast/slow analysis to investigate why specific transitions occur in representative individual models. This approach relies on the use of a graphics processing unit (GPU) to quickly map parameter space to model behavior and identify parameter sets for further analysis. Acceleration with modern low-cost GPUs is particularly well suited to exploring the moderate-sized (5-20) parameter spaces of excitable cell and signaling models.

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

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

Domanskyi, Sergii; Schilling, Joshua E.; Privman, Vladimir, E-mail: privman@clarkson.edu

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model wemore » describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of “stiff” equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.« less

Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models

PubMed Central

Burr, Tom

2013-01-01

Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the full data and is therefore computationally feasible. One technical challenge with ABC is that the quality of the approximation to the posterior distribution of model parameters depends on the user-chosen summary statistics. In this paper, the user requirement to choose effective summary statistics in order to accurately estimate the posterior distribution of model parameters is investigated and illustrated by example, using a model and corresponding real data of mitochondrial DNA population dynamics. We show that for some choices of summary statistics, the posterior distribution of model parameters is closely approximated and for other choices of summary statistics, the posterior distribution is not closely approximated. A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example. PMID:24288668

Selecting summary statistics in approximate Bayesian computation for calibrating stochastic models.

PubMed

Burr, Tom; Skurikhin, Alexei

2013-01-01

Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the "go-to" option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the full data and is therefore computationally feasible. One technical challenge with ABC is that the quality of the approximation to the posterior distribution of model parameters depends on the user-chosen summary statistics. In this paper, the user requirement to choose effective summary statistics in order to accurately estimate the posterior distribution of model parameters is investigated and illustrated by example, using a model and corresponding real data of mitochondrial DNA population dynamics. We show that for some choices of summary statistics, the posterior distribution of model parameters is closely approximated and for other choices of summary statistics, the posterior distribution is not closely approximated. A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example.

In Vivo Fluorescence Resonance Energy Transfer Imaging for Targeted Anti-Cancer Drug Delivery Kinetics

NASA Astrophysics Data System (ADS)

Webb, Kevin; Gaind, Vaibhav; Tsai, Hsiaorho; Bentz, Brian; Chelvam, Venkatesh; Low, Philip

2012-02-01

We describe an approach for the evaluation of targeted anti-cancer drug delivery in vivo. The method emulates the drug release and activation process through acceptor release from a targeted donor-acceptor pair that exhibits fluorescence resonance energy transfer (FRET). In this case, folate targeting of the cancer cells is used - 40 % of all human cancers, including ovarian, lung, breast, kidney, brain and colon cancer, over-express folate receptors. We demonstrate the reconstruction of the spatially-dependent FRET parameters in a mouse model and in tissue phantoms. The FRET parameterization is incorporated into a source for a diffusion equation model for photon transport in tissue, in a variant of optical diffusion tomography (ODT) called FRET-ODT. In addition to the spatially-dependent tissue parameters in the diffusion model (absorption and diffusion coefficients), the FRET parameters (donor-acceptor distance and yield) are imaged as a function of position. Modulated light measurements are made with various laser excitation positions and a gated camera. More generally, our method provides a new vehicle for studying disease at the molecular level by imaging FRET parameters in deep tissue, and allows the nanometer FRET ruler to be utilized in deep tissue.

Tuning a physically-based model of the air-sea gas transfer velocity

NASA Astrophysics Data System (ADS)

Jeffery, C. D.; Robinson, I. S.; Woolf, D. K.

Air-sea gas transfer velocities are estimated for one year using a 1-D upper-ocean model (GOTM) and a modified version of the NOAA-COARE transfer velocity parameterization. Tuning parameters are evaluated with the aim of bringing the physically based NOAA-COARE parameterization in line with current estimates, based on simple wind-speed dependent models derived from bomb-radiocarbon inventories and deliberate tracer release experiments. We suggest that A = 1.3 and B = 1.0, for the sub-layer scaling parameter and the bubble mediated exchange, respectively, are consistent with the global average CO 2 transfer velocity k. Using these parameters and a simple 2nd order polynomial approximation, with respect to wind speed, we estimate a global annual average k for CO 2 of 16.4 ± 5.6 cm h -1 when using global mean winds of 6.89 m s -1 from the NCEP/NCAR Reanalysis 1 1954-2000. The tuned model can be used to predict the transfer velocity of any gas, with appropriate treatment of the dependence on molecular properties including the strong solubility dependence of bubble-mediated transfer. For example, an initial estimate of the global average transfer velocity of DMS (a relatively soluble gas) is only 11.9 cm h -1 whilst for less soluble methane the estimate is 18.0 cm h -1.

Study the Cyclic Plasticity Behavior of 508 LAS under Constant, Variable and Grid-Load-Following Loading Cycles for Fatigue Evaluation of PWR Components

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

Mohanty, Subhasish; Barua, Bipul; Soppet, William K.

This report provides an update of an earlier assessment of environmentally assisted fatigue for components in light water reactors. This report is a deliverable in September 2016 under the work package for environmentally assisted fatigue under DOE’s Light Water Reactor Sustainability program. In an April 2016 report, we presented a detailed thermal-mechanical stress analysis model for simulating the stress-strain state of a reactor pressure vessel and its nozzles under grid-load-following conditions. In this report, we provide stress-controlled fatigue test data for 508 LAS base metal alloy under different loading amplitudes (constant, variable, and random grid-load-following) and environmental conditions (in airmore » or pressurized water reactor coolant water at 300°C). Also presented is a cyclic plasticity-based analytical model that can simultaneously capture the amplitude and time dependency of the component behavior under fatigue loading. Results related to both amplitude-dependent and amplitude-independent parameters are presented. The validation results for the analytical/mechanistic model are discussed. This report provides guidance for estimating time-dependent, amplitude-independent parameters related to material behavior under different service conditions. The developed mechanistic models and the reported material parameters can be used to conduct more accurate fatigue and ratcheting evaluation of reactor components.« less

Hyper- and viscoelastic modeling of needle and brain tissue interaction.

PubMed

Lehocky, Craig A; Yixing Shi; Riviere, Cameron N

2014-01-01

Deep needle insertion into brain is important for both diagnostic and therapeutic clinical interventions. We have developed an automated system for robotically steering flexible needles within the brain to improve targeting accuracy. In this work, we have developed a finite element needle-tissue interaction model that allows for the investigation of safe parameters for needle steering. The tissue model implemented contains both hyperelastic and viscoelastic properties to simulate the instantaneous and time-dependent responses of brain tissue. Several needle models were developed with varying parameters to study the effects of the parameters on tissue stress, strain and strain rate during needle insertion and rotation. The parameters varied include needle radius, bevel angle, bevel tip fillet radius, insertion speed, and rotation speed. The results will guide the design of safe needle tips and control systems for intracerebral needle steering.

The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

NASA Astrophysics Data System (ADS)

Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter

2018-02-01

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. About 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). The relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.

Model and observation comparison of the universal time and IMF by dependence of the ionospheric polar hole

NASA Technical Reports Server (NTRS)

Sojka, J. J.; Schunk, R. W.; Hoegy, W. R.; Grebowsky, J. M.

1991-01-01

The polar ionospheric F-region often exhibits regions of marked density depletion. These depletions have been observed by a variety of polar orbiting ionospheric satellites over a full range of solar cycle, season, magnetic activity, and universal time (UT). An empirical model of these observations has recently been developed to describe the polar depletion dependence on these parameters. Specifically, the dependence has been defined as a function of F10.7 (solar), summer or winter, Kp (magnetic), and UT. Polar cap depletions have also been predicted /1, 2/ and are, hence, present in physical models of the high latitude ionosphere. Using the Utah State University Time Dependent Ionospheric Model (TDIM) the predicted polar depletion characteristics are compared with those described by the above empirical model. In addition, the TDIM is used to predict the IMF By dependence of the polar hole feature.

An action potential-driven model of soleus muscle activation dynamics for locomotor-like movements

NASA Astrophysics Data System (ADS)

Kim, Hojeong; Sandercock, Thomas G.; Heckman, C. J.

2015-08-01

Objective. The goal of this study was to develop a physiologically plausible, computationally robust model for muscle activation dynamics (A(t)) under physiologically relevant excitation and movement. Approach. The interaction of excitation and movement on A(t) was investigated comparing the force production between a cat soleus muscle and its Hill-type model. For capturing A(t) under excitation and movement variation, a modular modeling framework was proposed comprising of three compartments: (1) spikes-to-[Ca2+]; (2) [Ca2+]-to-A; and (3) A-to-force transformation. The individual signal transformations were modeled based on physiological factors so that the parameter values could be separately determined for individual modules directly based on experimental data. Main results. The strong dependency of A(t) on excitation frequency and muscle length was found during both isometric and dynamically-moving contractions. The identified dependencies of A(t) under the static and dynamic conditions could be incorporated in the modular modeling framework by modulating the model parameters as a function of movement input. The new modeling approach was also applicable to cat soleus muscles producing waveforms independent of those used to set the model parameters. Significance. This study provides a modeling framework for spike-driven muscle responses during movement, that is suitable not only for insights into molecular mechanisms underlying muscle behaviors but also for large scale simulations.

Theoretical analysis of ozone generation by pulsed dielectric barrier discharge in oxygen

NASA Astrophysics Data System (ADS)

Wei, L. S.; Zhou, J. H.; Wang, Z. H.; Cen, K. F.

2007-08-01

The use of very short high-voltage pulses combined with a dielectric layer results in high-energy electrons that dissociate oxygen molecules into atoms, which are a prerequisite for the subsequent production of ozone by collisions with oxygen molecules and third particles. The production of ozone depends on both the electrical and the physical parameters. For ozone generation by pulsed dielectric barrier discharge in oxygen, a mathematical model, which describes the relation between ozone concentration and these parameters that are of importance in its design, is developed according to dimensional analysis theory. A formula considering the ozone destruction factor is derived for predicting the characteristics of the ozone generation, within the range of the corona inception voltage to the gap breakdown voltage. The trend showing the dependence of the concentration of ozone in oxygen on these parameters generally agrees with the experimental results, thus confirming the validity of the mathematical model.

On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship.

PubMed

Koyama, Shinsuke

2015-07-01

We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that the variance and the mean of interspike intervals are related by a power function characterized by two parameters: the scale factor and exponent. It is shown that this single assumption allows the variability of spike trains to have an arbitrary scale and various dependencies on the firing rate in the spike count statistics, as well as in the interval statistics, depending on the two parameters of the power function. We also propose a statistical model for spike trains that exhibits the variance-to-mean power relationship. Based on this, a maximum likelihood method is developed for inferring the parameters from rate-modulated spike trains. The proposed method is illustrated on simulated and experimental spike trains.

Progress Towards Spectroscopic Diagnostics of Plasma Parameters and Neutral Dynamics in Helicon Plasmas

NASA Astrophysics Data System (ADS)

Green, Jonathan; Schmitz, Oliver; Severn, Greg; van Ruremonde, Lars; Winters, Victoria

2017-10-01

The MARIA device at the UW-Madison is used primarily to investigate the dynamics and fueling of neutral particles in helicon discharges. A new systematic method is in development to measure key plasma and neutral particle parameters by spectroscopic methods. The setup relies on spectroscopic line ratios for investigating basic plasma parameters and extrapolation to other states using a collisional radiative model. Active pumping using a Nd:YAG pumped dye laser is used to benchmark and correct the underlying atomic data for the collisional radiative model. First results show a matching linear dependence between electron density and laser induced fluorescence on the magnetic field above 500G. This linear dependence agrees with the helicon dispersion relation and implies MARIA can reliably support the helicon mode and support future measurements. This work was funded by the NSF CAREER award PHY-1455210.

A general mixture theory. I. Mixtures of spherical molecules

NASA Astrophysics Data System (ADS)

Hamad, Esam Z.

1996-08-01

We present a new general theory for obtaining mixture properties from the pure species equations of state. The theory addresses the composition and the unlike interactions dependence of mixture equation of state. The density expansion of the mixture equation gives the exact composition dependence of all virial coefficients. The theory introduces multiple-index parameters that can be calculated from binary unlike interaction parameters. In this first part of the work, details are presented for the first and second levels of approximations for spherical molecules. The second order model is simple and very accurate. It predicts the compressibility factor of additive hard spheres within simulation uncertainty (equimolar with size ratio of three). For nonadditive hard spheres, comparison with compressibility factor simulation data over a wide range of density, composition, and nonadditivity parameter, gave an average error of 2%. For mixtures of Lennard-Jones molecules, the model predictions are better than the Weeks-Chandler-Anderson perturbation theory.

Regression dilution in the proportional hazards model.

PubMed

Hughes, M D

1993-12-01

The problem of regression dilution arising from covariate measurement error is investigated for survival data using the proportional hazards model. The naive approach to parameter estimation is considered whereby observed covariate values are used, inappropriately, in the usual analysis instead of the underlying covariate values. A relationship between the estimated parameter in large samples and the true parameter is obtained showing that the bias does not depend on the form of the baseline hazard function when the errors are normally distributed. With high censorship, adjustment of the naive estimate by the factor 1 + lambda, where lambda is the ratio of within-person variability about an underlying mean level to the variability of these levels in the population sampled, removes the bias. As censorship increases, the adjustment required increases and when there is no censorship is markedly higher than 1 + lambda and depends also on the true risk relationship.

Geomorphically based predictive mapping of soil thickness in upland watersheds

NASA Astrophysics Data System (ADS)

Pelletier, Jon D.; Rasmussen, Craig

2009-09-01

The hydrologic response of upland watersheds is strongly controlled by soil (regolith) thickness. Despite the need to quantify soil thickness for input into hydrologic models, there is currently no widely used, geomorphically based method for doing so. In this paper we describe and illustrate a new method for predictive mapping of soil thicknesses using high-resolution topographic data, numerical modeling, and field-based calibration. The model framework works directly with input digital elevation model data to predict soil thicknesses assuming a long-term balance between soil production and erosion. Erosion rates in the model are quantified using one of three geomorphically based sediment transport models: nonlinear slope-dependent transport, nonlinear area- and slope-dependent transport, and nonlinear depth- and slope-dependent transport. The model balances soil production and erosion locally to predict a family of solutions corresponding to a range of values of two unconstrained model parameters. A small number of field-based soil thickness measurements can then be used to calibrate the local value of those unconstrained parameters, thereby constraining which solution is applicable at a particular study site. As an illustration, the model is used to predictively map soil thicknesses in two small, ˜0.1 km2, drainage basins in the Marshall Gulch watershed, a semiarid drainage basin in the Santa Catalina Mountains of Pima County, Arizona. Field observations and calibration data indicate that the nonlinear depth- and slope-dependent sediment transport model is the most appropriate transport model for this site. The resulting framework provides a generally applicable, geomorphically based tool for predictive mapping of soil thickness using high-resolution topographic data sets.

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

NASA Astrophysics Data System (ADS)

Quinn Thomas, R.; Brooks, Evan B.; Jersild, Annika L.; Ward, Eric J.; Wynne, Randolph H.; Albaugh, Timothy J.; Dinon-Aldridge, Heather; Burkhart, Harold E.; Domec, Jean-Christophe; Fox, Thomas R.; Gonzalez-Benecke, Carlos A.; Martin, Timothy A.; Noormets, Asko; Sampson, David A.; Teskey, Robert O.

2017-07-01

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 105 km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.

Design of a broadband band-pass filter with notch-band using new models of coupled transmission lines.

PubMed

Daryasafar, Navid; Baghbani, Somaye; Moghaddasi, Mohammad Naser; Sadeghzade, Ramezanali

2014-01-01

We intend to design a broadband band-pass filter with notch-band, which uses coupled transmission lines in the structure, using new models of coupled transmission lines. In order to realize and present the new model, first, previous models will be simulated in the ADS program. Then, according to the change of their equations and consequently change of basic parameters of these models, optimization and dependency among these parameters and also their frequency response are attended and results of these changes in order to design a new filter are converged.

Microfissuring of Inconel 718

NASA Technical Reports Server (NTRS)

Nunes, A. C., Jr.

1983-01-01

A tentative mathematical computer model of the microfissuring process during electron beam welding of Inconel 718 has been constructed. Predictions of the model are compatible with microfissuring tests on eight 0.25-in. thick test plates. The model takes into account weld power and speed, weld loss (efficiency), parameters and material characteristics. Besides the usual material characteristics (thermal and strength properties), a temperature and grain size dependent critical fracture strain is required by the model. The model is based upon fundamental physical theory (i.e., it is not a mere data interpolation system), and can be extended to other metals by suitable parameter changes.

Global distribution of urban parameters derived from high-resolution global datasets for weather modelling

NASA Astrophysics Data System (ADS)

Kawano, N.; Varquez, A. C. G.; Dong, Y.; Kanda, M.

2016-12-01

Numerical model such as Weather Research and Forecasting model coupled with single-layer Urban Canopy Model (WRF-UCM) is one of the powerful tools to investigate urban heat island. Urban parameters such as average building height (Have), plain area index (λp) and frontal area index (λf), are necessary inputs for the model. In general, these parameters are uniformly assumed in WRF-UCM but this leads to unrealistic urban representation. Distributed urban parameters can also be incorporated into WRF-UCM to consider a detail urban effect. The problem is that distributed building information is not readily available for most megacities especially in developing countries. Furthermore, acquiring real building parameters often require huge amount of time and money. In this study, we investigated the potential of using globally available satellite-captured datasets for the estimation of the parameters, Have, λp, and λf. Global datasets comprised of high spatial resolution population dataset (LandScan by Oak Ridge National Laboratory), nighttime lights (NOAA), and vegetation fraction (NASA). True samples of Have, λp, and λf were acquired from actual building footprints from satellite images and 3D building database of Tokyo, New York, Paris, Melbourne, Istanbul, Jakarta and so on. Regression equations were then derived from the block-averaging of spatial pairs of real parameters and global datasets. Results show that two regression curves to estimate Have and λf from the combination of population and nightlight are necessary depending on the city's level of development. An index which can be used to decide which equation to use for a city is the Gross Domestic Product (GDP). On the other hand, λphas less dependence on GDP but indicated a negative relationship to vegetation fraction. Finally, a simplified but precise approximation of urban parameters through readily-available, high-resolution global datasets and our derived regressions can be utilized to estimate a global distribution of urban parameters for later incorporation into a weather model, thus allowing us to acquire a global understanding of urban climate (Global Urban Climatology). Acknowledgment: This research was supported by the Environment Research and Technology Development Fund (S-14) of the Ministry of the Environment, Japan.

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

Seljak, Uroš, E-mail: useljak@berkeley.edu

On large scales a nonlinear transformation of matter density field can be viewed as a biased tracer of the density field itself. A nonlinear transformation also modifies the redshift space distortions in the same limit, giving rise to a velocity bias. In models with primordial nongaussianity a nonlinear transformation generates a scale dependent bias on large scales. We derive analytic expressions for the large scale bias, the velocity bias and the redshift space distortion (RSD) parameter β, as well as the scale dependent bias from primordial nongaussianity for a general nonlinear transformation. These biases can be expressed entirely in termsmore » of the one point distribution function (PDF) of the final field and the parameters of the transformation. The analysis shows that one can view the large scale bias different from unity and primordial nongaussianity bias as a consequence of converting higher order correlations in density into 2-point correlations of its nonlinear transform. Our analysis allows one to devise nonlinear transformations with nearly arbitrary bias properties, which can be used to increase the signal in the large scale clustering limit. We apply the results to the ionizing equilibrium model of Lyman-α forest, in which Lyman-α flux F is related to the density perturbation δ via a nonlinear transformation. Velocity bias can be expressed as an average over the Lyman-α flux PDF. At z = 2.4 we predict the velocity bias of -0.1, compared to the observed value of −0.13±0.03. Bias and primordial nongaussianity bias depend on the parameters of the transformation. Measurements of bias can thus be used to constrain these parameters, and for reasonable values of the ionizing background intensity we can match the predictions to observations. Matching to the observed values we predict the ratio of primordial nongaussianity bias to bias to have the opposite sign and lower magnitude than the corresponding values for the highly biased galaxies, but this depends on the model parameters and can also vanish or change the sign.« less

WE-D-BRE-07: Variance-Based Sensitivity Analysis to Quantify the Impact of Biological Uncertainties in Particle Therapy

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

Kamp, F.; Brueningk, S.C.; Wilkens, J.J.

Purpose: In particle therapy, treatment planning and evaluation are frequently based on biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2). In the context of the linear-quadratic model, these quantities depend on biological parameters (α, β) for ions as well as for the reference radiation and on the dose per fraction. The needed biological parameters as well as their dependency on ion species and ion energy typically are subject to large (relative) uncertainties of up to 20–40% or even more. Therefore it is necessary to estimate the resulting uncertainties in e.g.more » RBE or EQD2 caused by the uncertainties of the relevant input parameters. Methods: We use a variance-based sensitivity analysis (SA) approach, in which uncertainties in input parameters are modeled by random number distributions. The evaluated function is executed 10{sup 4} to 10{sup 6} times, each run with a different set of input parameters, randomly varied according to their assigned distribution. The sensitivity S is a variance-based ranking (from S = 0, no impact, to S = 1, only influential part) of the impact of input uncertainties. The SA approach is implemented for carbon ion treatment plans on 3D patient data, providing information about variations (and their origin) in RBE and EQD2. Results: The quantification enables 3D sensitivity maps, showing dependencies of RBE and EQD2 on different input uncertainties. The high number of runs allows displaying the interplay between different input uncertainties. The SA identifies input parameter combinations which result in extreme deviations of the result and the input parameter for which an uncertainty reduction is the most rewarding. Conclusion: The presented variance-based SA provides advantageous properties in terms of visualization and quantification of (biological) uncertainties and their impact. The method is very flexible, model independent, and enables a broad assessment of uncertainties. Supported by DFG grant WI 3745/1-1 and DFG cluster of excellence: Munich-Centre for Advanced Photonics.« less

Seismic variability of subduction thrust faults: Insights from laboratory models

NASA Astrophysics Data System (ADS)

Corbi, F.; Funiciello, F.; Faccenna, C.; Ranalli, G.; Heuret, A.

2011-06-01

Laboratory models are realized to investigate the role of interface roughness, driving rate, and pressure on friction dynamics. The setup consists of a gelatin block driven at constant velocity over sand paper. The interface roughness is quantified in terms of amplitude and wavelength of protrusions, jointly expressed by a reference roughness parameter obtained by their product. Frictional behavior shows a systematic dependence on system parameters. Both stick slip and stable sliding occur, depending on driving rate and interface roughness. Stress drop and frequency of slip episodes vary directly and inversely, respectively, with the reference roughness parameter, reflecting the fundamental role for the amplitude of protrusions. An increase in pressure tends to favor stick slip. Static friction is a steeply decreasing function of the reference roughness parameter. The velocity strengthening/weakening parameter in the state- and rate-dependent dynamic friction law becomes negative for specific values of the reference roughness parameter which are intermediate with respect to the explored range. Despite the simplifications of the adopted setup, which does not address the problem of off-fault fracturing, a comparison of the experimental results with the depth distribution of seismic energy release along subduction thrust faults leads to the hypothesis that their behavior is primarily controlled by the depth- and time-dependent distribution of protrusions. A rough subduction fault at shallow depths, unable to produce significant seismicity because of low lithostatic pressure, evolves into a moderately rough, velocity-weakening fault at intermediate depths. The magnitude of events in this range is calibrated by the interplay between surface roughness and subduction rate. At larger depths, the roughness further decreases and stable sliding becomes gradually more predominant. Thus, although interplate seismicity is ultimately controlled by tectonic parameters (velocity of the plates/trench and the thermal regime), the direct control is exercised by the resulting frictional properties of the plate interface.

Analysis of the statistical thermodynamic model for nonlinear binary protein adsorption equilibria.

PubMed

Zhou, Xiao-Peng; Su, Xue-Li; Sun, Yan

2007-01-01

The statistical thermodynamic (ST) model was used to study nonlinear binary protein adsorption equilibria on an anion exchanger. Single-component and binary protein adsorption isotherms of bovine hemoglobin (Hb) and bovine serum albumin (BSA) on DEAE Spherodex M were determined by batch adsorption experiments in 10 mM Tris-HCl buffer containing a specific NaCl concentration (0.05, 0.10, and 0.15 M) at pH 7.40. The ST model was found to depict the effect of ionic strength on the single-component equilibria well, with model parameters depending on ionic strength. Moreover, the ST model gave acceptable fitting to the binary adsorption data with the fitted single-component model parameters, leading to the estimation of the binary ST model parameter. The effects of ionic strength on the model parameters are reasonably interpreted by the electrostatic and thermodynamic theories. The effective charge of protein in adsorption phase can be separately calculated from the two categories of the model parameters, and the values obtained from the two methods are consistent. The results demonstrate the utility of the ST model for describing nonlinear binary protein adsorption equilibria.

Comparing an annual and daily time-step model for predicting field-scale P loss

USDA-ARS?s Scientific Manuscript database

Several models with varying degrees of complexity are available for describing P movement through the landscape. The complexity of these models is dependent on the amount of data required by the model, the number of model parameters needed to be estimated, the theoretical rigor of the governing equa...

Distance-weighted city growth.

PubMed

Rybski, Diego; García Cantú Ros, Anselmo; Kropp, Jürgen P

2013-04-01

Urban agglomerations exhibit complex emergent features of which Zipf's law, i.e., a power-law size distribution, and fractality may be regarded as the most prominent ones. We propose a simplistic model for the generation of citylike structures which is solely based on the assumption that growth is more likely to take place close to inhabited space. The model involves one parameter which is an exponent determining how strongly the attraction decays with the distance. In addition, the model is run iteratively so that existing clusters can grow (together) and new ones can emerge. The model is capable of reproducing the size distribution and the fractality of the boundary of the largest cluster. Although the power-law distribution depends on both, the imposed exponent and the iteration, the fractality seems to be independent of the former and only depends on the latter. Analyzing land-cover data, we estimate the parameter-value γ≈2.5 for Paris and its surroundings.

An EOQ model of time quadratic and inventory dependent demand for deteriorated items with partially backlogged shortages under trade credit

NASA Astrophysics Data System (ADS)

Singh, Pushpinder; Mishra, Nitin Kumar; Singh, Vikramjeet; Saxena, Seema

2017-07-01

In this paper a single buyer, single supplier inventory model with time quadratic and stock dependent demand for a finite planning horizon has been studied. Single deteriorating item which suffers shortage, with partial backlogging and some lost sales is considered. Model is divided into two scenarios, one with non permissible delay in payment and other with permissible delay in payment. Latter is called, centralized system, where supplier offers trade credit to retailer. In the centralized system cost saving is shared amongst the two. The objective is to study the difference in minimum costs borne by retailer and supplier, under two scenarios including the above mentioned parameters. To obtain optimal solution of the problem the model is solved analytically. Numerical example and a comparative study are then discussed supported by sensitivity analysis of each parameter.

Functional Dependence of Thermodynamic and Thermokinetic Parameters of Refrigerants Used in Mine Air Refrigerators. Part 1 - Refrigerant R407C

NASA Astrophysics Data System (ADS)

Nowak, Bernard; Życzkowski, Piotr; Łuczak, Rafał

2017-03-01

The authors of this article dealt with the issue of modeling the thermodynamic and thermokinetic properties (parameters) of refrigerants. The knowledge of these parameters is essential to design refrigeration equipment, to perform their energy efficiency analysis, or to compare the efficiency of air refrigerators using different refrigerants. One of the refrigerants used in mine air compression refrigerators is R407C. For this refrigerant, 23 dependencies were developed, determining its thermodynamic and thermokinetic parameters in the states of saturated liquid, dry saturated vapour, superheated vapor, subcooled liquid, and in the two-phase region. The created formulas have been presented in Tables 2, 5, 8, 10 and 12, respectively. It should be noted that the scope of application of these formulas is wider than the range of changes of that refrigerant during the normal operation of mine refrigeration equipment. The article ends with the statistical verification of the developed dependencies. For this purpose, for each model correlation coefficients and coefficients of determination were calculated, as well as absolute and relative deviations between the given values from the program REFPROP 7 (Lemmon et al., 2002) and the calculated ones. The results of these calculations have been contained in Tables 14 and 15.

Link-topic model for biomedical abbreviation disambiguation.

PubMed

Kim, Seonho; Yoon, Juntae

2015-02-01

The ambiguity of biomedical abbreviations is one of the challenges in biomedical text mining systems. In particular, the handling of term variants and abbreviations without nearby definitions is a critical issue. In this study, we adopt the concepts of topic of document and word link to disambiguate biomedical abbreviations. We newly suggest the link topic model inspired by the latent Dirichlet allocation model, in which each document is perceived as a random mixture of topics, where each topic is characterized by a distribution over words. Thus, the most probable expansions with respect to abbreviations of a given abstract are determined by word-topic, document-topic, and word-link distributions estimated from a document collection through the link topic model. The model allows two distinct modes of word generation to incorporate semantic dependencies among words, particularly long form words of abbreviations and their sentential co-occurring words; a word can be generated either dependently on the long form of the abbreviation or independently. The semantic dependency between two words is defined as a link and a new random parameter for the link is assigned to each word as well as a topic parameter. Because the link status indicates whether the word constitutes a link with a given specific long form, it has the effect of determining whether a word forms a unigram or a skipping/consecutive bigram with respect to the long form. Furthermore, we place a constraint on the model so that a word has the same topic as a specific long form if it is generated in reference to the long form. Consequently, documents are generated from the two hidden parameters, i.e. topic and link, and the most probable expansion of a specific abbreviation is estimated from the parameters. Our model relaxes the bag-of-words assumption of the standard topic model in which the word order is neglected, and it captures a richer structure of text than does the standard topic model by considering unigrams and semantically associated bigrams simultaneously. The addition of semantic links improves the disambiguation accuracy without removing irrelevant contextual words and reduces the parameter space of massive skipping or consecutive bigrams. The link topic model achieves 98.42% disambiguation accuracy on 73,505 MEDLINE abstracts with respect to 21 three letter abbreviations and their 139 distinct long forms. Copyright © 2014 Elsevier Inc. All rights reserved.

Empirical evidence that metabolic theory describes the temperature dependency of within-host parasite dynamics.

PubMed

Kirk, Devin; Jones, Natalie; Peacock, Stephanie; Phillips, Jessica; Molnár, Péter K; Krkošek, Martin; Luijckx, Pepijn

2018-02-01

The complexity of host-parasite interactions makes it difficult to predict how host-parasite systems will respond to climate change. In particular, host and parasite traits such as survival and virulence may have distinct temperature dependencies that must be integrated into models of disease dynamics. Using experimental data from Daphnia magna and a microsporidian parasite, we fitted a mechanistic model of the within-host parasite population dynamics. Model parameters comprising host aging and mortality, as well as parasite growth, virulence, and equilibrium abundance, were specified by relationships arising from the metabolic theory of ecology. The model effectively predicts host survival, parasite growth, and the cost of infection across temperature while using less than half the parameters compared to modeling temperatures discretely. Our results serve as a proof of concept that linking simple metabolic models with a mechanistic host-parasite framework can be used to predict temperature responses of parasite population dynamics at the within-host level.

Radiative Dileptonic Decays of B-Meson in the General Two Higgs Doublet Model

NASA Astrophysics Data System (ADS)

Erkol, G.; Turan, G.

2002-05-01

We investigate the exclusive B → γ ℓ + ℓ - decay in the general two Higgs Doublet Model (model III) including the neutral Higgs boson effects with an emphasis on possible CP-violating effects. For this decay, we analyze the dependencies of the forward-backward asymmetry of the lepton pair, AFB, CP-violating asymmetry, ACP, and the CP-violating asymmetry in forward-backward asymmetry, ACP(AFB), on the model parameters and also on the neutral Higgs boson effects. We have found that AFB˜ 10-1, 10-2, ACP˜ 10-2, 10-1 and ACP(AFB) ˜ 10-2, 10-1 depending on the relative magnitude of the Yukawa couplings bar ξ N,ttU and bar ξ N,bbD in the model III. We also observe that these physical quantities are sensitive to the model parameters and neutral Higgs boson effects are quite sizable for some values of the coupling bar ξ N,τ τ D.

Empirical evidence that metabolic theory describes the temperature dependency of within-host parasite dynamics

PubMed Central

Jones, Natalie; Peacock, Stephanie; Phillips, Jessica; Molnár, Péter K.; Krkošek, Martin; Luijckx, Pepijn

2018-01-01

The complexity of host–parasite interactions makes it difficult to predict how host–parasite systems will respond to climate change. In particular, host and parasite traits such as survival and virulence may have distinct temperature dependencies that must be integrated into models of disease dynamics. Using experimental data from Daphnia magna and a microsporidian parasite, we fitted a mechanistic model of the within-host parasite population dynamics. Model parameters comprising host aging and mortality, as well as parasite growth, virulence, and equilibrium abundance, were specified by relationships arising from the metabolic theory of ecology. The model effectively predicts host survival, parasite growth, and the cost of infection across temperature while using less than half the parameters compared to modeling temperatures discretely. Our results serve as a proof of concept that linking simple metabolic models with a mechanistic host–parasite framework can be used to predict temperature responses of parasite population dynamics at the within-host level. PMID:29415043

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.

Improving smoothing efficiency of rigid conformal polishing tool using time-dependent smoothing evaluation model

NASA Astrophysics Data System (ADS)

Song, Chi; Zhang, Xuejun; Zhang, Xin; Hu, Haifei; Zeng, Xuefeng

2017-06-01

A rigid conformal (RC) lap can smooth mid-spatial-frequency (MSF) errors, which are naturally smaller than the tool size, while still removing large-scale errors in a short time. However, the RC-lap smoothing efficiency performance is poorer than expected, and existing smoothing models cannot explicitly specify the methods to improve this efficiency. We presented an explicit time-dependent smoothing evaluation model that contained specific smoothing parameters directly derived from the parametric smoothing model and the Preston equation. Based on the time-dependent model, we proposed a strategy to improve the RC-lap smoothing efficiency, which incorporated the theoretical model, tool optimization, and efficiency limit determination. Two sets of smoothing experiments were performed to demonstrate the smoothing efficiency achieved using the time-dependent smoothing model. A high, theory-like tool influence function and a limiting tool speed of 300 RPM were o

Maximizing the information learned from finite data selects a simple model

NASA Astrophysics Data System (ADS)

Mattingly, Henry H.; Transtrum, Mark K.; Abbott, Michael C.; Machta, Benjamin B.

2018-02-01

We use the language of uninformative Bayesian prior choice to study the selection of appropriately simple effective models. We advocate for the prior which maximizes the mutual information between parameters and predictions, learning as much as possible from limited data. When many parameters are poorly constrained by the available data, we find that this prior puts weight only on boundaries of the parameter space. Thus, it selects a lower-dimensional effective theory in a principled way, ignoring irrelevant parameter directions. In the limit where there are sufficient data to tightly constrain any number of parameters, this reduces to the Jeffreys prior. However, we argue that this limit is pathological when applied to the hyperribbon parameter manifolds generic in science, because it leads to dramatic dependence on effects invisible to experiment.

Deconvolution of complex differential scanning calorimetry profiles for protein transitions under kinetic control.

PubMed

Toledo-Núñez, Citlali; Vera-Robles, L Iraís; Arroyo-Maya, Izlia J; Hernández-Arana, Andrés

2016-09-15

A frequent outcome in differential scanning calorimetry (DSC) experiments carried out with large proteins is the irreversibility of the observed endothermic effects. In these cases, DSC profiles are analyzed according to methods developed for temperature-induced denaturation transitions occurring under kinetic control. In the one-step irreversible model (native → denatured) the characteristics of the observed single-peaked endotherm depend on the denaturation enthalpy and the temperature dependence of the reaction rate constant, k. Several procedures have been devised to obtain the parameters that determine the variation of k with temperature. Here, we have elaborated on one of these procedures in order to analyze more complex DSC profiles. Synthetic data for a heat capacity curve were generated according to a model with two sequential reactions; the temperature dependence of each of the two rate constants involved was determined, according to the Eyring's equation, by two fixed parameters. It was then shown that our deconvolution procedure, by making use of heat capacity data alone, permits to extract the parameter values that were initially used. Finally, experimental DSC traces showing two and three maxima were analyzed and reproduced with relative success according to two- and four-step sequential models. Copyright © 2016 Elsevier Inc. All rights reserved.

Thermodynamic modeling of transcription: sensitivity analysis differentiates biological mechanism from mathematical model-induced effects.

PubMed

Dresch, Jacqueline M; Liu, Xiaozhou; Arnosti, David N; Ay, Ahmet

2010-10-24

Quantitative models of gene expression generate parameter values that can shed light on biological features such as transcription factor activity, cooperativity, and local effects of repressors. An important element in such investigations is sensitivity analysis, which determines how strongly a model's output reacts to variations in parameter values. Parameters of low sensitivity may not be accurately estimated, leading to unwarranted conclusions. Low sensitivity may reflect the nature of the biological data, or it may be a result of the model structure. Here, we focus on the analysis of thermodynamic models, which have been used extensively to analyze gene transcription. Extracted parameter values have been interpreted biologically, but until now little attention has been given to parameter sensitivity in this context. We apply local and global sensitivity analyses to two recent transcriptional models to determine the sensitivity of individual parameters. We show that in one case, values for repressor efficiencies are very sensitive, while values for protein cooperativities are not, and provide insights on why these differential sensitivities stem from both biological effects and the structure of the applied models. In a second case, we demonstrate that parameters that were thought to prove the system's dependence on activator-activator cooperativity are relatively insensitive. We show that there are numerous parameter sets that do not satisfy the relationships proferred as the optimal solutions, indicating that structural differences between the two types of transcriptional enhancers analyzed may not be as simple as altered activator cooperativity. Our results emphasize the need for sensitivity analysis to examine model construction and forms of biological data used for modeling transcriptional processes, in order to determine the significance of estimated parameter values for thermodynamic models. Knowledge of parameter sensitivities can provide the necessary context to determine how modeling results should be interpreted in biological systems.

Scheme variations of the QCD coupling

NASA Astrophysics Data System (ADS)

Boito, Diogo; Jamin, Matthias; Miravitllas, Ramon

2017-03-01

The Quantum Chromodynamics (QCD) coupling αs is a central parameter in the Standard Model of particle physics. However, it depends on theoretical conventions related to renormalisation and hence is not an observable quantity. In order to capture this dependence in a transparent way, a novel definition of the QCD coupling, denoted by â, is introduced, whose running is explicitly renormalisation scheme invariant. The remaining renormalisation scheme dependence is related to transformations of the QCD scale Λ, and can be parametrised by a single parameter C. Hence, we call â the C-scheme coupling. The dependence on C can be exploited to study and improve perturbative predictions of physical observables. This is demonstrated for the QCD Adler function and hadronic decays of the τ lepton.

Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty

PubMed Central

Schiavazzi, Daniele E.; Baretta, Alessia; Pennati, Giancarlo; Hsia, Tain-Yen; Marsden, Alison L.

2017-01-01

Summary Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology. PMID:27155892

Exposure reconstruction for the TCDD-exposed NIOSH cohort using a concentration- and age-dependent model of elimination.

PubMed

Aylward, Lesa L; Brunet, Robert C; Starr, Thomas B; Carrier, Gaétan; Delzell, Elizabeth; Cheng, Hong; Beall, Colleen

2005-08-01

Recent studies demonstrating a concentration dependence of elimination of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) suggest that previous estimates of exposure for occupationally exposed cohorts may have underestimated actual exposure, resulting in a potential overestimate of the carcinogenic potency of TCDD in humans based on the mortality data for these cohorts. Using a database on U.S. chemical manufacturing workers potentially exposed to TCDD compiled by the National Institute for Occupational Safety and Health (NIOSH), we evaluated the impact of using a concentration- and age-dependent elimination model (CADM) (Aylward et al., 2005) on estimates of serum lipid area under the curve (AUC) for the NIOSH cohort. These data were used previously by Steenland et al. (2001) in combination with a first-order elimination model with an 8.7-year half-life to estimate cumulative serum lipid concentration (equivalent to AUC) for these workers for use in cancer dose-response assessment. Serum lipid TCDD measurements taken in 1988 for a subset of the cohort were combined with the NIOSH job exposure matrix and work histories to estimate dose rates per unit of exposure score. We evaluated the effect of choices in regression model (regression on untransformed vs. ln-transformed data and inclusion of a nonzero regression intercept) as well as the impact of choices of elimination models and parameters on estimated AUCs for the cohort. Central estimates for dose rate parameters derived from the serum-sampled subcohort were applied with the elimination models to time-specific exposure scores for the entire cohort to generate AUC estimates for all cohort members. Use of the CADM resulted in improved model fits to the serum sampling data compared to the first-order models. Dose rates varied by a factor of 50 among different combinations of elimination model, parameter sets, and regression models. Use of a CADM results in increases of up to five-fold in AUC estimates for the more highly exposed members of the cohort compared to estimates obtained using the first-order model with 8.7-year half-life. This degree of variation in the AUC estimates for this cohort would affect substantially the cancer potency estimates derived from the mortality data from this cohort. Such variability and uncertainty in the reconstructed serum lipid AUC estimates for this cohort, depending on elimination model, parameter set, and regression model, have not been described previously and are critical components in evaluating the dose-response data from the occupationally exposed populations.

Kinetic modeling of light limitation and sulfur deprivation effects in the induction of hydrogen production with Chlamydomonas reinhardtii: Part I. Model development and parameter identification.

PubMed

Fouchard, Swanny; Pruvost, Jérémy; Degrenne, Benoit; Titica, Mariana; Legrand, Jack

2009-01-01

Chlamydomonas reinhardtii is a green microalga capable of turning its metabolism towards H2 production under specific conditions. However this H2 production, narrowly linked to the photosynthetic process, results from complex metabolic reactions highly dependent on the environmental conditions of the cells. A kinetic model has been developed to relate culture evolution from standard photosynthetic growth to H2 producing cells. It represents transition in sulfur-deprived conditions, known to lead to H2 production in Chlamydomonas reinhardtii, and the two main processes then induced which are an over-accumulation of intracellular starch and a progressive reduction of PSII activity for anoxia achievement. Because these phenomena are directly linked to the photosynthetic growth, two kinetic models were associated, the first (one) introducing light dependency (Haldane type model associated to a radiative light transfer model), the second (one) making growth a function of available sulfur amount under extracellular and intracellular forms (Droop formulation). The model parameters identification was realized from experimental data obtained with especially designed experiments and a sensitivity analysis of the model to its parameters was also conducted. Model behavior was finally studied showing interdependency between light transfer conditions, photosynthetic growth, sulfate uptake, photosynthetic activity and O2 release, during transition from oxygenic growth to anoxic H2 production conditions.

The Rangeland Hydrology and Erosion Model: A Dynamic Approach for Predicting Soil Loss on Rangelands

NASA Astrophysics Data System (ADS)

Hernandez, Mariano; Nearing, Mark A.; Al-Hamdan, Osama Z.; Pierson, Frederick B.; Armendariz, Gerardo; Weltz, Mark A.; Spaeth, Kenneth E.; Williams, C. Jason; Nouwakpo, Sayjro K.; Goodrich, David C.; Unkrich, Carl L.; Nichols, Mary H.; Holifield Collins, Chandra D.

2017-11-01

In this study, we present the improved Rangeland Hydrology and Erosion Model (RHEM V2.3), a process-based erosion prediction tool specific for rangeland application. The article provides the mathematical formulation of the model and parameter estimation equations. Model performance is assessed against data collected from 23 runoff and sediment events in a shrub-dominated semiarid watershed in Arizona, USA. To evaluate the model, two sets of primary model parameters were determined using the RHEM V2.3 and RHEM V1.0 parameter estimation equations. Testing of the parameters indicated that RHEM V2.3 parameter estimation equations provided a 76% improvement over RHEM V1.0 parameter estimation equations. Second, the RHEM V2.3 model was calibrated to measurements from the watershed. The parameters estimated by the new equations were within the lowest and highest values of the calibrated parameter set. These results suggest that the new parameter estimation equations can be applied for this environment to predict sediment yield at the hillslope scale. Furthermore, we also applied the RHEM V2.3 to demonstrate the response of the model as a function of foliar cover and ground cover for 124 data points across Arizona and New Mexico. The dependence of average sediment yield on surface ground cover was moderately stronger than that on foliar cover. These results demonstrate that RHEM V2.3 predicts runoff volume, peak runoff, and sediment yield with sufficient accuracy for broad application to assess and manage rangeland systems.

The impact law of confining pressure and plastic parameter on Dilatancy of rock

NASA Astrophysics Data System (ADS)

Wang, Bin; Zhang, Zhenjie; Zhu, Jiebing

2017-08-01

Based on cyclic loading-unloading triaxle test of marble, the double parameter dilation angle model is established considering confining pressure effect and plastic parameter. Research shows that not only the strength but also the militancy behavior is highly depended on its confining pressure and plastic parameter during process of failure. Dilation angle evolution law shows obvious nonlinear characteristic almost with a rapid increase to the peak and then decrease gradually with plastic increasing, and the peak dilation angle value is inversely proportional with confining pressure. The proposed double parameter nonlinear dilation angle model can be used to well describe the Dilatancy of rock, which helps to understand the failure mechanism of surrounding rock mass and predict the range of plastic zone.

Modelling non-linear effects of dark energy

NASA Astrophysics Data System (ADS)

Bose, Benjamin; Baldi, Marco; Pourtsidou, Alkistis

2018-04-01

We investigate the capabilities of perturbation theory in capturing non-linear effects of dark energy. We test constant and evolving w models, as well as models involving momentum exchange between dark energy and dark matter. Specifically, we compare perturbative predictions at 1-loop level against N-body results for four non-standard equations of state as well as varying degrees of momentum exchange between dark energy and dark matter. The interaction is modelled phenomenologically using a time dependent drag term in the Euler equation. We make comparisons at the level of the matter power spectrum and the redshift space monopole and quadrupole. The multipoles are modelled using the Taruya, Nishimichi and Saito (TNS) redshift space spectrum. We find perturbation theory does very well in capturing non-linear effects coming from dark sector interaction. We isolate and quantify the 1-loop contribution coming from the interaction and from the non-standard equation of state. We find the interaction parameter ξ amplifies scale dependent signatures in the range of scales considered. Non-standard equations of state also give scale dependent signatures within this same regime. In redshift space the match with N-body is improved at smaller scales by the addition of the TNS free parameter σv. To quantify the importance of modelling the interaction, we create mock data sets for varying values of ξ using perturbation theory. This data is given errors typical of Stage IV surveys. We then perform a likelihood analysis using the first two multipoles on these sets and a ξ=0 modelling, ignoring the interaction. We find the fiducial growth parameter f is generally recovered even for very large values of ξ both at z=0.5 and z=1. The ξ=0 modelling is most biased in its estimation of f for the phantom w=‑1.1 case.

Updated observational constraints on quintessence dark energy models

NASA Astrophysics Data System (ADS)

Durrive, Jean-Baptiste; Ooba, Junpei; Ichiki, Kiyotomo; Sugiyama, Naoshi

2018-02-01

The recent GW170817 measurement favors the simplest dark energy models, such as a single scalar field. Quintessence models can be classified in two classes, freezing and thawing, depending on whether the equation of state decreases towards -1 or departs from it. In this paper, we put observational constraints on the parameters governing the equations of state of tracking freezing, scaling freezing, and thawing models using updated data, from the Planck 2015 release, joint light-curve analysis, and baryonic acoustic oscillations. Because of the current tensions on the value of the Hubble parameter H0, unlike previous authors, we let this parameter vary, which modifies significantly the results. Finally, we also derive constraints on neutrino masses in each of these scenarios.

Method for Constructing Composite Response Surfaces by Combining Neural Networks with Polynominal Interpolation or Estimation Techniques

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor); Madavan, Nateri K. (Inventor)

2007-01-01

A method and system for data modeling that incorporates the advantages of both traditional response surface methodology (RSM) and neural networks is disclosed. The invention partitions the parameters into a first set of s simple parameters, where observable data are expressible as low order polynomials, and c complex parameters that reflect more complicated variation of the observed data. Variation of the data with the simple parameters is modeled using polynomials; and variation of the data with the complex parameters at each vertex is analyzed using a neural network. Variations with the simple parameters and with the complex parameters are expressed using a first sequence of shape functions and a second sequence of neural network functions. The first and second sequences are multiplicatively combined to form a composite response surface, dependent upon the parameter values, that can be used to identify an accurate mode

Temperature and composition dependence of short-range order and entropy, and statistics of bond length: the semiconductor alloy (GaN)(1-x)(ZnO)(x).

PubMed

Liu, Jian; Pedroza, Luana S; Misch, Carissa; Fernández-Serra, Maria V; Allen, Philip B

2014-07-09

We present total energy and force calculations for the (GaN)1-x(ZnO)x alloy. Site-occupancy configurations are generated from Monte Carlo (MC) simulations, on the basis of a cluster expansion model proposed in a previous study. Local atomic coordinate relaxations of surprisingly large magnitude are found via density-functional calculations using a 432-atom periodic supercell, for three representative configurations at x = 0.5. These are used to generate bond-length distributions. The configurationally averaged composition- and temperature-dependent short-range order (SRO) parameters of the alloys are discussed. The entropy is approximated in terms of pair distribution statistics and thus related to SRO parameters. This approximate entropy is compared with accurate numerical values from MC simulations. An empirical model for the dependence of the bond length on the local chemical environments is proposed.

Uncertainty quantification in LES of channel flow

DOE PAGES

Safta, Cosmin; Blaylock, Myra; Templeton, Jeremy; ...

2016-07-12

Here, in this paper, we present a Bayesian framework for estimating joint densities for large eddy simulation (LES) sub-grid scale model parameters based on canonical forced isotropic turbulence direct numerical simulation (DNS) data. The framework accounts for noise in the independent variables, and we present alternative formulations for accounting for discrepancies between model and data. To generate probability densities for flow characteristics, posterior densities for sub-grid scale model parameters are propagated forward through LES of channel flow and compared with DNS data. Synthesis of the calibration and prediction results demonstrates that model parameters have an explicit filter width dependence andmore » are highly correlated. Discrepancies between DNS and calibrated LES results point to additional model form inadequacies that need to be accounted for.« less

Spiking and bursting patterns of fractional-order Izhikevich model

NASA Astrophysics Data System (ADS)

Teka, Wondimu W.; Upadhyay, Ranjit Kumar; Mondal, Argha

2018-03-01

Bursting and spiking oscillations play major roles in processing and transmitting information in the brain through cortical neurons that respond differently to the same signal. These oscillations display complex dynamics that might be produced by using neuronal models and varying many model parameters. Recent studies have shown that models with fractional order can produce several types of history-dependent neuronal activities without the adjustment of several parameters. We studied the fractional-order Izhikevich model and analyzed different kinds of oscillations that emerge from the fractional dynamics. The model produces a wide range of neuronal spike responses, including regular spiking, fast spiking, intrinsic bursting, mixed mode oscillations, regular bursting and chattering, by adjusting only the fractional order. Both the active and silent phase of the burst increase when the fractional-order model further deviates from the classical model. For smaller fractional order, the model produces memory dependent spiking activity after the pulse signal turned off. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. On the network level, the response of the neuronal network shifts from random to scale-free spiking. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.

A multiphysics 3D model of tissue growth under interstitial perfusion in a tissue-engineering bioreactor.

PubMed

Nava, Michele M; Raimondi, Manuela T; Pietrabissa, Riccardo

2013-11-01

The main challenge in engineered cartilage consists in understanding and controlling the growth process towards a functional tissue. Mathematical and computational modelling can help in the optimal design of the bioreactor configuration and in a quantitative understanding of important culture parameters. In this work, we present a multiphysics computational model for the prediction of cartilage tissue growth in an interstitial perfusion bioreactor. The model consists of two separate sub-models, one two-dimensional (2D) sub-model and one three-dimensional (3D) sub-model, which are coupled between each other. These sub-models account both for the hydrodynamic microenvironment imposed by the bioreactor, using a model based on the Navier-Stokes equation, the mass transport equation and the biomass growth. The biomass, assumed as a phase comprising cells and the synthesised extracellular matrix, has been modelled by using a moving boundary approach. In particular, the boundary at the fluid-biomass interface is moving with a velocity depending from the local oxygen concentration and viscous stress. In this work, we show that all parameters predicted, such as oxygen concentration and wall shear stress, by the 2D sub-model with respect to the ones predicted by the 3D sub-model are systematically overestimated and thus the tissue growth, which directly depends on these parameters. This implies that further predictive models for tissue growth should take into account of the three dimensionality of the problem for any scaffold microarchitecture.

On absence of steady state in the Bouchaud-Mézard network model

NASA Astrophysics Data System (ADS)

Liu, Zhiyuan; Serota, R. A.

2018-02-01

In the limit of infinite number of nodes (agents), the Itô-reduced Bouchaud-Mézard network model of economic exchange has a time-independent mean and a steady-state inverse gamma distribution. We show that for a finite number of nodes the mean is actually distributed as a time-dependent lognormal and inverse gamma is quasi-stationary, with the time-dependent scale parameter.

Upscaling of Solute Transport in Heterogeneous Media with Non-uniform Flow and Dispersion Fields

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

Xu, Zhijie; Meakin, Paul

2013-10-01

An analytical and computational model for non-reactive solute transport in periodic heterogeneous media with arbitrary non-uniform flow and dispersion fields within the unit cell of length ε is described. The model lumps the effect of non-uniform flow and dispersion into an effective advection velocity Ve and an effective dispersion coefficient De. It is shown that both Ve and De are scale-dependent (dependent on the length scale of the microscopic heterogeneity, ε), dependent on the Péclet number Pe, and on a dimensionless parameter α that represents the effects of microscopic heterogeneity. The parameter α, confined to the range of [-0.5, 0.5]more » for the numerical example presented, depends on the flow direction and non-uniform flow and dispersion fields. Effective advection velocity Ve and dispersion coefficient De can be derived for any given flow and dispersion fields, and . Homogenized solutions describing the macroscopic variations can be obtained from the effective model. Solutions with sub-unit-cell accuracy can be constructed by homogenized solutions and its spatial derivatives. A numerical implementation of the model compared with direct numerical solutions using a fine grid, demonstrated that the new method was in good agreement with direct solutions, but with significant computational savings.« less

Parameter variation of the one-diode model of a-Si and a- Si/μc-Si solar cells for modeling light-induced degradation

NASA Astrophysics Data System (ADS)

Weicht, J. A.; Hamelmann, F. U.; Behrens, G.

2014-11-01

For analyzing the long-term behavior of thin film a-Si/μc-Si photovoltaic modules, it is important to observe the light-induced degradation (LID) in dependence of the temperature for the parameters of the one-diode model for solar cells. According to the IEC 61646 standard, the impact of LID on module parameters of these thin film cells is determined at a constant temperature of 50°C with an irradiation of 1000 W/m2 at open circuit conditions. Previous papers examined the LID of thin film a-Si cells with different temperatures and some others are about a-Si/μc-Si. In these previous papers not all parameters of the one-diode model are examined. We observed the serial resistance (Rs), parallel resistance (Rp), short circuit current (Isc), open circuit voltage (Uoc), the maximum power point (MPP: Umpp, Impp and Pmpp) and the diode factor (n). Since the main reason for the LID of silicon-based thin films is the Staebler Wronski effect in the a-Si part of the cell, the temperature dependence of the healing of defects for all parameters of the one-diode model is also taken into account. We are also measuring modules with different kind of transparent conductive oxides: In a-Si thin film solar cells fluorine-doped tin oxide (FTO) is used and for thin film solar cells of a-Si/μc-Si boron- doped zinc oxide is used. In our work we describe an approach for transferring the parameters of a one-diode model for tandem thin film solar cells into the one-diode model for each part of the solar cell. The measurement of degradation and regeneration at higher temperatures is the necessary base for optimization of the different silicon-based thin films in warm hot climate.

Soft context clustering for F0 modeling in HMM-based speech synthesis

NASA Astrophysics Data System (ADS)

Khorram, Soheil; Sameti, Hossein; King, Simon

2015-12-01

This paper proposes the use of a new binary decision tree, which we call a soft decision tree, to improve generalization performance compared to the conventional `hard' decision tree method that is used to cluster context-dependent model parameters in statistical parametric speech synthesis. We apply the method to improve the modeling of fundamental frequency, which is an important factor in synthesizing natural-sounding high-quality speech. Conventionally, hard decision tree-clustered hidden Markov models (HMMs) are used, in which each model parameter is assigned to a single leaf node. However, this `divide-and-conquer' approach leads to data sparsity, with the consequence that it suffers from poor generalization, meaning that it is unable to accurately predict parameters for models of unseen contexts: the hard decision tree is a weak function approximator. To alleviate this, we propose the soft decision tree, which is a binary decision tree with soft decisions at the internal nodes. In this soft clustering method, internal nodes select both their children with certain membership degrees; therefore, each node can be viewed as a fuzzy set with a context-dependent membership function. The soft decision tree improves model generalization and provides a superior function approximator because it is able to assign each context to several overlapped leaves. In order to use such a soft decision tree to predict the parameters of the HMM output probability distribution, we derive the smoothest (maximum entropy) distribution which captures all partial first-order moments and a global second-order moment of the training samples. Employing such a soft decision tree architecture with maximum entropy distributions, a novel speech synthesis system is trained using maximum likelihood (ML) parameter re-estimation and synthesis is achieved via maximum output probability parameter generation. In addition, a soft decision tree construction algorithm optimizing a log-likelihood measure is developed. Both subjective and objective evaluations were conducted and indicate a considerable improvement over the conventional method.

Spin-dependent post-Newtonian parameters from EMRI computation in Kerr background

NASA Astrophysics Data System (ADS)

Friedman, John; Le Tiec, Alexandre; Shah, Abhay

2013-04-01

Because the extreme mass-ratio inspiral (EMRI) approximation is accurate to all orders in v/c, it can be used to find high order post-Newtonian parameters that are not yet analytically accessible. We report here on progress in computing spin-dependent, conservative, post-Newtonian parameters from a radiation-gauge computation for a particle in circular orbit in a family of Kerr geometries. For a particle with 4-velocity u^α= U k^α, with k^α the helical Killing vector of the perturbed spacetime, the renormalized perturbation δU, when written as a function of the particle's angular velocity, is invariant under gauge transformations generated by helically symmetric vectors. The EMRI computations are done in a modified radiation gauge. Extracted parameters are compared to previously known and newly computed spin-dependent post-Newtonian terms. This work is modeled on earlier computations by Blanchet, Detweiler, Le Tiec and Whiting of spin-independent terms for a particle in circular orbit in a Schwarzschild geometry.

An EOQ model for weibull distribution deterioration with time-dependent cubic demand and backlogging

NASA Astrophysics Data System (ADS)

Santhi, G.; Karthikeyan, K.

2017-11-01

In this article we introduce an economic order quantity model with weibull deterioration and time dependent cubic demand rate where holding costs as a linear function of time. Shortages are allowed in the inventory system are partially and fully backlogging. The objective of this model is to minimize the total inventory cost by using the optimal order quantity and the cycle length. The proposed model is illustrated by numerical examples and the sensitivity analysis is performed to study the effect of changes in parameters on the optimum solutions.

Nanoscale size dependence parameters on lattice thermal conductivity of Wurtzite GaN nanowires

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

Mamand, S.M., E-mail: soran.mamand@univsul.net; Omar, M.S.; Muhammad, A.J.

2012-05-15

Graphical abstract: Temperature dependence of calculated lattice thermal conductivity of Wurtzite GaN nanowires. Highlights: Black-Right-Pointing-Pointer A modified Callaway model is used to calculate lattice thermal conductivity of Wurtzite GaN nanowires. Black-Right-Pointing-Pointer A direct method is used to calculate phonon group velocity for these nanowires. Black-Right-Pointing-Pointer 3-Gruneisen parameter, surface roughness, and dislocations are successfully investigated. Black-Right-Pointing-Pointer Dislocation densities are decreases with the decrease of wires diameter. -- Abstract: A detailed calculation of lattice thermal conductivity of freestanding Wurtzite GaN nanowires with diameter ranging from 97 to 160 nm in the temperature range 2-300 K, was performed using a modified Callaway model.more » Both longitudinal and transverse modes are taken into account explicitly in the model. A method is used to calculate the Debye and phonon group velocities for different nanowire diameters from their related melting points. Effect of Gruneisen parameter, surface roughness, and dislocations as structure dependent parameters are successfully used to correlate the calculated values of lattice thermal conductivity to that of the experimentally measured curves. It was observed that Gruneisen parameter will decrease with decreasing nanowire diameters. Scattering of phonons is assumed to be by nanowire boundaries, imperfections, dislocations, electrons, and other phonons via both normal and Umklapp processes. Phonon confinement and size effects as well as the role of dislocation in limiting thermal conductivity are investigated. At high temperatures and for dislocation densities greater than 10{sup 14} m{sup -2} the lattice thermal conductivity would be limited by dislocation density, but for dislocation densities less than 10{sup 14} m{sup -2}, lattice thermal conductivity would be independent of that.« less

Model Selection in Systems Biology Depends on Experimental Design

PubMed Central

Silk, Daniel; Kirk, Paul D. W.; Barnes, Chris P.; Toni, Tina; Stumpf, Michael P. H.

2014-01-01

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis. PMID:24922483

Model selection in systems biology depends on experimental design.

PubMed

Silk, Daniel; Kirk, Paul D W; Barnes, Chris P; Toni, Tina; Stumpf, Michael P H

2014-06-01

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Solvable Catalyzed Birth-Death-Exchange Competition Model of Three Species

NASA Astrophysics Data System (ADS)

Wang, Hai-Feng; Lin, Zhen-Quan; Gao, Yan; Zhang, Heng

2009-10-01

A competition model of three species in exchange-driven aggregation growth is proposed. In the model, three distinct aggregates grow by exchange of monomers and in parallel, birth of species A is catalyzed by species B and death of species A is catalyzed by species C. The rates for both catalysis processes are proportional to kjν and kjω respectively, where ν(Ω) is a parameter reflecting the dependence of the catalysis reaction rate of birth (death) on the catalyst aggregate's size. The kinetic evolution behaviors of the three species are investigated by the rate equation approach based on the mean-field theory. The form of the aggregate size distribution of A-species ak(t) is found to be dependent crucially on the two catalysis rate kernel parameters. The results show that (i) in case of μ <= 0, the form of ak(t) mainly depends on the competition between self-exchange of species A and species-C-catalyzed death of species A; (ii) in case of ν > 0, the form of ak(t) mainly depends on the competition between species-B-catalyzed birth of species A and species-C-catalyzed death of species A.

Comparison of distributed reacceleration and leaky-box models of cosmic-ray abundances (Z = 3-28)

NASA Technical Reports Server (NTRS)

Letaw, John R.; Silberberg, Rein; Tsao, C. H.

1993-01-01

A large collection of elemental and isotopic cosmic-ray data has been analyzed using the leaky-box transport model with and without reacceleration in the interstellar medium. Abundances of isotopes and elements with charges Z = 3-28 and energies E = 10 MeV/nucleon-1 TeV/nucleon were explored. Our results demonstrate that reacceleration models make detailed and accurate predictions with the same number of parameters or fewer as standard leaky-box models. Ad hoc fitting parameters in the standard model are replaced by astrophysically significant reacceleration parameters. Distributed reacceleration models explain the peak in secondary-to-primary ratios around 1 GeV/nucleon. They diminish the discrepancy between rigidity-dependent leakage and energy-independent anisotropy. They also offer the possibility of understanding isotopic anomalies at low energy.

Using an ensemble smoother to evaluate parameter uncertainty of an integrated hydrological model of Yanqi basin

NASA Astrophysics Data System (ADS)

Li, Ning; McLaughlin, Dennis; Kinzelbach, Wolfgang; Li, WenPeng; Dong, XinGuang

2015-10-01

Model uncertainty needs to be quantified to provide objective assessments of the reliability of model predictions and of the risk associated with management decisions that rely on these predictions. This is particularly true in water resource studies that depend on model-based assessments of alternative management strategies. In recent decades, Bayesian data assimilation methods have been widely used in hydrology to assess uncertain model parameters and predictions. In this case study, a particular data assimilation algorithm, the Ensemble Smoother with Multiple Data Assimilation (ESMDA) (Emerick and Reynolds, 2012), is used to derive posterior samples of uncertain model parameters and forecasts for a distributed hydrological model of Yanqi basin, China. This model is constructed using MIKESHE/MIKE11software, which provides for coupling between surface and subsurface processes (DHI, 2011a-d). The random samples in the posterior parameter ensemble are obtained by using measurements to update 50 prior parameter samples generated with a Latin Hypercube Sampling (LHS) procedure. The posterior forecast samples are obtained from model runs that use the corresponding posterior parameter samples. Two iterative sample update methods are considered: one based on an a perturbed observation Kalman filter update and one based on a square root Kalman filter update. These alternatives give nearly the same results and converge in only two iterations. The uncertain parameters considered include hydraulic conductivities, drainage and river leakage factors, van Genuchten soil property parameters, and dispersion coefficients. The results show that the uncertainty in many of the parameters is reduced during the smoother updating process, reflecting information obtained from the observations. Some of the parameters are insensitive and do not benefit from measurement information. The correlation coefficients among certain parameters increase in each iteration, although they generally stay below 0.50.

A computer program (MODFLOWP) for estimating parameters of a transient, three-dimensional ground-water flow model using nonlinear regression

USGS Publications Warehouse

Hill, Mary Catherine

1992-01-01

This report documents a new version of the U.S. Geological Survey modular, three-dimensional, finite-difference, ground-water flow model (MODFLOW) which, with the new Parameter-Estimation Package that also is documented in this report, can be used to estimate parameters by nonlinear regression. The new version of MODFLOW is called MODFLOWP (pronounced MOD-FLOW*P), and functions nearly identically to MODFLOW when the ParameterEstimation Package is not used. Parameters are estimated by minimizing a weighted least-squares objective function by the modified Gauss-Newton method or by a conjugate-direction method. Parameters used to calculate the following MODFLOW model inputs can be estimated: Transmissivity and storage coefficient of confined layers; hydraulic conductivity and specific yield of unconfined layers; vertical leakance; vertical anisotropy (used to calculate vertical leakance); horizontal anisotropy; hydraulic conductance of the River, Streamflow-Routing, General-Head Boundary, and Drain Packages; areal recharge rates; maximum evapotranspiration; pumpage rates; and the hydraulic head at constant-head boundaries. Any spatial variation in parameters can be defined by the user. Data used to estimate parameters can include existing independent estimates of parameter values, observed hydraulic heads or temporal changes in hydraulic heads, and observed gains and losses along head-dependent boundaries (such as streams). Model output includes statistics for analyzing the parameter estimates and the model; these statistics can be used to quantify the reliability of the resulting model, to suggest changes in model construction, and to compare results of models constructed in different ways.

Investigation of stability in a two-delay model of the ultradian oscillations in glucose-insulin regulation

NASA Astrophysics Data System (ADS)

Huard, B.; Easton, J. F.; Angelova, M.

2015-09-01

In this paper, a two-delay model for the ultradian oscillatory behaviour of the glucose-insulin regulation system is studied. Hill functions are introduced to model nonlinear physiological interactions within this system and ranges on parameters reproducing biological oscillations are determined on the basis of analytical and numerical considerations. Local and global stability are investigated and delay-dependent conditions are obtained through the construction of Lyapunov-Krasovskii functionals. The effect of Hill parameters on these conditions, as well as the boundary of the stability region in the delay domain, are established for the first time. Numerical simulations demonstrate that the model with Hill functions represents well the oscillatory behaviour of the system with the advantage of incorporating new meaningful parameters. The influence of the time delays on the period of oscillations and the sensitivity of the latter to model parameters, in particular glucose infusion, are investigated. The model can contribute to the better understanding and treatment of diabetes.

The application of virtual prototyping methods to determine the dynamic parameters of mobile robot

NASA Astrophysics Data System (ADS)

Kurc, Krzysztof; Szybicki, Dariusz; Burghardt, Andrzej; Muszyńska, Magdalena

2016-04-01

The paper presents methods used to determine the parameters necessary to build a mathematical model of an underwater robot with a crawler drive. The parameters present in the dynamics equation will be determined by means of advanced mechatronic design tools, including: CAD/CAE software andMES modules. The virtual prototyping process is described as well as the various possible uses (design adaptability) depending on the optional accessories added to the vehicle. A mathematical model is presented to show the kinematics and dynamics of the underwater crawler robot, essential for the design stage.

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

Gronke, M.; Dijkstra, M., E-mail: maxbg@astro.uio.no

We perform Lyman- α (Ly α ) Monte-Carlo radiative transfer calculations on a suite of 2500 models of multiphase, outflowing media, which are characterized by 14 parameters. We focus on the Ly α spectra emerging from these media and investigate which properties are dominant in shaping the emerging Ly α profile. Multiphase models give rise to a wide variety of emerging spectra, including single-, double-, and triple-peaked spectra. We find that the dominant parameters in shaping the spectra include (i) the cloud covering factor, f {sub c} , which is in agreement with earlier studies, and (ii) the temperature andmore » number density of residual H i in the hot ionized medium. We attempt to reproduce spectra emerging from multiphase models with “shell models” which are commonly used to fit observed Ly α spectra, and investigate the connection between shell-model parameters and the physical parameters of the clumpy media. In shell models, the neutral hydrogen content of the shell is one of the key parameters controlling Ly α radiative transfer. Because Ly α spectra emerging from multiphase media depend much less on the neutral hydrogen content of the clumps, the shell-model parameters such as H i column density (but also shell velocity and dust content) are generally not well matched to the associated physical parameters of the clumpy media.« less

Characterization of material parameters for high speed forming and cutting via experiment and inverse simulation

NASA Astrophysics Data System (ADS)

Scheffler, Christian; Psyk, Verena; Linnemann, Maik; Tulke, Marc; Brosius, Alexander; Landgrebe, Dirk

2018-05-01

High speed velocity effects in production technology provide a broad range of technological and economic advantages [1, 2]. However, exploiting them necessitates the knowledge of strain rate dependent material behavior in process modelling. In general, high speed material data characterization features several difficulties and requires sophisticated approaches in order to provide reliable material data. This paper proposes two innovative concepts with electromagnetic and pneumatic drive and an approach for material characterization in terms of strain rate dependent flow curves and parameters of failure or damage models. The test setups have been designed for investigations of strain rates up to 105 s-1. In principle, knowledge about the temporary courses and local distributions of stress and strain in the specimen is essential for identifying material characteristics, but short process times, fast changes of the measurement values, small specimen size and frequently limited accessibility of the specimen during the test hinder directly measuring these parameters at high-velocity testing. Therefore, auxiliary test parameters, which are easier to measure, are recorded and used as input data for an inverse numerical simulation that provides the desired material characteristics, e.g. the Johnson-Cook parameters, as a result. These parameters are a force equivalent strain signal on a measurement body and the displacement of the upper specimen edge.

Experiments and modelling of rate-dependent transition delay in a stochastic subcritical bifurcation

NASA Astrophysics Data System (ADS)

Bonciolini, Giacomo; Ebi, Dominik; Boujo, Edouard; Noiray, Nicolas

2018-03-01

Complex systems exhibiting critical transitions when one of their governing parameters varies are ubiquitous in nature and in engineering applications. Despite a vast literature focusing on this topic, there are few studies dealing with the effect of the rate of change of the bifurcation parameter on the tipping points. In this work, we consider a subcritical stochastic Hopf bifurcation under two scenarios: the bifurcation parameter is first changed in a quasi-steady manner and then, with a finite ramping rate. In the latter case, a rate-dependent bifurcation delay is observed and exemplified experimentally using a thermoacoustic instability in a combustion chamber. This delay increases with the rate of change. This leads to a state transition of larger amplitude compared with the one that would be experienced by the system with a quasi-steady change of the parameter. We also bring experimental evidence of a dynamic hysteresis caused by the bifurcation delay when the parameter is ramped back. A surrogate model is derived in order to predict the statistic of these delays and to scrutinize the underlying stochastic dynamics. Our study highlights the dramatic influence of a finite rate of change of bifurcation parameters upon tipping points, and it pinpoints the crucial need of considering this effect when investigating critical transitions.

Experiments and modelling of rate-dependent transition delay in a stochastic subcritical bifurcation

PubMed Central

Noiray, Nicolas

2018-01-01

Complex systems exhibiting critical transitions when one of their governing parameters varies are ubiquitous in nature and in engineering applications. Despite a vast literature focusing on this topic, there are few studies dealing with the effect of the rate of change of the bifurcation parameter on the tipping points. In this work, we consider a subcritical stochastic Hopf bifurcation under two scenarios: the bifurcation parameter is first changed in a quasi-steady manner and then, with a finite ramping rate. In the latter case, a rate-dependent bifurcation delay is observed and exemplified experimentally using a thermoacoustic instability in a combustion chamber. This delay increases with the rate of change. This leads to a state transition of larger amplitude compared with the one that would be experienced by the system with a quasi-steady change of the parameter. We also bring experimental evidence of a dynamic hysteresis caused by the bifurcation delay when the parameter is ramped back. A surrogate model is derived in order to predict the statistic of these delays and to scrutinize the underlying stochastic dynamics. Our study highlights the dramatic influence of a finite rate of change of bifurcation parameters upon tipping points, and it pinpoints the crucial need of considering this effect when investigating critical transitions. PMID:29657803

Spatially-Dependent Modelling of Pulsar Wind Nebula G0.9+0.1

NASA Astrophysics Data System (ADS)

van Rensburg, C.; Krüger, P. P.; Venter, C.

2018-03-01

We present results from a leptonic emission code that models the spectral energy distribution of a pulsar wind nebula by solving a Fokker-Planck-type transport equation and calculating inverse Compton and synchrotron emissivities. We have created this time-dependent, multi-zone model to investigate changes in the particle spectrum as they traverse the pulsar wind nebula, by considering a time and spatially-dependent B-field, spatially-dependent bulk particle speed implying convection and adiabatic losses, diffusion, as well as radiative losses. Our code predicts the radiation spectrum at different positions in the nebula, yielding the surface brightness versus radius and the nebular size as function of energy. We compare our new model against more basic models using the observed spectrum of pulsar wind nebula G0.9+0.1, incorporating data from H.E.S.S. as well as radio and X-ray experiments. We show that simultaneously fitting the spectral energy distribution and the energy-dependent source size leads to more stringent constraints on several model parameters.

Spatially dependent modelling of pulsar wind nebula G0.9+0.1

NASA Astrophysics Data System (ADS)

van Rensburg, C.; Krüger, P. P.; Venter, C.

2018-07-01

We present results from a leptonic emission code that models the spectral energy distribution of a pulsar wind nebula by solving a Fokker-Planck-type transport equation and calculating inverse Compton and synchrotron emissivities. We have created this time-dependent, multizone model to investigate changes in the particle spectrum as they traverse the pulsar wind nebula, by considering a time and spatially dependent B-field, spatially dependent bulk particle speed implying convection and adiabatic losses, diffusion, as well as radiative losses. Our code predicts the radiation spectrum at different positions in the nebula, yielding the surface brightness versus radius and the nebular size as function of energy. We compare our new model against more basic models using the observed spectrum of pulsar wind nebula G0.9+0.1, incorporating data from H.E.S.S. as well as radio and X-ray experiments. We show that simultaneously fitting the spectral energy distribution and the energy-dependent source size leads to more stringent constraints on several model parameters.

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.

Parameter estimation for groundwater models under uncertain irrigation data

USGS Publications Warehouse

Demissie, Yonas; Valocchi, Albert J.; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen

2015-01-01

The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.

Computation of restoration of ligand response in the random kinetics of a prostate cancer cell signaling pathway.

PubMed

Dana, Saswati; Nakakuki, Takashi; Hatakeyama, Mariko; Kimura, Shuhei; Raha, Soumyendu

2011-01-01

Mutation and/or dysfunction of signaling proteins in the mitogen activated protein kinase (MAPK) signal transduction pathway are frequently observed in various kinds of human cancer. Consistent with this fact, in the present study, we experimentally observe that the epidermal growth factor (EGF) induced activation profile of MAP kinase signaling is not straightforward dose-dependent in the PC3 prostate cancer cells. To find out what parameters and reactions in the pathway are involved in this departure from the normal dose-dependency, a model-based pathway analysis is performed. The pathway is mathematically modeled with 28 rate equations yielding those many ordinary differential equations (ODE) with kinetic rate constants that have been reported to take random values in the existing literature. This has led to us treating the ODE model of the pathways kinetics as a random differential equations (RDE) system in which the parameters are random variables. We show that our RDE model captures the uncertainty in the kinetic rate constants as seen in the behavior of the experimental data and more importantly, upon simulation, exhibits the abnormal EGF dose-dependency of the activation profile of MAP kinase signaling in PC3 prostate cancer cells. The most likely set of values of the kinetic rate constants obtained from fitting the RDE model into the experimental data is then used in a direct transcription based dynamic optimization method for computing the changes needed in these kinetic rate constant values for the restoration of the normal EGF dose response. The last computation identifies the parameters, i.e., the kinetic rate constants in the RDE model, that are the most sensitive to the change in the EGF dose response behavior in the PC3 prostate cancer cells. The reactions in which these most sensitive parameters participate emerge as candidate drug targets on the signaling pathway. 2011 Elsevier Ireland Ltd. All rights reserved.

Global sensitivity analysis of a local water balance model predicting evaporation, water yield and drought

NASA Astrophysics Data System (ADS)

Speich, Matthias; Zappa, Massimiliano; Lischke, Heike

2017-04-01

Evaporation and transpiration affect both catchment water yield and the growing conditions for vegetation. They are driven by climate, but also depend on vegetation, soil and land surface properties. In hydrological and land surface models, these properties may be included as constant parameters, or as state variables. Often, little is known about the effect of these variables on model outputs. In the present study, the effect of surface properties on evaporation was assessed in a global sensitivity analysis. To this effect, we developed a simple local water balance model combining state-of-the-art process formulations for evaporation, transpiration and soil water balance. The model is vertically one-dimensional, and the relative simplicity of its process formulations makes it suitable for integration in a spatially distributed model at regional scale. The main model outputs are annual total evaporation (TE, i.e. the sum of transpiration, soil evaporation and interception), and a drought index (DI), which is based on the ratio of actual and potential transpiration. This index represents the growing conditions for forest trees. The sensitivity analysis was conducted in two steps. First, a screening analysis was applied to identify unimportant parameters out of an initial set of 19 parameters. In a second step, a statistical meta-model was applied to a sample of 800 model runs, in which the values of the important parameters were varied. Parameter effect and interactions were analyzed with effects plots. The model was driven with forcing data from ten meteorological stations in Switzerland, representing a wide range of precipitation regimes across a strong temperature gradient. Of the 19 original parameters, eight were identified as important in the screening analysis. Both steps highlighted the importance of Plant Available Water Capacity (AWC) and Leaf Area Index (LAI). However, their effect varies greatly across stations. For example, while a transition from a sparse to a closed forest canopy has almost no effect on annual TE at warm and dry sites, it increases TE by up to 100 mm/year at cold-humid and warm-humid sites. Further parameters of importance describe infiltration, as well as canopy resistance and its response to environmental variables. This study offers insights for future development of hydrological and ecohydrological models. First, it shows that although local water balance is primarily controlled by climate, the vegetation and soil parameters may have a large impact on the outputs. Second, it indicates that modeling studies should prioritize a realistic parameterization of LAI and AWC, while other parameters may be set to fixed values. Third, it illustrates to which extent parameter effect and interactions depend on local climate.

A Formal Approach to Empirical Dynamic Model Optimization and Validation

NASA Technical Reports Server (NTRS)

Crespo, Luis G; Morelli, Eugene A.; Kenny, Sean P.; Giesy, Daniel P.

2014-01-01

A framework was developed for the optimization and validation of empirical dynamic models subject to an arbitrary set of validation criteria. The validation requirements imposed upon the model, which may involve several sets of input-output data and arbitrary specifications in time and frequency domains, are used to determine if model predictions are within admissible error limits. The parameters of the empirical model are estimated by finding the parameter realization for which the smallest of the margins of requirement compliance is as large as possible. The uncertainty in the value of this estimate is characterized by studying the set of model parameters yielding predictions that comply with all the requirements. Strategies are presented for bounding this set, studying its dependence on admissible prediction error set by the analyst, and evaluating the sensitivity of the model predictions to parameter variations. This information is instrumental in characterizing uncertainty models used for evaluating the dynamic model at operating conditions differing from those used for its identification and validation. A practical example based on the short period dynamics of the F-16 is used for illustration.

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

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

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

Thomas, R. Quinn; Brooks, Evan B.; Jersild, Annika L.

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions,more » DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO 2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 10 5 km 2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO 2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO 2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO 2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO 2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.« less

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

DOE PAGES

Thomas, R. Quinn; Brooks, Evan B.; Jersild, Annika L.; ...

2017-07-26

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions,more » DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO 2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 10 5 km 2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO 2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO 2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO 2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO 2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.« less

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende

2014-01-01

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

Hole superconductivity in a generalized two-band model

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

Hong, X.Q.; Hirsch, J.E.

1992-06-01

We study superconductivity in a two-band model that generalizes the model introduced by Suhl, Matthias, and Walker: All possible interaction terms coupling both bands are included. The pairing interaction is assumed to originate in the momentum dependence of the intraband interactions that arises in the model of hole superconductivity. The model generically displays a single critical temperature and two gaps, with the larger gap associated with the band with strongest holelike character to the carriers. The dependence of the critical temperature and of the magnitudes of the gaps on the various parameters in the Hamiltonian is studied.

Time Delay Embedding Increases Estimation Precision of Models of Intraindividual Variability

ERIC Educational Resources Information Center

von Oertzen, Timo; Boker, Steven M.

2010-01-01

This paper investigates the precision of parameters estimated from local samples of time dependent functions. We find that "time delay embedding," i.e., structuring data prior to analysis by constructing a data matrix of overlapping samples, increases the precision of parameter estimates and in turn statistical power compared to standard…

On Medical Progress and Health Care Demand: A Ces Perspective Using the Grossman Model of Health Status.

PubMed

Batinti, Alberto

2015-12-01

I propose an application of the pure-consumption version of the Grossman model of health care demand, where utility depends on consumption and health status and health status on medical care and health technology. I derive the conditions under which an improvement in health care technology leads to an increase/decrease in health care consumption. In particular, I show how the direction of the effect depends on the relationship between the constant elasticity of substitution parameters of the utility and health production functions. I find that, under the constancy assumption, the ratio of the two elasticity of substitution parameters determines the direction of a technological change on health care demand. On the other hand, the technology share parameter in the health production function contributes to the size but not to the direction of the technological effect. I finally explore how the ratio of the elasticity of substitution parameters work in measurement and practice and discuss how future research may use the theoretical insight provided here. Copyright © 2014 John Wiley & Sons, Ltd.

SiC JFET Transistor Circuit Model for Extreme Temperature Range

NASA Technical Reports Server (NTRS)

Neudeck, Philip G.

2008-01-01

A technique for simulating extreme-temperature operation of integrated circuits that incorporate silicon carbide (SiC) junction field-effect transistors (JFETs) has been developed. The technique involves modification of NGSPICE, which is an open-source version of the popular Simulation Program with Integrated Circuit Emphasis (SPICE) general-purpose analog-integrated-circuit-simulating software. NGSPICE in its unmodified form is used for simulating and designing circuits made from silicon-based transistors that operate at or near room temperature. Two rapid modifications of NGSPICE source code enable SiC JFETs to be simulated to 500 C using the well-known Level 1 model for silicon metal oxide semiconductor field-effect transistors (MOSFETs). First, the default value of the MOSFET surface potential must be changed. In the unmodified source code, this parameter has a value of 0.6, which corresponds to slightly more than half the bandgap of silicon. In NGSPICE modified to simulate SiC JFETs, this parameter is changed to a value of 1.6, corresponding to slightly more than half the bandgap of SiC. The second modification consists of changing the temperature dependence of MOSFET transconductance and saturation parameters. The unmodified NGSPICE source code implements a T(sup -1.5) temperature dependence for these parameters. In order to mimic the temperature behavior of experimental SiC JFETs, a T(sup -1.3) temperature dependence must be implemented in the NGSPICE source code. Following these two simple modifications, the Level 1 MOSFET model of the NGSPICE circuit simulation program reasonably approximates the measured high-temperature behavior of experimental SiC JFETs properly operated with zero or reverse bias applied to the gate terminal. Modification of additional silicon parameters in the NGSPICE source code was not necessary to model experimental SiC JFET current-voltage performance across the entire temperature range from 25 to 500 C.

Analytic model of aurorally coupled magnetospheric and ionospheric electrostatic potentials

NASA Technical Reports Server (NTRS)

Cornwall, J. M.

1994-01-01

This paper describes modest but significant improvements on earlier studies of electrostatic potential structure in the auroral region using the adiabatic auroral arc model. This model has crucial nonlinearities (connected, for example. with aurorally produced ionization) which have hampered analysis; earlier work has either been linear, which I will show is a poor approximation or, if nonlinear, either numerical or too specialized to study parametric dependencies. With certain simplifying assumptions I find new analytic nonlinear solutions fully exhibiting the parametric dependence of potentials on magnetospheric (e.g.. cross-tail potential) and ionospheric (e.g., recombination rate) parameters. No purely phenomenological parameters are introduced. The results are in reasonable agreement with observed average auroral potential drops, inverted-V scale sizes, and dissipation rates. The dissipation rate is quite comparable to tail energization and transport rates and should have a major effect on tail and magnetospheric dynamics. This paper gives various relations between the cross-tail potential and auroral parameters (e.g., total parallel currents and potential drops) which can be studied with existing data sets.

Crystalline lens paradoxes revisited: significance of age-related restructuring of the GRIN.

PubMed

Sheil, Conor J; Goncharov, Alexander V

2017-09-01

The accommodating volume-constant age-dependent optical (AVOCADO) model of the crystalline lens is used to explore the age-related changes in ocular power and spherical aberration. The additional parameter m in the GRIN lens model allows decoupling of the axial and radial GRIN profiles, and is used to stabilise the age-related change in ocular power. Data for age-related changes in ocular geometry and lens parameter P in the axial GRIN profile were taken from published experimental data. In our age-dependent eye model, the ocular refractive power shows behaviour similar to the previously unexplained "lens paradox". Furthermore, ocular spherical aberration agrees with the data average, in contrast to the proposed "spherical aberration paradox". The additional flexibility afforded by parameter m , which controls the ratio of the axial and radial GRIN profile exponents, has allowed us to study the restructuring of the lens GRIN medium with age, resulting in a new interpretation of the origin of the power and spherical aberration paradoxes. Our findings also contradict the conceptual idea that the ageing eye is similar to the accommodating eye.

A simple model of hysteresis behavior using spreadsheet analysis

NASA Astrophysics Data System (ADS)

Ehrmann, A.; Blachowicz, T.

2015-01-01

Hysteresis loops occur in many scientific and technical problems, especially as field dependent magnetization of ferromagnetic materials, but also as stress-strain-curves of materials measured by tensile tests including thermal effects, liquid-solid phase transitions, in cell biology or economics. While several mathematical models exist which aim to calculate hysteresis energies and other parameters, here we offer a simple model for a general hysteretic system, showing different hysteresis loops depending on the defined parameters. The calculation which is based on basic spreadsheet analysis plus an easy macro code can be used by students to understand how these systems work and how the parameters influence the reactions of the system on an external field. Importantly, in the step-by-step mode, each change of the system state, compared to the last step, becomes visible. The simple program can be developed further by several changes and additions, enabling the building of a tool which is capable of answering real physical questions in the broad field of magnetism as well as in other scientific areas, in which similar hysteresis loops occur.

Finite Nuclei in the Quark-Meson Coupling Model.

PubMed

Stone, J R; Guichon, P A M; Reinhard, P G; Thomas, A W

2016-03-04

We report the first use of the effective quark-meson coupling (QMC) energy density functional (EDF), derived from a quark model of hadron structure, to study a broad range of ground state properties of even-even nuclei across the periodic table in the nonrelativistic Hartree-Fock+BCS framework. The novelty of the QMC model is that the nuclear medium effects are treated through modification of the internal structure of the nucleon. The density dependence is microscopically derived and the spin-orbit term arises naturally. The QMC EDF depends on a single set of four adjustable parameters having a clear physics basis. When applied to diverse ground state data the QMC EDF already produces, in its present simple form, overall agreement with experiment of a quality comparable to a representative Skyrme EDF. There exist, however, multiple Skyrme parameter sets, frequently tailored to describe selected nuclear phenomena. The QMC EDF set of fewer parameters, derived in this work, is not open to such variation, chosen set being applied, without adjustment, to both the properties of finite nuclei and nuclear matter.

Quarter-Wave buncher for NICA project

NASA Astrophysics Data System (ADS)

Trushin, M.; Fatkullin, R.; Sitnikov, A.; Seleznev, D.; Koshelev, V. A.; Plastun, A. S.; Barabin, S. V.; Kozlov, A. V.; Kuzmichev, V. G.; Kropachev, G. N.; Kulevoy, T.

2017-12-01

This paper represents the results of modeling the electrodynamic characteristics (EDC) for a quarter-wave coaxial beam buncher, simulation of thermal loads of the buncher, modeling of the mechanical changes in the geometric parameters caused by the thermal load of the buncher and modeling of the new EDC depended on this changes.

Random Predictor Models for Rigorous Uncertainty Quantification: Part 2

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean, the variance, and the range of the model's parameter, thus of the output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, is bounded rigorously.

Random Predictor Models for Rigorous Uncertainty Quantification: Part 1

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean and the variance of the model's parameters, thus of the predicted output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, can be bounded tightly and rigorously.

Model for the alpha and beta shear-mechanical properties of supercooled liquids and its comparison to squalane data

NASA Astrophysics Data System (ADS)

Hecksher, Tina; Olsen, Niels Boye; Dyre, Jeppe C.

2017-04-01

This paper presents data for supercooled squalane's frequency-dependent shear modulus covering frequencies from 10 mHz to 30 kHz and temperatures from 168 K to 190 K; measurements are also reported for the glass phase down to 146 K. The data reveal a strong mechanical beta process. A model is proposed for the shear response of the metastable equilibrium liquid phase of supercooled liquids. The model is an electrical equivalent-circuit characterized by additivity of the dynamic shear compliances of the alpha and beta processes. The nontrivial parts of the alpha and beta processes are each represented by a "Cole-Cole retardation element" defined as a series connection of a capacitor and a constant-phase element, resulting in the Cole-Cole compliance function well-known from dielectrics. The model, which assumes that the high-frequency decay of the alpha shear compliance loss varies with the angular frequency as ω-1 /2, has seven parameters. Assuming time-temperature superposition for the alpha and beta processes separately, the number of parameters varying with temperature is reduced to four. The model provides a better fit to the data than an equally parametrized Havriliak-Negami type model. From the temperature dependence of the best-fit model parameters, the following conclusions are drawn: (1) the alpha relaxation time conforms to the shoving model; (2) the beta relaxation loss-peak frequency is almost temperature independent; (3) the alpha compliance magnitude, which in the model equals the inverse of the instantaneous shear modulus, is only weakly temperature dependent; (4) the beta compliance magnitude decreases by a factor of three upon cooling in the temperature range studied. The final part of the paper briefly presents measurements of the dynamic adiabatic bulk modulus covering frequencies from 10 mHz to 10 kHz in the temperature range from 172 K to 200 K. The data are qualitatively similar to the shear modulus data by having a significant beta process. A single-order-parameter framework is suggested to rationalize these similarities.

Model for the alpha and beta shear-mechanical properties of supercooled liquids and its comparison to squalane data.

PubMed

Hecksher, Tina; Olsen, Niels Boye; Dyre, Jeppe C

2017-04-21

This paper presents data for supercooled squalane's frequency-dependent shear modulus covering frequencies from 10 mHz to 30 kHz and temperatures from 168 K to 190 K; measurements are also reported for the glass phase down to 146 K. The data reveal a strong mechanical beta process. A model is proposed for the shear response of the metastable equilibrium liquid phase of supercooled liquids. The model is an electrical equivalent-circuit characterized by additivity of the dynamic shear compliances of the alpha and beta processes. The nontrivial parts of the alpha and beta processes are each represented by a "Cole-Cole retardation element" defined as a series connection of a capacitor and a constant-phase element, resulting in the Cole-Cole compliance function well-known from dielectrics. The model, which assumes that the high-frequency decay of the alpha shear compliance loss varies with the angular frequency as ω -1/2 , has seven parameters. Assuming time-temperature superposition for the alpha and beta processes separately, the number of parameters varying with temperature is reduced to four. The model provides a better fit to the data than an equally parametrized Havriliak-Negami type model. From the temperature dependence of the best-fit model parameters, the following conclusions are drawn: (1) the alpha relaxation time conforms to the shoving model; (2) the beta relaxation loss-peak frequency is almost temperature independent; (3) the alpha compliance magnitude, which in the model equals the inverse of the instantaneous shear modulus, is only weakly temperature dependent; (4) the beta compliance magnitude decreases by a factor of three upon cooling in the temperature range studied. The final part of the paper briefly presents measurements of the dynamic adiabatic bulk modulus covering frequencies from 10 mHz to 10 kHz in the temperature range from 172 K to 200 K. The data are qualitatively similar to the shear modulus data by having a significant beta process. A single-order-parameter framework is suggested to rationalize these similarities.

Building a USGS National Crustal Model: Theoretical foundation, inputs, and calibration for the Western United States

NASA Astrophysics Data System (ADS)

Shah, A. K.; Boyd, O. S.; Sowers, T.; Thompson, E.

2017-12-01

Seismic hazard assessments depend on an accurate prediction of ground motion, which in turn depends on a base knowledge of three-dimensional variations in density, seismic velocity, and attenuation. We are building a National Crustal Model (NCM) using a physical theoretical foundation, 3-D geologic model, and measured data for calibration. An initial version of the NCM for the western U.S. is planned to be available in mid-2018 and for the remainder of the U.S. in 2019. The theoretical foundation of the NCM couples Biot-Gassmann theory for the porous composite with mineral physics calculations for the solid mineral matrix. The 3-D geologic model is defined through integration of results from a range of previous studies including maps of surficial porosity, surface and subsurface lithology, and the depths to bedrock and crystalline basement or seismic equivalent. The depths to bedrock and basement are estimated using well, seismic, and gravity data; in many cases these data are compiled by combining previous studies. Two parameters controlling how porosity changes with depth are assumed to be a function of lithology and calibrated using measured shear- and compressional-wave velocity and density profiles. Uncertainties in parameters derived from the model increase with depth and are dependent on the quantity and quality of input data sets. An interface to the model provides parameters needed for ground motion prediction equations in the Western U.S., including, for example, the time-averaged shear-wave velocity in the upper 30 meters (VS30) and the depths to 1.0 and 2.5 km/s shear-wave speeds (Z1.0 and Z2.5), which have a very rough correlation to the depths to bedrock and basement, as well as interpolated 3D models for use with various Urban Hazard Mapping strategies. We compare parameters needed for ground motion prediction equations including VS30, Z1.0, and Z2.5 between those derived from existing models, for example, 3-D velocity models for southern California available from the Southern California Earthquake Center, and those derived from the NCM and assess their ability to reduce the variance of observed ground motions.

Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models

NASA Astrophysics Data System (ADS)

Haslauer, C. P.; Bárdossy, A.

2017-12-01

A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.

Repetition priming in selective attention: A TVA analysis.

PubMed

Ásgeirsson, Árni Gunnar; Kristjánsson, Árni; Bundesen, Claus

2015-09-01

Current behavior is influenced by events in the recent past. In visual attention, this is expressed in many variations of priming effects. Here, we investigate color priming in a brief exposure digit-recognition task. Observers performed a masked odd-one-out singleton recognition task where the target-color either repeated or changed between subsequent trials. Performance was measured by recognition accuracy over exposure durations. The purpose of the study was to replicate earlier findings of perceptual priming in brief displays and to model those results based on a Theory of Visual Attention (TVA; Bundesen, 1990). We tested 4 different definitions of a generic TVA-model and assessed their explanatory power. Our hypothesis was that priming effects could be explained by selective mechanisms, and that target-color repetitions would only affect the selectivity parameter (α) of our models. Repeating target colors enhanced performance for all 12 observers. As predicted, this was only true under conditions that required selection of a target among distractors, but not when a target was presented alone. Model fits by TVA were obtained with a trial-by-trial maximum likelihood estimation procedure that estimated 4-15 free parameters, depending on the particular model. We draw two main conclusions. Color priming can be modeled simply as a change in selectivity between conditions of repetition or swap of target color. Depending on the desired resolution of analysis; priming can accurately be modeled by a simple four parameter model, where VSTM capacity and spatial biases of attention are ignored, or more fine-grained by a 10 parameter model that takes these aspects into account. Copyright © 2015 Elsevier B.V. All rights reserved.

Bsrightarrowtau+tau- decay in the general two Higgs doublet

NASA Astrophysics Data System (ADS)

Iltan, Erhan Onur; Turan, Gursevil

2002-11-01

We study the exclusive decay Bsrightarrowtau+tau- in the general two Higgs doublet model. We analyse the dependencies of the branching ratio on the model parameters, including the leading order QCD corrections. We found that there is an enhancement in the branching ratio, especially for rtb = bar xiN,ttU/bar xiN,bbD > 1 case. Further, the neutral Higgs effects are detectable for large values of the parameter bar xiN,tautauD.

SU-E-T-254: Optimization of GATE and PHITS Monte Carlo Code Parameters for Uniform Scanning Proton Beam Based On Simulation with FLUKA General-Purpose Code

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

Kurosu, K; Department of Medical Physics ' Engineering, Osaka University Graduate School of Medicine, Osaka; Takashina, M

Purpose: Monte Carlo codes are becoming important tools for proton beam dosimetry. However, the relationships between the customizing parameters and percentage depth dose (PDD) of GATE and PHITS codes have not been reported which are studied for PDD and proton range compared to the FLUKA code and the experimental data. Methods: The beam delivery system of the Indiana University Health Proton Therapy Center was modeled for the uniform scanning beam in FLUKA and transferred identically into GATE and PHITS. This computational model was built from the blue print and validated with the commissioning data. Three parameters evaluated are the maximummore » step size, cut off energy and physical and transport model. The dependence of the PDDs on the customizing parameters was compared with the published results of previous studies. Results: The optimal parameters for the simulation of the whole beam delivery system were defined by referring to the calculation results obtained with each parameter. Although the PDDs from FLUKA and the experimental data show a good agreement, those of GATE and PHITS obtained with our optimal parameters show a minor discrepancy. The measured proton range R90 was 269.37 mm, compared to the calculated range of 269.63 mm, 268.96 mm, and 270.85 mm with FLUKA, GATE and PHITS, respectively. Conclusion: We evaluated the dependence of the results for PDDs obtained with GATE and PHITS Monte Carlo generalpurpose codes on the customizing parameters by using the whole computational model of the treatment nozzle. The optimal parameters for the simulation were then defined by referring to the calculation results. The physical model, particle transport mechanics and the different geometrybased descriptions need accurate customization in three simulation codes to agree with experimental data for artifact-free Monte Carlo simulation. This study was supported by Grants-in Aid for Cancer Research (H22-3rd Term Cancer Control-General-043) from the Ministry of Health, Labor and Welfare of Japan, Grants-in-Aid for Scientific Research (No. 23791419), and JSPS Core-to-Core program (No. 23003). The authors have no conflict of interest.« less

The predictive performance of a path-dependent exotic-option credit risk model in the emerging market

NASA Astrophysics Data System (ADS)

Chen, Dar-Hsin; Chou, Heng-Chih; Wang, David; Zaabar, Rim

2011-06-01

Most empirical research of the path-dependent, exotic-option credit risk model focuses on developed markets. Taking Taiwan as an example, this study investigates the bankruptcy prediction performance of the path-dependent, barrier option model in the emerging market. We adopt Duan's (1994) [11], (2000) [12] transformed-data maximum likelihood estimation (MLE) method to directly estimate the unobserved model parameters, and compare the predictive ability of the barrier option model to the commonly adopted credit risk model, Merton's model. Our empirical findings show that the barrier option model is more powerful than Merton's model in predicting bankruptcy in the emerging market. Moreover, we find that the barrier option model predicts bankruptcy much better for highly-leveraged firms. Finally, our findings indicate that the prediction accuracy of the credit risk model can be improved by higher asset liquidity and greater financial transparency.

Quantifying parameter uncertainty in stochastic models using the Box Cox transformation

NASA Astrophysics Data System (ADS)

Thyer, Mark; Kuczera, George; Wang, Q. J.

2002-08-01

The Box-Cox transformation is widely used to transform hydrological data to make it approximately Gaussian. Bayesian evaluation of parameter uncertainty in stochastic models using the Box-Cox transformation is hindered by the fact that there is no analytical solution for the posterior distribution. However, the Markov chain Monte Carlo method known as the Metropolis algorithm can be used to simulate the posterior distribution. This method properly accounts for the nonnegativity constraint implicit in the Box-Cox transformation. Nonetheless, a case study using the AR(1) model uncovered a practical problem with the implementation of the Metropolis algorithm. The use of a multivariate Gaussian jump distribution resulted in unacceptable convergence behaviour. This was rectified by developing suitable parameter transformations for the mean and variance of the AR(1) process to remove the strong nonlinear dependencies with the Box-Cox transformation parameter. Applying this methodology to the Sydney annual rainfall data and the Burdekin River annual runoff data illustrates the efficacy of these parameter transformations and demonstrate the value of quantifying parameter uncertainty.

Identifying sensitive ranges in global warming precipitation change dependence on convective parameters

DOE PAGES

Bernstein, Diana N.; Neelin, J. David

2016-04-28

A branch-run perturbed-physics ensemble in the Community Earth System Model estimates impacts of parameters in the deep convection scheme on current hydroclimate and on end-of-century precipitation change projections under global warming. Regional precipitation change patterns prove highly sensitive to these parameters, especially in the tropics with local changes exceeding 3mm/d, comparable to the magnitude of the predicted change and to differences in global warming predictions among the Coupled Model Intercomparison Project phase 5 models. This sensitivity is distributed nonlinearly across the feasible parameter range, notably in the low-entrainment range of the parameter for turbulent entrainment in the deep convection scheme.more » This suggests that a useful target for parameter sensitivity studies is to identify such disproportionately sensitive dangerous ranges. Here, the low-entrainment range is used to illustrate the reduction in global warming regional precipitation sensitivity that could occur if this dangerous range can be excluded based on evidence from current climate.« less

Identifying sensitive ranges in global warming precipitation change dependence on convective parameters

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

Bernstein, Diana N.; Neelin, J. David

A branch-run perturbed-physics ensemble in the Community Earth System Model estimates impacts of parameters in the deep convection scheme on current hydroclimate and on end-of-century precipitation change projections under global warming. Regional precipitation change patterns prove highly sensitive to these parameters, especially in the tropics with local changes exceeding 3mm/d, comparable to the magnitude of the predicted change and to differences in global warming predictions among the Coupled Model Intercomparison Project phase 5 models. This sensitivity is distributed nonlinearly across the feasible parameter range, notably in the low-entrainment range of the parameter for turbulent entrainment in the deep convection scheme.more » This suggests that a useful target for parameter sensitivity studies is to identify such disproportionately sensitive dangerous ranges. Here, the low-entrainment range is used to illustrate the reduction in global warming regional precipitation sensitivity that could occur if this dangerous range can be excluded based on evidence from current climate.« less

Atomistic modelling of magnetic nano-granular thin films

NASA Astrophysics Data System (ADS)

Agudelo-Giraldo, J. D.; Arbeláez-Echeverry, O. D.; Restrepo-Parra, E.

2018-03-01

In this work, a complete model for studying the magnetic behaviour of polycrystalline thin films at nanoscale was processed. This model includes terms as exchange interaction, dipolar interaction and various types of anisotropies. For the first term, exchange interaction dependence of the distance n was used with purpose of quantify the interaction, mainly in grain boundaries. The third term includes crystalline, surface and boundary anisotropies. Special attention was paid to the disorder vector that determines the loss of cubic symmetry in the crystalline structure. For the case of the dipolar interaction, a similar implementation of the fast multiple method (FMM) was performed. Using these tools, modelling and simulations were developed varying the number of grains, and the results obtained presented a great dependence of the magnetic properties on this parameter. Comparisons between critical temperature and magnetization of saturation depending on the number of grains were performed for samples with and without factors as the surface and boundary anisotropies, and the dipolar interaction. It was observed that the inclusion of these parameters produced a decrease in the critical temperature and the magnetization of saturation; furthermore, in both cases, including and not including the disorder parameters, not only the critical temperature, but also the magnetization of saturation exhibited a range of values that also depend on the number of grains. This presence of a critical interval is due to each grain can transit toward the ferromagnetic state at different values of critical temperature. The processes of Zero field cooling (ZFC), Field cooling (FCC) and field cooling in warming mode (FCW) were necessary for understanding the mono-domain regime around of transition temperature, due to the high probabilities of a Super-paramagnetic (SPM) state.

A viscoelastic damage rheology and rate- and state-dependent friction

NASA Astrophysics Data System (ADS)

Lyakhovsky, Vladimir; Ben-Zion, Yehuda; Agnon, Amotz

2005-04-01

We analyse the relations between a viscoelastic damage rheology model and rate- and state-dependent (RS) friction. Both frameworks describe brittle deformation, although the former models localization zones in a deforming volume while the latter is associated with sliding on existing surfaces. The viscoelastic damage model accounts for evolving elastic properties and inelastic strain. The evolving elastic properties are related quantitatively to a damage state variable representing the local density of microcracks. Positive and negative changes of the damage variable lead, respectively, to degradation and recovery of the material in response to loading. A model configuration having an existing narrow zone with localized damage produces for appropriate loading and temperature-pressure conditions an overall cyclic stick-slip motion compatible with a frictional response. Each deformation cycle (limit cycle) can be divided into healing and weakening periods associated with decreasing and increasing damage, respectively. The direct effect of the RS friction and the magnitude of the frictional parameter a are related to material strengthening with increasing rate of loading. The strength and residence time of asperities (model elements) in the weakening stage depend on the rates of damage evolution and accumulation of irreversible strain. The evolutionary effect of the RS friction and overall change in the friction parameters (a-b) are controlled by the duration of the healing period and asperity (element) strengthening during this stage. For a model with spatially variable properties, the damage rheology reproduces the logarithmic dependency of the steady-state friction coefficient on the sliding velocity and the normal stress. The transition from a velocity strengthening regime to a velocity weakening one can be obtained by varying the rate of inelastic strain accumulation and keeping the other damage rheology parameters fixed. The developments unify previous damage rheology results on deformation localization leading to formation of new fault zones with detailed experimental results on frictional sliding. The results provide a route for extending the formulation of RS friction into a non-linear continuum mechanics framework.

SPOTting model parameters using a ready-made Python package

NASA Astrophysics Data System (ADS)

Houska, Tobias; Kraft, Philipp; Breuer, Lutz

2015-04-01

The selection and parameterization of reliable process descriptions in ecological modelling is driven by several uncertainties. The procedure is highly dependent on various criteria, like the used algorithm, the likelihood function selected and the definition of the prior parameter distributions. A wide variety of tools have been developed in the past decades to optimize parameters. Some of the tools are closed source. Due to this, the choice for a specific parameter estimation method is sometimes more dependent on its availability than the performance. A toolbox with a large set of methods can support users in deciding about the most suitable method. Further, it enables to test and compare different methods. We developed the SPOT (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of modules, to analyze and optimize parameters of (environmental) models. SPOT comes along with a selected set of algorithms for parameter optimization and uncertainty analyses (Monte Carlo, MC; Latin Hypercube Sampling, LHS; Maximum Likelihood, MLE; Markov Chain Monte Carlo, MCMC; Scuffled Complex Evolution, SCE-UA; Differential Evolution Markov Chain, DE-MCZ), together with several likelihood functions (Bias, (log-) Nash-Sutcliff model efficiency, Correlation Coefficient, Coefficient of Determination, Covariance, (Decomposed-, Relative-, Root-) Mean Squared Error, Mean Absolute Error, Agreement Index) and prior distributions (Binomial, Chi-Square, Dirichlet, Exponential, Laplace, (log-, multivariate-) Normal, Pareto, Poisson, Cauchy, Uniform, Weibull) to sample from. The model-independent structure makes it suitable to analyze a wide range of applications. We apply all algorithms of the SPOT package in three different case studies. Firstly, we investigate the response of the Rosenbrock function, where the MLE algorithm shows its strengths. Secondly, we study the Griewank function, which has a challenging response surface for optimization methods. Here we see simple algorithms like the MCMC struggling to find the global optimum of the function, while algorithms like SCE-UA and DE-MCZ show their strengths. Thirdly, we apply an uncertainty analysis of a one-dimensional physically based hydrological model build with the Catchment Modelling Framework (CMF). The model is driven by meteorological and groundwater data from a Free Air Carbon Enrichment (FACE) experiment in Linden (Hesse, Germany). Simulation results are evaluated with measured soil moisture data. We search for optimal parameter sets of the van Genuchten-Mualem function and find different equally optimal solutions with some of the algorithms. The case studies reveal that the implemented SPOT methods work sufficiently well. They further show the benefit of having one tool at hand that includes a number of parameter search methods, likelihood functions and a priori parameter distributions within one platform independent package.

Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates.

PubMed

Lord, Dominique; Park, Peter Young-Jin

2008-07-01

Traditionally, transportation safety analysts have used the empirical Bayes (EB) method to improve the estimate of the long-term mean of individual sites; to correct for the regression-to-the-mean (RTM) bias in before-after studies; and to identify hotspots or high risk locations. The EB method combines two different sources of information: (1) the expected number of crashes estimated via crash prediction models, and (2) the observed number of crashes at individual sites. Crash prediction models have traditionally been estimated using a negative binomial (NB) (or Poisson-gamma) modeling framework due to the over-dispersion commonly found in crash data. A weight factor is used to assign the relative influence of each source of information on the EB estimate. This factor is estimated using the mean and variance functions of the NB model. With recent trends that illustrated the dispersion parameter to be dependent upon the covariates of NB models, especially for traffic flow-only models, as well as varying as a function of different time-periods, there is a need to determine how these models may affect EB estimates. The objectives of this study are to examine how commonly used functional forms as well as fixed and time-varying dispersion parameters affect the EB estimates. To accomplish the study objectives, several traffic flow-only crash prediction models were estimated using a sample of rural three-legged intersections located in California. Two types of aggregated and time-specific models were produced: (1) the traditional NB model with a fixed dispersion parameter and (2) the generalized NB model (GNB) with a time-varying dispersion parameter, which is also dependent upon the covariates of the model. Several statistical methods were used to compare the fitting performance of the various functional forms. The results of the study show that the selection of the functional form of NB models has an important effect on EB estimates both in terms of estimated values, weight factors, and dispersion parameters. Time-specific models with a varying dispersion parameter provide better statistical performance in terms of goodness-of-fit (GOF) than aggregated multi-year models. Furthermore, the identification of hazardous sites, using the EB method, can be significantly affected when a GNB model with a time-varying dispersion parameter is used. Thus, erroneously selecting a functional form may lead to select the wrong sites for treatment. The study concludes that transportation safety analysts should not automatically use an existing functional form for modeling motor vehicle crashes without conducting rigorous analyses to estimate the most appropriate functional form linking crashes with traffic flow.

A rigorous multiple independent binding site model for determining cell-based equilibrium dissociation constants.

PubMed

Drake, Andrew W; Klakamp, Scott L

2007-01-10

A new 4-parameter nonlinear equation based on the standard multiple independent binding site model (MIBS) is presented for fitting cell-based ligand titration data in order to calculate the ligand/cell receptor equilibrium dissociation constant and the number of receptors/cell. The most commonly used linear (Scatchard Plot) or nonlinear 2-parameter model (a single binding site model found in commercial programs like Prism(R)) used for analysis of ligand/receptor binding data assumes only the K(D) influences the shape of the titration curve. We demonstrate using simulated data sets that, depending upon the cell surface receptor expression level, the number of cells titrated, and the magnitude of the K(D) being measured, this assumption of always being under K(D)-controlled conditions can be erroneous and can lead to unreliable estimates for the binding parameters. We also compare and contrast the fitting of simulated data sets to the commonly used cell-based binding equation versus our more rigorous 4-parameter nonlinear MIBS model. It is shown through these simulations that the new 4-parameter MIBS model, when used for cell-based titrations under optimal conditions, yields highly accurate estimates of all binding parameters and hence should be the preferred model to fit cell-based experimental nonlinear titration data.

The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

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

Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. Aboutmore » 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). In conclusion, the relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.« less

The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

DOE PAGES

Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter

2018-02-27

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. Aboutmore » 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). In conclusion, the relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.« less

Modeling of salt and pH gradient elution in ion-exchange chromatography.

PubMed

Schmidt, Michael; Hafner, Mathias; Frech, Christian

2014-01-01

The separation of proteins by internally and externally generated pH gradients in chromatofocusing on ion-exchange columns is a well-established analytical method with a large number of applications. In this work, a stoichiometric displacement model was used to describe the retention behavior of lysozyme on SP Sepharose FF and a monoclonal antibody on Fractogel SO3 (S) in linear salt and pH gradient elution. The pH dependence of the binding charge B in the linear gradient elution model is introduced using a protein net charge model, while the pH dependence of the equilibrium constant is based on a thermodynamic approach. The model parameter and pH dependences are calculated from linear salt gradient elutions at different pH values as well as from linear pH gradient elutions at different fixed salt concentrations. The application of the model for the well-characterized protein lysozyme resulted in almost identical model parameters based on either linear salt or pH gradient elution data. For the antibody, only the approach based on linear pH gradients is feasible because of the limited pH range useful for salt gradient elution. The application of the model for the separation of an acid variant of the antibody from the major monomeric form is discussed. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Synergistic effects in threshold models on networks

NASA Astrophysics Data System (ADS)

Juul, Jonas S.; Porter, Mason A.

2018-01-01

Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

Laser welding of polymers: phenomenological model for a quick and reliable process quality estimation considering beam shape influences

NASA Astrophysics Data System (ADS)

Timpe, Nathalie F.; Stuch, Julia; Scholl, Marcus; Russek, Ulrich A.

2016-03-01

This contribution presents a phenomenological, analytical model for laser welding of polymers which is suited for a quick process quality estimation for the practitioner. Besides material properties of the polymer and processing parameters like welding pressure, feed rate and laser power the model is based on a simple few parameter description of the size and shape of the laser power density distribution (PDD) in the processing zone. The model allows an estimation of the weld seam tensile strength. It is based on energy balance considerations within a thin sheet with the thickness of the optical penetration depth on the surface of the absorbing welding partner. The joining process itself is modelled by a phenomenological approach. The model reproduces the experimentally known process windows for the main process parameters correctly. Using the parameters describing the shape of the laser PDD the critical dependence of the process windows on the PDD shape will be predicted and compared with experiments. The adaption of the model to other laser manufacturing processes where the PDD influence can be modelled comparably will be discussed.

Sequential Markov chain Monte Carlo filter with simultaneous model selection for electrocardiogram signal modeling.

PubMed

Edla, Shwetha; Kovvali, Narayan; Papandreou-Suppappola, Antonia

2012-01-01

Constructing statistical models of electrocardiogram (ECG) signals, whose parameters can be used for automated disease classification, is of great importance in precluding manual annotation and providing prompt diagnosis of cardiac diseases. ECG signals consist of several segments with different morphologies (namely the P wave, QRS complex and the T wave) in a single heart beat, which can vary across individuals and diseases. Also, existing statistical ECG models exhibit a reliance upon obtaining a priori information from the ECG data by using preprocessing algorithms to initialize the filter parameters, or to define the user-specified model parameters. In this paper, we propose an ECG modeling technique using the sequential Markov chain Monte Carlo (SMCMC) filter that can perform simultaneous model selection, by adaptively choosing from different representations depending upon the nature of the data. Our results demonstrate the ability of the algorithm to track various types of ECG morphologies, including intermittently occurring ECG beats. In addition, we use the estimated model parameters as the feature set to classify between ECG signals with normal sinus rhythm and four different types of arrhythmia.

Parameter de-correlation and model-identification in hybrid-style terrestrial laser scanner self-calibration

NASA Astrophysics Data System (ADS)

Lichti, Derek D.; Chow, Jacky; Lahamy, Hervé

One of the important systematic error parameters identified in terrestrial laser scanners is the collimation axis error, which models the non-orthogonality between two instrumental axes. The quality of this parameter determined by self-calibration, as measured by its estimated precision and its correlation with the tertiary rotation angle κ of the scanner exterior orientation, is strongly dependent on instrument architecture. While the quality is generally very high for panoramic-type scanners, it is comparably poor for hybrid-style instruments. Two methods for improving the quality of the collimation axis error in hybrid instrument self-calibration are proposed herein: (1) the inclusion of independent observations of the tertiary rotation angle κ; and (2) the use of a new collimation axis error model. Five real datasets were captured with two different hybrid-style scanners to test each method's efficacy. While the first method achieves the desired outcome of complete decoupling of the collimation axis error from κ, it is shown that the high correlation is simply transferred to other model variables. The second method achieves partial parameter de-correlation to acceptable levels. Importantly, it does so without any adverse, secondary correlations and is therefore the method recommended for future use. Finally, systematic error model identification has been greatly aided in previous studies by graphical analyses of self-calibration residuals. This paper presents results showing the architecture dependence of this technique, revealing its limitations for hybrid scanners.

A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns

NASA Astrophysics Data System (ADS)

Ferdous, Nazneen; Bhat, Chandra R.

2013-01-01

This paper proposes and estimates a spatial panel ordered-response probit model with temporal autoregressive error terms to analyze changes in urban land development intensity levels over time. Such a model structure maintains a close linkage between the land owner's decision (unobserved to the analyst) and the land development intensity level (observed by the analyst) and accommodates spatial interactions between land owners that lead to spatial spillover effects. In addition, the model structure incorporates spatial heterogeneity as well as spatial heteroscedasticity. The resulting model is estimated using a composite marginal likelihood (CML) approach that does not require any simulation machinery and that can be applied to data sets of any size. A simulation exercise indicates that the CML approach recovers the model parameters very well, even in the presence of high spatial and temporal dependence. In addition, the simulation results demonstrate that ignoring spatial dependency and spatial heterogeneity when both are actually present will lead to bias in parameter estimation. A demonstration exercise applies the proposed model to examine urban land development intensity levels using parcel-level data from Austin, Texas.

Modelling of mechanical and filtration processes near the well with regard to anisotropy

NASA Astrophysics Data System (ADS)

Karev, V. I.; Klimov, D. M.; Kovalenko, Yu F.; Ustinov, K. B.

2018-04-01

A geomechanical approach to modeling deformation and seepage is presented. Three stages of modeling are described: choice of an appropriate mechanical model and its adaptation to the case in question, experimental determination of parameters of the model, simulation of processes of seepage for particular configurations of the well. The applied model allows describing the main specific characteristics of mechanical behavior of the collector: the influence of the pore pressure on deformation; the influence of not only shear but also comprehensive stresses and pore pressure on the transition to inelastic behavior; the appearance of inelastic volumetric deformation and its nontrivial dependence on the stress state; the anisotropy of elastic, strength and seepage properties; non-obvious dependence of permeability on the stress strain state. The model unites essential characteristics of Hill’s plastic flow theory for anisotropic materials and the Drucker–Prager theory for inelastic deformation of soils. The results of experimental determination of the involved parameters obtained using true triaxial loading system for the collector of Vladimir Filanovsky field in the Caspian Sea are presented.

The nuclear Thomas-Fermi model

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

Myers, W.D.; Swiatecki, W.J.

1994-08-01

The statistical Thomas-Fermi model is applied to a comprehensive survey of macroscopic nuclear properties. The model uses a Seyler-Blanchard effective nucleon-nucleon interaction, generalized by the addition of one momentum-dependent and one density-dependent term. The adjustable parameters of the interaction were fitted to shell-corrected masses of 1654 nuclei, to the diffuseness of the nuclear surface and to the measured depths of the optical model potential. With these parameters nuclear sizes are well reproduced, and only relatively minor deviations between measured and calculated fission barriers of 36 nuclei are found. The model determines the principal bulk and surface properties of nuclear mattermore » and provides estimates for the more subtle, Droplet Model, properties. The predicted energy vs density relation for neutron matter is in striking correspondence with the 1981 theoretical estimate of Friedman and Pandharipande. Other extreme situations to which the model is applied are a study of Sn isotopes from {sup 82}Sn to {sup 170}Sn, and the rupture into a bubble configuration of a nucleus (constrained to spherical symmetry) which takes place when Z{sup 2}/A exceeds about 100.« less

The Nuclear Thomas-Fermi Model

DOE R&D Accomplishments Database

Myers, W. D.; Swiatecki, W. J.

1994-08-01

The statistical Thomas-Fermi model is applied to a comprehensive survey of macroscopic nuclear properties. The model uses a Seyler-Blanchard effective nucleon-nucleon interaction, generalized by the addition of one momentum-dependent and one density-dependent term. The adjustable parameters of the interaction were fitted to shell-corrected masses of 1654 nuclei, to the diffuseness of the nuclear surface and to the measured depths of the optical model potential. With these parameters nuclear sizes are well reproduced, and only relatively minor deviations between measured and calculated fission barriers of 36 nuclei are found. The model determines the principal bulk and surface properties of nuclear matter and provides estimates for the more subtle, Droplet Model, properties. The predicted energy vs density relation for neutron matter is in striking correspondence with the 1981 theoretical estimate of Friedman and Pandharipande. Other extreme situations to which the model is applied are a study of Sn isotopes from {sup 82}Sn to {sup 170}Sn, and the rupture into a bubble configuration of a nucleus (constrained to spherical symmetry) which takes place when Z{sup 2}/A exceeds about 100.

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.

Bayesian Analysis of Non-Gaussian Long-Range Dependent Processes

NASA Astrophysics Data System (ADS)

Graves, T.; Franzke, C.; Gramacy, R. B.; Watkins, N. W.

2012-12-01

Recent studies have strongly suggested that surface temperatures exhibit long-range dependence (LRD). The presence of LRD would hamper the identification of deterministic trends and the quantification of their significance. It is well established that LRD processes exhibit stochastic trends over rather long periods of time. Thus, accurate methods for discriminating between physical processes that possess long memory and those that do not are an important adjunct to climate modeling. We have used Markov Chain Monte Carlo algorithms to perform a Bayesian analysis of Auto-Regressive Fractionally-Integrated Moving-Average (ARFIMA) processes, which are capable of modeling LRD. Our principal aim is to obtain inference about the long memory parameter, d,with secondary interest in the scale and location parameters. We have developed a reversible-jump method enabling us to integrate over different model forms for the short memory component. We initially assume Gaussianity, and have tested the method on both synthetic and physical time series such as the Central England Temperature. Many physical processes, for example the Faraday time series from Antarctica, are highly non-Gaussian. We have therefore extended this work by weakening the Gaussianity assumption. Specifically, we assume a symmetric α -stable distribution for the innovations. Such processes provide good, flexible, initial models for non-Gaussian processes with long memory. We will present a study of the dependence of the posterior variance σ d of the memory parameter d on the length of the time series considered. This will be compared with equivalent error diagnostics for other measures of d.

Determination of the glass-transition temperature of proteins from a viscometric approach.

PubMed

Monkos, Karol

2015-03-01

All fully hydrated proteins undergo a distinct change in their dynamical properties at glass-transition temperature Tg. To determine indirectly this temperature for dry albumins, the viscosity measurements of aqueous solutions of human, equine, ovine, porcine and rabbit serum albumin have been conducted at a wide range of concentrations and at temperatures ranging from 278 K to 318 K. Viscosity-temperature dependence of the solutions is discussed on the basis of the three parameters equation resulting from Avramov's model. One of the parameter in the Avramov's equation is the glass-transition temperature. For all studied albumins, Tg of a solution monotonically increases with increasing concentration. The glass-transition temperature of a solution depends both on Tg for a dissolved dry protein Tg,p and water Tg,w. To obtain Tg,p for each studied albumin the modified Gordon-Taylor equation was applied. This equation describes the dependence of Tg of a solution on concentration, and Tg,p and a parameter depending on the strength of the protein-solvent interaction are the fitting parameters. Thus determined the glass-transition temperature for the studied dry albumins is in the range (215.4-245.5)K. Copyright © 2014 Elsevier B.V. All rights reserved.

Temperature and depth dependence of positron annihilation parameters in YBa2Cu3O7-x and La1.85Sr0.15CuO4

NASA Astrophysics Data System (ADS)

Lynn, K. G.; Usmar, S. G.; Nielsen, B.; van der Kolk, G. J.; Kanazawa, I.; Sferlazzo, P.; Moodenbaugh, A. R.

1988-02-01

The temperature dependence of the positron annihilation parameters for YBa2Cu3O7-x x=0.7, 0.4 and 0.0 and La1.85Sr0.15CuO4 were measured. The depth dependence of the YBa2Cu3O7 was studied using a variable-energy positron beam showing a strong depth dependence in the Doppler line-shape extending up to an average depth of ˜5.0 μm. It was found that a transition in the Doppler line-shape parameter, ``S'', was associated with the superconducting transition temperature (Tc) in YBa2Cu3O7-x x=0.4 and 0.0 while no transition was observed in the nonsuperconducting YBa2Cu3O6.3. Positron lifetime parameters in YBa2Cu3O7 were found to be consistent with positrons localized at open volume regions (probably unoccupied crystallographic sites) in this material with a lifetime of 210 psec at 300 K. These results indicate that the electron density at these unoccupied sites increases, using a free electron model, approximately 9% between 100 and 12 K.

Parameter-induced uncertainty quantification of crop yields, soil N2O and CO2 emission for 8 arable sites across Europe using the LandscapeDNDC model

NASA Astrophysics Data System (ADS)

Santabarbara, Ignacio; Haas, Edwin; Kraus, David; Herrera, Saul; Klatt, Steffen; Kiese, Ralf

2014-05-01

When using biogeochemical models to estimate greenhouse gas emissions at site to regional/national levels, the assessment and quantification of the uncertainties of simulation results are of significant importance. The uncertainties in simulation results of process-based ecosystem models may result from uncertainties of the process parameters that describe the processes of the model, model structure inadequacy as well as uncertainties in the observations. Data for development and testing of uncertainty analisys were corp yield observations, measurements of soil fluxes of nitrous oxide (N2O) and carbon dioxide (CO2) from 8 arable sites across Europe. Using the process-based biogeochemical model LandscapeDNDC for simulating crop yields, N2O and CO2 emissions, our aim is to assess the simulation uncertainty by setting up a Bayesian framework based on Metropolis-Hastings algorithm. Using Gelman statistics convergence criteria and parallel computing techniques, enable multi Markov Chains to run independently in parallel and create a random walk to estimate the joint model parameter distribution. Through means distribution we limit the parameter space, get probabilities of parameter values and find the complex dependencies among them. With this parameter distribution that determines soil-atmosphere C and N exchange, we are able to obtain the parameter-induced uncertainty of simulation results and compare them with the measurements data.

R programming for parameters estimation of geographically weighted ordinal logistic regression (GWOLR) model based on Newton Raphson

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Saputro, Dewi Retno Sari

2017-03-01

GWOLR model used for represent relationship between dependent variable has categories and scale of category is ordinal with independent variable influenced the geographical location of the observation site. Parameters estimation of GWOLR model use maximum likelihood provide system of nonlinear equations and hard to be found the result in analytic resolution. By finishing it, it means determine the maximum completion, this thing associated with optimizing problem. The completion nonlinear system of equations optimize use numerical approximation, which one is Newton Raphson method. The purpose of this research is to make iteration algorithm Newton Raphson and program using R software to estimate GWOLR model. Based on the research obtained that program in R can be used to estimate the parameters of GWOLR model by forming a syntax program with command "while".

Methodology for modeling the devolatilization of refuse-derived fuel from thermogravimetric analysis of municipal solid waste components

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

Fritsky, K.J.; Miller, D.L.; Cernansky, N.P.

1994-09-01

A methodology was introduced for modeling the devolatilization characteristics of refuse-derived fuel (RFD) in terms of temperature-dependent weight loss. The basic premise of the methodology is that RDF is modeled as a combination of select municipal solid waste (MSW) components. Kinetic parameters are derived for each component from thermogravimetric analyzer (TGA) data measured at a specific set of conditions. These experimentally derived parameters, along with user-derived parameters, are inputted to model equations for the purpose of calculating thermograms for the components. The component thermograms are summed to create a composite thermogram that is an estimate of the devolatilization for themore » as-modeled RFD. The methodology has several attractive features as a thermal analysis tool for waste fuels. 7 refs., 10 figs., 3 tabs.« less

Wave propagation in the Lorenz-96 model

NASA Astrophysics Data System (ADS)

van Kekem, Dirk L.; Sterk, Alef E.

2018-04-01

In this paper we study the spatiotemporal properties of waves in the Lorenz-96 model and their dependence on the dimension parameter n and the forcing parameter F. For F > 0 the first bifurcation is either a supercritical Hopf or a double-Hopf bifurcation and the periodic attractor born at these bifurcations represents a traveling wave. Its spatial wave number increases linearly with n, but its period tends to a finite limit as n → ∞. For F < 0 and odd n, the first bifurcation is again a supercritical Hopf bifurcation, but in this case the period of the traveling wave also grows linearly with n. For F < 0 and even n, however, a Hopf bifurcation is preceded by either one or two pitchfork bifurcations, where the number of the latter bifurcations depends on whether n has remainder 2 or 0 upon division by 4. This bifurcation sequence leads to stationary waves and their spatiotemporal properties also depend on the remainder after dividing n by 4. Finally, we explain how the double-Hopf bifurcation can generate two or more stable waves with different spatiotemporal properties that coexist for the same parameter values n and F.

An information driven strategy to support multidisciplinary design

NASA Technical Reports Server (NTRS)

Rangan, Ravi M.; Fulton, Robert E.

1990-01-01

The design of complex engineering systems such as aircraft, automobiles, and computers is primarily a cooperative multidisciplinary design process involving interactions between several design agents. The common thread underlying this multidisciplinary design activity is the information exchange between the various groups and disciplines. The integrating component in such environments is the common data and the dependencies that exist between such data. This may be contrasted to classical multidisciplinary analyses problems where there is coupling between distinct design parameters. For example, they may be expressed as mathematically coupled relationships between aerodynamic and structural interactions in aircraft structures, between thermal and structural interactions in nuclear plants, and between control considerations and structural interactions in flexible robots. These relationships provide analytical based frameworks leading to optimization problem formulations. However, in multidisciplinary design problems, information based interactions become more critical. Many times, the relationships between different design parameters are not amenable to analytical characterization. Under such circumstances, information based interactions will provide the best integration paradigm, i.e., there is a need to model the data entities and their dependencies between design parameters originating from different design agents. The modeling of such data interactions and dependencies forms the basis for integrating the various design agents.

Sensitivity of viscosity Arrhenius parameters to polarity of liquids

NASA Astrophysics Data System (ADS)

Kacem, R. B. H.; Alzamel, N. O.; Ouerfelli, N.

2017-09-01

Several empirical and semi-empirical equations have been proposed in the literature to estimate the liquid viscosity upon temperature. In this context, this paper aims to study the effect of polarity of liquids on the modeling of the viscosity-temperature dependence, considering particularly the Arrhenius type equations. To achieve this purpose, the solvents are classified into three groups: nonpolar, borderline polar and polar solvents. Based on adequate statistical tests, we found that there is strong evidence that the polarity of solvents affects significantly the distribution of the Arrhenius-type equation parameters and consequently the modeling of the viscosity-temperature dependence. Thus, specific estimated values of parameters for each group of liquids are proposed in this paper. In addition, the comparison of the accuracy of approximation with and without classification of liquids, using the Wilcoxon signed-rank test, shows a significant discrepancy of the borderline polar solvents. For that, we suggested in this paper new specific coefficient values of the simplified Arrhenius-type equation for better estimation accuracy. This result is important given that the accuracy in the estimation of the viscosity-temperature dependence may affect considerably the design and the optimization of several industrial processes.

Fractal Model of Fission Product Release in Nuclear Fuel

NASA Astrophysics Data System (ADS)

Stankunas, Gediminas

2012-09-01

A model of fission gas migration in nuclear fuel pellet is proposed. Diffusion process of fission gas in granular structure of nuclear fuel with presence of inter-granular bubbles in the fuel matrix is simulated by fractional diffusion model. The Grunwald-Letnikov derivative parameter characterizes the influence of porous fuel matrix on the diffusion process of fission gas. A finite-difference method for solving fractional diffusion equations is considered. Numerical solution of diffusion equation shows correlation of fission gas release and Grunwald-Letnikov derivative parameter. Calculated profile of fission gas concentration distribution is similar to that obtained in the experimental studies. Diffusion of fission gas is modeled for real RBMK-1500 fuel operation conditions. A functional dependence of Grunwald-Letnikov derivative parameter with fuel burn-up is established.

Estimating system parameters for solvent-water and plant cuticle-water using quantum chemically estimated Abraham solute parameters.

PubMed

Liang, Yuzhen; Torralba-Sanchez, Tifany L; Di Toro, Dominic M

2018-04-18

Polyparameter Linear Free Energy Relationships (pp-LFERs) using Abraham system parameters have many useful applications. However, developing the Abraham system parameters depends on the availability and quality of the Abraham solute parameters. Using Quantum Chemically estimated Abraham solute Parameters (QCAP) is shown to produce pp-LFERs that have lower root mean square errors (RMSEs) of predictions for solvent-water partition coefficients than parameters that are estimated using other presently available methods. pp-LFERs system parameters are estimated for solvent-water, plant cuticle-water systems, and for novel compounds using QCAP solute parameters and experimental partition coefficients. Refitting the system parameter improves the calculation accuracy and eliminates the bias. Refitted models for solvent-water partition coefficients using QCAP solute parameters give better results (RMSE = 0.278 to 0.506 log units for 24 systems) than those based on ABSOLV (0.326 to 0.618) and QSPR (0.294 to 0.700) solute parameters. For munition constituents and munition-like compounds not included in the calibration of the refitted model, QCAP solute parameters produce pp-LFER models with much lower RMSEs for solvent-water partition coefficients (RMSE = 0.734 and 0.664 for original and refitted model, respectively) than ABSOLV (4.46 and 5.98) and QSPR (2.838 and 2.723). Refitting plant cuticle-water pp-LFER including munition constituents using QCAP solute parameters also results in lower RMSE (RMSE = 0.386) than that using ABSOLV (0.778) and QSPR (0.512) solute parameters. Therefore, for fitting a model in situations for which experimental data exist and system parameters can be re-estimated, or for which system parameters do not exist and need to be developed, QCAP is the quantum chemical method of choice.

Modeling and analysis of the solar concentrator in photovoltaic systems

NASA Astrophysics Data System (ADS)

Mroczka, Janusz; Plachta, Kamil

2015-06-01

The paper presents the Λ-ridge and V-trough concentrator system with a low concentration ratio. Calculations and simulations have been made in the program created by the author. The results of simulation allow to choose the best parameters of photovoltaic system: the opening angle between the surface of the photovoltaic module and mirrors, resolution of the tracking system and the material for construction of the concentrator mirrors. The research shows the effect each of these parameters on the efficiency of the photovoltaic system and method of surface modeling using BRDF function. The parameters of concentrator surface (eg. surface roughness) were calculated using a new algorithm based on the BRDF function. The algorithm uses a combination of model Torrance-Sparrow and HTSG. The simulation shows the change in voltage, current and output power depending on system parameters.

Soil mechanics: breaking ground.

PubMed

Einav, Itai

2007-12-15

In soil mechanics, student's models are classified as simple models that teach us unexplained elements of behaviour; an example is the Cam clay constitutive models of critical state soil mechanics (CSSM). 'Engineer's models' are models that elaborate the theory to fit more behavioural trends; this is usually done by adding fitting parameters to the student's models. Can currently unexplained behavioural trends of soil be explained without adding fitting parameters to CSSM models, by developing alternative student's models based on modern theories?Here I apply an alternative theory to CSSM, called 'breakage mechanics', and develop a simple student's model for sand. Its unique and distinctive feature is the use of an energy balance equation that connects grain size reduction to consumption of energy, which enables us to predict how grain size distribution (gsd) evolves-an unprecedented capability in constitutive modelling. With only four parameters, the model is physically clarifying what CSSM cannot for sand: the dependency of yielding and critical state on the initial gsd and void ratio.

Size-dependent geometrically nonlinear free vibration analysis of fractional viscoelastic nanobeams based on the nonlocal elasticity theory

NASA Astrophysics Data System (ADS)

Ansari, R.; Faraji Oskouie, M.; Gholami, R.

2016-01-01

In recent decades, mathematical modeling and engineering applications of fractional-order calculus have been extensively utilized to provide efficient simulation tools in the field of solid mechanics. In this paper, a nonlinear fractional nonlocal Euler-Bernoulli beam model is established using the concept of fractional derivative and nonlocal elasticity theory to investigate the size-dependent geometrically nonlinear free vibration of fractional viscoelastic nanobeams. The non-classical fractional integro-differential Euler-Bernoulli beam model contains the nonlocal parameter, viscoelasticity coefficient and order of the fractional derivative to interpret the size effect, viscoelastic material and fractional behavior in the nanoscale fractional viscoelastic structures, respectively. In the solution procedure, the Galerkin method is employed to reduce the fractional integro-partial differential governing equation to a fractional ordinary differential equation in the time domain. Afterwards, the predictor-corrector method is used to solve the nonlinear fractional time-dependent equation. Finally, the influences of nonlocal parameter, order of fractional derivative and viscoelasticity coefficient on the nonlinear time response of fractional viscoelastic nanobeams are discussed in detail. Moreover, comparisons are made between the time responses of linear and nonlinear models.

Angle-dependent spin-wave resonance spectroscopy of (Ga,Mn)As films

NASA Astrophysics Data System (ADS)

Dreher, L.; Bihler, C.; Peiner, E.; Waag, A.; Schoch, W.; Limmer, W.; Goennenwein, S. T. B.; Brandt, M. S.

2013-06-01

A modeling approach for standing spin-wave resonances based on a finite-difference formulation of the Landau-Lifshitz-Gilbert equation is presented. In contrast to a previous study [C. Bihler , Phys. Rev. BPRBMDO1098-012110.1103/PhysRevB.79.045205 79, 045205 (2009)], this formalism accounts for elliptical magnetization precession and magnetic properties arbitrarily varying across the layer thickness, including the magnetic anisotropy parameters, the exchange stiffness, the Gilbert damping, and the saturation magnetization. To demonstrate the usefulness of our modeling approach, we experimentally study a set of (Ga,Mn)As samples grown by low-temperature molecular-beam epitaxy by means of angle-dependent standing spin-wave resonance spectroscopy and electrochemical capacitance-voltage measurements. By applying our modeling approach, the angle dependence of the spin-wave resonance data can be reproduced in a simulation with one set of simulation parameters for all external field orientations. We find that the approximately linear gradient in the out-of-plane magnetic anisotropy is related to a linear gradient in the hole concentrations of the samples.

Calibration of Predictor Models Using Multiple Validation Experiments

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This paper presents a framework for calibrating computational models using data from several and possibly dissimilar validation experiments. The offset between model predictions and observations, which might be caused by measurement noise, model-form uncertainty, and numerical error, drives the process by which uncertainty in the models parameters is characterized. The resulting description of uncertainty along with the computational model constitute a predictor model. Two types of predictor models are studied: Interval Predictor Models (IPMs) and Random Predictor Models (RPMs). IPMs use sets to characterize uncertainty, whereas RPMs use random vectors. The propagation of a set through a model makes the response an interval valued function of the state, whereas the propagation of a random vector yields a random process. Optimization-based strategies for calculating both types of predictor models are proposed. Whereas the formulations used to calculate IPMs target solutions leading to the interval value function of minimal spread containing all observations, those for RPMs seek to maximize the models' ability to reproduce the distribution of observations. Regarding RPMs, we choose a structure for the random vector (i.e., the assignment of probability to points in the parameter space) solely dependent on the prediction error. As such, the probabilistic description of uncertainty is not a subjective assignment of belief, nor is it expected to asymptotically converge to a fixed value, but instead it casts the model's ability to reproduce the experimental data. This framework enables evaluating the spread and distribution of the predicted response of target applications depending on the same parameters beyond the validation domain.

LONG-TERM PROJECTIONS OF EASTERN OYSTER POPULATIONS UNDER VARIOUS MANAGEMENT SCENARIOS

EPA Science Inventory

Time series of fishery-dependent and fishery-independent data were used to parameterize a model of oyster population dynamics for Maryland's Chesapeake Bay. Model parameters are (1) fishing mortality, estimated from differences between predicted and reported landings scaled to a ...

COST VS. QUALITY IN DEMOGRAPHIC MODELLING: WHEN IS A VITAL RATE GOOD ENOUGH?

EPA Science Inventory

This presentation will focus on the assessment of quality for demographic parameters to be used in population-level risk assessment. Current population models can handle genetic, demographic, and environmental stochasticity, density dependence, and multiple stressors. However, cu...

Simple, distance-dependent formulation of the Watts-Strogatz model for directed and undirected small-world networks.

PubMed

Song, H Francis; Wang, Xiao-Jing

2014-12-01

Small-world networks-complex networks characterized by a combination of high clustering and short path lengths-are widely studied using the paradigmatic model of Watts and Strogatz (WS). Although the WS model is already quite minimal and intuitive, we describe an alternative formulation of the WS model in terms of a distance-dependent probability of connection that further simplifies, both practically and theoretically, the generation of directed and undirected WS-type small-world networks. In addition to highlighting an essential feature of the WS model that has previously been overlooked, namely the equivalence to a simple distance-dependent model, this alternative formulation makes it possible to derive exact expressions for quantities such as the degree and motif distributions and global clustering coefficient for both directed and undirected networks in terms of model parameters.

Simple, distance-dependent formulation of the Watts-Strogatz model for directed and undirected small-world networks

NASA Astrophysics Data System (ADS)

Song, H. Francis; Wang, Xiao-Jing

2014-12-01

Small-world networks—complex networks characterized by a combination of high clustering and short path lengths—are widely studied using the paradigmatic model of Watts and Strogatz (WS). Although the WS model is already quite minimal and intuitive, we describe an alternative formulation of the WS model in terms of a distance-dependent probability of connection that further simplifies, both practically and theoretically, the generation of directed and undirected WS-type small-world networks. In addition to highlighting an essential feature of the WS model that has previously been overlooked, namely the equivalence to a simple distance-dependent model, this alternative formulation makes it possible to derive exact expressions for quantities such as the degree and motif distributions and global clustering coefficient for both directed and undirected networks in terms of model parameters.

On the sensitivity analysis of porous material models

NASA Astrophysics Data System (ADS)

Ouisse, Morvan; Ichchou, Mohamed; Chedly, Slaheddine; Collet, Manuel

2012-11-01

Porous materials are used in many vibroacoustic applications. Different available models describe their behaviors according to materials' intrinsic characteristics. For instance, in the case of porous material with rigid frame, and according to the Champoux-Allard model, five parameters are employed. In this paper, an investigation about this model sensitivity to parameters according to frequency is conducted. Sobol and FAST algorithms are used for sensitivity analysis. A strong parametric frequency dependent hierarchy is shown. Sensitivity investigations confirm that resistivity is the most influent parameter when acoustic absorption and surface impedance of porous materials with rigid frame are considered. The analysis is first performed on a wide category of porous materials, and then restricted to a polyurethane foam analysis in order to illustrate the impact of the reduction of the design space. In a second part, a sensitivity analysis is performed using the Biot-Allard model with nine parameters including mechanical effects of the frame and conclusions are drawn through numerical simulations.

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

Kinetics of bacterial phospholipase C activity at micellar interfaces: effect of substrate aggregate microstructure and a model for the kinetic parameters.

PubMed

Singh, Jasmeet; Ranganathan, Radha; Hajdu, Joseph

2008-12-25

Activity at micellar interfaces of bacterial phospholipase C from Bacillus cereus on phospholipids solubilized in micelles was investigated with the goal of elucidating the role of the interface microstructure and developing further an existing kinetic model. Enzyme kinetics and physicochemical characterization of model substrate aggregates were combined, thus enabling the interpretation of kinetics in the context of the interface. Substrates were diacylphosphatidylcholine of different acyl chain lengths in the form of mixed micelles with dodecyldimethylammoniopropanesulfonate. An early kinetic model, reformulated to reflect the interfacial nature of the kinetics, was applied to the kinetic data. A better method of data treatment is proposed, use of which makes the presence of microstructure effects quite transparent. Models for enzyme-micelle binding and enzyme-lipid binding are developed, and expressions incorporating the microstructural properties are derived for the enzyme-micelle dissociation constant K(s) and the interface Michaelis-Menten constant, K(M). Use of these expressions in the interface kinetic model brings excellent agreement between the kinetic data and the model. Numerical values for the thermodynamic and kinetic parameters are determined. Enzyme-lipid binding is found to be an activated process with an acyl chain length dependent free energy of activation that decreases with micelle lipid molar fraction with a coefficient of about -15RT and correlates with the tightness of molecular packing in the substrate aggregate. Thus, the physical insight obtained includes a model for the kinetic parameters that shows that these parameters depend on the substrate concentration and acyl chain length of the lipid. Enzyme-micelle binding is indicated to be hydrophobic and solvent mediated with a dissociation constant of 1.2 mM.

Density functional and theoretical study of the temperature and pressure dependency of the plasmon energy of solids

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

Attarian Shandiz, M., E-mail: mohammad.attarianshandiz@mail.mcgill.ca; Gauvin, R.

The temperature and pressure dependency of the volume plasmon energy of solids was investigated by density functional theory calculations. The volume change of crystal is the major factor responsible for the variation of valence electron density and plasmon energy in the free electron model. Hence, to introduce the effect of temperature and pressure for the density functional theory calculations of plasmon energy, the temperature and pressure dependency of lattice parameter was used. Also, by combination of the free electron model and the equation of state based on the pseudo-spinodal approach, the temperature and pressure dependency of the plasmon energy wasmore » modeled. The suggested model is in good agreement with the results of density functional theory calculations and available experimental data for elements with the free electron behavior.« less

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

Bellomo, Nicola; Bellini, Emilio; Hu, Bin

Cosmological observables show a dependence with the neutrino mass, which is partially degenerate with parameters of extended models of gravity. We study and explore this degeneracy in Horndeski generalized scalar-tensor theories of gravity. Using forecasted cosmic microwave background and galaxy power spectrum datasets, we find that a single parameter in the linear regime of the effective theory dominates the correlation with the total neutrino mass. For any given mass, a particular value of this parameter approximately cancels the power suppression due to the neutrino mass at a given redshift. The extent of the cancellation of this degeneracy depends on themore » cosmological large-scale structure data used at different redshifts. We constrain the parameters and functions of the effective gravity theory and determine the influence of gravity on the determination of the neutrino mass from present and future surveys.« less

A versatile phenomenological model for the S-shaped temperature dependence of photoluminescence energy for an accurate determination of the exciton localization energy in bulk and quantum well structures

NASA Astrophysics Data System (ADS)

Dixit, V. K.; Porwal, S.; Singh, S. D.; Sharma, T. K.; Ghosh, Sandip; Oak, S. M.

2014-02-01

Temperature dependence of the photoluminescence (PL) peak energy of bulk and quantum well (QW) structures is studied by using a new phenomenological model for including the effect of localized states. In general an anomalous S-shaped temperature dependence of the PL peak energy is observed for many materials which is usually associated with the localization of excitons in band-tail states that are formed due to potential fluctuations. Under such conditions, the conventional models of Varshni, Viña and Passler fail to replicate the S-shaped temperature dependence of the PL peak energy and provide inconsistent and unrealistic values of the fitting parameters. The proposed formalism persuasively reproduces the S-shaped temperature dependence of the PL peak energy and provides an accurate determination of the exciton localization energy in bulk and QW structures along with the appropriate values of material parameters. An example of a strained InAs0.38P0.62/InP QW is presented by performing detailed temperature and excitation intensity dependent PL measurements and subsequent in-depth analysis using the proposed model. Versatility of the new formalism is tested on a few other semiconductor materials, e.g. GaN, nanotextured GaN, AlGaN and InGaN, which are known to have a significant contribution from the localized states. A quantitative evaluation of the fractional contribution of the localized states is essential for understanding the temperature dependence of the PL peak energy of bulk and QW well structures having a large contribution of the band-tail states.

Updated determination of stress parameters for nine well-recorded earthquakes in eastern North America

USGS Publications Warehouse

Boore, David M.

2012-01-01

Stress parameters (Δσ) are determined for nine relatively well-recorded earthquakes in eastern North America for ten attenuation models. This is an update of a previous study by Boore et al. (2010). New to this paper are observations from the 2010 Val des Bois earthquake, additional observations for the 1988 Saguenay and 2005 Riviere du Loup earthquakes, and consideration of six attenuation models in addition to the four used in the previous study. As in that study, it is clear that Δσ depends strongly on the rate of geometrical spreading (as well as other model parameters). The observations necessary to determine conclusively which attenuation model best fits the data are still lacking. At this time, a simple 1/R model seems to give as good an overall fit to the data as more complex models.

Accurate Modeling Method for Cu Interconnect

NASA Astrophysics Data System (ADS)

Yamada, Kenta; Kitahara, Hiroshi; Asai, Yoshihiko; Sakamoto, Hideo; Okada, Norio; Yasuda, Makoto; Oda, Noriaki; Sakurai, Michio; Hiroi, Masayuki; Takewaki, Toshiyuki; Ohnishi, Sadayuki; Iguchi, Manabu; Minda, Hiroyasu; Suzuki, Mieko

This paper proposes an accurate modeling method of the copper interconnect cross-section in which the width and thickness dependence on layout patterns and density caused by processes (CMP, etching, sputtering, lithography, and so on) are fully, incorporated and universally expressed. In addition, we have developed specific test patterns for the model parameters extraction, and an efficient extraction flow. We have extracted the model parameters for 0.15μm CMOS using this method and confirmed that 10%τpd error normally observed with conventional LPE (Layout Parameters Extraction) was completely dissolved. Moreover, it is verified that the model can be applied to more advanced technologies (90nm, 65nm and 55nm CMOS). Since the interconnect delay variations due to the processes constitute a significant part of what have conventionally been treated as random variations, use of the proposed model could enable one to greatly narrow the guardbands required to guarantee a desired yield, thereby facilitating design closure.

An opinion-driven behavioral dynamics model for addictive behaviors

NASA Astrophysics Data System (ADS)

Moore, Thomas W.; Finley, Patrick D.; Apelberg, Benjamin J.; Ambrose, Bridget K.; Brodsky, Nancy S.; Brown, Theresa J.; Husten, Corinne; Glass, Robert J.

2015-04-01

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual's behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. This has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.

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.

Software for Estimating Costs of Testing Rocket Engines

NASA Technical Reports Server (NTRS)

Hines, Merlon M.

2004-01-01

A high-level parametric mathematical model for estimating the costs of testing rocket engines and components at Stennis Space Center has been implemented as a Microsoft Excel program that generates multiple spreadsheets. The model and the program are both denoted, simply, the Cost Estimating Model (CEM). The inputs to the CEM are the parameters that describe particular tests, including test types (component or engine test), numbers and duration of tests, thrust levels, and other parameters. The CEM estimates anticipated total project costs for a specific test. Estimates are broken down into testing categories based on a work-breakdown structure and a cost-element structure. A notable historical assumption incorporated into the CEM is that total labor times depend mainly on thrust levels. As a result of a recent modification of the CEM to increase the accuracy of predicted labor times, the dependence of labor time on thrust level is now embodied in third- and fourth-order polynomials.

Software for Estimating Costs of Testing Rocket Engines

NASA Technical Reports Server (NTRS)

Hines, Merion M.

2002-01-01

A high-level parametric mathematical model for estimating the costs of testing rocket engines and components at Stennis Space Center has been implemented as a Microsoft Excel program that generates multiple spreadsheets. The model and the program are both denoted, simply, the Cost Estimating Model (CEM). The inputs to the CEM are the parameters that describe particular tests, including test types (component or engine test), numbers and duration of tests, thrust levels, and other parameters. The CEM estimates anticipated total project costs for a specific test. Estimates are broken down into testing categories based on a work-breakdown structure and a cost-element structure. A notable historical assumption incorporated into the CEM is that total labor times depend mainly on thrust levels. As a result of a recent modification of the CEM to increase the accuracy of predicted labor times, the dependence of labor time on thrust level is now embodied in third- and fourth-order polynomials.

Software for Estimating Costs of Testing Rocket Engines

NASA Technical Reports Server (NTRS)

Hines, Merlon M.

2003-01-01

A high-level parametric mathematical model for estimating the costs of testing rocket engines and components at Stennis Space Center has been implemented as a Microsoft Excel program that generates multiple spreadsheets. The model and the program are both denoted, simply, the Cost Estimating Model (CEM). The inputs to the CEM are the parameters that describe particular tests, including test types (component or engine test), numbers and duration of tests, thrust levels, and other parameters. The CEM estimates anticipated total project costs for a specific test. Estimates are broken down into testing categories based on a work-breakdown structure and a cost-element structure. A notable historical assumption incorporated into the CEM is that total labor times depend mainly on thrust levels. As a result of a recent modification of the CEM to increase the accuracy of predicted labor times, the dependence of labor time on thrust level is now embodied in third- and fourth-order polynomials.

Chiral magnetic conductivity and surface states of Weyl semimetals in topological insulator ultra-thin film multilayer.

PubMed

Owerre, S A

2016-06-15

We investigate an ultra-thin film of topological insulator (TI) multilayer as a model for a three-dimensional (3D) Weyl semimetal. We introduce tunneling parameters t S, [Formula: see text], and t D, where the former two parameters couple layers of the same thin film at small and large momenta, and the latter parameter couples neighbouring thin film layers along the z-direction. The Chern number is computed in each topological phase of the system and we find that for [Formula: see text], the tunneling parameter [Formula: see text] changes from positive to negative as the system transits from Weyl semi-metallic phase to insulating phases. We further study the chiral magnetic effect (CME) of the system in the presence of a time dependent magnetic field. We compute the low-temperature dependence of the chiral magnetic conductivity and show that it captures three distinct phases of the system separated by plateaus. Furthermore, we propose and study a 3D lattice model of Porphyrin thin film, an organic material known to support topological Frenkel exciton edge states. We show that this model exhibits a 3D Weyl semi-metallic phase and also supports a 2D Weyl semi-metallic phase. We further show that this model recovers that of 3D Weyl semimetal in topological insulator thin film multilayer. Thus, paving the way for simulating a 3D Weyl semimetal in topological insulator thin film multilayer. We obtain the surface states (Fermi arcs) in the 3D model and the chiral edge states in the 2D model and analyze their topological properties.

Uncertainty Estimation in Elastic Full Waveform Inversion by Utilising the Hessian Matrix

NASA Astrophysics Data System (ADS)

Hagen, V. S.; Arntsen, B.; Raknes, E. B.

2017-12-01

Elastic Full Waveform Inversion (EFWI) is a computationally intensive iterative method for estimating elastic model parameters. A key element of EFWI is the numerical solution of the elastic wave equation which lies as a foundation to quantify the mismatch between synthetic (modelled) and true (real) measured seismic data. The misfit between the modelled and true receiver data is used to update the parameter model to yield a better fit between the modelled and true receiver signal. A common approach to the EFWI model update problem is to use a conjugate gradient search method. In this approach the resolution and cross-coupling for the estimated parameter update can be found by computing the full Hessian matrix. Resolution of the estimated model parameters depend on the chosen parametrisation, acquisition geometry, and temporal frequency range. Although some understanding has been gained, it is still not clear which elastic parameters can be reliably estimated under which conditions. With few exceptions, previous analyses have been based on arguments using radiation pattern analysis. We use the known adjoint-state technique with an expansion to compute the Hessian acting on a model perturbation to conduct our study. The Hessian is used to infer parameter resolution and cross-coupling for different selections of models, acquisition geometries, and data types, including streamer and ocean bottom seismic recordings. Information about the model uncertainty is obtained from the exact Hessian, and is essential when evaluating the quality of estimated parameters due to the strong influence of source-receiver geometry and frequency content. Investigation is done on both a homogeneous model and the Gullfaks model where we illustrate the influence of offset on parameter resolution and cross-coupling as a way of estimating uncertainty.

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.

Quantitative Rheological Model Selection

NASA Astrophysics Data System (ADS)

Freund, Jonathan; Ewoldt, Randy

2014-11-01

The more parameters in a rheological the better it will reproduce available data, though this does not mean that it is necessarily a better justified model. Good fits are only part of model selection. We employ a Bayesian inference approach that quantifies model suitability by balancing closeness to data against both the number of model parameters and their a priori uncertainty. The penalty depends upon prior-to-calibration expectation of the viable range of values that model parameters might take, which we discuss as an essential aspect of the selection criterion. Models that are physically grounded are usually accompanied by tighter physical constraints on their respective parameters. The analysis reflects a basic principle: models grounded in physics can be expected to enjoy greater generality and perform better away from where they are calibrated. In contrast, purely empirical models can provide comparable fits, but the model selection framework penalizes their a priori uncertainty. We demonstrate the approach by selecting the best-justified number of modes in a Multi-mode Maxwell description of PVA-Borax. We also quantify relative merits of the Maxwell model relative to powerlaw fits and purely empirical fits for PVA-Borax, a viscoelastic liquid, and gluten.